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Prosumer-centric demand side management for minimizing electricity bills in a DC residential PV-battery system: An Australian household case study

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  • Mulleriyawage, U.G.K.
  • Wang, P.
  • Rui, T.
  • Zhang, K.
  • Hu, C.
  • Shen, W.X.

Abstract

Technological advancements lead to the development of a home energy management system (HEMS) to perform demand side management (DSM) in residential houses. The current study proposes a DSM strategy to minimize electricity bills in a DC residential house considering the predictions of electrical load consumption and solar photovoltaic (PV) power based on long short-term memory networks. A simulation study compares the proposed DSM strategy against four alternate scenarios, the result shows that the proposed DSM strategy has the lowest annual energy cost increment (5.81%) compared to the benchmark scenario where it is assumed to have the ideal predictions of the load consumption and solar PV power. It also shows that the predictions of the load consumption and solar PV power are essential for economically sensible DSM, however further analysis reveals that the prediction errors do not correlate with the differences in daily energy costs (or daily peak powers) resulting from the proposed DSM strategy and benchmark scenario. Finally, the proposed DSM strategy is experimentally validated on a small-scale hardware-in-the-loop test platform to demonstrate the feasibility of its practical implementation in a HEMS.

Suggested Citation

  • Mulleriyawage, U.G.K. & Wang, P. & Rui, T. & Zhang, K. & Hu, C. & Shen, W.X., 2023. "Prosumer-centric demand side management for minimizing electricity bills in a DC residential PV-battery system: An Australian household case study," Renewable Energy, Elsevier, vol. 205(C), pages 800-812.
    • Handle: RePEc:eee:renene:v:205:y:2023:i:c:p:800-812
    DOI: 10.1016/j.renene.2023.01.029
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    References listed on IDEAS

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    1. Zang, Haixiang & Xu, Ruiqi & Cheng, Lilin & Ding, Tao & Liu, Ling & Wei, Zhinong & Sun, Guoqiang, 2021. "Residential load forecasting based on LSTM fusing self-attention mechanism with pooling," Energy, Elsevier, vol. 229(C).
    2. Mulleriyawage, U.G.K. & Shen, W.X., 2021. "Impact of demand side management on optimal sizing of residential battery energy storage system," Renewable Energy, Elsevier, vol. 172(C), pages 1250-1266.
    3. Mulleriyawage, U.G.K. & Shen, W.X., 2020. "Optimally sizing of battery energy storage capacity by operational optimization of residential PV-Battery systems: An Australian household case study," Renewable Energy, Elsevier, vol. 160(C), pages 852-864.
    4. Marvin Barivure Sigalo & Ajit C. Pillai & Saptarshi Das & Mohammad Abusara, 2021. "An Energy Management System for the Control of Battery Storage in a Grid-Connected Microgrid Using Mixed Integer Linear Programming," Energies, MDPI, vol. 14(19), pages 1-14, September.
    5. Khan, Ahsan Raza & Mahmood, Anzar & Safdar, Awais & Khan, Zafar A. & Khan, Naveed Ahmed, 2016. "Load forecasting, dynamic pricing and DSM in smart grid: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 1311-1322.
    6. Ranaweera, Iromi & Midtgård, Ole-Morten, 2016. "Optimization of operational cost for a grid-supporting PV system with battery storage," Renewable Energy, Elsevier, vol. 88(C), pages 262-272.
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