An optimization method and system for a distribution network energy storage system and soft switch configuration

Abstract

The present invention discloses an optimization method and system for the configuration of a distribution network energy storage system and soft switches. The method includes: randomly generating a plurality of first configuration schemes through a preset energy storage-soft switch planning model; constructing an operation target through the preset energy storage-soft switch planning model according to the historical operation data of the distribution network; calculating the optimal operation mode corresponding to each first configuration scheme under each operation target function according to the operation target; screening the optimal operation mode according to a preset configuration scheme evaluation index to obtain an optimal energy storage-soft switch configuration scheme; and optimizing the configuration of the distribution network energy storage system and soft switches according to the optimal energy storage-soft switch configuration scheme. The present invention comprehensively considers the comprehensive evaluation system of the distribution network energy storage system and soft switches and various operation scenarios of the distribution network, reduces the cost required for configuring soft switches, and enables the configuration of the distribution network energy storage system and soft switches to be widely applied.

Description

Technical Field

[0001] The present invention belongs to the field of power grid energy regulation, and particularly relates to an optimization method and system for the configuration of a distribution network energy storage system and a soft switch. Background Art

[0002] With the continuous development of China's new power system, the installed capacity of distributed power sources mainly composed of renewable energy units such as solar power generation and wind power generation has been increasing. However, distributed power sources inherently have adverse characteristics such as intermittency, uncertainty, and volatility, and it is necessary to introduce an energy storage system (ESS) into the distribution network to suppress the output fluctuations of renewable energy and track the planned output, so as to improve the output characteristics of renewable energy and counter the adverse effects brought by intermittency, uncertainty, and volatility. In addition, the intelligent soft switch, abbreviated as SOP, realizes the flexible connection between distribution network feeders, can accurately control the load transfer between feeders and the system power flow, and can also alleviate the fluctuation impact brought by the large-scale access of renewable energy, improving the flexibility of the distribution network. Therefore, SOP is commonly used to configure the distribution network ESS to improve the regulation ability of the distribution network. However, the configuration costs of the distribution network ESS and SOP are high and it is difficult to implement them on a large scale, so it is necessary to optimize the configuration of the distribution network ESS and SOP.

[0003] In the existing research on the optimal configuration of the distribution network ESS and SOP, most of them establish models using a two-layer structure of upper-layer planning investment - lower-layer simulation operation, which fails to consider the comprehensive evaluation of the distribution network ESS and SOP, and has low solution efficiency and does not consider the comprehensive operation scenarios of the distribution network. Summary of the Invention

[0004] The present invention proposes an optimization method and system for the configuration of a distribution network energy storage system and a soft switch, which comprehensively considers the comprehensive evaluation system of the distribution network energy storage system and the soft switch as well as various operation scenarios of the distribution network, reduces the cost required for configuring the soft switch, and enables the configuration of the distribution network energy storage system and the soft switch to be widely applied.

[0005] The first aspect of the present invention provides an optimization method for the configuration of a distribution network energy storage system and a soft switch, and the method includes:

[0006] Randomly generate a number of first configuration schemes through a preset energy storage - soft switch planning model; wherein, the energy storage - soft switch planning model is configured according to the equipment data of the distribution network; the equipment data of the distribution network includes distribution network node parameters, energy storage system parameters, and soft switch parameters;

[0007] Based on the historical operation data of the distribution network, an operation target is constructed through a preset energy storage - soft - switch planning model; among them, the operation target includes an operation scenario set, an operation objective function, and operation constraint conditions; the operation objective function includes an energy consumption objective function, an operation efficiency objective function, an optimal node voltage objective function, a configuration utilization rate objective function, and a configuration economic benefit objective function;

[0008] According to the operation target, the optimal operation mode corresponding to the first configuration scheme under each operation objective function is calculated;

[0009] The optimal operation mode is screened according to the preset configuration scheme evaluation index to obtain the optimal energy storage - soft - switch configuration scheme;

[0010] According to the optimal energy storage - soft - switch configuration scheme, the configuration of the distribution network energy storage system and soft - switch is optimized.

[0011] The above - mentioned scheme generates a number of first configuration schemes through a preset energy storage - soft - switch planning model, calculates the optimal operation mode of each first configuration scheme under each operation objective function through the operation target, then calculates the comprehensive score of the first configuration scheme according to the optimal operation mode, and takes the first configuration scheme with the highest comprehensive score within the preset maximum number of iterations as the optimal energy storage - soft - switch configuration scheme, so that the distribution network can obtain the optimal configuration of the energy storage system and soft - switch at a relatively low cost, and the configuration of the distribution network ESS and SOP can be widely applied.

[0012] In a possible implementation method of the first aspect, based on the historical operation data of the distribution network, constructing an operation target through a preset energy storage - soft - switch planning model is specifically as follows:

[0013] The historical operation data of the distribution network is clustered through the k - means clustering algorithm to obtain an operation scenario set; among them, the clustering centers of the operation scenario set include the wind power output, photovoltaic power output, and load size under each operation scenario, as well as the probability of occurrence of each operation scenario;

[0014] Combining the historical operation data of the distribution network and the operation scenario set to obtain an operation objective function;

[0015] According to the historical operation data of the distribution network, operation constraint conditions are obtained; among them, the operation constraint conditions include power flow constraint, power balance constraint, node voltage constraint, line current constraint, wind and light abandonment power constraint, upper - level grid tie - line power constraint, energy storage system operation constraint, and energy storage - soft - switch operation constraint.

[0016] The above solution provides screening conditions and data support for screening the first configuration plan by constructing the operation scenarios of the distribution network, and then combining the operation scenarios with the actual operation data of the distribution network to construct the operation objective function and operation constraints.

[0017] In a possible implementation method of the first aspect, by combining the historical operation data of the distribution network and the set of operation scenarios, the operation objective function is obtained, specifically as follows:

[0018] According to the set of operation scenarios and the power supply side data of the distribution network, with the goal of optimizing new energy consumption, an energy consumption objective function is constructed;

[0019] According to the set of operation scenarios and the grid side data of the distribution network, with the goal of optimizing the operation efficiency of the distribution network, an operation efficiency objective function is constructed;

[0020] According to the set of operation scenarios and the grid side data of the distribution network, with the goal of the optimal node voltage of the distribution network, an optimal node voltage objective function is constructed;

[0021] According to the set of operation scenarios and the configuration of the energy storage system and soft switch, with the goal of the optimal utilization rate of the energy storage system and soft switch, a configuration utilization rate objective function is constructed;

[0022] According to the set of operation scenarios and the configuration of the energy storage system and soft switch, with the goal of the optimal investment of the energy storage system and soft switch, a configuration economic benefit objective function is constructed.

[0023] The above solution obtains the most economical and practical objective function for the distribution network after configuring the energy storage system and soft switch from aspects such as energy consumption, operation efficiency, stability, energy consumption, utilization rate and investment of the energy storage system and soft switch of the distribution network, providing technical support for calculating the comprehensive score of the first configuration plan.

[0024] In a possible implementation method of the first aspect, the energy consumption objective function, operation efficiency objective function, optimal node voltage objective function, configuration utilization rate objective function, and configuration economic benefit objective function are specifically as follows:

[0025] The energy consumption objective function, the specific formula is:

[0026]

[0027] Among them, T is the total number of daily scheduling cycles, is the duration of a single scheduling cycle, N DG is the total number of distributed power sources in the distribution network, is the curtailment of wind and light of the distributed power source under the operation scenario s, scheduling period t, and number g, Pr(Z s ) is the probability of the occurrence of the clustering center Z s ;

[0028] The operating efficiency objective function, and the specific formula is:

[0029]

[0030] Among them, N line is the total number of lines in the distribution network, is the square of the line current under the operating scenario s, scheduling period t, and line number l. N SOP is the total number of converters of the soft switch, is the active power of the converter loss of the soft switch under the operating scenario s, scheduling period t, and line number l. k is the total number of operating scenarios, and r l is the resistance of the line numbered l;

[0031] The optimal node voltage objective function, and the specific formula is:

[0032]

[0033] Among them, N bus is the total number of nodes in the distribution network, is the square of the node voltage under the operating scenario s, scheduling period t, and node number i;

[0034] The configuration utilization rate objective function is specifically:

[0035]

[0036] Among them, N ESS is the total number of distributed power sources in the distribution network, and are respectively the charging power and discharging power of the distributed power source under the operating scenario s, scheduling period t, and node number i, is the energy conversion efficiency of the soft switch.

[0037] The configuration economic benefit objective function, and the specific formula is:

[0038]

[0039] Among them, N NOP is the number of traditional mechanical tie switches NOP in the distribution network, c SOP is the configuration cost per unit capacity of the soft switch, is the capacity of the soft switch converter configured at node j, is the installation cost of the soft switch converter configured at node j, is the configuration cost per unit capacity of the energy storage system, is the power of the energy storage system installed at node j, and t ESS is the configuration duration of the energy storage system, $C_{unit}$ is the configuration cost of the unit power energy storage system, $i$ is the node where the energy storage system is installed, and $j$ is the node where the soft-switching converter is installed.

[0040] In a possible implementation method of the first aspect, the optimal operation mode is screened according to the preset configuration scheme evaluation index to obtain the optimal energy storage-soft-switch configuration scheme, specifically:

[0041] According to the preset configuration scheme evaluation index, the target score of each optimal operation mode of the first configuration scheme is calculated iteratively;

[0042] According to the target score of each optimal operation mode, the comprehensive score of the corresponding first configuration scheme is obtained;

[0043] If the current iteration number is greater than the preset maximum iteration number, the first configuration scheme with the highest comprehensive score is output as the optimal energy storage-soft-switch configuration scheme;

[0044] Otherwise, update the current iteration number, the calculation parameters of the preset configuration scheme evaluation index, and update the first configuration scheme according to the calculation parameters, and then continue to iteratively calculate the comprehensive score of the updated first configuration scheme.

[0045] The above scheme iteratively calculates the comprehensive score of the first configuration scheme, and finds the first configuration scheme with the highest comprehensive score within the preset maximum iteration number as the optimal energy storage-soft-switch configuration scheme; this optimal energy storage-soft-switch configuration scheme considers the operation scenarios of the entire distribution network and can greatly reduce the cost of configuring the energy storage system and the soft-switch connection.

[0046] In a possible implementation method of the first aspect, the target score and the comprehensive score are specifically:

[0047] The target score of each optimal operation mode, the specific formula is:

[0048]

[0049] where $x$ m,o is the target score of the $o$-th optimal operation mode of the $m$-th first configuration scheme, $Obj$ m,o is the target function value of the $o$-th optimal operation mode of the $m$-th first configuration scheme, and are respectively the maximum and minimum values of the $o$-th optimal operation mode among all the first configuration schemes;

[0050] The comprehensive score of the first configuration scheme, the specific formula is:

[0051]

[0052] where x m is the comprehensive score of the m-th first configuration scheme.

[0053] In a possible implementation method of the first aspect, the calculation parameters of the preset configuration scheme evaluation index are specifically:

[0054] The inertia weight w, the first learning factor c1, and the second learning factor c2 of the preset configuration scheme evaluation index, the specific formulas are:

[0055]

[0056]

[0057]

[0058] where is the maximum number of iterations, is the maximum value of the inertia weight, is the minimum value of the inertia weight, is the maximum value of the first learning factor, is the minimum value of the first learning factor, is the maximum value of the second learning factor, is the minimum value of the second learning factor, N iter is the current number of iterations.

[0059] The second aspect of the present invention provides an optimization system for a distribution network energy storage system and soft switch configuration. The system includes: a configuration scheme generation module, an operation target generation module, an optimal operation mode calculation module, an optimal operation mode screening module, and an energy storage system and soft switch configuration optimization module;

[0060] Among them, the configuration scheme generation module is used to randomly generate a number of first configuration schemes through a preset energy storage-soft switch planning model;

[0061] The operation target generation module is used to construct an operation target according to the historical operation data of the distribution network through a preset energy storage-soft switch planning model; among them, the operation target includes an operation scenario set, an operation target function, and an operation constraint condition; the operation target function includes an energy consumption target function, an operation efficiency target function, an optimal node voltage target function, a configuration utilization rate target function, and a configuration economic benefit target function;

[0062] The optimal operation mode calculation module is used to calculate the optimal operation mode corresponding to the first configuration scheme under each operation target function according to the operation target;

[0063] The optimal operation mode screening module is used to screen the optimal operation mode according to the preset configuration scheme evaluation index, and obtain the optimal energy storage-soft switch configuration scheme;

[0064] The energy storage system and soft switch configuration optimization module is used to optimize the configuration of the energy storage system and soft switch in the distribution network according to the optimal energy storage-soft switch configuration scheme.

[0065] In a possible implementation manner of the second aspect, the operation target generation module includes: an operation target generation unit;

[0066] The operation target generation unit is used to cluster the historical operation data of the distribution network through the k-means clustering algorithm to obtain a set of operation scenarios; among them, the clustering centers of the set of operation scenarios include the wind power output, photovoltaic power output and load size under each operation scenario, as well as the probability of occurrence of each operation scenario;

[0067] Combining the historical operation data of the distribution network and the set of operation scenarios to obtain an operation target function;

[0068] According to the historical operation data of the distribution network, obtain operation constraint conditions; among them, the operation constraint conditions include power flow constraints, power balance constraints, node voltage constraints, line current constraints, wind and light abandonment power constraints, upper-level grid tie line power constraints, energy storage system operation constraints, and energy storage-soft switch operation constraints.

[0069] In a possible implementation manner of the second aspect, the operation target generation unit includes: a target function generation unit;

[0070] The target function generation unit is used to construct an energy consumption target function with the optimal new energy consumption as the goal according to the set of operation scenarios and the power supply side data of the distribution network; construct an operation efficiency target function with the optimal operation efficiency of the distribution network as the goal according to the set of operation scenarios and the grid side data of the distribution network; construct an optimal node voltage target function with the optimal node voltage of the distribution network as the goal according to the set of operation scenarios and the grid side data of the distribution network; construct a configuration utilization rate target function with the optimal utilization rate of the energy storage system and soft switch as the goal according to the set of operation scenarios and the configuration of the energy storage system and soft switch; construct a configuration economic benefit target function with the optimal investment of the energy storage system and soft switch as the goal according to the set of operation scenarios and the configuration of the energy storage system and soft switch.

[0071] In a possible implementation manner of the second aspect, the optimal operation mode screening module includes: a highest comprehensive score calculation unit;

[0072] The highest comprehensive score calculation unit is used to iteratively calculate the target scores of each optimal operation mode of the first configuration scheme according to the preset configuration scheme evaluation indicators; obtain the comprehensive score of the corresponding first configuration scheme according to the target scores of each optimal operation mode; if the current iteration count is greater than the preset maximum iteration count, output the first configuration scheme with the highest comprehensive score as the optimal energy storage - soft switch configuration scheme; otherwise, update the current iteration count, the calculation parameters of the preset configuration scheme evaluation indicators, and update the first configuration scheme according to the calculation parameters, and then continue to iteratively calculate the comprehensive score of the updated first configuration scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the technical solutions of the present invention, the accompanying drawings required for implementation will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0074] Figure 1 FIG. is a schematic flow chart of a specific optimization method for a distribution network energy storage system and soft switch configuration provided by an embodiment of the present invention;

[0075] Figure 2 FIG. is a structural diagram of an optimization system for a distribution network energy storage system and soft switch configuration provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0076] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0077] It should be understood that the step numbers used in the text are only for convenience of description and are not intended to limit the execution order of the steps.

[0078] As Figure 1 shown, Figure 1 FIG. is a schematic flow chart of a specific optimization method for a distribution network energy storage system and soft switch configuration provided by an embodiment of the present invention. The optimization method for the distribution network energy storage system and soft switch configuration in this embodiment includes steps S1 to S5, which are described in detail as follows:

[0079] Step S1, randomly generate a number of first configuration schemes through a preset energy storage - soft switch planning model;

[0080] In this step, first input the device data of the distribution network into a preset energy storage - soft switch planning model, configure the model parameters and the preset maximum number of iterations; then, according to the preset energy storage - soft switch planning model, randomly generate several first configuration schemes.

[0081] In some embodiments, step S1 is specifically as follows:

[0082] Input the numbers, rated voltages, voltage upper limits, voltage lower limits of each node of the distribution network; the numbers, starting node numbers of the lines, ending node numbers of the lines, resistances of each line, reactances of each line, maximum currents flowing through each line; the branches where each tie switch is located, the nodes where each user is located, the operating historical data of active power, the operating historical data of reactive power; the types of each distributed power source, the nodes where they are located, the operating historical data of the output power and the maximum transmission power of the upper - level power grid tie line into the preset energy storage - soft switch planning model. Configure the preset energy storage - soft switch planning model according to these data;

[0083] Then input the device data of the energy storage system and the soft switch into the preset energy storage - soft switch planning model, including: the configured duration, charging power, discharging power, upper and lower limits of the allowable state of charge of the energy storage system, and the maximum installed power of a single energy storage system; the energy conversion efficiency of the soft switch, the upper - limit coefficient of reactive power, and the maximum installed power of a single soft switch. Construct the configuration environment of the energy storage system and the soft switch according to these data.

[0084] Finally, input the calculation parameters of the preset evaluation index of the configuration scheme for iterative calculation and the preset maximum number of iterations, including: the preset maximum number of iterations, the maximum value of the inertia weight, the minimum value of the inertia weight, the maximum and minimum values of the first learning factor, the maximum and minimum values of the second learning factor, and the number of preset first configuration schemes; in this embodiment, the preset maximum number of iterations can take an integer from 20 to 40, the maximum value of the inertia weight takes a number from 1 to 1.2, the minimum value of the inertia weight is a number from 0.2 to 0.5, the maximum values of the first learning factor and the second learning factor are the same and are within 3 to 4, the minimum values of the first learning factor and the second learning factor are the same and are within 1 to 2, and the number of preset first configuration schemes is generally a positive integer between 6 and 9; before the iterative calculation starts, the current number of iterations is 0.

[0085] According to the above settings, through the preset energy storage - soft switch planning model, first determine the continuous variable of the capacity of the energy storage system installed at the i - th node, and determine the continuous variable of the apparent power of the soft switch installed between the i - th node and the j - th node; among them, the installed power of a single energy storage system and the soft switch does not exceed the maximum operating installed power;

[0086] Determine the continuous variable of the apparent power of the soft switch installed between the i-th node and the j-th node according to the continuous variable of the capacity of the energy storage system installed at the i-th node, and generate a number of first configuration schemes.

[0087] Step S2: According to the historical operation data of the distribution network, construct an operation target through a preset energy storage-soft switch planning model.

[0088] In this step, first use the k-means clustering algorithm to cluster the historical data of distributed power sources and load historical data in the distribution network to obtain a set of operation scenarios for the complete distribution network. Then, according to the set of operation scenarios, from multiple perspectives such as the power supply side, grid side, and energy storage of the distribution network, combined with the historical operation data of the distribution network, construct an operation target function. Next, according to the historical operation data of the distribution network, combined with the actual operation situation of the distribution network, obtain a number of operation constraint conditions.

[0089] Construct an operation target according to the set of operation scenarios, operation target function, and operation constraint conditions.

[0090] In some embodiments, step S2 is specifically:

[0091] The steps of using the k-means clustering algorithm to cluster the historical data of distributed power sources and load historical data in the distribution network are as follows:

[0092] (1) Denote the output power of each distributed power source from 1 to 24 hours on the i-th day in the historical data of distributed power sources as , and the load of each node as , then the historical data of the distribution network on the i-th day can be expressed as , , and the set of historical data of the distribution network can be expressed as ; Set the number of clustering clusters k, the convergence accuracy and the error ;

[0093] (2) Randomly select k days of data from the set of historical data X of the distribution network as the initial clustering centers, denoted as ;

[0094] (3) Then calculate the Euclidean distance from the daily historical data X i to each clustering center Z j , and the specific formula is:

[0095]

[0096] (4) Classify the historical data X i into the cluster class l where the clustering center Z l with the smallest Euclidean distance is located.among them, namely:

[0097]

[0098] (5) Then use the average value of the historical data X of each day within the cluster i to update the clustering center of each cluster:

[0099] ;

[0100] where n j is the number of historical data included in the j-th cluster;

[0101] (6) According to the updated clustering centers of each cluster, calculate the convergence error, and the specific formula is:

[0102] ;

[0103] (7) If Error > , then return to (3) to continue the iteration; otherwise, output the k clustering centers obtained by clustering , and each clustering center is the wind power output, photovoltaic power output and load size under the operating scenario, and calculate the probability Pr of the occurrence of each clustering center. The specific formula is as follows:

[0104]

[0105] where k is the total number of clustering clusters;

[0106] According to each clustering center, obtain the operating scenario corresponding to each clustering center, and obtain the operating scenario set.

[0107] According to the operating scenario set and the power source side data of the distribution network, with the goal of optimal new energy consumption, that is, the goal of minimizing wind and light abandonment, construct the energy consumption objective function Obj1, and the specific formula is:

[0108]

[0109] where T is the total number of daily scheduling cycles, is the duration of a single scheduling cycle, N DG is the total number of distributed power sources in the distribution network, is the wind and light abandonment amount of the distributed power source under the operating scenario s, scheduling period t, and number g, Pr(Z s ) is the probability of the occurrence of the clustering center Z s .

[0110] According to the operating scenario set and the grid side data of the distribution network, with the goal of optimal distribution network operation efficiency, that is, the goal of minimizing network loss, construct the operating efficiency objective function Obj2, and the specific formula is:

[0111]

[0112] Among them, N line is the total number of lines in the distribution network, is the square of the line current under the operation scenario s, dispatch period t, and line number l, N SOP is the total number of converters of the soft switch, is the active power of the converter loss of the soft switch under the operation scenario s, dispatch period t, and line number l, k is the total number of operation scenarios, r l is the resistance of the line numbered l;

[0113] According to the operation scenario set and the grid-side data of the distribution network, with the optimal node voltage of the distribution network as the goal, that is, the minimum node voltage deviation, an optimal node voltage objective function Obj3 is constructed. The specific formula is:

[0114]

[0115] Among them, N bus is the total number of nodes in the distribution network, is the square of the node voltage under the operation scenario s, dispatch period t, and node number i;

[0116] According to the operation scenario set and the configuration of the energy storage system and the soft switch, with the optimal utilization rate of the energy storage system and the soft switch and the maximum charge and discharge power as the goal, that is, the minimum negative value of the sum of the powers of the energy storage system and the soft switch, a configuration utilization rate objective function Obj4 is constructed. The specific formula is:

[0117]

[0118] Among them, N ESS is the total number of distributed power sources in the distribution network, and are the charging power and discharging power of the distributed power source under the operation scenario s, dispatch period t, and node number i respectively, is the energy conversion efficiency of the soft switch;

[0119] According to the operation scenario set and the configuration of the energy storage system and the soft switch, with the optimal investment of the energy storage system and the soft switch as the goal, a configuration economic benefit objective function Obj5 is constructed. The specific formula is:

[0120]

[0121] Among them, N NOP is the number of traditional mechanical tie switches NOP in the distribution network, c SOP is the configuration cost per unit capacity of the soft switch, is the capacity of the soft-switching converter configured at node j, is the installation cost of the soft-switching converter configured at node j, is the configuration cost of the unit-capacity energy storage system, is the power of the energy storage system installed at node j, t ESS is the configuration duration of the energy storage system, is the configuration cost of the unit-power energy storage system, i is the node where the energy storage system is installed, and j is the node where the soft-switching converter is installed.

[0122] According to the energy consumption objective function, operation efficiency objective function, optimal node voltage objective function, configuration utilization objective function, and configuration economic benefit objective function, an operation objective function is constructed.

[0123] Then, based on the historical operation data of the distribution network and combined with the actual operation conditions of the distribution network, operation constraint conditions are constructed; among them, the operation constraint conditions include power flow constraints, power balance constraints, node voltage constraints, line current constraints, wind and light abandonment power constraints, upper-level grid tie-line power constraints, energy storage system operation constraints, and energy storage-soft-switch operation constraints.

[0124] Among them, the specific formula for the power flow constraint is:

[0125]

[0126] Among them, and are the active power and reactive power of the node under the operation scenario s, scheduling period t, and number j respectively, and are the active power and reactive power flowing from node i to node j on line ij respectively;

[0127] The specific formula for the power balance constraint is:

[0128]

[0129] Among them, N G is the total number of energy storage systems in the distribution network, is the transmission power of the upper-level grid tie-line under the operation scenario s, scheduling period t, and number g, is the output of the distributed power source, is the load size;

[0130] The specific formula for the node voltage constraint is:

[0131]

[0132] Among them, is the square of the voltage of node i in operation scenario s and scheduling period t, is the maximum value of the voltage of node i, is the minimum value of the voltage of node i.

[0133] The specific formula for the line current constraint is:

[0134]

[0135] Among them, is the current flowing through line ij in operation scenario s and scheduling period t, is the maximum value of the current that can flow through line ij;

[0136] The specific formula for the curtailment of wind and solar power constraint is:

[0137]

[0138] The specific formula for the power constraint of the upper-level power grid connection line is:

[0139]

[0140] Among them, is the active power transmitted by the upper-level connection line numbered g in operation scenario s and scheduling period t, is the maximum power that can be transmitted by the upper-level connection line numbered g;

[0141] The specific formula for the operation constraint of the energy storage system is:

[0142]

[0143]

[0144]

[0145]

[0146]

[0147]

[0148]

[0149] Among them, and are the 0-1 auxiliary variables of the charge and discharge states of the energy storage system under operation scenario s, scheduling period t, and number i respectively, indicates that the energy storage system is in the charging state, indicates that the energy storage system is in the discharging state, The state of charge of the energy storage system under the operating scenario s, scheduling period t, and number i. The maximum state of charge allowed for the energy storage system. The minimum state of charge allowed for the energy storage system. The charging efficiency of the energy storage system. The discharging efficiency of the energy storage system. The duration of a scheduling period. The total number of scheduling periods; generally, can be taken as 0.85, can be taken as 0.15, and can be taken as 15 minutes and 96 respectively;

[0150] The specific formula for the energy storage - soft - switch operation constraint is:

[0151]

[0152] Among them, is the reactive power of the soft - switch converter under the operating scenario s, scheduling period t, and number i. is the active power of the soft - switch converter. is the active - power loss of the soft - switch converter. is the upper - limit coefficient of the reactive - power of the soft - switch variable. is the charging power of the energy storage system installed in the energy storage - soft - switch under the operating scenario s, scheduling period t. is the discharging power of the energy storage system..

[0153] Step S3: According to the operating objective, calculate the optimal operation mode corresponding to the first configuration scheme under each operating objective function.

[0154] In this step, according to the several first configuration schemes generated in step S1, solve the optimal operation mode corresponding to each first configuration scheme under 5 operating objective functions, and then generate the corresponding objective - function value according to each optimal operation mode.

[0155] Suppose there are M first configuration schemes. For the m - th first configuration scheme, solve the optimization problem with the power , , of the energy storage system and the soft - switch at each moment as decision variables, aiming to minimize the operating objective function, and satisfying the power - flow constraint, power - balance constraint, node - voltage constraint, line - current constraint, wind - abandonment and light - abandonment power constraint, upper - level power - grid tie - line power constraint, energy - storage system operation constraint, and energy - storage - soft - switch operation constraint described in step S2.

[0156] Among them, solving the optimal operation mode is a mixed-integer second-order cone programming problem, and in some embodiments, a classical analytical algorithm of commercial solvers such as Gurobi and CPLEX is used for solving.

[0157] For M first configuration schemes, there are a total of M×5 corresponding objective function values, and the objective function values are the objective scores of each optimal operation mode of the first configuration scheme.

[0158] Step S4: Screen the optimal operation mode according to a preset configuration scheme evaluation index to obtain an optimal energy storage-soft switch configuration scheme;

[0159] In this step, first, according to the preset configuration scheme evaluation index, iteratively calculate the objective scores of each optimal operation mode of the first configuration scheme, and then perform an addition operation on the objective scores of each optimal operation mode to obtain the comprehensive score of the corresponding first configuration scheme; if the current iteration number is greater than the preset maximum iteration number, output the first configuration scheme with the highest comprehensive score as the optimal energy storage-soft switch configuration scheme; otherwise, update the current iteration number, the calculation parameters of the preset configuration scheme evaluation index, and update the first configuration scheme according to the calculation parameters, and then continue to iteratively calculate the comprehensive score of the updated first configuration scheme.

[0160] In some embodiments, step S4 is specifically:

[0161] For the m-th first configuration scheme, first normalize the objective scores of the corresponding optimal operation modes, and the specific formula is:

[0162]

[0163] where x m,o is the objective score of the o-th optimal operation mode of the m-th first configuration scheme, Obj m,o is the objective function value of the o-th optimal operation mode of the m-th first configuration scheme, and are respectively the maximum and minimum values of the o-th optimal operation mode among all first configuration schemes.

[0164] Then sum up all the objective scores of the m-th first configuration scheme to obtain the comprehensive score x m of the m-th first configuration scheme, and the specific formula is:

[0165] ;

[0166] Then judge the current iteration number. If the current iteration number N iter is greater than the preset maximum iteration number , the first configuration plan with the highest current comprehensive score is output as the optimal energy storage-soft switch configuration plan; otherwise, the following steps are performed:

[0167] (1) Set N iter = N iter + 1;

[0168] (2) Calculate the inertia weight w, the first learning factor c1, and the second learning factor c2 in the preset configuration plan evaluation index:

[0169]

[0170]

[0171]

[0172] Among them, is the maximum number of iterations, is the maximum value of the inertia weight, is the minimum value of the inertia weight, is the maximum value of the first learning factor, is the minimum value of the first learning factor, is the maximum value of the second learning factor, is the minimum value of the second learning factor, N iter is the current number of iterations. Generally speaking, can be taken as an integer from 20 to 40, is taken as 1 to 1.2, is taken as 0.2 to 0.5, and are generally taken to be the same and taken as 3 to 4, and are generally taken to be the same and taken as 1 to 2.

[0173] (3) Among all the first configuration plans, select the first configuration plan with the highest comprehensive score and record it as the global optimal configuration plan ; each first configuration plan selects the optimal operation mode with the highest comprehensive score as the current configuration plan and records it as .

[0174] (4) Update the inertia weight w, the first learning factor c1, and the second learning factor c2 in the preset configuration plan evaluation index:

[0175]

[0176] Among them, is the correction amount of the first configuration plan, recorded during the previous iteration. When N iter = 0, then set = 0; and are respectively random numbers obeying uniform distribution in the interval ;

[0177] (5) According to the updated inertia weight, the first learning factor and the second learning factor, iteratively calculate the comprehensive scores of each first configuration scheme.

[0178] Step S5, according to the optimal energy storage - soft switch configuration scheme, optimize the configuration of the distribution network energy storage system and the soft switch;

[0179] In this step, optimize the configuration of the distribution network energy storage system and the soft switch through the optimal energy storage - soft switch configuration scheme, so that the configuration of the distribution network energy storage system and the soft switch adapts to the operation environment of most distribution networks and reduces the configuration cost.

[0180] Furthermore, in order to execute the optimization system for the configuration of the distribution network energy storage system and the soft switch corresponding to the above - mentioned method embodiments to achieve the corresponding functions and technical effects, Figure 2 a structural diagram of an optimization system for the configuration of a distribution network energy storage system and a soft switch is provided. For the sake of convenience of description, only the parts related to this embodiment are shown. The optimization system for the configuration of the distribution network energy storage system and the soft switch provided by the embodiments of the present invention includes:

[0181] A configuration scheme generation module 201, configured to randomly generate a plurality of first configuration schemes through a preset energy storage - soft switch planning model; wherein, the energy storage - soft switch planning model is configured according to the equipment data of the distribution network; the equipment data of the distribution network includes distribution network node parameters, energy storage system parameters, and soft switch parameters;

[0182] An operation target generation module 202, configured to construct an operation target through a preset energy storage - soft switch planning model according to the historical operation data of the distribution network; wherein, the operation target includes an operation scenario set, an operation target function, and an operation constraint condition; the operation target function includes an energy consumption target function, an operation efficiency target function, an optimal node voltage target function, a configuration utilization target function, and a configuration economic benefit target function;

[0183] An optimal operation mode calculation module 203, configured to calculate the optimal operation mode of each first configuration scheme under each operation target function according to the operation target;

[0184] An optimal operation mode screening module 204, configured to screen the optimal operation mode according to a preset configuration scheme evaluation index to obtain the optimal energy storage - soft switch configuration scheme;

[0185] The energy storage system and the soft-switching configuration optimization module 205 are used to optimize the configuration of the energy storage system and the soft-switching in the distribution network according to the optimal energy storage-soft-switching configuration scheme.

[0186] In some embodiments, the operation target generation module 202 further includes:

[0187] An operation target generation unit, which is used to cluster the historical operation data of the distribution network through the k-means clustering algorithm to obtain an operation scenario set; wherein, the clustering centers of the operation scenario set include the wind power output, the photovoltaic power output and the load size under each operation scenario, as well as the probability of occurrence of each operation scenario;

[0188] Combining the historical operation data of the distribution network and the operation scenario set to obtain an operation target function;

[0189] According to the historical operation data of the distribution network, obtain the operation constraint conditions; wherein, the operation constraint conditions include power flow constraints, power balance constraints, node voltage constraints, line current constraints, curtailment of wind and light power constraints, power constraints of the connection line to the superior power grid, energy storage system operation constraints, and energy storage-soft-switching operation constraints.

[0190] Among them, the operation target generation unit further includes:

[0191] A target function generation unit, which is used to construct an energy consumption target function with the optimal new energy consumption as the target according to the operation scenario set and the power source side data of the distribution network; construct an operation efficiency target function with the optimal operation efficiency of the distribution network as the target according to the operation scenario set and the grid side data of the distribution network; construct an optimal node voltage target function with the optimal node voltage of the distribution network as the target according to the operation scenario set and the grid side data of the distribution network; construct a configuration utilization rate target function with the optimal utilization rate of the energy storage system and the soft-switching as the target according to the operation scenario set and the configuration of the energy storage system and the soft-switching; construct a configuration economic benefit target function with the optimal investment of the energy storage system and the soft-switching as the target according to the operation scenario set and the configuration of the energy storage system and the soft-switching.

[0192] In some embodiments, the optimal operation mode screening module 204 further includes:

[0193] The highest comprehensive score calculation unit is used to iteratively calculate the target scores of each optimal operation mode of the first configuration scheme according to the preset configuration scheme evaluation indicators; obtain the comprehensive score of the corresponding first configuration scheme according to the target scores of each optimal operation mode; if the current iteration number is greater than the preset maximum iteration number, output the first configuration scheme with the highest comprehensive score as the optimal energy storage-soft switch configuration scheme; otherwise, update the current iteration number, the calculation parameters of the preset configuration scheme evaluation indicators, and update the first configuration scheme according to the calculation parameters, and then continue to iteratively calculate the comprehensive score of the updated first configuration scheme.

[0194] This embodiment proposes an optimization method and system for the configuration of a distribution network energy storage system and a soft switch: randomly generate a number of first configuration schemes through a preset energy storage-soft switch planning model; construct an operation target through the preset energy storage-soft switch planning model according to the historical operation data of the distribution network; calculate the optimal operation mode corresponding to the first configuration scheme under each operation target function according to the operation target; screen the optimal operation mode according to the preset configuration scheme evaluation indicators to obtain the optimal energy storage-soft switch configuration scheme; optimize the configuration of the distribution network energy storage system and the soft switch according to the optimal energy storage-soft switch configuration scheme. The beneficial effects are as follows: comprehensively consider the comprehensive evaluation system of the distribution network energy storage system and the soft switch and various operation scenarios of the distribution network, reduce the cost required for configuring the soft switch, and enable the configuration of the distribution network energy storage system and the soft switch to be widely applied.

[0195] The above specific embodiments have further elaborated on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. In particular, for those skilled in the art, any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An optimization method for the configuration of a distribution network energy storage system and soft switches, characterized in that Including: Randomly generate a number of first configuration schemes through a preset energy storage - soft - switch planning model; wherein, the energy storage - soft - switch planning model is configured according to the equipment data of the distribution network; the equipment data of the distribution network includes distribution network node parameters, energy storage system parameters, and soft - switch parameters; According to the historical operation data of the distribution network, construct an operation target through a preset energy storage - soft - switch planning model; wherein, the operation target includes an operation scenario set, an operation objective function, and operation constraint conditions; the operation objective function includes an energy consumption target function, an operation efficiency target function, an optimal node voltage target function, a configuration utilization rate target function, and a configuration economic benefit target function; According to the operation target, calculate the optimal operation mode corresponding to the first configuration scheme under each operation objective function; Screen the optimal operation mode according to the preset configuration scheme evaluation index to obtain the optimal energy storage - soft - switch configuration scheme; Optimize the configuration of the energy storage system and soft - switch in the distribution network according to the optimal energy storage - soft - switch configuration scheme.

2. The optimization method for the configuration of a distribution network energy storage system and soft switches according to claim 1, wherein The step of constructing an operation target through a preset energy storage - soft - switch planning model according to the historical operation data of the distribution network is specifically as follows: Cluster the historical operation data of the distribution network through the k - means clustering algorithm to obtain an operation scenario set; wherein, the cluster centers of the operation scenario set include the wind power output, photovoltaic power output, and load size under each operation scenario, as well as the probability of occurrence of each operation scenario; Combine the historical operation data of the distribution network and the operation scenario set to obtain an operation objective function; According to the historical operation data of the distribution network, obtain operation constraint conditions; wherein, the operation constraint conditions include power flow constraints, power balance constraints, node voltage constraints, line current constraints, wind and light abandonment power constraints, upper - level power grid tie - line power constraints, energy storage system operation constraints, and energy storage - soft - switch operation constraints.

3. The optimization method for the distribution network energy storage system and soft switch configuration according to claim 2, characterized in that The step of combining the historical operation data of the distribution network and the operation scenario set to obtain an operation objective function is specifically as follows: Construct an energy consumption target function with the goal of optimal new - energy consumption according to the operation scenario set and the power - source - side data of the distribution network; Construct an operation efficiency target function with the goal of optimal operation efficiency of the distribution network according to the operation scenario set and the grid - side data of the distribution network; Construct an optimal node voltage target function with the goal of the optimal node voltage of the distribution network according to the operation scenario set and the grid - side data of the distribution network; Construct a configuration utilization rate target function with the goal of the optimal utilization rate of the energy storage system and soft - switch according to the operation scenario set and the configuration of the energy storage system and soft - switch; Construct a configuration economic benefit target function with the goal of the optimal investment of the energy storage system and soft - switch according to the operation scenario set and the configuration of the energy storage system and soft - switch.

4. The optimization method for the distribution network energy storage system and soft switch configuration according to claim 3, characterized in that The energy consumption target function, operation efficiency target function, optimal node voltage target function, configuration utilization rate target function, and configuration economic benefit target function are specifically as follows: The energy consumption target function, the specific formula is: Among them, T is the total number of daily scheduling cycles, is the duration of a single scheduling cycle, N DG is the total number of distributed power sources in the distribution network, is the amount of abandoned wind and light of the distributed power source under the operating scenario s, scheduling period t, and number g. Pr(Z s ) is the probability of the occurrence of the clustering center Z s ; The operation efficiency target function, the specific formula is: Among them, N line is the total number of lines in the distribution network, is the square of the line current under the operating scenario s, scheduling period t, and line number l, N SOP is the total number of converters of the soft switch, is the active power of the converter loss of the soft switch under the operating scenario s, scheduling period t, and line number l, k is the total number of operating scenarios, r l is the resistance of the line numbered l; The optimal node voltage target function, the specific formula is: Among them, N bus is the total number of nodes in the distribution network, is the square of the node voltage under the operation scenario s, the scheduling period t, and the number i; The configuration utilization rate target function, specifically: Among them, N ESS is the total number of distributed power sources in the distribution network, and are the charging power and discharging power of the distributed power source under the operating scenario s, scheduling period t, and number i respectively, is the energy conversion efficiency of the soft switch; The configuration economic benefit target function, the specific formula is: Among them, N NOP is the number of traditional mechanical tie switches NOP in the distribution network, c SOP is the configuration cost of the soft switch per unit capacity, is the capacity of the soft switch converter configured at node j, is the installation cost of the soft switch converter configured at node j, is the configuration cost of the energy storage system per unit capacity, is the power of the energy storage system installed at node j, t ESS is the configuration duration of the energy storage system, is the configuration cost of the energy storage system per unit power, i is the node where the energy storage system is installed, and j is the node where the soft switch converter is installed.

5. The optimization method for the configuration of a distribution network energy storage system and soft switches according to claim 1, characterized in that, The optimal energy storage - soft switch configuration scheme is obtained by screening the optimal operation modes according to the preset evaluation indexes of the configuration scheme, specifically as follows: According to the preset evaluation indexes of the configuration scheme, iteratively calculate the target scores of each optimal operation mode of the first configuration scheme; According to the target scores of each optimal operation mode, obtain the comprehensive score of the corresponding first configuration scheme; If the current iteration number is greater than the preset maximum iteration number, output the first configuration scheme with the highest comprehensive score as the optimal energy storage - soft switch configuration scheme; Otherwise, update the current iteration number, the calculation parameters of the preset evaluation indexes of the configuration scheme, and update the first configuration scheme according to the calculation parameters, and then continue to iteratively calculate the comprehensive score of the updated first configuration scheme.

6. The optimization method for the configuration of a distribution network energy storage system and soft switches according to claim 5, characterized in that The target score and the comprehensive score are specifically as follows: The target score of each optimal operation mode, the specific formula is: Among them, x m,o is the objective score of the o-th optimal operation mode of the m-th first configuration scheme, Obj m,o is the objective function value of the o-th optimal operation mode of the m-th first configuration scheme, and are respectively the maximum and minimum values of the o-th optimal operation mode among all first configuration schemes; The comprehensive score of the first configuration scheme, the specific formula is: where x m is the comprehensive score of the m-th first configuration scheme.

7. The optimization method for the configuration of a distribution network energy storage system and soft switches according to claim 4, characterized in that The calculation parameters of the preset evaluation indexes of the configuration scheme are specifically as follows: The inertia weight w, the first learning factor c1, and the second learning factor c2 of the preset evaluation indexes of the configuration scheme, the specific formula is: Among them, is the maximum number of iterations, is the maximum inertia weight, is the minimum inertia weight, is the maximum first learning factor, is the minimum first learning factor, is the maximum second learning factor, is the minimum second learning factor, N iter is the current number of iterations.

8. An optimization system for a distribution network energy storage system and soft switch configuration, characterized in that, Including: A configuration scheme generation module, an operation target generation module, an optimal operation mode calculation module, an optimal operation mode screening module, and an energy storage system and soft switch configuration optimization module; Among them, the configuration scheme generation module is used to randomly generate a number of first configuration schemes through a preset energy storage - soft switch planning model; among them, the energy storage - soft switch planning model is configured according to the equipment data of the distribution network; the equipment data of the distribution network includes distribution network node parameters, energy storage system parameters, and soft switch parameters; The operation target generation module is used to construct an operation target through a preset energy storage - soft switch planning model according to the historical operation data of the distribution network; among them, the operation target includes an operation scenario set, an operation target function, and an operation constraint condition; the operation target function includes an energy consumption target function, an operation efficiency target function, an optimal node voltage target function, a configuration utilization rate target function, and a configuration economic benefit target function; The optimal operation mode calculation module is used to calculate the optimal operation mode corresponding to the first configuration scheme under each operation target function according to the operation target; The optimal operation mode screening module is used to screen the optimal operation mode according to the preset evaluation indexes of the configuration scheme to obtain the optimal energy storage - soft switch configuration scheme; The energy storage system and soft switch configuration optimization module is used to optimize the configuration of the energy storage system and soft switch of the distribution network according to the optimal energy storage - soft switch configuration scheme.

9. The optimization system for the distribution network energy storage system and soft switch configuration according to claim 8, characterized in that The operation target generation module includes: an operation target generation unit; The operation target generation unit is used to cluster the historical operation data of the distribution network through the k - means clustering algorithm to obtain an operation scenario set; among them, the clustering centers of the operation scenario set include the wind power output, photovoltaic output, and load size under each operation scenario, and the probability of occurrence of each operation scenario; Combining the historical operation data of the distribution network and the operation scenario set, obtain the operation target function; Based on the historical operation data of the distribution network, operating constraint conditions are obtained; among them, the operating constraint conditions include power flow constraints, power balance constraints, node voltage constraints, line current constraints, wind and light abandonment power constraints, power constraints of the connection lines to the superior power grid, operating constraints of the energy storage system, and operating constraints of the energy storage - soft switch.

10. The optimization system for the distribution network energy storage system and soft switch configuration according to claim 9, characterized in that, The operating objective generation unit includes: an objective function generation unit; The objective function generation unit is used to construct an energy consumption objective function with the optimal new - energy consumption as the goal according to the operating scenario set and the power - source - side data of the distribution network; construct an operating efficiency objective function with the optimal operating efficiency of the distribution network as the goal according to the operating scenario set and the grid - side data of the distribution network; construct an optimal node voltage objective function with the optimal node voltage of the distribution network as the goal according to the operating scenario set and the grid - side data of the distribution network; construct a configuration utilization rate objective function with the optimal utilization rate of the energy storage system and the soft switch as the goal according to the operating scenario set and the configuration of the energy storage system and the soft switch; construct a configuration economic benefit objective function with the optimal investment of the energy storage system and the soft switch as the goal according to the operating scenario set and the configuration of the energy storage system and the soft switch.

11. The optimization system for the distribution network energy storage system and soft switch configuration according to claim 8, characterized in that, The optimal operation mode screening module includes: a highest comprehensive score calculation unit; The highest comprehensive score calculation unit is used to iteratively calculate the objective scores of each optimal operation mode of the first configuration plan according to the preset evaluation indexes of the configuration plan; obtain the comprehensive score of the corresponding first configuration plan according to the objective scores of each optimal operation mode; if the current iteration number is greater than the preset maximum iteration number, output the first configuration plan with the highest comprehensive score as the optimal energy storage - soft switch configuration plan; otherwise, update the current iteration number, the calculation parameters of the preset evaluation indexes of the configuration plan, and update the first configuration plan according to the calculation parameters, and then continue to iteratively calculate the comprehensive score of the updated first configuration plan.

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