Abstract
The rapid growth of electric vehicles (EV) in cities has led to the development of microgrids (MGs) combined with photovoltaics (PV) and the energy storage system (ESS) as charging stations. Traditional sizing methods cannot efficiently evaluate large-scale scenarios through nonlinear optimization models to ensure the economy and reliability of the design. This paper combines the physical-economical model (PEM) and data-driven model (DDM) for fast-speed and accurate determination of MG sizes, considering nonlinear battery degradation and optimal power dispatch under variant EV charging profiles. The DDM predicts a truncated initial solution subspace where the optimal result is likely to reside, which performs 90 % time savings while maintaining over 95 % optimality compared with the benchmark PEM. The artificial neural network (ANN) is adopted for DDM with the input of load features and economic parameters, and the accuracy can be continuously improved with more data input. The EV load training set is extracted from charging piles and enlarged two orders of magnitude larger than the original based on reversed Monte Carlo (RMC) and feature space expansion. Through sensitivity analysis of over 40 scenarios, we show that the optimal results are mainly related to load features, correlation with solar irradiation, PV and ESS prices. Most adequately designed stations will pay back around five years. Projects with poor revenue will become profitable when the ESS price and PV price are below 154 $/kWh and 615 $/kW, and all the studied cases are worth the investment with the ESS price lower than 77 $/kWh.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call