The proposed study incorporates a stochastic demand side management (SDSM) strategy for a self-sufficient residential system powered from a PV source with a hybrid battery–hydrogen storage system to minimize the total degradation costs associated with key components, including Li-io batteries, fuel cells, and PEM electrolyzers. The uncertainty in demand forecasting is addressed through a scenario-based generation to enhance the robustness and accuracy of the proposed method. Then, stochastic optimization was employed to determine the optimal operating schedules for deferable appliances and optimal water heater (WH) settings. The optimization problem was solved using a genetic algorithm (GA), which efficiently explores the solution space to determine the optimal operating schedules and reduce degradation costs. The proposed SDSM technique is validated through MATLAB 2020 simulations, demonstrating its effectiveness in reducing component degradation costs, minimizing load shedding, and reducing excess energy generation while maintaining user comfort. The simulation results indicate that the proposed method achieved total degradation cost reductions of 16.66% and 42.6% for typical summer and winter days, respectively, in addition to a reduction of the levelized cost of energy (LCOE) by about 22.5% compared to the average performance of 10,000 random operation scenarios.

This paper proposes a time-based no-cost category of energy conservation measures (ECMs) enabled by audit-driven building energy modeling. The study presents an audit-to-simulation framework applied to an academic building (Electrical Engineering Department, Assiut University, Egypt) following the audit levels and requirements of ASHRAE Standard 100-2024. The building operation is characterized via audit findings, high-resolution electrical monitoring, and occupancy profiling, then translated into a calibrated building energy model (BEM) developed using SketchUp, OpenStudio, and EnergyPlus. The validated BEM serves as a decision-support testbed to evaluate the proposed ECMs prior to implementation, enabling quantification of their impacts on annual and daily energy use, peak reduction, and load-profile shape. The proposed ECMs are classified into two subcategories: working-day ECMs and time-slot-modification ECMs. The first category involves adjusting the number of working days per week. The second category includes several scheduling-based strategies, namely seasonal time shifts, modification of lecture and tutorial session durations, rearrangement of lectures and tutorial sessions, and shifting peak-demand time slots. The simulation results show that modifying lecture and tutorial durations (ECM3) is the most effective measure, achieving 6.2% annual energy savings, followed by seasonal time shifts (ECM2) with 5.8%. For peak demand, reducing operation during peak periods (ECM5) lowers the daily peak load by 25.9%. The combined implementation of the proposed ECMs reduces annual energy consumption by up to 16% and daily peak demand by 29.4%. The findings highlight the substantial potential of structured audit-informed operational strategies in university buildings, emphasizing their role as low-risk, high-impact interventions for peak management and energy performance enhancement.