Abstract

The share of renewable energy sources (RES)’ production in energy mixes, especially the ones of PV panels and wind farms, has been continuously increasing during the last few years. Similarly, a strong development of battery electric vehicles (EV) is expected within the next years. However, these two new innovations could trigger security issues on electrical grids, in particular local congestions and voltage deviations from the required standards. One way to mitigate these problems could be to combine the charging periods of the EVs with the local RES production. This paper aims at analysing the possibility to implement this kind of smart charging strategy in France by 2020, taking into account the wide diversity of local energy mixes in France and their seasonal dependencies. First, the authors identify four different energy mixes representative of most of the local French mixes, each one related to a region in France. Then, we model the charging load curves of the EVs at the substation level for the same regions. We deduce the “green charging ratio” (GCR) without any charging management strategy. The same ratio is then computed assuming that an Energy Management System (EMS), which goal is to maximize this ratio, controls the EV charging patterns. The results are provided for each region and seasons. They show a wide diversity of green charging ratio achievable: in very windy regions, the GCR without any EMS is already rather high on the contrary, in sunny regions, the GCR is much improved with the implementation of the EMS. In such regions, the GCR can also be enhanced with the penetration of charging stations at working places. Document type: Article

Abstract

The recent rise of renewable energy sources connected to the distribution networks and the high peak consumptions requested by electric vehicle-charging bring new challenges for network operators. To operate smart electricity grids, cooperation between grid-owned and third-party assets becomes crucial. In this paper, we propose a methodology that combines machine learning with multi-objective optimization to accurately plan the exploitation of the energy district&rsquo s flexibility with the objective of reducing peak consumption and avoiding reverse power flow. Using historical data, acquired by the smart meters deployed on the pilot district, the district&rsquo s power profile can be predicted daily and analyzed to identify potentially critical issues on the network. District&rsquo s resources, such as electric vehicles, charging stations, photovoltaic panels, buildings energy management systems, and energy storage systems, have been modeled by taking into account their operational constraints and the multi-objective optimization has been adopted to identify the usage pattern that better suits the distribution operator&rsquo s (DSO) needs. The district is subject to incentives and penalties based on its ability to respond to the DSO request. Analysis of the results shows that this methodology can lead to a substantial reduction of both the reverse power flow and peak consumption. Document type: Article

Abstract

Recently, the use of renewable energy sources (RES) and electric vehicles (EVs) has been rapidly increased worldwide. As a result of the highly fluctuating nature of RES, the charging and discharging rates of EVs significantly have to be increased, and so the lifespan of EV batteries decreases. In this paper, an optimization-based method is proposed to smooth voltage fluctuations due to various RES types by optimally controlling the charging and discharging power of EVs and the reactive power of the RES inverters. To extend the lifespan of the EV battery, EV power fluctuations and their minimum preset state of charge (SOC) are considered in the proposed optimization model. For this purpose, a new multi-objective function is formulated, including (1) voltage fluctuations, (2) EV power fluctuations, and (3) the deviation of SOC of EVs from their minimum desired level. The use of the hull moving average (HMA) is proposed to mitigate voltage fluctuations, which eliminates the lag problem of the widely used movingaverage methods. The gravitational search algorithm (GSA) is utilized to accurately solve the optimization model. The simulation results demonstrate the effectiveness of the proposed method to smooth voltage fluctuations while considering degradation and charging plan of EV batteries. Peer reviewed