Research On Power Efficiency Optimization Strategy For New Generation Intelligent Campus

With the vigorous development of smart grid projects in China,smart parks relying on smart grid have also attracted the attention of the society.With the rise of new generation of artificial intelligence technology,Internet +,big data integration and other emerging technologies,the new generation of intelligent parks has become the development direction of intelligent parks in the future.With a high starting point and high requirements as the starting point of construction,the new generation of intelligent park aims to build a new type of park integrating five industries,namely artificial intelligence,intelligent electronics,big data,intelligent manufacturing and new energy.Including the construction of a new generation of intelligent campus grid,is beneficial to optimizing the energy integration,on the one hand can meet its energy terminal users with diverse needs,guide the user to the power of the economic and reasonable,the response peak electricity grid system,realize the power load cut peak pinggu,improve the utilization rate of electrical equipment to make the operation of the power grid is more stable;On the other hand,it can effectively promote social energy sharing,realize the construction goal of smart grid security,economy,energy conservation and efficiency,promote social energy construction,and better realize the policy of energy sustainable development.For the load prediction of the active distribution network in the new generation of intelligent park,combined with the role of load aggregators,the load calculation and analysis involving demand response are introduced,and the load prediction method suitable for the active distribution network in the park is proposed.This paper analyzes the response characteristics and internal mechanism of users to the power load participating in the demand response plan,selects the appropriate mathematical model for the response degree of users to the demand plan and linearizes it to establish the load prediction model based on the response degree of users.First of the park's active power load according to the different load mechanism is divided into traditional seasonal base load and demand response signal and random factors affect the load curve,using similar day forecast load model forecast seasonal base load,the depth of neural network model of user demand response signal and random factors such as load forecast,finally will be two parts weighted superposition forecasting load,total active power load.By comparing with other methods,the validity and accuracy of the combined load prediction model proposed in this paper are proved.In the context of load aggregators participating in the construction of new generation intelligent park,the influences of various measures on the optimization of power efficiency in the park are firstly considered.From based on electricity price and load demand response to impact load mechanism,puts forward the suitable for the demand of a new generation of intelligent park response load model,to improve the electricity price and establishment of incentive and then optimization model is established,and the simulation results show that,under the demand response measures,can effectively realize load cut peak pinggu in the park and the park of the power system reliability,reduce the overall cost of the system.Then,considering the characteristics of wind power generation in the park,an economic operation model of wind power consumption based on electricity price and incentive measures is established.Through this model,it is found that demand response measures can effectively reduce the cost of wind power generation,reduce wind abandon and improve the utilization rate of wind power.