Charge Scheduling of an Energy Storage System under Time-of-use Pricing and a Demand Charge

TL;DR

This paper presents a genetic algorithm-based method for scheduling energy storage system charging to minimize electricity costs under time-of-use pricing and demand charges, demonstrating significant cost savings in simulations.

Contribution

The paper introduces a real-coded genetic algorithm for ESS charge scheduling that accounts for both time-of-use pricing and demand charges, improving cost efficiency.

Findings

Cost reduction of approximately 17% compared to no ESS

8% savings over net power-based scheduling

Effective scheduling strategy for residential energy management

Abstract

A real-coded genetic algorithm is used to schedule the charging of an energy storage system (ESS), operated in tandem with renewable power by an electricity consumer who is subject to time-of-use pricing and a demand charge. Simulations based on load and generation profiles of typical residential customers show that an ESS scheduled by our algorithm can reduce electricity costs by approximately 17%, compared to a system without an ESS, and by 8% compared to a scheduling algorithm based on net power.