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

Giampaolo Manzolini (), Andrea Fusco, Domenico Gioffrè, Silvana Matrone, Riccardo Ramaschi, Marios Saleptsis, Riccardo Simonetti, Filip Sobic, Michael James Wood, Emanuele Ogliari and Sonia Leva
Additional contact information
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

Forecasting, 2024, vol. 6, issue 3, 1-25

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%).

Keywords: optimal energy management system; LSTM; EV forecast; microgrid operation; PV forecast; MILP (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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