Demand-side management solutions reward flexible customers for achieving desired goals. However, under price-based demand-response programs, uncoordinated load shifting among many customers may lead to rebound peaks, thus incurring financial costs on the energy service provider (ESP). This study addresses this issue from both the ESP (utility company) and customer’s perspectives in a two-step approach. First, each customer solves a local multi-objective load scheduling problem. Thereafter, the ESP solves a system-wide demand profile optimization problem. More precisely, we formulate in the first step a multi-objective optimization model to minimize consumption costs and load schedule discomfort while considering multiple energy sources and customer preferences. In the second step, we design an approach that combines Pareto-optimal solutions from all customers and minimizes their aggregate demand profile’s peak-to-average ratio (PAR). Experimental results show that the proposed approach outperforms the equivalent single-objective optimization models, and it can reduce the PAR metric by up to 11%. In other words, the proposed approach successfully improves the ESP’s system-wide demand profile aggregation while reducing customers’ expenses and discomfort.

需求方管理解决方案奖励灵活的客户实现预期目标。然而，在基于价格的需求响应计划下，许多客户之间不协调的负载转移可能会导致峰值反弹，从而给能源服务提供商 (ESP) 带来财务成本。本研究以两步法从 ESP（公用事业公司）和客户的角度解决了这个问题。首先，每个客户解决一个本地多目标负载调度问题。此后，ESP 解决了系统范围的需求曲线优化问题。更准确地说，我们在第一步中制定了一个多目标优化模型，以在考虑多种能源和客户偏好的同时，最大限度地减少消耗成本和负载调度不适。第二步，我们设计了一种方法，该方法结合了所有客户的帕累托最优解决方案，并将其总需求曲线的峰均比 (PAR) 最小化。实验结果表明，所提出的方法优于等效的单目标优化模型，并且可以将 PAR 度量降低多达 11%。换句话说，所提出的方法成功地改善了 ESP 的系统范围需求概况聚合，同时减少了客户的费用和不适。