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A knee-guided algorithm to solve multi-objective economic emission dispatch problem

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  • Yu, Xiaobing
  • Duan, Yuchen
  • Luo, Wenguan

Abstract

Environmental protection and climate change have addressed tremendous pressure on thermal plants. So, the Economic Emission Dispatch (EED) problem has to consider bi-objective: the fuel cost and emission dispatch, which can be solved by the conventional Multi-Objective Evolutionary Algorithms (MOEAs). However, these MOEAs often provide well-distributed Pareto Optimal Front (POF), which may be a burden to thermal plants policymakers to select an optimal solution from a lot of candidate solutions. We develop a Knee-Guided Algorithm (KGA) to handle the EED problem, in which the knee solution is defined as the optimal by using the minimum Manhattan distance approach. The proposed KGA searches around the knee solution to boost the convergence and outputs the knee solution instead of the whole POF, which is convenient to thermal plant policymakers. Through four test cases, including six-unit, ten-unit, eleven-unit, and fourteen-unit, the proposed KGA is compared with some latest algorithms. The results have demonstrated that the KGA is superior.

Suggested Citation

  • Yu, Xiaobing & Duan, Yuchen & Luo, Wenguan, 2022. "A knee-guided algorithm to solve multi-objective economic emission dispatch problem," Energy, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:energy:v:259:y:2022:i:c:s0360544222017790
    DOI: 10.1016/j.energy.2022.124876
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    References listed on IDEAS

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    2. Zhang, Qiang & Zou, Dexuan & Duan, Na, 2023. "An improved differential evolution using self-adaptable cosine similarity for economic emission dispatch," Energy, Elsevier, vol. 283(C).

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