A method and device for optimal power flow calculation of hydrogen-electric comprehensive energy system

Abstract

The application discloses a kind of optimal power flow calculation method and device of hydrogen-electric comprehensive energy system, belong to electrical engineering technical field, the method is determined projection space by the coupling relationship of each described subsystem and the demand of privacy protection;In the projection space, the initial vertex set of the operation space of each described subsystem is sought;By projection transformation, the vertex set corresponding to each described subsystem is obtained only containing public variable, hides the private variable of each subsystem, effectively protects the privacy of each subsystem.In the projection space, the operation space of the hydrogen-electric comprehensive energy system is reconstructed, and the projected operation space is an equivalent operation space containing the optimality information of the original optimization problem, thus achieving high accuracy, which can improve the accuracy of the entire power flow optimization.In addition, the method avoids iteration, solving the problem of large output scheduling resource loss and high operating cost in the traditional distributed optimization method for power flow optimization.

Description

Technical Field

[0001] This invention belongs to the field of electrical engineering technology, and more specifically, relates to an optimal power flow calculation method and apparatus for a hydrogen-electric integrated energy system. Background Technology

[0002] Hydrogen can be obtained from renewable energy sources through water electrolysis, offering advantages such as cleanliness, low carbon footprint, and high energy density, and is considered a substitute for traditional fossil fuels. Furthermore, hydrogen energy is a crucial secondary energy source supporting the low-carbon transition of energy systems. Hydrogen-electric integrated energy systems achieve diversified energy utilization and optimized allocation by integrating hydrogen energy with power subsystems. With the development of new power subsystems, the installed capacity of wind turbines within these subsystems is continuously increasing. While large-scale wind turbine grid connection brings significant environmental and economic benefits, the strong volatility and uncertainty of wind power also pose challenges to the scheduling and operation of power subsystems.

[0003] Centralized scheduling methods can be used to solve the optimal power flow problem of integrated energy systems. However, for integrated energy systems composed of multiple coupled subsystems, the above centralized methods require all data from each subsystem. Since different subsystems (such as hydrogen and electricity subsystems) are typically managed by independent operators, this may lead to privacy issues. Therefore, solving the optimal power flow problem of integrated energy systems requires distributed scheduling methods. Subsystems only exchange boundary information and public information, and do not disclose private information to other operators, thus avoiding the aforementioned privacy issues.

[0004] However, distributed scheduling methods all involve iteration, which often leads to high output scheduling and operation costs and significant resource losses in hydrogen-electric integrated energy systems. Summary of the Invention

[0005] In view of the above-mentioned defects or improvement needs of the existing technology, the present invention provides a method and apparatus for a hydrogen-electric integrated energy system. Its purpose is to solve the technical problems of high output scheduling operation cost and large resource loss caused by using distributed scheduling methods for power flow optimization of hydrogen-electric integrated energy systems.

[0006] To achieve the above objectives, according to one aspect of the present invention, an optimal power flow calculation method for a hydrogen-electric integrated energy system is provided, comprising:

[0007] S1. With the goal of minimizing the operating cost under the worst-case scenario of wind power uncertainty, a primitive centralized operation model of the hydrogen-electric integrated energy system is constructed. The constraints of the primitive centralized operation model include: the constraint corresponding to the deviation of coal-fired unit output from determinism and the constraint corresponding to the deviation of hydrogen fuel cell output from determinism.

[0008] S2. The original centralized operation model of the hydrogen-electric integrated energy system is reconstructed and simplified to obtain the simplified centralized operation model corresponding to the wind power deterministic set of the hydrogen-electric integrated energy system.

[0009] S3. Decompose the simplified centralized operation model of the hydrogen-electric integrated energy system into sub-problems corresponding to each subsystem, wherein the subsystems include the power subsystem and the hydrogen subsystem;

[0010] S4. Determine the projection space based on the coupling relationship of each subsystem and the requirements for privacy protection;

[0011] S5. Find the initial vertex set of the operating space of each subsystem in the projection space;

[0012] S6. Find other vertices based on the initial vertex set corresponding to each subsystem;

[0013] S7. Using the new constraints formed by all vertices found in all the subsystems, the operating space of the hydrogen-electric integrated energy system is reconstructed in the projection space. The reconstructed operating space contains the equivalent operating space of all information of the centralized model.

[0014] S8. Solve the optimization problem of the hydrogen-electric integrated energy system obtained by reconstructing each of the sub-problems in the reconstructed operating space to obtain the optimal solution corresponding to the power output scheduling;

[0015] S9. Obtain the power flow parameters corresponding to each subsystem under the optimal solution.

[0016] In one embodiment, the objective function of the original centralized running model is expressed as:

[0017] ;

[0018] The constraint corresponding to the deterministic deviation of the output of the coal-fired unit is: ;

[0019] The constraint corresponding to the deterministic deviation of the hydrogen fuel cell output is expressed as follows: ;in, , and The coefficients of the quadratic function of the operating cost of coal-fired power units are... For coal-fired power units i exist t Constant effort For wind turbines w exist t Deviation at any given time; Coal-fired power units i exist t Constant effort For coal-fired power units i exist t Deviation at any given time; The price of hydrogen per standard cubic meter. Hydrogen load p exist t Hydrogen gas is constantly being consumed; For hydrogen fuel cells k exist t The amount of hydrogen consumed at any given time. For hydrogen fuel cells k exist t The offset that constantly consumes hydrogen. The magnitude of the deviation of wind power output from the predicted value due to wind power uncertainties. and For coal-fired power units i Minimum and maximum output values; For coal-fired power units i The balance factor For hydrogen fuel cells k exist t Deviation at any given time; For hydrogen fuel cells k The balance factor .

[0020] In one embodiment, S2 includes:

[0021] S201. The original centralized operation model of the hydrogen-electric integrated energy system is rewritten in the following form: ; It is a vector consisting of variables in the power subsystem, where the elements are private variables of the power subsystem; Let be a vector consisting of the variables of the hydrogen subsystem, where the elements are private variables of the hydrogen subsystem; Let be a vector consisting of the coupling variables of the power subsystem and the hydrogen subsystem, where the elements are common variables; , and This is the vector of coefficient parameters corresponding to each variable in the objective function; , , and This is the coefficient matrix corresponding to each variable in the constraints; and It is a vector composed of the constants in each constraint;

[0022] S202. The original centralized operation model of the hydrogen-electric integrated energy system is reconstructed and simplified. The simplified centralized operation model of the hydrogen-electric integrated energy system is expressed as follows: ; This represents the minimum deviation of wind power output from the predicted value. This represents the maximum deviation of wind power output from the predicted value. and Indicating constraints The corresponding coefficient matrix, intermediate variables , , , for , , , , and The item indicates that the wind power output fluctuated. The resulting fluctuations in other variables.

[0023] In one embodiment, the subproblem of the power subsystem is represented as: , For the common variables of the subproblems of the power subsystem, This is a custom value defined to avoid the runtime space becoming unbounded after rewriting.

[0024] In one embodiment, the subproblem of the hydrogen subsystem is expressed as: , For the common variables of the subproblems of the hydrogen subsystem, This is a custom value defined to avoid the runtime space becoming unbounded after rewriting.

[0025] In one embodiment, S5 includes: setting an initial vertex set for each of the subsystems. For an empty set; for each public variable y, perform the following steps: for all public variables other than y... Assign initial values The initial value is in the variable Within the feasible region; find the maximum value of the selected common variable y. and minimum value ,Will and The initial values ​​of these two shared variables constitute the two initial vertices. and Add the two initial vertices to the initial vertex set. Finally, a complete initial vertex set is obtained. .

[0026] In one embodiment, S6 includes:

[0027] S601, Set the loop count and vertex set Initialize to ;

[0028] S602, Order ;

[0029] S603, Solving the problem using vertex sets The convex hull formed;

[0030] S604, using formula Find the external normal vector corresponding to each boundary surface of the convex hull in S603, where, For the first The convex hull boundary surface is formed in the next iteration. s The vertex matrix, For the first The convex hull boundary surface in the next loop s The external normal vector;

[0031] S605, along each boundary face of the convex hull s external normal vector Search outwards from the boundary surface s farthest convex hull vertex ;

[0032] S606, the vertex obtained in S605 satisfy When, it means that a new vertex has been found. Then corresponding vertex Add to vertex set ;

[0033] S607, Repeat S602-S606, until the condition is met. The loop terminates when the time is reached.

[0034] In one embodiment, S7 includes:

[0035] S701, Constructing Vertex Sets The convex hull is represented by;

[0036] S702. Use formula Solve for the external normal vector of the convex hull obtained in S701; where, To form the convex hull boundary surface s The vertex matrix, convex hull boundary surface s The external normal vector;

[0037] S703. Use formula This represents the constraints after projection into the operating space corresponding to the sub-problems of the power subsystem; using the formula This represents the constraints projected onto the operating space corresponding to the subproblem of the hydrogen subsystem.

[0038] According to another aspect of the present invention, an optimal power flow calculation device for a hydrogen-electric integrated energy system is provided, comprising:

[0039] The module is used to construct the original centralized operation model of the hydrogen-electric integrated energy system with the goal of minimizing the operating cost under the worst case of wind power uncertainty. The constraints of the original centralized operation model include: the constraint corresponding to the deviation of coal-fired unit output from determinism and the constraint corresponding to the deviation of hydrogen fuel cell output from determinism.

[0040] A simplification module is used to reconstruct and simplify the original centralized operation model of the hydrogen-electric integrated energy system to obtain a simplified centralized operation model corresponding to the wind power deterministic set of the hydrogen-electric integrated energy system.

[0041] The decomposition module is used to decompose the simplified centralized operation model of the hydrogen-electric integrated energy system into sub-problems corresponding to each subsystem, including the power subsystem and the hydrogen subsystem.

[0042] A determination module is used to determine the projection space based on the coupling relationship of each of the subsystems and the requirements for privacy protection.

[0043] The first search module is used to search for the initial vertex set of the operating space of each of the subsystems in the projection space;

[0044] The second search module searches for other vertices based on the initial vertex set corresponding to each subsystem;

[0045] The first reconstruction module is used to reconstruct the operating space of the hydrogen-electric integrated energy system in the projection space by utilizing the new constraints formed by all vertices found by all the subsystems. The reconstructed operating space includes the equivalent operating space containing all information of the centralized model.

[0046] The second reconstruction module is used to solve the optimization problem of the hydrogen-electric integrated energy system obtained by reconstructing each of the sub-problems in the reconstructed operating space, and obtain the optimal solution corresponding to the power output scheduling.

[0047] The acquisition module is used to acquire the power flow parameters corresponding to each of the subsystems under the optimal solution.

[0048] According to another aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0049] According to another aspect of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0050] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0051] (1) This invention provides an optimal power flow calculation method for a hydrogen-electric integrated energy system. The projection space is determined by the coupling relationship of each subsystem and the need for privacy protection. The initial vertex set of the operating space of each subsystem is found in the projection space. The vertex set corresponding to each subsystem containing only public variables is obtained through projection transformation, which hides the private variables of each subsystem and effectively protects the privacy of each subsystem. The operating space of the hydrogen-electric integrated energy system is reconstructed in the projection space. The projected operating space is an equivalent operating space containing the optimality information of the original optimization problem, thus achieving high accuracy and improving the accuracy of the entire power flow optimization. In addition, this invention projects and reduces the dimensionality of the entire operating space through mathematical means. The boundary information of each subsystem is managed uniformly after projection. The process does not involve iteration. Compared with the traditional distributed optimization method that uses iteration, which has too many iterations and may have non-convergence problems, this invention can quickly and accurately obtain the optimal solution of output scheduling for operation configuration, reducing the operating cost and resource consumption of the hydrogen-electric integrated energy system in the output scheduling process.

[0052] (2) The constraint corresponding to the deviation of the coal-fired unit output from certainty in this scheme is: The constraint corresponding to the deviation of hydrogen fuel cell output from determinism is expressed as follows: This design takes into account that each power generation device absorbs the fluctuations in wind power output in proportion to its own capacity. The advantage is that it can make reasonable use of each power generation device.

[0053] (3) In this scheme, the original centralized operation model of the hydrogen-electric integrated energy system is reconstructed and simplified to: This design takes into account the transformation of uncertain operating models into deterministic operating models. The advantage is that the methods for solving deterministic models can be applied to solve uncertain problems.

[0054] (4) The sub-problem of the power subsystem described in this scheme is expressed as: This design considers isolating the problems of the power subsystem from other problems, which has the advantage of achieving privacy protection.

[0055] (5) The subproblem of the hydrogen subsystem described in this scheme is expressed as: This design isolates the hydrogen subsystem from other issues, which has the advantage of protecting privacy.

[0056] (6) In this scheme, the maximum value of the selected common variable y is determined. and minimum value ,Will and The initial values ​​of these two shared variables constitute the two initial vertices. and Add the two initial vertices to the initial vertex set. Finally, a complete initial vertex set is obtained. This design takes into account the initial vertices in each common variable dimension. The advantage is that the initial vertex set can be obtained with a simple solution process.

[0057] (7) In this scheme, along each boundary surface of the convex hull s external normal vector Search outwards from the boundary surface s farthest convex hull vertex When the obtained vertex satisfy At that time, then corresponding vertex Add to vertex set This design considers adding only the vertices that meet the conditions to the vertex set. The advantage is that it avoids increasing the computational cost by adding irrelevant vertices inside the convex hull into the vertex set.

[0058] (8) The formula used in this scheme This represents the constraints after projection into the operating space corresponding to the sub-problems of the power subsystem; using the formula The constraints are represented by the projection of the runtime space corresponding to the subproblem of the hydrogen subsystem. This design considers using a vertex set to represent the constraints after the runtime space projection. The advantage is that the representation is simple and it is easy to use loops to construct and reconstruct constraints in the program. Attached Figure Description

[0059] Figure 1 This is a flowchart of the optimal power flow calculation method for the hydrogen-electric integrated energy system provided in Embodiment 1 of the present invention;

[0060] Figure 2 This is a 6-6 node hydrogen-electric integrated energy system topology diagram of the optimal power flow calculation method for the hydrogen-electric integrated energy system provided in Embodiment 1 of the present invention;

[0061] Figure 3The constraint number and computation time of solving the 40-118 node hydrogen-electric integrated energy system using the dimension reduction projection method provided in Embodiment 1 of this invention vary with the remaining residual. A diagram illustrating the changes. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0063] Example 1

[0064] The optimal power flow calculation method for the hydrogen-electric integrated energy system provided in this embodiment was tested in a 6-6 node hydrogen-electric integrated energy system. The topology diagram of the 6-6 node hydrogen-electric integrated energy system is shown below. Figure 2 As shown, the hydrogen subsystem has 6 nodes, 5 hydrogen transmission pipelines, 2 hydrogen sources, and 4 hydrogen loads; the power subsystem has 6 nodes, 7 power transmission lines, 2 coal-fired generator sets, 1 wind turbine, and 3 power loads. The hydrogen and power subsystems operate collaboratively via a single hydrogen fuel cell. Two scenarios are presented to illustrate the effectiveness of the invention:

[0065] First scenario: Solve directly using a centralized operation model;

[0066] Second case: Solve using the dimension reduction projection method;

[0067] In this 6-6 node hydrogen-electric integrated energy system, the optimal power flow calculation method for the second case of the hydrogen-electric integrated energy system is as follows: Figure 1 As shown, the specific steps include:

[0068] S1. Introduce wind power uncertainty into the operating model that does not consider wind power uncertainty.

[0069] The mathematical expression for the operating model that does not consider wind power uncertainties is as follows:

[0070] (1)

[0071] (2)

[0072] (3)

[0073] (4)

[0074] (5)

[0075] (6)

[0076] (7)

[0077] (8)

[0078] (9)

[0079] (10)

[0080] (11)

[0081] (12)

[0082] (13)

[0083] (14)

[0084] (15)

[0085] (16)

[0086] (17)

[0087] (18)

[0088] (19)

[0089] (20)

[0090] (twenty one)

[0091] (twenty two)

[0092] (twenty three)

[0093] (twenty four)

[0094] (25)

[0095] Equation (1) represents the objective function for the optimal power flow of the hydrogen-electric integrated energy system. The objective function minimizes the operating cost of the hydrogen-electric integrated energy system, which includes the fuel cost of the coal-fired unit. and the cost of hydrogen .

[0096] Equation (2) represents the fuel cost of a coal-fired power unit, which is a quadratic function. The output of coal-fired unit i at time t; , and The coefficients of the quadratic function of the operating cost of coal-fired power units.

[0097] Equation (3) represents the output power constraint of the coal-fired unit. and These represent the minimum and maximum output values ​​of coal-fired unit i.

[0098] Equation (4) represents the transmission line capacity constraint. Let be the power flow rate of tie line l at time t; This represents the maximum transmission power of transmission line l.

[0099] Equation (5) represents the DC power flow constraint of the transmission line. For the starting node of the connection line l The phase angle at time t; Termination node of the connection line l The phase angle at time t; Let l be the impedance of the connecting line.

[0100] Equation (6) represents the node power balance constraint. The predicted output of wind turbine w at time t is given without considering wind power uncertainties. Let be the power generation of hydrogen fuel cell k at time t; Let be the power consumed by electrical load j at time t.

[0101] Equation (7) represents the climbing constraint. Let i be the rate of downward ramp of coal-fired unit i; Let be the upward ramp rate of coal-fired unit i.

[0102] Equations (8)-(10) represent the upper rotational spare constraints. Let be the on-spinning reserve capacity of coal-fired unit i at time t; The minimum value of the upward rotation reserve of the entire system at time t.

[0103] Equations (11)-(13) represent the lower rotational spare constraints. Let be the rotating reserve capacity of coal-fired unit i at time t; Let t be the minimum value of the downward rotation reserve of the entire system at time t.

[0104] Equation (14) represents the operating cost of the hydrogen subsystem. The price of hydrogen per standard cubic meter; This represents the hydrogen consumed by the hydrogen load p at time t. Let be the amount of hydrogen consumed by hydrogen fuel cell k at time t.

[0105] Equation (15) represents the mass conservation equation for hydrogen in the hydrogen pipeline. Let f be the length of the hydrogen pipeline; Let f be the cross-sectional area of ​​the hydrogen pipeline. Let be the mass flow rate at time t of the starting node of hydrogen pipeline f; Let f be the mass flow rate at time t at the termination node of hydrogen pipeline f. The speed of sound in hydrogen gas; Let be the hydrogen pressure at time t at the starting node of hydrogen pipeline f; Let be the hydrogen pressure at time t at the termination node of the hydrogen pipeline f.

[0106] Equation (16) represents the Navier-Stokes equation describing the conservation of momentum. Let f be the coefficient of friction of the hydrogen pipeline; Let be the flow velocity of hydrogen in hydrogen pipeline f; Let f be the diameter of the hydrogen pipeline.

[0107] Equation (17) represents the hydrogen constraint output from the hydrogen source. Let be the amount of hydrogen output by hydrogen source o at time t; This represents the maximum output of hydrogen from hydrogen source O. This is the minimum amount of hydrogen output from the hydrogen source.

[0108] Equation (18) represents the node mass flow balance. Let f be the starting node of the hydrogen pipeline; Let n be the termination node of the hydrogen pipeline f; n is the node number in the hydrogen subsystem.

[0109] Equation (19) represents the node pressure constraint at the hydrogen source. Let be the hydrogen pressure at node n at time t.

[0110] Equation (20) represents the hydrogen pressure constraint at the node. Let be the hydrogen pressure at node n at time t; This represents the minimum hydrogen pressure at node n. This represents the maximum hydrogen pressure at node n.

[0111] Equation (21) represents the integration of nodal pressure variables.

[0112] Equation (22) represents the mass flow constraint at the starting node of the hydrogen pipeline. Let f be the minimum mass flow rate of the hydrogen pipeline; This represents the maximum mass flow rate f in the hydrogen pipeline.

[0113] Equation (23) represents the mass flow constraint at the hydrogen pipeline termination node.

[0114] Equation (24) represents the relationship between hydrogen consumption and power generation in a hydrogen fuel cell. The amount of electricity generated per standard cubic meter of hydrogen consumed.

[0115] Equation (25) represents the hydrogen consumption constraint for hydrogen fuel cells. This represents the minimum hydrogen consumption of the hydrogen fuel cell k. This represents the maximum hydrogen consumption of the hydrogen fuel cell k.

[0116] The operating model considering the uncertainties of wind power is as follows:

[0117] (26)

[0118] (27)

[0119] (28)

[0120] Equation (26) represents the objective function considering the uncertainty of wind power, with the objective of minimizing the operating cost under the worst-case scenario of the wind power uncertainty set.

[0121] Equation (27) represents the amount by which the output of a coal-fired unit deviates from the deterministic model. Let be the deviation of coal-fired unit i at time t; Let w be the deviation of the wind turbine unit at time t; Let be the balance factor of coal-fired unit i, and be a constant related to the unit capacity.

[0122] Equation (28) represents the amount by which the output of the hydrogen fuel cell deviates from the deterministic model. Let k be the deviation of the hydrogen fuel cell at time t; is the balance factor for hydrogen fuel cell k. Wherein, and satisfy: .

[0123] Constraints (2)-(25) , and Replace with , and In addition, other variables were also replaced with values ​​that deviated from the predicted values, such as... Replace with , Replace with The constraints of the hydrogen-electric integrated energy system consist of constraints (27)-(28) and the replaced constraints (2)-(25).

[0124] S2. Reconstruct and simplify the operating model that considers wind power uncertainties. This includes the following steps:

[0125] S201. The centralized operation model of the non-hydrogen-electric integrated energy system is written in the following form:

[0126] (29)

[0127] in, It is a vector consisting of variables in the power subsystem, where the elements are private variables of the power subsystem; Let be a vector consisting of the variables of the hydrogen subsystem, where the elements are private variables of the hydrogen subsystem; Let be a vector consisting of the coupling variables of the power subsystem and the hydrogen subsystem, where the elements are common variables; , and This is the vector of coefficient parameters corresponding to each variable in the objective function; , , and This is the coefficient matrix corresponding to each variable in the constraints; and It is a vector composed of the constants in each constraint.

[0128] S202. Considering the uncertainty of wind power, the centralized operation model of the hydrogen-electric integrated energy system is rewritten as equation (30):

[0129] (30)

[0130] in, This refers to the magnitude of the deviation of wind power output from the predicted value due to uncertainties in wind power. This represents the minimum deviation of wind power output from the predicted value. This represents the maximum deviation of wind power output from the predicted value. and The item indicates that the wind power output fluctuated. The resulting fluctuations in other variables.

[0131] S203, Considering only The two worst-case scenarios, namely and .like and If the time constraint can be satisfied, then All constraints can be satisfied. Therefore, the uncertainty model (30) can be simplified to the deterministic model (31):

[0132] (31)

[0133] S3. Decompose the reconstructed and simplified hydrogen-electric integrated energy system model considering wind power uncertainties into power subsystem subproblems and hydrogen subsystem subproblems.

[0134] Sub-problems of power subsystems:

[0135] (32)

[0136] To hide the private variable information in the objective function, a new public variable is defined. The power subsystem subproblem is rewritten as follows:

[0137] (33)

[0138] in, This is a custom value based on actual conditions. This setting is to avoid the runtime space becoming unbounded after rewriting.

[0139] Sub-problem of the hydrogen subsystem:

[0140] (34)

[0141] Similarly, to hide the private variable information in the objective function, a new public variable is defined. The subproblem of the hydrogen subsystem is rewritten as follows:

[0142] (35)

[0143] in, This is a custom value based on actual conditions. This setting is to avoid the runtime space becoming unbounded after rewriting.

[0144] S4. Determine the projection space based on the coupling relationship of each subsystem and the need for privacy protection.

[0145] In the context of the problem discussed in this invention, to protect the privacy of each subsystem, it is necessary to hide their respective private variables. Therefore, public variables are chosen. and As variables constituting the projection space, private variables can thus be hidden.

[0146] S5. Locate the initial vertex set of the hydrogen and electron system's operating space in the projected space. This includes the following steps:

[0147] S501, Set the initial vertex set It is an empty set.

[0148] S502, Select one of the public variables (using...) (For example).

[0149] S503 refers to other common variables besides those in step S502. Assign a suitable initial value This initial value must be in the variable Within the feasible domain.

[0150] S504. Based on the conditions in step S503, find the maximum and minimum values ​​of the selected variables in step S502, that is, solve the following problem:

[0151] (36)

[0152] Received and The initial values ​​of these two shared variables constitute the two initial vertices. and Add these two initial vertices to the initial vertex set. .

[0153] S505. Repeat steps S502-S504, applying the same steps to other common variables (such as vectors). Perform the same operation on the variables in the table to obtain the complete initial vertex set. The number of vertices in the initial vertex set is denoted as N0.

[0154] S506. To facilitate the subsequent calculation of the boundary normal vector, the origin of the coordinate system is translated to the interior of the convex hull formed by the initial vertex set:

[0155] (37)

[0156] in, For the initial vertex set The i-th vertex in the array.

[0157] S6. Based on the initial vertex set of each subsystem, find other vertices of that subsystem by translating the boundary. The steps are as follows:

[0158] S601, Set the loop count and vertex set Initialize to .

[0159] S602,

[0160] S603, Solving the problem using vertex sets The convex hull formed by this.

[0161] S604. Solve for the external normal vector corresponding to each boundary surface of the convex hull in S603:

[0162] (38)

[0163] in, For the first The vertex matrix that forms the convex hull boundary surface s in each iteration. For the first The external normal vector of the convex hull boundary surface s in the second cycle. When the origin of the coordinate system is located inside the convex hull, the normal vector obtained by applying equation (38) is the external normal vector, and this condition has been guaranteed by equation (37).

[0164] S605, the external normal vector along each boundary surface s of the convex hull. Search outwards for the vertex of the convex hull farthest from the boundary surface s, that is, solve the objective function (39) under the constraints of model (33).

[0165] (39)

[0166] In addition, corresponding The value is denoted as .

[0167] S606, because the vertex obtained in step S605 It must be on or outside the boundary surface of the convex hull obtained in step S603, therefore we have Established. When At the boundary surface , Not a new peak; when When outside the boundary plane , This is the newly found vertex. That is, when... When equation (40) is satisfied, it means that a new vertex has been found.

[0168] (40)

[0169] in, It is a very small positive number. This will satisfy equation (40). corresponding vertex Add to vertex set .

[0170] S607, repeat steps S602-S606). When equation (41) is satisfied, it means that there are no new vertices outside the accuracy requirement range, and the loop terminates.

[0171] (41)

[0172] in, This represents the remaining residual from the vertex search. The larger the value, the shorter the computation time, but the lower the solution accuracy, and vice versa. If The vertex set in the obtained projection space It is completely accurate, and it can describe the operating space of the original optimization problem after projection with perfect precision. Figure 3 The constraint number and computation time of solving the 40-118 node hydrogen-electric integrated energy system using the dimension reduction projection method provided in Embodiment 1 of this invention vary with the remaining residual. A diagram illustrating the changes.

[0173] S7. Using the new constraints formed by the sought vertices, reconstruct the operating space of the hydrogen-electric integrated energy system in the projected space. This operating space is the equivalent operating space containing all information from the centralized model. The steps are as follows:

[0174] S701, Constructing Vertex Sets The convex hull is represented.

[0175] S702. Solve for the external normal vector of the convex hull:

[0176] (42)

[0177] in, To form the vertex matrix of the convex hull boundary surface s, Let be the external normal vector of the convex hull boundary surface s.

[0178] S703, the constraints after projection of the runtime space for the power subsystem subproblem are as follows:

[0179] (43)

[0180] Similarly, the projected runtime constraints of the hydrogen subsystem subproblem are:

[0181] (44)

[0182] As can be seen, private variables have been hidden, and the constraints after the runtime space projection only contain public variables.

[0183] S704. Each subsystem provides the information obtained in step S703 to the central operator.

[0184] S8. Solve the optimization problem in the reconstructed runtime space to obtain the optimal solution for output scheduling. The steps are as follows:

[0185] The central operator projects the runtime constraint information of each sub-problem and combines it into a reconstructed convex hull (46), and further obtains the optimization problem of reconstruction (45)-(46).

[0186] (45)

[0187] (46)

[0188] The reconfiguration optimization problem (45)-(46) obtained by the central operator contains only common variables. Therefore, the privacy of each subsystem is effectively protected. In addition, the reconfiguration optimization problem (45)-(46) is completely equivalent to model (31) and contains the optimality information of the original model. The central operator can obtain the common variables under the optimal cost by solving the reconfiguration optimization problem (45)-(46). , and The value is denoted as , and .

[0189] S9. Reconstruct the hydrogen and electron problems to obtain the power flow parameters under the optimal solution corresponding to the power output scheduling. The steps are as follows:

[0190] The central operator will seek Returned to the respective subsystem operators. The respective subsystem operators will... Substituting the values ​​into subproblems (32) and (34) respectively, we obtain the reconstructed subproblems. Substituting the public variables Once the value is determined, each sub-problem can be solved independently of other subsystems. By solving the reconstruction sub-problems, each subsystem operator can obtain the optimal power flow for their own system.

[0191] As shown in Tables 1-3, the calculation results for the first and second cases in the 6-6 node hydrogen-electric integrated energy system are consistent, verifying the effectiveness of the present invention. Based on the above analysis, it is evident that the present invention can ensure accuracy while avoiding iteration and protecting privacy. Specifically, Table 1 shows the system operating cost ($) obtained under different solution methods for the 6-6 node hydrogen-electric integrated energy system; Table 2 shows the coal-fired generator output (MW) obtained under different solution methods for the 6-6 node hydrogen-electric integrated energy system; Table 3 shows the hydrogen fuel cell hydrogen consumption (10³ SCM) obtained under different solution methods for the 6-6 node hydrogen-electric integrated energy system; and Table 4 shows the number of variables, constraints, calculation time, and total cost obtained under different solution methods for the 40-118 node hydrogen-electric integrated energy system.

[0192] Table 1

[0193]

[0194] Table 2

[0195]

[0196] Table 3

[0197]

[0198] As shown in Table 4, the number of variables, number of constraints, computation time and total cost under predicted output are compared for the first and second cases in the 40-118 node hydrogen-electric integrated energy system.

[0199] Table 4

[0200]

[0201] The computation time for the second case is the time taken after the initial vertex set search is completed. It can be seen that the number of variables in the second case using the dimension reduction projection method is significantly reduced, greatly simplifying the complexity of the optimization problem. Furthermore, the privacy of each subsystem is fully protected. The number of constraints in the second case solved using the dimension reduction projection method is much larger than in example A, making the transformed runtime space more compact. The computation time for the second case is longer than that for the first case, but still within an acceptable range. In the 40-118 node hydrogen-electric integrated energy system, the total cost calculated under the predicted output differs slightly between the two cases. Using the total cost calculation result under the predicted output of the first case directly solved by the centralized operation model as a benchmark, the error of the second case using the dimension reduction projection method is calculated to be 0.13%. The small error ensures the accuracy of the dimension reduction projection method. Moreover, iteration is avoided throughout the entire calculation process, solving the problems of excessive iterations and potential non-convergence in traditional iterative distributed optimization methods. In summary, the optimal power flow calculation method based on projection in this invention has the advantages of privacy protection, high accuracy, and non-iterative operation.

[0202] Example 2

[0203] This embodiment provides an optimal power flow calculation device for a hydrogen-electric integrated energy system, including:

[0204] The module is used to construct the original centralized operation model of the hydrogen-electric integrated energy system with the goal of minimizing the operating cost under the worst case of wind power uncertainty. The constraints of the original centralized operation model include: the constraint corresponding to the deviation of coal-fired unit output from determinism and the constraint corresponding to the deviation of hydrogen fuel cell output from determinism.

[0205] A simplification module is used to reconstruct and simplify the original centralized operation model of the hydrogen-electric integrated energy system to obtain a simplified centralized operation model corresponding to the wind power deterministic set of the hydrogen-electric integrated energy system.

[0206] The decomposition module is used to decompose the simplified centralized operation model of the hydrogen-electric integrated energy system into sub-problems corresponding to each subsystem, including the power subsystem and the hydrogen subsystem.

[0207] A determination module is used to determine the projection space based on the coupling relationship of each of the subsystems and the requirements for privacy protection.

[0208] The first search module is used to search for the initial vertex set of the operating space of each of the subsystems in the projection space;

[0209] The second search module searches for other vertices based on the initial vertex set corresponding to each subsystem;

[0210] The first reconstruction module is used to reconstruct the operating space of the hydrogen-electric integrated energy system in the projection space by utilizing the new constraints formed by all vertices found by all the subsystems. The reconstructed operating space includes the equivalent operating space containing all information of the centralized model.

[0211] The second reconstruction module is used to solve the optimization problem of the hydrogen-electric integrated energy system obtained by reconstructing each of the sub-problems in the reconstructed operating space, and obtain the optimal solution corresponding to the power output scheduling.

[0212] The acquisition module is used to acquire the power flow parameters corresponding to each of the subsystems under the optimal solution.

[0213] Example 3

[0214] This embodiment provides an electronic device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method.

[0215] Example 4

[0216] This embodiment provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0217] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for calculating the optimal power flow of a hydrogen-electric integrated energy system, characterized in that, include: S1. With the goal of minimizing the operating cost under the worst-case scenario of wind power uncertainty, a primitive centralized operation model of the hydrogen-electric integrated energy system is constructed. The constraints of the primitive centralized operation model include the constraints corresponding to the output deviation of coal-fired units and the output deviation of hydrogen fuel cells, respectively. S2. The original centralized operation model of the hydrogen-electric integrated energy system is reconstructed and simplified to obtain the simplified centralized operation model corresponding to the wind power deterministic set of the hydrogen-electric integrated energy system. S3. Decompose the simplified centralized operation model of the hydrogen-electric integrated energy system into sub-problems corresponding to each subsystem, wherein the subsystems include the power subsystem and the hydrogen subsystem; S4. Determine the projection space based on the coupling relationship of each subsystem and the requirements for privacy protection; S5. Find the initial vertex set of the operating space of each subsystem in the projection space; S6. Find other vertices based on the initial vertex set corresponding to each subsystem; S7. Using the new constraints formed by all the vertices found in all the subsystems, the operating space of the hydrogen-electric integrated energy system is reconstructed in the projection space; S8. Solve the optimization problem of the hydrogen-electric integrated energy system obtained by reconstructing each of the sub-problems in the reconstructed operating space to obtain the optimal solution corresponding to the power output scheduling; S9. Obtain the power flow parameters corresponding to each subsystem under the optimal solution; Wherein, S2 includes: S201, rewriting the original centralized operation model of the hydrogen-electric integrated energy system into the following form: ; It is a vector consisting of variables in the power subsystem, where the elements are private variables of the power subsystem; Let be a vector consisting of the variables of the hydrogen subsystem, where the elements are private variables of the hydrogen subsystem; Let be a vector consisting of the coupling variables of the power subsystem and the hydrogen subsystem, where the elements are common variables; , and This is the vector of coefficient parameters corresponding to each variable in the objective function; , , and This is the coefficient matrix corresponding to each variable in the constraints; and S202. The original centralized operation model of the hydrogen-electric integrated energy system is reconstructed and simplified, and the simplified centralized operation model of the hydrogen-electric integrated energy system is expressed as follows: ; This represents the minimum deviation of wind power output from the predicted value. This represents the maximum deviation of wind power output from the predicted value. and Indicating constraints The corresponding coefficient matrix, intermediate variables , , , for , , , , and The item indicates that due to fluctuations in wind power output... The resulting fluctuations in other variables; S5 includes the following steps: S501, setting the initial vertex set It is an empty set; S502, select one from the public variables. S503, for the purpose of removing Other public variables Assign appropriate initial values , In variables Within the feasible region; S504, solve for the selected common variables. maximum value and minimum value ,Will The maximum and minimum values ​​of the variables and the initial values ​​of other common variables. Forming two initial vertices and Add the two initial vertices to the initial vertex set. S505, for the exception of Other public variables Repeat steps S502-S504 to obtain the complete initial vertex set. S506. Translate the origin of the coordinate system into the convex hull formed by the initial set of vertices.

2. The optimal power flow calculation method for a hydrogen-electric integrated energy system as described in claim 1, characterized in that, The objective function of the original centralized operation model is expressed as: ； The constraint corresponding to the deterministic deviation of the output of the coal-fired unit is: ; The constraint corresponding to the deterministic deviation of the hydrogen fuel cell output is expressed as follows: ; in, , and The coefficients of the quadratic function of the operating cost of coal-fired power units are... For coal-fired power units i exist t Constant effort For wind turbines w exist t Deviation at any given time; For coal-fired power units i exist t Deviation at any given time; The price of hydrogen per standard cubic meter. Hydrogen load p exist t Hydrogen gas is constantly being consumed; For hydrogen fuel cells k exist t The amount of hydrogen consumed at any given time. For hydrogen fuel cells k exist t The offset that constantly consumes hydrogen. The magnitude of the deviation of wind power output from the predicted value due to wind power uncertainties. and For coal-fired power units i Minimum and maximum output values; For coal-fired power units i The balance factor For hydrogen fuel cells k exist t Deviation at any given time; For hydrogen fuel cells k The balance factor .

3. The optimal power flow calculation method for a hydrogen-electric integrated energy system as described in claim 1, characterized in that, when The subproblem of the power subsystem is expressed as follows: , For the common variables of the subproblems of the power subsystem, This is a custom value defined to avoid the runtime space becoming unbounded after rewriting.

4. The optimal power flow calculation method for a hydrogen-electric integrated energy system as described in claim 1, characterized in that, when The subproblem of the hydrogen subsystem is expressed as follows: For the common variables of the subproblems of the hydrogen subsystem, This is a custom value defined to avoid the runtime space becoming unbounded after rewriting.

5. The optimal power flow calculation method for a hydrogen-electric integrated energy system as described in claim 4, characterized in that, S6 includes: S601, Set the loop count and vertex set Initialize to ; S602, Order ; S603, Solving the problem using vertex sets The convex hull formed by it; S604, using formula Find the external normal vector corresponding to each boundary surface of the convex hull in S603, where, For the first The convex hull boundary surface is formed in the next iteration. s The vertex matrix, For the first The convex hull boundary surface in the next loop s The external normal vector; S605, along each boundary face of the convex hull s external normal vector Search outwards from the boundary surface s farthest convex hull vertex ; S606, the vertex obtained in S605 satisfy At that time, then corresponding vertex Add to vertex set ; It is a very small positive number. Vertices of the convex hull corresponding , , for , For the initial vertex set The i-th vertex in For the initial vertex set The number of vertices in the middle; S607, Repeat S602-S606, until the condition is met. When the loop terminates, This represents the remaining residual from the vertex search.

6. The optimal power flow calculation method for a hydrogen-electric integrated energy system as described in claim 5, characterized in that, S7 includes: S701, Constructing Vertex Sets The convex hull is represented by; S702. Use formula Solve for the external normal vector of the convex hull obtained in S701; where, To form the convex hull boundary surface s The vertex matrix, The convex hull boundary surface corresponding to the subproblem of the power subsystem s The external normal vector; S703. Use formula This represents the constraints after projection into the operating space corresponding to the sub-problems of the power subsystem; using the formula This represents the constraints projected onto the operating space corresponding to the subproblem of the hydrogen subsystem. The convex hull boundary surface corresponding to the subproblem of the hydrogen subsystem s The external normal vector.

7. An optimal power flow calculation device for a hydrogen-electric integrated energy system, characterized in that, The method for performing the optimal power flow calculation method according to any one of claims 1-6 includes: The module is used to construct the original centralized operation model of the hydrogen-electric integrated energy system with the goal of minimizing the operating cost under the worst case of wind power uncertainty. The constraints of the original centralized operation model include: the constraint corresponding to the deviation of coal-fired unit output from determinism and the constraint corresponding to the deviation of hydrogen fuel cell output from determinism. A simplification module is used to reconstruct and simplify the original centralized operation model of the hydrogen-electric integrated energy system to obtain a simplified centralized operation model corresponding to the wind power deterministic set of the hydrogen-electric integrated energy system. The decomposition module is used to decompose the simplified centralized operation model of the hydrogen-electric integrated energy system into sub-problems corresponding to each subsystem, including the power subsystem and the hydrogen subsystem. A determination module is used to determine the projection space based on the coupling relationship of each of the subsystems and the requirements for privacy protection. The first search module is used to search for the initial vertex set of the operating space of each of the subsystems in the projection space; The second search module searches for other vertices based on the initial vertex set corresponding to each subsystem; The first reconstruction module is used to reconstruct the operating space of the hydrogen-electric integrated energy system in the projection space by utilizing the new constraints formed by all vertices found by all the subsystems. The reconstructed operating space includes the equivalent operating space containing all information of the centralized model. The second reconstruction module is used to solve the optimization problem of the hydrogen-electric integrated energy system obtained by reconstructing each of the sub-problems in the reconstructed operating space, and obtain the optimal solution corresponding to the power output scheduling. The acquisition module is used to acquire the power flow parameters corresponding to each of the subsystems under the optimal solution.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

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