With the increase in the power system scale, the identification of electromechanical oscillation mode parameters by traditional numerical methods can no longer meet the requirements of complete real-time analysis. Therefore, a method based on machine learning (multilayer artificial neural networks) is proposed to identify the electromechanical oscillation mode parameters of the power system. This method can take the monitorable variables of the WAMS as the input of the model and the key characteristic information such as frequency and damping ratio as the output. After processing the input and output data with randomized dynamic mode decomposition (randomized-DMD), their mapping relationship can be analyzed by using the multilayer neuron architecture. The simulation results of the 4-generator 2-area system and the IEEE 16-generator 5-area system show that this method can accurately calculate the key characteristic parameters of the system without considering the change in the control parameters and after the offline training of historical data, which shows higher accuracy, stronger robustness, and sensitive online tracking ability.

At present, most studies use the direct method to analyze the oscillation problem of modern power systems. However, these studies often only simplify the external characteristics of the wind turbine and lack an in-depth understanding of its internal refined energy structure. In this paper, based on the direct-drive permanent magnetic synchronous generator’s detailed model (D-PMSG), combined with the dynamic energy of its port, layers of analysis are performed on the wind turbine’s internal connections, and a detailed model of the energy structure is created. Then, the interaction mechanism of each control link in the wind turbine is analyzed by combining the energy function of the wind turbine with the improved perturbation method. Finally, this paper constructs a sub-synchronous oscillation (SSO) scenario of weak damping and a forcing type and proves the accuracy and effectiveness of the traceability method based on the refined energy of D-PMSG. This traceability method based on refined energy is expected to provide a new solution to the stability problem caused by the integration of new energy.

This paper proposes a Stackelberg game-based collaborative optimization strategy for Park-Level Integrated Energy Systems (PIESs) operating in carbon and green certificate markets. The strategy addresses interest conflicts and low-carbon transition challenges in multi-agent optimization by integrating a carbon capture, power-to-gas, and liquid carbon dioxide energy storage technology chain. Innovatively integrates LCES into the CCS-P2G-LCES chain, achieving internal carbon cycling and energy storage. First, a market environment for PIESs integrating carbon trading and green certificate trading is constructed, and a deeply coupled low-carbon technology chain model of CCS-P2G-LCES is established to realize internal carbon resource cycling and energy time shifting. Second, a one-leader, multiple-follower Stackelberg game framework is developed with the Integrated Energy Service Provider (IESP) as the leader and the User Load Aggregator (ULA) and Electric Vehicle Aggregator (EVA) as followers. The IESP guides demand response on the user and electric vehicle sides by formulating differentiated energy prices. On this basis, a collaborative optimization dispatch model is constructed with the objective of maximizing the comprehensive revenue of the IESP. Finally, case study analysis verifies that the proposed method not only enhances operational revenue and reduces user energy costs, but also significantly reduces system carbon emissions and improves renewable energy consumption rates. The results demonstrate the feasibility and superiority of integrating market mechanisms, low-carbon technologies, and multi-agent game-based collaborative optimization.