Abstract
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.
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