Electrical joint dispatching method and device based on variational inequality, equipment and medium

Abstract

The application discloses an electrical joint scheduling method and device based on variational inequality, equipment and medium, the method comprises the following steps: constructing a monotone variational inequality electrical-gas comprehensive energy system scheduling model according to preset electrical-gas comprehensive energy system parameters, the preset electrical-gas comprehensive energy system parameters include power system parameters, natural gas system parameters and electrical-gas coupling device parameters, and the electrical-gas comprehensive energy system scheduling model includes a piecewise tangent linearization quasi-steady-state natural gas network considering the pipe storage effect; the electrical-gas comprehensive energy system scheduling model is solved in a distributed manner by using a balanced splitting-contraction algorithm to obtain an electrical-gas joint optimization scheduling result; and the cooperation surplus of the electrical-gas comprehensive energy system scheduling model is distributed fairly based on a double-sided Shapley value incentive mechanism.The application can solve the technical problems of high wind power curtailment, privacy leakage, incentive incompatibility and the like in the electrical-gas comprehensive energy system.

Description

Technical Field

[0001] This invention relates to distributed joint scheduling technology for integrated electric and gas energy systems, specifically to an electrical joint scheduling method, apparatus, equipment, and medium based on variational inequalities. Background Technology

[0002] Integrated energy systems, by establishing connections between different energy systems, effectively enhance the operational flexibility of the power system and promote the high-proportion consumption of renewable energy. The widespread application of gas turbine units in power systems has led to increasingly close coupling between power and natural gas systems, gradually forming an integrated electricity-gas energy system. Joint optimization scheduling of this system helps improve overall system economy and energy utilization efficiency, and reduces wind and electricity curtailment. Electricity-gas joint optimization scheduling mainly includes two modes: centralized and distributed. Distributed algorithms can achieve joint optimization scheduling while protecting the privacy of operating entities; in practice, distributed scheduling is often used instead of centralized scheduling.

[0003] Natural gas network modeling requires a balance between accuracy and efficiency. The Weymouth equation is often linearized using piecewise linear (PWL) approximation or Taylor series expansion (TSE). In PWL, introducing binary variables transforms the optimal scheduling of the integrated electricity-gas energy system into a mixed-integer linear programming problem. However, higher accuracy necessitates more binary variables, leading to computational inefficiency. TSE simplifies computation by using a tangent approximation of the Weymouth equation near fixed points, but its accuracy still needs further verification. Tangents often lack compactness, making it difficult to select suitable expansion points. Furthermore, natural gas is compressible, and pipeline storage effects are common, making it an effective source of renewable energy for the grid. Therefore, for the optimal scheduling of integrated electricity-gas energy systems, a linear quasi-steady-state gas network model that balances accuracy and efficiency is crucial.

[0004] The optimal scheduling of integrated power and gas energy systems requires an independent and coordinated approach to protect privacy, including user information, operating parameters, and topology of both the power and gas networks. Typically, distributed algorithms are used to transform the optimal scheduling problem of integrated power and gas energy systems into sub-problems of the power grid and gas grid, and each sub-problem is solved independently with limited information exchange.

[0005] Existing research on joint power-gas scheduling is typically based on collective rationality, i.e., maximizing the total utility of the integrated power-gas energy system. While joint power-gas scheduling based on collective rationality can reduce the total cost of the integrated energy system, it cannot simultaneously guarantee the interests of both the power grid and the gas grid. Therefore, joint power-gas scheduling based on collective rationality cannot achieve incentive compatibility.

[0006] To achieve incentive compatibility, existing research falls into two main categories: market game theory methods and transfer payment methods. Market game theory methods may not maximize overall benefits. Transfer payment methods involve the natural gas system sharing some of its cooperative surplus with the power system, reducing the total operating costs for both. However, calculating the optimal allocation ratio of the cooperative surplus is currently relatively complex and difficult to implement. Summary of the Invention

[0007] This application provides an electrical joint scheduling method, device, equipment, and medium based on variational inequalities. It combines monotone variational inequalities with cooperative game theory for distributed optimal electricity-gas integrated energy optimization scheduling. This method aims to solve technical problems such as individual privacy protection, low renewable energy grid connection rate, and incentive compatibility under the joint scheduling of existing electricity-gas integrated energy systems with a more efficient distributed algorithm and cooperative game mechanism.

[0008] Therefore, the first aspect of the present invention provides an electrical joint scheduling method based on variational inequalities, comprising: Step 101: Construct a monotone variational inequality-based scheduling model for the integrated electric-gas energy system based on pre-set integrated electric-gas energy system parameters. The pre-set integrated electric-gas energy system parameters include: power system parameters, natural gas system parameters, and electric-gas coupling equipment parameters. The integrated electric-gas energy system scheduling model includes a piecewise tangential linearized quasi-steady-state natural gas network that considers pipeline storage effects.

[0009] Step 102: The balanced split-shrink algorithm is used to solve the power-gas integrated energy system scheduling model in a distributed manner to obtain the power-gas joint optimization scheduling results; Step 103: Fairly allocate the cooperative surplus of the electricity-gas integrated energy system scheduling model based on the incentive mechanism of bilateral Shapley values; The results of the combined power-gas optimization scheduling include: output of thermal power units, output of gas turbine units, wind power grid connection, gas production of natural gas wells, power system scheduling cost, natural gas system scheduling cost, and power-gas integrated energy system scheduling cost.

[0010] Preferably, the step of constructing a monotone variational inequality-based integrated energy system scheduling model based on pre-set integrated energy system parameters includes a piecewise tangential linearized quasi-steady-state natural gas network considering pipeline storage effects, and further includes: Obtain preset integrated electric-gas energy system parameters, which include power system parameters, natural gas system parameters, and electric-gas coupling equipment parameters; The power system parameters include bus parameters, branch parameters, thermal power unit parameters, wind turbine unit parameters, and electrical load parameters; The natural gas system parameters include node parameters, pipeline parameters, natural gas well parameters, compressor parameters, and gas load parameters; The parameters of the electro-pneumatic coupling equipment include the operating parameters of the gas turbine unit.

[0011] Preferably, the step of using the balanced split-shrink algorithm to perform a distributed solution on the electricity-gas integrated energy system scheduling model to obtain the electricity-gas joint optimal scheduling result includes: S21: Based on the balanced split-contraction algorithm, the solution problem of the power-gas integrated energy system scheduling model is transformed into a power grid scheduling sub-problem and a gas grid scheduling sub-problem. Solving the power grid scheduling sub-problem yields the power grid scheduling solution.

[0012] S22: The power grid scheduling solution is sent to all gas networks respectively, and each gas network solves its own gas network scheduling subproblem to obtain all gas network scheduling solutions. S23: The power grid collects all the gas grid scheduling solutions, updates the power grid scheduling subproblem, and solves the power grid scheduling subproblem. The above process is repeated iteratively to obtain the scheduling solution of the integrated power-gas energy system.

[0013] Preferably, the step of using the balanced split-shrink algorithm to solve the electricity-gas integrated energy system scheduling model in a distributed manner to obtain the electricity-gas joint optimal scheduling result further includes: Based on the power system dispatch cost, the natural gas system dispatch cost, and the power-gas integrated energy system dispatch cost, a cooperative game calculation analysis is performed to obtain the bilateral Shapley value of the connection between the power grid and the gas grid. The grid dispatch cost and the gas grid dispatch cost are calculated based on the bilateral Shapley values ​​of the grid and gas grid connection relationship.

[0014] A second aspect of the present invention provides a scheduling apparatus based on the electrical joint scheduling method based on variational inequalities described in the first aspect, comprising: The model building unit is used to construct a monotone variational inequality electric-gas integrated energy system scheduling model based on pre-set electric-gas integrated energy system parameters. The pre-set electric-gas integrated energy system parameters include power system parameters, natural gas system parameters, and electric-gas coupling equipment parameters. The electric-gas integrated energy system scheduling model includes a piecewise tangential linearized quasi-steady-state natural gas network that considers pipeline storage effects. The model solving unit, connected to the model building unit, is used to perform distributed solving of the electric-gas integrated energy system scheduling model using the balanced split-shrink algorithm to obtain the joint electric-gas optimization scheduling result. The combined power-gas optimization scheduling results include the output of thermal power units, the output of gas turbine units, the grid-connected wind power, the gas production of natural gas wells, the scheduling cost of the power system, the scheduling cost of the natural gas system, and the scheduling cost of the integrated power-gas energy system.

[0015] Preferably, the distributed dispatching device for the integrated electric-gas energy system further includes: The parameter acquisition unit is used to acquire preset electric-gas integrated energy system parameters and transmit them to the model building unit. The preset electric-gas integrated energy system parameters include power system parameters, natural gas system parameters and electric-gas coupling equipment parameters. The power system parameters include bus parameters, branch parameters, thermal power unit parameters, wind turbine unit parameters, and electrical load parameters; The natural gas system parameters include node parameters, pipeline parameters, natural gas well parameters, compressor parameters, and gas load parameters; The parameters of the electro-pneumatic coupling equipment include the operating parameters of the gas turbine unit.

[0016] Preferably, the model solving unit includes: The problem transformation subunit is used to transform the solution problem of the integrated electricity-gas energy system scheduling model into a power grid scheduling subproblem and a gas grid scheduling subproblem based on the equilibrium split-contraction algorithm.

[0017] The iterative solution subunit, connected to the problem transformation subunit, is used to first solve the power grid scheduling subproblem to obtain the power grid scheduling solution; and then send the power grid scheduling solution to all gas networks respectively. Each gas network solves its own gas network scheduling subproblem to obtain all gas network scheduling solutions; the power grid collects all the gas network scheduling solutions, updates the power grid scheduling subproblem, and solves the power grid scheduling subproblem. The above process is repeated iteratively to obtain the scheduling solution of the integrated electricity-gas energy system.

[0018] Preferably, the distributed dispatching device for the integrated electric-gas energy system further includes: The game analysis unit connected to the model solving unit is used to perform cooperative game calculation and analysis based on the power system dispatch cost, the natural gas system dispatch cost and the power-gas integrated energy system dispatch cost, to obtain the bilateral Shapley value of the connection relationship between the power grid and the gas grid; The cost calculation unit is used to calculate the grid dispatch cost and the gas grid dispatch cost based on the bilateral Shapley value of the connection relationship between the power grid and the gas grid. The cost calculation unit is connected to the game analysis unit.

[0019] A third aspect of the present invention provides a scheduling device based on the electrical joint scheduling method based on variational inequalities described in the first aspect, the device comprising a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the distributed scheduling method for the integrated electric-gas energy system described in the first aspect according to the instructions in the program code.

[0020] A fourth aspect of the present invention provides a computer-readable storage medium for storing program code for executing the electrical joint scheduling method based on variational inequalities described in the first aspect.

[0021] As can be seen from the above technical solutions, the embodiments of this application have the following advantages: This invention provides a method for scheduling an integrated electricity-gas energy system based on a distributed cooperative mechanism, comprising: constructing a monotone variational inequality-based scheduling model for the integrated electricity-gas energy system according to pre-set integrated electricity-gas energy system parameters, including power system parameters, natural gas system parameters, and parameters of electricity-gas coupling equipment; the scheduling model including a piecewise tangential linearized quasi-steady-state natural gas network considering pipeline storage effects; using a balanced split-contraction algorithm to solve the scheduling model in a distributed manner to obtain the joint optimization scheduling result of electricity and gas; and fairly distributing the cooperative surplus of the integrated electricity-gas energy system scheduling model based on a bilateral Shapley value incentive mechanism. The results of the joint optimization scheduling of electricity and gas include the output of thermal power units, the output of gas turbine units, the grid-connected wind power, the gas production of natural gas wells, the scheduling cost of the power system, the scheduling cost of the natural gas system, and the scheduling cost of the integrated electricity and gas energy system. With a more efficient distributed algorithm and cooperative game mechanism, and under the premise of ensuring the independent operation of the power system and the information privacy of the natural gas system, efficient distributed optimization scheduling of the integrated electricity and gas energy system is carried out to improve the overall energy utilization efficiency. This invention provides a distributed power-gas integrated energy optimization scheduling method based on monotone variational inequalities and a balanced split-contraction algorithm. The method employs a balanced split-contraction algorithm to solve the constructed power-gas integrated energy system scheduling model in a distributed manner. During this process, the bilateral Shapley values ​​of the power grid and gas grid are calculated based on their marginal contributions, establishing a cooperative surplus allocation mechanism based on these bilateral Shapley values. This ensures that the incentives for the power system and the natural gas system align with the expected results, overcoming the problem of incompatible incentives in the joint scheduling of the power-gas integrated energy system and guaranteeing the compatibility of the incentive mechanism. Therefore, this application can solve the technical problems of privacy leakage, high wind and electricity curtailment, and incentive incompatibility caused by the power grid and gas grid belonging to different operators under the existing electricity-carbon market mechanism and the high proportion of renewable energy grid connection. Attached Figure Description

[0022] Figure 1This is a flowchart illustrating the scheduling method for an integrated electricity-gas energy system based on monotone variational inequalities and cooperative game theory, as provided in an embodiment of the present invention.

[0023] Figure 2 This is a schematic diagram of the structure of the integrated electric-gas energy system scheduling device based on monotone variational inequalities and cooperative game mechanism provided in an embodiment of the present invention.

[0024] Figure 3 This is a schematic diagram of the scheduling framework for an integrated electric-gas energy system based on monotone variational inequalities and cooperative game theory, provided in an embodiment of the present invention.

[0025] Figure 4 Provided for embodiments of the present invention and The relationship between them.

[0026] Figure 5 The role of the piecewise tangent linearized quasi-steady-state natural gas network model considering pipeline storage effects provided in this embodiment of the invention for wind power grid connection. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0028] For easier understanding, please refer to Figure 1 This invention provides an embodiment of an electrical joint scheduling method based on variational inequalities, which includes the following steps: Step 101: Construct a monotone variational inequality-based scheduling model for the integrated electric-gas energy system based on the pre-set parameters of the integrated electric-gas energy system. The pre-set parameters of the integrated electric-gas energy system include the parameters of the power system, the parameters of the natural gas system, and the parameters of the electric-gas coupling equipment. The scheduling model of the integrated electric-gas energy system includes a piecewise tangential linearized quasi-steady-state natural gas network that considers the pipeline storage effect.

[0029] Furthermore, step 101, preceding the following, also includes: Obtain the pre-set parameters of the integrated electric-gas energy system, which include the parameters of the power system, the natural gas system, and the electric-gas coupling equipment. The power system parameters include bus parameters, branch parameters, thermal power unit parameters, wind turbine unit parameters, and electrical load parameters; The natural gas system parameters include node parameters, pipeline parameters, natural gas well parameters, compressor parameters, and gas load parameters; The parameters of the electro-pneumatic coupling equipment include the operating parameters of the gas turbine unit.

[0030] It should be noted that the parameters of the power system, natural gas system, and electro-gas coupling equipment include, but are not limited to, the above parameters. Other relevant parameters can also be obtained as needed. This embodiment only provides a few examples and does not limit the specifics.

[0031] In power system dispatching models, the objective is to minimize the dispatching cost of thermal power plants. The cost of penalties for wind power curtailment and total natural gas procurement costs :

[0032] Among them, the dispatching cost of thermal power plants Given by the following formula:

[0033] in, Indicates the serial number of the thermal power unit; A collection of thermal power units; t For scheduling time; T For the set of scheduling times; , , These are the quadratic, linear, and constant term coefficients for the power generation cost of thermal power units, respectively. Indicates the first Taiwan thermal power units t Power generation during the dispatch period.

[0034] The cost of penalties for wind power curtailment Increases with increasing power rationing:

[0035] in, Indicates the serial number of the wind turbine unit; A collection of wind turbine units; Indicates wind turbine unit The power curtailment penalty coefficient; This represents the maximum value of wind power generation. This represents the actual value of wind power generation.

[0036] From the Natural gas procurement cost of a natural gas system Defined as:

[0037] in, Indicates the serial number of the gas turbine unit; Indicates gas network A collection of gas turbine units; Indicates natural gas system medium-gas turbine units Cost coefficient; Indicates the first Gas turbine units of a natural gas system exist t Gas consumption during the scheduling period.

[0038] Power system dispatch is affected by the following constraints: , , , , , , , , ,

[0039] The above constraints define the power balance constraint for each bus, the capacity limit constraint for transmission lines, the voltage angle constraint for the reference bus, the power regulation capability constraint for thermal power units, the power regulation capability constraint for gas turbine units, the power output limit constraint for thermal power units, the power output limit constraint for gas turbine units, the power output limit constraint for wind farm units, and the energy conversion efficiency constraint for gas turbine units.

[0040] in, Indicates the natural gas system serial number; Represents a collection of natural gas systems; Represents the set of scheduling times; Indicates connection to the busbar A collection of thermal power units; Indicates connection to the busbar A collection of wind turbine units; Indicates connection to the busbar It belongs to the natural gas system A collection of gas turbine units; Represented as the first Each thermal power unit t- Electrical output during time period 1; Indicates gas network Middle Each gas turbine unitt Electricity output during the -1 time period; Indicates the scheduling period t Internal natural gas system medium-gas turbine units l Output power; Indicates the scheduling period t Inner busbar The load absorbed power; Represents the set of busbars; Indicates scheduling time t Inner busbar The voltage phase angle; Indicates connecting busbar and The line impedance; Indicates connecting busbar and Maximum power limit; Represents the set of busbars; Indicates the voltage phase angle of the reference bus; Indicates thermal power unit Maximum downhill power limit; Indicates thermal power unit Maximum uphill power limit; Indicates natural gas system Internal combustion engine unit Maximum downhill power limit; Indicates natural gas system Internal combustion engine unit Maximum uphill power limit; Indicates thermal power unit Minimum output power; Indicates thermal power unit Maximum output power; Indicates natural gas system Internal combustion engine unit Minimum output power; Indicates natural gas system Internal combustion engine unit Maximum output power; Indicates scheduling time t Internal natural gas system medium-gas turbine units ; output power; Indicates the efficiency of the gas turbine unit; This indicates the calorific value of natural gas.

[0041] To facilitate understanding, we first construct a piecewise tangent-linearized quasi-steady-state natural gas network that considers pipeline storage effects. First, we perform piecewise tangent-linearization of the Weymouth equation, which is expressed as follows: , in Indicated as from the pipe m Flow direction n airflow rate; Here, represents the state coefficient of the natural gas pipeline, and is a constant term. For nodes m The nodal pressure, For nodes n The node pressure.

[0042] use and Represent and That is, the respective nodes m and n The squared value of the nodal pressure; define a new variable. express m and n The difference in the square of air pressure is expressed as: , The Weymouth equation is restated as follows: , According to the mean square theorem, we can obtain:

[0043] in, and They are The first and second derivatives; This indicates the point chosen for the expansion of the Mean Value Theorem; Indicates in and Any value that exists between the given conditions that satisfies the above equation. The expression is always non-negative. Therefore, we can approximate the Weymouth equation using the mean value theorem, as follows:

[0044] in, The number of the expansion point; This represents the number of expansion points; Indicates the expansion point Connecting nodes m and n And the difference in squared pressure.

[0045] The constraints on the piecewise tangent linearized quasi-steady-state natural gas network, including those considering pipeline storage effects, are as follows: , , , , , , , , , , ,

[0046] The above constraints are respectively the gas flow balance constraint of each node, the gas flow loss constraint of the compressor, the maximum gas pressure compression ratio constraint of the compressor, the gas pressure constraint of the node, the gas pressure square difference constraint, the piecewise tangent linearized Weymouth equation constraint, the pipeline gas flow constraint considering the pipe storage, the storage gas flow constraint due to the pipe storage effect, and the gas consumption gas flow constraint of the gas turbine unit.

[0047] in, The subscript indicates that this parameter refers to a natural gas system. Parameters; t The subscript indicates that the parameter is the scheduling time. t Parameters; This refers to the gas well serial number; This indicates the index of the expansion point chosen for the Mean Value Theorem; Represents a node m The upper connected gas well assembly; Represents a node m The gas well connected to the upper part is in the scheduling time t Production gas flow rate; The compressor serial number; For nodes m The compressor assembly connected above; and Scheduling time t The air flow rate out of the compressor and the air flow rate into the compressor; m For node sequence number; n For connecting nodes m The remaining node numbers; m For node sequence number; n For connecting nodes m The remaining node numbers Indicates natural gas system Set of internal nodes; Represented as a connection m The rest of the node set; Represented as scheduling time t Internal natural gas system a Pipeline mn In the The airflow rate at the expansion point chosen by the median value theorem; For scheduling time t Internal outflow pipe mn air flow rate For scheduling time t Inflow pipe mn airflow rate; Indicates natural gas system Connecting nodes m A collection of gas turbine units; Represented as scheduling time t Internal combustion engine unit Gas consumption; Indicates scheduling time t internal nodes m Gas load and gas consumption flow rate; This is expressed as the compressor's loss coefficient; and These represent the gas pressure flowing out of and into the compressor, respectively. This is expressed as the maximum air pressure compression ratio of the compressor; Represents a node m The square value of air pressure; and Representing nodes respectively m Minimum and maximum limits of the square of atmospheric pressure; For a collection of pipes; Represented as the dispatch time in a piecewise tangent linearized quasi-steady-state natural gas network considering pipeline storage effects. t Internal flow through connection nodes m and n The average gas flow rate of the pipeline; and Represented as nodes m The gas well connected to the upper part is in the scheduling time t Minimum and maximum limiting gas flow rates for production; This indicates the gas flow rate consumed by the gas turbine generator in a natural gas system.

[0048] Under the above constraints, the target of natural gas system optimization scheduling is the gas production of gas wells, with the first... Taking a natural gas system as an example, the dispatch cost of gas wells is given by the following formula: , in, This indicates the cost of gas production from a gas well; This represents the cost coefficient for gas production from a gas well. Represented as a natural gas system Collection of medium gas wells.

[0049] To improve the accuracy of the piecewise tangent linearized quasi-steady-state natural gas network considering pipeline storage effects, and to make the calculated natural gas flow rate closer to the actual value, a penalty term is introduced. Piecewise tangent linearization relaxation of the tightening Weymouth equation: , in, This is the penalty coefficient; Indicates natural gas system Central pipeline assembly.

[0050] In summary, the objective of natural gas system scheduling is to minimize the scheduling cost of gas wells and the penalty term associated with the piecewise tangential linearization of the Weymouth equation. It also includes the profit obtained from selling natural gas to gas turbine units.

[0051] The dispatch target for the integrated electricity-gas energy system is the sum of the dispatch target for the power system and the dispatch target for natural gas:

[0052] For ease of understanding, we will first express the integrated electricity-gas energy system scheduling model in a compact form: , , , , The first formula is the objective function of the integrated electricity-gas energy system scheduling model, which is to minimize the scheduling cost of thermal power units, the penalty cost of wind power curtailment, and the gas production cost of natural gas wells. It is a quadratic function, including the dispatching cost of thermal power units, the penalty cost of wind power curtailment, and the dispatching cost of gas turbine units; It is a linear function, including the gas production cost of natural gas wells and the scheduling revenue of gas turbine units; This represents the power grid inequality constraints; Represents the power grid equality constraints; Indicates gas network Inequality constraints; Indicates gas network Equality constraints; Indicates coupling constraints; Represents the internal variables of the power grid, and ;in This is the electrical output of a conventional thermal power unit. It is the electrical output of the gas turbine unit. It is the electrical output of the wind turbine; It is the phase angle of the busbar; Represents the internal variables of the gas network. Indicates gas network , and ; Represented as power grid boundary variables; Represented as gas network Boundary variables; Represented as boundary variables of the gas network; where Indicates the gas production of a natural gas well. This indicates the gas consumption of the gas turbine unit; From the connection m and n Pipes flow from node to node n airflow rate; From node m Flow to connection m and n The air flow rate of the pipe at the node; It is a connection m and n The average gas flow rate in the pipes at the node; This indicates the airflow rate from the node into the compressor. This indicates the airflow rate from the compressor to the node; It is a node m The square of the air pressure; It is a node m With nodes n The difference in the square of air pressure; It is the coefficient matrix of the power grid inequality constraints. It is the coefficient vector of the power grid inequality constraints. It is the coefficient matrix of the power grid equality constraints. It is the coefficient vector of the power grid equality constraints. It is a gas network The coefficient matrix of the inequality constraints. It is a gas network The coefficient vector of the inequality constraints. It is a gas network The coefficient matrix of the equality constraints, It is a gas network The coefficient vector of the equality constraints, It is the power grid and gas grid The coupling constraint power grid coefficient matrix, It is the power grid and gas grid The coupling constraint gas network coefficient matrix, It is the power grid and gas grid The coupling constraint coefficient vector.

[0053] Based on the above compact model of the integrated electricity-gas energy system scheduling, we can transform it into a monotone variational inequality model. The Lagrangian function of the above compact model is expressed as follows: , in, and The superscript indicates the first Each gas network's internal variables and their corresponding dual variables; and These are the original variable and the dual variable, respectively. A It refers to the number of air networks. yes A saddle point if the following assumption inequality holds.

[0054]

[0055] Saddle Point It can be described by the following characteristics:

[0056] in, Represents the feasible region of the power system; Represents the feasible region of the natural gas system; Let m represent the real number space of dimension m. Based on the Lagrangian function of the compact model of the integrated electric-gas energy system scheduling, the above-mentioned assumptions, inequalities, and the above-mentioned saddle point characteristics, we can define the saddle point as... Rephrased as: , Where the superscript T denotes matrix transpose, Indicates the corresponding natural gas system of Feasible region; Indicates the corresponding gas network m-dimensional real number space; Indicates the first The optimal solution for the gas network; Indicates the first The optimal multipliers. The above formula, as constraint variables, can be restated into the following monotone variational inequality model:

[0057] in, ; This is the optimization objective of the integrated electricity-gas energy system scheduling model; This represents the gradient at the optimal solution. Represented as all variables The optimal solution; This is the optimization objective of the integrated electricity-gas energy system scheduling model; and .

[0058]

[0059] in, , The subscript indicates which gas network's coefficient matrix this is. The subscript indicates which gas network's coefficient vector this vector belongs to. The superscript indicates which gas network the variable belongs to, ranging from 1 to 1. A ; express The gradient at the given location; the subscript 'a' indicates the number from 1 to A, meaning there are a total of A gas networks, and 'a' represents the a-th gas network; T indicates matrix transpose; other missing terms have been added above; the subscripts A, B, b (bold indicates matrix) indicate which gas network the variable belongs to, and the total number is from 1 to A (thin indicates quantity); in summary, the saddle point of the Lagrangian function of the integrated electricity-gas energy dispatch model, i.e., its optimal solution, is also the solution to the monotone variational inequality.

[0060] Step 102: The balanced split-shrink algorithm is used to solve the power-gas integrated energy system scheduling model in a distributed manner to obtain the power-gas joint optimization scheduling results.

[0061] The combined power-gas optimization scheduling results include thermal power unit output, gas turbine unit output, wind power grid connection, natural gas well production, power system scheduling cost, natural gas system scheduling cost, and power-gas integrated energy system scheduling cost. Further, step 102 includes: The step of using a balanced split-shrink algorithm to perform a distributed solution on the electricity-gas integrated energy system scheduling model to obtain the joint optimization scheduling results for electricity and gas includes: Based on the balanced split-contraction algorithm, the solution problem of the power-gas integrated energy system scheduling model is transformed into a power grid scheduling subproblem and a gas grid scheduling subproblem. Solving the power grid scheduling subproblem yields the power grid scheduling solution.

[0062] The power grid scheduling solution is sent to each gas grid, and each gas grid solves its own gas grid scheduling subproblem to obtain all gas grid scheduling solutions. The power grid collects all the gas grid scheduling solutions, updates the power grid scheduling subproblem, and solves the power grid scheduling subproblem. The above process is repeated iteratively to obtain the scheduling solution of the integrated power-gas energy system.

[0063] The specific principle of the balanced split-shrink algorithm is as follows: set up In order to be in The vector, set Let be a positive semi-definite matrix, defined , Represented as a vector corresponding norm Given at the beginning , Represented as core variables The k The value of the next iteration; Represented as core variables The k +1 iteration value, found Make the following conditions true:

[0064] in, Represented as all variables The k +1 iteration value; Represented as all variables The k The next iteration value; a positive semidefinite matrix Defined as:

[0065] in, Represented as a penalty factor, It is the identity matrix. It represents an n-dimensional real number space.

[0066] The above conditions can be equivalently expressed as:

[0067] in, Representing variables The k +1 iteration value; Representing variables Calculated power grid costs; Representing variables The k +1 iteration value; Representing variables The calculation of the first Cost of individual gas network; Indicates multiplier The k The value of the next iteration; Indicates multiplier The k +1 iteration value; Representing variables The k The value of the next iteration; Representing variables The k The value of the next iteration. The above conditions can be transformed into the following steps: The first step is to solve the power grid dispatching subproblem and update the decision variables of the gas grid dispatching subproblem. .

[0068] , in Representing variables The k The next iteration value.

[0069] The second step is for the power grid to update the decision variables. Send to all gas networks, then update the power grid and the first Lagrange multipliers of the gas network :

[0070] The third step involves updating the decision variables sent by the power grid to address the gas grid dispatch subproblem. With the updated Lagrange multipliers Update # Decision variables of the gas network scheduling subproblem :

[0071] No. The sub-problem of gas network scheduling will and This is then sent to the power grid dispatch subproblem. Finally, the power grid dispatch subproblem receives input from all the gas grid dispatch subproblems. and And update the decision variables of the power grid dispatch sub-problem. .

[0072] To improve efficiency, we further expand the relevant variables as follows:

[0073] in, Indicates the step size; This represents the extended variable; efficiency can be further improved based on the above extended variables, as shown below: set up From the initial assumptions, we can obtain:

[0074] Apply the following identity:

[0075] We can obtain:

[0076] According to the original monotone variational inequality model, we have:

[0077] The above formula shows It is an unknown distance function exist The upward direction at that point. The above formula can be written as:

[0078] Then we can obtain:

[0079] To simplify the above formula, let:

[0080] in It depends on A new variable. We want to maximize the difference in distance from the optimal point between two consecutive iterations, its use... express:

[0081] Notice:

[0082] The above formula is A quadratic function.

[0083] We cannot directly maximize ,because The coefficients contain unknown solutions .

[0084] Will Substituting into the simplified formula above, we get:

[0085] To further improve computational efficiency, we set:

[0086] This is about The second lower bound function. Figure 4 Showing and The relationship between them.

[0087] quadratic function exist When the maximum value is reached, we want to maximize it. Therefore, we expand

[0088] in, In short, in expanding Then, the distance function It decreases more in each iteration and approaches the optimal value faster, thus improving efficiency.

[0089] The above method is the split-shrink algorithm; however, it uses a fixed penalty factor. Unable to balance the original residuals of the monotone variational inequality model and dual residual Therefore, we set specific criteria to update the penalty factor, as follows:

[0090] in, For a given ratio, where Indicates the penalty factor The k The value of the next iteration; Indicates the penalty factor The k +1 iteration value. By using the above criteria, the ratio of the original error to the dual error can be more balanced, making the iterative value closer to the solution of the monotone variational inequality model and accelerating the convergence speed. We call this method the Balanced Split-Contraction Algorithm.

[0091] Step 103: Based on the incentive mechanism of bilateral Shapley values, the cooperative surplus of the electricity-gas integrated energy system scheduling model is fairly distributed.

[0092] Based on the power system dispatch cost, the natural gas system dispatch cost, and the power-gas integrated energy system dispatch cost calculated in step 102, a cooperative game analysis is performed to obtain the bilateral Shapley value of the connection between the power grid and the gas grid. The grid dispatch cost and the gas grid dispatch cost are calculated based on the bilateral Shapley values ​​of the grid and gas grid connection relationship.

[0093] Considering an integrated electricity-gas energy system consisting of multiple gas grids connected to the power grid, the Grand Alliance represents it as... , Indicates grid operator, Indicates the first A gas grid operator. While profit redistribution based on Shapley values ​​can indeed achieve incentive compatibility in the dispatching problem of the integrated electricity-gas energy system, the computational complexity increases exponentially with the number of collaborators. To achieve a fair distribution of cooperative surplus while reducing computational complexity, we adopt a bilateral Shapley value method. In the bilateral Shapley value method, the integrated electricity-gas energy system alliance is divided into a grid set and all gas grid sets, where the initial cost allocated to the grid is... for:

[0094] Assigned to the Initial cost of a gas network for:

[0095] in Indicates the first The cost of joint dispatching of the remaining gas networks and the power grid when one gas network does not cooperate with the power grid. When indicating independent scheduling, the first Variables in the gas network; These represent variables of the power grid during independent dispatch.

[0096] Residual cost after initial allocation for:

[0097] Assuming the remaining costs are allocated proportionally, the final cost of the power grid and the The final cost of individual gas networks The allocation is as follows: , ,

[0098] For including For an integrated electricity-gas energy system alliance of individual gas networks, the incentive mechanism based on bilateral Shapley values ​​only requires calculating (1+2+2). a By calculating the cost of one scenario, the cost allocation for the power grid and all gas networks can be determined. In contrast, the Shapley value requires calculation of (…). The cost of this scenario is reduced. The bilateral Shapley value reduces computational complexity and significantly improves efficiency, making it more suitable for large-scale integrated power-gas energy system optimization and scheduling problems. This incentive mechanism based on the bilateral Shapley value allocates cooperative surpluses according to marginal contributions, reducing costs for both the power grid and gas grid while promoting cooperation between the power and gas industries.

[0099] For ease of understanding, this application also provides small-scale and large-scale integrated electric-gas energy systems that apply the distributed cooperative mechanism-based scheduling method of the integrated electric-gas energy system in this embodiment. To further quantify the accuracy of the piecewise tangent linearized quasi-steady-state natural gas network considering pipeline storage effects, the relative error of pipeline natural gas flow is defined as follows: , The average relative error is defined as the average of the relative errors of all pipelines over all scheduling time periods.

[0100] For details on the Weymouth linearization in the piecewise tangent linearization quasi-steady-state natural gas network considering the pipeline storage effect, please refer to Tables 1 and 2.

[0101] Table 1. Comparison of Linearization Methods for Weymouth Equations in Small-Scale Electric-Gas Integrated Energy Systems

[0102] Table 1 shows that, in this comparison, with one hour as a scheduling period, the average relative errors of all pipeline gas flow velocities in all gas networks of the large-scale integrated electric-gas energy system within 24 hours, using the Taylor series expansion method, are 31.77%, 10.33%, and 29.30%, respectively; the maximum relative errors are 54.84%, 35.75%, and 48.88%, respectively. In the piecewise linearization of the quasi-steady-state natural gas network considering pipeline storage effects, the average relative errors of all pipeline gas flow rates in the three gas networks of the large-scale integrated electric-gas energy system within 24 hours are 0.40%, 0.07%, and 0.08%, respectively; the maximum relative errors are 0.87%, 0.42%, and 2.03%, respectively.

[0103] Table 2. Comparison of Linearization Methods for Weymouth Equations in Large-Scale Electric-Gas Integrated Energy Systems

[0104] Table 2 shows that, in this comparison, with one hour as a scheduling period, using the Taylor series expansion method, the average relative error of gas flow velocity in all pipelines of the large-scale integrated electric-gas energy system exceeds 23.00% within 24 hours, and the maximum relative error exceeds 69.00%. In the piecewise linearization of the quasi-steady-state natural gas network considering pipeline storage effects, the average relative error of gas flow rate in all pipelines of the five gas networks of the large-scale integrated electric-gas energy system does not exceed 2.00 × 10⁻⁶ within 24 hours. -3 The maximum relative error does not exceed 5.00%. This confirms that the piecewise linearization method for the quasi-steady-state natural gas network considering the pipeline storage effect is superior to the Taylor series expansion method.

[0105] For the application of the piecewise tangent linearized quasi-steady-state natural gas network model considering pipeline storage effects to wind power grid connection, please refer to [link / reference needed]. Figure 5 ,like Figure 5 As shown in section (a), the storage effect significantly increases the amount of wind power connected to the grid, especially during periods of abundant wind, such as nighttime and early morning. When the storage effect is taken into account, the amount of wind power curtailment is reduced by 50.64% compared to the case without the storage effect. Figure 5 Parts (b), (c), and (d) further demonstrate that, considering the storage effect, the increase in wind power grid connection will reduce the power generation of gas turbine units in each gas grid. Specifically: Gas grid 1: Power generation of gas turbine units decreases by 25.80% when considering the storage effect; Gas grid 2: Power generation of gas turbine units decreases by 6.46% when considering the storage effect; Gas grid 3: Power generation of gas turbine units decreases by 9.38% when considering the storage effect. In conclusion, incorporating the gas grid storage effect can improve wind power grid connection capacity and reduce gas turbine unit power generation.

[0106] Table 3. Economic benefits of large-scale integrated electric-gas energy systems considering pipeline storage effects.

[0107] Table 3 compares dispatch costs with and without considering the storage effect. With the storage effect considered, the cost of the integrated electricity-gas energy system decreased from $8,792,886 to $7,966,172. Although the grid achieved the largest absolute savings ($395,582), its relative reduction (6.62%) was less than that of the gas grid. Each gas grid achieved a stabilization cost reduction of 14.05% to 16.01%, highlighting the economic benefits of utilizing the storage effect in the optimal dispatch of the integrated electricity-gas energy system. These results demonstrate that considering the storage effect can improve economic efficiency and enhance coordination between the grid and gas grids.

[0108] Please refer to Tables 4 and 5 for a comparison of the economic benefits of small-scale and large-scale integrated electric-gas energy systems based on Shapley values.

[0109] Table 4. Comparison of Economic Benefits of Small-Scale Electric-Pneumatic Integrated Energy Systems

[0110] Table 4 illustrates the economic performance of a small-scale integrated electric-gas energy system. Under the joint dispatch mode, the cost of the integrated electric-gas energy system is $2,820,313, significantly lower than the $4,498,282 under the independent dispatch mode. However, the grid cost increases from $1,901,601 to $1,957,361, primarily due to increased gas consumption by the gas grid during joint dispatch. This may diminish the grid's willingness to participate, highlighting the need to redistribute the cooperative surplus.

[0111] To address this issue, a Shapley-based incentive mechanism was adopted, reducing grid costs to $1,028,254. However, the traditional Shapley method requires input of 15 possible participant scenarios, resulting in excessive computational complexity and limiting its practical application. To simplify the calculation, a bilateral Shapley-based incentive mechanism was employed. This method only requires evaluating 9 participant scenarios, reducing grid costs to $1,083,679 while also increasing participant willingness. Compared to the traditional Shapley method, this method has a relative error of 5.39% in grid costs. The cost errors for gas network 1, gas network 2, and gas network 3 are 5.76%, 4.02%, and 0.73%, respectively. In summary, the bilateral Shapley-based incentive mechanism significantly reduces computational complexity while lowering grid and gas network costs.

[0112] Table 5. Comparison of Economic Benefits of Large-Scale Electric-Gas Integrated Energy Systems

[0113] Table 5 illustrates the economic performance of large-scale integrated electric-gas energy systems. Under the joint dispatch mode, the cost of the integrated electric-gas energy system is $7,966,172, a significant reduction compared to $12,745,317 under the independent dispatch mode. However, the grid cost increases from $5,320,359 to $5,579,736. In contrast, gas turbine generators show a significant cost reduction under joint dispatch, with savings of up to 70%. The costs of these gas turbine generators under independent dispatch range from $1,399,259 to $1,554,768, showing a continuous downward trend across all joint dispatch methods. However, calculating the Shapley value requires considering 63 possible scenarios, resulting in high computational costs.

[0114] In comparison, the bilateral Shapley value method only requires evaluating 13 participant scenarios to maintain equivalent accuracy. The "Error" column shows the relative cost error between the Shapley value method and the bilateral Shapley value method. Both methods minimize the observation cost bias in the traditional cooperative model, with the grid error being only 1.00%. The cost errors for gas networks 1 to 5 are 1.15%, 0.15%, 0.10%, 0.29%, and 1.14%, respectively. In summary, the incentive mechanism based on the bilateral Shapley value effectively promotes collaborative cooperation between the power grid and the gas network while reducing computational complexity.

[0115] The distributed optimal power-gas integrated energy scheduling method based on a combination of monotone variational inequalities and cooperative game theory provided in this application employs a balanced split-contraction algorithm for distributed optimization of a power-gas integrated energy system scheduling model that includes a piecewise tangential linearized quasi-steady-state natural gas network considering pipeline storage effects. During this process, bilateral Shapley values ​​are used to allocate revenue, ensuring that the incentives for the power system and the natural gas system are consistent with the expected results. This overcomes the problem of incentive incompatibility in joint scheduling of the power-gas integrated energy system, ensuring the compatibility of the incentive mechanism. Therefore, this application can solve technical problems such as privacy leaks and incentive incompatibility caused by the high proportion of new energy system access and the high wind power curtailment under the existing electricity-carbon market mechanism, and the fact that the power grid and gas grid belong to different operators.

[0116] For easier understanding, please refer to Figure 2 The present invention provides an embodiment of an electrical joint method scheduling device based on variational inequalities, comprising: Model building unit 201 is used to construct a monotone variational inequality-based scheduling model for the integrated electric-gas energy system based on pre-set integrated electric-gas energy system parameters. The pre-set integrated electric-gas energy system parameters include power system parameters, natural gas system parameters, and electric-gas coupling equipment parameters. The integrated electric-gas energy system scheduling model includes a piecewise tangential linearized quasi-steady-state natural gas network considering pipeline storage effects. Further, it includes: Weymouth Linearization Subunit 2011 is used to establish a piecewise tangential linearized quasi-steady-state natural gas network that takes into account pipeline storage effects. The model solving unit 202, which is connected to the model building unit 201, is used to perform distributed solution of the electricity-gas integrated energy system scheduling model using the balanced split-shrink algorithm to obtain the electricity-gas joint optimization scheduling result. The combined power-gas optimization scheduling results include thermal power unit output, gas turbine unit output, wind power grid connection, natural gas well production, power system scheduling cost, natural gas system scheduling cost, and power-gas integrated energy system scheduling cost. Furthermore, it also includes: The parameter acquisition unit 203, connected to the model building unit 201, is used to acquire preset electric-gas integrated energy system parameters, which include power system parameters, natural gas system parameters, and electric-gas coupling equipment parameters. The power system parameters include bus parameters, branch parameters, thermal power unit parameters, wind turbine unit parameters, and electrical load parameters; The natural gas system parameters include node parameters, pipeline parameters, natural gas well parameters, compressor parameters, and gas load parameters; The parameters of the electro-pneumatic coupling equipment include the operating parameters of the gas turbine unit; Furthermore, the model solving unit 202 includes: Problem transformation subunit 2021 is used to transform the solution problem of the electric-gas integrated energy system scheduling model into a power grid scheduling subproblem and a gas grid scheduling subproblem based on the balance split-contraction algorithm; The iterative solution subunit 2022, connected to the problem transformation subunit 2021, is used to first solve the power grid scheduling subproblem to obtain the power grid scheduling solution; and then send the power grid scheduling solution to all gas networks respectively, and each gas network solves its own gas network scheduling subproblem to obtain all gas network scheduling solutions; the power grid collects all the gas network scheduling solutions, updates the power grid scheduling subproblem, and solves the power grid scheduling subproblem, repeating the above process iteratively to obtain the power-gas integrated energy system scheduling solution; Furthermore, it also includes: The game analysis unit 204, connected to the model solving unit 202, is used to perform cooperative game calculation and analysis based on the power system dispatch cost, the natural gas system dispatch cost, and the power-gas integrated energy system dispatch cost to obtain the bilateral Shapley value of the connection relationship between the power grid and the gas grid. The cost calculation unit 205 is used to calculate the grid dispatch cost and the gas grid dispatch cost based on the bilateral Shapley value of the connection relationship between the power grid and the gas grid. The cost calculation unit 205 is connected to the game analysis unit 204.

[0117] This application also provides a scheduling device based on a variational inequality-based electrical joint scheduling method, the device including a processor and a memory; The memory is used to store program code and transfer the program code to the processor; The processor is used to execute the distributed scheduling method for the integrated electric-gas energy system in the above method embodiment according to the instructions in the program code.

[0118] The present invention also provides a computer-readable storage medium for storing program code for executing the electrical joint scheduling method based on variational inequalities in the above method embodiments.

[0119] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0120] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0121] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0122] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods described in the various embodiments of this application through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0123] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An electrical joint scheduling method based on variational inequalities, characterized in that, include: Step 101: The integrated electric-gas energy system includes an electric system and a natural gas system. Based on the acquired pre-set integrated electric-gas energy system parameters, a scheduling compact model of the integrated electric-gas energy system is constructed. Based on the Lagrangian function and its saddle point characteristics of the scheduling compact model, the scheduling compact model is transformed into a monotone variational inequality model. The saddle point of the Lagrangian function is its optimal solution, which is also the solution of the monotone variational inequality model. The natural gas system is a piecewise tangential linearized quasi-steady-state natural gas system that takes into account the pipeline storage effect. Specifically, the optimization scheduling object of the natural gas system is the gas production of natural gas wells. The Weymouth equation is piecewise tangential linearized, and a penalty term is introduced to tighten the piecewise tangential linearization relaxation of the Weymouth equation, so that the calculated gas production is closer to the actual value. The constraints of the natural gas system include gas flow balance constraints at each node, gas flow loss constraints of the compressor, maximum gas pressure compression ratio constraints of the compressor, gas pressure constraints at the nodes, gas pressure squared difference constraints, piecewise tangent linearized Weymouth equation constraints, pipeline gas flow constraints considering pipeline storage, storage gas flow constraints due to pipeline storage effect, and gas flow constraints consumed by the gas turbine unit. Step 102: The monotone variational inequality model is solved in a distributed manner using the balanced split-shrink algorithm to obtain the joint electric-gas optimization scheduling result; Specifically, a specific criterion is set to update the penalty factor in the split-shrink algorithm. This makes the original error of the monotone variational inequality model... With dual error The ratios are more balanced, as shown in the following formula: ,in, For a given ratio, , They represent The kth and (k+1)th iteration values ​​are used to make the iteration values ​​closer to the solution of the monotone variational inequality model and accelerate the convergence speed, thus forming the balanced split-contraction algorithm. The combined power-gas optimization scheduling results include: thermal power unit output, gas turbine unit output, wind power grid connection, natural gas well production, power system scheduling cost, natural gas system scheduling cost, and power-gas integrated energy system scheduling cost. Step 103: Based on the incentive mechanism of bilateral Shapley values, the cooperative surplus of the electricity-gas integrated energy system scheduling model is fairly distributed.

2. The electrical joint scheduling method based on variational inequalities according to claim 1, characterized in that, The pre-set integrated electric-gas energy system parameters include power system parameters, natural gas system parameters, and electric-gas coupling equipment parameters; The power system parameters include bus parameters, branch parameters, thermal power unit parameters, wind turbine unit parameters, and electrical load parameters; The natural gas system parameters include node parameters, pipeline parameters, natural gas well parameters, compressor parameters, and gas load parameters; The parameters of the electro-pneumatic coupling equipment include the operating parameters of the gas turbine unit.

3. The electrical joint scheduling method based on variational inequalities according to claim 1, characterized in that, Step 102 includes the following sub-steps: S21: Based on the balanced split-contraction algorithm, the solution problem of the power-gas integrated energy system scheduling model is transformed into a power grid scheduling sub-problem and a gas grid scheduling sub-problem. Solving the power grid scheduling sub-problem yields the power grid scheduling solution. S22: The power grid scheduling solution is sent to all gas networks respectively, and each gas network solves its own gas network scheduling subproblem to obtain all gas network scheduling solutions; S23: The power grid collects all the gas grid scheduling solutions, updates and solves the power grid scheduling subproblem; S24: Repeat the above sub-steps iteratively to obtain the scheduling solution of the integrated electric-gas energy system.

4. The electrical joint scheduling method based on variational inequalities according to claim 1, characterized in that, In step 103, a cooperative game analysis is performed based on the power system dispatch cost, the natural gas system dispatch cost, and the power-gas integrated energy system dispatch cost to obtain the bilateral Shapley value of the connection between the power grid and the gas grid; the power grid dispatch cost and the gas grid dispatch cost are calculated based on the bilateral Shapley value of the connection between the power grid and the gas grid.

5. An electrical joint dispatching device based on variational inequalities, employing the electrical joint dispatching method based on variational inequalities as described in any one of claims 1-4, characterized in that, The device includes: The parameter acquisition unit is used to acquire preset electric-gas integrated energy system parameters and transmit them to the model building unit; The model building unit is used to construct a compact scheduling model of the integrated electric-gas energy system based on the preset parameters of the integrated electric-gas energy system and transform it into a monotone variational inequality model. The integrated electric-gas energy system scheduling model includes the power system and the piecewise tangent linearized quasi-steady-state natural gas system considering pipeline storage effects. The model solving unit, connected to the model building unit, is used to perform distributed solving of the monotone variational inequality model using the equilibrium split-shrink algorithm to obtain the joint optimization scheduling results of the power and gas systems. The joint optimization scheduling results of the power and gas systems include the output of thermal power units, the output of gas turbine units, the grid-connected wind power, the gas production of natural gas wells, the scheduling cost of the power system, the scheduling cost of the natural gas system, and the scheduling cost of the integrated power and gas energy system. The game analysis unit, connected to the model solving unit, is used to perform cooperative game calculation and analysis based on the power system dispatch cost, the natural gas system dispatch cost, and the power-gas integrated energy system dispatch cost, to obtain the bilateral Shapley value of the connection relationship between the power grid and the gas grid; The cost calculation unit, connected to the game analysis unit, is used to calculate the power grid dispatch cost and the gas grid dispatch cost based on the bilateral Sharpe ratio.

6. An electrical joint dispatching device based on variational inequalities, characterized in that, The device includes a processor and a memory; The memory is used to store program code and transmit the program code to the processor; The processor is used to execute the electrical joint scheduling method based on variational inequalities as described in any one of claims 1-4 according to the instructions in the program code.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program code for executing the electrical joint scheduling method based on variational inequalities as described in any one of claims 1-4.

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