Two-stage robust optimization configuration method of integrated energy system and related components

Abstract

The invention relates to a two-stage robust optimization configuration method of an integrated energy system and related components, and belongs to the technical field of integrated energy system optimization, and the method comprises the steps: constructing output models of a plurality of devices based on the integrated energy system; determining an optimization target based on the operation cost of the integrated energy system, and constructing a two-stage robust optimization configuration model according to the optimization target; a column sum constraint method Camp is adopted; and solving the two-stage robust optimal configuration model by the CG to obtain an optimal configuration result of the integrated energy system. According to the method, the conservative degree of the system can be flexibly adjusted, and a conservative economic optimal solution which still meets the safe operation constraint of the system in a severe environment is provided for investors; a near-zero-carbon electricity-heat-cold comprehensive energy system model is provided, an air source heat pump is adopted for providing cold / heat loads, fossil fuel is prevented from being used, and carbon emission is effectively reduced.

Description

Technical Field

[0001] The present application relates to the technical field of integrated energy system optimization, and in particular to a two-stage robust optimization configuration method for an integrated energy system and related components. Background Art

[0002] According to the 28th Conference of the Parties to the United Nations Framework Convention on Climate Change, by 2030, the global installed capacity of renewable energy power generation will increase to three times the current level (11 billion kW), and the global average annual energy efficiency improvement rate will increase from 2% to 4%. "Carbon neutrality" has become a global consensus, and the trend of carbon emission reduction is irreversible.

[0003] The integrated energy system can make comprehensive use of multiple energy sources to meet the needs of different electricity / cooling / heat loads, coordinate the relationships between multiple energy sources, and maximize economic, environmental and social benefits through system-level optimization, thereby improving energy utilization efficiency, promoting the consumption of renewable energy, and reducing total carbon emissions. However, the uncertainty of the output and load of renewable energy such as solar and wind energy will affect the stable operation of the integrated energy system. How to find the optimal solution for economy and stability under the influence of source-load uncertainty is an urgent problem to be solved. Summary of the invention

[0004] Based on this, it is necessary to provide a two-stage robust optimization configuration method and related components for an integrated energy system that can obtain the optimal solution for economy and stability under the influence of source and load uncertainty in response to the above technical problems.

[0005] In a first aspect, a two-stage robust optimization configuration method for an integrated energy system is provided, the method comprising: constructing output models of multiple devices based on the integrated energy system; determining optimization targets based on the operating cost of the integrated energy system, and constructing a two-stage robust optimization configuration model according to the optimization targets; and solving the two-stage robust optimization configuration model using a column and constraint method C&CG to obtain an optimized configuration result of the integrated energy system.

[0006] Optionally, based on the integrated energy system, constructing multiple output models includes: based on the electricity-cold-heat integrated energy system, constructing output models of multiple devices, the output models including at least one of the following: photovoltaic output model, wind power output model, electric boiler output model, electric refrigeration and air conditioning output model, air source heat pump output model, power grid interaction model and output models of multiple types of energy storage equipment.

[0007] Optionally, the photovoltaic output model includes:

[0008] P t PV +P t cur =APV ω t η PV

[0009] Among them, P t PV Represents the active power of the photovoltaic power generation system, P t cur Represents the reactive power of the photovoltaic power generation system, A PV Represents the photovoltaic system installation area, ω t represents the light intensity at time t, η PV Indicates the conversion efficiency of photovoltaic power generation system;

[0010] The wind power output model includes:

[0011]

[0012] Among them, v min 、v max 、v ra Represents cut-in, cut-out and rated wind speed, P t wt represents the power generated by the wind power system at time t, I wt represents the installed capacity of the wind turbine, v t represents the wind speed at time t;

[0013] The electric boiler output model includes:

[0014] Q t EB =η EB P t EB

[0015] Among them, Q t EB Indicates the heat supplied by the electric boiler, P t EB represents the input power of the electric boiler at time t, η EB Indicates the thermal efficiency of the electric boiler;

[0016] The electric refrigeration air conditioning output model includes:

[0017] Q t EC =COP EC P t EC

[0018] Among them, COP EC Indicates the energy efficiency ratio of electric refrigeration and air conditioning, Q t EC represents the cooling capacity of the electric refrigeration air conditioner at time t, P tEC represents the power of the electric refrigeration air conditioner at time t;

[0019] The air source heat pump output model includes:

[0020] Q t gh =COP gh P t gh

[0021] Q t gc =COP gc P t gc

[0022] Among them, Q t gh , Q t gc They represent the design hourly heating and cooling capacity of the air source heat pump, COP gh , COP gc Respectively represent the heating energy efficiency ratio and cooling energy efficiency ratio of air source heat pump, P t gh , P t gc They represent the input heating power and cooling power of the air source heat pump respectively;

[0023] The grid interaction model includes:

[0024]

[0025] Among them, P t buy , P t sell They represent the amount of electricity purchased and sold from the power grid by the system at time t, U buy t , U sell t They represent the state of purchasing electricity from the power grid and the state of selling electricity, respectively. They are integer variables with values ​​of 0-1. The sum of the two state variables at any time t is equal to 1, that is, at time t, it can only be one of the state of purchasing electricity or selling electricity. buymax , P sellmax They represent the maximum amount of electricity purchased and sold respectively;

[0026] The output models of various types of energy storage devices include:

[0027]

[0028] Wherein, the subscript i indicates the energy storage type, and Respectively represent the capacity of multiple types of energy storage at time t and time t+1, η i,loss Represents the self-consumption rate of multiple types of energy storage, and Represents the charging and discharging power of multiple types of energy storage, η i,ch and η i,dis Respectively represent the charging and discharging efficiency of multiple types of energy storage.

[0029] Optionally, the method further comprises: constructing constraints corresponding to a plurality of output models, including at least one of the following: power balance constraints, photovoltaic unit output constraints, wind turbine unit output constraints, energy storage device output constraints, electric boiler output constraints, air source heat pump output constraints, and electric refrigeration air conditioner output constraints;

[0030] The power balance constraints include:

[0031]

[0032] in, They represent the discharge power and charging power of the energy storage device at time t, L t E is the electrical load at time t, They represent the heat release power and heat storage power of the thermal energy storage device at time t, L t H is the heat load at time t, They represent the cooling power and storage power of the cold storage device at time t, L t C is the cooling load at time t;

[0033] The photovoltaic unit output constraints include:

[0034]

[0035] in, Indicates the upper limit of the photovoltaic unit power;

[0036] The wind turbine output constraints include:

[0037]

[0038] in, Indicates the upper limit of wind turbine power;

[0039] The energy storage device output constraints include:

[0040]

[0041] Among them, U ch i,t Indicates the charging status of multiple types of energy storage devices. When the value is 1, it means charging.chmax i Indicates the maximum charging power of multiple types of energy storage devices, U dis i,t Indicates the energy release status of multiple types of energy storage devices. When the value is 1, it means energy release. dismax i Indicates the maximum energy release power of multiple types of energy storage devices, Respectively represent the minimum capacity and maximum capacity of the energy storage device;

[0042] The electric boiler output constraints include:

[0043]

[0044] in, Indicates the upper limit of the electric boiler input power;

[0045] The air source heat pump output constraints include:

[0046]

[0047] in, They represent the maximum heating power and the maximum cooling power of the air source heat pump respectively;

[0048] The electric refrigeration air conditioning output constraints include:

[0049]

[0050] in, Indicates the upper limit of input power of electric refrigeration air conditioner.

[0051] Optionally, based on the operating cost of the integrated energy system, the optimization objectives include:

[0052] The optimization goal of the integrated energy system is defined as minimizing the daily operating cost. Based on the optimization goal, the objective function is determined to include:

[0053]

[0054] Among them, C t OM represents the operation and maintenance cost of each device in period t, C t grid represents the grid interaction cost in period t, C t pen represents the penalty cost of time period t, and n represents the number of time periods in a scheduling cycle;

[0055] The calculation method of the operation and maintenance cost of each device includes:

[0056]

[0057] Among them, c OM,pv 、c OM,WT 、c OM,i 、c OM,gh 、c OM,gc 、c OM,EC 、c OM,EB They represent the operation and maintenance costs of photovoltaic, wind power, various types of energy storage equipment, air source heat pump heating, air source heat pump cooling, air conditioning, and electric boiler equipment, respectively. n represents the number of scheduling periods in a day, and m represents the types of various types of energy storage equipment.

[0058] The grid interaction costs include:

[0059] C t grid =c t buy P t buy Δt-c t sell P t sell Δt

[0060] Among them, c t buy represents the unit electricity purchase price, c t sell represents the unit electricity selling price, Δt represents the dispatching time interval;

[0061] The penalty costs include:

[0062] C t pen =c p,WT (P t re,WT -P t wt )+c p,PV (P t re,PV -P t PV )

[0063] Among them, c p,WT is the wind abandonment penalty coefficient, c p,PV is the light abandonment penalty coefficient, P t re,WT is the wind power forecast value in period t, P t re,PV is the photovoltaic forecast value in period t.

[0064] Optionally, the two-stage robust optimization configuration model includes:

[0065] Define the main problem expression:

[0066]

[0067] Define the subproblem expression:

[0068]

[0069] The first stage is the outer layer minimization, with x as the optimization variable, u and y as the known variables, and the second stage is the inner layer maximum minimization, with u and y as the optimization variables, u represents the uncertain variable, U represents the range of the uncertain variable, and c T represents the set of coefficients of the objective function;

[0070] Based on the Big-M method, the uncertain variables are linearized, including:

[0071]

[0072] Among them, γ, λ, ν, π are all dual variables corresponding to each constraint condition, B = [B WT ,B PV (t),B EL (t),B HL (t),B CL (t)] T represents a binary variable, Γ WT and Γ PV represent the uncertainty adjustment parameters of wind power output and photovoltaic output respectively, Γ EL , Γ HL and Γ CL They represent the uncertainty adjustment parameters of the electric, heating and cooling load powers, c represents the coefficient vector of the objective function, D, F, G represent the coefficient vectors of the inequality constraint variables, K, I u represents the coefficient vector of the equality constraint variables, d and h represent the constant vectors of the corresponding constraints, and B' represents the continuous auxiliary variable.

[0073] Optionally, the column and constraint method C&CG is used to solve the two-stage robust optimization configuration model to obtain the optimization configuration result of the integrated energy system, including: defining the value of the target scheduling data u of the target date as the initial scenario, defining the number of iterations k=1, the lower bound LB=-∞, and the upper bound UB=+∞ of the objective function; solving the main problem expression according to the predicted values ​​of wind power, photovoltaic power, and load, and calculating the optimal output plan of each unit within the target date Among them, the lower bound of the objective function value of the main problem expression is updated as The optimal solution of the main problem expression Substitute into the subproblem expression and solve to get the objective function value of the subproblem expression and the value of the corresponding uncertain variable u And update the upper bound to In response to detecting that UB-LB≤ε, output the optimized configuration result and And exit the loop, where ε represents the running deviation; in response to detecting that UB-LB>ε, continue to iterate and add target constraints; let k=k+1, and jump to the step of calculating and solving the main problem expression, repeat the iteration until the algorithm converges and the calculation ends.

[0074] In the second aspect, a two-stage robust optimization configuration device for an integrated energy system is provided, the device comprising: a first model building module, for building an output model of multiple devices based on the integrated energy system; a second model building module, for determining an optimization target based on the operating cost of the integrated energy system, and building a two-stage robust optimization configuration model according to the optimization target; an optimization configuration module, for solving the two-stage robust optimization configuration model using a column and constraint method C&CG to obtain an optimization configuration result of the integrated energy system.

[0075] According to a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: constructing output models of multiple devices based on an integrated energy system; determining optimization targets based on the operating costs of the integrated energy system, and constructing a two-stage robust optimization configuration model according to the optimization targets; and solving the two-stage robust optimization configuration model using a column and constraint method C&CG to obtain an optimization configuration result of the integrated energy system.

[0076] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: based on an integrated energy system, an output model of multiple devices is constructed; based on the operating cost of the integrated energy system, an optimization target is determined, and a two-stage robust optimization configuration model is constructed according to the optimization target; the two-stage robust optimization configuration model is solved using a column and constraint method C&CG to obtain an optimized configuration result of the integrated energy system.

[0077] In a fifth aspect, a computer program product is provided, which includes a computer program, and when the computer program is executed by a processor, the following steps are implemented: based on an integrated energy system, an output model of multiple devices is constructed; based on the operating cost of the integrated energy system, an optimization target is determined, and a two-stage robust optimization configuration model is constructed according to the optimization target; the two-stage robust optimization configuration model is solved using a column and constraint method C&CG to obtain an optimized configuration result of the integrated energy system.

[0078] The two-stage robust optimization configuration method and related components of the above-mentioned integrated energy system, the method includes: based on the integrated energy system, constructing an output model of multiple equipment; based on the operating cost of the integrated energy system, determining the optimization target, and constructing a two-stage robust optimization configuration model according to the optimization target; using the column and constraint method C&CG to solve the two-stage robust optimization configuration model to obtain the optimization configuration result of the integrated energy system. This application is based on the synergistic optimization of multiple energy sources, and comprehensively considers the effect of different absorption forms of solar energy, cold, heat, and electricity on the improvement of the system absorption capacity; by establishing an uncertainty set and adjusting the uncertainty of uncertain factors, the system conservatism can be flexibly adjusted, providing investors with a conservative economic optimal solution that still meets the system safety operation constraints under harsh environments; a near-zero-carbon electric-heat-cold integrated energy system model is proposed, which uses air source heat pumps to provide cold / heat loads, avoids the use of fossil fuels, and effectively reduces carbon emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 It is a flowchart of a two-stage robust optimization configuration method of an integrated energy system in one embodiment;

[0080] Figure 2 A schematic diagram of the structure of an electric-cold-heat integrated energy system of an integrated energy system in one embodiment;

[0081] Figure 3 (a)-3(b) are schematic diagrams of the power of each energy storage device on a typical cooling day and a typical heating day of a two-stage robust optimization configuration method for an integrated energy system in one embodiment;

[0082] Figure 4 (a)-4(b) are schematic diagrams comparing actual values ​​and predicted values ​​of typical daily loads for cooling and heating in a two-stage robust optimization configuration method for an integrated energy system in one embodiment;

[0083] Figure 4 (c)-4(d) are schematic diagrams comparing the actual and predicted values ​​of the photovoltaic load on a typical cooling day and the photovoltaic load on a typical heating day of a two-stage robust optimization configuration method for an integrated energy system in one embodiment;

[0084] Figure 4 (e)-4(f) are schematic diagrams comparing actual and predicted values ​​of wind power load on a typical day for cooling and wind power load on a typical day for heating in a two-stage robust optimization configuration method for an integrated energy system in one embodiment;

[0085] Figure 4 (g)-4(h) are schematic diagrams comparing actual and predicted values ​​of a typical daily cooling load for cooling and a typical daily cooling load for heating in a two-stage robust optimization configuration method for an integrated energy system in one embodiment;

[0086] Figure 5 (a)-5(b) are schematic diagrams of grid interaction power on a typical cooling day and a typical heating day of a two-stage robust optimization configuration method for an integrated energy system in one embodiment;

[0087] Figure 6 A structural block diagram of a two-stage robust optimization configuration device for an integrated energy system in one embodiment;

[0088] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0089] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0090] It should be understood that in the description of the present application, unless the context clearly requires otherwise, words such as "include", "comprises", and the like throughout the specification should be interpreted as including rather than being exclusive or exhaustive; that is, as including but not limited to.

[0091] It should also be understood that the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of this application, unless otherwise specified, "plurality" means two or more.

[0092] It should be noted that the terms "S1", "S2", etc. are only used for the purpose of describing the steps, and do not specifically refer to the order or sequence, nor are they used to limit the present application. They are only for the convenience of describing the method of the present application, and cannot be understood as indicating the order of the steps. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present application.

[0093] According to the background technology, the uncertainty of the output and load of renewable energy sources such as solar energy and wind energy will affect the stable operation of the integrated energy system. To solve the above technical problems, a two-stage robust optimization method can be used to plan the wind and solar power consumption equipment for a zero-carbon integrated energy system, and to find the optimal solution for economy and stability under the influence of source and load uncertainty, so as to provide investors with an adjustable conservative planning result to deal with source and load uncertainty.

[0094] In one embodiment, Figure 1 As shown in the figure, a two-stage robust optimization configuration method for an integrated energy system is provided. Figure 1 The terminal in is used as an example to illustrate, including the following steps:

[0095] S1: Based on the integrated energy system, build output models of multiple devices.

[0096] It should be noted that the integrated energy system is an electricity-cooling-heat integrated energy system, such as Figure 2 As shown, it includes multiple devices, such as fans, photovoltaics and electric boilers.

[0097] In some specific implementations, constructing multiple output models based on the integrated energy system includes: constructing output models of multiple devices based on the electric-cold-heat integrated energy system, wherein the output models include at least one of the following: a photovoltaic output model, a wind power output model, an electric boiler output model, an electric refrigeration and air conditioning output model, an air source heat pump output model, a power grid interaction model, and multiple types of energy storage equipment output models, specifically:

[0098] The photovoltaic output model includes:

[0099] P t PV +P t cur =A PV ω t η PV

[0100] Among them, P t PV Represents the active power of the photovoltaic power generation system, P t cur Represents the reactive power of the photovoltaic power generation system, A PV Represents the photovoltaic system installation area, ω t represents the light intensity at time t, η PV Indicates the conversion efficiency of photovoltaic power generation system;

[0101] The wind power output model includes:

[0102]

[0103] Among them, v min 、v max 、v ra Respectively represent the cut-in, cut-out and rated wind speed. The preferred values ​​in this application are 3m / s, 20m / s, 10m / s, P t wt represents the power generated by the wind power system at time t, I wt represents the installed capacity of the wind turbine, v t represents the wind speed at time t;

[0104] The electric boiler output model includes:

[0105] Q t EB =η EB P t EB

[0106] Among them, Q t EB Indicates the heat supplied by the electric boiler, P t EB represents the input power of the electric boiler at time t, η EB Indicates the thermal efficiency of the electric boiler;

[0107] The electric refrigeration air conditioning output model includes:

[0108] Q t EC =COP EC P t EC

[0109] Among them, COP EC Indicates the energy efficiency ratio of electric refrigeration and air conditioning, Q t EC represents the cooling capacity of the electric refrigeration air conditioner at time t, P t EC represents the power of the electric refrigeration air conditioner at time t;

[0110] The air source heat pump output model includes:

[0111] Q t gh =COP gh P t gh

[0112] Q t gc =COP gc P t gc

[0113] Among them, Qt gh , Q t gc They represent the design hourly heating and cooling capacity of the air source heat pump, COP gh , COP gc Respectively represent the heating energy efficiency ratio and cooling energy efficiency ratio of the air source heat pump. The preferred values ​​of this application are: 4.2 / 4.6 in spring, summer and autumn, and 2.4 / 2.2 in winter. t gh , P t gc They represent the input heating power and cooling power of the air source heat pump respectively;

[0114] The grid interaction model includes:

[0115]

[0116] Among them, P t buy , P t sell They represent the amount of electricity purchased and sold from the power grid by the system at time t, U buy t , U sell t They represent the state of purchasing electricity from the power grid and the state of selling electricity, respectively. They are integer variables with values ​​of 0-1. The sum of the two state variables at any time t is equal to 1, that is, at time t, it can only be one of the state of purchasing electricity or selling electricity. buymax , P sellmax They represent the maximum amount of electricity purchased and sold respectively;

[0117] The output models of various types of energy storage devices include:

[0118]

[0119] Wherein, the subscript i indicates the energy storage type, and Respectively represent the capacity of multiple types of energy storage at time t and time t+1, η i,loss Represents the self-consumption rate of multiple types of energy storage, and Represents the charging and discharging power of multiple types of energy storage, η i,ch and η i,dis Respectively represent the charging and discharging efficiency of multiple types of energy storage.

[0120] In some specific implementations, the method further includes: constructing constraints corresponding to multiple output models, including at least one of the following: power balance constraints, photovoltaic unit output constraints, wind turbine unit output constraints, energy storage device output constraints, electric boiler output constraints, air source heat pump output constraints, and electric refrigeration air conditioning output constraints, specifically:

[0121] The power balance constraints include:

[0122]

[0123] in, They represent the discharge power and charging power of the energy storage device at time t, L t E is the electrical load at time t, They represent the heat release power and heat storage power of the thermal energy storage device at time t, L t H is the heat load at time t, They represent the cooling power and storage power of the cold storage device at time t, L t C is the cooling load at time t;

[0124] The photovoltaic unit output constraints include:

[0125]

[0126] in, Indicates the upper limit of the photovoltaic unit power;

[0127] The wind turbine output constraints include:

[0128]

[0129] in, Indicates the upper limit of wind turbine power;

[0130] The energy storage device output constraints include:

[0131]

[0132] Among them, U ch i,t Indicates the charging status of multiple types of energy storage devices. When the value is 1, it means charging. chmax i Indicates the maximum charging power of multiple types of energy storage devices, U dis i,t Indicates the energy release status of multiple types of energy storage devices. When the value is 1, it means energy release. dismax i Indicates the maximum energy release power of multiple types of energy storage devices, Respectively represent the minimum capacity and maximum capacity of the energy storage device;

[0133] The electric boiler output constraints include:

[0134]

[0135] in, Indicates the upper limit of the electric boiler input power;

[0136] The air source heat pump output constraints include:

[0137]

[0138] in, They represent the maximum heating power and the maximum cooling power of the air source heat pump respectively;

[0139] The electric refrigeration air conditioning output constraints include:

[0140]

[0141] in, Indicates the upper limit of input power of electric refrigeration air conditioner.

[0142] S2: Based on the operating cost of the integrated energy system, an optimization target is determined, and a two-stage robust optimization configuration model is constructed according to the optimization target.

[0143] It should be noted that the optimization goal is to minimize the daily operating cost. The two-stage robust optimization configuration model divides the optimization configuration of the integrated energy system into main problems and sub-problems, establishes an uncertainty variable model, and linearizes the uncertain variables using the Big-M method.

[0144] In some specific embodiments, based on the operating cost of the integrated energy system, determining the optimization target includes:

[0145] The optimization goal of the integrated energy system is defined as minimizing the daily operating cost. Based on the optimization goal, the operation optimization objective function of the integrated energy system is determined to include:

[0146]

[0147] Among them, C t OM represents the operation and maintenance cost of each device in period t, C t grid represents the grid interaction cost in period t, C t pen represents the penalty cost of time period t, n represents the number of time periods in a scheduling cycle, and the preferred value is 24 hours;

[0148] Furthermore, the calculation method of the operation and maintenance cost of each device includes:

[0149]

[0150] Among them, c OM,pv 、cOM,WT 、c OM,i 、c OM,gh 、c OM,gc 、c OM,EC 、c OM,EB They represent the operation and maintenance costs of photovoltaic, wind power, various types of energy storage equipment, air source heat pump heating, air source heat pump cooling, air conditioning, and electric boiler equipment, respectively. n represents the number of scheduling periods in a day, and m represents the types of various types of energy storage equipment.

[0151] The grid interaction costs include:

[0152] C t grid =c t buy P t buy Δt-c t sell P t sell Δt

[0153] Among them, c t buy represents the unit electricity purchase price, c t sell represents the unit electricity selling price, Δt represents the dispatching time interval;

[0154] The penalty costs include:

[0155] C t pen =c p,WT (P t re,WT -P t wt )+c p,PV (P t re,PV -P t PV )

[0156] Among them, c p,WT is the wind abandonment penalty coefficient, c p,PV is the light abandonment penalty coefficient, P t re,WT is the wind power forecast value in period t, P t re,PV is the photovoltaic forecast value in period t.

[0157] In some specific implementations, for linear optimization problems, traditional robust optimization methods take values ​​of uncertain variables at the boundaries, and the resulting solutions are relatively robust, but at the same time the resulting solutions are too conservative, and it is difficult to achieve the proposed "worst" scenario in actual engineering, resulting in cost waste. The purpose of the two-stage robust optimization model is to make the uncertain variable u obtain a balance value between the ideal scenario and the "worst" scenario within the uncertainty set U, so that the objective function is optimal. The model decomposes the two-stage robust optimization configuration model into a main problem and sub-problems according to the column and constraint method C&CG, and obtains the system optimization configuration result by iterative solution using CPLEX (mathematical optimization technology). Based on this, the two-stage robust optimization configuration model includes: defining the main problem expression:

[0158]

[0159] Define the subproblem expression:

[0160]

[0161] The first stage is the outer layer minimization, with x as the optimization variable, u and y as the known variables, and the second stage is the inner layer maximum minimization, with u and y as the optimization variables, u represents the uncertain variable, U represents the range of the uncertain variable, and c T represents the set of coefficients of the objective function;

[0162] Furthermore, the main problem constraints include:

[0163]

[0164] Among them, x and y are optimization variables, and the specific expression is:

[0165]

[0166] Among them, k is the current iteration number, y l is the value of the optimization variable after the lth iteration, is the value of the uncertain variable u obtained after the lth iteration, c is the coefficient vector of the objective function, D, F, G are the coefficient vectors of the inequality constraint variables, K, I u is the coefficient vector of the equality constraint variables, d, g, h are the constant vectors of the corresponding constraints, and the expressions of the objective function, equipment operation and maintenance cost, grid interaction cost, and penalty cost are the second line of the constraint conditions, i.e., stα≥c T y l , the output model expression of each device is the third line of the constraint condition, namely Ky l =g, the constraint condition expression of each output model is the fourth line of the constraint condition, that is, Fx+Gy l ≥h;

[0167] Furthermore, the fifth line of the constraint, is the uncertainty optimization model constraint, where u is the uncertainty parameter. Since the output of renewable energy and load power are the basic data of the predicted values, and their actual values ​​may produce errors due to various reasons, they are uncertain. The predicted values ​​of the uncertainty parameters are expressed as follows:

[0168]

[0169]

[0170] Among them, U WT (t) represents the predicted value of wind power output, U PV (t) represents the predicted value of photovoltaic output, L E (t) represents the predicted value of electric load power, L C (t) represents the predicted value of cooling load power, L H (t) represents the predicted value of heat load power, U t WT , U t PV , L t E , L t H , L t C They represent the uncertain variables of wind power output, photovoltaic output, electric load, thermal load and cooling load at time t respectively. They represent the predicted values ​​of wind power output, photovoltaic output, electric load, thermal load, and cooling load, respectively, ΔP WT , ΔP PV , ΔP EL , ΔP HL , ΔP CL They represent the maximum fluctuation values ​​of wind power, photovoltaic power, electric load, thermal load, and cooling load respectively. T represents the maximum dispatch period in a dispatch cycle, and the preferred value is 24;

[0171] Based on the subproblem form, the constraints of the subproblem are determined as follows: (x,u) is a fixed value in a given scenario, so the inner minimization of the above equation is a linear problem, which can be converted into a max form according to the strong duality theory to obtain the following dual form:

[0172]

[0173] Among them, γ, λ, ν, π are the dual variables corresponding to each constraint condition;

[0174] Linear modeling of uncertain variables includes: when the above formula reaches the maximum value, the value of u should be the predicted value expression of the uncertainty parameter (i.e. U WT (t), U PV (t), L E (t), L C (t) and L H (t) corresponds to the boundary value of the expression). Within the range of values, when the wind and solar power output is the minimum and the load power is the maximum, the system cost is the maximum, which is closer to the "worst" scenario. Therefore, the range of uncertain variables can be improved to the following form:

[0175]

[0176]

[0177] Where B = [B WT ,B PV (t),B EL (t),B HL (t),B CL (t)] T is a binary variable. When the value is 1, it means that the uncertain variable in the corresponding period reaches the boundary value. WT and Γ PV are the “uncertainty adjustment parameters” of wind power output and photovoltaic output, Γ EL ,Γ HL and Γ CL The "uncertainty adjustment parameter" of the power of electric, heating and cooling loads is an integer ranging from 0 to 24 in this application, which represents the total number of time periods when the uncertain variable described by the improved uncertain variable value range expression takes the boundary of the uncertain interval. The larger the value, the stronger the conservatism. After substituting the improved uncertain variable value range expression into the dual form expression, the form of the product of the binary variable and the continuous variable is linearized by the Big-M method here, and the following can be obtained:

[0178]

[0179] Among them, this formula represents the relationship between the introduced auxiliary variables and the original variables, γ, λ, ν, π all represent the dual variables corresponding to each constraint condition, B = [B WT ,B PV (t),B EL (t),B HL (t),B CL (t)] T is a binary variable, Γ WT and Γ PV represent the uncertainty adjustment parameters of wind power output and photovoltaic output respectively, Γ EL , ΓHL and Γ CL They represent the uncertainty adjustment parameters of the electric, heating and cooling load powers, c is the coefficient vector of the objective function, D, F, G represent the coefficient vectors of the inequality constraint variables, K, I u represents the coefficient vector of the equality constraint variables, d and h represent the constant vectors of the corresponding constraints, The continuous auxiliary variable represents the uncertainty of wind, solar and load. π is the upper bound of the dual variable. Taking a sufficiently large positive real number can realize the linear transformation of the function.

[0180] S3: The column and constraint method C&CG is used to solve the two-stage robust optimization configuration model to obtain the optimal configuration result of the integrated energy system.

[0181] It should be noted that the column and constraint method C&CG is a two-stage robust optimization and column and constraint generation algorithm.

[0182] In some specific implementations, the column and constraint method C&CG is used to solve the two-stage robust optimization configuration model, and the optimization configuration results of the integrated energy system include:

[0183] After decoupling the linearized expression, we get the main problem expression and sub-problem expression in linear form. We can use the column constraint generation algorithm to interactively iterate and solve the main and sub-problems. The specific steps are as follows:

[0184] (1) Define the value of the target scheduling data u on the target date as the initial scenario, define the number of iterations k = 1, the lower bound LB of the objective function = -∞, and the upper bound UB = +∞, where the target date refers to the typical day, and the target scheduling data refers to the scheduling data before the typical day, that is, the data planned and predicted before the scheduling date. Figure 3 (a)-3(b), Figure 4 (a)-4(h) and Figure 5 (a)-5(b);

[0185] (2) Solve the main problem expression based on the predicted values ​​of wind power, photovoltaic power, and load, and calculate the optimal output plan for each unit within the target date Among them, the lower bound of the objective function value of the main problem expression is updated as

[0186] (3) The optimal solution of the main problem expression Substitute into the subproblem expression and solve to get the objective function value of the subproblem expression and the value of the corresponding uncertain variable u And update the upper bound to

[0187] (4) In response to detecting that UB-LB ≤ ε, output the optimized configuration result and And exit the loop, where ε represents the set running deviation; in response to detecting that UB-LB>ε, continue to iterate and add the following constraints:

[0188]

[0189] Let k = k + 1, and jump to the step of calculating and solving the main problem expression, that is, step (2), and repeat the iteration until the algorithm converges, and the calculation ends.

[0190] In the above-mentioned two-stage robust optimization configuration method of the integrated energy system, the method includes: based on the integrated energy system, constructing an output model of multiple equipment; based on the operating cost of the integrated energy system, determining the optimization target, and constructing a two-stage robust optimization configuration model according to the optimization target; using the column and constraint method C&CG to solve the two-stage robust optimization configuration model to obtain the optimization configuration result of the integrated energy system. This application is based on the synergistic optimization of multiple energy sources and comprehensively considers the effect of different absorption forms of solar energy, cold, heat, and electricity on the improvement of the system absorption capacity; by establishing an uncertainty set and adjusting the uncertainty of uncertain factors, the system conservatism can be flexibly adjusted, providing investors with a conservative economic optimal solution that still meets the system safety operation constraints under harsh environments; a near-zero-carbon electric-heat-cold integrated energy system model is proposed, which uses air source heat pumps to provide cold / heat loads, avoids the use of fossil fuels, and effectively reduces carbon emissions.

[0191] It should be understood that although Figure 1-Figure 2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1-Figure 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0192] In one embodiment, Figure 6As shown, a two-stage robust optimization configuration device for an integrated energy system is provided, comprising: a first model building module, a second model building module and an optimization configuration module, wherein: the first model building module is used to build an output model of multiple devices based on the integrated energy system; the second model building module is used to determine the optimization target based on the operating cost of the integrated energy system, and to build a two-stage robust optimization configuration model according to the optimization target; the optimization configuration module is used to solve the two-stage robust optimization configuration model using a column and constraint method C&CG to obtain an optimization configuration result of the integrated energy system.

[0193] As a preferred implementation, in the embodiment of the present invention, the first model building module is specifically used for:

[0194] Based on the electricity-cooling-heat integrated energy system, construct output models of multiple devices, wherein the output models include at least one of the following: photovoltaic output model, wind power output model, electric boiler output model, electric refrigeration and air conditioning output model, air source heat pump output model, power grid interaction model and multiple types of energy storage equipment output models;

[0195] The photovoltaic output model includes:

[0196] P t PV +P t cur =A PV ω t η PV

[0197] Among them, P t PV Represents the active power of the photovoltaic power generation system, P t cur Represents the reactive power of the photovoltaic power generation system, A PV Represents the photovoltaic system installation area, ω t represents the light intensity at time t, η PV Indicates the conversion efficiency of photovoltaic power generation system;

[0198] The wind power output model includes:

[0199]

[0200] Among them, v min 、v max 、v ra Represents cut-in, cut-out and rated wind speed, P t wt represents the power generated by the wind power system at time t, I wt represents the installed capacity of the wind turbine, v t represents the wind speed at time t;

[0201] The electric boiler output model includes:

[0202] Q t EB =η EB P t EB

[0203] Among them, Q t EB Indicates the heat supplied by the electric boiler, P t EB represents the input power of the electric boiler at time t, η EB Indicates the thermal efficiency of the electric boiler;

[0204] The electric refrigeration air conditioning output model includes:

[0205] Q t EC =COP EC P t EC

[0206] Among them, COP EC Indicates the energy efficiency ratio of electric refrigeration and air conditioning, Q t EC represents the cooling capacity of the electric refrigeration air conditioner at time t, P t EC represents the power of the electric refrigeration air conditioner at time t;

[0207] The air source heat pump output model includes:

[0208] Q t gh =COP gh P t gh

[0209] Q t gc =COP gc P t gc

[0210] Among them, Q t gh , Q t gc They represent the design hourly heating and cooling capacity of the air source heat pump, COP gh , COP gc Respectively represent the heating energy efficiency ratio and cooling energy efficiency ratio of air source heat pump, P t gh , P t gc They represent the input heating power and cooling power of the air source heat pump respectively;

[0211] The grid interaction model includes:

[0212]

[0213] Among them, P t buy , P t sell They represent the amount of electricity purchased and sold from the power grid by the system at time t, U buy t , U sell t They represent the state of purchasing electricity from the power grid and the state of selling electricity, respectively. They are integer variables with values ​​of 0-1. The sum of the two state variables at any time t is equal to 1, that is, at time t, it can only be one of the state of purchasing electricity or selling electricity. buymax , P sellmax They represent the maximum amount of electricity purchased and sold respectively;

[0214] The output models of various types of energy storage devices include:

[0215]

[0216] Among them, the subscript i represents the energy storage type, and Respectively represent the capacity of multiple types of energy storage at time t and time t+1, η i,loss Represents the self-consumption rate of multiple types of energy storage, and Represents the charging and discharging power of multiple types of energy storage, η i,ch and η i,dis Respectively represent the charging and discharging efficiency of multiple types of energy storage.

[0217] As a preferred implementation manner, in the embodiment of the present invention, the first model building module is further used to:

[0218] Constructing constraints corresponding to multiple output models, including at least one of the following: power balance constraint, photovoltaic unit output constraint, wind turbine unit output constraint, energy storage device output constraint, electric boiler output constraint, air source heat pump output constraint, and electric refrigeration air conditioning output constraint;

[0219] The power balance constraints include:

[0220]

[0221] in, They represent the discharge power and charging power of the energy storage device at time t, L t E is the electrical load at time t, They represent the heat release power and heat storage power of the thermal energy storage device at time t, L t H is the heat load at time t, They represent the cooling power and storage power of the cold storage device at time t, L t C is the cooling load at time t;

[0222] The photovoltaic unit output constraints include:

[0223]

[0224] in, Indicates the upper limit of the photovoltaic unit power;

[0225] The wind turbine output constraints include:

[0226]

[0227] in, Indicates the upper limit of wind turbine power;

[0228] The energy storage device output constraints include:

[0229]

[0230] Among them, U ch i,t Indicates the charging status of multiple types of energy storage devices. When the value is 1, it means charging. chmax i Indicates the maximum charging power of multiple types of energy storage devices, U dis i,t Indicates the energy release status of multiple types of energy storage devices. When the value is 1, it means energy release. dismax i Indicates the maximum energy release power of multiple types of energy storage devices, Respectively represent the minimum capacity and maximum capacity of the energy storage device;

[0231] The electric boiler output constraints include:

[0232]

[0233] in, Indicates the upper limit of the electric boiler input power;

[0234] The air source heat pump output constraints include:

[0235]

[0236] in, They represent the maximum heating power and the maximum cooling power of the air source heat pump respectively;

[0237] The electric refrigeration air conditioning output constraints include:

[0238]

[0239] in, Indicates the upper limit of input power of electric refrigeration air conditioner.

[0240] As a preferred implementation, in the embodiment of the present invention, the second model building module is specifically used for:

[0241] The optimization goal of the integrated energy system is defined as minimizing the daily operating cost. Based on the optimization goal, the objective function is determined to include:

[0242]

[0243] Among them, C t OM represents the operation and maintenance cost of each device in period t, C t grid represents the grid interaction cost in period t, C t pen represents the penalty cost of time period t, and n represents the number of time periods in a scheduling cycle;

[0244] The calculation method of the operation and maintenance cost of each device includes:

[0245]

[0246] Among them, c OM,pv 、c OM,WT 、c OM,i 、c OM,gh 、c OM,gc 、c OM,EC 、c OM,EB They represent the operation and maintenance costs of photovoltaic, wind power, various types of energy storage equipment, air source heat pump heating, air source heat pump cooling, air conditioning, and electric boiler equipment, respectively. n represents the number of scheduling periods in a day, and m represents the types of various types of energy storage equipment.

[0247] The grid interaction costs include:

[0248] C t grid =c t buy P t buy Δt-c t sell P t sell Δt

[0249] Among them, ct buy represents the unit electricity purchase price, c t sell represents the unit electricity selling price, Δt represents the dispatching time interval;

[0250] The penalty costs include:

[0251] C t pen =c p,WT (P t re,WT -P t wt )+c p,PV (P t re,PV -P t PV )

[0252] Among them, c p,WT is the wind abandonment penalty coefficient, c p,PV is the light abandonment penalty coefficient, P t re,WT is the wind power forecast value in period t, P t re,PV is the photovoltaic forecast value in period t.

[0253] As a preferred implementation manner, in the embodiment of the present invention, the first model building module is further used to:

[0254] Define the main problem expression:

[0255]

[0256] Define the subproblem expression:

[0257]

[0258] The first stage is the outer layer minimization, with x as the optimization variable, u and y as the known variables, and the second stage is the inner layer maximum minimization, with u and y as the optimization variables, u represents the uncertain variable, U represents the range of the uncertain variable, and c T represents the set of coefficients of the objective function;

[0259] Based on the Big-M method, the uncertain variables are linearized, including:

[0260]

[0261] Among them, γ, λ, ν, π are all dual variables corresponding to each constraint condition, B = [B WT ,B PV (t),B EL (t),BHL (t),B CL (t)] T represents a binary variable, Γ WT and Γ PV represent the uncertainty adjustment parameters of wind power output and photovoltaic output respectively, Γ EL , Γ HL and Γ CL They represent the uncertainty adjustment parameters of the electric, heating and cooling load powers, c represents the coefficient vector of the objective function, D, F, G represent the coefficient vectors of the inequality constraint variables, K, I u represents the coefficient vector of the equality constraint variables, d, g, h represent the constant vectors of the corresponding constraints, and B' represents the continuous auxiliary variable.

[0262] As a preferred implementation mode, in the embodiment of the present invention, the optimization configuration module is specifically used to: define the value of the target scheduling data u of the target date as the initial scenario, define the number of iterations k=1, the lower bound LB=-∞, and the upper bound UB=+∞ of the objective function; solve the main problem expression according to the predicted values ​​of wind power, photovoltaic power, and load, and calculate the optimal output plan of each unit within the target date Among them, the lower bound of the objective function value of the main problem expression is updated as The optimal solution of the main problem expression Substitute into the subproblem expression and solve to get the objective function value of the subproblem expression and the value of the corresponding uncertain variable u And update the upper bound to In response to detecting that UB-LB≤ε, output the optimized configuration result and And exit the loop, where ε represents the running deviation; in response to detecting that UB-LB>ε, continue to iterate and add target constraints; let k=k+1, and jump to the step of calculating and solving the main problem expression, repeat the iteration until the algorithm converges and the calculation ends.

[0263] For the specific definition of the two-stage robust optimization configuration device for the integrated energy system, please refer to the definition of the two-stage robust optimization configuration method for the integrated energy system in the above text, which will not be repeated here. Each module in the above-mentioned two-stage robust optimization configuration device for the integrated energy system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0264] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 7As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a two-stage robust optimization configuration method for an integrated energy system is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0265] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0266] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program:

[0267] S1: Based on the integrated energy system, construct output models of multiple devices; S2: Based on the operating cost of the integrated energy system, determine the optimization target, and construct a two-stage robust optimization configuration model according to the optimization target; S3: Use the column and constraint method C&CG to solve the two-stage robust optimization configuration model to obtain the optimization configuration result of the integrated energy system.

[0268] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0269] S1: Based on the integrated energy system, construct output models of multiple devices; S2: Based on the operating cost of the integrated energy system, determine the optimization target, and construct a two-stage robust optimization configuration model according to the optimization target; S3: Use the column and constraint method C&CG to solve the two-stage robust optimization configuration model to obtain the optimization configuration result of the integrated energy system.

[0270] In one embodiment, a computer program product is provided, the computer program product comprising a computer program, the computer program when executed by a processor implements the following steps:

[0271] S1: Based on the integrated energy system, construct output models of multiple devices; S2: Based on the operating cost of the integrated energy system, determine the optimization target, and construct a two-stage robust optimization configuration model according to the optimization target; S3: Use the column and constraint method C&CG to solve the two-stage robust optimization configuration model to obtain the optimization configuration result of the integrated energy system.

[0272] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0273] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0274] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A two-stage robust optimization configuration method for an integrated energy system, characterized in that: The method comprises: Based on the integrated energy system, build output models of multiple devices; Based on the operation cost of the integrated energy system, an optimization target is determined, and a two-stage robust optimization configuration model is constructed according to the optimization target; The column and constraint method C&CG is used to solve the two-stage robust optimization configuration model and obtain the optimal configuration result of the integrated energy system.

2. The two-stage robust optimization configuration method for the integrated energy system according to claim 1 is characterized in that: Based on the integrated energy system, multiple output models are constructed including: Based on the electricity-cooling-heat integrated energy system, output models of multiple devices are constructed, and the output models include at least one of the following: photovoltaic output model, wind power output model, electric boiler output model, electric refrigeration and air conditioning output model, air source heat pump output model, power grid interaction model and output models of multiple types of energy storage devices.

3. The two-stage robust optimization configuration method for the integrated energy system according to claim 2 is characterized in that: The photovoltaic output model includes: P t PV +P t cur =A PV oh t or PV Among them, P t PV Represents the active power of the photovoltaic power generation system, P t cur Represents the reactive power of the photovoltaic power generation system, A PV represents the photovoltaic system installation area, ω t represents the light intensity at time t, η PV Indicates the conversion efficiency of photovoltaic power generation system; The wind power output model includes: Among them, v min 、v max 、v ra Represents cut-in, cut-out and rated wind speed, P t wt represents the power generated by the wind power system at time t, I wt represents the installed capacity of the wind turbine, v t represents the wind speed at time t; The electric boiler output model includes: Q t EB =η EB P t EB Among them, Q t EB Indicates the heat supplied by the electric boiler, P t EB represents the input power of the electric boiler at time t, η EB Indicates the thermal efficiency of the electric boiler; The electric refrigeration air conditioning output model includes: Q t EC =COP EC P t EC Among them, COP EC Indicates the energy efficiency ratio of electric refrigeration and air conditioning, Q t EC represents the cooling capacity of the electric refrigeration air conditioner at time t, P t EC represents the power of the electric refrigeration air conditioner at time t; The air source heat pump output model includes: Q t gh =COP gh P t gh Q t gc =COP gc P t gc Among them, Q t gh , Q t gc They represent the design hourly heating and cooling capacity of the air source heat pump, COP gh , COP gc Respectively represent the heating energy efficiency ratio and cooling energy efficiency ratio of air source heat pump, P t gh , P t gc They represent the input heating power and cooling power of the air source heat pump respectively; The grid interaction model includes: Among them, P t buy , P t sell They represent the amount of electricity purchased and sold from the power grid by the system at time t, U buy t , U sell t They represent the state of purchasing electricity from the power grid and the state of selling electricity, respectively. They are integer variables with values ​​of 0-1. The sum of the two state variables at any time t is equal to 1, that is, at time t, it can only be one of the state of purchasing electricity or selling electricity. buymax , P sellmax They represent the maximum amount of electricity purchased and sold respectively; The output models of various types of energy storage devices include: Wherein, the subscript i indicates the energy storage type, and Respectively represent the capacity of multiple types of energy storage at time t and time t+1, η i,loss Represents the self-consumption rate of multiple types of energy storage, and Represents the charging and discharging power of multiple types of energy storage, η i,ch and η i,dis Respectively represent the charging and discharging efficiency of multiple types of energy storage.

4. The two-stage robust optimization configuration method for the integrated energy system according to claim 3 is characterized in that: The method further comprises: Constructing constraints corresponding to multiple output models, including at least one of the following: power balance constraint, photovoltaic unit output constraint, wind turbine unit output constraint, energy storage device output constraint, electric boiler output constraint, air source heat pump output constraint, and electric refrigeration air conditioning output constraint; The power balance constraints include: in, They represent the discharge power and charging power of the energy storage device at time t, L t E is the electrical load at time t, They represent the heat release power and heat storage power of the thermal energy storage device at time t, L t H is the heat load at time t, They represent the cooling power and storage power of the cold storage device at time t, L t C is the cooling load at time t; The photovoltaic unit output constraints include: in, Indicates the upper limit of the photovoltaic unit power; The wind turbine output constraints include: in, Indicates the upper limit of wind turbine power; The energy storage device output constraints include: Among them, U ch i,t Indicates the charging status of multiple types of energy storage devices. When the value is 1, it means charging. chmax i Indicates the maximum charging power of multiple types of energy storage devices, U dis i,t Indicates the energy release status of multiple types of energy storage devices. When the value is 1, it means energy release. dismax i Indicates the maximum energy release power of multiple types of energy storage devices, Respectively represent the minimum capacity and maximum capacity of the energy storage device; The electric boiler output constraints include: in, Indicates the upper limit of the electric boiler input power; The air source heat pump output constraints include: in, They represent the maximum heating power and the maximum cooling power of the air source heat pump respectively; The electric refrigeration air conditioning output constraints include: in, Indicates the upper limit of input power of electric refrigeration air conditioner.

5. The two-stage robust optimization configuration method for the integrated energy system according to claim 4 is characterized in that: Based on the operating cost of the integrated energy system, the optimization objectives include: The optimization goal of the integrated energy system is defined as minimizing the daily operating cost. Based on the optimization goal, the objective function is determined to include: Among them, C t OM represents the operation and maintenance cost of each device in period t, C t grid represents the grid interaction cost in period t, C t pen represents the penalty cost of time period t, and n represents the number of time periods in a scheduling cycle; The calculation method of the operation and maintenance cost of each device includes: Among them, c OM,pv 、c OM,WT 、c OM,i 、c OM,gh 、c OM,gc 、c OM,EC 、c OM,EB They represent the operation and maintenance costs of photovoltaic, wind power, various types of energy storage equipment, air source heat pump heating, air source heat pump cooling, air conditioning, and electric boiler equipment, respectively. n represents the number of scheduling periods in a day, and m represents the types of various types of energy storage equipment. The grid interaction costs include: C t grid =c t buy P t buy Δt-c t sell P t sell Δt Among them, c t buy represents the unit electricity purchase price, c t sell represents the unit electricity selling price, Δt represents the dispatching time interval; The penalty costs include: C t pen =c p,WT (P t re,WT -P t wt )+c p,PV (P t re,PV -P t PV ) Among them, c p,WT is the wind abandonment penalty coefficient, c p,PV is the light abandonment penalty coefficient, P t re,WT is the wind power forecast value in period t, P t re,PV is the photovoltaic forecast value in period t.

6. The two-stage robust optimization configuration method for the integrated energy system according to claim 1 is characterized in that: The two-stage robust optimization configuration model includes: Define the main problem expression: Define the subproblem expression: The first stage is the outer layer minimization, with x as the optimization variable, u and y as the known variables, and the second stage is the inner layer maximum minimization, with u and y as the optimization variables, u represents the uncertain variable, U represents the range of the uncertain variable, and c T represents the set of coefficients of the objective function; Based on the Big-M method, the uncertain variables are linearized, including: Among them, γ, λ, ν, π are all dual variables corresponding to each constraint condition, B = [B WT ,B PV (t),B EL (t),B HL (t),B CL (t)] T represents a binary variable, Γ WT and Γ PV represent the uncertainty adjustment parameters of wind power output and photovoltaic output respectively, Γ EL , Γ HL and Γ CL They represent the uncertainty adjustment parameters of the electric, heating and cooling load powers, c represents the coefficient vector of the objective function, D, F, G represent the coefficient vectors of the inequality constraint variables, K, I u represents the coefficient vector of the equality constraint variables, d and h represent the constant vectors of the corresponding constraints, and B' represents the continuous auxiliary variable.

7. The two-stage robust optimization configuration method for the integrated energy system according to claim 6 is characterized in that: The column and constraint method C&CG is used to solve the two-stage robust optimization configuration model, and the optimization configuration results of the integrated energy system include: Define the value of the target scheduling data u on the target date as the initial scenario, define the number of iterations k = 1, the lower bound LB of the objective function = -∞, and the upper bound UB = +∞; Solve the main problem expression based on the predicted values ​​of wind power, photovoltaic power and load, and calculate the optimal output plan for each unit within the target date Among them, the lower bound of the objective function value of the main problem expression is updated as The optimal solution of the main problem expression Substitute it into the subproblem expression and solve it to get the objective function value f of the subproblem expression k * (x k * ) and the value of the corresponding uncertain variable u And update the upper bound to UB = min{UB,f k * (x k * )}; In response to detecting that UB-LB≤ε, output the optimized configuration result and And exit the loop, where ε represents the running deviation; In response to detecting that UB-LB>ε, continue iterating and add target constraints; Let k=k+1, and jump to the step of calculating and solving the main problem expression, repeat the iteration until the algorithm converges and the calculation ends.

8. A two-stage robust optimization configuration device for an integrated energy system, characterized in that: The device comprises: A first model building module is used to build output models of multiple devices based on the integrated energy system; A second model building module is used to determine an optimization target based on the operating cost of the integrated energy system, and to build a two-stage robust optimization configuration model according to the optimization target; The optimization configuration module is used to solve the two-stage robust optimization configuration model by using the column and constraint method C&CG to obtain the optimization configuration result of the comprehensive energy system.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images