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Intelligent demand side management for optimal energy scheduling of grid connected microgrids

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

Abstract

The incorporation of renewables and communication technologies to the utility paves a way for self-sustained microgrids (MG). The volatile nature of these resources, uncertainties associated with the time-varying load, and market prices impose the significance of an efficient energy management system (EMS). So far, the MG optimal operation has been referred to optimize the operating costs only. However, the prospects of incorporating demand-side management (DSM) with the EMS problem and its effect on total operating cost and peak reduction is needed to be evaluated. To fill this gap, the impact of utility induced flexible load shaping strategy on non-dispatchable energy sources is investigated in this paper. A three-stage stochastic EMS framework is proposed for solving optimal day-ahead scheduling and minimizing the operational cost of grid-connected MG. In the first stage, four possible scenarios for solar and wind power generation profiles are created to address the uncertainty problem by considering real-time meteorological data. The second stage deals with the MG system configuration, operational constraints, and assigning DSM load participation data to be incorporated with the objective function. In this regard, the Quantum Particle Swarm Optimization is devised at stage three to obtain the optimal power dispatch configuration for DG units, maximizing the power export to the utility and compare the results with and without incorporating DSM participation for all scenarios. The obtained simulation results show the competence of the proposed stochastic framework about cost reduction by 43.81% with the implementation of the load participation level of 20% DSM.

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  • Kumar, R. Seshu & Raghav, L. Phani & Raju, D. Koteswara & Singh, Arvind R., 2021. "Intelligent demand side management for optimal energy scheduling of grid connected microgrids," Applied Energy, Elsevier, vol. 285(C).
    • Handle: RePEc:eee:appene:v:285:y:2021:i:c:s0306261921000040
    DOI: 10.1016/j.apenergy.2021.116435
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    12. 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.
    13. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    14. Huang, Yan & Ju, Yuntao & Ma, Kang & Short, Michael & Chen, Tao & Zhang, Ruosi & Lin, Yi, 2022. "Three-phase optimal power flow for networked microgrids based on semidefinite programming convex relaxation," Applied Energy, Elsevier, vol. 305(C).
    15. Cai, Qiran & Xu, Qingyang & Qing, Jing & Shi, Gang & Liang, Qiao-Mei, 2022. "Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities," Energy, Elsevier, vol. 261(PB).
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    17. Hanaa Feleafel & Jovana Radulovic & Michel Leseure, 2024. "Should We Have Selfish Microgrids?," Energies, MDPI, vol. 17(16), pages 1-24, August.
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    19. Zhou, Xu & Ma, Zhongjing & Zou, Suli & Zhang, Jinhui, 2022. "Consensus-based distributed economic dispatch for Multi Micro Energy Grid systems under coupled carbon emissions," Applied Energy, Elsevier, vol. 324(C).
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    21. Tang, Hong & Wang, Shengwei, 2022. "A model-based predictive dispatch strategy for unlocking and optimizing the building energy flexibilities of multiple resources in electricity markets of multiple services," Applied Energy, Elsevier, vol. 305(C).

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