Power distribution system operation optimization method and device, terminal equipment and storage medium

Abstract

The invention discloses a power distribution system operation optimization method and device, terminal equipment and a storage medium, and belongs to the field of power distribution systems, and the method comprises the steps: obtaining the operation scene data of a to-be-optimized power distribution system; based on the operation scene data and a preset constraint condition, constructing an operation cost model; the method comprises the following steps: obtaining a reconstruction neighborhood structure, a capacitor bank neighborhood structure and an energy storage neighborhood structure based on an initial topology basic variable and an operation cost model of a to-be-optimized power distribution system, and further calculating target function values of the operation cost model under different topology basic variables, different capacitor bank states and different energy storage states; and selecting the topology basic variable with the minimum target function value, the capacitor bank state and the energy storage state, generating a control strategy, and controlling the to-be-optimized power distribution system based on the control strategy, so that the problem of low control precision in operation of the power distribution network can be solved.

Description

Technical Field

[0001] This invention relates to the field of power distribution systems, and more particularly to a method, apparatus, terminal equipment, and storage medium for optimizing the operation of power distribution systems. Background Technology

[0002] In modern power systems, existing research has proposed intelligent soft switches to replace traditional tie switches, enabling flexible interconnection between distribution network feeders and making system operation more reliable. SOPs combined with energy storage devices can perform peak shaving and valley filling, and achieve continuous regulation of active and reactive power in the distribution network, allowing for flexible adjustments based on the real-time operating status of the distribution network. However, research on distribution network optimization still faces some challenges. Existing research rarely fully considers multiple controllable resources for distribution network operation optimization; most studies only consider SOPs, distributed generation (DG) output, capacitor banks (CB), energy storage devices (ESS), and demand response (DR) individually. Therefore, the lack of research on distribution network optimization using existing technologies leads to low control accuracy in distribution network operation. Summary of the Invention

[0003] This invention provides a method, apparatus, terminal equipment, and storage medium for optimizing the operation of a power distribution system, which can solve the problem of low control accuracy in the operation of power distribution networks.

[0004] The power distribution system operation optimization method provided by this invention includes:

[0005] Obtain operational scenario data of the power distribution system to be optimized;

[0006] Based on the aforementioned operational scenario data and preset constraints, an operational cost model is constructed.

[0007] Based on the initial topology basic variables of the power distribution system to be optimized, the operating cost model is initially calculated to obtain the initial capacitor bank state and the initial energy storage state of the operating cost model; wherein, the initial topology basic variables are the initial topology values ​​of the topology structure of the power distribution system to be optimized, the initial capacitor bank state is the initial state of each capacitor in the power distribution system to be optimized, and the initial energy storage state is the initial state of each energy storage device in the power distribution system to be optimized.

[0008] A reconstructed neighborhood structure is constructed based on the initial topological basic variables, a capacitor bank neighborhood structure is constructed based on the initial capacitor bank state, and an energy storage neighborhood structure is constructed based on the initial energy storage state.

[0009] Based on the reconstructed neighborhood structure, the capacitor bank neighborhood structure, and the energy storage neighborhood structure, the objective function value of the operating cost model is calculated under different topological basic variables, different capacitor bank states, and different energy storage states. The topological basic variables, capacitor bank states, and energy storage states with the smallest objective function values ​​are selected to generate a control strategy, and the power distribution system to be optimized is controlled based on the control strategy.

[0010] Furthermore, the step of constructing a reconstructed neighborhood structure based on the initial topological basic variables, constructing a capacitor bank neighborhood structure based on the initial capacitor bank state, and constructing an energy storage neighborhood structure based on the initial energy storage state includes:

[0011] Line state adjustment operations are performed on the lines in the topology of the power distribution system to be optimized to obtain several candidate topology basic variables that are different from the initial topology basic variables. The initial topology basic variables and all candidate topology basic variables are then combined to form a reconstructed neighborhood structure. Specifically, the line state adjustment operation is to change the line state from on to off, or change the line state from off to on.

[0012] Based on the constraint of the number of capacitor banks, the capacitor state adjustment operation is performed on the capacitors in the power distribution system to be optimized to obtain several candidate capacitor bank states that are different from the initial capacitor bank states. The initial capacitor bank states and all candidate capacitor bank states are combined into a capacitor bank neighborhood structure. Specifically, the capacitor state adjustment operation is to change the capacitor state from on to off, or change the capacitor state from off to on.

[0013] Based on the energy storage change constraint, an energy storage adjustment operation is performed on the energy storage device in the power distribution system to be optimized to obtain several candidate energy storage states that are different from the initial energy storage state, and the initial energy storage state and all candidate energy storage states are combined into an energy storage neighborhood structure; wherein, the energy storage adjustment operation specifically involves adjusting the number of charge and discharge modes of the energy storage device.

[0014] Furthermore, the capacitor bank control quantity constraint includes:

[0015]

[0016] In the formula, Let t be the number of capacitors connected in a group of switched capacitors connected at node i under scenarios t and s; where t is the time, i is the node, and s is the clustered scenario. η represents the number of capacitors installed; η represents the number of modules in the control system. Ω represents the number of capacitors connected in a set of switched capacitors connected at node i under scenarios t-1 and s. scb It is a set of nodes.

[0017] Furthermore, the energy storage variation constraint includes:

[0018]

[0019] In the formula, binary variables This represents the working state of node i connected to ESS under scenarios t and s; t is the time; i is the node; s is the clustered scenario; The working status of node i connected to ESS under scenario t-1, s; Ω represents the maximum number of operational changes across all ESSs from one cycle to the next. ess For the ESS collection.

[0020] Furthermore, the acquisition of operational scenario data for the power distribution system to be optimized includes:

[0021] Obtain the operating data of the power distribution system to be optimized;

[0022] The operational data is clustered to obtain operational scenario data; specifically, the operational scenario data includes vectors of energy cost, energy demand, and solar irradiance within a preset time period, as well as the probability of scenario occurrence.

[0023] Furthermore, the objective function of the operating cost model satisfies the following condition:

[0024]

[0025] In the formula, ψ is the objective function value. The cost of power loss in the system; Cost of gas emissions from the power transmission system and the dispatchable fossil fuel DG units within the system; Cost of responding to demand; To cut costs for DG; ρ t,s Let T be the probability of scenario t and s occurring; t,s To analyze the duration of the time period; π emis and π ens These are the energy coefficient, carbon dioxide emission coefficient, and DG reduction cost coefficient, respectively; π dr R is the demand response cost discount factor; ij The resistance of branch ij; e is the square of the current in branch ij; ss and e dg These are the carbon dioxide emission coefficients of substations and distributed power generation nodes, respectively. and These are, respectively, the power injected into the substation, the power injected by distributed generation, the increase in active load demand during demand response, and the unutilized power; Ω t For time sets; Ω s For scene collection; Ω b For the set of branches; Ω ss For the set of substation nodes; Ω dg It is a set of distributed power nodes.

[0026] Furthermore, the constraints include: system operation constraints, line radial constraints, distributed power output constraints, capacitor bank constraints, power electronic device and energy storage constraints, and demand response constraints.

[0027] Another embodiment of the present invention provides a power distribution system operation optimization device, including: a data acquisition module, a data construction module, a data processing module, a neighborhood search module, and a result generation module;

[0028] The data acquisition module is used to acquire the operating scenario data of the power distribution system to be optimized;

[0029] The data construction module is used to construct an operating cost model based on the operating scenario data and preset constraints.

[0030] The data processing module is used to perform initial calculations on the operating cost model based on the initial topology basic variables of the power distribution system to be optimized, and to obtain the initial capacitor bank state and the initial energy storage state of the operating cost model; wherein, the initial topology basic variables are the initial topology values ​​of the topology structure of the power distribution system to be optimized, the initial capacitor bank state is the initial state of each capacitor in the power distribution system to be optimized, and the initial energy storage state is the initial state of each energy storage device in the power distribution system to be optimized.

[0031] The neighborhood search module is used to construct a reconstructed neighborhood structure based on the initial topological basic variables, construct a capacitor bank neighborhood structure based on the initial capacitor bank state, and construct an energy storage neighborhood structure based on the initial energy storage state.

[0032] The result generation module is used to calculate the objective function value of the operating cost model under different topological basic variables, different capacitor bank states, and different energy storage states based on the reconstructed neighborhood structure, the capacitor bank neighborhood structure, and the energy storage neighborhood structure; select the topological basic variable, capacitor bank state, and energy storage state with the smallest objective function value; generate a control strategy; and control the power distribution system to be optimized based on the control strategy.

[0033] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the steps of the power distribution system operation optimization method provided by the present invention.

[0034] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the power distribution system operation optimization method provided by the present invention.

[0035] The following benefits can be obtained by implementing the present invention:

[0036] This invention acquires operational scenario data of a power distribution system to be optimized. Based on the operational scenario data and preset constraints, an operational cost model is constructed. The optimization objective for the power distribution system to be optimized is then determined based on the operational cost model. First, based on the initial topological variables of the power distribution system to be optimized, the operational cost model is initially calculated to obtain the initial capacitor bank state and the initial energy storage state. Then, a reconstructed neighborhood structure is constructed based on the initial topological variables, a capacitor bank neighborhood structure is constructed based on the initial capacitor bank state, and an energy storage neighborhood structure is constructed based on the initial energy storage state. This allows for the search of different results for the topological variables, capacitor bank state, and energy storage state that affect the optimization objective, and the different search results are applied to the subsequent operational cost model. This invention constructs an operating cost model to determine the optimization objective of the power distribution system to be optimized. Based on the neighborhood structure, it adds different results for topology basic variables, capacitor bank states, and energy storage states. Under different topology basic variables, capacitor bank states, and energy storage states, it determines the topology basic variables, capacitor bank states, and energy storage states that minimize the objective function value of the operating cost model. This achieves optimized control of the power distribution system to be optimized and improves the control accuracy of the topology basic variables, capacitor bank states, and energy storage states of the power distribution system to be optimized while minimizing operating costs. Attached Figure Description

[0037] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0038] Figure 1 This is a flowchart illustrating a power distribution system operation optimization method according to an embodiment of the present invention;

[0039] Figure 2This is a schematic diagram of the structure of a power distribution system operation optimization device provided in an embodiment of the present invention;

[0040] Figure 3 This is a schematic flowchart of a branch-swapping algorithm provided in an embodiment of the present invention;

[0041] Figure 4 This is a schematic diagram of a power distribution system node provided in an embodiment of the present invention;

[0042] Figure 5 This is a schematic diagram of the neighborhood search process provided in an embodiment of the present invention;

[0043] Figure 6 This is a schematic diagram of a power distribution system provided in an embodiment of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0046] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0047] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0048] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0049] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0050] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0051] See Figure 1 To address the problem of low control accuracy in power distribution network operation, an embodiment of the present invention provides a power distribution system operation optimization method, comprising:

[0052] 101. Obtain the operation scenario data of the power distribution system to be optimized.

[0053] Furthermore, the acquisition of operational scenario data for the power distribution system to be optimized includes:

[0054] Obtain the operating data of the power distribution system to be optimized;

[0055] The operational data is clustered to obtain operational scenario data; specifically, the operational scenario data includes vectors of energy cost, energy demand, and solar irradiance within a preset time period, as well as the probability of scenario occurrence.

[0056] In one specific embodiment, integrating distributed energy resources into a modern power grid requires modeling uncertainties such as energy costs, load demand, and distributed power sources. Among various uncertainty handling techniques, clustering is a suitable and easily implemented alternative. This method groups a set of observational data and extracts the most representative information from a large dataset. Due to the applicability and proven performance of the k-means method in modeling uncertainties in power systems, this embodiment selects the k-means method.

[0057] Standardized values ​​including energy costs, demand, and solar irradiance are measured, and the daily observation set is reduced to clusters using the k-means algorithm through the following steps:

[0058] (1) Daily data curve: The entire dataset is divided into data groups belonging to 24 hours according to the required number of days;

[0059] (2) Time period grouping: Each data group is further divided into 24 equal time periods;

[0060] (3) k-means clustering: k-means clustering was performed on the 24 time periods to obtain vectors containing energy costs, energy demand, and solar irradiance, thus obtaining operational scenario data in the following form: Energy cost within a preset time period, τ t，s For energy demand, G t，s The vector of solar irradiance and ρ t，s This represents the probability of the scenario occurring.

[0061] 102. Based on the aforementioned operational scenario data and preset constraints, construct an operational cost model.

[0062] In one specific embodiment, an operating cost model is constructed using the MISOCP model to determine the operating state that reduces system operating costs within the planned scope. Therefore, the objective function includes system losses, CO2 emissions, demand response incentives, and DG reduction costs.

[0063] Furthermore, the objective function of the operating cost model satisfies the following condition:

[0064]

[0065]

[0066] In the formula, Ψ is the objective function value. The cost of power loss in the system; Cost of gas emissions from the power transmission system and the dispatchable fossil fuel DG units within the system; Cost of responding to demand; To cut costs for DG; ρ t,s Let T be the probability of scenario t and s occurring; t,s To analyze the duration of the time period; π emis and π ens These are the energy coefficient, carbon dioxide emission coefficient, and DG reduction cost coefficient, respectively; π dr R is the demand response cost discount factor; ij The resistance of branch ij; e is the square of the current in branch ij; ss and e dg These are the carbon dioxide emission coefficients of substations and distributed power generation nodes, respectively. and These are, respectively, the power injected into the substation, the power injected by distributed generation, the increase in active load demand during demand response, and the unutilized power; Ω t For time sets; Ω s For scene collection; Ω b For the set of branches; Ω ss For the set of substation nodes; Ω dg It is a set of distributed power nodes.

[0067] It should be noted that, Demand response costs are the total discount on electricity prices resulting from changes in energy supply plans. System operators can supply power to a particular type of load at any convenient time to meet users' daily electricity needs.

[0068] Furthermore, the constraints include: system operation constraints, line radial constraints, distributed power output constraints, capacitor bank constraints, power electronic device and energy storage constraints, and demand response constraints.

[0069] In a specific embodiment, the system operating constraints are as follows:

[0070]

[0071]

[0072] In the formula, P ij,t,s and Q ij,t,s The active and reactive power transmitted by branch ij under scenarios t and s; and The active and reactive power injected into the substation under scenarios t and s; and The active and reactive power injected into the distributed power source under scenarios t and s; and Provides discharge and charging power for the ESS; and The active and reactive power demand after demand response; The reactive power compensation of the fixed capacitor at node i; The reactive power injected into the switched capacitor bank at scenarios t and s; X ij The reactance of branch ij; Let the square of the voltage at node i be the voltage under scenarios t and s. μ is the square of the voltage at node j under scenarios t and s; ij,t,s This is a slack variable used in voltage calculations; Let be the square of the impedance of branch ij; and V k represents the upper and lower limits of voltage amplitude. ij It is a binary variable, where 0 represents an open circuit and 1 represents a closed circuit; This represents the upper limit of substation capacity; P ki，t，s Let be the active power transmitted by branch ki under scenarios t and s. Inject active power into the SOP of node i. Let Q be the SOP loss power of node i. ki，t，s The reactive power transmitted by branch ki under scenarios t and s. Inject reactive power into the SOP of node i. μ is the upper limit of the line transmission current. ij，t，s k is the slack variable for voltage calculation. ij It is a 0-1 variable, representing the switch of the circuit.

[0073] The radial constraint of the line is as follows:

[0074] β ij +β ji =k ij

[0075]

[0076] β ij =0|j∈Ω ss

[0077] β ji =0|i∈Ω ss

[0078] β ij ,β ji ,k ij ∈{0,1}

[0079] In the formula, β ij β represents the parent node of j being i. ji Represents the parent node of i for j; if branch ij is part of the spanning tree, it represents k. ij It is 1, or β ij It is 1, or β ji =1; Ω n Let k be the set of nodes. If branch ij is closed, i.e., k ij If the value is 1, the above constraint forces one of these nodes to be set as the parent node.

[0080] The output constraints of distributed power sources are as follows:

[0081] The distributed power source proposed in this patent is photovoltaic:

[0082]

[0083] In the formula, P i std δ represents the voltage at node i under standard conditions. i The power / temperature coefficient of node i; For the photovoltaic temperature at node i; G t,s Let t be the solar radiation in scene s; and The capacitive and inductive power factors for distributed power generation output; T amb For ambient temperature; NOCT i The nominal operating cell temperature of the photovoltaic node i.

[0084] The capacitor bank constraint is as follows:

[0085]

[0086] In the formula, Let the initial time be t ini The number of capacitors connected in a group of switched capacitors under scenario s; For the final moment t ini The number of capacitors connected in a group of switched capacitors under scenario s; t ini t is the initial time; end For the final moment; The reactive power capacity of each capacitor; The number of capacitors installed for node i; and An auxiliary variable characterizing the increase or decrease of the capacitor installed at node i; This represents the maximum permissible switching amount for the switched capacitor bank.

[0087] Power electronic devices and energy storage constraints, specifically:

[0088]

[0089] In the formula, Inject active power into the SOP of node i; Inject active power into the SOP of node j; Let SOP be the power loss at node i; Let SOP be the power loss at node j; The maximum active power limit for SOP; Inject reactive power into the SOP of node i; and These are the lower and upper limits of the reactive power injected into node i at its SOP, respectively. Inject reactive power into the SOP of node j; and These are the lower and upper limits of the reactive power injected into node j at its SOP, respectively. The maximum SOP capacity at node i; The maximum SOP capacity at node j; Let be the loss coefficient for node i; Let be the loss coefficient for node j; This is a binary variable representing the charge / discharge state of the ESS; and Charge and discharge power of ESS; and These are the upper and lower limits of the discharge power; and These are the upper and lower limits of the charging power; The energy stored in the ESS connecting node i at time t; The energy stored in the ESS connecting node i at time t-1; For in t ini The energy stored in the ESS of node i at all times; For in t end The energy stored in the ESS of node i at any given time; T t,s The time required for analysis; and For charge and discharge efficiency; For ESS self-discharge efficiency; and The upper and lower limits of the energy stored in the ESS; and As an auxiliary variable, it represents the change in the charging and discharging of the ESS; Ω represents the maximum permissible variation of ESS. T For time combination;

[0090] The demand response constraints are as follows:

[0091] Demand response load can alleviate the consumption patterns of heavy load periods, reduce overall power loss, and improve system quality and reliability. Customers who have pre-agreed to shift a certain percentage of their load from periods of heavy load to periods of light load can effectively reduce system losses.

[0092]

[0093] In the formula, P i d and Let τ be the active and reactive power requirements of node i;t,s For scenario t and s, the demand factors; and The increase or decrease in the active power load demand of node i after the demand response; Let be the load power factor of node i; This represents the maximum permissible percentage reduction in load after demand response; This is a binary variable representing whether or not the entity participates in the demand response. 1 represents participation, and 0 represents non-participation.

[0094] 103. Based on the initial topology basic variables of the power distribution system to be optimized, perform initial calculations on the operating cost model to obtain the initial capacitor bank state and the initial energy storage state of the operating cost model; wherein, the initial topology basic variables are the initial topology values ​​of the topology structure of the power distribution system to be optimized, the initial capacitor bank state is the initial state of each capacitor in the power distribution system to be optimized, and the initial energy storage state is the initial state of each energy storage device in the power distribution system to be optimized.

[0095] In one specific embodiment, mathematical algorithms are effective tools for heuristic strategies and mathematical optimization to decompose and solve highly complex problems. The strategy proposed in this embodiment generates candidate solutions for the optimization process in the search space. These solutions, called neighboring solutions, are obtained by slightly modifying the initial / current solution. This strategy involves fixing some variables by assigning a predetermined value or retaining them as variables determined by a commercial solver. Doing so reduces the number of variables in the problem, obtaining neighboring solutions from the solutions of the sub-models derived from the above model. Since binary and integer variables increase the complexity of the problem, the following neighborhood structure is used to handle them.

[0096] In one specific embodiment, the initial calculation of the operating cost model is performed as follows:

[0097] First, set a set of variables to initial values, as shown below:

[0098] (1) Keep k ij The value remains unchanged, preserving the original topology;

[0099] (2) For SCB Perform relaxation integrity;

[0100] (3) In ESS Perform relaxation;

[0101] (4) Cancel demand response and disconnect load mode:

[0102] (5) Solve using a solver.

[0103] (6) and Choose the integer value that is closest to them.

[0104] In mixed integer programming (MIP), relaxation integrity refers to: (1) the number of modules in a capacitor bank is allowed to take continuous values ​​in the interval [0, ni scb]; and (2) the charge and discharge states of energy storage are allowed to take continuous values ​​in the interval [0, 1].

[0105] The steps to implement relaxation operation:

[0106] (1) Model adjustment:

[0107] In the initial stage, based on the initial topological basic variables, the mixed integer second-order cone programming (MISOCP) model (i.e., the operating cost model) is modified to remove integer constraints. By controlling the quantity constraints of capacitor banks and the energy storage change constraints, discrete constraints are removed, allowing them to take continuous values.

[0108] (2) Solving the continuity problem after relaxation:

[0109] The solution is used to solve the relaxed continuous optimization problem, and an initial solution is obtained: the initial state of the capacitor bank and the initial state of the energy storage.

[0110] (3) Variable rounding:

[0111] The relaxed continuous solutions are restored to integer or binary values. For the number of capacitor modules, the nearest integer is taken; for the energy storage charging and discharging state, if the continuous value is ≥0.5, it is set to 1 (discharging), otherwise it is set to 0 (charging).

[0112] (4) Neighborhood search guidance:

[0113] Based on the repaired integer solutions, candidate solutions are generated using the neighborhood structure:

[0114] 1) Reconstructing the neighborhood structure (NB1): Closing or disconnecting certain lines to generate a new topology.

[0115] 2) Capacitor Bank Neighborhood Structure (NB2): Limits the range of module number variation.

[0116] 3) Energy storage neighborhood structure (NB3): Limits the number of times the charging and discharging modes change.

[0117] 104. Construct a reconstructed neighborhood structure based on the initial topological basic variables, construct a capacitor bank neighborhood structure based on the initial capacitor bank state, and construct an energy storage neighborhood structure based on the initial energy storage state.

[0118] Furthermore, the step of constructing a reconstructed neighborhood structure based on the initial topological basic variables, constructing a capacitor bank neighborhood structure based on the initial capacitor bank state, and constructing an energy storage neighborhood structure based on the initial energy storage state includes:

[0119] Line state adjustment operations are performed on the lines in the topology of the power distribution system to be optimized to obtain several candidate topology basic variables that are different from the initial topology basic variables. The initial topology basic variables and all candidate topology basic variables are then combined to form a reconstructed neighborhood structure. Specifically, the line state adjustment operation is to change the line state from on to off, or change the line state from off to on.

[0120] Based on the constraint of the number of capacitor banks, the capacitor state adjustment operation is performed on the capacitors in the power distribution system to be optimized to obtain several candidate capacitor bank states that are different from the initial capacitor bank states. The initial capacitor bank states and all candidate capacitor bank states are combined into a capacitor bank neighborhood structure. Specifically, the capacitor state adjustment operation is to change the capacitor state from on to off, or change the capacitor state from off to on.

[0121] Based on the energy storage change constraint, an energy storage adjustment operation is performed on the energy storage device in the power distribution system to be optimized to obtain several candidate energy storage states that are different from the initial energy storage state, and the initial energy storage state and all candidate energy storage states are combined into an energy storage neighborhood structure; wherein, the energy storage adjustment operation specifically involves adjusting the number of charge and discharge modes of the energy storage device.

[0122] In one specific embodiment:

[0123] (1) Reconstructing the neighborhood structure (NB1)

[0124] 1) Operation: Close 1-3 lines (k ij =1), after forming a loop, disconnect other lines to restore radiation.

[0125] 2) Constraint Adjustment: Only optimize k within the closed loop. ij and β ij The remaining variables are fixed.

[0126] 3) Impact on the objective function: After changing the network topology, the current distribution I... ij,t,s and loss C loss change.

[0127] (2) Neighborhood structure of capacitor bank (NB2)

[0128] 1) Operation: Limit the change in the number of capacitor modules, and turn on or off the capacitors whose number changes.

[0129] 2) Impact on the objective function: Optimize capacitor switching to reduce reactive power loss and voltage deviation.

[0130] (3) Energy storage neighborhood structure (NB3)

[0131] 1) Operation: Limit the number of times the energy storage device changes its charging and discharging modes.

[0132] 2) Impact on objective function: Optimize energy storage charging and discharging timing to reduce peak-hour electricity purchase costs.

[0133] Furthermore,

[0134] The capacitor bank control quantity constraint includes:

[0135]

[0136] In the formula, Let t be the number of capacitors connected in a group of switched capacitors connected at node i under scenarios t and s; where t is the time, i is the node, and s is the clustered scenario. η represents the number of capacitors installed; η represents the number of modules in the control system. Ω represents the number of capacitors connected in a set of switched capacitors connected at node i under scenarios t-1 and s. scb It is a set of nodes.

[0137] Furthermore, the energy storage variation constraint includes:

[0138]

[0139] In the formula, binary variables This represents the working state of node i connected to ESS under scenarios t and s; t is the time; i is the node; s is the clustered scenario; The working status of node i connected to ESS under scenario t-1, s; Ω represents the maximum number of operational changes across all ESSs from one cycle to the next. ess For the ESS collection.

[0140] In one specific embodiment, the energy storage device is an ESS, or Energy Storage System.

[0141] In one specific embodiment, the reconstruction of the neighborhood structure is achieved by generating a set of radial topologies for the neighborhood. In this case, the neighborhood can be obtained using a branch-switching technique, which has been widely applied to network reconstruction problems. The algorithm first closes a switch in the network to form a closed loop, and then opens a segmented switch to restore the radiality of the network.

[0142] The branch-exchange algorithm proposed in this embodiment creates a radial neighborhood topology by solving the proposed mathematical model. It uses k ij and β ij Integer variables in the model have been removed. When an open line ij closes, i.e., k is preserved... ij=1 (i.e., the line state, 1 for on, 0 for off), then we only need to solve for k of the lines that form the loop. ij and β ij The model can then be solved. Otherwise, keep the remaining k constants. ij and β ij The variables are their current values. The algorithm identifies formed loops and selects the optimal path to break, such as... Figure 3 The flowchart is shown.

[0143] In one specific embodiment, Figure 4 This demonstrates a system with two substations, three tie switches, and integer variable states associated with the lines. If 6-12 is closed (bold line), then k6 12 is set to 1, forming a loop. Figure 3 We know that node i is 6 and node j is 12, so the temporary variable a takes the value 6. The algorithm finds a closed loop with node b as its parent node, satisfying ab→6b or ba→b6 and k6b or kb6=1. Among all variables, only loop 2-6 satisfies the conditions (k26=1 and β62=1), indicating that 2 is the parent node of 6 and 2-6 is a closed loop. Since node 2 is not a substation, variable a=2. Now, the algorithm repeats this process to find a closed loop of 2b or b2, ensuring that it is a child node of b. Loops 1-2 and 2-3 are closed, but the parent node of 2 is 1 (β21=1), while 3 is a child node (β32=1). At this point, the algorithm identifies node 1 as a substation node, and node j is assigned the value 12. The algorithm identifies the loop connecting node 12 to the substation.

[0144] The closed-loop circuits are candidate circuits to be opened, marked in red. Through solving the mathematical model, only the variables k12, k26, k9 10, k10 11, k11 12, β12, β21, β26, β62, β9 10, β10 9, β10 11, β11 10, β1112, and β12 11 were optimized; the remaining variables k and b remained unchanged from their existing values.

[0145] This algorithm can be extended to simultaneously shut down several lines, such as... Figure 4 As shown, by introducing a vector Nl of dimension nl... nl×1 , where nl represents the number of lines that need to be closed to obtain the neighborhood solution, and thus obtain the reconstructed neighborhood structure NB1.

[0146] In a specific embodiment, obtaining the neighborhood structure of the capacitor bank specifically involves: converting the integer variable k... ij β ij and By fixing their current values ​​and adding constraints on the number of capacitor bank controls, the search space of the problem is narrowed, resulting in the capacitor bank neighborhood structure NB2. Consider the current solution of the neighborhood mathematical algorithm for the switched capacitor bank. It can control the closing or opening of up to η capacitor banks between consecutive scenes. Similarly, with Limited to a single unit, it can be used to control the number of operable capacitor banks in a system.

[0147] In a specific embodiment, the acquisition of the energy storage neighborhood structure specifically involves: the operating behavior of the energy storage device being determined by binary variables. This indicates that the variable is assumed to have values ​​of 1 and 0 during the discharge and charge processes, respectively. Therefore, the integer variable k... ij β ij and By fixing its current value and adding the following constraints to the proposed model, the energy storage neighborhood structure NB3 is obtained. The energy storage variation constraint controls the total variation between two consecutive cycles. The maximum operational change of all ESSs from one cycle to the next is...

[0148] 105. Based on the reconstructed neighborhood structure, the capacitor bank neighborhood structure, and the energy storage neighborhood structure, calculate the objective function value of the operating cost model under different topological basic variables, different capacitor bank states, and different energy storage states. Select the topological basic variable, capacitor bank state, and energy storage state with the smallest objective function value, generate a control strategy, and control the power distribution system to be optimized based on the control strategy.

[0149] In a specific embodiment, the search space can be effectively reduced by applying a neighborhood reduction strategy (NB1, NB2, NB3) to achieve a local optimum of the network. Because NB1 has a greater impact on the solution, NB1 is used first to determine the topology, and then NB2 and NB3 are used to determine the SCB and ESS operating states. Each neighborhood structure is applied only to variables of the corresponding type, defining the topology x. k ≡k ij ∪β ij ∪β ji These variables are fixed, and then the SCB integer variable is defined. Finally, for x scb Fixation to determine ESS variables

[0150] Capacitor banks (CB) include static capacitor banks (SCB).

[0151] To implement a memory concept similar to the tabu search algorithm, this embodiment uses a tabu list (TL) for the visited topologies. Each visited topology has an "ol" vector containing its on / off switch. During the several iterations defined by the tabu cycle (TP) parameter, the "ol" vector is stored in the "TL," thus avoiding reverting to that topology during the "TP" iterations.

[0152] After obtaining the system state and the first objective function, mathematical operations are performed. The current objective function value is a time variable that stores the best objective function found before the current iteration, initially set as the objective function for the initial system state. The "iter" parameter serves as an iteration counter, equivalent to the total number of neighborhoods created. Then, the "incit" parameter counts consecutive iterations where the current objective function value has not improved, and uses this as a stopping criterion. The search ends if the current objective function value has not improved after the incmax iteration. "nv" represents the number of neighborhoods created in each iteration, and "nl" is the number of lines that need to be closed to apply NB1. Note that the vector "ol" contains the disconnected lines for each visited topology.

[0153] See Figure 5 The search process involves creating a random matrix "ngb(nv×nl)" where each row contains "nl" rows to be closed, representing "nv" neighborhoods. The algorithm then forms neighborhoods by iteratively closing the switches using different "nl" vectors. After neighborhood creation, they are sorted to generate the objective function vector "Sngb" (dimensional nv×1). The algorithm selects the best objective function topology that does not belong to TL and compares its objective function Ψ with the solution of the current objective function value. If the value of Ψ is larger, the current objective function value is updated; otherwise, it retains its original value, and the counter "incit" is incremented. Integer and binary variables are saved each time the bit node is updated. The counter "incit" is then reset. The process stops when the objective function has not improved after the "incmax" iteration. As shown in the diagram above, after each comparison of the current objective function value, "nl" is incremented by one unit and reset after reaching a predetermined value to avoid impacting CPU computation time. This process ensures efficient exploration of the solution space, adjusting based on the performance of adjacent solutions while considering the memory-like mechanisms of Time Limits (TL).

[0154] In a specific embodiment, the solution of the operating cost model is as follows: (1) Initialization: This process is started by setting the initial topology basic variables and parameters, including the power distribution system, and then using these initial conditions to solve the optimization problem to obtain the initial capacitor bank state and the initial energy storage state.

[0155] (2) Neighborhood Creation: This algorithm creates a set of neighborhoods by iteratively closing different line sets defined by the "nv" vector. State variables such as switch positions and line states represent different potential configurations of the power distribution system. Based on the initial topology basic variables, the initial capacitor bank state, and the initial energy storage state, the algorithm constructs the reconstructed neighborhood structure, the capacitor bank neighborhood structure, and the energy storage neighborhood structure.

[0156] (3) Objective function evaluation: For each generated neighborhood, the algorithm evaluates the corresponding objective function.

[0157] (4) Current objective function value update: The algorithm maintains a current objective function value variable, which stores the best objective function value found. If a newly evaluated neighborhood produces a better solution, the current objective function value will be updated.

[0158] (5) Memory mechanism: To enhance the exploration capability, the algorithm employs a time-limited (TL) mechanism to prevent duplicate solutions from occurring within a certain time frame. This mechanism helps avoid premature convergence and allows for a more thorough exploration of the solution space.

[0159] (6) Termination condition: Iteration continues until the stopping condition is met. This algorithm monitors the objective function value for optimization within a specified number of iterations (incmax), avoiding unnecessary computational burden.

[0160] In one specific embodiment, the constraint design of the neighborhood structure is as follows: for the capacitor bank neighborhood structure (NB2), the variation range of the number of modules and the total number of modules in adjacent time periods are limited; for the energy storage neighborhood structure (NB3), the total number of changes in the charging and discharging modes is limited.

[0161] A tabu list records visited topologies (identified by vector markers for "broken lines"), preventing redundant searches. Setting a tabu period (TP) ensures that local optima are not returned within a certain number of iterations.

[0162] Computational efficiency optimization can significantly reduce solution time by solving in stages (first determining the network topology, then optimizing the capacitor bank, and finally optimizing energy storage) and reducing the size of subproblems (optimizing only variables related to the current neighborhood).

[0163] To better illustrate this, the following example is provided:

[0164] Step 1: See Figure 6This embodiment uses a typical IEEE 33-node system as the research object for illustration. Nodes 7, 9, 23, and 28 are equipped with 500kVA distributed generation (PV) power sources, node 31 has a 100kVar FCB, and node 17 has a SCB consisting of four 150kVar units. The NMA parameters are set as follows: nv = 5, 1 ≤ nl ≤ 3, TL = 5, incmax = 3.

[0165] Step 2: Set up 4 comparison schemes

[0166] Option 1: Initial network topology, without network reconstruction or demand response;

[0167] Option 2: Implement network reconstruction and demand response.

[0168] Option 3: Implement network reconstruction and demand response.

[0169] Option 4: Implement network reconstruction and demand response.

[0170] Step 3: Calculate and compare the costs of the four options. The results are shown in Table 1.

[0171] Table 1

[0172]

[0173] Table 1 compares the simulation results of the four schemes. In schemes 2, 3, and 4, network reconfiguration and demand response can effectively reduce system losses and CO2 emission costs compared to scheme 1. Scheme 4, employing the method proposed in this patent, achieves the best loss reduction effect while also having the lowest CO2 emission cost. Although it is slightly inferior to other schemes in terms of demand response incentive costs and DG reduction costs, from the perspective of total cost iteration, scheme 4 remains the most economical.

[0174] like Figure 2 As shown, based on the above method embodiments, corresponding apparatus embodiments are provided;

[0175] An embodiment of the present invention provides a power distribution system operation optimization device, including: a data acquisition module 201, a data construction module 202, a data processing module 203, a neighborhood search module 204, and a result generation module 205;

[0176] The data acquisition module is used to acquire the operating scenario data of the power distribution system to be optimized;

[0177] The data construction module is used to construct an operating cost model based on the operating scenario data and preset constraints.

[0178] The data processing module is used to perform initial calculations on the operating cost model based on the initial topology basic variables of the power distribution system to be optimized, and to obtain the initial capacitor bank state and the initial energy storage state of the operating cost model; wherein, the initial topology basic variables are the initial topology values ​​of the topology structure of the power distribution system to be optimized, the initial capacitor bank state is the initial state of each capacitor in the power distribution system to be optimized, and the initial energy storage state is the initial state of each energy storage device in the power distribution system to be optimized.

[0179] The neighborhood search module is used to construct a reconstructed neighborhood structure based on the initial topological basic variables, construct a capacitor bank neighborhood structure based on the initial capacitor bank state, and construct an energy storage neighborhood structure based on the initial energy storage state.

[0180] The result generation module is used to calculate the objective function value of the operating cost model under different topological basic variables, different capacitor bank states, and different energy storage states based on the reconstructed neighborhood structure, the capacitor bank neighborhood structure, and the energy storage neighborhood structure; select the topological basic variable, capacitor bank state, and energy storage state with the smallest objective function value; generate a control strategy; and control the power distribution system to be optimized based on the control strategy.

[0181] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the power distribution system operation optimization method provided by any of the above-described method embodiments of the present invention.

[0182] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0183] Based on the above embodiments of the power distribution system operation optimization method, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the power distribution system operation optimization method of any embodiment of the present invention.

[0184] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0185] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0186] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0187] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the power distribution system operation optimization method described in any of the above-described method embodiments of the present invention.

[0188] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0189] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for optimizing the operation of a power distribution system, characterized in that, include: Obtain operational scenario data of the power distribution system to be optimized; Based on the aforementioned operational scenario data and preset constraints, an operational cost model is constructed. Based on the initial topology basic variables of the power distribution system to be optimized, the operating cost model is initially calculated to obtain the initial capacitor bank state and the initial energy storage state of the operating cost model; wherein, the initial topology basic variables are the initial topology values ​​of the topology structure of the power distribution system to be optimized, the initial capacitor bank state is the initial state of each capacitor in the power distribution system to be optimized, and the initial energy storage state is the initial state of each energy storage device in the power distribution system to be optimized. A reconstructed neighborhood structure is constructed based on the initial topological basic variables, a capacitor bank neighborhood structure is constructed based on the initial capacitor bank state, and an energy storage neighborhood structure is constructed based on the initial energy storage state. Based on the reconstructed neighborhood structure, the capacitor bank neighborhood structure, and the energy storage neighborhood structure, the objective function value of the operating cost model is calculated under different topological basic variables, different capacitor bank states, and different energy storage states. The topological basic variables, capacitor bank states, and energy storage states with the smallest objective function values ​​are selected to generate a control strategy, and the power distribution system to be optimized is controlled based on the control strategy.

2. The power distribution system operation optimization method as described in claim 1, characterized in that, The process of constructing a reconstructed neighborhood structure based on the initial topological basic variables, constructing a capacitor bank neighborhood structure based on the initial capacitor bank state, and constructing an energy storage neighborhood structure based on the initial energy storage state includes: Line state adjustment operations are performed on the lines in the topology of the power distribution system to be optimized to obtain several candidate topology basic variables that are different from the initial topology basic variables. The initial topology basic variables and all candidate topology basic variables are then combined to form a reconstructed neighborhood structure. Specifically, the line state adjustment operation is to change the line state from on to off, or change the line state from off to on. Based on the constraint of the number of capacitor banks, the capacitor state adjustment operation is performed on the capacitors in the power distribution system to be optimized to obtain several candidate capacitor bank states that are different from the initial capacitor bank states. The initial capacitor bank states and all candidate capacitor bank states are combined into a capacitor bank neighborhood structure. Specifically, the capacitor state adjustment operation is to change the capacitor state from on to off, or change the capacitor state from off to on. Based on the energy storage change constraint, an energy storage adjustment operation is performed on the energy storage device in the power distribution system to be optimized to obtain several candidate energy storage states that are different from the initial energy storage state, and the initial energy storage state and all candidate energy storage states are combined into an energy storage neighborhood structure; wherein, the energy storage adjustment operation specifically involves adjusting the number of charge and discharge modes of the energy storage device.

3. The power distribution system operation optimization method as described in claim 2, characterized in that, The capacitor bank control quantity constraint includes: In the formula, Let t be the number of capacitors connected in a group of switched capacitors connected at node i under scenarios t and s; where t is the time, i is the node, and s is the clustered scenario. η represents the number of capacitors installed; η represents the number of modules in the control system. Ω represents the number of capacitors connected in a set of switched capacitors connected at node i under scenarios t-1 and s. scb It is a set of nodes.

4. The power distribution system operation optimization method as described in claim 3, characterized in that, The energy storage variation constraints include: In the formula, binary variables This represents the working state of node i connected to ESS under scenarios t and s; t is the time; i is the node; s is the clustered scenario; The working status of node i connected to ESS under scenario t-1, s; Ω represents the maximum number of operational changes across all ESSs from one cycle to the next. ess For the ESS collection.

5. The power distribution system operation optimization method as described in claim 4, characterized in that, The acquisition of operational scenario data for the power distribution system to be optimized includes: Obtain the operating data of the power distribution system to be optimized; The operational data is clustered to obtain operational scenario data; specifically, the operational scenario data includes vectors of energy cost, energy demand, and solar irradiance within a preset time period, as well as the probability of scenario occurrence.

6. The power distribution system operation optimization method as described in claim 5, characterized in that, The objective function of the operating cost model satisfies the following condition: In the formula, ψ is the objective function value. The cost of power loss in the system; Cost of gas emissions from the power transmission system and the dispatchable fossil fuel DG units within the system; Cost of responding to demand; To cut costs for DG; ρ t,s Let T be the probability of scenario t and s occurring; t,s To analyze the duration of the time period; π emis and π ens These are the energy coefficient, carbon dioxide emission coefficient, and DG reduction cost coefficient, respectively; π dr R is the demand response cost discount factor; ij The resistance of branch ij; e is the square of the current in branch ij; ss and e dg These are the carbon dioxide emission coefficients of substations and distributed power generation nodes, respectively. and These are, respectively, the power injected into the substation, the power injected by distributed generation, the increase in active load demand during demand response, and the unutilized power; Ω t For time sets; Ω s For scene collection; Ω b For the set of branches; Ω ss For the set of substation nodes; Ω dg It is a set of distributed power nodes.

7. The power distribution system operation optimization method as described in claim 6, characterized in that, The constraints include: system operation constraints, line radial constraints, distributed power output constraints, capacitor bank constraints, power electronic device and energy storage constraints, and demand response constraints.

8. A power distribution system operation optimization device, characterized in that, include: The module includes a data acquisition module, a data construction module, a data processing module, a neighborhood search module, and a result generation module. The data acquisition module is used to acquire the operating scenario data of the power distribution system to be optimized; The data construction module is used to construct an operating cost model based on the operating scenario data and preset constraints. The data processing module is used to perform initial calculations on the operating cost model based on the initial topology basic variables of the power distribution system to be optimized, and to obtain the initial capacitor bank state and the initial energy storage state of the operating cost model; wherein, the initial topology basic variables are the initial topology values ​​of the topology structure of the power distribution system to be optimized, the initial capacitor bank state is the initial state of each capacitor in the power distribution system to be optimized, and the initial energy storage state is the initial state of each energy storage device in the power distribution system to be optimized. The neighborhood search module is used to construct a reconstructed neighborhood structure based on the initial topological basic variables, construct a capacitor bank neighborhood structure based on the initial capacitor bank state, and construct an energy storage neighborhood structure based on the initial energy storage state. The result generation module is used to calculate the objective function value of the operating cost model under different topological basic variables, different capacitor bank states, and different energy storage states based on the reconstructed neighborhood structure, the capacitor bank neighborhood structure, and the energy storage neighborhood structure; select the topological basic variable, capacitor bank state, and energy storage state with the smallest objective function value; generate a control strategy; and control the power distribution system to be optimized based on the control strategy.

9. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the power distribution system operation optimization method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the power distribution system operation optimization method as described in any one of claims 1-7.

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