DOI:10.35833/MPCE.2020.000764 |
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Economic Dispatch with High Penetration of Wind Power Using Extreme Learning Machine Assisted Group Search Optimizer with Multiple Producers Considering Upside Potential and Downside Risk |
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Author:
Yuanzheng Li1, Jingjing Huang2, Yun Liu3, Zhixian Ni2, Yu Shen4, Wei Hu4, Lei Wu5
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Author Affiliation:
1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China 2. China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan 430074, China 3. School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China 4. State Grid Hubei Power Electric Research Institute, Wuhan 430077, China 5. Department of Electrical & Computer Engineering, Stevens Institute of Technology, Hoboken USA
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Foundation: |
This work is supported by the Key Scientific and Technological Research Project of State Grid Corporation of China (No. 5400-202022113A-0-0-00). |
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Abstract: |
The power system with high penetration of wind power is gradually formed, and it would be difficult to determine the optimal economic dispatch (ED) solution in such an environment with significant uncertainties. This paper proposes a multi-objective ED (MuOED) model, in which the expected generation cost (EGC), upside potential (USP), and downside risk (DSR) are simultaneously considered. The heterogeneous indices of upside potential and downside risk mean the potential economic gains and losses brought by high penetration of wind power, respectively. Then, the MuOED model is formulated as a tri-objective optimization problem, which is related to uncertain multi-criteria decision-making against uncertainties. Afterwards, the tri-objective optimization problem is solved by an extreme learning machine (ELM) assisted group search optimizer with multiple producers (GSOMP). Pareto solutions are obtained to reflect the trade-off among the expected generation cost, the upside potential, and the downside risk. And a fuzzy decision-making method is used to choose the final ED solution. Case studies based on the Midwestern US power system verify the effectiveness of the proposed MuOED model and the developed optimization algorithm. |
Keywords: |
Economic dispatch (ED) ; wind power ; extreme learning machine ; optimization algorithm |
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Received:October 26, 2020
Online Time:2022/11/21 |
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