Charge Scheduling of an Energy Storage System under Time-of-use Pricing and a Demand Charge
This work addresses cost savings for residential electricity consumers using ESS, but it is incremental as it applies an existing method (genetic algorithm) to a specific energy management scenario.
The paper tackles the problem of scheduling an energy storage system (ESS) under time-of-use pricing and demand charges to reduce electricity costs, achieving a 17% cost reduction compared to no ESS and an 8% improvement over a net power-based scheduling algorithm.
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.