Low-carbon Scheduling Of Virtual Power Plants Containing Large-scale Electric Vehicles

The virtual power plant can achieve energy conservation and emission reduction through effective management of internal cleaning entities.Currently,the virtual power plant uses the electricity price mechanism to optimize the operating conditions of each entity,which has limited role in promoting the clean entity to exert low-carbon characteristics.Therefore,it has important research significance that a variety of green trading mechanisms are introduced to virtual power plants to mobilize the enthusiasm of various entities to participate in low-carbon dispatch,so as to give full play to the clean characteristics of electric vehicles and new energy.The low-carbon scheduling of virtual power plants with large-scale electric vehicles is the main research object.The research has been carried out from building multi-agent full life cycle carbon emission models of the virtual power plant,low-carbon dispatching the virtual power plant found on carbon trading mechanism which considers tiered carbon price,low-carbon dispatching the virtual power plant based on generation-right green trading and green-certificate green trading,and rollingly correcting scheduling result taking into account forecast errors.Firstly,multi-agent full life cycle carbon emission models of the virtual power plant are built.Considering manufacturing,driving,and scrap recycling,full life cycle carbon emission models of electric vehicles in charging disorderly and orderly are constructed.After forecasting the output of wind turbines and photovoltaic units,full life cycle carbon emission models of wind turbines,photovoltaic units,and gas turbines are established,and the carbon emission factors of the above units are calculated based on actual data which establish the basis for later research.Secondly,the low-carbon dispatch strategy for virtual power plants ground on tiered carbon price carbon trading is proposed.Fusing multi-type and multi-scenario electric vehicle charging models to obtain disorderly charging data and orderly charging data of large-scale electric vehicles.Carbon emission credits are allocated to entities of the virtual power plant,the carbon trading low-carbon scheduling model with tiered carbon price is established and solved by Elitist Non-Dominated Sorting Genetic Algorithm.The comparison between the results of carbon trading with tiered carbon price and traditional carbon trading shows that tiered carbon price can increase the carbon emission reduction revenue of virtual power plants.The effects of adding energy types of cars,number,types,and travel scenarios,and charging modes of electric vehicles on the scheduling results are analyzed.The results show that more electric vehicles in orderly charging can reduce carbon emissions and promote the output of new energy units.Electric buses and traveling on holidays can guide the clean development of the virtual power plant.Thirdly,the low-carbon dispatch strategies of virtual power plants based on generation-right green trading and green-certificate green trading are proposed.By analyzing the coupling effect between the tiered carbon price and the price of the generation right,and between the tiered carbon price and the price of the green certificate,the generation-right green trading mechanism including carbon trading and generation right trading and the green-certificate green trading mechanism including carbon trading and green certificate trading are proposed.Examples show that compared with carbon trading,the two proposed trading mechanisms can further enhance the carbon emission reduction efficiency of the virtual power plant.The increase in the price of carbon emission rights,the number of electric vehicles and the scale of new energy can improve the environmental benefits of the virtual power plant.Finally,the low-carbon scheduling of the virtual power plant considering forecast errors is rollingly corrected.The daily charging load error prediction model of orderly charging electric vehicles is established,the new energy output error forecast model with error penalty cost is proposed.The low-carbon scheduling rollingly correcting model is solved by the model predictive control method,and compared with day-ahead scheduling considering the forecast error,it can be seen that the rolling correction has brought higher economic and environmental benefits to the entities of the virtual power plant.The greater the degree of prediction error is,the more significant the advantage is.