In this work, a preference-based, demand response (DR) multi-objective optimization model based on real-time electricity price is presented to solve the problem of optimal residential load management. The purpose of such a model is threefold: 1) to minimize the costs associated with consumption; 2) to minimize the inconvenience caused to consumers; and 3) to minimize environmental pollution. Potential solutions to the underlying multi-objective optimization problem are subject to a set of electrical and operational constraints related to home appliances categories and the utilization of distributed energy resources (DER) and energy storage systems (ESS). The use of the proposed model is illustrated in a realistic microgrid scenario where suitable solutions were found by the Non-Dominated Sorting Genetic Algorithm III (NSGA-III). These solutions determine new operational periods for home appliances as well as the utilization of DER and ESS for the planning horizon while considering consumer preferences. Besides helping consumers to take advantage of the benefits offered by DR, the experimental results show that the multi-objective DR model together with the NSGA-III algorithm can effectively minimize energy-consumption costs as well as reduce inconvenience costs and environmental pollution.

在这项工作中，基于实时电价的基于偏好的需求响应（DR）多目标优化模型被提出，以解决最佳的住宅负荷管理问题。这种模型的目的有三方面：1）最小化与消费相关的成本；2）尽量减少给消费者带来的不便；3）尽量减少环境污染。潜在的多目标优化问题的潜在解决方案受到一系列与家用电器类别以及分布式能源（DER）和能源存储系统（ESS）的使用有关的电气和操作约束。在一个现实的微电网场景中说明了所建议模型的使用，其中非控制排序遗传算法III（NSGA-III）找到了合适的解决方案。这些解决方案确定了家用电器的新运营周期，以及在考虑消费者偏好的同时为计划范围使用了DER和ESS。实验结果表明，除了可以帮助消费者充分利用灾难恢复所带来的好处外，多目标灾难恢复模型与NSGA-III算法一起可以有效地降低能耗成本，并减少不便成本和环境污染。