Energy and Buildings 86 (2015) 222–232
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Economic feasibility of residential electricity storage systems in Ontario, Canada considering two policy scenarios Ivan Kantor ∗ , Ian H. Rowlands, Paul Parker, Bronwyn Lazowski University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada N2L 3G1
a r t i c l e
i n f o
Article history: Received 8 September 2014 Received in revised form 7 October 2014 Accepted 9 October 2014 Available online 22 October 2014 Keywords: Residential Electricity Storage system Energy policy Buildings Household Arbitrage Economics Storage Time of use
a b s t r a c t Excessive electricity consumption during peak demand periods has been shown to be expensive for utility companies and can affect the stability of the electricity grid. Shifting peak electricity consumption to offpeak periods has attracted the interest of governments, utility companies, equipment manufacturers and residents. Individual, hourly, household data from Ontario, Canada are used to explore the potential for households to install electricity storage systems by manipulating two financial policy triggers. Results show that households with higher daily and on-peak consumption realize net benefits at lower deviations from the current pricing regimes than do those with lower consumption. Benefits for households can be realized by manipulating either of the policy triggers considered, although the feasibility of these policy decisions is not explored. Repurposed Li-ion batteries require complete subsidy on re-purposing and installation of the system with a $29 kWhcapacity −1 subsidy or a differential of 19.5 ¢ kWh−1 between on- and off-peak commodity rates for 10% of households to achieve net benefits. Systems with new ZnMnO2 batteries require a $44 kWhcapacity −1 subsidy in addition to a complete installation subsidy or a differential of 16.5 ¢ kWh−1 between on- and off-peak commodity prices to achieve the same proportion of households with net benefits. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Increased peak electricity consumption can cause negative economic, social and environmental impacts. This was experienced during California’s energy crisis in 2000 and China’s large-scale power shortages of 2004 and 2011, where high costs and power shortages were some of the consequences of inadequate energy supply and demand management [1,2]. Electricity storage systems (ESSs) provide a solution to these issues by charging during off-peak, low-demand periods and utilizing this stored electricity during on-peak, high-demand periods. This strategy of load-shifting improves grid efficiencies and allows for increased flexibility in demand management [3]. At the residential scale, ESSs enable homeowners to reduce household electricity costs and peak energy demand, given a differential pricing scheme between high-and low-demand periods. Battery-based household systems, consisting of a battery and an inverter/charger, reduce the household load during high-demand periods by cycling the battery to take advantage of abundant electricity during low-demand periods [4]. The battery technologies
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used for ESSs include: lead-acid, nickel cadmium, nickel metal hydride and lithium-ion [5]. Non-battery options for energy storage include: super capacitors, flywheels, compressed air energy storage, super conducting magnetic energy storage and fuel cells [5,6]. Lithium-ion batteries for electric vehicles (EV) are a developing point of interest for residential electricity storage systems. Used EV batteries retain 80% of their initial amp-hour capacity at the end of their vehicle-life and have potential secondary applications [7,8]. Studies have indicated that storage systems can aid in smoothening demand [9,10]. Incentives for householders to invest in ESSs can be found in the cost differential for pricing between low- and high-demand periods under a time-of-use pricing regime in which off-peak commodity pricing is significantly lower than that during on-peak periods. This pricing arbitrage provides economic benefits to the homeowner by reducing electricity costs, while also creating system benefits by reducing the grid demand during peak hours. Ultimately, this allows households to benefit from differences in electricity pricing without the need to change their consumption levels [11]. However, the current costs of residential ESSs present a barrier to implementation. To test how effective ESSs would be to create household and grid scale benefits, policies supporting the implementation of these technologies would be required to decrease barriers (e.g., provide subsidies and incentives to reduce initial
2. Literature review In this section, existing publications on policies stimulating the uptake of ESSs are reviewed, with a focus on existing electricity pricing and ESS development policies in the residential sector. Economic instruments are frequently the policy tool chosen to influence energy decisions [12]. As noted by Taylor et al., establishing mandatory energy or emissions performance standards or sufficiently high energy costs are potential policy measures to encourage the implementation of ESSs [13]. Peak commodity prices occur at times of peak demand and jurisdictions such as California have implemented a cost structure for these extreme cases known as critical peak pricing (CPP) [14]. CPP events occur only for a limited number of days each year, when the system or market exhibits predetermined cost or demand criteria. Herter et al. found that CPP pricing in California was three times more expensive than on-peak pricing [15]. Currently, three major Californian utilities have implemented a CPP price of over $1.00 kWh−1 , which is much higher than the conventional on-peak summer rate of 10–20 ¢ kWh−1 [16]. This is further evidence that greater fluctuations in electricity rates are being implemented to encourage demand-shifting and these alternative regimes exhibit opportunities for electricity pricing arbitrage for households with an ESS. Heymans et al. identified that ESS systems in Ontario were found to be most effective for net savings when off-peak electricity rates were reduced by 75% [17]. Subsidies are another method to stimulate the adoption of new energy technologies such as ESSs. As identified by Taylor et al., the transition of ESSs into the market will not take place if they are not economically beneficial [13]. Heymans et al. recognized through their study that annual savings through residential ESSs were most consistent when auxiliary costs such as regulatory, delivery and debt fees are eliminated; therefore, they identify that incentives are needed for the adoption of ESSs. Their study also determined that the Province of Ontario, Canada would require $104–155 million for the adoption of a subsidy program [17]. The majority of the research into proposed policies for ESSs involves large-scale systems implemented at the national or subnational level [13,18–20]. These studies stress the importance of storage implementation for national energy security and for energy efficiency measures [13,19,20]. Additionally, the International Energy Agency (IEA) report on energy storage systems identifies that the deployment of ESSs allows for the supply of
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investment costs, or enact disincentives, such as higher prices, for consumption during peak hours). An additional opportunity to decrease the capital cost of ESSs is through the secondary use of electric vehicle (EV) batteries. These refurbished EV batteries, available at a lower cost, provide a promising prospect for residential storage, and are thus included in this research. The two scenarios explored in this work are financial incentives to decrease the capital costs of ESS installation and alternative commodity pricing that would encourage ESSs based on net savings from differentials between on- and off-peak price levels considering two promising ESS technologies as identified by the literature. This paper provides a techno-economic assessment of both new battery and second-use EV ESSs to determine the size of the incentives or policy interventions required to generate net household benefits. These net benefits are considered a prerequisite for the creation of a residential ESS market and it is also desired to determine what characteristics are typical of households that may achieve net benefits from installing an ESS. In this article, Section 2.0 provides analysis of existing literature, followed by the methodology in Section 3.0, results and discussion in Section 4.0 and the conclusions in Section 5.0.
Annual household consumpon (kWh)
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Household Annual Household Consumpon (kWh) Household average annual consumpon (kWh) 2013 Ontario average annual consumpon (kWh)
Fig. 1. Annual household consumption with group average and Ontario average.
multiple energy and power services and helps to support the uptake of renewable energy supply systems [18]. According to the IEA, government policies have been implemented to encourage adoption of large-scale ESSs in various countries including: Canada (Ontario), China, European Union, Japan, South Korea and the United States [18]. The IEA also identified government initiatives focused on small-scale residential ESSs. First, the procurement model of Ontario’s 2013 revised Feed-In-Tariff (FIT) program [21] includes opportunities to incorporate energy storage with renewable energy generation. Second, Germany enacted a subsidy for small-scale ESS projects to promote distributed energy storage to balance the large implementation of small-scale photo-voltaic (PV) projects [18]. These examples highlight the opportunity to integrate small-scale ESSs with renewable electricity generation but these opportunities have not been explored in detail in the existing literature. The current research in this field and associated development of policies for implementing residential ESSs is limited, thus providing an opportunity to explore the potential for residential electricity storage under time-of-use (ToU) pricing, making use of advanced battery technologies. This work considers options of subsidized ESSs and alternative ToU commodity pricing required to yield net economic benefits to residential households. Additionally, this research incorporates real residential consumption data instead of national or sub-national averages, as the decisions and behavior of individual households will also affect the potential benefits of implementing an ESS [22]. 3. Methodology Hourly smart meter data were obtained from a local electrical utility company, with consent of the householders as part of an on-going smart grid project to assess the interactions of residential electricity consumers with the electricity grid in Milton, Ontario, Canada. These data were obtained for a period of 49 months beginning on January 1, 2010 through to February 6, 2014 and are considered to be the most accurate available data for hourly electricity consumption as the quality is regularly assessed by the electrical utility to ensure proper delivery and billing. The annual consumption for the participant households averages 9760 kWh and the Ontario average is 9370 kWh [23]. Given the wide range of consumption levels and the similar averages, the households reported here are an appropriate representation of urban households in Ontario, Canada. The distribution of the consumption is shown in Fig. 1 with the group average and Ontario average also plotted for context. The annualized results throughout this work are constructed by aggregating the data from each household and hour over the entire range of data. These values are then divided by the fractional years of data for the individual households to adjust for those with less than the full data complement as
4.4 5.3 5.1 5.9 6.2 6.5 6.3 7.8 7.2 8 8 8.1 8.9 9.2 10 9.9 10.4 10.9 9.3 9.9 9.9 10.7 10.8 11.7 11.8 12.4 12.9 21:00–7:00 21:00–7:00 21:00–7:00 19:00–7:00 19:00–7:00 19:00–7:00 19:00–7:00 19:00–7:00 19:00–7:00 11:00–17:00 7:00–11:0017:00–21:00 11:00–17:00 7:00–11:0017:00–19:00 11:00–17:00 7:00–11:0017:00–19:00 11:00–17:00 7:00–11:0017:00–19:00 11:00–17:00 7:00–11:0017:00–21:00 11:00–17:00 7:00–11:0017:00–21:00 11:00–17:00 7:00–11:0017:00–19:00 11:00–17:00 7:00–11:0017:00–19:00 11:00–17:00 7:00–11:0017:00–19:00 Winter Summer Winter Summer Winter Summer Winter Summer Winter 2009–2010 2010 2010–2011 2011 2011–2012 2012 2012–2013 2013 2013–2014
Off-peak price (¢/kWh) Mid-peak price (¢/kWh) On-peak price (¢/kWh) Off-peak period Mid-peak period(s) On-peak period(s)
Table 1 Ontario time-of-use electricity periods and rates. Winter season is November 1st–April 30th each year while summer is May 1st–October 31st.
2.11:1 1.87:1 1.94:1 1.81:1 1.74:1 1.8:1 1.87:1 1.59:1 1.79:1
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On-:off-peak price ratio
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well as to account for the differences between individual years. The ID used throughout this paper is an identifier generated to maintain anonymity of the participants in this project. Initial steps in analyzing the data were to adjust the timestamp and rate period according to the ToU pricing regime and daylight savings time schedule as applicable in Ontario. The ToU pricing periods and costs are shown in Table 1. Weekends and holidays are assessed at the off-peak pricing level; thus, the periods and prices shown are for non-holiday weekdays only. The usage data were aggregated for each household and day according to the on-, mid- and off-peak usage to gain insight into the demand profiles of each individual case. Climate and the electricity pricing model influence the results of such an analysis and it is important to note that this work is completed in the context of Ontario, Canada with the consumption data specifically being provided from Milton, Ontario. For alternative jurisdictions, the procedure for sizing residential electricity storage would be consistent but regional factors would influence the results and should be accounted for. Household characteristics were determined by calculating average usage, percentage of on-peak usage and daily on-peak usage for the entire study period and also utilizing the summer consumption as a subset of this dataset. Average consumption figures throughout this work are found by considering the consumption for each hour and day and then aggregated over the study period. These aggregates are then divided by the fractional years or number of days to determine annual and daily values, respectively. These characteristics are utilized to illustrate household behaviour for determining which types of consumption patterns can be related to maximum effectiveness of electricity storage. These characteristics were determined using all of the available data for each household in the study so as to provide an accurate, individual assessment of ESS options. Each household was also assessed for the maximum annual cost savings over the study period that could have been experienced by shifting on- and mid-peak usage to off-peak times. This calculation was made by comparing the commodity price for the ToU period to the off-peak rate for all consumption throughout the study period. The difference between these values for the entire study period is divided by the fractional years of data to define the maximum annual potential for installing an ESS. This provides a basis for the maximum economic benefit potential for implementing an ideal electricity storage unit in each household. The results of this are shown in Table 3. Such ideal storage would consist of a unit that yielded 100% charge/discharge efficiency with a capacity and discharge rate perfectly aligned with the demand. Although current technology is far from providing this ideal scenario, this analysis illustrates the maximum savings potential for investment in an electrical storage solution. Financial values calculated in this project are in Canadian Dollars (CDN). Storage technology and installation costs are found in the literature in United States Dollars (USD) but the average exchange between these two currencies over the study period is 0.99CDN: 1USD and thus the currencies are treated as equivalent [24]. The storage technologies identified from the literature as demonstrating promise for use in residential electricity storage are ZnMnO2 batteries, as discussed by Zheng [25] to potentially yield cost savings, and repurposed EV Li-ion batteries identified in literature to have second-life potential in stationary storage applications [9,17,26]. The characteristics of these two technologies as implemented in this work are exhibited in Table 2. ZnMnO2 is a developmental flow battery technology and is the only chemical electricity storage technology shown by Zheng to have potential for applications as an ESS [25]. As this technology is developmental, some uncertainties exist and the average expected values are utilized for this work. Repurposed Li-ion batteries from EVs have
I. Kantor et al. / Energy and Buildings 86 (2015) 222–232 Table 2 Summary of storage technology specifications used in this study.
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Table 3 Maximum savings potential for each of the 25 study households.
Technology New ZnMnO2
Repurposed Li-ion
Price ($ kWhcapacity −1 ) Installation cost ($)
150 2000
Useful lifetime (years) Nominal pack capacity (kWh) Maximum number of packs Round-trip efficiency (%) Depth of discharge (%) Literature references
16 1 30 80 90 [25]
102 2300 (per pack, includes re-purposing) 12 19.6 3 64 80 [9,17,26]
been studied for second-use potential and are also considered as an option for low-cost electricity storage. Uncertainty remains in long-term capacity fade for these batteries and the current best estimates for the operational parameters according to Heymans et al. (2014) are used in this work. Additional refinement of uncertain quantities throughout the lifetime of both technologies would yield higher confidence in the results of this work. The preliminary step in assessing the cost-effectiveness of these storage technologies in residential electricity storage was to assess the current savings potential. The smart meter data were analyzed in conjunction with implementation of storage technology according to the following rules: 1. One battery charge cycle per day is completed during off-peak hours to the capacity of the battery while discharge can be any portion of the capacity that will not violate the depth of discharge limits and is dependent upon the individual day. 2. Stored electricity is prioritized to offset on-peak hours with only the remainder applied to mid-peak periods. 3. Household demand is perfectly predictable, such that discharging in mid-peak periods is only acceptable if the on-peak demand capacity for all on-peak periods in a day is retained. 4. Storage charge is not utilized during off-peak periods. 5. Battery self-discharge would not occur. 6. Battery capacity would not degrade. 7. Multiple repurposed Li-ion packs could be utilized to increase storage capacity in discrete nominal increments of 19.6 kWh, whereas a new ZnMnO2 battery could be constructed to have capacity ranging between 1 and 30 kWh in increments of 1 kWh. 8. Storage capital costs are amortized over the battery’s useful lifetime with an annual compounded interest rate of 5%. The evaluation of the impact of storage was completed by importing the usage data from 25 households into Microsoft SQL Server 2008 R2 as it is capable of rapid, extensive and complex calculations using very large data sets as was required for this analysis. Each household was evaluated by applying the decision rules shown above with the historical pricing regime shown in Table 1 and the battery technologies presented in Table 2. Prediction of future electricity rates is not included as part of this work as the intention is to provide results based upon the current storage technology cost and electricity pricing. The electricity consumption and cost for each household were evaluated with and without storage to find the economic benefits from applying residential storage technologies. Storage capacities were varied through a range representing 0–300% of average on-peak consumption for the households to explore a range of scenarios and potential capacities. The results from this initial analysis were that installation of storage in any allowable capacity was not economically viable given the 2010–2014 pricing of electricity and storage; therefore, arbitrage
ID
Maximum annual savings potential
H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H23 H24 H25 Average Standard deviation
$118.50 $158.92 $208.29 $74.64 $171.30 $96.59 $116.45 $194.08 $55.28 $100.49 $194.06 $186.66 $119.38 $266.64 $74.12 $171.54 $117.29 $122.04 $188.50 $147.94 $157.79 $257.21 $168.16 $122.30 $187.16 $151.01 $53.75
between the commodity pricing during on- and off-peak times is not economically feasible for either battery technology. Next, consideration was given to the conditions required to incentivize investment in residential electricity storage. The two areas identified were a reduction in storage cost, potentially as an incentive program from government or utility agencies, or a change in electricity pricing. Subsidies on the cost of installing residential electricity storage could impact either the fixed or variable costs of the storage technologies. A subsidy program to offset the cost of storage installation would be reasonable in light of historical incentive programs in Ontario to reduce on-peak consumption or to increase distributed generation during on-peak hours (PeakSaver, PeakSaver Plus, Feedin-tariff [21]). The analysis was repeated with an applied incentive of removing the installation cost for the storage. Again the cost of the storage was too great to yield a net savings from this technology. By eliminating the installation cost and adjusting the capacity cost through an iterative process from the current price (shown in Table 2) down to $1 kWhstorage −1 , a profile for these two storage technologies was developed in order to find the point at which storage would become economically attractive for each individual household. The other policy option evaluated is to create a greater difference between on- and off-peak pricing following the time-of-use periods implemented in Ontario. Using the household electrical demand profiles from the past four years of data, the on-peak price was adjusted iteratively from its current level up to a five-fold increase. The break-even on-peak electricity price for each household is thus calculated for each of the two storage technologies. 4. Results and discussion The maximum annual savings potential for each household is calculated by aggregating the differences in commodity pricing between off-peak hours and the on-/mid-peak hours for all of the data available. This is annualized by dividing the aggregate consumption and price differential by the fractional years of data considered. The results from this analysis are shown in Table 3.
I. Kantor et al. / Energy and Buildings 86 (2015) 222–232
60
60
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40 y = -0.74x + 58.56 R² = 0.73
30 20
y = -0.23x + 17.71 R² = 0.76
30 20
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0 15
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55
Li-ion capacity cost subsidy ($
65
75
kWh-1)
Percentage of households with net savings (%)
Daily consumpon (kWh)
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100%
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90%
90%
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80% y = 0.0325x - 0.8551 R² = 0.9828
70%
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Average Daily On-peak Consumpon
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Storage capacity subsidy ($)
Percentage of On-peak Consumpon
Daily consumpon (kWh)
Fig. 2. Household consumption characteristics related to individual break-even storage capacity cost subsidies for repurposed Li-ion batteries based on year-round consumption.
60
60
50
50
40
40 y = -0.90x + 68.16 R² = 0.73
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30
20 10
20 y = -0.27x + 20.36 R² = 0.67
10
0
0 15
25
35
45
55
65
75
Li-ion capacity cost subsidy ($ kWh-1) Average Summer Daily Consumpon Average Daily Summer On-peak Consumpon Percentage of Summer On-peak Consumpon Fig. 3. Household consumption characteristics related to individual break-even storage capacity cost subsidies for repurposed Li-ion batteries based on summer consumption.
It is clear from this analysis that the potential savings from installing electricity storage are small for many households, even given an ideal storage technology. Table 3 shows that the potential annual savings from implementing storage to shift consumption to off-peak periods and pricing is approximately $151 but varies by a factor of five from $55 to $266. This broad range is a clear indication that a household-specific assessment of residential electricity storage and the associated market potential for implementing such storage is required to better understand the broad range of consumption behaviour among households. Annual cost savings calculations for the remainder of this work reflect the savings for a household from charging an ESS in offpeak times to offset usage during on-peak periods while making an annual payment on the amortized capital cost of the equipment. A preliminary step with this analysis is to identify and characterize the households that are most likely to achieve cost savings with an ESS installed. The characteristics of these hubs can be used by policy-makers to reach decisions as to the likelihood of certain households to adopt this technology from an economic standpoint. Figs. 2 and 3 show the storage capacity subsidy required to achieve net benefits for a household related to the its daily consumption, on-peak consumption and percentage of consumption on peak year-round and in summer, respectively. The average daily on-peak consumption and average daily consumption show linear relations with the break-even price of implementing repurposed Li-ion storage. The percentage of on-peak consumption does not exhibit a significant relationship with the break-even subsidy for storage; thus, for repurposed Li-ion batteries, the point at which this technology becomes economically viable is related to the total
Fig. 4. Percentage of households with net benefits from repurposed Li-ion storage related to capacity cost subsidy, eliminating upper and lower tails.
use and on-peak use in a particular household. Similar relationships are observed by examining the entire year of household consumption, shown in Fig. 2. Data from the entire year show a better linear fit with the break-even level of subsidy and the daily on-peak consumption than do the summer data alone. A relationship between daily consumption and a cost subsidy to achieve a break-even economic point for households is logical as the electricity storage is implemented to take advantage of the arbitrage between on- and off-peak pricing without changing demand patterns. As such, the on-peak usage can be used as an indicator to determine when a household would find economic incentive in applying electricity storage. To determine the subsidy required on the capacity costs of storage, beyond the subsidy for installation and re-purposing of the battery packs, the cost of the storage is manipulated in increments of $1 kWhcapacity −1 in order to express the economic break-even point as a function of the subsidy. The results shown in Fig. 4 show that the break-even range for repurposed Li-ion batteries falls between a subsidy level of $27 and $56 kWhcapacity −1 for the majority of households. Subsidies below $27 will not generate positive returns to households to encourage installing storage and a subsidy above $56 will encourage a very high uptake with increased savings for each household but would not yield additional benefits for the subsidy providers. Considering only the linear uptake range of $2756 kWhcapacity −1 yields the strong linear relationship presented in Fig. 4. The linear relationship observed for this technology shows that increasing subsidy levels will yield a corresponding increase in the percentage of households with net benefits. Households with positive net savings at levels reflecting ∼10%, ∼50% and ∼90% of the total households are used to assess low, moderate and high adoption levels, respectively. The capacity cost of the storage to yield these percentages with net benefits of repurposed Li-ion batteries as residential electricity storage units are $73 kWhcapacity −1 , $63 kWhcapacity −1 and $50 kWhcapacity −1 , respectively. These levels require a capacity subsidy in addition to the installation subsidy of $29/kWh, \$39 kWhcapacity −1 and $52 kWhcapacity −1 , respectively. The economic results for these scenarios are shown in Table 4 with the capacity listed being the usable capacity of the battery. The range of usable capacities considered here are 10, 20 and 30 kWh, representing 1, 2 and 3 repurposed Li-ion packs. It can be observed from Table 4 that although the savings are positive for implementing storage at these pricing levels, the values are small. Given additional implementation barriers of imperfect information, risks (technical and financial), inertia, etc., larger subsidies may be required to make the implementation more attractive for householders. As mentioned previously, an alternative option for incentivizing residential electricity storage would be to increase the arbitrage
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Table 4 Annual savings and storage capacity with useful storage capacity pricing for levels corresponding to adoption by 10%, 50% and 90% of households using repurposed Li-ion batteries. ID
Storage capacity cost $73 kWhcapacity −1
H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H23 H24 H25
$63 kWhcapacity −1
$50 kWhcapacity −1
Annual cost savings
Storage capacity (kWh)
Annual cost savings
Storage capacity (kWh)
Annual cost savings
Storage capacity (kWh)
$0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $1.17 $0.00 $0.00 $0.00 $0.00 $0.00 $2.86 $0.00 $0.60 $0.00 $0.00 $0.00
0 0 0 0 0 0 0 0 0 0 0 0 0 10 0 0 0 0 0 10 0 10 0 0 0
$0.00 $0.00 $7.77 $0.00 $2.98 $0.00 $0.00 $1.07 $0.00 $0.00 $5.20 $2.71 $0.00 $12.40 $0.00 $0.00 $0.00 $0.00 $1.48 $14.08 $0.88 $11.82 $0.10 $0.00 $10.45
0 0 10 0 10 0 0 10 0 0 10 10 0 10 0 0 0 0 10 10 10 10 10 0 10
$4.65 $11.96 $22.36 $0.00 $17.58 $0.00 $6.16 $15.66 $0.00 $2.01 $19.80 $17.30 $0.30 $26.99 $0.57 $12.98 $6.35 $5.96 $16.07 $28.68 $15.47 $26.42 $14.70 $6.36 $25.04
10 10 10 0 10 0 10 10 0 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
Daily consumpon (kWh)
60
60
50
50
40
40 y = -0.89x + 56.18 R² = 0.62
30
30
20
20
10
10
y = -0.28x + 17.14 R² = 0.67
0 20
30
40
50
0 60
70
Daily consumpon (kWh)
Note: These levels of 10%, 50% and 90% adoption correspond to n = 3, 12 and 22, respectively.
60
60
50
50 y = -1.09x + 65.48 R² = 0.63
40
40
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30
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20
10 0 20
30
y = -0.34x + 19.89 R² = 0.62 40 50
10 0 60
70
On-peak electricity Price (¢ kWh-1)
On-peak electricity price (¢ kWh-1) Average Summer Daily Consumpon Average Daily Consumpon
Average Daily Summer On-peak Consumpon
Average Daily On-peak Consumpon Percentage of On-peak Consumpon Fig. 5. Break-even on-peak electricity price with repurposed Li-ion batteries based on year-round consumption.
opportunity for households, i.e., to increase the difference between on- and off-peak pricing. Within Ontario, the 2014 ratio for on-:offpeak pricing is 1.8:1 or an absolute differential of 6 ¢ kWh−1 . Future demands from the electricity grid during peak hours may cause policy-makers to consider an increase in the on-peak electricity price to a level that may incentivize the adoption of storage to shift demand from on-peak periods. The break-even point for storage in each household is calculated by assessing a range of potential onpeak electricity prices, from the current level up to five times the current level. A plot of the break-even point with the on-peak electricity price is shown in Fig. 5 for year-round consumption patterns and Fig. 6 for summer consumption. Similar trends are observed for this case as in the case of a storage cost subsidy, although one outlying household with very low on-peak consumption shifts the linear fit of the data. As in the analysis with the storage capacity subsidy, the level of household participation relative to the on-peak electricity cost is
Percentage of Summer On-peak Consumpon Fig. 6. Break-even on-peak electricity price with repurposed Li-ion batteries based on summer consumption.
shown in Fig. 7. The on-:off-peak ratio is also plotted to show the relation between the two prices as it corresponds to the percentage of households with net benefits. Fig. 7 shows that an on-peak electricity price between 25 ¢ kWh−1 and 40 ¢ kWh−1 is sufficient to create a net benefit for the majority of households. The on-peak prices corresponding to net savings gained by 10%, 50% and 90% of households are 27, 30 and 38 ¢ kWh−1 , respectively. The economic assessment for each household at these pricing levels is shown in Table 5. It is clear from Table 5 that the economic benefit to households from an increased on-peak electricity price is greatly increased compared to that observed when applying a subsidy on the cost of storage. As such, jurisdictions with a large price disparity between on- and off-peak pricing have an attractive market structure to stimulate residential investment in electricity storage. Similar analysis was conducted for the ZnMnO2 battery ESS as for the repurposed Li-ion battery. The preliminary step was to correlate the uptake from households with a subsidy on the capacity
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Table 5 Annual savings and storage capacity with set on-peak electricity pricing corresponding to adoption by 10%, 50% and 90% of households using repurposed Li-ion batteries. ID
On-peak electricity price 27 ¢ kWh−1
H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H23 H24 H25
30 ¢ kWh−1
38 ¢ kWh−1
Annual savings ($)
Storage capacity (kWh)
Annual savings
Storage capacity (kWh)
Annual savings
Storage capacity (kWh)
$0.00 $0.00 $7.60 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $96.24 $0.00 $0.00 $0.00 $0.00 $0.00 $3.81 $0.00 $73.74 $0.00 $0.00 $0.00
0 0 20 0 0 0 0 0 0 0 0 0 0 20 0 0 0 0 0 10 0 20 0 0 0
$0.00 $0.00 $105.89 $0.00 $29.88 $0.00 $0.00 $28.59 $0.00 $0.00 $43.95 $29.75 $0.00 $211.75 $0.00 $0.00 $0.00 $0.00 $17.92 $79.86 $5.05 $184.34 $3.47 $0.00 $75.03
0 0 20 0 10 0 0 20 0 0 10 20 0 20 0 0 0 0 10 10 10 20 10 0 10
$37.32 $113.25 $330.55 $0.00 $186.45 $0.00 $59.29 $223.66 $0.00 $3.52 $240.84 $224.47 $28.38 $475.77 $3.43 $174.90 $52.57 $49.07 $204.02 $254.86 $151.02 $437.16 $149.10 $64.50 $271.78
10 20 20 0 10 0 10 20 0 10 20 20 10 20 10 20 10 10 20 20 10 20 10 10 20
10 9 8 7 6 5 4 3 2 1 0 0
20
40
60
On-peak electricity price (¢ Parcipaon percentage
Daily consumpon (kWh)
100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%
On-:off-peak prcie rao
Percentage of households with net benefits (%)
Note: These levels of 10%, 50% and 90% of households with net benefits correspond to n = 3, 12 and 22, respectively.
60
60
50
50
40
40
y = 0.28x + 11.28 R² = 0.06
30
30 20
20 y = 0.10x + 2.51 R² = 0.09
10
10 0
0 15
25
35
45
55
65
ZnMnO2 break-even capacity subsidy ($ kWh-1)
80
kWh-1)
Average Daily Consumpon Average Daily On-peak Consumpon
On-:off-peak rao
Percentage of On-peak Consumpon
portion of the costs, assuming that installation is also fully subsidized. The results for this analysis are presented in Figs. 8 and 9 for whole-year and summer-only consumption, respectively. These results are dramatically different from those for the re-purposed Li-ion batteries as there is a very clear point at which the storage exhibits a net savings for many of the households. The level of subsidy for battery installation is not correlated with consumption characteristics from the household, as evidenced by a weak linear fit of the data in Figs. 8 and 9, showing that the economic case for new ZnMnO2 batteries is very different than the case of repurposed Li-ion batteries. The results show that there is a threshold subsidy level at which the economic case for storage shifts from non-viable to viable and at that point, the majority of households have a financial incentive to install storage. The subsidy levels for net benefits to 10% and 90% of participants are $44 kWhcapacity −1 and $59 kWhcapacity −1 , an intermediate level of 50% is unavailable due to the majority-shift of households experiencing net benefits at the subsidy level of $59 kWhcapacity −1 . The results of households with net benefits corresponding to
Fig. 8. Household consumption characteristics related to individual break-even storage capacity subsidies using ZnMnO2 batteries based on whole-year consumption.
Daily consumpon (kWh)
Fig. 7. Percentage of households with net benefits from repurposed Li-ion storage related to variable on-peak electricity pricing.
60
60
50
50 y = 0.33x + 10.99 R² = 0.06
40
40 30
30
20
20 y = 0.11x + 2.76 R² = 0.07
10
10 0
0 15
25
35
45
55
65
ZnMnO2 break-even capacity subsidy ($ kWh-1) Average Summer Daily Consumpon Average Daily Summer On-peak Consumpon Percentage of Summer On-peak Consumpon Fig. 9. Household consumption characteristics related to individual break-even storage capacity subsidies based on summer consumption using ZnMnO2 batteries based on summer consumption.
100%
90%
90%
80%
80%
70%
70%
60%
60%
50%
50%
40%
40% y = 0.01x - 0.41 R² = 0.32
30% 20%
30% 20%
10%
10%
0%
0% 0
20
40
60
80
100
Storage capacity price subsidy ($
120
ZnMnO2 capacity cost $91 kWhcapacity −1
$106 kWhcapacity −1
H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H23 H24 H25
50
50 y = -1.55x + 71.12 R² = 0.72
40
Annual savings
Storage capacity (kWh)
Annual savings
Storage capacity (kWh)
$0.00 $0.00 $0.00 $0.15 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $2.31 $0.00 $0.00 $0.00 $0.00 $3.51 $0.00 $0.00 $0.00 $0.00 $0.00
0 0 0 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 4 0 0 0 0 0
$0.13 $0.11 $0.27 $2.60 $0.24 $0.00 $0.00 $0.13 $0.00 $0.15 $0.23 $0.23 $0.15 $0.46 $5.91 $0.18 $0.16 $0.15 $0.15 $10.02 $0.20 $0.41 $0.16 $0.16 $3.51
2 2 4 3 3 0 0 2 0 2 3 3 2 6 3 3 2 2 2 5 3 6 2 2 6
Note: These levels of 10% and 90% adoption correspond to n = 3 and 22, respectively.
battery capacity subsidies are shown in Fig. 10. From this figure, it can be seen that uptake of the batteries is modest for low subsidies, up to $44 kWhcapacity −1 , and additional incentives are marginally effective until reaching the majority uptake threshold level of $59 kWhcapacity −1 at which point there is a large proportion with net benefits, representing 90% of the sample studied. Due to the distribution of the data points and the seemingly disparate portions of the graph, the data cannot be fit linearly. From visual inspection, it is observed that the distribution could be described by a piecewise linear function with the transition at a subsidy level of $59 kWhcapacity −1 . For the ZnMnO2 battery, the level of subsidy to incentivize the majority (90%) of households to install storage is 39% of the capacity cost of the battery in addition to fully-subsidized installation. Table 6 shows the annual savings and the implemented storage capacity for each of the households at the two subsidy levels for participation levels of 10% and 90%. As with the case of repurposed
40
30
30
20
20
10
10
y = -0.48x + 21.66 0 R² = 0.75 25 20
0 30
35
40
45
50
Break-even on-peak electricity price (¢ kWh-1) Average Daily Consumpon
140
Table 6 Annual savings and storage capacity with set storage capacity pricing corresponding to adoption by 10% and 90% of households using ZnMnO2 batteries. ID
60
Average Daily On-peak Consumpon
kWhcapacity-1)
Fig. 10. Percentage of households with net benefits from installing ZnMnO2 ESS related to capacity cost subsidy, eliminating upper and lower tails.
229
60 Daily consumpon (kWh)
100%
Percentage of On-peak Consumpon Fig. 11. Break-even on-peak electricity price with ZnMnO2 batteries based on whole-year consumption.
Daily consumpon (kWh)
Percentage of households with net benefits (%)
I. Kantor et al. / Energy and Buildings 86 (2015) 222–232
60
60
50
50 y = -1.89x + 83.52 R² = 0.72
40
40
30
30
20
20
10 y = -0.57x + 25.14 0 R² = 0.67 20 25
10 0 30
35
40
45
50
Break-even on-peak electricity price (¢ kWh-1) Average Summer Daily Consumpon Average Daily Summer On-peak Consumpon Percentage of Summer On-peak Consumpon Fig. 12. Break-even on-peak electricity price with ZnMnO2 batteries based on summer consumption.
Li-ion batteries, the annual savings are marginal for most households and considerations of additional barriers for implementation would not likely lead to widespread adoption of ZnMnO2 batteries for residential storage at these modest returns. Variable electricity rates are also considered in the context of ZnMnO2 battery implementation to identify the on-peak price at which this technology could be economically implemented. Considering the costs for procurement and installation of the storage shown in Table 2, the break-even economic case is examined for each household and those encouraged to implement storage are related to their consumption characteristics in Figs. 11 and 12 for whole-year and summer-only consumption, respectively. The break-even point for these households is related to the daily consumption and also the daily on-peak consumption in both the whole-year and summer-only cases. The goodness of fit is slightly higher for the linear trend following the year-round consumption characteristics and thus it would be advisable to utilize year-round consumption data in sizing this type of storage for residential households. The proportion of households with net savings from installing storage is shown in Fig. 13 related to the on-peak electricity price to determine the threshold rates for 10%, 50% and 90% participation in electricity storage using ZnMnO2 batteries. These rates correspond to on-peak electricity prices of 24, 28 and 32 ¢ kWh−1 , respectively. As with the analysis for capacity price subsidies, there is a gap for a range of on-peak electricity prices but this gap is less pronounced that in the results for the capacity cost subsidy. Incremental advances in the proportion of households with net benefits do not occur between 56% and 72% participation, though the difference in on-peak pricing is only 1 ¢ kWh−1 .
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Table 7 Annual savings and storage capacity with set on-peak electricity pricing corresponding to adoption by 10%, 50% and 90% of households using ZnMnO2 batteries. ID
On-peak electricity price 24 ¢ kWh−1
H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H23 H24 H25
28 ¢ kWh−1
32 ¢ kWh−1
Annual cost savings
Storage capacity (kWh)
Annual cost savings
Storage capacity (kWh)
Annual cost savings
Storage capacity (kWh)
$0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $54.70 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $0.00 $35.83 $0.00 $0.00 $0.00
0 0 0 0 0 0 0 0 0 0 0 0 0 15 0 0 0 0 0 0 0 14 0 0 0
$0.00 $0.00 $94.96 $0.00 $39.42 $0.00 $0.00 $35.12 $0.00 $0.00 $61.16 $45.04 $0.00 $179.96 $0.00 $7.60 $0.00 $0.00 $33.03 $85.84 $17.90 $153.74 $18.21 $0.00 $84.86
0 0 14 0 10 0 0 13 0 0 13 12 0 16 0 11 0 0 12 11 10 16 11 0 12
$18.45 $72.18 $211.97 $0.00 $129.99 $0.00 $33.07 $132.37 $0.00 $3.68 $163.23 $143.11 $3.54 $322.80 $7.36 $94.23 $29.01 $29.77 $126.80 $188.56 $101.82 $289.84 $105.37 $33.03 $191.41
7 12 16 0 11 0 8 14 0 7 13 14 9 17 6 13 8 8 13 12 11 17 12 9 13
100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%
10 9 8 7 6 5 4 3 2 1 0 0
20
40
60
80
On-:off-peak electricity price rao
Percentage of households with net benefits (%)
Note: These levels of 10%, 50% and 90% adoption correspond to n = 3, 12 and 22, respectively.
On-peak electricity price (¢ kWh-1) Parcipaon percentage
on:off-peak rao
Fig. 13. Percentage of households with net benefits from ZnMnO2 battery storage related to variable on-peak electricity pricing.
The annual savings and storage capacities for the threshold participation rates are shown in Table 7. Compared to the storage cost subsidy case for the same ZnMnO2 technology, the capacities and corresponding savings are much greater. Comparing these cases to those for repurposed Li-ion batteries, it is clear that the ZnMnO2 batteries become economically viable at lower electricity rates and provide greater annual savings. The commercial availability and technical specifications of ZnMnO2 batteries are still somewhat uncertain; therefore, adoption of this technology could not be immediate and may vary from the specifications employed in this analysis. The commodity pricing regime explored here is specific to Ontario, Canada and would need to be evaluated in other jurisdictions before installation could be recommended in those areas. However, the framework developed for assessing economic breakeven points based on storage capacity subsidies could be applied globally. Similarly, the framework for investigation regarding alternative electricity pricing could be applied wherever differential electricity pricing schemes exist.
Utilizing household data for assessing the economic benefits from installation of a residential ESS is required to account for the unique demands profiles in each household. This detailed analysis allows for individualized assessment of net economic benefit potential for each household which may be overshadowed by national or sub-national averaging as discussed by Carlson et al. [22]. Repurposed Li-ion ESSs will be increasingly available for secondlife applications as EV batteries approach end-of-life within vehicles. These batteries represent an immediate opportunity for implementing a residential ESS but require installation and equipment subsidies to be economically attractive for householders or a radically different electricity pricing regime within the study area of Ontario, Canada. Critical assumptions such as capacity fade and battery degradation should be assessed in more detail for repurposing Li-ion batteries, in addition to a thorough assessment of market potential to determine the price point for these repurposed batteries. ZnMnO2 batteries are an emerging flow-battery technology that could be employed as a residential ESS although further refinements of the technology are required to obtain firm details on pricing and capability of these batteries. Interest rate assumptions were identified in the literature to have a major impact on the economic viability of installing ESSs [25] and the work presented here uses an interest rate of 5%. Variations in this interest rate could have a large impact on the economic benefits of installing residential ESSs and thus may be subject to future sensitivity analysis.
5. Conclusions The two considerations in this analysis to encourage adoption of residential ESSs based on net economic benefits are an incentive on the installation and equipment costs of the ESS or an alternative ToU pricing regime. Subsidy programs on the cost of purchasing and installing an ESS system yield marginal savings for householders
I. Kantor et al. / Energy and Buildings 86 (2015) 222–232
given the current ToU pricing scheme in Ontario, Canada. Elevated pricing during on-peak times yields a much greater potential for savings in the households studied. From this analysis, it is clear that technology subsidies or drastically different commodity pricing could allow householders within the province of Ontario to realize economic benefits from installing a residential ESS. Repurposed Li-ion batteries are a current option for residential ESSs and will become increasingly available as more EVs are introduced. The technology subsidies required to reach an economic break-even point for repurposed Li-ion batteries are installation costs in addition to a capacity subsidy of $29 kWh−1 , $39 kWh−1 and $52 kWh−1 for 10%, 50% and 90% of households studied, respectively. These subsidies represent 28%, 38% and 51% of the repurposed battery capacity cost. New ZnMnO2 batteries require an installation subsidy in addition to a capacity subsidy of $44 kWhcapacity −1 to reach economic break-even for 10% of households and $59 kWhcapacity −1 for 90% of households. These subsidies are equivalent to 29% and 39% of the capacity cost of the ESS. On-peak electricity pricing can also influence the viability of installing residential ESSs. Repurposed Li-ion ESS systems, at current estimated pricing, require on-peak electricity pricing of 27, 30 and 37 ¢ kWh−1 for 10%, 50% and 90% of households to reach an economic break-even. The commodity cost differentials for these proportions of households to experience net benefits are 19.5, 22.5 and 29.5 ¢ kWh−1 , respectively. For ZnMnO2 ESSs, the commodity price differentials between on- and off-peak required for breakeven economics in 10, 50 and 90% of households are 16.5, 20.5 and 24.5 ¢ kWh−1 , respectively. The two scenarios considered of equipment subsidies and alternative commodity pricing yield differing results based on the battery technology considered. For the subsidy scenario, repurposed Li-ion ESSs would require less of a subsidy than new ZnMnO2 batteries for householders to experience net benefits. However, in the case of alternative electricity pricing, ZnMnO2 batteries yield net benefits for higher proportions of households at comparably lower commodity prices than repurposed Li-ion batteries. This difference can be attributed to the higher initial cost of the ZnMnO2 system but with correspondingly higher round-trip efficiency. Daily consumption and daily on-peak consumption are found to be better indicators of which households would realize net economic benefits from an ESS than is the percentage of on-peak consumption from these households. Households with high overall consumption and high on-peak electricity consumption require less equipment subsidy to find net savings using an ESS based on repurposed Li-ion batteries. Similarly, these households experience net savings with a smaller differential between on- and off-peak pricing and are thus more able to take advantage of this pricing arbitrage. The share of on-peak consumption in each household was not found to have a significant correlation with the break-even economic point for either the equipment subsidy or alternative ToU pricing scenario. This result may be attributable to the small range of on-peak usage percentages but is nonetheless found to be less indicative of net savings in a household than are the measures of daily consumption and daily on-peak consumption. ZnMnO2 -based ESSs do not follow the same trends as those based on repurposed Li-ion batteries. ZnMnO2 systems with a subsidy program exhibit break-even economics which are not correlated with the household characteristics of consumption or on-peak consumption. Instead, residential ESSs using ZnMnO2 technology exhibit break-even economics in a piecewise linear fashion with two tails representing few households with breakeven economics at low subsidy levels and many households at high subsidy levels but without an intermediate area. For the cases of alternative electricity pricing, ZnMnO2 -based ESSs do result in similar trends of break-even economics as seen with the repurposed Li-ion systems, where the observation is that households
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with higher consumption are more likely to have net savings from an ESS under higher on-peak electricity rates. Households with higher consumption make more frequent utilization of the ESS and in higher capacities which yields net savings on commodity pricing without adjusting consumption behaviour. Acknowledgments This research was completed under the auspices of the Energy Hub Management System project, which is supported by the Ontario Centres of Excellence, Hydro One Networks Incorporated, Milton Hydro Distribution Incorporated and Energent Incorporated. Valuable work on related topics by graduate students and other members of the Sustainable Energy Policy group at the University of Waterloo helped to inform thinking in this area. The authors are grateful for this support; they, however, remain solely responsible for the contents of this article. References [1] K. Herter, S. Wayland, Residential response to critical-peak pricing of electricity: California evidence, Energy 35 (2010) 1561–1567, http://dx.doi.org/ 10.1016/j.energy.2009.07.022. [2] Z. Ming, X. Song, L. Lingyun, W. Yuejin, W. Yang, L. Ying, China’s largescale power shortages of 2004 and 2011 after the electricity market reforms of 2002: explanations and differences, Energy Policy 61 (2013) 610–618, http://dx.doi.org/10.1016/j.enpol.2013.06.116. [3] I. Koutsopoulos, L. Tassiulas, Control and optimization meet the smart power grid: scheduling of power demands for optimal energy management, in: Proceedings of the 2nd International Conference on Energy-Efficient Computing and Networking, ACM, New York, NY, USA, 2011, pp. 41–50, http://dx.doi.org/10.1145/2318716.2318723. [4] J. Leadbetter, L. Swan, Battery storage system for residential electricity peak demand shaving, Energy and Buildings 55 (2012) 685–692, http://dx.doi.org/ 10.1016/j.enbuild.2012.09.035. [5] N.-K.C. Nair, N. Garimella, Battery energy storage systems: assessment for small-scale renewable energy integration, Energy and Buildings 42 (2010) 2124–2130, http://dx.doi.org/10.1016/j.enbuild.2010.07.002. [6] L. Sigrist, E. Lobato, L. Rouco, Energy storage systems providing primary reserve and peak shaving in small isolated power systems: an economic assessment, International Journal of Electrical Power & Energy Systems 53 (2013) 675–683, http://dx.doi.org/10.1016/j.ijepes.2013.05.046. [7] C. Narula, R. Martinez, O. Onar, M.R. Starke, G. Andrews, Economic Analysis of Deploying Used Batteries in Power Systems, Oak Ridge National Laboratory, Oak Ridge, Tennessee, 2011, http://web.ornl.gov/sci/physical sciences directorate/mst/pcm/pdf/Publication%2030540.pdf. [8] P. Wolfs, An economic assessment of second use lithium-ion batteries for grid support, in: Proceedings of the Universities Power Engineering Conference (AUPEC), 2010 20th Australasian, 2010, pp. 1–6. [9] J. Neubauer, A. Pesaran, The ability of battery second use strategies to impact plug-in electric vehicle prices and serve utility energy storage applications, Journal of Power Sources 196 (2011) 10351–10358, http://dx.doi.org/10.1016/j.jpowsour.2011.06.053. [10] R. Walawalkar, J. Apt, R. Mancini, Economics of electric energy storage for energy arbitrage and regulation in New York, Energy Policy 35 (2007) 2558–2568, http://dx.doi.org/10.1016/j.enpol.2006.09.005. [11] T. Erseghe, A. Zanella, C.G. Codemo, Optimal and compact control policies for energy storage units with single and multiple batteries, IEEE Transactions on Smart Grid 5 (2014) 1308–1317, http://dx.doi.org/10.1109/TSG.2014.2303824. [12] P. Linares, X. Labandeira, Energy efficiency: economics and policy, Journal of Economic Surveys 24 (2010) 573–592, http://dx.doi.org/10.1111/ j.1467-6419.2009.00609.x. [13] P.G. Taylor, R. Bolton, D. Stone, P. Upham, Developing pathways for energy storage in the UK using a coevolutionary framework, Energy Policy 63 (2013) 230–243, http://dx.doi.org/10.1016/j.enpol.2013.08.070. [14] K. Herter, Residential implementation of critical-peak pricing of electricity, Energy Policy 35 (2007) 2121–2130, http://dx.doi.org/10.1016/ j.enpol.2006.06.019. [15] K. Herter, P. McAuliffe, A. Rosenfeld, An exploratory analysis of California residential customer response to critical peak pricing of electricity, Energy 32 (2007) 25–34, http://dx.doi.org/10.1016/j.energy.2006.01.014. [16] CA Utility Approaches to Critical Peak Pricing and Capacity Reservation, Joule Assets. (n.d.). http://www.jouleassets.com/california-utilities-criticalpeak-pricing-capacity-reservation (accessed 16.07.14). [17] C. Heymans, S.B. Walker, S.B. Young, M. Fowler, Economic analysis of second use electric vehicle batteries for residential energy storage and load-levelling, Energy Policy 71 (2014) 22–30, http://dx.doi.org/10.1016/j.enpol.2014.04.016. [18] International Energy Agency, Technology Roadmap: Energy Storage, IEA, Paris, France, 2014, http://www.iea.org/publications/freepublications/publication/ TechnologyRoadmapEnergystorage.pdf.
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