Stochastic Optimization a New Method Based on for Solving Dynamic Reactive Power Optimization Problems Involving Renewable Energy and Storage

• Published in: 2024 Second International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE) pp. 1 - 6
• Main Authors: Hu, Xu, Feng, Jian, Yang, Feiran
• Format: Conference Proceeding
• Language: English
• Published: IEEE 17.05.2024
• Subjects: chance constraints; Dynamic reactive power optimization; Energy storage; Optimization; partial sample average approximation; Power generation; Power system dynamics; Reactive power; renewable energy; Renewable energy sources; sample average approximation; stochastic optimization; Stochastic processes; Uncertainty; Wind energy
• DOI: 10.1109/ICCSIE61360.2024.10698590

The use of renewable energy for power generation is receiving increasing attention. However, the high uncertainty of renewable energy increases the difficulty of maintaining stable operation of the power system; On the contrary, some constraints of the power system can only be satisfied in high probability situations. In this paper, a dynamic reactive power optimization model with chance constraints is proposed, which effectively integrates energy storage and renewable energy to meet the requirements of renewable energy integration. Specifically, this paper requires that wind energy account for at least a predetermined proportion of total demand, and assumes that wind energy can complete this power generation plan. We propose a new sampling method based on the partial sample average approximate (PSAA) framework to effectively solve large-scale chance constrained dynamic reactive power optimization problems. The computational experiments on the modified IEEE-14 bus system show that the proposed method is more accurate and the computational time cost is less than the average approximate value of the standard sample.