Optimized Configuration And Intelligent Collaboration Of Peak-cutting And Valley-filling Of The Combined Cooling,Heating And Power System

The combined cooling heating and power(CCHP)system has the advantageous characteristics such as low network loss,utilization of new energy,and improved energy efficiency,which in general play an important role in promoting social and economic development and ecological protection.In view of the inaccurate description of the target function in the current research on the optimization of the CCHP system,this paper first gives the energy flow diagram of the constructed CCHP system,and the structural requirements of each part,and the power model of each part from the system such as distributed power supply are briefly described.The target functi on of the system is constructed with the parameters of equipment procurement cost,fuel cost,equipment maintenance cost and environmental cost.Then take the system stability,equipment output limit and energy storage system capacity etc.as the constraints of the optimal configuration.By using the various loads on a typical day in a sample function,the optimized configuration capacity of each device in the CCHP system can be solved by using the improved 2nd generation of non-dominant sequencing genetic algorithm(NSGA-II)and the traditional particle swarm optimization(PSO)respectively,which eventually proves that the constructed CCHP system is economical efficient and environmentally friendly,and its stability index is verified.The results of the two algorithms show that NSGA-II is superior in avoiding falling into local optimum or non-convergence,it also provides fast operation speed for the optimal configuration in CCHP system.The optimized configuration of the CCHP system and the intelligent collaborative technology of peak-cutting and valley-filling shall ensure the economical efficiency and environmental protection from the early configured construction,while taking into account the reliability of the entire system.Based on the obtained values in the optimal configuration model,this paper aims to establish an intelligent collaborative peak-cutting and valley-filling model in the CCHP system.The constructed model takes the load standard deviation of the optimized system electrical load as th e objective optimization of target function,the battery energy storage system is constrained by power charging and discharging,and residual power,meanwhile the evaluation index of the intelligent collaborative effect of peak-cutting and valley-filling is given.Using the method of quadratic programming to describe the constructed model and prove its convexity,i.e.the optimization.Take the wind power generation consumption as the optimal decision-making variables to finally optimize and establish the model.The peak-cutting and valley-filling effect based on the quadratic planning algorithm is obvious,reducing the peak value and increasing the valley value,so that the absolute peak-valley difference is reduced,thereby reducing the peak-valley coefficient and the peak-valley difference rate,at the same time,the standard deviation of load fluctuation is reduced,and the load curve will be flattened on the basis of guaranteed load.Comparing to the peak-cutting and valley-filling optimization aimed for direct economy,the performance index of load peak-cutting and valley-filling optimization is lower,however the overall effect still stands out,which proves that battery energy storage can participate in the intelligent collaborative peak-cutting and valley-filling,and it gives good peak-cutting and valley-filling effect while contributes to the direct economy.