Optimal power flow calculation method and device for hydrogen-electricity integrated energy system

Abstract

The invention discloses an optimal power flow calculation method and device for a hydrogen-electricity integrated energy system, and belongs to the technical field of electrical engineering, and the method comprises the steps: determining a projection space through the coupling relation of subsystems and the privacy protection demand; searching an initial vertex set of the operation space of each subsystem in the projection space; according to the method, the vertex sets corresponding to the subsystems and only containing the public variables are obtained through projection transformation, the private variables of the subsystems are hidden, and the privacy of the subsystems is effectively protected. According to the method, the operation space of the hydrogen-electricity integrated energy system is reconstructed in the projection space, and the projected operation space is an equivalent operation space containing the optimality information of the original optimization problem, so that higher precision is achieved, and the accuracy of the whole power flow optimization can be improved; in addition, according to the method, iteration is avoided, and the problems of large output scheduling resource loss and high operation cost existing in power flow optimization by adopting a traditional iterative distributed optimization method are solved.

Description

Technical Field

[0001] The present invention belongs to the field of electrical engineering technology, and more specifically, relates to an optimal power flow calculation method and device for a hydrogen-electricity integrated energy system. Background Art

[0002] Hydrogen can be obtained from renewable energy through the electrolysis of water. It has the advantages of being clean, low-carbon, and having high energy density, and is considered to be a substitute for traditional fossil fuels. In addition, hydrogen energy is an important secondary energy source that supports the low-carbon transformation of the energy system. The hydrogen-electricity integrated energy system achieves diversified utilization and optimal allocation of energy by integrating hydrogen energy with the power subsystem. With the development of new power subsystems, the proportion of wind turbine installed capacity in the power subsystem continues to increase. While the large-scale grid-connected wind turbines bring huge environmental and economic benefits, the strong volatility and uncertainty of wind power also bring difficulties to the dispatching and operation of the power subsystem.

[0003] Centralized scheduling methods can be used to solve the optimal power flow calculation problem of integrated energy systems. However, for integrated energy systems composed of multiple coupled subsystems, the above centralized methods require all data of each subsystem, and different subsystems (such as hydrogen subsystems and power subsystems) are usually managed by independent operators, which may lead to privacy issues. Therefore, a distributed scheduling method is needed to solve the optimal power flow of integrated energy systems. Subsystems only exchange boundary information and public information, and private information is not disclosed to other operators, which can avoid the above privacy issues.

[0004] However, distributed scheduling methods all involve iterations, which often lead to high operating costs and large resource losses in output scheduling of hydrogen-electricity integrated energy systems. Summary of the invention

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a method and device for a hydrogen-electricity integrated energy system, which aims to solve the technical problems of high output scheduling operating costs and large resource losses caused by using a distributed scheduling method to optimize the flow of a hydrogen-electricity integrated energy system.

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

[0007] S1. Taking the minimum operation cost of the worst case of the wind power uncertainty set as the goal, an original centralized operation model of the hydrogen-electricity integrated energy system is constructed, wherein the constraints of the original centralized operation model include: constraints corresponding to the certainty of the output deviation of the coal-fired unit and constraints corresponding to the certainty of the output deviation of the hydrogen fuel cell;

[0008] S2. Reconstruct and simplify the original centralized operation model of the hydrogen-electricity integrated energy system to obtain a simplified centralized operation model corresponding to the wind power deterministic set of the hydrogen-electricity integrated energy system;

[0009] S3, decomposing the simplified centralized operation model of the hydrogen-electricity integrated energy system into sub-problems corresponding to each subsystem, wherein the subsystem includes a power subsystem and a hydrogen subsystem;

[0010] S4. Determine the projection space according to the coupling relationship of each subsystem and the privacy protection requirement;

[0011] S5, searching for an initial vertex set of the operation space of each of the subsystems in the projection space;

[0012] S6. Finding other vertices based on the initial vertex set corresponding to each of the subsystems;

[0013] S7, using new constraints formed by all vertices found by all the subsystems to reconstruct the operating space of the hydrogen-electricity integrated energy system in the projection space, wherein the reconstructed operating space contains an equivalent operating space of all information of the centralized model;

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

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

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

[0017] The constraint corresponding to the output deviation determinism of the coal-fired unit is: The constraint corresponding to the hydrogen fuel cell output deviation certainty is expressed as:

[0018] Among them, b 0,i 、b 1,i and b 2,i is the quadratic function coefficient of the coal-fired unit operating cost, PG i,t is the output of coal-fired unit i at time t, ΔPW w,t is the deviation of wind turbine w at time t; PG i,t The output of coal-fired unit i at time t, ΔPG i,t is the deviation of coal-fired unit i at time t; ρ G is the price of hydrogen per standard cubic meter, GB p,t is the hydrogen consumed by hydrogen load p at time t; GHk,t is the amount of hydrogen consumed by hydrogen fuel cell k at time t, ΔGH k,t is the offset of hydrogen consumption by hydrogen fuel cell k at time t, ΔPW is the deviation of wind power output from the predicted value caused by wind power uncertainty, and is the minimum and maximum output of coal-fired unit i; KG i is the balance factor of coal-fired unit i, ΔPH k,t KH is the deviation of hydrogen fuel cell k at time t; k is the balance factor of the hydrogen fuel cell k,

[0019] In one embodiment, the S2 includes:

[0020] S201. Rewrite the original centralized operation model of the hydrogen-electricity integrated energy system into the following form: α is a vector composed of variables in the power subsystem, where the elements are private variables of the power subsystem; β is a vector composed of variables in the hydrogen subsystem, where the elements are private variables of the hydrogen subsystem; χ is a vector composed of coupled variables of the power subsystem and the hydrogen subsystem, where the elements are public variables; A1, A2 and A3 are coefficient parameter vectors corresponding to each variable in the objective function; B1, B2, B3 and B4 are coefficient matrices corresponding to each variable in the constraints; C1 and C2 are vectors composed of constants in each constraint;

[0021] S202, reconstructing and simplifying the original centralized operation model of the hydrogen-electricity integrated energy system, and obtaining a simplified centralized operation model of the hydrogen-electricity integrated energy system as follows: ΔPW min ΔPW is the minimum deviation of wind power output from the predicted value; max is the maximum deviation of wind power output from the predicted value, B5 and B6 represent the coefficient matrix corresponding to ΔPW in the constraint, and the intermediate variable for The terms B5·ΔPW and B6·ΔPW represent the fluctuation of other variables due to the fluctuation of wind power output by ΔPW.

[0022] In one embodiment, the sub-problem of the power subsystem is expressed as: y e are the public variables of the subproblems of the electrical subsystem, This is a custom value to avoid unbounded running space after rewriting.

[0023] In one embodiment, the subproblem of the hydrogen subsystem is expressed as: y his the public variable of the subproblem of the hydrogen subsystem, This is a custom value to avoid unbounded running space after rewriting.

[0024] In one embodiment, the S5 includes: setting the initial vertex set of each subsystem is an empty set; for each public variable y, perform the following steps: assign initial values ​​χ to other public variables χ except y init , the initial value is within the feasible domain of the variable x; solve the maximum value of the selected public variable y and minimum value Will and Together with the initial values ​​of other public variables, two initial vertices are formed. and Add the two initial vertices to the initial vertex set Finally, we get the complete initial vertex set

[0025] In one embodiment, the S6 includes:

[0026] S601, set the number of cycles r e = 0, and the vertex set Initialize to

[0027] S602, let r e =r e +1;

[0028] S603, solve the vertex set The convex hull of

[0029] S604, using formula Solve the external normal vector corresponding to each boundary surface of the convex hull in S603, where: For the rth e The vertex matrix that forms the convex hull boundary surface s in the second cycle, For the rth e The external normal vector of the convex hull boundary surface s in the secondary cycle;

[0030] S605, the external normal vector of each boundary surface s along the convex hull Search outward for the convex hull vertex farthest from the boundary surface s

[0031] S606: When the vertex obtained in S605 satisfy When , it means that a new vertex is searched Then The corresponding vertex Add to Vertex Set

[0032] S607, repeat S602-S606, when satisfied , terminates the loop.

[0033] In one embodiment, the S7 includes:

[0034] S701, build vertex set The convex hull of denoted by ;

[0035] S702. Use formula Solve the normal vector outside the boundary of the convex hull obtained in S701; wherein, is the vertex matrix that constitutes the convex hull boundary surface s, is the external normal vector of the convex hull boundary surface s;

[0036] S703. Use formula Represents the constraints of the projected operation space corresponding to the subproblem of the power subsystem; using the formula Represents the constraints of the projected operating space corresponding to the subproblem of the hydrogen subsystem.

[0037] According to another aspect of the present invention, there is provided an optimal power flow calculation device for a hydrogen-electricity integrated energy system, comprising:

[0038] A construction module is used to construct an original centralized operation model of a hydrogen-electricity integrated energy system with the goal of minimizing the operation cost under the worst-case scenario of the wind power uncertainty set, wherein the constraints of the original centralized operation model include: constraints corresponding to the certainty of the output deviation of the coal-fired unit and constraints corresponding to the certainty of the output deviation of the hydrogen fuel cell;

[0039] A simplification module, used to reconstruct and simplify the original centralized operation model of the hydrogen-electricity integrated energy system to obtain a simplified centralized operation model corresponding to the wind power deterministic set of the hydrogen-electricity integrated energy system;

[0040] A decomposition module, used to decompose the simplified centralized operation model of the hydrogen-electricity integrated energy system into sub-problems corresponding to each subsystem, wherein the subsystem includes an electric power subsystem and a hydrogen subsystem;

[0041] A determination module, used to determine the projection space according to the coupling relationship of each subsystem and the privacy protection requirement;

[0042] A first search module, used for searching the initial vertex set of the operation space of each subsystem in the projection space;

[0043] A second search module searches for other vertices based on the initial vertex set corresponding to each of the subsystems;

[0044] A first reconstruction module is used to utilize new constraints formed by all vertices found by all the subsystems to reconstruct the operation space of the hydrogen-electricity integrated energy system in the projection space, wherein the reconstructed operation space contains an equivalent operation space of all information of the centralized model;

[0045] A second reconstruction module is used to solve the optimization problem of the hydrogen-electricity integrated energy system obtained by reconstructing each of the sub-problems in the reconstructed operation space to obtain the optimal solution corresponding to the output scheduling;

[0046] The acquisition module is used to obtain the power flow parameters corresponding to each of the subsystems under the optimal solution.

[0047] According to another aspect of the present invention, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0048] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0049] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0050] (1) The present invention provides an optimal power flow calculation method for a hydrogen-electricity integrated energy system, which determines the projection space through the coupling relationship of each subsystem and the need for privacy protection; finds the initial vertex set of the operating space of each subsystem in the projection space; obtains the vertex set corresponding to each subsystem containing only public variables through projection transformation, hides the private variables of each subsystem, and effectively protects the privacy of each subsystem. The operating space of the hydrogen-electricity integrated energy system is reconstructed in the projection space, and the projected operating space is an equivalent operating space containing the optimality information of the original optimization problem, thereby achieving a higher accuracy and improving the accuracy of the entire power flow optimization; in addition, the present invention mathematically projects the entire operating space for dimensionality reduction, and the boundary information of each subsystem is uniformly managed after projection, and no iteration is involved in the process. Compared with the traditional distributed optimization method using iteration, which has too many iterations and possible non-convergence problems, the present invention can quickly and accurately obtain the optimal solution for output scheduling for operation configuration, thereby reducing the operating cost and resource loss of the hydrogen-electricity integrated energy system during the output scheduling process.

[0051] (2) The constraints corresponding to the output deviation of coal-fired units in this scheme are: The constraint corresponding to the deviation of the hydrogen fuel cell output from determinism is expressed as: Such a design takes into account that each power generation equipment absorbs wind power output fluctuations in proportion to its own capacity. The advantage is that each power generation equipment can be used reasonably.

[0052] (3) In this scheme, the original centralized operation model of the hydrogen-electricity integrated energy system is reconstructed and simplified as follows: This design takes into account the transformation of the uncertain operating model into a deterministic operating model. The advantage is that the solution method of the deterministic model can be applied to solve the uncertainty problem.

[0053] (4) The subproblem of the power subsystem described in this scheme is expressed as:

[0054] This design considers separating the power subsystem issues from other issues, and has the benefit of achieving privacy protection.

[0055] (5) The subproblem of the hydrogen subsystem in this scheme is expressed as:

[0056] This design considers the issue of the hydrogen subsystem to be independent of other issues. The benefit is that it can achieve the effect of privacy protection.

[0057] (6) In this solution, we solve the maximum value of the selected public variable y and minimum value Will and Together with the initial values ​​of other public variables, two initial vertices are formed. and Add the two initial vertices to the initial vertex set Finally, we get the complete initial vertex set This design takes into account the initial vertices on the dimensions of each public variable. The advantage is that the initial vertex set can be obtained through a simple solution process.

[0058] (7) In this scheme, the external normal vector of each boundary surface s along the convex hull is Search outward for the convex hull vertex farthest from the boundary surface s When the vertex satisfy When The corresponding vertex Add to Vertex Set This design considers adding only vertices that meet the conditions into the vertex set. The advantage is that it avoids adding irrelevant vertices inside the convex hull into the vertex set, which causes an increase in calculation amount.

[0059] (8) In this scheme, the formula Represents the constraints of the projected operation space corresponding to the subproblem of the power subsystem; using the formula The constraints after the projection of the operating space corresponding to the subproblem of the hydrogen subsystem are represented in this way. Such a design considers using a vertex set to represent the constraints after the projection of the operating space. The advantage is that the representation method is simple and it is convenient to use loops to construct and reconstruct constraints in the program. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a flow chart of an optimal power flow calculation method for a hydrogen-electricity integrated energy system provided in Example 1 of the present invention;

[0061] Figure 2 It is a 6-6 node hydrogen-electricity integrated energy system topology diagram of the optimal power flow calculation method for the hydrogen-electricity integrated energy system provided in Example 1 of the present invention;

[0062] Figure 3 This is a schematic diagram of the change in the number of constraints and calculation time with the remaining residual ε2 when solving the 40-118 node hydrogen-electricity integrated energy system provided in Example 1 of the present invention using the dimensionality reduction projection method. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0064] Example 1

[0065] The optimal power flow calculation method for the hydrogen-electricity integrated energy system provided in this embodiment is tested in a 6-6 node hydrogen-electricity integrated energy system. The topology diagram of the 6-6 node hydrogen-electricity integrated energy system is shown in 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 generators, 1 wind turbine and 3 power loads, and the hydrogen subsystem and the power subsystem operate in coordination through 1 hydrogen fuel cell. Two situations are set to illustrate the effectiveness of the present invention:

[0066] The first case: using the centralized operation model to directly solve;

[0067] The second case: using the dimension reduction projection method to solve;

[0068] In the 6-6 node hydrogen-electricity integrated energy system, the optimal power flow calculation method of the hydrogen-electricity integrated energy system proposed in the second case is as follows: Figure 1 As shown, the specific steps include:

[0069] S1. Introducing wind power uncertainty into the operation model that does not consider wind power uncertainty.

[0070] The mathematical expression of the operation model without considering the uncertainty of wind power is as follows:

[0071]

[0072]

[0073]

[0074] Wherein, formula (1) represents the objective function of the optimal power flow of the hydrogen-electricity integrated energy system. The objective function minimizes the operating cost of the hydrogen-electricity integrated energy system. The operating cost includes the fuel cost of the coal-fired unit: and hydrogen cost C G .

[0075] Formula (2) represents the fuel cost of coal-fired units, which is a quadratic function, PG i,t is the output of coal-fired unit i at time t; b 0,i 、b 1,i and b 2,i is the quadratic function coefficient of the coal-fired unit operating cost.

[0076] Formula (3) represents the output power constraint of coal-fired units, and is the minimum and maximum output of coal-fired unit i.

[0077] Formula (4) represents the transmission line capacity constraint, PL l,t is the power flow of tie line l at time t; is the maximum transmission power of transmission line l.

[0078] Formula (5) represents the DC power flow constraint of the transmission line, The starting node s of the tie line l l The phase angle at time t; The end node e of the tie line l l The phase angle at time t; x l is the impedance of the tie line l.

[0079] Formula (6) represents the node power balance constraint, PW w,t is the predicted output of wind turbine w at time t without considering the uncertainty of wind power; PH k,t is the power generation of hydrogen fuel cell k at time t; PB j,t is the power consumed by the power load j at time t.

[0080] Formula (7) represents the climbing constraint, RD i is the downward ramp rate of coal-fired unit i; RU i is the upward climbing rate of coal-fired unit i.

[0081] Equations (8)-(10) represent the upper spinning reserve constraint, ru i,t is the upper spinning reserve capacity of coal-fired unit i at time t; SRU t It is the minimum value of the upper rotation reserve of the whole system at time t.

[0082] Equations (11)-(13) represent the lower spinning reserve constraint, rd i,t is the lower spinning reserve capacity of coal-fired unit i at time t; SRD t It is the minimum value of the lower rotation reserve of the whole system at time t.

[0083] Equation (14) represents the operating cost of the hydrogen subsystem, ρ G is the price of hydrogen per standard cubic meter; GB p,t is the hydrogen consumed by hydrogen load p at time t; GH k,t is the amount of hydrogen consumed by hydrogen fuel cell k at time t.

[0084] Formula (15) represents the mass conservation equation of hydrogen in the hydrogen pipeline, L f is the length of the hydrogen pipeline f; A f is the cross-sectional area of ​​the hydrogen pipeline f; is the mass flow rate of the starting node of the hydrogen pipeline f at time t; is the mass flow rate of the hydrogen pipeline f at the end node at time t; c H is the speed of sound in hydrogen; is the hydrogen pressure at the starting node of the hydrogen pipeline f at time t; is the hydrogen pressure at the terminal node of hydrogen pipeline f at time t.

[0085] Equation (16) represents the Navier-Stokes equation describing the conservation of momentum, λ f is the friction coefficient of the hydrogen pipeline f; ω f is the flow velocity of hydrogen in the hydrogen pipeline f; d f is the diameter of the hydrogen pipeline f.

[0086] Formula (17) represents the hydrogen output constraint of the hydrogen source, G o,t is the amount of hydrogen output by the hydrogen source o at time t; is the maximum value of the hydrogen output from the hydrogen source o; The minimum value of the hydrogen output from the hydrogen source o.

[0087] Formula (18) represents the node mass flow balance, s f is the starting node of the hydrogen pipeline f; e f is the terminal node of the hydrogen pipeline f; n is the node number in the hydrogen subsystem.

[0088] Formula (19) represents the node pressure constraint at the hydrogen source, p n,t is the hydrogen pressure at node n at time t.

[0089] Formula (20) represents the node hydrogen pressure constraint, p n,t is the hydrogen pressure at node n at time t; is the minimum value of hydrogen pressure at node n; is the maximum value of hydrogen pressure at node n.

[0090] Formula (21) represents the integration of node pressure variables.

[0091] Formula (22) represents the mass flow constraint of the starting node of the hydrogen pipeline, is the minimum value of the mass flow rate of hydrogen pipeline f; is the maximum value of the mass flow rate of the hydrogen pipeline f.

[0092] Formula (23) represents the mass flow constraint of the hydrogen pipeline terminal node.

[0093] Formula (24) represents the relationship between hydrogen consumption and power generation of hydrogen fuel cells, η P The amount of electricity generated per standard cubic meter of hydrogen consumed.

[0094] Formula (25) represents the hydrogen consumption constraint of hydrogen fuel cell, is the minimum value of hydrogen consumption of hydrogen fuel cell k; is the maximum value of hydrogen consumption of hydrogen fuel cell k.

[0095] The operation model considering wind power uncertainty is as follows:

[0096]

[0097] Among them, formula (26) represents the objective function considering wind power uncertainty, and the goal is to minimize the operating cost of the worst case wind power uncertainty set.

[0098] Formula (27) represents the amount by which the output of the coal-fired unit deviates from the deterministic model, ΔPG i,t is the deviation of coal-fired unit i at time t; ΔPW w,t KG is the deviation of wind turbine w at time t; i is the balance factor of coal-fired unit i and is a constant related to the unit capacity.

[0099] Equation (28) represents the amount by which the hydrogen fuel cell output deviates from the deterministic model, ΔPH k,t KH is the deviation of hydrogen fuel cell k at time t; k is the balance factor of the hydrogen fuel cell k. Among them, KG i and KH k satisfy:

[0100] Constrain the PW in (2)-(25) w,t PG i,t and PH k,t Replace with (PW w,t +ΔPW w,t ), (PG i,t +ΔPG i,t ) and (PH k,t +ΔPH k,t ). In addition, other variables are also replaced with values ​​that deviate from the predicted values, such as PL l,t Replace with (PL l,t +ΔPL l,t ), p n,t Replace with (p n,t +Δp n,t ) etc. The constraints of the hydrogen-electricity integrated energy system consist of constraints (27)-(28) and replaced constraints (2)-(25).

[0101] S2. Reconstruct and simplify the operation model considering the uncertainty of wind power. This includes the following steps:

[0102] S201. Write the centralized operation model of the non-hydrogen-electricity integrated energy system into the following form:

[0103]

[0104] Among them, α is a vector composed of variables in the power subsystem, where the elements are private variables of the power subsystem; β is a vector composed of variables in the hydrogen subsystem, where the elements are private variables of the hydrogen subsystem; χ is a vector composed of coupled variables of the power subsystem and the hydrogen subsystem, where the elements are public variables; A1, A2 and A3 are coefficient parameter vectors corresponding to each variable in the objective function;

[0105] B1, B2, B3 and B4 are the coefficient matrices corresponding to the variables in the constraints; C1 and C2 are vectors composed of constants in each constraint.

[0106] S202. Considering the uncertainty of wind power, the centralized operation model of the hydrogen-electricity integrated energy system is rewritten as formula (30):

[0107]

[0108] Where ΔPW is the deviation of wind power output from the predicted value caused by wind power uncertainty; ΔPW min ΔPW is the minimum deviation of wind power output from the predicted value; max The maximum amount by which wind power output deviates from the predicted value. Among them, B5·ΔPW and B6·ΔPW represent the fluctuation of other variables caused by the fluctuation of wind power output by ΔPW.

[0109] S203, only consider the two worst cases of ΔPW, namely, ΔPW min and ΔPW max If ΔPW min and ΔPW max When the constraints can be satisfied, then All constraints can be satisfied. Therefore, the uncertainty model (30) can be simplified to the deterministic model (31):

[0110]

[0111] S3. Decompose the reconstructed and simplified hydrogen-electricity integrated energy system model considering wind power uncertainty into power subsystem subproblems and hydrogen subsystem subproblems.

[0112] Power subsystem sub-problems:

[0113]

[0114] In order to hide the private variable information in the objective function, a new public variable y is defined e , rewrite the power subsystem subproblem as follows:

[0115]

[0116] in, This is a customized value based on actual conditions. This setting is to avoid the situation where the running space is unbounded after rewriting.

[0117] Hydrogen subsystem subproblems:

[0118]

[0119] Similarly, in order to hide the private variable information in the objective function, a new public variable y is defined h , rewrite the hydrogen subsystem subproblem as follows:

[0120]

[0121] in, This is a customized value based on actual conditions. This setting is to avoid the situation where the running space is unbounded after rewriting.

[0122] S4. Determine the projection space based on the coupling relationship between each subsystem and the need for privacy protection.

[0123] In the context of the problem discussed in this invention, in order to protect the privacy of each subsystem, it is necessary to hide the private variables of each subsystem. Therefore, the public variables x and y are selected. e As variables that constitute the projection space, private variables can be hidden.

[0124] S5. Find the initial vertex set of the operating space of the hydrogen and electron system in the projection space. This includes the following steps:

[0125] S501, set initial vertex set Is an empty set.

[0126] S502, select one of the public variables (with y e for example).

[0127] S503, assign a suitable initial value x to other public variables x except those in step S502 init , this initial value must be within the feasible region of the variable χ.

[0128] S504. Combined with the conditions of step S503, solve the maximum and minimum values ​​of the variables selected in step S502, that is, solve the following problem:

[0129]

[0130] What you get and Together with the initial values ​​of other public variables, two initial vertices are formed. and Add these two initial vertices to the initial vertex set

[0131] S505, repeat steps S502-S504, and perform the same operation on other public variables (such as variables in vector x) to obtain a complete initial vertex set The number of vertices in the initial vertex set is denoted as N0.

[0132] S506. In order to facilitate the subsequent calculation of the normal vector outside the boundary, the coordinate origin is translated to the inside of the convex hull formed by the initial vertex set:

[0133]

[0134] in, is the initial vertex set The i-th vertex in .

[0135] S6. Find other vertices of each subsystem by translating the boundary based on the initial vertex set of each subsystem. The steps are as follows:

[0136] S601, set the number of cycles r e = 0, and the vertex set Initialize to

[0137] S602, r e =r e +1

[0138] S603, solve the vertex set Constructed convex hull.

[0139] S604, respectively solve the external normal vector corresponding to each boundary surface of the convex hull in S603:

[0140]

[0141] in, For the rth e The vertex matrix that forms the convex hull boundary surface s in the second cycle, For the rth e The external normal vector of the convex hull boundary surface s in the second cycle. When the coordinate origin is inside the convex hull, the normal vector obtained by applying equation (38) is the external normal vector. This condition is guaranteed by equation (37).

[0142] S605, respectively along the outer normal vector of each boundary surface s of the convex hull The convex hull vertex farthest from the boundary surface s is searched outward, that is, the objective function (39) is solved under the constraints of the model (33).

[0143]

[0144] In addition, Corresponding The value is recorded as

[0145] 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, so Established. On the boundary surface

[0146] Not a new vertex; when Outside the boundary surface is the new vertex found. When equation (40) is satisfied, it means that a new vertex is searched.

[0147]

[0148] Among them, ε1 is a very small positive number. The corresponding vertex Add to Vertex Set

[0149] S607, repeat steps S602-S606), when equation (41) is satisfied, it means that there are no new vertices outside the precision requirement range, and the loop is terminated.

[0150]

[0151] Where ε2 is the residual error of vertex search. The larger ε2 is, the shorter the calculation time is, but the lower the solution accuracy is, and vice versa. If ε2 = 0, the vertex set in the projection space is obtained. It is completely precise and can fully and accurately describe the operating space of the original optimization problem after projection. Figure 3 This is a schematic diagram of the change in the number of constraints and calculation time with the remaining residual ε2 when solving the 40-118 node hydrogen-electricity integrated energy system provided in Example 1 of the present invention using the dimensionality reduction projection method.

[0152] S7. Use the new constraints formed by the found vertices to reconstruct the operating space of the hydrogen-electricity integrated energy system in the projection space. This operating space is the equivalent operating space that contains all the information of the centralized model. The steps are as follows:

[0153] S701, build vertex set Represents the convex hull of .

[0154] S702, solving the normal vector outside the boundary of the convex hull:

[0155]

[0156] in, is the vertex matrix that constitutes the convex hull boundary surface s, is the external normal vector of the convex hull boundary surface s.

[0157] S703, the constraints after running the power subsystem subproblem in spatial projection are as follows:

[0158]

[0159] Similarly, the projected running space constraint of the hydrogen subsystem subproblem is:

[0160]

[0161] It can be seen that private variables have been hidden, and the constraints after running space projection only contain public variables.

[0162] S704. Each subsystem provides the information obtained in step S703 to the central operator.

[0163] S8. Solve the optimization problem in the reconstructed operating space and obtain the optimal solution corresponding to the output scheduling.

[0164] Here are the steps:

[0165] The central operator projects the spatial constraint information of each subproblem into a reconstructed convex hull (46), and further obtains the reconstructed optimization problems (45)-(46).

[0166] miny e +y h +A3·χ(45)

[0167] Constraints(43)-(44)(46)

[0168] The reconstruction optimization problem (45)-(46) obtained by the central operator contains only public variables. Therefore, the privacy of each subsystem is effectively protected. In addition, the reconstruction optimization problem (45)-(46) is completely equivalent to model (31) and contains the optimality information of the original model. By solving the reconstruction optimization problem (45)-(46), the central operator can obtain the public variable y under the optimal cost e ,y h and the value of χ, denoted as and χ * .

[0169] S9. Reconstruct the hydrogen and electron problems and obtain the power flow parameters under the optimal solution corresponding to the output scheduling.

[0170] Here are the steps:

[0171] The central operator will find χ * Return to each subsystem operator. Each subsystem operator will * Substitute the values ​​of into subproblems (32) and (34) to obtain the reconstructed subproblems. Substitute the public variable χ * After the value of , each sub-problem can be solved independently of other sub-systems. By solving the reconstruction sub-problem, each sub-system operator can obtain the optimal power flow of their own system.

[0172] According to the calculation, as shown in Tables 1-3, the calculation results of the first and second cases in the 6-6 node hydrogen-electricity integrated energy system are consistent, which verifies the effectiveness of the present invention. According to the above analysis, it can be seen that the present invention can avoid iteration and protect privacy while ensuring accuracy. Among them, Table 1 is the system operating cost ($) obtained under different solution methods for the 6-6 node hydrogen-electricity integrated energy system; Table 2 is the coal-fired generator output (MW) obtained under different solution methods for the 6-6 node hydrogen-electricity integrated energy system; Table 3 is the hydrogen consumption of hydrogen fuel cells (103SCM) obtained under different solution methods for the 6-6 node hydrogen-electricity integrated energy system; Table 4 is the number of variables, number of constraints, calculation time and total cost obtained under different solution methods for the 40-118 node hydrogen-electricity integrated energy system.

[0173] Table 1

[0174]

[0175] Table 2

[0176]

[0177] Table 3

[0178]

[0179] As shown in Table 4, the number of variables, number of constraints, calculation time, and total cost under predicted output of the first and second cases in the 40-118 node hydrogen-electric integrated energy system are compared.

[0180] Table 4

[0181]

[0182]

[0183] Among them, the calculation time of the second case is the calculation time after the initial vertex set search is completed. It can be seen that the number of variables in the second case using the dimensionality reduction projection method is greatly reduced, which greatly simplifies the complexity of the optimization problem. In addition, the privacy of each subsystem is also fully protected. The number of constraints in the second case solved by the dimensionality reduction projection method is much larger than that of Example A, which also makes the operation space more compact after the transformation. The calculation time of the second case is longer than that of the first case, but it is still within an acceptable range. In the 40-118 node hydrogen-electric integrated energy system, the total cost calculated under the predicted output in the two cases is slightly different. Taking the calculation result of the total cost 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 dimensionality reduction projection method is calculated to be 0.13%. The error is small, and the accuracy of the dimensionality reduction projection method can be guaranteed. In addition, iteration is avoided throughout the calculation process, which solves the problem of excessive iterations and possible non-convergence of traditional distributed optimization methods using iterations. In summary, the optimal power flow calculation method based on projection of the present invention has the advantages of privacy protection, high accuracy and non-iterative.

[0184] Example 2

[0185] This embodiment provides an optimal power flow calculation device for a hydrogen-electricity integrated energy system, comprising:

[0186] A construction module is used to construct an original centralized operation model of a hydrogen-electricity integrated energy system with the goal of minimizing the operation cost under the worst-case scenario of the wind power uncertainty set, wherein the constraints of the original centralized operation model include: constraints corresponding to the certainty of the output deviation of the coal-fired unit and constraints corresponding to the certainty of the output deviation of the hydrogen fuel cell;

[0187] A simplification module, used to reconstruct and simplify the original centralized operation model of the hydrogen-electricity integrated energy system to obtain a simplified centralized operation model corresponding to the wind power deterministic set of the hydrogen-electricity integrated energy system;

[0188] A decomposition module, used to decompose the simplified centralized operation model of the hydrogen-electricity integrated energy system into sub-problems corresponding to each subsystem, wherein the subsystem includes an electric power subsystem and a hydrogen subsystem;

[0189] A determination module, used to determine the projection space according to the coupling relationship of each subsystem and the privacy protection requirement;

[0190] A first search module, used for searching the initial vertex set of the operation space of each subsystem in the projection space;

[0191] A second search module searches for other vertices based on the initial vertex set corresponding to each of the subsystems;

[0192] A first reconstruction module is used to utilize new constraints formed by all vertices found by all the subsystems to reconstruct the operation space of the hydrogen-electricity integrated energy system in the projection space, wherein the reconstructed operation space contains an equivalent operation space of all information of the centralized model;

[0193] A second reconstruction module is used to solve the optimization problem of the hydrogen-electricity integrated energy system obtained by reconstructing each of the sub-problems in the reconstructed operation space to obtain the optimal solution corresponding to the output scheduling;

[0194] The acquisition module is used to obtain the power flow parameters corresponding to each of the subsystems under the optimal solution.

[0195] Example 3

[0196] This embodiment provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0197] Example 4

[0198] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0199] It will be easily understood by those skilled in the art that the above description is only 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 in the protection scope of the present invention.

Claims

1. A method for calculating the optimal power flow of a hydrogen-electricity integrated energy system, characterized in that: include: S1. Taking the minimum operation cost of the worst-case wind power uncertainty set as the goal, an original centralized operation model of the hydrogen-electricity integrated energy system is constructed, wherein the constraints of the original centralized operation model include: constraints corresponding to the output deviation certainty of the coal-fired unit and the output deviation certainty of the hydrogen fuel cell; S2. Reconstruct and simplify the original centralized operation model of the hydrogen-electricity integrated energy system to obtain a simplified centralized operation model corresponding to the wind power deterministic set of the hydrogen-electricity integrated energy system; S3, decomposing the simplified centralized operation model of the hydrogen-electricity integrated energy system into sub-problems corresponding to each subsystem, wherein the subsystem includes a power subsystem and a hydrogen subsystem; S4. Determine the projection space according to the coupling relationship of each subsystem and the privacy protection requirement; S5, searching for an initial vertex set of the operation space of each of the subsystems in the projection space; S6. Finding other vertices based on the initial vertex set corresponding to each of the subsystems; S7, using new constraints formed by all vertices found by all the subsystems to reconstruct the operating space of the hydrogen-electricity integrated energy system in the projection space; S8. Solve the optimization problem of the hydrogen-electricity integrated energy system obtained by reconstructing each of the sub-problems in the reconstructed operation space to obtain the optimal solution corresponding to the output scheduling; S9. Obtain the power flow parameters corresponding to each of the subsystems under the optimal solution.

2. The optimal power flow calculation method for the hydrogen-electricity integrated energy system according to claim 1, characterized in that: The objective function of the original centralized operation model is expressed as: The constraint corresponding to the output deviation determinism of the coal-fired unit is: The constraint corresponding to the hydrogen fuel cell output deviation certainty is expressed as: Among them, b 0,i 、b 1,i and b 2,i is the quadratic function coefficient of the coal-fired unit operating cost, PG i,t is the output of coal-fired unit i at time t, ΔPW w,t is the deviation of wind turbine w at time t; PG i,t The output of coal-fired unit i at time t, ΔPG i,t is the deviation of coal-fired unit i at time t; ρ G is the price of hydrogen per standard cubic meter, GB p,t is the hydrogen consumed by hydrogen load p at time t; GH k,t is the amount of hydrogen consumed by hydrogen fuel cell k at time t, ΔGH k,t is the offset of hydrogen consumption by hydrogen fuel cell k at time t, ΔPW is the deviation of wind power output from the predicted value caused by wind power uncertainty, and is the minimum and maximum output of coal-fired unit i; KG i is the balance factor of coal-fired unit i, ΔPH k,t KH is the deviation of hydrogen fuel cell k at time t; k is the balance factor of the hydrogen fuel cell k, 3. The optimal power flow calculation method for the hydrogen-electricity integrated energy system according to claim 1, characterized in that: The S2 includes: S201. Rewrite the original centralized operation model of the hydrogen-electricity integrated energy system into the following form: α is a vector composed of variables in the power subsystem, where the elements are private variables of the power subsystem; β is a vector composed of variables in the hydrogen subsystem, where the elements are private variables of the hydrogen subsystem; χ is a vector composed of coupled variables of the power subsystem and the hydrogen subsystem, where the elements are public variables; A1, A2 and A3 are coefficient parameter vectors corresponding to each variable in the objective function; B1, B2, B3 and B4 are coefficient matrices corresponding to each variable in the constraints; C1 and C2 are vectors composed of constants in each constraint; S202, reconstructing and simplifying the original centralized operation model of the hydrogen-electricity integrated energy system, and obtaining a simplified centralized operation model of the hydrogen-electricity integrated energy system as follows: ΔPW min ΔPW is the minimum deviation of wind power output from the predicted value; max is the maximum deviation of wind power output from the predicted value, B5 and B6 represent the coefficient matrix corresponding to ΔPW in the constraint, and the intermediate variable for The terms B5·ΔPW and B6·ΔPW represent the fluctuation of other variables due to the fluctuation of wind power output by ΔPW.

4. The optimal power flow calculation method for the hydrogen-electricity integrated energy system according to claim 3, characterized in that: The subproblem of the power subsystem is expressed as: y e are the public variables of the subproblems of the electrical subsystem, This is a custom value to avoid unbounded running space after rewriting.

5. The optimal power flow calculation method for the hydrogen-electricity integrated energy system according to claim 3, characterized in that: The subproblem of the hydrogen subsystem is expressed as: y h is the public variable of the subproblem of the hydrogen subsystem, This is a custom value to avoid unbounded running space after rewriting.

6. The optimal power flow calculation method for the hydrogen-electricity integrated energy system according to claim 1, characterized in that: The S5 comprises: setting the initial vertex set of each subsystem is an empty set; for each public variable y, perform the following steps: assign initial values ​​χ to other public variables χ except y init , the initial value is within the feasible domain of the variable x; solve the maximum value of the selected public variable y and minimum value Will and Together with the initial values ​​of other public variables, two initial vertices are formed. and Add the two initial vertices to the initial vertex set Finally, we get the complete initial vertex set 7. The optimal power flow calculation method for the hydrogen-electricity integrated energy system according to claim 5, characterized in that: The S6 includes: S601, set the number of cycles r e = 0, and the vertex set Initialize to S602, let r e =r e +1; S603, solve the vertex set The convex hull of S604. Use formula Solve the external normal vector corresponding to each boundary surface of the convex hull in S603, where: is the vertex matrix that constitutes the convex hull boundary surface s in the re-th cycle, is the external normal vector of the convex hull boundary surface s in the re-th cycle; S605, the external normal vector of each boundary surface s along the convex hull Search outward for the convex hull vertex farthest from the boundary surface s S606: When the vertex obtained in S605 satisfy When The corresponding vertex Add to Vertex Set S607, repeat S602-S606, when satisfied , terminates the loop.

8. The optimal power flow calculation method for the hydrogen-electricity integrated energy system according to claim 5, characterized in that: The S7 includes: S701, build vertex set The convex hull of denoted by ; S702. Use formula Solve the normal vector outside the boundary of the convex hull obtained in S701; wherein, is the vertex matrix that constitutes the convex hull boundary surface s, is the external normal vector of the convex hull boundary surface s; S703. Use formula Represents the constraints of the projected operation space corresponding to the subproblem of the power subsystem; using the formula Represents the constraints of the projected operating space corresponding to the subproblem of the hydrogen subsystem.

9. An optimal power flow calculation device for a hydrogen-electricity integrated energy system, characterized in that: include: A construction module is used to construct an original centralized operation model of a hydrogen-electricity integrated energy system with the goal of minimizing the operation cost under the worst-case scenario of the wind power uncertainty set, wherein the constraints of the original centralized operation model include: constraints corresponding to the certainty of the output deviation of the coal-fired unit and constraints corresponding to the certainty of the output deviation of the hydrogen fuel cell; A simplification module, used to reconstruct and simplify the original centralized operation model of the hydrogen-electricity integrated energy system to obtain a simplified centralized operation model corresponding to the wind power deterministic set of the hydrogen-electricity integrated energy system; A decomposition module, used to decompose the simplified centralized operation model of the hydrogen-electricity integrated energy system into sub-problems corresponding to each subsystem, wherein the subsystem includes an electric power subsystem and a hydrogen subsystem; A determination module, used to determine the projection space according to the coupling relationship of each subsystem and the privacy protection requirement; A first search module, used for searching the initial vertex set of the operation space of each subsystem in the projection space; A second search module searches for other vertices based on the initial vertex set corresponding to each of the subsystems; A first reconstruction module is used to utilize new constraints formed by all vertices found by all the subsystems to reconstruct the operation space of the hydrogen-electricity integrated energy system in the projection space, wherein the reconstructed operation space contains an equivalent operation space of all information of the centralized model; A second reconstruction module is used to solve the optimization problem of the hydrogen-electricity integrated energy system obtained by reconstructing each of the sub-problems in the reconstructed operation space to obtain the optimal solution corresponding to the output scheduling; The acquisition module is used to obtain the power flow parameters corresponding to each of the subsystems under the optimal solution.

10. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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