TY - JOUR
T1 - Dynamic energy storage pricing for virtual power plants via bilevel game optimization under multiple uncertainties
AU - Zhao, Zihao
AU - Yu, Biying
AU - Wu, Yun
AU - Fu, Jiahao
AU - Dai, Ying
AU - Wang, Peng
AU - Zhao, Guangpu
N1 - Publisher Copyright:
© 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
PY - 2026/4/10
Y1 - 2026/4/10
N2 - Energy storage systems are crucial for enhancing Virtual Power Plant (VPP) profitability and operational flexibility. However, existing research predominantly focuses on external market bidding strategies, neglecting energy storage pricing as a core decision variable, which limits comprehensive characterization of price-driven internal VPP ecosystems. To address this gap, this study proposes a bilevel Stackelberg game model where the upper-level VPP operator establishes optimal charge and discharge prices as the leader, while lower-level users adjust their energy demand to minimize costs as followers. A hyper-parameter-tuned differential evolution algorithm is employed to solve this bilevel optimization problem. The approach is validated through Monte Carlo simulations across 200 distinct scenarios. Results demonstrate that the dynamic pricing model increases VPP's average daily revenue by 16.0%, of which physical energy storage arbitrage contributes 9.0% and the dynamic pricing strategy alone contributes an additional 6.4%. Value at Risk (VaR) analysis validates the strategy's robustness, achieving VaR95 of 1254.3 RMB and VaR99 of 1138.0 RMB that guarantee profitability even under extreme market conditions, demonstrating practical feasibility for real-world VPP deployments.
AB - Energy storage systems are crucial for enhancing Virtual Power Plant (VPP) profitability and operational flexibility. However, existing research predominantly focuses on external market bidding strategies, neglecting energy storage pricing as a core decision variable, which limits comprehensive characterization of price-driven internal VPP ecosystems. To address this gap, this study proposes a bilevel Stackelberg game model where the upper-level VPP operator establishes optimal charge and discharge prices as the leader, while lower-level users adjust their energy demand to minimize costs as followers. A hyper-parameter-tuned differential evolution algorithm is employed to solve this bilevel optimization problem. The approach is validated through Monte Carlo simulations across 200 distinct scenarios. Results demonstrate that the dynamic pricing model increases VPP's average daily revenue by 16.0%, of which physical energy storage arbitrage contributes 9.0% and the dynamic pricing strategy alone contributes an additional 6.4%. Value at Risk (VaR) analysis validates the strategy's robustness, achieving VaR95 of 1254.3 RMB and VaR99 of 1138.0 RMB that guarantee profitability even under extreme market conditions, demonstrating practical feasibility for real-world VPP deployments.
KW - Bilevel game
KW - Energy storage pricing strategy
KW - Multiple uncertainties
KW - Storage arbitrage
KW - Virtual power plant
UR - https://www.scopus.com/pages/publications/105033901110
U2 - 10.1016/j.est.2026.121259
DO - 10.1016/j.est.2026.121259
M3 - Article
AN - SCOPUS:105033901110
SN - 2352-152X
VL - 154
JO - Journal of Energy Storage
JF - Journal of Energy Storage
M1 - 121259
ER -