Domain contraction type electrical integrated energy system affine energy flow calculation method

Abstract

The application provides an affine power flow calculation method for a domain contraction type electrical comprehensive energy system, establishes an affine model of an electrical network, a gas network and a coupling element considering the uncertainty of distributed power output fluctuation and load change; around the network basic operating point under the determined performance flow, firstly, the state quantity is completely predicted based on network sensitivity analysis, and a compression coefficient optimization model is introduced to compress the domain of the predicted state quantity, so as to reduce the conservativeness of the state quantity solution. In addition, an electrical affine power flow column iteration method is provided, the power matching at the coupling unit is used to realize the power flow interaction in the calculation, the calculation sequence of the network in the complex coupling system does not need to be combed, and efficient and simple electrical-gas multi-power flow solving is realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of electrical integrated energy systems, and particularly relates to a domain contraction type affine energy flow calculation method of an electrical integrated energy system. BACKGROUND

[0002] An integrated electricity-gas system (IEGS) can realize interconnection and mutual aid of electric power and natural gas energy, and provide effective support for clean energy grid connection, and improve the comprehensive utilization and collaborative optimization level of energy. Energy flow calculation can evaluate the operation state of the IEGS, and is an important basis for system optimization scheduling and fault analysis research. However, the output fluctuation of grid-connected new energy and the load change bring strong uncertainty to the IEGS, and in addition to the interactive influence of electric and gas energy flow, the system energy flow analysis tends to be complex, and the existing deterministic energy flow analysis method cannot be applied to uncertain systems. Therefore, an effective uncertain energy flow calculation method is needed to clarify the transmission process of uncertain factors and quantitatively evaluate the influence of uncertain factors on the system operation state.

[0003] Currently, the traditional interval energy flow algorithm of the integrated electricity-gas system has the following two deficiencies: first, the interval algorithm cannot represent the correlation between uncertain quantities, which will lead to interval expansion, and the strong nonlinearity of the natural gas pipeline equation not only aggravates the interval expansion effect, but also may not be able to normally operate in some operations; second, the existing interval energy flow algorithm cannot identify the source of uncertainty in the state quantity according to the result, and it is difficult to directly quantitatively analyze the influence degree of uncertain factors on the system state. In the existing solving method, the interval iteration method needs to solve the problem that the interval Jacobian matrix cannot be inverted by constructing an operator, and the convergence is poor and the repeated iteration process is easy to lead to interval expansion; the direct optimization method based on the nonlinear energy flow optimization model is difficult to solve, and is easy to fall into a local optimal solution. In addition, the traditional multi-energy flow decomposition method needs to determine the solving order of electric and gas energy flow according to the control mode of the coupled element, and the application in the electrical interconnected network with complex coupling relationship is difficult, which limits the expandability. SUMMARY

[0004] Based on the above analysis, and in view of the defects and deficiencies existing in the prior art, the present application proposes an affine energy flow calculation method for an electrical integrated energy system based on domain contraction, or an affine interval energy flow calculation method for an electrical integrated energy system based on the domain contraction idea. The method establishes an affine model of the electrical network, the gas network and the coupling element considering the uncertainty of the distributed power output fluctuation and the load change; around the basic operating point of the network under the determined performance flow, firstly, the state quantity is perfectly predicted based on the network sensitivity analysis, and a compression coefficient optimization model is introduced to compress the domain of the predicted state quantity, thereby reducing the conservativeness of the state quantity solution. In addition, an electrical affine energy flow column iteration method is proposed, and the power matching at the coupling unit is used to realize the energy flow interaction in the calculation, without the need to sort out the calculation sequence of the network in the complex coupling system, thereby realizing efficient and simple electrical-gas multi-energy flow solution.

[0005] The main design points include:

[0006] (1) Based on affine arithmetic, an affine energy flow model of the electrical network and the gas network considering the uncertainty of the distributed power output and the load is established.

[0007] (2) Based on the initial operating point under the steady-state energy flow calculation, the influence of the multi-element uncertainty factor on the network state quantity is preliminarily predicted through the sensitivity relationship between the network variables.

[0008] (3) The calculation result of the uncertainty factor is obtained from the predicted state quantity through affine operation, and the perfectness thereof is taken as a constraint condition, and the minimum compression coefficient is optimized and solved to compress the domain of the predicted state quantity.

[0009] (4) For the complex energy flow interaction of the electrical-gas integrated energy system under bidirectional coupling, the column iteration method is used to calculate the electrical-gas uncertainty affine energy flow considering the distribution autonomy characteristic thereof.

[0010] The technical solution adopted by the present application to solve the technical problems is:

[0011] An affine energy flow calculation method for an electrical integrated energy system based on domain contraction, characterized in that:

[0012] An affine model of the electrical network, the gas network and the coupling element is established based on affine arithmetic, and an affine expression of the uncertainty factors such as the distributed power output fluctuation and the load change is obtained; on the basis of the affine model, firstly, the sensitivity relationship between the network variables is established to predict the state quantity under the action of the uncertainty factors and obtain a preliminary result satisfying the perfectness; secondly, the calculation value of the uncertainty factor is obtained through affine operation of the predicted state quantity, and a compression coefficient optimization model is solved under the condition that the calculation value satisfies the perfectness, and the region of the predicted state quantity is compressed and corrected by the coefficient; at the same time, an electrical affine energy flow column iteration method is used to complete the multi-energy flow interaction calculation by exchanging the boundary conditions of the electrical network, the gas network and the coupling element.

[0013] Further, the affine model specifically includes:

[0014] (1) Power grid affine model

[0015] The affine model of the node injection power in the power grid is shown in equation (1):

[0016]

[0017] In the equation: represents the affine quantity of the node active power and the node reactive power; P i,mid , Q i,mid represents the central value of the node active power and the node reactive power affine quantity; M E,P , M E,Q respectively represent the number of uncertain factors affecting the active power and the reactive power in the power grid; ε j is a noise element, defined in the range of [-1, 1], representing the jth independent uncertainty source affecting the uncertainty quantity, caused by calculation error or external factors; is a noise element representing the jth active power uncertainty factor; is a noise element representing the jth reactive power uncertainty factor; p ij represents the influence degree of the jth active power uncertainty factor on the ith node active power; q ij represents the influence of the jth reactive power uncertainty factor on the ith node reactive power;

[0018] (2) Gas grid affine model

[0019] In the natural gas network, the gas source and the load are represented in the form of gas flow, and the affine model of the node injection flow is as follows:

[0020]

[0021] In the equation: represents the affine node injection flow of the ith gas grid node; L i,mid is the central value of the node injection flow affine quantity; M G is the number of uncertainty factors affecting the gas grid; l ij is a noise element coefficient, representing the influence degree of the jth uncertainty factor on the ith node injection flow;

[0022] The square of the node gas pressure П is used instead of the node gas pressure π as the state quantity of the natural gas network, and its affine form is represented as follows:

[0023]

[0024] In the equation: represents the affine gas pressure of the ith node; Πi,mid is the central value of the gas pressure affine quantity; τ ij is the noise element coefficient representing the influence degree of the jth uncertain factor on the gas pressure of the ith node;

[0025] The affine energy flow equation of the natural gas network pipeline is represented as formula (4):

[0026]

[0027] In the formula: represents the flow of the mth pipeline; represents the gas pressure of the starting node of the mth pipeline; represents the gas pressure of the ending node of the mth pipeline; c m represents the pipeline parameter of the mth pipeline, which is related to the length, radius and friction coefficient of the pipeline;

[0028] The natural gas network pipeline flow satisfies the flow continuity equation at the node, that is, the injected gas flow at the node is equal to the outflowing gas flow, that is:

[0029]

[0030] In the formula: A G is the node-branch association matrix of the natural gas network;

[0031] (3) Coupling element affine model

[0032] The energy conversion relationship of the coupling element of the electrical integrated energy system is as follows:

[0033]

[0034] In the formula: is the GT consumed natural gas flow, with the unit of m 3 / h; is the P2G produced natural gas flow; is the GT produced electric power, with the unit of kW; is the P2G consumed electric power; η represents the conversion efficiency of the coupling unit; H G is the natural gas heat value.

[0035] Further, based on the network sensitivity, the specific process of the prediction calculation is as follows:

[0036] Firstly, the affine model of the uncertain factor is established, which is specifically shown in formula (7):

[0037]

[0038] In the formula: M is the number of uncertain factors; the uncertain factor affects the network state in the form of power; in the electrical power network Corresponding power In the natural gas network, Corresponding air flow rate

[0039] The affine form of the state variables is represented as a linear combination of the central value and a set of independent noise elements. The central value of the predicted state variables is solved by the Newton-Raphson method, and the noise element coefficients are predicted by the uncertainty factor and the sensitivity. As shown in Equation (8), the j-th noise element coefficient of the i-th state variable is represented as the product of the sensitivity and the j-th uncertainty factor noise element coefficient.

[0040]

[0041] In the formula: N is the number of network nodes; in a power network, Corresponding voltage amplitude and voltage phase angle In natural gas networks Corresponding air pressure

[0042] Furthermore, the compression correction of the predicted state variables after the prediction calculation specifically includes:

[0043] Using the network topology and energy flow equations, the calculated values ​​of uncertain factors are obtained from the predicted values ​​of state variables. The specific variable relationships are shown in equation (9):

[0044]

[0045] because The prediction results are complete, but the nonlinear affine operations in the energy flow equations will cause a certain degree of interval expansion, and the calculation results of uncertain factors... The region will contain uncertainties in the real world. The region; construct the affine optimization model shown in equation (10), and solve for the minimum compression coefficient K in the range [0,1]. c After the noise element coefficient is compressed The region always satisfies completeness:

[0046]

[0047] In the formula: Y calc,j,mid Calculate the value for the j-th uncertainty factor. The center value of y, j = 1, 2, ..., M; jk for The coefficient of the k-th noise element; Z is The number of noise elements included, including M original noise elements ε ori And ZM newly generated noise elements ε during the nonlinear affine operation. new ;

[0048] The compression coefficient K is adopted c The noise element coefficient in the predicted state quantity is corrected to obtain a final state quantity result of the affine energy flow calculation:

[0049]

[0050] Further, in the iteration process, an electric and gas affine energy flow parallel iteration method is adopted, the electric and gas networks only exchange boundary information with the coupling device to realize parallel calculation of the energy flow, the calculation sequence of the electric and gas energy flow does not need to be determined according to the control mode of the coupling element, and the characteristics of the distribution autonomy of the electric and gas networks are met.

[0051] The specific process of iteration is as follows:

[0052] Firstly, the network parameters including the branches of the IEGN and the coupling element efficiency are input, and the variation intervals of each uncertain factor are obtained; the initial values of the coupling units are set, at least including the natural gas flow of the GT unit and the electric power of the P2G unit;

[0053] Secondly, the affine energy flow algorithm of the prediction correction type proposed in the application is adopted to perform affine energy flow calculation on the electric network and the gas network respectively to obtain the network state quantity.

[0054] Then, the power and flow of the coupling element are calculated: for the GT unit, the electric power of the GT is calculated from the electric network energy flow first, and then the gas flow required by the GT is calculated through the energy conversion relationship; for the P2G unit, the gas production of the P2G is calculated from the gas network energy flow first, and then the electric power of the P2G is calculated through the energy conversion relationship;

[0055] Finally, whether the electric power and the gas flow of the coupling element are matched is judged: the electric power of the GT obtained in the previous two iterations and the flow of the P2G obtained in the previous two iterations are taken, if the upper and lower limit errors of the power / flow in the two times are within the preset accuracy, it is considered that the power is matched, and the iteration is ended; if the power matching condition is not met, the gas flow of the GT calculated in the current iteration is taken as the initial setting of the GT gas flow in the next iteration; the electric power of the P2G calculated in the current iteration is taken as the initial setting of the P2G electric power in the next iteration; the above steps are repeated until the convergence condition is met.

[0056] The application provides an affine energy flow calculation method for an electrical comprehensive energy system based on a domain contraction idea. Affine models of the electrical network, the gas network and coupling elements are established based on affine arithmetic, and affine expressions of uncertain factors such as fluctuation of distributed power output and load change are given. In the algorithm, firstly, the sensitivity relationship between network variables is established to predict the state quantity under the action of uncertain factors and obtain a preliminary result meeting the completeness; secondly, the calculation value of the uncertain factor is obtained by affine operation on the predicted state quantity, and the compression coefficient optimization model is solved under the condition that the calculation value meets the completeness, and the region of the predicted state quantity is compressed and corrected by the coefficient. The electrical affine energy flow column iteration method can realize efficient and simple electrical multi-energy flow calculation, realizes energy flow interaction in the calculation through power matching at the coupling unit, does not need to sort out the calculation sequence of the network in the complex coupling system, and conforms to the current management form of the distributed autonomy of the electrical and gas systems.

[0057] Compared with the traditional interval energy flow calculation method, the affine energy flow calculation method provided by the application and the preferred scheme thereof adopts affine arithmetic, considers the correlation between uncertain quantities to effectively suppress the interval expansion of the strong nonlinear electrical comprehensive energy system, so that the calculation result has lower conservativeness, and at the same time, the influence of the uncertain factor on the system can be analyzed by means of the noise element quantity. In addition, the method reduces the complexity of the traditional interval energy flow calculation method and improves the calculation efficiency through the prediction and domain contraction links. BRIEF DESCRIPTION OF DRAWINGS

[0058] The application will be further described in detail below with reference to the drawings and specific embodiments:

[0059] Figure 1 It is an affine energy flow calculation flowchart of the electrical comprehensive energy system of the embodiment of the application. DETAILED DESCRIPTION

[0060] In order to make the features and advantages of the patent more obvious and easy to understand, the following examples are specifically described as follows:

[0061] It should be pointed out that the following detailed description is all exemplary and is intended to provide further description of the present application. Unless otherwise specified, all technical and scientific terms used in the specification have the same meaning as understood by ordinary skilled persons in the technical field to which the present application belongs.

[0062] It should be noted that the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form unless the context clearly indicates otherwise, and furthermore, it should be understood that when the terms "comprise" and / or "include" are used in the specification, there is a feature, step, operation, device, component and / or combination thereof.

[0063] The embodiment will be further described in detail below with reference to the accompanying drawings:

[0064] As Figure 1 shown, the following detailed description of the domain contraction type electrical comprehensive energy system affine energy flow calculation method proposed in the embodiment of the application.

[0065] 1. Affine model

[0066] (1) Power grid affine model

[0067] The affine model of the node injection power in the power network is shown in formula (1):

[0068]

[0069] In the formula: represents the affine quantity of the node active power and the node reactive power; P i,mid , Q i,mid represents the center value of the node active and node reactive affine quantity; M E,P , M E,Q respectively represent the number of uncertain factors affecting active and reactive power in the power grid; ε j is a noise element, defined in the range of [-1, 1], representing the jth independent uncertainty source affecting the uncertainty, caused by calculation error or external factors. The noise element can represent the relationship between multiple uncertainties. When multiple uncertainties have the same noise element, it means that there is some connection and mutual dependence between the uncertainties. is a noise element representing the jth active uncertainty factor; is a noise element representing the jth reactive uncertainty factor; p ij represents the influence degree of the jth active uncertainty factor on the ith node active power; q ij represents the influence of the jth reactive uncertainty factor on the ith node reactive power.

[0070] (2) Gas network affine model

[0071] In the natural gas network, the gas source and the load are represented in the form of gas flow. The affine model of the node injection flow is as follows:

[0072]

[0073] In the formula: represents the affine node injection flow of the ith gas network node; L i,mid is the center value of the node injection flow affine quantity; M G is the number of uncertainty factors affecting the gas network; l ijis the noise element coefficient, which represents the influence degree of the jth uncertain factor on the flow injected into the ith node.

[0074] The pressure difference between the two ends of the pipeline in the natural gas network is the driving force for the transmission of natural gas in the network, so the node pressure can accurately describe the state of the natural gas network. Generally, the square of the node pressure, denoted as, is used instead of the node pressure, denoted as, as the state quantity of the natural gas network, which will be referred to as pressure in the following. The affine form of is expressed as follows:

[0075]

[0076] In the formula: denotes the affine pressure of the ith node; denotes the center value of the pressure affine quantity; denotes the influence degree of the jth uncertain factor on the pressure of the ith node. i,mid ij

[0077] The affine energy flow equation of the pipeline in the natural gas network can be expressed as formula (4):

[0078]

[0079] In the formula: denotes the flow of the mth pipeline; denotes the pressure of the starting node of the mth pipeline; denotes the pressure of the terminal node of the mth pipeline; denotes the pipeline parameter of the mth pipeline, which is related to the length, radius and friction coefficient of the pipeline, etc. m

[0080] The pipeline flow of the natural gas network satisfies the flow continuity equation at the node, that is, the flow injected into the node is equal to the flow out of the node, that is:

[0081]

[0082] In the formula: A G is the node-branch incidence matrix of the natural gas network.

[0083] (3) Affine model of coupling element

[0084] The coupling elements of the electrical integrated energy system mainly include gas turbines (GT) and power to gas (P2G) units, and the energy conversion relationship is as follows:

[0085]

[0086] In the formula: is the GT consumption natural gas flow, with the unit of m 3 / h; is the P2G output natural gas flow;​​​ GT output electric power, in kW; P2G consumed electric power; η represents the conversion efficiency of the coupled unit; H G The heating value of natural gas.

[0087] 2. An electric-gas affine power flow calculation method based on domain contraction

[0088] 2.1. Prediction part

[0089] The affine state quantity is represented in a certain region in the state space. A rough prediction can be made for the region to obtain a complete but relatively conservative result, and then the region is compressed to reduce the conservatism of the solution. The sensitivity can reflect the influence of the system disturbance on the state quantity under the current state of the system. Therefore, based on the current operating point of the system determined by the performance flow calculation, the influence of the uncertain factors on the network state quantity can be preliminarily predicted through the sensitivity. First, an affine model of the uncertain factors is established, which is specifically shown in equation (7):

[0090]

[0091] In the equation, M represents the number of uncertain factors. The uncertain factors affect the network state in the form of power. In the electric power network, corresponding to the electric power In the natural gas network, corresponding to the gas flow

[0092] The affine form of the state quantity is represented as the linear combination of the center value and a set of independent noise elements. The center value of the predicted state quantity is solved by the Newton-Raphson method, and the noise element coefficient is predicted by the uncertain factors and the sensitivity. As shown in equation (8), the jth noise element coefficient of the ith state quantity can be represented as the product of the sensitivity and the jth noise element coefficient of the uncertain factor.

[0093]

[0094] In the equation, N represents the number of network nodes. In the electric power network, corresponding to the voltage amplitude and the voltage phase angle In the natural gas network, corresponding to the gas pressure

[0095] 2.2. Domain contraction part

[0096] The state quantity is a variable that describes the operating state of the system. Through the network topology and the power flow equation, the calculation value of the uncertain factor can be obtained from the predicted value of the state quantity. The specific variable relationship is shown in equation (9):

[0097]

[0098] Since is a complete prediction result, and the nonlinear affine operation in the energy equation will cause a certain degree of interval expansion, the calculation result of the uncertainty factor The area will contain the area of the uncertainty factor of the real situation. The affine optimization model shown in formula (10) is constructed, and the minimum compression coefficient K c in the range of [0, 1] is solved The area of the noise element coefficient compressed by Always meet the completeness:

[0099]

[0100] In the formula: Y calc,j,mid The central value of the jth uncertainty factor calculation value , j=1, 2, …, M; y jk The coefficient of the kth noise element; Z is The number of noise elements contained, including M original noise elements ε ori And Z-M new noise elements ε new generated in the nonlinear affine operation process.

[0101] The compression coefficient K c is used to correct the noise element coefficient in the predicted state quantity, and the final state quantity result of the affine energy flow calculation is obtained:

[0102]

[0103] 3 Electric affine energy flow column iteration method

[0104] The affine type interval energy flow calculation method of the electric comprehensive energy system proposed in the application is as shown in Figure 1 :

[0105] First, input the network parameters of IEGN branch, coupling element efficiency, etc. and obtain the variation interval of each uncertainty factor. Set the initial value of the coupling unit, including the natural gas flow of the GT unit and the electric power of the P2G unit.

[0106] Second, the prediction correction type affine energy flow algorithm proposed in the application is used to perform affine energy flow calculation on the power grid and the gas grid respectively, and the network state quantity is obtained.

[0107] Then, the power and flow of the coupling element are calculated. For the GT unit, the electrical power of the GT is calculated from the grid energy flow, and the gas flow required by the GT is calculated from the energy conversion relationship. For the P2G unit, the gas production of the P2G is calculated from the gas grid energy flow, and the electrical power of the P2G is calculated from the energy conversion relationship.

[0108] Finally, it is determined whether the electrical power and the gas flow of the coupling element match. The electrical power of the GT unit and the flow of the P2G unit obtained in the previous two iterations are taken, and if the upper and lower limit errors of the power / flow in the two iterations are within a certain accuracy, it is considered that the power matches, and the iteration ends. If the power matching condition is not met, the gas flow of the GT calculated in the current iteration is taken as the initial setting of the gas flow of the GT in the next iteration, and the electrical power of the P2G calculated in the current iteration is taken as the initial setting of the electrical power of the P2G in the next iteration. The above steps are repeated until the convergence condition is met.

[0109] Those skilled in the art will understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0110] The present application is described with reference to flowcharts and / or block diagrams according to the methods, devices (systems), and computer program products of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in the flow or flows and / or blocks. Figure 1 The functions specified in the flow or flows and / or blocks.

[0111] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction devices that implement the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in the flow or flows and / or blocks. Figure 1 The functions specified in the flow or flows and / or blocks.

[0112] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

[0113] The above descriptions are only the preferred embodiments of the present application, not intended to limit the present application to other forms. Any person skilled in the art can make changes or modifications to the above-mentioned technical contents, or make equivalent embodiments. However, any simple modification, equivalent change and modification made according to the technical essence of the present application, without departing from the technical solution of the present application, shall fall within the protection scope of the present application.

[0114] The present application is not limited to the above-mentioned preferred embodiments, and any person skilled in the art can derive other various forms of domain contraction type electric comprehensive energy system affine energy flow calculation method based on the disclosure of the present application. Any equivalent change and modification made according to the scope of the present application shall fall within the scope of the present application.

Claims

1. An affine power flow calculation method for a domain contraction type electrical integrated energy system, characterized in that: An affine model of the electrical and gas networks and the coupling elements is established based on affine arithmetic to obtain an affine expression of uncertain factors including distributed power output fluctuations and load changes; on the basis of the affine model, firstly, a sensitivity relationship between network variables is established to predict state quantities under the action of uncertain factors and obtain a preliminary result satisfying completeness; secondly, the calculated values of the uncertain factors are obtained by affine operation on the predicted state quantities, and a compression coefficient optimization model is solved under the condition that the calculated values satisfy completeness, and the region of the predicted state quantities is compressed and corrected by using the coefficient; at the same time, an electrical affine power flow column iteration method is used to complete multi-energy flow interaction calculation by exchanging boundary conditions of the electrical and gas networks and the coupling elements; Based on network sensitivity, the specific process of prediction calculation is as follows: Firstly, the affine model of the uncertain factors is established, which is specifically shown in formula (7): where: M is the number of uncertainties; the uncertainties affect the network state in the form of power; in the electric power network corresponding electric power in the natural gas network, corresponding gas flow The affine form of the state quantity is a linear combination of a center value and a group of independent noise elements, the center value of the predicted state quantity is solved by the Newton-Raphson method, and the noise element coefficient is predicted by the uncertain factors and the sensitivity; as shown in formula (8), the jth noise element coefficient of the ith state quantity is expressed as the product of the sensitivity and the jth noise element coefficient of the uncertain factor: where N is the number of network nodes; in the electric power network, corresponding voltage amplitudes and voltage phase angles in the natural gas network corresponding gas pressures 2. The affine power flow calculation method for a domain contraction type electrical integrated energy system according to claim 1, characterized in that: The affine model specifically includes: (1) Affine model of the electrical network The affine model of the node injection power in the electrical network is shown in formula (1): In the formula: represents the affine quantity of node active power and node reactive power; P i,mid , Q i,mid represents the central value of node active, node reactive affine quantity; M E,P , M E,Q respectively represent the number of uncertain factors affecting active power and reactive power in the power grid; ε j is a noise element, defined in the range of [-1, 1], representing the jth independent uncertainty source affecting the uncertainty quantity, caused by calculation error or external factors; is a noise element representing the jth active uncertainty factor; is a noise element representing the jth reactive uncertainty factor; p ij represents the influence degree of the jth active uncertainty factor on the active power of the ith node; q ij represents the influence of the jth reactive uncertainty factor on the reactive power of the ith node; (2) Affine model of the gas network In the natural gas network, the gas source and the load are represented in the form of gas flow, and the affine model of the node injection flow is as follows: In the formula, represents the affine node injection flow of the ith gas network node; L i,mid is the central value of the node injection flow affine quantity; M G is the number of uncertain factors affecting the gas network; l ij is the noise element coefficient, which represents the influence degree of the jth uncertain factor on the injection flow of the ith node; The square of the node gas pressure П is used instead of the node gas pressure π as the state quantity of the natural gas network, and its affine form is represented as follows: wherein: represents the affine pressure of the i-th node; Π i,mid is the central value of the pressure affine quantity; τ ij represents the influence degree of the j-th uncertain factor on the pressure of the i-th node; The affine power flow equation of the natural gas network pipeline is represented by formula (4): wherein: Qm represents the flow rate of the mth pipe; Pm0 represents the gas pressure at the start node of the mth pipe; Pm1 represents the gas pressure at the end node of the mth pipe;c m Pm represents the pipe parameter of the mth pipe, which is related to the length, radius and friction coefficient of the pipe; The natural gas network pipeline flow satisfies the flow continuity equation at the node, and the node injection gas flow is equal to the outflow gas flow, that is: In the formula, A G is the node-branch incidence matrix of the natural gas network; (3) Affine model of the coupling element The energy conversion relationship of the coupling element of the electrical integrated energy system is as follows: wherein: is the GT consumed natural gas flow rate in m3 / h; 3 is the GT consumed natural gas flow rate in m3 / h; is the P2G produced natural gas flow rate in m3 / h; is the GT produced electric power in kW; is the P2G consumed electric power; η represents the coupled unit conversion efficiency; H G is the natural gas heating value.

3. The domain-reduction type electrical integrated energy system affine energy flow calculation method according to claim 2, characterized in that: The specific compression and correction of the region of the predicted state quantity after prediction calculation includes: The calculated values of the uncertain factors are obtained from the state quantity prediction values by network topology and power flow equation, and the specific variable relationship is shown in formula (9): Since is the complete prediction result, and the nonlinear affine operation in the energy equation will cause a certain degree of interval expansion, the calculation result of the uncertainty factor The area will contain the area of the uncertainty factor in the real situation; construct the affine optimization model shown in equation (10) to solve the minimum compression coefficient K c in the range of [0, 1], so that the area of the noise element coefficient after compression always satisfies the completeness: wherein: Y calc,j,mid value of the jth uncertain factor central value of the jth uncertain factor, j = 1, 2, …, M; y jk value of the jth uncertain factor coefficient of the kth noise element; Z is number of noise elements contained, including M original noise elements ε ori and Z-M new noise elements ε generated in the process of nonlinear affine operation new ; The compression coefficient K c The noise element coefficients in the predicted state quantities are corrected to obtain the final state quantity results for the affine energy flow calculation:

4. The affine power flow calculation method for a domain contraction type electrical integrated energy system according to claim 1, characterized in that: The specific process of using the electrical affine power flow column iteration method to complete multi-energy flow interaction calculation by exchanging boundary conditions of the electrical and gas networks and the coupling elements is as follows: Firstly, input the network parameters including the branches of the IEGN, the efficiency of the coupling element, and obtain the variation interval of each uncertain factor; set the initial value of the coupling unit, including at least the natural gas flow of the GT unit and the electrical power of the P2G unit; Secondly, the affine power flow calculation method of the prediction correction type proposed is used to perform affine power flow calculation on the electrical network and the gas network respectively to obtain the network state quantities; Then, the power and flow of the coupling element are calculated: for the GT unit, the electric power of the GT is calculated from the energy flow of the power grid, and the gas flow required by the GT is calculated through the energy conversion relationship; for the P2G unit, the gas production of the P2G is calculated from the energy flow of the gas grid, and the electric power of the P2G is calculated through the energy conversion relationship; Finally, it is determined whether the electric power and the gas flow of the coupling element match: the electric power of the GT unit obtained in the previous two iterations and the flow of the P2G unit obtained in the previous two iterations are taken, if the upper and lower limit errors of the power / flow in the two iterations are within the preset accuracy, it is considered that the power matches, and the iteration ends; if the power matching condition is not met, the gas flow of the GT calculated in the current iteration is taken as the initial setting of the gas flow of the GT in the next iteration; the electric power of the P2G calculated in the current iteration is taken as the initial setting of the electric power of the P2G in the next iteration; the above steps are repeated until the convergence condition is met.

Images