Design Considerations of a Coordinative Demand Charge Mitigation Strategy

TL;DR

This paper introduces a mixed integer linear programming algorithm for demand charge mitigation that effectively reduces peak electricity consumption by coordinating various controllable resources, considering load forecast discrepancies.

Contribution

It develops a novel optimization algorithm for demand charge mitigation that accounts for load forecast errors and resource payback effects, improving system peak management.

Findings

The algorithm effectively reduces demand charges in simulations.

It prevents peak shifting to payback hours.

Identifies diminishing returns for resource sizing.

Abstract

This paper presents a coordinative demand charge mitigation (DCM) strategy for reducing electricity consumption during system peak periods. Available DCM resources include batteries, diesel generators, controllable loads, and conservation voltage reduction. All resources are directly controlled by load serving entities. A mixed integer linear programming based energy management algorithm is developed to optimally coordinate of DCM resources considering the load payback effect. To better capture system peak periods, two different kinds of load forecast are used: the day-ahead load forecast and the peak-hour probability forecast. Five DCM strategies are compared for reconciling the discrepancy between the two forecasting results. The DCM strategies are tested using actual utility data. Simulation results show that the proposed algorithm can effectively mitigate the demand charge while preventing the system peak from being shifted to the payback hours. We also identify the diminishing return effect, which can help load serving entities optimize the size of their DCM resources.