Sizing Study On Hybrid Renewable Energy Systems Using Multi-objective Evolutionary Algorithm

With the rapid depletion of conventional fossil fuels and the increasing price of coal,natural gas and crude oil,energy demand has become a challenge for the development of economy and society.The usage of renewable energy has attracted increasingly public attention as a result of energy crisis and environment protection.The common drawback of renewable energy such as solar and wind is their intermittence and unpredictable nature,hindering their wide usage.A hybrid renewable energy system(HRES),integrating different energy resources in a proper combination,can overcome the uncertainty problems of solar and wind,thus leading to a more reliable system.This paper studies the sizing and design problems of hybrid renewable energy systems based on multi-objective evolutionary algorithm.First of all,this paper analyzes the structure of HRES and establishes the models of system components.It studies the structures of common HRES like PV-wind-battery system,PV-wind-diesel-battery system and so on.Then the mathematical models of system components,including PV panel,wind turbine,battery bank and diesel generator,are established.Second,the system model of HRES is demonstrated on the basis of component models.The paper establishes the stand-alone system model and the grid-connected system model,respectively.Meanwhile,the optimization objectives of this two system models are introduced,these objectives are the annualized cost of system(ACS),loss of power supply probability(LPSP),fuel emissions and the net cost of purchasing power from the grid.Finally,the preference-inspired coevolutionary algorithm using goal vectors(PICEA-g)is researched.With an enhanced fitness assignment method of candidate solutions,the performance of PICEA-g is improved.Then the modified algorithm is applied to solve both the stand-alone and grid-connected system model of HRES,obtaining the optimum system configurations.To validate the effectiveness of the modified algorithm and the correctness of system model,a case study is carried out and the optimization results are analyzed.