Data-Knowledge Hybrid-Driven Approach for Power Grid Optimal Dispatch Modelling and Decision-Making

The operation of power grid has intensively complicated due to grid expansion and renewable resources, posing unprecedented challenges for power grid dispatching. Traditional data-driven dispatching methods are highly dependent on the quantity and quality of data samples. To address the optimization problem of regional power grid dispatching, this paper proposes a data-knowledge hybrid-driven multi-agent optimization scheduling model. The model combines the multi-agent deep reinforcement learning framework with scheduling knowledge by incorporating scheduling experience knowledge into the agent's loss function as regularization terms, guiding the agent to take reasonable and effective actions during the optimization process and improving learning efficiency. The model aims to minimize system operation costs, reduce wind and solar curtailment and load shedding, and considers constraints such as nodal power balance, line flow constraints, and unit output limits. Experimental results show that compared with traditional data-driven reinforcement learning algorithms, this method has significant advantages in terms of training time, convergence performance, cost-effectiveness, and security.