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Real-Time Economic Dispatch of CHP Systems with Battery Energy Storage for Behind-the-Meter Applications

Author

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  • Marvin B. Sigalo

    (Faculty of Environment, Science and Economy, University of Exeter, Penryn Campus, Cornwall TR10 9FE, UK)

  • Saptarshi Das

    (Faculty of Environment, Science and Economy, University of Exeter, Penryn Campus, Cornwall TR10 9FE, UK)

  • Ajit C. Pillai

    (Faculty of Environment, Science and Economy, University of Exeter, Penryn Campus, Cornwall TR10 9FE, UK)

  • Mohammad Abusara

    (Faculty of Environment, Science and Economy, University of Exeter, Penryn Campus, Cornwall TR10 9FE, UK)

Abstract

The use of combined heat and power (CHP) systems has recently increased due to their high combined efficiency and low emissions. Using CHP systems in behind-the-meter applications, however, can introduce some challenges. Firstly, the CHP system must operate in load-following mode to prevent power export to the grid. Secondly, if the load drops below a predefined threshold, the engine will operate at a lower temperature and hence lower efficiency, as the fuel is only half-burnt, creating significant emissions. The aforementioned issues may be solved by combining CHP with a battery energy storage system (BESS); however, the dispatch of CHP and BESS must be optimised. Offline optimisation methods based on load prediction will not prevent power export to the grid due to prediction errors. Therefore, this paper proposes a real-time Energy Management System (EMS) using a combination of Long Short-Term Memory (LSTM) neural networks, Mixed Integer Linear Programming (MILP), and Receding Horizon (RH) control strategy. The RH control strategy is suggested to reduce the impact of prediction errors and enable real-time implementation of the EMS exploiting actual generation and demand data on the day. Simulation results show that the proposed method can prevent power export to the grid and reduce the operational cost by 8.75% compared to the offline method.

Suggested Citation

  • Marvin B. Sigalo & Saptarshi Das & Ajit C. Pillai & Mohammad Abusara, 2023. "Real-Time Economic Dispatch of CHP Systems with Battery Energy Storage for Behind-the-Meter Applications," Energies, MDPI, vol. 16(3), pages 1-20, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1274-:d:1046058
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    References listed on IDEAS

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    1. Khawaja Haider Ali & Marvin Sigalo & Saptarshi Das & Enrico Anderlini & Asif Ali Tahir & Mohammad Abusara, 2021. "Reinforcement Learning for Energy-Storage Systems in Grid-Connected Microgrids: An Investigation of Online vs. Offline Implementation," Energies, MDPI, vol. 14(18), pages 1-18, September.
    2. Landelle, Arnaud & Tauveron, Nicolas & Haberschill, Philippe & Revellin, Rémi & Colasson, Stéphane, 2017. "Organic Rankine cycle design and performance comparison based on experimental database," Applied Energy, Elsevier, vol. 204(C), pages 1172-1187.
    3. 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.
    4. Abdulazeez Rotimi & Ali Bahadori-Jahromi & Anastasia Mylona & Paulina Godfrey & Darren Cook, 2018. "Optimum Size Selection of CHP Retrofitting in Existing UK Hotel Building," Sustainability, MDPI, vol. 10(6), pages 1-17, June.
    5. Fragaki, Aikaterini & Andersen, Anders N. & Toke, David, 2008. "Exploration of economical sizing of gas engine and thermal store for combined heat and power plants in the UK," Energy, Elsevier, vol. 33(11), pages 1659-1670.
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