Cooperative Game Theory for Grid Service Pricing: A Utility-Centric Approach

Volume 10, Issue 3, Page No 21-28, 2025

Author’s Name: Faraz Farhidi ¹*, Yahia Baghzouz ², Maxim Rusakov ³

View Affiliations

¹ Adjunct Professor, Department of Economics, Georgia State University, Atlanta, 30303, USA
² Professor, Department of Electrical & Computer Engineering, University of Nevada Las Vegas, Las Vegas, 89154, USA
³ Principal Engineer, Evolution Networks, Ramat Gan, Israel

^a)whom correspondence should be addressed. E-mail: faraz.farhidi@gmail.com

Adv. Sci. Technol. Eng. Syst. J. 10(3), 21-28 (2025); DOI: 10.25046/aj100304

Keywords: Cooperative game, Energy arbitrage, Peak load management, Price schemes

Download Now!

21 Downloads

Export Citations

Abstract

This study presents a novel alternative to traditional Net Energy Metering (NEM) by proposing a set of innovative pricing schemes for solar customers participating in utility-led grid service programs through the aggregation of Distributed Energy Resources (DERs). Grounded in cooperative game theory, the proposed framework facilitates equitable and efficient value allocation among key stakeholders, namely customers, utilities, and aggregators—based on their respective marginal contributions to grid performance and system cost reductions. In contrast to legacy NEM structures, which typically remunerate customers at retail rates and inadequately incentivize storage adoption, load flexibility, or temporal optimization, this approach enables new revenue opportunities by embedding DERs within coordinated grid service portfolios. The pricing mechanisms developed herein are centered on two critical grid services: energy arbitrage and peak load management. These services are provisioned by the excess capacity of customer-owned DERs, particularly rooftop photovoltaic systems and behind-the-meter battery storage. Through the implementation of a Grid Services Set (GSS) and a complementary Grid Services Rider (GSR) tariff structure, participating customers voluntarily permit automated utility coordination of their devices in return for performance-based compensation. An integrated optimization algorithm co-optimizes DER dispatch across both distribution-level operational requirements and real-time wholesale market opportunities, such as those found in the Energy Imbalance Market. This enables strategic charging during periods of surplus or negative pricing and discharging during price peaks. The proposed model contributes to the advancement of Non-Wires Alternatives (NWAs) by reducing reliance on conventional infrastructure upgrades and enhancing grid flexibility and resilience. It also offers a regulatory-aligned pathway for harmonizing DER integration with utility planning objectives, renewable energy targets, and climate adaptation strategies. By fostering a cooperative paradigm between utilities and customers, the framework promotes prosocial grid behavior, scalable DER participation, and innovation in the evolving landscape of decentralized energy systems.

Received: 18 April 2025 Revised: 22 May 2025 Accepted: 24 May 2025 Online: 28 May 2025

Full Text

References (21)

[1] P. Denholm, R. M. Margolis, J.M. Milford, ―Production cost modeling for high levels of photovoltaics penetration,‖ National Renewable Energy Laboratory 2010, doi: 10.2172/924642
T. Navidi, A. El Gamal, R. Rajagopal, ―Coordinating distributed energy resources for reliability can significantly reduce future distribution grid upgrades and peak load,‖ Joule, 7(8), 1769-1792, 2023, DOI: 10.1016/j.joule.2023.06.015.
S. Borenstein, ―The Private Net Benefits of Residential Solar PV: The Role of Electricity Tariffs, Tax Incentives, and Rebates,‖ Journal of the Association of Environmental and Resource Economists, 4(S1), S85–S122, 2017, DOI: 10.1086/691978.
R. Matsuda-Dunn, L. Leddy, E. Hotchkiss, M. Gautam, M. Abdelmalak, ―What Role Do Aggregators Play in Power System Security and Resilience?‖ Preprint. Golden, CO: National Renewable Energy Laboratory. NREL/CP6A40-85649, 2023, DOI: 10.1109/RWS58133.2023.10284615.
S.P. Burger, M. Luke, ―Business models for distributed energy resources: A review and empirical analysis,‖ Energy Policy, 109, 230–248, 2017, DOI: 10.1016/j.enpol.2017.07.007.
J.L. Mathieu, D.S. Callaway, S. Kiliccote, ―Examining uncertainty in demand response baseline models and variability in automated responses to dynamic pricing,‖ In IEEE Conference on Decision and Control, 2011, DOI: 10.1109/CDC.2011.6160795.
R. Hledik, T. Lee, ―Load flexibility: Market potential and opportunities in the United States,‖ In Variable Generation, Flexible Demand (pp. 195-210). Academic Press, 2021, DOI: 10.1016/B978-0-12-823810-3.00001-7.
E. Hittinger, J. Siddiqui, ―The challenging economics of US residential grid defection.” Utilities Policy 45: 27-35, 2017, DOI: 10.1016/j.jup.2016.11.003.
F. Farhidi, M. Rusakov, M. ―An Application of Excess Solar and Storage Capacity Optimization for Grid Services,‖ Journal of Energy and Power Technology, 6(3), 1-25, 2024, DOI: 10.21926/jept.2403017.
W. Saad, Z. Han, H.V. Poor, T. Başar, ―A noncooperative game for double auction-based energy trading in microgrids,‖ In 2011 IEEE International Conference on Smart Grid Communications, 2011, DOI: 10.1109/SmartGridComm.2011.6102323.
K. Khezeli, H. Firoozi, ―A Cooperative Game Theory Approach for Fair Cost Allocation in Peer-to-Peer Energy Trading,‖ Sustainable Energy, Grids and Networks, 21, 100299, 2020, DOI: 10.1016/j.segan.2020.100299.
R. Sioshansi, P. Denholm, T. Jenkin, J. Weiss, J. ―Estimating the value of electricity storage in PJM: Arbitrage and some welfare effects,‖ Markets Operated by Regional Transmission Organizations and Independent System Operators, 2020, Docket No. RM18-9-000.
F. Farhidi, K. Madani, ―A game theoretic analysis of the conflict over Iran’s nuclear program,‖ IEEE International Conference on Systems, Man, and Cybernetics, pp. 617-622, 2015, DOI: 10.1109/SMC.2015.118.
K. Madani, F. Farhidi, S. Gholizadeh. ―Bargaining Power in Cooperative Resource Allocations Games,‖ Algorithms 15, no. 12: 445, 2022, DOI: 10.3390/a15120445.
S. Iqbal, M. Sarfraz, M., Ayyub, M., Tariq, R.K. Chakrabortty, M.J. Ryan, B. Alamri, ―A comprehensive review on residential demand side management strategies in smart grid environment,‖ Sustainability, 13(13), 7170, 2021, DOI: 10.3390/su13137170.
S. Borenstein, ―The Private Net Benefits of Residential Solar PV: The Role of Electricity Tariffs, Tax Incentives and Rebates,‖ Journal of the Association of Environmental and Resource Economists, 4(S1), S85-S122. 2017, DOI: 10.1086/691978.
L.S. Shapley, ―A Value for n-person Games,‖ Ann. Math. Stud. 28, 307–318, 1953, DOI: 10.1515/9781400881970-018.
H. Gao, T. Jin, C. Feng, C. Li, Q. Chen, C. Kang, ―Review of virtual power plant operations: Resource coordination and multidimensional interaction,‖ Applied energy, 357, 122284, 2024, DOI: 10.1016/j.apenergy.2023.122284.
M. Najafi, A. Akhavein, A. Akbari, M. Dashtdar, ―Value of the lost load with consideration of the failure probability,‖ Ain Shams Engineering Journal, 12(1), 659-663, 2021, DOI: 10.1016/j.asej.2020.05.012.
Energy Economics, 31(2), 269–277, 2009, DOI: 10.1016/j.eneco.2008.10.005.
E. Hittinger, J.F. Whitacre, J. Apt, J. ―What properties of grid energy storage are most valuable?‖ Journal of Power Sources, 206(1), 436–449, 2010, DOI: 10.1016/j.jpowsour.2011.12.023.

Cooperative Game Theory for Grid Service Pricing: A Utility-Centric Approach