2017/05/31 by Jeremy Every, Li Li, Every, Jeremy +3
Engineering · Environmental Science · Mathematics · #Electric Vehicles and Infrastructure #Energy and Environment Impacts #FOS: Mathematics #Optimization and Control (math.OC) #Smart Grid Energy Management #math.OC
paper · pdf · doi:10.48550/arxiv.1705.10949
To appear in the proceedings of the 12th International Conference on Industrial Electronics and Applications (ICIEA2017), Siem Reap, Cambodia
arxiv created 2017/05/31 · openalex publication_date 2017/05/31 · arxiv updated 2017/06/01 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
Continued advances in PV and battery energy storage technologies have made hybrid PV-battery systems an attractive prospect for residential energy consumers. However the process to select an appropriate system is complicated by the relatively high cost of batteries, a multitude of available retail electricity plans and the removal of PV installation incentive schemes. In this paper, an optimization strategy based on an individual customer's temporal load profile is established to maximize electricity cost savings through optimal selection of PV-battery system size, orientation and retail electricity plan. Quantum-behaved particle swarm optimization is applied as the underlying algorithm given its well-suited application to problems involving hybrid energy system specification. The optimization strategy is tested using real-world residential consumption data, current system pricing and available retail electricity plans to establish the efficacy of a hybrid PV-battery solution.