Deferrable Load Scheduling under Demand Charge: A Block Model-Predictive Control Approach

TL;DR

This paper introduces a block model-predictive control method for scheduling deferrable loads like electric vehicle charging to minimize demand charges, improving efficiency and reliability despite forecast inaccuracies.

Contribution

It proposes a novel block MPC approach that decomposes demand charges into stage costs, enhancing real-time dispatch under demand charge constraints.

Findings

Outperforms benchmark methods in numerical tests

Effective in managing demand charge penalties

Applicable to electric vehicle charging scenarios

Abstract

Optimal scheduling of deferrable electrical loads can reshape the aggregated load profile to achieve higher operational efficiency and reliability. This paper studies deferrable load scheduling under demand charge that imposes a penalty on the peak consumption over a billing period. Such a terminal cost poses challenges in real-time dispatch when demand forecasts are inaccurate. A block model-predictive control approach is proposed by breaking demand charge into a sequence of stage costs. The problem of charging electric vehicles is used to illustrate the efficacy of the proposed approach. Numerical examples show that the block model-predictive control outperforms benchmark methods in various settings.