Power distribution network energy storage configuration method and system based on carbon potential balance

Abstract

The invention discloses a power distribution network energy storage configuration method and system based on carbon potential balance, and the method comprises the steps: obtaining a network topology structure of a target power distribution network, historical power flow distribution data, and historical output data of a renewable distributed power supply; calculating an electrical distance index between any two nodes, a carbon emission reduction potential index of each node and a carbon potential equilibrium index of each node; performing clustering analysis on the electrical distance index, the carbon emission reduction potential index and the carbon potential balance index of each node by adopting a clustering algorithm, and determining a matched power distribution network cluster group according to a clustering analysis result; constructing an energy storage configuration double-layer optimization model of each power distribution network cluster group, and solving the energy storage configuration double-layer optimization model to obtain configuration information data of the energy storage system; and configuring the energy storage system of the target power distribution network based on the configuration information data. According to the method provided by the invention, local consumption of regional resources is realized, the layout and capacity of an energy storage system are optimized, and the greening low-carbon level of a power system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid management, and in particular to a distribution network energy storage configuration method and system based on carbon potential balancing. Background Art

[0002] In modern power systems, with the increase in the proportion of renewable distributed power sources connected, the intermittency and uncertainty of renewable distributed power sources have led to an imbalance in power supply and demand and increased complexity in grid scheduling, bringing new challenges to the stability of the power system and the effective allocation of energy storage resources.

[0003] Traditional energy storage configuration methods usually only focus on the balance of power supply and demand, while ignoring the carbon emission effect in the power grid. With the global focus on greenhouse gas emission reduction, the carbon emission problem in the power system has received increasing attention. However, traditional energy storage configuration methods cannot meet the requirements of reducing carbon emissions in the power grid and improving the green and low-carbon level of the power system.

[0004] It can be seen that how to improve the low-carbon nature of energy storage resource configuration has become a technical problem that technical personnel in this field need to solve urgently. Summary of the invention

[0005] The present invention provides a distribution network energy storage configuration method and system based on carbon potential balance to solve the technical problem of how to improve the low-carbon nature of energy storage resource configuration, optimize the layout and capacity of the energy storage system, and improve the green and low-carbon level of the power system.

[0006] In a first aspect, the present invention provides a distribution network energy storage configuration method based on carbon potential balancing, the method comprising:

[0007] Acquire a network topology structure of a target distribution network, historical power flow distribution data of the network topology structure, and historical output data of renewable distributed power sources;

[0008] Based on the historical power flow distribution data, the electrical distance between any two nodes in the network topology structure is calculated, wherein the electrical distance includes at least an independent branch electrical distance and a related branch electrical distance, and based on the proportion relationship between the independent branch electrical distance and the related branch electrical distance between any two nodes, the electrical distance index between any two nodes is calculated;

[0009] Calculating the carbon emission reduction potential index of each of the nodes according to the matching relationship between the rated output power of the renewable distributed power source and the demand side load of each of the nodes;

[0010] Calculating the carbon emission of each of the nodes according to the historical power flow distribution data, and calculating the carbon potential balance index of each of the nodes according to the difference between the carbon emission and the node carbon emission threshold;

[0011] Using a clustering algorithm to perform cluster analysis on the electrical distance index, the carbon emission reduction potential index and the carbon potential balance index of each of the nodes, and determining a matching distribution network cluster group according to the cluster analysis results;

[0012] Constructing a two-layer optimization model for energy storage configuration of each of the distribution network cluster groups, and solving the two-layer optimization model for energy storage configuration to obtain configuration information data of the energy storage system;

[0013] The energy storage system of the target distribution network is configured based on the configuration information data.

[0014] Preferably, the calculating of the electrical distance index between any two nodes based on the ratio of the independent branch electrical distance and the related branch electrical distance between any two nodes comprises:

[0015] Based on the network topology, determining independent branches and related branches between any two of the nodes;

[0016] Determining the sum of the electrical distances of the independent branches based on the electrical distances;

[0017] Determining the sum of the electrical distances of the relevant branches based on the electrical distances;

[0018] Calculating the electrical distance index between each node according to the sum of the electrical distances of the independent branches and the sum of the electrical distances of the independent branches;

[0019] The calculation formula of the electrical distance index is:

[0020]

[0021] Where m is the sum of the electrical distances of the independent branches between node i and node j, d ij is the electrical distance between node i and node j, k i is the sum of the electrical distances of the relevant branches of node i, k j is the sum of the electrical distances of the relevant branches of node j.

[0022] Preferably, the calculation formula of the carbon emission reduction potential index is:

[0023]

[0024] in, is the sum of the rated output power of the renewable distributed generation at node i, is the sum of the demand side load power of node i, N is the number of nodes, t represents the time, and T is 8760 hours in a year;

[0025] The calculation formula of the carbon potential balance index is:

[0026]

[0027] Among them, C i is the difference between the carbon emission of node i and the node carbon emission threshold.

[0028] Preferably, the clustering algorithm is used to perform cluster analysis on the electrical distance index, the carbon emission reduction potential index and the carbon potential balance index of each node, and a matching distribution network cluster group is determined according to the cluster analysis result, including:

[0029] Taking the electrical distance index, the carbon emission reduction potential index and the carbon potential balance index of each of the nodes as characteristic values ​​of each of the nodes;

[0030] Mapping the eigenvalues ​​of each of the nodes into a unified space matrix to obtain a vector expression of each of the nodes in the space matrix;

[0031] Performing cluster analysis on the vector expressions of each of the nodes to obtain a cluster analysis result;

[0032] According to the cluster analysis result, each of the nodes is clustered to obtain a distribution network cluster group that matches the cluster analysis result.

[0033] Preferably, the upper model of the energy storage configuration double-layer optimization model takes the location and rated capacity of the energy storage system as variables, the lowest annual comprehensive cost of the target distribution network as the objective function, and the constraint condition of the upper model is the power balance constraint;

[0034] The lower model of the energy storage configuration double-layer optimization model takes the charging and discharging power of the energy storage system at each moment as a variable, and the minimization of the network loss of the target distribution network as an objective function. The constraints of the lower model include: branch flow constraints of the target distribution network, node voltage constraints, branch power constraints, reverse power constraints of the main network interconnection branch of the target distribution network, charging and discharging power constraints of the energy storage system, charging and discharging efficiency of the energy storage system, and state of charge constraints of the energy storage system.

[0035] In a second aspect, the present invention further provides a distribution network energy storage configuration system based on carbon potential balance, which implements the above-mentioned distribution network energy storage configuration method based on carbon potential balance, and the system includes: a data acquisition unit, an electrical distance index calculation unit, a carbon emission reduction potential index calculation unit, a carbon potential balance index calculation unit, a distribution network cluster division unit, an energy storage system optimization unit and an energy storage system configuration unit;

[0036] The data acquisition unit is used to obtain the network topology of the target distribution network, the historical power flow distribution data of the network topology and the historical output data of the renewable distributed power source;

[0037] The electrical distance index calculation unit is used to calculate the electrical distance between any two nodes in the network topology structure based on the historical power flow distribution data, wherein the electrical distance at least includes an independent branch electrical distance and a related branch electrical distance, and the electrical distance index between any two nodes is calculated based on the proportion relationship between the independent branch electrical distance and the related branch electrical distance between any two nodes;

[0038] The carbon emission reduction potential index calculation unit is used to calculate the carbon emission reduction potential index of each of the nodes according to the matching relationship between the rated output power of the renewable distributed power source and the demand side load of each of the nodes;

[0039] The carbon potential balance index calculation unit is used to calculate the carbon emissions of each of the nodes according to the historical power flow distribution data, and calculate the carbon potential balance index of each of the nodes according to the difference between the carbon emissions and the node carbon emission threshold;

[0040] The distribution network cluster division unit is used to perform cluster analysis on the electrical distance index, the carbon emission reduction potential index and the carbon potential balance index of each of the nodes using a clustering algorithm, and determine a matching distribution network cluster group according to the cluster analysis result;

[0041] The energy storage system optimization unit is used to construct a two-layer optimization model for energy storage configuration of each of the distribution network cluster groups, and solve the two-layer optimization model for energy storage configuration to obtain configuration information data of the energy storage system;

[0042] The energy storage system configuration unit is used to configure the energy storage system of the target distribution network based on the configuration information data.

[0043] Preferably, the calculating of the electrical distance index between any two nodes based on the ratio of the independent branch electrical distance and the related branch electrical distance between any two nodes comprises:

[0044] Based on the network topology, determining independent branches and related branches between any two of the nodes;

[0045] Determining the sum of the electrical distances of the independent branches based on the electrical distances;

[0046] Determining the sum of the electrical distances of the relevant branches based on the electrical distances;

[0047] Calculating the electrical distance index between each node according to the sum of the electrical distances of the independent branches and the sum of the electrical distances of the independent branches;

[0048] The calculation formula of the electrical distance index is:

[0049]

[0050] Where m is the sum of the electrical distances of the independent branches between node i and node j, d ij is the electrical distance between node i and node j, k i is the sum of the electrical distances of the relevant branches of node i, k j is the sum of the electrical distances of the relevant branches of node j.

[0051] Preferably, the calculation formula of the carbon emission reduction potential index is:

[0052]

[0053] in, is the sum of the rated output power of the renewable distributed generation at node i, is the sum of the demand side load power of node i, N is the number of nodes, t represents the time, and T is 8760 hours in a year;

[0054] The calculation formula of the carbon potential balance index is:

[0055]

[0056] Among them, C i is the difference between the carbon emission of node i and the node carbon emission threshold.

[0057] Preferably, the clustering algorithm is used to perform cluster analysis on the electrical distance index, the carbon emission reduction potential index and the carbon potential balance index of each node, and a matching distribution network cluster group is determined according to the cluster analysis result, including:

[0058] Taking the electrical distance index, the carbon emission reduction potential index and the carbon potential balance index of each of the nodes as characteristic values ​​of each of the nodes;

[0059] Mapping the eigenvalues ​​of each of the nodes into a unified space matrix to obtain a vector expression of each of the nodes in the space matrix;

[0060] Performing cluster analysis on the vector expressions of each of the nodes to obtain a cluster analysis result;

[0061] According to the cluster analysis result, each of the nodes is clustered to obtain a distribution network cluster group that matches the cluster analysis result.

[0062] Preferably, the upper model of the energy storage configuration double-layer optimization model takes the location and rated capacity of the energy storage system as variables, the lowest annual comprehensive cost of the target distribution network as the objective function, and the constraint condition of the upper model is the power balance constraint;

[0063] The lower model of the energy storage configuration double-layer optimization model takes the charging and discharging power of the energy storage system at each moment as a variable, and the minimization of the network loss of the target distribution network as an objective function. The constraints of the lower model include: branch flow constraints of the target distribution network, node voltage constraints, branch power constraints, reverse power constraints of the main network interconnection branch of the target distribution network, charging and discharging power constraints of the energy storage system, charging and discharging efficiency of the energy storage system, and state of charge constraints of the energy storage system.

[0064] The present invention provides a distribution network energy storage configuration method and system based on carbon potential balancing. Compared with the prior art, the embodiments of the present invention have at least the following beneficial effects:

[0065] (1) Improve the matching degree between the demand-side load and renewable distributed power sources within the cluster group, balance the carbon potential of the cluster group, optimize the layout and capacity of the energy storage system, and improve the green and low-carbon level of the power system.

[0066] (2) Based on the electrical distance index, carbon emission reduction potential index and carbon potential balance index, the target distribution network is divided into multiple relatively independent small cluster groups to realize the local consumption of regional resources and improve the operating efficiency and stability of the target distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a schematic diagram of the steps of a distribution network energy storage configuration method based on carbon potential balancing provided by a preferred embodiment of the present invention;

[0068] Figure 2 It is a structural schematic diagram of a distribution network energy storage configuration system based on carbon potential balancing provided by a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0069] The following is a detailed explanation of the implementation mode of the present invention in conjunction with the accompanying drawings. The embodiments are provided only for illustrative purposes and cannot be understood as limiting the present invention. The accompanying drawings are only for reference and illustration purposes and do not constitute a limitation on the scope of protection of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In the description of the present invention, the terms "first", "second", "third", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second", "third", etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, the meaning of "multiple" is two or more.

[0070] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. The terms "vertical", "horizontal", "left", "right", "upper", "lower" and similar expressions used herein are only for illustrative purposes, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0071] In the description of the present invention, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood by specific circumstances.

[0072] In an embodiment of the present invention, a distribution network energy storage configuration method based on carbon potential balancing is provided. Figure 1 , the method comprising:

[0073] S1. Obtaining a network topology structure of a target distribution network, historical power flow distribution data of the network topology structure, and historical output data of renewable distributed power sources.

[0074] S2. Based on the historical flow distribution data, the electrical distance between any two nodes in the network topology structure is calculated, wherein the electrical distance includes at least an independent branch electrical distance and a related branch electrical distance. Based on the ratio of the independent branch electrical distance and the related branch electrical distance between any two nodes, the electrical distance index between any two nodes is calculated.

[0075] S3. Calculate the carbon emission reduction potential index of each of the nodes according to the matching relationship between the rated output power of the renewable distributed power source and the demand-side load of each of the nodes.

[0076] S4. Calculate the carbon emission of each of the nodes according to the historical flow distribution data, and calculate the carbon potential balance index of each of the nodes according to the difference between the carbon emission and the node carbon emission threshold.

[0077] S5. Use a clustering algorithm to perform cluster analysis on the electrical distance index, the carbon emission reduction potential index and the carbon potential balance index of each of the nodes, and determine a matching distribution network cluster group according to the cluster analysis results.

[0078] S6. Constructing a two-layer optimization model for energy storage configuration of each of the distribution network cluster groups, and solving the two-layer optimization model for energy storage configuration to obtain configuration information data of the energy storage system.

[0079] S7. Configure the energy storage system of the target distribution network based on the configuration information data.

[0080] In the distribution network energy storage configuration method based on carbon potential balancing disclosed in the preferred embodiment of the present invention, the network topology structure of the target distribution network and the historical flow distribution data of the network topology structure are collected, wherein the network topology structure is used to represent the topological relationship between the nodes and branches of the target distribution network, as well as the transmission data and loss data of the nodes and branches. The parameters and historical output data of the renewable distributed power sources connected to the target distribution network are collected, and the renewable distributed power source parameters include the type of renewable distributed power source, such as wind power and photovoltaic power, the scale of the renewable distributed power source, such as the rated output power of the renewable distributed power source, and the daily output characteristic curve of the renewable distributed power source.

[0081] Based on the collected historical power flow distribution data and historical output data of renewable distributed power sources, the target distribution network is clustered into multiple relatively independent small cluster groups, which can effectively reduce the transmission distance of power on the target distribution network, thereby realizing the local consumption of regional resources, and at the same time narrowing the search space of the energy storage control strategy, improving the accuracy of the control strategy, minimizing the impact of power flow on the entire target distribution network, and improving the operating efficiency and stability of the target distribution network.

[0082] The electrical distance reflects the closeness between any two nodes in the target distribution network by measuring the sensitivity of the power change between any two nodes in the distribution network to the change of voltage amplitude. It is a structural indicator for cluster division.

[0083] In a preferred embodiment of the present invention, the voltage sensitivity matrix of the network topology is derived according to the power flow equation under the Newton-Raphson method. The power flow equation under the Newton-Raphson method is expressed as follows:

[0084]

[0085] Among them, Δδ is the node power angle change matrix, ΔU is the node voltage change matrix, ΔP is the active power change matrix, ΔQ is the reactive power change matrix, and H, N, J, and L are the corresponding elements in the Jacobian matrix respectively.

[0086] According to the power flow equation under the Newton-Raphson method, the expression of the voltage sensitivity matrix is ​​derived:

[0087] ΔU=S UP ΔP+S UQ ΔQ

[0088]

[0089] Among them, S UP is the active power-voltage sensitivity matrix; S UQ is the reactive power-voltage sensitivity matrix; S U is the voltage sensitivity comprehensive matrix.

[0090] According to the voltage sensitivity matrix, the electrical distance between any two nodes in the network topology is calculated. The electrical distance calculation formula between any two nodes is:

[0091]

[0092] Among them, d ij is the electrical distance between node i and node j, N is the number of nodes, S ij is the voltage sensitivity between node i and node j in the voltage sensitivity matrix, max j S ij is the maximum value of the voltage sensitivity between node j and other nodes, L ij represents the ratio of the voltage sensitivity between node i and node j to the maximum voltage sensitivity between node j and other nodes, k represents the summation index, d ij The larger the value, the smaller the coupling between the two nodes.

[0093] Based on the network topology, the independent branches and related branches between any two nodes in the network topology are determined, where an independent branch is a branch that does not share current with other branches, and a related branch is a branch that is electrically connected or mutually influenced with other branches. The calculated electrical distance includes at least the independent branch electrical distance and the related branch electrical distance. Based on the determined independent branch electrical distance and related branch electrical distance, the electrical distance index between any two nodes is calculated. The calculation formula of the electrical distance index is:

[0094]

[0095] Where m is the sum of the electrical distances of the independent branches between node i and node j, k i is the sum of the electrical distances of the relevant branches of node i, k j is the sum of the electrical distances of the relevant branches of node j.

[0096] In order to ensure that the demand-side load of nodes in the clusters divided by the target distribution network matches the renewable distributed power generation, and to avoid the situation where the number of nodes in some cluster groups is small under a single electrical distance indicator, resulting in excessive penetration of renewable distributed power generation within the cluster and a large energy storage configuration capacity in the cluster, a carbon emission reduction potential indicator is introduced to represent the matching degree between the demand-side load of the node and the renewable distributed power generation, as well as the carbon emission reduction capacity of the target distribution network when absorbing renewable distributed power generation. The calculation formula of the carbon emission reduction potential indicator is:

[0097]

[0098] in, is the sum of the rated output power of renewable distributed generation at node i, is the sum of the demand side load powers of node i, t represents the time, and T is 8760 hours in a year.

[0099] In a preferred embodiment of the present invention, a carbon potential balance index is used to measure whether the carbon potential of each node in the network topology is similar. The carbon potential balance index comprehensively considers the relationship between the carbon emissions of each node and the node carbon emission threshold, and calculates the degree of balance between each node.

[0100] First, the net carbon potential of each node is calculated. The net carbon potential of each node is the difference between the carbon emissions of the node and the node carbon emissions threshold. The calculation formula is:

[0101] C i =E i -A i

[0102] Among them, C i is the net carbon potential of node i, E iis the carbon emission of node i, A i is the node carbon emission threshold.

[0103] According to the net carbon potential, the carbon potential balance index is calculated. The calculation formula of the carbon potential balance index is:

[0104]

[0105] Among them, C i is the net carbon potential of node i, that is, the difference between the carbon emissions of node i and the node carbon emission threshold.

[0106] Furthermore, a clustering algorithm is used to perform cluster analysis on the electrical distance index, carbon emission reduction potential index and carbon potential balance index of each node, and a matching distribution network cluster group is determined according to the cluster analysis results. Specifically, the number of clusters k for cluster analysis is set, and the electrical distance index, carbon emission reduction potential index and carbon potential balance index of each node are used as the eigenvalues ​​of each node. The eigenvalues ​​of each node are mapped to a unified spatial matrix to obtain the vector expression of each node in the spatial matrix, and the electrical distance index, carbon emission reduction potential index and carbon potential balance index of each node are expressed in the form of vectors. Based on the number of clusters k, k points are randomly selected as cluster centers, and the distance between each node and each cluster center is calculated according to the vector expression of each node, and then the corresponding points are divided into the nearest cluster center to obtain the cluster analysis results. According to the cluster analysis results, each node is clustered to obtain a distribution network cluster group that matches the cluster analysis results.

[0107] By performing cluster analysis on the electrical distance index, carbon emission reduction potential index and carbon potential balance index of each node, any two nodes with high correlation in the target distribution network can be divided into the same cluster to obtain several distribution network cluster groups.

[0108] After obtaining the distribution network cluster groups, the average value of the net carbon potential of each node in each cluster group is calculated as:

[0109]

[0110] Furthermore, the standard deviation of the net carbon potential of each node in each cluster group is calculated to evaluate the degree of dispersion of the carbon potential of each node in each cluster group. The calculation formula of the standard deviation is:

[0111]

[0112] The reciprocal of the standard deviation, namely the carbon potential balance index, is used to measure the similarity of the carbon potential of each node in each cluster group. To avoid infinite values ​​when S is 0, the carbon potential balance index is expressed as:

[0113]

[0114] When the CPBI value is close to 1, it means that the carbon potential of each node in each cluster group is very close, and the power grid system has achieved a good carbon balance; when the CPBI value is far less than 1, it means that the carbon potential of each node in each cluster group is quite different, and the distribution network cluster group needs to be adjusted to achieve a better balance. The cluster analysis results can be adjusted by adjusting the parameters in the cluster analysis, and the distribution network cluster groups can be re-divided.

[0115] The introduction of CPBI can accurately detect and adjust the carbon potential balance of each cluster group, and improve the green and low-carbon level of the entire power grid system.

[0116] After the target distribution network is divided into cluster groups, a two-layer optimization model for energy storage configuration is constructed for each distribution network cluster group. The upper model of the two-layer optimization model for energy storage configuration takes the location and capacity of the energy storage system as variables, and the annual comprehensive cost of the distribution network cluster is minimized as the objective function. The constraint of the upper model is the power balance constraint. The lower model of the two-layer optimization model for energy storage configuration takes the charging and discharging power of the energy storage system at each moment as a variable, and the network loss of the distribution network cluster is minimized as the objective function. The constraints of the lower model include: target distribution network branch flow constraints, node voltage constraints, node power constraints, target distribution network main network interconnection branch reverse power constraints, ESS charging and discharging power constraints, ESS charging and discharging efficiency, and ESS charge state constraints.

[0117] The mathematical expression of the two-layer optimization model for energy storage configuration is:

[0118]

[0119] Among them, X, Y are the independent variables of the upper model, representing the location and rated capacity of the energy storage system, x, y are the independent variables of the lower model, representing the charging and discharging power of the energy storage system at each time, F, f are the objective functions of the upper and lower models, G(X,Y)≤0 is the inequality constraint of the upper model, H(X,Y)=0 is the equality constraint of the upper model, g(x,y)≤0 is the inequality constraint of the lower model, and h(x,y) is the equality constraint of the lower model.

[0120] For the upper model, the optimization goal is to minimize the annual comprehensive cost of the target distribution network. The annual comprehensive cost includes the installation cost of the energy storage system, the operation and maintenance cost of the energy storage system, the main grid power purchase cost and the photovoltaic subsidy, which can be specifically expressed as:

[0121] minC=C1+C2+C3-C4

[0122] Among them, C1 is the installation cost of the energy storage system, C2 is the operation and maintenance cost of the energy storage system, C3 is the main grid electricity purchase cost, and C4 represents the photovoltaic subsidy.

[0123] The installation cost of the energy storage system is expressed as:

[0124]

[0125] Where M is the number of distribution network clusters, ξ is the discount rate, h is the service life of the energy storage system, and is the unit capacity and unit power investment cost of the energy storage system, and are the rated capacity and rated power of the energy storage system of cluster group z respectively.

[0126] The operation and maintenance cost of the energy storage system is expressed as

[0127]

[0128] Among them, C ESS is the unit charge and discharge operation and maintenance cost of the energy storage system, is the charging and discharging power of the energy storage system in cluster group z at time t. A positive value represents discharging of the energy storage system, and a negative value represents charging of the energy storage system.

[0129] The main grid electricity purchase cost is expressed as:

[0130]

[0131] Among them, n cp is the number of main network liaison branches, is the real-time electricity price of the main network at time t, is the power of the main network interconnection branch g at time t.

[0132] The power balance constraint is expressed as:

[0133]

[0134] Among them, n z is the number of nodes in cluster group z, is the load active power of node j at time t, n SI is the number of branches of the target distribution network, is the network loss of each branch of the target distribution network at time t, which is determined by the power flow calculation results of the lower model.

[0135] For the lower model, the optimization objective is to minimize the target distribution network loss, and the mathematical expression is:

[0136]

[0137] in, It is the distribution network loss.

[0138] The target distribution network branch power flow constraint is expressed as:

[0139]

[0140] Among them, P ij , Q ij are the active power and reactive power transmitted between nodes i and j, respectively. ij , R ij , X ij are branch current, branch resistance and branch reactance respectively, P j , Q j and U j are the active power, reactive power and voltage amplitude injected into node j respectively, θ(j) is the branch with node j as the head node, and κ(j) is the branch with node j as the end node.

[0141] The node voltage constraint is expressed as:

[0142]

[0143] Among them, U j,z,t is the voltage of node j in cluster z at time t; are the upper and lower limits of the voltage amplitude of node j in cluster z respectively.

[0144] The branch power constraint is expressed as:

[0145]

[0146] Among them, S l,z,t is the transmission capacity of branch l in cluster group z at time t, are the upper and lower limits of the transmission power of branch l in cluster group z, n II,z is the number of branches within cluster group z.

[0147] The reverse power constraint of the main interconnection branch of the target distribution network is expressed as:

[0148]

[0149] in, The maximum reverse power allowed to pass through the main network contact branch g.

[0150] The charging and discharging power constraints of the energy storage system, the charging and discharging efficiency constraints of the energy storage system, and the state of charge constraints of the energy storage system are expressed as:

[0151]

[0152] in, is the maximum output power of the energy storage system in cluster group z, η z,t is the charging and discharging efficiency of the energy storage system in cluster group z at time t, is the discharge efficiency of the energy storage system in cluster group z, is the charging efficiency of the energy storage system in cluster group z, Represents the charge and discharge state of the energy storage system in cluster group z at time t, with 1 for discharge and -1 for charge; SOC z,t is the state of charge of the energy storage system in cluster group z at time t, are the minimum and maximum state of charge of the energy storage system, SOC z,0 is the initial charge state of the energy storage system.

[0153] Furthermore, Gurobi is used to solve the two-level optimization model of energy storage configuration to obtain the optimal location, optimal rated capacity and optimal charging and discharging power of the energy storage system at each moment.

[0154] Gurobi is a high-performance optimization solver that is widely used in multi-level optimization problems. It has the characteristics of efficient solution, flexible modeling, rich parameter settings and easy integration. In this application, Gurobi is used to solve the two-level optimization model of energy storage configuration, which can improve the accuracy of the solution results and improve the efficiency of the solution.

[0155] In a preferred embodiment of the present invention, the network topology of the target distribution network, the historical power flow distribution data of the network topology and the historical output data of the renewable distributed power source are obtained; based on the historical power flow distribution data, the electrical distance between any two nodes in the network topology is calculated, wherein the electrical distance at least includes the independent branch electrical distance and the related branch electrical distance; based on the proportion relationship between the independent branch electrical distance and the related branch electrical distance between any two nodes, the electrical distance index between any two nodes is calculated; according to the matching relationship between the rated output power of the renewable distributed power source and the demand side load of each node, Calculate the carbon emission reduction potential index of each node; calculate the carbon emissions of each node according to the historical flow distribution data, and calculate the carbon potential balance index of each node according to the difference between the carbon emissions and the node carbon emission threshold; use a clustering algorithm to perform cluster analysis on the electrical distance index, carbon emission reduction potential index and carbon potential balance index of each node, and determine the matching distribution network cluster group according to the cluster analysis results; construct a two-layer optimization model for energy storage configuration of each distribution network cluster group, and solve the two-layer optimization model for energy storage configuration to obtain the configuration information data of the energy storage system; configure the energy storage system of the target distribution network based on the configuration information data. The distribution network energy storage configuration method based on carbon potential balance provided in the present application divides the target distribution network into multiple relatively independent small cluster groups based on the electrical distance index, carbon emission reduction potential index and carbon potential balance index, realizes the local consumption of regional resources, improves the matching degree of the node demand side load and renewable distributed power source in the cluster group, balances the carbon potential of the cluster group, optimizes the layout and capacity of the energy storage system, and improves the greening and low-carbon level of the power system.

[0156] Accordingly, if Figure 2 As shown, based on a distribution network energy storage configuration method based on carbon potential balance, an embodiment of the present invention further provides a distribution network energy storage configuration system based on carbon potential balance, to implement the distribution network energy storage configuration method based on carbon potential balance disclosed in an embodiment of the present invention, the system comprising: a data acquisition unit 1, an electrical distance index calculation unit 2, a carbon emission reduction potential index calculation unit 3, a carbon potential balance index calculation unit 4, a distribution network cluster division unit 5, an energy storage system optimization unit 6 and an energy storage system configuration unit 7;

[0157] The data acquisition unit 1 is used to obtain the network topology of the target distribution network, the historical power flow distribution data of the network topology and the historical output data of the renewable distributed power source;

[0158] The electrical distance index calculation unit 2 is used to calculate the electrical distance between any two nodes in the network topology structure based on the historical power flow distribution data, wherein the electrical distance at least includes an independent branch electrical distance and a related branch electrical distance, and the electrical distance index between any two nodes is calculated based on the proportion relationship between the independent branch electrical distance and the related branch electrical distance between any two nodes;

[0159] The carbon emission reduction potential index calculation unit 3 is used to calculate the carbon emission reduction potential index of each node according to the matching relationship between the rated output power of the renewable distributed power source and the demand side load of each node;

[0160] The carbon potential balance index calculation unit 4 is used to calculate the carbon emissions of each of the nodes according to the historical flow distribution data, and calculate the carbon potential balance index of each of the nodes according to the difference between the carbon emissions and the node carbon emission threshold;

[0161] The distribution network cluster division unit 5 is used to perform cluster analysis on the electrical distance index, the carbon emission reduction potential index and the carbon potential balance index of each node using a clustering algorithm, and determine a matching distribution network cluster group according to the cluster analysis result;

[0162] The energy storage system optimization unit 6 is used to construct a two-layer optimization model for energy storage configuration of each of the distribution network cluster groups, and solve the two-layer optimization model for energy storage configuration to obtain configuration information data of the energy storage system;

[0163] The energy storage system configuration unit 7 is used to configure the energy storage system of the target distribution network based on the configuration information data.

[0164] Further, the calculating of the electrical distance index between any two nodes based on the ratio of the independent branch electrical distance and the related branch electrical distance between any two nodes includes:

[0165] Based on the network topology, determining independent branches and related branches between any two of the nodes;

[0166] Determining the sum of the electrical distances of the independent branches based on the electrical distances;

[0167] Determining the sum of the electrical distances of the relevant branches based on the electrical distances;

[0168] Calculating the electrical distance index between each node according to the sum of the electrical distances of the independent branches and the sum of the electrical distances of the independent branches;

[0169] The calculation formula of the electrical distance index is:

[0170]

[0171] Where m is the sum of the electrical distances of the independent branches between node i and node j, d ij is the electrical distance between node i and node j, k i is the sum of the electrical distances of the relevant branches of node i, k j is the sum of the electrical distances of the relevant branches of node j.

[0172] The calculation formula of the carbon emission reduction potential index is:

[0173]

[0174] in, is the sum of the rated output power of the renewable distributed generation at node i, is the sum of the demand side load power of node i, N is the number of nodes, t represents the time, and T is 8760 hours in a year;

[0175] The calculation formula of the carbon potential balance index is:

[0176]

[0177] Among them, C i is the difference between the carbon emission of node i and the node carbon emission threshold.

[0178] Furthermore, the clustering algorithm is used to perform cluster analysis on the electrical distance index, the carbon emission reduction potential index and the carbon potential balance index of each node, and a matching distribution network cluster group is determined according to the cluster analysis result, including:

[0179] Taking the electrical distance index, the carbon emission reduction potential index and the carbon potential balance index of each of the nodes as characteristic values ​​of each of the nodes;

[0180] Mapping the eigenvalues ​​of each of the nodes into a unified space matrix to obtain a vector expression of each of the nodes in the space matrix;

[0181] Performing cluster analysis on the vector expressions of each of the nodes to obtain a cluster analysis result;

[0182] According to the cluster analysis result, each of the nodes is clustered to obtain a distribution network cluster group that matches the cluster analysis result.

[0183] Furthermore, the upper model of the energy storage configuration double-layer optimization model takes the location and rated capacity of the energy storage system as variables, the lowest annual comprehensive cost of the target distribution network as the objective function, and the constraint condition of the upper model is the power balance constraint;

[0184] The lower model of the energy storage configuration double-layer optimization model takes the charging and discharging power of the energy storage system at each moment as a variable, and the minimization of the network loss of the target distribution network as an objective function. The constraints of the lower model include: branch flow constraints of the target distribution network, node voltage constraints, branch power constraints, reverse power constraints of the main network interconnection branch of the target distribution network, charging and discharging power constraints of the energy storage system, charging and discharging efficiency of the energy storage system, and state of charge constraints of the energy storage system.

[0185] For the specific definition of a distribution network energy storage configuration system based on carbon potential balance, please refer to the above-mentioned definition of a distribution network energy storage configuration method based on carbon potential balance, which will not be repeated here. A person of ordinary skill in the art can appreciate that the modules and steps described in conjunction with the embodiments disclosed in the present invention can be implemented in hardware, software, or a combination of both. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0186] A distribution network energy storage configuration method and system based on carbon potential balance provided in this embodiment is used to solve the technical problem of how to improve the low-carbon nature of energy storage resource configuration. Obtain the network topology structure of the target distribution network, the historical flow distribution data of the network topology structure, and the historical output data of renewable distributed power sources; based on the historical flow distribution data, calculate the electrical distance between any two nodes in the network topology structure, where the electrical distance includes at least the independent branch electrical distance and the related branch electrical distance; calculate the electrical distance index between any two nodes based on the ratio of the independent branch electrical distance and the related branch electrical distance between any two nodes; calculate the load of each node based on the matching relationship between the rated output power of the renewable distributed power source and the demand side load of each node. Carbon emission reduction potential index; calculate the carbon emissions of each node according to the historical flow distribution data, and calculate the carbon potential balance index of each node according to the difference between the carbon emissions and the node carbon emission threshold; use a clustering algorithm to perform cluster analysis on the electrical distance index, carbon emission reduction potential index and carbon potential balance index of each node, and determine the matching distribution network cluster group according to the cluster analysis results; construct a two-layer optimization model for energy storage configuration of each distribution network cluster group, and solve the two-layer optimization model for energy storage configuration to obtain the configuration information data of the energy storage system; configure the energy storage system of the target distribution network based on the configuration information data. The distribution network energy storage configuration method based on carbon potential balance provided in the present application divides the target distribution network into multiple relatively independent small cluster groups based on the electrical distance index, carbon emission reduction potential index and carbon potential balance index, realizes the local consumption of regional resources, improves the matching degree of load and renewable distributed power source within the cluster, balances the carbon potential of the cluster group, optimizes the layout and capacity of the energy storage system, and improves the green and low-carbon level of the power system.

[0187] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0188] The above-described embodiments only express several preferred implementations of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the invention. It should be noted that, for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be regarded as the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A distribution network energy storage configuration method based on carbon potential balance, characterized in that: The method comprises: Acquire a network topology structure of a target distribution network, historical power flow distribution data of the network topology structure, and historical output data of renewable distributed power sources; Based on the historical power flow distribution data, the electrical distance between any two nodes in the network topology structure is calculated, wherein the electrical distance includes at least an independent branch electrical distance and a related branch electrical distance, and based on the proportion relationship between the independent branch electrical distance and the related branch electrical distance between any two nodes, the electrical distance index between any two nodes is calculated; Calculating the carbon emission reduction potential index of each of the nodes according to the matching relationship between the rated output power of the renewable distributed power source and the demand side load of each of the nodes; Calculating the carbon emission of each of the nodes according to the historical power flow distribution data, and calculating the carbon potential balance index of each of the nodes according to the difference between the carbon emission and the node carbon emission threshold; Using a clustering algorithm to perform cluster analysis on the electrical distance index, the carbon emission reduction potential index and the carbon potential balance index of each of the nodes, and determining a matching distribution network cluster group according to the cluster analysis results; Constructing a two-layer optimization model for energy storage configuration of each of the distribution network cluster groups, and solving the two-layer optimization model for energy storage configuration to obtain configuration information data of the energy storage system; The energy storage system of the target distribution network is configured based on the configuration information data.

2. The method for configuring energy storage in a distribution network based on carbon potential balance according to claim 1, characterized in that: The calculating the electrical distance index between any two nodes based on the ratio of the independent branch electrical distance and the related branch electrical distance between any two nodes comprises: Based on the network topology, determining independent branches and related branches between any two of the nodes; Determining the sum of the electrical distances of the independent branches based on the electrical distances; Determining the sum of the electrical distances of the relevant branches based on the electrical distances; Calculating the electrical distance index between each node according to the sum of the electrical distances of the independent branches and the sum of the electrical distances of the independent branches; The calculation formula of the electrical distance index is: Where m is the sum of the electrical distances of the independent branches between node i and node j, d ij is the electrical distance between node i and node j, k i is the sum of the electrical distances of the branches related to node i, k j is the sum of the electrical distances of the relevant branches of node j.

3. The method for configuring energy storage in a distribution network based on carbon potential balance according to claim 1, characterized in that: The calculation formula of the carbon emission reduction potential index is: in, is the sum of the rated output power of the renewable distributed generation at node i, is the sum of the demand side load power of node i, N is the number of nodes, t represents the time, and T is 8760 hours in a year; The calculation formula of the carbon potential balance index is: Among them, C i is the difference between the carbon emission of node i and the node carbon emission threshold.

4. The method for configuring energy storage in a distribution network based on carbon potential balance according to claim 1, characterized in that: The clustering algorithm is used to perform cluster analysis on the electrical distance index, the carbon emission reduction potential index and the carbon potential balance index of each node, and a matching distribution network cluster group is determined according to the cluster analysis result, including: Taking the electrical distance index, the carbon emission reduction potential index and the carbon potential balance index of each of the nodes as characteristic values ​​of each of the nodes; Mapping the eigenvalues ​​of each of the nodes into a unified space matrix to obtain a vector expression of each of the nodes in the space matrix; Performing cluster analysis on the vector expressions of each of the nodes to obtain a cluster analysis result; According to the cluster analysis result, each of the nodes is clustered to obtain a distribution network cluster group that matches the cluster analysis result.

5. The method for configuring energy storage in a distribution network based on carbon potential balance according to claim 1, characterized in that: The upper model of the energy storage configuration double-layer optimization model takes the location and rated capacity of the energy storage system as variables, the lowest annual comprehensive cost of the target distribution network as the objective function, and the constraint condition of the upper model is the power balance constraint; The lower model of the energy storage configuration double-layer optimization model takes the charging and discharging power of the energy storage system at each moment as a variable, and the minimization of the network loss of the target distribution network as an objective function. The constraints of the lower model include: branch flow constraints of the target distribution network, node voltage constraints, branch power constraints, reverse power constraints of the main network interconnection branch of the target distribution network, charging and discharging power constraints of the energy storage system, charging and discharging efficiency of the energy storage system, and state of charge constraints of the energy storage system.

6. A distribution network energy storage configuration system based on carbon potential balance, characterized in that: The system comprises: a data acquisition unit, an electrical distance index calculation unit, a carbon emission reduction potential index calculation unit, a carbon potential balance index calculation unit, a distribution network cluster division unit, an energy storage system optimization unit and an energy storage system configuration unit; The data acquisition unit is used to obtain the network topology of the target distribution network, the historical power flow distribution data of the network topology and the historical output data of the renewable distributed power source; The electrical distance index calculation unit is used to calculate the electrical distance between any two nodes in the network topology structure based on the historical power flow distribution data, wherein the electrical distance at least includes an independent branch electrical distance and a related branch electrical distance, and the electrical distance index between any two nodes is calculated based on the proportion relationship between the independent branch electrical distance and the related branch electrical distance between any two nodes; The carbon emission reduction potential index calculation unit is used to calculate the carbon emission reduction potential index of each of the nodes according to the matching relationship between the rated output power of the renewable distributed power source and the demand side load of each of the nodes; The carbon potential balance index calculation unit is used to calculate the carbon emissions of each of the nodes according to the historical power flow distribution data, and calculate the carbon potential balance index of each of the nodes according to the difference between the carbon emissions and the node carbon emission threshold; The distribution network cluster division unit is used to perform cluster analysis on the electrical distance index, the carbon emission reduction potential index and the carbon potential balance index of each of the nodes using a clustering algorithm, and determine a matching distribution network cluster group according to the cluster analysis result; The energy storage system optimization unit is used to construct a two-layer optimization model for energy storage configuration of each of the distribution network cluster groups, and solve the two-layer optimization model for energy storage configuration to obtain configuration information data of the energy storage system; The energy storage system configuration unit is used to configure the energy storage system of the target distribution network based on the configuration information data.

7. The distribution network energy storage configuration system based on carbon potential balance according to claim 6, characterized in that: The calculating the electrical distance index between any two nodes based on the ratio of the independent branch electrical distance and the related branch electrical distance between any two nodes comprises: Based on the network topology, determining independent branches and related branches between any two of the nodes; Determining the sum of the electrical distances of the independent branches based on the electrical distances; Determining the sum of the electrical distances of the relevant branches based on the electrical distances; Calculating the electrical distance index between each node according to the sum of the electrical distances of the independent branches and the sum of the electrical distances of the independent branches; The calculation formula of the electrical distance index is: Where m is the sum of the electrical distances of the independent branches between node i and node j, d ij is the electrical distance between node i and node j, k i is the sum of the electrical distances of the branches related to node i, k j is the sum of the electrical distances of the relevant branches of node j.

8. The distribution network energy storage configuration system based on carbon potential balancing according to claim 6, characterized in that: The calculation formula of the carbon emission reduction potential index is: in, is the sum of the rated output power of the renewable distributed generation at node i, is the sum of the demand side load power of node i, N is the number of nodes, t represents the time, and T is 8760 hours in a year; The calculation formula of the carbon potential balance index is: Among them, C i is the difference between the carbon emission of node i and the node carbon emission threshold.

9. The distribution network energy storage configuration system based on carbon potential balancing according to claim 6, characterized in that: The clustering algorithm is used to perform cluster analysis on the electrical distance index, the carbon emission reduction potential index and the carbon potential balance index of each node, and a matching distribution network cluster group is determined according to the cluster analysis result, including: Taking the electrical distance index, the carbon emission reduction potential index and the carbon potential balance index of each of the nodes as characteristic values ​​of each of the nodes; Mapping the eigenvalues ​​of each of the nodes into a unified space matrix to obtain a vector expression of each of the nodes in the space matrix; Performing cluster analysis on the vector expressions of each of the nodes to obtain a cluster analysis result; According to the cluster analysis result, each of the nodes is clustered to obtain a distribution network cluster group that matches the cluster analysis result.

10. The distribution network energy storage configuration system based on carbon potential balancing according to claim 6, characterized in that: The upper model of the energy storage configuration double-layer optimization model takes the location and rated capacity of the energy storage system as variables, the lowest annual comprehensive cost of the target distribution network as the objective function, and the constraint condition of the upper model is the power balance constraint; The lower model of the energy storage configuration double-layer optimization model takes the charging and discharging power of the energy storage system at each moment as a variable, and the minimization of the network loss of the target distribution network as an objective function. The constraints of the lower model include: branch flow constraints of the target distribution network, node voltage constraints, branch power constraints, reverse power constraints of the main network interconnection branch of the target distribution network, charging and discharging power constraints of the energy storage system, charging and discharging efficiency of the energy storage system, and state of charge constraints of the energy storage system.

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