Two-stage Optimal Research Of CCHP Considering Wind/Solar/Load Uncertainty

Environmental pollution and energy scarcity are two major issues around the world today,developing wind and solar energy-based renewable energy sources and distributed co-generation system is an effective way to solve the problem.However,due to the frequent changes of wind and light intensity and the random fluctuation of load,it is difficult to accurately predict the output and load of the distributed power in combined cooling and heating power system,which may cause the optimization strategy to deviate from the actual situation.Different from photovoltaic and load,wind power output has obvious anti-peaking characteristics.Therefore,it is of great significance to study the probability distribution model of wind power output under irregular forms.First of all,this thesis gives the basic composition structure of the traditional sub-supply system and the cogeneration system,clarifies the relationship between its efficiency and output,builds a mathematical model.Secondly,considering the active control capability of wind power,the thesis proposes an improved stochastic model predictive control method.In view of the influence of the "wind curtailment and curtailment" process on the probability density function of wind power output,this method establishes an irregular probability distribution model that takes into account the active control capacity of wind power.Then,the thesis integrates the entropy weight method and the expert index weight method into the evaluation process,proposes an improved multi-attribute evaluation method for approximating ideal solutions.Finally,the thesis takes the cold and heat load of a typical day in an urban area of Qingdao as an example to verify the feasibility of the optimization method proposed in this paper.The results show that the optimized scheduling method proposed in this thesis can effectively improve the prediction accuracy of the wind power prediction model and the overall benefit of the CCHP system,which verifies the effectiveness of the proposed model and method.