An energy storage roadmap study incorporating government subsidies based on a combined tripartite evolutionary game and Monte Carlo simulation approach: evidence from China

The strategic coordination of government subsidies with energy storage development and source-grid-load-storage (SGLS) integration represents a pivotal challenge in achieving carbon neutrality. This study pioneers a tripartite evolutionary game framework integrating government regulators, energy storage operators, and power system entities to analyze their strategic interdependencies. Through numerical simulation and Monte Carlo forecasting, we quantify that stability thresholds of subsidy-driven interactions, parametric sensitivity of market equilibrium, and temporal evolution of industry profitability. Key findings reveal that: (1) Strategic complementarity governs system dynamics. Subsidy intensity (a-value) exhibits nonlinear effects: increasing a from 0 to 0.8 reduces stabilization time by 46.91 %, yet exceeding 0.8 in western China triggers a 49.93 % rebound due to diminishing marginal returns. With penalty funds of 200 M CNY, penalty mechanisms accelerate equilibrium attainment by 81.22 % in eastern regions, outperforming western counterparts by 4.09 %, which reveals market-driven incentive heterogeneity. (2) Benefit allocation asymmetry follows an 0.8:0.1:0.1 ratio among regulators, operators, and grid entities, validated by multi-agent ROI linkage effects (eastern regions achieve 46.06 % stabilization improvement vs. 17.82 % in western regions). (3) Techno-economic thresholds dictate viability: probabilistic profitability rises from 35.74 % (2024–2032) to 42.78 % (2032–2040) under system cost benchmarks (<1500 CNY/kWh), while disruptive technologies (e.g., sodium-ion batteries) elevate short-term gains by 1.31 %, albeit constrained by commercialization cycles. This study proposes a subsidy mechanism optimizing fiscal interventions for energy storage development, coupled with Monte Carlo-based revenue projections generating risk-informed strategies. By integrating game theory with probabilistic forecasting, we establish an innovative framework addressing governance challenges in low-carbon transitions under uncertainty.