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Impact of PV and EV Forecasting in the Operation of a Microgrid

Author

Listed:
  • Giampaolo Manzolini

    (Department of Energy, Politecnico di Milano, 20156 Milan, Italy)

  • Andrea Fusco

    (Department of Energy, Politecnico di Milano, 20156 Milan, Italy)

  • Domenico Gioffrè

    (Department of Energy, Politecnico di Milano, 20156 Milan, Italy)

  • Silvana Matrone

    (Department of Energy, Politecnico di Milano, 20156 Milan, Italy)

  • Riccardo Ramaschi

    (Department of Energy, Politecnico di Milano, 20156 Milan, Italy)

  • Marios Saleptsis

    (Department of Energy, Politecnico di Milano, 20156 Milan, Italy)

  • Riccardo Simonetti

    (Department of Energy, Politecnico di Milano, 20156 Milan, Italy)

  • Filip Sobic

    (Department of Energy, Politecnico di Milano, 20156 Milan, Italy)

  • Michael James Wood

    (Department of Energy, Politecnico di Milano, 20156 Milan, Italy)

  • Emanuele Ogliari

    (Department of Energy, Politecnico di Milano, 20156 Milan, Italy)

  • Sonia Leva

    (Department of Energy, Politecnico di Milano, 20156 Milan, Italy)

Abstract

The electrification of the transport sector together with large renewable energy deployment requires powerful tools to efficiently use energy assets and infrastructure. In this framework, the forecast of electric vehicle demand and solar photovoltaic (PV) generation plays a fundamental role. This paper studies the impact of forecast accuracy on total electric cost of a simulated electric vehicles (EVs) charging station coupled with true solar PV and stationary battery energy storage. The optimal energy management system is based on the rolling horizon approach implemented in with a mixed integer linear program which takes as input the EV load forecast using long short-term memory (LSTM) neural network and persistence approaches and PV production forecast using a physical hybrid artificial neural network. The energy management system is firstly deployed and validated on an existing multi-good microgrid by achieving a discrepancy of state variables below 10% with respect to offline simulations. Then, eight weeks of simulations from each of the four seasons show that the accuracy of the forecast can increase operational costs by 10% equally distributed between the PV and EV forecasts. Finally, the accuracy of the combined PV and EV forecast matters more than single accuracies: LSTM outperforms persistence to predict the EV load (−30% root mean squared error), though when combined with PV forecast it has higher error (+15%) with corresponding higher operational costs (up to 5%).

Suggested Citation

  • Giampaolo Manzolini & Andrea Fusco & Domenico Gioffrè & Silvana Matrone & Riccardo Ramaschi & Marios Saleptsis & Riccardo Simonetti & Filip Sobic & Michael James Wood & Emanuele Ogliari & Sonia Leva, 2024. "Impact of PV and EV Forecasting in the Operation of a Microgrid," Forecasting, MDPI, vol. 6(3), pages 1-25, July.
  • Handle: RePEc:gam:jforec:v:6:y:2024:i:3:p:32-615:d:1446942
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    References listed on IDEAS

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    1. Ali Jawad Alrubaie & Mohamed Salem & Khalid Yahya & Mahmoud Mohamed & Mohamad Kamarol, 2023. "A Comprehensive Review of Electric Vehicle Charging Stations with Solar Photovoltaic System Considering Market, Technical Requirements, Network Implications, and Future Challenges," Sustainability, MDPI, vol. 15(10), pages 1-26, May.
    2. Pascual, Julio & Barricarte, Javier & Sanchis, Pablo & Marroyo, Luis, 2015. "Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting," Applied Energy, Elsevier, vol. 158(C), pages 12-25.
    3. Trinadh Pamulapati & Muhammed Cavus & Ishioma Odigwe & Adib Allahham & Sara Walker & Damian Giaouris, 2022. "A Review of Microgrid Energy Management Strategies from the Energy Trilemma Perspective," Energies, MDPI, vol. 16(1), pages 1-34, December.
    4. Parisio, Alessandra & Rikos, Evangelos & Tzamalis, George & Glielmo, Luigi, 2014. "Use of model predictive control for experimental microgrid optimization," Applied Energy, Elsevier, vol. 115(C), pages 37-46.
    5. Marzband, Mousa & Sumper, Andreas & Ruiz-Álvarez, Albert & Domínguez-García, José Luis & Tomoiagă, Bogdan, 2013. "Experimental evaluation of a real time energy management system for stand-alone microgrids in day-ahead markets," Applied Energy, Elsevier, vol. 106(C), pages 365-376.
    6. Andu Dukpa & Boguslaw Butrylo, 2022. "MILP-Based Profit Maximization of Electric Vehicle Charging Station Based on Solar and EV Arrival Forecasts," Energies, MDPI, vol. 15(15), pages 1-14, August.
    7. Michael Wood & Emanuele Ogliari & Alfredo Nespoli & Travis Simpkins & Sonia Leva, 2023. "Day Ahead Electric Load Forecast: A Comprehensive LSTM-EMD Methodology and Several Diverse Case Studies," Forecasting, MDPI, vol. 5(1), pages 1-18, March.
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    1. Jun Han & Chao Cai & Wenjie Pan & Hong Liu & Zhengyang Xu, 2024. "Hybrid Proximal Policy Optimization—Wasserstein Generative Adversarial Network Framework for Hosting Capacity Optimization in Renewable-Integrated Power Systems," Energies, MDPI, vol. 17(24), pages 1-22, December.

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