Comparing electric vehicle charging strategies in stochastic microgrid optimization

Abstract

Renewable energy sources, e.g. solar energy and wind energy, have gained popularity as an alternative means of energy production as they do not reinforce global warming. In addition, more and more electrical appliances (e.g. electric vehicles, induction cookers, and heat pumps) are used as a substitute for appliances that need non-renewable energy sources. This increase in the use of renewable energy resources pushes the electricity grid to its limits due to new induced load peaks. The grid is not designed for these developments and as a result, asset deterioration, higher transport losses, and outages are expected to occur. The most straightforward solution for the distributed system operator, i.e. the operating manager of the distribution network, is to expand the grid. However, grid expansion is a costly operation and there are additional promising methods to decrease grid load peaks, e.g. by using different charging strategies for electric vehicles. The conventional charging strategy for electric vehicles is uncontrolled charging. With uncontrolled charging, the charging of the electric vehicle immediately commences once a connection with the charging pole is established. The smart charging strategy, however, is able to delay the charging moment to a more optimal time instant in view of, e.g. variable electricity prices. The vehicle-to-home (V2H) charging strategy is similar to smart charging, but in addition, the V2H strategy allows the electric vehicle to discharge electricity to power a nearby residential home. This research aims to compare smart charging and V2H charging on their economical effects for their users. The charging strategies are implemented using two control algorithms: a rule-based controller and a model predictive control (MPC) algorithm. The rule-based controller is implemented due to its simplicity and the MPC algorithm is used for its ability to take into account predictions of system related variables, e.g. household loads. The MPC algorithm is implemented with two different forecasts namely, perfect information, i.e. uncertain variables are forecasted perfectly, and certainty equivalent, i.e. uncertain variables are predicted using a persistence forecast model. The persistence forecast model assumes that the future values of an uncertain variable remain equal to the latest measurements, e.g. the solar generation of tomorrow is expected to be equal to that of today. The control problem is non-linear as an electric vehicle behaves differently depending on its status, e.g. driving or charging. The control problem is therefore reformulated into a mixed logical dynamical framework such that it can be solved efficiently using mixed integer linear programming. An extensive comparison in performance for a microgrid case study is done using real data of solar generation, electric vehicles, and household loads for simulation. The results show that the V2H charging strategy can outperform smart charging by reducing both the peak loads and the electricity costs. However, the V2H strategy only gives a minor extra decrease in costs compared to smart charging and the performance of V2H charging is highly dependent on the quality of the forecasts. Therefore, it is concluded that, in practice, smart charging is the most effective charging strategy. Further research is done to investigate whether the microgrid costs, using a smart charging strategy, can be reduced further by taking into account the uncertainty of some variables such as the electricity price and the household load. This is implemented through a scenario-based MPC algorithm due to its ability to incorporate multiple forecasts, i.e. scenarios, for each uncertain variable. Six different scenario generation methods are implemented which are distinguished by two characteristics: the period from which the historical error between the certainty equivalent case and the true realization of the uncertain variable is collected (i.e. yearly, seasonal, or daily) and the addition method of these errors to the persistence forecast model to generate new scenarios, i.e. as a variable or as a constant. An extensive comparison in performance for a microgrid case study is done. The results show that the certainty equivalent MPC case can be outperformed if a low number of scenarios is generated. This is achieved most effectively by collecting the persistence forecast model error from a rolling horizon of the past 24 hours and adding the error as a constant to the persistence forecast model.