Abstract
In the coming years, several transformations in the transport sector are expected, associated with the increase in electric vehicles (EVs). These changes directly impact electrical distribution systems (EDSs), introducing new challenges in their planning and operation. One way to assist in the desired integration of this technology is to allocate EV charging stations (EVCSs). Efforts have been made towards the development of EVCSs, with the ability to recharge the vehicle at a similar time than conventional vehicle filling stations. Besides, EVs can bring environmental benefits by reducing greenhouse gas emissions. However, depending on the energy matrix of the country in which the EVs fleet circulates, there may be indirect emissions of polluting gases. Therefore, the development of this technology must be combined with the growth of renewable generation. Thus, this proposal aims to develop a mathematical model that includes EVs integration in the distribution system. To this end, a mixed-integer linear programming (MILP) model is proposed to solve the allocation problem of EVCSs including renewable energy sources. The model addresses the environmental impact and uncertainties associated with demand (conventional and EVs) and renewable generation. Moreover, an EV charging forecast method is proposed, subject to the uncertainties related to the driver's behavior, the energy required by these vehicles, and the state of charge of the EVs. The proposed model was implemented in the AMPL modelling language and solved via the commercial solver CPLEX. Tests with a 24-node system allow evaluating the proposed method application.
Highlights
Concerns about climate change and initiatives like the Paris agreement motivate many sectors of the economy to reduce CO2 emissions
A two-stage stochastic programming model for the simultaneous allocation of electric vehicles (EVs) charging stations (EVCSs) and renewable energy sources (RES) that deals with uncertainties of renewable generation (wind turbine (WT) and photovoltaic (PV) generator) along with environmental issues related to CO2 emissions
The proposed mixed-integer linear programming (MILP) model is based on (Tabares et al 2016) and assumes that: the operation of the electrical distribution systems (EDSs) is represented by a balanced AC linearized power flow model and the loads are modeled as constant powers; CO2 emissions are penalized in the objective function with an emission cost; and the RES that will be allocated belong to the distribution system operator
Summary
Concerns about climate change and initiatives like the Paris agreement motivate many sectors of the economy to reduce CO2 emissions. A mixed-integer linear programming (MILP) model to solve the allocation problem of EVCSs, aiming to minimize system costs, is proposed in (Neyestani et al 2015). To this end, a two-stage model was developed. A two-stage stochastic programming MILP model is used to solve the problem, which considers stochastic behavior of conventional demand, EV demand, and renewable generation. A two-stage stochastic programming model for the simultaneous allocation of EVCSs and RES that deals with uncertainties of renewable generation (wind turbine (WT) and photovoltaic (PV) generator) along with environmental issues related to CO2 emissions.
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