Optimal economic dispatch model based on risk management for wind‐integrated power system. Issue 15 (1st November 2015)

• Record Type: Journal Article
• Authors: Wu, Junli; Zhang, Buhan; Wang, Ke; Shao, Jian; Yao, Jianguo; Zeng, Dan; Ge, Tengyu
• Is Part Of: IET generation, transmission & distribution. Volume 9:Issue 15(2015)
• Page Start: 2152
• Page End: 2158
• Publication Date: 2015-11-01
• Subjects: wind power -- risk management -- power generation dispatch -- air pollution control -- probability -- particle swarm optimisation; optimal economic dispatch model -- wind‐integrated power system -- carbon emissions reduction -- wind power generation -- thermal power generation -- probabilistic model -- cost‐risk model -- multiple objective particle swarm optimisation -- Pareto‐optimal solutions -- risk management method -- IEEE 30‐bus power system; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2015.0209
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : In recent years, the proportion of wind power capacity is increased dramatically for carbon emissions reduction in several countries. However, wind power generation cannot be dispatched as conventional thermal power generation because of its randomness and intermittency. To deal with the increased uncertainty, a probabilistic model is established to analyse the uncertainty of wind power and load. Conditional value at risk index is applied to assess risk including the loss of load and 'spilling' wind energy associated with unpredictable imbalances between generation and load. The cost–risk model is proposed by minimising the operation cost and risk in optimal economic dispatch problem. The study uses multiple objective particle swarm optimisation to solve the model and obtain the Pareto‐optimal solutions, which can reflect the relationship between risk and cost. The optimal solution can be determined in Pareto‐optimal set by risk management method based on analysis of the risk marginal cost. The model and methods are tested on the IEEE 30‐bus power system. The results demonstrate the proposed method can control the cost and risk in effective way.