Distributed multi-objective optimization method and device suitable for electricity-hydrogen coupling system

Abstract

The invention relates to the technical field of energy optimization, and discloses a distributed multi-objective optimization method and device suitable for an electricity-hydrogen coupling system. Specifically, a dual-target optimization model between a power supplier and a hydrogen donor is constructed to effectively solve the problem of competition conflict between the power supplier and the hydrogen donor in the scheduling process in the existing electro-hydrogen system, economic benefits between the power supplier and the hydrogen donor are achieved, and it is ensured that the bidirectional energy coupling process between electric power and a hydrogen energy system is more efficient. And designing a distributed Xi-constraint optimization algorithm based on an alternating direction multiplier method to solve the dual-objective optimization model. The algorithm can capture the optimal Pareto frontier and limit the target function of each participant in a reasonable range on the premise of protecting the privacy of each participant of the electro-hydrogen system, ensures that the distributed calculation process of scheduling is stable and efficient, improves the coordination between energy suppliers while meeting the target optimization of electric power and hydrogen energy suppliers, and improves the optimization efficiency of the energy suppliers. And the operation cost of the system is minimized.

Description

Technical Field

[0001] The present application relates to the field of energy optimization technology, and in particular to a distributed multi-objective optimization method and device suitable for an electric-hydrogen coupling system. Background Art

[0002] With the rapid development of hydrogen energy technology and the access to large-scale renewable energy, the integration of power systems and hydrogen energy systems has gradually become an important direction in the energy field. The electric-hydrogen coupling system not only improves the flexibility and utilization efficiency of energy through bidirectional energy conversion, but also provides the possibility of solving the volatility problem caused by the access of a high proportion of renewable energy to the power system. However, in this integrated system, the economic interests of power suppliers and hydrogen energy suppliers often conflict, making it difficult to reach a unified scheduling plan.

[0003] In addition, in the electric-hydrogen coupling system, not only the energy conversion between electricity and hydrogen needs to be considered, but also the various physical constraints of the power network and the hydrogen pipeline network need to be met. These complex physical couplings increase the difficulty of scheduling, especially when the electricity and hydrogen suppliers are operated by different entities. How to achieve effective optimization scheduling without leaking the privacy of all parties becomes a major problem. Although the existing centralized scheduling method can uniformly consider the overall optimization of the system, it faces information security and privacy protection issues due to the need to obtain the private data of all participants, and it is difficult to adapt to actual application needs.

[0004] Therefore, how to design a distributed scheduling method that can coordinate the scheduling needs of electricity and hydrogen suppliers without leaking privacy and ensure that the operating cost of the system is minimized has become a key challenge in the optimization and scheduling of the electric-hydrogen coupling system. In particular, in multi-objective optimization, how to balance the conflicts of interest between different participants and efficiently capture the Pareto optimal solution through distributed computing has become an important research direction in the electric-hydrogen coupling system. Summary of the invention

[0005] The present application provides a distributed multi-objective optimization method suitable for an electric-hydrogen coupling system to solve the problem in the prior art that the economic interests of electricity suppliers and hydrogen suppliers often conflict, making it difficult to reach a unified scheduling plan, as well as information security and privacy protection issues.

[0006] Correspondingly, the present application also provides a distributed multi-objective optimization device suitable for an electric-hydrogen coupling system, an electronic device, and a computer-readable storage medium, which are used to ensure the implementation and application of the above method.

[0007] In order to solve the above technical problems, the present application discloses a distributed multi-objective optimization method applicable to an electric-hydrogen coupling system, the method comprising:

[0008] Based on the bidirectional energy coupling relationship between the power system and the hydrogen energy system, and the competitive relationship between power suppliers and hydrogen suppliers, a dual-objective optimization model between power suppliers and hydrogen suppliers is constructed; the dual-objective optimization model aims to minimize the operating costs of power suppliers and hydrogen suppliers;

[0009] The distributed ξ-constrained optimization algorithm based on the alternating direction multiplier method is used to solve the dual-objective optimization model to obtain the operating costs of the power supplier and the hydrogen supplier and the corresponding optimal values ​​of the decision variables.

[0010] Preferably, based on the bidirectional energy coupling relationship between the power system and the hydrogen energy system, and the competitive relationship between the power supplier and the hydrogen supplier, a dual-objective optimization model between the power supplier and the hydrogen supplier is constructed, including:

[0011] Determine the objective function of the target optimization model, including the objective function of the electricity supplier and the objective function of the hydrogen supplier;

[0012] Determine the constraints of the target optimization model, including power supplier constraints, hydrogen supplier constraints, and coupling constraints between the power system and the hydrogen energy system;

[0013] Construct a bi-objective optimization model based on the objective function and constraints:

[0014]

[0015] Among them, obj 1 and obj 2 are the optimization model for power supplier and the optimization model for hydrogen supplier, respectively. f(x) and f(y) are the objective functions of power supplier and hydrogen supplier, respectively. x is the decision variable of power system, y is the decision variable of hydrogen energy system, and g i is the coupling constraint between the power system and the hydrogen energy system, h j for and k are the inequality and equality constraints for safe operation of the power system and hydrogen energy system, respectively. cq is the number of coupling constraints, N eq is the number of equality constraints, N ieq is the number of inequality constraints.

[0016] Preferably, a distributed ξ-constrained optimization algorithm based on the alternating direction multiplier method is used to solve the dual-objective optimization model to obtain the operating costs of the power supplier and the hydrogen supplier and the corresponding optimal values ​​of the decision variables, including:

[0017] Ignore the objective function of the electricity supplier and only optimize the objective function of the hydrogen supplier to obtain the maximum and minimum operating costs acceptable to the hydrogen supplier;

[0018] Limit the operating cost of hydrogen suppliers to increase linearly from the minimum operating cost to the maximum operating cost, and determine the operating cost that hydrogen suppliers can accept;

[0019] The operating cost acceptable to the hydrogen supplier is added as a constraint to the power supplier's optimization model;

[0020] By optimizing the power supplier model multiple times, the operating costs of the power supplier and hydrogen supplier and the corresponding optimal values ​​of decision variables are obtained.

[0021] Preferably, the form of the distributed ξ-constrained optimization algorithm in the dual-objective optimization model is:

[0022]

[0023] Among them, C Gaccept The maximum operating cost acceptable to hydrogen suppliers is C. Gaccept .

[0024] Preferably, the objective function of the electricity supplier is ignored, and only the objective function of the hydrogen supplier is optimized. Before obtaining the maximum operating cost and the minimum operating cost acceptable to the hydrogen supplier, the method further includes:

[0025] Add replication variables for variable information exchanged between power suppliers and hydrogen suppliers to obtain replication variable constraints;

[0026] The replicated variable constraints are relaxed into the original objective function in the form of augmented Lagrangian, and the augmented Lagrangian forms of the objective functions of the electricity supplier and the hydrogen supplier are obtained.

[0027] Preferably, determining the objective function of the dual-objective optimization model includes:

[0028] Constructing the objective function of power suppliers based on their operating costs;

[0029] Construct the hydrogen supplier's objective function based on the hydrogen supplier's operating costs;

[0030] The operating costs of power suppliers include the wholesale electricity purchase cost from the upper power grid, the operation and maintenance cost of the electrolyzer, and the electricity purchase cost from the fuel cell of the hydrogen supplier.

[0031] The operating costs of hydrogen suppliers include the wholesale hydrogen purchase cost from hydrogen source plants, the operation and maintenance costs of hydrogen fuel cells, and the cost of purchasing hydrogen from electrolyzers of power suppliers.

[0032] Preferably, the supplier's constraints include distribution network constraints, power purchase constraints, and power sales constraints;

[0033] The constraints on hydrogen suppliers include hydrogen network constraints, gas pressure constraints and flow constraints.

[0034] The present application also discloses a distributed multi-objective optimization device applicable to an electric-hydrogen coupling system, the device comprising:

[0035] A model building module is used to build a dual-objective optimization model between power suppliers and hydrogen suppliers based on the bidirectional energy coupling relationship between the power system and the hydrogen energy system, as well as the competitive relationship between the power suppliers and the hydrogen suppliers; wherein the dual-objective optimization model aims to minimize the operating costs of the power suppliers and the hydrogen suppliers;

[0036] The optimization solution module is used to solve the dual-objective optimization model using a distributed ξ-constrained optimization algorithm based on the alternating direction multiplier method to obtain the operating costs of the power supplier and the hydrogen supplier and the corresponding optimal values ​​of the decision variables.

[0037] The present application also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, one or more methods described in the present application are implemented.

[0038] The present application also discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, one or more methods described in the present application are implemented.

[0039] In this application, based on the bidirectional energy coupling relationship between the power system and the hydrogen energy system, and the competitive relationship between the power supplier and the hydrogen supplier, a dual-objective optimization model between the power supplier and the hydrogen supplier is constructed. It can effectively solve the competitive conflict problem between the power supplier and the hydrogen supplier in the existing electric hydrogen system in the scheduling process. Among them, the dual-objective optimization model aims to minimize the operating costs of the power supplier and the hydrogen supplier, which can achieve the economic benefits between the power supplier and the hydrogen supplier, and ensure that the bidirectional energy coupling process between the power and hydrogen energy systems is more efficient. In addition, a distributed ξ-constrained optimization algorithm based on the alternating direction multiplier method is designed to solve the dual-objective optimization model. The algorithm can capture the optimal Pareto frontier under the premise of protecting the privacy of each participant in the electric hydrogen system, limit the objective function of each participant within a reasonable range, ensure that the distributed computing process of scheduling is stable and efficient, and improve the coordination between energy suppliers while meeting the optimization goals of the power and hydrogen suppliers, so as to minimize the system operation cost.

[0040] Additional aspects and advantages of the present application will be given in the following description, which will become apparent from the following description, or will be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0042] Figure 1 A flow chart of a distributed multi-objective optimization method applicable to an electric-hydrogen coupling system provided in an embodiment of the present application;

[0043] Figure 2 A topological diagram of the electric-hydrogen coupling system provided in an embodiment of the present application;

[0044] Figure 3 The Pareto front result obtained by the weighted summation method provided in the embodiment of the present application;

[0045] Figure 4 The Pareto frontier result obtained by the mathematical programming method provided in the embodiment of the present application;

[0046] Figure 5 The Pareto front result obtained by the NSGA-II method provided in the embodiment of the present application;

[0047] Figure 6 The Pareto frontier result obtained by the distributed ξ-constraint method provided in the embodiment of the present application;

[0048] Figure 7 The algorithm convergence process under different penalty factors provided in the embodiment of the present application;

[0049] Figure 8 A schematic diagram of the structure of a distributed multi-objective optimization device applicable to an electric-hydrogen coupling system provided in an embodiment of the present application;

[0050] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0051] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be interpreted as limiting the present application.

[0052] It will be understood by those skilled in the art that, unless expressly stated, the singular forms "one", "said", and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be an intermediate element. In addition, the "connection" or "coupling" used herein may include wireless connection or wireless coupling. The term "and / or" used herein includes all or any unit and all combinations of one or more associated listed items.

[0053] Those skilled in the art will appreciate that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the field to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0054] The solution provided in the embodiments of the present application can be executed by any electronic device, such as a terminal device or a server, wherein the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected via wired or wireless communication, and the present application does not limit this. With regard to the technical problems existing in the prior art, the distributed multi-objective optimization method and device for the electric-hydrogen coupling system provided in the present application are intended to solve at least one of the technical problems of the prior art.

[0055] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0056] The present application embodiment provides a possible implementation method, such as Figure 1As shown, a flow chart of a distributed multi-objective optimization method applicable to an electric-hydrogen coupling system is provided, which can be executed by any electronic device, and optionally, can be executed on a server or terminal device.

[0057] like Figure 1 As shown in , the method may include the following steps:

[0058] Step 101, based on the bidirectional energy coupling relationship between the power system and the hydrogen energy system, and the competitive relationship between the power supplier and the hydrogen supplier, a dual-objective optimization model between the power supplier and the hydrogen supplier is constructed.

[0059] Among them, the dual-objective optimization model aims to minimize the operating costs of electricity and hydrogen suppliers.

[0060] Step 102, using a distributed ξ-constrained optimization algorithm based on an alternating direction multiplier method to solve the dual-objective optimization model, and obtaining the operating costs of the electricity supplier and the hydrogen supplier and the corresponding optimal values ​​of the decision variables.

[0061] Among them, the distributed ξ-constrained optimization algorithm optimizes and solves the models of each participating entity in the dual-objective optimization model, and can effectively capture the optimal Pareto frontier.

[0062] In the embodiment of the present application, based on the bidirectional energy coupling relationship between the power system and the hydrogen energy system, and the competitive relationship between the power supplier and the hydrogen supplier, a dual-objective optimization model between the power supplier and the hydrogen supplier is constructed. It can effectively solve the competitive conflict problem between the power supplier and the hydrogen supplier in the existing electric hydrogen system in the scheduling process. Among them, the dual-objective optimization model aims to minimize the operating costs of the power supplier and the hydrogen supplier, which can achieve the economic benefits between the power supplier and the hydrogen supplier, and ensure that the bidirectional energy coupling process between the power and hydrogen energy systems is more efficient. In addition, a distributed ξ-constrained optimization algorithm based on the alternating direction multiplier method is designed to solve the dual-objective optimization model. The algorithm can capture the optimal Pareto frontier under the premise of protecting the privacy of each participant in the electric hydrogen system, limit the objective function of each participant within a reasonable range, ensure that the distributed computing process of the scheduling is stable and efficient, and meet the optimization of the objectives of the power and hydrogen suppliers. At the same time, it improves the coordination between energy suppliers and minimizes the system operation cost. This method can not only adapt to complex electric hydrogen coupling systems, but also flexibly deal with the conflict of interests in multi-objective optimization, and has a strong practical application value.

[0063] In an optional embodiment, based on the bidirectional energy coupling relationship between the power system and the hydrogen energy system, and the competitive relationship between the power supplier and the hydrogen supplier, a dual-objective optimization model between the power supplier and the hydrogen supplier is constructed, including:

[0064] Determine the objective function of the target optimization model, including the objective function of the electricity supplier and the objective function of the hydrogen supplier;

[0065] Determine the constraints of the target optimization model, including power supplier constraints, hydrogen supplier constraints, and coupling constraints between the power system and the hydrogen energy system;

[0066] Construct a bi-objective optimization model based on the objective function and constraints:

[0067]

[0068] Among them, obj 1 and obj 2 are the optimization model for power supplier and the optimization model for hydrogen supplier, respectively. f(x) and f(y) are the objective functions of power supplier and hydrogen supplier, respectively. x is the decision variable of power system, y is the decision variable of hydrogen energy system, and g i is the coupling constraint between the power system and the hydrogen energy system, h j for and k are the inequality and equality constraints for safe operation of the power system and hydrogen energy system, respectively. cq is the number of coupling constraints, N eq is the number of equality constraints, N ieq is the number of inequality constraints.

[0069] The dual-objective optimization model takes x and y as optimization variables, considers the internal constraints of each system and the coupling constraints between systems, and optimizes the minimum operating cost of each system under the premise of ensuring constraint safety, and obtains the optimal Pareto frontier.

[0070] In an optional embodiment, determining the objective function of the dual-objective optimization model includes:

[0071] Constructing the objective function of power suppliers based on their operating costs;

[0072] Construct the hydrogen supplier's objective function based on the hydrogen supplier's operating costs;

[0073] The operating costs of power suppliers include the wholesale electricity purchase cost from the upper power grid, the operation and maintenance cost of the electrolyzer, and the electricity purchase cost from the fuel cell of the hydrogen supplier.

[0074] The operating costs of hydrogen suppliers include the wholesale hydrogen purchase cost from hydrogen source plants, the operation and maintenance costs of hydrogen fuel cells, and the cost of purchasing hydrogen from electrolyzers of power suppliers.

[0075] Electricity suppliers make profits by selling electricity to users, and hydrogen suppliers make profits by selling hydrogen to users.

[0076] Based on the above, the objective functions of the electricity supplier and the hydrogen supplier can be expressed as:

[0077]

[0078]

[0079] Among them, C P and C G are the operating costs of the electricity supplier and the hydrogen supplier, respectively, W,e,t and p W,g,t are the wholesale electricity price of the upper power grid and the wholesale hydrogen price of the hydrogen source plant during period t, P grid,t and G grid,t are the wholesale electricity and wholesale hydrogen in period t, ξ EG and GE are the unit maintenance costs of the electrolyzer and hydrogen fuel cell, respectively, j,EG,t and G j,GE,t are the amount of electricity input to the electrolyzer and the amount of hydrogen input to the hydrogen fuel cell during period t, respectively. j,GE,t and p j,EG,t are the electricity price from the hydrogen fuel cell and the hydrogen price from the electrolyzer during period t, respectively, e,t and p g,t are the electricity price sold by the power supplier to the user and the hydrogen price sold by the hydrogen supplier to the user during period t, respectively. j,t and G j,t are the electricity and hydrogen purchases of node j in period t, respectively. B is the set formed by all nodes j in the system, and ΔT is the unit scheduling period.

[0080] In an optional embodiment, the supplier's constraints include distribution network constraints, power purchase constraints, and power sales constraints, which can be expressed as follows:

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] Among them, j, u, and v are all system node indexes, ju and vj represent the branch from node j to node u and the branch from node v to node j respectively, P ju,t , Q ju,t They represent the active power and reactive power flowing through branch ju during period t, P vj,t , Qvj,t ,I vj,t They represent the active power, reactive power and current flowing through branch vj during period t, r vj 、x vj 、r ju 、x ju Respectively represent the resistance and reactance of branch vj and the resistance and reactance of branch ju, V j,t and V u,t They represent the voltages of nodes j and u at time period t, respectively, and P j,load,t , P j,EG,t , P j,GE,t They represent the electrical load, electrolyzer output, and hydrogen fuel cell power consumption of node j in period t, respectively. and are the sets formed by all nodes and all branches in the distribution network, respectively. Equations (4)-(8) are distribution network constraints, where equations (4)-(5) are distribution network node power balance constraints, equation (6) is the voltage drop constraint, equation (7) is the branch power constraint after second-order cone relaxation, and equation (8) is the coupling node power balance constraint. The first part of equation (9) is the power purchase constraint, and the second part is the power sales constraint.

[0088] The constraints of hydrogen suppliers include hydrogen network constraints, gas pressure constraints and flow constraints, which can be expressed as:

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] Among them, f p,t 、f p,min and f p,max They represent the flow rate of pipe p in period t, the minimum flow rate allowed, and the maximum flow rate, respectively. i,t and π j,t Respectively represent the air pressure of node i and node j during time period t, π n,min and π n,max They represent the minimum and maximum air pressure of node n, sgn is the sign function, and f k,t , G s,t and Gl,t represent the flow rates of pipeline k, hydrogen source node s and hydrogen load node l during period t, respectively, and B nk , G ns and D nl Respectively represent the corresponding coefficient matrix, Φ p is the conversion factor of pipeline p, n i,t and n j,t They represent the square of air pressure at nodes i and j during period t respectively. and They represent the pipeline set and the node set respectively. Equations (10)-(16) are all hydrogen network constraints, among which Equation (13) is the gas pressure constraint, and Equations (10)-(12) and Equations (14)-(16) are flow constraints.

[0097] The power supplier and hydrogen supplier are connected to each other at the coupling node through energy conversion equipment such as electrolyzers and hydrogen fuel cells for energy conversion and trading. Therefore, the coupling constraint can be expressed as:

[0098]

[0099]

[0100]

[0101]

[0102] Among them, η EG and η GE are the conversion efficiencies of the electrolyzer and hydrogen fuel cell, and are the maximum output powers of the electrolyzer and hydrogen fuel cell, respectively. Equations (17)-(18) represent the electrolyzer equipment constraints, and equations (19)-(20) represent the hydrogen fuel cell constraints.

[0103] In an optional embodiment, a distributed ξ-constrained optimization algorithm based on an alternating direction multiplier method is used to solve the dual-objective optimization model to obtain the operating costs of the power supplier and the hydrogen supplier and the corresponding optimal values ​​of the decision variables, including:

[0104] Ignore the objective function of the electricity supplier and only optimize the objective function of the hydrogen supplier to obtain the maximum and minimum operating costs acceptable to the hydrogen supplier;

[0105] Limit the operating cost of hydrogen suppliers to increase linearly from the minimum operating cost to the maximum operating cost according to a fixed step length, and determine the operating cost that hydrogen suppliers can accept;

[0106] The operating cost acceptable to the hydrogen supplier is added as a constraint to the power supplier's optimization model;

[0107] By optimizing the power supplier model multiple times, the operating costs of the power supplier and hydrogen supplier and the corresponding optimal values ​​of decision variables are obtained.

[0108] In an optional embodiment, the distributed ξ-constrained optimization algorithm in the dual-objective optimization model is in the form of:

[0109]

[0110] Among them, C Gaccept is the maximum operating cost that the hydrogen supplier can accept. Under this algorithm, the operating cost of the power supplier is taken as the main objective function, and the operating cost of the hydrogen supplier is constrained not to exceed C Gaccept , and add this constraint to the original power supplier optimization model, thereby converting the dual-objective optimization model into a single-objective form.

[0111] In an optional embodiment, the objective function of the electricity supplier is ignored, and only the objective function of the hydrogen supplier is optimized. Before obtaining the maximum operating cost and the minimum operating cost acceptable to the hydrogen supplier, the method further includes:

[0112] Add replication variables for variable information exchanged between power suppliers and hydrogen suppliers to obtain replication variable constraints;

[0113] The replicated variable constraints are relaxed to the original objective function in the form of augmented Lagrangian, and the augmented Lagrangian forms of the objective functions of the electricity supplier and the hydrogen supplier are obtained.

[0114] Among them, the augmented Lagrangian form of the power supplier's objective function is:

[0115]

[0116] Where L is the augmented Lagrangian form of the power supplier, C P is the objective function of the power supplier, λ is the Lagrange multiplier, ρ is the penalty factor, and T is the scheduling period, which is 24h.

[0117] Before optimization, the power supplier's objective function is converted into its augmented Lagrangian form, and the objective function is restricted by a penalty term. During each iterative optimization process, local variable information is exchanged for consensus matching. After multiple iterations, the consensus constraints between the power supplier and the hydrogen supplier are gradually reached, and the optimized values ​​of the decision variables are obtained while effectively protecting the privacy of the participating entities.

[0118] Based on the above, the process of solving the bi-objective optimization model using the distributed ξ-constrained optimization algorithm is as follows:

[0119] First, in order to protect the user privacy of the power supplier and the hydrogen supplier, the replication variables of the interactive power between the power supplier and the hydrogen supplier are added, that is, the replication variables of the four variables involved in equations (17) and (19) are added respectively:

[0120]

[0121] in, and are replicated variables representing the electricity consumption and hydrogen output of the electrolyzer; and are replicated variables representing the consumed hydrogen energy and output electrical energy of the hydrogen fuel cell respectively.

[0122] Secondly, the equality constraint of equation (23) is relaxed into the original objective function through the augmented Lagrangian form, and the augmented Lagrangian form of the objective function of the power supplier and hydrogen supplier is obtained:

[0123]

[0124]

[0125] Again, without considering the power supplier, the maximum operating cost C is obtained by optimizing the hydrogen supplier objective function. Gmax and minimum operating cost C Gmin . And establish the expected number of non-dominated optimal solution sets N.

[0126] Then, according to the increasing order of the operating costs of hydrogen suppliers from the smallest to the largest, the operating costs that hydrogen suppliers can accept at the current step are determined as follows:

[0127] C Gaccept =C Gmin +n·(C Gmax -C Gmin ) / N (26)

[0128] Among them, n represents the nth set of non-dominated optimal solutions. The acceptable cost of the hydrogen supplier at this time is added as a constraint to the optimization model of the power supplier. As a result, the original dual-objective optimization problem is converted into a single-objective optimization problem with a depressed solution:

[0129]

[0130] Next, initialize the relevant parameters, the maximum number of iterations and the acceptable original residual convergence threshold, optimize equation (27) iteratively, calculate the original residual after each iteration, and determine whether it is less than the convergence threshold:

[0131] ||r k+1 || 2 =||X S,k+1 -X C,k+1 || 2 <ξ (28)

[0132] If it is less than, stop the iteration and output the objective function value and the optimized variable value;

[0133] If not less than, update the Lagrange multiplier to:

[0134] λ k+1 =λ k +ρ(X S,k+1 -X C,k+1 ) (29)

[0135] Continue iterating until the residual error is less than the convergence threshold or the maximum number of iterations is reached.

[0136] Finally, according to the expected number of non-dominated optimal solution sets, the objective function of the power supplier is optimized and calculated multiple times to obtain the objective function values ​​of the power supplier and the hydrogen supplier after each optimization, as well as the corresponding decision variables, and the residual convergence of each set of solution iteration process is recorded until N sets of solutions are obtained.

[0137] The distributed ξ-constrained optimization algorithm based on the alternating direction multiplier method proposed in the embodiment of the present application effectively solves the problem while protecting the privacy of the participating entities by exchanging local information and multiple iterations. By limiting the objective function of each participating entity, the stability and efficiency of the scheduling process are ensured, avoiding the information security issues brought about by the traditional centralized scheduling method.

[0138] In the embodiment of the present application, multiple iterations are used to solve the problem of privacy protection for electricity and hydrogen suppliers. By utilizing its model characteristics and algorithm flow, a distributed ξ-constrained optimization algorithm based on the alternating direction multiplier method is modeled and solved using the Matlab language.

[0139] Exemplarily, after the algorithm designed in the embodiment of the present application is implemented based on Matlab programming, the following example scenario is set: in the example, a 33-node distribution system (distribution network) and a 20-node hydrogen energy system (hydrogen energy network) are set, and nodes 17, 24, and 31 of the distribution system and nodes 17, 19, and 16 of the hydrogen energy system are set as coupling nodes. Corresponding energy conversion devices (i.e., coupling devices) are set in the two systems to be connected. The system topology is as follows: Figure 2 As shown. The maximum number of single iterations is set to 50, the residual convergence threshold is 1e-8, the penalty factor is set to 100, and the expected number of non-dominated optimal solution sets N = 100. In order to reflect the advantages of the embodiments of the present application, the uniformity of the Pareto front obtained by each method is also compared with the weighted summation method, mathematical programming method and NSGA-II.

[0140] (1) Pareto front uniformity analysis

[0141] The 100 sets of non-dominated optimal solutions obtained by the weighted sum method, mathematical programming method, NSGA-II method and the method proposed by the present invention are as follows: Figure 3 , Figure 4 , Figure 5 and Figure 6 As shown. Among them, Figure 3 is the Pareto frontier result obtained by weighted summation method, Figure 4 is the Pareto frontier result obtained by mathematical programming method, Figure 5 is the Pareto frontier result obtained by NSGA-II method, Figure 6 This is the Pareto frontier result obtained by the distributed ξ-constraint method in the embodiment of the present application. The horizontal axis in the figure represents the cost of the hydrogen supplier (thousand yuan), and the vertical axis represents the cost of the electricity supplier (thousand yuan). A negative operating cost indicates that the electricity supplier or hydrogen supplier is profitable.

[0142] As can be seen from the results, the Pareto frontier obtained by the method in the embodiment of the present application is more uniform, while the uniformity of the Pareto frontier obtained by the weighted summation method and the mathematical programming method is relatively poor. For the weighted summation method, this is caused by the limitations of the method. The weighted summation method merges multiple targets into one target. Although the weight distribution of each objective function value is different, the performance is the same, which is not necessarily a different Pareto optimal solution, that is, the diversity of the Pareto frontier is not enough. In actual practice, this means that the interaction strength of the power supplier and the hydrogen supplier in the time period is inconsistent, but both parties can obtain the same benefits, which is not desirable; from the perspective of operating costs, the effect is consistent, but this will increase the workload of scheduling planning. The non-uniformity of the mathematical programming method is caused by the limitations of the objective function. The mathematical programming method uses the square of the distance between the two objective functions and their respective optimal values ​​as the new objective function. In order to make the new objective function as small as possible, the result always tends to be distributed in the area close to the optimal value of both, which inevitably leads to the area far away from the single target value. It can also be seen from the results that under the mathematical programming method, the maximum cost achieved by the power supplier and the hydrogen supplier is about -10 and -7, respectively, and it can never be 0. In other words, it is difficult for mathematical programming methods to capture solutions on the edge of the Pareto boundary. However, in practical applications, such extreme situations are possible. Under certain conditions, only electricity suppliers or hydrogen suppliers may be responsible for the energy supply of both the power grid and the hydrogen network during the entire scheduling period. At this time, a critical situation with high operating costs will occur. Therefore, mathematical programming methods are difficult to promote and the feasible domain is relatively small. For the NSGA-II algorithm, its optimization process is a multi-stage evolutionary process. Due to the different number of iterations, the results will not be completely consistent, and it is greatly affected by the crossover operator and the mutation operator. In addition, in order to protect the diversity of the boundary, the algorithm introduces the concept of crowding distance. Unqualified individuals will be eliminated during the evolution process. Therefore, the number of solution sets obtained is inconsistent with the number of populations, which increases the instability of the algorithm results. In other words, if the NSGA-II algorithm wants to obtain a more stable solution, it requires more iterations and a larger population, which undoubtedly increases computing resources.

[0143] On the contrary, Figure 6As shown, the method proposed in the embodiment of the present application can obtain a relatively uniform Pareto front. From the perspective of density estimation, the solution set is evenly distributed in the feasible domain, and there will be no situation where the density is very large in the local range. This is mainly because the method proposed in the embodiment of the present application fully considers the interests of both competing parties. Optimize the cost of one party and limit the cost range of the other party at the same time. As long as the cost range is reasonable, both parties will be able to achieve a more satisfactory competition result. In the actual optimization problem, the power supplier and the hydrogen supplier can exchange acceptable cost ranges with each other, for example, using the operating cost of the power supplier as the main objective function, that is, the hydrogen supplier is satisfied with the objective function of optimizing the power supplier. At this time, the objective function is a single objective, which avoids the conflict of interest caused by unequal weight distribution during the optimization process. In addition, as long as different ranges are restricted in the feasible domain, different solutions can be obtained, and decision makers can choose the restricted step size and range according to actual conditions. In theory, any set of Pareto solutions within the acceptable cost range of both parties can be obtained, which is very useful for maintaining the diversity of the Pareto front.

[0144] (2) Algorithm performance analysis

[0145] First, the convergence characteristics of the algorithm proposed in the embodiment of the present application are analyzed. When changing the values ​​of different penalty factors, the system converges from the original residual as shown in the following figure: Figure 7 As shown, it can be seen that when the penalty factor ρ is fixed, as the penalty factor increases, the residual decreases faster. When the number of iterations is the same, as the penalty factor increases, the final residual result continues to decrease. When the penalty factor reaches 100, only about 10 iterations are needed to achieve an accuracy of 1e-8, which reflects the higher convergence speed and reliability of the algorithm in the embodiment of the present application. In addition, the calculation time required for the four methods of distributed ξ-constraint method, weighted summation method, mathematical programming method and NSGA-II in the embodiment of the present application under different groups of non-dominated optimal solution sets is analyzed and compared, and the number of non-dominated optimal solution sets is set to 10, 20, 50 and 100 respectively. The calculation time of the four different methods is compared and analyzed, and the results are shown in Table 1.

[0146] Table 1 Calculation time of different methods under different solution sets (unit: seconds)

[0147]

[0148] As can be seen from the results, the calculation time of the method proposed in the embodiment of the present application is longer than that of the weighted summation method and the NSGA-II algorithm, which is mainly because the weighted summation method and the NSGA-II do not generate iterative steps inside each set of optimal solutions, and there is only one external loop when searching for the next set of optimal solutions, so the time is relatively short. On the other hand, the calculation time of the mathematical programming method and the proposed method is relatively long, and the calculation time increases rapidly with the increase of the number of solution sets. When the number of solution sets is small, such as 10 and 20, the time spent by the proposed method is slightly longer than that of the mathematical programming method, which is mainly because the number of solution sets is small, the number of outer loops is small, and the loop calculation time of the residual in the proposed method is similar to the mathematical programming method for optimizing quadratic functions. When the number of solution sets increases, such as 50 and 100, the time spent by the proposed method is shorter. This is because when the number of solution sets is large, the inner loop of the proposed method only needs to optimize one objective function, and as the iteration proceeds, the residual becomes smaller and smaller, and the optimization time is also reduced. The mathematical programming method needs to optimize the objective function of a square term each time it is optimized, which is time-consuming and will not change with the increase of the number of solution sets. Therefore, when the number of solution sets is large, the calculation time of the method proposed in the embodiment of the present application is shorter than that of the mathematical programming method. In general, due to the introduction of the distributed iterative process, the method proposed in the embodiment of the present application is not as good as the centralized and heuristic multi-objective optimization algorithm in terms of solution time. However, since the privacy of the participants can be effectively protected, to a certain extent, even sacrificing a certain solution time is acceptable.

[0149] From the above example analysis, it can be concluded that the distributed ξ-constraint method proposed in the embodiment of the present application has simple mathematical principles, a reliable theoretical basis, and strong interpretability. It introduces the idea of ​​distributed algorithms, effectively protects the privacy of participants by increasing the interaction weight replication variables, and achieves higher convergence accuracy within a smaller number of iterations. Compared with traditional centralized and heuristic algorithms, regardless of the number of solution sets, a Pareto front with higher uniformity can be obtained, providing a better geometry for multi-objective optimization results. In addition, decision makers can choose appropriate penalty factors according to the complexity of the problem and the scale of parameters to obtain fast and high-precision convergence effects.

[0150] Based on the same principle as the method provided in the embodiment of the present application, the embodiment of the present application also provides a distributed multi-objective optimization device suitable for an electric-hydrogen coupling system, such as Figure 8 As shown, the device comprises:

[0151] The model building module 801 is used to build a dual-objective optimization model between the power supplier and the hydrogen supplier based on the bidirectional energy coupling relationship between the power system and the hydrogen energy system, and the competitive relationship between the power supplier and the hydrogen supplier; wherein the dual-objective optimization model aims to minimize the operating costs of the power supplier and the hydrogen supplier;

[0152] The optimization solution module 802 is used to solve the dual-objective optimization model using a distributed ξ-constrained optimization algorithm based on the alternating direction multiplier method to obtain the operating costs of the power supplier and the hydrogen supplier and the corresponding optimal values ​​of the decision variables.

[0153] In the embodiment of the present application, based on the bidirectional energy coupling relationship between the power system and the hydrogen energy system, and the competitive relationship between the power supplier and the hydrogen supplier, a dual-objective optimization model between the power supplier and the hydrogen supplier is constructed. It can effectively solve the competitive conflict problem between the power supplier and the hydrogen supplier in the existing electric hydrogen system in the scheduling process. Among them, the dual-objective optimization model aims to minimize the operating costs of the power supplier and the hydrogen supplier, which can achieve the economic benefits between the power supplier and the hydrogen supplier, and ensure that the bidirectional energy coupling process between the power and hydrogen energy systems is more efficient. In addition, a distributed ξ-constrained optimization algorithm based on the alternating direction multiplier method is designed to solve the dual-objective optimization model. The algorithm can capture the optimal Pareto frontier under the premise of protecting the privacy of each participant in the electric hydrogen system, limit the objective function of each participant within a reasonable range, ensure that the distributed computing process of the scheduling is stable and efficient, and meet the optimization of the objectives of the power and hydrogen suppliers. At the same time, it improves the coordination between energy suppliers and minimizes the system operation cost. This method can not only adapt to complex electric hydrogen coupling systems, but also flexibly deal with the conflict of interests in multi-objective optimization, and has a strong practical application value.

[0154] The distributed multi-objective optimization device for the electric-hydrogen coupling system provided in the embodiment of the present application can achieve Figures 1 to 7 To avoid repetition, the various processes implemented in the method embodiment are not described here.

[0155] The distributed multi-objective optimization device applicable to the electric-hydrogen coupling system of the embodiment of the present application can execute the distributed multi-objective optimization method applicable to the electric-hydrogen coupling system provided in the embodiment of the present application, and its implementation principle is similar. The actions performed by each module and unit in the distributed multi-objective optimization device applicable to the electric-hydrogen coupling system in each embodiment of the present application correspond to the steps in the distributed multi-objective optimization method applicable to the electric-hydrogen coupling system in each embodiment of the present application. For the detailed functional description of each module of the distributed multi-objective optimization device applicable to the electric-hydrogen coupling system, please refer to the description in the corresponding distributed multi-objective optimization method applicable to the electric-hydrogen coupling system shown in the previous text, which will not be repeated here.

[0156] Based on the same principle as the method shown in the embodiment of the present application, the embodiment of the present application also provides an electronic device, which may include but is not limited to: a processor and a memory; a memory for storing a computer program; a processor for executing the distributed multi-objective optimization method for the electric-hydrogen coupling system shown in any optional embodiment of the present application by calling a computer program. Compared with the prior art, the distributed multi-objective optimization method for the electric-hydrogen coupling system provided by the present application constructs a dual-objective optimization model between the power supplier and the hydrogen supplier, which can effectively solve the competition conflict problem between the power supplier and the hydrogen supplier in the scheduling process in the existing electric-hydrogen system, and can achieve the economic benefits between the power supplier and the hydrogen supplier, and ensure that the two-way energy coupling process between the electric power and hydrogen energy systems is more efficient. The designed distributed ξ-constrained optimization algorithm can capture the optimal Pareto frontier under the premise of protecting the privacy of each participant in the electric-hydrogen system, limit the objective function of each participant within a reasonable range, ensure that the distributed computing process of the scheduling is stable and efficient, and meet the optimization of the goals of the power and hydrogen suppliers. At the same time, it improves the coordination between energy suppliers and minimizes the system operation cost.

[0157] In an optional embodiment, an electronic device is also provided, such as Fig. 9 As shown, Fig. 9 The electronic device 900 shown may be a server, including: a processor 901 and a memory 903. The processor 901 and the memory 903 are connected, such as through a bus 902. Optionally, the electronic device 900 may further include a transceiver 904. It should be noted that in actual applications, the transceiver 904 is not limited to one, and the structure of the electronic device 900 does not constitute a limitation on the embodiments of the present application.

[0158] Processor 901 may be a CPU (Central Processing Unit), a general purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 901 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0159] The bus 902 may include a path to transmit information between the above components. The bus 902 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 902 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig. 9 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0160] The memory 903 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0161] The memory 903 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 901. The processor 901 is used to execute the application code stored in the memory 903 to implement the contents shown in the above method embodiment.

[0162] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs and desktop computers. Fig. 9 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0163] The server provided in this application can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected by wired or wireless communication, and this application does not limit this.

[0164] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0165] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0166] It should be noted that the computer-readable storage medium mentioned above in the present application can also be a computer-readable signal medium or a combination of a computer-readable storage medium and a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0167] The computer-readable medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0168] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0169] According to one aspect of the present application, a computer program product or a computer program is provided, the computer program product or the computer program comprising computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the distributed multi-objective optimization method and apparatus applicable to an electric-hydrogen coupling system provided in the above-mentioned various optional implementations.

[0170] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0171] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0172] The modules involved in the embodiments described in this application can be implemented by software or hardware. The name of the module does not limit the module itself in some cases. For example, the model building module can also be described as "a model building module for building a dual-objective optimization model between the power supplier and the hydrogen supplier based on the bidirectional energy coupling relationship between the power system and the hydrogen energy system, and the competitive relationship between the power supplier and the hydrogen supplier".

[0173] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A distributed multi-objective optimization method suitable for an electric-hydrogen coupling system, characterized in that: The method comprises: Based on the bidirectional energy coupling relationship between the power system and the hydrogen energy system, and the competitive relationship between the power supplier and the hydrogen supplier, a dual-objective optimization model between the power supplier and the hydrogen supplier is constructed; wherein the dual-objective optimization model aims to minimize the operating costs of the power supplier and the hydrogen supplier; The dual-objective optimization model is solved using a distributed ξ-constrained optimization algorithm based on the alternating direction multiplier method to obtain the operating costs of the power supplier and the hydrogen supplier and the corresponding optimal values ​​of the decision variables.

2. The distributed multi-objective optimization method applicable to the electric-hydrogen coupling system according to claim 1, characterized in that: Based on the bidirectional energy coupling relationship between the power system and the hydrogen energy system, and the competitive relationship between the power supplier and the hydrogen supplier, a dual-objective optimization model between the power supplier and the hydrogen supplier is constructed, including: Determining the objective function of the target optimization model, including the objective function of the electricity supplier and the objective function of the hydrogen supplier; Determining the constraints of the target optimization model, including constraints of power suppliers, constraints of hydrogen suppliers, and coupling constraints between the power system and the hydrogen energy system; Constructing the dual-objective optimization model based on the objective function and the constraint conditions: Where obj1 and obj2 are the optimization models for power suppliers and hydrogen suppliers, respectively; f(x) and f(y) are the objective functions for power suppliers and hydrogen suppliers, respectively; x is the decision variable for the power system, y is the decision variable for the hydrogen energy system, and g is the decision variable for the hydrogen energy system. i is the coupling constraint between the power system and the hydrogen energy system, h j for and k are the inequality and equality constraints for safe operation of the power system and hydrogen energy system, respectively. cq is the number of coupling constraints, N eq is the number of equality constraints, N ieq is the number of inequality constraints.

3. The distributed multi-objective optimization method applicable to the electric-hydrogen coupling system according to claim 2, characterized in that: The distributed ξ-constrained optimization algorithm based on the alternating direction multiplier method is used to solve the dual-objective optimization model to obtain the operating costs of the power supplier and the hydrogen supplier and the corresponding optimal values ​​of the decision variables, including: Ignore the objective function of the electricity supplier, and only optimize the objective function of the hydrogen supplier to obtain the maximum operating cost and the minimum operating cost acceptable to the hydrogen supplier; Limit the operating cost of hydrogen suppliers to increase linearly from the minimum operating cost to the maximum operating cost, and determine the operating cost that hydrogen suppliers can accept; Adding the operating cost acceptable to the hydrogen supplier as a constraint condition for the power supplier to the power supplier optimization model; By optimizing the power supplier model multiple times, the operating costs of the power supplier and the hydrogen supplier and the corresponding optimal values ​​of the decision variables are obtained.

4. The distributed multi-objective optimization method applicable to the electric-hydrogen coupling system according to claim 3 is characterized in that: The form of the distributed ξ-constrained optimization algorithm in the dual-objective optimization model is: Among them, C Gaccept The maximum operating cost acceptable to hydrogen suppliers is C. Gaccept .

5. The distributed multi-objective optimization method applicable to the electric-hydrogen coupling system according to claim 3, characterized in that: Before ignoring the electricity supplier objective function and optimizing only the hydrogen supplier objective function to obtain the maximum operating cost and the minimum operating cost acceptable to the hydrogen supplier, the method further includes: Add replication variables for variable information exchanged between power suppliers and hydrogen suppliers to obtain replication variable constraints; The replicated variable constraint is relaxed into the original objective function in the form of augmented Lagrangian, and the augmented Lagrangian forms of the objective function of the electricity supplier and the objective function of the hydrogen supplier are obtained.

6. The distributed multi-objective optimization method applicable to the electric-hydrogen coupling system according to claim 2, characterized in that: Determining the objective function of the dual-objective optimization model includes: Constructing the objective function of power suppliers based on their operating costs; Construct the hydrogen supplier's objective function based on the hydrogen supplier's operating costs; The operating costs of power suppliers include the wholesale electricity purchase cost from the upper power grid, the operation and maintenance cost of the electrolyzer, and the electricity purchase cost from the fuel cell of the hydrogen supplier. The operating costs of hydrogen suppliers include the wholesale hydrogen purchase cost from hydrogen source plants, the operation and maintenance costs of hydrogen fuel cells, and the cost of purchasing hydrogen from electrolyzers of power suppliers.

7. The distributed multi-objective optimization method applicable to the electric-hydrogen coupling system according to claim 2, characterized in that: Suppliers’ constraints include distribution network constraints, power purchase constraints, and power sales constraints; The constraints on hydrogen suppliers include hydrogen network constraints, gas pressure constraints and flow constraints.

8. A distributed multi-objective optimization device suitable for an electric-hydrogen coupling system, characterized in that: The device comprises: A model building module, for building a dual-objective optimization model between the power supplier and the hydrogen supplier based on the bidirectional energy coupling relationship between the power system and the hydrogen energy system, and the competitive relationship between the power supplier and the hydrogen supplier; wherein the dual-objective optimization model aims to minimize the operating costs of the power supplier and the hydrogen supplier; The optimization solution module is used to solve the dual-objective optimization model using a distributed ξ-constrained optimization algorithm based on the alternating direction multiplier method to obtain the operating costs of the power supplier and the hydrogen supplier and the corresponding optimized values ​​of the decision variables.

9. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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