Multiobjective Model Predictive Control for Residential Demand Response Management Under Uncertainty

TL;DR

This paper introduces a multiobjective model predictive control method for residential demand response that effectively balances costs and user satisfaction under uncertainty, using advanced optimization techniques.

Contribution

It develops a novel multiobjective control framework with efficient optimization algorithms for home energy management under uncertainty.

Findings

Outperforms existing methods in cost reduction.

Limits cost increase to 0.52% under uncertainties.

Uses Laguerre functions for control signal parameterization.

Abstract

Residential users in demand response programs must balance electricity costs and user dissatisfaction under real-time pricing. This study proposes a multiobjective model predictive control approach for home energy management systems with battery storage, aiming to minimize both objectives while mitigating uncertainties. Laguerre functions parameterize control signals, transforming the optimization problem into one with linear inequalities for efficient exploration. A constrained multiobjective evolutionary algorithm, incorporating convex sampler-based crossover and mutation, is developed to ensure feasible solutions. Simulations show that the proposed method outperforms existing approaches, limiting cost increases to 0.52\% under uncertainties, compared to at least 2.3\% with other methods.