Modeling and multi-objective optimization of a photovoltaic-thermal based multigeneration system

Abstract

In this thesis, a novel multi generation integrated system is introduced, analyzed and optimized. It consists of GaAs based PV (Photo-Voltaic) arrays connected in series. Light is being concentrated on those arrays using lenses or mirrors. Concentrated light increases the cell temperature significantly. The cell is cooled down using a heat transfer fluid. This heat is being transferred to an organic Rankine cycle which not only produces electricity but also produces hot water for domestic applications. The organic Rankine cycle is also coupled with quadruple ammonia water absorption chiller. A part of the net electricity is used a PEM electrolyzer which produces hydrogen. So, using a single heat source, four useful commodities are produced, namely electricity, hot water, cooling, and hydrogen. An integrated thermal model is developed to analyze this system. Five parameters, namely pressure in primary loop, pressure and temperature of organic Rankine cycle, pressure and temperature of ammonia water chiller system are varied in order to investigate their effects on energy and exergy efficiencies, cost of electricity, enviro economic parameter and on different exergoenvironmental factors. The results indicate that the PV array should be tilted at 30˚ this will give maximum radiation intensity on the surface of PV array. I-V (Current and Volt) curves shows that increasing the radiation intensity increases both the current and voltage. For sake of analysis, the system is developed for Toronto, ON, Canada. At a concentration factor of 15 in a 100 cell PV array, 196.2 V and 8 A can be achieved at a radiation intensity of 1000 W/m². Similarly, increasing the radiation intensity can increase the cell temperature up to 700 K. The maximum calculated energy and exergy efficiencies are 46% and 40.5%, respectively. Three objective functions are developed using the data obtained from parametric study namely exergy efficiency, cost of electricity ($/kWs) and exergoenvironmental impact coefficient. These objective functions were optimized using a genetic algorithm called NSGA-II. I have a multiple solutions which is called Pareto-front. The results shows that the optimum efficiency is 38.14%, optimum cost of coefficient of electricity is 13.4 cents/kWh and optimized exergoenvironmental impact coefficient is 2.6. The multigeneration energy system discussed in this thesis and similar other alternatives (with renewable energy sources) can significantly contribute in addressing world energy needs and related challenges (e.g., human wealth, human welfare and environmental impact).