Tag: water management

Energy Ponds – l’état des lieux

I drift away from investor-relations sites, and I go back to my ‘Energy Ponds’ concept. I feel like going through it once again. First thing first, I want to lay out the idea such as I have figured it out so far. The second of the first things is that I am describing a yet-unimplemented, tentative technological solution, which combines water management with the generation of renewable energies. Ram-pumps are installed in the stream of a river. Kinetic energy of the river creates a by-flow through the ram-pumps, endowed with its own kinetic energy and flow rate, derived from those of the river. That by-flow is utilized in two ways. Some of it, within the limits of environmental sustainability of the riverine ecosystem, is pumped into wetland-type structures, which serve the purpose of water retention. On the way to wetlands, that sub-stream passes through elevated tanks, which create a secondary hydraulic head and allow placing hydroelectric turbines on pipes leading from elevated tanks to wetlands, i.e. back to ground level. The remaining part of the ram-pumped by-flow, before going back into the river, is recirculated through hydroelectric turbines located close to the ram-pump. The basic idea is shown graphically in Figure 1.

The remaining part of the article develops a complex proof of concept for ‘Energy Ponds’. Component solutions are discussed against the background of relevant literature, and a quantitative simulation of its basic parameters is presented, for simulated location in Poland, author’s home country.

Figure 1

‘Energy Ponds’ are supposed to create a system of water retention with as little invasive change in the landscape as possible. Typical reservoirs, such as artificial ponds and lakes, need space, and that space ought to be taken away from other employments thereof. This is a dilemma in land management: do we use a given portion of terrain for a retention reservoir or do we use it for other purposes? In densely populated regions, that dilemma becomes acute, when it comes to mutual arrangement of human architectural structures, agricultural land, and something which, fault of a better word, can be called ‘natural landscape’ (quotation marks refer to the fact that many landscapes which we intuitively gauge as ‘natural’ are actually man-made and artificially maintained).  

Water management, as a distinct field of technology, with an environmental edge, could benefit from innovation-boosting tools typical for other technological fields (Wehn & Montalvo 2018[1]; Mvulirwenande & Wehn 2020[2]). Technological change as regards water management is needed both in rural areas and in cities. Interestingly enough, urban environments seem to be more conservative than agriculture in terms of water management (Wong, Rogers & Brown 2020[3]). There is a quest for scientifically based constants in water management, such as the required quantity of water per person per day; Hogeboom (2020[4]) argues it is roughly 3800 liters after covering all the conventional uses of water. On the other hand, Mohamed et al. (2020[5]) claim that the concept of ‘arid region’, so far reserved for desertic environments, is de facto a type of environmental context when we systematically lack accurate environmental data as regards quickly changing availability of water. Kumar et al. (2020[6]) go even further and postulate something called ‘socio-hydrology’: human societies seem to develop characteristically differentiated patterns of collective behaviour in different hydrological conditions. Other research suggests that societies adapt to increased use of water by visibly increasing the productivity of that use, whilst increased income per capita seems being correlated with increased productivity in the use of water (Bagstad et al. 2020[7]).

In the ‘Energy Ponds’ concept, retention of water is supposed to be a slow, looped recirculation through local ecosystems, rather than accumulation in fixed reservoirs. Once water has been ram-pumped from a river into wetlands, the latter allow slow runoff, via ground waters, back into the hydrological system of the river basin. It is as if rain was falling once again in that river basin, with rainwater progressively collected by the drainage of the river. In hydrology, such a system is frequently referred to as quasi-reservoirs (Harvey et al. 2009[8]; Phiri et al. 2021[9]). Groundwater seems being the main factor of the observable water storage anomalies (Neves, Nunes, & Monteiro 2020[10]). Purposeful control of the size and density in the patches of green vegetation seems to be a good practical regulator of water availability, whence the interest in using wetlands as reservoirs (Chisola, Van der Laan, & Bristow 2020[11]).

Ram-pumps placed in the stream of a river become distributed energy resources based on renewable energy: they collect and divert part of the kinetic energy conveyed by the flow of water. Of course, one can ask: isn’t it simpler to put just hydroelectric turbines in that stream, without ram-pumps as intermediary? A ram-pump, properly instrumented with piping, becomes a versatile source of kinetic energy, which can be used for many purposes. In ‘Energy Ponds’, the idea is to use that energy both for creating a system of water retention, and for generating electricity. The former has its constraints. The amount of water to adsorb from the river is limited by the acceptable impact on ecosystems downstream. That impact can be twofold. Excessive adsorption upstream can expose the downstream ecosystems to dangerously low water levels, yet the opposite can happen as well: when we consider wetlands as pseudo-reservoirs, and thus as a reserve of water, its presence can stabilize the flow downstream (Hunt et al. 2022[12]), and the biological traits of ecosystems downstream are not necessarily at risk (Zhao et al. 2020[13]). Strong local idiosyncrasies can appear in that respect (Xu et al. 2020[14]).

Major rivers, even those in plains, have a hydraulic head counted in dozens of meters and a flow rate per second in the hundreds of cubic meters per second. With the typical efficiency of ram-pumps ranging from 35% to 66%, the basic physical model of ram-pumping (Fatahi-Alkouhi et al. 2019[15];  Zeidan & Ostfeld 2021[16]) allows pumping from the river more water than it is possible to divert into the local wetlands.  

Thus, two sub-streams are supposed to be ram-pumped in the ‘Energy Ponds’ system: one sub-stream ultimately directed to and retained in the local wetlands, and another one being the non-retainable excess, to be redirected immediately back into the river. The latter can immediately flow through hydroelectric turbines placed as close as possible to ram-pumps, in order not to lose kinetic energy. The other goes further, through the system of elevated tanks. Why introducing elevated tanks in the system? Isn’t it better to direct the retainable flow of water as directly as possible to wetlands, thus along a trajectory as flat as is feasible in the given terrain? The intuition behind elevated tanks is the general concept of Roman siphon (e.g. Angelakis et al. 2020[17]). When we place an artificially elevated tank along the flow of water from the river to the wetlands, it allows transporting water over a longer distance without losing kinetic energy in that flow. Therefore, the wetlands themselves, as well as the points of discharge from the piping of ‘Energy Ponds’ can be spread over a greater radius from the point of intake from the river. That gives flexibility as regards adapting the spatial distribution of the whole installation to the landscape at hand. Elevated water tanks can increase the efficiency of water management (Abunada et al. 2014[18]; Njepu, Zhang & Xia 2019[19]).

A water tank placed at the top of the Roman siphon is a reserve of energy in pumped storage, and thus allows creating a flow with the same kinetic energy as was generated by ram-pumps, yet further away from the point of intake in the river. Depending on the volumetric capacity and the relative height of the elevated tank, various amounts of energy can be stored. Two points are to consider in that respect. ‘Energy Ponds’ is supposed to be a solution as friendly as possible to the landscape. Big water towers are probably to exclude, and the right solution for elevated tanks seems closer to those encountered in farms, with relatively light structure. If there is need to store more energy in a local installation of ‘Energy Ponds’, there can be more such elevated tanks, scattered across the landscape. With respect to the relative height, documented research indicates a maximum cost-effective elevation between 30 and 50 meters, with 30 looking like a pragmatically conservative estimate (Inthachot et al. 2015[20]; Guo et al. 2018[21]; Li et al. 2021[22]). Elevated tanks such as conceived in ‘Energy Ponds’ can be various combinations of small equalizing tanks (serving just to level up intermittence in the flow of water), and structures akin raingardens (see e.g. Bortolini & Zanin 2019[23]), thus elevated platforms with embedded vegetal structures, capable of retaining substantial amounts of water.

As an alternative to artificial elevated tanks, and to the extent of possibilities offered by natural landscape, a mutation of the STORES technology (short-term off-river energy storage) can be used (Lu et al. 2021[24]; Stocks et al. 2021[25]). The landscape needed for that specific solution is a low hill, located next to the river and to the wetland. The top of such hill can host a water reservoir.

The whole structure of ‘Energy Ponds’, such as conceptually set for now, looks like a wetland, adjacent to the channel of a river, combined with tubular structures for water conduction, ram pumps and hydroelectric turbines. As for the latter, we keep in mind the high likelihood of dual stream: one close to ram-pumps, the other one after the elevated tanks. Proper distribution of power between the generating units can substantially reduce the amount of water used to generate the same amount of energy (Cordova et al. 2014[26]).

Hydroelectricity is collected in energy storage installations, which sell electricity to its end users. The whole concept follows the general stream of research on creating distributed energy resources coupled with landscape restoration (e.g.  Vilanova & Balestieri 2014[27]; Vieira et al. 2015[28]; Arthur et al. 2020[29] ; Cai, Ye & Gholinia 2020[30] ; Syahputra & Soesanti 2021[31]). In that path of research, combinations of energy sources (small hydro, wind and photovoltaic) plus battery backup, seem to be privileged as viable solutions, and seem allowing investment in RES installations with an expected payback time of approximately 10 – 11 years (Ali et al. 2021[32]). For the sake of clarity in the here-presented conceptualization of ‘Energy Ponds’, only the use of hydropower is considered, whilst, of course, the whole solution is open to adding other power sources to the mix, with limitations imposed by the landscape. As wetlands are an integral component of the whole concept, big windfarms seem excluded from the scope of energy sources, as they need solid support in the ground. Still, other combinations are possible. Once there is one type of RES exploited in the given location, it creates, with time, a surplus of energy which can be conducive to installing other types of RES power stations. The claim seems counterintuitive (if one source of energy is sufficient, then it is simply sufficient and crowds out other sources), yet there is evidence that local communities can consider RES according to the principle that ‘appetite grows as we eat’ (Sterl et al. 2020[33]). Among different solutions, floating solar farms, located on the surface of the wetland, could be an interesting extension of the ‘Energy Ponds’ concept (Farfan & Breyer 2018[34]; Sanchez et al. 2021[35]).

Material and methods

The general method of studying the feasibility of ‘Energy Ponds’ is always specific to a location, and it unfolds at two levels: a) calibrating the basic physical properties of the installation and b) assessing its economic viability. Hydrological determinants are strongly idiosyncratic, especially the amount of water possible do adsorb from the local river and to retain in wetlands, and the impact of retention in wetlands upon the ecosystems downstream. With wetlands as a vital component, the conceptual scheme of the ‘Energy Ponds’ naturally belongs to plains, as well as to wide river valleys surrounded by higher grounds. That kept in mind, there are conceptual developments as regards artificially created wetlands in the mountains (Shih & Hsu 2021[36]).

The local feasibility of ‘Energy Ponds’ starts with the possible location and size of wetlands. Places where wetlands either already exist or used to exist in the past, before being drained, seem to be the most natural, as local ecosystems are likely to be more receptive to newly created or expanded wetlands. Conflicts in land management between wetlands and, respectively, farmland and urban settlements, should be studied. It seems that the former type is sharper than the latter. There is documented technological development as regards the so-called Sponge Cities, where urban and peri-urban areas can be transformed into water-retaining structures, including the wetland-type ones (Sun et al. 2020[37]; Köster 2021[38]; Hamidi, Ramavandi & Sorial 2021[39]). On the other hand, farmland is a precious resource and conflicts between retention of water and agriculture are (and probably should be) settled in favour of agriculture.   

Quantitatively, data regarding rivers boils down to the flow rate in cubic meters per second, and to the hydraulic head. Flow and head are the elementary empirical observables of the here-presented method, and they enter into the basic equation of ram-pumping, as introduced by Zeidan & Ostfeld (2021 op.cit.), namely:

HR*QR*η = HT*QT                     (1)

…where HR is the hydraulic head of the river, QR is its flow rate in m3/s, HT is the relative elevation where water is being ram-pumped, QT is the quantity of pumped water, and η is a coefficient of general efficiency in the ram-pumps installed. That efficiency depends mostly on the length of pipes and their diameter, and ranges between 35% and 66% in the solutions currently available in the market. Knowing that QT is a pre-set percentage p of QR and, given the known research, p = QT/QR ≤ 20%, it can be written that QT = QRp. Therefore, equation (1) can be transformed:  

η = [HT*QR*p] / [HR*QR] = HT*p / HR              (2)

The coefficient η is predictable within the range that comes with the producer’s technology. It is exogenous to the ‘Energy Ponds’ system as such unless we assume producing special ram-pumps in the project. With a given flow per second in the river, efficiency η natural dictates the amount of water being ram-pumped, thus the coefficient of adsorption p.  Dual utilization of the ram-pumped flow (i.e. retention in wetlands and simple recirculation through proximate turbines) allows transforming equation (1) into equivalence (3):

{[HRQRHTp / HR] = [HRQC + HTQW] } {QRHTp = [HRQC + HTQW]}         (3)

…where Q­C stands for the sub-flow that is just being recirculated through turbines, without retention in wetlands, and QW is the amount retained. The balance of proportions between QC and QW is an environmental cornerstone in the ‘Energy Ponds’ concept, with QW being limited by two factors: the imperative of supplying enough water to ecosystems downstream, and the retentive capacity of the local wetlands. The latter is always a puzzle, and its thorough understanding requires many years of empirical observation. Still, a more practical method is proposed here: observable change in the surface of wetlands informs about changes in the amount of water stored. Of course, this is a crude observable, yet it can serve for regulating the amount of water conducted into the wetland.      

The hydraulic head of the river (HR) is given by the physical properties thereof, and thus naturally exogenous. Therefore, the fundamental technological choice in ‘Energy Ponds’ articulates into four ‘big’ variables: a) the producer-specific technology of ram-pumping b) the relative height HT of elevated tanks c) the workable fork of magnitudes in the amount of water QW to store in wetlands, and d) the exact technology of energy storage for hydroelectricity. These 4 decisions together form the first level of feasibility as regards the ‘Energy Ponds’ concept. They are essentially adaptive: they manifest the right choice for the given location, with its natural and social ecosystem.

A local installation of ‘Energy Ponds’ impacts the local environment at two levels, namely the retention of water, and the supply of energy. Water retained in wetlands has a beneficial impact on the ecosystem, yet it is not directly consumable: it needs to pass through the local system of supply in potable water first. The direct consumable generated by ‘Energy Ponds’ is hydroelectricity. Besides, there is some empirical evidence for a positive impact of wetlands upon the value of the adjacent, residential real estate (Mahan et al. 2000[40]; Tapsuwan et al. 2009[41]; Du & Huang 2018[42]). Thus comes the second level of feasibility for ‘Energy Ponds’, namely the socio-economic one. As ‘Energy Ponds’ is an early-stage concept, bearing significant uncertainty, the Net Present Value (NPV) of discounted cash flows seems suitable in that respect. Can the thing pay its own bills, and bring a surplus?

Answering that question connects once again to the basic hydrological metrics, namely head and flow. Hydroelectric power is calculated, in watts, as: water density (1000 kg/m3) * gravity acceleration constant (9,8 m/s2) * Net Head (meters) * Q (water flow rate m3/s). The output of electricity is derived from the power generated. It is safe to assume 50 weeks per year of continuous hydrogeneration, with the remaining time reserved for maintenance, which gives 50*7*24 = 8400 hours. Based on the previous formulations, power W generated in an installation of ‘Energy Ponds’ can be expressed with equation (4), and the resulting output E of electricity is given by equation (5):

W[kW] = ρ * g * (HRQC + HTQW) = 9,81 * (HRQC + HTQW)      (4)

E[kWh] = 8400 * W           (5)

The Net Present Value (NPV) of cash flow in an ‘Energy Ponds’ project is the residual part of revenue from the sales of electricity, as in equation (6).

The revenue is calculated as RE = PE*E , with PE standing for the price of electricity per 1 kWh. Investment outlays and the current costs of maintenance can be derived from the head and the flow specific to the given location. In that respect, the here-presented method, including parameters in equation (6), follows that by Hatata, El-Saadawi, & Saad (2019[43]). A realistic, technological lifecycle of an installation can be estimated at 12 years. Crossflow turbines seem optimal for flow rates inferior or equal to 20 m3 per second, whilst above 20 m3 Kaplan turbines look like the best choice. Investment and maintenance costs relative to ram pumps, elevated tanks, and the necessary piping remain largely uncertain, and seemingly idiosyncratic as regards the exact location and its physical characteristics. That methodological difficulty, seemingly inherent to the early conceptual phase of development in the ‘Energy Ponds’ concept, can be provisionally bypassed with the assumption that those hydraulic installations will consume the cost which would normally correspond to the diversion weir and intake, as well as to the cost of the powerhouse building. The variable IH corresponds to investment outlays in the strictly hydrological part of ‘Energy Ponds’ (i.e. ram-pumps, piping, and elevated tanks), whilst the ITU component, on the other hand, represents investment in the strictly spoken turbines and the adjacent power equipment (generator, electrical and mechanical auxiliary, transformer, and switchyard). The LCOS variable in equation (6) is the Levelized Cost of Storage, estimated for a discharge capacity of 6 hours in Li-Ion batteries, at €0,28 per 1 kWh (Salvini & Giovannelli 2022[44]; Chadly et al. 2022[45]). The ‘0,000714’ factor in equation (6) corresponds to the 6 hours of discharge as a fraction of the total 8400 working hours of the system over 1 year.

Case study with calculations

The here presented case study simulates the environmental and economic impact which could possibly come from the hypothetical development of the ‘Energy Ponds’ concept in author’s own country, namely Poland. The concept is simulated in the mouths of 32 Polish rivers, namely: Wisła, Odra, Warta, Narew, Bug, Noteć, San, Wieprz, Pilica, Bzura, Biebrza, Bóbr, Łyna, Drwęca, Barycz, Wkra, Gwda, Prosna, Dunajec, Brda, Pisa, Wisłoka, Nida, Nysa Kłodzka, Wisłok, Drawa, Krzna, Parsęta, Rega, Liwiec, Wełna, Nysa Łużycka (the spelling is original Polish). Flow rates, in cubic meters per second, as observed in those mouths, are taken as the quantitative basis for further calculations, and they are provided in Table 1, below. Figure 1, further below, presents the same graphically, on the map of Poland. The corresponding locations are marked with dark ovals. There are just 28 ovals on the map, as those located near Warsaw encompass more than one river mouth. That specific place in Poland is exceptionally dense in terms of fluvial network. Further in this section, one particular location is studied, namely the mouth of the Narew River, and it is marked on the map as a red oval.  

Table 1

River nameMouth opening on…Average flow rate [m3/s]River nameMouth opening on…Average flow rate [m3/s]
WisłaBaltic Sea1080ProsnaWarta17,4
OdraBaltic Sea567DunajecWisła85,5
WartaOdra216BrdaWisła28
NarewWisła313PisaNarew26,8
BugNarew155WisłokaWisła35,5
NotećWarta76,6NidaWisła21,1
SanWisła129Nysa KłodzkaOdra37,7
WieprzWisła36,4WisłokSan24,5
PilicaWisła47,4DrawaNoteć21,3
BzuraWisła28,6KrznaBug11,4
BiebrzaNarew35,3ParsętaBaltic Sea29,1
BóbrOdra44,8RegaBaltic Sea21,1
ŁynaPregoła[46]34,7LiwiecBug12,1
DrwęcaWisła30WełnaWarta9,2
BaryczOdra18,8Nysa ŁużyckaOdra31
WkraNarew22,3

Figure 2

The hypothetical location of Energy Ponds installations at the mouths of rivers is based on the availability of hydrological data, and more specifically the flow rate in cubic meters per second. That variable is being consistently observed at the mouths of rivers, for the most part, unless some specific research is conducted. Thus, locating the simulated Energy Ponds at the mouths of rivers is not a substantive choice. Real locations should be selected on the grounds of observable properties in the local ecosystem, mostly as regards the possibility of storing water in wetlands. 

The map of location chosen for this simulation goes pretty much across all the available fluvial valleys in Poland, the physical geography of which naturally makes rivers grow as they head North, and therefore the northern part of the country gives the most water to derive from rivers. Once again, when choosing real locations for the Energy Ponds installations, more elevated ground is a plausible location as well. Most of the brown ovals on the map are in the broad vicinity of cities. This is another feature of the Polish geography: high density of population, the latter being clearly concentrated along rivers. This is also an insight into the function of Energy Ponds in real life. Such as it is simulated in Poland, i.e. in a densely populated environment, it is a true challenge to balance environmental services provided by wetlands, on the one hand, and the need for agricultural land, on the other hand. The simulation allows guessing that Energy Ponds can give more to city dwellers than to those living in the countryside.  

Another important trait of this specific simulation for Energy Ponds is the fact that virtually all the locations on the map correspond to places where wetlands used to exist in the past, before being drained and dried for the needs of human settlements. Geological and hydrological conditions are naturally conducive to swamp and pond formation in these ecosystems. It is important to prevent any ideological take on these facts. The present article is far from pushing simplistic claims such as “nature is better than civilisation”. Still, the draining and drying of wetlands in the past happened in the context of technologies which did not really allow reliable construction amidst a wetland-type environment. Today, we dispose of a much better technological base, such as comfortable barge-based houses, for example. The question of cohabitation between wetlands and human habitat can be reconsidered productively.   

Three levels of impact upon the riverine ecosystem are simulated as three hypothetical percentages of adsorption from the river through ram pumping: 5% of the flow, 10% and 20%, where 20% corresponds to the maximum possible pump-out as regards environmental impact. With these assumptions, the complete hypothetical set of 32 installations would yield 5 163 231 600 m3 a year at 5% of adsorption, and, respectively 10 326 463 200 m3 and 20 652 926 400 m3 with the adsorption rates at 10% and 20%.  In 2020, the annual precipitations were around 201,8 billion of m3, which means the 32 hypothetical installations of Energy Ponds could recirculate from 2,5% to 10% of that total volume, and that, in turn, translates into a significant environmental impact. 

Let’s focus on one particular location in order to further understand the environmental impact: the place where the Narew River mouths into Vistula River, north of Warsaw. The town of Nowy Dwór Mazowiecki, population 28 615, is located right at this junction of rivers. With the average consumption of water at the level of households being around 33,7 m3 a year, that local population consumes annually some 964 326 m3 of water. The flow rate in the Narew River close to its mouth into Vistula is 313 m3 per second, which amounts to a total of 9 870 768 000,00 m3 a year. Adsorbing 5%, 10% or 20% from that total flow amounts to, respectively, 493 538 400 m3, 987 076 800 m3, and 1 974 153 600 m3. From another angle, the same annual consumption of water in households, in Nowy Dwór Mazowiecki, corresponds to 0,0098% of the annual waterflow in the river mouth. The ‘Energy Ponds’ concept would allow to recirculate easily into the surrounding ecosystem the entire annual household consumption of water in this one single town.           

Let’s stay in this specific location and translate water into energy, and further into investment. The first issue to treat is a workable approach to using the capacity of ram-pumps in that specific location, or, in other words, a realistic estimation of the total pumped volume QC + QW . Metric flow per second at the mouth of the Narew River is 313 m3 on average. It is out of the question to install ram-pumps across the entire width of the stream, i.e. some 300 metres on average, as Narew is a navigable river. Still, what if we replaced width with length, i.e. what if a row of ram-pumps was placed along one bank of the river, over a total length of 1 km? Then, with the average efficiency of ram-pumps pegged at 50,5%, it can be assumed that 50,5% of the total flow per second, thus 50,5%*313 m3/s = 158,065 m3/s would flow through ram-pumps. With the baseline head of the Narew River being 92 meters, Table 2 below offers an estimation of the electric power thus possible to generate at the mouth, in an installation of ‘Energy Ponds’, according to equation (4).

Table 2 – Electric power [kW] possible to generate in an installation of ‘Energy Ponds’ at the mouth of the Narew River, Poland.

 Percentage of the total flow to be stored in wetlands (QW)
The relative height of elevated tanks5%10%20%
10 meters130 067,65117 478,4892 300,13
20 meters131 602,92120 549,0198 441,19
30 meters133 138,18123 619,54104 582,25

Source: author’s

When the highest possible elevated tanks are chosen (30 m), combined with the lowest percentage of the flow retained in wetlands (5%), electric power generated is the greatest, i.e. 133,138 MW. The optimal point derives logically from natural conditions. Comparatively to the artificially elevated tanks and their 30 meters maximum, the head of the river itself (92 meters) is an overwhelming factor. An interesting aspect of the ‘Energy Ponds’ concept comes out: the most power can be derived from the natural denivelation of terrain, with elevated tanks and their Roman siphon being just an additional source of potential energy. Further calculations, as regards the necessary investment outlays and the cost of storage, demonstrate that the lowest investment per unit of useful power – 987,16 Polish Zloty (PLN) per 1 kW – is reached precisely at the same point. Comparatively, the lowest power – generated at 20% of the flow adsorbed into wetlands and the lowest height of 10 meters of elevated tanks – is connected to the highest investment per unit, namely 1 111,13 PLN per 1 kW.       

The local urban population in Nowy Dwór Mazowiecki represents an annual consumption of electricity amounting to 828 752 048 kWh, and electricity hypothetically generated at that greatest power amounts to 1 118 360 718,72 kWh a year, thus covering, with an overhead, the local needs. This output of energy would be currently worth PLN 845,48 million a year at the retail prices of electricity[47] (i.e. when sold in a local peer-to-peer market). Should it be sold at wholesale prices[48], to the national power grid, it would represent PLN 766,08 million annually. Corrected with an annual Levelized Cost of Storage estimated at 107 161,99 PLN for Li-Ion batteries, that stream of revenue gives a 12-year discounted present value of PLN 5 134 million at retail prices, and PLN 4 647 million at wholesale prices.  With investment outlays estimated, according to the method presented earlier in this article, at some PLN 131,43 million, the project seems largely profitable. As a matter of fact, it could reach a positive Net Present Value already on the first year.

Comparatively, at the point of lowest power and highest investment per unit thereof, thus at 20% of adsorption into wetlands and 10 meters of height in elevated tanks, the 12-year discounted stream of revenue corrected for LCOS would be PLN 3 555,3 million (retail) and PLN 3 217,8 million (wholesale), against an investment of PLN 102,56 million.                   

Conclusion

The above-presented case study in the hypothetical implementation of the ‘Energy Ponds’ concept sums up optimistically. Still, the ‘Energy Ponds’ is still just a concept, and the study of its possible feasibility is hypothetical. That suggests caution, and the need to take a devil’s advocate’s stance. The case study can be utilized for both outlining the positive potential in ‘Energy Ponds’ and showing the possible angles of stress-testing the concept. The real financial value and the real engineering difficulty of investment in the basic hydraulic infrastructure of ‘Energy Ponds’ has been just touched upon, and essentially bypassed with a few assumptions. Those assumptions seem to be holding when written down, but confrontation with real life can bring about unpredicted challenges. This superficiality stems from the author’s own limitations, as an economist attempting to form an essentially engineering solution. Still, even from the economic point of view, one factor of uncertainty emerges: the pace of technological change. The method used for this feasibility study is a textbook one, similar to calculating the Levelized Cost of Energy: there is an initial investment, which we spread over the expected lifecycle of the technology in question. However, technologies can change at an unexpected pace, and the actual lifecycle of an installation – especially a highly experimental one – might be much shorter than expected. In the author’s (very intuitive) perspective, technological uncertainty is a big pinch of salt to add to the results of the case study.

Another factor of uncertainty is the real possibility of restoring wetlands in densely populated areas. Whilst new technologies in construction and agriculture do allow better overlapping between wetlands, cities, and farming, this is still just overlapping. At the bottom line, wetlands objectively take land away from many other possible uses. Literature is far from decisive about solutions in that respect. The great majority of known projects in the restoration of wetlands aim at and end up in restoring wildlife, not in assuring smooth coexistence between nature and civilisation. Some serious socio-economic experimentation might be involved in projects such as ‘Energy Ponds’.

Hydrogeneration in ‘Energy Ponds’ belongs to the category of Distributed Energy Resources (DER). DER systems become more and more popular, across the world, and they prove to be workable solutions in very different conditions of physical geography (McIlwaine et al. 2021[49]). Connection to local markets of energy, and into local smart grids, seems critical for the optimization of DER systems (Zakeri et al. 2021[50]; Touzani et al. 2021[51]; Haider et al. 2021[52]; Zhang et al. 2022[53]). How necessary is the co-existence – or pre-existence – of such a local network for the economically successful deployment of ‘Energy Ponds’?  What happens when the local installation of ‘Energy Ponds’ is the only Distributed Energy Resource around?

[1] Wehn, U., & Montalvo, C. (2018). Exploring the dynamics of water innovation: Foundations for water innovation studies. Journal of Cleaner Production, 171, S1-S19. https://doi.org/10.1016/j.jclepro.2017.10.118

[2] Mvulirwenande, S., & Wehn, U. (2020). Fostering water innovation in Africa through virtual incubation: Insights from the Dutch VIA Water programme. Environmental Science & Policy, 114, 119-127. https://doi.org/10.1016/j.envsci.2020.07.025

[3] Wong, T. H., Rogers, B. C., & Brown, R. R. (2020). Transforming cities through water-sensitive principles and practices. One Earth, 3(4), 436-447. https://doi.org/10.1016/j.oneear.2020.09.012

[4] Hogeboom, R. J. (2020). The Water Footprint Concept and Water’s Grand Environmental Challenges. One earth, 2(3), 218-222. https://doi.org/10.1016/j.oneear.2020.02.010

[5] Mohamed, M. M., El-Shorbagy, W., Kizhisseri, M. I., Chowdhury, R., & McDonald, A. (2020). Evaluation of policy scenarios for water resources planning and management in an arid region. Journal of Hydrology: Regional Studies, 32, 100758. https://doi.org/10.1016/j.ejrh.2020.100758

[6] Kumar, P., Avtar, R., Dasgupta, R., Johnson, B. A., Mukherjee, A., Ahsan, M. N., … & Mishra, B. K. (2020). Socio-hydrology: A key approach for adaptation to water scarcity and achieving human well-being in large riverine islands. Progress in Disaster Science, 8, 100134. https://doi.org/10.1016/j.pdisas.2020.100134

[7] Bagstad, K. J., Ancona, Z. H., Hass, J., Glynn, P. D., Wentland, S., Vardon, M., & Fay, J. (2020). Integrating physical and economic data into experimental water accounts for the United States: Lessons and opportunities. Ecosystem Services, 45, 101182. https://doi.org/10.1016/j.ecoser.2020.101182

[8] 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.

[9] 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.

[10] Neves, M. C., Nunes, L. M., & Monteiro, J. P. (2020). Evaluation of GRACE data for water resource management in Iberia: a case study of groundwater storage monitoring in the Algarve region. Journal of Hydrology: Regional Studies, 32, 100734. https://doi.org/10.1016/j.ejrh.2020.100734

[11] Chisola, M. N., Van der Laan, M., & Bristow, K. L. (2020). A landscape hydrology approach to inform sustainable water resource management under a changing environment. A case study for the Kaleya River Catchment, Zambia. Journal of Hydrology: Regional Studies, 32, 100762. https://doi.org/10.1016/j.ejrh.2020.100762

[12] Hunt, J. D., Nascimento, A., ten Caten, C. S., Tomé, F. M. C., Schneider, P. S., Thomazoni, A. L. R., … & Senne, R. (2022). Energy crisis in Brazil: Impact of hydropower reservoir level on the river flow. Energy, 239, 121927. https://doi.org/10.1016/j.energy.2021.121927

[13] Zhao, P., Li, Z., Zhang, R., Pan, J., & Liu, Y. (2020). Does water diversion project deteriorate the water quality of reservoir and downstream? A case-study in Danjiangkou reservoir. Global Ecology and Conservation, 24, e01235. https://doi.org/10.1016/j.gecco.2020.e01235

[14] Xu, D., Lyon, S. W., Mao, J., Dai, H., & Jarsjö, J. (2020). Impacts of multi-purpose reservoir construction, land-use change and climate change on runoff characteristics in the Poyang Lake basin, China. Journal of Hydrology: Regional Studies, 29, 100694. https://doi.org/10.1016/j.ejrh.2020.100694

[15] Fatahi-Alkouhi, R., Lashkar-Ara, B., & Keramat, A. (2019). On the measurement of ram-pump power by changing in water hammer pressure wave energy. Ain Shams Engineering Journal, 10(4), 681-693. https://doi.org/10.1016/j.asej.2019.05.001

[16] Zeidan, M., & Ostfeld, A. (2021). Hydraulic Ram Pump Integration into Water Distribution Systems for Energy Recovery Application. Water, 14(1), 21, https://doi.org/10.3390/w14010021   

[17] Angelakis, A. N., Voudouris, K. S., & Tchobanoglous, G. (2020). Evolution of water supplies in the Hellenic world focusing on water treatment and modern parallels. Water Supply, 20(3), 773-786 https://doi.org/10.3390/w13081069

[18] Abunada, M., Trifunović, N., Kennedy, M., & Babel, M. (2014). Optimization and reliability assessment of water distribution networks incorporating demand balancing tanks. Procedia Engineering, 70, 4-13. https://doi.org/10.1016/j.proeng.2014.02.002

[19] Njepu, A., Zhang, L., & Xia, X. (2019). Optimal tank sizing and operation of energy-water supply systems in residences. Energy Procedia, 159, 352-357. https://doi.org/10.1016/j.egypro.2019.01.003

[20] Inthachot, M., Saehaeng, S., Max, J. F., Müller, J., & Spreer, W. (2015). Hydraulic ram pumps for irrigation in Northern Thailand. Agriculture and Agricultural Science Procedia, 5, 107-114. https://doi.org/10.1016/j.aaspro.2015.08.015

[21] 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

[22] 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

[23] 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

[24] 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

[25] 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

[26] Cordova M, Finardi E, Ribas F, de Matos V, Scuzziato M. Performance evaluation and energy production optimization in the real-time operation of hydropower plants. Electr Pow Syst Res 2014;116:201–7.  https://doi.org/10.1016/j.epsr.2014.06.012

[27] Vilanova, M. R. N., & Balestieri, J. A. P. (2014). Hydropower 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

[28] 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

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

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

[31] 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

[32] 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

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

[34] Farfan, J., & Breyer, C. (2018). Combining floating solar photovoltaic power plants and hydropower reservoirs: a virtual battery of great global potential. Energy Procedia, 155, 403-411.

[35] Sanchez, R. G., Kougias, I., Moner-Girona, M., Fahl, F., & Jäger-Waldau, A. (2021). Assessment of floating solar photovoltaics potential in existing hydropower reservoirs in Africa. Renewable Energy, 169, 687-699. https://doi.org/10.1016/j.renene.2021.01.041

[36] Shih, S. S., & Hsu, Y. W. (2021). Unit hydrographs for estimating surface runoff and refining the water budget model of a mountain wetland. Ecological Engineering, 173, 106435, https://doi.org/10.1016/j.ecoleng.2021.106435

[37] Sun, Y., Deng, L., Pan, S. Y., Chiang, P. C., Sable, S. S., & Shah, K. J. (2020). Integration of green and gray infrastructures for sponge city: Water and energy nexus. Water-Energy Nexus, 3, 29-40. https://doi.org/10.1016/j.wen.2020.03.003

[38] Köster, S. (2021). How the Sponge City becomes a supplementary water supply infrastructure. Water-Energy Nexus, 4, 35-40. https://doi.org/10.1016/j.wen.2021.02.002

[39] Hamidi, A., Ramavandi, B., & Sorial, G. A. (2021). Sponge city—an emerging concept in sustainable water resource management: A scientometric analysis. Resources, Environment and Sustainability, 5, 100028. https://doi.org/10.1016/j.resenv.2021.100028

[40] Mahan, B. L., Polasky, S., & Adams, R. M. (2000). Valuing urban wetlands: a property price approach. Land economics, 100-113. https://doi.org/10.2307/3147260

[41] Tapsuwan, S., Ingram, G., Burton, M., & Brennan, D. (2009). Capitalized amenity value of urban wetlands: a hedonic property price approach to urban wetlands in Perth, Western Australia. Australian Journal of Agricultural and Resource Economics, 53(4), 527-545. https://doi.org/10.1111/j.1467-8489.2009.00464.x

[42] Du, X., & Huang, Z. (2018). Spatial and temporal effects of urban wetlands on housing prices: Evidence from

Hangzhou, China. Land use policy, 73, 290-298. https://doi.org/10.1016/j.landusepol.2018.02.011

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

[44] Salvini, C., & Giovannelli, A. (2022). Techno-economic comparison of diabatic CAES with artificial air reservoir and battery energy storage systems. Energy Reports, 8, 601-607.

[45] Chadly, A., Azar, E., Maalouf, M., & Mayyas, A. (2022). Techno-economic analysis of energy storage systems using reversible fuel cells and rechargeable batteries in green buildings. Energy, 247, 123466. https://doi.org/10.1016/j.energy.2022.123466

[46] The Pregoła river is located in the Russian Kaliningrad district, whilst much of the Łyna river is located in Poland. The hypothetical location of Energy Ponds is assumed to be in Poland, thus upstream from the mouth into Pregoła. 

[47] https://www.globalpetrolprices.com/Poland/electricity_prices/ last access May 17th, 2022

[48] https://www.statista.com/statistics/1066654/poland-wholesale-electricity-prices/ last access May 17th, 2022

[49] McIlwaine, N., Foley, A. M., Morrow, D. J., Al Kez, D., Zhang, C., Lu, X., & Best, R. J. (2021). A state-of-the-art techno-economic review of distributed and embedded energy storage for energy systems. Energy, 229, 120461. https://doi.org/10.1016/j.energy.2021.120461

[50] Zakeri, B., Gissey, G. C., Dodds, P. E., & Subkhankulova, D. (2021). Centralized vs. distributed energy storage–Benefits for residential users. Energy, 236, 121443. https://doi.org/10.1016/j.energy.2021.121443

[51] Touzani, S., Prakash, A. K., Wang, Z., Agarwal, S., Pritoni, M., Kiran, M., … & Granderson, J. (2021). Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency. Applied Energy, 304, 117733. https://doi.org/10.1016/j.apenergy.2021.117733

[52] Haider, R., D’Achiardi, D., Venkataramanan, V., Srivastava, A., Bose, A., & Annaswamy, A. M. (2021). Reinventing the utility for distributed energy resources: A proposal for retail electricity markets. Advances in Applied Energy, 2, 100026. https://doi.org/10.1016/j.adapen.2021.100026

[53] Zhang, S., May, D., Gül, M., Musilek, P., (2022) Reinforcement learning-driven local transactive energy market for distributed energy resources. Energy and AI (2022), doi: https://doi.org/10.1016/j.egyai.2022.100150

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

A test pitch of my ‘Energy Ponds’ business concept

I am returning to a business concept I have been working on for many months, and which I have provisionally labelled ‘Energy Ponds’. All that thinking about new economic solutions for a world haunted by insidious pathogens – no, not selfie sticks, I am talking about the other one, COVID-19 – pushed me to revisit fundamentally the concept of Energy Ponds, and you, my readers, you are my rubber duck.

The rubber duck (Latin: anas flexilis), also known as bath duck (anas balneum) is a special semi-aquatic avian species, whose valour I know from my son, IT engineer by profession. Every now and then, he says, on the phone: ‘Dad, focus, you are going to be my rubber duck’. The rubber duck is an imaginary animal. It feeds on discursive waters. You talk to it in order to get your own thoughts straight. When I am my son’s rubber duck, he explains me some programming problems and solutions, he checks if I understand what he says, and when I test positive, it means that he can get the message across to any moderately educated hominid.

I am going to proceed along the path of discursive equilibrium, in a cycle made of three steps. First, I will try to describe my idea in 1 – 2 sentences, in a simple and intelligible way. Then, I develop on that short description, with technical details. In the third step, I look for gaps and holes in the so-presented concept, and then I go again: short description, development, critical look etc. I think I will repeat the cycle until I reach the Subjective Feeling of Having Exhausted the Matter. Nelson Goodman and John Rawls proposed something slightly similar (Goodman 1955[1]; Rawls 1999[2]): when I talk long enough to myself, and to an imaginary audience, my concepts sharpen.   

Here I go. First attempt. I synthesize. The concept of ‘Energy Ponds’ consists in ram-pumping water from rivers into retentive, semi-natural wetlands, so as to maximize the retention of water, and, in the same time, in using the elevation created through ram-pumping so as to generate hydroelectricity. At the present stage of conceptual development, ‘Energy Ponds’ require optimization at two levels, namely that of adequately choosing and using the exact geographical location, and that of making the technology of ram-pumping economically viable.  

I develop. We are increasingly exposed to hydrological effects of climate change, namely to recurrent floods and droughts, and it starts being a real pain in the ass. We need to figure out new ways of water management, so as to retain a maximum of rainwater, whilst possibly alleviating occasional flood-flows. Thus, we need to figure out good ways of capturing rainwater, and of retaining it. Rivers are the drainpipes of surrounding lands, whence the concept of draining basin: this is the expanse of land, adjacent to a river, where said river collects (drains) water from. That water comes from atmospheric precipitations. When we collect water from rivers, we collect rainwater, which fell on the ground, trickled underground, and then, under the irresistible force of grandpa Newton, flew towards the lowest point in the whereabouts, that lowest point being the river.

Thus, when we collect water from the river, we collect rainwater, just drained through land. We can collect it in big artificial reservoirs, which has been done for decades. An alternative solution is to retain water in wetlands. This is something that nature has been doing for millions of years. We have sort of a ready-made recipe from. Wetlands are like sponges covered with towels. A layer of spongy ground, allowing substantial accumulation of water, is covered with a dense, yet not very thick layer of shallowly rooted vegetation. That cover layer prevents the evaporation of water.  

Now, I go into somehow novel a form of expression, i.e. novel for me. The age I am, 52, I have that slightly old school attachment to writing, and for the last 4 years, I have been mostly writing on my blog. Still, as a university professor, I work with young people – students – and those young people end up, every now and then, by teaching me something. I go more visual in my expression, which this whole written passage can be considered as an introduction to. Under the two links below, you will find:

  1. The Power Point Presentation with a regular pitch of my idea

That would be all in this update. Just as with my other ideas, in the times we have, i.e. with the necessity to figure out new s**t in the presence of pathogens, you are welcome to contact me with any intellectual contribution you feel like supplying.  

If you want to contact me directly, you can mail at: goodscience@discoversocialsciences.com .

[1] Goodman, N. (1955) Fact, Fiction, and Forecast, Cambridge, Mass., Harvard University Press, pp. 65–68

[2] Rawls J. (1999) A Theory of Justice. Revised Edition, President and Fellows of Harvard College, ISBN 0-674-00078-1, p. 18