Optimal planning and design method for complex polygeneration systems : A case study for a residential building in Italy

Polygeneration energy systems using multiple energy sources (e.g., wind, biomass, solar) and delivering multiple energy services (i.e., heating, cooling, and electricity) have potential economic and environmental benefits over traditional energy generation systems. However, for maximized benefits, such systems must be the correct size and have a suitable operating strategy implemented. In this study, an optimization model is proposed to identify the optimal design and operating strategy of a complex polygeneration system. The system includes photovoltaic modules, solar thermal units, wind turbines, combined heat and power units, energy storages (hot, cold, and electric), vapor compression and absorption chillers, and a boiler. The interactions between these units are managed based on the integrated operating strategies: following thermal load, following electric load and modified base load. A particle swarm optimization is used as an optimization algorithm and the objective function is defined to minimize the annualized total cost, fuel consumption, and carbon dioxide emissions using a weighting factor method. The careful incorporation of the realistic operation of the CHP is considered in the theoretical model. This includes the effects of the part-load operation and outdoor temperature on the efficiency and power output of the CHP. In addition, the size dependency of the unit cost of the chillers and CHP units over the search space is taken into account. With this approach, the achieved results would be as close to real conditions as possible. Six configuration scenarios are examined for a case study in a residential building complex located in northern Italy. It is concluded that implementation of the optimized polygeneration system has energetic, economic, and environmental conservation benefits in all these scenarios. The annualized cost and fuel consumption of the optimal solutions decreased by 3–19% and 10–37%, respectively, for the various scenarios compared to the separate generation system.QC 20180529