Abstract
Recently, an increasing number of photovoltaic/battery energy storage/electric vehicle charging stations (PBES) have been established in many cities around the world. This paper proposes a PBES portfolio optimization model with a sustainability perspective. First, various decision-making criteria are identified from perspectives of economy, society, and environment. Secondly, the performance of alternatives with respect to each criterion is evaluated in the form of trapezoidal intuitionistic fuzzy numbers (TrIFN). Thirdly, the alternatives are ranked based on cumulative prospect theory. Then, a multi-objective optimization model is built and solved by multi-objective particle swarm optimization (MOPSO) algorithm to determine the optimal PBES portfolio. Finally, a case in South China is studied and a scenario analysis is conducted to verify the effectiveness of the proposed model.
Highlights
In recent years, with the rapid increase of electric vehicles (EV), a lot of EV charging stations are built but the problem of high pressure on the power grid cannot be ignored
It can be concluded from the above research that photovoltaic/battery energy storage/electric vehicle charging stations (PBES) has attracted a lot of attention and proved to be a feasible solution to reduce the stress of EV charging load on the utility grid
The cumulative prospect theory is used to evaluate the performance of potential PBES projects and multi-objective particle swarm optimization (MOPSO) algorithm is applied to select the optimal portfolio
Summary
With the rapid increase of electric vehicles (EV), a lot of EV charging stations are built but the problem of high pressure on the power grid cannot be ignored. García-Triviño et al [7] proposed a control strategy of a fast EV charging station consisting of a PV system, batteries, fast charging units, and a connection to the local grid It can be concluded from the above research that PBES has attracted a lot of attention and proved to be a feasible solution to reduce the stress of EV charging load on the utility grid. Based on the existing research, this study proposes a hybrid decision framework under trapezoidal intuitionistic fuzzy environment to optimize the PBES portfolio from a sustainability perspective. In this method, the cumulative prospect theory is used to evaluate the performance of potential PBES projects and multi-objective particle swarm optimization (MOPSO) algorithm is applied to select the optimal portfolio. A better PBES should have less impact on the environment
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