Research on Distributed Optimal Scheduling Technology for Distribution Networks with High-Proportion Photovoltaic Integration

This chapter delves into the challenges of optimal scheduling in distribution networks with high photovoltaic (PV) integration. It compares centralized and distributed control systems, highlighting the advantages of distributed control in terms of flexibility and reliability. The study introduces a distributed optimal scheduling strategy that uses a genetic algorithm for network clustering based on modularity and self-consumption indices. The strategy aims to minimize network loss and wasted solar energy, utilizing the SADMM algorithm for optimization. The effectiveness of the approach is validated through simulations on the IEEE 33-node system, demonstrating improved PV consumption, reduced network losses, and enhanced voltage quality. The chapter also explores the impact of energy storage on the distribution network's performance, showing significant benefits in terms of operating costs and voltage regulation. The findings underscore the importance of clustering techniques and the integration of energy storage in optimizing the operation of distribution networks with high PV penetration.