Dynamic robust feasible region solving method and system for scheduling robust optimization of integrated energy system

Abstract

The invention discloses a dynamic robust feasible region solving method for scheduling robust optimization of an integrated energy system. The method comprises the following steps: S1, constructing an integrated energy system model according to the integrated energy system; s2, converting the integrated energy system model into a compact form to obtain a compact model; constructing an uncertainty set according to the compact model; s3, defining correlation and continuity of decision variables in a preset number of time periods according to the uncertainty set to obtain a first set; the first set comprises robust feasible regions of a preset number of time periods; s4, updating the robust feasible regions of the preset number of time periods in the first set in a rolling manner; optimizing the first constraint according to the first set to obtain a second constraint; s5, solving the dynamic robust feasible region according to the second constraint and the first set to obtain a second set; the second set comprises robust feasible regions of a preset number of time periods. The invention further discloses a dynamic robust feasible region solving system for scheduling robust optimization of the integrated energy system.

Description

Technical Field

[0001] The present invention relates to the field of integrated energy system scheduling, and in particular to a dynamic robust feasible domain solving method and system for robust optimization of integrated energy system scheduling. Background Art

[0002] With the development of Integrated Energy System (IES), renewable energy resources can be used more efficiently by coordinating the real-time dispatch of multiple energy sources such as electricity and heat. However, due to the volatility and unpredictability of renewable energy, the real-time dispatch of integrated energy systems faces great uncertainty and robustness challenges, especially under the premise that the system needs to balance economy and operational reliability. In the coordinated dispatch of multiple energy sources, it is often difficult to balance the safety and economy of system operation. Due to the randomness and volatility of renewable energy and the dynamic changes in load demand, the optimization problem of energy dispatch becomes more complicated.

[0003] Existing integrated energy system scheduling optimization methods mainly focus on two categories: deterministic optimization and robust optimization. Deterministic optimization assumes that the system operating environment is known. Although it can provide efficient scheduling solutions, its effectiveness is greatly reduced when facing energy fluctuations and demand changes in actual operation. Therefore, deterministic optimization is difficult to apply to actual working conditions. In traditional robust optimization, two-stage robust optimization is usually used for system scheduling, and the system's operating strategy under extreme conditions is preset to ensure the safety of operation. However, although the two-stage robust optimization has a certain degree of security, this method is too conservative and cannot flexibly respond to actual real-time fluctuations. In particular, when load demand and renewable energy output change frequently, it is difficult to balance the robustness of the system and the economy of scheduling. In the prior art, there are few technical solutions for constructing a dynamic robust feasible domain for robust optimization of integrated energy systems.

[0004] Therefore, a new technical solution is urgently needed to solve the technical problem of how to construct a dynamic robust feasible domain for robust optimization of integrated energy system scheduling. Summary of the invention

[0005] The present invention provides a method and system for solving a dynamic robust feasible domain for robust optimization of integrated energy system scheduling, so as to solve the technical problem of how to construct a dynamic robust feasible domain for robust optimization of integrated energy system scheduling.

[0006] To achieve the above object, the present invention provides a dynamic robust feasible region solution method for robust optimization of integrated energy system scheduling, comprising:

[0007] S1. Construct an integrated energy system model according to the integrated energy system; the integrated energy system model includes an objective function and a first constraint; the first constraint includes an operation constraint, a wind power generation constraint, a cogeneration and transmission constraint, an electric energy storage system constraint, and a thermal energy storage system constraint;

[0008] S2, transforming the integrated energy system model into a compact form to obtain a compact model; constructing an uncertainty set based on the compact model;

[0009] S3. Define the correlation and continuity of the decision variables in a preset number of time periods according to the uncertainty set to obtain a first set; the first set includes a robust feasible domain of the preset number of time periods;

[0010] S4. Rollingly updating the robust feasible domain of a preset number of time periods in the first set; optimizing the first constraint according to the first set to obtain a second constraint;

[0011] S5. Solve the dynamic robust feasible region according to the second constraint and the first set to obtain a second set; the second set includes robust feasible regions of a preset number of time periods.

[0012] Preferably, the objective function includes:

[0013] Total system operating cost:

[0014] minf=C buy +C fuel +C op +C punish

[0015] Among them, C buy represents the cost of purchasing electricity from the main grid; C fuel represents the fuel cost of the gas turbine; C op represents the operation and maintenance cost of the energy storage system; C punish represents the penalty cost; f represents the total cost of system operation;

[0016] The costs of buying electricity from the mains grid include:

[0017] The integrated energy system is connected to the main grid, and electricity is purchased and sold between the main grid and the integrated energy system. The purchase and sales costs can be expressed as:

[0018]

[0019] Among them, c grid,t is the electricity price of the relaxed node at time t; P 0,t is the active power imported from the grid at the slack node; Δt is one cycle; T is the total number of time periods in the scheduling cycle;

[0020] The fuel costs for gas turbines include:

[0021] The fuel cost curve of a gas turbine is described by a quadratic function and can be expressed as:

[0022]

[0023] Where a, b and c are the fuel cost coefficients of the gas turbine, where a is the fixed cost, b is the linear term coefficient, and c is the quadratic term coefficient; P CHP,g,t is the power generation of the g-th cogeneration unit at time t; G is the set of gas turbines;

[0024] The operation and maintenance costs of energy storage systems include:

[0025] The operation and maintenance cost mainly considers the charging and discharging losses of the energy storage system, including the losses of the electric energy storage system and the thermal energy storage tank, which can be expressed as:

[0026]

[0027] Among them, c bess-E and c bess-H denote the operation and maintenance costs of electric energy storage and thermal energy storage, respectively; S is the set of electric energy storage systems; and are the charging and discharging power of the sth energy storage system at time t respectively; and are the charging and discharging efficiency of the electric energy storage system s respectively; Φ S It is a collection of thermal energy storage systems; and are the heat absorption and release powers of thermal energy storage system j at time t respectively; and are the heat absorption and release efficiencies of the thermal energy storage system S, respectively;

[0028] Penalty costs include:

[0029] The penalty costs include wind curtailment penalty, voltage deviation penalty and temperature deviation penalty:

[0030]

[0031] Where: c pun,r 、c pun,v and c pun,τ are the penalty coefficients for wind power abandonment, voltage deviation and temperature deviation respectively; H, B and Φ B They are respectively the set of wind turbines, the set of grid nodes and the set of heat network nodes; is the abandoned wind power of the wind turbine at time t; U j,t is the square value of the voltage at the grid node j at time t; U refis the voltage reference value; τ k,t is the actual temperature of the heating network node k at time t; τ ref is the temperature reference value;

[0032] C fuel and C punish It is nonlinear. To facilitate the solution, we use the binary variable u=[u1,u2,...,u m ]The objective function C fuel Perform piecewise linearization and obtain:

[0033]

[0034] Where m is the number of segments; P CHP,α,g,t is the power generation of the g-th generator set in the αth segment at time t; k CHP,α is the slope of the αth segment, b n and b n-1 are the lengths of the nth and n-1th segments respectively; u n and u n+1 is a binary variable used to control the selection of segments; b1 is the length of the first segment; u1 is a variable that controls the segmentation of the first segment; P CHP,1,g,t is the power generated by the g-th generator set in the first section at time t; P CHP,n,g,t is the power generation of the g-th generator set in the n-th section at time t; u j-1 is the variable controlling the j-1 segment; P CHP,m,g,t is the power generation of the g-th generator set in the m-th section at time t; b m is the length of the mth segment; b m-1 is the length of the m-1th segment; u m-1 is the variable controlling the segmentation of segment m-1;

[0035] For C punish C pun,v ∑ j∈B |U j,t -U ref |, introduce variable μ j,t , so that it satisfies:

[0036]

[0037] Among them, μ j,t is the auxiliary variable introduced to linearize the absolute value of the voltage deviation;

[0038] And introduce the variable ψ k,t , for C punish In Perform linearization;

[0039] Get the optimized C punish, which can be expressed as:

[0040]

[0041] Among them, ψ k,t It is an auxiliary variable introduced to linearize the absolute value of temperature deviation.

[0042] Preferably, the operation constraints, wind power generation constraints, cogeneration and transmission constraints, power storage system constraints and thermal storage system constraints include:

[0043] Operational constraints include:

[0044] The active balance constraint, reactive balance constraint, linearized voltage drop constraint, voltage amplitude constraint and branch transmission power constraint in the distribution network can be expressed as:

[0045]

[0046] Among them, P l,t and Q l,t are the active power and reactive power requirements of branch l at time t respectively; and are the active power and reactive power injected into the system by node j at time t; P k,t and Q k,t are the active power output of node k at time t; λ(j) is the branch set starting from node j; r l and x l are the resistance and reactance of the line connecting nodes i and j, respectively; and is the voltage lower limit and upper limit of node i; S l is the apparent power capacity of line l;

[0047] The branch transmission power constraint is a second-order cone constraint. After approximate linearization, the linearization result can be expressed as:

[0048]

[0049] Where α is the edge index of the line capacity constraint approximation; Q i,t is the reactive power output of node i at time t; P i,t is the active power output of node i at time t.

[0050] Wind power generation constraints include:

[0051]

[0052] Where: is the power generated by turbine h at time t; is the abandoned wind power of wind turbine h at time t;

[0053] Cogeneration and transmission constraints include:

[0054] Power output constraints and heat production constraints for cogeneration units:

[0055]

[0056] Among them, H CHP,g,t is the heat generated by the g-th CHP unit at time t; and are the maximum and minimum power of the g-th cogeneration unit respectively; and are the maximum and minimum heat generated by the g-th cogeneration unit, respectively;

[0057] Constraints on the relationship between output electrical power and thermal power:

[0058]

[0059] Where R is the thermoelectric ratio;

[0060] In order to limit the change of output power in adjacent time periods, the slope constraint is adopted:

[0061]

[0062] Among them, P up,g and P down,g are the maximum upward and downward climbing rates of generator set g, respectively;

[0063] In the district heating network, the heating system is regulated by adjusting the mass flow. On the basis of the reasonable assumption of stable heating, the mass flow has a size and a fixed direction, and a linear heating network model is adopted that ignores heat loss. The heating network constraints consider the balance of thermal power and mass flow at each node and the transmission capacity limit of the heating pipeline.

[0064] Thermal balance constraints:

[0065]

[0066] Among them, H G,i,t and H L,i,t are the heat generation and heat load of node i at time t respectively; Heat flow from node j to node i at time t; is the heat outflow from node i to node k at time t; Φ in (i) and Φ out (i) is the set of all nodes flowing into and out of node i in the heat network;

[0067] Pipeline mass flow balance constraints:

[0068]

[0069] Among them, m ji,t is the mass flow of water in the heating network from node j to node i; m ik,t is the mass flow of water in the heating network from node k to node i;

[0070] After accounting for heat losses in the heating pipe, the constraints limiting the temperature drop during the transport process include:

[0071]

[0072] Among them, L ji is the length of the pipeline from node j to node i; V ji is the fluid flow rate from node j to node i; c is the specific heat capacity parameter related to the pipe connecting node i and node j at time t; Φ L It is a collection of pipes in the heating network; is the heat outflow from node j to node i at time t; τ a,t is the temperature of node a at time t;

[0073] The range constraints of the heating pipe temperature amplitude:

[0074]

[0075] Among them, τ i,t is the temperature of node i at time t; τ ji,t is the temperature in the pipe from node j to node i at time t; and are the minimum and maximum allowable temperatures of node i at time t, respectively; and are the minimum and maximum allowable temperatures in the pipeline from node j to node i at time t, respectively;

[0076] The range constraint of the temperature amplitude of the heating pipe is a nonlinear constraint, which is approximated as a linear constraint through Taylor expansion:

[0077]

[0078] in It includes the heat load demand of each node, the net heat release power of thermal energy storage and H CHP,h,t ;

[0079] Pipe heat transfer constraints:

[0080]

[0081] Among them, ΦL It is a collection of pipes in the heating network; is the minimum allowable temperature of node i at time j; is the maximum allowable temperature of node i at time t;

[0082] The mass flow rate should be less than the maximum capacity of the heating pipe:

[0083]

[0084] in, and m ji,t The upper and lower limits of

[0085] Limitations between heat, node temperature and mass flow:

[0086]

[0087] By McCormick convex relaxation method, To perform linearization:

[0088]

[0089] in, and m ik,t The upper and lower limits of

[0090] Electric energy storage system constraints include:

[0091]

[0092] in, is the maximum charging or discharging power of the energy storage system s; is the energy storage state of the energy storage system s at time t; and are the highest and lowest thresholds of energy storage of energy storage system s, respectively; is the energy storage state of the energy storage system s at time t-1.

[0093] Thermal energy storage system constraints include:

[0094]

[0095] in, is the stored heat of the thermal energy storage system S at time t; The stored heat of node S at time t-1; and are the upper and lower limits of stored heat, respectively; is the maximum charging or discharging heat of the energy storage system S.

[0096] Preferably, converting the integrated energy system model into a compact form to obtain a compact model includes:

[0097] The integrated energy system model is transformed into a compact form to obtain a compact model, which can be expressed as:

[0098]

[0099] Among them, x t is the state variable, y t is the independent decision variable for all time periods from time t to the end of the integrated energy system model; c t is the cost coefficient at time t; b t is the fixed constraint at time t; is the uncertain variable of wind power output; a, b, c and d represent the uncertain variables related to variable x t ,y t 、x t-1 and w t The coefficient of correlation.

[0100] Preferably, constructing an uncertainty set according to the compact model comprises:

[0101] Uncertainty set W t include:

[0102] W t ={w t ∣0≤w t ≤w max}

[0103] in, The maximum wind power output.

[0104] The correlation of decision variables in adjacent time periods is expressed as (x t ,y t )∈θ(x t-1 ,w t ),Right now

[0105] Preferably, the first set is obtained by defining the correlation and continuity of the decision variables in a preset number of time periods according to the uncertainty set, including:

[0106] Assume that the dynamic robust feasible region at each moment is F t , get the first set, including:

[0107]

[0108] By satisfying the constraints of the next moment, the robust feasible region of the previous moment can be derived; F t-1is the dynamic robust feasible region at time t-1; F t-1 Based on the first constraint and F t Assume that the robust feasible region of the last T cycle is F T ={x T ∣X n ≤x T ≤X m}, and reverse recursively derive the robust feasible region at each moment.

[0109] Preferably, the robust feasible region for rolling update of a preset number of time periods in the first set includes:

[0110] Since 24 hours is a long time, the rolling update method is adopted to effectively reduce conservatism and increase the economy and flexibility of operation. The whole process is divided into a preset number of time periods. In the first period, the dynamic feasible area of ​​time periods 1 to 12 is calculated and applied to time periods 1 to 6. The system status is updated according to the actual situation of time periods 1 to 6. Based on the updated status and new requirements, the dynamic feasible area of ​​time periods 6 to 12 is calculated again, and so on in each new time period.

[0111] Preferably, optimizing the first constraint according to the first set to obtain the second constraint includes:

[0112] θ(x t-1 ,w t ) and F t is a polyhedral set, and the dynamic robust feasible domain of each cycle is a polyhedral set, that is, a convex set; therefore, the first constraint and the first set are expressed as a compact model, which can be expressed as:

[0113]

[0114] in, and is the coefficient of the constraint; P is the slack variable for checking the feasibility of the constraint. If all constraints are satisfied, then P = 0. For a given This is equivalent to solving:

[0115]

[0116] Among them, γ is the corresponding dual variable;

[0117] Construct the Lagrangian function:

[0118]

[0119] The dual function g(γ) is defined as the minimum of the Lagrangian function:

[0120]

[0121] The dual problem includes:

[0122]

[0123] in, is the set of relaxations in the feasible domain, and each cycle is initially set to [X n ,X m ].

[0124] Preferably, the dynamic robust feasible region is solved according to the second constraint and the first set, and the second set is obtained including:

[0125] A1. Considering x t-1 The low dimension is solved by enumerating the vertices: if x t-1 Contains three decision variables, then its initial feasible region is a cuboid, enumerate all the vertices of the state variables at this time, and substitute them into the optimization problem V in turn * Perform optimization and obtain optimization results;

[0126] A2. Feasibility verification based on optimization results: calculate the optimal value V * and the optimal solution If V * =0, indicating that all decision variables are in the feasible region and meet the feasibility requirements, then return to A1 and perform a robust feasibility test at the next moment; if V * >0, indicating that there is a condition that is not met, then proceed to A3;

[0127] A3. Generate cutting planes and perform feasible area cutting: If V * >0, then there exists a set Construct the cutting plane H t , constrain Add to As x t-1 Constraints; By combining the intersection of the cutting plane and the original feasible region, the intersection of the plane and the boundary of the feasible region and the subsequent newly generated cutting surface boundary are calculated, and the obtained intersection is marked as a set Substitute the new vertices generated after cutting into X one by one n ,X m Solve the problem; then return to step A2 for testing;

[0128] A4. Complete the solution and store the feasible region constraints for each cycle: After solving the feasible region at each moment, we get the second set F = F1, F2, ..., F T-1 ].

[0129] The present invention also provides a dynamic robust feasible domain solution system for robust optimization of integrated energy system scheduling, which is used in the method of the present invention. The system includes a model construction module, a model conversion and uncertainty set construction module, a robust feasible domain construction module, a robust feasible domain update module and a robust feasible domain solution module;

[0130] The model building module is used to build an integrated energy system model according to the integrated energy system; the integrated energy system model includes an objective function and a first constraint; the first constraint includes an operation constraint, a wind power generation constraint, a cogeneration and transmission constraint, an electric energy storage system constraint, and a thermal energy storage system constraint;

[0131] The model conversion and uncertainty set construction module is used to convert the integrated energy system model into a compact form to obtain a compact model; and to construct an uncertainty set based on the compact model;

[0132] The robust feasible domain construction module is used to define the correlation and continuity of the decision variables in a preset number of time periods according to the uncertainty set to obtain a first set; the first set includes the robust feasible domain of the preset number of time periods;

[0133] The robust feasible domain update module is used to roll over the robust feasible domain of a preset number of time periods in the first set; optimize the first constraint according to the first set to obtain the second constraint;

[0134] The robust feasible domain solving module is used to solve the dynamic robust feasible domain according to the second constraint and the first set to obtain the second set; the second set includes the robust feasible domain of a preset number of time periods.

[0135] The present invention has the following beneficial effects:

[0136] The dynamic robust feasible domain solution method for integrated energy system scheduling robust optimization of the present invention ensures the real-time feasibility of system operation by constructing the dynamic robust feasible domain hour by hour, and improves the scheduling flexibility and economy of the system by optimizing the feasible domain, providing strong support for the integrated energy system to achieve efficient and stable scheduling in a complex and changeable environment. The method can cope with various extreme situations and has better robustness.

[0137] The dynamic robust feasible domain solving system for robust optimization of integrated energy system scheduling of the present invention is used in the method of the present invention and has the same beneficial effects as the method of the present invention.

[0138] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0139] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0140] Figure 1 It is a schematic diagram of a method flow of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0141] The embodiments of the present invention are described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0142] See also Figure 1 In a preferred embodiment of the present invention, a dynamic robust feasible region solution method for robust optimization of integrated energy system scheduling is provided, comprising:

[0143] S1. Construct an integrated energy system model based on the integrated energy system; the integrated energy system model includes an objective function and a first constraint; the first constraint includes an operation constraint, a wind power generation constraint, a cogeneration and transmission constraint, an electric energy storage system constraint, and a thermal energy storage system constraint.

[0144] In a preferred embodiment of the present invention, the objective function includes:

[0145] Total system operating cost:

[0146] minf=C buy +C fuel +C op +C punish

[0147] Among them, C buy represents the cost of purchasing electricity from the main grid; C fuel represents the fuel cost of the gas turbine; C op represents the operation and maintenance cost of the energy storage system; C punish represents the penalty cost; f represents the total cost of system operation;

[0148] The costs of buying electricity from the mains grid include:

[0149] The integrated energy system is connected to the main grid, and electricity is purchased and sold between the main grid and the integrated energy system. The purchase and sales costs can be expressed as:

[0150]

[0151] Among them, c grid,t is the electricity price of the relaxed node at time t; P 0,t is the active power imported from the grid at the slack node; Δt is one cycle; T is the total number of time periods in the scheduling cycle;

[0152] The fuel costs for gas turbines include:

[0153] The fuel cost curve of a gas turbine is described by a quadratic function and can be expressed as:

[0154]

[0155] Where a, b and c are the fuel cost coefficients of the gas turbine, where a is the fixed cost, b is the linear term coefficient, and c is the quadratic term coefficient; P CHP,g,t is the power generation of the g-th cogeneration unit at time t; G is the set of gas turbines;

[0156] The operation and maintenance costs of energy storage systems include:

[0157] The operation and maintenance cost mainly considers the charging and discharging losses of the energy storage system, including the losses of the electric energy storage system and the thermal energy storage tank, which can be expressed as:

[0158]

[0159] Among them, c bess-E and c bess-H denote the operation and maintenance costs of electric energy storage and thermal energy storage, respectively; S is the set of electric energy storage systems; and are the charging and discharging power of the sth energy storage system at time t respectively; and are the charging and discharging efficiency of the electric energy storage system s respectively; Φ S It is a collection of thermal energy storage systems; and are the heat absorption and release powers of thermal energy storage system j at time t respectively; and are the heat absorption and release efficiencies of the thermal energy storage system S, respectively;

[0160] Penalty costs include:

[0161] The penalty cost includes wind abandonment penalty, voltage deviation penalty and temperature deviation penalty. By setting the penalty cost, a compromise strategy is sought for the integrated energy microgrid to balance the economy of system operation and the quality of energy supply:

[0162]

[0163] Where: c pun,r 、c pun,v and c pun,τ are the penalty coefficients for wind power abandonment, voltage deviation and temperature deviation respectively; H, B and Φ B They are respectively the set of wind turbines, the set of grid nodes and the set of heat network nodes; is the abandoned wind power of the wind turbine at time t; U j,t is the square value of the voltage at the grid node j at time t; U ref is the voltage reference value; τ k,t is the actual temperature of the heating network node k at time t; τ ref is the temperature reference value;

[0164] C fuel and C punish It is nonlinear. To facilitate the solution, we use the binary variable u=[u1,u2,...,u m ]The objective function C fuel Perform piecewise linearization and obtain:

[0165]

[0166] Where m is the number of segments; P CHP,α,g,t is the power generation of the g-th generator set in the αth segment at time t; k CHP,α is the slope of the αth segment, b n and b n-1 are the lengths of the nth and n-1th segments respectively; u n and u n+1 is a binary variable used to control the selection of segments; b1 is the length of the first segment; u1 is a variable that controls the segmentation of the first segment; P CHP,1,g,t is the power generated by the g-th generator set in the first section at time t; P CHP,n,g,t is the power generation of the g-th generator set in the n-th section at time t; u j-1 is the variable controlling the j-1 segment; P CHP,m,g,t is the power generation of the g-th generator set in the m-th section at time t; b m is the length of the mth segment; b m-1 is the length of the m-1th segment; u m-1 is the variable controlling the segmentation of segment m-1;

[0167] For C punish C pun,v ∑ j∈B |U j,t -U ref |, introduce variable μ j,t , so that it satisfies:

[0168]

[0169] Among them, μ j,t is the auxiliary variable introduced to linearize the absolute value of the voltage deviation;

[0170] And introduce the variable ψ k,t , for Cpunish In Perform linearization;

[0171] Get the optimized C punish , which can be expressed as:

[0172]

[0173] Among them, ψ k,t It is an auxiliary variable introduced to linearize the absolute value of temperature deviation.

[0174] In a preferred embodiment of the present invention, the operation constraints, wind power generation constraints, cogeneration and transmission constraints, power storage system constraints and thermal storage system constraints include:

[0175] Operational constraints are used to describe the balance conditions of active and reactive power, voltage and power transfer restrictions.

[0176] Operational constraints include:

[0177] The active balance constraint, reactive balance constraint, linearized voltage drop constraint, voltage amplitude constraint and branch transmission power constraint in the distribution network can be expressed as:

[0178]

[0179] Among them, P l,t and Q l,t are the active power and reactive power requirements of branch l at time t respectively; and are the active power and reactive power injected into the system by node j at time t; P k,t and Q k,t are the active power output of node k at time t; λ(j) is the branch set starting from node j; r l and x l are the resistance and reactance of the line connecting nodes i and j, respectively; and is the voltage lower limit and upper limit of node i; S l is the apparent power capacity of line l;

[0180] The branch transmission power constraint is a second-order cone constraint. After approximate linearization, the linearization result can be expressed as:

[0181]

[0182] Where α is the edge index of the line capacity constraint approximation; Q i,t is the reactive power output of node i at time t; P i,t is the active power output of node i at time t.

[0183] Wind power generation constraints include:

[0184]

[0185] Where: is the power generated by turbine h at time t; is the abandoned wind power of wind turbine h at time t;

[0186] Cogeneration and transmission constraints include:

[0187] Power output constraints and heat production constraints for cogeneration units:

[0188]

[0189] Among them, H CHP,g,t is the heat generated by the g-th CHP unit at time t; and are the maximum and minimum power of the g-th cogeneration unit respectively; and are the maximum and minimum heat generated by the g-th cogeneration unit, respectively;

[0190] Constraints on the relationship between output electrical power and thermal power:

[0191]

[0192] Where R is the thermoelectric ratio;

[0193] In order to limit the change of output power in adjacent time periods, the slope constraint is adopted:

[0194]

[0195] Among them, P up,g and P down,g are the maximum upward and downward climbing rates of generator set g, respectively;

[0196] In the district heating network, the heating system is regulated by adjusting the mass flow. On the basis of the reasonable assumption of stable heating, the mass flow has a size and a fixed direction, and a linear heating network model is adopted that ignores heat loss. The heating network constraints consider the balance of thermal power and mass flow at each node and the transmission capacity limit of the heating pipeline.

[0197] Thermal balance constraints:

[0198]

[0199] Among them, H G,i,t and H L,i,t are the heat generation and heat load of node i at time t respectively; Heat flow from node j to node i at time t; is the heat outflow from node i to node k at time t; Φ in (i) and Φ out (i) is the set of all nodes flowing into and out of node i in the heat network;

[0200] Pipeline mass flow balance constraints:

[0201]

[0202] Among them, m ji,t is the mass flow of water in the heating network from node j to node i; m ik,t is the mass flow of water in the heating network from node k to node i;

[0203] After accounting for heat losses in the heating pipe, the constraints limiting the temperature drop during the transport process include:

[0204]

[0205] Among them, L ji is the length of the pipeline from node j to node i; V ji is the fluid flow rate from node j to node i; c is the specific heat capacity parameter related to the pipe connecting node i and node j at time t; Φ L It is a collection of pipes in the heating network; is the heat outflow from node j to node i at time t; τ a,t is the temperature of node a at time t;

[0206] The range constraints of the heating pipe temperature amplitude:

[0207]

[0208] Among them, τ i,t is the temperature of node i at time t; τ ji,t is the temperature in the pipe from node j to node i at time t; and are the minimum and maximum allowable temperatures of node i at time t, respectively; and are the minimum and maximum allowable temperatures in the pipeline from node j to node i at time t, respectively;

[0209] The range constraint of the temperature amplitude of the heating pipe is a nonlinear constraint, which is approximated as a linear constraint through Taylor expansion:

[0210]

[0211] in It includes the heat load demand of each node, the net heat release power of thermal energy storage and H CHP,g,t ;

[0212] Pipe heat transfer constraints:

[0213]

[0214]

[0215] Among them, Φ L It is a collection of pipes in the heating network; is the minimum allowable temperature of node i at time t; is the maximum allowable temperature of node i at time t;

[0216] The mass flow rate should be less than the maximum capacity of the heating pipe:

[0217]

[0218] in, and m ji,t The upper and lower limits of

[0219] Limitations between heat, node temperature and mass flow:

[0220]

[0221] By McCormick convex relaxation method, To perform linearization:

[0222]

[0223] in, and m ik,t The upper and lower limits of

[0224] Electric energy storage system constraints include:

[0225]

[0226] in, is the maximum charging or discharging power of the energy storage system s; is the energy storage state of the energy storage system s at time t; and are the highest and lowest thresholds of energy storage of energy storage system s, respectively; is the energy storage state of the energy storage system s at time t-1.

[0227] Thermal energy storage system constraints include:

[0228]

[0229] in, is the stored heat of the thermal energy storage system S at time t; The stored heat of node S at time t-1; and are the upper and lower limits of stored heat, respectively; is the maximum charging or discharging heat of the energy storage system S.

[0230] S2. Convert the integrated energy system model into a compact form to obtain a compact model; and construct an uncertainty set based on the compact model.

[0231] In a preferred embodiment of the present invention, the integrated energy system model is converted into a compact form, and the compact model obtained includes:

[0232] The integrated energy system model is transformed into a compact form to obtain a compact model, which can be expressed as:

[0233]

[0234] Among them, x t is the state variable, y t is the independent decision variable for all time periods from time t to the end of the integrated energy system model; c t is the cost coefficient at time t; b t is the fixed constraint at time t; is the uncertain variable of wind power output; a, b, c and d represent the uncertain variables related to variable x t ,y t 、x t-1 and w t The coefficient of correlation.

[0235] In a preferred embodiment of the present invention, constructing an uncertainty set according to a compact model includes:

[0236] Uncertainty set W t include:

[0237] W t ={w t ∣0≤w t ≤w max}

[0238] in, The maximum wind power output.

[0239] The correlation of decision variables in adjacent time periods is expressed as (x t ,y t )∈θ(x t-1 ,w t ),Right now

[0240] S3. Define the correlation and continuity of decision variables in a preset number of time periods according to the uncertainty set to obtain a first set; the first set includes a robust feasible domain of a preset number of time periods.

[0241] In a preferred embodiment of the present invention, the correlation and continuity of decision variables in a preset number of time periods are defined according to the uncertainty set, and the first set includes:

[0242] Assume that the dynamic robust feasible region at each moment is F t , get the first set, including:

[0243]

[0244] By satisfying the constraints of the next moment, the robust feasible region of the previous moment can be derived; F t-1 is the dynamic robust feasible region at time t-1; F t-1 Based on the first constraint and F t Assume that the robust feasible region of the last T cycle is F T ={x T ∣X n ≤x T ≤X m}, and reverse recursively derive the robust feasible region at each moment.

[0245] S4. Rollingly update the robust feasible domain of a preset number of time periods in the first set; optimize the first constraint according to the first set to obtain the second constraint.

[0246] In a preferred embodiment of the present invention, the robust feasible region for rolling update of a preset number of time periods in the first set includes:

[0247] Since 24 hours is a long time, the rolling update method is adopted to effectively reduce conservatism, increase the economy and flexibility of operation, and divide the whole process into a preset number of time periods. For example, 24 hours can be divided into 4 periods, each of which is 6 hours. In the first period, the dynamic feasible area of ​​time periods 1 to 12 is calculated and applied to time periods 1 to 6. The system status is updated according to the actual situation of time periods 1 to 6. Based on the updated status and new requirements, the dynamic feasible area of ​​time periods 6 to 12 is calculated again, and so on in each new time period.

[0248] In a preferred embodiment of the present invention, optimizing the first constraint according to the first set to obtain the second constraint includes:

[0249] θ(x t-1 ,w t ) and Ft is a polyhedral set, and the dynamic robust feasible domain of each cycle is a polyhedral set, that is, a convex set; therefore, the first constraint and the first set are expressed as a compact model, which can be expressed as:

[0250]

[0251] in, and is the coefficient of the constraint; P is the slack variable for checking the feasibility of the constraint. If all constraints are satisfied, then P = 0. For a given This is equivalent to solving:

[0252]

[0253] Among them, γ is the corresponding dual variable;

[0254] Construct the Lagrangian function:

[0255]

[0256] The dual function g(γ) is defined as the minimum of the Lagrangian function:

[0257]

[0258] The dual problem includes:

[0259]

[0260] in, is the set of relaxations in the feasible domain, and each cycle is initially set to [X n ,X m ].

[0261] S5. Solve the dynamic robust feasible region according to the second constraint and the first set to obtain a second set; the second set includes robust feasible regions of a preset number of time periods.

[0262] In a preferred embodiment of the present invention, the dynamic robust feasible region is solved according to the second constraint and the first set, and the second set includes:

[0263] A1. Considering x t-1 The low dimension is solved by enumerating the vertices: if x t-1 Contains three decision variables, then its initial feasible region is a cuboid, enumerate all the vertices of the state variables at this time, and substitute them into the optimization problem V in turn * Perform optimization and obtain optimization results;

[0264] A2. Feasibility verification based on optimization results: calculate the optimal value V * and the optimal solution If V * =0, indicating that all decision variables are in the feasible region and meet the feasibility requirements, then return to A1 and perform a robust feasibility test at the next moment; if V * >0, indicating that there is a condition that is not met, then proceed to A3;

[0265] A3. Generate cutting planes and perform feasible area cutting: If V * >0, then there exists a set Construct the cutting plane H t , constrain Add to As x t-1 Constraints; By combining the intersection of the cutting plane and the original feasible region, the intersection of the plane and the boundary of the feasible region and the subsequent newly generated cutting surface boundary are calculated, and the obtained intersection is marked as a set Substitute the new vertices generated after cutting into X one by one n ,X m Solve the problem; then return to step A2 for testing;

[0266] A4. Complete the solution and store the feasible region constraints for each cycle: After solving the feasible region at each moment, we get the second set F = [F1, F2, ..., F T-1 ].

[0267] The dynamic robust feasible domain solution method for integrated energy system scheduling robust optimization of the present invention ensures the real-time feasibility of system operation by constructing the dynamic robust feasible domain hour by hour, and improves the scheduling flexibility and economy of the system by optimizing the feasible domain, providing strong support for the integrated energy system to achieve efficient and stable scheduling in a complex and changeable environment. The method can cope with various extreme situations and has better robustness.

[0268] The present invention proposes a multi-level robust optimization framework for an integrated energy microgrid system. Compared with conventional economic dispatch, the proposed method can cope with various extreme situations and has better robustness.

[0269] The invention proposes a projection algorithm based on linear programming (LP). Different from the traditional McCormick relaxation method, the method transforms the bilinear programming (BP) problem into multiple LP problems through vertex enumeration, which greatly improves the solution speed.

[0270] The present invention also provides a dynamic robust feasible domain solution system for robust optimization of integrated energy system scheduling, which is used in the method of the present invention. The system includes a model construction module, a model conversion and uncertainty set construction module, a robust feasible domain construction module, a robust feasible domain update module and a robust feasible domain solution module;

[0271] The model building module is used to build an integrated energy system model according to the integrated energy system; the integrated energy system model includes an objective function and a first constraint; the first constraint includes an operation constraint, a wind power generation constraint, a cogeneration and transmission constraint, an electric energy storage system constraint, and a thermal energy storage system constraint;

[0272] The model conversion and uncertainty set construction module is used to convert the integrated energy system model into a compact form to obtain a compact model; and to construct an uncertainty set based on the compact model;

[0273] The robust feasible domain construction module is used to define the correlation and continuity of the decision variables in a preset number of time periods according to the uncertainty set to obtain a first set; the first set includes the robust feasible domain of the preset number of time periods;

[0274] The robust feasible domain update module is used to roll over the robust feasible domain of a preset number of time periods in the first set; optimize the first constraint according to the first set to obtain the second constraint;

[0275] The robust feasible domain solving module is used to solve the dynamic robust feasible domain according to the second constraint and the first set to obtain the second set; the second set includes the robust feasible domain of a preset number of time periods.

[0276] The dynamic robust feasible domain solving system for robust optimization of integrated energy system scheduling of the present invention is used in the method of the present invention and has the same beneficial effects as the method of the present invention.

[0277] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A dynamic robust feasible region solution method for robust optimization of integrated energy system scheduling, characterized in that: include: S1. Constructing an integrated energy system model according to the integrated energy system; the integrated energy system model includes an objective function and a first constraint; the first constraint includes an operation constraint, a wind power generation constraint, a cogeneration and transmission constraint, an electric energy storage system constraint, and a thermal energy storage system constraint; S2. converting the integrated energy system model into a compact form to obtain a compact model; and constructing an uncertainty set according to the compact model; S3. Defining the correlation and continuity of decision variables in a preset number of time periods according to the uncertainty set to obtain a first set; the first set includes a robust feasible domain of a preset number of time periods; S4. Rollingly updating the robust feasible domain of the preset number of time periods in the first set; Optimize the first constraint according to the first set to obtain a second constraint; S5. Solve the dynamic robust feasible domain according to the second constraint and the first set to obtain a second set; the second set includes the robust feasible domain of the preset number of time periods.

2. The dynamic robust feasible region solution method for robust optimization of integrated energy system scheduling according to claim 1 is characterized in that: The objective function includes: Total system operating cost: minf=C buy +C fuel +C op +C punish Among them, C buy represents the cost of purchasing electricity from the main grid; C fuel represents the fuel cost of the gas turbine; C op represents the operation and maintenance cost of the energy storage system; C punish represents the penalty cost; f represents the total cost of system operation; The cost of purchasing electricity from the main grid includes: The integrated energy system is connected to the main grid, and electricity is purchased and sold between the main grid and the integrated energy system. The purchase and sales costs can be expressed as: Among them, c grid,t is the electricity price of the relaxed node at time t; P 0,t is the active power imported from the grid at the slack node; Δt is one cycle; T is the total number of time periods in the scheduling cycle; The fuel cost of the gas turbine includes: The fuel cost curve of a gas turbine is described by a quadratic function and can be expressed as: Where a, b and c are the fuel cost coefficients of the gas turbine, where a is the fixed cost, b is the linear term coefficient, and c is the quadratic term coefficient; P CHP,g,t is the power generation of the g-th cogeneration unit at time t; G is the set of gas turbines; The operation and maintenance costs of the energy storage system include: The operation and maintenance cost mainly considers the charging and discharging losses of the energy storage system, including the losses of the electric energy storage system and the thermal energy storage tank, which can be expressed as: Among them, c bess-E and c bess-H denote the operation and maintenance costs of electric energy storage and thermal energy storage, respectively; S is the set of electric energy storage systems; and are the charging and discharging power of the sth energy storage system at time t respectively; and are the charging and discharging efficiency of the electric energy storage system s respectively; Φ S It is a collection of thermal energy storage systems; and are the heat absorption and release powers of thermal energy storage system j at time t, respectively; and are the heat absorption and release efficiencies of the thermal energy storage system S, respectively; The penalty costs include: The penalty costs include wind curtailment penalty, voltage deviation penalty and temperature deviation penalty: Where: c pun,r 、c pun,v and c pun,τ are the penalty coefficients for wind power abandonment, voltage deviation and temperature deviation respectively; H, B and Φ B They are respectively the set of wind turbines, the set of grid nodes and the set of heat network nodes; is the abandoned wind power of the wind turbine at time t; U j,t is the square value of the voltage at the grid node j at time t; U ref is the voltage reference value; τ k,t is the actual temperature of the heating network node k at time t; τ ref is the temperature reference value; C fuel and C punish It is nonlinear. To facilitate the solution, we use the binary variable u=[u1,u2,...,u m ]The objective function C fuel Perform piecewise linearization and obtain: Where m is the number of segments; P CHP,α,g,t is the power generation of the g-th generator set in the αth segment at time t; k CHP,α is the slope of the αth segment, b n and b n-1 are the lengths of the nth and n-1th segments respectively; u n and u n+1 is a binary variable used to control the selection of segments; b1 is the length of the first segment; u1 is a variable that controls the segmentation of the first segment; P CHP,1,g,t is the power generated by the g-th generator set in the first section at time t; P CHP,n,g,t is the power generation of the g-th generator set in the n-th section at time t; u j-1 is the variable controlling the j-1 segment; P CHP,m,g,t is the power generation of the g-th generator set in the m-th section at time t; b m is the length of the mth segment; b m-1 is the length of the m-1th segment; u m-1 is the variable controlling the segmentation of segment m-1; For C punish In Introducing the variable μ j,t , so that it satisfies: Among them, μ j,t is the auxiliary variable introduced to linearize the absolute value of the voltage deviation; And introduce the variable ψ k,t , for C punish In Perform linearization; Get the optimized C punish , which can be expressed as: Among them, ψ k,t It is an auxiliary variable introduced to linearize the absolute value of temperature deviation.

3. The dynamic robust feasible region solution method for robust optimization of integrated energy system scheduling according to claim 2 is characterized in that: The operation constraints, wind power generation constraints, cogeneration and transmission constraints, power storage system constraints and thermal storage system constraints include: The operating constraints include: The active balance constraint, reactive balance constraint, linearized voltage drop constraint, voltage amplitude constraint and branch transmission power constraint in the distribution network can be expressed as: Among them, P l,t and Q l,t are the active power and reactive power requirements of branch l at time t respectively; and are the active power and reactive power injected into the system by node j at time t; P k,t and Q k,t are the active power output of node k at time t; λ(j) is the branch set starting from node j; r l and x l are the resistance and reactance of the line connecting nodes i and j, respectively; and is the voltage lower limit and upper limit of node i; S l is the apparent power capacity of line l; The branch transmission power constraint is a second-order cone constraint, and the linearization result obtained after approximate linearization can be expressed as: Where α is the edge index of the line capacity constraint approximation; Q i,t is the reactive power output of node i at time t; P i,t is the active power output of node i at time t. The wind power generation constraints include: Where: is the power generated by turbine h at time t; is the abandoned wind power of wind turbine h at time t; The CHP and transmission constraints include: Power output constraints and heat production constraints for cogeneration units: Among them, H CHP,g,t is the heat generated by the g-th CHP unit at time t; and are the maximum and minimum power of the g-th cogeneration unit respectively; and are the maximum and minimum heat generated by the g-th cogeneration unit, respectively; Constraints on the relationship between output electrical power and thermal power: Where R is the thermoelectric ratio; In order to limit the change of output power in adjacent time periods, the slope constraint is adopted: Among them, P up,g and P down,g are the maximum upward and downward climbing rates of generator set g, respectively; In the district heating network, the heating system is regulated by adjusting the mass flow. On the basis of the reasonable assumption of stable heating, the mass flow has a size and a fixed direction, and a linear heating network model is adopted that ignores heat loss. The heating network constraints consider the balance of thermal power and mass flow at each node and the transmission capacity limit of the heating pipeline. Thermal balance constraints: Among them, H G,i,t and H L,i,t are the heat generation and heat load of node i at time t respectively; Heat flow from node j to node i at time t; is the heat outflow from node i to node k at time t; Φ in (i) and Φ out (i) is the set of all nodes flowing into and out of node i in the heat network; Pipeline mass flow balance constraints: Among them, m ji,t is the mass flow of water in the heating network from node j to node i; m ik,t is the mass flow of water in the heating network from node k to node i; After accounting for heat losses in the heating pipe, the constraints limiting the temperature drop during the transport process include: Among them, L ji is the length of the pipeline from node j to node i; V ji is the fluid flow rate from node j to node i; c is the specific heat capacity parameter related to the pipe connecting node i and node j at time t; Φ L It is a collection of pipes in the heating network; is the heat outflow from node j to node i at time t; τ a,t is the temperature of node a at time t; The range constraints of the heating pipe temperature amplitude: Among them, τ i,t is the temperature of node i at time t; τ ji,t is the temperature in the pipe from node j to node i at time t; and are the minimum and maximum allowable temperatures of node i at time t, respectively; and are the minimum and maximum allowable temperatures in the pipeline from node j to node i at time t, respectively; The range constraint of the temperature amplitude of the heating pipe is a nonlinear constraint, which is approximated as a linear constraint through Taylor expansion: in It includes the heat load demand of each node, the net heat release power of thermal energy storage and H CHP,g,t ; Pipe heat transfer constraints: Among them, Φ L It is a collection of pipes in the heating network; is the minimum allowable temperature of node i at time j; is the maximum allowable temperature of node i at time t; The mass flow rate should be less than the maximum capacity of the heating pipe: in, and m ji,t The upper and lower limits of Limitations between heat, node temperature and mass flow: By McCormick convex relaxation method, To perform linearization: in, and m ik,t The upper and lower limits of The power storage system constraints include: in, is the maximum charging or discharging power of the energy storage system s; is the energy storage state of the energy storage system s at time t; and are the highest and lowest thresholds of energy storage of energy storage system s, respectively; is the energy storage state of the energy storage system s at time t-1. The thermal energy storage system constraints include: in, is the stored heat of the thermal energy storage system S at time t; The stored heat of node S at time t-1; and are the upper and lower limits of stored heat, respectively; is the maximum charging or discharging heat of the energy storage system S.

4. The dynamic robust feasible region solution method for robust optimization of integrated energy system scheduling according to claim 3 is characterized in that: The integrated energy system model is converted into a compact form, and the obtained compact model includes: The integrated energy system model is converted into a compact form to obtain a compact model, which can be expressed as: Among them, x t is the state variable, y t is the independent decision variable for all time periods from time t to the end of the integrated energy system model; c t is the cost coefficient at time t; b t is the fixed constraint at time t; is the uncertain variable of wind power output; a, b, c and d represent the uncertain variables related to variable x t ,y t 、x t-1 and w t The coefficient of correlation.

5. The dynamic robust feasible region solution method for robust optimization of integrated energy system scheduling according to claim 4 is characterized in that: Constructing the uncertainty set according to the compact model includes: The uncertainty set W t include: IN t ={in t ∣0≤in t ≤in max } in, The maximum wind power output. The correlation of decision variables in adjacent time periods is expressed as (x t ,y t )∈θ(x t-1 ,w t ),Right now 6. The dynamic robust feasible region solution method for integrated energy system scheduling robust optimization according to claim 5 is characterized in that: According to the uncertainty set, the correlation and continuity of the decision variables in a preset number of time periods are defined, and the first set includes: Assume that the dynamic robust feasible region at each moment is F t , get the first set, including: By satisfying the constraints of the next moment, the robust feasible region of the previous moment can be derived; F t-1 is the dynamic robust feasible region at time t-1; F t-1 Based on the first constraint and F t Assume that the robust feasible region of the last T cycle is F T ={x T ∣X n ≤x T ≤X m }, and reverse recursively derive the robust feasible region at each moment.

7. The dynamic robust feasible region solution method for integrated energy system scheduling robust optimization according to claim 6 is characterized in that: The robust feasible domain for rolling update of the preset number of time periods in the first set includes: Since 24 hours is a long time, the rolling update method is adopted to effectively reduce conservatism and increase the economy and flexibility of operation. The whole process is divided into a preset number of time periods. In the first period, the dynamic feasible area of ​​time periods 1 to 12 is calculated and applied to time periods 1 to 6. The system status is updated according to the actual situation of time periods 1 to 6. Based on the updated status and new requirements, the dynamic feasible area of ​​time periods 6 to 12 is calculated again, and so on in each new time period.

8. The dynamic robust feasible region solution method for integrated energy system scheduling robust optimization according to claim 7 is characterized in that: Optimizing the first constraint according to the first set to obtain the second constraint includes: θ(x t-1 ,w t ) and F t is a polyhedral set, and the dynamic robust feasible domain of each cycle is a polyhedral set, that is, a convex set; therefore, the first constraint and the first set are expressed as a compact model, which can be expressed as: in, and is the coefficient of the constraint; P is the slack variable for checking the feasibility of the constraint. If all constraints are satisfied, then P = 0. For a given This is equivalent to solving: Among them, γ is the corresponding dual variable; Construct the Lagrangian function: The dual function g(γ) is defined as the minimum of the Lagrangian function: The dual problem includes: in, is the set of relaxations in the feasible domain, and each cycle is initially set to [X n ,X m ].

9. The dynamic robust feasible region solution method for robust optimization of integrated energy system scheduling according to claim 8, characterized in that: The dynamic robust feasible region is solved according to the second constraint and the first set, and the second set is obtained, including: A1. Considering x t-1 The low dimension is solved by enumerating the vertices: if x t-1 Contains three decision variables, then its initial feasible region is a cuboid, enumerate all the vertices of the state variables at this time, and substitute them into the optimization problem V in turn * Perform optimization and obtain optimization results; A2. Perform feasibility verification based on the optimization results: calculate and obtain the optimal value V * and the optimal solution If V * =0, indicating that all decision variables are in the feasible region and meet the feasibility requirements, then return to A1 and perform a robust feasibility test at the next moment; if V * >0, indicating that there is a condition that is not met, then proceed to A3; A3. Generate cutting planes and perform feasible area cutting: If V * >0, then there exists a set Construct the cutting plane H t , constrain Add to As x t-1 Constraints; By combining the intersection of the cutting plane and the original feasible region, the intersection of the plane and the boundary of the feasible region and the subsequent newly generated cutting surface boundary are calculated, and the obtained intersection is marked as a set Substitute the new vertices generated after cutting into X one by one n ,X m Solve the problem; then return to step A2 for testing; A4. Complete the solution and store the feasible region constraints for each cycle: After solving the feasible region at each moment, the second set F = [F1,F2,…,F T-1 ]。 10. A dynamic robust feasible domain solution system for robust optimization of integrated energy system scheduling, used in the method according to any one of claims 1 to 9, characterized in that: The system includes a model building module, a model conversion and uncertainty set building module, a robust feasible domain building module, a robust feasible domain updating module and a robust feasible domain solving module; The model building module is used to build an integrated energy system model according to the integrated energy system; the integrated energy system model includes an objective function and a first constraint; the first constraint includes an operation constraint, a wind power generation constraint, a cogeneration and transmission constraint, an electric energy storage system constraint, and a thermal energy storage system constraint; The model conversion and uncertainty set construction module is used to convert the integrated energy system model into a compact form to obtain a compact model; and to construct an uncertainty set according to the compact model; The robust feasible domain construction module is used to define the correlation and continuity of decision variables in a preset number of time periods according to the uncertainty set to obtain a first set; the first set includes the robust feasible domain of the preset number of time periods; The robust feasible domain updating module is used to rollingly update the robust feasible domain of the preset number of time periods in the first set; optimize the first constraint according to the first set to obtain a second constraint; The robust feasible domain solving module is used to solve the dynamic robust feasible domain according to the second constraint and the first set to obtain a second set; the second set includes the robust feasible domain of the preset number of time periods.

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