A dynamic robust feasible region solving method and system for integrated energy system scheduling robust optimization

Abstract

The application discloses a dynamic robust feasible region solving method for integrated energy system scheduling robust optimization, comprising 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, 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 comprises robust feasible regions of the preset number of time periods; S4, rolling updating the robust feasible regions of the preset number of time periods in the first set; optimizing first constraints according to the first set to obtain second constraints; S5, solving the dynamic robust feasible region according to the second constraints and the first set to obtain a second set; the second set comprises robust feasible regions of the preset number of time periods. The application further discloses a dynamic robust feasible region solving system for integrated energy system scheduling robust optimization.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system scheduling, and in particular to a method and system for solving the dynamic robust feasible region for robust optimization of integrated energy system scheduling. Background Technology

[0002] With the development of Integrated Energy Systems (IES), renewable energy resources can be utilized more efficiently through the real-time scheduling of various energy sources such as electricity and heat. However, due to the volatility and unpredictability of renewable energy, the real-time scheduling of IES faces significant uncertainties and robustness challenges, especially when the system needs to balance economic efficiency and operational reliability. In the coordinated scheduling 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 scheduling becomes even more complex.

[0003] Existing integrated energy system scheduling optimization methods mainly fall into two categories: deterministic optimization and robust optimization. Deterministic optimization assumes a known system operating environment and, while providing efficient scheduling schemes, its effectiveness is significantly reduced when faced with energy fluctuations and demand changes in actual operation, making it difficult to apply to real-world conditions. Traditional robust optimization typically employs a two-stage robust optimization approach for system scheduling, pre-setting operating strategies for extreme conditions to ensure operational safety. However, while two-stage robust optimization offers a degree of safety, this approach is overly conservative and cannot flexibly address real-time fluctuations, especially when load demand and renewable energy output change frequently, making it difficult to balance system robustness and scheduling economics. Currently, there are few technical solutions for constructing dynamic robust feasible regions for robust optimization of integrated energy systems.

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

[0005] This invention provides a method and system for solving dynamic robust feasible regions for robust optimization of integrated energy system scheduling, in order to solve the technical problem of how to construct a dynamic robust feasible region for robust optimization of integrated energy system scheduling.

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

[0007] 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 operational constraints, wind power generation constraints, combined heat and power and transmission constraints, electric energy storage system constraints, and thermal energy storage system constraints.

[0008] S2. Transform the integrated energy system model into a compact form to obtain a compact model; construct an uncertainty set based on the compact model.

[0009] S3. Define the correlation and continuity of decision variables over a predetermined number of time periods based on the uncertainty set to obtain the first set; the first set includes the robust feasible region over a predetermined number of time periods.

[0010] S4. Roll out the robust feasible region of the first set for a preset number of time periods; optimize the first constraint based on the first set to obtain the second constraint;

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

[0012] Preferably, the objective function includes:

[0013] Total system operating cost:

[0014] ;

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

[0016] The cost of purchasing electricity from the main grid includes:

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

[0018] ;

[0019] in, Let be the electricity price at time t, which is the relaxation node. This refers to the active power imported from the grid at the relaxation node; One cycle is defined as T; T represents the total number of time periods in the scheduling cycle.

[0020] The fuel cost of a gas turbine includes:

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

[0022] ;

[0023] in, , and Here, represents the fuel cost coefficient for the gas turbine, where... For fixed costs, The coefficients of the linear term, The coefficient of the quadratic term; Let G be the power generation of the g-th cogeneration unit at time t; G is the collection of gas turbines.

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

[0025] Operation and maintenance costs mainly consider the charging and discharging losses of the energy storage system, including the losses of the electrical energy storage system and the thermal energy storage tank, which can be expressed as:

[0026] ;

[0027] in, and These represent the operation and maintenance costs of electrical energy storage and thermal energy storage, respectively; M is the collection of electrical energy storage systems. and At time respectively No. The charging and discharging power of an energy storage system; and These are respectively electrical energy storage systems The charging and discharging efficiency; A collection of thermal energy storage systems; and At time respectively Thermal energy storage system The heat absorption and release power; and These are the heat absorption and heat release efficiencies of the thermal energy storage system S, respectively.

[0028] The costs of punishment include:

[0029] Penalty costs include wind curtailment penalties, voltage deviation penalties, and temperature deviation penalties:

[0030] ;

[0031] In the formula: , and These are the penalty coefficients for wind curtailment, voltage deviation, and temperature deviation, respectively. , and These are respectively a set of wind turbine generators, a set of power grid nodes, and a set of heating network nodes; Let h be the wind curtailment power of the wind turbine at time t. For at any time power grid nodes The square of the voltage; This is the voltage reference value; For at any time heating network nodes The actual temperature; This is a temperature reference value;

[0032] and Since it is nonlinear, a binary variable is used for ease of solution. The objective function After piecewise linearization, we get:

[0033] ;

[0034] ;

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] in, This represents the number of segments; For time t, the g-th generator unit is at time t. The power generation capacity of the section; For the first The slope of the segment and These are the lengths of the nth and (n-1th)th segments, respectively. and It is a binary variable used to control the selection of segments; The length of the first segment; To control the variables in the first segment; Let g be the power output of the g-th generator unit at time t in the first segment; Let g be the power output of the g-th generator unit at time t in segment n. Variables to control the (j-1)th segment; Let g be the power output of the g-th generator unit at time t in segment m. Let m be the length of the m-th segment; Let m be the length of the (m-1)th segment; Variables to control the (m-1)th segment;

[0040] for In Introducing variables To satisfy:

[0041] ;

[0042] in, An auxiliary variable introduced to linearize the absolute value of the voltage deviation;

[0043] And introduce variables ,right In Perform linearization;

[0044] Optimized , can be represented as:

[0045] ;

[0046] ;

[0047] ;

[0048] in, This is an auxiliary variable introduced to linearize the absolute value of the temperature deviation.

[0049] Preferably, the operational constraints, wind power generation constraints, combined heat and power (CHP) and transmission constraints, electrical energy storage system constraints, and thermal energy storage system constraints include:

[0050] Operational constraints include:

[0051] The active power balance constraint, reactive power balance constraint, linearized voltage drop constraint, voltage magnitude constraint, and branch transmission power constraint in the distribution network can be expressed as follows:

[0052] ;

[0053] in, and Branch roads exist The active and reactive power requirements at any given time; and They are nodes exist The active and reactive power constantly injected into the system; and They are nodes exist Active power output at any given time; For nodes The initial set of branches; and These are the connection nodes. and The resistance and reactance of the circuit; and For nodes The lower and upper limits of the voltage; For the line Apparent power capacity; This represents the line in the distribution network branch set L that is connected to node i and node j; This represents the set of nodes corresponding to the downstream branch originating from node j;

[0054] The branch transmission power constraint is a second-order cone constraint. After approximate linearization, the linearized result can be expressed as:

[0055] ;

[0056] in, The edge index is used to approximate the linear capacity constraint. For node i in Reactive power output at any given moment; For node i in Active power output at any given moment.

[0057] Wind power generation constraints include:

[0058] ;

[0059] In the formula: Let h be the power generated by the steam turbine at time t; Let h be the wind curtailment power of the wind turbine at time t;

[0060] Cogeneration and transmission constraints include:

[0061] Power output constraints and thermal energy production constraints of the cogeneration unit:

[0062] ;

[0063] in, Let be the heat generated by the g-th cogeneration unit at time t; and These are the maximum and minimum power of the g-th cogeneration unit, respectively; and These are the maximum and minimum heat output of the g-th cogeneration unit, respectively;

[0064] Constraints on the relationship between output electrical power and thermal power:

[0065] ;

[0066] Where R is the thermoelectric ratio;

[0067] To limit the variation in output power between adjacent time periods, a ramp constraint is used:

[0068] ;

[0069] in, and These represent the maximum upward and downward climb rates of generator set g, respectively; This represents the output power of the g-th radio unit at time t; This represents the output power of the g-th generator unit at time t-1;

[0070] In a district heating network, the heating system is regulated by adjusting the mass flow rate. Based on the reasonable assumption of stable heating, the mass flow rate has magnitude and fixed direction, and a linear heating network model that ignores heat loss is adopted. The heating network constraints take into account the balance of heat power and mass flow rate at each node as well as the transmission capacity limit of the heating pipes.

[0071] Thermal equilibrium constraint:

[0072] ;

[0073] in, and Let be the heat generation and heat load of node i at time t, respectively; Heat flow from node j to node i at time t; For a moment From node To the node The heat outflow rate; and Let i be the set of all nodes in the heating network that flow into and out of node i.

[0074] Pipeline mass flow balance constraints:

[0075] ;

[0076] in, Let be the mass flow rate of water in the heating network from node j to node i. Let K be the mass flow rate of water in the heating network from node k to node i.

[0077] After considering heat loss in the heating pipes, the constraints limiting temperature drop during transmission include:

[0078] ;

[0079] in, Let be the length of the pipe from node j to node i; For the node To the node The fluid velocity; For time Connecting nodes and nodes Specific heat capacity parameters related to the pipeline; For the collection of pipes in the heating network; For a moment From node To the node The heat outflow rate; For nodes exist Temperature at any moment;

[0080] Temperature range constraints for heating pipes:

[0081] ;

[0082] in, For nodes exist Temperature at any moment; For the node To the node exist The temperature inside the pipe at any given time; and They are nodes exist Minimum and maximum permissible temperatures at any given time; and They are slave nodes To the node exist The minimum and maximum permissible temperatures in the pipe at any given time;

[0083] The range constraint on the temperature amplitude of the heating pipe is a nonlinear constraint, which can be approximated as a linear constraint through Taylor expansion:

[0084] ;

[0085] in This includes the heat load requirement of each node, the net heat release power of thermal energy storage, and ; This represents the fluid velocity within the pipe from node j to node i; This represents the temperature of node j at time t;

[0086] Pipeline heat transfer constraints:

[0087] ;

[0088] ;

[0089] in, A collection of pipes in a heating network; For nodes exist Minimum permissible temperature at any given time; For nodes exist The maximum permissible temperature at any given time;

[0090] The mass flow rate should be less than the maximum capacity of the heating pipeline.

[0091] ;

[0092] in, and They are respectively The upper and lower limits;

[0093] Limitations between heat, node temperature, and mass flow rate:

[0094] ;

[0095] Using McCormick's convex relaxation method Perform linearization:

[0096] ;

[0097] in, and They are respectively The upper and lower limits;

[0098] Constraints of power storage systems include:

[0099] ;

[0100] in, The maximum charging or discharging power of the energy storage system s; Let be the energy storage state of energy storage system s at time t; and These are the highest and lowest threshold values ​​for energy storage in energy storage system s, respectively. Let represent the energy storage state of energy storage system s at time t-1.

[0101] Constraints of thermal energy storage systems include:

[0102] ;

[0103] in, Let S be the stored heat of energy in thermal energy storage system S at time t; The stored heat of energy at node S at time t-1; and These are the upper and lower limits of thermal energy storage, respectively. The maximum heat generated during charging or discharging of the energy storage system S.

[0104] Preferably, the integrated energy system model is transformed into a compact form, resulting in a compact model including:

[0105] Transforming the integrated energy system model into a compact form, we obtain the compact model, which can be represented as:

[0106] ;

[0107] in, For state variables, ; For all time periods from time t to the end of the integrated energy system model, these are the independent decision variables. Let be the cost coefficient at time t; For a fixed constraint at time t; For the uncertain variables of wind power output; This represents the wind power output of the first wind turbine at time t. This represents the wind power output of the Hth wind turbine at time t; , , and Representing variables respectively , and The correlation coefficient; T is the total number of time periods in the scheduling cycle; This indicates the energy storage state of energy storage system s at time t; This represents the power generation of the g-th combined heat and power unit at time t; This represents the stored heat of energy in the thermal energy storage system S at time t;

[0108] Preferably, the uncertainty set constructed based on the compact model includes:

[0109] Uncertainty set include:

[0110] ;

[0111] in, This represents the maximum wind power output.

[0112] The correlation of decision variables within adjacent time periods is expressed as follows: ,Right now ; Let represent the set of all feasible decision variables at time t; Represents the state variables at time t-1; This represents the uncertain wind power output at time t;

[0113] Preferably, based on the uncertainty set, the correlation and continuity of decision variables over a predetermined number of time periods are defined, resulting in a first set including:

[0114] Assume the dynamic robust feasible region at each time step is F. t The first set is obtained, which includes:

[0115] ;

[0116] ;

[0117] ;

[0118] in, and These represent the maximum and minimum allowable output of the g-th cogeneration unit, respectively; and These represent the highest and lowest threshold values ​​for energy storage in energy storage system s, respectively. and These represent the upper and lower limits of thermal energy storage, respectively; A1, B1, C1, and D1 represent the variables... , and The relevant coefficient matrix;

[0119] By satisfying the constraints of the next time step, the robust feasible region of the previous time step can be derived. for Dynamic robust feasible region at any given time; Based on the first constraint and F t The obtained result is: assuming the robust feasible region for the last T-cycle is... The robust feasible region at each time step is obtained by reverse recursion.

[0120] Preferably, the robust feasible domain for rolling updates of a preset number of time periods in the first set includes:

[0121] Since 24 hours is a long time, a rolling update method is adopted to effectively reduce conservatism and increase the economy and flexibility of operation. The entire process is divided into a preset number of time periods. In the first period, the dynamic feasible region 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 region of time periods 6 to 12 is calculated again, and so on in each new time period.

[0122] Preferably, the second constraint is obtained by optimizing the first constraint based on the first set, including:

[0123] and F t Given a set of polyhedra, the dynamic robust feasible region for each period is also a set of polyhedra, i.e., a convex set; therefore, the first constraint and the first set can be represented as a compact model, which can be expressed as:

[0124] ;

[0125] in, , , , and Let be the coefficient of the constraint; P is the slack variable used to test the feasibility of the constraints. If all constraints are satisfied, then P = 0. This is equivalent to solving:

[0126] ;

[0127] in, For the corresponding dual variable;

[0128] Construct the Lagrangian function:

[0129] ;

[0130] Dual function Defined as the minimum value of the Lagrange function:

[0131] ;

[0132] Duality problems include:

[0133] ;

[0134] in, ; The set of relaxations within the feasible region is initially set to [value] for each period. .

[0135] Preferably, the dynamic robust feasible region is solved based on the second constraint and the first set, and the second set includes:

[0136] A1, considering The low-dimensionality of the problem can be solved by enumerating vertices: if If a problem involves three decision variables, its initial feasible region is a cuboid. We enumerate all vertices of the state variable at this point and substitute them sequentially into the optimization problem. Optimize and obtain the optimized result;

[0137] A2. Verify feasibility based on optimization results: calculate the optimal value. and optimal solution ;like =0, indicating that all decision variables are in the feasible region and meet the feasibility requirements, then return to A1 to perform a robust feasibility check at the next time step; if If the value is greater than 0, it indicates that there is a condition that is not met, so proceed to A3;

[0138] A3. Generate the cutting plane and perform feasible region cutting: If Then there exists a set To construct the cutting plane , will constrain Add to As The constraints are defined; by combining the intersections of the cutting plane and the original feasible region, the intersections of the plane and the boundary of the feasible region, as well as the boundaries of the newly generated cutting plane, are calculated. The resulting intersections are labeled as a set. Substitute the newly generated vertices after the cut into the following values: Perform the solution; then return to step A2 to perform the test;

[0139] A4. Complete the solution and store the feasible region constraints for each cycle: After solving the feasible region at each time step, the second set is obtained. .

[0140] The present invention also provides a dynamic robust feasible region solving system for robust optimization of integrated energy system scheduling, which is used in the method of the present invention. The system includes a model building module, a model transformation and uncertainty set building module, a robust feasible region building module, a robust feasible region updating module, and a robust feasible region solving module.

[0141] The model building module is used to build 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 operational constraints, wind power generation constraints, combined heat and power and transmission constraints, electric energy storage system constraints, and thermal energy storage system constraints.

[0142] The model transformation and uncertainty set construction module is used to transform the integrated energy system model into a compact form, resulting in a compact model; and to construct an uncertainty set based on the compact model.

[0143] The robust feasible region construction module is used to define the correlation and continuity of decision variables over a preset number of time periods based on the uncertainty set, and obtain the first set; the first set includes the robust feasible regions over a preset number of time periods;

[0144] The robust feasible region update module is used to continuously update the robust feasible region of the first set for a preset number of time periods; optimize the first constraint based on the first set to obtain the second constraint;

[0145] The robust feasible region solution module is used to solve the dynamic robust feasible region based on the second constraint and the first set, and obtain the second set; the second set includes a preset number of robust feasible regions for time periods.

[0146] The present invention has the following beneficial effects:

[0147] This invention presents a dynamic robust feasible region solution method for robust optimization of integrated energy system scheduling. By constructing a dynamic robust feasible region hourly, it ensures the real-time feasibility of system operation. Furthermore, by optimizing the feasible region, it enhances the scheduling flexibility and economy of the system, providing strong support for achieving efficient and stable scheduling of integrated energy systems in complex and ever-changing environments. This method can cope with various extreme conditions and exhibits better robustness.

[0148] The dynamic robust feasible region solving system for robust optimization of integrated energy system scheduling of the present invention, used in the method of the present invention, has the same beneficial effects as the method of the present invention.

[0149] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0150] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0151] Figure 1 This is a schematic diagram of the method flow of a preferred embodiment of the present invention. Detailed Implementation

[0152] The embodiments of the present invention will be 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.

[0153] See Figure 1In a preferred embodiment of the present invention, a method for solving the dynamic robust feasible region for robust optimization of integrated energy system scheduling is provided, comprising:

[0154] 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 operational constraints, wind power generation constraints, combined heat and power and transmission constraints, power storage system constraints, and thermal storage system constraints.

[0155] In a preferred embodiment of the present invention, the objective function includes:

[0156] Total system operating cost:

[0157] ;

[0158] Among them, C buy This represents the cost of purchasing electricity from the main grid; C fuel Indicates the fuel cost of a gas turbine; C op Indicates the operation and maintenance cost of the energy storage system; C punish Indicates the cost of punishment; This represents the total operating cost of the system;

[0159] The cost of purchasing electricity from the main grid includes:

[0160] The integrated energy system is connected to the main power grid, and electricity is purchased and sold between the main power grid and the integrated energy system. The purchase and sale costs can be expressed as:

[0161] ;

[0162] in, Let be the electricity price at time t, which is the relaxation node. This refers to the active power imported from the grid at the relaxation node; One cycle is defined as T; T represents the total number of time periods in the scheduling cycle.

[0163] The fuel cost of a gas turbine includes:

[0164] The fuel cost curve of a gas turbine is described by a quadratic function, which can be expressed as:

[0165] ;

[0166] in, , and Here, represents the fuel cost coefficient for the gas turbine, where... For fixed costs, The coefficients of the linear term, The coefficient of the quadratic term; Let G be the power generation of the g-th cogeneration unit at time t; G is the collection of gas turbines.

[0167] The operation and maintenance costs of energy storage systems include:

[0168] Operation and maintenance costs mainly consider the charging and discharging losses of the energy storage system, including the losses of the electrical energy storage system and the thermal energy storage tank, which can be expressed as:

[0169] ;

[0170] in, and These represent the operation and maintenance costs of electrical energy storage and thermal energy storage, respectively; M is the collection of electrical energy storage systems. and At time respectively No. The charging and discharging power of an energy storage system; and These are respectively electrical energy storage systems The charging and discharging efficiency; A collection of thermal energy storage systems; and At time respectively Thermal energy storage system The heat absorption and release power; and These are the heat absorption and heat release efficiencies of the thermal energy storage system S, respectively.

[0171] The costs of punishment include:

[0172] Penalty costs include wind curtailment penalties, voltage deviation penalties, and temperature deviation penalties. By setting penalty costs, a trade-off strategy is sought for integrated energy microgrids to balance the economics of system operation and the quality of energy supply.

[0173] ;

[0174] In the formula: , and These are the penalty coefficients for wind curtailment, voltage deviation, and temperature deviation, respectively. , and These are respectively a set of wind turbine generators, a set of power grid nodes, and a set of heating network nodes; Let h be the wind curtailment power of the wind turbine at time t. For at any time The square of the voltage at grid node j; This is the voltage reference value; For at any time heating network nodes The actual temperature; This is a temperature reference value;

[0175] and Since it is nonlinear, a binary variable is used for ease of solution. The objective function After piecewise linearization, we get:

[0176] ;

[0177] ;

[0178] ;

[0179] ;

[0180] ;

[0181] ;

[0182] in, This represents the number of segments; For time t, the g-th generator unit is at time t. The power generation capacity of the section; For the first The slope of the segment and These are the lengths of the nth and (n-1th)th segments, respectively. and It is a binary variable used to control the selection of segments; The length of the first segment; To control the variables in the first segment; Let g be the power output of the g-th generator unit at time t in the first segment; Let g be the power output of the g-th generator unit at time t in segment n. Variables to control the (j-1)th segment; Let g be the power output of the g-th generator unit at time t in segment m. Let m be the length of the m-th segment; Let m be the length of the (m-1)th segment; Variables to control the (m-1)th segment;

[0183] for In Introducing variables To satisfy:

[0184] ;

[0185] in, An auxiliary variable introduced to linearize the absolute value of the voltage deviation;

[0186] And introduce variables ,right In Perform linearization;

[0187] Optimized , can be represented as:

[0188] ;

[0189] ;

[0190] ;

[0191] in, This is an auxiliary variable introduced to linearize the absolute value of the temperature deviation.

[0192] In a preferred embodiment of the present invention, the operational constraints, wind power generation constraints, combined heat and power (CHP) and transmission constraints, electrical energy storage system constraints, and thermal energy storage system constraints include:

[0193] Operating constraints are used to describe the balance conditions of active and reactive power, and the limitations on voltage and power transmission.

[0194] Operational constraints include:

[0195] The active power balance constraint, reactive power balance constraint, linearized voltage drop constraint, voltage magnitude constraint, and branch transmission power constraint in the distribution network can be expressed as follows:

[0196] ;

[0197] in, and Branch roads exist The active and reactive power requirements at any given time; and They are nodes exist The active and reactive power constantly injected into the system; and They are nodes exist Active power output at any given time; For nodes The initial set of branches; and These are the connection nodes. and The resistance and reactance of the circuit; and For nodes The lower and upper limits of the voltage; For the line Apparent power capacity; This represents the line in the distribution network branch set L that is connected to node i and node j; This represents the set of nodes corresponding to the downstream branch originating from node j;

[0198] The branch transmission power constraint is a second-order cone constraint. After approximate linearization, the linearized result can be expressed as:

[0199] ;

[0200] in, The edge index is used to approximate the linear capacity constraint. For node i in Reactive power output at any given moment; For node i in Active power output at any given moment.

[0201] Wind power generation constraints include:

[0202] ;

[0203] In the formula: Let h be the power generated by the steam turbine at time t; Let h be the wind curtailment power of the wind turbine at time t;

[0204] Cogeneration and transmission constraints include:

[0205] Power output constraints and thermal energy production constraints of the cogeneration unit:

[0206] ;

[0207] in, Let be the heat generated by the g-th cogeneration unit at time t; and These are the maximum and minimum power of the g-th cogeneration unit, respectively; and These are the maximum and minimum heat output of the g-th cogeneration unit, respectively;

[0208] Constraints on the relationship between output electrical power and thermal power:

[0209] ;

[0210] Where R is the thermoelectric ratio;

[0211] To limit the variation in output power between adjacent time periods, a ramp constraint is used:

[0212] ;

[0213] in, and These represent the maximum upward and downward climb rates of generator set g, respectively; This represents the output power of the g-th radio unit at time t; This represents the output power of the g-th generator unit at time t-1;

[0214] In a district heating network, the heating system is regulated by adjusting the mass flow rate. Based on the reasonable assumption of stable heating, the mass flow rate has magnitude and fixed direction, and a linear heating network model that ignores heat loss is adopted. The heating network constraints take into account the balance of heat power and mass flow rate at each node as well as the transmission capacity limit of the heating pipes.

[0215] Thermal equilibrium constraint:

[0216] ;

[0217] in, and Let be the heat generation and heat load of node i at time t, respectively; Heat flow from node j to node i at time t; For a moment From node To the node The heat outflow rate; and Let i be the set of all nodes in the heating network that flow into and out of node i.

[0218] Pipeline mass flow balance constraints:

[0219] ;

[0220] in, Let be the mass flow rate of water in the heating network from node j to node i. Let K be the mass flow rate of water in the heating network from node k to node i.

[0221] After considering heat loss in the heating pipes, the constraints limiting temperature drop during transmission include:

[0222] ;

[0223] in, Let be the length of the pipe from node j to node i; For the node To the node The fluid velocity; For time Connecting nodes and nodes Specific heat capacity parameters related to the pipeline; For the collection of pipes in the heating network; For a moment Heat outflow from node j to node 𝑖; For nodes exist Temperature at any moment;

[0224] Temperature range constraints for heating pipes:

[0225] ;

[0226] in, For nodes exist Temperature at any moment; For the node To the node exist The temperature inside the pipe at any given time; and They are nodes exist Minimum and maximum permissible temperatures at any given time; and They are slave nodes To the node exist The minimum and maximum permissible temperatures in the pipe at any given time;

[0227] The range constraint on the temperature amplitude of the heating pipe is a nonlinear constraint, which can be approximated as a linear constraint through Taylor expansion:

[0228] ;

[0229] in This includes the heat load requirement of each node, the net heat release power of thermal energy storage, and ; This represents the fluid velocity within the pipe from node j to node i; This represents the temperature of node j at time t;

[0230] Pipeline heat transfer constraints:

[0231] ;

[0232] ;

[0233] in, A collection of pipes in a heating network; For nodes exist Minimum permissible temperature at any given time; For nodes exist The maximum permissible temperature at any given time;

[0234] The mass flow rate should be less than the maximum capacity of the heating pipeline.

[0235] ;

[0236] in, and They are respectively The upper and lower limits;

[0237] Limitations between heat, node temperature, and mass flow rate:

[0238] ;

[0239] Using McCormick's convex relaxation method Perform linearization:

[0240] ;

[0241] in, and They are respectively The upper and lower limits;

[0242] Constraints of power storage systems include:

[0243] ;

[0244] in, The maximum charging or discharging power of the energy storage system s; Let be the energy storage state of energy storage system s at time t; and These are the highest and lowest threshold values ​​for energy storage in energy storage system s, respectively. Let represent the energy storage state of energy storage system s at time t-1.

[0245] Constraints of thermal energy storage systems include:

[0246] ;

[0247] in, Let S be the stored heat of energy in thermal energy storage system S at time t; The stored heat of energy at node S at time t-1; and These are the upper and lower limits of thermal energy storage, respectively. The maximum heat generated during charging or discharging of the energy storage system S.

[0248] S2. Transform the integrated energy system model into a compact form to obtain a compact model; construct an uncertainty set based on the compact model.

[0249] In a preferred embodiment of the present invention, the integrated energy system model is transformed into a compact form to obtain a compact model, which includes:

[0250] Transforming the integrated energy system model into a compact form, we obtain the compact model, which can be represented as:

[0251] ;

[0252] in, For state variables, ; For all time periods from time t to the end of the integrated energy system model, these are the independent decision variables. Let be the cost coefficient at time t; For a fixed constraint at time t; For the uncertain variables of wind power output; This represents the wind power output of the first wind turbine at time t. This represents the wind power output of the Hth wind turbine at time t; , , and Representing variables respectively , and The correlation coefficient; T is the total number of time periods in the scheduling cycle; This indicates the energy storage state of energy storage system s at time t; This represents the power generation of the g-th combined heat and power unit at time t; This represents the stored heat of energy in the thermal energy storage system S at time t;

[0253] In a preferred embodiment of the present invention, constructing the uncertainty set based on the compact model includes:

[0254] Uncertainty set include:

[0255] ;

[0256] in, This represents the maximum wind power output.

[0257] The correlation of decision variables within adjacent time periods is expressed as follows: ,Right now ; Let represent the set of all feasible decision variables at time t; Represents the state variables at time t-1; This represents the uncertain wind power output at time t;

[0258] S3. Define the correlation and continuity of decision variables over a preset number of time periods based on the uncertainty set to obtain the first set; the first set includes the robust feasible region over a preset number of time periods.

[0259] In a preferred embodiment of the present invention, the correlation and continuity of decision variables over a predetermined number of time periods are defined according to the uncertainty set, resulting in a first set including:

[0260] Assume the dynamic robust feasible region at each time step is F. t The first set is obtained, which includes:

[0261] ;

[0262] ;

[0263] ;

[0264] in, and These represent the maximum and minimum allowable output of the g-th cogeneration unit, respectively; and These represent the highest and lowest threshold values ​​for energy storage in energy storage system s, respectively. and These represent the upper and lower limits of thermal energy storage, respectively; A1, B1, C1, and D1 represent the variables... , and The relevant coefficient matrix;

[0265] By satisfying the constraints of the next time step, the robust feasible region of the previous time step can be derived. for Dynamic robust feasible region at any given time; Based on the first constraint and F t The obtained result is: assuming the robust feasible region for the last T-cycle is... The robust feasible region at each time step is obtained by reverse recursion.

[0266] S4. Roll out the robust feasible domain of the first set for a preset number of time periods; optimize the first constraint based on the first set to obtain the second constraint.

[0267] In a preferred embodiment of the present invention, the robust feasible domain for rolling updates of a preset number of time periods in the first set includes:

[0268] Since 24 hours is a long time, a rolling update method is adopted to effectively reduce conservatism and increase the economy and flexibility of operation. The entire process is divided into a preset number of time periods, such as 4 segments of 6 hours each within 24 hours. In the first segment, the dynamic feasible region of time period 1 to 12 is calculated and applied to time period 1 to 6. The system status is updated according to the actual situation of time period 1 to 6. Based on the updated status and new requirements, the dynamic feasible region of time period 6 to 12 is calculated again, and so on in each new time period.

[0269] In a preferred embodiment of the present invention, optimizing the first constraint based on the first set to obtain the second constraint includes:

[0270] and F t Given a set of polyhedra, the dynamic robust feasible region for each period is also a set of polyhedra, i.e., a convex set; therefore, the first constraint and the first set can be represented as a compact model, which can be expressed as:

[0271] ;

[0272] in, , , , and Let be the coefficient of the constraint; P is the slack variable used to test the feasibility of the constraints. If all constraints are satisfied, then P = 0. This is equivalent to solving:

[0273] ;

[0274] in, For the corresponding dual variable;

[0275] Construct the Lagrangian function:

[0276] ;

[0277] Dual function Defined as the minimum value of the Lagrange function:

[0278] ;

[0279] Duality problems include:

[0280] ;

[0281] ;

[0282] ;

[0283] in, ; The set of relaxations within the feasible region is initially set to [value] for each period. .

[0284] S5. Solve for the dynamic robust feasible region based on the second constraint and the first set to obtain the second set; the second set includes a preset number of robust feasible regions for time periods.

[0285] In a preferred embodiment of the present invention, the dynamic robust feasible region is solved based on the second constraint and the first set, and the second set includes:

[0286] A1, considering The low-dimensionality of the problem can be solved by enumerating vertices: if If a problem involves three decision variables, its initial feasible region is a cuboid. We enumerate all vertices of the state variable at this point and substitute them sequentially into the optimization problem. Optimize and obtain the optimized result;

[0287] A2. Verify feasibility based on optimization results: calculate the optimal value. and optimal solution ;like =0, indicating that all decision variables are in the feasible region and meet the feasibility requirements, then return to A1 to perform a robust feasibility check at the next time step; if If the value is greater than 0, it indicates that there is a condition that is not met, so proceed to A3;

[0288] A3. Generate the cutting plane and perform feasible region cutting: If Then there exists a set To construct the cutting plane , will constrain Add to As The constraints are defined; by combining the intersections of the cutting plane and the original feasible region, the intersections of the plane and the boundary of the feasible region, as well as the boundaries of the newly generated cutting plane, are calculated. The resulting intersections are labeled as a set. Substitute the newly generated vertices after the cut into the following values: Perform the solution; then return to step A2 to perform the test;

[0289] A4. Complete the solution and store the feasible region constraints for each cycle: After solving the feasible region at each time step, the second set is obtained. .

[0290] This invention presents a dynamic robust feasible region solution method for robust optimization of integrated energy system scheduling. By constructing a dynamic robust feasible region hourly, it ensures the real-time feasibility of system operation. Furthermore, by optimizing the feasible region, it enhances the scheduling flexibility and economy of the system, providing strong support for achieving efficient and stable scheduling of integrated energy systems in complex and ever-changing environments. This method can cope with various extreme conditions and exhibits better robustness.

[0291] This invention proposes a multi-level robust optimization framework for integrated energy microgrid systems. Compared with conventional economic dispatch, the proposed method can cope with various extreme situations and has better robustness.

[0292] This invention proposes a projection algorithm based on linear programming (LP). Unlike the traditional McCormick relaxation method, this method transforms the bilinear programming (BP) problem into multiple LP problems through vertex enumeration, greatly improving the solution speed.

[0293] The present invention also provides a dynamic robust feasible region solving system for robust optimization of integrated energy system scheduling, which is used in the method of the present invention. The system includes a model building module, a model transformation and uncertainty set building module, a robust feasible region building module, a robust feasible region updating module, and a robust feasible region solving module.

[0294] The model building module is used to build 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 operational constraints, wind power generation constraints, combined heat and power and transmission constraints, electric energy storage system constraints, and thermal energy storage system constraints.

[0295] The model transformation and uncertainty set construction module is used to transform the integrated energy system model into a compact form, resulting in a compact model; and to construct an uncertainty set based on the compact model.

[0296] The robust feasible region construction module is used to define the correlation and continuity of decision variables over a preset number of time periods based on the uncertainty set, and obtain the first set; the first set includes the robust feasible regions over a preset number of time periods;

[0297] The robust feasible region update module is used to continuously update the robust feasible region of the first set for a preset number of time periods; optimize the first constraint based on the first set to obtain the second constraint;

[0298] The robust feasible region solution module is used to solve the dynamic robust feasible region based on the second constraint and the first set, and obtain the second set; the second set includes a preset number of robust feasible regions for time periods.

[0299] The dynamic robust feasible region solving system for robust optimization of integrated energy system scheduling of the present invention, used in the method of the present invention, has the same beneficial effects as the method of the present invention.

[0300] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for solving the dynamic robust feasible region for robust optimization of integrated energy system scheduling, characterized in that, include: 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 operational constraints, wind power generation constraints, combined heat and power and transmission constraints, power storage system constraints, and thermal storage system constraints. S2. Transform the integrated energy system model into a compact form to obtain a compact model; construct an uncertainty set based on the compact model; the compact model is represented as: ； in, For state variables, ; For all time periods from time t to the end of the integrated energy system model, these are the independent decision variables. Let be the cost coefficient at time t; For a fixed constraint at time t; For the uncertain variables of wind power output, This represents the wind power output of the first wind turbine at time t. This represents the wind power output of the Hth wind turbine at time t; , , and Representing variables respectively , and The correlation coefficient; T is the total number of time periods in the scheduling cycle; This indicates the energy storage state of energy storage system s at time t; This represents the power generation of the g-th combined heat and power unit at time t; This represents the stored heat of energy in the thermal energy storage system S at time t; Constructing an uncertainty set based on the compact model includes: The uncertainty set include: ； in, Maximum wind power output; The correlation of decision variables within adjacent time periods is expressed as follows: ,Right now ; Let represent the set of all feasible decision variables at time t; Represents the state variables at time t-1; This represents the uncertain wind power output at time t; S3. Define the correlation and continuity of decision variables over a preset number of time periods based on the uncertainty set to obtain a first set; the first set includes the robust feasible region over a preset number of time periods. Assume the dynamic robust feasible region at each time step is F. t The first set is obtained, which includes: ； ； ； in, and These represent the maximum and minimum allowable output of the g-th cogeneration unit, respectively; and These represent the highest and lowest threshold values ​​for energy storage in energy storage system s, respectively. and These represent the upper and lower limits of thermal energy storage, respectively; A1, B1, C1, and D1 represent the variables... , and The relevant coefficient matrix; By satisfying the constraints of the next time step, the robust feasible region of the previous time step can be derived. for Dynamic robust feasible region at any given time; Based on the first constraint and F t The obtained result is: assuming the robust feasible region for the last T-cycle is... The robust feasible region at each time step is obtained by reverse recursion. S4. Rollwise update the robust feasible region of the first set for the preset number of time periods; optimize the first constraint based on the first set to obtain the second constraint; S5. Based on the second constraint and the first set, the dynamic robust feasible region is solved by combining the enumeration vertex method and region cutting to obtain the second set; the second set includes the robust feasible region of the preset number of time periods.

2. The method for solving the dynamic robust feasible region for robust optimization of integrated energy system scheduling according to claim 1, characterized in that, The objective function includes: Total system operating cost: ； Among them, C buy This represents the cost of purchasing electricity from the main grid; C fuel Indicates the fuel cost of a gas turbine; C op Indicates the operation and maintenance cost of the energy storage system; C punish Indicates the cost of punishment; This represents the total operating cost of the system; The cost of purchasing electricity from the main grid includes: The integrated energy system is connected to the main power grid, and electricity is purchased and sold between the main power grid and the integrated energy system. The purchase and sale costs can be expressed as: ； in, Let be the electricity price at time t, which is the relaxation node. This refers to the active power imported from the grid at the relaxation node; One cycle is defined as T; T represents 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, which can be expressed as: ； in, , and Here, represents the fuel cost coefficient for the gas turbine, where... For fixed costs, The coefficients of the linear term, The coefficient of the quadratic term; Let G be the power generation of the g-th cogeneration unit at time t; G is the collection of gas turbines. The operation and maintenance costs of the energy storage system include: Operation and maintenance costs mainly consider the charging and discharging losses of the energy storage system, including the losses of the electrical energy storage system and the thermal energy storage tank, which can be expressed as: ； in, and These represent the operation and maintenance costs of electrical energy storage and thermal energy storage, respectively; M is the collection of electrical energy storage systems. and At time respectively No. The charging and discharging power of an energy storage system; and These are respectively electrical energy storage systems The charging and discharging efficiency; A collection of thermal energy storage systems; and At time respectively Thermal energy storage system The heat absorption and release power; and These are the heat absorption and heat release efficiencies of the thermal energy storage system S, respectively. The cost of the penalty includes: Penalty costs include wind curtailment penalties, voltage deviation penalties, and temperature deviation penalties: ； In the formula: , and These are the penalty coefficients for wind curtailment, voltage deviation, and temperature deviation, respectively. , and These are respectively a set of wind turbine generators, a set of power grid nodes, and a set of heating network nodes; Let h be the wind curtailment power of the wind turbine at time t. For at any time power grid nodes The square of the voltage; This is the voltage reference value; For at any time heating network nodes The actual temperature; This is a temperature reference value; and Since it is nonlinear, a binary variable is used for ease of solution. The objective function After piecewise linearization, we get: ； ； ； ； ； ； in, This represents the number of segments; For time t, the g-th generator unit is at time t. The power generation capacity of the section; For the first The slope of the segment and These are the lengths of the nth and (n-1th)th segments, respectively. and It is a binary variable used to control the selection of segments; The length of the first segment; To control the variables in the first segment; Let g be the power output of the g-th generator unit at time t in the first segment; Let g be the power output of the g-th generator unit at time t in segment n. Variables to control the (j-1)th segment; Let g be the power output of the g-th generator unit at time t in segment m. Let m be the length of the m-th segment; Let m be the length of the (m-1)th segment; Variables to control the (m-1)th segment; for In Introducing variables To satisfy: ； in, An auxiliary variable introduced to linearize the absolute value of the voltage deviation; And introduce variables ,right In Perform linearization; Optimized , can be represented as: ； ； ； in, This is an auxiliary variable introduced to linearize the absolute value of the temperature deviation.

3. The method for solving the dynamic robust feasible region for robust optimization of integrated energy system scheduling according to claim 2, characterized in that, The operational constraints, wind power generation constraints, combined heat and power and transmission constraints, power energy storage system constraints, and thermal energy storage system constraints include: The operational constraints include: The active power balance constraint, reactive power balance constraint, linearized voltage drop constraint, voltage magnitude constraint, and branch transmission power constraint in the distribution network can be expressed as follows: ； in, and Branch roads exist The active and reactive power requirements at any given time; and They are nodes exist The active and reactive power constantly injected into the system; and They are nodes exist Active power output at any given time; For nodes The initial set of branches; and These are the connection nodes. and The resistance and reactance of the circuit; and For nodes The lower and upper limits of the voltage; For the line Apparent power capacity; This represents the line in the distribution network branch set L that is connected to node i and node j; This represents the set of nodes corresponding to the downstream branch originating from node j; The branch transmission power constraint is a second-order cone constraint. After approximate linearization, the linearized result can be expressed as: ； in, The edge index is used to approximate the linear capacity constraint. For node i in Reactive power output at any given moment; For node i in Active power output at any given time; The wind power generation constraints include: ； In the formula: Let h be the power generated by the steam turbine at time t; Let h be the wind curtailment power of the wind turbine at time t; The cogeneration and transmission constraints include: Power output constraints and thermal energy production constraints of the cogeneration unit: ； in, Let be the heat generated by the g-th cogeneration unit at time t; and These are the maximum and minimum power of the g-th cogeneration unit, respectively; and These are the maximum and minimum heat output of the g-th cogeneration unit, respectively; Constraints on the relationship between output electrical power and thermal power: ； Where R is the thermoelectric ratio; To limit the variation in output power between adjacent time periods, a ramp constraint is used: ； in, and These represent the maximum upward and downward climb rates of generator set g, respectively; This represents the output power of the g-th radio unit at time t; This represents the output power of the g-th generator unit at time t-1; In a district heating network, the heating system is regulated by adjusting the mass flow rate. Based on the reasonable assumption of stable heating, the mass flow rate has magnitude and fixed direction, and a linear heating network model that ignores heat loss is adopted. The heating network constraints take into account the balance of heat power and mass flow rate at each node as well as the transmission capacity limit of the heating pipes. Thermal equilibrium constraint: ； in, and Let be the heat generation and heat load of node i at time t, respectively; Heat flow from node j to node i at time t; For a moment From node To the node The heat outflow rate; and Let i be the set of all nodes in the heating network that flow into and out of node i. Pipeline mass flow balance constraints: ； in, Let be the mass flow rate of water in the heating network from node j to node i. Let K be the mass flow rate of water in the heating network from node k to node i. After considering heat loss in the heating pipes, the constraints limiting temperature drop during transmission include: ； in, Let be the length of the pipe from node j to node i; For the node To the node The fluid velocity; For time Connecting nodes and nodes Specific heat capacity parameters related to the pipeline; For the collection of pipes in the heating network; For a moment From node j to node The heat outflow rate; For nodes exist Temperature at any moment; Temperature range constraints for heating pipes: ； in, For nodes exist Temperature at any moment; For the node To the node exist The temperature inside the pipe at any given time; and They are nodes exist Minimum and maximum permissible temperatures at any given time; and They are slave nodes To the node exist The minimum and maximum permissible temperatures in the pipe at any given time; The range constraint on the temperature amplitude of the heating pipe is a nonlinear constraint, which can be approximated as a linear constraint through Taylor expansion: ； in This includes the heat load requirement of each node, the net heat release power of thermal energy storage, and ; This represents the fluid velocity within the pipe from node j to node i; This represents the temperature of node j at time t; Pipeline heat transfer constraints: ； ； in, A collection of pipes in a heating network; The mass flow rate should be less than the maximum capacity of the heating pipeline. ； in, and They are respectively The upper and lower limits; Limitations between heat, node temperature, and mass flow rate: ； Using McCormick's convex relaxation method Perform linearization: ； in, and They are respectively The upper and lower limits; The constraints of the energy storage system include: ； in, The maximum charging or discharging power of the energy storage system s; Let be the energy storage state of energy storage system s at time t; and These are the highest and lowest threshold values ​​for energy storage in energy storage system s, respectively. Let be the energy storage state of energy storage system s at time t-1; The constraints of the thermal energy storage system include: ； in, Let S be the stored heat of energy in thermal energy storage system S at time t; The stored heat of energy at node S at time t-1; and These are the upper and lower limits of thermal energy storage, respectively. The maximum heat generated during charging or discharging of the energy storage system S.

4. The method for solving the dynamic robust feasible region for robust optimization of integrated energy system scheduling according to claim 3, characterized in that, The robust feasible domain for the preset number of time periods in the first set that is continuously updated includes: Since 24 hours is a long time, a rolling update method is adopted to effectively reduce conservatism and increase the economy and flexibility of operation. The entire process is divided into a preset number of time periods. In the first period, the dynamic feasible region 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 region of time periods 6 to 12 is calculated again, and so on in each new time period.

5. The method for solving the dynamic robust feasible region for robust optimization of integrated energy system scheduling according to claim 4, characterized in that, Optimizing the first constraint based on the first set yields the following second constraints: and F t Given a set of polyhedra, the dynamic robust feasible region for each period is a set of polyhedra, i.e., a convex set; therefore, the first constraint and the first set can be represented as a compact model, which can be expressed as: ； in, , , , and Let P be the coefficient of the constraint; P is the slack variable used to test the feasibility of the constraints. If all constraints are satisfied, then P = 0 for the given set of uncertainties. The k-th wind power output scenario used for feasibility verification This is equivalent to solving: ； in, For the corresponding dual variable; Construct the Lagrangian function: ； Dual function Defined as the minimum value of the Lagrange function: ； Duality problems include: ； in, ; The set of relaxations within the feasible region is initially set to [value] for each period. .

6. The method for solving the dynamic robust feasible region for robust optimization of integrated energy system scheduling according to claim 5, characterized in that, Solving for the dynamic robust feasible region based on the second constraint and the first set yields the second set, which includes: A1, considering The low-dimensionality of the problem can be solved by enumerating vertices: if If a problem involves three decision variables, its initial feasible region is a cuboid. We enumerate all vertices of the state variable at this point and substitute them sequentially into the optimization problem. Optimize and obtain the optimized result; A2. Feasibility verification based on the optimization results: calculate the optimal value. and optimal solution ;like =0, indicating that all decision variables are in the feasible region and meet the feasibility requirements, then return to A1 to perform a robust feasibility check at the next time step; if If the value is greater than 0, it indicates that there is a condition that is not met, so proceed to A3; A3. Generate the cutting plane and perform feasible region cutting: If Then there exists a set To construct the cutting plane , will constrain Add to As The constraints are defined; by combining the intersections of the cutting plane and the original feasible region, the intersections of the plane and the boundary of the feasible region, as well as the boundaries of the newly generated cutting plane, are calculated. The resulting intersections are labeled as a set. Substitute the newly generated vertices after the cut into the following values: Perform the solution; then return to step A2 to perform the test; A4. Complete the solution and store the feasible region constraints for each cycle: After solving the feasible region at each time step, the second set is obtained. .

7. A dynamic robust feasible region solving system for robust optimization of integrated energy system scheduling, used in the method described in any one of claims 1 to 6, characterized in that, The system includes a model building module, a model transformation and uncertainty set construction module, a robust feasible region construction module, a robust feasible region update module, and a robust feasible region solution module; The model building module is used to build 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 operational constraints, wind power generation constraints, combined heat and power and transmission constraints, power storage system constraints, and thermal storage system constraints. The model transformation and uncertainty set construction module is used to transform 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. The robust feasible region construction module is used to define the correlation and continuity of decision variables over a preset number of time periods based on the uncertainty set, and obtain a first set; the first set includes the robust feasible regions over a preset number of time periods; The robust feasible region update module is used to continuously update the robust feasible region of the first set for the preset number of time periods; and to optimize the first constraint according to the first set to obtain the second constraint; The robust feasible region solving module is used to solve the dynamic robust feasible region based on the second constraint and the first set to obtain a second set; the second set includes the robust feasible regions of the preset number of time periods.

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