Incorporating a Load-Shifting Algorithm for Optimal Energy Storage Capacity Design in Smart Homes

• Published in: Designs Vol. 8; no. 1; p. 11
• Main Authors: Khwanrit, Ruengwit, Lim, Yuto, Javaid, Saher, Charoenlarpnopparut, Chalie, Tan, Yasuo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2024
• Subjects: Algorithms; Alternative energy sources; Communication; Costs; distributed power-flow assignment; Electrical loads; Electricity; Energy consumption; Energy efficiency; Energy management systems; Energy resources; Energy sources; Energy storage; energy storage loss; Ethernet; Home environment; Internet of Things; Linear programming; Literature reviews; Load shifting; Loss reduction; Mathematical optimization; optimal storage capacity; Optimization; Power flow; renewable energy resources; Smart buildings; smart home; Smart houses; Storage capacity; Storage units; Weather; Wind farms; Winter; Japan
• ISSN: 2411-9660; 2411-9660
• DOI: 10.3390/designs8010011

In today’s power system landscape, renewable energy (RE) resources play a pivotal role, particularly within the residential sector. Despite the significance of these resources, the intermittent nature of RE resources, influenced by variable weather conditions, poses challenges to their reliability as energy resources. Addressing this challenge, the integration of an energy storage system (ESS) emerges as a viable solution, enabling the storage of surplus energy during peak-generation periods and subsequent release during shortages. One of the great challenges of ESSs is how to design ESSs efficiently. This paper focuses on a distributed power-flow system within a smart home environment, comprising uncontrollable power generators, uncontrollable loads, and multiple energy storage units. To address the challenge of minimizing energy loss in ESSs, this paper proposes a novel approach, called energy-efficient storage capacity with loss reduction (SCALE) scheme, that combines multiple-load power-flow assignment with a load-shifting algorithm to minimize energy loss and determine the optimal energy storage capacity. The optimization problem for optimal energy storage capacity is formalized using linear programming techniques. To validate the proposed scheme, real experimental data from a smart home environment during winter and summer seasons are employed. The results demonstrate the efficacy of the proposed algorithm in significantly reducing energy loss, particularly under winter conditions, and determining optimal energy storage capacity, with reductions of up to 11.4% in energy loss and up to 62.1% in optimal energy storage capacity.