In order to realize massive residential consumers participating in demand response (DR), load aggregator (LA) can be employed to aggregate flexible loads. In this article, a distributed optimization approach based on state-based potential game (SPG) is proposed to describe the bidding behavior among LAs in day-ahead DR market for load shifting amount. Under the consideration of DR resource's energy consumption satisfaction, profit-oriented bidding decision problem is modeled from the perspective of individual LA and DR market. Basically, the establishment and proof of an SPG model is presented in details. Then, the best-strategy-response iterative algorithm is proposed to search for the Nash equilibrium in a distributed way. Finally, simulation is carried out to verify the efficiency and effectiveness of the proposed approach. Simulation results show that the whole profit of all LAs increases 0.15%, while the running time of the algorithm reduces 59.53% compared with the conventional algorithm in the noncooperative game.

Ensuring frequency stability in microgrids (MGs) is becoming increasingly challenging due to the openness of networked control systems, the volatility of renewable energy sources, and the growing threat of hybrid cyber-physical attacks. This paper presents a delay-tolerant resilient model predictive control (MPC) approach for load frequency stability in MGs. First, a comprehensive MG load frequency control (LFC) model is developed, explicitly accounting for communication time delays. A control strategy that combines resilient MPC with delay compensation is proposed to enhance system resilience and dynamic performance. To address hybrid cyber-physical attacks, a resilient MPC-based output feedback controller is designed based on a hybrid $H_{2}/H_{\infty }$ performance index, in which the $H_{2}$ criterion is optimized online under a guaranteed $H_{\infty }$ performance bound, ensuring robust frequency regulation. Feasibility and safety of the controller are guaranteed by reformulating the non-convex constraints via cone complementary linearization, and solving the controller using linear matrix inequality techniques. Stability and feasibility analyses of the algorithm are also provided. Finally, a series of comparative simulations under various attack scenarios and perturbation conditions are conducted to validate the robustness of the proposed method and demonstrate its effectiveness in enhancing the resilience of the MG system.

The optimal dispatching of renewable energy power stations is particularly crucial in scenarios where the stations face energy rationing due to the large proportion of renewable energy integrated into the power system. In order to achieve safe, economical, and fair scheduling of renewable energy power stations, this paper proposes a two-stage scheduling framework. Specifically, in the initial stage, the maximum consumption space of renewable energy for the system can be optimized by optimizing the formulated safe-economic dispatch model. In the second stage, the fair allocation mechanism of renewable energy power stations is proposed based on the game-fairness empowerment approach. In order to obtain a comprehensive evaluation of renewable energy power stations, an evaluation index system is constructed considering equipment performance, output characteristics, reliability, flexibility, and economy. Subsequently, the cooperative game weighting method is proposed to rank the performance of renewable energy power stations as the basis for fair dispatching. Simulation results show that the proposed scheduling strategy can effectively ensure the priority of renewable energy power stations based on their comprehensive ranking, and improve the safety, economy, and fairness of power station participation in scheduling.

Inertia is the measure of a power system’s ability to resist power interference. The accurate estimation and prediction of inertia are crucial for the safe operation of the power system. To obtain the accurate power system inertia provided by generators, this paper proposes an estimation method considering the influence of frequency and voltage characteristics on the power deficit during transients. Specifically, the traditional swing equations-based inertia estimation model is improved by embedding linearized frequency and voltage factors. On this basis, the snake optimization algorithm is utilized to identify the power system inertia constant due to its strong global search ability and fast convergence speed. Finally, the proposed inertia estimation method is validated in four test systems, and the results show the effectiveness of the proposed method.