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><span class="fontstyle0">This paper constructs an electric vehicle (EV) charging optimization model considering demand response and the uncertainties of source and load. First, after obtaining the charging load by using the Markov chain, the charging time is divided into peaks, flats, and valleys, and the charging load is adjusted through price-based demand response. Secondly, aiming at minimizing user charging cost, greenhouse gas (GHG) emissions, and load fluctuation, and maximizing the revenue of power supplier, a deterministic EV charging optimization model is established. Third, by modeling information gap decision theory (IGDT), the uncertainty of wind and photovoltaic powers (WP and PV) is introduced into the charging optimization model to analyze the impact of WP and PV</span><span class="fontstyle2">’</span><span class="fontstyle0">s fluctuations on risk aversion decision makers. Finally, a joint algorithm for solving a many-objective problem based on </span><span class="fontstyle2" style="font-size:8pt;">ε</span><span class="fontstyle0">-constraint and NSGA-II algorithm is proposed. The results of case study show that: (1) rolling forecast based on Markov chain has a better effect on dealing with the uncertainty of charging load than Monte Carlo; (2) the price-based demand response combined with time division model can be used to shave peaks and fill valleys and reduce user charging cost; (3) optimized by using the deterministic model, the charging load curve is smoother; (4) with the uncertainty of power generation involved in the many-objective optimization, the PV</span><span class="fontstyle2">’</span><span class="fontstyle0">s fluctuation is greater, and the power supplier revenue increases compared with the deterministic model, but the GHG emissions also increase; (5) In the same range of expected benefit changes, the fluctuation values acceptable to decision makers are different for different objective functions; (6) the user response, the normalized number of the selected sub-objective, and the maximum acceptable benefit deviation have different influences on the ordered charging strategy; (7) the joint algorithm proposed in this paper is an effective method to solve many-objective problems that contain conflictive aims.</span>
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