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Optimal midterm peak shaving cost in an electricity management system using behind customers’ smart meter configuration

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  • Nieta, Agustín A. Sánchez de la
  • Ilieva, Iliana
  • Gibescu, Madeleine
  • Bremdal, Bernt
  • Simonsen, Stig
  • Gramme, Eivind

Abstract

This paper analyses a local electricity system (LES) comprising photovoltaic production (PV), a connection to the distribution network, local loads and an energy storage system (ESS). Given the flexibility of the ESS, the LES can provide a peak shaving service (PSS) to the grid operator based on the actual monthly power tariff. This paper proposes a stochastic mixed-integer linear programming problem that maximises the expected operating profit of the LES midterm. Assuming a behind customers’ smart meter configuration, income is derived from selling the energy of prosumers to other external electrical areas. If the costs are higher than the income, the net profit will be negative, i.e. a net loss. The cost component of the objective function can be reduced through the management of local resources and by providing PSS to the distribution network operator to minimise the power cost of the monthly power tariff. The model is tested for 720 h (considering a month of 30 days) in three cases: (i) without PV and ESS; (ii) with PV and ESS, where losses are 0%; (iii) with PV and ESS, where losses are 18%. Due to the monthly power tariff, the net loss of the LES is reduced through the optimal management of local resources when the ESS losses are lower than 18%. To assess seasonal implications about the LES, the 12 months of the year are also tested. The month of October indicated the highest peak shaving, while the lowest peak shaving depended on the ESS losses.

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  • Nieta, Agustín A. Sánchez de la & Ilieva, Iliana & Gibescu, Madeleine & Bremdal, Bernt & Simonsen, Stig & Gramme, Eivind, 2021. "Optimal midterm peak shaving cost in an electricity management system using behind customers’ smart meter configuration," Applied Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:appene:v:283:y:2021:i:c:s0306261920316706
    DOI: 10.1016/j.apenergy.2020.116282
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    1. Álvarez-Arroyo, César & Vergine, Salvatore & de la Nieta, Agustín Sánchez & Alvarado-Barrios, Lázaro & D’Amico, Guglielmo, 2024. "Optimising microgrid energy management: Leveraging flexible storage systems and full integration of renewable energy sources," Renewable Energy, Elsevier, vol. 229(C).
    2. Ahmadi, Bahman & Ceylan, Oguzhan & Ozdemir, Aydogan & Fotuhi-Firuzabad, Mahmoud, 2022. "A multi-objective framework for distributed energy resources planning and storage management," Applied Energy, Elsevier, vol. 314(C).
    3. Heidi S. Nygård & Stig Ødegaard Ottesen & Olav Henrik Skonnord, 2024. "Profitability Analyses for Residential Battery Investments: A Norwegian Case Study," Energies, MDPI, vol. 17(16), pages 1-18, August.
    4. Henni, Sarah & Becker, Jonas & Staudt, Philipp & vom Scheidt, Frederik & Weinhardt, Christof, 2022. "Industrial peak shaving with battery storage using a probabilistic forecasting approach: Economic evaluation of risk attitude," Applied Energy, Elsevier, vol. 327(C).

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