Optimal control of integrated photovoltaic and water distribution systems is essential for maximizing community benefits. This study investigates the optimization of such controls in a real-world installation in Makassar, Indonesia, where achieving optimal performance is critical given the absence of grid export capabilities for residential users. Building on previously developed Modelica models of the existing system, an integrated hierarchical control strategy is designed to coordinate the operations of the battery, pump, and inverter. Daily simulations illustrate the interactions between the supervisory and local controllers. Annual simulations show that the proposed control consumes 14\% less energy than the existing methodologies. When evaluated under dynamic utility pricing structures, the control strategy further reduces energy usage by an additional 6\% and can save up to 25\% in utility costs. The results highlight the advantages of implementing robust hierarchical controls in integrated systems and their potential to be optimized and calibrated for greater community support.

This study builds upon previous research that proposed a multifunctional building cladding-panel integrating photovoltaic cells and a proton exchange membrane electrolyzer to simultaneously harvest solar energy and convert surplus electricity to hydrogen. While prior work has shown good correlation between simulation and experimental results of a PV-hydrogen system, few studies have focused on systems installed on vertical building facades. Since the electrolyzer, as the energy conversion element, is assumed to be installed alongside the PV panels on the façade, it is particularly sensitive to environmental conditions. Therefore, an experimental setup that accounts for variations in ambient climate is preferred to better reflect real-world energy conversion performance. A MATLAB/Simulink simulation model had been developed to evaluate the system's performance in a built environment. An experimental setup was constructed to validate the simulation and gain deeper insights. Results indicate strong alignment between simulation and experiment under high irradiance conditions (>400 W/m²). However, under low irradiance and frequent cloud fluctuations, notable discrepancies were observed.

Energy storage systems are critical for Antarctic stations seeking to integrate renewable generation, where intermittent supply and the high cost of diesel fuel transport make reliable storage solutions essential. This study examines how a 1.5 MWh containerized Battery Energy Storage System (BESS) enclosure performs under South Pole conditions using EnergyPlus simulations. Though the modeling follows typical building energy modeling (BEM) procedures, the analysis provides rare quantitative evidence of an enclosure behavior in extreme cold. Results show internal battery heat dominates, requiring cooling rather than heating even at −60 °C. A simple air-side economizer reduced cooling energy by up to 100 %. These findings offer practical guidance for reliable BESS operation in similarly cold, isolated climates.

Modern buildings are increasingly interconnected with occupancy, heating, ventilation, and air-conditioning (HVAC) systems, distributed energy resources (DERs), and power grids. Modeling, control, and optimization of such multi-domain systems play a critical role in achieving building-sector decarbonization. However, most existing tools lack scalability and physical consistency for addressing these complex, multi-scale ecosystem problems. To bridge this gap, this study presents BESTOpt, a modular, physics-informed machine learning (PIML) framework that unifies building applications, including benchmarking, evaluation, diagnostics, control, optimization, and performance simulation. The framework adopts a cluster–domain–system/building–component hierarchy and a standardized state–action–disturbance–observation data typology. By embedding physics priors into data-driven modules, BESTOpt improves model accuracy and physical consistency under unseen conditions. Case studies on single-building and cluster scenarios demonstrate its capability for multi-level centralized and decentralized control. Looking ahead, BESTOpt lays the foundation for an open, extensible platform that accelerates interdisciplinary research toward smart, resilient, and decarbonized building ecosystems.