A robust mean variance optimization model for economic dispatch with wind power integration and evenly distributed Pareto front generation. (24th January 2017)

• Record Type: Journal Article
• Authors: Ding, Tao; Kou, Yu; Ma, Zhoujun; Zhang, Yiyang; Yan, Huan
• Is Part Of: International transactions on electrical energy systems. Volume 27:Number 7(2017:Oct.)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-01-24
• Subjects: biobjective optimization -- economic dispatch -- mean variance optimization -- Pareto front -- second‐order cone programming -- uncertainty; Electric power -- Periodicals; Electric power systems -- Periodicals; Electrical engineering -- Periodicals; 621.3
• DOI: 10.1002/etep.2324
• Languages: English
• ISSNs: 2050-7038

Abstract

Summary: A robust mean variance optimization model is set up for an economic dispatch problem integrating uncertain wind power in a real‐time electricity market. The objective of the model is to find a robust optimal solution so as to minimize both the expected total generation cost and hedge the risk resulting from the robustness. Furthermore, the proposed model is transformed into a biobjective second‐order cone programming. To generate an evenly distributed Pareto front set that will not overrepresent one region of the design space or neglect others, we propose a normalized‐constraint method in this work. The Pareto front, demonstrated in the Institute of Electrical and Electronics Engineers 118‐bus case study, shows a trade‐off between total generation cost and risk. An important observation during the case study is that the Pareto front of the robust mean variance model has a special shape that, by sacrificing only a little total generation cost, can greatly reduce the risk in the robust real‐time economic dispatch.