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Optimal Operation Programming of Distributed Generation Resources and Storage Systems in Active Distribution Networks Using a Training-Learning-Based Optimization Algorithm
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Majid Moazzami *1   , Mohammad Javad Sonboli2 |
1- Department of Electrical Engineering, Na.C., Islamic Azad University, Najafabad, Iran , m_moazzami79@yahoo.com
2- Department of Electrical Engineering, Na.C., Islamic Azad University, Najafabad, Iran, Department of Electrical Engineering, Na.C., Islamic Azad University, Najafabad, Iran |
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Abstract: (4 Views) |
In recent years, due to the undeniable benefits of renewable energy resources, energy storage and demand response, the penetration of these resources in distribution networks has increased. With the addition of distributed generation and storage resources to the two-way distribution grid, the active distribution grid is formed. In this situation, with the challenges of active distribution networks, past methods and techniques have not been responsive to economically and securely exploiting the network and more efficient methods and tools are needed to adapt to the changing nature of distribution networks. This paper proposes an optimal new energy management strategy and daily grid scheduling in the presence of small-scale compressed air energy storage (MCAES) and taking into account the uncertainties of renewable energy resources. Minimizing the cost of operating energy storage systems, environmental pollutions, the cost of unsupplied energy, and energy surplus cost taking into account the load supply constraints, are among the goals of this new strategy. The technical limitations considered in this article include the constraints of distributed generation resources and energy storage systems. The demand response program is also used to flatten load pattern and optimize the utilization of the micro grid. Using the proposed Teaching–learning-based optimization (TLBO) algorithm on a micro-grid model, the results of simulation of this model show that the use of MCAES storage system and demand response program reduce operating costs, pollution propagation, unsupplied energy and micro-grid energy surplus cost.
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| Keywords: demand response program, energy storage, optimal energy management, renewable energy resources, uncertainty |
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Type of Study: Research |
Subject:
Special
Received: 2026/07/14 | Accepted: 2026/10/2
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| * Corresponding Author Address: Department of Electrical Engineering, Na.C., Islamic Azad University, Najafabad, Iran |
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