Research On The Response Characteristic And Application Of Multi-energy Demand Response For Energy Connection

Recently,with the generalization of Energy Connection in China,Energy hub deemed as the critical joint in multi-energy coupling,has become commonly adopted by large industrial users,commercial users and campus users for its high energy flexibility.In comparison with traditional electrical users who consume electricity only,integrated energy users equipped with energy hub,will be prosumers who consume electricity,gas from upward energy system and produce electricity,cooling,heating for downward end users.Due to the feasibility of energy substituation among multi-energy inputs,integrated energy users have multiple options for energy.That is,users can select the most economic combination of energy alternatives considering different energy prices.Therefore,integrated energy users have the flexible response with respect to the varying energy price.Above mentioned type of demand response（DR）is named as integrated energy demand response（IDR）.Comparing with traditional one,IDR is implemented assuming that the energy use of connected users remained fixed so that IDR has such advantages like low uncertainty and high response potential.Aiming at making IDR participate in the operation and planning of energy system,it’s necessary to model response characteristics of IDR in a tangible and comprehensive way.This paper focuses on the modeling of IDR as well as its application.Based on analyzing response mechanism of integrated users as well as its affecting factors,the single-time-interval and multi-time-intervals response models are proposed based on the energy substitution and energy transferring among multi-time intervals.Then,the method of parameter identification of IDR response model is proposed.Based on the above response model,the applications of IDR are considered including the pricing strategies of large-scale integrated energy users as well as the planning methods of district energy hub.Based on the proposed response model,the complicated non-linear pricing and planning optimizing problems are much easier to solve,avoiding the low resolving speed,poor convergence and low robustness.The study of this paper makes some foundation for the generalization and application of IDR.The summary is illustrated as follows.（1）The response characteristics of IDR at single time interval and among multi-time intervals are studied.This work is expected to learn how integrated energy users react and alter the purchase combination of electricity and natual gas under their varying prices.Aimed at response at single time interval,a model called Electricity Substituted Curve（ESC）based on consumer psychology theory is proposed to characterize the response of integrated energy user at single time interval;and aimed at reponse among multi-time intervals,a model called demand elasticity matrix based on electricity,heating coefficients is proposed to characterize the response among multi-time intervals.The modeling approach of ESC as well as Utility curve is proposed based on consumer choice theory in microeconomics,and the modeling approach of demand elasticity matrix is proposed based on the deduction of KKT conditions.Then,the single-time-interval and multi-time-intervals response characteristics of different users in different seasonal scenarios are analyzed.The proposed response model can deduce the impact factors of IDR,and lies the foundation for the research on the schedule,pricing,and planning of IDR.（2）The parameter identification of IDR response model is studied.This work is expected to learn how to determine the response model based on the observed the response amount of users and the announced electricity and gas prices.The identification method based on least square method is proposed according to the multi-time-interval response model proposed in（1）.The proposed method of parameter identification can help energy system operator to acquire the response characteristic of IDR users,and also provide basis for learning energy pricing strategy and planning approach for Transmission system operator（TSO）.（3）The energy pricing strategy of large-scale integrated energy users is studied.This work is expected to learn how to make energy prices for large-scale integrated energy users to realize the minimum supply cost from the perspective of TSO.Combining the multi-time-intervals response model proposed in（1）with pricing model,the TOU pricing model for large-scale integrated energy users as well as its corresponding solving approach based on interior point method is proposed.The optimal TOU pricing strategy is obtained through the proposed model and solving approach.Comparing with the two major approaches for solving stackelberg game problem such as bi-level iteration method and single-level linearizing method,our solving approach is more efficient and the solution is more optimal.（4）The device capacity planning of district energy hub（DEH）is studied.The work is aiming to make investment decisions of DEH devices capacity from the perspective of district integrated energy serving entity（ILSE）.The electric demand model is extended to be available in multi-oprational scenarios and generalizing DEH system.Based on the extended demand model,a multiple-scenario bi-level stochastic DEH planning model is proposed to consider the uncertainties of multi-energy price and multi-energy load.Then,the corresponding solving approach including dimension reduction and variable unimodal searching is proposed to avoid curse of dimensions caused by multiple scenarios and broad range of device capacity.Comparing with Benders decomposition（BD）dealing with scenario-based stochastic problems,our solving approach is more adaptive and efficient for the planning problems with the large number of scenario and broad range of device capacity.（5）The coordinated planning of multi district integrated energy systems considering the interactions between ILSE and TSO is studied from the view of ILSE.The work is to help ILSE to invest and plan several DEHs in electric and gas transmission systems.Based on the work in（3）and（4）,a tri-level optimization model which is appeared as ILSE’s investment(1^st level),TSO’s pricing(2^nd level),DEH’s response to energy price(3^rd level),is proposed.Based on the multi-time-intervals response model proposed in（1）and some other transformations,the primal optimization model can be transformed as a bi-level second-order cone optimization model,which can be solved by CCG.This planning model is more approaching with the actual operation and planning of integrated energy supply and excavate more profitable ways to maximize investment returns of ILSE.