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Integration of energy storage systems with adjustable curtailment of photovoltaic generation in power distribution networks for enhanced grid efficiency

Author

Listed:
  • Ammasaikutti, Pradeep
  • Stanley Raja Thaveedhu, Alex
  • Jayapalan, Muruganantham
  • Palanisamy, Kannan

Abstract

Although renewable energy sources can produce electricity efficiently, there are certain operational challenges when integrating them into the power grid. To mitigate these issues, this manuscript proposes a new approach for integrating Energy Storage Systems (ESS) with adjustable curtailment of photovoltaic generation in power distribution networks to develop grid efficiency, reduce energy costs, and improve system reliability. The proposed technique involves the Logarithmic Differential Convolutional Neural Network (LDCNN) and Eel and Grouper Optimizer (EGO) technique, which is commonly referred to as LDCNN-EGO. The objective is to improve grid efficiency and reduce costs and enhance system reliability. The LDCNN method is used to predict the load demand, and the EGO is used to optimize the locations and size of ESS in power distribution networks. The proposed method is implemented using MATLAB to evaluate its performances and compared with the existing methods like sizing algorithms (SA), Coot Bird Search Algorithm-Genetic Algorithm (CBSA) and Crayfish Optimization Algorithm (COA). The proposed method attains the lowest operational cost of $27.2, outperforming SA ($28.3), CBSA ($29.3), and COA ($30.3). This approach effectively improves grid reliability while supporting a smoother transition toward a sustainable energy system.

Suggested Citation

  • Ammasaikutti, Pradeep & Stanley Raja Thaveedhu, Alex & Jayapalan, Muruganantham & Palanisamy, Kannan, 2026. "Integration of energy storage systems with adjustable curtailment of photovoltaic generation in power distribution networks for enhanced grid efficiency," Renewable Energy, Elsevier, vol. 259(C).
    • Handle: RePEc:eee:renene:v:259:y:2026:i:c:s096014812502600x
    DOI: 10.1016/j.renene.2025.124936
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    References listed on IDEAS

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