Modeling Stochastic Multi-Agent Interaction in Intraday Battery Energy Storage Dispatch with Market Power

Paper: arXiv 2605.01178

Authors: Ruimeng Hu, Mike Ludkovski, Hezhong Zhang

Abstract

We develop a stochastic game-theoretic model for intraday dispatch of grid-scale battery energy storage systems (BESSs). We assume that each BESS operator competitively manages her state-of-charge to maximize energy arbitrage revenues, driven by the endogenized electricity price that depends on the sum of the charging rates. We characterize the Nash equilibrium of the resulting finite-player linear-quadratic differential game with a shared stochastic driver, obtaining semi-explicit representations of equilibrium feedback controls and equilibrium prices both in the general heterogeneous and the simplified homogeneous BESS setting, via a system of Riccati equations. We then analyze competitive effects, including the marginal externality of additional BESS entering the market, the benefit of coordination and the corresponding market power of large operators, and supply effects from hybrid-type BESSs. We further study the asymptotic regime as the number of agents grows large. Our model provides a quantitative testbed to study the impact of decentralized BESS deployment on the grid and the resulting reduction in daily price spreads.

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper provides a rigorous mathematical framework utilizing stochastic differential games, systems of Riccati equations, and mean-field limits to model battery energy storage dispatch. While mathematically dense and novel in its application to market power and energy arbitrage, it is primarily a theoretical framework lacking real-world backtesting or empirical market data validation.

Research Flowchart

  flowchart TD
    A["Research Goal: Model intraday BESS dispatch & market power via stochastic differential games"] --> B["Inputs: Shared stochastic electricity price driver & BESS operational constraints"]
    B --> C["Methodology: Formulate finite-player linear-quadratic differential game with endogenized prices"]
    C --> D["Computation: Solve system of Riccati equations to obtain Nash equilibrium feedback controls"]
    D --> E["Findings: Quantified market power, coordination benefits, and reduced daily price spreads"]
    D --> F["Asymptotic Analysis: Derived market behavior as number of agents grows large (N → ∞)"]