# Potential Arbitrage Revenue of Energy Storage Systems in PJM

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## Abstract

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## 1. Introduction

- Frequency Regulation (217 MW);
- Reserve Capacity (116 MW);
- Energy Time Shift (131 MW);
- Billing Management (95 MW).

## 2. PJM Electricity Markets

#### 2.1. Locational Marginal Price

#### 2.2. Electricity Prices in the PJM Real-Time Market

#### 2.3. Volatility of Electricity Prices in PJM

#### 2.4. Seasonality of Electricity Prices for PJM

#### 2.5. Electricity Prices in PJM Zones

## 3. Potential Arbitrage Revenue in RTM

#### 3.1. Generic ESS Model

_{t}: energy price in hour t, κ: power capacity of storage device, h: number of hours of discharge at rated power, d

_{t}: discharge power in hour t of storage device, c

_{t}: charge power in hour t of storage device, ${s}_{t}$: state of charge in hour t of storage device, η: round trip efficiency of storage device.

#### 3.2. The Energy Arbitrage Revenue in PJM

#### 3.3. Seasonality of Revenue in PJM

#### 3.4. Spatial Distribution of Revenue Across Locations

## 4. Arbitrage Revenue in RTM and DAM

#### 4.1. Sensitivity to Round Trip Efficiency for Dispatching in RTM

#### 4.2. Additional Arbitrage Revenue in RTM

#### 4.3. Price Forecast Impact on Arbitrage in RTM

## 5. Breakeven Overnight Installed Cost

## 6. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Eyer, J.; Corey, G. Energy Storage for the Electricity Grid: Benefits and Market Potential Assessment Guide; SAND2010-0815, Technical Report; Sandia National Laboratories: Livermore, CA, USA, 2010.
- Akhil, A.A.; Huff, G.; Currier, A.B.; Kaun, B.C.; Rastler, D.M.; Chen, S.B.; Cotter, A.L.; Bradshaw, D.T.; Gauntlett, W.D. DOE/EPRI 2013 Electricity Storage Handbook in Collaboration with NRECA; SAND2013-5131, Technical Report; Sandia National Laboratories: Livermore, CA, USA, 2013.
- US Development of Energy (DOE), Global Energy Storage Database. Available online: http://www.energystorageexchange.org/projects (accessed on 9 January 2015).
- Luo, X.; Wang, J.; Dooner, M.; Clarke, J. Overview of current development in electrical energy storage technologies and the application potential in power system operation. Appl. Energy
**2015**, 137, 511–536. [Google Scholar] [CrossRef] - Sioshansi, R.; Denholm, P.; Jenkins, T.; Weiss, J. Estimating the value of electricity storage in PJM: Arbitrage and some welfare effects. Energy Econ.
**2009**, 31, 269–277. [Google Scholar] [CrossRef] - Shear, T. Wholesale Power Prices Increase Across the Country in 2014, Today in Energy—U.S. Energy Information Administration. Available online: http://www.eia.gov/todayinenergy/detail.cfm?id=19531 (accessed on 12 January 2015).
- Drury, E.; Denholm, P.; Sioshansi, R. The value of compressed air energy storage in energy and reserve markets. Energy
**2011**, 36, 4959–4973. [Google Scholar] [CrossRef] - Bradbury, K.; Pratson, L.; Patiño-Echeverri, D. Economic viability of energy storage systems based on price arbitrage potential in real-time US electricity markets. Appl. Energy
**2014**, 114, 512–519. [Google Scholar] [CrossRef] - Monitoring Analytics, LLC. 2014 State of the Market Report for PJM. In Independent Market Monitor for PJM Report; Monitoring Analytics, LLC: Southeastern, PA, USA, 2015; Volume 2. [Google Scholar]
- Ott, A.L. Experience with PJM Market Operation, System Design and Implementation. IEEE Trans. Power Syst.
**2003**, 18, 528–534. [Google Scholar] [CrossRef] - Fan, Z.; Horger, T.; Bastian, J.; Ott, A. An overview of PJM energy market design and development. In Proceedings of the 3rd International Conference Electric Utility Deregulation and Restructuring and Power Technologies, NanJing, China, 6–9 April 2008; pp. 12–17. [Google Scholar]
- Litvinov, E. Design and operation of the locational marginal prices-based electricity markets. IET Gener. Transm. Distrib.
**2010**, 4, 315–323. [Google Scholar] [CrossRef] - Zareipour, H.; Bhattacharya, K.; Canizares, C. Electricity market price volatility: The case of Ontario. Energy Policy
**2007**, 35, 4739–4748. [Google Scholar] [CrossRef] - Electricity Data Browser. Average Cost of Fossil-Fuels for Electricity Generation—Natural Gas and Coal. Available online: http://www.eia.gov/electricity/data.cfm (accessed on 9 November 2015).
- U.S. Energy Storage Monitor. U.S. Energy Storage Monitor Q2 2015: Executive Summary; GTM Research and Energy Storage Association (ESA): Washington, DC, USA, 2015. [Google Scholar]
- Byrne, R.H.; Silva-Monroy, C.A. Estimating the Maximum Potential Revenue for Grid Connected Electricity Storage: Arbitrage and Regulation; SAND2012-3863, Technical Report; Sandia National Laboratories: Livermore, CA, USA, 2012.
- Hittinger, E.; Lueken, R. Is inexpensive natural gas hindering the grid energy storage industry? Energy Policy
**2015**, 87, 140–152. [Google Scholar] - Salles, M.B.C.; Aziz, M.J.; Hogan, W.W. Potential Arbitrage Revenue of Energy Storage Systems in PJM during 2014. In Proceedings of the IEEE Power & Energy Society General Meeting, Boston, MA, USA, 17–21 July 2016. [Google Scholar]
- McConnell, D.; Forcey, T.; Sandiford, M. Estimating the value of electricity storage in an energy-only wholesale market. Appl. Energy
**2015**, 159, 422–432. [Google Scholar] [CrossRef] - Connolly, D.; Lund, H.; Finn, P.; Mathiesen, B.V.; Leahy, M. Practical operation strategies for pumped hydroelectric energy storage (PHES) utilising electricity price arbitrage. Energy Policy
**2011**, 39, 4189–4196. [Google Scholar] [CrossRef] - Lueken, R.; Apt, J. The effects of bulk electricity storage on the PJM market. Energy Syst.
**2014**, 5, 677–704. [Google Scholar] [CrossRef] - Walawalkar, R.; Apt, J.; Mancini, R. Economics of electric energy storage for energy arbitrage and regulation in New York. Energy Policy
**2007**, 35, 255–2568. [Google Scholar] [CrossRef] - Distributed Generation Renewable Energy Estimate of Costs (Updated February 2016). Available online: http://www.nrel.gov/analysis/tech_lcoe_re_cost_est.html (accessed on 21 June 2017).
- Denholm, P.; Ela, E.; Kirby, B.; Milliqan, M. The Role of Energy Storage with Renewable Electricity Generation; Technical Report; NREL: Golden, CO, USA, 2010. [Google Scholar]
- Lazard. Lazard’s Levelized Cost of Storage Analysis—Version 1.0. Available online: https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwi_7bLTu4vKAhUMRiYKHbeGD8sQFggdMAA&url=https://www.lazard.com/media/2391/lazards-levelized-cost-of-storage-analysis-10.pdf&usg=AFQjCNFgvdlFQpMpaLWTSNncWrtlbQEl0Q (accessed on 1 February 2017).
- U.S. Energy Information Administration (EIA). Spreadsheet AEO2014_financial.xls. 2014. Available online: https://www.eia.gov/opendata/excel/ (accessed on 21 July 2017).
- Internal Revenue Service “How to Depreciate Property”. Available online: http://www.irs.gov/pub/irs-pdf/p946.pdf (accessed on 1 February 2017).
- Sakti, A.; Gallagher, K.G.; Sepulveda, N.; Uckun, C.; Vergara, C.; de Sistemes, F.J.; Dees, D.W.; Botterud, A. Enhanced representations of lithium-ion batteries in power systems models and their effect on the valuation of energy arbitrage applications. J. Power Sources
**2017**, 342, 279–291. [Google Scholar] [CrossRef] - Mohsenian-Rad, H. Coordinated price-maker operation of large energy storage units in nodal energy markets. IEEE Trans. Power Syst.
**2016**, 31, 786–797. [Google Scholar] [CrossRef] - Mohsenian-Rad, H. Optimal bidding, scheduling, and deployment of battery systems in California day-ahead energy market. IEEE Trans. Power Syst.
**2016**, 31, 422–453. [Google Scholar] [CrossRef] - Byrne, R.H.; Concepcion, R.J.; Silva-Monroy, C.A. Estimating potential revenue from electrical energy storage in PJM. In Proceedings of the IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, USA, 17–21 July 2016. [Google Scholar]

**Figure 1.**Electrochemical energy storage systems (ESS) installations over 1 MW in the United States (August 2015) classified by their primary use, based on the data provided in [3].

**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).

**Figure 3.**Hourly-based average prices of electricity in the real-time market (RTM), encompassing 7395 PJM nodes.

**Figure 4.**Average electricity prices of RTM in PJM, encompassing 7395 nodes. The abscissa is the percentage of nodes with average price less than the value given by the ordinate. The values are independently sorted by year.

**Figure 5.**Annual standard deviation of real-time prices in PJM, encompassing 7395 nodes. The values are independently sorted by year.

**Figure 6.**Average of the 10 highest daily standard deviation of real-time prices in PJM, encompassing 7395 nodes. The values are independently sorted by year.

**Figure 7.**Daily-based standard deviation (Std) and the mean value of real-time prices at the price node NEWMERED69 KV N-MERD.

**Figure 8.**Monthly-based PJM average prices of electricity in RTM, considering the previous same nodes.

**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 12.**Yearly-based average prices of electricity in RTM across PJM zones. The dotted line represents the average per zone in the period.

**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.

**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 than the value given by the ordinate; the values are independently sorted by year.

**Figure 15.**Potential revenue in RTM in 2008 from 1 to 14 MWh, considering 7395 nodes with price-taking and perfect forecast.

**Figure 16.**Monthly-based average percentage of annual potential arbitrage revenue for PJM in RTM normalized per year, considering 7395 locations. The dotted line connects the multi-year monthly average.

**Figure 19.**Sensitivity analysis for the revenue in dollars per kW, considering round trip efficiency from 10% to 90%, increasing in increments of 10%. LMP data for 2014 in RTM, 10 h discharge duration.

**Figure 20.**Additional revenue in energy arbitrage in RTM relative to day-ahead market (DAM) in 2008 and 2014 for different energy capacities with 1 MW power capacity.

**Figure 21.**Potential revenue of a 10 MWh ESS dispatched in the RTM using DAM settlement prices as forecast. The results are presented as a percentage of the revenue captured by the same device in the RTM with perfect foresight.

**Figure 22.**Potential revenue of a 10 MWh ESS dispatched in the RTM using DAM settlement prices as forecast divided by the potential revenue in DAM.

**Figure 23.**Distribution of breakeven overnight installed cost in the PJM RT-Market for an ESS vs. discharge duration at rated power (1–14 h) for each of the years studied. The thin vertical bars represent the entire range over the 7395 nodes. The thick vertical bars are bounded by the 25th and 75th percentile of the nodes. The circles represent the 50% percentile of the nodes and the black crosses indicate the 95th percentile of the nodes. Assumed round trip efficiency is 90% and project lifetime is 20 years.

**Figure 24.**Distribution of breakeven overnight installed cost in the PJM DA-Market for an ESS vs. discharge duration at rated power (1–14 h) for each of the years studied. The thin vertical bars represent the entire range over the 7395 nodes. The thick vertical bars are bounded by the 25th and 75th percentile of the nodes. The circles represent the 50% percentile of the nodes and the black crosses indicate the 95th percentile of the nodes. Assumed round trip efficiency is 90% and project lifetime is 20 years.

**Figure 25.**Distribution of breakeven overnight installed cost using DA prices as forecast for dispatch in the PJM RT-Market for an ESS vs. discharge duration at rated power (1–14 h) for each of the years studied. The thin vertical bars represent the entire range over the 7395 nodes. The thick vertical bars are bounded by the 25th and 75th percentile of the nodes. The circles represent the 50% percentile of the nodes and the black crosses indicate the 95th percentile of the nodes. Assumed round trip efficiency is 90% and project lifetime is 20 years.

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**MDPI and ACS Style**

Salles, M.B.C.; Huang, J.; Aziz, M.J.; Hogan, W.W.
Potential Arbitrage Revenue of Energy Storage Systems in PJM. *Energies* **2017**, *10*, 1100.
https://doi.org/10.3390/en10081100

**AMA Style**

Salles MBC, Huang J, Aziz MJ, Hogan WW.
Potential Arbitrage Revenue of Energy Storage Systems in PJM. *Energies*. 2017; 10(8):1100.
https://doi.org/10.3390/en10081100

**Chicago/Turabian Style**

Salles, Mauricio B. C., Junling Huang, Michael J. Aziz, and William W. Hogan.
2017. "Potential Arbitrage Revenue of Energy Storage Systems in PJM" *Energies* 10, no. 8: 1100.
https://doi.org/10.3390/en10081100