A method and system for assessing flexibility of an integrated energy system

Abstract

The application discloses a kind of methods and systems for evaluating the flexibility of integrated energy system, belong to integrated energy system technical field.The method of the present application comprises: for integrated energy system, the upper limit optimization target and lower limit optimization target of external power demand of the integrated energy system are determined, and the fluctuation range of the integrated energy system multivariate load is determined based on box type uncertainty set;Based on the upper limit optimization target and lower limit optimization target of external power demand of the integrated energy system, and the fluctuation range of the integrated energy system multivariate load, the evaluation model for evaluating the flexibility of the integrated energy system is constructed;The evaluation model is solved, and the flexibility of the integrated energy system is determined based on the optimal solution of the evaluation model obtained.The flexibility of integrated energy system can be effectively evaluated by the evaluation model established, and the evaluation method is efficient and accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy systems, and more particularly, to a method and system for evaluating the flexibility of an integrated energy system. Background Art

[0002] At present, in the research on the overall flexibility evaluation of integrated energy systems, some scholars regard multiple loads as deterministic boundary conditions or only consider the uncertainty of electric loads. However, in actual operation, they are affected by many random factors and the prediction accuracy is difficult to guarantee. Taking multiple loads as deterministic boundary conditions often makes the resulting solutions appear to be more "risky". Therefore, it is necessary to take into account the influence of multiple load uncertainties in the model.

[0003] To address the issue of load uncertainty, some researchers have used stochastic programming and scenario analysis to model uncertain variables. Stochastic programming uses random variables to describe uncertain information, while scenario analysis, based on probability theory, describes the uncertainty of the research object using scenarios. The key to both approaches is to simulate the characteristics of uncertain variables using limited scenarios. Both stochastic programming and scenario analysis require deterministic probability curves to generate scenarios, but accurate probability distributions are generally difficult to obtain in real-world projects. Summary of the Invention

[0004] To address the above issues, the present invention proposes a method for evaluating the flexibility of an integrated energy system, comprising:

[0005] For an integrated energy system, determining an upper bound optimization target and a lower bound optimization target of the integrated energy system for external power demand, and determining a fluctuation range of multiple loads of the integrated energy system based on a boxed uncertainty set;

[0006] Based on the upper and lower bound optimization targets of the integrated energy system's external power demand and the fluctuation range of the multi-load of the integrated energy system, an evaluation model for evaluating the flexibility of the integrated energy system is constructed;

[0007] The evaluation model is solved, and the flexibility of the integrated energy system is determined based on the obtained optimal solution of the evaluation model.

[0008] Optionally, estimate the model, including: optimization variables, objective function, and constraints.

[0009] Optional, optimization variables, as follows:

[0010]

[0011] Among them, x and y are optimization variables, are the start and stop status 0-1 variables of CCHP power generation, waste heat cooling, and waste heat heating at time t, They are the start and stop status 0-1 variables of the electric cooling and heating equipment at time t, They are the start and stop status 0-1 variables of the gas cooling and heating equipment at time t, are the 0-1 control variables for the charging and discharging operations of the energy storage device at time t, is the purchased municipal power power at time t, are the electricity, cooling and heating power output by CCHP at time t, are the cooling and heating power output by the electric cooling and heating equipment at time t, are the cooling and heating power output of the gas cooling and heating equipment at time t, are the charging and discharging power of the energy storage device at time t, They are the electricity, cooling and heating loads corresponding to the worst scenario at time t.

[0012] Optionally, the objective function includes: an upper bound function of the power demand interval of the integrated energy system and a lower bound function of the power demand interval of the integrated energy system;

[0013] The upper bound function of the power demand interval of the integrated energy system is as follows:

[0014]

[0015] The lower bound function of the power demand interval of the integrated energy system is as follows:

[0016]

[0017] Among them, x and y are optimization variables, N T is the total number of moments in the evaluation period, u is a multivariate load scenario, U is the uncertainty set of the multivariate load scenario, c T is the coefficient matrix of the objective function, and Ω(u,x) is the feasible region of the given optimization variable y.

[0018] Optional constraints, including: flexible operation constraints of controllable energy supply equipment, flexible operation constraints of energy storage equipment, supply and demand balance constraints of integrated energy system, and conservative constraints;

[0019] The flexible operation constraints of the controllable energy supply equipment are as follows:

[0020]

[0021] in, are the output and input power of device i at time t, η iis the energy conversion efficiency of device i, θ i is the start and stop control variable of device i at time t, are the upper and lower limits of the output power allowed for device i, respectively, i is the ramp rate allowed for device i;

[0022] The flexible operation constraints of the energy storage equipment are as follows:

[0023]

[0024] in, The upper limit of the charging and discharging power of the energy storage device. is the rated capacity of the energy storage device, are the 0-1 control variables for the charging and discharging operations of the energy storage device at time t, are the charging and discharging power of the energy storage device at time t respectively;

[0025] The supply and demand balance constraints of the integrated energy system are as follows:

[0026]

[0027] in, are the electric power consumed by the electric heating and electric cooling equipment at time t, They are CCHP waste heat production, waste heat consumed by heating, and waste heat consumed by cooling at time t, is the purchased municipal power power at time t, are the electricity, cooling and heating power output by CCHP at time t, are the cooling and heating power output by the electric cooling and heating equipment at time t, are the cooling and heating power output of the gas cooling and heating equipment at time t, are the electricity, cooling and heating loads corresponding to the worst scenario at time t;

[0028] The conservative constraints are as follows:

[0029]

[0030] Among them, N T is the total number of moments in the evaluation period, u is a multivariate load scenario, U is the uncertainty set of the multivariate load scenario, c T is the coefficient matrix of the objective function, are the uncertain variables of electric, cooling and heating load power after considering uncertainty at time t, They are the maximum fluctuation deviations allowed for the power, cooling, and heating loads at time t, which are positive numbers. are the predicted values ​​of power, cooling and heating loads at time t, are the variables that control the worst electricity, cooling, and heating load scenarios to fall within the lower bound of the uncertainty set; are the variables that control the worst electricity, cooling and heating load scenarios to fall within the lower bound of the uncertainty set, Γ is the uncertainty adjustment parameter, Corresponding to and are the lower and upper limits of conservative parameters for different loads, respectively; e, c, and h are electricity, cooling, and heating, respectively.

[0031] Optionally, solving the evaluation model includes:

[0032] Decomposing the evaluation model to determine the main problem and subproblems for solving the upper and lower bounds of the external power demand of the integrated energy system;

[0033] The sub-problems are dualized using KKT conditions, and the dualized sub-problems are processed using the Big-M method.

[0034] The main problem and the sub-problems processed by the Big-M method are solved by the CCG algorithm to obtain the optimal solution of the evaluation model.

[0035] Alternatively, the main problem can be expressed as follows:

[0036]

[0037] Among them, x and y are optimization variables, u is a multivariate load scenario, and c T is the coefficient matrix of the objective function, x and y are the optimization variables, c T is the coefficient matrix of the objective function, α is the main problem objective function, D up , K up 、F up , G up 、 are the coefficient matrices of the corresponding constraints when solving the upper bound of IES external power demand; d up 、k up 、h up 、 They are the constant matrices in the constraints corresponding to solving the upper bound of IES external power demand. Replacing the superscript from up to down corresponds to solving the lower bound of IES external power demand.

[0038] Optional, sub-problem expression is as follows:

[0039]

[0040] Among them, x and y are optimization variables, u is a multivariate load scenario, U is the uncertainty set of the multivariate load scenario, c T is the coefficient matrix of the objective function, Ω(u,x) is the feasible region of the given optimization variable y, D, K, F, G, I u are the coefficient matrices of the constraints, d, k, h, and u are the constant matrices in the constraints, and γ, λ, υ, and π are the dual variables.

[0041] The optional expression of the sub-problem after the even processing is as follows:

[0042]

[0043] Among them, x and y are optimization variables, U is the uncertainty set of the multivariate load scenario, γ, λ, h, v, π are dual variables, and T is the transposed matrix of the coefficient matrix.

[0044] In another aspect, the present invention further provides a system for evaluating the flexibility of an integrated energy system, comprising:

[0045] An initialization unit is used to determine, for an integrated energy system, an upper bound optimization target and a lower bound optimization target of the integrated energy system for external power demand, and determine a fluctuation range of a multi-element load of the integrated energy system based on a boxed uncertainty set;

[0046] a modeling unit, configured to construct an evaluation model for evaluating the flexibility of the integrated energy system based on an upper bound optimization target and a lower bound optimization target of the integrated energy system for external power demand and a fluctuation range of the multi-element load of the integrated energy system;

[0047] A solving unit is used to solve the evaluation model and determine the flexibility of the integrated energy system based on the obtained optimal solution of the evaluation model.

[0048] Optionally, evaluate the model, including: optimization variables, objective function, and constraints.

[0049] Optional, optimization variables, as follows:

[0050]

[0051] Among them, x and y are optimization variables, are the start and stop status 0-1 variables of CCHP power generation, waste heat cooling, and waste heat heating at time t, They are the start and stop status 0-1 variables of the electric cooling and heating equipment at time t, They are the start and stop status 0-1 variables of the gas cooling and heating equipment at time t, are the 0-1 control variables for the charging and discharging operations of the energy storage device at time t, is the purchased municipal power power at time t, are the electricity, cooling and heating power output by CCHP at time t, are the cooling and heating power output by the electric cooling and heating equipment at time t, are the cooling and heating power output of the gas cooling and heating equipment at time t, are the charging and discharging power of the energy storage device at time t, They are the electricity, cooling and heating loads corresponding to the worst scenario at time t.

[0052] Optionally, the objective function includes: an upper bound function of the power demand interval of the integrated energy system and a lower bound function of the power demand interval of the integrated energy system;

[0053] The upper bound function of the power demand interval of the integrated energy system is as follows:

[0054]

[0055] The lower bound function of the power demand interval of the integrated energy system is as follows:

[0056]

[0057] Among them, x and y are optimization variables, N T is the total number of moments in the evaluation period, u is a multivariate load scenario, U is the uncertainty set of the multivariate load scenario, c T is the coefficient matrix of the objective function, and Ω(u,x) is the feasible region of the given optimization variable y.

[0058] Optional constraints, including: flexible operation constraints of controllable energy supply equipment, flexible operation constraints of energy storage equipment, supply and demand balance constraints of integrated energy system, and conservative constraints;

[0059] The flexible operation constraints of the controllable energy supply equipment are as follows:

[0060]

[0061] in, are the output and input power of device i at time t, η i is the energy conversion efficiency of device i, θ i is the start and stop control variable of device i at time t, are the upper and lower limits of the output power allowed for device i, respectively, iis the ramp rate allowed for device i;

[0062] The flexible operation constraints of the energy storage equipment are as follows:

[0063]

[0064] in, The upper limit of the charging and discharging power of the energy storage device. is the rated capacity of the energy storage device, are the 0-1 control variables for the charging and discharging operations of the energy storage device at time t, are the charging and discharging power of the energy storage device at time t respectively;

[0065] The supply and demand balance constraints of the integrated energy system are as follows:

[0066]

[0067] in, are the electric power consumed by the electric heating and electric cooling equipment at time t, They are CCHP waste heat production, waste heat consumed by heating, and waste heat consumed by cooling at time t, is the purchased municipal power power at time t, are the electricity, cooling and heating power output by CCHP at time t, are the cooling and heating power output by the electric cooling and heating equipment at time t, are the cooling and heating power output of the gas cooling and heating equipment at time t, are the electricity, cooling and heating loads corresponding to the worst scenario at time t;

[0068] The conservative constraints are as follows:

[0069]

[0070] Among them, N T is the total number of moments in the evaluation period, u is a multivariate load scenario, U is the uncertainty set of the multivariate load scenario, c T is the coefficient matrix of the objective function, are the uncertain variables of electric, cooling and heating load power after considering uncertainty at time t, They are the maximum fluctuation deviations allowed for the power, cooling, and heating loads at time t, which are positive numbers. are the predicted values ​​of power, cooling and heating loads at time t, are the variables that control the worst electricity, cooling, and heating load scenarios to fall within the lower bound of the uncertainty set; are the variables that control the worst electricity, cooling and heating load scenarios to fall within the lower bound of the uncertainty set, Γ is the uncertainty adjustment parameter, Corresponding to and are the lower and upper limits of conservative parameters for different loads, respectively; e, c, and h are electricity, cooling, and heating, respectively.

[0071] Optionally, solving the evaluation model includes:

[0072] Decomposing the evaluation model to determine the main problem and subproblems for solving the upper and lower bounds of the external power demand of the integrated energy system;

[0073] The sub-problems are dualized using KKT conditions, and the dualized sub-problems are processed using the Big-M method.

[0074] The main problem and the sub-problems processed by the Big-M method are solved by the CCG algorithm to obtain the optimal solution of the evaluation model.

[0075] Alternatively, the main problem can be expressed as follows:

[0076]

[0077] Among them, x and y are optimization variables, u is a multivariate load scenario, and c T is the coefficient matrix of the objective function, x and y are the optimization variables, c T is the coefficient matrix of the objective function, α is the main problem objective function, D up , K up 、F up , G up 、 are the coefficient matrices of the corresponding constraints when solving the upper bound of IES external power demand; d up 、k up 、h up 、 They are the constant matrices in the constraints corresponding to solving the upper bound of IES external power demand. Replacing the superscript from up to down corresponds to solving the lower bound of IES external power demand.

[0078] Optional, sub-problem expression is as follows:

[0079]

[0080] Among them, x and y are optimization variables, u is a multivariate load scenario, U is the uncertainty set of the multivariate load scenario, ct is the coefficient matrix of the objective function, Ω(u,x) is the feasible region of the given optimization variable y, D, K, F, G, I u are the coefficient matrices of the constraints, d, k, h, and u are the constant matrices in the constraints, and γ, λ, υ, and π are the dual variables.

[0081] The optional expression of the sub-problem after the even processing is as follows:

[0082]

[0083] Among them, x and y are optimization variables, U is the uncertainty set of the multivariate load scenario, γ, λ, h, v, π are dual variables, and T is the transposed matrix of the coefficient matrix.

[0084] In yet another aspect, the present invention further provides a computing device comprising: one or more processors;

[0085] a processor for executing one or more programs;

[0086] When the one or more programs are executed by the one or more processors, the above-described method is implemented.

[0087] In another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, the method described above is implemented.

[0088] Compared with the prior art, the present invention has the following beneficial effects:

[0089] The present invention provides a method for evaluating the flexibility of an integrated energy system, the method comprising: determining, for the integrated energy system, an upper bound optimization target and a lower bound optimization target of the integrated energy system for external power demand, and determining the fluctuation range of the multi-element load of the integrated energy system based on a boxed uncertainty set; constructing an evaluation model for evaluating the flexibility of the integrated energy system based on the upper bound optimization target and the lower bound optimization target of the integrated energy system for external power demand, and the fluctuation range of the multi-element load of the integrated energy system; solving the evaluation model, and determining the flexibility of the integrated energy system based on the obtained optimal solution of the evaluation model. The present invention can effectively evaluate the flexibility of the integrated energy system through the established evaluation model, and the evaluation method is highly efficient and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 is a flow chart of the method of the present invention;

[0091] Figure 2 A schematic diagram of the flexibility range of the integrated energy system from the perspective of the superior power system according to an embodiment of the method of the present invention;

[0092] Figure 3 This is a schematic diagram of the worst load scenario without taking into account the conservativeness parameter according to an embodiment of the method of the present invention;

[0093] Figure 4 A schematic diagram of the worst load scenario taking into account the conservativeness parameter of an embodiment of the method of the present invention;

[0094] Figure 5 A flowchart for solving the upper bound of the flexibility interval of an integrated energy system according to an embodiment of the method of the present invention;

[0095] Figure 6 It is a structural diagram of the system of the present invention. DETAILED DESCRIPTION

[0096] Exemplary embodiments of the present invention will now be described with reference to the accompanying drawings. However, the present invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to provide a thorough and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments shown in the accompanying drawings is not intended to limit the present invention. In the accompanying drawings, identical elements are denoted by the same reference numerals.

[0097] Unless otherwise specified, the terms used herein (including technical terms) have the meanings commonly understood by those skilled in the art. In addition, it is understood that terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0098] Example 1:

[0099] This invention proposes a method for evaluating the flexibility of an integrated energy system, such as Figure 1 Shown, including:

[0100] Step 1: For an integrated energy system, determine the upper and lower bound optimization targets of the integrated energy system for external power demand, and determine the fluctuation range of the multi-element load of the integrated energy system based on a boxed uncertainty set;

[0101] Step 2: Based on the upper and lower optimization targets of the integrated energy system's external power demand and the fluctuation range of the multi-load of the integrated energy system, an evaluation model for evaluating the flexibility of the integrated energy system is constructed;

[0102] Step 3: Solve the evaluation model and determine the flexibility of the integrated energy system based on the obtained optimal solution of the evaluation model.

[0103] The evaluation model includes: optimization variables, objective functions and constraints.

[0104] Among them, the optimization variables are as follows:

[0105]

[0106] Among them, x and y are optimization variables, are the start and stop status 0-1 variables of CCHP power generation, waste heat cooling, and waste heat heating at time t, They are the start and stop status 0-1 variables of the electric cooling and heating equipment at time t, They are the start and stop status 0-1 variables of the gas cooling and heating equipment at time t, are the 0-1 control variables for the charging and discharging operations of the energy storage device at time t, is the purchased municipal power power at time t, are the electricity, cooling and heating power output by CCHP at time t, are the cooling and heating power output by the electric cooling and heating equipment at time t, are the cooling and heating power output of the gas cooling and heating equipment at time t, are the charging and discharging power of the energy storage device at time t, They are the electricity, cooling and heating loads corresponding to the worst scenario at time t.

[0107] The objective function includes: an upper bound function of the power demand interval of the integrated energy system and a lower bound function of the power demand interval of the integrated energy system;

[0108] The upper bound function of the power demand interval of the integrated energy system is as follows:

[0109]

[0110] The lower bound function of the power demand interval of the integrated energy system is as follows:

[0111]

[0112] Among them, x and y are optimization variables, N T is the total number of moments in the evaluation period, u is a multivariate load scenario, U is the uncertainty set of the multivariate load scenario, c T is the coefficient matrix of the objective function, and Ω(u,x) is the feasible region of the given optimization variable y.

[0113] Among them, the constraints include: flexible operation constraints of controllable energy supply equipment, flexible operation constraints of energy storage equipment, supply and demand balance constraints of integrated energy system and conservative constraints;

[0114] The flexible operation constraints of the controllable energy supply equipment are as follows:

[0115]

[0116]

[0117] in, are the output and input power of device i at time t, η i is the energy conversion efficiency of device i, θ i is the start and stop control variable of device i at time t, are the upper and lower limits of the output power allowed for device i, respectively, i is the ramp rate allowed for device i;

[0118] The flexible operation constraints of the energy storage equipment are as follows:

[0119]

[0120] in, The upper limit of the charging and discharging power of the energy storage device. is the rated capacity of the energy storage device, are the 0-1 control variables for the charging and discharging operations of the energy storage device at time t, are the charging and discharging power of the energy storage device at time t respectively;

[0121] The supply and demand balance constraints of the integrated energy system are as follows:

[0122]

[0123] in, are the electric power consumed by the electric heating and electric cooling equipment at time t, They are CCHP waste heat production, waste heat consumed by heating, and waste heat consumed by cooling at time t, is the purchased municipal power power at time t, are the electricity, cooling and heating power output by CCHP at time t, are the cooling and heating power output by the electric cooling and heating equipment at time t, are the cooling and heating power output of the gas cooling and heating equipment at time t, are the electricity, cooling and heating loads corresponding to the worst scenario at time t;

[0124] The conservative constraints are as follows:

[0125]

[0126] Among them, N T is the total number of moments in the evaluation period, u is a multivariate load scenario, U is the uncertainty set of the multivariate load scenario, c T is the coefficient matrix of the objective function, are the uncertain variables of electric, cooling and heating load power after considering uncertainty at time t, They are the maximum fluctuation deviations allowed for the power, cooling, and heating loads at time t, which are positive numbers. are the predicted values ​​of power, cooling and heating loads at time t, are the variables that control the worst electricity, cooling, and heating load scenarios to fall within the lower bound of the uncertainty set; are the variables that control the worst electricity, cooling and heating load scenarios to fall within the lower bound of the uncertainty set, Γ is the uncertainty adjustment parameter, Corresponding to and are the lower and upper limits of conservative parameters for different loads, respectively; e, c, and h are electricity, cooling, and heating, respectively.

[0127] Wherein, solving the evaluation model includes:

[0128] Decomposing the evaluation model to determine the main problem and subproblems for solving the upper and lower bounds of the external power demand of the integrated energy system;

[0129] The sub-problems are dualized using KKT conditions, and the dualized sub-problems are processed using the Big-M method.

[0130] The main problem and the sub-problems processed by the Big-M method are solved by the CCG algorithm to obtain the optimal solution of the evaluation model.

[0131] The main problem is expressed as follows:

[0132]

[0133] Among them, x and y are optimization variables, u is a multivariate load scenario, and c T is the coefficient matrix of the objective function, x and y are the optimization variables, c T is the coefficient matrix of the objective function, α is the main problem objective function, D up , K up 、F up , G up 、 are the coefficient matrices of the corresponding constraints when solving the upper bound of IES external power demand; d up 、k up、h up 、 They are the constant matrices in the constraints corresponding to solving the upper bound of IES external power demand. Replacing the superscript from up to down corresponds to solving the lower bound of IES external power demand.

[0134] The expression of the sub-problem is as follows:

[0135]

[0136] Among them, x and y are optimization variables, u is a multivariate load scenario, U is the uncertainty set of the multivariate load scenario, c T is the coefficient matrix of the objective function, Ω(u,x) is the feasible region of the given optimization variable y, D, K, F, G, I u are the coefficient matrices of the constraints, d, k, h, and u are the constant matrices in the constraints, and γ, λ, υ, and π are the dual variables.

[0137] Among them, the expression of the sub-problem after the even processing is as follows:

[0138]

[0139] Among them, x and y are optimization variables, U is the uncertainty set of the multivariate load scenario, γ, λ, h, v, π are dual variables, and T is the transposed matrix of the coefficient matrix.

[0140] The present invention will be further described below in conjunction with embodiments:

[0141] To address the "multi-load uncertainty" issue in integrated energy system flexibility assessment research, this paper uses a boxed uncertainty set to describe the uncertainty of multi-loads and establishes a two-stage robust optimization model with max-min-max and min-max-min structures. This model can obtain the maximum and minimum bounds of the integrated energy system's upper-level power demand under severe load scenarios. The model uses the Column-and-Constraint Generation (C&CG) algorithm to decompose the two-stage robust optimization model into a main problem and subproblems for cyclical solution. KKT conditions and the big-M method are used to simplify and linearize the double-layer structure and nonlinear terms in the subproblems.

[0142] The evaluation of the overall flexibility of the integrated energy system starts from the perspective of the superior power system and quantifies the external power demand range of the integrated energy system. The integrated energy system has a variety of different operating strategies 1, 2, 3, 4, etc. Different operating strategies correspond to different external energy demands. For any moment in the evaluation cycle, as long as there is an operating strategy that makes the external energy demand at that moment reach the maximum / minimum, then the energy demand is the upper / lower limit of the adjustable capacity range at that moment. Therefore, solving the adjustable capacity range in the operation cycle is to solve the envelope of the external energy demand under all its operating strategies, such as Figure 2 Therefore, the external power demand range of the integrated energy system is used as the overall flexibility evaluation indicator of the integrated energy system.

[0143] Among them, the optimization target of the upper bound of the external power demand of the integrated energy system at time t is shown in formula (1), and the optimization target of the lower bound of the external power demand is shown in formula (2).

[0144]

[0145] In the overall flexibility assessment model of the integrated energy system, if multiple loads are used as the deterministic boundary, the resulting solution often appears too "risky" and the multiple load forecast results are not accurate. Therefore, it is necessary to take into account the impact of multiple load uncertainty in the model. The fluctuation range of multiple loads is within the box uncertainty set U formed by formula (3):

[0146]

[0147] Where: are the uncertain variables of electric, cooling and heating load power after considering uncertainty at time t; The maximum fluctuation deviations allowed for the power, cooling, and heating loads at time t are positive numbers. are the predicted values ​​of power, cooling and heating loads at time t respectively.

[0148] Establish an overall flexibility assessment model for an integrated energy system that considers multi-load uncertainty, including:

[0149] 1) Optimize variables as follows:

[0150]

[0151] Where: are the start and stop status 0-1 variables of CCHP power generation, waste heat cooling, and waste heat heating at time t; They are the start and stop status 0-1 variables of the electric cooling and heating equipment at time t; They are 0-1 variables for the start and stop status of the gas cooling and heating equipment at time t; are the 0-1 control variables for the charging and discharging operations of the energy storage device at time t; is the purchased municipal power power at time t; are the electrical, cooling and heating power output by CCHP at time t respectively; are the cooling and heating power output by the electric cooling and heating equipment at time t respectively; are the cooling and heating power output of the gas cooling and heating equipment at time t respectively; are the charging and discharging power of the energy storage device at time t respectively; They are the electricity, cooling and heating loads corresponding to the worst scenario at time t.

[0152] 2) The objective function is as follows:

[0153] The upper bound of the power demand range of the integrated energy system is as follows:

[0154]

[0155] The lower bound of the power demand range of the integrated energy system is as follows:

[0156]

[0157] Where: N T is the total number of moments in the evaluation period; u is a multivariate load scenario; U is the uncertainty set of the multivariate load scenario; c T is the coefficient matrix of the objective function; Ω(u,x) represents the feasible domain of optimizing variable y given a set of u and x.

[0158] 3) Constraints are as follows:

[0159] Controllable energy supply equipment includes gas power generation, electric refrigeration, electric heating, gas refrigeration, gas heating and other equipment. The flexibility operation constraints of these equipment include formulas (8)-(10).

[0160]

[0161] Where: are the output and input power of device i at time t; η i is the energy conversion efficiency of device i; θ i (t) is the start and stop control variable of device i at time t; are the upper and lower limits of the output power allowed for device i; λ i is the ramp rate allowed for device i.

[0162] The flexibility operation constraints of energy storage equipment include formulas (11)-(12).

[0163]

[0164]

[0165] Where: The upper limit of the charging and discharging power of the energy storage device; It is the rated capacity of the energy storage device.

[0166] The supply and demand balance constraints of the integrated energy system include the following formulas:

[0167]

[0168] Where: are the electric power consumed by the electric heating and electric cooling equipment at time t respectively; They are CCHP waste heat production, waste heat consumed for heating, and waste heat consumed for cooling at time t respectively.

[0169] The conservativeness constraints are as follows:

[0170] The inner layer of the subproblem is a linearized model. Combined with the strong duality theory, u* will be obtained at the boundary of U. That is, when solving the upper bound of the external power demand interval of the integrated energy system, the multivariate load values ​​are all the maximum values ​​of the uncertainty set, which also meets the definition of the "worst" scenario ( Figure 3 ). However, the above solution is too conservative, so we introduce "uncertainty control variable B" and "uncertainty adjustment parameter Γ". B is a 0-1 control variable that controls whether the result falls into the "worst scenario". Γ is in the range of 0 to N T The integer in Γ represents the total number of time periods in which the load power reaches the minimum or maximum value of the fluctuation range within the scheduling period. It is used to adjust the conservatism of the optimal solution. The larger the value of Γ, the more conservative the solution. Conversely, the more risky the solution.

[0171]

[0172] After adding the conservatism constraint, the “worst case” results are as follows: Figure 4 As shown. Figure 3 The degree of conservatism in the most conservative severe scenario is significantly reduced.

[0173] (2) Solution method for overall flexibility evaluation of integrated energy systems considering multi-load uncertainty

[0174] When the uncertainty of multiple loads is not considered, the upper and lower bounds of the overall flexibility evaluation model of the integrated energy system can be expressed in compact form as follows:

[0175]

[0176] If the uncertainty of multi-element loads is considered, taking Equation (5) as an example: the outer maximization problem is the first-stage problem, where the optimization variables are the control variables x of different devices; the inner min-max problem is the second-stage problem, where the optimization variables are the robust scenario u and the output y of different devices. The maximization problem is equivalent to the objective function of Equation (18), which represents the maximum demand for external power. Then, for each given set of u, Equation (5) can be transformed into Equation (18). The purpose of the min structure in the second-stage optimization problem of the two-stage robust model is to find the worst multi-element load scenario that minimizes the external power demand of the integrated energy system.

[0177] The column and constraint generation algorithm (C&CG) can be used to solve the above two-stage robust optimization model. Decomposing (5) and (6), we can obtain the main problem of solving the upper and lower bounds of the external power demand of the integrated energy system:

[0178]

[0179] The expression of the decomposed subproblem is shown in formula (20), which is dualized using the KKT condition. The auxiliary variable setting is shown in formula (21), and the dual expression is shown in formula (22).

[0180]

[0181] There is a bilinear u in formula (22) T π, and the Big-M method is used to process it. After processing, the main problem (19) and subproblem (22) of the model are mixed integer linear programming problems, which can be solved by CCG algorithm. The process is as follows Figure 5 As shown below:

[0182] 1) Given a set of uncertain variables u as the initial worst scenario, set the upper bound LB = -∞ and the upper bound UB = +∞;

[0183] 2) Solve the main problem according to u* and get the optimal (x*,y*,obj 主 *), keep x*, and update LB=obj 主 *;

[0184] 3) Bring the main problem x* into the subproblem to obtain the worst scenario u* and optimal solution obj corresponding to x* 子 *, and update the upper bound UB = obj 子 *;

[0185] 4) Given the convergence threshold of the algorithm as ε, if UB - LB ≤ ε, stop the iteration and return the optimal solutions x* and y* and the worst scenario u*. Otherwise, return to 2 and continue iterating until convergence.

[0186] The following verification is carried out using a single-time, pure power system (electric load, generator, and power storage equipment) as an example:

[0187] The solution objective is to minimize the power demand from the upstream (min-max-min) in the "worst-case scenario." The uncertainty set of the load at a single moment is [700, 1000], the generator output range is [100, 500], and the upper limit of the charge and discharge power of the energy storage device is 100. The optimal solution is for the generator to operate at full power and the energy storage device to discharge at full power. The worst-case scenario is a load of 1000 kW.

[0188] Step 1: Assume that the initial u takes the electric load as 700kW, the upper bound LB = -∞ and the upper bound UB = +∞;

[0189] Step 2: Solve the main problem. The optimal solution is that the generator runs at full power and the energy storage discharges at full power. At this time, x 1 =[1,0,1],y 1 =[500,0,100],LB 1 =obj 1 主 =700-500-100=100;

[0190] Step 3, put x 1 = [1,0,1], bring it into the subproblem and solve it, y* = [500,0,100], and find the worst scenario u 1 =1000,UB 1 =obj 1 子 =1000-600=400, UB 1 -LB 1 =400-100=300;

[0191] Step 4, iterate k = 2 to solve the main problem, and replace u in the third step 1 Substitute in, now x 2 =[1,0,1],y 2 =[500,0,100],LB=obj 2 主 =1000-500-100=400;

[0192] Step 5, iterate k=2 subproblems and solve them, substituting x 2 =[1,0,1] The rest is the same as step 3, UB 2 =obj 2 子 =1000-600=400, UB 2 -LB 2 =400-400=0, stop iteration and output the result.

[0193] For the overall flexibility of the integrated energy system during the entire evaluation period, the model solution results are: obtaining the worst load scenario and its corresponding overall flexibility of the integrated energy system.

[0194] The method of the present invention quantifies the overall flexibility of an integrated energy system by using its "feasible region" of external power demand. Compared to other methods, this patented method provides more intuitive assessment results, and its handling of multi-load uncertainty is more aligned with actual engineering needs. By replacing the probability distribution of multi-loads with an uncertainty set, the method uses optimization to determine the flexibility assessment results of the integrated energy system under the "worst load scenario." This method accurately assesses the overall flexibility of the integrated energy system and the impact of multi-load uncertainty on the assessment results.

[0195] Example 2:

[0196] The present invention also provides a system 200 for evaluating the flexibility of an integrated energy system. Figure 6 Shown, including:

[0197] The initialization unit 201 is configured to determine, for an integrated energy system, an upper bound optimization target and a lower bound optimization target of the integrated energy system for external power demand, and determine a fluctuation range of multiple loads of the integrated energy system based on a boxed uncertainty set;

[0198] A modeling unit 202 is configured to construct an evaluation model for evaluating the flexibility of the integrated energy system based on an upper optimization target and a lower optimization target of the integrated energy system for external power demand and a fluctuation range of multiple loads of the integrated energy system;

[0199] The solving unit 203 is configured to solve the evaluation model and determine the flexibility of the integrated energy system based on an obtained optimal solution of the evaluation model.

[0200] The evaluation model includes: optimization variables, objective functions and constraints.

[0201] Among them, the optimization variables are as follows:

[0202]

[0203] Among them, x and y are optimization variables, are the start and stop status 0-1 variables of CCHP power generation, waste heat cooling, and waste heat heating at time t, They are the start and stop status 0-1 variables of the electric cooling and heating equipment at time t, They are the start and stop status 0-1 variables of the gas cooling and heating equipment at time t, are the 0-1 control variables for the charging and discharging operations of the energy storage device at time t, is the purchased municipal power power at time t, are the electricity, cooling and heating power output by CCHP at time t, are the cooling and heating power output by the electric cooling and heating equipment at time t, are the cooling and heating power output of the gas cooling and heating equipment at time t, are the charging and discharging power of the energy storage device at time t, They are the electricity, cooling and heating loads corresponding to the worst scenario at time t.

[0204] The objective function includes: an upper bound function of the power demand interval of the integrated energy system and a lower bound function of the power demand interval of the integrated energy system;

[0205] The upper bound function of the power demand interval of the integrated energy system is as follows:

[0206]

[0207] The lower bound function of the power demand interval of the integrated energy system is as follows:

[0208]

[0209]

[0210] Among them, x and y are optimization variables, N T is the total number of moments in the evaluation period, u is a multivariate load scenario, U is the uncertainty set of the multivariate load scenario, c T is the coefficient matrix of the objective function, and Ω(u,x) is the feasible region of the given optimization variable y.

[0211] Among them, the constraints include: flexible operation constraints of controllable energy supply equipment, flexible operation constraints of energy storage equipment, supply and demand balance constraints of integrated energy system and conservative constraints;

[0212] The flexible operation constraints of the controllable energy supply equipment are as follows:

[0213]

[0214] in, are the output and input power of device i at time t, η i is the energy conversion efficiency of device i, θ i is the start and stop control variable of device i at time t, are the upper and lower limits of the output power allowed for device i, respectively, iis the ramp rate allowed for device i;

[0215] The flexible operation constraints of the energy storage equipment are as follows:

[0216]

[0217] in, The upper limit of the charging and discharging power of the energy storage device. is the rated capacity of the energy storage device, are the 0-1 control variables for the charging and discharging operations of the energy storage device at time t, are the charging and discharging power of the energy storage device at time t respectively;

[0218] The supply and demand balance constraints of the integrated energy system are as follows:

[0219]

[0220]

[0221] in, are the electric power consumed by the electric heating and electric cooling equipment at time t, They are CCHP waste heat production, waste heat consumed by heating, and waste heat consumed by cooling at time t, is the purchased municipal power power at time t, are the electricity, cooling and heating power output by CCHP at time t, are the cooling and heating power output by the electric cooling and heating equipment at time t, are the cooling and heating power output of the gas cooling and heating equipment at time t, are the electricity, cooling and heating loads corresponding to the worst scenario at time t;

[0222] The conservative constraints are as follows:

[0223]

[0224] Among them, N T is the total number of moments in the evaluation period, u is a multivariate load scenario, U is the uncertainty set of the multivariate load scenario, c T is the coefficient matrix of the objective function, are the uncertain variables of electric, cooling and heating load power after considering uncertainty at time t, They are the maximum fluctuation deviations allowed for the power, cooling, and heating loads at time t, which are positive numbers. are the predicted values ​​of power, cooling and heating loads at time t, are the variables that control the worst electricity, cooling, and heating load scenarios to fall within the lower bound of the uncertainty set; are the variables that control the worst electricity, cooling and heating load scenarios to fall within the lower bound of the uncertainty set, Γ is the uncertainty adjustment parameter, Corresponding to and are the lower and upper limits of conservative parameters for different loads, respectively; e, c, and h are electricity, cooling, and heating, respectively.

[0225] Wherein, solving the evaluation model includes:

[0226] Decomposing the evaluation model to determine the main problem and subproblems for solving the upper and lower bounds of the external power demand of the integrated energy system;

[0227] The sub-problems are dualized using KKT conditions, and the dualized sub-problems are processed using the Big-M method.

[0228] The main problem and the sub-problems processed by the Big-M method are solved by the CCG algorithm to obtain the optimal solution of the evaluation model.

[0229] The main problem is expressed as follows:

[0230]

[0231] Among them, x and y are optimization variables, u is a multivariate load scenario, and c T is the coefficient matrix of the objective function, x and y are the optimization variables, c T is the coefficient matrix of the objective function, α is the main problem objective function, D up , K up 、F up , G up 、 are the coefficient matrices of the corresponding constraints when solving the upper bound of IES external power demand; d up 、k up 、h up 、 They are the constant matrices in the constraints corresponding to solving the upper bound of IES external power demand. Replacing the superscript from up to down corresponds to solving the lower bound of IES external power demand.

[0232] The expression of the sub-problem is as follows:

[0233]

[0234] Among them, x and y are optimization variables, u is a multivariate load scenario, U is the uncertainty set of the multivariate load scenario, c T is the coefficient matrix of the objective function, Ω(u,x) is the feasible region of the given optimization variable y, D, K, F, G, I u are the coefficient matrices of the constraints, d, k, h, and u are the constant matrices in the constraints, and γ, λ, υ, and π are the dual variables.

[0235] Among them, the expression of the sub-problem after the even processing is as follows:

[0236]

[0237] Among them, x and y are optimization variables, U is the uncertainty set of the multivariate load scenario, γ, λ, h, v, π are dual variables, and T is the transposed matrix of the coefficient matrix.

[0238] The present invention can effectively evaluate the flexibility of the integrated energy system through the established evaluation model, and the evaluation method is highly efficient and accurate.

[0239] Example 3:

[0240] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of the method in the above embodiment.

[0241] Example 4:

[0242] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiment.

[0243] It will be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention may be implemented in various computer languages, for example, the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0244] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0245] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0246] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0247] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0248] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for evaluating the flexibility of an integrated energy system, characterized in that The method comprises: For an integrated energy system, determining an upper bound optimization target and a lower bound optimization target of the integrated energy system for external power demand, and determining a fluctuation range of multiple loads of the integrated energy system based on a boxed uncertainty set; Based on the upper and lower bound optimization targets of the integrated energy system's external power demand and the fluctuation range of the multi-load of the integrated energy system, an evaluation model for evaluating the flexibility of the integrated energy system is constructed; Solving the evaluation model and determining the flexibility of the integrated energy system based on an obtained optimal solution of the evaluation model; The evaluation model includes: optimization variables, objective functions and constraints; The objective function includes: an upper bound function of the power demand interval of the integrated energy system and a lower bound function of the power demand interval of the integrated energy system; The upper bound function of the power demand interval of the integrated energy system is as follows: The lower bound function of the power demand interval of the integrated energy system is as follows: Among them, x and y are optimization variables, N T is the total number of moments in the evaluation period, u is a multivariate load scenario, U is the uncertainty set of the multivariate load scenario, c T is the coefficient matrix of the objective function, Ω(u, x) is the feasible region of the given optimization variable y, is the purchased municipal power power at time t; The constraints include: flexible operation constraints of controllable energy supply equipment, flexible operation constraints of energy storage equipment, supply and demand balance constraints of the integrated energy system, and conservative constraints; Solving the evaluation model includes: Decomposing the evaluation model to determine the main problem and subproblems for solving the upper and lower bounds of the external power demand of the integrated energy system; The sub-problems are dualized using KKT conditions, and the dualized sub-problems are processed using the Big-M method. The main problem and the sub-problems processed by the Big-M method are solved by the CCG algorithm to obtain the optimal solution of the evaluation model.

2. The method according to claim 1, characterized in that The optimization variables are as follows: Among them, x and y are optimization variables, are the start and stop status 0-1 variables of CCHP power generation, waste heat cooling, and waste heat heating at time t, They are the start and stop status 0-1 variables of the electric cooling and heating equipment at time t, They are the start and stop status 0-1 variables of the gas cooling and heating equipment at time t, are the 0-1 control variables for the charging and discharging operations of the energy storage device at time t, is the purchased municipal power power at time t, are the electricity, cooling and heating power output by CCHP at time t, are the cooling and heating power output by the electric cooling and heating equipment at time t, are the cooling and heating power output of the gas cooling and heating equipment at time t, are the charging and discharging power of the energy storage device at time t, They are the electricity, cooling and heating loads corresponding to the worst scenario at time t.

3. The method according to claim 1, characterized in that The flexible operation constraints of the controllable energy supply equipment are as follows: in, are the output and input power of device i at time t, η i is the energy conversion efficiency of device i, θ i is the start and stop control variable of device i at time t, are the upper and lower limits of the output power allowed for device i, respectively, i is the ramp rate allowed for device i; The flexible operation constraints of the energy storage equipment are as follows: in, The upper limit of the charging and discharging power of the energy storage device. is the rated capacity of the energy storage device, are the 0-1 control variables for the charging and discharging operations of the energy storage device at time t, are the charging and discharging power of the energy storage device at time t respectively; The supply and demand balance constraints of the integrated energy system are as follows: in, are the electric power consumed by the electric heating and electric cooling equipment at time t, They are CCHP waste heat production, waste heat consumed by heating, and waste heat consumed by cooling at time t, is the purchased municipal power power at time t, are the electricity, cooling and heating power output by CCHP at time t, are the cooling and heating power output by the electric cooling and heating equipment at time t, are the cooling and heating power output of the gas cooling and heating equipment at time t, are the electricity, cooling and heating loads corresponding to the worst scenario at time t; The conservative constraints are as follows: Among them, N T is the total number of moments in the evaluation period, u is a multivariate load scenario, U is the uncertainty set of the multivariate load scenario, c T is the coefficient matrix of the objective function, are the uncertain variables of electric, cooling and heating load power after considering uncertainty at time t, They are the maximum fluctuation deviations allowed for the power, cooling, and heating loads at time t, which are positive numbers. are the predicted values ​​of power, cooling and heating loads at time t, are the variables that control the worst electricity, cooling, and heating load scenarios to fall within the lower bound of the uncertainty set; are the variables that control the worst electricity, cooling and heating load scenarios to fall within the lower bound of the uncertainty set, Γ is the uncertainty adjustment parameter, Corresponding to and are the lower and upper limits of conservative parameters for different loads, respectively; e, c, and h are electricity, cooling, and heating, respectively.

4. The method according to claim 1, wherein The main problem is expressed as follows: Upper bound Nether Among them, x and y are optimization variables, u is a multivariate load scenario, and c T is the coefficient matrix of the objective function, x and y are the optimization variables, c T is the coefficient matrix of the objective function, α is the main problem objective function, D up , K up 、F up , G up 、 are the coefficient matrices of the corresponding constraints when solving the upper bound of IES external power demand; d up 、k up 、h up 、 They are the constant matrices in the constraints corresponding to solving the upper bound of IES external power demand. Replacing the superscript from up to down corresponds to solving the lower bound of IES external power demand.

5. The method according to claim 1, wherein The expression of the sub-problem is as follows: Upper bound Nether Among them, x and y are optimization variables, u is a multivariate load scenario, U is the uncertainty set of the multivariate load scenario, c T is the coefficient matrix of the objective function, Ω(u, x) is the feasible region of the given optimization variable y, D, K, F, G, I u are the coefficient matrices of the constraints, d, k, h, and u are the constant matrices in the constraints, and γ, λ, υ, and π are the dual variables.

6. The method according to claim 5, characterized in that The expression of the sub-problem after the dualization process is as follows: Upper bound Nether Among them, x and y are optimization variables, U is the uncertainty set of the multivariate load scenario, γ, λ, h, v, π are dual variables, and T is the transposed matrix of the coefficient matrix.

7. A system for evaluating the flexibility of an integrated energy system, characterized in that: The system comprises: An initialization unit is used to determine, for an integrated energy system, an upper bound optimization target and a lower bound optimization target of the integrated energy system for external power demand, and determine a fluctuation range of a multi-element load of the integrated energy system based on a boxed uncertainty set; a modeling unit, configured to construct an evaluation model for evaluating the flexibility of the integrated energy system based on an upper bound optimization target and a lower bound optimization target of the integrated energy system for external power demand and a fluctuation range of the multi-element load of the integrated energy system; a solving unit, configured to solve the evaluation model and determine the flexibility of the integrated energy system based on an obtained optimal solution of the evaluation model; The evaluation model includes: optimization variables, objective functions and constraints; The objective function includes: an upper bound function of the power demand interval of the integrated energy system and a lower bound function of the power demand interval of the integrated energy system; The upper bound function of the power demand interval of the integrated energy system is as follows: The lower bound function of the power demand interval of the integrated energy system is as follows: Among them, x and y are optimization variables, N T is the total number of moments in the evaluation period, i is a multivariate load scenario, U is the uncertainty set of the multivariate load scenario, c T is the coefficient matrix of the objective function, Ω(u, x) is the feasible region of the given optimization variable y, is the purchased municipal power power at time t; The constraints include: flexible operation constraints of controllable energy supply equipment, flexible operation constraints of energy storage equipment, supply and demand balance constraints of the integrated energy system, and conservative constraints; Solving the evaluation model includes: Decomposing the evaluation model to determine the main problem and subproblems for solving the upper and lower bounds of the external power demand of the integrated energy system; The sub-problems are dualized using KKT conditions, and the dualized sub-problems are processed using the Big-M method. The main problem and the sub-problems processed by the Big-M method are solved by the CCG algorithm to obtain the optimal solution of the evaluation model.

8. The system according to claim 7, characterized in that The optimization variables are as follows: Among them, x and y are optimization variables, are the start and stop status 0-1 variables of CCHP power generation, waste heat cooling, and waste heat heating at time t, They are the start and stop status 0-1 variables of the electric cooling and heating equipment at time t, They are the start and stop status 0-1 variables of the gas cooling and heating equipment at time t, are the 0-1 control variables for the charging and discharging operations of the energy storage device at time t, is the purchased municipal power power at time t, are the electricity, cooling and heating power output by CCHP at time t, are the cooling and heating power output by the electric cooling and heating equipment at time t, are the cooling and heating power output of the gas cooling and heating equipment at time t, are the charging and discharging power of the energy storage device at time t, They are the electricity, cooling and heating loads corresponding to the worst scenario at time t.

9. The system according to claim 7, wherein: The flexible operation constraints of the controllable energy supply equipment are as follows: in, are the output and input power of device i at time t, η i is the energy conversion efficiency of device i, θ i is the start and stop control variable of device i at time t, are the upper and lower limits of the output power allowed for device i, respectively, i is the ramp rate allowed for device i; The flexible operation constraints of the energy storage equipment are as follows: in, The upper limit of the charging and discharging power of the energy storage device. is the rated capacity of the energy storage device, are the 0-1 control variables for the charging and discharging operations of the energy storage device at time t, are the charging and discharging power of the energy storage device at time t respectively; The supply and demand balance constraints of the integrated energy system are as follows: in, are the electric power consumed by the electric heating and electric cooling equipment at time t, They are CCHP waste heat production, waste heat consumed by heating, and waste heat consumed by cooling at time t, is the purchased municipal power power at time t, are the electricity, cooling and heating power output by CCHP at time t, are the cooling and heating power output by the electric cooling and heating equipment at time t, are the cooling and heating power output of the gas cooling and heating equipment at time t, are the electricity, cooling and heating loads corresponding to the worst scenario at time t; The conservative constraints are as follows: Among them, N T is the total number of moments in the evaluation period, u is a multivariate load scenario, U is the uncertainty set of the multivariate load scenario, c T is the coefficient matrix of the objective function, are the uncertain variables of electric, cooling and heating load power after considering uncertainty at time t, They are the maximum fluctuation deviations allowed for the power, cooling, and heating loads at time t, which are positive numbers. are the predicted values ​​of power, cooling and heating loads at time t, are the variables that control the worst electricity, cooling, and heating load scenarios to fall within the lower bound of the uncertainty set; are the variables that control the worst electricity, cooling and heating load scenarios to fall within the lower bound of the uncertainty set, Γ is the uncertainty adjustment parameter, Corresponding to and are the lower and upper limits of conservative parameters for different loads, respectively; e, c, and h are electricity, cooling, and heating, respectively.

10. The system according to claim 7, wherein: The main problem is expressed as follows: Upper bound Nether Among them, x and y are optimization variables, u is a multivariate load scenario, and c T is the coefficient matrix of the objective function, x and y are the optimization variables, c T is the coefficient matrix of the objective function, α is the main problem objective function, D up , K up 、F up , G up 、 are the coefficient matrices of the corresponding constraints when solving the upper bound of IES external power demand; d up 、k up 、h up 、 They are the constant matrices in the constraints corresponding to solving the upper bound of IES external power demand. Replacing the superscript from up to down corresponds to solving the lower bound of IES external power demand.

11. The system according to claim 7, wherein: The expression of the sub-problem is as follows: Upper bound Nether Among them, x and y are optimization variables, u is a multivariate load scenario, U is the uncertainty set of the multivariate load scenario, c T is the coefficient matrix of the objective function, Ω(u,x) is the feasible region of the given optimization variable y, D, K, F, G, I u are the coefficient matrices of the constraints, d, k, h, and u are the constant matrices in the constraints, and γ, λ, υ, and π are the dual variables.

12. The system according to claim 11, wherein: The expression of the sub-problem after the dualization process is as follows: Upper bound Nether Among them, x and y are optimization variables, U is the uncertainty set of the multivariate load scenario, γ, λ, h, v, π are dual variables, and T is the transposed matrix of the coefficient matrix.

13. A computer device, characterized in that: include: one or more processors; a processor for executing one or more programs; When the one or more programs are executed by the one or more processors, the method according to any one of claims 1 to 6 is implemented.

14. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed, the method according to any one of claims 1 to 6 is implemented.

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