Risk-preference-aware optimal scheduling and profit allocation of load aggregators and charging operators

With the increasing penetration of renewable energy and the ongoing deepening of electricity market liberalization, distributed flexible resources, such as electric vehicles and energy storage systems, are becoming key enablers for enhancing grid regulation capabilities and improving system operational efficiency. However, under current market mechanisms, these resources still suffer from misaligned incentives and insufficient coordination in scheduling, risk sharing, and profit allocation, resulting in their potential flexibility value not being fully exploited. To address these challenges, this paper focuses on the interaction between load aggregators (LAs) and charging operators (COs) and develops a risk-preference-aware framework for optimal scheduling and profit allocation in an LA-CO coupled system. First, a joint probabilistic model of wind and photovoltaic output is established using kernel density estimation and copula theory, and representative generation scenarios are produced via Monte Carlo sampling and K-means clustering. Second, a Stackelberg game-based optimisation model incorporating Conditional Value-at-Risk (CVaR) is formulated to characterise multi-actor strategic responses under risk constraints. Third, an improved weighted Nash bargaining scheme is designed for cooperative profit allocation, in which bargaining weights integrate marginal contribution, revenue contribution, profit growth rate, and output forecast accuracy to ensure both fairness and incentive compatibility. Finally, a case study for the Qingdao region in Shandong Province demonstrates that the proposed model can reduce the system peak-valley difference and imbalance penalty costs by 11.27% and 27.23%, respectively, while increasing the profits of the LA and CO by 24.08% and 19.21%. These results indicate that the proposed approach significantly enhances system operational stability and risk resilience, providing an effective methodological basis for constructing a multi-stakeholder-coordinated, risk-controllable, and renewable-energy-friendly power system.