Multi-Objective Optimal Dispatch Of Combined Cooling Heating And Power Systems With Strengthen Firefly Algorithm

With the rapid development and the strong independent control characteristic of the distributed energy system,the combined cooling,heating and power systems(CCHP)considering as a distributed energy system are developing rapidly.Nevertheless,problems of low energy utilization,high operating costs and instability in the application of the CCHP system are seriously affected the economic and security.Thereupon,in this study,it is extremely significance that the object should consist of power system and the CCHP system so as to optimize operational control and provide essential theoretical basics.Synthetical energy system(SES),incorporating solar heat collector,wind,photovoltaic power generation,and the CCHP system,are proposed to generate electricity,heating,and cooling.SES not only reduces operational costs and conductive to environmental protection,but also ensures its stability.An original multi-objective optimization model for SES is presented to minimize the operational cost,primary energy consumption,and carbon dioxide emission of the SES and to optimally reduce the power loss and voltage magnitude deviation of the power system.A novel Pareto optimization algorithm,named multi-objective strength firefly algorithm based on generalized normalized normal constraint(GNNCSFA)method,is proposed to solve the multi-objective operation problem of synthetically energy system.The algorithm mainly includes two parts: one part that generalized normalized normal constraint method is developed to convert the multi-objective operation problem into a series of constrained single-objective optimization sub-problems.Another part that the strength firefly algorithm(SFA)that a Boltzmann distribution,the internal population selection,and application of scale genes can be effectively solved by the single-objective optimization sub-problems.A hyper-plane-based decision making strategy is introduced to identify the best compromise solution for the obtained Pareto frontiers.The developed method in this paper is simulated and analyzed in MATLAB2014 platform.Firstly,simulation results show that the high rate of energy utilization,the low operational costs and eco-friendly for the proposed system contrasted with the CCHP system are extraordinarily obvious.In additional,the numerical results demonstrate that the proposed SFA and the strength firefly algorithm based on generalized normalized normal constraint method exhibits competitive performance in the spacing,span and convergence metric when compared to the algorithms of the dual-objective optimization,and that the proposed SFA obviously improves searching speed when compared to other single-objective optimization algorithms.Finally,the best compromise solution by the hyperplane-based decision making strategy is also obviously a better choice when compared to the strategy of the state.