This study proposed the operation planning of the battery energy storage systems (BESS) to maximize the economic value in terms of life-cycle cost considering both the electric power self-consumption and peak load reduction. Toward this end, a bi-objective optimization model was developed in consideration of the economic net profit as well as the battery aging. An economic simulation was then conducted to create a configuration of the most cost-effective operation planning. As a result of the case study, the operation with limits on selfsufficiency rate and peak load reduction could raise the self-sufficiency rate by up to 22.1% and reduce the peak load by up to 29%, while the net present value (NPV) of the BESS was US$7,067.9 lower compared to the operation without such limits. The customers of the BESS with the PV systems can maximize their economic profits and the policy makers can establish plans for economic support schemes to improve the environmental performance of the BESS with the PV system.

Windows are a key design element that can affect the building energy performance and occupant psychological satisfaction. While smaller windows can increase building energy performance, they can also lower occupant psychological satisfaction. Despite the importance of determining the optimal window size by considering the building energy performance and occupant psychological satisfaction and their trade-off relationship, few studies have proposed a window size that considers both aspects. To solve this problem, this study proposed the following framework capable of accounting for both aspects in determining the optimal window size: (i) experimental settings for measuring the occupant psychological satisfaction based on the window size; (ii) virtual environment creation using SketchUp, 3dsMax, and the Unreal engine; (iii) measurement of occupant psychological satisfaction using questionnaire survey; (iv) measurement of building energy performance using SketchUp and EnergyPlus; and (v) selection of the optimal window size using the Pareto optimal solution. Using the proposed framework, even non-specialists of virtual reality or energy simulations can easily measure building energy performance and occupant psychological satisfaction by SketchUp modeling. Based on the building energy performance and occupant psychological satisfaction measured as such, the optimal window size can be determined according to building usage and conditions as well as client requirements.