AI Article Synopsis
- A real-coded genetic algorithm optimizes the charging schedule of an energy storage system (ESS) for residential consumers using renewable energy.
- Simulations demonstrate that using this genetic algorithm can lead to about 17% cost savings on electricity when compared to systems without an ESS, and 8% compared to a traditional scheduling algorithm.
- The approach takes into account time-of-use pricing and demand charges, showcasing the potential for significant economic benefits in energy management.
Article 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.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4147358 | PMC |
| http://dx.doi.org/10.1155/2014/937329 | DOI Listing |
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