Solid oxide fuel cells are an emerging energy conversion technology suitable for high-temperature power generation with proper auxiliary heat. Combining SOFCs and polygeneration has produced practical applications for modern energy system designs. Even though many researchers have reviewed these systems’ technologies, opportunities and challenges, reviews regarding the optimal strategy for designing and operating the systems are limited. Polygeneration is more complicated than any other energy generation type due to its ability to generate many types of energy from various prime movers. Moreover, integration with other applications, such as vehicle charging and fueling stations, increases the complication in making the system optimally serve the loads. This study elaborates on the energy planning and guidelines for designing a polygeneration system, especially for residential applications. The review of polygeneration technologies also aligns with the current research trend of developing green technology for modern and smart homes in residential areas. The proposed guideline is expected to solve the complication in other applications and technologies and design the polygeneration system optimally.

The strength of combined heat and power (CHP) systems in covering the electricity and heat demands has been proven over the last two decades. However, due to their complexity, CHP systems overcome challenges in optimally managing energy sources. This study aims to develop the best energy management model for a residential micro-CHP system that combines photovoltaics, thermal, and fuel cells, employing particle swarm optimization to optimize the model and incorporating fuzzy constraints to reduce power losses. To confirm its superiority, the proposed management model was compared to the conventional following-electrical-load approach. At the same time, the best candidate was selected using the Analytical Hierarchy Process. The results indicate that particle swarm optimization (PSO) significantly enhanced source operations and improved all objective criteria—primary energy saving (PES) (86.4%), net present value (NPV) (101.0%), and CO₂ reduction (5.1%)—compared to the conventional load-following approach; however, it exhibited a loss of power supply probability (LPSP) of approximately 41.7%. Additionally, the fuzzy constraints on the PSO model (PSO–FL (fuzzy logic)) have successfully eliminated the LPSP (0%) while maintaining all criteria at their best values. It has improved the PES, NPV, and CO2 by approximately 9.9%, 47.1%, and 7.1%, respectively. The analytical hierarchy process (AHP) with the best weights of 0.3, 0.4, and 0.3 for the PES, NPV, and CO2 criteria was chosen as the best solution. Prospects for the real implementation of the best model were validated through the operation of a real micro-CHP system.