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Impact of multiple demand side management programs on the optimal operation of grid-connected microgrids

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  • Seshu Kumar, R.
  • Phani Raghav, L.
  • Koteswara Raju, D.
  • Singh, Arvind R.

Abstract

With the rapid proliferation of non-dispatchable energy sources, the need for demand-side management (DSM) strategies has become crucial to ensure affordability and reliability for end-users. The application of various diversified DSM strategies in the microgrid energy management system (EMS) is gaining popularity. This paper aims to solve the energy management problem of the microgrid in conjunction with both customer-oriented and utility-oriented DSM strategies for the first time in the literature. In light of this, a stochastic EMS framework is developed to implement and analyze the flexible load shaping DSM strategy, price-based, and incentive-based demand response programs (DRPs) in the presence of non-dispatchable energy resources. Further, the flexible price-oriented load model is adopted for price-driven and incentive-driven DRPs to depict the realistic assessment of consumers’ sensitivity to market prices. The scenario construction approach is employed to address the stochastic nature of renewable power generation, market prices, and load demand. With the complexities as mentioned above, the problem needs to be solved with a powerful optimizer sufficiently to enhance energy efficiency and optimize energy utilization. Hence, the recently reported novel metaheuristic algorithm (Black Widow Optimization) is applied to solve the proposed MG energy management problem in the MATLAB environment. The IEEE-34 node distribution feeder-based MG network is modified to study the proposed algorithm's performance, and a detailed analysis of various techno-economic indices is presented. The obtained simulation results are compared with existing popular algorithms to prove the efficacy of the proposed algorithm in terms of convergence, computational time, an optimum solution and the real-time market bid prices were considered in analysis for day-ahead scheduling of microgrid network.

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  • Seshu Kumar, R. & Phani Raghav, L. & Koteswara Raju, D. & Singh, Arvind R., 2021. "Impact of multiple demand side management programs on the optimal operation of grid-connected microgrids," Applied Energy, Elsevier, vol. 301(C).
    • Handle: RePEc:eee:appene:v:301:y:2021:i:c:s0306261921008540
    DOI: 10.1016/j.apenergy.2021.117466
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    5. Hegazy Rezk & A. G. Olabi & Enas Taha Sayed & Tabbi Wilberforce, 2023. "Role of Metaheuristics in Optimizing Microgrids Operating and Management Issues: A Comprehensive Review," Sustainability, MDPI, vol. 15(6), pages 1-27, March.
    6. Ali M. Jasim & Basil H. Jasim & Habib Kraiem & Aymen Flah, 2022. "A Multi-Objective Demand/Generation Scheduling Model-Based Microgrid Energy Management System," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    7. Dey, Bishwajit & Misra, Srikant & Garcia Marquez, Fausto Pedro, 2023. "Microgrid system energy management with demand response program for clean and economical operation," Applied Energy, Elsevier, vol. 334(C).
    8. Cui, Jindong & Ran, Zihan & Shen, Wei & Xin, Yechun, 2024. "Study on multi-type flexible load control method of active distribution network based on dynamic time-sharing electricity price," Applied Energy, Elsevier, vol. 357(C).
    9. Navid Rezaei & Abdollah Ahmadi & Mohammadhossein Deihimi, 2022. "A Comprehensive Review of Demand-Side Management Based on Analysis of Productivity: Techniques and Applications," Energies, MDPI, vol. 15(20), pages 1-28, October.
    10. Masoud Dashtdar & Aymen Flah & Seyed Mohammad Sadegh Hosseinimoghadam & Hossam Kotb & Elżbieta Jasińska & Radomir Gono & Zbigniew Leonowicz & Michał Jasiński, 2022. "Optimal Operation of Microgrids with Demand-Side Management Based on a Combination of Genetic Algorithm and Artificial Bee Colony," Sustainability, MDPI, vol. 14(11), pages 1-26, May.
    11. Phani Raghav, L. & Seshu Kumar, R. & Koteswara Raju, D. & Singh, Arvind R., 2022. "Analytic Hierarchy Process (AHP) – Swarm intelligence based flexible demand response management of grid-connected microgrid," Applied Energy, Elsevier, vol. 306(PB).

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