Economic dispatch of interconnected renewable power systems via D3QN-based differential evolution

To address the challenges of high dimensionality, nonlinearity, and complex constraints in the economic dispatch of new interconnected power systems (ED), a differential evolution algorithm driven by a dueling double deep Q-network (D3QN-DE) is proposed. In this algorithm, the D3QN serves as the intelligent core, utilizing a five-dimensional state space to monitor population and individual rankings. This multi-dimensional perception enables the agent to adaptively tune the scaling factor and crossover probability based on the population's evolutionary state, effectively balancing global exploration and local exploitation for superior global search. Furthermore, by introducing a multi-segment reward function mechanism, the algorithm provides more precise feedback during evolution, accelerating convergence and improving solution quality. Concurrently, to address challenges posed by the uncertainty from high renewable energy integration, an ED model is constructed that incorporates interconnected spinning reserve capacity across multiple areas. This model establishes an inter-area reserve mutual assistance mechanism, considers area-specific differences in generation resources, and effectively absorbs load and renewable forecast errors. Consequently, it enhances system operational flexibility while ensuring efficient renewable energy accommodation. The algorithm is then employed to solve this dispatch model. Simulation results indicate that the D3QN-DE algorithm achieves higher convergence accuracy in multi-area scheduling, facilitating the optimal allocation of cross-area resources and reducing operating costs.