Optimization of cross-regional virtual power plant collaborative response strategy based on multi-agent reinforcement learning

The collaborative response efficiency of cross-regional virtual power plants has become an important factor affecting the operational quality of the power system. In view of complex conditions such as resource differences, communication limitations and inconsistent goals, the multi-agent reinforcement learning method is introduced to construct a distributed model with autonomous decision-making ability, improving response consistency and system stability in aspects such as state modeling, incentive design, information interaction and transfer training, and supporting the construction of the intelligent scheduling.