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Combined model predictive control and ANN-based forecasters for jointly acting renewable self-consumers: An environmental and economical evaluation

Author

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  • Negri, Simone
  • Giani, Federico
  • Blasuttigh, Nicola
  • Massi Pavan, Alessandro
  • Mellit, Adel
  • Tironi, Enrico

Abstract

Recent European Community directives introduce Renewable Energy Communities (REC) and Jointly Acting Renewable Self-Consumers (JARSC). Both entities are constituted by communities of residential and/or non-residential prosumers, located in proximity of renewable generators and Electrical Storage Systems (ESS) owned and managed by the REC/JARSCs. These aggregations of prosumers are aimed at providing environmental and economic benefits by maximizing their global self-consumption. In this frame, it is relevant to introduce a control strategy which considers the whole system represented by the REC/JARSCs and performs optimal management of energy production, storage and consumption. The present paper proposes a Model Predictive Control (MPC) based control design, targeted at the minimization of electricity cost and equivalent CO2 emissions, considering the whole ensemble of loads included in the REC/JARSCs over a 24-h prediction horizon. To exploit the MPC ability of including forecasts in the optimization problem, predictors including Artificial Neural Networks (ANN) are developed for solar irradiance, air temperature, electricity price and carbon intensity. The proposed control performance is evaluated considering a case study located in Milan, Italy, and its advantages with respect to traditional control algorithms are highlighted by comprehensive numerical simulations. Lastly, an economic evaluation of the considered system is presented.

Suggested Citation

  • Negri, Simone & Giani, Federico & Blasuttigh, Nicola & Massi Pavan, Alessandro & Mellit, Adel & Tironi, Enrico, 2022. "Combined model predictive control and ANN-based forecasters for jointly acting renewable self-consumers: An environmental and economical evaluation," Renewable Energy, Elsevier, vol. 198(C), pages 440-454.
    • Handle: RePEc:eee:renene:v:198:y:2022:i:c:p:440-454
    DOI: 10.1016/j.renene.2022.07.065
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    Cited by:

    1. Hu, Jiaxiang & Hu, Weihao & Cao, Di & Sun, Xinwu & Chen, Jianjun & Huang, Yuehui & Chen, Zhe & Blaabjerg, Frede, 2024. "Probabilistic net load forecasting based on transformer network and Gaussian process-enabled residual modeling learning method," Renewable Energy, Elsevier, vol. 225(C).
    2. Nicola Blasuttigh & Simone Negri & Alessandro Massi Pavan & Enrico Tironi, 2023. "Optimal Sizing and Environ-Economic Analysis of PV-BESS Systems for Jointly Acting Renewable Self-Consumers," Energies, MDPI, vol. 16(3), pages 1-25, January.
    3. Calise, Francesco & Cappiello, Francesco Liberato & Cimmino, Luca & Dentice d’Accadia, Massimo & Vicidomini, Maria, 2023. "Renewable smart energy network: A thermoeconomic comparison between conventional lithium-ion batteries and reversible solid oxide fuel cells," Renewable Energy, Elsevier, vol. 214(C), pages 74-95.
    4. Tanja M. Kneiske, 2023. "Reducing CO 2 Emissions for PV-CHP Hybrid Systems by Using a Hierarchical Control Algorithm," Energies, MDPI, vol. 16(17), pages 1-24, August.

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