Optimized Operation Of Regional Integrated Energy System With Integrated Demand Response

The increasing shortage of fossil fuels and the environmental problems have become the sword of Damocles hanging over the rapidly developing human society.Faced with the world trend of energy transformation and revolution,China has vigorously developed the Regional Integrated Energy System(RIES),aiming at solving the problems of excessive energy consumption and local consumption of renewable Energy.Under the new situation,the demand side is also undergoing revolutionary changes.With the enhancement of the coupling relationship between production and consumption,the Integrated Demand Response(IDR)is expanded on the basis of traditional Demand Response,and has become an effective means to explore the demand elasticity and flexibility of the user side of RIES.First,the typical operation framework of RIES with CHP as the core capacity equipment is taken as the starting point.On the demand side,IDR will participate in comprehensive demand corresponding cold,heat and electric load.Models applied to optimal operation strategy are put forward.Secondly,to solve the phenomenon of new energy abandonment and the imbalance between the supply and demand of heat and power,a day-ahead optimal operation model of RIES,including peak-valley heat price and IDR,is proposed to reduce the operation cost of RIES and reduce the phenomenon of new energy abandonment.However,the prediction of new energy output and heating load in the day-ahead stage is affected by the weather and other uncertain factors,such as the temperature deviation leading to the shortage of available capacity of building heating load,thus resulting in the situation that the optimal operation strategy is not in line with the optimal energy distribution of actual scheduling.Accordingly based on the analysis of the optimal operation at present,using controllable power and thermal load joint scheduling,establish a RIES two-stage optimization scheduling policy considering uncertainty and IDR.The first phase in RIES optimized the load curve with target of minimizing the overall energy cost.The second phase of real-time correction aiming at the lowest cost of compensation to users to optimize the scheduling.Example analysis and results show the effectiveness of the proposed strategy and model.At the day-ahead stage,the IDR considering peak-valley heat price is better than the IDR strategy without considering the thermal load’s response to heat price,and both the energy cost and the abandonment rate of new energy are reduced.In the real-time scheduling stage,the total compensation cost is related to the scheduling compensation rate of different loads,the prediction error value and the building heating load schedulable capacity.The errors caused by uncertainties in day-ahead optimization can be eliminated by reasonable scheduling of several types of controllable load.