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Hybrid renewable energy systems: Influence of short term forecasting on model predictive control performance

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  • Bartolucci, Lorenzo
  • Cordiner, Stefano
  • Mulone, Vincenzo
  • Santarelli, Marina

Abstract

Energy Management Systems (EMS) strategies aim at matching energy production with the request, as they are off-phased and highly variable whenever LV networks are considered. This work demonstrates how an EMS based on a Model Predictive Control (MPC) strategy can perform better improving the accuracy of the load forecasting algorithm. To that aim a novel approach is presented, that is characterized by the correlation between real time and historical consumption data. The technique has been tested for over a year of operation. Three test cases have been compared (low error load forecasting, higher error load forecasting and correlation-corrected load forecasting) and techno-economic advantages have been obtained with the new approach. Indeed, a reduction of 14,1% in energy unbalance with the grid and of 8,7% in annual operational costs have been obtained when the load forecast correction is performed. Moreover, the critical components of the system (Electrochemical Energy Storage and Fuel Cell) result to work in less stressful operating conditions, another positive effective of the technique.

Suggested Citation

  • Bartolucci, Lorenzo & Cordiner, Stefano & Mulone, Vincenzo & Santarelli, Marina, 2019. "Hybrid renewable energy systems: Influence of short term forecasting on model predictive control performance," Energy, Elsevier, vol. 172(C), pages 997-1004.
    • Handle: RePEc:eee:energy:v:172:y:2019:i:c:p:997-1004
    DOI: 10.1016/j.energy.2019.01.104
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    1. Zhou, Yuan & Wang, Jiangjiang & Liu, Yi & Yan, Rujing & Ma, Yanpeng, 2021. "Incorporating deep learning of load predictions to enhance the optimal active energy management of combined cooling, heating and power system," Energy, Elsevier, vol. 233(C).
    2. Sachajdak, Andrzej & Lappalainen, Jari & Mikkonen, Hannu, 2019. "Dynamic simulation in development of contemporary energy systems – oxy combustion case study," Energy, Elsevier, vol. 181(C), pages 964-973.
    3. Sara Bellocchi & Michele Manno & Michel Noussan & Michela Vellini, 2019. "Impact of Grid-Scale Electricity Storage and Electric Vehicles on Renewable Energy Penetration: A Case Study for Italy," Energies, MDPI, vol. 12(7), pages 1-32, April.
    4. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    5. Wang, Xiaokui & Bamisile, Olusola & Chen, Shuheng & Xu, Xiao & Luo, Shihua & Huang, Qi & Hu, Weihao, 2022. "Decarbonization of China's electricity systems with hydropower penetration and pumped-hydro storage: Comparing the policies with a techno-economic analysis," Renewable Energy, Elsevier, vol. 196(C), pages 65-83.
    6. Bellocchi, Sara & De Falco, Marcello & Gambini, Marco & Manno, Michele & Stilo, Tommaso & Vellini, Michela, 2019. "Opportunities for power-to-Gas and Power-to-liquid in CO2-reduced energy scenarios: The Italian case," Energy, Elsevier, vol. 175(C), pages 847-861.
    7. Lorenzo Bartolucci & Stefano Cordiner & Vincenzo Mulone & Marina Santarelli, 2019. "Ancillary Services Provided by Hybrid Residential Renewable Energy Systems through Thermal and Electrochemical Storage Systems," Energies, MDPI, vol. 12(12), pages 1-18, June.

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