Multi-objective Design Optimization For Small Distributed Energy System Based On Genetic Algorithm NSGA-?

Small distributed energy system refers to capacity below 50 k W,providing cold,hot,electric and hot water loads on site.Due to the reduction of energy delivery links,small distributed energy systems can efficiently provide load requirements for small users.However,due to many power supply equipment,and load fluctuations,the need to optimize the design of system components.So this paper studies the design optimization of small distributed energy system based on NSGA-? multi-objective genetic algorithm.In this paper,a small residential building in Dalian as the object,in accordance with the identified envelope,using De ST software,the cold,heat,electricity and domestic hot water load of the building were 15.2k W,16.5k W,14.2k W and 1.9k W.By determining the hourly and external thermal disturbances of buildings during the working days and weekends,the hourly load of typical summer and winter days are calculated.Based on the analysis of the climate characteristics and load characteristics of the calculation object,a small distributed energy system with solar energy and natural gas mixed energy supply is proposed.And the system in the dish solar thermal power generation module for Stirling engine,VM cycle heat pump and a storage device and a heat exchanger for modeling.Under the given heat source temperature,the average circle pressure,the volume ratio and the speed range of the hot and cold cavity,the NSGA-? genetic algorithm is used to optimize the VM cycle heat pump.When the heat source temperature for 973.84 K,the average cycle pressure for 7.06×108Pa,hot and cold chamber volume ratio 26.72 and the speed of 408.98 rpm,COP and exergy efficiency system has the best comprehensive.The exergy efficiency is 0.215,COP is 3.17.The sensitivity analysis shows that the influence of heat source temperature on the objective function is relatively high,the volume ratio of hot and cold chamber volume ratio is smaller than that of rotating speed,and the change caused by circulating average pressure is the least.Therefore,in the actual operation,should first ensure the constant temperature of the heat source.Taking the annual total cost and annual CO₂ and NO₂ emissions as the objective function,the Pareto optimal solution set of system equipment capacity allocation is obtained based on the balance of energy flow and equipment performance.The total annual emission of scheme 1 is the smallest,and the total annual cost of scheme 3 is the smallest.The TOPSIS method to get the optimal scheme for scheme 2,the capacity of the system configuration: capacity of dis h solar power generation system for the Stirling engine 14.2k W,VM cycle heat pump 23 k W,power storage equipment for 10.5k Wh and storage tank 9.89 k Wh.Finally,compared with the traditional subsystem,the optimal solution set has the advantages of energy saving and environmental protection.The annual operating costs from scheme 1 to scheme 3 were saved by 50.59%,51.60%,52.13%,and pollutant emissions were significantly smaller than the sub supply system.