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<p>The widespread integration of distributed power generation plants (DGPs) into distribution networks has become a standard practice in many countries worldwide. The benefits gained from DGP deployment are heavily influenced by their optimal planning and placement. This study introduces an innovative approach that combines the Energy Valley Optimizer (EVO) with Fuzzy logic to determine the best access points and penetration levels for wind power plants (WTPs), solar power plants (SPPs), and micro-turbine power plants (MTPs) within the IEEE 69-node distribution network, while ensuring technical, economic, and environmental harmony. The research focuses on three key scenarios: (1) a single-objective optimization for maximum power loss reduction, (2) a multi-objective optimization aimed at simultaneously minimizing four sub-objectives, and (3) a multi-objective optimization considering seasonal variations in time-dependent generation and demand. Specifically, the multi-objective framework prioritizes reducing total power losses in distribution branches, improving the voltage deviation index, minimizing electricity procurement costs, and curbing CO2, SO2, and NOX emissions. The proposed EVO-Fuzzy method was benchmarked against several robust optimization techniques. Results demonstrate the superior efficiency and reliability of the proposed approach compared to existing methods. In the third case study, which incorporates the simultaneous connection of WTPs, SPPs, and MTPs under time-varying conditions, the method yielded a fitness value of 0.2926—better than the sun flower optimization (SFO) method (0.2956) and the standalone EVO method (0.2936). This indicates an improved quality of solutions achieved through EVO-Fuzzy logic integration. Moreover, the study substantiated multiple advantages of DGP integration using this approach. By implementing the optimized solution, annual electricity procurement costs were reduced by $1.36&#xa0;million, with a DGP penetration level of 65.63%. Power losses in distribution branches fell by 84.64%, voltage levels improved from a range of [0.909, 1.00] to [0.965, 1.031], and emissions were reduced by 68.55%. Thus, connecting DGPs has proven to deliver significant financial, operational, and environmental benefits, affirming that EVO with Fuzzy logic represents an effective tool for tackling optimization challenges associated with DGP integration.</p>

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Investigating distributed generator high penetration in improving technical, emission and economic constraints of distribution network

  • 期刊论文

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  • 发表日期：

    2026年2月28日

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  • Fares suliaman alromithy,
  • Hamed Hosseinnia,
  • Rana Rostami,
  • Mohammad Rashed M Altimania

摘要

The widespread integration of distributed power generation plants (DGPs) into distribution networks has become a standard practice in many countries worldwide. The benefits gained from DGP deployment are heavily influenced by their optimal planning and placement. This study introduces an innovative approach that combines the Energy Valley Optimizer (EVO) with Fuzzy logic to determine the best access points and penetration levels for wind power plants (WTPs), solar power plants (SPPs), and micro-turbine power plants (MTPs) within the IEEE 69-node distribution network, while ensuring technical, economic, and environmental harmony. The research focuses on three key scenarios: (1) a single-objective optimization for maximum power loss reduction, (2) a multi-objective optimization aimed at simultaneously minimizing four sub-objectives, and (3) a multi-objective optimization considering seasonal variations in time-dependent generation and demand. Specifically, the multi-objective framework prioritizes reducing total power losses in distribution branches, improving the voltage deviation index, minimizing electricity procurement costs, and curbing CO2, SO2, and NOX emissions. The proposed EVO-Fuzzy method was benchmarked against several robust optimization techniques. Results demonstrate the superior efficiency and reliability of the proposed approach compared to existing methods. In the third case study, which incorporates the simultaneous connection of WTPs, SPPs, and MTPs under time-varying conditions, the method yielded a fitness value of 0.2926—better than the sun flower optimization (SFO) method (0.2956) and the standalone EVO method (0.2936). This indicates an improved quality of solutions achieved through EVO-Fuzzy logic integration. Moreover, the study substantiated multiple advantages of DGP integration using this approach. By implementing the optimized solution, annual electricity procurement costs were reduced by $1.36 million, with a DGP penetration level of 65.63%. Power losses in distribution branches fell by 84.64%, voltage levels improved from a range of [0.909, 1.00] to [0.965, 1.031], and emissions were reduced by 68.55%. Thus, connecting DGPs has proven to deliver significant financial, operational, and environmental benefits, affirming that EVO with Fuzzy logic represents an effective tool for tackling optimization challenges associated with DGP integration.