Abstract:

With the large-scale grid integration of distributed renewable energy, the traditional distribution network has gradually developed from a single flow to a complex two-way flow. In view of the problem that the traditional dispatching method in active distribution network technology cannot be directly applied, this study explores improvement measures from two aspects of intelligent algorithm and optimization model. On the basis of considering the relevance of “source network load and storage”, aiming at improving the effect of peak shaving and valley filling, improving the economy of distribution network, and reducing the loss of distribution network, the forecast of wind and solar output has been carried out to improve the validity of the data, and a two-stage two-layer joint optimal dispatch model has been established. The study analyzes the advantages and disadvantages of the traditional particle swarm algorithm, and proposes to use the improved HE-MOPSO algorithm to solve the model. By solving the ZDT1~4 test function and using the extended IEEE33 node to perform the simulation calculation, the experimental results proved the superiority of the improved algorithm and model.

Key words: active distribution network, multi-objective optimization, particle swarm algorithm, grey forecast, demand response, time-of-use price, congestion sorting, dynamic weighting