Economic and Low-Carbon-Oriented Distribution Network Planning Considering the Uncertainties of Photovoltaic Generation and Load Demand to Achieve Their Reliability

Abstract

The integration of renewable resources with distribution networks (DNs) is an effective way to reduce carbon emissions in energy systems. In this paper, an economic and low-carbon-oriented optimal planning solution for the integration of photovoltaic generation (PV) and an energy storage system (ESS) in DNs is proposed. A convolutional neural network (CNN)-based prediction model is adopted to characterize the uncertainties of PV and load demand in advance. Then, taking the lowest total economic cost, the largest carbon emission reduction, and the highest system power supply reliability as the optimization objectives, the optimal distribution network planning model is constructed. The improved multi-objective particle swarm optimization (MOPSO) algorithm is used to solve the optimization model, and the effectiveness of the proposed solution is confirmed through a comparative case study on the IEEE-33 bus system. Simulation results show that the proposed solution can better maintain the balance between economic cost and carbon emissions in DNs.