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Designing an energy system using multiple energy sources including renewables and providing multiple energy services (e.g. electricity, heating) can enhance the reliability and efficiency of the system while mitigating the environmental footprint. However, interaction among various components, variation of the energy demand profile, and local ambient conditions make design optimization a complex task, and suggesting that efficient simulation tools and optimization techniques can help designers to determine the best solutions within a reasonable timeframe and budget. Previous work on a dynamic microgrid simulation tool called "u-Grid" used an exhaustive search technique to find optimum configurations. However, the high computational cost of the exhaustive search was a motivation to explore alternative optimization methods to improve the optimization process and also to enhance search speed. In this paper Particle Swarm Optimization (PSO) has been presented as a global optimizer and incorporated within the problem context. Results from the exhaustive search have been used as a benchmark for testing and validation of the newly introduced optimization technique. The result shows that the PSO method is an efficient technique which has the ability to determine a high quality design solution for an optimized microgrid with a relatively low computational cost. Applying this PSO-based algorithm to the case study has reduced the total computation time a factor of about 6 in a significantly smaller computational platform.

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Small-scale decentralized polygeneration systems have several energetic, economic and environmental benefits. However, using multiple energy sources and providing multiple energy services can lead to complicated studies which require advanced optimization techniques for determining optimal solutions. Furthermore, several parameters can influence the design and performance of a polygeneration system. In this study, the effects of heat load, renewable generation and storage units on the optimal design and performance of a polygeneration system for a hypothetical hospital located in northern Italy are investigated. The polygeneration system shows higher performance compared to the reference system, which is based on the separate generation of heat and power. It reduces fuel consumption by 14-32%, CO 2 emissions by 10-29% and annualized total cost by 7-19%, for various studied scenarios. The avoided fuel and electricity purchase of the polygeneration system has a positive impact on the economy. This, together with the environmental and energetic benefits if the renewable generation and use of storage devices, indicate the viability and competitiveness of the system.

Design and performance of polygeneration energy systems are highly influenced by several variables, including the climate zone, which can affect the load profile as well as the availability of renewable energy sources. To investigate the effects, in this study, the design of a polygeneration system for identical residential buildings that are located in three different climate zones in Iran has been investigated. To perform the study, a model has previously developed by the author is used. The performance of the polygeneration system in terms of energy, economy and environment were compared to each other. The results show significant energetic and environmental benefits of the implementation of polygeneration systems in Iran, especially in the building that is located in a hot climate, with a high cooling demand and a low heating demand. Optimal polygeneration system for an identical building has achieved a 27% carbon dioxide emission reduction in the cold climate, while this value is around 41% in the hot climate. However, when considering the price of electricity and gas in the current energy market in Iran, none of the systems are feasible and financial support mechanisms or other incentives are required to promote the application of decentralized polygeneration energy systems.

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