Techno-Economic Analysis and Optimization of Distributed Energy Systems

As a promising approach for sustainable development, distributed energy systems have receive increasing attention worldwide and have become a key topic explored by researchers in the areas of building energy systems and smart grid. In line with this research trend, this dissertation presents a techno-economic analysis and optimization of distributed energy systems including combined heat and power (CHP), photovoltaics (PV), battery energy storage (BES), and thermal energy storage (TES) for commercial buildings. First, the techno-economic performance of the CHP system is analyzed and evaluated for four building types including hospital, large office, large hotel, and secondary school, located in different U.S. regions. The energy consumption of each building is obtained by EnergyPlus simulation software. The simulation models of CHP system are established for each building type. From the simulation results, the payback period (PBP) of the CHP system in different locations is calculated. The parameters that have an influence on the PBP of the CHP system are analyzed. Second, PV system and integrated PV and BES (PV-BES) system are investigated for several commercial building types, respectively. The effects of the variation in key parameters, such as PV system capacity, capital cost of PV, sell back ratio, battery capacity, and capital cost of battery, on the performance of PV and/or PV-BES system are explored. Finally, subsystems in previous chapters (CHP, PV, and BES) along with TES system are integrated together based on a proposed control strategy to meet the electric and thermal energy demand of commercial buildings (i.e., hospital and large hotel). A multi-objective particle swarm optimization (PSO) is conducted to determine the optimal size of each subsystem with the objective to minimize the payback period and maximize the reduction of carbon dioxide emissions. The results reveal how the key factors affect the performance of distributed energy system and demonstrate the proposed optimization can be effectively utilized to obtain an optimized design of distributed energy systems that can get a tradeoff between the environmental and economic impacts for different buildings