Research On Distributed Low-carbon Economic Dispatch Considering Renewable Energy Outpu

Due to the growing numbers of problems on the energy scarcity and greenhouse effect,clean energy sources such as wind and light power generation have become the key to solve the problem due to their non-polluting and sustainable advantages.However,as the wind and light power output are random and fluctuating,it brings uncertainty to the operation and dispatch of power system,which leads to wind and photovoltaic power abandonment.With the development of low-carbon electricity,the participation of electric vehicles in power system dispatching could provide part of the wind and photovoltaic power consumption space and provide important ideas to solve the environmental problems and low energy utilization problems.Therefore,based on a distributionally robust optimization method of general moment uncertainty,this paper describes the uncertainty of wind and solar power output,and establishes a two-stage distributionally robust low-carbon economic dispatch model considering carbon trading and electric vehicles to realize the economy and low-carbon of system operation.The research in this paper mainly includes the following aspects.(1)Firstly,the characteristics,principles and general representation of a distributionally robust optimization method are briefly described,follow which,a more detailed classification summary of the distributionally robust ambiguity set is elaborated regarding its construction methods,advantages and disadvantages and application fields.At last,the solution strategy of the distributionally robust optimization model is briefly summarized.(2)Secondly,a two-stage distributionally robust low-carbon economic dispatch model considering the characteristics of carbon trading and electric vehicle energy storage is established based on a general moment uncertainty distributionally robust optimization method to characterize the uncertain wind power output.The carbon trading cost is introduced into the objective function,while the system carbon emission is reduced and the wind power consumption is increased through the synergistic cooperation of electric vehicle energy storage and wind power generation.The ambiguity set of wind power output uncertainty is constructed based on the generic uncertainty moments so that more probabilistic statistical information of uncertain parameters could be covered to make the ambiguity set more complete.Linear decision rules and duality theory are used to convert the original model into an easy-to-solve quadratic programming model.The simulation results verify the effectiveness of introducing carbon transaction costs and electric vehicles to improve the economic benefits of the system and reduce carbon emissions,and improve the efficiency of wind power consumption.Furthermore,compared with the robust optimization model and the deterministic model,a two-stage distributionally robust model established in this paper can better balance the economy and robustness of the system operation.(3)Finally,in order to solve the pressure of peaks on the power system caused by disorderly charging of electric vehicles,this paper establishes a two-stage distributionally robust low-carbon economic dispatch model for coordinated and optimized operation between electric vehicle clusters and power systems containing wind and photovoltaic based on the concept of low-carbon economy and comprehensive consideration of the driving demand of electric vehicles.An ambiguity set is constructed to describe the uncertainty of wind and photovoltaic power output.In this paper,linear decision rules and the duality theory are used to convert the original model into an easy-to-solve quadratic programming model.The simulation example shows that the model effectively smooths out the load fluctuation of the system,consumes renewable energy,reduces the peak-to-valley difference and reduces carbon emissions.It also effectively improves the utilization rate of wind and light resources,balancing the economy and low carbon of system operation.