Multi-Objective Day-Ahead Optimization Scheduling Based on MOEA/D for Active Distribution Networks with Distributed Wind and Photovoltaic Power Integration

The high proportion of renewable energy connected to the grid poses new challenges to the safe and economic operation of active distribution networks (ADNs). However, most of the existing research focuses on single-objective optimization or ignores the influence of the uncertainty of renewable energy output and the demand response mechanism, and lacks verification of the scalability of models in large-scale systems. For an active distribution network system with distributed wind power and photovoltaic access, this paper establishes a multi-objective day-ahead optimal dispatching model that takes into account economy, reliability, and safety. The research adopts a scenario-based method and chance-constrained programming (CCP) to handle the uncertainty of wind and solar output. It combines the quasi-Monte Carlo (QMC) method and Kantorovich distance to achieve scenario generation and reduction, and introduces price-based and incentivized demand response mechanisms to form four combined optimization models. The multi-objective optimization solution was carried out based on the multi-objective evolutionary algorithm based on decomposition (MOEA/D), verifying the effectiveness of the proposed method in terms of operation cost, load shedding expectation, and node voltage limit control. The case study is based on the improved IEEE 30-node and 200-node 49-generator systems. The results indicate that this method can effectively balance multiple objectives such as operation costs, load shedding expectations, and node voltage limit; can significantly enhance the renewable energy consumption capacity of active distribution networks; and can provide an effective solution for the optimal dispatching of active distribution networks with a high proportion of renewable energy.