Bi-level stochastic optimization for load aggregator participating in energy and reserve markets based on conditional value at risk - Rijun Wang; Bing Gu; Zhenglong Sun; Cheng Liu; Chao Jiang; Shuyu Zhou; Zhichong Cao (International Journal of Electrical Power & Energy Systems, 2025)
The integration of large-scale renewable energy into the power system imposes higher demands on the flexible regulation capability of the new power system, urgently necessitating the exploration of the regulatory potential of load-side resources. With the continuous advancement of electricity market reforms, the load aggregator (LA) has emerged as a significant new market entity. To fully leverage the flexibility value of LA, this paper constructs a bi-level stochastic optimization model for LA participation in energy and reserve markets, considering the uncertainties of market prices and controllable load resources within the aggregated region. The upper-level model aims to maximize the total benefit of LA in energy and reserve markets, while quantifying the potential risks arising from market price fluctuations and controllable load uncertainties using conditional value-at-risk theory. The lower-level model achieves the joint clearing of energy and reserve markets. By employing Karush-Kuhn-Tucker conditions and duality theory, the bi-level model is transformed into a mixed-integer linear programming problem for solution. Simulation results demonstrate that the proposed model can optimize the trading strategy of LA in multiple types of electricity markets, mitigate potential risks, and enhance benefit. Compared to participating solely in the energy market, LA’s benefit increases significantly when simultaneously engaging in both energy and reserve markets. Additionally, the nodal location of LA’s integration into the system has a substantial impact on its market benefit.