Low carbon solar-based sustainable energy system planning for residential buildings

• Published in: Renewable & sustainable energy reviews 2025-01, Vol.207, p.114942, Article 114942
• Main Authors: Noorollahi, Younes, Zahedi, Rahim, Ahmadi, Esmaeil, Khaledi, Arian
• Format: Article
• Language: English
• Subjects: Energy economics; Energy systems planning; Multi-objective optimization; Renewable energy; Residential energy hub
• ISSN: 1364-0321
• DOI: 10.1016/j.rser.2024.114942

Demand-side policy support has a significant role in energy systems. System profitability will widely hinge on planning and programming under conventional systems with no support. On the other hand, in smart grids, buildings are no longer passive consumers, but they can be used for demand-side energy management. In this study, two energy systems are assumed for an on-grid smart building. The power grid and PV panels are the first system’s electricity suppliers, and the thermal load is fulfilled by an electric boiler, Solar Collector (SC), and thermal storage in this system. The second energy system, instead of an electric boiler, is equipped with a gas boiler. As a consequence of investigating this system as an energy hub, the best combination and optimized size of each design are achieved using the Particle Swarm Optimization heuristic method (PSO) via MATLAB software based on technical and economic benchmarks. The payback period for the system equipped with a gas boiler is equal to 3.3 years, compared to 4.3 years of the electric boiler system payback and their net present value is 4633.9$ and 3909.5$ respectively. The results of optimization inditated that the PV panels should be 15 m2 in size, the solar collector should have a size of 2.68 m2, and the gas boiler should be 3.09 kW in capacity. The optimization results indicate that the solar collector is not beneficial in the system with the gas boiler, so all of the thermal loads have been provided with a gas boiler in this system. [Display omitted] •Choosing PSO to solve the optimization problem.•Accompanying energy systems planning by optimal sizing.•Calculating the optimal size of SC and PV and the required size of the storage tank and boilers based on NPV.•Considering environmental factors and emission tax in this modeling.