energies Article
Potential Arbitrage Revenue of Energy Storage Systems in PJM Mauricio B. C. Salles 1, * 1 2 3
*
ID
, Junling Huang 2 , Michael J. Aziz 3 and William W. Hogan 2
Laboratory of Advanced Electric Grids - LGrid, Polytechnic School, University of São Paulo, São Paulo 05508-010, Brazil John F. Kennedy School of Government, Harvard University, Cambridge, MA 02138, USA;
[email protected] (J.H.);
[email protected] (W.W.H.) John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA;
[email protected] Correspondence:
[email protected]; Tel.: +55-11-3091-5533
Academic Editor: Haolin Tang Received: 15 May 2017; Accepted: 21 July 2017; Published: 27 July 2017
Abstract: The volatility of electricity prices is attracting interest in the opportunity of providing net revenue by energy arbitrage. We analyzed the potential revenue of a generic Energy Storage System (ESS) in 7395 different locations within the electricity markets of Pennsylvania-New Jersey-Maryland interconnection (PJM), the largest U.S. regional transmission organization, using hourly locational marginal prices over the seven-year period 2008–2014. Assuming a price-taking ESS with perfect foresight in the real-time market, we optimized the charge-discharge profile to determine the maximum potential revenue for a 1 MW system as a function of energy/power ratio, or rated discharge duration, from 1 to 14 h, including a limited analysis of sensitivity to round-trip efficiency. We determined minimum potential revenue with a similar analysis of the day-ahead market. We presented the distribution over the set of nodes and years of price, price volatility, and maximum potential arbitrage revenue. From these results, we determined the breakeven overnight installed cost of an ESS below which arbitrage would be profitable, its dependence on rated discharge duration, its distribution over grid nodes, and its variation over the years. We showed that dispatch into real-time markets based on day-ahead market settlement prices is a simple, feasible method that raises the lower bound on the achievable arbitrage revenue. Keywords: battery; electricity market; energy arbitrage; energy storage; real-time market
1. Introduction Energy storage systems (ESS) offer benefits to grid operations from distributed generation-to-utility scale installations. The costs of ESS have been decreasing, and new technologies are still in development. They can be physically connected at generation, transmission, and distribution, or at the customer side. In general, the benefits provided by an ESS are related to energy time shift, ancillary services, upgrade deferral, power quality, reliability, and variable generation smoothing [1,2]. The United States (including all territories) has already installed 52 units (411 MW) of electrochemical ESS over 1 MW of rated power (i.e., power capacity); 12 units (74.5 MW) are under construction and 26 units (330 MW) are already contracted or announced [3]. Among 90 installations (totaling 815 MW), the primary services provided are concentrated on four types [3]:
• • •
Frequency Regulation (217 MW); Reserve Capacity (116 MW); Energy Time Shift (131 MW);
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Whereas Whereas electrochemical electrochemical ESS can operate to provide more than one service at the same time, such such as as voltage voltage support support and and frequency frequency regulation, some services are mutually exclusive. Currently, the the focus focus is is on on ancillary services, which include frequency regulation and reserve capacity. capacity. Ancillary services represent more than 40% of the power capacity of those 90 installations. The services represent more than 40% of the power capacity of those 90 installations. The characteristics characteristics of of frequency frequency regulation regulation and and reserve reserve capacity capacity do do not not require require great great energy energy capacity capacity from from the the storage storage system, system, as as shown shown in in Figure Figure 1. 1. For ease of of comparison, comparison, we present present the the discharge discharge duration duration at at rated rated power, power,i.e., i.e.,the theenergy/power energy/power ratio, instead of the energy capacity capacity for for the the abscissa. abscissa. Most of of the the units units for for energy energy time time shift shift have have more morethan thanone onehour hourof ofdischarge dischargeduration durationat atthe therated ratedpower powercapacity. capacity.
Figure 1. 1. Electrochemical Electrochemical energy energy storage storage systems systems (ESS) (ESS) installations installations over over 11 MW MW in in the the United United States States Figure (August 2015) classified by their primary use, based on the data provided in [3]. (August 2015) classified by their primary use, based on the data provided in [3].
The lithium-ion battery has the highest percentage in number of units, as shown in Figure 2, but The lithium-ion battery has the highest percentage in number of units, as shown in Figure 2, but the total power capacity is dominated by lead-acid batteries. Figure 2 shows that the most commonly the total power capacity is dominated by lead-acid batteries. Figure 2 shows that the most commonly used technologies for longer duration are the flow and the sodium-sulfur batteries, which are also used technologies for longer duration are the flow and the sodium-sulfur batteries, which are also used used for energy time shift (Figure 1). Both technologies are considered to be promising options for for energy time shift (Figure 1). Both technologies are considered to be promising options for energy energy time shift and peak shaving [2,4]. Because flow batteries, in principle, can be designed with time shift and peak shaving [2,4]. Because flow batteries, in principle, can be designed with arbitrarily arbitrarily large energy/power ratios, they become more interesting for long discharge duration large energy/power ratios, they become more interesting for long discharge duration applications. applications. This paper investigates the requirements for economic viability at discharge durations This paper investigates the requirements for economic viability at discharge durations up to 14 h, in up to 14 h, in anticipation of growth in photovoltaic and wind supply leading to increased need for anticipation of growth in photovoltaic and wind supply leading to increased need for energy time-shift energy time-shift services. The analyses, however, are pertinent to all ESS technologies. services. The analyses, however, are pertinent to all ESS technologies. The round trip efficiency of EES technologies is continuously improving. This manuscript The round trip efficiency of EES technologies is continuously improving. This manuscript considers instead a foreseeable future in which costs should be lower and efficiencies higher. considers instead a foreseeable future in which costs should be lower and efficiencies higher. Flywheels, Flywheels, at 95% [4], excluding power electronics losses, inspired an aspirational target of 95%. at 95% [4], excluding power electronics losses, inspired an aspirational target of 95%. Values around Values around 95% are realistic for some ESS technologies (for example some Li-ion batteries, 95% are realistic for some ESS technologies (for example some Li-ion batteries, flywheels, and flywheels, and supercapacitors). However, the process of choosing a suitable ESS may not be only a supercapacitors). However, the process of choosing a suitable ESS may not be only a question question of investment, efficiency, maturity, and reliability, but it is also a question of the of investment, efficiency, maturity, and reliability, but it is also a question of the environmental impact environmental impact of the chemical materials. This paper does not concentrate on a specific ESS of the chemical materials. This paper does not concentrate on a specific ESS type of technology, but type of technology, but rather on a generic model. rather on a generic model. The wholesale electricity markets in the United States have been affected by increases in average electricity prices and in their volatility, before 2007 [5]. In 2008, the prices started to decline because of the exploitation of shale gas. A significant increase occurred in 2014 due to the extremely cold weather in the beginning of the year [6], but the average price subsequently returned to the values of 2013. The use of ESS for energy time shift has a great potential for creating revenue in these scenarios, buying energy when the prices are low and selling back to the grid when prices are high. The estimation of this potential arbitrage revenue is presented in [5] for a period from 2002 to 2007 for a few nodes in Figure 2. The electrochemical technology of ESS installations, and their relation with power capacity and discharge duration at rated power (same units as in Figure 1).
up to 14 h, in anticipation of growth in photovoltaic and wind supply leading to increased need for energy time-shift services. The analyses, however, are pertinent to all ESS technologies. The round trip efficiency of EES technologies is continuously improving. This manuscript considers instead a foreseeable future in which costs should be lower and efficiencies higher. Flywheels, at 95% [4], excluding power electronics losses, inspired an aspirational target of 395%. Energies 2017, 10, 1100 of 19 Values around 95% are realistic for some ESS technologies (for example some Li-ion batteries, flywheels, and supercapacitors). However, the process of choosing a suitable ESS may not be only a the PJM day-ahead marketefficiency, (DAM). This work reported a method but of short-term of hourly question of investment, maturity, and reliability, it is alsoforecasting a question of the prices to maximize revenue, using a one day back-casting dispatch. The theoretical value capture environmental impact of the chemical materials. This paper does not concentrate on a specificfrom ESS that approach was close to thaton obtainable perfect foresight. type of technology, but rather a genericwith model.
Figure Figure 2. 2. The The electrochemical electrochemical technology technology of of ESS ESS installations, installations, and and their their relation relation with with power power capacity capacity and discharge duration at rated power (same units as in Figure 1). and discharge duration at rated power (same units as in Figure 1).
A similar approach was presented in [7] highlighting that energy arbitrage and operating reserves revenues could make conventional compressed air energy storage (CAES) devices profitable in several day-ahead electricity markets. An economic evaluation of 14 ESS technologies was performed in [8], considering real-time energy prices in 2008 (the highest of the past decade) from seven different markets. The studies performed in the current work extend the analysis presented in [5–8] and evaluate the potential arbitrage revenue of a generic ESS using real-time prices from 2008 to 2014, including more than 7,300 different locations in PJM. The organization of the paper is as follows. Section 2 gives an overview and statistical description of the energy prices in the PJM wholesale markets. In Section 3, the potential arbitrage revenue of an ESS in the market over the period from 2008 to 2014 is determined for a price-taking ESS with perfect forecast of hourly locational marginal prices, providing an upper limit to arbitrage revenue. Section 4 evaluates the impact of forecast on potential arbitrage revenue, providing a lower limit on revenue from the day-ahead market and raising the lower limit with a novel approach to dispatch. In Section 5 we determine the breakeven overnight installed cost that an ESS must fall below for profitability, its dependence on rated discharge duration, its variation over grid nodes, and its variation over the years. Finally, the main conclusions are presented in Section 6. 2. PJM Electricity Markets The PJM Interconnection is a regional transmission organization (RTO) in the United States coordinating the wholesale electricity market in 13 states and in the District of Columbia. In 2014, the electricity generation in PJM was 780 TWh for an installed power capacity of 183 GW. The total billings were $50.03 billion in 2014 and $35.89 billion in 2013 [9]. The high demand and high prices in the beginning of 2014 (consequences of extremely cold weather) caused the 40% increase [9]. This section is dedicated to discussing the wholesale electricity prices from 2008 to 2014. 2.1. Locational Marginal Price Locational marginal price (LMP) can be defined as the price of supplying an additional MW of load at a specific location, or node (likely a bus) in the power system. The total LMP value is based on three pricing components: system energy price, transmission congestion cost and cost of marginal losses. The values are determined by an economic dispatch algorithm following operation security constraints. In PJM, there are two energy markets with separate settlements: the day-ahead market (DAM) and the real-time market (RTM). The DAM produces bid-based schedules with hourly-based prices and the RTM is a physical market with a five minute interval price. Both market settlements are performed on hourly-based LMPs, but the RTM is based on actual system condition deviations from the day-ahead schedule [10–12].
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2.2. Electricity Prices in the PJM Real-Time Market PJM has a special tool called Data Miner to download the historical electricity price data4 of for19RTM Energies 2017, 10, 1100 Energies 2017, 10, 1100 19 and DAM. We selected all of the nodes that have a complete data set from 2008 through 2014.4 of The PJM downloaded data must bemust treated carefully to identify changes in the node (node IDs PJM downloaded downloaded data must be treated treated carefully to identify identify changes in the the nodeidentifications identifications (node (node PJM data be carefully to changes in node identifications IDs can be changed for various reasons) and to find missing data within the period analyzed. We can be changed for various reasons) and to find missing data within the period analyzed. We IDs can be changed for various reasons) and to find missing data within the period analyzed.analyzed We analyzed 906 million price observations, including both RTM and DAM. 906 million price observations, including both RTM and DAM. analyzed 906 million price observations, including both RTM and DAM. The price price deviations between DAMand andRTM RTM should should be be small nono significant events happen in in The price deviations between DAM should besmall smallifif if significant events happen The deviations between DAM and RTM no significant events happen in the system. The annual average electricity price of all the PJM nodes analyzed over a seven-year the system. The annual average electricity price of the all the nodes analyzed over a seven-year the system. The annual average electricity price of all PJMPJM nodes analyzed over a seven-year period period is is presented presented in Figure Figure 3. 3. The prices prices in in 2008 2008 were were the the highest highest observed. observed. Figure 33 suggests suggests aa period is presented in Figure 3.inThe pricesThe in 2008 were the highest observed. FigureFigure 3 suggests a possible possible simplified simplified model model considering considering two two periods periods of of charging charging (22:00 (22:00 to to 6:00 6:00 and and 12:00 12:00 to 16:00) 16:00) and and possible simplified model of considering two periods of16:00 charging (22:00 to 6:00However, and 12:00 tosimulations 16:00)to and showed two periods two periods discharging (to 12:00 and to 22:00) per day. the two periods of discharging (to 12:00 and 16:00 to 22:00) per day. However, the simulations showed of discharging (to 12:00 andleads 16:00 22:00) day. However, theoptimization simulationsanalysis showed thatathis simple that this this simple simple model to to very poorper performance, and an an using linear that model leads to very poor performance, and optimization analysis using a linear modelprogram leads towas veryrequired. poor performance, and an optimization analysis using a linear program was required. program was required.
Figure 3. Hourly-based average pricesofofelectricity electricity in the real-time market (RTM), encompassing Figure 3. Hourly-based average in the thereal-time real-timemarket market (RTM), encompassing Figure 3. Hourly-based averageprices prices of electricity in (RTM), encompassing 7395 PJM nodes. 7395 7395 PJM nodes. PJM nodes.
Another way way to to study study price price distribution distribution is is using using the the average average of of RTM RTM prices prices per per year year per per PJM PJM Another
Another to study distribution is using the RTM prices per Figure year pershows PJM node. node. The Theway average priceprice is aa variable variable that influences influences the average potentialof arbitrage revenue. node. average price is that the potential arbitrage revenue. Figure 44 shows The average priceofisnodes a variable that influences the potential arbitrage revenue. Figure 4 shows the number the number for which the average electricity price fell below a given value for a given year. the number of nodes for which the average electricity price fell below a given value for a given year. It demonstrates that the average LMP values vary significantly with location. The nodes can be of nodes for which the average electricity price fell below a given value for a given year. It demonstrates It demonstrates that the average LMP values vary significantly with location. The nodes can be divided into three groups, considering price: (a) 50% with low prices; (b) 45% with medium prices that the average vary significantly with location. be divided into three groups, divided intoLMP threevalues groups, considering price: (a) 50% with The low nodes prices; can (b) 45% with medium prices and (c) (c) 5% 5% with high prices. considering price: (a)high 50%prices. with low prices; (b) 45% with medium prices and (c) 5% with high prices. and with
Figure 4. Average electricity prices of RTM in PJM, encompassing 7395 nodes. The abscissa is the Figure 4. Average electricity prices of RTM in PJM, encompassing 7395 nodes. The abscissa is the
Figure 4. Average electricity prices of RTM PJM, nodes. The percentage of nodes with average price lessinthan theencompassing value given by7395 the ordinate. The abscissa values areis the percentage of nodes with average price less than the value given by the ordinate. The values are percentage of nodes withbyaverage price less than the value given by the ordinate. The values are independently sorted year. independently sorted by year. independently sorted by year.
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2.3. 2.3. Volatility of Electricity Prices Prices in in PJM PJM 2.3. Volatility of Electricity Prices in PJM In In Figure Figure 5, 5, the the historical historical volatility volatility of all the 7395 PJM nodes was measured measured by annual standard standard In Figure 5, the historical volatility of all the 7395 PJM nodes was measured by annual standard deviation deviation [13]. [13]. The values in 2014 2014 were were the the highest highest in in all all nodes, nodes, followed followed by by the the values values of of 2008. 2008. deviation [13]. The values in 2014 were the highest in all nodes, followed by the values of 2008.
Figure 5. Annual standard deviation of real-time prices in PJM, encompassing 7395 nodes. The values Figure Figure 5. 5. Annual Annual standard standard deviation deviation of of real-time real-time prices prices in in PJM, PJM, encompassing encompassing7395 7395nodes. nodes. The The values values are independently sorted by year. are are independently independently sorted sorted by byyear. year.
The volatility of the electricity prices can also influence the potential arbitrage revenue of ESS. The volatility volatility of of the the electricity electricity prices can also influence the potential arbitrage revenue of ESS. The However, an annual calculation might not have information on the effects resulting from severe However, an an annual annual calculation calculation might might not not have have information information on on the the effects effects resulting resulting from from severe severe However, contingences or congestions to consider the potential revenue in specific days. contingences or congestions to consider the potential revenue in specific days. contingences or congestions to consider the potential revenue in specific days. Figure 6 sheds light on the distribution of the standard deviation. The standard deviation was Figure 66 sheds sheds light light on on the the distribution distribution of of the thestandard standarddeviation. deviation. The The standard standard deviation deviation was was Figure calculated for every day within the period of analysis and for all the nodes. The average of the 10 calculated for every day The average of of thethe 10 calculated day within within the the period periodofofanalysis analysisand andfor forallallthe thenodes. nodes. The average highest values in each node is shown in Figure 6. The values in 2014 were even higher than the year highest values in each node is shown in Figure 6. The values in 2014 werewere eveneven higher thanthan the year 10 highest values in each node is shown in Figure 6. The values in 2014 higher the values. values. year values.
Figure 6. Average of the 10 highest daily standard deviation of real-time prices in PJM, encompassing Figure 6. 6. Average Average of of the the 10 10 highest highest daily daily standard standard deviation deviation of of real-time real-time prices prices in in PJM, PJM, encompassing encompassing Figure 7395 nodes. The values are independently sorted by year. 7395 nodes. The values are independently sorted by year. 7395 nodes. The values are independently sorted by year.
The highest standard deviation value in 2014 (from Figure 5) happened in a load bus in The highest higheststandard standard deviation value2014 in 2014 5) happened a in load bus in The deviation value (from(from FigureFigure 5) happened a loadinbus Delaware, price node number 49984 andinnode name NEWMERED69 KVinN-MERD. TheDelaware, standard Delaware, price node number 49984 andNEWMERED69 node name NEWMERED69 KV N-MERD. The standard price node number 49984 and node name KV N-MERD. The standard deviation and deviation and the average price per day for the two years with the highest yearly-based values (2008 deviation and theper average price per day forwith the two years with the highestvalues yearly-based values (2008 the average price day for the two years the highest yearly-based (2008 and 2014) are and 2014) are presented in Figure 7. The same node had very different profiles for daily-based and 2014) in are presented insame Figure 7. had The very samedifferent node had very for different profiles for daily-based presented Figure 7. The node profiles daily-based investigation and investigation and only small spikes appeared in 2008. investigation andappeared only smallinspikes only small spikes 2008. appeared in 2008.
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(a) During 2008
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(b) During 2014
(a) During 2008 (b) 2014 (a) During 2008 (b)During During 2014 Figure 7. Daily-based standard deviation (Std) and the mean value of real-time prices at the price 7. Daily-based standard deviation (Std) and the mean value of real-time prices at the price nodeFigure NEWMERED69 KV N-MERD. Figure 7. Daily-based standard deviation (Std)and and mean value of real-time prices atprice the price Figure standard deviation (Std) thethe mean value of real-time prices at the node node NEWMERED69 KV N-MERD.
node NEWMERED69 KV N-MERD. NEWMERED69 KV N-MERD.
2.4. Seasonality of Electricity Prices for PJM 2.4. Seasonality of Electricity Prices for PJM 2.4. of Electricity Prices PJM 2.4. Seasonality Seasonality Electricity Prices for forbecause PJM of different reasons; however, the impact of the extreme Electricity ofprices can increase Electricity prices can increase because of different reasons; however, the impact of the extreme
weather at the 2014 isbecause 8. In In almostall all years, the prices in and Electricity prices can increase of different reasons; however, the impact of the Electricity prices can of increase because of different reasons; however, the impact of January the extreme extreme weather at beginning the beginning of 2014 isshown shownin in Figure Figure 8. almost years, the prices in January and July are to be than presumably because a higher demand. weather at the beginning of is in Figure 8. all the prices in Julyverified are verified to higher be higher than inother othermonths, months, because of of a higher demand. weather at the beginning of 2014 2014 isinshown shown in Figurepresumably 8. In In almost almost all years, years, the prices in January January and and July July are are verified verified to be higher than in other months, presumably because of a higher demand.
Figure 8. Monthly-based PJM average prices of electricity in RTM, considering the previous same nodes. Figure 8. Monthly-based PJM average prices of electricity in RTM, considering the previous same
nodes. 8. Monthly-based Figure Monthly-basedPJM PJMaverage averageprices prices of electricity in RTM, considering the previous same Figure of electricity in RTM, considering the previous same nodes. Figure 8 also shows that the price of electricity in 2008 was the highest in the period analyzed. nodes. The reason could be the high prices of natural gas for electricity generation, as shown in Figure 9. Figure 88 also also shows that that the price price of of electricity electricity in was the highest in period analyzed. Figure in 2008 2008gas was the highestprices in the the period analyzed. From the same shows figure, a verythe strong relation between natural and electricity can be verified. The reason could be the high prices of natural gas for electricity generation, as shown in Figure 9. Figure 8 also shows that the price of electricity in 2008 was the highest in the period analyzed. The price of coal nothigh volatile [14]. of natural gas for electricity generation, as shown in Figure 9. The reason could beisthe prices
From the same figure, aa very between natural gas electricity prices can verified. The reason could be the highstrong pricesrelation of natural gas for electricity generation, shown 9. From the same figure, very strong relation between natural gas and and electricityas prices caninbe beFigure verified. The price of coal is not volatile [14]. From the same figure, very strong The price of coal is nota volatile [14].relation between natural gas and electricity prices can be verified. The price of coal is not volatile [14].
Figure 9. Monthly-based PJM average price of electricity in RTM and the average cost of natural gas for electricity generation in the U.S. The average price of electricity was calculated from the 7395 nodes and the cost of natural gas was based on the data provided in [14].
Figure 9. Monthly-based PJM average price of electricity in RTM and the average cost of natural gas for electricity generation theaverage U.S. Theprice generation in in average price of electricity wasthe calculated 7395 nodes Figure 9. Monthly-based PJM of electricity in RTM and averagefrom cost the of natural gas andelectricity the cost of natural gas wasU.S. based the provided in the data data provided in [14]. [14].was calculated from the 7395 nodes for generation in the Theon average price of electricity
and the cost of natural gas was based on the data provided in [14].
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The seasonality shown in Figure 8 was also observed in the daily profile of electricity prices The seasonality shown in Figure 8 was also observed in the daily profile of electricity prices in The seasonality in Figure was also observed in the daily of electricity prices in different months of shown the year. Figure810 shows the average price ofprofile electricity in January forinthe different months of the year. Figure 10 shows the average price of electricity in January for the 7395 different months of the year. Figure 10 shows the average price of electricity in January for the 7395 7395 nodes in RTM. were twoper peaks per day inand January and only(Figure one in11). July nodes in RTM. ThereThere were two peaks day in January only one in July The(Figure revenue11). nodes in RTM. There were two peaks per day in January and only one in July (Figure 11). The revenue Thewas revenue was probably affected notofonly because ofbut thealso high prices, also because of the probably affected not only because the high prices, because of but the difference between was probably affected noton-peak only because ofThe theJanuary high prices, butwould also because of the difference between difference between offand price. profile have had more potential revenue off- and on-peak price. The January profile would have had more potential revenue if the average off-average and on-peak price. Thesame, January profile would have had more potential revenue if the average if the price were and they lead to different values for the optimum price were the same, andthe they would lead to would different values for the optimum energy capacity ofenergy the price were the same, and they would lead to different values for the optimum energy capacity of the capacity of the ESS. The optimum energy capacity with only one peak per day would be much higher ESS. The optimum energy capacity with only one peak per day would be much higher than for two ESS. The optimum energy capacity with only one peak per day would be much higher than for two than for two peaks per day. peaks per day. peaks per day.
Figure Januaryhourly-based hourly-based average prices prices of electricity electricity in Figure 10.10. January inRTM, RTM,encompassing encompassing7395 7395nodes. nodes. Figure 10. January hourly-basedaverage average prices of of electricity in RTM, encompassing 7395 nodes.
Figure 11. July hourly-based average prices of electricity in RTM, encompassing 7395 nodes. Figure July hourly-basedaverage averageprices prices of of electricity electricity in Figure 11.11. July hourly-based in RTM, RTM,encompassing encompassing7395 7395nodes. nodes.
2.5. Electricity Prices in PJM Zones 2.5. Electricity Prices in PJM Zones 2.5. Electricity Prices in PJM Zones The nodal prices varied in time and location in wholesale markets based on LMP. Observation The nodal prices varied in time and location in wholesale markets based on LMP. Observation of The the PJM price profile across important markets for understanding and where nodal prices varied in time and and location locationwas in wholesale based on when LMP. Observation of the PJM price profile across time and location was important for understanding when and where the potential revenue could be captured. Figure 12 shows the average price of electricity 18 PJM of the pricerevenue profile could acrossbe time and location important for understanding whenin the PJM potential captured. Figure was 12 shows the average price of electricity inand 18 where PJM zones (zones ATSI and Duke joined PJM in 2011 and are not analyzed in this paper). The dotted line thezones potential revenue could be captured. Figure 12 shows the average price of electricity in PJM (zones ATSI and Duke joined PJM in 2011 and are not analyzed in this paper). The dotted18line represents the average per zone in the period. zones (zones the ATSI and Duke joined PJMperiod. in 2011 and are not analyzed in this paper). The dotted line represents average per zone in the Thethe COMED and BGE zones were observed to have the lowest (34.74 $/MWh) and the highest represents average per zone in the period. The COMED and BGE zones were observed to have the lowest (34.74 $/MWh) and the highest (51.53 $/MWh) prices, respectively. The high prices of natural gas in 2008 (Figure 9) had moderate The $/MWh) COMEDprices, and BGE zones were to have the lowest $/MWh) and moderate the highest (51.53 respectively. Theobserved high prices of natural gas in(34.74 2008 (Figure 9) had impact in the COMED, DEOK, DUQ zones. Another interesting point was that the impact of extreme (51.53 $/MWh) prices, respectively. high prices interesting of natural point gas inwas 2008 (Figure 9) had moderate impact in the COMED, DEOK, DUQThe zones. Another that the impact of extreme weather in the early 2014 was stronger in the BGE and DPL zones. impact in the COMED, DEOK, zones. Another interesting point was that the impact of extreme weather in the early 2014 wasDUQ stronger in the BGE and DPL zones. weather in the early 2014 was stronger in the BGE and DPL zones.
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Figure 12. Yearly-based average prices of electricity in RTM across PJM zones. The dotted line Figure 12. Yearly-based average prices of electricity in RTM across PJM zones. The dotted line represents the average per zone in the period. represents the average per zone in the period.
3. Potential Arbitrage Revenue in RTM
3. Potential Arbitrage Revenue in RTM
PJM is one the most engaged RTOs in the United States in stimulating the use of ESS at utility
PJM oneConsidering the most engaged RTOs ESS, in the United States in thecapacity use of ESS at utility scale is [15]. electrochemical there are already 123stimulating MW of power (14 units MW) in operation in PJM and 28 MW CAISO Independent Systemcapacity Operator)(14 [3].units scale over [15].1 Considering electrochemical ESS,in there are(California already 123 MW of power The installed ESSs in CAISO have more hours of discharge duration at rated power capacity than in [3]. over 1 MW) in operation in PJM and 28 MW in CAISO (California Independent System Operator) PJM, but no electrochemical ESS is used exclusively for energy arbitrage to provide revenue. The installed ESSs in CAISO have more hours of discharge duration at rated power capacity than in In CAISO, there is one multiple-purpose project (the Tehachapi Wind Energy Storage Project), PJM, but no electrochemical ESS is used exclusively for energy arbitrage to provide revenue. which also aims to verify the energy arbitrage potential [3]. However, no report of a real energy In CAISO, there is one multiple-purpose project (the Tehachapi Wind Energy Storage Project), arbitrage operation has been found, except for a report regarding a simulation for Tehachapi [16]. whichThe also aimstechnology to verify the energy arbitrage [3].byHowever, no report of a real energy battery of the Tehachapi projectpotential is provided the A123 Systems (Lithium-ion, 8 arbitrage operation has been found, except for a report regarding a simulation for Tehachapi MW, 32 MWh, 4 h, 80% round trip efficiency) [3,16]. They have found that for the DAM, the potential [16]. The battery the Tehachapi projecttheoretical is provided by the Systems (Lithium-ion, 8 MW, revenuetechnology with perfectofforecast (i.e., maximum values) forA123 2010 and 2011 are $198,330 and 32 MWh, 4 h,respectively 80% round[16]. trip efficiency) [3,16]. They have found that for the DAM, the potential $331,992, revenue with perfect forecast (i.e., maximum theoretical values) for 2010 and 2011 are $198,330 and 3.1. Generic ESS Model $331,992, respectively [16]. A generic linear optimization model of price-taking ESS was adopted, considering a perfect
3.1. Generic forecastESS of a Model year of hourly-based price data to maximize arbitrage revenue, as presented in [5]. The energy arbitrage model in [5] describes a perfect foresight analysis of optimal storage policy. The A generic linear optimization model of price-taking ESS was adopted, considering a perfect price-taking model assumes that the individual storage facility has no impact on the equilibrium price forecast of a year of hourly-based price data to maximize arbitrage revenue, as presented in [5]. outcomes. Equation (1), presented below, represents the implemented model: The energy arbitrage model in [5] describes a perfect foresight analysis of optimal storage policy. T The price-taking model assumes that the individual storage facility has no impact on the equilibrium Max pt ( d t − ct ) c,d ,s price outcomes. Equation (1), presented below, the implemented model: t =represents 1 st = st −1 + η ct − dt (1) T
Max dt] − ct ) d t , ct∑∈ [p0, t (κ
,
c,d,s t=1
hκ ] stst=∈s[t0, −1 + ηct − dt , dt , ct ∈ p[t0, κ] where T: number of hours in dispatch horizon, : energy price in hour t, κ: power capacity of storage
(1)
device, h: number of hours of discharge sat rated dt: discharge power in hour t of storage device, ∈ [0, power, hκ ] t
ct: charge power in hour t of storage device,
st : state of charge in hour t of storage device, η: round
wheretrip T: efficiency number of dispatch horizon, pt : energy price in hour t, κ: power capacity of storage of hours storageindevice. device, h: Application number of hours discharge at rated power, estimates dt : discharge power in hour tthat of storage device, of thisof model provided preliminary of the net revenues would be ct : charge power in hour t of storage device, s : state of charge in hour t of storage device, η: round t solution always had charging and discharging at either trip accrued through price arbitrage. The optimal efficiency ofthe storage device. zero or maximum power rate. The choice a round efficiencypreliminary of 95% was estimates taken to represent a forward-looking Application of of this modeltrip provided of the net revenues that upper would be limitthrough of revenue forarbitrage. highly efficient ESS technologies; to efficiency presented in Section accrued price The optimal solution sensitivity always had charging isand discharging at either 4.1. Direct current/alternating current (DC/AC) conversion efficiencies are improving, and new zero or the maximum power rate. The choice of a round trip efficiency of 95% was taken to represent a forward-looking upper limit of revenue for highly efficient ESS technologies; sensitivity to efficiency is presented in Section 4.1. Direct current/alternating current (DC/AC) conversion efficiencies are improving, and new storage
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technologies (for example some Li-ion batteries, flywheels and supercapacitors) can have such high Energies 2017, 10, 1100 9 of 19 storage (forofexample somethe Li-ion batteries, flywheels and supercapacitors) can have was efficiency [4].technologies Without loss generality, maximum power capacity of charge and discharge such Without generality, the maximum power capacity of charge and selected tohigh betechnologies 1efficiency MW for [4]. all Theofenergy capacity of the ESS forsupercapacitors) the base case was 10 MWh, storage (foranalyses. example loss some Li-ion batteries, flywheels and can have discharge was selected to be 1 MW for all analyses. The energy capacity of the ESS for the base case such high it efficiency [4].upWithout loss of generality, the maximum power capacity of charge and and we varied from 1 to to, in some analyses, 14 MWh. was 10 MWh, and we varied fromfor 1 toallupanalyses. to, in some 14 MWh. discharge selected be 1it MW Theanalyses, energy capacity of the the base case on Figure 13was shows the to dependence of annual arbitrage revenue per kWESS of for power capacity Figure 13 shows the dependence of annual arbitrage revenue per kW of power capacity on was 10 MWh, and we varied it from 1 to up to, in some analyses, 14 MWh. energy/power ratio forfor a selected node with high pricestandard standarddeviation. deviation. It also shows the of lack of energy/power a selected node with high price It also showscapacity the lack on Figure 13 ratio shows the dependence of annual arbitrage revenue per kW of power sensitivity of the result, for this particular node, to the period of perfect forecasting. sensitivity of the result, this particular node, toprice the period of perfect forecasting. energy/power ratio for afor selected node with high standard deviation. It also shows the lack of The perfect forecast considering the simulation period of24 24hh(365 (365simulations/year) simulations/year) forMWh 10 MWh The perfect forecast considering the simulation period of for 10 sensitivity of the result, for this particular node, to the period of perfect forecasting. captured only 6.4% less than for the period of 8760 h (one simulation/year), and for 4 MWh captured captured only 6.4% less than for the period of 8760 h (one simulation/year), and for 4 MWh captured The perfect forecast considering the simulation period of 24 h (365 simulations/year) for 10 MWh only 3.2%only less6.4% than for onefor simulation/year. Figure 13simulation/year), also shows the and diminishing of only 3.2% less than for one simulation/year. 13 also shows the diminishing of increasing captured less than the periodFigure of 8760 h (one for 4returns MWhreturns captured increasing the energy/power ratio. the energy/power only 3.2% lessratio. than for one simulation/year. Figure 13 also shows the diminishing returns of increasing the energy/power ratio.
Figure 13. Potential annual arbitrage roundtrip tripefficiency) efficiency) energy/power Figure 13. Potential annual arbitragerevenue revenue(1 (1 MW, MW, 95% 95% round vs.vs. energy/power a particular node differentperiods periodsof ofperfect perfect forecast ratio ratio for a for particular node forfor different forecastoptimization. optimization. Figure 13. Potential annual arbitrage revenue (1 MW, 95% round trip efficiency) vs. energy/power ratio for a particular node for different periods of perfect forecast optimization.
3.2.Energy The Energy Arbitrage Revenue PJM 3.2. The Arbitrage Revenue ininPJM
3.2. The Arbitrage Revenue in PJM TheEnergy potential arbitrage revenue of an ESS using a yearly-based perfect forecast in the PJM RTM
The potential arbitrage revenue of an ESS using a yearly-based perfect forecast in the PJM RTM for the period of 2008 to 2014 is presented in Figure 14. It shows that the highest potential revenue The potential arbitrage revenue of an in ESS using a14. yearly-based perfect the PJM RTM for the period of2008, 2008followed to 2014by is presented Figure It shows that theforecast highestinpotential revenue occurred in 2014. Considering the presented yearly-based standard deviation and for the period of 2008 to 2014 is presented in Figure 14. It shows that the highest potential revenue occurred in 2008, followed by 2014. Considering the presented yearly-based standard deviation and average curves, the potential revenue has the a strong correlation with high average electricity occurredprice in 2008, followed by 2014. Considering presented yearly-based standard deviation and average price curves, the potential revenue has a strong correlation with high average electricity prices prices (asprice shown for 2008 recently foundhas in [17]) and acorrelation weaker correlation highelectricity standard average curves, the and potential revenue a strong with highwith average (as shown for 2008 and recently found in [17]) and a weaker correlation with high standard deviation deviation (as shown for 2014 in Figure 5). prices (as shown for 2008 and recently found in [17]) and a weaker correlation with high standard (as shown for 2014 in Figure 5). deviation (as shown for 2014 in Figure 5).
Figure 14. Yearly-based potential arbitrage revenue of a 10 h. ESS with 95% round trip efficiency for RTM in PJM, encompassing 7395 nodes. The abscissa is the percentage of nodes with revenue less Figure 14. Yearly-based potential arbitrage revenue of a 10 h. ESS with 95% round trip efficiency for Figure 14.the Yearly-based potential arbitrage revenue of a 10 h. ESSsorted with by 95% round trip efficiency for than value given by the ordinate; the values are independently year. RTM in PJM, encompassing 7395 nodes. The abscissa is the percentage of nodes with revenue less RTM in PJM, encompassing 7395 nodes. The abscissa is the percentage of nodes with revenue less than than the value given by the ordinate; the values are independently sorted by year.
the value given by the ordinate; the values are independently sorted by year.
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different energy/power energy/power ratios The potential arbitrage revenue for different ratios in in 2008 2008 is is shown shown in in Figure Figure 15. 15. The potential arbitrage revenue for different energy/power ratios in 2008 is shown in Figure 15. The potential potential revenue revenue presents presentsvery verysmall smallincrements incrementsfor forenergy/power energy/power ratio ratio above above 10 10 h. This pattern The The potential revenue presents very small increments for energy/power ratio above 10 h. This pattern presented in in [18], [18], with with small small differences differences in in the the higher higherpotential potentialrevenue revenuenodes. nodes. is similar to 2014, as presented is similar to 2014, as presented in [18], with small differences in the higher potential revenue nodes.
Figure 15. Potential revenue in RTM in in 2008 from 1 to 14 MWh, considering 7395 nodes with priceFigure 15. Potential Potentialrevenue revenue RTM 2008 from 14 MWh, considering 7395 nodes with Figure 15. in in RTM in 2008 from 1 to114toMWh, considering 7395 nodes with pricetaking and perfect forecast. price-taking and perfect forecast. taking and perfect forecast.
3.3. Seasonality of Revenue Revenue in in PJM PJM 3.3. Seasonality of The average average revenue revenue was was evaluated evaluated to investigate investigate themonthly monthly distributionfor for a 1 MW/5 MWh MWh The evaluated to to investigate the the monthly distribution distribution foraa11MW/5 MW/5 ESS. Figure 16 shows the percentage normalized per year. June and July are very important months ESS. Figure 16 shows the percentage normalized per year. June and July are very important months for revenue capture. In 2014, the potential revenue was higher still for the first two months of the the In 2014, 2014, the the potential potential revenue revenue was was higher higher still still for for the for revenue capture. In first two months of year. The cold weather in the beginning of 2014 (between 1 January and 28 February) was probably year. The cold weather in the beginning of 2014 (between 1 January and 28 February) was probably responsible for 38% of the year’s total revenue. Recently, similar finding has also been reported by Recently, aa similar responsible for 38% of the year’s total revenue. Recently, finding has also been reported by McConnell et etal. al.[19]. [19].The TheESS ESSwould would provide an analogous service to peak generators and would anan analogous service to peak generators and and would also McConnell et al. [19]. The ESS wouldprovide provide analogous service to peak generators would also depend on extreme price events for a significant amount of its revenue. In 2008, the highest depend on extreme price events for a significant amountamount of its revenue. In 2008, In the2008, highest also depend on extreme price events for a significant of its revenue. the revenue highest revenue occurred in June and July; during this two-month period the ESS would have captured 26% occurred in June and July;and during two-month period the ESSthe would have captured 26% of26% the revenue occurred in June July; this during this two-month period ESS would have captured of the year’s total. year’s total. of the year’s total.
Figure 16. Monthly-based average percentage of annual potential arbitrage revenue for PJM in RTM Figure percentage of annual potential arbitrage revenue for PJM RTM Figure 16. 16. Monthly-based Monthly-basedaverage average percentage of annual potential arbitrage revenue for in PJM in normalized per year, considering 7395 locations. The dotted line connects the multi-year monthly normalized per year, 7395 locations. The dotted connects the multi-year monthly RTM normalized per considering year, considering 7395 locations. The line dotted line connects the multi-year average. average. monthly average.
Figures 14 and 16 shows two different profiles for the best two years of potential arbitrage Figures 14 and 16 shows two different profiles for the best two years of potential arbitrage Figures 14 and 16 shows different profiles for the best two years of potential arbitrage revenue: concentrated in fewtwo months (2014) or distributed along the year (2008). The dottedrevenue: line for revenue: concentrated in few months (2014) or distributed along the year (2008). The dotted line for concentrated in few months (2014) or distributed along the year (2008). The dotted line for the monthly the monthly average over the years in Figure 16 showed that the three best months to explore the the monthly average over the years in Figure 16 showed that the three best months to explore the potential of arbitrage revenue are January, June and July. potential of arbitrage revenue are January, June and July.
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average over Energies 2017, 10, the 1100 years in Figure 16 showed that the three best months to explore the potential 11 of of 19 Energies 2017, 10, 1100 are January, June and July. 11 of 19 arbitrage revenue 3.4. Spatial Distribution of Revenue Across Locations 3.4. Spatial Distribution of Revenue Across Locations The potential revenue presented in Figure 14 is reorganized to include location information, potential revenue presented Figure 14reorganized is Figures reorganized to18 include location information, potential revenue presented in in Figure 14 is toand include location information, using usingThe node zip codes obtained from the PJM website. 17 show, respectively, revenue using node zip codes obtained from the PJM website. Figures 17 and 18 show, respectively, revenue node zip codes obtained from the PJM website. Figures 17 and 18 show, respectively, revenue for 2008 for 2008 and 2014 across locations. The color scale is normalized to the maximum revenue of each for 2008 2014 across locations. scale is normalized to the revenue maximum revenue and 2014 and across locations. The color The scalecolor is normalized to the maximum of each year.of each year. year.
Figure 17. The variation of potential revenue in RTM for 2008 with 7395 PJM node locations. Figure 17. 17. The The variation variation of of potential potential revenue revenue in Figure in RTM RTM for for 2008 2008 with with 7395 7395 PJM PJM node node locations. locations.
Figure 18. The variation of potential revenue in RTM for 2014 with 7395 PJM node locations. Figure 18. The variation of potential revenue in RTM for 2014 with 7395 PJM node locations. Figure 18. The variation of potential revenue in RTM for 2014 with 7395 PJM node locations.
From these figures, it was apparent that the best arbitrage opportunities were not always at the these was apparent that the bestrevenue arbitrage opportunities were not always at the sameFrom nodes. We figures, selecteditthe best (highest) potential nodes in 2008 and categorized them in From these figures, itthe was apparent that the bestrevenue arbitragenodes opportunities were not always at the same nodes. We selected best (highest) potential in 2008 and categorized them in percentage groups of the best 5%, 10% and 20%. We followed them across the seven years and we same nodes.groups We selected the best (highest) potential revenue nodes in 2008the and categorized them percentage of the best 5%, 10% and 20%. We followed them across seven years and we found that only 19 nodes stayed in the same group of 5%, 185 nodes stayed in the best 10% and 577 in percentage groups of the best in 5%, and 20%.ofWe them across thebest seven years found that only 19 nodes stayed the10% same group 5%,followed 185 nodes stayed in the 10% andand 577 nodes stayed in the best 20%. we found that only 19 nodes stayed in the same group of 5%, 185 nodes stayed in the best 10% and nodes stayed in the best 20%. 577 nodes stayed in the best 20%. 4. Arbitrage Revenue in RTM and DAM 4. Arbitrage Revenue in RTM and DAM 4. Arbitrage Revenue in in RTM DAM The price volatility theand RTM was higher than in the DAM. This fact reflected generator or The price volatility in the RTM was higher than in the DAM. This fact reflected generator or transmission line outages, generator bid changes inDAM. energyThis demand, and transmission The price volatility in the RTM wasstrategies, higher than in the fact reflected generatorline or transmissionhowever, line outages, generator bid strategies, changes in energy demand, and transmission line congestion; daily average prices in RTM and DAM did not vary as much. The price transmission line outages, generator bid strategies, changes in energy demand, and transmission line congestion; however, dailythe average prices in RTM and DAMhigher did not vary as than much. TheDAM, price volatility is however, the reasondaily why potential revenue RTM in the congestion; average prices arbitrage in RTM and DAM was did not varyinasthe much. The price volatility volatility is the reason why the potential arbitrage revenue was higher in the RTM than in the DAM, asthe reported PJM [18]. is reasonfor why theinpotential arbitrage revenue was higher in the RTM than in the DAM, as reported as reported for PJM in [18]. for PJM in [18]. 4.1. Sensitivity to Round Trip Efficiency for Dispatching in RTM 4.1. Sensitivity to Round Trip Efficiency for Dispatching in RTM 4.1. Sensitivity to Round Trip Efficiency for Dispatching in RTM The round trip efficiency of 95% meant that in each 10 h of charge, one may have 9.5 h of The round trip efficiency ofWe 95%analyzed meant that in each 10 htoofround charge, may have 9.5 h of discharge, bothtrip at efficiency rated power. sensitivity tripone efficiency order to The round of 95% meant that inthe each 10 h of charge, one may have 9.5 h ofindischarge, discharge, both at rated power. We analyzed the sensitivity to round trip efficiency in order to quantify the decrease in potential when efficiency using data 2014the in both at rated power. We analyzedrevenue the sensitivity to round decreases, trip efficiency in the order to from quantify quantify in potential when efficiency using the data from 2014the in RTM for athe 10 decrease MWh energy capacityrevenue device (Figure 19). At thedecreases, upper end of the efficiency range, RTM for a 10 MWh energy capacity device (Figure 19). At the upper end of the efficiency range, the potential revenue was nearly proportional to efficiency at most of the nodes. Reference [8] previously potential revenue was nearly proportional to efficiency at most of the nodes. Reference [8] previously found a proportionality relationship between revenue and efficiency using different data. found a proportionality relationship between revenue and efficiency using different data.
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decrease in potential revenue when efficiency decreases, using the data from 2014 in RTM for a 10 MWh energy capacity device (Figure 19). At the upper end of the efficiency range, the potential revenue was nearly proportional to efficiency at most of the nodes. Reference [8] previously found a proportionality relationship between revenue and efficiency using different data. Energies 2017, 10, 1100 12 of 19 Energies 2017, 10, 1100
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Figure 19. Sensitivity analysis for the revenue dollars considering round trip Figure revenue in in dollars perper kW,kW, considering round trip efficiency from Figure 19. 19. Sensitivity Sensitivityanalysis analysisfor forthe the revenue in dollars per kW, considering round trip efficiency efficiency from 10% to 90%, increasing in increments of 10%. LMP data for 2014 in RTM, 10 h discharge duration. 10% 90%, in increments of 10%. LMPLMP data data for 2014 in RTM, 10 h10 discharge duration. fromto10% to increasing 90%, increasing in increments of 10%. for 2014 in RTM, h discharge duration.
4.2. Additional Additional Arbitrage Arbitrage Revenue Revenue in in RTM RTM 4.2. RTM The additional additional arbitrage arbitrage revenue revenue in in RTM RTM compared compared to to DAM, DAM, both both assuming assuming perfect perfect foresight, foresight, The depended on on the the energy/power profile during during the the year year also also depended ratio as as shown shown in the energy/power energy/power ratio ratio as shown in Figure Figure 20. 20. The The price price profile also influenced the potential revenue. influenced influenced the the potential potential revenue. revenue. Figure 20 shows that percentage of revenue RTM to Figure that thethe percentage of extra revenue gainedgained in RTM in compared to DAM decreased Figure 20 20shows shows that the percentage of extra extra revenue gained in RTM compared compared to DAM DAM decreased as discharge duration increases. Figure 20 shows that for 2008 (solid line) the additional as discharge Figure 20 Figure shows 20 that for 2008 line) the additional revenue decreased as duration dischargeincreases. duration increases. shows that(solid for 2008 (solid line) the additional revenue was higher for the 55% of nodes from 45% 100%) for and of revenue was higher for 55% the best best 55% of the the nodes (abscissa (abscissa from 45% to tofor 100%) for 1, 1, 2MWh and 33ofMWh MWh of was higher for the best of the nodes (abscissa from 45% to 100%) 1, 2 and 32 energy energy capacity. Significantly higher additional revenue can be captured more likely in the best 5% energy capacity. Significantly higher additional be captured morein likely in the 5% capacity. Significantly higher additional revenuerevenue can be can captured more likely the best 5%best of the of (abscissa 95% to in 2008 2014 compared the other 95% of the of the the nodes nodes (abscissa from 95% to 100%) 100%) in RTM RTM for 2008 and 2014 compared tothe theother other95% 95%of of the the nodes (abscissa from from 95% to 100%) in RTM for for 2008 andand 2014 compared toto nodes (abscissa from 0% to 95%). 95%). This are more more frequent frequent than than nodes (abscissa (abscissa from from 0% 0% to This reflects reflects that that short-duration short-duration fluctuations fluctuations are nodes long-duration fluctuations in the RTM. long-duration fluctuations fluctuations in in the the RTM. RTM. long-duration
Figure Figure 20. 20. Additional Additional revenue revenue in in energy energy arbitrage arbitrage in in RTM RTM relative relative to to day-ahead day-ahead market market (DAM) (DAM) in in Figure 20.2014 Additional revenue in energy arbitrage in1 RTM relativecapacity. to day-ahead market (DAM) in 2008 2008 and for different energy capacities with MW power 20082014 and for 2014 for different energy capacities MW power capacity. and different energy capacities with 1with MW1power capacity.
As As a a general general rule, rule, an an increment increment on on the the energy energy capacity capacity of of the the ESS ESS decreases decreases the the percentage percentage of of additional revenue that could be captured in the RTM relative to the DAM; however, additional revenue that could be captured in the RTM relative to the DAM; however, the the absolute absolute revenue revenue is is higher higher for for greater greater energy energy capacity. capacity. The The starting starting points points of of the the curves curves in in Figure Figure 20 20 showed showed that that there there was was at at least least 20% 20% additional additional potential potential revenue revenue in in RTM RTM for for aa 10 10 MWh MWh ESS ESS (the (the magenta magenta line) line) and and around around 50% 50% more more for for aa 33 MWh MWh ESS ESS (blue (blue line). line).
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As a general rule, an increment on the energy capacity of the ESS decreases the percentage of additional revenue that could be captured in the RTM relative to the DAM; however, the absolute revenue is higher for greater energy capacity. The starting points of the curves in Figure 20 showed that there was at least 20% additional potential revenue in RTM for a 10 MWh ESS (the magenta line) and around 50% Energies 2017, 10, 1100more for a 3 MWh ESS (blue line). 13 of 19 4.3. Price Forecast Forecast Impact Impact on on Arbitrage Arbitrage in 4.3. Price in RTM RTM The use of ofhistorical historicalprice price data been often used in optimal ESS optimal dispatch analysis as an The use data hashas been often used in ESS dispatch analysis as an upper upper [7,8,16–22]. This of analysis an indication of the theoretical of storage. bound bound [7,8,16–22]. This kind ofkind analysis gives angives indication of the theoretical value ofvalue storage. Simple Simple forecasting methods haveproposed been proposed to represent the imperfections of guessing price forecasting methods have been to represent the imperfections of guessing the the price in in advance for the DAM. McConnell et al. [19] achieved 85% of perfect-foresight revenue using advance for the DAM. McConnell et al. [19] achieved 85% of perfect-foresight revenue using prepre-dispatch prices the AEMO (Australian Energy Market Operator) available day ahead of dispatch prices fromfrom the AEMO (Australian Energy Market Operator) available a dayaahead of time time for six hours of storage. Sioshansi et al. [5] evaluates the use of the PJM historic prices (from 2002 for six hours of storage. Sioshansi et al. [5] evaluates the use of the PJM historic prices (from 2002 to to 2007) previous two weeks for1212hhofofstorage, storage,achieving achievingvalues valuesfrom from84% 84% to to almost almost 90% 90% of 2007) of of thethe previous two weeks for of perfect-foresight-in-DAM perfect-foresight-in-DAM values values (depending (depending on on the the year). year). Analogous results results have have been also reported recently in [17,20,21]. Our approach was similar to the above; however, we applied a method that can capture the equivalent of ofmore morethan than100% 100%ofofthe the potential revenue opportunities in the DAM. It would an potential revenue opportunities in the DAM. It would be anbe easy easy matter to exactly capture the DAM price arbitrage schedule, such as by dispatching in real-time matter to exactly capture the DAM price arbitrage schedule, such as by dispatching in optimally for for the theknown knownDAM DAMprices, prices,which whichwould would feasible under PJM market definition. bebe feasible under thethe PJM market definition. By By contrast, capturing the deviations embodied in the real-time market prices presents a high payoff contrast, capturing the deviations embodied in the real-time market prices presents a high payoff but but a greater challenge in optimizing the storage in the RTM presents more a greater challenge in optimizing the storage profile.profile. BiddingBidding in the RTM presents more risk and risk this and thishas market has higher volatility to the The marginal locationalprices marginal on a market higher volatility comparedcompared to the DAM. TheDAM. locational on aprices day-ahead day-ahead easilyafter takenthe after the DAM settlement. dispatch resultingfrom fromthe the linear basis couldbasis be could easily be taken DAM settlement. The The dispatch resulting optimization analysis for the DAM for the next day could be applied directly to dispatching in the RTM additional revenue appearing in Figure 20. RTM in in real realtime. time.This Thisapproach approachcan cancapture capturea aportion portionofofthe the additional revenue appearing in Figure Figure 21 shows the evaluated accuracy of the dispatch using the proposed method, expressed as a 20. Figure 21 shows the evaluated accuracy of the dispatch using the proposed method, expressed as percentage of the from from a perfect forecastforecast of the prices in the RTM The best performance a percentage of revenue the revenue a perfect of the prices initself. the RTM itself. The best of the proposed was achieved 2012; however, similar accuracya was achieved for 2009, performance of forecast the proposed forecastfor was achieved fora2012; however, similar accuracy was 2011 and 2013. The worst performance of the forecasting method was reached for 2014. The accuracy achieved for 2009, 2011 and 2013. The worst performance of the forecasting method was reached for for 2008 amongfor the2008 highest 40% the of the nodes. 2014. Thewas accuracy was for among highest for 40% of the nodes.
Figure 21. 21. Potential Potential revenue revenue of ofaa10 10MWh MWhESS ESSdispatched dispatchedininthe theRTM RTM using DAM settlement prices Figure using DAM settlement prices as as forecast. results presented a percentage of the revenue captured by the same device in forecast. TheThe results areare presented as aaspercentage of the revenue captured by the same device in the the RTM perfect foresight. RTM withwith perfect foresight.
Figure 22 shows another way to look at the same results presented in Figure 20. The curves presented in Figure 22 show the potential revenue in the RTM using DAM settlement prices as forecast for optimizing dispatch, divided by the potential revenue in the DAM with perfect forecast. Most of the nodes reached more than 100% of the theoretical arbitrage opportunity in the DAM. The exception is 2014, during which the method slightly underperforms participation in the DAM. This feasible forecast method allowed the ESS owner to capture more revenue opportunities
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Figure 22 shows another way to look at the same results presented in Figure 20. The curves presented in Figure 22 show the potential revenue in the RTM using DAM settlement prices as forecast for optimizing dispatch, divided by the potential revenue in the DAM with perfect forecast. Most of the nodes reached more than 100% of the theoretical arbitrage opportunity in the DAM. The exception is 2014, during which the method slightly underperforms participation in the DAM. This feasible forecast method allowed the ESS owner to capture more revenue opportunities than are provided in the DAM. However, a more sophisticated forecast method may be necessary to deal with scarcity Energies 2017, 10,and 1100high price spikes during extreme weather conditions (as in 2014). 14 of 19
Figure 22. 22. Potential Potential revenue revenueof ofaa10 10MWh MWhESS ESSdispatched dispatchedininthe theRTM RTM using DAM settlement prices Figure using DAM settlement prices as as forecast divided by the potential revenue in DAM. forecast divided by the potential revenue in DAM.
5. Breakeven Installed Cost Cost 5. Breakeven Overnight Overnight Installed We analyzed analyzed the the breakeven breakeven overnight overnight installed installed cost of an an ESS ESS using using the the PJM PJM net net revenue revenue We cost [23] [23] of analysis described described above for the the various various years years and and locations. locations. The to derive derive the the analysis above for The adopted adopted approach approach is is to net installed costs (after taxes and depreciation) to obtain an annualized capital recovery factor (CRF) net installed costs (after taxes and depreciation) to obtain an annualized capital recovery factor (CRF) that would would return return the the present present value value of of the the investment over the the life life of that investment over of the the project. project. This This capital capital recovery recovery factor can be compared with the net revenues based on the project storage profile [24]. factor can be compared with the net revenues based on the project storage profile [24]. The PJM PJM revenue revenue data data applied applied to to the the price price arbitrage arbitrage model model of of storage storage economics economics is is based based on on The several input assumptions. Although the approach is independent of the particular ESS technology several input assumptions. Although the approach is independent of the particular ESS technology deployed, parameter parameter values values will choose parameter parameter values values deployed, will differ differ from from one one technology technology to to another. another. We We choose anticipated to characterize a future-state mature flow battery technology, rather than any current anticipated to characterize a future-state mature flow battery technology, rather than any current technology, because because the the latter latter are are too too costly costly for for immediate immediate implementation. implementation. The new technology, The total total life life of of aa new ESS is uncertain. The analysis in [25] assumes 20-year life for flow batteries. A range of battery ESS is uncertain. The analysis in [25] assumes 20-year life for flow batteries. A range of battery technologies analyzed technologies analyzed in in [8] [8] have have lifetimes lifetimes of of 5–15 5–15 years. years. Here Here we we considered considered aa 20-year 20-year project project life. life. The basic application assumed no long-term policy support such as through special subsidies. The The basic application assumed no long-term policy support such as through special subsidies. The Net Operating Operating Revenue (NR) was was equal equal to to the the PJM PJM net net revenue revenue analysis analysis for for Net Revenue Before Before Corporate Corporate Taxes Taxes (NR) the various years and locations. Hence, the energy purchase costs, round trip losses, and pricing the various years and locations. Hence, the energy purchase costs, round trip losses, and pricing assumptions followed followed the the various various sensitivity sensitivity cases cases for for the the net net revenues. revenues. assumptions Typically, the annual Fixed Operating and Maintenance Cost (OM) (νassumed ) is assumed to besmall very Typically, the annual Fixed Operating and Maintenance Cost (OM) (ν) is to be very small for scale large flow scale based flow based batteries. For example, reference adopts a range from 0.9% to 3% for large batteries. For example, reference [25] [25] adopts a range from 0.9% to 3% of of the initial capital costs. The present calculations use 2%. The assumptions for tax rates and related the initial capital costs. The present calculations use 2%. The assumptions for tax rates and related financial parameters parameterscome comefrom from analysis of Energy the Energy Information Administration for the financial thethe analysis of the Information Administration for the Annual Annual Outlook Energy Outlook All quantities areterms in real termswhere exceptspecified: where specified: Energy [26]. All [26]. quantities are in real except i = Nominal corporate borrowing rate = 7.1% τ = Corporate tax rate = 38% d = Share of investment financed by debt = 45% E = Risk adjusted real return on equity = 9.3% π = Expected inflation rate = 2.0%
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i = Nominal corporate borrowing rate = 7.1% τ = Corporate tax rate = 38% d = Share of investment financed by debt = 45% E = Risk adjusted real return on equity = 9.3% π = Expected inflation rate = 2.0% The Real Corporate Discount Rate (e r) can be calculated as in Equation (2): e r = d ( i (1 − τ ) − π ) + (1 − d ) E
(2)
Tax Depreciation (D) requires separate treatment for each year. The IRS (Internal Revenue Service) applies different rules for deprecation of capital expenditures, accessed in Table 4-1 for Modified Accelerated Cost Recovery System (MACRS) in [27]. The depreciation rate applies to the nominal capital values. Hence, the real capital depreciation must be discounted by the anticipated inflation rate. We assume in our analysis 7-year depreciation, as suggested in [25]. The base case assumption for storage is no investment tax credit (ITC), i.e., ρ ITC = 0%. Here, with no ITC, the full investment is subject to depreciation deductions. With these elements of the model, for given net revenue (NR), assumed constant in real terms over the life of the facility (N) in years, the resulting breakeven overnight installed cost (CC) satisfies Equation (3):
N
CC = ρ ITC CC +
( NR − νCC )(1 − τ ) +
∑
δtMACRS
(1 + π ) t
CCτ (3)
(1 + er )t
t =1
The implied tax Adjusted Capital Recovery Factor (ACRF) is presented in Equation (4): N
ACRF =
NR = CC
1
1 − ρ ITC + ν(1 − τ ) ∑
r) t =1 (1 + e N 1 ∑
t =1
=
e r (1 + e r) N
(1 + er ) N − 1
"
t
δtMACRS
t =1
(1 + er )t (1 + π )t (4)
(1 + er )t #
N
δtMACRS
t =1
(1 + er )t (1 + π )t
1 − ρ ITC − τ ∑
N
−τ ∑
+ ν (1 − τ )
Under the base case assumptions of 0% ITC, a 20-year project life and a 15-year tax depreciation life, the Adjusted Capital Recovery Factor is equal to 8.66%. Applying this capital recovery rate to data in the PJM net revenue analysis produces the implied breakeven installed cost. For a flow battery, the cost is a linear combination of the marginal cost per unit power capacity and the marginal cost per unit energy capacity. To simplify the comparison with other storage systems, the total requirement is converted to (total system cost)/(total energy capacity) in $/kWh. We performed the analysis for an ESS with perfect forecast, 90% round trip efficiency, and 20-year project life for Real-time (Figure 23) and Day-ahead (Figure 24) Markets. The distribution of breakeven costs, spanned by the thin vertical bars, was dependent on discharge duration and year. The 95th percentile of the nodes, by revenue, are marked by the black crosses, which indicate the installed cost below which ESS penetration into the grid would first become very significant. The 50th percentile of the nodes are marked by the circles, which indicated the installed cost below which ESS penetration might be entirely transformative. This latter statement is only qualitative because by the time this level of penetration is reached, the price-taking assumption would certainly have broken down.
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interspersed of recharging. Nevertheless, the actual cost of flow batteries drops markedly Energies 2017, 10,periods 1100 16 of 19 interspersed periods of recharging. thestudy actualis cost of flow drops with increasing discharge duration.Nevertheless, Thus, further required to batteries determine the markedly discharge with increasing discharge duration. Thus, further study ismight required determine durations at which the cost of a flow battery installation first to penetrate intothe thedischarge range of The assumption of perfect forecast in the RT-market implied an upper limit on the breakeven cost, and durations which cost ofshown a flow installation might first penetrate into the range of breakevenat costs suchthe as those in battery these figures. the assumption perfect forecast in the DA-market breakeven costs of such as those shown in these figures.implied a lower limit on the breakeven cost.
Figure 23. Distribution of breakeven overnight installed cost in the PJM RT-Market for an ESS vs. Figure 23. duration Distribution of overnight PJM an vs. Distribution of breakeven breakeven overnight installed cost inthe thestudied. PJMRT-Market RT-Market for an ESS ESSbars vs. discharge at rated power (1–14 h) forinstalled each of cost the in years The thinfor vertical discharge duration at rated power (1–14 h) for each of the years studied. The thin vertical bars duration at rated power (1–14 h) for each of the years studied. The thin vertical bars represent represent the entire range over the 7395 nodes. The thick vertical bars are bounded by the 25th and represent the entire over the 7395 nodes. The vertical bars arenodes bounded by75th the 25th and the range over the 7395 nodes. Therepresent thick vertical barspercentile are bounded by the 25th and percentile 75thentire percentile of therange nodes. The circles the thick 50% of the and the black crosses 75th percentile of percentile the nodes. The circles represent theround 50% percentile of the and the black crosses of the nodes. The circles represent the 50% percentile of the nodes and the black crosses indicate the indicate the 95th of the nodes. Assumed trip efficiency isnodes 90% and project lifetime is indicate the 95thofpercentile the nodes. Assumed round trip is 90%lifetime and project 95th percentile the nodes.ofAssumed round trip efficiency is efficiency 90% and project is 20lifetime years. is 20 years. 20 years.
Figure 24. 24. Distribution Distribution of Figure of breakeven breakeven overnight overnight installed installed cost cost in in the the PJM PJM DA-Market DA-Market for for an an ESS ESS vs. vs. Figure 24. duration Distribution of breakeven overnight installed cost in the PJM DA-Market forvertical an ESSbars vs. discharge at rated power (1–14 h) for each of the years studied. The thin discharge duration at rated power (1–14 h) for each of the years studied. The thin vertical bars represent discharge duration at rated power (1–14nodes. h) forThe each of the years studied. The thin vertical represent the entire over the 7395 vertical bars arethe bounded thepercentile 25thbars and the entire range over range the 7395 nodes. The thick verticalthick bars are bounded by 25th andby 75th represent the entire range over the 7395 nodes. The thick vertical bars are bounded by the 25th and 75th of the nodes. The circles represent the 50% percentile of the the black crosses of thepercentile nodes. The circles represent the 50% percentile of the nodes and thenodes blackand crosses indicate the 75th percentile of percentile the nodes. of The circles represent theround 50% percentile of theisnodes andproject the black crosses indicate the 95th the nodes. Assumed trip efficiency 90% and lifetime 95th percentile of the nodes. Assumed round trip efficiency is 90% and project lifetime is 20 years. is indicate 20 years.the 95th percentile of the nodes. Assumed round trip efficiency is 90% and project lifetime is 20 years.
An additional analysis, performed using DA prices as a forecast for dispatch into the RT-Market, Future works on this topic should consider detailed ESS dispatch models, including bids for is presented in Figure These overnight installed costs are somewhat higher than bids thosefor in Future works on25. this topicbreakeven should consider detailed dispatch models, including each five minute period in RTM [28], to determine the extentESS to which increased temporal granularity the DA-Market. each five minute period in of RTM [28], toAn determine extent to which increased temporal can achieve higher values revenue. optimal the bidding strategy across both DAM and granularity RTM could All ESS installed costs are currently higher than the breakeven costs indicated by Figure 25 for can achieve higher values of revenue. An optimal bidding strategy across both DAM and RTM could also be attractive when both choices are available to the same optimization process [29,30]. Finally, the vast majority of nodes, and thus the opportunities for profitability are extremely limited. also be attractive when choices are current available to the same optimization process [29,30]. Finally, the participation in the both frequency regulation market as an addition to the energy arbitrage market However, ESS costs are falling and are anticipated to fall significantly farther, bringing an increasing the participation in the regulation market as an addition to the energy arbitrage market should also increase thefrequency revenues [31]. percentage nodes into profitability should alsoof increase the revenues [31].over time. All of Figures 23–25 indicate rapidly declining ESS breakeven cost/kWh with increasing energy/power ratio. This trend reflects the diminishing returns in revenue/kWh with increasing discharge duration, as fewer occasions arise requiring full discharge without the opportunity for interspersed periods of recharging. Nevertheless, the actual cost of flow batteries drops markedly with
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increasing discharge duration. Thus, further study is required to determine the discharge durations at which the cost of a flow battery installation might first penetrate into the range of breakeven costs such as those shown in these figures. Energies 2017, 10, 1100 17 of 19
Figure Figure 25. 25. Distribution Distribution of of breakeven breakeven overnight overnight installed installed cost cost using using DA DA prices prices as as forecast forecast for for dispatch dispatch in for an an ESS ESS vs. vs. discharge in the the PJM PJM RT-Market RT-Market for discharge duration duration at at rated rated power power (1–14 (1–14 h) h) for for each each of of the the years years studied. The thin vertical bars represent the entire range over the 7395 nodes. The thick vertical studied. The thin vertical bars represent the entire range over the 7395 nodes. The thick vertical bars bars are are bounded bounded by by the the 25th 25th and and 75th 75th percentile percentile of of the the nodes. nodes. The The circles circles represent represent the the 50% 50% percentile percentile of of the nodesand andthe the black crosses indicate the percentile 95th percentile of the Assumed nodes. Assumed round trip the nodes black crosses indicate the 95th of the nodes. round trip efficiency efficiency 90% and project is 90% andisproject lifetime is lifetime 20 years.is 20 years.
6. Conclusions Future works on this topic should consider detailed ESS dispatch models, including bids for each This paper hasinanalyzed thetopotential arbitrage revenue of energy storage systems in PJM dayfive minute period RTM [28], determine the extent to which increased temporal granularity can ahead real-time fromAn 2008 to 2014bidding for all 7395 locations which complete hourly achieveand higher valuesmarkets of revenue. optimal strategy acrossfor both DAM and RTM couldLMP also data are available. energyto arbitrage through storageprocess arises from the scale of the be attractive whenThe bothopportunity choices arefor available the same optimization [29,30]. Finally, energy market the volatility of prices. The potential revenue generated a 1-MW system with participation inand the frequency regulation market as an addition to the energy by arbitrage market should various energy/power ratios was determined as the solution of an optimization problem assuming also increase the revenues [31]. perfect foresight. Perfect foresight represents an upper limit to arbitrage revenue in the RTM and 6. Conclusions represents essentially a lower limit to revenue in the DAM. The main characteristics the potential best nodesarbitrage to installrevenue an ESS were verified through the presented This paper has analyzedofthe of energy storage systems in PJM analysis. They were: high average prices, periods of high price volatility, and high forecast accuracy day-ahead and real-time markets from 2008 to 2014 for all 7395 locations for which complete hourly to exploit in the RTM. Increasing the durationthrough at ratedstorage power arises increases potential LMP dataspikes are available. The opportunity fordischarge energy arbitrage fromthe the scale of revenue per kW of power capacity, but returns diminish rapidly after 4–6 h of discharge duration at the energy market and the volatility of prices. The potential revenue generated by a 1-MW system with rated power. Net revenue is approximately proportional to round-trip energy efficiency for assuming the upper various energy/power ratios was determined as the solution of an optimization problem portion of the range of energy efficiencies, as shown in Figure 19. perfect foresight. Perfect foresight represents an upper limit to arbitrage revenue in the RTM and A simple, implementable forecast method proposed in order to capture a portion of the represents essentially a lower limit to revenue in was the DAM. additional revenue opportunity in the RTM compared in thethrough DAM using the dayThe main characteristics of the best nodes to installtoanthat ESSavailable were verified the presented ahead price settlement as a forecast for optimizing dispatch into the RTM. This approach increases analysis. They were: high average prices, periods of high price volatility, and high forecast accuracy to the revenue over thatRTM. available in the DAM for the vast majority nodes, as shown in Figure 22, and exploit spikes in the Increasing the discharge duration at of rated power increases the potential thus can be considered a new lower bound on the potential revenues. revenue per kW of power capacity, but returns diminish rapidly after 4–6 h of discharge duration at theNet revenue forecasts we have evaluated the distribution, over all 7395 nodes, for of breakeven ratedFrom power. revenue is approximately proportional to round-trip energy efficiency the upper overnight installed cost for an ESS vs. its discharge duration when arbitrage is the only source of portion of the range of energy efficiencies, as shown in Figure 19. revenue. The results are presented in Figures 23–25. Although breakeven costs per kWh of A simple, implementable forecast method was proposed in order to capture a portionenergy of the capacity markedly with discharge actual cost ofinflow batteries per drops additionaldrop revenue opportunity in the RTMduration, comparedthe to that available the DAM using thekWh day-ahead markedly with discharge duration as well. price settlement as a forecast for optimizing dispatch into the RTM. This approach increases the revenue ESS costs are not low enough for profitability atas the vast majority nodes, but can recent over Current that available in the DAM for the vast majority of nodes, shown in Figureof22, and thus be ESS cost reductions are expected to continue, bringing an increasing percentage of nodes into considered a new lower bound on the potential revenues. profitability over time. Acknowledgments: We acknowledge contributions by Sandesh Kataria to the collection and cleaning of data and by Na Li for helpful discussions. This work was supported by São Paulo Research Foundation (FAPESP)— grant #2014/05261-0, U.S. Department of Energy Advanced Research Projects Agency—Energy award no. DEAR0000348, the Massachusetts Clean Energy Technology Center and the Harvard School of Engineering and Applied Sciences.
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From the revenue forecasts we have evaluated the distribution, over all 7395 nodes, of breakeven overnight installed cost for an ESS vs. its discharge duration when arbitrage is the only source of revenue. The results are presented in Figures 23–25. Although breakeven costs per kWh of energy capacity drop markedly with discharge duration, the actual cost of flow batteries per kWh drops markedly with discharge duration as well. Current ESS costs are not low enough for profitability at the vast majority of nodes, but recent ESS cost reductions are expected to continue, bringing an increasing percentage of nodes into profitability over time. Acknowledgments: We acknowledge contributions by Sandesh Kataria to the collection and cleaning of data and by Na Li for helpful discussions. This work was supported by São Paulo Research Foundation (FAPESP)—grant #2014/05261-0, U.S. Department of Energy Advanced Research Projects Agency—Energy award no. DE-AR0000348, the Massachusetts Clean Energy Technology Center and the Harvard School of Engineering and Applied Sciences. Author Contributions: William W. Hogan and Michael J. Aziz conceived the study; Mauricio B. C. Salles and Junling Huang collected the data; Mauricio B. C. Salles analyzed the data with commentary from William W. Hogan and Michael J. Aziz; and Mauricio B. C. Salles, Michael J. Aziz and William W. Hogan wrote the paper. Conflicts of Interest: The authors declare no conflict of interest.
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