%0 Journal Article
%T 双碳目标下考虑居民不同意愿下充电的优化调度策略<br>The Optimal Charging Scheduling Strategy Considering Different Wills of Residents under the Dual Carbon Target
%A 史亚峰
%A 党亚峥
%A 薛中会
%J Pure Mathematics
%P 465-478
%@ 2160-7605
%D 2024
%I Hans Publishing
%R 10.12677/pm.2024.145201
%X 针对电动汽车(electrical vehicle, EV)在居民小区无序充电对电网系统容易产生严重隐患等问题，本文提出了考虑车主的不同意愿和性格的优化调度策略。研究电动汽车用户意愿具有多样性和不确定性的特点，考虑到居民用户的不同意愿以及用户的性格因素制定的动态分时电价，引导EV在多模式电价中做出选择。在满足用户意愿的前提下，进行优化调度，使其充电成本最低化。采用遗传算法(GA)求解优化模型，获得最优多模式动态电价和EV充放电策略。仿真结果表明了不仅可以有效地减小电网的峰谷差，还可以降低电动汽车用户的充电费用，充分验证了该策略的有效性与合理性。<br />
In order to solve the problem that electric vehicle (EV) unordered charging in residential areas is easy to cause serious hidden dangers to the power grid system, this paper proposes an optimal scheduling strategy that takes into account the different wishes and personalities of owners. This paper studies the diversity and uncertainty of electric vehicle users’ wishes, and takes into account the different wishes of resident users and the personality factors of users to make a dynamic TOU price to guide EV to make a choice among multi-mode electricity prices. Under the premise of meeting the wishes of users, the optimal scheduling is carried out to minimize the charging cost. Genetic algorithm (GA) was used to solve the optimization model, and the optimal multi-mode dynamic electricity price and EV charging and discharging strategy were obtained. The simulation results show that not only the peak-valley difference of the power grid can be effectively reduced, but also the charging cost of EV users can be reduced, which fully verifies the effectiveness and rationality of the strategy.
%K 有序充电，动态电价，蒙特卡洛，性格因子，电动汽车，意愿指数<br>Orderly Charging
%K Dynamic Electricity Price
%K Monte Carlo
%K Personality Factors
%K Electric Vehicles
%K Willingness Index
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=88718