Optimal Scheduling of Active Distribution Considering Uncertainties in Wind and Solar Power Generation

This article aims to optimally schedule an active distribution considering uncertainties in both wind and solar power generations. In this respect, an optimal scheduling model to minimize electricity interaction and maximize the autonomous defense capability of active distribution, achieving economical and efficient operation is firstly developed; a sampling and clustering approach is then introduced to quantify the uncertainty in both wind and solar power generations; and an improved genetic algorithm is finally proposed to solve the corresponding optimal scheduling problem. The numerical results from the case study demonstrate that the proposed approach effectively manages the uncertainty in wind and solar power integration, leading to more stable and cost-efficient power system scheduling.

I. Introduction

Since the proposal of the “dual carbon” goal in September 2020 in China, the environmental importance of new green energy sources such as wind power and solar power has been increasingly demonstrated. As a result, there has been an increasing investment in developing new green energy sources like wind and solar power. Nevertheless, the unpredictable nature, inconsistency, and variability of wind and solar energy generation considerably affect the power grid's operation. The high proportion of renewable energy sources connected to the power system induces many challenges to the stable and safe operation of the power grid, leading to a substantial abandonment of the wind and solar power, which imposes a higher demand on the power network operation.