Many-objective charging optimization for electric vehicles considering demand response and multi-uncertainties based on Markov chain and information gap decision theory
Qingyou Yan
<p>This paper constructs an electric vehicle (EV) charging optimization model considering demand response and the<br>uncertainties of source and load. First, after obtaining the charging load by using the Markov chain, the charging<br>time is divided into peaks, flats, and valleys, and the charging load is adjusted through price-based demand<br>response. Secondly, aiming at minimizing user charging cost, greenhouse gas (GHG) emissions, and load fluctuation,<br>and maximizing the revenue of power supplier, a deterministic EV charging optimization model is<br>established. Third, by modeling information gap decision theory (IGDT), the uncertainty of wind and photovoltaic<br>powers (WP and PV) is introduced into the charging optimization model to analyze the impact of WP and<br>PV’s fluctuations on risk aversion decision makers. Finally, a joint algorithm for solving a many-objective<br>problem based on ε-constraint and NSGA-II algorithm is proposed. The results of case study show that: (1)<br>rolling forecast based on Markov chain has a better effect on dealing with the uncertainty of charging load than<br>Monte Carlo; (2) the price-based demand response combined with time division model can be used to shave<br>peaks and fill valleys and reduce user charging cost; (3) optimized by using the deterministic model, the charging<br>load curve is smoother; (4) with the uncertainty of power generation involved in the many-objective optimization,<br>the PV’s fluctuation is greater, and the power supplier revenue increases compared with the deterministic<br>model, but the GHG emissions also increase; (5) In the same range of expected benefit changes, the fluctuation<br>values acceptable to decision makers are different for different objective functions; (6) the user response, the<br>normalized number of the selected sub-objective, and the maximum acceptable benefit deviation have different<br>influences on the ordered charging strategy; (7) the joint algorithm proposed in this paper is an effective method<br>to solve many-objective problems that contain conflictive aims.<br></p>
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