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?

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 1554 155
20094 8414 841
20105 7855 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
2008253253
2009254254
2010309309
20117 6697 669
20129 5279 527
201313 66913 669
201410 04910 049
201528 61028 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
20082727
20091212
2010123123
2011128128
2012286286
2013376376
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
200855
200955
201066
201177
201288
20134747
20145858
20157171
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

Ça semble expérimenter toujours

Je continue avec l’idée que j’avais baptisée « Projet Aqueduc ». Je suis en train de préparer un article sur ce sujet, du type « démonstration de faisabilité ». Je le prépare en anglais et je me suis dit que c’est une bonne idée de reformuler en français ce que j’ai écrit jusqu’à maintenant, l’histoire de changer l’angle intellectuel, me dégourdir un peu et prendre de la distance.

Une démonstration de faisabilité suit une logique similaire à tout autre article scientifique, sauf qu’au lieu d’explorer et vérifier une hypothèse théorique du type « les choses marchent de façon ABCD, sous conditions RTYU », j’explore et vérifie l’hypothèse qu’un concept pratique, comme celui du « Projet Aqueduc », a des fondements scientifiques suffisamment solides pour que ça vaille la peine de travailler dessus et de le tester en vie réelle. Les fondements scientifiques viennent en deux couches, en quelque sorte. La couche de base consiste à passer en revue la littérature du sujet pour voir si quelqu’un a déjà décrit des solutions similaires et là, le truc c’est explorer des différentes perspectives de similarité. Similaire ne veut pas dire identique, n’est-ce pas ? Cette revue de littérature doit apporter une structure logique – un modèle – applicable à la recherche empirique, avec des variables et des paramètres constants. C’est alors que vient la couche supérieure de démonstration de faisabilité, qui consiste à conduire de la recherche empirique proprement dite avec ce modèle.    

Moi, pour le moment, j’en suis à la couche de base. Je passe donc en revue la littérature pertinente aux solutions hydrologiques et hydroélectriques, tout en formant, progressivement, un modèle numérique du « Projet Aqueduc ». Dans cette mise à jour, je commence par une brève récapitulation du concept et j’enchaîne avec ce que j’ai réussi à trouver dans la littérature. Le concept de base du « Projet Aqueduc » consiste donc à placer dans le cours d’une rivière des pompes qui travaillent selon le principe du bélier hydraulique et qui donc utilisent l’énergie cinétique de l’eau pour pomper une partie de cette eau en dehors du lit de la rivière, vers des structures marécageuses qui ont pour fonction de retenir l’eau dans l’écosystème local. Le bélier hydraulique à la capacité de pomper à la verticale aussi bien qu’à l’horizontale et donc avant d’être retenue dans les marécages, l’eau passe par une structure similaire à un aqueduc élevé (d’où le nom du concept en français), avec des réservoirs d’égalisation de flux, et ensuite elle descend vers les marécages à travers des turbines hydroélectriques. Ces dernières produisent de l’énergie qui est ensuite emmagasinée dans une installation de stockage et de là, elle est vendue pour assurer la survie financière à la structure entière. On peut ajouter des installations éoliennes et/ou photovoltaïques pour optimiser la production de l’énergie sur le terrain occupé par la structure entière.  Vous pouvez trouver une description plus élaborée du concept dans ma mise à jour intitulée « Le Catch 22 dans ce jardin d’Eden ». La faisabilité dont je veux faire une démonstration c’est la capacité de cette structure à se financer entièrement sur la base des ventes d’électricité, comme un business régulier, donc de se développer et durer sans subventions publiques. La solution pratique que je prends en compte très sérieusement en termes de créneau de vente d’électricité est une station de chargement des véhicules électriques.   

L’approche de base que j’utilise dans la démonstration de faisabilité – donc mon modèle de base – consiste à représenter le concept en question comme une chaîne des technologies :

>> TCES – stockage d’énergie

>> TCCS – station de chargement des véhicules électriques

>> TCRP – pompage en bélier hydraulique

>> TCEW – réservoirs élevés d’égalisation

>> TCCW – acheminement et siphonage d’eau

>> TCWS – l’équipement artificiel des structures marécageuses

>> TCHE – les turbines hydroélectriques

>> TCSW – installations éoliennes et photovoltaïques     

Mon intuition de départ, que j’ai l’intention de vérifier dans ma recherche à travers la littérature, est que certaines de ces technologies sont plutôt prévisibles et bien calibrées, pendant qu’il y en a d’autres qui sont plus floues et sujettes au changement, donc moins prévisibles. Les technologies prévisibles sont une sorte d’ancrage pour the concept entier et celles plus floues sont l’objet d’expérimentation.

Je commence la revue de littérature par le contexte environnemental, donc avec l’hydrologie. Les variations au niveau de la nappe phréatiques, qui est un terme scientifique pour les eaux souterraines, semblent être le facteur numéro 1 des anomalies au niveau de rétention d’eau dans les réservoirs artificiels (Neves, Nunes, & Monteiro 2020[1]). D’autre part, même sans modélisation hydrologique détaillée, il y a des preuves empiriques substantielles que la taille des réservoirs naturels et artificiels dans les plaines fluviales, ainsi que la densité de placement de ces réservoirs et ma manière de les exploiter ont une influence majeure sur l’accès pratique à l’eau dans les écosystèmes locaux. Il semble que la taille et la densité des espaces boisés intervient comme un facteur d’égalisation dans l’influence environnementale des réservoirs (Chisola, Van der Laan, & Bristow 2020[2]). Par comparaison aux autres types de technologie, l’hydrologie semble être un peu en arrière en termes de rythme d’innovation et il semble aussi que des méthodes de gestion d’innovation appliquées ailleurs avec succès peuvent marcher pour l’hydrologie, par exemple des réseaux d’innovation ou des incubateurs des technologies (Wehn & Montalvo 2018[3]; Mvulirwenande & Wehn 2020[4]). L’hydrologie rurale et agriculturale semble être plus innovatrice que l’hydrologie urbaine, par ailleurs (Wong, Rogers & Brown 2020[5]).

Ce que je trouve assez surprenant est le manque apparent de consensus scientifique à propos de la quantité d’eau dont les sociétés humaines ont besoin. Toute évaluation à ce sujet commence avec « beaucoup et certainement trop » et à partir de là, le beaucoup et le trop deviennent plutôt flous. J’ai trouvé un seul calcul, pour le moment, chez Hogeboom (2020[6]), qui maintient que la personne moyenne dans les pays développés consomme 3800 litres d’eau par jour au total, mais c’est une estimation très holistique qui inclue la consommation indirecte à travers les biens et les services ainsi que le transport. Ce qui est consommé directement via le robinet et la chasse d’eau dans les toilettes, ça reste un mystère pour la science, apparemment, à moins que la science ne considère ce sujet comment trop terre-à-terre pour s’en occuper sérieusement.     

Il y a un créneau de recherche intéressant, que certains de ses représentants appellent « la socio-hydrologie », qui étudie les comportements collectifs vis-à-vis de l’eau et des systèmes hydrologiques et qui est basée sur l’observation empirique que lesdits comportements collectifs s’adaptent, d’une façon profonde et pernicieuse à la fois, aux conditions hydrologiques que la société en question vit avec (Kumar et al. 2020[7]). Il semble que nous nous adaptons collectivement à la consommation accrue de l’eau par une productivité croissante dans l’exploitation de nos ressources hydrologiques et le revenu moyen par tête d’habitant semble être positivement corrélé avec cette productivité (Bagstad et al. 2020[8]). Il paraît donc que l’accumulation et superposition de nombreuses technologies, caractéristique aux pays développés, contribue à utiliser l’eau de façon de plus en plus productive. Dans ce contexte, il y a une recherche intéressante conduite par Mohamed et al. (2020[9]) qui avance la thèse qu’un environnement aride est non seulement un état hydrologique mais aussi une façon de gérer les ressources hydrologiques, sur ma base des données qui sont toujours incomplètes par rapport à une situation qui change rapidement.

Il y a une question qui vient plus ou moins naturellement : dans la foulée de l’adaptation socio-hydrologique quelqu’un a-t-il présenté un concept similaire à ce que moi je présente comme « Projet Aqueduc » ? Eh bien, je n’ai rien trouvé d’identique, néanmoins il y a des idées intéressement proches. Dans l’hydrologie descriptive il y a ce concept de pseudo-réservoir, qui veut dire une structure comme les marécages ou des nappes phréatiques peu profondes qui ne retiennent pas l’eau de façons statique, comme un lac artificiel, mais qui ralentissent la circulation de l’eau dans le bassin fluvial d’une rivière suffisamment pour modifier les conditions hydrologiques dans l’écosystème (Harvey et al. 2009[10]; Phiri et al. 2021[11]). D’autre part, il y a une équipe des chercheurs australiens qui ont inventé une structure qu’ils appellent par l’acronyme STORES et dont le nom complet est « short-term off-river energy storage » (Lu et al. 2021[12]; Stocks et al. 2021[13]). STORES est une structure semi-artificielle d’accumulation par pompage, où on bâtit un réservoir artificiel au sommet d’un monticule naturel placé à une certaine distance de la rivière la plus proche et ce réservoir reçoit l’eau pompée artificiellement de la rivière. Ces chercheurs australiens avancent et donnent des preuves scientifiques pour appuyer la thèse qu’avec un peu d’astuce on peut faire fonctionner ce réservoir naturel en boucle fermée avec la rivière qui l’alimente et donc de créer un système de rétention d’eau. STORES semble être relativement le plus près de mon concept de « Projet Aqueduc » et ce qui est épatant est que moi, j’avais inventé mon idée pour l’environnement des plaines alluviales de l’Europe tandis que STORES avait été mis au point pour l’environnement aride et quasi-désertique d’Australie. Enfin, il y a l’idée des soi-disant « jardins de pluie » qui sont une technologie de rétention d’eau de pluie dans l’environnement urbain, dans des structures horticulturales, souvent placées sur les toits d’immeubles (Bortolini & Zanin 2019[14], par exemple).

Je peux conclure provisoirement que tout ce qui touche à l’hydrologie strictement dite dans le cadre du « Projet Aqueduc » est sujet aux changements plutôt imprévisible. Ce que j’ai pu déduire de la littérature ressemble à un potage bouillant sous couvercle. Il y a du potentiel pour changement technologique, il y a de la pression environnementale et sociale, mais il n’y pas encore de mécanismes institutionnels récurrents pour connecter l’un à l’autre. Les technologies TCEW (réservoirs élevés d’égalisation), TCCW (acheminement et siphonage d’eau), et TCWS (l’équipement artificiel des structures marécageuses) démontrant donc un avenir flou, je passe à la technologie TCRP de pompage en bélier hydraulique. J’ai trouvé deux articles chinois, qui se suivent chronologiquement et qui semblent par ailleurs avoir été écrits par la même équipe de chercheurs : Guo et al. (2018[15]), and Li et al. (2021[16]). Ils montrent la technologie du bélier hydraulique sous un angle intéressant. D’une part, les Chinois semblent avoir donné du vrai élan à l’innovation dans ce domaine spécifique, tout au moins beaucoup plus d’élan que j’ai pu observer en Europe. D’autre part, les estimations de la hauteur effective à laquelle l’eau peut être pompée avec les béliers hydrauliques dernier cri sont respectivement de 50 mètres dans l’article de 2018 et 30 mètres dans celui de 2021. Vu que les deux articles semblent être le fruit du même projet, il y a eu comme une fascination suivie par une correction vers le bas. Quoi qu’il en soit, même l’estimation plus conservative de 30 mètres c’est nettement mieux que les 20 mètres que j’assumais jusqu’à maintenant.

Cette élévation relative possible à atteindre avec la technologie du bélier hydraulique est importante pour la technologie suivante de ma chaîne, donc celle des petites turbines hydroélectriques, la TCHE. L’élévation relative de l’eau et le flux par seconde sont les deux paramètres clés qui déterminent la puissance électrique produite (Cai, Ye & Gholinia 2020[17]) et il se trouve que dans le « Projet Aqueduc », avec l’élévation et le flux largement contrôlés à travers la technologie du bélier hydraulique, les turbines deviennent un peu moins dépendantes sur les conditions naturelles.

J’ai trouvé une revue merveilleusement encyclopédique des paramètres pertinents aux petites turbines hydroélectriques chez Hatata, El-Saadawi, & Saad (2019[18]). La puissance électrique se calcule donc comme : Puissance = densité de l’eau (1000 kg/m3) * constante d’accélération gravitationnelle (9,8 m/s2) * élévation nette (mètres) * Q (flux par seconde m3/s).

L’investissement initial en de telles installations se calcule par unité de puissance, donc sur la base de 1 kilowatt et se divise en 6 catégories : la construction de la prise d’eau, la centrale électrique strictement dite, les turbines, le générateur, l’équipement auxiliaire, le transformateur et enfin le poste extérieur. Je me dis par ailleurs que – vu la structure du « Projet Aqueduc » – l’investissement en la construction de prise d’eau est en quelque sorte équivalent au système des béliers hydrauliques et réservoirs élevés. En tout cas :

>> la construction de la prise d’eau, par 1 kW de puissance  ($) 186,216 * Puissance-0,2368 * Élévation -0,597

>> la centrale électrique strictement dite, par 1 kW de puissance  ($) 1389,16 * Puissance-0,2351 * Élévation-0,0585

>> les turbines, par 1 kW de puissance  ($)

@ la turbine Kaplan: 39398 * Puissance-0,58338 * Élévation-0,113901

@ la turbine Frances: 30462 * Puissance-0,560135 * Élévation-0,127243

@ la turbine à impulsions radiales: 10486,65 * Puissance-0,3644725 * Élévation-0,281735

@ la turbine Pelton: 2 * la turbine à impulsions radiales

>> le générateur, par 1 kW de puissance  ($) 1179,86 * Puissance-0,1855 * Élévation-0,2083

>> l’équipement auxiliaire, par 1 kW de puissance  ($) 612,87 * Puissance-0,1892 * Élévation-0,2118

>> le transformateur et le poste extérieur, par 1 kW de puissance 

($) 281 * Puissance0,1803 * Élévation-0,2075

Une fois la puissance électrique calculée avec le paramètre d’élévation relative assurée par les béliers hydrauliques, je peux calculer l’investissement initial en hydro-génération comme la somme des positions mentionnées ci-dessus. Hatata, El-Saadawi, & Saad (2019 op. cit.) recommandent aussi de multiplier une telle somme par le facteur de 1,13 (c’est donc un facteur du type « on ne sait jamais ») et d’assumer que les frais courants d’exploitation annuelle vont se situer entre 1% et 6% de l’investissement initial.

Syahputra & Soesanti (2021[19]) étudient le cas de la rivière Progo, dotée d’un flux tout à fait modeste de 6,696 mètres cubes par seconde et située dans Kulon Progo Regency (une region spéciale au sein de Yogyakarta, Indonesia). Le système des petites turbines hydroélectriques y fournit l’électricité aux 962 ménages locaux, et crée un surplus de 4 263 951 kWh par an d’énergie à revendre aux consommateurs externes. Dans un autre article, Sterl et al. (2020[20]) étudient le cas de Suriname et avancent une thèse intéressante, notamment que le développement d’installations basées sur les énergies renouvelables crée un phénomène d’appétit d’énergie qui croît à mesure de manger et qu’un tel développement en une source d’énergie – le vent, par exemple – stimule l’investissement en installations basées sur d’autres sources, donc l’hydraulique et le photovoltaïque.  

Ces études relativement récentes corroborent celles d’il y a quelques années, comme celle de Vilanova & Balestieri (2014[21]) ou bien celle de Vieira et al. (2015[22]), avec une conclusion générale que les petites turbines hydroélectriques ont atteint un degré de sophistication technologique suffisante pour dégager une quantité d’énergie économiquement profitable. Par ailleurs, il semble qu’il y a beaucoup à gagner dans ce domaine à travers l’optimisation de la distribution de puissance entre les turbines différentes. De retour aux publications les plus récentes, j’ai trouvé des études de faisabilité tout à fait robustes pour les petites turbines hydroélectriques, qui indiquent que – pourvu qu’on soit prêt à accepter un retour d’environ 10 à 11 ans sur l’investissement initial – le petit hydro peut être exploité profitablement même avec une élévation relative en dessous de 20 mètres (Arthur et al. 2020[23] ; Ali et al. 2021[24]).

C’est ainsi que j’arrive donc à la portion finale dans la chaîne technologique du « Projet Aqueduc », donc au stockage d’énergie (TCES) ainsi que TCCS ou la station de chargement des véhicules électriques. La puissance à installer dans une station de chargement semble se situer entre 700 et 1000 kilowatts (Zhang et al. 2018[25]; McKinsey 2018[26]). En dessous de 700 kilowatt la station peut devenir si difficile à accéder pour le consommateur moyen, due aux files d’attente, qu’elle peut perdre la confiance des clients locaux. En revanche, tout ce qui va au-dessus de 1000 kilowatts est vraiment utile seulement aux heures de pointe dans des environnements urbains denses. Il y a des études de concept pour les stations de chargement où l’unité de stockage d’énergie est alimentée à partir des sources renouvelables (Al Wahedi & Bicer 2020[27]). Zhang et al. (2019[28]) présentent un concept d’entreprise tout fait pour une station de chargement située dans le milieu urbain. Apparemment, le seuil de profitabilité se situe aux environs de 5 100 000 kilowatt heures vendues par an.  

En termes de technologie de stockage strictement dite, les batteries Li-ion semblent être la solution de base pour maintenant, quoi qu’une combinaison avec les piles à combustible ou bien avec l’hydrogène semble prometteuse (Al Wahedi & Bicer 2020 op. cit. ; Sharma, Panvar & Tripati 2020[29]). En général, pour le moment, les batteries Li-Ion montrent le rythme d’innovation relativement le plus soutenu (Tomaszewska et al. 2019[30] ; de Simone & Piegari 2019[31]; Koohi-Fayegh & Rosen 2020[32]). Un article récent par Elmeligy et al. (2021[33]) présente un concept intéressant d’unité mobile de stockage qui pourrait se déplacer entre plusieurs stations de chargement. Quant à l’investissement initial requis pour une station de chargement, ça semble expérimenter toujours mais la marge de manœuvre se rétrécit pour tomber quelque part entre $600 ÷ $800 par 1 kW de puissance (Cole & Frazier 2019[34]; Cole, Frazier, Augustine 2021[35]).


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[10] 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.

[11] 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.

[12] 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

[13] 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

[14] Bortolini, L., & Zanin, G. (2019). Reprint of: Hydrological behaviour of rain gardens and plant suitability: A study in the Veneto plain (north-eastern Italy) conditions. Urban forestry & urban greening, 37, 74-86. https://doi.org/10.1016/j.ufug.2018.07.003

[15] Guo, X., Li, J., Yang, K., Fu, H., Wang, T., Guo, Y., … & Huang, W. (2018). Optimal design and performance analysis of hydraulic ram pump system. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Puissance and Energy, 232(7), 841-855. https://doi.org/10.1177%2F0957650918756761

[16] Li, J., Yang, K., Guo, X., Huang, W., Wang, T., Guo, Y., & Fu, H. (2021). Structural design and parameter optimization on a waste valve for hydraulic ram pumps. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Puissance and Energy, 235(4), 747–765. https://doi.org/10.1177/0957650920967489

[17] Cai, X., Ye, F., & Gholinia, F. (2020). Application of artificial neural network and Soil and Water Assessment Tools in evaluating Puissance generation of small hydroPuissance stations. Energy Reports, 6, 2106-2118. https://doi.org/10.1016/j.egyr.2020.08.010.

[18] Hatata, A. Y., El-Saadawi, M. M., & Saad, S. (2019). A feasibility study of small hydro Puissance for selected locations in Egypt. Energy Strategy Reviews, 24, 300-313. https://doi.org/10.1016/j.esr.2019.04.013

[19] Syahputra, R., & Soesanti, I. (2021). Renewable energy systems based on micro-hydro and solar photovoltaic for rural areas: A case study in Yogyakarta, Indonesia. Energy Reports, 7, 472-490. https://doi.org/10.1016/j.egyr.2021.01.015

[20] Sterl, S., Donk, P., Willems, P., & Thiery, W. (2020). Turbines of the Caribbean: Decarbonising Suriname’s electricity mix through hydro-supported integration of wind Puissance. Renewable and Sustainable Energy Reviews, 134, 110352. https://doi.org/10.1016/j.rser.2020.110352

[21] Vilanova, M. R. N., & Balestieri, J. A. P. (2014). HydroPuissance recovery in water supply systems: Models and case study. Energy conversion and management, 84, 414-426. https://doi.org/10.1016/j.enconman.2014.04.057

[22] Vieira, D. A. G., Guedes, L. S. M., Lisboa, A. C., & Saldanha, R. R. (2015). Formulations for hydroelectric energy production with optimality conditions. Energy Conversion and Management, 89, 781-788. https://doi.org/10.1016/j.enconman.2014.10.048

[23] Arthur, E., Anyemedu, F. O. K., Gyamfi, C., Asantewaa-Tannor, P., Adjei, K. A., Anornu, G. K., & Odai, S. N. (2020). Potential for small hydroPuissance development in the Lower Pra River Basin, Ghana. Journal of Hydrology: Regional Studies, 32, 100757. https://doi.org/10.1016/j.ejrh.2020.100757

[24] Ali, M., Wazir, R., Imran, K., Ullah, K., Janjua, A. K., Ulasyar, A., … & Guerrero, J. M. (2021). Techno-economic assessment and sustainability impact of hybrid energy systems in Gilgit-Baltistan, Pakistan. Energy Reports, 7, 2546-2562. https://doi.org/10.1016/j.egyr.2021.04.036

[25] Zhang, Y., He, Y., Wang, X., Wang, Y., Fang, C., Xue, H., & Fang, C. (2018). Modeling of fast charging station equipped with energy storage. Global Energy Interconnection, 1(2), 145-152. DOI:10.14171/j.2096-5117.gei.2018.02.006

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

[27] Al Wahedi, A., & Bicer, Y. (2020). Development of an off-grid electrical vehicle charging station hybridized with renewables including battery cooling system and multiple energy storage units. Energy Reports, 6, 2006-2021. https://doi.org/10.1016/j.egyr.2020.07.022

[28] 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

[29] Sharma, S., Panwar, A. K., & Tripathi, M. M. (2020). Storage technologies for electric vehicles. Journal of traffic and transportation engineering (english edition), 7(3), 340-361. https://doi.org/10.1016/j.jtte.2020.04.004

[30] Tomaszewska, A., Chu, Z., Feng, X., O’Kane, S., Liu, X., Chen, J., … & Wu, B. (2019). Lithium-ion battery fast charging: A review. ETransportation, 1, 100011. https://doi.org/10.1016/j.etran.2019.100011

[31] De Simone, D., & Piegari, L. (2019). Integration of stationary batteries for fast charge EV charging stations. Energies, 12(24), 4638. https://doi.org/10.3390/en12244638

[32] Koohi-Fayegh, S., & Rosen, M. A. (2020). A review of energy storage types, applications and recent developments. Journal of Energy Storage, 27, 101047. https://doi.org/10.1016/j.est.2019.101047

[33] 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

[34] 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

[35] 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.

We keep going until we observe

I keep working on a proof-of-concept paper for my idea of ‘Energy Ponds’. In my last two updates, namely in ‘Seasonal lakes’, and in ‘Le Catch 22 dans ce jardin d’Eden’, I sort of refreshed my ideas and set the canvas for painting. Now, I start sketching. What exact concept do I want to prove, and what kind of evidence can possibly confirm (or discard) that concept? The idea I am working on has a few different layers. The most general vision is that of purposefully storing water in spongy structures akin to swamps or wetlands. These can bear various degree of artificial construction, and can stretch from natural wetlands, through semi-artificial ones, all the way to urban technologies such as rain gardens and sponge cities. The most general proof corresponding to that vision is a review of publicly available research – peer-reviewed papers, preprints, databases etc. – on that general topic.

Against that general landscape, I sketch two more specific concepts: the idea of using ram pumps as a technology of forced water retention, and the possibility of locating those wetland structures in the broadly spoken Northern Europe, thus my home region. Correspondingly, I need to provide two streams of scientific proof: a review of literature on the technology of ram pumping, on the one hand, and on the actual natural conditions, as well as land management policies in Europe, on the other hand.  I need to consider the environmental impact of creating new wetland-like structures in Northern Europe, as well as the socio-economic impact, and legal feasibility of conducting such projects.

Next, I sort of build upwards. I hypothesise a complex technology, where ram-pumped water from the river goes into a sort of light elevated tanks, and from there, using the principle of Roman siphon, cascades down into wetlands, and through a series of small hydro-electric turbines. Turbines generate electricity, which is being stored and then sold outside.

At that point, I have a technology of water retention coupled with a technology of energy generation and storage. I further advance a second hypothesis that such a complex technology will be economically sustainable based on the corresponding sales of electricity. In other words, I want to figure out a configuration of that technology, which will be suitable for communities which either don’t care at all, or simply cannot afford to care about the positive environmental impact of the solution proposed.

Proof of concept for those two hypotheses is going to be complex. First, I need to pass in review the available technologies for energy storage, energy generation, as well as for the construction of elevated tanks and Roman siphons. I need to take into account various technological mixes, including the incorporation of wind turbines and photovoltaic installation into the whole thing, in order to optimize the output of energy. I will try to look for documented examples of small hydro-generation coupled with wind and solar. Then, I have to rack the literature as regards mathematical models for the optimization of such power systems and put them against my own idea of reverse engineering back from the storage technology. I take the technology of energy storage which seems the most suitable for the local market of energy, and for the hypothetical charging from hydro-wind-solar mixed generation. I build a control scenario where that storage facility just buys energy at wholesale prices from the power grid and then resells it. Next, I configure the hydro-wind-solar generation so as to make it economically competitive against the supply of energy from the power grid.

Now, I sketch. I keep in mind the levels of conceptualization outlined above, and I quickly move through published science along that logical path, quickly picking a few articles for each topic. I am going to put those nonchalantly collected pieces of science back-to-back and see how and whether at all it all makes sense together. I start with Bortolini & Zanin (2019[1]), who study the impact of rain gardens on water management in cities of the Veneto region in Italy. Rain gardens are vegetal structures, set up in the urban environment, with the specific purpose to retain rainwater.  Bortolini & Zanin (2019 op. cit.) use a simplified water balance, where the rain garden absorbs and retains a volume ‘I’ of water (‘I’ stands for infiltration), which is the difference between precipitations on the one hand, and the sum total of overflowing runoff from the rain garden plus evapotranspiration of water, on the other hand. Soil and plants in the rain garden have a given top capacity to retain water. Green plants typically hold 80 – 95% of their mass in water, whilst trees hold about 50%. Soil is considered wet when it contains about 25% of water. The rain garden absorbs water from precipitations at a rate determined by hydraulic conductivity, which means the relative ease of a fluid (usually water) to move through pore spaces or fractures, and which depends on the intrinsic permeability of the material, the degree of saturation, and on the density and viscosity of the fluid.

As I look at it, I can see that the actual capacity of water retention in a rain garden can hardly be determined a priori, unless we have really a lot of empirical data from the given location. For a new location of a new rain garden, it is safe to assume that we need an experimental phase when we empirically assess the retentive capacity of the rain garden with different configurations of soil and vegetation used. That leads me to generalizing that any porous structure we use for retaining rainwater, would it be something like wetlands, or something like a rain garden in urban environment, has a natural constraint of hydraulic conductivity, and that constraint determines the percentage of precipitations, and the metric volume thereof, which the given structure can retain.

Bortolini & Zanin (2019 op. cit.) bring forth empirical results which suggest that properly designed rain gardens located on rooftops in a city can absorb from 87% to 93% of the total input of water they receive. Cool. I move on and towards the issue of water management in Europe, with a working paper by Fribourg-Blanc, B. (2018[2]), and the most important takeaway from that paper is that we have something called European Platform for Natural Water Retention Measures AKA http://nwrm.eu , and that thing have both good properties and bad properties. The good thing about http://nwrm.eu is that it contains loads of data and publications about projects in Natural Water Retention in Europe. The bad thing is that http://nwrm.eu is not a secure website. Another paper, by Tóth et al. (2017[3]) tells me that another analytical tool exists, namely the European Soil Hydraulic Database (EU‐ SoilHydroGrids ver1.0).

So far, so good. I already know there is data and science for evaluating, with acceptable precision, the optimal structure and the capacity for water retention in porous structures such as rain gardens or wetlands, in the European context. I move to the technology of ram pumps. I grab two papers: Guo et al. (2018[4]), and Li et al. (2021[5]). They show me two important things. Firstly, China seems to be burning the rubber in the field of ram pumping technology. Secondly, the greatest uncertainty as for that technology seems to be the actual height those ram pumps can elevate water at, or, when coupled with hydropower, the hydraulic head which ram pumps can create. Guo et al. (2018 op. cit.) claim that 50 meters of elevation is the maximum which is both feasible and efficient. Li et al. (2021 op. cit.) are sort of vertically more conservative and claim that the whole thing should be kept below 30 meters of elevation. Both are better than 20 meters, which is what I thought was the best one can expect. Greater elevation of water means greater hydraulic head, and more hydropower to be generated. It pays off to review literature.

Lots of uncertainty as for the actual capacity and efficiency of ram pumping means quick technological change in that domain. This is economically interesting. It means that investing in projects which involve ram pumping means investment in quickly changing a technology. That means both high hopes for an even better technology in immediate future, and high needs for cash in the balance sheet of the entities involved.

I move to the end-of-the-pipeline technology in my concept, namely to energy storage. I study a paper by Koohi-Fayegh & Rosen (2020[6]), which suggests two things. Firstly, for a standalone installation in renewable energy, whatever combination of small hydropower, photovoltaic and small wind turbines we think of, lithium-ion batteries are always a good idea for power storage, Secondly, when we work with hydrogeneration, thus when we have any hydraulic head to make electricity with, pumped storage comes sort of natural. That leads me to an idea which looks even crazier than what I have imagined so far: what if we create an elevated garden with strong capacity for water retention. Ram pumps take water from the river and pump it up onto elevated platforms with rain gardens on it. Those platforms can be optimized as for their absorption of sunlight and thus as regards their interaction with whatever is underneath them.  

I move to small hydro, and I find two papers, namely Couto & Olden (2018[7]), and Lange et al. (2018[8]), which are both interestingly critical as regards small hydropower installations. Lange et al. (2018 op. cit.) claim that the overall environmental impact of small hydro should be closely monitored. Couto & Olden (2018 op. cit.) go further and claim there is a ‘craze’ about small hydro, and that craze has already lead to overinvestment in the corresponding installations, which can be damaging both environmentally and economically (overinvestment means financial collapse of many projects). Those critical views in mind, I turn to another paper, by Zhou et al. (2019[9]), who approach the issue as a case for optimization, within a broader framework called ‘Water-Food-Energy’ Nexus, WFE for closer friends. This paper, just as a few others it cites (Ming et al. 2018[10]; Uen et al. 2018[11]), advocates for using artificial intelligence in order to optimize for WFE.

Zhou et al. (2019 op.cit.) set three hydrological scenarios for empirical research and simulation. The baseline scenario corresponds to an average hydrological year, with average water levels and average precipitations. Next to it are: a dry year and a wet year. The authors assume that the cost of installation in small hydropower is $600 per kW on average.  They simulate the use of two technologies for hydro-electric turbines: Pelton and Vortex. Pelton turbines are optimized paddled wheels, essentially, whilst the Vortex technology consists in creating, precisely, a vortex of water, and that vortex moves a rotor placed in the middle of it.

Zhou et al. (2019 op.cit.) create a multi-objective function to optimize, with the following desired outcomes:

>> Objective 1: maximize the reliability of water supply by minimizing the probability of real water shortage occurring.

>> Objective 2: maximize water storage given the capacity of the reservoir. Note: reservoir is understood hydrologically, as any structure, natural or artificial, able to retain water.

>> Objective 3: maximize the average annual output of small hydro-electric turbines

Those objectives are being achieved under the corresponding sets of constraints. For water supply those constraints all turn around water balance, whilst for energy output it is more about the engineering properties of the technologies taken into account. The three objectives are hierarchized. First, Zhou et al. (2019 op.cit.) perform an optimization regarding Objectives 1 and 2, thus in order to find the optimal hydrological characteristics to meet, and then, on the basis of these, they optimize the technology to put in place, as regards power output.

The general tool for optimization used by Zhou et al. (2019 op.cit.) is a genetic algorithm called NSGA-II, AKA Non-dominated Sorting Genetic Algorithm. Apparently, NSGA-II has a long and successful history of good track in engineering, including water management and energy (see e.g. Chang et al. 2016[12]; Jain & Sachdeva 2017[13];  Assaf & Shabani 2018[14]). I want to stop for a while here and have a good look at this specific algorithm. The logic of NSGA-II starts with creating an initial population of cases/situations/configurations etc. Each case is a combination of observations as regards the objectives to meet, and the actual values observed in constraining variables, e.g. precipitations for water balance or hydraulic head for the output of hydropower. In the conventional lingo of this algorithm, those cases are called chromosomes. Yes, I know, a hydro-electric turbine placed in the context of water management hardly looks like a chromosome, but it is a genetic algorithm, and it just sounds fancy to use that biologically marked vocabulary.

As for me, I like staying close to real life, and therefore I call those cases solutions rather than chromosomes. Anyway, the underlying math is the same. Once I have that initial population of real-life solutions, I calculate two parameters for each of them: their rank as regards the objectives to maximize, and their so-called ‘crowded distance’. Ranking is done with the procedure of fast non-dominated sorting. It is a comparison in pairs, where the solution A dominates another solution B, if and only if there is no objective of A worse than that objective of B and there is at least one objective of A better than that objective of B. The solution which scores the most wins in such peer-to-peer comparisons is at the top of the ranking, the one with the second score of wins is the second etc. Crowding distance is essentially the same as what I call coefficient of coherence in my own research: Euclidean distance (or other mathematical distance) is calculated for each pair of solutions. As a result, each solution is associated with k Euclidean distances to the k remaining solutions, which can be reduced to an average distance, i.e. the crowded distance.

In the next step, an off-spring population is produced from that original population of solutions. It is created by taking relatively the fittest solutions from the initial population, recombining their characteristics in a 50/50 proportion, and adding them some capacity for endogenous mutation. Two out of these three genetic functions are de facto controlled. We choose relatively the fittest by establishing some kind of threshold for fitness, as regards the objectives pursued. It can be a required minimum, a quantile (e.g. the third quartile), or an average. In the first case, we arbitrarily impose a scale of fitness on our population, whilst in the latter two the hierarchy of fitness is generated endogenously from the population of solutions observed. Fitness can have shades and grades, by weighing the score in non-dominated sorting, thus the number of wins over other solutions, on the one hand, and the crowded distance on the other hand. In other words, we can go for solutions which have a lot of similar ones in the population (i.e. which have a low average crowded distance), or, conversely, we can privilege lone wolves, with a high average Euclidean distance from anything else on the plate.  

The capacity for endogenous mutation means that we can allow variance in all or in just the selected variables which make each solution. The number of degrees of freedom we allow in each variable dictates the number of mutations that can be created. Once again, discreet power is given to the analyst: we can choose the genetic traits which can mutate and we can determine their freedom to mutate. In an engineering problem, technological and environmental constraints should normally put a cap on the capacity for mutation. Still, we can think about an algorithm which definitely kicks the lid off the barrel of reality, and which generates mutations in the wildest registers of variables considered. It is a way to simulate a process when the presence of strong outliers has a strong impact on the whole population.

The same discreet cap on the freedom to evolve is to be found when we repeat the process. The offspring generation of solutions goes essentially through the same process as the initial one, to produce further offspring: ranking by non-dominated sorting and crowded distance, selection of the fittest, recombination, and endogenous mutation. At the starting point of this process, we can be two alternative versions of the Mother Nature. We can be a mean Mother Nature, and we shave off from the offspring population all those baby-solutions which do not meet the initial constraints, e.g. zero supply of water in this specific case. On the other hand, we can be even meaner a Mother Nature and allow those strange, dysfunctional mutants to keep going and see what happens to the whole species after a few rounds of genetic reproduction.

With each generation, we compute an average crowded distance between all the solutions created, i.e. we check how diverse is the species in this generation. As long as diversity grows or remains constant, we assume that the divergence between the solutions generated grows or stays the same. Similarly, we can compute an even more general crowded distance between each pair of generations, and therefore to assess how far has the current generation gone from the parent one. We keep going until we observe that the intra-generational crowded distance and the inter-generational one start narrowing down asymptotically to zero. In other words, we consider resuming evolution when solutions in the game become highly similar to each other and when genetic change stops bringing significant functional change.

Cool. When I want to optimize my concept of Energy Ponds, I need to add the objective of constrained return on investment, based on the sales of electricity. In comparison to Zhou et al. (2019 op.cit.), I need to add a third level of selection. I start with selecting environmentally the solutions which make sense in terms of water management. In the next step, I produce a range of solutions which assure the greatest output of power, in a possible mix with solar and wind. Then I take those and filter them through the NSGA-II procedure as regards their capacity to sustain themselves financially. Mind you, I can shake it off a bit by fusing together those levels of selection. I can simulate extreme cases, when, for example, good economic sustainability becomes an environmental problem. Still, it would be rather theoretical. In Europe, non-compliance with environmental requirements makes a project a non-starter per se: you just can get the necessary permits if your hydropower project messes with hydrological constraints legally imposed on the given location.     

Cool. It all starts making sense. There is apparently a lot of stir in the technology of making semi-artificial structures for retaining water, such as rain gardens and wetlands. That means a lot of experimentation, and that experimentation can be guided and optimized by testing the fitness of alternative solutions for meeting objectives of water management, power output and economic sustainability. I have some starting data, to produce the initial generation of solutions, and then try to optimize them with an algorithm such as NSGA-II.


[1] Bortolini, L., & Zanin, G. (2019). Reprint of: Hydrological behaviour of rain gardens and plant suitability: A study in the Veneto plain (north-eastern Italy) conditions. Urban forestry & urban greening, 37, 74-86. https://doi.org/10.1016/j.ufug.2018.07.003

[2] Fribourg-Blanc, B. (2018, April). Natural Water Retention Measures (NWRM), a tool to manage hydrological issues in Europe?. In EGU General Assembly Conference Abstracts (p. 19043). https://ui.adsabs.harvard.edu/abs/2018EGUGA..2019043F/abstract

[3] Tóth, B., Weynants, M., Pásztor, L., & Hengl, T. (2017). 3D soil hydraulic database of Europe at 250 m resolution. Hydrological Processes, 31(14), 2662-2666. https://onlinelibrary.wiley.com/doi/pdf/10.1002/hyp.11203

[4] Guo, X., Li, J., Yang, K., Fu, H., Wang, T., Guo, Y., … & Huang, W. (2018). Optimal design and performance analysis of hydraulic ram pump system. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 232(7), 841-855. https://doi.org/10.1177%2F0957650918756761

[5] Li, J., Yang, K., Guo, X., Huang, W., Wang, T., Guo, Y., & Fu, H. (2021). Structural design and parameter optimization on a waste valve for hydraulic ram pumps. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 235(4), 747–765. https://doi.org/10.1177/0957650920967489

[6] Koohi-Fayegh, S., & Rosen, M. A. (2020). A review of energy storage types, applications and recent developments. Journal of Energy Storage, 27, 101047. https://doi.org/10.1016/j.est.2019.101047

[7] Couto, T. B., & Olden, J. D. (2018). Global proliferation of small hydropower plants–science and policy. Frontiers in Ecology and the Environment, 16(2), 91-100. https://doi.org/10.1002/fee.1746

[8] Lange, K., Meier, P., Trautwein, C., Schmid, M., Robinson, C. T., Weber, C., & Brodersen, J. (2018). Basin‐scale effects of small hydropower on biodiversity dynamics. Frontiers in Ecology and the Environment, 16(7), 397-404.  https://doi.org/10.1002/fee.1823

[9] 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

[10] Ming, B., Liu, P., Cheng, L., Zhou, Y., & Wang, X. (2018). Optimal daily generation scheduling of large hydro–photovoltaic hybrid power plants. Energy Conversion and Management, 171, 528-540. https://doi.org/10.1016/j.enconman.2018.06.001

[11] Uen, T. S., Chang, F. J., Zhou, Y., & Tsai, W. P. (2018). Exploring synergistic benefits of Water-Food-Energy Nexus through multi-objective reservoir optimization schemes. Science of the Total Environment, 633, 341-351. https://doi.org/10.1016/j.scitotenv.2018.03.172

[12] 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

[13] 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

[14] 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

Seasonal lakes

Once again, been a while since I last blogged. What do you want, I am having a busy summer. Putting order in my own chaos, and, over the top of that, putting order in other people’s chaos, this is all quite demanding in terms of time and energy. What? Without trying to put order in chaos, that chaos might take less time and energy? Well, yes, but order look tidier than chaos.

I am returning to the technological concept which I labelled ‘Energy Ponds’ (or ‘projet Aqueduc’ in French >> see: Le Catch 22 dans ce jardin d’Eden). You can find a description of that concept onder the hyperlinked titles provided. I am focusing on refining my repertoire of skills in scientific validation of technological concepts. I am passing in review some recent literature, and I am trying to find good narrative practices in that domain.

The general background of ‘Energy Ponds’ consists in natural phenomena observable in Europe as the climate change progresses, namely: a) long-term shift in the structure of precipitations, from snow to rain b) increasing occurrence of floods and droughts c) spontaneous reemergence of wetlands. All these phenomena have one common denominator: increasingly volatile flow per second in rivers. The essential idea of Energy Ponds is to ‘financialize’ that volatile flow, so to say, i.e. to capture its local surpluses, store them for later, and use the very mechanism of storage itself as a source of economic value.

When water flows downstream, in a river, its retention can be approached as the opportunity for the same water to loop many times over the same specific portion of the collecting basin (of the river). Once such a loop is created, we can extend the average time that a liter of water spends in the whereabouts. Ram pumps, connected to storage structures akin to swamps, can give such an opportunity. A ram pump uses the kinetic energy of flowing water in order to pump some of that flow up and away from its mainstream. Ram pumps allow forcing a process, which we now as otherwise natural. Rivers, especially in geological plains, where they flow relatively slowly, tend to build, with time, multiple ramifications. Those branchings can be directly observable at the surface, as meanders, floodplains or seasonal lakes, but much of them is underground, as pockets of groundwater. In this respect, it is useful to keep in mind that mechanically, rivers are the drainpipes of rainwater from their respective basins. Another basic hydrological fact, useful to remember in the context of the Energy Ponds concept, is that strictly speaking retention of rainwater – i.e. a complete halt in its circulation through the collecting basin of the river – is rarely possible, and just as rarely it is a sensible idea to implement. Retention means rather a slowdown to the flow of rainwater through the collecting basin into the river.

One of the ways that water can be slowed down consists in making it loop many times over the same section of the river. Let’s imagine a simple looping sequence: water from the river is being ram-pumped up and away into retentive structures akin to swamps, i.e. moderately deep spongy structures underground, with high capacity for retention, covered with a superficial layer of shallow-rooted vegetation. With time, as the swamp fills with water, the surplus is evacuated back into the river, by a system of canals. Water stored in the swamp will be ultimately evacuated, too, minus evaporation, it will just happen much more slowly, by the intermediary of groundwaters. In order to illustrate the concept mathematically, let’ s suppose that we have water in the river flowing at the pace of, e.g. 45 m3 per second. We make it loop once via ram pumps and retentive swamps, and, if as a result of that looping, the speed of the flow is sliced by 3. On the long run we slow down the way that the river works as the local drainpipe: we slow it from 43 m3 per second down to [43/3 = 14,33…] m3 per second.  As water from the river flows slower overall, it can yield more environmental services: each cubic meter of water has more time to ‘work’ in the ecosystem.  

When I think of it, any human social structure, such as settlements, industries, infrastructures etc., needs to stay in balance with natural environment. That balance is to be understood broadly, as the capacity to stay, for a satisfactorily long time, within a ‘safety zone’, where the ecosystem simply doesn’t kill us. That view has little to do with the moral concepts of environment-friendliness or sustainability. As a matter of fact, most known human social structures sooner or later fall out of balance with the ecosystem, and this is how civilizations collapse. Thus, here comes the first important assumption: any human social structure is, at some level, an environmental project. The incumbent social structures, possible to consider as relatively stable, are environmental projects which have simply hold in place long enough to grow social institutions, and those institutions allow further seeking of environmental balance.

I am starting my review of literature with an article by Phiri et al. (2021[1]), where the authors present a model for assessing the way that alluvial floodplains behave. I chose this one because my concept of Energy Ponds is supposed to work precisely in alluvial floodplains, i.e. in places where we have: a) a big river b) a lot of volatility in the amount of water in that river, and, as a consequence, we have (c) an alternation of floods and droughts. Normal stuff where I come from, i.e. in Northern Europe. Phiri et al. use the general model, acronymically called SWAT, which comes from ‘Soil and Water Assessment Tool’ (see also: Arnold et al. 1998[2]; Neitsch et al. 2005[3]), and with that general tool, they study the concept of pseudo-reservoirs in alluvial plains. In short, a pseudo-reservoir is a hydrological structure which works like a reservoir but does not necessarily look like one. In that sense, wetlands in floodplains can work as reservoirs of water, even if from the hydrological point of view they are rather extensions of the main river channel (Harvey et al. 2009[4]).

Analytically, the SWAT model defines the way a reservoir works with the following equation: V = Vstored + Vflowin − Vflowout + Vpcp − Vevap − Vseep . People can rightly argue that it is a good thing to know what symbols mean in an equation, and therefore V stands for the volume of water in reservoir at the end of the day, Vstored corresponds to the amount of water stored at the beginning of the day, Vflowin means the quantity of water entering reservoir during the day, Vflowout is the metric outflow of water during the day, Vpcp is volume of precipitation falling on the water body during the day, Vevap is volume of water removed from the water body by evaporation during the day, Vseep is volume of water lost from the water body by seepage.

This is a good thing to know, as well, once we have a nice equation, what the hell are we supposed to do with it in real life. Well, the SWAT model has even its fan page (http://www.swatusers.com ), and, as Phiri et al. phrase it out, it seems that the best practical use is to control the so-called ‘target release’, i.e. the quantity of water released at a given point in space and time, designated as Vtarg. The target release is mostly used as a control metric for preventing or alleviating floods, and with that purpose in mind, two decision rules are formulated. During the non-flood season, no reservation for flood is needed, and target storage is set at emergency spillway volume. In other words, in the absence of imminent flood, we can keep the reservoir full. On the other hand, when the flood season is on, flood control reservation is a function of soil water content. This is set to maximum and 50 % of maximum for wet and dry grounds, respectively. In the context of the V = Vstored + Vflowin − Vflowout + Vpcp − Vevap − Vseep equation, Vtarg is a specific value (or interval of values) in the Vflowout component.

As I am wrapping my mind around those conditions, I am thinking about the opposite application, i.e. about preventing and alleviating droughts. Drought is recognizable by exceptionally low values in the amount of water stored at the end of the given period, thus in the basic V, in the presence of low precipitation, thus low Vpcp, and high evaporation, which corresponds to high Vevap. More generally, both floods and droughts occur when – or rather after – in a given Vflowin − Vflowout balance, precipitation and evaporation take one turn or another.

I feel like moving those exogenous meteorological factors on one side of the equation, which goes like  – Vpcp + Vevap =  – V + Vstored + Vflowin − Vflowout − Vseep and doesn’t make much sense, as there are not really many cases of negative precipitation. I need to switch signs, and then it is more presentable, as Vpcp – VevapV – Vstored – Vflowin + Vflowout + Vseep . Weeell, almost makes sense. I guess that Vflowin is sort of exogenous, too. The inflow of water into the basin of the river comes from a melting glacier, from another river, from an upstream section of the same river etc. I reframe: Vpcp – Vevap + Vflowin V – Vstored + Vflowout + Vseep  . Now, it makes sense. Precipitations plus the inflow of water through the main channel of the river, minus evaporation, all that stuff creates a residual quantity of water. That residual quantity seeps into the groundwaters (Vseep), flows out (Vflowout), and stays in the reservoir-like structure at the end of the day (V – Vstored).

I am having a look at how Phiri et al. (2021 op. cit.) phrase out their model of pseudo-reservoir. The output value they peg the whole thing on is Vpsrc, or the quantity of water retained in the pseudo-reservoir at the end of the day. The Vpsrc is modelled for two alternative situations: no flood (V ≤ Vtarg), or flood (V > Vtarg). I interpret drought as particularly uncomfortable a case of the absence of flood.

Whatever. If V ≤ Vtarg , then Vpsrc = Vstored + Vflowin − Vbaseflowout + Vpcp − Vevap − Vseep  , where, besides the already known variables, Vbaseflowoutstands for volume of water leaving PSRC during the day as base flow. When, on the other hand, we have flood, Vpsrc = Vstored + Vflowin − Vbaseflowout − Voverflowout + Vpcp − Vevap − Vseep .

Phiri et al. (2021 op. cit.) argue that once we incorporate the phenomenon of pseudo-reservoirs in the evaluation of possible water discharge from alluvial floodplains, the above-presented equations perform better than the standard SWAT model, or V = Vstored + Vflowin − Vflowout + Vpcp − Vevap − Vseep

My principal takeaway from the research by Phiri et al. (2021 op. cit.) is that wetlands matter significantly for the hydrological balance of areas with characteristics of floodplains. My concept of ‘Energy Ponds’ assumes, among other things, storing water in swamp-like structures, including urban and semi-urban ones, such as rain gardens (Sharma & Malaviya 2021[5] ; Li, Liu & Li 2020[6] ; Venvik & Boogaard 2020[7],) or sponge cities (Ma, Jiang & Swallow 2020[8] ; Sun, Cheshmehzangi & Wang 2020[9]).  

Now, I have a few papers which allow me to have sort of a bird’s eye view of the SWAT model as regards the actual predictability of flow and retention in fluvial basins. It turns out that identifying optimal sites for hydropower installations is a very complex task, prone to a lot of error, and only the introduction of digital data such as GIS allows acceptable precision. The problem is to estimate accurately both the flow and the head of the waterway in question at an exact location (Liu et al., 2017[10]; Gollou and Ghadimi 2017[11]; Aghajani & Ghadimi 2018[12]; Yu & Ghadimi 2019[13]; Cai, Ye & Gholinia 2020[14]). My concept of ‘Energy Ponds’ includes hydrogeneration, but makes one of those variables constant, by introducing something like Roman siphons, with a constant head, apparently possible to peg at 20 metres. The hydro-power generation seems to be pseudo-concave function (i.e. it hits quite a broad, concave peak of performance) if the hydraulic head (height differential) is constant, and the associated productivity function is strongly increasing. Analytically, it can be expressed as a polynomial, i.e. as a combination of independent factors with various powers (various impact) assigned to them (Cordova et al. 2014[15]; Vieira et al. 2015[16]). In other words, by introducing, in my technological concept, that constant head (height) makes the whole thing more prone to optimization.

Now, I take on a paper which shows how to present a proof of concept properly: Pradhan, A., Marence, M., & Franca, M. J. (2021). The adoption of Seawater Pump Storage Hydropower Systems increases the share of renewable energy production in Small Island Developing States. Renewable Energy, https://doi.org/10.1016/j.renene.2021.05.151 . This paper is quite close to my concept of ‘Energy Ponds’, as it includes the technology of pumped storage, which I think about morphing and changing into something slightly different. Such as presented by Pradhan, Marence & Franca (2021, op. cit.), the proof of concept is structured in two parts: the general concept is presented, and then a specific location is studied  – the island of Curaçao, in this case – as representative for a whole category. The substance of proof is articulated around the following points:

>> the basic diagnosis as for the needs of the local community in terms of energy sources, with the basic question whether Seawater Pumped Storage Hydropower System is locally suitable as technology. In this specific case, the main criterium was the possible reduction of dependency on fossils. Assumptions as for the electric power required have been made, specifically for the local community.  

>> a GIS tool has been tested for choosing the optimal location. GIS stands for Geographic Information System (https://en.wikipedia.org/wiki/Geographic_information_system ). In this specific thread the proof of concept consisted in checking whether the available GIS data, and the software available for processing it are sufficient for selecting an optimal location in Curaçao.

At the bottom line, the proof of concept sums up to checking, whether the available GIS technology allows calibrating a site for installing the required electrical power in a Seawater Pumped Storage Hydropower System.

That paper by Pradhan, Marence & Franca (2021, op. cit.) presents a few other interesting traits for me. Firstly, the author’s prove that combining hydropower with windmills and solar modules is a viable solution, and this is exactly what I thought, only I wasn’t sure. Secondly, the authors consider a very practical issue: corrosion, and the materials recommended in order to bypass that problem. Their choice is fiberglass. Secondly, they introduce an important parameter, namely the L/H aka ‘Length to Head’ ratio. This is the proportion between the length of water conductors and the hydraulic head (i.e. the relative denivelation) in the actual installation. Pradhan, Marence & Franca recommend distinguishing two types of installations: those with L/H < 15, on the one hand, and those with 15 ≤ L/H ≤ 25. However accurate is that assessment of theirs, it is a paremeter to consider. In my concept of ‘Energy Ponds’, I assume an artificially created hydraulic head of 20 metres, and thus the conductors leading from elevated tanks to the collecting wetland-type structure should be classified in two types, namely [(L/H < 15) (L < 15*20) (L < 300 metres)], on the one hand, and [(15 ≤ L/H ≤ 25) (300 metres ≤ L ≤ 500 metres)], on the other hand.  

Still, there is bad news for me. According to a report by Botterud, Levin & Koritarov (2014[17]), which Pradhan, Marence & Franca quote as an authoritative source, hydraulic head for pumped storage should be at least 100 metres in order to make the whole thing profitable. My working assumption with ‘Energy Ponds’ is 20 metres, and, obviously, I have to work through it.

I think I have the outline of a structure for writing a decent proof-of-concept article for my ‘Energy Ponds’ concept. I think I should start with something I have already done once, two years ago, namely with compiling data as regards places in Europe, located in fluvial plains, with relatively the large volatility in water level and flow. These places will need water retention.

Out of that list, I select locations eligible for creating wetland-type structures for retaining water, either in the form of swamps, or as porous architectural structures. Once that second list prepared, I assess the local need for electrical power. From there, I reverse engineer. With a given power of X megawatts, I reverse to the storage capacity needed for delivering that power efficiently and cost-effectively. I nail down the storage capacity as such, and I pass in review the available technologies of power storage.

Next, I choose the best storage technology for that specific place, and I estimate the investment outlays necessary for installing it. I calculate the hydropower required in hydroelectric turbines, as well as in adjacent windmills and photovoltaic. I check whether the local river can supply the amount of water that fits the bill. I pass in review literature as regards optimal combinations of those three sources of energy. I calculate the investment outlays needed to install all that stuff, and I add the investment required in ram pumping, elevated tanks, and water conductors.  

Then, I do a first approximation of cash flow: cash from sales of electricity, in that local installation, minus the possible maintenance costs. After I calculate that gross margin of cash,  I compare it to the investment capital I had calculated before, and I try to estimate provisionally the time of return on investment. Once this done, I add maintenance costs to my sauce. I think that the best way of estimating these is to assume a given lifecycle of complete depreciation in the technology installed, and to count maintenance costs as the corresponding annual amortization.         


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