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<p>This study investigates the integration of distributed energy resources (DERs) to enhance the efficiency and cost-effectiveness of distribution networks (DNs) while considering both meteorological and metrological variability. The primary objective is to optimize the operation of battery energy storage systems (BESS) in coordination with demand response (DR) strategies, addressing uncertainties in wind power generation and the integration of shunt capacitors (SCs). A key aspect of this research is the precise measurement and calibration of wind speed data to ensure accurate stochastic modeling, which is critical for optimizing power system operations. The study employs metrological techniques to validate wind power predictions, ensuring the reliability of input data used for optimization. A multi-objective optimization framework is proposed, integrating time-of-use demand response mechanisms while maintaining voltage stability across all nodes. The stochastic nature of wind generation is modeled based on historical meteorological data, which is refined through metrological validation methods. The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is utilized to derive the Pareto optimal front (POF), which is further evaluated using the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) to identify the most balanced solution. The methodology is tested on two systems: a standard 33-bus DN and a realistic 108-bus radial DN representing Indian conditions. Results indicate that the coordinated operation of DERs significantly reduces power losses and grid demand costs while maintaining operational performance under varying meteorological conditions. By incorporating metrological validation in wind power scenario modeling, this study ensures data accuracy and enhances the technical and economic performance of DNs. The findings highlight the importance of integrating demand response programs, stochastic wind generation models, and BESS with SCs, providing a robust strategy for sustainable and resilient power system operations.</p>

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Efficient DER Coordination for Grid Reliability in the Indian Power Sector Under Meteorological and Metrological Variability

  • 期刊论文

  • 发表日期：

    2025年3月20日

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  • Praveen Kumar,
  • Anil Kumar Yadav,
  • S. K. Jha,
  • Vivek Saxena

摘要

This study investigates the integration of distributed energy resources (DERs) to enhance the efficiency and cost-effectiveness of distribution networks (DNs) while considering both meteorological and metrological variability. The primary objective is to optimize the operation of battery energy storage systems (BESS) in coordination with demand response (DR) strategies, addressing uncertainties in wind power generation and the integration of shunt capacitors (SCs). A key aspect of this research is the precise measurement and calibration of wind speed data to ensure accurate stochastic modeling, which is critical for optimizing power system operations. The study employs metrological techniques to validate wind power predictions, ensuring the reliability of input data used for optimization. A multi-objective optimization framework is proposed, integrating time-of-use demand response mechanisms while maintaining voltage stability across all nodes. The stochastic nature of wind generation is modeled based on historical meteorological data, which is refined through metrological validation methods. The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is utilized to derive the Pareto optimal front (POF), which is further evaluated using the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) to identify the most balanced solution. The methodology is tested on two systems: a standard 33-bus DN and a realistic 108-bus radial DN representing Indian conditions. Results indicate that the coordinated operation of DERs significantly reduces power losses and grid demand costs while maintaining operational performance under varying meteorological conditions. By incorporating metrological validation in wind power scenario modeling, this study ensures data accuracy and enhances the technical and economic performance of DNs. The findings highlight the importance of integrating demand response programs, stochastic wind generation models, and BESS with SCs, providing a robust strategy for sustainable and resilient power system operations.