Regional Integrated Energy System Planning Under Multiple Uncertainty Scenarios

Regional integrated energy system(RIES)involves the utilization of multiple energy,which can improve energy efficiency and the ability of energy interconnection.The RIES has attracted the attention of more and more domestic and foreign scholars.However,more and more renewable energy and multi energy loads are connected to the energy system,which increases the multiple uncertainties of the system,which will have a great impact on the planning and operation of the energy system.To solve this problem,this paper focuses on the generation of multiple uncertainty scenarios and the medium and short-term,medium and long-term stochastic planning of RIES.The main work includes:(1)Aiming at the uncertainty of renewable energy and multi energy load in medium and short-term planning,a multi-dimensional correlation scenario generation method for medium and short-term planning is proposed.This method generates multidimensional correlation scene sets by analyzing the characteristics of time series probability distribution parameters,autocorrelation and cross-correlation of various renewable energy and multi energy load.Case study show that this method can effectively generate scenes that fit the characteristics of historical data,and can provide scene data for medium and short-term stochastic planning.(2)Considering the medium and short-term uncertainty of renewable energy and multi energy load,and considering the risk of pipeline out of limit,a multi-objective medium and short-term stochastic expansion planning model of RIES based on opportunity constraints is proposed.With reference to the edge betweenness theory,the importance index of pipeline is defined.The upper layer aims to minimize the cost of pipeline expansion planning and the risk of pipeline out of limit,and the lower layer aims to minimize the daily operation cost of energy station.The simplified case study of Xinba town demonstration area in Yangzhong,Jiangsu Province shows that the planning model is effective in improving economy and promoting renewable energy consumption.(3)Aiming at the multi energy load growth and energy price uncertainty in medium and long-term planning,a multi-stage scenario tree generation and energy price interval prediction method for medium and long-term planning is proposed.Based on the methods of conditional generative adversarial network,random forest and markov chain,a multi-stage scenario tree generation model which can characterize the load evolution trend is proposed.Based on artificial neural network,interval prediction evaluation index and particle swarm optimization algorithm,an energy price interval prediction model which can characterize the fluctuation range of energy price is proposed.The case study shows that the multi-stage scenario tree method can cover the evolution possibility of future load under the condition of small sample data,the energy price interval prediction model can quantify the fluctuation range of energy price,and can provide data support for medium and long-term stochastic planning.Considering the medium and long-term load and price uncertainty and the influence of energy facility construction time,a medium and long-term multi-stage stochastic planning model of RIES is proposed.Firstly,considering the practical engineering constraints,a new pipeline topology planning model based on block simplification is proposed.On this basis,considering the planning investment cost,regional operation cost,energy station income and carbon emission reduction,a medium and long-term multi-stage stochastic planning model of RIES is proposed.Through the simplified case study of Dazhangzhuang town demonstration area in Beichen,Tianjin,the effectiveness of this method in reasonably planning the construction sequence of energy facilities and accelerating the investment recovery cycle is verified.