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<p>The optimization of combined heat and power economic dispatch (CHPED) presents a critical, complex, nonlinear, and non-convex challenge vital for achieving optimal economic performance in modern power systems. The difficulty of CHPED increases further when factors such as valve-point loading effect (VPLE), prohibited zones, and system losses are considered. This study presents an enhanced&#xa0;weighted mean of vectors optimizer&#xa0;(EINFO) to address the&#xa0;CHPED in small-scale systems, including 4-unit, 7-unit, and 24-unit setups, and accommodates VPLE for large-scale systems. The EINFO improves the global search capability of the conventional INFO method by incorporating three strategies: fitness distance balance (FDB), a chaotic mechanism (CM), and quasi-oppositional based learning (QOBL). Evaluations using the CEC 2022 benchmark functions involve statistical comparisons, convergence analyses, and boxplot assessments against several established methods, including SCA, BDO, AVOA, GTO, MGO, ARO, BWO, FFA, and traditional INFO. The results show that EINFO achieves cost reductions ranging from 0.0000324 to 2.0643% for 4-unit systems, 0.00234–2.2346% for 7-unit systems, and 0.01568–17.5413% for 24-unit systems compared to the best outcomes from other methods.</p>

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An enhanced weighted mean of vectors optimizer: addressing combined heat and power economic dispatch with system losses and valve point loading effect

  • 期刊论文

  • 发表日期：

    2025年6月4日

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  • Mohamed Ebeed,
  • Mosaed Elnaka,
  • Noor Habib Khan,
  • Raheela Jamal,
  • Adel Bedair Abdel-Rahman,
  • Francisco Jurado,
  • Salah Kamel,
  • Mahmoud Rihan

摘要

The optimization of combined heat and power economic dispatch (CHPED) presents a critical, complex, nonlinear, and non-convex challenge vital for achieving optimal economic performance in modern power systems. The difficulty of CHPED increases further when factors such as valve-point loading effect (VPLE), prohibited zones, and system losses are considered. This study presents an enhanced weighted mean of vectors optimizer (EINFO) to address the CHPED in small-scale systems, including 4-unit, 7-unit, and 24-unit setups, and accommodates VPLE for large-scale systems. The EINFO improves the global search capability of the conventional INFO method by incorporating three strategies: fitness distance balance (FDB), a chaotic mechanism (CM), and quasi-oppositional based learning (QOBL). Evaluations using the CEC 2022 benchmark functions involve statistical comparisons, convergence analyses, and boxplot assessments against several established methods, including SCA, BDO, AVOA, GTO, MGO, ARO, BWO, FFA, and traditional INFO. The results show that EINFO achieves cost reductions ranging from 0.0000324 to 2.0643% for 4-unit systems, 0.00234–2.2346% for 7-unit systems, and 0.01568–17.5413% for 24-unit systems compared to the best outcomes from other methods.