Journal of Energy Storage
School of Engineering
Photovoltaic (PV) systems in residential buildings require energy storage to enhance their productivity; however, in present technology, battery storage systems (BSSs) are not the most cost-effective solutions. Comparatively, thermal storage systems (TSSs) can provide opportunities to enhance PV self-consumption while reducing life cycle costs. This paper proposes a new framework for optimal sizing design and real-time operation of energy storage systems in a residential building equipped with a PV system, heat pump (HP), thermal and electrical energy storage systems. For simultaneous optimal sizing of BSS and TSS, a particle swarm optimization (PSO) algorithm is applied to minimize daily electricity and life cycle costs of the smart building. A model predictive controller is then developed to manage energy flow of storage systems to minimize electricity costs for end-users. The main objective of the controller is to optimally control HP operation and battery charge/discharge actions based on a demand response program. The controller regulates the flow of water in the storage tank to meet designated thermal energy requirements by controlling HP operation. Furthermore, the power flow of battery is controlled to supply all loads during peak-load hours to minimize electricity costs. The results of this paper demonstrate to rooftop PV system owners that investment in combined TSS and BSS can be more profitable as this system can minimize life cycle costs. The proposed methods for optimal sizing and operation of electrical and thermal storage system can reduce the annual electricity cost by more than 80% with over 42% reduction in the life cycle cost. Simulation and experimental results are presented to validate the effectiveness of the proposed framework and controller.
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