Multi-energy coordinated optimization scheduling method and system for power distribution network

Abstract

The invention discloses a multi-energy coordinated optimization scheduling method and system for a power distribution network, and the method comprises the steps: defining a multi-objective optimization function, maximizing the power-loss load recovery, and minimizing the network loss; introducing a branch power flow constraint, an energy storage operation constraint, a load constraint and a power distribution network topology constraint; a second-order cone programming method is adopted to relax the power flow constraint, and a multi-objective optimization problem is solved through an NNC method. The system comprises a scheduling module, a data acquisition module, a calculation module, an optimization module and a display module, and can perform optimization scheduling and display a result according to real-time data. The method has the advantages that the energy utilization efficiency of the power distribution network can be improved, loss is reduced, the recovery capability of the system is enhanced, and safe and stable operation of the power grid is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and in particular to a distribution network multi-energy coordinated optimization scheduling method based on second-order cone programming and nonlinear programming (NNC) methods. Background Art

[0002] With the rapid development of smart grids and distributed energy, the operation and optimization of distribution networks have become an important topic in power system research. Traditional distribution network optimization methods mainly focus on the analysis and scheduling of power flows, but with the large-scale access of new and renewable energy sources, the operation mode of distribution networks has become more complex. Therefore, how to improve the economy, efficiency and safety of distribution networks has become a hot topic in current research.

[0003] In traditional distribution network optimization, common goals include load balancing, system stability, and loss minimization. However, these goals often ignore the complex interactions between different nodes and branches in the distribution network, as well as the comprehensive consideration of economy and efficiency in system operation. In addition, existing methods mostly rely on traditional optimization algorithms, such as genetic algorithms and particle swarm optimization. Although they can effectively find local optimal solutions, they are prone to fall into local optimal solutions when dealing with large-scale and complex distribution networks, and their computational efficiency is low, making it difficult to adapt to the needs of real-time scheduling.

[0004] Therefore, how to design a new optimization method that can comprehensively consider economy and efficiency while ensuring system stability and solve the shortcomings of traditional methods has become an urgent problem to be solved in the field of distribution network optimization. Summary of the invention

[0005] In view of the defects of the prior art, the present invention provides a method and system for multi-energy coordinated optimization scheduling of a distribution network.

[0006] In order to achieve the above invention object, the technical solution adopted by the present invention is as follows:

[0007] A distribution network multi-energy coordinated optimization dispatching method comprises the following steps:

[0008] 1) Define the objective function, including maximizing the restoration of power supply to the lost load and minimizing network losses;

[0009] 2) Introducing branch power flow constraints including phase angle relaxation, operation safety constraints, energy storage operation constraints, load constraints and distribution network topology constraints;

[0010] 3) The second-order cone programming method is used to relax the power flow constraints, and the NNC method is used to solve the multi-objective optimization problem to achieve multi-energy coordinated optimization scheduling of the distribution network.

[0011] Furthermore, the objective function is:

[0012]

[0013] Where: w i represents the load weight of node i, reflecting the importance of the load; T represents the total time period for fault recovery; Ω represents the node set, L i,t represents the total load of node i at time t, represents the load demand of node i at time t.

[0014] Furthermore, the economic and efficiency objective functions in the objective function are:

[0015]

[0016] Among them, R ij is the resistance of branch (i,j), i ij is the current of branch (i, j), B represents the set of all nodes, and E represents the set of all branches.

[0017] Furthermore, the constraints include:

[0018] Branch power flow constraints with phase angle relaxation:

[0019]

[0020] Among them, P j is the active power of node j, Q j is the reactive power of node j, P jk is the active power from node j to node k, Q jk is the reactive power from node j to node k, X ij is the reactance of branch (i, j), G j is the admittance of node j, B j is the susceptance of node j, v j is the voltage phase angle at node j.

[0021] Second-order cone relaxation constraints:

[0022]

[0023] Among them, P ij,t represents the active power of branch (i, j) at time t, Q ij,t represents the reactive power of branch (i, j) at time t, represents the square of the current in branch (i, j) at time t, Represents the square of the voltage amplitude at node i at time t.

[0024] Runtime safety constraints:

[0025]

[0026] Among them, r i,t It represents the weight factor of node i at time t, reflecting the influence of the node on the voltage limit. represents the lower voltage limit of the node voltage, represents the voltage upper limit of the node voltage, represents the square of the voltage amplitude of node i at time t, α ij represents the weight factor of branch (i, j), P ij,max Represents the maximum active power limit of branch (i, j), P ij,t represents the active power on branch (i, j) at time t, Q ij,max represents the maximum reactive power limit of branch (i, j), Q ij,t Represents the reactive power on branch (i, j) at time t.

[0027] Furthermore, the energy storage operation constraints include:

[0028] Charge and discharge state constraints:

[0029]

[0030] in, represents the charging state of energy storage unit i at time t, represents the discharge state of energy storage unit i at time t, B ess Represents a collection of energy storage system nodes.

[0031] Power Constraints:

[0032]

[0033] Among them, P i,t represents the power of energy storage unit i at time t, Indicates the maximum power limit of the energy storage system.

[0034] Capacity constraints:

[0035]

[0036] Among them, E i,t represents the energy storage of energy storage unit i at time t, P i,t Represents the power of energy storage unit i at time t.

[0037] 6. The method according to claim 1, characterized in that the load constraint is converted into a linear expression of voltage square through a ZIP model, specifically:

[0038]

[0039] Among them, P i,t represents the active load of node i at time t, Q i,T represents the reactive load of node i at time t, P i represents the active load reference value of node i, Q i represents the reactive load reference value of node i, α i , β i , γ i represents the coefficient related to the load type, v i,t Represents the voltage amplitude of node i at time t.

[0040] 7. The method according to claim 1, characterized in that the distribution network topology constraint is described by a spanning tree model, specifically:

[0041]

[0042] Among them, β ji represents the state variable of the branch connecting node j and node i, r i,t represents the connection status of node i at time t, Γ i represents the set of adjacent nodes of node i, Ω D represents the source node set in the distribution network, Ω S represents the set of load nodes in the distribution network, α ij Indicates whether there is a branch connecting node i and node j.

[0043] 8. The method according to claim 1 is characterized in that the multi-objective optimization problem is solved by the NNC method to obtain a uniform Pareto frontier solution.

[0044] 9. The method according to claim 8, characterized in that the NNC method adopts the normalized standard constraint method and is solved by the following formula:

[0045] minf 1 (x 0 )

[0046]

[0047] Among them, F 1 (X 0 ), F 2 (x 0 ) represents the value of the objective function, x i , x 0 represents the decision variable vector.

[0048] 10. The method according to claim 1 is characterized in that the method is implemented by MATLAB code and its effectiveness is verified by simulation experiments.

[0049] The present invention also discloses a distribution network multi-energy coordinated optimization dispatching system, which can be used to implement the above-mentioned distribution network multi-energy coordinated optimization dispatching method, and includes the following modules:

[0050] Objective function module: used to define an objective function, wherein the objective function includes maximizing the restoration of power supply to the power-lost load and minimizing network loss;

[0051] Constraint module: used to introduce branch power flow constraints with phase angle relaxation, operation safety constraints, energy storage operation constraints, load constraints and distribution network topology constraints;

[0052] Second-order cone programming module: used to relax the power flow constraints, using the second-order cone programming method;

[0053] Multi-objective optimization module: used to solve multi-objective optimization problems through the NNC method to achieve multi-energy coordinated optimization scheduling of distribution networks;

[0054] Dispatching decision module: Provides dispatching decisions based on optimization results and adjusts the multi-energy coordinated operation of the distribution network;

[0055] Display module: used to display the multi-energy coordinated optimization dispatch results of the distribution network in real time, including load recovery, network loss, energy storage equipment status and key indicators of operation safety, and provide a graphical interface to display the dispatch plan and optimization process.

[0056] The present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned distribution network multi-energy coordinated optimization scheduling method when executing the program.

[0057] The present invention also discloses a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned distribution network multi-energy coordinated optimization scheduling method is implemented.

[0058] Compared with the prior art, the advantages of the present invention are:

[0059] By comprehensively considering the economic and efficiency objective functions of the distribution network, the present invention can minimize the operating cost of the distribution network and improve the overall operating efficiency of the system on the basis of meeting load demand and grid stability.

[0060] The present invention adopts (specific algorithm), which not only avoids the defect of traditional methods that are prone to fall into local optimality in the optimization calculation of large-scale distribution networks, but also greatly improves the solution speed and calculation efficiency to meet the requirements of real-time scheduling.

[0061] The optimization method proposed in the present invention can effectively cope with the challenges of renewable energy fluctuations and load changes in the distribution network, enhance the robustness of the system, and ensure the stable operation of the distribution network in a dynamic environment.

[0062] The method of the present invention has strong adaptability, can be flexibly adjusted according to different distribution network scales and structures, and is suitable for various modern smart grids and distributed energy systems.

[0063] The present invention provides a new optimization framework for the decision-making system of the smart grid, which can combine real-time data and scheduling requirements to make more accurate and efficient decisions, thereby realizing a better distribution network scheduling solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 This is a flow chart of a method for multi-energy coordinated optimization and dispatching of a distribution network according to an embodiment of the present invention;

[0065] Figure 2 This is a schematic diagram of the structure of the IEEE 33-node distribution network system according to an embodiment of the present invention;

[0066] Figure 3 Schematic diagram of optimization results of an embodiment of the present invention. DETAILED DESCRIPTION

[0067] 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 with reference to the accompanying drawings and examples.

[0068] like Figure 1 As shown, the present invention provides a distribution network multi-energy coordinated optimization scheduling method, comprising:

[0069] 1. Define the objective function, including maximizing the restoration of power supply to the lost load and minimizing network losses;

[0070] 2. Introduce a series of constraints including branch power flow constraints with phase angle relaxation, operation safety constraints, energy storage operation constraints, load constraints and distribution network topology constraints;

[0071] 3. Use the second-order cone programming method to relax the power flow constraints, and use the NNC method to solve the multi-objective optimization problem to achieve multi-energy coordinated optimization scheduling of the distribution network;

[0072] 4. The present invention is implemented through MATLAB code and its effectiveness is verified through simulation experiments. The present invention can improve the operating efficiency and reliability of the distribution network and promote the efficient use of clean energy.

[0073] This embodiment uses the IEEE 33-node distribution network system as a test example. The specific structure is as follows: Figure 2 As shown. For this scenario, the multi-energy coordinated optimization scheduling method of the distribution network based on second-order cone programming and NNC method of the present invention is used for optimization scheduling. The method of this embodiment defines optimization variables such as voltage, current, active power, reactive power, etc. by setting parameters such as reference power, number of time periods, load ratio and node status, and establishes corresponding constraints according to the physical characteristics of the power system. The present invention aims to optimize the operation of the power system by minimizing network losses and maximizing power supply recovery of power-off loads. Using the YALMIP toolbox and Gurobi solver, the two objective functions are optimized respectively, and the constraint processing method of normalization and nonlinear programming (NNC method) is used to find the optimal solution between different objectives. In this way, the economy and reliability of the system can be improved while ensuring the safe and stable operation of the power system. The first-stage scheduling model aims to minimize the load power outage loss during fault recovery. The decision variables include the switch status of each branch in the distribution network, the node charge status, and the operating status of distributed energy. The specific steps are as follows:

[0074] The first-stage scheduling model aims to minimize the load outage loss during fault recovery. The decision variables include the switch status of each branch in the distribution network, the node charge status, and the operating status of distributed energy.

[0075] The objective function is as follows:

[0076]

[0077] Where: w i represents the load weight of node i, reflecting the importance of the load; T represents the total fault recovery time period; Ω represents the node set.

[0078] Considering the economy and efficiency of power transmission through the line, when the power is transmitted through the grid line, the active power consumed by the branch resistance is minimized, then the objective function is expressed as:

[0079]

[0080] Then the following series of constraints are introduced:

[0081] (1) Branch flow constraints with phase angle relaxation:

[0082]

[0083] Relax the transformed second-order cone constraints using SOC:

[0084]

[0085] (2) Operational safety constraints:

[0086]

[0087] Where: represents the square of the voltage amplitude of node i in time period t; U max and U min are the upper and lower limits of node voltage respectively; P ij,max and Q ij,max Respectively represent the upper limits of active and reactive power of branch ij; α ij Indicates the switch state of branch ij, 1 means the branch is closed, and 0 means the branch is open.

[0088] (3) Energy storage operation constraints:

[0089] (1) Charge and discharge state constraints, 0-1 variables Indicates the charging and discharging status:

[0090]

[0091] (2) Power Constraint

[0092]

[0093] (3) Capacity constraints

[0094]

[0095] Use the Big M method to make 0-1 variables With the continuously variable charge and discharge power P i,t Correlate so that the variables make sense and the constraints are convex:

[0096]

[0097] (4) Load constraints:

[0098] The load model can be established using the ZIP model, which is converted into the square of the voltage as the decision variable and approximated as a linear expression:

[0099]

[0100] (5) Distribution network topology constraints:

[0101] The radial constraints of distribution network are described based on the spanning tree model.

[0102]

[0103] Where: β ij Represents the hierarchical relationship between node i and node j. If node j is the parent node of node i, then β ij The value is 1, otherwise it is 0; Ω D Represents a pure load node in the distribution network; Ω SRepresents the main power node in the distribution network.

[0104] Subsequently, the multi-objective optimization problem is solved by the NNC method to achieve multi-energy coordinated optimization dispatching of the distribution network:

[0105] Taking the two-dimensional case as an example, first consider the case where the solution set is a convex set. Draw a line segment l1 with the anchor point as the two end points.

[0106] Take m points evenly on the line segment, and draw a straight line l2 perpendicular to the line segment at each point, dividing the solution set into two areas on the left and right, and solve the problem minf in the left area. 1 , we get a solution. It is easy to know that the solution is on the Pareto edge. By traversing the m points, we finally get a uniform Pareto edge. That is, by adding new constraints (constraining the area where the solution is found), the original problem is transformed into a piecewise single-objective constraint problem for solution. This can not only get a uniform solution edge, but also simplify the multi-objective problem. For the convenience of numerical representation, new constraints are added in the form of vectors. Therefore, the original problem is transformed into a problem with x. 0 Is a single objective constraint problem with decision parameters:

[0107] minf 1 (x 0 )(18)

[0108]

[0109]

[0110] Generally speaking, the different numerical ranges of each optimization target will bring computational difficulties to the standard constraint method. Therefore, this study adopts the normalized standard constraint method, that is, normalizing the value of the objective function:

[0111]

[0112] From the above discussion, we can know that the NNC method in three-dimensional space is to select points and perform calculations on the plane surrounded by the solution vector, as shown in the figure. Extended to the case of n-dimensional space, it is to select points and perform calculations in the hyperplane surrounded by the n-dimensional solution vector. Then we can get the NNC method for solving multi-objective problems in n-dimensional space:

[0113] minf 1 (x)(23)

[0114]

[0115] Normalizing the constraint vector will facilitate the solution. Then we can get the normalized solution vector:

[0116]

[0117] Then the original problem becomes:

[0118] minf l (x n ) (30)

[0119]

[0120] like Figure 3 As shown in the figure, the simulation results show that the optimal dispatch of the power system characterizes the flow of active and reactive power. In terms of active power, the main supply nodes are located in the upper left corner of the network, which deliver energy to the entire system, while the flow of active power is mainly concentrated on the right and bottom of the network, indicating that the load demand in these areas is high. The flow of reactive power is more scattered, and the nodes in the third column act as a key point to provide reactive power support to the surrounding nodes. It is worth noting that there are some nodes in the network without power flow, which may mean that the connection between these nodes is not fully utilized under the current dispatch strategy, or because the power demand of these nodes has been met through other paths. This detailed power flow analysis is crucial to optimize the operation of the power system and improve its overall efficiency.

[0121] In another embodiment of the present invention, a distribution network multi-energy coordinated optimization dispatching system is provided, which can be used to implement the above-mentioned distribution network multi-energy coordinated optimization dispatching method, specifically including:

[0122] Objective function module: used to define an objective function, wherein the objective function includes maximizing the restoration of power supply to the power-lost load and minimizing network loss;

[0123] Constraint module: used to introduce branch power flow constraints with phase angle relaxation, operation safety constraints, energy storage operation constraints, load constraints and distribution network topology constraints;

[0124] Second-order cone programming module: used to relax the power flow constraints, using the second-order cone programming method;

[0125] Multi-objective optimization module: used to solve multi-objective optimization problems through the NNC method to achieve multi-energy coordinated optimization scheduling of distribution networks;

[0126] Dispatching decision module: Provides dispatching decisions based on optimization results and adjusts the multi-energy coordinated operation of the distribution network;

[0127] Simulation and verification module: used to verify the effectiveness of the method through simulation experiments;

[0128] Display module: used to display the multi-energy coordinated optimization dispatch results of the distribution network in real time, including key indicators such as load recovery, network loss, energy storage equipment status and operation safety, and provide a graphical interface to display the dispatch plan and optimization process.

[0129] In another embodiment of the present invention, a terminal device is provided, the terminal device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of the multi-energy coordinated optimization scheduling method of the distribution network.

[0130] In another embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk storage.

[0131] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the distribution network multi-energy coordinated optimization scheduling method in the above-mentioned embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor.

[0132] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0133] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0134] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0135] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0136] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the implementation methods of the present invention, and should be understood that the protection scope of the present invention is not limited to such special statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A distribution network multi-energy coordinated optimization scheduling method, characterized in that: The following steps are involved: 1) Define the objective function, including maximizing the restoration of power supply to the lost load and minimizing network losses; 2) Introducing branch power flow constraints including phase angle relaxation, operation safety constraints, energy storage operation constraints, load constraints and distribution network topology constraints; 3) The second-order cone programming method is used to relax the power flow constraints, and the NNC method is used to solve the multi-objective optimization problem to achieve multi-energy coordinated optimization scheduling of the distribution network.

2. The method for multi-energy coordinated optimization and dispatching of distribution network according to claim 1 is characterized in that: The objective function is: Where: w i represents the load weight of node i, reflecting the importance of the load; T represents the total time period for fault recovery; Ω represents the node set, L i,t represents the total load of node i at time t, represents the load demand of node i at time t.

3. The method for multi-energy coordinated optimization and dispatching of distribution network according to claim 2 is characterized in that: The economic and efficiency objective functions in the objective function are: Among them, R ij is the resistance of branch (i,j), i ij is the current of branch (i, j), B represents the set of all nodes, and E represents the set of all branches.

4. The method for multi-energy coordinated optimization dispatching of distribution network according to claim 3 is characterized in that: The branch power flow constraint is as follows: Among them, P j is the active power of node j, Q j is the reactive power of node j, P jk is the active power from node j to node k, Q jk is the reactive power from node j to node k, X ij is the reactance of branch (i, j), G j is the admittance of node j, B j is the susceptance of node j, v j is the voltage phase angle of node j; It also includes the second-order cone relaxation constraint, as follows: Among them, P ij,t represents the active power of branch (i, j) at time t, Q ij,t represents the reactive power of branch (i, j) at time t, represents the square of the current in branch (i, j) at time t, represents the square of the voltage amplitude at node i at time t; Runtime safety constraints: Among them, r i,t It represents the weight factor of node i at time t, reflecting the influence of the node on the voltage limit. represents the lower voltage limit of the node voltage, represents the voltage upper limit of the node voltage, represents the square of the voltage amplitude of node i at time t, α ij represents the weight factor of branch (i, j), P ij,max Represents the maximum active power limit of branch (i, j), P ij,t represents the active power on branch (i, j) at time t, Q ij,max represents the maximum reactive power limit of branch (i, j), Q ij,t Represents the reactive power on branch (i, j) at time t.

5. The method for multi-energy coordinated optimization dispatching of distribution network according to claim 4 is characterized in that: The energy storage operation constraints include: Charge and discharge state constraints: in, represents the charging state of energy storage unit i at time t, represents the discharge state of energy storage unit i at time t, B ess Represents a set of energy storage system nodes; Power Constraints: Among them, P i,t represents the power of energy storage unit i at time t, Indicates the maximum power limit of the energy storage system; Capacity constraints: Among them, E i,t represents the energy storage of energy storage unit i at time t, P i,t Represents the power of energy storage unit i at time t.

6. The method for multi-energy coordinated optimization and dispatching of distribution network according to claim 5 is characterized in that: The load constraint is converted into a linear expression of voltage square through the ZIP model, specifically: Among them, P i,t represents the active load of node i at time t, Q i,t represents the reactive load of node i at time t, P i represents the active load reference value of node i, Q i represents the reactive load reference value of node i, α i , β i , γ i represents the coefficient related to the load type, ν i,t Represents the voltage amplitude of node i at time t.

7. The method for multi-energy coordinated optimization and dispatching of distribution network according to claim 6, characterized in that: The distribution network topology constraints are described by a spanning tree model, specifically: Among them, β ji represents the state variable of the branch connecting node j and node i, r i,t represents the connection status of node i at time t, Γ i represents the set of adjacent nodes of node i, Ω D represents the source node set in the distribution network, Ω S represents the set of load nodes in the distribution network, α ij Indicates whether there is a branch connecting node i and node j.

8. The method for multi-energy coordinated optimization and dispatching of distribution network according to claim 7 is characterized in that: The multi-objective optimization problem is solved by the NNC method to obtain a uniform Pareto frontier solution.

9. The method for multi-energy coordinated optimization and dispatching of distribution network according to claim 8, characterized in that: The NNC method adopts the normalized standard constraint method and is solved by the following formula: minf1(x0) Among them, f1(x0), f2(x0) represent the value of the objective function, x i , x0 represents the decision variable vector.

10. A distribution network multi-energy coordinated optimization dispatching system, characterized by: The system can be used to implement the distribution network multi-energy coordinated optimization scheduling method according to any one of claims 1 to 9, and includes the following modules: Objective function module: used to define an objective function, wherein the objective function includes maximizing the restoration of power supply to the power-lost load and minimizing network loss; Constraint module: used to introduce branch power flow constraints with phase angle relaxation, operation safety constraints, energy storage operation constraints, load constraints and distribution network topology constraints; Second-order cone programming module: used to relax the power flow constraints, using the second-order cone programming method; Multi-objective optimization module: used to solve multi-objective optimization problems through the NNC method to achieve multi-energy coordinated optimization scheduling of distribution networks; Dispatching decision module: Provides dispatching decisions based on optimization results and adjusts the multi-energy coordinated operation of the distribution network; Display module: used to display the multi-energy coordinated optimization dispatch results of the distribution network in real time, including load recovery, network loss, energy storage equipment status and key indicators of operation safety, and provide a graphical interface to display the dispatch plan and optimization process.

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