A comprehensive demand response method and system that considers user response risks

Abstract

A comprehensive demand response method and system considering user response risk is disclosed, serving the implementation of demand response technology in the integrated energy market. The process includes: integrating the equipment characteristics and information of integrated energy service providers; establishing a user selection mechanism for integrated energy service providers considering user response risk; and establishing a cost model for integrated energy service providers. Next, user energy consumption information and response characteristics are collected, and the response fatigue phenomenon caused by continuous user participation is considered, establishing a comprehensive user demand response benefit model. Finally, the integrated energy service provider cost model and the comprehensive user demand response benefit model are integrated, and based on their interaction mechanism, a comprehensive demand response optimization problem is established. The unique equilibrium solution is then sought with the goal of reducing integrated energy service provider costs and improving user benefits. This invention improves the accuracy and effectiveness of the comprehensive demand response strategy by considering the uncertainty risk of user response and the fatigue effect of continuous response over multiple time periods, achieving a win-win situation for both integrated energy service providers and users.

Description

Technical Field

[0001] This invention relates to the field of integrated energy and demand response, and more specifically to an integrated demand response method and system that takes into account user response risks. Background Technology

[0002] In recent years, with the continuous development of integrated energy systems and integrated energy services, integrated demand response, as an extension of electricity demand response, can achieve coordinated complementarity among multiple energy sources, promote the consumption of renewable energy, and advance the coordination and optimization between energy service providers and users. It is an important means to achieve my country's energy transition. Integrated demand response can be divided into price-based demand response and incentive-based demand response. Compared with price-based demand response, incentive-based demand response has greater regulatory potential due to the ability to formulate differentiated incentive strategies, and has become a current research hotspot.

[0003] The interaction between integrated energy service providers and users is a crucial aspect of integrated demand response. In incentive-based integrated demand response, after receiving response tasks from multiple energy trading markets, integrated energy service providers issue incentive prices to users to guide their participation. Users, upon receiving the pricing strategy, formulate their own response strategies to maximize their own benefits. However, user response strategies are influenced by multiple factors and possess a degree of uncontrollability, i.e., user response risk. User response risk is mainly caused by the uncertainty of user response and the response fatigue phenomenon that arises from continuous user participation. User uncertainty can be further divided into uncertainty in user participation and uncertainty in user response; user response fatigue also means that the response characteristics of users at different times are related to the response characteristics of previous times, exhibiting a multi-time-period coupling characteristic. User response risk can lead to deviations between the actual response volume and the response target, incurring risk costs for integrated energy service providers and significantly impacting the accuracy of integrated demand response strategy formulation. Therefore, there is an urgent need to develop an integrated demand response optimization strategy that considers user response risk.

[0004] Regarding research on integrated demand response methods and systems, CN115146811A, by Sun Yi et al., in "An Integrated Demand Response Method and System Involving Multiple Energy Aggregators," proposed an integrated demand response method and system. However, it did not consider the actual user response characteristics, i.e., users have response risks, nor did it consider the risk costs caused to multiple energy aggregators by user uncertainty and response fatigue. CN109727158A, by Zhang Xiaohui et al., in "A Scheduling Method for Integrated Electric and Thermal Energy Systems Based on Improved Weak Robust Optimization," established an integrated electric and thermal demand response model, but did not consider the participation of gas energy, nor did it consider the interaction between integrated energy service providers and users in actual situations. CN112200343A, by Gao Hongjun et al., in "An Operation Optimization System and Method Based on the Distribution of Benefits between Energy Service Providers and Users," considered the interaction mechanism between energy service providers and users, but did not consider the risk issues of user-side response. In CN 111951046 A, Wang Lei et al. proposed an incentive optimization strategy for integrated energy service providers in their paper "A Method and System for Prioritizing Load Control Subsidies in Integrated Energy Systems," but did not consider the impact of user response characteristics on integrated energy service providers in practice.

[0005] Publication numbers: CN 109286187 A, Gong Jianfeng et al.'s "A Microgrid Day-ahead Economic Dispatch Method Oriented to Multi-Subject Interest Balance", CN 114708030 A, Gao Hongjun et al.'s "A Comprehensive Energy Service Provider Retail Package Design Method Based on Multi-Master Multi-Slave Game Theory", CN 113870053 A, Long Chuan et al.'s "A Multi-Park Electricity-Gas Interconnection System Optimization Operation Method and System", and CN 111860965 A, Xu Qingshan et al.'s "A User Comprehensive Energy System Optimization Dispatch Method Considering Multiple Types of Energy Storage Services" all proposed benefit models and methods, but they did not target the field of comprehensive demand response optimization, did not consider user risk characteristics, and did not propose practical methods to solve user risks.

[0006] Regarding research on considering user uncertainty in integrated demand response optimization strategies, Chinese patent applications include: Lin Wenzhi et al.'s "Day-ahead Economic Optimization Dispatch of Integrated Energy System in Industrial Park Considering Demand Response Uncertainty" uses fuzzy expressions to model the uncertainty of flexible loads on the user side and sets a user satisfaction index to reflect the acceptable response range for users; Dong Xiaoying et al.'s "Optimization Matching Strategy between Distribution Network and Thermal Storage Electric Heating Load" uses a building thermodynamic model to estimate users' heat load demand to address the uncertainty of users' heat load demand; Liu Wenxia et al.'s "Collaborative Optimization Configuration of Integrated Energy System Considering Demand Response Uncertainty" is based on evidence theory and simultaneously considers user subjective uncertainty and stochastic uncertainty in IDR, making the strategy formulation more accurate. Sun Yi et al.'s "Incentive Strategy for Integrated Demand Response Optimization Considering User Response Characteristics" considers changes in user response willingness to optimize the integrated demand response strategy; however, it does not consider the uncertainty of user participation and the associated risk costs, nor does it propose a practical and effective solution.

[0007] Chinese patent applications: Wang Lei et al., in "An Optimization Method and System for Integrated Energy Systems Considering Demand-Side Response Uncertainty," considered the uncertainty of user response, but did not consider the uncertainty of user participation, nor the impact of user uncertainty on energy companies, and did not propose any practical solutions. Wang Wei et al., in "An Optimization Method for Energy Operation of Regional Integrated Service Providers Considering Risk Costs," considered the risk costs of user energy consumption, but the proposed risk costs did not reflect the uncertainty of user response and response fatigue.

[0008] In summary, the above-mentioned studies still have the following shortcomings: 1) Most studies only consider the uncertainty of user response, without considering the uncertainty of user participation and the response fatigue phenomenon caused by users participating in responses at multiple time periods. Furthermore, the analysis of user response characteristics does not consider effectively modeling the correlation between user response characteristics at multiple time periods. 2) Current studies fail to consider the risk cost problem of integrated energy service providers caused by user response risk. 3) Current studies mostly use methods such as price adjustment or incentives by integrated energy service providers to optimize strategies, without proposing a practical mechanism to reduce risk costs.

[0009] Therefore, this invention proposes a comprehensive demand response method and system that takes into account user response risks, aiming to reduce the costs for integrated energy service providers while improving the benefits of user participation in comprehensive demand response. Summary of the Invention

[0010] The purpose of this invention is to address the issue of user response risk by proposing a user selection mechanism for integrated energy service providers, thereby reducing their risk and incentive costs. It also considers user uncertainty and response fatigue caused by users participating in responses continuously over multiple time periods, thereby reducing the total cost for integrated energy service providers while improving users' energy comfort and efficiency.

[0011] To achieve the above objectives, a comprehensive demand response method considering user response risks is characterized by comprising the following steps:

[0012] Step 1: Integrate the equipment characteristics and information of integrated energy service providers, consider user response risks, establish a user selection mechanism for integrated energy service providers, and establish a cost model for integrated energy service providers;

[0013] Step 2: Collect user energy consumption information and response characteristics, consider the response fatigue phenomenon caused by continuous user participation, and establish a comprehensive user demand response benefit model;

[0014] Step 3: Integrate the integrated energy service provider cost model and the user integrated demand response benefit model. Based on the interaction mechanism between the two, establish an integrated demand response optimization problem, and solve for the unique equilibrium solution with the goal of reducing the integrated energy service provider cost and improving user benefits.

[0015] This invention provides a comprehensive demand response system that considers user response risks. The system includes: a comprehensive energy service provider model building module, a user model building module, and a comprehensive demand response optimization problem building and solving module. Each of the three modules has a specific function, and they are interconnected to complete data transmission between modules, thereby realizing the overall system functionality.

[0016] The integrated energy service provider model building module is responsible for establishing the integrated energy service provider cost model. By considering user response risk, it establishes a user selection mechanism for integrated energy service providers and a total incentive cost model for them. Based on the included energy storage and energy coupling equipment, it separately establishes operating cost models for energy storage and energy coupling equipment. By integrating the total incentive cost model, the energy storage operating cost model, and the energy coupling equipment operating cost model, the integrated energy service provider cost model is established and then passed to the integrated demand response optimization problem establishment and solution module.

[0017] The user model building module is responsible for establishing a user benefit model based on user participation in the comprehensive demand response. First, it establishes a user dissatisfaction cost model. Then, by considering response fatigue caused by users participating in the comprehensive demand response over multiple time periods, it establishes a user response willingness model. Combining the user dissatisfaction cost model and the user response willingness model, it establishes a user benefit model and passes this model to the comprehensive demand response optimization problem establishment and solution module.

[0018] The module for establishing and solving the comprehensive demand response optimization problem is responsible for establishing the comprehensive demand response optimization problem based on the interaction between users and integrated energy service providers, and solving the comprehensive demand response optimization problem. The result is the optimal equilibrium solution that achieves a win-win situation for integrated energy service providers and users.

[0019] Beneficial effects

[0020] (1) To address the uncertainty of user participation and response, a mechanism for integrated energy service providers to select users to participate in the response is proposed. By selecting some users with lower response risk and cost to participate in the response, the controllability of users is improved and the cost of integrated energy service providers is reduced.

[0021] (2) To address the response fatigue problem caused by continuous user participation, a multi-period coupled user response intention model was established, which improved the user's energy comfort and efficiency in the optimization of comprehensive demand response problem, making the strategies formulated by integrated energy service providers more effective. Attached Figure Description

[0022] Figure 1 This is a diagram of a comprehensive demand response system architecture that takes into account user response risks.

[0023] Figure 2 A flowchart illustrating the steps of a comprehensive demand response method that considers user response risks;

[0024] Figure 3 This is a typical energy flow diagram of a comprehensive demand response system that takes into account user response risks. Detailed Implementation

[0025] The present invention will now be described in detail with reference to the embodiments shown in the accompanying drawings. However, it should be noted that these embodiments are not intended to limit the present invention. All equivalent changes or substitutions in function, method, or structure made by those skilled in the art based on these embodiments are within the scope of protection of the present invention.

[0026] Example 1

[0027] refer to Figure 1 This invention discloses a comprehensive demand response system that considers user response risk, comprising: a comprehensive energy service provider model establishment module, a user model establishment module, and a comprehensive demand response optimization problem establishment and solution module, characterized by:

[0028] The integrated energy service provider model building module is responsible for establishing the integrated energy service provider cost model. By considering user response risk, it establishes a user selection mechanism for integrated energy service providers and a total incentive cost model for them. Based on the included energy storage and energy coupling equipment, it separately establishes operating cost models for energy storage and energy coupling equipment. By integrating the total incentive cost model, the energy storage operating cost model, and the energy coupling equipment operating cost model, the integrated energy service provider cost model is established and then passed to the integrated demand response optimization problem establishment and solution module.

[0029] The user model building module is responsible for establishing a user benefit model based on user participation in the comprehensive demand response. First, it establishes a user dissatisfaction cost model. Then, by considering response fatigue caused by users participating in the comprehensive demand response over multiple time periods, it establishes a user response willingness model. Combining the user dissatisfaction cost model and the user response willingness model, it establishes a user benefit model and passes this model to the comprehensive demand response optimization problem establishment and solution module.

[0030] The module for establishing and solving the comprehensive demand response optimization problem is responsible for establishing the comprehensive demand response optimization problem based on the interaction between users and integrated energy service providers, and solving the comprehensive demand response optimization problem. The result is the optimal equilibrium solution that achieves a win-win situation for integrated energy service providers and users.

[0031] Furthermore, the integrated energy service provider model building module includes a user selection mechanism that considers user response risk. By introducing selection decision factors, the integrated energy service provider can select users with lower response risk costs to respond.

[0032] Furthermore, the user model building module considers the response fatigue phenomenon in scenarios where the same user participates in the response continuously, and makes the user benefit model more accurate by building a user response willingness model.

[0033] Furthermore, the integrated demand response optimization problem establishment and solution module establishes the problem as a two-level programming problem based on a master-slave game problem, and transforms it into a single-objective programming problem for solution.

[0034] Example 2

[0035] refer to Figure 2 This invention discloses a comprehensive demand response method that considers user response risk, characterized by comprising the following steps:

[0036] Step 1: Integrate the equipment characteristics and information of integrated energy service providers, consider user response risks, establish a user selection mechanism for integrated energy service providers, and build a cost model for integrated energy service providers.

[0037] First, based on the theory of random distribution, a risk cost model is established for integrated energy service providers regarding user participation in integrated demand response:

[0038] Let the user set be . The time set of the comprehensive demand response is

[0039] The risk cost model for integrated energy service providers brought about by user i's participation in integrated demand response is shown in equation (1):

[0040]

[0041] Where, r = {r 1,t ,r 2,t ,...,r n,t Risk cost is a random variable that follows a normal distribution. The higher the risk cost, the greater the risk to the user's response. Conversely, the lower the risk cost, the more controllable the user's response is. The mean risk cost of user response represents the central risk cost that a user's participation in integrated demand response brings to the integrated energy service provider; σ r The standard deviation of risk cost reflects the dispersion of user response risk; the larger the value, the more difficult it is to control the risk of user response.

[0042] Secondly, based on user response risk, user selection decision variables are introduced to establish a comprehensive energy service provider user selection mechanism that considers risk costs:

[0043] Based on the aforementioned response risk and cost, integrated energy service providers select only users with lower response risk to participate in the response, excluding users with higher response risk from participating. Therefore, a decision variable x is introduced. i,t This represents the integrated energy service provider's decision to select a user, specifically whether the integrated energy service provider selects user i to respond during time period t.

[0044] The mechanism for integrated energy service providers to select users is shown in equation (2):

[0045]

[0046] Where, r c This represents the risk cost threshold, indicating the risk cost for user i during time period t. ri,t If the cost is less than this threshold, it means that the cost incurred by the user's response is within the acceptable range for the integrated energy service provider, and the integrated energy service provider can choose to respond to this user, i.e., x. i,t =1, conversely, x i,t =0.

[0047] Secondly, establish a total incentive cost model for integrated energy service providers that considers user response risk:

[0048] Total incentive cost for integrated energy service providers It includes both the cost of incentives distributed to users and the risk cost of bearing the uncertainty risks of users.

[0049]

[0050] In the formula: j∈{1,2,3} represents the type of energy, corresponding to electricity, heat, and gas respectively; during time period t, ξ i,j,t Let π be the actual response amount of user i to energy j during time period t. i,j,t That is, time period t is the incentive unit price issued by the integrated energy service provider to user i when responding to energy j; The response types for user participation in IDR are respectively classified as reduction IDR and absorption IDR.

[0051] Regarding incentive distribution, the incentive price issued by integrated energy service providers should firstly be higher than the minimum price expected by users, so that users will participate; secondly, it should be lower than the subsidy price provided by the energy market, so that integrated energy service providers will not suffer losses.

[0052]

[0053] In the formula: This refers to the subsidized price provided in the energy market, ρ i,j,t λ i,j Let ρ be the minimum incentive price expected for user i to participate in the response. To better reflect the pattern of integrated energy service providers setting incentives based on actual user needs, let ρ be defined. i,j,t ∈{0.2,1} are the excitation type parameters, corresponding to the absorption and reduction type IDR respectively.

[0054] Next, an operating cost model for energy storage equipment is established:

[0055] The operating cost model of energy storage equipment can be equivalent to a quadratic function of charging and discharging power, as shown in equation (5):

[0056]

[0057] During time period t, This refers to the operating cost of the energy storage equipment; for the j-th energy source, α j This represents the cost coefficient for energy storage equipment. The charging and discharging power of energy storage devices.

[0058] The operation of energy storage devices should meet the following constraints:

[0059]

[0060]

[0061]

[0062] In the formula: Indicates the maximum capacity of the energy storage device; Let represent the remaining capacity of the j-th energy source in the energy storage device during time period t. This is the initial capacity. To better reflect the energy loss phenomenon of actual energy storage devices, η ES These represent the charge / discharge efficiency coefficients of the energy storage device in the charging and discharging states, respectively. When the energy storage device is in the charging state, η... ES =0.9, when the energy storage device is in the discharge state, η ES =1.1. u min u max These represent the minimum and maximum energy states of the energy storage device, respectively. Equation (8) implies that the energy storage device must maintain energy conservation throughout the entire time period.

[0063] Then, an operating cost model for energy coupling equipment consisting of an electric boiler, a gas turbine, and a combined heat and power unit is established:

[0064] like Figure 3 The diagram shows a typical energy coupling system, consisting of an electric boiler, a gas turbine, and a combined heat and power (CHP) unit. The electric boiler can convert a portion of the electrical energy into heat energy; gas energy can be converted into electrical energy through the gas turbine, or into both electrical and heat energy through the CHP unit. Therefore, the operating cost of the energy coupling system includes the operating costs of the electric boiler, gas turbine, and CHP unit.

[0065] The operating cost model for energy coupling equipment is shown in equation (9):

[0066]

[0067] In the formula, The baseline load for time period t when the user does not participate in the j-th energy response; For the j-th type of energy response target that the integrated energy service provider needs to achieve during time period t, when When this happens, integrated energy service providers need to guide users to achieve reduced IDR; conversely, users need to achieve absorbed IDR. EB μ GT μ CHP These represent the unit operating price of the energy coupling equipment. r1, r2, and r3 are the scheduling factors of the energy coupling equipment, which determine the input-output relationship of the energy coupling equipment. For example... Figure 3 The coupling matrix of the energy coupling device is shown below:

[0068]

[0069]

[0070] In the formula, B e,in B h,in B g,in B e,out B h,out B g,out ε represents the input and output of electrical energy, thermal energy, and gas energy, respectively; D is the energy coupling matrix characterizing the energy conversion relationship within the energy coupling device; ε jk That is, the elements in the energy coupling matrix; η GT η EB , These represent the energy conversion efficiency between various energy conversion devices.

[0071] Based on the characteristics and input-output relationships of the energy coupling equipment, r1, r2, and r3 are the scheduling factors in the energy hub, used to optimize the output power of electric boilers, gas turbines, and combined heat and power units, and reshape the response to demand. The scheduling factors of each coupling device should be positive and not higher than 1, i.e.:

[0072]

[0073]

[0074] These represent the rated output power of electric boilers, gas turbines, and combined heat and power units, respectively.

[0075] Finally, a cost model for integrated energy service providers is established.

[0076] In a comprehensive demand response scenario, in order to achieve the target response volume for each time period The relationship between the actual total response from user participation and the response provided by energy storage should satisfy is shown in equation (14):

[0077]

[0078] in, Let t be the expected total response target of the integrated energy service provider to the j-th energy source.

[0079] Based on the above models, the cost model for integrated energy service providers is as follows:

[0080]

[0081] Step 2: Collect user energy consumption information and response characteristics, consider the response fatigue phenomenon caused by continuous user participation, and establish a comprehensive user demand response benefit model;

[0082] First, a user dissatisfaction cost model is established based on user response volume, user willingness to respond, and users' expected minimum incentive price:

[0083] Based on the collected user energy consumption information and response characteristics, a user dissatisfaction cost model is established using user response volume, user response willingness, and user expected minimum incentive price, as shown in Equation (16):

[0084]

[0085] in, P represents the cost function for user dissatisfaction. i,j,t The willingness of user i to respond to the j-th energy source during time period t is one of the important manifestations of user response characteristics and also one of the important factors causing uncertainty in user response and participation. Specifically, it can be expressed as the size of the user's response under a unit incentive price.

[0086] Secondly, considering the response fatigue phenomenon caused by continuous user participation, a user response willingness model is established:

[0087] Since continuous user participation in responses can lead to response fatigue, i.e., a decrease in response willingness, resulting in response bias, user response willingness is related to whether the user participated in the response in the previous period, the type of response, and the size of the response. A user response willingness model is established as shown in Equation (17):

[0088]

[0089] In particular, P i,j,0 γ represents the degree of user willingness in the initial period. i The user's willingness to respond coefficient is a known parameter.

[0090] Finally, by combining the user dissatisfaction cost model and the user response willingness model, a comprehensive user demand response benefit model is established:

[0091] In comprehensive demand response, the actual total response target should equal the sum of the actual responses from all users:

[0092]

[0093] In summary, the benefit model of user participation in integrated demand response is shown in equation (19):

[0094]

[0095] st(16), (18)

[0096] Step 3: Integrate the integrated energy service provider cost model and the user integrated demand response benefit model. Based on the interaction mechanism between the two, establish an integrated demand response optimization problem, and solve for the unique equilibrium solution with the goal of reducing the integrated energy service provider cost and improving user benefits.

[0097] First, the outputs of the integrated energy service provider model building module and the user model building module are integrated to establish an integrated demand response optimization problem, which is a two-level optimization problem.

[0098] The comprehensive demand response optimization problem is shown in equation (20):

[0099]

[0100] As can be seen from the above model, this is a two-level optimization problem and a Stackelberg game model. The upper-level integrated energy service provider is the leader, which aims to minimize costs by issuing incentive prices to users. The lower-level users are the followers, who aim to maximize benefits by determining their own response based on the incentive prices they receive.

[0101] From equations (15) and (19), it can be seen that the strategy sets of both integrated energy service providers and users are non-empty sets. Regarding equation (19) with respect to ξ... i,j,t Find the partial derivative:

[0102]

[0103] Continue with ξ i,j, Taking the second-order partial derivative of t, we can obtain:

[0104]

[0105] Because of P i,j,t Since it is a positive value, when x i,t When =1, the value of equation (22) is always less than 0, that is, the objective function equation (19) is a concave function and has a unique maximum value. Therefore, given the strategy of the upper-level leader, the follower users have one and only one optimal solution.

[0106] Because x i,t ∈{0,1}, when (21) equals zero, we get:

[0107]

[0108] The variable for the upper-level leader is π. i,j,t Expanding the constraints of equation (14) and solving for r1, r2, and r3 respectively, we can obtain:

[0109]

[0110]

[0111]

[0112] in, These are all auxiliary variables established to simplify the expression.

[0113] From equations (24)-(26), it can be seen that when the strategy of the lower-level user is given, r1, r2, and r3 are related to... The function. Therefore, It is also a variable for upper-level leaders.

[0114] The objective function of equation (15) is applied to π respectively. i,j,t and Taking the partial derivative, we get:

[0115]

[0116]

[0117] By substituting equations (23) and (9), we obtain that the Hessian matrix of the objective function of the integrated energy service provider is positive, and is a positive definite matrix. Therefore, once the lower-level follower strategy is formulated, the upper-level leader's strategy has one and only one optimal solution.

[0118] In summary, the comprehensive demand response optimization problem has one and only one optimal equilibrium solution.

[0119] Secondly, the bi-level optimization problem is transformed into an equivalent single-objective optimization problem.

[0120] From equation (23), we can see that:

[0121]

[0122] Substituting equation (29) into equation (3), the original bi-level optimization problem becomes equivalent to a single-objective optimization problem. The single-objective optimization problem is shown in equation (30):

[0123]

[0124] Finally, the single-objective optimization problem is solved using the IPOPT solver to obtain the optimal equilibrium solution of the comprehensive demand response optimization problem.

[0125] Solving equation (30) using the IPOPT solver, the decision variable is obtained as ξ. i,j,t , r1, r2, r3. This yields the optimal equilibrium solution to the comprehensive demand response optimization problem.

[0126] In summary, this invention discloses a comprehensive demand response method and system that considers user response risk. By considering user response risk, it reduces the costs for integrated energy service providers and improves user benefits. Addressing the uncertainty of user participation and response, a mechanism for integrated energy service providers to select user participation in the response is proposed, and user response fatigue is considered, establishing a user response willingness model. Through simulation analysis and comparison with methods and systems that do not consider user response risk or the mechanism for integrated energy service providers to select user participation in the response, simulation results verify that the method and system of this invention perform better and can more effectively achieve a win-win situation for both integrated energy service providers and users. Therefore, the comprehensive demand response method and system that considers user response risk proposed in this invention effectively solves the user response risk problem and achieves beneficial results.

[0127] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A comprehensive demand response method that considers user response risk, characterized in that, Includes the following steps: (1) Integrate the equipment characteristics and information of integrated energy service providers, consider user response risks, establish a user selection mechanism for integrated energy service providers, and establish a cost model for integrated energy service providers; (2) Collect user energy consumption information and response characteristics, consider the response fatigue phenomenon of continuous user participation in response, and establish a comprehensive user demand response benefit model; (10-1) Establish a user dissatisfaction cost model based on user response volume, user response willingness and user expected minimum incentive price. (10-2) Considering the response fatigue phenomenon of users continuously participating in the response, establish a user response willingness model; (10-3) Combining (10-1) and (10-2), establish a comprehensive user demand response benefit model; (3) Integrate the integrated energy service provider cost model and the user integrated demand response benefit model, establish an integrated demand response optimization problem based on the interaction mechanism between the two, and solve for the unique equilibrium solution with the goal of reducing the integrated energy service provider cost and improving the user benefit; step (1) further includes the following: (3-1) Based on the theory of random distribution, a risk cost model for integrated energy service providers is established for users participating in integrated demand response; the risk cost model for integrated energy service providers for user i participating in integrated demand response is as follows: Where, r = {r 1,t ,r 2,t ,...,r n,t This refers to risk cost. The mean risk cost of user response, representing the central risk cost that the user's participation in integrated demand response brings to the integrated energy service provider; σ r The standard deviation of risk cost; (3-2) Based on user response risk, user selection decision variables are introduced to establish a user selection mechanism for integrated energy service providers that considers risk costs; the integrated energy service provider user selection mechanism is characterized as follows: x i,t For integrated energy service providers to make user selection decisions, r c Indicates the risk cost threshold; (3-3) Establish a total incentive cost model for integrated energy service providers that considers user response risk; the total incentive cost model for integrated energy service providers is characterized as follows: In the formula: j∈{1,2,3} represents the type of energy, corresponding to electricity, heat, and gas respectively; during time period t, ξ i,j,t Let π be the actual response amount of user i to energy j during time period t. i,j,t That is, time period t is the incentive unit price issued by the integrated energy service provider to user i when responding to energy j; The response type for user participation in IDR; (3-4) Establish an operating cost model for energy storage equipment for integrated energy service providers; the model representing the operating cost of energy storage equipment is as follows: During time period t, This refers to the operating cost of the energy storage equipment; for the j-th energy source, α j This represents the cost coefficient for energy storage equipment. The charging and discharging power of energy storage devices; (3-5) Establish an operating cost model for energy coupling equipment consisting of an electric boiler, a gas turbine, and a combined heat and power unit; the operating cost model for energy coupling equipment is characterized as follows: In the formula, For the operating costs of energy coupling equipment, The baseline load for time period t when the user does not participate in the j-th energy response; For time period t, the integrated energy service provider needs to achieve the j-th type of energy response target; μ EB μ GT μ CHP These represent the unit operating price of the energy coupling equipment; r1, r2, and r3 are the scheduling factors of the energy coupling equipment, which determine the input-output relationship of the energy coupling equipment. (3-6) Combining (3-1) and (3-5), establish a cost model for integrated energy service providers that takes into account user selection mechanisms.

2. The comprehensive demand response method considering user response risk according to claim 1, characterized in that, Step (3) further includes the following: (14-1) Based on steps (1) and (2), integrate the outputs of the integrated energy service provider model building module and the user model building module to establish an integrated demand response optimization problem, which is a two-level optimization problem; (14-2) Transforms the bi-level optimization problem in (14-1) into a single-objective optimization problem; (14-3) Use IPOPT to solve the single-objective optimization problem obtained in (14-2) to obtain the optimal equilibrium solution of the comprehensive demand response optimization problem.

3. A comprehensive demand response system that considers user response risk, the system applying the method of claim 1, comprising: The integrated energy service provider model building module, user model building module, and integrated demand response optimization problem building and solving module are characterized by: Integrated Energy Service Provider Model Building Module: This module is responsible for building the integrated energy service provider cost model. By considering user response risk, it establishes a user selection mechanism for integrated energy service providers and constructs a total incentive cost model for integrated energy service providers. Based on the included energy storage equipment and energy coupling equipment, it establishes separate operating cost models for energy storage equipment and energy coupling equipment. By integrating the total incentive cost model, energy storage equipment operating cost model, and energy coupling equipment operating cost model, it constructs the integrated energy service provider cost model and passes it to the integrated demand response optimization problem establishment and solution module. User Model Building Module: This module is responsible for building a user benefit model obtained by users participating in the comprehensive demand response. First, it establishes a user dissatisfaction cost model, and then establishes a user response willingness model by considering the response fatigue phenomenon of users participating in the comprehensive demand response at multiple time periods. Combining the user dissatisfaction cost model and the user response willingness model, it establishes a user benefit model and passes the user benefit model to the comprehensive demand response optimization problem establishment and solution module. Integrated Demand Response Optimization Problem Establishment and Solution Module: This module is responsible for establishing an integrated demand response optimization problem based on the interaction between users and integrated energy service providers, and solving the integrated demand response optimization problem. The result is the optimal equilibrium solution that achieves a win-win situation for integrated energy service providers and users.

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