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

Abstract

The invention discloses a variational inequality-based electricity-gas combined dispatching method, device and equipment and a medium, and the method comprises the steps: constructing a monotonous variational inequality electricity-gas integrated energy system dispatching model according to preset parameters of an electricity-gas integrated energy system, the preset power-gas integrated energy system parameters comprise power system parameters, natural gas system parameters and power-gas coupling equipment parameters, and the power-gas integrated energy system scheduling model comprises a segmented tangent linearization quasi-steady-state natural gas network considering the management and storage effect; carrying out distributed solution on the power-gas integrated energy system scheduling model by adopting an equilibrium split-contraction algorithm to obtain a power-gas joint optimization scheduling result; and fairly distributing cooperation surplus of the dispatching model of the power-gas integrated energy system based on an incentive mechanism of a bilateral Shapley value. According to the invention, the technical problems of high wind power abandoned electricity quantity, privacy leakage, incompatibility of excitation and the like of an electricity-gas integrated energy system can be solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of distributed joint scheduling of electric-gas integrated energy system, and particularly relates to an electric-gas joint scheduling method and device based on variational inequality, equipment and medium. BACKGROUND

[0002] Integrated energy system effectively improves the operation flexibility of power system and promotes high proportion of renewable energy consumption by establishing the connection between different energy systems. The wide application of gas turbine units in power system makes the coupling relationship between power system and natural gas system increasingly close, and gradually forms an electric-gas integrated energy system. The joint optimization scheduling of the system helps to improve the overall economy and energy utilization efficiency of the system and reduce the curtailment of wind and electricity. The electric-gas joint optimization scheduling mainly includes centralized and distributed modes. Distributed algorithm can realize joint optimization scheduling while protecting the privacy of operation subject, and distributed scheduling is often used instead of centralized scheduling in reality.

[0003] Natural gas network modeling needs to balance accuracy and efficiency. Weymouth equation is often linearized by piecewise linear (PWL) approximation or Taylor series expansion (TSE). In PWL, binary variables are introduced to convert the electric-gas integrated energy system optimization scheduling into a mixed integer linear programming. However, the higher the accuracy, the more binary variables are required to be introduced, resulting in low computational efficiency. TSE simplifies the calculation complexity by using the tangent to approximate Weymouth equation near the fixed point, but its accuracy still needs to be further verified. The tangent often lacks compactness, and it is difficult to choose a suitable expansion point. In addition, natural gas is compressible, and the pipe storage effect is often seen in the pipeline, which can effectively improve the new energy consumption of the power grid as a gas network energy storage feature. Therefore, for the optimization scheduling of electric-gas integrated energy system, a linear quasi-steady-state gas network model that takes into account accuracy and efficiency is crucial.

[0004] Electric-gas integrated energy system optimization scheduling needs an independent and coordinated method to protect privacy, including user information, operating parameters and topological structure of power network and natural gas network, etc. Distributed algorithm is often used to convert the electric-gas integrated energy system optimization scheduling problem into power grid sub-problems and gas network sub-problems, and each optimization sub-problem is solved independently under the condition of limited information interaction.

[0005] Existing electric-gas joint optimization scheduling research is usually based on collective rationality, that is, maximizing the total utility of electric-gas integrated energy system. Although the electric-gas joint optimization scheduling based on collective rationality can reduce the total cost of integrated energy system, it cannot guarantee the interests of power grid and gas network respectively. Therefore, the electric-gas joint optimization scheduling based on collective rationality cannot achieve incentive compatibility.

[0006] To achieve incentive compatibility, existing researches are divided into two categories: market game method and transfer payment method. Market game method may not achieve overall benefit maximization. Transfer payment method is that natural gas system shares some cooperative surplus with power system, so that the total operation cost of both sides is reduced. However, the calculation of the optimal allocation ratio of the current cooperative surplus is relatively complex and not easy to operate. SUMMARY

[0007] The application provides an electrical joint scheduling method and device based on variational inequality, which combines monotone variational inequality and cooperative game with distributed optimal electricity-gas integrated energy optimization scheduling. The method and device are used to solve the technical problems of individual privacy protection, low new energy grid connection rate and incentive compatibility in the existing electricity-gas integrated energy system joint scheduling by using a more efficient distributed algorithm and cooperative game mechanism.

[0008] To this end, the application provides an electrical joint scheduling method based on variational inequality, which comprises the following steps: Step 101: constructing a monotone variational inequality electricity-gas integrated energy system scheduling model according to preset electricity-gas integrated energy system parameters, wherein the preset electricity-gas integrated energy system parameters comprise power system parameters, natural gas system parameters and electricity-gas coupling device parameters, and the electricity-gas integrated energy system scheduling model comprises a piecewise tangent linearization quasi-steady-state natural gas network considering pipe storage effect.

[0009] Step 102: performing distributed solution on the electricity-gas integrated energy system scheduling model by using a balanced splitting-contracting algorithm to obtain an electricity-gas joint optimization scheduling result; Step 103: performing fair distribution on cooperative surplus of the electricity-gas integrated energy system scheduling model based on a double-sided Shapley value incentive mechanism; The electricity-gas joint optimization scheduling result comprises thermal power unit output, gas turbine unit output, wind power grid connection amount, natural gas well gas production amount, power system scheduling cost, natural gas system scheduling cost and electricity-gas integrated energy system scheduling cost.

[0010] Preferably, the step of constructing a monotone variational inequality electricity-gas integrated energy system scheduling model according to preset electricity-gas integrated energy system parameters, wherein the electricity-gas integrated energy system scheduling model comprises a piecewise tangent linearization quasi-steady-state natural gas network considering pipe storage effect, further comprises the following steps: acquiring preset electricity-gas integrated energy system parameters, wherein the preset electricity-gas integrated energy system parameters comprise power system parameters, natural gas system parameters and electricity-gas coupling device parameters; The power system parameters comprise bus parameters, branch parameters, thermal power unit parameters, wind power unit parameters and electric load parameters. The natural gas system parameters include node parameters, pipeline parameters, natural gas well parameters, compressor parameters and gas load parameters. The electric-gas coupling device parameters include operation parameters of gas turbine units.

[0011] Preferably, the electric-gas comprehensive energy system scheduling model is distributedly solved by using a balanced split-contraction algorithm to obtain an electric-gas joint optimization scheduling result, which comprises the following steps: S21: The solving problem of the electric-gas comprehensive energy system scheduling model is converted into an electric grid scheduling sub-problem and a gas grid scheduling sub-problem based on a balanced split-contraction algorithm, and the electric grid scheduling sub-problem is solved to obtain an electric grid scheduling solution.

[0012] S22: The electric grid scheduling solution is sent to all gas grids respectively, and all gas grids solve their respective gas grid scheduling sub-problems to obtain all gas grid scheduling solutions. S23: The electric grid collects all the gas grid scheduling solutions, updates the electric grid scheduling sub-problem, and solves the electric grid scheduling sub-problem, and the above process is repeated to obtain an electric-gas comprehensive energy system scheduling solution.

[0013] Preferably, the electric-gas comprehensive energy system scheduling model is distributedly solved by using a balanced split-contraction algorithm to obtain an electric-gas joint optimization scheduling result, and then the following steps are further included: A bilateral Shapley value of the electric grid and the gas grid connection relationship is obtained by performing cooperative game calculation and analysis based on the electric power system scheduling cost, the natural gas system scheduling cost and the electric-gas comprehensive energy system scheduling cost. The electric grid scheduling cost and the gas grid scheduling cost are respectively calculated based on the bilateral Shapley value of the electric grid and the gas grid connection relationship.

[0014] The second aspect of the present application provides a scheduling device based on the electric-gas joint scheduling method based on the variational inequality of the first aspect, which comprises: A model construction unit is configured to construct a single variational inequality electric-gas comprehensive energy system scheduling model according to preset electric-gas comprehensive energy system parameters, wherein the preset electric-gas comprehensive energy system parameters include electric power system parameters, natural gas system parameters and electric-gas coupling device parameters, and the electric-gas comprehensive energy system scheduling model comprises a piecewise tangent linearization quasi-steady-state natural gas network considering pipe storage effect. A model solving unit connected with the model construction unit is configured to distributedly solve the electric-gas comprehensive energy system scheduling model by using a balanced split-contraction algorithm to obtain an electric-gas joint optimization scheduling result. The electric-gas combined optimal scheduling result comprises a thermal power unit output, a gas turbine unit output, a wind power grid connection amount, a natural gas well gas production amount, a power system scheduling cost, a natural gas system scheduling cost and an electric-gas comprehensive energy system scheduling cost.

[0015] Preferably, the electric-gas comprehensive energy system distributed scheduling device further comprises: a parameter acquisition unit configured to acquire preset electric-gas comprehensive energy system parameters and deliver the preset electric-gas comprehensive energy system parameters to the model construction unit, wherein the preset electric-gas comprehensive energy system parameters comprise power system parameters, natural gas system parameters and electric-gas coupling device parameters; the power system parameters comprise bus parameters, branch parameters, thermal power unit parameters, wind power unit parameters and electric load parameters; the natural gas system parameters comprise node parameters, pipeline parameters, natural gas well parameters, compressor parameters and gas load parameters; the electric-gas coupling device parameters comprise operation parameters of the gas turbine unit.

[0016] Preferably, the model solving unit comprises: a problem transformation subunit configured to transform a solving problem of the electric-gas comprehensive energy system scheduling model into a power grid scheduling subproblem and a gas grid scheduling subproblem based on a balance split-contraction algorithm.

[0017] an iterative solving subunit connected with the problem transformation subunit, configured to solve the power grid scheduling subproblem to obtain a power grid scheduling solution, and send the power grid scheduling solution to all gas grids, respectively, so that all gas grids solve respective gas grid scheduling subproblems to obtain all gas grid scheduling solutions, and the power grid collects the all gas grid scheduling solutions, updates the power grid scheduling subproblem, solves the power grid scheduling subproblem, and iteratively repeats the above process to obtain an electric-gas comprehensive energy system scheduling solution.

[0018] Preferably, the electric-gas comprehensive energy system distributed scheduling device further comprises: a game analysis unit connected with the model solving unit, configured to perform cooperative game calculation analysis based on the power system scheduling cost, the natural gas system scheduling cost and the electric-gas comprehensive energy system scheduling cost to obtain a bilateral Shapley value of a power grid and gas grid connection relationship; a cost calculation unit configured to calculate a power grid scheduling cost and a gas grid scheduling cost based on the bilateral Shapley value of the power grid and gas grid connection relationship, wherein the cost calculation unit is connected with the game analysis unit.

[0019] The third aspect of the present application provides a scheduling device based on the electric-gas combined scheduling method based on a variational inequality of the first aspect, and the device comprises a processor and a memory. The memory is configured to store program code and transmit the program code to the processor. The processor is configured to execute the power-gas integrated energy system distributed scheduling method according to the instructions in the program code.

[0020] The fourth aspect of the present application provides a computer readable storage medium for storing program code, the program code being used to execute the power-gas integrated energy system distributed scheduling method according to the fourth aspect of the present application.

[0021] From the above technical solutions, the embodiments of the present application have the following advantages: The present application provides a power-gas integrated energy system scheduling method based on a distributed cooperation mechanism, which comprises the following steps: constructing a monotonic variational inequality power-gas integrated energy system scheduling model according to preset power-gas integrated energy system parameters, the preset power-gas integrated energy system parameters including power system parameters, natural gas system parameters and power-gas coupling device parameters, and the power-gas integrated energy system scheduling model including a piecewise tangent linearization quasi-steady-state natural gas network considering the pipe storage effect; performing distributed solving on the power-gas integrated energy system scheduling model by using a balanced splitting-contracting algorithm to obtain a power-gas joint optimization scheduling result; and performing fair distribution of cooperation surpluses of the power-gas integrated energy system scheduling model based on a bilateral Shapley value incentive mechanism. The power-gas joint optimization scheduling result includes thermal power unit output, gas turbine unit output, wind power grid-connected amount, natural gas well gas production, power system scheduling cost, natural gas system scheduling cost and power-gas integrated energy system scheduling cost. The present application uses a more efficient distributed algorithm and cooperation game mechanism to perform efficient power-gas integrated energy system distributed optimization scheduling under the premise of ensuring independent operation of the power system and the natural gas system and information privacy, thereby improving energy comprehensive utilization efficiency. The present application provides a power-gas integrated energy system optimization scheduling method based on monotonic variational inequality and balanced splitting-contracting algorithm, which uses the balanced splitting-contracting algorithm to perform distributed solving on the constructed power-gas integrated energy system scheduling model. In this process, the bilateral Shapley values of the power grid and the gas grid are calculated according to marginal contributions of the connection relationship between the power grid and the gas grid, a cooperation surplus distribution mechanism based on the bilateral Shapley values is established, so that the incentives of the power system and the natural gas system are consistent with the expected results, the problem of incompatible incentives in the power-gas integrated energy system joint scheduling is overcome, and the compatibility of the incentive mechanism is ensured. Therefore, the present application can solve the technical problems of privacy leakage, high abandoned wind and abandoned electricity, incompatible incentives and the like caused by the fact that the power grid and the gas grid belong to different operators under the condition of high proportion of new energy grid connection and existing power-carbon market mechanism. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1A flowchart of an electric-gas integrated energy system scheduling method based on monotone variational inequality and cooperative game mechanism is provided for the embodiment of the present application.

[0023] Figure 2 A structural diagram of an electric-gas integrated energy system scheduling device based on monotone variational inequality and cooperative game mechanism is provided for the embodiment of the present application.

[0024] Figure 3 A scheduling framework diagram of an electric-gas integrated energy system based on monotone variational inequality and cooperative game mechanism is provided for the embodiment of the present application.

[0025] Figure 4 The relationship between the electric-gas integrated energy system scheduling method based on monotone variational inequality and cooperative game mechanism and the electric-gas integrated energy system scheduling device based on monotone variational inequality and cooperative game mechanism is provided for the embodiment of the present application. And The relationship between the electric-gas integrated energy system scheduling method based on monotone variational inequality and cooperative game mechanism and the electric-gas integrated energy system scheduling device based on monotone variational inequality and cooperative game mechanism is provided for the embodiment of the present application.

[0026] Figure 5 The effect of a piecewise tangent linearization quasi-steady-state natural gas network model considering pipe storage effect on wind power grid integration is provided for the embodiment of the present application. DETAILED DESCRIPTION

[0027] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0028] For ease of understanding, please refer to Figure 1 The present application provides an embodiment of an electric joint scheduling method based on variational inequality, which comprises the following steps: Step 101: Construct a monotone variational inequality electric-gas integrated energy system scheduling model according to preset electric-gas integrated energy system parameters, the preset electric-gas integrated energy system parameters including power system parameters, natural gas system parameters and electric-gas coupling device parameters, and the electric-gas integrated energy system scheduling model including a piecewise tangent linearization quasi-steady-state natural gas network considering pipe storage effect.

[0029] Further, step 101 further comprises: Obtaining preset electric-gas integrated energy system parameters, the preset electric-gas integrated energy system parameters including power system parameters, natural gas system parameters and electric-gas coupling device parameters; The power system parameters include bus parameters, branch parameters, thermal power unit parameters, wind power unit parameters and electric load parameters; The natural gas system parameters include node parameters, pipeline parameters, natural gas well parameters, compressor parameters, and gas load parameters. The electro-gas coupling device parameters include operation parameters of gas turbine units.

[0030] It should be noted that the power system parameters, the natural gas system parameters, and the electro-gas coupling device parameters include but are not limited to the above parameters, and other related parameters can also be obtained as needed, and the embodiments only give several examples, and the specific embodiments are not limited.

[0031] In the power system scheduling model, the target is to minimize the scheduling cost of the thermal power plant , the penalty cost of wind power curtailment , and the total natural gas procurement cost :

[0032] The scheduling cost of the thermal power plant is given by the following formula:

[0033] wherein, denotes the serial number of the thermal power unit; is the set of thermal power units; t is the scheduling time; T is the set of scheduling times; , , are the quadratic term coefficient, the linear term coefficient, and the constant term coefficient of the power generation cost of the thermal power unit, respectively; denotes the power generation of the th thermal power unit in the t th scheduling period.

[0034] The penalty cost of wind power curtailment increases with the increase of the curtailed power:

[0035] wherein, denotes the serial number of the wind power unit; is the set of wind power units; denotes the curtailment penalty coefficient of the wind power unit ; is the maximum value of wind power generation; is the actual value of wind power generation.

[0036] The natural gas procurement cost from the th natural gas system is 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 The Middle Each gas turbine unitt -1 period of electrical output; representing a dispatch period t within the natural gas system representing a gas unit l output power; representing a dispatch period t within the bus load absorption power; representing a bus set representing a dispatch time t within the bus voltage phase angle; representing a line impedance connecting a bus and ; representing a maximum power limit connecting a bus and ; representing a bus set representing a voltage phase angle of a reference bus; representing a maximum downward ramping power limit of a thermal unit ; representing a maximum upward ramping power limit of a thermal unit ; representing a maximum downward ramping power limit of a gas unit within the natural gas system ; representing a maximum upward ramping power limit of a gas unit within the natural gas system ; representing a minimum output power of a thermal unit ; representing a maximum output power of a thermal unit ; representing a minimum output power of a gas unit within the natural gas system ; representing a maximum output power of a gas unit within the natural gas system ; representing a dispatch time t within the natural gas system output power of a gas unit ; representing an efficiency of a gas unit; representing a natural gas heat value.

[0041] For the convenience of understanding, we first establish a piecewise tangent linearization quasi-steady-state natural gas network considering pipe storage effect. First, we first segment the tangent linearization of Weymouth equation, which is expressed as follows: , wherein represents the gas flow rate from the pipeline m to n ; is the natural gas pipeline state coefficient, which is a constant term; is the node gas pressure of node m , is the node gas pressure of node n .

[0042] adopt and to represent and , that is, the square values of the node gas pressures of nodes m and n respectively; define a new variable to represent the square difference of the gas pressures of m and n , which is expressed as: , Weymouth equation is re-expressed as: , According to the mean value theorem, we can obtain:

[0043] wherein, and are the first derivative and the second derivative of respectively; represents the expansion point selected by the mean value theorem; represents any value existing between and that satisfies the above equation. is always non-negative. Therefore, we can use the mean value theorem to approximate the Weymouth equation, as follows:

[0044] wherein, is the expansion point number; is the number of expansion points; represents the expansion point connecting nodes m and n and the square difference of the gas pressures.

[0045] The constraints of the piecewise tangent linearization quasi-steady-state natural gas network considering pipe storage effect 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 linearization Weymouth equation constraint, the pipeline gas flow constraint considering pipe storage, the pipe storage effect storage gas flow constraint and the gas consumption flow constraint of the gas turbine unit.

[0047] Wherein, is a subscript indicating that the parameter is a parameter of the natural gas system ; t is a subscript indicating that the parameter is a parameter of the scheduling time t ; is a gas well serial number; is an expansion point serial number selected by the mean value theorem; is a set of gas wells connected to the node m ; is a gas flow produced by the gas well connected to the node m at the scheduling time t ; is a compressor serial number; is a set of compressors connected to the node m ; and are respectively a gas flow flowing out of the compressor at the scheduling time t and a gas flow flowing into the compressor; m is a node serial number; n is a remaining node serial number connected to the node m ; m is a node serial number; n is a remaining node serial number connected to the node m ; is a natural gas system the set of inner nodes; the set of remaining nodes represented as connected m to the set of inner nodes; the gas flow rate through the pipeline t in the scheduling time a ; mn at the expansion point selected in the first mean value theorem; the gas flow rate through the pipeline t out of the scheduling time mn ; the gas flow rate through the pipeline t into the scheduling time mn ; the set of gas turbine units connected to the node ; m the gas flow rate consumed by the gas turbine units in the scheduling time t ; the gas flow rate consumed by the node in the scheduling time t ; m the gas flow rate consumed by the node ; and represent the gas pressure of the gas flowing into and out of the compressor, respectively; the maximum gas pressure compression ratio of the compressor; the square value of the gas pressure of the node m ; and represent the minimum and maximum limit values of the square value of the gas pressure of the node m , respectively; the set of pipelines; the average gas flow rate through the pipelines connected to the node t and m in the scheduling time n , considering the pipe storage effect and the quasi-steady state of the natural gas network; and represent the minimum and maximum limit gas flow rates produced by the connected gas well on the node m in the scheduling time t ; the gas flow rate consumed by the gas turbine units in the natural gas system.

[0048] Under the above constraints, the optimization scheduling object of the natural gas system is the gas production of the gas well. Taking the first natural gas system as an example, the scheduling cost of the gas well is given by the following formula: , where, represents the cost of gas production of the gas well; represents the cost coefficient of gas production of the gas well, represents the set of gas wells in the natural gas system .

[0049] In order to improve the accuracy of the piecewise tangent linearization quasi-steady-state natural gas network considering the pipe storage effect, and make the calculated natural gas flow closer to the actual value, a penalty term is introduced to tighten the relaxation of the piecewise tangent linearization of the Weymouth equation: , where, is the penalty coefficient; represents the set of pipelines in the natural gas system .

[0050] In summary, the scheduling objective of the natural gas system is to minimize the scheduling cost of the gas well and the penalty term related to the piecewise tangent linearization Weymouth equation, which also includes the profit obtained by selling natural gas to the gas turbine unit:

[0051] And the scheduling objective of the electricity-gas integrated energy system is the sum of the scheduling objective of the power system and the scheduling objective of the natural gas:

[0052] For ease of understanding, we first express the scheduling model of the electricity-gas integrated energy system in a compact form: , , , , where the first formula is the objective function of the scheduling model of the electricity-gas integrated energy system, which is to minimize the scheduling cost of the thermal power unit, the penalty cost of wind power curtailment and the cost of gas production of the gas well; is a quadratic function, including the scheduling cost of the thermal power unit, the penalty cost of wind power curtailment and the scheduling cost of the gas turbine unit; is a linear function, including the cost of gas production of the gas well and the scheduling benefit of the gas turbine unit; represents the inequality constraint of the power grid; represents the equality constraint of the power grid; represents the inequality constraint of the gas grid ; represents the equality constraint of the gas grid ; 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 value of the next iteration.

[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 residuals 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 surpluses 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 night 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. A method for electrical joint dispatching based on variational inequality, characterized in that, The method comprises the steps of: Step 101: constructing a monotone variational inequality power-gas integrated energy system scheduling model according to preset power-gas integrated energy system parameters, wherein the preset power-gas integrated energy system parameters comprise power system parameters, natural gas system parameters and power-gas coupling device parameters, and the power-gas integrated energy system scheduling model comprises a piecewise tangent linearization quasi-steady-state natural gas network considering pipe storage effect; Step 102: solving the power-gas integrated energy system scheduling model in a distributed manner by using a balanced splitting-contracting algorithm to obtain power-gas joint optimization scheduling results; The power-gas joint optimization scheduling results comprise thermal power unit output, gas turbine unit output, wind power grid-connected amount, natural gas well gas production, power system scheduling cost, natural gas system scheduling cost and power-gas integrated energy system scheduling cost; The step 102 comprises the following steps: S21: converting a solving problem of the power-gas integrated energy system scheduling model into a power grid scheduling sub-problem and a gas grid scheduling sub-problem based on the balanced splitting-contracting algorithm, and solving the power grid scheduling sub-problem to obtain a power grid scheduling solution; S22: sending the power grid scheduling solution to all gas grids, respectively, and solving the respective gas grid scheduling sub-problems by all the gas grids to obtain all gas grid scheduling solutions; S23: collecting the all gas grid scheduling solutions by the power grid, updating the power grid scheduling sub-problem, and solving the power grid scheduling sub-problem to obtain a power-gas integrated energy system scheduling solution by repeating the above process iteratively.

2. The variational inequality based electrical co-scheduling method of claim 1, wherein, The step of constructing a monotone variational inequality power-gas integrated energy system scheduling model according to preset power-gas integrated energy system parameters further comprises the following steps: Obtaining preset power-gas integrated energy system parameters, wherein the preset power-gas integrated energy system parameters comprise power system parameters, natural gas system parameters and power-gas coupling device parameters; The power system parameters comprise bus parameters, branch parameters, thermal power unit parameters, wind power unit parameters and electric load parameters; The natural gas system parameters comprise node parameters, pipeline parameters, natural gas well parameters, compressor parameters and gas load parameters; The power-gas coupling device parameters comprise operation parameters of a gas turbine unit; The power-gas integrated energy system scheduling model comprises a piecewise tangent linearization quasi-steady-state natural gas network considering pipe storage effect.

3. The variational inequality based electrical co-scheduling method of claim 1, wherein, The step 102 further comprises the following steps: Step 103: performing fair distribution of cooperation surplus of the power-gas integrated energy system scheduling model based on a double-sided Shapley value incentive mechanism; The double-sided Shapley value of a power grid-gas grid connection relationship is obtained by performing cooperative game calculation and analysis based on the power system scheduling cost, the natural gas system scheduling cost and the power-gas integrated energy system scheduling cost; The power grid scheduling cost and the gas grid scheduling cost are calculated based on the double-sided Shapley value of the power grid-gas grid connection relationship, respectively.

4. An electrical joint dispatching device based on variational inequality, characterized by, The method comprises the steps of: The model construction unit is configured to construct a monotone variational inequality power-gas integrated energy system scheduling model according to preset power-gas integrated energy system parameters, the preset power-gas integrated energy system parameters including power system parameters, natural gas system parameters and power-gas coupling device parameters, and the power-gas integrated energy system scheduling model including a piecewise tangent linearization quasi-steady-state natural gas network considering pipe storage effect; The model solving unit connected with the model construction unit is configured to solve the power-gas integrated energy system scheduling model in a distributed manner by using a balance splitting-contraction algorithm to obtain power-gas joint optimization scheduling results. The power-gas joint optimization scheduling results include thermal power unit output, gas turbine unit output, wind power grid-connected amount, natural gas well gas production, power system scheduling cost, natural gas system scheduling cost and power-gas integrated energy system scheduling cost.

5. The variational inequality based electrical co-scheduling apparatus according to claim 4, characterized by Further comprising: The parameter acquisition unit is configured to acquire preset power-gas integrated energy system parameters and deliver the preset power-gas integrated energy system parameters to the model construction unit, the preset power-gas integrated energy system parameters including power system parameters, natural gas system parameters and power-gas coupling device parameters; The power system parameters include bus parameters, branch parameters, thermal power unit parameters, wind power 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 power-gas coupling device parameters include operation parameters of the gas turbine unit.

6. The variational inequality based electrical co-scheduling apparatus according to claim 4, wherein, The model solving unit comprises: The problem transformation subunit is configured to transform a solving problem of the power-gas integrated energy system scheduling model into power grid scheduling subproblems and gas grid scheduling subproblems based on the balance splitting-contraction algorithm; The iterative solving subunit connected with the problem transformation subunit is configured to solve the power grid scheduling subproblems to obtain power grid scheduling solutions, send the power grid scheduling solutions to all gas grids respectively, and solve the gas grid scheduling subproblems of all gas grids respectively to obtain all gas grid scheduling solutions, collect the all gas grid scheduling solutions by the power grid, update the power grid scheduling subproblems, solve the power grid scheduling subproblems, and repeat the above process to obtain power-gas integrated energy system scheduling solutions.

7. The variational inequality based electrical co-scheduling apparatus according to claim 4, wherein, Further comprising: The game analysis unit connected with the model solving unit is configured to perform cooperative game calculation and analysis based on the power system scheduling cost, the natural gas system scheduling cost and the power-gas integrated energy system scheduling cost to obtain a bilateral Shapley value of a power grid-gas grid connection relationship; The cost calculation unit is configured to calculate power grid scheduling cost and gas grid scheduling cost based on the bilateral Shapley value of the power grid-gas grid connection relationship, and the cost calculation unit is connected with the game analysis unit.

8. An electrical joint dispatching device based on variational inequality, characterized by, The device comprises a processor and a memory; The memory is configured to store program code and transmit the program code to the processor; The processor is configured to execute the power-gas integrated energy system distributed scheduling method according to instructions in the program code.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium is configured to store program code, and the program code is configured to execute the power-gas joint scheduling method based on variational inequality.

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