Optimal Configuration Of Distributed Energy Supply System With Multiple Complementary Energy Under Uncertain Conditions

In order to achieve the goal of carbon peak and carbon neutralization,it is imperative to adjust the energy structure.This requires improving the utilization rate of fossil energy and increasing the development and utilization of renewable energy.Distributed energy supply system with multiple complementary energy can integrate renewable energy and fossil energy at the same time,which not only improves security,but also reduces carbon emissions.However,the volatility of renewable energy and the users’ random behavior have brought challenges to the safe operation of the system,studying the uncertain factors of distributed energy supply system will help to further promote the development of distributed system and provide theoretical support for the adjustment of energy structure.Therefore,aiming at the distributed energy supply system with multiple complementary energy,this paper proposes a multi-objective optimization model considering the uncertainty on both sides of resources and loads.Considering the comprehensive performance of the system,the optimal configuration scheme of the system for the whole year is obtained.The specific contents are as follows:Firstly,this paper takes the distributed energy supply system with renewable energy input as the research object,establishes the energy balance equation and models of main components on the energy side of the system based on the relationship between energy supply and load demand.On this basis,according to the state of gas turbine,a reasonable operation strategy is formulated to obtain the best optimal configuration results.Secondly,the uncertain factors exist in source and load sides are studied,and the Latin hypercube sampling is used to generate typical scenarios including electricity,cooling,heating load and solar radiation;because the huge scenario data will make the subsequent calculation fall into dimensional disaster,the clustering method combining adaptive fuzzy c-means clustering and k-means clustering is used to reduce the generated scenarios.Then the chance constraint programming is used to further deal with the uncertain factors in the energy balance equation.According to the characteristics of each uncertain factor,the chance constraint programming model is transformed into a deterministic model.Finally,considering the economy,energy,environmental protection and independence of the system,a multi-objective optimization model is established.Based on the characteristics of mixed integer linear programming problem,the improved ε-constraint method is used to solve the multi-objective optimization problem,and uses the multi-objective decision making to screen the final solution in the generated Pareto solution set.Then,the effectiveness and accuracy of the chance constrained programming model,operation strategy and solution method in this paper are tested,and the influence of energy storage equipment on system ’s configuration and performance is explored.Multi scenario analysis method proposed in this paper not only retains the authenticity of the data,but also greatly reduces the amount of calculation,which provides a data basis for the subsequent optimal configuration.The chance constraint model further improves the comprehensive performance of the system compared with the deterministic model.Improved ε-constraint method and multi-objective decisionmaking method are used to solve the multi-objective optimization model,and the ideal optimal configuration scheme under different confidence levels is obtained.Compared with the confidence level of 0.5,the optimal capacity of gas turbine under the confidence level of 0.99 increases by 62 kw and 10.49%;the annual total cost saving rate decreases by 1.81%,and the primary energy saving rate,carbon dioxide emission reduction rate and net interaction increases by 15.07%,2.44% and 29.55%,respectively.