How much energy storage is needed to incorporate ...

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Energy (2017) 000–000 283–293 EnergyProcedia Procedia135 00 (2017) www.elsevier.com/locate/procedia

11th International Renewable Energy Storage Conference, IRES 2017, 14-16 March 2017, 11th International Renewable Energy Storage Germany Conference, IRES 2017, 14-16 March 2017, Düsseldorf, Düsseldorf, Germany

How much energy storage is needed to incorporate very large How much energy storage is needed to incorporate very large The 15th International Symposium on District Heating and Cooling intermittent renewables? intermittent renewables? Assessing the feasibility of using the heat demand-outdoor A.A. Solomon**, Michel Child, Upeksha Caldera, Christian Breyer temperature function for Skinnarilankatu aUpeksha long-term district heat forecast A.A. Solomon , Michel Child, Caldera, Christian Breyerdemand Lappeenranta University of Technology, 34, 53850 Lappeenranta, Finland Lappeenranta University of Technology, Skinnarilankatu 34, 53850 Lappeenranta, Finland

I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc

Abstract a IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal Abstract b Veolia Recherche & Innovation, 291 Avenue Dreyfous Daniel, 78520 Limay, France

In this paper,cDépartement we presentSystèmes issuesÉnergétiques of electricity storage requirements based on comparative studies of various results. It et Environnement - IMT Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France In paper, present issues ofenergy electricity requirements based onits comparative studies of various results. It wasthis found thatwe when we increase fromstorage VRE, the use of storage and capacity increases until we reach some was found that when we increase energy from VRE, the use of storage and its capacity increases until we reach some threshold. After that threshold, the storage use starts to decline even if we increase the size. An optimally utilized threshold. After daily that threshold, the storage to decline we increase the size. utilized storage of about average demand woulduse be starts sufficient to reacheven gridifpenetration of about 90%Anofoptimally the total demands storage of about average demand would be sufficient to reach grid penetration of aboutis90% of the total demands Abstract from VRE. The daily understanding of the physics and economics of the future energy system mandatory to build and from VRE. The understanding of the physics and economics of the future energy system is mandatory to build and operate it optimally. District itheating networks are commonly addressed in the literature as one of the most effective solutions for decreasing the operate optimally.

gas emissions fromby theElsevier buildingLtd. sector. These systems require high investments which are returned through the heat ©greenhouse 2017 The Authors. Published ©sales. 2017 Due The Authors. Published by Elsevier Ltd. and building renovation policies, heat demand in the future could decrease, to thethe changed climate conditions © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of EUROSOLAR The European European Association Association for for Renewable Renewable Energy. Energy. Peer-review under the responsibility of EUROSOLAR -- The prolonging the investment return period. Peer-review under the responsibility of EUROSOLAR - The European Association for Renewable Energy. The mainEnergy scope storage; of this paper is to assess feasibility using the heat demand – outdoor temperature function for heat demand Keywords: Wind Energy; Solar the energy; Storageof Capacity; forecast. Energy The district Alvalade, in Lisbon Keywords: storage;of Wind Energy; located Solar energy; Storage (Portugal), Capacity; was used as a case study. The district is consisted of 665 buildings that vary in both construction period and typology. Three weather scenarios (low, medium, high) and three district scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were 1.renovation Introduction with results from a dynamic heat demand model, previously developed and validated by the authors. 1.compared Introduction The resultsby showed that wheninterest only weather change is considered, the margin of error could be acceptable for technology some applications Driven an increased in transitioning to low-carbon energy systems, energy storage has (the error significant in demand was lower than 20% for allenergy scenarios considered). introducing renovation Driven byannual an increased interest transitioning toweather low-carbon energy systems, energyafter storage has garnered attention. For in many experts, storage technology is However, considered one oftechnology the disruptive scenarios,significant the error value increased to 59.5% (depending on storage the weather and renovation scenarios combination garnered attention. Forup many energy technology considered one ofseveral the considered). disruptive technologies that could change the way we experts, generate and consume energy. In the is past decades [1-18], research The value of that slopecould coefficient increased average the range energy. of 3.8%Inupthe to past 8% decades per decade, that corresponds to the technologies change the wayofon we generatewithin and consume [1-18], activities dealing with some scenarios low-carbon energy future have somehow examined the role ofseveral energyresearch storage decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and activities dealing some scenarios of low-carbon energy future have somehow the role of energy storage technology in thewith corresponding systems. Many researchers [1-10] estimated examined storage capacity requirements for renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the technology in the corresponding systems. Many researchers [1-10] estimated storage capacity requirements for renewable energy based grids that dominantly depends on wind and solar. These studies uses diverging methodologies coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and renewable energy based grids that dominantly depends on wind and solar. These studies uses diverging methodologies improve the accuracy of heat demand estimations. 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. © 2017 The Authors. Published by Elsevier Ltd. 1876-6102 2017the Theresponsibility Authors. Published by Elsevier -Ltd. Peer-review©under of EUROSOLAR The European Association for Renewable Energy. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Peer-review under the responsibility of EUROSOLAR - The European Association for Renewable Energy. Cooling. *Corresponding author E-mail: [email protected] *Corresponding author E-mail: [email protected] Keywords: Heat demand; Forecast; Climate change

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under the responsibility of EUROSOLAR - The European Association for Renewable Energy. 10.1016/j.egypro.2017.09.520

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A.A. Solomon et al. / Energy Procedia 135 (2017) 283–293 Solomon A. A.,et al./ Energy Procedia 00 (2017) 000–000

for modeling while also studying the cases at different geographic regions. As regards to modelling techniques, we could have three major categories. Namely, (i) those estimating required energy capacity for very high shares of renewable energy with no or little attention to the power capacity of storage [1-3]; (ii) economic models assessing storage as a key technology in a low-carbon energy future [4-11]; and (iii) those studying factors affecting storage design and the corresponding capacity requirements [12-15]. As regards to the diversities in geographic location, it is possible to find studies covering several parts of the world such as entire regions (or a part) of Europe [1,2,5,9], Japan [3], Kingdom of Saudi Arabia (KSA) [6], Asia [7], Israel [12,13], USA [4,10,14-16]. Yet, most have a different level of emphasis on the spatial and temporal resolution as well as the share of wind and solar in the studied scenarios. It is well known that wind and solar profiles as well as load profiles demonstrate certain common typical characteristics globally as much as they have certain location specific characteristics. As a result, one may expect convergence towards a certain global picture about storage requirement from these studies. Unfortunately, this is not occurring due to several reasons. In this paper, we will make a closer examination of various results in order to extract insights regarding storage requirements and their design as well as options to high Variable Renewable Energy (VRE) systems. This study analyzes results of various hourly models using physical parameters related to storage design criteria. From several studies, we included studies dealing with the case of the Kingdom of Saudi Arabia (KSA) [10], and Finland [9] as well as studies based on the Israeli [19] and Californian [5] grids because of the ready availability of the result data. We have also included 2 other studies [8,4,6] (namely a study covering the eastern United states PJM interconnection [8] and Europe [4,6]) that are based on an hourly modeling strategy and presented useful typical data for the intended analysis. We believe that achieving 100% VRE is not a necessity to reach to 100% RE (net zero emission energy system). However, in order to clarify the complexity of storage design to the possible breadth and address the case of some resource constrained regions, we will extend our discussion to 100% VRE grid or energy systems. 2. Bases for Comparison Energy storage modeling is currently one of the most unsettled areas due to complexities arising during modeling of these technologies. The major challenge relates to the requirement of several abstraction parameters to correctly characterize the technology as well as issues related to uncertainties around the operational policies and pricing of the future grid. On top of this, large scale modeling results in simplifications that are intended to overcome computational resource limitations. Due to the above challenges, there are several modeling approaches, which are often difficult to compare [1-16]. The first of the above three modeling categories deals with estimating the energy capacity of the storage by assuming a limitless capacity without any/little attention to power capacity of the storage [1-3]. Such studies are often based on aggregated wind and solar profiles over a large geographic swat such as Europe [1,2]. Such approaches overrun the possibility of an optimized storage with a disaggregated several node optimization model. The second group, which is basically an economic optimization, assesses economic performance of storage in the future energy system [4-11]. There are several versions of these categories but for our interest on large scale renewables, we will focus on two of the sub-categories, i.e., those relying on sampled time [9,10] and those which model storage based on the hourly time dynamics [4-8]. Due to the implementation of exogenous limits on the energy-to-power capacity ratio of the storage and other simplifications, the optimal design of storage is still in question in most of these models. However, those optimizing on an hourly time scale approach the problem in far-better-way than the other. In a third group, where storage design requirements were studied (regardless of economics) by increasing renewable energy from low penetration to very high penetration, several lessons regarding factors affecting energy storage design for very high penetration were identified. These models were crafted to study both the power and energy capacity requirements for various levels of penetration and several high penetration enabling strategies such as energy curtailment [12-15]. These studies identify the important constraints to be considered for a proper design of large scale energy storage to enable high use of VREs. These constraints are the building blocks of any model that aims to better design energy systems based on high shares of renewable energy and thus should compromise the bases for our

A.A. Solomon et al. / Energy Procedia 135 (2017) 283–293 Solomon A. A., et al./ Energy Procedia 00 (2017) 000–000

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criterion for comparison. Consequently, we will first present how these constraints affect storage requirements and briefly state the manner of our comparison in the next section. 3. Factors affecting energy storage capacity To do a proper comparison, it is important to know the bases for proper storage design criteria and factors affecting it. The present consensus seems that if larger and larger storage is available then higher and higher grid penetration of renewables would be possible. Under such proposition, the only challenges to achieving high grid penetration with large storage are economics. But this is half the truth because effective storage size depends on the energy and power capacity of the storage [12-15], the nature of the local renewable energy resources [1, 2, 14, 15], the level of grid penetration of renewables [12-15] and several other factors. Understanding the link between these factors could help in creating a ground for justifying our economic models performance as their result also depends on these physical constraints. At the same time, the magnitude of certainty in the physics of the future energy system is far better than the economic forecast. In the following, a detailed summary of (non-economic) factors affecting storage requirements will be presented. These are: 3.1. Level of grid penetration:Energy storage has little role in increasing bulk penetration of renewables to the energy system at low penetration of the variable renewable resources [14, 19-21]. Note that the term penetration represents the percentage of the total demand supplied by direct VRE energy plus the stored one. But it emerges as a key player as the VRE system size increases to a level where energy curtailment becomes prevalent [12-16]. At that time, storage plays the role of transferring the excess energy to a later time when the generation from VRE becomes lower than the demand. Fig. 1 presents storage size requirements versus VRE penetration to the respective grids produced using a data set from California (2011) [14, 15] and Israel (2006) [12,13]. California’s grid (which is more than 6 times larger in energy consumption and 20 times larger in land coverage than Israel) was represented as a 12 load area system as compared to the single load area system for Israel. The figure shows that the required storage size approximately linearly increases with penetration as storage emerges as a key player in increasing grid penetration. However, the increase in grid penetration starts to level off as we increase storage capacity to accommodate more surplus VRE generation. Once the storage reaches that turning point, further increase in capacity results in lesser and lesser grid penetration. This could be seen from the corresponding usefulness index (an index that shows the ratio of the energy delivered by storage to storage energy capacity) presented in the same figure, which shows an initially increasing use of storage with an increase in capacity that started decreasing after reaching some peak value. In the present study, both cases represent the condition in which little or no energy curtailment was allowed. Consequently, the above change in storage use is attributed to the corresponding change in dispatch strategy. When relatively smaller storage capacities are built, the storage could be dispatched more actively based on the short term generation and demand profiles (diurnal and weekly conditions), but larger storage capacities store massive energy which could be transferred from one season to the other often leading to large unused stored energy at the end of the year and causing their least utilization. The above result points to the importance of aiming at finding properly sized storage while increasing its use rather than going for a very large storage as discussed in [13]. Note that storage use starts to decrease for storage capacity much lower than daily average demand of the local grid, specifically above a capacity of about 70% and 22% of the daily demands of Israel and California, respectively. This was termed as peak Energy Storage Capacity (peak EC) due to the observed peak usefulness index (UI) value corresponding to these storage capacities. The lower peak

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EC for California was due to an effect of wind-solar complementarities and the impact of transmission lines between the 12 load areas as compared to the solar based and single load area systems of the Israeli grid [14].

Fig. 1. Grid penetration (left axis) and UI (right) versus (Network) Energy storage Capacity. Network energy capacity represents total storage capacity over the entire network.

3.2. Energy curtailment As a result of the foregoing discussions, we can understand that applying the strict no curtailment strategy to design a storage system with 100% VRE penetration will lead to over building of both VRE and storage systems, and leave massive unused stored energy at the end of the year. In a model that allows energy curtailment, it was found that models naturally starts to curtail energy in order to reduce the storage capacity requirement once it reached sufficiently large storage (creating another backward inflection at the end of the level off region in Fig. 1). This is first reported by [15]. In that paper, it was shown that the trend described above was observed for wind energy only scenarios after the storage capacity reached approximately 6.6 times the daily average demand. It was noted that if further VRE size increase was enforced, similar conditions could occur for any other wind-solar combinations. In [12-15], it was shown that energy curtailment increases the use of energy storage to increase VRE grid penetration. For the Israeli and Californian grids, allowing 20% total VRE energy loss (including the loss due to energy storage efficiency), lead to grid penetration of 85% of the annual demand or better by storage size termed as “peak EC” in the above [13-15]. Under similar conditions, depending on grid size and diversity of VRE resources, a storage system with a size of daily average demand can achieve grid penetration as high as 93% of the annual demand as in the case of California. If we push for 100% grid penetration of VRE, the required storage size will also increase by some daily average demand. Energy curtailment has also shown additional benefit of reducing the balancing requirement that comes from conventional power plants, often termed as backup [2,14,15]. Note that the term “balancing” and “backup” capacity were used by different groups to refer to the same thing; i.e. any generation resources available to fill in the shortfalls of the VRE generators and storage. Due to some negative connotation that term “backup” is interpreted as fossil fuel generators. We will use the term “balancing” instead. In 100% RE energy systems balancing could come from a mix


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of generators, such as hydropower, biomass, conventional generators running on synthetic natural gas, and marine resources, if they are co-optimized. Fig. 2 shows how renewable energy curtailment increase leads to a reduced balancing capacity need while increasing grid penetration. The figure shows that using the 186 GWh/22 GW storage, which at 20% total energy loss achieves grid penetration of approximately 85% of the annual demand, the corresponding conventional balancing capacity was reduced to 59% of the peak demand. The total energy loss stands for the loss due to curtailment plus loss due to storage efficiency. The loss due to storage efficiency is about 8% and 3% of the total renewable generation for the Israeli and California grid, respectively. A decrease in curtailment could lead to an increase in the energy storage size and vice versa, but the best option is to find the optimal condition by considering various constraints.

Fig. 2. Grid penetration (left axis) and UI (right) versus (Network) Energy storage Capacity. Network energy capacity represents total storage capacity over the entire network.

3.3. Storage design and dispatch An important question of the future renewable energy based grid is designing a proper storage system (one of the flexibility options along with demand response, supply side management, sector coupling, transmission interconnection, etcetera), which consists of identifying a proper storage power and energy capacity to be placed at a given location in a power grid, and to enable an efficient use of local renewable energy resources. Proper sizing of energy storage power and energy capacity requires the ability to capture the storage time dynamics because of their role in matching a time varying renewable power output to a time varying load [14,15]. The hourly study of the Israeli

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and California grid shows that the inter-link between the power and energy capacity of the storage depends on the seasonal and diurnal interaction of VRE resource output and the local electricity demand [12-15]. Economics could somehow move it to one or the other side based on the resource complementarity, wind/solar and storage cost, energy curtailment, etcetera subject to these physical constraints. Storage dispatch also has its own impact on storage design and balancing capacity requirement. As discussed above, the peak ECs are storage systems that are mostly undergoing charge and discharge cycles in short periods of time resulting in high use. But much larger storage may transfer energy from one season to the other without undergoing significant discharging, when seen collectively, resulting in a limited role in increasing grid penetration. This will be much worse if the power and energy capacity is not well matched to the required systems. This indicates that going for very large storage does not always mean large benefits both economically and technically. As shown above, for a total energy loss at 20% of the VRE generation, a storage system size of about daily average demand suffices to reach grid penetration of about 90% of the annual demand. Consequently, to reach a certain penetration level it is important to push for a dispatch approach that will provide an efficient use of all resources. 3.4. Resources complementarity Complementarities between wind and solar were shown to give a multi-dimensional advantage to the future grid as compared to wind/solar technologies as a stand-alone [15]. As regards storage design [15], it was shown that windsolar complementarities could lead to higher VRE penetration with smaller storage energy and power capacity as compared to wind and solar as a stand-alone. For example, to arrive at 52% VRE penetration without any energy curtailment, the required storage energy capacity for wind and solar as a stand-alone was 30 and 2 folds larger than the corresponding storage for a 50-50 wind–solar mix, respectively [15]. Overall, it was shown that the both storage EC and PC are smaller for the wind-solar mixture than any of the technologies as a stand-alone. Similar observation was reported by [2] regarding the storage Energy Capacity requirement for European grid. 3.5. Relevant reliability and reserve criteria The present supply reliability conditions, which are based on peak demand, may not be applicable to the future grid. This is due to the observation that the significant balancing needs were required outside the traditional peak load time due to the flexible dispatching possibility of the energy storage and the matching of the renewable resources to the local peak load times [13,15]. In addition, several issues related to the dispatch of such systems are not yet known. Economic models that involve several energy storage technologies do not always have any clear dispatch merit order and sometimes even a clear limit on impacts of cycling on the lifetime of the battery is missing. Lack of clarity on this and other matters, such as market structure, may reduce the accuracy of the storage design. As a side note, we want to remind the reader that creating a constraint that will enforce the above criteria in an economic model is not easy. Future work is necessary in order to further understand and synchronize this into economic models. Returning to the manner of comparison, it is important to focus on studies that are based on hourly models. From those, we included studies dealing with the case of the Kingdom of Saudi Arabia [6, 23], and Finland [5] as well as studies based on the Israeli [12,13] and California [14,15] grid because of the ready availability of the result data. We have also included two other studies [2,4] (namely a study covering the eastern United states PJM interconnection [4] and Europe [2]) that are based on an hourly modeling strategy and those presented useful, typical data for the intended comparison. Yet, comparison is not easy because the studies dealt with scenarios of their own interest. For example, the study dealing with Finland’s energy system examines the entire energy system (power, heating/cooling and transport), while the KSA study has both a power only and integrated energy system scenario (power and desalination). This is important as integrated energy systems covering all energy sectors will be the future reality that could provide major flexibility, and that power only scenarios neglect. All other studies considered the case of power systems alone. In their studies, the economic models consider various storage technologies [4-7] while others use generic storage with assumed energy efficiencies [1,2,12-15]. As regards to the modeling technique, [4] used a model called Regional Renewable Electricity Economic Optimization Model (RREEOM); [5] was based on EnergyPLAN; [6] was based on large high spatial and temporal resolution model detailed in [7]; while studies covering the remaining regions [1,2,12-


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15] rely on independent non-economic models. As a result, we have to look at typical parameters (sometimes with simplified adjustment) that may help us to make informed comparisons to arrive at meaningful conclusions. 4. How large can storage be? Due to complexities discussed above, there is no simple way to estimate the largest storage capacity one would need. In this analysis, we have examined the lowest storage size based on technical studies investigating various conditions to find alternatives while also comparing it to the highest cases approximated using economically optimized models with projected economic parameters. In the following, we will provide a summary of important data and make our own estimates based on principles discussed above. Table 1. Typical parameters and corresponding values Parameters of interest Region/country of the study Finland integrated [5]

KSA integrated [6]

KSA [6]

Israel [13]

California [14]

Europe [2]

PJM+ [4,22]

VRE Penetration [% of annual demand]

70

99

98

90

85

100

100

Energy storage capacity [GWh]

3990

37473

38381

113

186

16,000

891

Energy storage capacity [ daily average demand]

8.6

18.7

21.6

0.83

0.22

1.8

1.2

Total Energy loss [% of total VRE generation]

6 (2.5% storage loss)

16 (10% storage loss)




50*

>50*,+*

Annual demand during the studied year [TWh]

105

729

650

50.2

302

3240

276

Usefulness index [a.u.]

6.3X

11.6X

10X

186

220

NA**

24

Storage Efficiency

mix2

mix2

mix2

75%

75%

100%

81%

Share of other RE resources [% of annual demand]

30 (hydro, biomass)

1 (geothermal and biomass)

2 (geother mal and biomass) power

103

153

0

0

Energy sectors investigated

power, heat power, power power power power sector desalination + this is based on their GIV storage scenario, which was selected for data completeness. * Estimate may not include loss due to storage efficiency +* estimate based on the given data [4], in [22] it was shown that at 95% penetration, the loss was 51% X UI could be lower for low efficiency storage because of the resulting larger energy storage need, UI for KSA was readjusted for gas storage (the improvement in UI was to maximum 5 points). For Finland, direct use of synthetic gas makes it less important (but even if necessary, the change will be a maximum 5 point) ** At 50% curtailment, for a diverse resource such as theirs, a penetration of approximately 80% is possible, one could then see the UI will at maximum be 40.5. 1 resource type not specified, but considered dispatchable renewable technologies (at the same time, data also show 98% VRE penetration at 25% total energy loss as well as storage capacity of about 1.3 and 6 times daily average demand, respectively. 2 The dominant capacities and their efficiencies are power-to-gas (electrolyzer, 61% [5], 77% [6], CCGT 58% and OCGT 43%), battery (>90%), thermal storage = 90%

Table 1 shows typical parameters collected or calculated based on the corresponding studies. The data for [5, 6, 13, 14] was obtained from the result database acquired from the authors, while the estimates corresponding to [2,4] were

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made based on information given in the papers. Due to significant difference between resource qualities from place to place, it is not easy to create an index related to VRE generation capacity. But indicators related to their matching capability to the local demand for energy provide important insights because of similarities of the matching characteristics [12-15,19-21]. As a result, we collect/create indicators such as VRE penetration, total energy loss, energy storage as a fraction of daily average demand, usefulness index and storage efficiency. As seen in previous sub-sections, these parameters are related to factors affecting storage design and use. Table 1 shows significantly varying parameters by geographic regions covered and the different studies. If we simply compare the locations targeting 100% or closer from VRE, we see that they correspond to significantly divergent storage sizes. Note that even if the corresponding storage energy capacities were given by an absolute unit of GWh, we rely on the capacity given in daily average demand for the purpose of the comparison. Considering the power only studies, it can be seen that the energy storage capacity per grid varies from 1.2 to 22 times daily average demand. Note that the KSA integrated scenario relied on comparatively lower storage size, due to a reduction that occurred as a result of the added flexibility coming from sector coupling. In Budischak et al. [4], it was concluded that economics prefers huge curtailment over increasing storage sizing and claimed that with certain policies future cost of VRE electricity could be comparable to the present cost of electricity. However, the cost of shadow thermal backup capacity in their study was not well accounted for due to the estimation of cost of its electricity at the present price rate regardless of negligible dispatch time. On the contrary, the data for the KSA study shows total electricity cost even lower to the present market though with massive storage and approximately 6% curtailment [6]. This is mainly a consequence of not ignoring energy subsidies for the current energy system and strict cost optimization of the future 100% RE system leading to a balance of RE generation, curtailment, flexibility due to sector coupling and a mix of short- and long-term storage options. In [2], the storage requirement was assessed by aggregating the wind and solar resources over entire Europe into one per resource type. They reported smaller storage at the cost of massive energy curtailment and optimal wind-solar complementarity. When it comes to relatively lower penetration, the Finish study reports VRE penetration of about 70% of the annual demand with storage energy capacity of 8.6 times daily average demand and at 6% total energy loss while complemented by other RE generation to reach 100% RE in the energy system. Note that the study of the Israeli and Californian grids showed a penetration of up to 90% of the annual demand, with energy capacity less than or approximately equal to daily average demand. Caution should be made not to make an absolute comparison between each results. For example Finland, which is in a temperate zone, has solar resources for two-third of a year as compared to Israel and California. The solar resource in this area exists mainly from March to October (during spring to autumn) but the remaining half-year was well complemented by good wind resources. Compared to California, where both wind and solar show better diurnal and seasonal complementarity, it could be expected that by comparison the Finish system may require more storage capacity. This may be further clarified if we compare the Finnish scenario with the achieved 73% VRE penetration for California at approximately 6% total loss and storage capacity of about 0.5 times daily average demand. But the possibility for improvement will be discussed later. Which one is correct, promoting the very high curtailment or large energy storage? The answer is neither. The significant dissimilarity between results may be the evidence that shows that present economic optimizations are not good to find a global optimal solution. The reason may be related to poor understanding of the future grid and difficulty to accurately model some of the parameters. A good example of misunderstanding may be evident from the tendency to design a grid for 100% VRE penetration and the corresponding result summarized in Table 1, which may be clear from results presented in [13,14]. At such a perfect 100% VRE penetration, the system should overcome the mismatching challenge either by building significantly large storage (very significant as compared to the 1 times daily average capacity and 20% total loss estimated for about 90% penetration) or depend on excessive energy loss of up to 50% of the generation for an exchange in storage capacity reduction. Due to the aforementioned challenges, researchers employ various tools to arrive at their result. For example, in a study by [4], the requirement that VRE and storage should supply the load 99.9% of the time may have lead to an over constraining that resulted in such an excess generation. On the contrary, the model used in [6] does not have such a constraint. Instead, it applies a priori limits on energy to power ratio of the storage, which could limit the flexibility in arriving at the optimal storage. Though, that approach is mandatory to correctly account for the cost of storage, at least for current battery technologies. No evidence was given on how the related non-linearity for such storage was overcame in [4]. Moreover, it is worth noting that the KSA study resulted in a system that dominantly depended on solar as compared to the dependence on wind


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observed for the PJM area. In short, it is possible to say that a grid optimized for high shares of renewables has yet to emerge even if the possibility of an economical renewable future is projected in those studies. This may show that optimizing the grid may guarantee a competitive renewable future. If not, one should expect a grid which will be extremely material intensive and may face the same sustainability question as the present grid, or alternatively face challenges of being constructed due to a material production limit in some sector. The ways to improve this challenge is to pursue a grid optimized for variable renewable energy systems. Yes, curtailment is mandatory to increase grid penetration and reduce storage needs, but the level of curtailment must have a limit. It is no surprise that it required very small storage at a curtailment value of about 50% of the total generation as more than 80% grid penetration was reported [14] for California without storage. It was also shown that the physical benefits of energy curtailment in increasing grid penetration by increasing storage use peaks at approximately 15% total energy loss [12-15]. The reduction in balancing capacity need levels off when total energy loss approaches 20% of the total VRE generation [15]. This puts some upper limit on its technical benefit where it starts to significantly diminish, and the upper limit may be dependent on level of penetration. Note that poor storage efficiency may make a little more curtailment more economical depending on VRE penetration. However, this should be seen against a report by Caldera et al. [23], where a 100% VRE system with less curtailment and large power-to-gas storage cost 10% less than the otherwise battery dominated system. In the present studies, the designed large storage are the least utilized (as can be seen from the UI values). However, one can conclude that well utilized storage size of about daily average demand could have led to a grid penetration higher than 90% of the annual demand at 20% energy loss. This shows that the solution to the above problem may require a different approach. First, there is a possibility for an alternative mix between the two extremes even if we still push reaching closer to 100%. Second, rather than targeting 100% from VRE alone it would be better to optimize the grid to achieve approximately 90% penetration, while other renewable resources are co-optimized to reach 100% RE (net zero emissions). Thirdly, it is important to work to arrive at an optimal storage design and dispatch to reduce the significant curtailment or significantly larger storage and balancing need. Returning to the question of storage requirement, one can see that a storage approximately equal to the daily average demand (at 20% total energy loss) is enough to arrive at grid penetration of 90% of the annual demand. Depending on resources, a further grid penetration target may require increased storage capacity or massive energy curtailment or a combination of both. The large storage capacity in Finland at (70% VRE penetration) may be due to wind driven storage capacity during the winter season (as wind appears to prefer a higher energy to power ratio than solar PV). But we have seen that an increase in energy curtailment could reduce the energy storage capacity need significantly below what is currently reported but might increase the cost for the energy system. We have indicated above that for wind alone systems some technical model started preferring curtailment over an increase in storage at energy capacity approximately 6.6 times the daily average demand [15] (note that the corresponding penetration and total energy loss was 57% and 0%, respectively). For the solar only Israeli grid scenario, at 25% total energy loss, storage capacity of about 6 daily average demand was sufficient to reach to grid penetration of about 98% of the annual demand. Note that the UI for such storage will be one of the lowest. Such storage gives a significant opportunity to reach 100% by using smaller other types of resources if an optimal dispatch is implemented. Thus, one may take this storage capacity (6 times daily average demand) as a loose approximation of the largest storage required for resource constrained regions with moderate curtailment. Even though, due to its hydro and other renewable resources, California does need to push for that high VRE share as it could be used as a showcase for other resource rich areas. The data found in the result database shows that when keeping above 25% total energy loss, a penetration higher than 98% was achieved by a storage capacity of about 1.3 times daily average demand. In summary, the mismatch between the VRE and load profile leads to least efficient use of resources, if a 100% renewable grid was aspired from wind and solar systems alone. However, optimal designing for VRE penetration up to 90% complemented with other renewable resources could provide an efficient energy system relying on lower energy storage and modest curtailment. Thus, designing a future power grid should involve broader principles as compared to the existing market economic and “reliability of supply” lead design strategy. At the core of this principle should be the critical material limitations of the future energy system. To properly deal with the challenge, detailed understanding of the physics of the future grid will be mandatory to design and operate such an efficient system. Finally, it is important to note that in the context of this paper, discussing large storage capacity as seasonal storage


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(since they are limited to a few days even in a loose approximation), is wrong. However, depending on the level of penetration and technology type, seasonal dispatching may be necessary for achieving a 100% RE system which supports the net zero emission target. It is also important to remind that the present economic model, such as the one reported for KSA [6, 23], shows that if the forecasted economic and technological advancement goes as expected, we may build storage as large as 22 times daily average demand. 5. Conclusions We have made a thorough comparison between several studies evaluating storage requirements based on hourly models. A number of interesting lessons surfaced at the end of our analysis. First, economic models promoted high penetration of VRE based on two varying techniques. The first one relied on large storage capacity at low energy curtailment, while others used significantly smaller storage at the expense of massive VRE curtailment. However, both groups reported cost of electricity comparable to the present cost. Closer study of the data and a comparison with other results show that an energy system optimized for VRE has yet to emerge. The data set for the Israeli and Californian grid studies show that a storage energy capacity of about 1 times daily average demand could suffice to arrive at VRE penetration of approximately 90% of the annual demand at total energy loss of about 20% of the total VRE generation. Depending on resources, a further VRE grid penetration target may require increased storage capacity, massive energy curtailment or a combinations of both. For example, for the solar only Israeli grid scenario, at 25% total energy loss, storage capacity of about 6 times daily average demand was required to reach to grid penetration of about 98% of the annual demand while a capacity of about 1.3 times daily average demand to reach the same penetration under the same loss requirement for the case of California. The mismatch between the VRE and load profile lead to the least efficient use of resources if a 100% renewable grid was aspired from wind and solar systems alone. However, optimal designing for VRE penetration up to 90% complemented with other renewable resources, in order to reach to 100% RE (net zero emission energy system), could provide an efficient energy system relying on lower energy storage and modest curtailment. Thus, designing a future energy systems should involve broader principles as compared to the existing market economic and “reliability of supply” lead design strategy. At the core of this principle of reaching to a net zero emission energy system should be a commitment to limit material intensity of the future energy system. To properly deal with the challenge, detailed understanding of the physics of the future energy system will be mandatory to design and operate such an efficient system. Acknowledgements The authors gratefully acknowledge the public financing of Tekes, the Finnish Funding Agency for Innovation, for the ‘Neo-Carbon Energy’ project under the number 40101/14 and support from LUT internally research platform REFLEX. The third author would like to thank Reiner Lemoine-Foundation for the valuable scholarship. References 1. 2. 3. 4. 5. 6.

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