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Microgrid system energy management with demand response program for clean and economical operation

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  • Dey, Bishwajit
  • Misra, Srikant
  • Garcia Marquez, Fausto Pedro

Abstract

A fundamental microgrid system's load demand often fluctuates hourly. Utilities establish different prices at various times based on the fluctuation of the load demand curve, this is referred to as electricity price based on time-of-use (TOU). The overall contribution of the paper is manifold which involves the techno-economic impacts of grid participation, electricity pricing and renewables. However, the primary goal is to offer a demand-response (DR) model that maximizes the benefits of energy retailers, in this case the microgrid customers. DR models examine the utility and elasticity of various customers, taking into account their different behaviors during both peak and valley periods. Considering that 40 % of the customers participate in the DR program, an exhaustive optimization process is initiated to calculate the optimal incentive value. Thereafter, a novel intelligent algorithm is implemented to minimize the overall cost of a microgrid system and analyze the outcome with and without DR program. The many cost factors taken into account include fuel costs, fined pollution costs, operating and maintenance costs, depreciation costs, etc. The use of a DR-based energy management microgrid system resulted in a significant decrease in overall generating costs from 880¥ to 872¥ along with pollutants released as compared to those described in the literature, according to numerical data. Furthermore, the peak demand was lowered by 5.13 % from 180 kW to 170.754 kW. The suggested optimization method is claimed to be superior by measures of central tendency analysis.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:334:y:2023:i:c:s0306261923000818
    DOI: 10.1016/j.apenergy.2023.120717
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    3. Pinciroli, Luca & Baraldi, Piero & Compare, Michele & Zio, Enrico, 2023. "Optimal operation and maintenance of energy storage systems in grid-connected microgrids by deep reinforcement learning," Applied Energy, Elsevier, vol. 352(C).
    4. Ahmed T. Hachemi & Fares Sadaoui & Abdelhakim Saim & Mohamed Ebeed & Hossam E. A. Abbou & Salem Arif, 2023. "Optimal Operation of Distribution Networks Considering Renewable Energy Sources Integration and Demand Side Response," Sustainability, MDPI, vol. 15(24), pages 1-34, December.

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