Tag: innovation

Small Modular Reactors in Poland – first approach to the topic

I am returning to my blog, after months of break. I have been busy with the strictly scientific writing, and I discover every day, over and over again, that I have a finite amount of energy and focus. I keep circling around the topic of business models and business structures in the energy sector. I’ve read a lot of annual reports published by companies from the energy sector, and I have learnt a lot. Something like a year ago, when I essentially stopped blogging regularly, I would say I was all about renewable energy and about the ‘let’s save the planet’ thing. Now, I am much less sectarian. Warren Buffett is right: studying corporate reports is eye-opening. One of my re-discoveries as I have been looking inside businesses is that we can collectively do only the things which we know how to do, and therefore which we have practiced and learnt how to do. The rest is marketing slogans, applied to control crowds of useful idiots, by steering their outrage at their own existence. Therefore, now, when somebody approaches me with the ‘let’s save the planet’ story, I ask politely: “My friend, do you know how to save the planet? As a matter of fact, do you know how to save any planet? How many planets have you saved so far? How can you substantiate your claims?”.

Yet, bullshit put aside, energy transition is interesting. I think we are experiencing a somehow unprecedented multitude of energy sources. We have fossil fuels under many forms. We have wind. We have at least two different forms of solar: the photovoltaic and the concentrated solar power (CSP). We have nuclear coming in three sizes: the L (large scale reactors), the M (Small Modular Reactors), and the S (microreactors).

Another thing which I learnt from the comparative reading of corporate reports, on the one hand, and scientific articles, on the other hand, is that published social science is lagging far behind real-life. Just to give a quick example, many of my fellow scientists are firmly convinced that the development of solar farms is a technological breakthrough, and on the path of that breakthrough, the champions of planet-saving change struggle to keep their head above the water when fighting the villains of status quo. No. The business of solar farms is among the fastest growing businesses in the world, and in my home Europe, and in my even more intimate home Poland. The bulk of that business is based on well-known technologies, tested to the point of being officially certified for use in big power systems. The business is mostly about acquiring the really attractive solar assets, and possibly about trading them subsequently to someone else. Solar assets are land, in the first place. The solar installation is cheap and easy to build. The hard part is to acquire land, which: a) is acceptably close to consumers of energy b) is withing reach of bulk connection points in the power grid c) is abnormally cheap and, at the same time, there are no legal problems coming with it.

There are two technologies in the energy sector which somehow fascinate me with their economics: Small Modular Reactors (SMRs) and hydrogen. The former has recently started to grow very fast, after years of seemingly sterile discussions. In my home country, Poland, two different projects of SMR have been initiated. Hydrogen, on the other hand, is fascinating because it evolves so slowly, in spite of great hopes.

I am focusing on the SMRs, as my government has significantly changed the national energy policy in that respect. You can consult https://www.gov.pl/web/climate/energy-policy-of-poland-until-2040-epp2040 to know more. Suddenly, the development of SMRs has become a national priority. Two projects are on the table:

  1. Project #1, based on a technology called VOYAGR (https://www.nuscalepower.com/en/products/voygr-smr-plants ). This is a joint-venture between the Polish mining company  KGHM (https://kghm.com/en/investors ) and the U.S.-based company NuScale Power (https://www.nuscalepower.com/en/investors )

In 2022, the Polish government significantly modified the national energy policy, by putting strong emphasis on distributed energy resources, and, among them, an even stronger emphasis on Small Modular Reactors (https://www.gov.pl/web/climate/energy-policy-of-poland-until-2040-epp2040). Two projects are currently on the table:

For the sake of simplicity, these two projects will be further labelled by the names of the respective technologies involved, i.e. VOYAGR and BWRX-300. They display very different degrees of institutional development. The VOYAGR project seems to be stuck at the level of preliminary formalities. In the annual report of KGHM for 2022, we can read that “”On  14  February  2022,  KGHM  signed  the  Early  Works  Agreement  with  NuScale  Power,  LLC  (the  nuclear  technology provider), representing the first step in the implementation of SMR technology within the KGHM Group’s business operations.  On 20 April 2022, KGHM and TAURON Polska Energia S.A.  signed a letter of intent on cooperation in the scope of construction of low-emission energy sources, including using small modular nuclear reactors (SMRs). On 8 July 2022, the Company submitted the first application concerning the safety assessment of the small modular reactor technology considered by the Company to the  State Atomic  Agency.  On  6  September  2022,  KGHM  Polska  Miedź  S.A.  and  SN  Nuclearelectrica  SA  signed  a memorandum on cooperation on the development of SMR. A preliminary site assessment report for the SMR reactor is currently being developed“. In a later presentation by KGHM, dating from April 2023 (https://kghm.com/en/investor-presentation-april-2023 ), we can read that ” KGHM plans to build a small modular light water nuclear unit with a capacity of up to 500 MW by 2030. In 2021, KGHM Polska Miedź S.A. established a Nuclear Energy Department and in February 2022 a contract was signed with NuScale Power, LLC (“NuScale”) to commence work on implementing the SMR technology in Poland.“

Additionally, in April 2022, KGHM signed a letter of intent with Tauron Polska Energia S.A. regarding research, development and future investment projects with respect to the construction of energy generation sources utilising SMR nuclear power technology (https://kghm.com/en/signing-letter-intent-regarding-cooperation-generation-sources-utilising-smr-nuclear-power-0 ).

As regards the provider of technology in the VOYAGR project, namely NuScale Power Corp., there is a trace of cooperation with KGHM since September 2021, when both parties signed a first memorandum of understanding (

https://www.nuscalepower.com/en/news/press-releases/2021/nuscale-signs-mou-with-kghm-and-pbe ). On February 14th, 2022, a further going agreement was signed, to initiate the Deployment of the First Small Modular Reactor in Poland (

https://www.nuscalepower.com/en/news/press-releases/2022/nuscale-to-announce-historic-agreement-with-kghm ). Finally, on September 12th, 2022, the parties signed a task order to further this cooperation (

https://www.nuscalepower.com/en/news/press-releases/2022/nuscale-and-kghm-sign-task-order-to-initiate-the-deployment-of-first-smr-in-poland ). Since then, no significant further development has been communicated to the public. NuScale Power owns 87 patents pending in Europe.

As regards the BWRX-300 project, there has been more institutional development. In December 2021, GE Hitachi Nuclear Energy, BWXT Canada and Synthos Green Energy announced their intention to Support Deployment of Small Modular Reactors in Poland (https://www.ge.com/news/press-releases/ge-hitachi-nuclear-energy-bwxt-canada-and-synthos-green-energy-announce-intention-to ).

In June 2022, PKN Orlen SA and Synthos Green Energy SA formed a new entity, Orlen Synthos Green Energy sp z o.o. (KRS 0000975672), with an initial paid-in equity of 20 000 000 PLN, 50% from each of the partners. In November 2022, Synthos Green Energy SA and GE-Hitachi Nuclear Energy International LLC formed a new entity, BWRX Europe sp. z o.o. (KRS 0001004329), with an initial paid-in equity of 2 000 000 PLN, 50% from each of the partners. In March 2023, GE Hitachi Nuclear Energy (GEH), Tennessee Valley Authority (TVA), Ontario Power Generation (OPG) and Synthos Green Energy (SGE) announced they are forming a consortium to advance the global deployment of the GEH BWRX-300 small modular reactor (https://www.ge.com/news/press-releases/tennessee-valley-authority-ontario-power-generation-and-synthos-green-energy-invest ).

On April 21st, 2023, Orlen Synthos Green Energy sp z o.o. formed 19 affiliate entities, each as a limited liability partnership (PL: sp. z o.o.), each with an initial paid-in equity of 500 000 PLN, for the purposes of developing 19 local installations of the BWRX-300 technology:

  1. BWRX-300 Tarnobrzeg (KRS 0001031820)
  2. BWRX-300 Stalowa Wola (KRS 0001032414)
  3. BWRX-300 Nowa Huta (KRS 0001031830)
  4. BWRX-300 Ostrołęka (KRS 0001031827)
  5. BWRX-300 Połaniec (KRS 0001032980)
  6. BWRX-300 Bełchatów (KRS 0001031650)
  7. BWRX-300 Dąbrowa Górnicza (KRS 0001032435)
  8. BWRX-300 Grudziądz (KRS 0001031818)
  9. BWRX-300 Kozienice (KRS 0001032027)
  10. BWRX-300 Kujawy (KRS 0001032386)
  11. BWRX-300 Łaziska (KRS 0001032519)
  12. BWRX-300 Łódź (KRS 0001031895)
  13. BWRX-300 Pomorze (KRS 0001032683)
  14. BWRX-300 Poznań (KRS 0001031631)
  15. BWRX-300 Rybnik (KRS 0001032520)
  16. BWRX-300 Stawy Monowskie (KRS 0001031321)
  17. BWRX-300 Warszawa (KRS 0001032415)
  18. BWRX-300 Warta (KRS 0001032686)
  19. BWRX-300 Włocławek (KRS 0001031647)

GE-Hitachi Nuclear owns 181 patents pending in Europe.

According to recent research (Asuega, Limb & Quinn 2023[1]), the capital cost of constructing a small modular reactor is between $3 985 and $4 844 per 1 kW of electrical power. The BWRX-300 reactor has a nominal power of 300 MW, whence a unitary capital cost between $1,2 bln and $1,45 bln. Therefore, the total capital cost of 19 local deployments in the BWRX-300 technology in Poland can be estimated between $22,72 bln and 27,61 bln. The state-controlled company PKN Orlen, one of the two involved in the project from the Polish side, has currently a total equity of $33,4 bln. As regards the VOYAGR project, and if its Polish partner, KGHM, is to be held to their word of implementing an SMR of 500 MW, their capital needs for the venture will be somewhere between $2 bln and $2,42 bln for the reactor alone, with present equity of $7,15 bln.

The deployment of SMRs in Poland is vital to national security and therefore the Polish government takes a fully understandable care of controlling the flow of information on that topic. Nevertheless, with full respect for the security concerns, the deployment of SMRs in Poland means a huge absorption of capital by the Polish energy sector. It is important to start an informed public discussion about institutional vehicles for that capital flow, which, by its sheer size, will be disruptive for the capital market no matter what exact solutions will emerge. It is to bear in mind that deploying Small Modular Reactors in Poland spells breakthrough technological change. None of the technologies involved, i.e. VOYAGR and BWRX-300, has ever been fully implemented with conclusive results. Generally, SMR projects across the world are at the stage of optimal site selection (Liu et al. 2022[2]).

Such a discussion should be combined with economic research and elucidate, among others and not exclusively, the following questions:

  1. How predictable is the budget of the whole SMR programme in Poland? According to literature, nuclear projects notoriously go over budget and over schedule, even with otherwise much better-tested technologies (Mignacca & Locatelli 2020[3]; Mignacca et al. 2020[4]).
  2. Will those new investments be financed mostly with equity or mostly with corporate debt? What exact “debt to equity” ratio is to expect?
  3. How will be the required equity raised?
  4. How will the necessary borrowing be orchestrated?
  5. How will the SMR programme coexist with offshore wind projects conducted by the Polish partners of both projects? Literature suggests that the Levelized Cost of Energy (LCOE) from SMRs might not necessarily be competitive as compared to offshore wind (Fattahi et al. 2022[5]).
  6. What will be the involvement of the government, both fiscal and monetary?
  7. What will be the impact of SMRs on their local markets of energy? Just to illustrate the importance of the question, we can mention the two BWRX-300 reactors are planned in the Podkarpackie voivodship, namely in Tarnobrzeg and in Stalowa Wola. Once completed, they will add 600 MW of power to the voivodship-wide energy market, which will be a game changer for the local economy.

[1] Asuega, A., Limb, B. J., & Quinn, J. C. (2023). Techno-economic analysis of advanced small modular nuclear reactors. Applied Energy, 334, 120669. https://doi.org/10.1016/j.apenergy.2023.120669

[2] Liu, Y., Huang, G., Chen, J., Zhang, X., Zheng, X., & Zhai, M. (2022). Development of an optimization-aided small modular reactor siting model–A case study of Saskatchewan, Canada. Applied Energy, 305, 117867. https://doi.org/10.1016/j.apenergy.2021.117867

[3] Mignacca, B., & Locatelli, G. (2020). Economics and finance of Small Modular Reactors: A systematic review and research agenda. Renewable and Sustainable Energy Reviews, 118, 109519. https://doi.org/10.1016/j.rser.2019.109519

[4] Mignacca, B., Locatelli, G., & Sainati, T. (2020). Deeds not words: Barriers and remedies for Small Modular nuclear Reactors. Energy, 206, 118137. https://doi.org/10.1016/j.energy.2020.118137

[5] Fattahi, A., Sijm, J., Van den Broek, M., Gordón, R. M., Dieguez, M. S., & Faaij, A. (2022). Analyzing the techno-economic role of nuclear power in the Dutch net-zero energy system transition. Advances in Applied Energy, 7, 100103. https://doi.org/10.1016/j.adapen.2022.100103

Tesla first in line

Once again, a big gap in my blogging. What do you want – it happens when the academic year kicks in. As it kicks in, I need to divide my attention between scientific research and writing, on the one hand, and my teaching on the other hand.

I feel like taking a few steps back, namely back to the roots of my observation. I observe two essential types of phenomena, as a scientist: technological change, and, contiguously to that, the emergence of previously unexpected states of reality. Well, I guess we all observe the latter, we just sometimes don’t pay attention. I narrow it down a bit. When it comes to technological change, I am just bewildered with the amounts of cash that businesses have started holding, across the board, amidst an accelerating technological race. Twenty years ago, any teacher of economics would tell their students: ‘Guys, cash is the least productive asset of all. Keep just the sufficient cash to face the most immediate expenses. All the rest, invest it in something that makes sense’. Today, when I talk to my students, I tell them: ‘Guys, with the crazy speed of technological change we are observing, cash is king, like really. The greater reserves of cash you hold, the more flexible you stay in your strategy’.

Those abnormally big amounts of cash that businesses tend to hold, those last years, it has two dimensions in terms of research. On the one hand, it is economics and finance, and yet, on the other hand, it is management. For quite some time, digital transformation has been about the only thing worth writing about in management science, but that, namely the crazy accumulation of cash balances in corporate balance sheets, is definitely something worth writing about. Still, there is amazingly little published research on the general topic of cash flow and cash management in business, just as there is very little on financial liquidity in business. The latter topic is developed almost exclusively in the context of banks, mostly the central ones. Maybe it is all that craze about the abominable capitalism and the general claim that money is evil. I don’t know.

Anyway, it is interesting. Money, when handled at the microeconomic level, tells the hell of a story about our behaviour, our values, our mutual trust, and our emotions. Money held in corporate balance sheets tells the hell of a story about decision making. I explain. Please, consider the amount of money you carry around with you, like the contents of your wallet (credit cards included) plus whatever you have available instantly on your phone. Done? Visualised? Good. Now, ask yourself what percentage of all those immediately available monetary balances you use during your one average day. Done? Analysed? Good. In my case, it would be like 0,5%. Yes, 0,5%. I did that intellectual exercise with my students, many time. They usually hit no more than 10%, and they are gobsmacked. Their first reaction is WOKEish: ‘So I don’t really need all that money, right. Money is pointless, right?’. Not quite, my dear students. You need all that money; you just need it in a way which you don’t immediately notice.

There is a model in the theory of complex systems, called the ants’ colony (see for example: (Chaouch, Driss & Ghedira 2017[1]; Asghari & Azadi 2017[2]; Emdadi et al. 2019[3]; Gupta & Srivastava 2020[4]; Di Caprio et al. 2021[5]). Yes, Di Caprio. Not the Di Caprio you intuitively think about, though. Ants communicate with pheromones. They drop pheromones somewhere they sort of know (how?) it is going to be a signal for other ants. Each ant drops sort of a standard parcel of pheromones. Nothing to write home about, really, and yet enough to attract the attention of another ant which could drop its individual pheromonal parcel in the same location. With any luck, other ants will discover those chemical traces and validate them with their individual dumps of pheromones, and this is how the colony of ants maps its territories, mostly to find and exploit sources of food. This is interesting to find out that in order for all that chemical dance to work, there needs to be a minimum number of ants on the job. In there are not enough ants per square meter of territory, they just don’t find each other’s chemical imprints and have no chance to grab hold of the resources available. Yes, they all die prematurely. Money in human societies could be the equivalent of a pheromone. We need to spread it in order to carry out complex systemic changes. Interestingly, each of us, humans, is essentially blind to those complex changes: we just cannot wrap our mind around quickly around the technical details of something apparently as simple as the manufacturing chain of a gardening rake (do you know where exactly and in what specific amounts all the ingredients of steel come from? I don’t).  

All that talk about money made me think about my investments in the stock market. I feel like doing things the Warren Buffet’s way: going to the periodical financial reports of each company in my portfolio, and just passing in review what they do and what they are up to. By the way, talking about Warren Buffet’s way, I recommend my readers to go to the source: go to https://www.berkshirehathaway.com/ first, and then to  https://www.berkshirehathaway.com/2020ar/2020ar.pdf as well as to https://www.berkshirehathaway.com/qtrly/3rdqtr21.pdf . For now, I focus on studying my own portfolio according to the so called “12 immutable tenets by Warren Buffet”, such as I allow myself to quote them:

>> Business Tenets: Is the business simple and understandable? Does the business have a consistent operating history? Does the business have favourable long-term prospects?

>> Management Tenets: Is management rational? Is management candid with its shareholders? Does management resist the institutional imperative?

>> Financial Tenets Focus on return on equity, not earnings per share. Calculate “owner earnings.” Look for companies with high profit margins. For every dollar retained, make sure the company has created at least one dollar of market value.

>> Market Tenets: What is the value of the business? Can the business be purchased at a significant discount to its value?

(Hagstrom, Robert G.. The Warren Buffett Way (p. 98). Wiley. Kindle Edition.)

Anyway, here is my current portfolio:

>> Tesla (https://ir.tesla.com/#tab-quarterly-disclosure),

>> Allegro.eu SA (https://about.allegro.eu/ir-home ),

>> Alten (https://www.alten.com/investors/ ),

>> Altimmune Inc (https://ir.altimmune.com/ ),

>> Apple Inc (https://investor.apple.com/investor-relations/default.aspx ),

>> CureVac NV (https://www.curevac.com/en/investor-relations/overview/ ),

>> Deepmatter Group PLC (https://www.deepmatter.io/investors/ ), 

>> FedEx Corp (https://investors.fedex.com/home/default.aspx ),

>> First Solar Inc (https://investor.firstsolar.com/home/default.aspx )

>> Inpost SA (https://www.inpost.eu/investors )

>> Intellia Therapeutics Inc (https://ir.intelliatx.com/ )

>> Lucid Group Inc (https://ir.lucidmotors.com/ )

>> Mercator Medical SA (https://en.mercatormedical.eu/investors/ )

>> Nucor Corp (https://www.nucor.com/investors/ )

>> Oncolytics Biotech Inc (https://ir.oncolyticsbiotech.com/ )

>> Solaredge Technologies Inc (https://investors.solaredge.com/ )

>> Soligenix Inc (https://ir.soligenix.com/ )

>> Vitalhub Corp (https://www.vitalhub.com/investors )

>> Whirlpool Corp (https://investors.whirlpoolcorp.com/home/default.aspx )

>> Biogened (https://biogened.com/ )

>> Biomaxima (https://www.biomaxima.com/325-investor-relations.html )

>> CyfrPolsat (https://grupapolsatplus.pl/en/investor-relations )

>> Emtasia (https://elemental-asia.biz/en/ )

>> Forposta (http://www.forposta.eu/relacje_inwestorskie/dzialalnosc_i_historia.html )

>> Gameops (http://www.gameops.pl/en/about-us/ )

>> HMInvest (https://grupainwest.pl/relacje )

>> Ifirma (https://www.ifirma.pl/dla-inwestorow )

>> Moderncom (http://moderncommercesa.com/wpmccom/en/dla-inwestorow/ )

>> PolimexMS (https://www.polimex-mostostal.pl/en/reports/raporty-okresowe )

>> Selvita (https://selvita.com/investors-media/ )

>> Swissmed (https://swissmed.com.pl/?menu_id=8 )   

Studying that whole portfolio of mine through the lens of Warren Buffet’s tenets looks like a piece of work, really. Good. I like working. Besides, as I have been reading Warren Buffett’s annual reports at https://www.berkshirehathaway.com/ , I realized that I need a real strategy for investment. So far, I have developed a few efficient hacks, such as, for example, the habit of keeping my s**t together when other people panic or when they get euphoric. Still, hacks are not the same as strategy.

I feel like adding my own general principles to Warren Buffet’s tenets. Principle #1: whatever I think I do my essential strategy consists in running away from what I perceive as danger. Thus, what am I afraid of, in my investment? What subjective fears and objective risks factors shape my actions as investor? Once I understand that, I will know more about my own actions and decisions. Principle #2: the best strategy I can think of is a game with nature, where each move serves to learn something new about the rules of the game, and each move should be both decisive and leaving me with a margin of safety. What am I learning as I make my moves? What my typical moves actually are?

Let’s rock. Tesla (https://ir.tesla.com/#tab-quarterly-disclosure), comes first in line, as it is the biggest single asset in my portfolio. I start my digging with their quarterly financial report for Q3 2021 (https://www.sec.gov/Archives/edgar/data/1318605/000095017021002253/tsla-20210930.htm ), and I fish out their Consolidated Balance Sheets (in millions, except per share data, unaudited: https://www.sec.gov/Archives/edgar/data/1318605/000095017021002253/tsla-20210930.htm#consolidated_balance_sheets ).

Now, I assume that if I can understand why and how numbers change in the financial statements of a business, I can understand the business itself. The first change I can spot in that balance sheet is property, plant and equipment, net passing from $12 747 million to $17 298 million in 12 months. What exactly has happened? Here comes Note 7 – Property, Plant and Equipment, Net, in that quarterly report, and it starts with a specification of fixed assets comprised in that category. Good. What really increased in this category of assets is construction in progress, and here comes the descriptive explanation pertinent thereto: “Construction in progress is primarily comprised of construction of Gigafactory Berlin and Gigafactory Texas, expansion of Gigafactory Shanghai and equipment and tooling related to the manufacturing of our products. We are currently constructing Gigafactory Berlin under conditional permits in anticipation of being granted final permits. Completed assets are transferred to their respective asset classes, and depreciation begins when an asset is ready for its intended use. Interest on outstanding debt is capitalized during periods of significant capital asset construction and amortized over the useful lives of the related assets. During the three and nine months ended September 30, 2021, we capitalized $14 million and $52 million, respectively, of interest. During the three and nine months ended September 30, 2020, we capitalized $13 million and $33 million, respectively, of interest.

Depreciation expense during the three and nine months ended September 30, 2021 was $495 million and $1.38 billion, respectively. Depreciation expense during the three and nine months ended September 30, 2020 was $403 million and $1.13 billion, respectively. Gross property, plant and equipment under finance leases as of September 30, 2021 and December 31, 2020 was $2.60 billion and $2.28 billion, respectively, with accumulated depreciation of $1.11 billion and $816 million, respectively.

Panasonic has partnered with us on Gigafactory Nevada with investments in the production equipment that it uses to manufacture and supply us with battery cells. Under our arrangement with Panasonic, we plan to purchase the full output from their production equipment at negotiated prices. As the terms of the arrangement convey a finance lease under ASC 842, Leases, we account for their production equipment as leased assets when production commences. We account for each lease and any non-lease components associated with that lease as a single lease component for all asset classes, except production equipment classes embedded in supply agreements. This results in us recording the cost of their production equipment within Property, plant and equipment, net, on the consolidated balance sheets with a corresponding liability recorded to debt and finance leases. Depreciation on Panasonic production equipment is computed using the units-of-production method whereby capitalized costs are amortized over the total estimated productive life of the respective assets. As of September 30, 2021 and December 31, 2020, we had cumulatively capitalized costs of $1.89 billion and $1.77 billion, respectively, on the consolidated balance sheets in relation to the production equipment under our Panasonic arrangement.”

Good. I can try to wrap my mind around the contents of Note 7. Tesla is expanding its manufacturing base, including a Gigafactory in my beloved Europe. Expansion of the manufacturing capacity means significant, quantitative growth of the business. According to Warren Buffett’s philosophy: “The question of where to allocate earnings is linked to where that company is in its life cycle. As a company moves through its economic life cycle, its growth rates, sales, earnings, and cash flows change dramatically. In the development stage, a company loses money as it develops products and establishes markets. During the next stage, rapid growth, the company is profitable but growing so fast that it cannot support the growth; often it must not only retain all of its earnings but also borrow money or issue equity to finance growth” (Hagstrom, Robert G.. The Warren Buffett Way (p. 104). Wiley. Kindle Edition).  Tesla looks like they are in the phase of rapid growth. They have finally nailed down how to generate profits (yes, they have!), and they are expanding capacity-wise. They are likely to retain earnings and to be in need of cash, and that attracts my attention to another passage in Note 7: “Interest on outstanding debt is capitalized during periods of significant capital asset construction and amortized over the useful lives of the related assets”. If I understand correctly, the financial strategy consists in not servicing (i.e. not paying the interest due on) outstanding debt when that borrowed money is really being used to finance the construction of productive assets, and starting to service that debt only after the corresponding asset starts working and paying its bills. That means, in turn, that lenders are being patient and confident with Tesla. They assume their unconditional claims on Tesla’s future cash flows (this is one of the possible ways to define outstanding debt) are secure.   

Good. Now, I am having a look at Tesla’s Consolidated Statements of Operations (in millions, except per share data, unaudited: https://www.sec.gov/Archives/edgar/data/1318605/000095017021002253/tsla-20210930.htm#consolidated_statements_of_operations ). It is time to have a look at Warren Buffett’s Business Tenets as regards Tesla. Is the business simple and understandable? Yes, I think I can understand it. Does the business have a consistent operating history? No, operational results changed in 2020 and they keep changing. Tesla is passing from the stage of development (which took them a decade) to the stage of rapid growth. Does the business have favourable long-term prospects? Yes, they seem to have good prospects. The market of electric vehicles is booming (EV-Volumes[6]; IEA[7]).

Is Tesla’s management rational? Well, that’s another ball game. To develop in my next update.

[1] Chaouch, I., Driss, O. B., & Ghedira, K. (2017). A modified ant colony optimization algorithm for the distributed job shop scheduling problem. Procedia computer science, 112, 296-305. https://doi.org/10.1016/j.procs.2017.08.267

[2] Asghari, S., & Azadi, K. (2017). A reliable path between target users and clients in social networks using an inverted ant colony optimization algorithm. Karbala International Journal of Modern Science, 3(3), 143-152. http://dx.doi.org/10.1016/j.kijoms.2017.05.004

[3] Emdadi, A., Moughari, F. A., Meybodi, F. Y., & Eslahchi, C. (2019). A novel algorithm for parameter estimation of Hidden Markov Model inspired by Ant Colony Optimization. Heliyon, 5(3), e01299. https://doi.org/10.1016/j.heliyon.2019.e01299

[4] Gupta, A., & Srivastava, S. (2020). Comparative analysis of ant colony and particle swarm optimization algorithms for distance optimization. Procedia Computer Science, 173, 245-253. https://doi.org/10.1016/j.procs.2020.06.029

[5] Di Caprio, D., Ebrahimnejad, A., Alrezaamiri, H., & Santos-Arteaga, F. J. (2021). A novel ant colony algorithm for solving shortest path problems with fuzzy arc weights. Alexandria Engineering Journal. https://doi.org/10.1016/j.aej.2021.08.058

[6] https://www.ev-volumes.com/

[7] https://www.iea.org/reports/global-ev-outlook-2021/trends-and-developments-in-electric-vehicle-markets

The batteries we don’t need anymore

I continue on the thread I started to develop in my last update in French, titled ‘De quoi parler à la prochaine réunion de faculté’, i.e. I am using that blog, and the fact of writing, to put some order in the almost ritual mess that happens at the beginning of the academic year. New calls for tenders start in the ministerial grant programs, new syllabuses need to be prepared, new classes start. Ordinary stuff, mind you, this is just something about September, as if I were in Vivaldi’s ‘Four seasons’: the hot, tumultuous Summer slowly folds into the rich, textured, and yet implacably realistic Autumn.

My central idea is to use some of the science which I dove into during the summer holidays as an intellectual tool for putting order in that chaos. That almost new science of mine is mostly based on the theory of complex systems, and my basic claim is that technological change is an emergent phenomenon in complex social systems. We don’t know why exactly our technologies change the way they change. We can trace the current technologies back to their most immediate ancestors and sometimes we can predict their most immediate successors, but that’s about it. Futuristic visions of technologies that could be there in 50 years from now are already some kind of traditional entertainment. The concept of technological progress, when we try to find a developmental logic in the historically known technological change, is usually standing on wobbly legs, on the other hand. Yes, electricity allowed the emergence of medical technologies used in hospitals, and that saved a lot of human lives, but there is no way Thomas Edison could have known that. The most spectacular technological achievements of mankind, such as the Egyptian pyramids, the medieval cathedrals, the Dutch windmills from the 16th century, or the automobile, seen from the historical distance, look ambiguous. Yes, it all solved some problems, but it facilitated the emergence of new problems. The truly unequivocal benefit of those technological leaps, which could have been actually experienced by the people who made them, was to learn how to develop technologies.

The studies I did during the Summer holidays 2021 focused on four essential, mathematical models of emergent technological change: cellular automata, flock of birds AKA particle swarm, ants’ nest, and imperfect Markov chains. I start with passing in review the model of cellular automata. At any given moment, the social complexity can be divided into a finite number of social entities (agents). They can be individual humans, businesses, NGOs, governments, local markets etc. Each such entity has an immediate freedom of movement, i.e. a finite number of one-step moves. The concept is related to the theory of games and corresponds to what happens in real life. When we do something social, we seldom just rush forwards. Most frequently, we make one step, observe the outcomes, adjust, then we make the next step etc. When all social agents do it, the whole social complexity can be seen as a collection of cells, or pixels. Each such cell (pixel) is a local state of being in society. A social entity can move into that available state, or not, at their pleasure and leisure. All the one-step moves a social entity can make translate into a trajectory it can follow across the social space. Collective outcomes we strive for and achieve can be studied as temporary complex states of those entities following their respective trajectories. The epistemological trick here is that individual moves and their combinations can be known for sure only ex post. All we can do ex ante is to define the possible states, and just wait where does the reality go.

As we are talking about the possible states of social complexity, I found an interesting mathematical mindf**k at quite an unexpected source, namely in the book titled ‘Aware. The Science and Practice of Presence. The Groundbreaking Meditation Practice’ by Daniel J. Siegel [Penguin Random House LLC, 2018, Identifiers: LCCN 2018016987 (print), LCCN 2018027672 (ebook), ISBN 9780143111788, ISBN 9781101993040 (hardback)]. This is a mathematical way of thinking, apparently taken from quantum physics. Here is the essence of it. Everything that happens does so as 100% probability of the given thing happening. Each phenomenon which takes place is the actualization of the same phenomenon being just likely to happen.

Actualization of probability can be seen as collision of two vehicles in traffic. When the two vehicles are at a substantial distance from each other, the likelihood of them colliding is zero, for all practical purposes. As they converge towards each other, there comes a point when they become sort of provisionally entangled, e.g. they find themselves heading towards the same crossroads. The probability of collision increases slightly, and yet it is not even the probability of collision, it is just the probability that these two might find themselves in a vicinity conducive to a possible collision. Nothing to write home about, yet, like really. It can be seen as a plateau of probability slowly emerging out of the initial soup of all the things which can possibly happen.

As the two cars drive closer and closer to the crossroads in question, the panoply of possible states narrows down. There is a very clear chunk of reality which gains in likelihood, as if it was a mountain range pushing up from the provisional plateau. There comes a point where the two cars (and their drivers) just come on collision course, and there is no way around it, and this is a peak of 100% probability. Boom! Probability is being consumed.

What do those cars have in common with meditation and with the emergence of technological change? As regards meditation, thought can be viewed as a progressively emerging actualization of something that was just a weak probability, sort of a month ago it was just weakly probable that today I would think what I think, it became much more likely yesterday, as the thoughts from yesterday have an impact on the thoughts of today, and today it all comes to fruition, i.e. to the 100% probability. As regards emergent technological change, the way technology changes today can be viewed as actualization of something that was highly probable last year, just somehow probable 10 years ago, and had been just part of the amorphous soup of probability 30 years ago. Those trajectories followed by individual agents inside social complexity, as defined in the theory of cellular automata, are entangled together precisely according to that pattern of emergent probabilities. Two businesses coming up with two mutually independent, and yet similar technologies, are like two peak actualizations of 100% probability in a plateau of probable technological change, which, in turn, has been slowly emerging for some time.

Those other theories I use explain and allow to model mathematically that entanglement. The theory of particle swarm, pertinent to flocks of birds, assumes that autonomous social agents strive for a certain level of behavioural coupling. We expect some level of predictability from others, and we can cooperate with others when we are satisfactorily predictable in our actions. The strive for social coherence is, therefore, one mechanism of entanglement between individual trajectories of cellular automata. The theory of ants’ nest focuses on a specific category of communication systems in societies, working like pheromones. Ants organize by marking, reinforcing and following paths across their environment, and their pheromones serve as markers and reinforcement agents for those paths. In human societies, there are social pheromones. Money and financial markets make probably the most obvious example, but scientific publications are another one. The more scientific articles are being published on a given topic, the more likely are other articles being written on the same topic, until the whole thing reaches a point of saturation, when some ants (pardon me, scientists) start thinking about another path to mark with intellectual pheromones.

Cool. I have (OK, we have) complex social states, made of entangled probabilities that something specific happens, and they encompass technology. Those complex states change, i.e. one complex state morphs into another. Now, how the hell can I know, as a researcher, what is happening exactly? Such as the theory of complex systems suggests it, I can never know exactly, for one, and I need to observe, for two. As I don’t know exactly what is it exactly, that thing which I label ‘technological change’, it is problematic to set too many normative assumptions as for which specific path that technological change should take. I think this is the biggest point of contention as I apply my theory, such as I have just outlined it, to my main field of empirical research, namely energy economics, and technological change in the sector of energy. The more I do that research, the more convinced I am that the so-called ‘energy policies’, ‘climate policies’ etc. are politically driven bullshit based on wishful thinking, with not much of a chance to bring the positive change we expect. I have that deep feeling that setting a strategy for future innovations in our business/country/world is very much like that Polish expression ‘sharing the skin of a bear which is still running in the woods’. First, you need to kill the bear, only then you can bicker about who takes what part of the skin. In the case of innovation, long-term strategies in that domain consist in predicting what we will do when we have something we don’t even know yet what is it exactly.

I am trying to apply this general theory in the grant applications which I am in charge of preparing now, and in my teaching. We have that idea, at the faculty, to apply for funding to study the market of electric vehicles in Europe and in Poland. This is an interesting situation as regards business models. In the US, the market of electric cars is clearly divided among three categories of players. There is Tesla, which is a category and an industry in itself, with its peculiar strategy of extreme vertical integration. Then there are the big, classical car makers, such as Toyota, General Motors etc., with their business models based on rather a short vertical chain of value added inside the business, and a massive supply chain upstream of the house. Finally, there is a rising tide of small start-ups in the making of electric vehicles. I wonder what I could be in Europe. As our European market of electric vehicles is taking off, it is dominated by the incumbent big manufacturers, the old school ones, with Tesla building a factory in Germany, and progressively building a beachhead in the market. There is some timid movement towards small start-up businesses in the field, but it is really timid. In my home country, Poland, the most significant attempt at starting up an electric vehicle made in Poland is a big consortium of state-controlled companies, running under the name of ‘Electromobility Poland’.  

I have that intuition, which I provisionally express as a working hypothesis, namely that business models are an emergent property of technologies which they use. As regards the market of electric vehicles, it means that Tesla’s business model is not an accidental explosion of Elon Musk’s genius mind: it is an emergent characteristic of the technologies involved.

Good. I have some theory taking shape, nice and easy. I let it ripen a bit, and I start sniffing around for facts. What is a business model, in my mind? It is the way of operating the chain of value added, and getting paid for it, in the first place. Then, it is the way of using capital. I noticed that highly innovative environments force businesses to build up and keep large amounts of cash money, arguably to manage the diverse uncertainties emerging as technologies around morph like hell. In some cases, e.g. in biotech, the right business model for rapid innovation is a money-sucker, with apparently endless pay-ins of additional equity by the shareholders, and yet with a big value in terms of technological novelty created. I can associate that phenomenon of vacuum cleaning equity with the case of Tesla, who just recently started being profitable, and had gone through something like a decade in permanent operational loss. That is all pertinent to fixed costs, thus to the cash we need to build up and keep in place the organizational structure required for managing the value chain the way we want to manage it.

I am translating those loose remarks of mine into observable phenomena. Everything I have just mentioned is to be found in the annual financial reports. This is my first source of information. When I want to study business models in the market of electric vehicles, I need to look into financial and corporate reports of businesses active in the market. I need to look into the financial reports of Mercedes Benz, BMW, Renault, PSA, Volkswagen, Fiat, Volvo, and Opel – thus the European automotive makers – and see how it is going, and whether whatever is going on can be correlated with changes in the European market of electric vehicles. Then, it is useful to look into the financial reports of global players present in the European market, e.g. Tesla, Toyota, Honda and whatnot, just to see what changes in them as the European market of electric vehicles is changing.

If my intuition is correct, i.e. if business models are truly an emergent property of technologies used, the fact of engaging into the business of electric vehicles should be correlated with some sort of recurrent pattern in those companies.         

Good. This is about the big boys in the playground. Now, I turn toward the small ones, the start-up businesses. As I already said, it is not like we have a crowd of them in the European industry of electric vehicles. The intuitive axis of research which comes to my mind is to look at start-ups active in the U.S., study their business models, and see if there is any chance of something similar emerging in Europe. Somehow tangentially to that, I think it would be interesting to check whether the plan of Polish government regarding ‘Electromobility Poland’, that is the plan to develop it with public and semi-public money, and then sell it to private investors, has any grounds and under what conditions it can be a workable plan.

Good. I have rummaged a bit in my own mind, time to do the same to other people. I mean, I am passing to reviewing the literature. I type ‘electric vehicles Europe business model’ at the https://www.sciencedirect.com/ platform, and I look at what’s popping up. Here comes the paper by Pardo-Bosch, F., Pujadas, P., Morton, C., & Cervera, C. (2021). Sustainable deployment of an electric vehicle public charging infrastructure network from a city business model perspective. Sustainable Cities and Society, 71, 102957., https://doi.org/10.1016/j.scs.2021.102957 . The abstract says: ‘The unprecedented growth of global cities together with increased population mobility and a heightened concern regarding climate change and energy independence have increased interest in electric vehicles (EVs) as one means to address these challenges. The development of a public charging infrastructure network is a key element for promoting EVs, and with them reducing greenhouse gas emissions attributable to the operation of conventional cars and improving the local environment through reductions in air pollution. This paper discusses the effectiveness, efficiency, and feasibility of city strategic plans for establishing a public charging infrastructure network to encourage the uptake and use of EVs. A holistic analysis based on the Value Creation Ecosystem (VCE) and the City Model Canvas (CMC) is used to visualise how such plans may offer public value with a long-term and sustainable approach. The charging infrastructure network implementation strategy of two major European cities, Nantes (France) and Hamburg (Germany), are analysed and the results indicate the need to involve a wide range of public and private stakeholders in the metropolitan areas. Additionally, relevant, and fundamental patterns and recommendations are provided, which may help other public managers effectively implement this service and scale-up its use and business model.’

Well, I see there is a lot of work to do, as I read that abstract. I rarely find a paper where I have so much to argue with, just after having read the abstract. First of all, ‘the unprecedented growth of global cities’ thing. Actually, if you care to have a look at the World Bank data on urban land (https://data.worldbank.org/indicator/AG.LND.TOTL.UR.K2 ), as well as that on urban population (https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS ), you will see that urbanization is an ambiguous phenomenon, strongly region-specific. The central thing is that cities become increasingly distinct from the countryside, as types of human settlements. The connection between electric vehicles and cities is partly clear, but just partly. Cities are the most obvious place to start with EVs, because of the relatively short distance to travel between charging points. Still, moving EVs outside the cities, and making them functional in rural areas, is the next big challenge.

Then comes the ‘The development of a public charging infrastructure network is a key element for promoting EVs’ part. As I studied the thing in Europe, the network of charging stations, as compared to the fleet of EVs in the streets is so dense that we have like 12 vehicles per charging station on average, across the European Union. There is no way a private investor can have it for their money, when financing a private charging station, with that average density. We face a paradox: there are so many publicly funded charging stations, in relation to the car fleet out there, that private investment gets discouraged. I agree that it could be an acceptable transitory state in the market, although it begs the question whether private charging stations are a viable business in Europe. Tesla has based a large part of its business model in the US precisely on the development of their own charging stations. Is it a viable solution in Europe?

Here comes another general remark, contingent to my hypothesis of business models being emergent on the basis of technologies. Automotive technologies in general, thus the technology of a vehicle moving by itself, regardless the method of propulsion (i.e. internal combustion vs electric) is a combination of two component technologies. Said method of propulsion is one of them, and the other one is the technology of distributing the power source across space. Electric vehicles can be viewed as cousins to tramways and electric trains, with just more pronounced a taste for independence: instead of drinking electricity from a permanent wiring, EVs carry their electricity around with them, in batteries.

As we talk about batteries, here comes another paper in my cursory rummaging across other people’s science: Albertsen, L., Richter, J. L., Peck, P., Dalhammar, C., & Plepys, A. (2021). Circular business models for electric vehicle lithium-ion batteries: An analysis of current practices of vehicle manufacturers and policies in the EU. Resources, Conservation and Recycling, 172, 105658., https://doi.org/10.1016/j.resconrec.2021.105658 . Yes, indeed, the advent of electric vehicles creates a problem to solve, namely what to do with all those batteries. I mean two categories of batteries. Those which we need, and hope to acquire easily when the time comes for changing them in our vehicles, in the first place, and those we don’t need anymore and expect someone to take care of them swiftly and elegantly.       

De quoi parler à la prochaine réunion de faculté

Je change un peu de style par rapport à mes dernières mises à jour sur ce blog et je change parce que c’est le début de l’année académique, chez moi. C’est donc le chaos habituel que je commence à aimer, par ailleurs. Cette mise à jour est donc une mise en ordre dans mes idées et mes projets.

Je commence par essayer de résumer la recherche que j’ai faite pendant les vacances. Je pose donc l’hypothèse générale que le changement technologique est un phénomène émergent, qui survient comme intégration des phénomènes partiels dans le système social dont la complexité est essentiellement insaisissable pour nous. L’hypothèse d’émergence impose par ailleurs une distinction entre le terme de changement technologique et celui de progrès technologique. Le progrès technologique est un terme fortement subjectif, basé sur un système des valeurs communes dans une société. Le progrès c’est donc du changement, comme phénomène émergent, plus notre interprétation de ce phénomène.

Cette hypothèse a d’autres conséquences, dont la plus importante et la plus pratique c’est une mise en question radicale du concept de politique technologique ou politique d’innovation, au niveau agrégé d’état, de communauté internationale ou même au niveau des grandes entreprises. Dans les soi-disant « politiques technologiques » leurs créateurs assument que nous pouvons collectivement décider de développer nos technologies dans une direction donnée et que ça va marcher. Pour autant que je sache, c’est problématique déjà au niveau des grandes entreprises. Les politiques d’innovation des années 1990 dans l’industrie automobile, par exemple, étaient dans ce style. C’était comme « tout le monde se concentre sur la création de la technologie AVBG pour les 10 années à venir et pas de discussion ». Dans la plupart des cas, les résultats étaient désastreux. Des milliards des dollars dépensés sur des projets qui échouaient de façon spectaculaire au moment de confrontation au marché. Je pense que la seule technologie fondamentale dans l’automobile qui avait émergé avec succès des années 1990 c’était la propulsion hybride (moteur électrique plus moteur à combustion interne). C’était une technologie à laquelle personne de donnait arbitrairement la priorité dans les stratégies des grandes entreprises du secteur. Le pionnier dans ce domaine, Toyota, approchait le développement de la propulsion hybride de façon très prudente et pragmatique, comme une série heuristique d’expériences contrôlées, très loin des stratégies du type « tout le monde à bord ».  

Après les échecs des politiques centralisées d’innovation des années 1990, les grandes entreprises sont devenues beaucoup plus prudentes dans ce domaine. La meilleure politique semble être celle d’expérimentation abondante avec plusieurs idées nouvelles à la fois, avec une composante de compétition interne. Eh bien, lorsque je regarde les politiques qui se donnent l’étiquette « climatiques », quoi que cela veuille dire au juste, je me dis que ces hommes et femmes politiques (à propos, peut-on dire « les gens politiques » ?) feraient bien de prendre exemple des grandes entreprises. Les politiques qui commencent avec « Nous devons tous… » sont déjà suspectes. Si une telle politique assume, en plus, que la meilleure façon de stimuler l’innovation est d’introduire des nouveaux impôts, je suis contre. Oui, pour être clair : je suis vigoureusement contre le soi-disant « impôt carbone ». Je trouve cette idée dysfonctionnelle sur plusieurs niveaux, mais j’y reviendrai à une autre occasion.

Mon plat scientifique à emporter, après les vacances d’été AD 2021, est donc celui de changement technologique comme phénomène émergent qui intègre un système social complexe en ce qui concerne l’usage des ressources. Je me réfère fortement à la théorie des systèmes complexes en général et plus particulièrement à quatre modèles mathématiques là-dedans : le modèle d’automates cellulaires, celui d’essaim d’oiseaux, celui de fourmilière, et enfin celui des chaînes imparfaites de Markov.   

Avec cette perspective générale dans l’esprit, je me tourne vers un projet de recherche qui est en train de prendre forme parmi moi et mes collègues à l’université. Nous pensons qu’il serait intéressant d’étudier les développements possibles dans le marché européen des véhicules électriques, plus particulièrement en ce qui concerne les modèles d’entreprise. Par « modèle d’entreprise » je veux dire la même chose que le terme anglais « business model », donc la façon d’intégrer et de gérer la chaîne de valeur ajoutée dans le marché en question.

J’explique. Si on prend le marché global des véhicules électriques, on a essentiellement trois modèles d’entreprise : Tesla, les sociétés automobiles classiques et les startups. Tesla est idiosyncratique, c’est pratiquement une industrie en soi. Leur modèle d’entreprise est basé sur une intégration verticale très poussée, aussi bien au niveau des technologies qu’à celui d’organisation. Tesla avait commencé par faire des bagnoles électriques, puis ils ont enchaîné avec des stations de chargement et du photovoltaïque. C’est une chaîne de plus en plus longue des technologies verticalement connectées. D’autre part, le concept de « giga factory », chez Tesla, c’est de l’intégration verticale opérationnelle. L’idée consiste à convaincre les fournisseurs de localiser, dans la mesure du possible, leurs centres de fabrication dans la même usine où Tesla fait ses voitures. Simple et génial, j’ai envie de dire.

Tesla a donc un modèle d’entreprise très fortement intégré à la verticale et – comme j’ai pu le constater en observant leurs finances au fil des années – ça a pris du temps d’apprendre comment gérer cette chaîne de façon à capter proprement la valeur ajoutée. Ce n’est que récemment que tout ce bazar a commencé à être profitable. Là, il y a une question qui me fascine : pourquoi est-ce qu’autant de gens avaient mis autant d’effort dans l’expérimentation tellement coûteuse avec un modèle d’entreprise qui, pendant des années (pendant presque une décennie, en fait), semblait n’avoir aucune perspective réaliste de dégager du profit ?

Oui, je sais, Elon Musk. Le gars est fascinant, je suis d’accord. Seulement une organisation de la taille de Tesla, ça ne se crée pas autour d’une seule personne, même aussi géniale qu’Elon Musk, qui, par ailleurs, tout en étant un génie, n’est pas vraiment charismatique. Les grandes organisations, ça émerge de la complexité du tissu social, en intégrant certaines parties de ce tissu. Il est très dur de créer une grande organisation et il est encore plus dur de la tenir en place à long terme. Il doit y avoir des mécanismes sociaux sous-jacents qui soutiennent et stimulent ce phénomène.      

A côté de Tesla, il y a les producteurs automobiles établis, comme Daimler Chrysler, Toyota, le groupe PSA etc. Aujourd’hui, c’est devenu presque un impératif pour un producteur automobile de faire des véhicules électriques. Seulement ces producteurs-là, ils maintiennent le modèle d’entreprise bien morcelé verticalement, avec du outsourcing poussé et aucune tentative marquée d’adopter le modèle super-intégré de Tesla.

A côté de tout ça, il y a beaucoup des startups dans le marché des voitures électriques et ça, c’est même plus fascinant que Tesla. Pendant des décennies, l’automobile semblait être complétement fermé à ce type de petite entreprise créative, agile et coriace dont en voit plein dans l’informatique, la nanotechnologie ou bien la biotechnologie. Paradoxalement, le moment où Tesla a réussi de stabiliser financièrement sont modèle d’entreprise super intégrée, des startups ont commencé à proliférer. C’est comme si l’émergence d’un organisme géant très spécifique avait enclenché l’émergence des petites bestioles expérimentales de toute sorte.

Je me demande donc quel peut bien être ce mécanisme sous-jacent d’émergence des modèles nouveaux d’entreprise avec l’avènement des véhicules électriques. Voilà mon hypothèse de travail no. 1 à ce sujet : l’émergence rapide de nouveaux modèles d’entreprise manifeste une tendance poussée de la société à expérimenter et ceci, à son tour, témoigne de l’orientation collective sur un certain type de résultat.

Il y a ce principe, formulé, je crois, par Sigmund Freud. Si nous voulons découvrir les motivations réelles et profondes d’une personne qui ne sait pas comment les articuler, regardons les conséquences de ses actions. Ces conséquences disent beaucoup sur les valeurs et les tendances personnelles. La civilisation est une histoire. C’est un peu comme une personnalité. La prolifération des véhicules électriques à deux conséquences majeures. D’une part, nous réduisons notre dépendance du pétrole. Ceci contribue à protéger l’environnement, mais ça permet aussi de remuer un peu l’équilibre géopolitique. En Europe, par exemple, nous n’avons pas de pétrole local et aussi longtemps que nous roulons sur des moteurs à combustion interne, notre système de transport routier est stratégiquement dépendant d’une ressource que nous n’avons pas. A l’échelle globale, l’abandon du combustible en faveur des véhicules électriques, ça réduit la dépendance stratégique vis-à-vis des pays pétroliers et c’est in changement géopolitique majeur.   

La seconde conséquence majeure de la transition vers le véhicule électrique est une accélération spectaculaire dans le développement des technologies de stockage d’énergie. Remarquons, par ailleurs, que chaque voiture est un réservoir mobile d’énergie. Ça concerne toutes les voitures, celles à combustion interne aussi. Le réservoir d’essence est un réservoir mobile d’énergie. Le développement d’automobile en général, donc des moyens de transport qui bougent avec leur propre énergie, équivaut au développement d’un réseau géant de petits réservoirs mobiles d’énergie.

Notre civilisation s’est largement développée, à travers des millénaires, sur la base des technologies de stockage. Le stockage de nourriture semble avoir joué un rôle crucial, mais le stockage d’énergie est important aussi. Toute l’industrie des carburants fossiles est largement l’histoire de découverte comment stocker et transporter une source d’énergie. Les véhicules électriques, ça peut être la génération 2.0 dans ce domaine.

Voilà donc que je peaufine mon hypothèse de travail, comme no. 2 : l’émergence rapide de nouveaux modèles d’entreprise dans l’industrie des véhicules électriques est un phénomène émergent d’expérimentation collective orientée sur le réaménagement des relations géopolitiques basées sur la dépendance du pétrole ainsi que sur le développement des technologies de stockage d’énergie.

Eh bien voilà une jolie hypothèse. De quoi parler à la prochaine réunion de la faculté.

Living next door to such small success

Just two updates ago, I was trying to combine my work on the technological concept which I labelled ‘Energy Ponds’ AKA ‘Project Aqueduct’, with more theoretical a strand of research on collective intelligence in human societies. A third component thread has come into the game, a bit as a surprise. The editor of ‘International Journal of Energy Sector Management’ has just asked me to give a final revision to the manuscript which I am about to publish with them, titled ‘Climbing the right hill – an evolutionary approach to the European market of electricity’. More specifically, the editor asks me to refine the style of the paper, so as to make it more accessible to non-initiated readers.

I think I am smart. Many people think they are. I know I tend to overestimate my work capacity, though. I need an intellectual synthesis for all the three things: ‘Energy Ponds’, research on collective intelligence, and the final revision of my article. I need some kind of common denominator over which I could put and denominate all that intellectual stuff. I focus on the phenomenon of technological change. My most fundamental intuition about technological change is that it happens as a by-product of us, humans, collectively pursuing some other outcomes. I perceive technology as an emergence (not to confound with emergency) which happens when human societies reach a given level of complexity. Technologies are complex ways human interaction with the broadly spoken natural environment, i.e. with both natural resources and natural constraints.

I am rummaging in my most personal cases of technological change, namely my idea of ‘Energy Ponds’, and my investment decisions in the stock market. Non-linearity of change keeps floating to the surface. When the most obvious path of development in a technology is tight optimization through a sequence of small incremental improvements in efficiency, that technology is close to maturity in its lifecycle, and is not much of a big deal anymore. The truly promising technologies, those able to wake up the neighbours, are those with yet unclear prospects for optimization, with different alternative paths of experimentation in view.

Deep technological change occurs as non-linear path of experimentation in collective human interaction with both natural resources and natural constraints. Non-linearity means uncertainty, and uncertainty implies alternative states of nature, spread over a broad spectrum of outcomes. Them Black Swans are just waiting around the street corner. Deep technological change can play out according to different scenarios. We tend to think about scenarios as sequences, only with technological change the sequence is highly speculative, and the more uncertain the further we go from the starting point. There is another way of defining a scenario, namely as an orientation, a social force which pushes in a specific direction.

I start connecting the dots. Deep, break-through technological change practically never happens as a clearly purposeful collective strategy. It is always a disruption, and it takes us by surprise. Technological change happens as a sudden shortcut to achieve whatever collective outcomes we are after. People who invented the wheel probably didn’t want to invent the wheel as such, they were after a better way of transportation by land. Internet was invented because scientists started to work in large, dispersed networks of labs and needed a fast communication system for a lot of content.

Thus, we are after something, and, accidentally, we invent something else, which makes ripples across the social structure. We use the transformational force conveyed in those ripples to keep pursuing the same collective outcomes. It is interesting to notice that a new technology is practically never superior per se to the incumbent solutions. Social improvement happens only when human societies wrap themselves around that novel stuff and learn how to use it. Let’s suppose that a benevolent and very advanced alien race hands out to us a technology to travel between parallel universes. Looks cool, at the first sight. When we think about it longer, though, questions arise. What are the practical benefits of travelling between parallel universes? It is only when we figure out those benefits that we start absorbing that otherwise revolutionary technology.

I double back a bit on my own words. Deep technological change is essentially disruptive and surprising, and yet there is more to technological change than just the deep and disruptive kind. Periods of grinding, progressive optimization come after and between deep technological ripples. Here, I ask: why? Why the hell having all that business of technological change? It is interesting to notice that rapid technological change makes rifts in space just as it does in time. There are places on this planet where humans have been living for quite a few millennia without inventing s**t. It is even more interesting to notice that some among those no-progress lands used to be quite the opposite in the past. Amazonian jungle is a good example. Pre-Colombian people (i.e. people who used to live there before they learnt they had just been discovered) had a thriving civilization, with a lot of innovations up their sleeve, such as altitude specific agriculture in terraced fields, or written communication using pieces of string. Afghanistan (hic!) is another example. Centuries before Pythagoras figured out his angles and them square roots from sums of square powers, the place which we call ‘Afghanistan’ today used to be a huge mining hub, providing tin to all of the Bronze Age civilization in the Mediterranean and the Levant.

My point is that we, humans, need a good kick where it really hurts, plus some favourable conditions to recover when it really hurts, and then we start inventing stuff. Still, many of us can pass entire epochs (literally epochs) without figuring out anything new. As I like surfing through literature as I write, a few quotes come to my mind, out of the books I am reading now. Out of ‘The Black Swan. The impact of the highly improbable’ by Nassim Nicolas Taleb , Penguin, 2010, I have that passage from page 114: “Consider the following: of all the colorful adventurers who have lived on our planet, many were occasionally crushed, and a few did bounce back repeatedly. It is those who survive who will tend to believe that they are indestructible; they will have a long and interesting enough experience to write books about it. Until, of course … Actually, adventurers who feel singled out by destiny abound, simply because there are plenty of adventurers, and we do not hear the stories of those down on their luck”. The point is that we mostly know about technological change we know, as it were. The folds of history, which we tend to smooth out ex post, cover thousands of episodes when inventions simply didn’t work. I wonder how many people got mauled to death before someone finally nailed down the right way to make big, strong oxen pull heavy carts on wheels.

The adventure of technological change plays out favourably just sometimes, and yet we keep trying. Here come two quotes from another book: ‘The Knowledge Illusion. Why we never think alone’ by Steven Sloman and Philip Fernbach, RIVERHEAD BOOKS (An imprint of Penguin Random House LLC, Ebook ISBN: 9780399184345, Kindle Edition). On page page 133 thereof a whole new chapter starts under the provocative title: Technology as an Extension of Thought. It goes: ‘The mastery of new technology has gone hand in hand with the evolution of our species. According to Ian Tattersall, curator emeritus with the American Museum of Natural History in New York, “cognitive capacity and technology reinforced each other” as civilization developed. Genetic evolution and technological change have run in tandem throughout our evolutionary history. As brains increased in size from one hominid species to its descendants, tools became more sophisticated and more common. Our predecessors started using rocks with sharp edges. Later generations discovered fire, stone axes, and knives, followed by harpoons and spears, then nets, hooks, traps, snares, and bows and arrows, and eventually farming. Each of these technological changes was accompanied by all the other changes that led to the modern human being: cultural, behavioral, and genetic changes’. A few pages further, p. 150, the same authors write about the modern technology of crowdsourcing: ‘The power of crowdsourcing and the promise of collaborative platforms suggest that the place to look for real superintelligence is not in a futuristic machine that can outsmart human beings. The superintelligence that is changing the world is in the community of knowledge’.

It seems that we, humans, invent new things just because we can, just because we are biologically wired for it. Still, that creative interaction with our environment is full of failures, which, from time to time, produce some timid successes. The local humans, living next door to such small success, have the drive and the capacity to put a big fire up, starting from such a small spark. Once it has worked, deep technological rift happens, which transforms civilizations.

As I return to the final revision of the manuscript which I am about to publish with them, titled ‘Climbing the right hill – an evolutionary approach to the European market of electricity’, for the ‘International Journal of Energy Sector Management’, I wonder how to describe the kind of technological change which I write about in that paper, namely the development of renewable energies and the transition to electricity from the straightforward use of fossil thermal energy, in the European market. What I see in the empirical data is a historically short window of progress which, whilst being a bit bumpy, generally follows an upward trend. As I look at all of my so-far research on collective intelligence, it is largely the same. I have been studying historically short windows of technological change which generally looks like progress with some minor accidents on the way. On the other hand, when I refer to my ‘Energy Ponds’ concept and to the feasibility studies I am running for it, it is the deep-ripple type. I propose to implement a complex solution whose outcomes will be more environmental (water management and landscape management) more than straightforwardly financial. Yes, the whole thing has a chance to earn a living by selling electricity from hydroelectric turbines, but this is like Nicola Tesla earning a living by repairing people’s house equipment.

Is there any theoretical way I can use my toolbox of collective intelligence – tested on incremental technological change – to represent the socio-economic absorption of ‘Energy Ponds’? Good question. It is about social structures reacting to something disturbing. The general intuition I have in that respect, and which I developed through simulations described in my draft paper: ‘Behavioral absorption of Black Swans: simulation with an artificial neural network’  is that social structures tend to smooth out disturbances, for one. New things enter the game easier and faster than old things get pushed out of it, for two. I think that both cases, namely technological change in the European market of electricity and the possible development of ‘Energy Ponds’ are the kind of story, when new technologies sort of pile up on the top of old ones. Increased complexity is created. Increasing complexity means the build-up of some kind of non-equilibrium, which either gest smoothed out, and the corresponding technological change is nicely absorbed, or it doesn’t, and we have the Schumpeterian creative destruction.

I pretty much know how social structures wrap themselves around new power installations. There is one Black Swan, though, swimming surreptitiously around: the nuclear. In Europe, we have a keen interest in passing from combustion engines to electric vehicles. Combustion engines run on gasoline or on diesel, which all boils down to oil, which we don’t have and need to import. Transportation based on electricity makes us much less dependent on imported fuels, and that means more strategic security. Still, I think we will need to come back to developing nuclear power plants if we want to have enough juice for all those batteries on wheels.  

As regards ‘Energy Ponds’, the big question is how will urban and peri-urban structures get along with swamp-like reservoirs of water. That is really a deep question. For centuries, cities in Europe have been developing by drying out and draining down swamps. Swamps and buildings do not really like each other. Do we have the technologies to make their mutual neighbourhood liveable?

Que je finisse de façon bien élégante

Me revoilà avec l’idée de faire un rapprochement théorique entre mon « Projet Aqueduc » et ma recherche sur le phénomène d’intelligence collective. Comme je passe en revue ce que j’ai écrit jusqu’à maintenant sur les deux sujets, des conclusions provisoires se forment dans ma tête. L’idée primordiale est que je ne suis pas sûr du tout si mon « Projet Aqueduc » n’est pas, par le plus grand des hasards, une connerie déguisée. Comme je fais de la recherche sur le business d’énergies renouvelables, j’ai pu constater maintes fois qu’à part l’écologie fonctionnelle et pragmatique il y a une écologie religieuse, quelque chose comme l’Église Généralisée de la Mère Nature. Moi je veux rester dans le fonctionnel. Je n’achète pas vraiment l’argument que toute solution « naturelle » est meilleure qu’une « artificielle ». Le retour à la nature que tellement de mes amis prêchent comme une confession de foi c’est aussi le retour à la tuberculose et à l’absence de canalisation.

Je pense donc que « Projet Aqueduc » est une idée intéressante pour expérimenter avec mais pas vraiment une solution complète à optimaliser. L’idée d’utiliser un algorithme génétique du type NSGA-II ou « Non-dominated Sorting Genetic Algorithm » (comparez : Chang et al. 2016[1]; Jain & Sachdeva 2017[2];  Assaf & Shabani 2018[3]; Zhou et al. 2019[4]), que j’avais formulée au début de juillet ( We keep going until we observe du 5 juillet) serait prématurée. Le « Projet Aqueduc » est une chaîne complexe des technologies dont certaines sont plutôt certaines – pardonnez le jeu de mots – pendant que d’autres sont très incertaines dans leur développement général ainsi que leur application exacte dans ce contexte spécifique.    

Je pense que je vais me concentrer sur la mise au point d’une méthode de vérifier la faisabilité du concept en question dans la phase où il est actuellement, donc dans la phase d’expérimentation initiale, tôt dans le cycle de développement. Une telle méthode pourrait être appliquée à l’étude de faisabilité d’autres technologies en phase de développement expérimental. Le « Projet Aqueduc » présente un défi intellectuel intéressant de ce point de vue. L’une des dimensions importantes de ce concept est sa taille physique, surtout la quantité d’eau retenue dans les structures marécageuses qui agissent comme des pseudo-réservoirs hydrologiques (Harvey et al. 2009[5]; Phiri et al. 2021[6] ; Lu et al. 2021[7]; Stocks et al. 2021[8]). Plus nous expérimentons avec la taille physique des installations, plus grands sont les risques liés à cette expérimentation. Tester une installation de petite taille pour le « Projet Aqueduc » engendre des risques négligeables mais en même temps n’apporte pas vraiment de données empiriques sur ce qui se passe dans une installation de taille autrement plus substantielle. C’est donc un cas où – apparemment au moins – je dois expérimenter avec des risques de plus en plus élevés pour acquérir des données sur ce qui peut se passer si ces risques se consument en vie réelle. Voilà un casse-tête digne de ce nom.

En termes de défi intellectuel, j’en ai un autre, tombé un peu à l’improviste. Le journal « International Journal of Energy Sector Management » vient de me demander de donner un dernier coup de pinceau, avant la publication, à mon article intitulé « Climbing the right hill – an evolutionary approach to the European market of electricity ». Les recommandations du réviseur ainsi que celles du rédacteur responsable sont à peu près homogènes et se résument à donner plus de clarté à mon texte, de façon à le rendre plus facile à approcher pour des lecteurs non-initiés à la méthode que j’y utilise. Je relève le gant pour ainsi dire et je vais essayer de résumer le manuscrit en français, de façon aussi claire que possible. J’espère que ça va me donner un bon point de départ pour faire la révision finale de mon manuscrit en anglais.

Je commence par le résumé d’en-tête, donc ce qui s’appelle « abstract » en jargon scientifique anglais. L’article étudie changement socio-économique comme phénomène évolutif et plus précisément comme une marche adaptative en paysage rugueux, avec l’assomption d’intelligence collective et en vue d’optimiser la participation d’électricité dans la consommation totale de l’énergie ainsi que la participation des sources renouvelables dans la consommation d’électricité. Une méthode originale est présentée, où un réseau neuronal artificiel est utilisé pour produire des réalités alternatives à partir de l’ensemble originel de données empiriques. La distance Euclidienne entre ces réalités alternatives et la réalité empirique est utilisée comme base pour évaluer les objectifs collectifs. La variance de distance Euclidienne entre variables est utilisée comme base pour évaluer l’intensité d’interactions épistatiques entre les phénomènes représentés avec les variables. La méthode est testée dans un échantillon de 28 pays européens, entre 2008 et 2017, en présence d’imperfections du marché au niveau des prix de détail d’électricité. Les variables-clés, pertinentes à l’énergie, semblent être instrumentales par rapport à la poursuite d’autres valeurs collectives et ces dernières semblent se concentrer sur l’intensité de travail ainsi que sa rémunération.

Voilà un résumé bien scientifique. J’avoue : si je n’avais pas écrit cet article moi-même, je n’y comprendrais que dalle, à ce résumé. Pas étonnant que le réviseur et le rédacteur responsable me demandent gentiment de simplifier et de clarifier. Où commence-je donc ? Voilà une question qui mérite un peu de réflexion. Je pense qu’il faut que recule dans le temps et que je me souvienne le cheminement logique que j’avais pris, il y a un an et demi, lorsque j’avais écrit la première version de cet article. Oui, un an et demi. La science, ça traine parfois. Les idées-éclair, ça ralentit considérablement dans la phase de publication.

Je recule donc dans le temps. Il y avait deux trucs en concours, pour ainsi dire. D’une part, j’étais content après la publication d’un article chez « Energy », un journal bien respectable, sous le titre « Energy efficiency as manifestation of collective intelligence in human societies ». La méthode que j’y avais utilisée était largement la même que dans cet article chez « International Journal of Energy Sector Management », auquel je suis en train de donner une touche finale. Un réseau neuronal artificiel produit des simulations d’intelligence collective des sociétés humaines. Chaque simulation est une sorte de réalité alternative orientée sur l’optimisation d’une variable spécifique. Chaque réalité alternative demeure à une certaine distance mathématique de la réalité empirique. J’assume que celles qui sont les plus proches reflètent le mieux les rapports entre les variables empiriques. Puisque ce rapport est en fait une orientation – la poursuite d’optimisation d’une variable précise – j’interprète le tout comme une évolution collectivement intelligente avec un système de valeurs collectives.

Les résultats empiriques que j’avais obtenus dans cet article chez « Energy » étaient un peu surprenants, mais juste un peu. Les économies nationales que j’étudiais semblaient être orientés sur l’optimisation de rapport entre le flux d’invention scientifique et la capitalisation des entreprises (coefficient du nombre des demandes domestique de brevet par un million de dollars en actifs productifs fixes) plus que tout le reste. L’efficience énergétique, mesurée à l’échelle d’économies nationales, semblait être le cadet des soucis de lesdites économies nationales, pour ainsi dire. En général, ces 59 pays que j’avais pris sous ma loupe, démontraient bien une croissance d’efficience énergétique, mais cette amélioration semblait être un effet secondaire obtenu dans la poursuite d’équilibre local entre la science mûre (pour demander des brevets) et l’investissement.

Le catalogue des variables que j’avais pris en considération dans « Energy efficiency as manifestation of collective intelligence in human societies » était plutôt restreint. J’avais étudié 14 variables, dont la plupart étaient là en raison d’assomptions que j’avais prises à propos du contexte socio-économique de l’efficience énergétique. Alors, je m’étais posé la question suivante : qu’est-ce qui va se passer si je prends une poignée des variables pertinentes au secteur d’énergie, dans un contexte plus ou moins environnemental, et je les plonge dans un bain commun avec un catalogue vraiment large des variables macroéconomiques ? Côté méthode, c’est une approche classique dans la science. Un truc marche avec des assomptions bien serrées et le pas suivant est de tester le même truc avec des assomptions plus relax, genre pas trop d’idées préconçues.

En ce qui concerne le catalogue exhaustif des variables macroéconomiques, Penn Tables 9.1. (Feenstra et al. 2015[9]), avec 49 variables du type classique (Produit National, inflation, le marché d’emploi etc.) semblaient être une source convenable. J’avais déjà expérimenté avec cette base des données et ma méthode d’étudier l’intelligence collective en produisant des réalités alternatives avec un réseau neuronal et j’avais obtenu des résultats intéressants. Je les avais décrits dans un manuscrit plutôt technique intitulé « The Labour Oriented, Collective Intelligence of Ours : Penn Tables 9.1 Seen Through the Eyes of A Neural Network ». Il semble que les économies nationales de quelques 168 pays décrits dans Penn Tables 9.1 sont orientées sur l’optimalisation du marché de l’emploi plus que sur quoi que ce soit d’autre. Les variables dont l’optimalisation produit des réalités alternatives les plus proches de la réalité empirique sont, dans ce cas : le nombre moyen d’heures ouvrables par année par personne, la participation des salaires dans le Revenu National Brut et finalement le coefficient de capital humain, qui mesure le nombre moyen d’années d’éducation que les jeunes gens ont dans leur CV au moment d’entrer le marché d’emploi.

Encore une fois : lorsque la plupart d’économistes développent sur le sort horrible des travailleurs dans un monde dominé par des capitalistes rapaces sans pitié ni conscience, ma méthode suggérait le contraire, donc un monde orienté sur le travail et les travailleurs, beaucoup plus que sur l’optimalisation du retour interne sur l’investissement, par exemple. Ma méthode donnait donc des résultats surprenants avec des données empiriques tout à fait classiques. J’étais donc bien sûr que les résultats tout aussi surprenants que j’avais présenté dans « Energy efficiency as manifestation of collective intelligence in human societies » n’étaient pas le résultat de mon propre biais cognitif au niveau du matériel empirique de base mais bel et bien le résultat d’une méthode originale de recherche.

Ces résultats en main, je me demandais comment faire un rapprochement avec le secteur d’énergie. A l’époque, j’avais participé à un colloque public à propos des voitures électriques. Le colloque lui-même n’était pas vraiment excitant, mais après j’ai eu une discussion très intéressante avec mon fils. Le fiston avait dit : « En Europe, on n’a pas de notre pétrole bien à nous. Nous avons un système de transport routier très dense, presque entièrement dépendant d’une source d’énergie que nous devons importer, donc sur le pétrole. Comme risque stratégique, c’en est un gros ». Je me suis dit : il a raison, mon fiston. Encore une fois. C’est agaçant. Faut que je fasse quelque chose. Les voitures électriques, ça a besoin d’électricité et donc la participation d’électricité dans la consommation totale d’énergie serait un bon indicateur de notre préparation à passer vers les véhicules électriques, en Europe. Je peux prendre Penn Tables 9.1. (Feenstra et al. 2015 op. cit.), en extraire les données à propos des pays Européens, ajouter des variables pertinentes au secteur d’énergie et voilà : je peux tester l’hypothèse générale que ces variables énergétiques sont des orientations significative dans l’intelligence collective des pays Européens.    

Il y avait un autre truc, en fait. Ça fait déjà un bout de temps que j’ai fait attention aux prix d’électricité en Europe, et plus précisément à la différence très marquée entre les prix pour petits consommateurs d’énergie, calibre ménages, d’une part, et les prix réservés aux usagers plus grands. Vous pouvez consulter, à ce sujet, ma mise à jour du 28 Juin 2018 : « Deux cerveaux, légèrement différents l’un de l’autre ». C’est une imperfection du marché en une forme classique. J’avais donc décidé d’ajouter les prix d’électricité en Europe à cet ensemble déjà bien hétéroclite et voilà que ça a commencé.

Bon, j’ai reconstitué à peu de choses près le raisonnement originel qui m’a poussé à écrire cet article « Climbing the right hill – an evolutionary approach to the European market of electricity ». Si je sais comment j’avais commencé, il y a des chances que je finisse de façon bien élégante.  

[1] Chang, F. J., Wang, Y. C., & Tsai, W. P. (2016). Modelling intelligent water resources allocation for multi-users. Water resources management, 30(4), 1395-1413. https://doi.org/10.1007/s11269-016-1229-6

[2] Jain, V., & Sachdeva, G. (2017). Energy, exergy, economic (3E) analyses and multi-objective optimization of vapor absorption heat transformer using NSGA-II technique. Energy Conversion and Management, 148, 1096-1113. https://doi.org/10.1016/j.enconman.2017.06.055

[3] Assaf, J., & Shabani, B. (2018). Multi-objective sizing optimisation of a solar-thermal system integrated with a solar-hydrogen combined heat and power system, using genetic algorithm. Energy Conversion and Management, 164, 518-532. https://doi.org/10.1016/j.enconman.2018.03.026

[4] Zhou, Y., Chang, L. C., Uen, T. S., Guo, S., Xu, C. Y., & Chang, F. J. (2019). Prospect for small-hydropower installation settled upon optimal water allocation: An action to stimulate synergies of water-food-energy nexus. Applied Energy, 238, 668-682. https://doi.org/10.1016/j.apenergy.2019.01.069

[5] Harvey, J.W., Schaffranek, R.W., Noe, G.B., Larsen, L.G., Nowacki, D.J., O’Connor, B.L., 2009. Hydroecological factors governing surface water flow on a low-gradient floodplain. Water Resour. Res. 45, W03421, https://doi.org/10.1029/2008WR007129.

[6] Phiri, W. K., Vanzo, D., Banda, K., Nyirenda, E., & Nyambe, I. A. (2021). A pseudo-reservoir concept in SWAT model for the simulation of an alluvial floodplain in a complex tropical river system. Journal of Hydrology: Regional Studies, 33, 100770. https://doi.org/10.1016/j.ejrh.2020.100770.

[7] Lu, B., Blakers, A., Stocks, M., & Do, T. N. (2021). Low-cost, low-emission 100% renewable electricity in Southeast Asia supported by pumped hydro storage. Energy, 121387. https://doi.org/10.1016/j.energy.2021.121387

[8] Stocks, M., Stocks, R., Lu, B., Cheng, C., & Blakers, A. (2021). Global atlas of closed-loop pumped hydro energy storage. Joule, 5(1), 270-284. https://doi.org/10.1016/j.joule.2020.11.015

[9] Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), “The Next Generation of the Penn World Table” American Economic Review, 105(10), 3150-3182, available for download at http://www.ggdc.net/pwt 

Tax on Bronze

I am trying to combine the line of logic which I developed in the proof-of-concept for the idea I labelled ‘Energy Ponds’ AKA ‘Project Aqueduct’ with the research on collective intelligence in human societies. I am currently doing serious review of literature as regards the theory of complex systems, as it looks like just next door to my own conceptual framework. The general idea is to use the theory of complex systems – within the general realm of which the theory of cellular automata looks the most promising, for the moment – to simulate the emergence and absorption of a new technology in the social structure.  

I started to sketch the big lines of that picture in my last update in French, namely in ‘L’automate cellulaire respectable’. I assume that any new technology burgeons inside something like a social cell, i.e. a group of people connected by common goals and interests, together with some kind of institutional vehicle, e.g. a company, a foundation etc. It is interesting to notice that new technologies develop through the multiplication of such social cells rather than through linear growth of just one cell. Up to a point this is just one cell growing, something like the lone wolf of Netflix in the streaming business, and then ideas start breeding and having babies with other people.

I found an interesting quote in the book which is my roadmap through the theory of complex systems, namely in ‘What Is a Complex System?’ by James Landyman and Caroline Wiesner (Yale University Press 2020, ISBN 978-0-300-25110-4). On page 56 (Kindle Edition), Landyman and Wiesner write something interesting about the collective intelligence in colonies of ants: ‘What determines a colony’s survival is its ability to grow quickly, because individual workers need to bump into other workers often to be stimulated to carry out their tasks, and this will happen only if the colony is large. Army ants, for example, are known for their huge swarm raids in pursuit of prey. With up to 200 000 virtually blind foragers, they form trail systems that are up to 20 metres wide and 100 metres long (Franks et al. 1991). An army of this size harvests prey of 40 grams and more each day. But if a small group of a few hundred ants accidentally gets isolated, it will go round in a circle until the ants die from starvation […]’.

Interesting. Should nascent technologies have an ant-like edge to them, their survival should be linked to them reaching some sort of critical size, which allows the formation of social interactions in the amount which, in turn, an assure proper orientation in all the social cells involved. Well, looks like nascent technologies really are akin to ant colonies because this is exactly what happens. When we want to push a technology from its age of early infancy into the phase of development, a critical size of the social network is required. Customers, investors, creditors, business partners… all that lot is necessary, once again in a threshold amount, to give a new technology the salutary kick in the ass, sending it into the orbit of big business.

I like jumping quickly between ideas and readings, with conceptual coherence being an excuse just as frequently as it is a true guidance, and here comes an article on urban growth, by Yu et al. (2021[1]). The authors develop a model of urban growth, based on the empirical data on two British cities: Oxford and Swindon. The general theoretical idea here is that strictly speaking urban areas are surrounded by places which are sort of in two minds whether they like being city or countryside. These places can be represented as spatial cells, and their local communities are cellular automatons which move cautiously, step by step, into alternative states of being more urban or more rural. Each such i-th cellular automaton displays a transition potential Ni, which is a local balance between the benefits of urban agglomeration Ni(U), as opposed to the benefits Ni(N) of conserving scarce non-urban resources. The story wouldn’t be complete without the shit-happens component Ri of randomness, and the whole story can be summarized as: Ni = Ni(U) – Ni(N) + Ri.

Yu et al. (2021 op. cit.) add an interesting edge to the basic theory of cellular automata, such as presented e.g. in Bandini, Mauri & Serra (2001[2]), namely the component of different spatial scales. A spatial cell in a peri-urban area can be attracted to many spatial aspects of being definitely urban. Those people may consider the possible benefits of sharing the same budget for local schools in a perimeter of 5 kilometres, as well as the possible benefits of connecting to a big hospital 20 km away. Starting from there, it looks a bit gravitational. Each urban cell has a power of attraction for non-urban cells, however that power decays exponentially with physical distance.

I generalize. There are many technologies spreading across the social space, and each of them is like a city. I mean, it does not necessarily have a mayor, but it has dense social interactions inside, and those interactions create something like a gravitational force for external social cells. When a new technology gains new adherents, like new investors, new engineers, new business entities, it becomes sort of seen and known. I see two phases in the development of a nascent technology. Before it gains enough traction in order to exert significant gravitational force on the temporarily non-affiliated social cells, a technology grows through random interactions of the initially involved social cells. If those random interactions exceed a critical threshold, thus if there are enough forager ants in the game, their simple interactions create an emergence, which starts coagulating them into a new industry.

I return to cities and their growth, for a moment. I return to the story which Yu et al. (2021[3]) are telling. In my own story on a similar topic, namely in my draft paper ‘The Puzzle of Urban Density And Energy Consumption’, I noticed an amazing fact: whilst individual cities grow, others decay or even disappear, and the overall surface of urban areas on Earth seems to be amazingly stationary over many decades. It looks as if the total mass, and hence the total gravitational attraction of all the cities on Earth was a constant over at least one human generation (20 – 25 years). Is it the same with technologies? I mean, is there some sort of constant total mass that all technologies on Earth have, within the lifespan of one human generation, and there are just specific technologies getting sucked into that mass whilst others drop out and become moons (i.e. cold, dry places with not much to do and hardly any air to breathe).

What if a new technology spreads like Tik-Tok, i.e. like a wildfire? There is science for everything, and there is some science about fires in peri-urban areas as well. That science is based on the same theory of cellular automata. Jiang et al. (2021[4]) present a model, where territories prone to wildfires are mapped into grids of square cells. Each cell presents a potential to catch fire, through its local properties: vegetation, landscape, local climate. The spread of a wildfire from a given cell R0 is always based on the properties of the cells surrounding the fire.

Cirillo, Nardi & Spitoni (2021[5]) present an interesting mathematical study of what happens when, in a population of cellular automata, each local automaton updates itself into a state which is a function of the preceding state in the same cell, as well as of the preceding states in the two neighbouring cells. It means, among other things, that if we add the dimension of time to any finite space Zd where cellular automata dwell, the immediately future state of a cell is a component of the available neighbourhood for the present state of that cell. Cirillo, Nardi & Spitoni (2021) demonstrate, as well, that if we know the number and the characteristics of the possible states which one cellular automaton can take, like (-1, 0, 1), we can compute the total number of states that automaton can take in a finite number of moves. If we make many such cellular automatons move in the same space Zd , a probabilistic chain of complex states emerge.

As I wrote in ‘L’automate cellulaire respectable’, I see a social cell built around a new technology, e.g. ‘Energy Ponds’, moving, in the first place, along two completely clear dimensions: physical size of installations and financial size of the balance sheet. Movements along these two axes are subject to the influence happening along some foggy, unclear dimensions connected to preferences and behaviour: expected return on investment, expected future value of the firm, risk aversion as opposed to risk affinity etc. That makes me think, somehow, about a theory next door to that of cellular automata, namely the theory of swarms. This is a theory which explains complex changes in complex systems through changes in strength of correlation between individual movements. According to the swarm theory, a complex set which behaves like a swarm can adapt to external stressors by making the moves of individual members more or less correlated with each other. A swarm in routine action has its members couple their individual behaviour rigidly, like marching in step. A swarm alerted by a new stressor can loosen it a little, and allow individual members some play in their behaviour, like ‘If I do A, you do B or C or D, anyway one out of these three’. A swarm in mayhem loses it completely and there is no behavioural coupling whatsoever between members.

When it comes to the development and societal absorption of a new technology, the central idea behind the swarm-theoretic approach is that in order to do something new, the social swarm has to shake it off a bit. Social entities need to loosen their mutual behavioural coupling so as to allow some of them to do something else than just ritually respond to the behaviour of others. I found an article which I can use to transition nicely from the theory of cellular automata to the swarm theory: Puzicha & Buchholz (2021[6]). The paper is essentially applicable to the behaviour of robots, yet it is about a swarm of 60 distributed autonomous mobile robots which need to coordinate through a communication network with low reliability and restricted capacity. In other words, sometimes those robots can communicate with each other, and sometimes they don’t. When some robots out of the 60 are having a chat, they can jam the restricted capacity of the network and thus bar the remaining robots from communicating. Incidentally, this is how innovative industries work. When a few companies, let’s say the calibre of unicorns, are developing a new technology. They absorb the attention of investors, governments, potential business partners and potential employees. They jam the restricted field of attention available in the markets of, respectively, labour and capital.      

Another paper from the same symposium ‘Intelligent Systems’, namely Serov, Voronov & Kozlov (2021[7]), leads in a slightly different direction. Whilst directly derived from the functioning of communication systems, mostly the satellite-based ones, the paper suggests a path of learning in a network, where the capacity for communication is restricted, and the baseline method of balancing the whole thing is so burdensome for the network that it jams communication even further. You can compare it to a group of people who are all so vocal about the best way to allow each other to speak that they have no time and energy left for speaking their mind and listening to others. I have found another paper, which is closer to explaining the behaviour of those individual agents when they coordinate just sort of. It is Gupta & Srivastava (2020[8]), who compare two versions of swarm intelligence: particle swarm and ant colony. The former (particle swarm) generalises a problem applicable to birds. Simple, isn’t it? A group of birds will randomly search for food. Birds don’t know where exactly the food is, so they follow the bird which is nearest to the food.  The latter emulates the use of pheromones in a colony of ants. Ants selectively spread pheromones as they move around, and they find the right way of moving by following earlier deposits of pheromones. As many ants walk many times a given path, the residual pheromones densify and become even more attractive. Ants find the optimal path by following maximum pheromone deposition.

Gupta & Srivastava (2020) demonstrate that the model of ant colony, thus systems endowed with a medium of communication which acts by simple concentration in space and time are more efficient for quick optimization than the bird-particle model, based solely on observing each other’s moves. From my point of view, i.e. from that of new technologies, those results reach deeper than it could seem at the first sight. Financial capital is like a pheromone. One investor-ant drops some financial deeds at a project, and it can hopefully attract further deposits of capital etc. Still, ant colonies need to reach a critical size in order for that whole pheromone business to work. There needs to be a sufficient number of ants per unit of available space, in order to create those pheromonal paths. Below the critical size, no path becomes salient enough to create coordination and ants starve to death fault of communicating efficiently. Incidentally, the same is true for capital markets. Some 11 years ago, right after the global financial crisis, a fashion came to create small, relatively informal stock markets, called ‘alternative capital markets’. Some of them were created by the operators of big stock markets (e.g. the AIM market organized by the London Stock Exchange), some others were completely independent ventures. Now, a decade after that fashion exploded, the conclusion is similar to ant colonies: fault of reaching a critical size, those alternative capital markets just don’t work as smoothly as the big ones.

All that science I have quoted makes my mind wander, and it starts walking down the path of hilarious and absurd. I return, just for a moment, to another book: ‘1177 B.C. THE YEAR CIVILIZATION COLLAPSED. REVISED AND UPDATED’ by Eric H. Cline (Turning Points in Ancient History, Princeton University Press, 2021, ISBN 9780691208022). The book gives in-depth an account of the painful, catastrophic end of a whole civilisation, namely that of the Late Bronze Age, in the Mediterranean and the Levant. The interesting thing is that we know that whole network of empires – Egypt, Hittites, Mycenae, Ugarit and whatnot – collapsed at approximately the same moment, around 1200 – 1150 B.C., we know they collapsed violently, and yet we don’t know exactly how they collapsed.

Alternative history comes to my mind. I imagine the transition from Bronze Age to the Iron Age similarly to what we do presently. The pharaoh-queen VanhderLeyenh comes up with the idea of iron. Well, she doesn’t, someone she pays does. The idea is so seducing that she comes, by herself this time, with another one, namely tax on bronze. ‘C’mon, Mr Brurumph, don’t tell me you can’t transition to iron within the next year. How many appliances in bronze do you have? Five? A shovel, two swords, and two knives. Yes, we checked. What about your rights? We are going through a deep technological change, Mr Brurumph, this is not a moment to talk about rights. Anyway, this is not even the new era yet, and there is no such thing as individual rights. So, Mr Brurumph, a one-year notice for passing from bronze to iron is more than enough. Later, you pay the bronze tax on each bronze appliance we find. Still, there is a workaround. If you officially identify as a non-Bronze person, and you put the corresponding sign over your door, you have a century-long prolongation on that tax’.

Mr Brurumph gets pissed off. Others do too. They feel lost in a hostile social environment. They start figuring s**t out, starting from the first principles of their logic. They become cellular automata. They focus on nailing down the next immediate move to make. Errors are costly. Swarm behaviour forms. Fights break out. Cities get destroyed. Not being liable to pay the tax on bronze becomes a thing. It gets support and gravitational attraction. It becomes tempting to join the wandering hordes of ‘Tax Free People’ who just don’t care and go. The whole idea of iron gets postponed like by three centuries.  

[1] Yu, J., Hagen-Zanker, A., Santitissadeekorn, N., & Hughes, S. (2021). Calibration of cellular automata urban growth models from urban genesis onwards-a novel application of Markov chain Monte Carlo approximate Bayesian computation. Computers, environment and urban systems, 90, 101689. https://doi.org/10.1016/j.compenvurbsys.2021.101689

[2] Bandini, S., Mauri, G., & Serra, R. (2001). Cellular automata: From a theoretical parallel computational model to its application to complex systems. Parallel Computing, 27(5), 539-553. https://doi.org/10.1016/S0167-8191(00)00076-4

[3] Yu, J., Hagen-Zanker, A., Santitissadeekorn, N., & Hughes, S. (2021). Calibration of cellular automata urban growth models from urban genesis onwards-a novel application of Markov chain Monte Carlo approximate Bayesian computation. Computers, environment and urban systems, 90, 101689. https://doi.org/10.1016/j.compenvurbsys.2021.101689

[4] Jiang, W., Wang, F., Fang, L., Zheng, X., Qiao, X., Li, Z., & Meng, Q. (2021). Modelling of wildland-urban interface fire spread with the heterogeneous cellular automata model. Environmental Modelling & Software, 135, 104895. https://doi.org/10.1016/j.envsoft.2020.104895

[5] Cirillo, E. N., Nardi, F. R., & Spitoni, C. (2021). Phase transitions in random mixtures of elementary cellular automata. Physica A: Statistical Mechanics and its Applications, 573, 125942. https://doi.org/10.1016/j.physa.2021.125942

[6] Puzicha, A., & Buchholz, P. (2021). Decentralized model predictive control for autonomous robot swarms with restricted communication skills in unknown environments. Procedia Computer Science, 186, 555-562. https://doi.org/10.1016/j.procs.2021.04.176

[7] Serov, V. A., Voronov, E. M., & Kozlov, D. A. (2021). A neuro-evolutionary synthesis of coordinated stable-effective compromises in hierarchical systems under conflict and uncertainty. Procedia Computer Science, 186, 257-268. https://doi.org/10.1016/j.procs.2021.04.145

[8] Gupta, A., & Srivastava, S. (2020). Comparative analysis of ant colony and particle swarm optimization algorithms for distance optimization. Procedia Computer Science, 173, 245-253. https://doi.org/10.1016/j.procs.2020.06.029

L’automate cellulaire respectable

J’essaie de développer une jonction entre deux créneaux de ma recherche : l’étude de faisabilité pour mon « Projet Aqueduc » d’une part et ma recherche plus théorique sur le phénomène d’intelligence collective d’autre part. Question : comment prédire et prévoir l’absorption d’une technologie nouvelle dans une structure sociale ? En des termes plus concrets, comment puis-je prévoir l’absorption de « Projet Aqueduc » dans l’environnement socio-économique ? Pour me rendre la vie plus difficile – ce qui est toujours intéressant – je vais essayer de construire le modèle de cette absorption à partir d’une base théorique relativement nouvelle pour moi, notamment la théorie d’automates cellulaires. En termes de littérature, pour le moment, je me réfère à deux articles espacés de 20 ans l’un de l’autre : Bandini, Mauri & Serra (2001[1]) ainsi que Yu et al. (2021[2]).

Pourquoi cette théorie précise ? Pourquoi pas, en fait ? Sérieusement, la théorie d’automates cellulaires essaie d’expliquer des phénomènes très complexes – qui surviennent dans des structures qui ont l’air d’être vraiment intelligentes – à partir d’assomptions très faibles à propos du comportement individuel d’entités simples à l’intérieur de ces structures. En plus, cette théorie est déjà bien traduite en termes d’intelligence artificielle et se marie donc bien avec mon but général de développer une méthode de simuler des changements socio-économiques avec des réseaux neuronaux.

Il y a donc un groupe des gens qui s’organisent d’une façon ou d’une autre autour d’une technologie nouvelle. Les ressources économiques et la structure institutionnelle de ce groupe peuvent varier : ça peut être une société de droit, un projet public-privé, une organisation non-gouvernementale etc. Peu importe : ça commence comme une microstructure sociale. Remarquez : une technologie existe seulement lorsque et dans la mesure qu’une telle structure existe, sinon une structure plus grande et plus complexe. Une technologie existe seulement lorsqu’il y a des gens qui s’occupent d’elle.

Il y a donc ce groupe organisé autour d’une technologie naissante. Tout ce que nous savons sur l’histoire économique et l’histoire des technologies nous dit que si l’idée s’avère porteuse, d’autres groupes plus ou moins similaires vont se former. Je répète : d’autres groupes. Lorsque la technologie des voitures électriques avait finalement bien mordu dans le marché, ceci n’a pas entraîné l’expansion monopolistique de Tesla. Au contraire : d’autres entités ont commencé à bâtir de façon indépendante sur l’expérience de Tesla. Aujourd’hui, chacun des grands constructeurs automobiles vit une aventure plus ou moins poussée avec les bagnoles électriques et il y a toute une vague des startups crées dans le même créneau. En fait, la technologie du véhicule électrique a donné une deuxième jeunesse au modèle de petite entreprise automobile, un truc qui semblait avoir été renvoyé à la poubelle de l’histoire.

L’absorption d’une technologie nouvelle peut donc être représentée comme la prolifération des cellules bâties autour de cette technologie. A quoi bon, pouvez-vous demander. Pourquoi inventer un modèle théorique de plus pour le développement des technologies nouvelles ? Après tout, il y en a déjà pas mal, de tels modèles. Le défi théorique consiste à simuler le changement technologique de façon à cerner des Cygnes Noirs possibles. La différence entre un cygne noir tout simple et un Cygne Noir écrit avec des majuscules est que ce dernier se réfère au livre de Nassim Nicolas Taleb « The Black Swan. The impact of the highly improbable », Penguin, 2010. Oui, je sais, il y a plus que ça. Un Cygne Noir en majuscules peut bien être le Cygne Noir de Tchaïkovski, donc une femme (Odile) autant attirante que dangereuse par son habileté d’introduire du chaos dans la vie d’un homme. Je sais aussi que si j’arrangerai une conversation entre Tchaïkovski et Carl Gustav Jung, les deux messieurs seraient probablement d’accord qu’Odile alias Cygne Noir symbolise le chaos, en opposition à l’ordre fragile dans la vie de Siegfried, donc à Odette. Enfin, j’fais pas du ballet, moi, ici. Je blogue. Ceci implique une tenue différente, ainsi qu’un genre différent de flexibilité. Je suis plus âgé que Siegfried, aussi, comme par une génération.  

De tout en tout, mon Cygne Noir à moi est celui emprunté à Nassim Nicolas Taleb et c’est donc un phénomène qui, tout en étant hors d’ordinaire et surprenant pour les gens concernés, est néanmoins fonctionnellement et logiquement dérivé d’une séquence des phénomènes passés. Un Cygne Noir se forme autour des phénomènes qui pendant un certain temps surviennent aux extrémités de la courbe Gaussienne, donc à la frange de probabilité. Les Cygnes Noirs véhiculent du danger et des opportunités nouvelles, à des doses aussi variées que le sont les Cygnes Noirs eux-mêmes. L’intérêt pratique de cerner des Cygnes Noirs qui peuvent surgir à partir de la situation présente est donc celui de prévenir des risques du type catastrophique d’une part et de capter très tôt des opportunités exceptionnelles d’autre part.

Voilà donc que, mine de rien, je viens d’enrichir la description fonctionnelle de ma méthode de simuler l’intelligence collective des sociétés humaines avec les réseaux neuronaux artificiels. Cette méthode peut servir à identifier à l’avance des développements possibles du type de Cygne Noir : significatifs, subjectivement inattendus et néanmoins fonctionnellement enracinées dans la réalité présente.

Il y a donc cette technologie nouvelle et il y a des cellules socio-économiques qui se forment autour d’elle. Il y a des espèces distinctes des cellules et chaque espèce correspond à une technologie différente. Chaque cellule peut être représentée comme un automate cellulaire A = (Zd, S, n, Sn+1 -> S), dont l’explication commence avec Zd, donc l’espace à d dimensions ou les cellules font ce qu’elles ont à faire. L’automate cellulaire ne sait rien sur cet espace, tout comme une truite n’est pas vraiment forte lorsqu’il s’agit de décrire une rivière. Un automate cellulaire prend S états différents et ces états sont composés des mouvements du type un-pas-à-la-fois, dans n emplacements cellulaires adjacents. L’automate sélectionne ces S états différents dans un catalogue plus large Sn+1 de tous les états possibles et la fonction Sn+1 -> S alias la règle locale de l’automate A décrit de façon générale le quotient de cette sélection, donc la capacité de l’automate cellulaire d’explorer toutes les possibilités de bouger son cul (cellulaire) juste d’un cran à partir de la position actuelle.

Pourquoi distinguer ces quatre variables structurelles dans l’automate cellulaire ? Pourquoi n’assumons-nous pas que le nombre possible des mouvements « n » est une fonction constante des dimensions offertes par l’espace Zd ? Pourquoi ne pas assumer que le nombre réel d’états S est égal au total possible de Sn+1 ? Eh bien parce que la théorie d’automates cellulaires a des ambitions de servir à quelque chose d’utile et elle s’efforce de simuler la réalité. Il y a donc une technologie nouvelle encapsulée dans une cellule sociale A. L’espace social autour d’A est vaste, mais il peut y avoir des portes verrouillées. Des marchés oligopoles, des compétiteurs plus rapides et plus entreprenants, des obstacles légaux et mêmes des obstacles purement sociaux. Si une société à qui vous proposez de coopérer dans votre projet innovant craint d’être exposée à 10 000 tweets enragés de la part des gens qui n’aiment pas votre technologie, cette porte-là est fermée, quoi que la dimension où elle se trouve est théoriquement accessible.

Si je suis un automate cellulaire tout à fait ordinaire et j’ai la possibilité de bouger dans n emplacements sociaux adjacents à celui où je suis maintenant, je commence par choisir juste un mouvement et voir ce qui se passe. Lorsque tout se passe de façon satisfaisante, j’observe mon environnement immédiat nouveau – j’observe donc le « n » nouveau visible à partir de la cellule où je viens de bouger – je fais un autre mouvement dans un emplacement sélectionné dans ce nouveau « n » et ainsi de suite. Dans un environnement immédiat « n » moi, l’automate cellulaire moyen, j’explore plus qu’un emplacement possible de parmi n seulement lorsque je viens d’essuyer un échec dans l’emplacement précédemment choisi et j’avais décidé que la meilleure stratégie est de retourner à la case départ tout en reconsidérant les options possibles.         

La cellule sociale bâtie autour d’une technologie va donc se frayer un chemin à travers l’espace social Zd, en essayant de faire des mouvement réussis, donc en sélectionnant une option de parmi les « n » possibles. Oui, les échecs ça arrive et donc parfois la cellule sociale va expérimenter avec k > 1 mouvements immédiats. Néanmoins, la situation où k = n c’est quand les gens qui travaillent sur une technologie nouvelle ont essayé, en vain, toutes les options possibles sauf une dernière et se jettent la tête en avant dans celle-ci, qui s’avère une réussite. De telles situations arrivent, je le sais. Je crois bien que Canal+ était une aventure de ce type à ces débuts. Néanmoins, lorsqu’un truc marche, dans le lancement d’une technologie nouvelle, on juste continue dans la foulée sans regarder par-dessus l’épaule.

Le nombre réel S d’états que prend un automate cellulaire est donc largement sujet à l’hystérèse. Chaque mouvement réussi est un environnement immédiat de moins à exploiter, donc celui laissé derrière nous.  En même temps, c’est un défi nouveau de faire l’autre mouvement réussi au premier essai sans s’attarder dans des emplacements alternatifs. L’automate cellulaire est donc un voyageur plus qu’un explorateur. Bref, la formulation A = (Zd, S, n, Sn+1 -> S) d’un automate cellulaire exprime donc des opportunités et des contraintes à la fois.

Ma cellule sociale bâtie autour de « Projet Aqueduc » coexiste avec des cellules sociales bâties autour d’autres technologies. Comme tout automate cellulaire respectable, je regarde autour de moi et je vois des mouvements évidents en termes d’investissement. Je peux bouger ma cellule sociale en termes de capital accumulé ainsi que de l’échelle physique des installations. Je suppose que les autres cellules sociales centrées sur d’autres technologies vont faire de même : chercher du capital et des opportunités de croître physiquement. Excellent ! Voilà donc que je vois deux dimensions de Zd : l’échelle financière et l’échelle physique. Je me demande comment faire pour y bouger et je découvre d’autres dimensions, plus comportementales et cognitives celles-là : le retour interne (profit) espéré sur l’investissement ainsi que le retour externe (croissance de valeur d’entreprise), la croissance générale du marché de capital d’investissement etc.

Trouver des dimensions nouvelles, c’est fastoche, par ailleurs. Beaucoup plus facile que c’est montré dans les films de science-fiction. Il suffit de se demander ce qui peut bien gêner nos mouvements, regarder bien autour, avoir quelques conversations et voilà ! Je peux découvrir des dimensions nouvelles même sans accès à un téléporteur inter-dimensionnel à haute énergie. Je me souviens d’avoir vu sur You Tube une série de vidéos dont les créateurs prétendaient savoir à coup sûr que le grand collisionneur de hadrons (oui, celui à Genève) a ouvert un tunnel vers l’enfer. Je passe sur des questions simplissimes du genre : « Comment savez-vous que c’est un tunnel, donc un tube avec une entrée et une sortie ? Comment savez-vous qu’il mène en enfer ? Quelqu’un est-il allé de l’autre côté et demandé les locaux où ça où ils habitent ? ». Le truc vraiment épatant est qu’il y a toujours des gens qui croient dur comme fer que vous avez besoin des centaines de milliers de dollars d’investissement et des années de recherche scientifique pour découvrir un chemin vers l’enfer. Ce chemin, chacun de nous l’a à portée de la main. Suffit d’arrêter de découvrir des dimensions nouvelles dans notre existence.

Bon, je suis donc un automate cellulaire respectable qui développe le « Projet Aqueduc » à partir d’une cellule d’enthousiastes et en présence d’autres automates cellulaires. On bouge, nous, les automates cellulaires, le long de deux dimensions bien claires d’échelle – capital accumulé et taille physique des installations – et on sait que bouger dans ces dimensions-ci exige un effort dans d’autres dimensions moins évidentes qui s’entrelacent autour d’intérêt général pour notre idée de la part des gens extra – cellulaires. Notre Zd est en fait un Zd eh ben alors !. Le fait d’avoir deux dimensions bien visibles et un nombre discutable de dimensions plus floues fait que le nombre « n » des mouvements possibles est tout aussi discutable et on évite d’en explorer toutes les nuances. On saute sur le premier emplacement possible de parmi « n », ce qui nous transporte dans un autre « n », puis encore et encore.

Lorsque tous les automates cellulaires démontrent des règles locales Sn+1 -> S à peu près cohérentes, il est possible d’en faire une description instantanée Zd -> S, connue aussi comme configuration de A ou bien son état global. Le nombre d’états possibles que mon « Projet Aqueduc » peut prendre dans un espace rempli d’automates cellulaires va dépendre du nombre d’états possibles d’autres automates cellulaires. Ces descriptions instantanées Zd -> S sont, comme le nom l’indique, instantanées, donc temporaires et locales. Elles peuvent changer. En particulier, le nombre S d’états possibles de mon « Projet Aqueduc » change en fonction de l’environnement immédiat « n » accessible à partir de la position courante t. Une séquence de positions correspond donc à une séquence des configurations ct = Zd -> S (t) et cette séquence est désignée comme comportement de l’automate cellulaire A ou bien son évolution.        

[1] Bandini, S., Mauri, G., & Serra, R. (2001). Cellular automata: From a theoretical parallel computational model to its application to complex systems. Parallel Computing, 27(5), 539-553. https://doi.org/10.1016/S0167-8191(00)00076-4

[2] Yu, J., Hagen-Zanker, A., Santitissadeekorn, N., & Hughes, S. (2021). Calibration of cellular automata urban growth models from urban genesis onwards-a novel application of Markov chain Monte Carlo approximate Bayesian computation. Computers, environment and urban systems, 90, 101689. https://doi.org/10.1016/j.compenvurbsys.2021.101689

Plusieurs bouquins à la fois, comme d’habitude

Je suis en train de finir la première version, encore un peu rudimentaire, de mon article sur la faisabilité du « Projet Aqueduc » : un concept technologique en phase de naissance que j’essaie de développer et de promouvoir. Je pense que j’ai fait tous les calculs de base et j’ai l’intention d’en donner un compte rendu sommaire dans cette mise à jour. Je vais présenter ces résultats dans une structure logique qui est en train de faire sa percée dans le monde de la science : je commence par présenter l’idée de base et je l’associe avec du matériel empirique que je juge pertinent ainsi qu’avec la méthode d’analyse de ce matériel. Seulement après la description méthodologique je fais une revue de la littérature à propos des points saillants de la méthode et de l’idée de base. Ces trois composantes de base – introduction, matériel empirique et méthode d’analyse, revue de la littérature – forment la base de ce qui suit, donc de la présentation des calculs et leurs résultats ainsi que la discussion finale du tout. C’est une forme de composition qui est en train de remplacer une structure plus traditionnelle, qui était bâtie autour d’une transition rigoureuse de la théorie vers la partie empirique.

Je commence donc par reformuler et réaffirmer mon idée de base, donc l’essence même de « Projet Aqueduc ». Le travail de recherche que je viens de faire m’a fait changer les idées à ce propos. Initialement, je voyais le « Project Aqueduc » de la façon que vous pouvez voir décrite dans une mise à jour antérieure : « Ça semble expérimenter toujours ». Maintenant, je commence à apprécier la valeur cognitive et pratique de la méthode que j’ai mise au point pour conduire l’étude de faisabilité elle-même. La méthode en question est une application créative (enfin, j’espère) du rasoir d’Ockham : je divise mon concept entier en technologies composantes spécifiques et j’utilise la revue de littérature pour évaluer le degré d’incertitude attaché à chacune de parmi elles. Je concentre l’étude de faisabilité économique sur ce que peux dire de façon à peu près fiable à propos des technologies relativement le plus certaines et j’assume que ces technologies-là doivent générer un surplus de liquidité financière suffisant pour financer le développement de celles relativement plus incertaines.

Dans le cadre du « Projet Aqueduc », ce qui semble le mieux enraciné en termes de calcul ces coûts et d’investissement c’est la technologie de hydro-génération. Celle-ci est bien documentée et bien connue. Pas vraiment beaucoup d’innovation, par ailleurs. ça semble tourner tout seul. Les technologies de, respectivement, stockage d’énergie ainsi que chargement des voitures électriques viennent juste après en termes de prévisibilité : ça bouge, mais ça bouge de façon plutôt organisée. Il y a des innovations à espérer mais je pense que je suis capable de prédire plus ou moins de quelle direction elles vont venir.

Quoi qu’il en soit, j’ai simulé des installations hypothétiques de « Projet Aqueduc » dans les embouchures de 32 rivières de mon pays, la Pologne. J’ai pris les données officielles sur le débit par seconde, en mètres cubes, et j’ai simulé trois niveaux d’adsorption à partir de ce courant, à travers les béliers hydrauliques du « Projet Aqueduc » : 5%, 10% et 20%. En parallèle, j’ai simulé trois élévations possibles des réservoirs d’égalisation : 10 mètres, 20 mètres et 30 mètres. Avec les 654 millimètres de précipitations annuelles moyennes en Pologne, donc avec un ravitaillement hydrologique des précipitations avoisinant 201,8 milliards mètres cubes, ces 32 installations hypothétiques pourraient faire re-circuler entre 2,5% et 10% de ce total. Ceci fait un impact hydrologique substantiel pendant que l’impact sur le marché d’énergie n’est pas vraiment important. Avec l’adsorption d’eau au maximum, soit 20% du débit des rivières en question, ainsi qu’avec l’élévation des réservoirs d’égalisation fixée à 30 mètres (donc le maximum rationnellement possible vu la littérature du sujet), la puissance électrique totale de ces 32 installations hypothétiques serait de quelques 128,9 mégawatts, contre les 50 gigawatts déjà installés dans le système énergétique de la Pologne.

J’écrivais, dans mes mises à jour précédentes, que le « Projet Aqueduc » combine l’impact hydrologique avec celui sur le marché d’énergies renouvelables. Faut que je corrige. La production d’hydro-énergie est tout juste un moyen d’assurer la faisabilité économique du projet et puisque j’en suis là, encore quelques résultats de calculs. Vu les données d’Eurostat sur les prix d’énergie, le « Projet Aqueduc » semble faisable financièrement plutôt avec les prix moyens enregistrés en Europe qu’avec les prix minimum. Avec les prix moyens, l’exploitation des turbines hydroélectriques ainsi que celle d’installations de stockage d’énergie peut dégager quelques 90% de marge brute qui, à son tour, peut servir à financer les autres technologies du projet (pompage avec les béliers hydrauliques, infrastructure hydrologique etc.) et à créer un surplus net de trésorerie. En revanche, lorsque je simule les prix d’énergie à leur minimum empirique, ça donne un déficit brut de -18% après le coût d’énergie et de son stockage. Du coup, le « Projet Aqueduc » n’est pas vraiment du genre « énergies renouvelables pour tous et bon marché ». Le truc a des chances de marcher sans financement publique seulement lorsqu’il touche un marché de consommateurs prêts à payer plus que le minimum pour leur électricité.

En ce qui concerne la station de chargement de véhicules électriques, comme créneau marketing pour l’hydro-énergie produite, je répète tout simplement les conclusions que j’avais déjà exprimées dans la mise à jour intitulée « I have proven myself wrong » : ça n’a pas l’air de pouvoir marcher. A moins de créer une station de chargement hyper-demandée, avec des centaines de chargements par mois, il n’y aura tout simplement pas de trafic suffisant, au moins pas avec les proportions présentes entre la flotte de véhicules électriques en Europe et le réseau des stations de chargement. En revanche, il y a cette idée alternative de stations mobiles de chargement, développé de façon rigoureuse par Elmeligy et al. (2021[1]), par exemple. C’est un changement profond d’approche. Au lieu de construire une station puissante de chargement rapide, couplée avec un magasin d’énergie performant (et cher), on construit un système de batteries mobiles à puissance un peu moins élevée (200 kW dans la solution citée) et on les déplace à travers des parkings fréquentés dans un véhicule spécialement adapté à cette fin.

Maintenant, je change de sujet, mais alors complètement. Hier, j’ai reçu un courriel de la part d’une maison d’édition américaine, Nova Science Publishers, Inc., avec l’invitation à proposer un manuscrit de livre sur le sujet général d’intelligence collective. Apparemment, ils ont lu mon article dans le journal « Energy », intitulé « Energy efficiency as manifestation of collective intelligence in human societies ». Il est aussi possible que quelqu’un chez Nova suit mon blog et ce que je publie sur le phénomène d’intelligence collective. Écrire un livre est différent d’écrire un article. Ce dernier privilégie la concision et la brévité pendant que le premier exige un flot abondant d’idées tout comme un contexte riche et structuré.

En faisant un peu de lecture, ces dernières semaines, je me suis rendu compte que mon hypothèse générale d’intelligence collective des sociétés humaines – donc l’hypothèse d’apprentissage collectif à travers l’expérimentation avec plusieurs versions alternatives de la même structure sociale de base – se marie bien avec l’hypothèse des systèmes complexes. J’ai trouvé cette intersection intéressante comme je lisais le livre intitulé « 1177 B.C. The Year Civilisation Collapsed. Revised and Updated », publié par Eric H. Cline chez Princeton University Press en 2021[2]. En étudiant les mécanismes possibles de la décomposition des grands empires de l’âge de Bronze, Eric Cline cite la théorie des systèmes complexes. Si un ensemble est composé d’entités qui différent dans leur complexité – donc si nous observons entité dans entité et tout ça dans encore une autre entité – les connections fonctionnelles entre ces entités peuvent en quelque sorte stocker l’information et donc générer l’apprentissage spontané. De façon tout à fait surprenante, j’ai trouvé une référence scientifiquement sérieuse à la théorie des systèmes complexes dans un autre bouquin que je suis en train de lire (oui, j’ai l’habitude de lire plusieurs livres à la fois), donc dans « Aware. The Science and Practice of Presence. The Groundbreaking Meditation Practice », publié par Daniel J. Siegel chez TarcherPerigee en 2018[3].  Daniel J. Siegel developpe sur l’hypothèse que la conscience humaine est un système complexe et comme tel est capable d’auto-organisation. Je me permets de traduire ad hoc un court passage du début de ce livre : « L’une des caractéristiques émergentes fondamentales des systèmes complexes dans cette réalité qui est la nôtre est désignée comme auto-organisation. C’est un concept que vous pourriez croire être crée par quelqu’un en psychologie ou même dans les affaires, mais c’est un terme mathématique. La forme ou les contours du déploiement d’un système complexe sont déterminés par cette propriété émergente d’auto-organisation. Ce déploiement peut être optimisé ou bien il peut être contraint. Lorsqu’il ne s’optimise pas, il passe vers chaos ou vers la rigidité. Lorsqu’il s’optimise, il passe vers l’harmonie, en étant flexible, adaptable, cohérent, énergétique et stable ».

Intéressant : une étude systématique du développement et de la chute d’une civilisation peut trouver la même base théorique que l’étude scientifique de la méditation et cette base et la théorie des systèmes complexes. La façon do cette théorie se présente ressemble beaucoup à mes simulations de changement social et technologique où j’utilise des réseaux neuronaux comme représentation d’intelligence collective. Je suis en train de réfléchir sur la façon la plus générale possible d’exprimer et englober mon hypothèse d’intelligence collective. Je pense que le brouillon intitulé « Behavioral absorption of Black Swans: simulation with an artificial neural network », en combinaison avec la théorie des chaînes imparfaites de Markov (Berghout & Verbitskiy 2021[4]) sont peut-être le meilleur point de départ. J’assume donc que toute réalité sociale est une collection des phénomènes que nous ne percevons que de façon partielle et imparfaite et que nous estimons comme saillants lorsque leur probabilité d’occurrence dépasse un certain niveau critique.

Mathématiquement, la réalité sociale intelligible est donc un ensemble de probabilités. Je ne fais aucune assomption à priori quant à la dépendance mutuelle formelle de ces probabilités, mais je peux assumer que nous percevons tout changement de réalité sociale comme passage d’un ensemble des probabilités à un autre, donc comme une chaîne complexe d’états. Ici et maintenant, nous sommes dans une chaîne complexe A et à partir de là, tout n’est pas possible. Bien sûr, je ne veux pas dire que tout est impossible : j’assume tout simplement que la complexité d’ici et maintenant peut se transformer en d’autres complexités sous certaines conditions et contraintes. L’assomption la plus élémentaire à ce propos est que nous envisageons de bouger notre cul collectif seulement vers des états complexes qui nous rapprochent de ce que nous poursuivons ensemble et ceci quelles que soient les contraintes exogènes à notre choix. Je dirais même qu’en présence de contraintes sévères nous devenons particulièrement attentifs à l’état complexe prochain vers lequel nous transigeons. Une société constamment menacée par la pénurie de nourriture, par exemple, va être très tatillonne et en même temps très ingénieuse dans sa propre croissance démographique, en allant même jusqu’à la régulation culturelle du cycle menstruel des femmes.

Bon, ce sera tout dans cette mise à jour. Je m’en vais réfléchir et lire (plusieurs bouquins à la fois, comme d’habitude).

[1] Elmeligy, M. M., Shaaban, M. F., Azab, A., Azzouz, M. A., & Mokhtar, M. (2021). A Mobile Energy Storage Unit Serving Multiple EV Charging Stations. Energies, 14(10), 2969. https://doi.org/10.3390/en14102969

[2] LCCN 2020024530 (print) | LCCN 2020024531 (ebook) | ISBN 9780691208015 (paperback) | ISBN 9780691208022 (ebook) ; Cline, Eric H.. 1177 B.C.: 6 (Turning Points in Ancient History, 1) . Princeton University Press. Kindle Edition.

[3] LCCN 2018016987 (print) | LCCN 2018027672 (ebook) | ISBN 9780143111788 | ISBN 9781101993040 (hardback) ; Siegel, Daniel J.. Aware (p. viii). Penguin Publishing Group. Kindle Edition.

[4] Berghout, S., & Verbitskiy, E. (2021). On regularity of functions of Markov chains. Stochastic Processes and their Applications, Volume 134, April 2021, Pages 29-54, https://doi.org/10.1016/j.spa.2020.12.006

I have proven myself wrong

I keep working on a proof-of-concept paper for the idea I baptized ‘Energy Ponds’. You can consult two previous updates, namely ‘We keep going until we observe’ and ‘Ça semble expérimenter toujours’ to keep track of the intellectual drift I am taking. This time, I am focusing on the end of the technological pipeline, namely on the battery-powered charging station for electric cars. First, I want to make myself an idea of the market for charging.

I take the case of France. In December 2020, they had a total of 119 737 electric vehicles officially registered (matriculated), which made + 135% as compared to December 2019[1]. That number pertains only to 100% electrical ones, with plug-in hybrids left aside for the moment. When plug-in hybrids enter the game, France had, in December 2020, 470 295 vehicles that need or might need the services of charging stations. According to the same source, there were 28 928 charging stations in France at the time, which makes 13 EVs per charging station. That coefficient is presented for 4 other European countries: Norway (23 EVs per charging station), UK (12), Germany (9), and Netherlands (4).

I look up into other sources. According to Reuters[2], there was 250 000 charging stations in Europe by September 2020, as compared to 34 000 in 2014. That means an average increase by 36 000 a year. I find a different estimation with Statista[3]: 2010 – 3 201; 2011 – 7 018; 2012 – 17 498; 2013 – 28 824; 2014 – 40 910; 2015 – 67 064; 2016 – 98 669; 2017 – 136 059; 2018 – 153 841; 2019 – 211 438; 2020 – 285 796.

On the other hand, the European Alternative Fuels Observatory supplies their own data at https://www.eafo.eu/electric-vehicle-charging-infrastructure, as regards European Union.

Number of EVs per charging station (source: European Alternative Fuels Observatory):

EVs per charging station
201014
20116
20123
20134
20145
20155
20165
20175
20186
20197
20209

The same EAFO site gives their own estimation as regards the number of charging stations in Europe:

Number of charging stations in Europe (source: European Alternative Fuels Observatory):

High-power recharging points (more than 22 kW) in EUNormal charging stations in EUTotal charging stations
201225710 25010 507
201375117 09317 844
20141 47424 91726 391
20153 39644 78648 182
20165 19070 01275 202
20178 72397 287106 010
201811 138107 446118 584
201915 136148 880164 016
202024 987199 250224 237

Two conclusions jump to the eye. Firstly, there is just a very approximate count of charging stations. Numbers differ substantially from source to source. I can just guess that one of the reasons for that discrepancy is the distinction between officially issued permits to build charging points, on the one hand, and the actually active charging points, on the other hand. In Europe, building charging points for electric vehicles has become sort of a virtue, which governments at all levels like signaling. I guess there is some boasting and chest-puffing in the numbers those individual countries report.  

Secondly, high-power stations, charging with direct current, with a power of at least 22 kWh,  gain in importance. In 2012, that category made 2,45% of the total charging network in Europe, and in 2020 that share climbed to 11,14%. This is an important piece of information as regards the proof-of-concept which I am building up for my idea of Energy Ponds. The charging station I placed at the end of the pipeline in the concept of Energy Ponds, and which is supposed to earn a living for all the technologies and installations upstream of it, is supposed to be powered from a power storage facility. That means direct current, and most likely, high power.   

On the whole, the www.eafo.eu site seems somehow more credible that Statista, with all the due respect for the latter, and thus I am reporting some data they present on the fleet of EVs in Europe. Here it comes, in a few consecutive tables below:

Passenger EVs in Europe (source: European Alternative Fuels Observatory):

BEV (pure electric)PHEV (plug-in-hybrid)Total
20084 155–4 155
20094 841–4 841
20105 785–5 785
201113 39516313 558
201225 8913 71229 603
201345 66232 47478 136
201475 47956 745132 224
2015119 618125 770245 388
2016165 137189 153354 290
2017245 347254 473499 820
2018376 398349 616726 014
2019615 878479 7061 095 584
20201 125 485967 7212 093 206

Light Commercial EVs in Europe (source: European Alternative Fuels Observatory):

BEV (pure electric)PHEV (plug-in-hybrid)Total
2008253–253
2009254–254
2010309–309
20117 669–7 669
20129 527–9 527
201313 669–13 669
201410 049–10 049
201528 610–28 610
201640 926140 927
201752 026152 027
201876 286176 287
201997 36311797 480
2020120 7111 054121 765

Bus EVs in Europe (source: European Alternative Fuels Observatory):

BEV (pure electric)PHEV (plug-in-hybrid)Total
200827–27
200912–12
2010123–123
2011128–128
2012286–286
2013376–376
201438940429
2015420145565
2016686304990
20178884451 333
20181 6084862 094
20193 6365254 161
20205 3115505 861

Truck EVs in Europe (source: European Alternative Fuels Observatory):

BEV (pure electric)PHEV (plug-in-hybrid)Total
20085–5
20095–5
20106–6
20117–7
20128–8
201347–47
201458–58
201571–71
201611339152
2017544094
201822240262
201959538633
2020983291 012

Structure of EV fleet in Europe as regards the types of vehicles (source: European Alternative Fuels Observatory):

Passenger EVLight commercial EVBus EVTruck EV
200893,58%5,70%0,61%0,11%
200994,70%4,97%0,23%0,10%
201092,96%4,97%1,98%0,10%
201163,47%35,90%0,60%0,03%
201275,09%24,17%0,73%0,02%
201384,72%14,82%0,41%0,05%
201492,62%7,04%0,30%0,04%
201589,35%10,42%0,21%0,03%
201689,39%10,33%0,25%0,04%
201790,34%9,40%0,24%0,02%
201890,23%9,48%0,26%0,03%
201991,46%8,14%0,35%0,05%
202094,21%5,48%0,26%0,05%

Summing it up a bit. The market of Electric Vehicles in Europe seems being durably dominated by passenger cars. There is some fleet in other categories of vehicles, and there is even some increase, but, for the moment, in all looks more like an experiment. Well, maybe electric buses turn up sort of more systemically.

The proportion between the fleet of electric vehicles and the infrastructure of charging stations still seems to be in the phase of adjustment in the latter to the abundance of the former. Generally, the number of charging stations seems to be growing slower than the fleet of EVs. Thus, for my own concept, I assume that the coefficient of 9 EVs per charging station, on average, will stand still or will slightly increase. For the moment, I take 9. I assume that my charging stations will have like 9 habitual customers, plus a fringe of incidental ones.

From there, I think in the following terms. The number of times the average customer charges their car depends on the distance they cover. Apparently, there is like a 100 km  50 kWh equivalence. I did not find detailed statistics as regards distances covered by electric vehicles as such, however I came by some Eurostat data on distances covered by all passenger vehicles taken together: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Passenger_mobility_statistics#Distance_covered . There is a lot of discrepancy between the 11 European countries studied for that metric, but the average is 12,49 km per day. My average 9 customers would do, in total, an average of 410,27 of 50 kWh charging purchases per year. I checked the prices of fast charging with direct current: 2,3 PLN per 1 kWh in Poland[4],  €0,22 per 1 kWh in France[5], $0,13 per 1 kWh in US[6], 0,25 pence per 1 kWh in UK[7]. Once converted to US$, it gives $0,59 in Poland, $0,26 in France, $0,35 in UK, and, of course, $0,13 in US. Even at the highest price, namely that in Poland, those 410,27 charging stops give barely more than $12 000 a year.

If I want to have a station able to charge 2 EVs at the same time, fast charging, and counting 350 kW per charging pile (McKinsey 2018[8]), I need 700 kW it total. Investment in batteries is like $600 ÷ $800 per 1 kW (Cole & Frazier 2019[9]; Cole, Frazier, Augustine 2021[10]), thus 700 * ($600 ÷ $800) = $420 000 ÷ $560 000. There is no way that investment pays back with $12 000 a year in revenue, and I haven’t even started talking about paying off on investment in all the remaining infrastructure of Energy Ponds: ram pumps, elevated tanks, semi-artificial wetlands, and hydroelectric turbines.

Now, I revert my thinking. Investment in the range of $420 000 ÷ $560 000, in the charging station and its batteries, gives a middle-of-the-interval value of $490 000. I found a paper by Zhang et al. (2018[11]) who claim that a charging station has chances to pay off, as a business, when it sells some 5 000 000 kWh a year. When I put it back-to-back with the [50 kWh / 100 km] coefficient, it gives 10 000 000 km. Divided by the average annual distance covered by European drivers, thus by 4 558,55 km, it gives 2 193,68 customers per year, or some 6 charging stops per day. That seems hardly feasible with 9 customers. I assume that one customer would charge their electric vehicle no more than twice a week, and 6 chargings a day make 6*7 = 42 chargings, and therefore 21 customers.

I need to stop and think. Essentially, I have proven myself wrong. I had been assuming that putting a charging station for electric vehicles at the end of the internal value chain in the overall infrastructure of Energy Ponds will solve the problem of making money on selling electricity. Turns out it makes even more problems. I need time to wrap my mind around it.

[1] http://www.avere-france.org/Uploads/Documents/161011498173a9d7b7d55aef7bdda9008a7e50cb38-barometre-des-immatriculations-decembre-2020(9).pdf

[2] https://www.reuters.com/article/us-eu-autos-electric-charging-idUSKBN2C023C

[3] https://www.statista.com/statistics/955443/number-of-electric-vehicle-charging-stations-in-europe/

[4] https://elo.city/news/ile-kosztuje-ladowanie-samochodu-elektrycznego

[5] https://particulier.edf.fr/fr/accueil/guide-energie/electricite/cout-recharge-voiture-electrique.html

[6] https://afdc.energy.gov/fuels/electricity_charging_home.html

[7] https://pod-point.com/guides/driver/cost-of-charging-electric-car

[8] McKinsey Center for Future Mobility, How Battery Storage Can Help Charge the Electric-Vehicle Market?, February 2018,

[9] Cole, Wesley, and A. Will Frazier. 2019. Cost Projections for Utility-Scale Battery Storage.

Golden, CO: National Renewable Energy Laboratory. NREL/TP-6A20-73222. https://www.nrel.gov/docs/fy19osti/73222.pdf

[10] Cole, Wesley, A. Will Frazier, and Chad Augustine. 2021. Cost Projections for UtilityScale Battery Storage: 2021 Update. Golden, CO: National Renewable Energy

Laboratory. NREL/TP-6A20-79236. https://www.nrel.gov/docs/fy21osti/79236.pdf.

[11] Zhang, J., Liu, C., Yuan, R., Li, T., Li, K., Li, B., … & Jiang, Z. (2019). Design scheme for fast charging station for electric vehicles with distributed photovoltaic power generation. Global Energy Interconnection, 2(2), 150-159. https://doi.org/10.1016/j.gloei.2019.07.003