Research methodology for optimal energy scheduling of virtual power plants based on TD3PG

Virtual power plants play a crucial role in enhancing energy efficiency and reducing carbon emissions. However, the dynamic and complex energy landscape presents significant challenges for optimal scheduling. In this paper, we propose an optimal scheduling method based on the TD3PG algorithm to address the multi-objective optimization challenges associated with virtual power plants. Firstly, the sampling process is enhanced through a prioritized experience replay mechanism, and the absolute mean approach is employed to calculate the TD error, ensuring reliable sample values. Secondly, a system scheduling model is developed with the total system operating cost as the key metric, while environmental states, scheduling actions, and reward functions are designed in accordance with the Markov decision-making principle. Lastly, the economic efficiency of the algorithm and the feasibility of the model are validated. This study employs various evaluation metrics, and the results demonstrate that the performance of the proposed method is comparable to or exceeds that of several classical algorithms.