A power distribution network day-ahead active power adjustment control method, device, equipment and medium

Abstract

The present application belongs to the technical field of power distribution network dispatching, and particularly relates to a power distribution network day-ahead active power control method, device, equipment and medium. Obtain power distribution network information, and day-ahead prediction values of each new energy power station and power load; form a power distribution network operation state index set with maximum load power consumption guarantee as a target; obtain the tie switch state of the flexible loop device formed by individuals in the population according to a swarm intelligence algorithm; construct a typical day operation model of the power distribution network and optimize it, form the latest power distribution network operation state index set according to the optimized typical day operation model of the power distribution network; calculate the individual fitness value, obtain the tie switch state of the access flexible loop device represented by the optimal individual under the maximum iteration number, and form the power distribution network day-ahead optimal active power control plan. The present application improves the power distribution network resource utilization efficiency and reduces resource waste by obtaining the day-ahead optimal active power control plan.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power distribution network dispatching, and particularly relates to a power distribution network day-ahead active power dispatching control method, device, equipment and medium. BACKGROUND

[0002] The traditional power distribution network adopts an open-loop operation and closed-loop design mode for planning and operation maintenance, and utilizes the loop closing device and the tie switch to ensure the power supply of users during maintenance or fault transfer, but after the loop closing of the two power distribution feeders, the feeder current is composed of the initial current of each branch before the loop closing and the loop current caused by the difference between the voltage phase quantities at both ends of the loop closing switch, which will result in excessively large loop closing flow or impact current. Therefore, the loop closing device and the tie switch in the traditional power distribution network are only reasonably operated during the grid fault or necessary maintenance to complete the uninterrupted power supply of users.

[0003] With the access of large-scale multi-type distributed power sources, new energy systems and energy storage systems in the power distribution network, the power supply complexity and uncertainty of the power distribution network are greatly increased, the resource utilization efficiency is not high, and a large amount of resources is wasted. SUMMARY

[0004] The application aims to provide a power distribution network day-ahead active power dispatching control method, device, equipment and medium to solve the problem of low resource utilization efficiency and large resource waste of the power distribution network under the access of large-scale multi-type distributed power sources, new energy systems and energy storage systems.

[0005] In order to solve the above technical problems, the application adopts the following technical scheme:

[0006] In a first aspect, the application provides a power distribution network day-ahead active power dispatching control method, which comprises the following steps:

[0007] S1. Obtain the power distribution network information, and the day-ahead prediction values of each new energy power station and power load;

[0008] S2. According to the power distribution network information and the day-ahead prediction values of each new energy power station and power load, calculate the technical indicators describing the operation state of the power distribution network when all the flexible loop closing devices and tie switches in the power distribution network are disconnected, with the maximum load power consumption guarantee as the target, to form a power distribution network operation state indicator set;

[0009] S3. Obtain the tie switch state of the flexible loop closing device formed by the individual in the population according to the swarm intelligence algorithm;

[0010] S4. According to the power distribution network information and the day-ahead prediction values of each new energy power station and power load, consider the tie switch state of the flexible loop closing device formed by the individual obtained in step S3, construct a typical day operation model of the power distribution network and optimize it, and form the latest power distribution network operation state indicator set according to the optimized typical day operation model of the power distribution network.

[0011] S5. Calculate individual fitness value according to the power distribution network operation state index set in step S2 and the latest power distribution network operation state index set in step S4;

[0012] S6. Obtain the state of the tie switch of the access flexible loop device represented by the optimal individual under the maximum number of iterations according to the swarm intelligence algorithm in step S3 and the individual fitness value obtained in step S5, and form a day-ahead optimal active power control plan of the power distribution network.

[0013] Optionally, the step of calculating the technical index describing the operation state of the power distribution network when all the flexible loop devices and tie switches in the power distribution network are disconnected to form a power distribution network operation state index set with the maximum load power consumption guarantee as the target according to the power distribution network information and the day-ahead predicted values of each new energy power station and power load, specifically comprises:

[0014] The power distribution network information includes: energy storage system capacity state, energy storage system charging efficiency, energy storage system discharging efficiency, number of power grid nodes, output active power, reactive power, current, resistance and reactance between power grid nodes, installed capacity of new energy power station;

[0015] The day-ahead predicted value of the new energy power station includes: the day-ahead predicted output active power of the new energy power station;

[0016] The optimization variables of the optimal power flow calculation method of the power distribution network are selected, including: actual output active power and reactive power of the new energy power station, output active power and reactive power of the energy storage system, voltage of each power grid node on the power network, current on each power network branch, actual obtained active power and reactive power of the power load;

[0017] The optimization objective function of the power distribution network to guarantee the load power consumption to the greatest extent is defined, and the formula is as follows:

[0018]

[0019] Wherein: J represents the objective function; Node represents the number of nodes of the power grid; X load,i (t) represents the actual obtained active power of the power load on the i-th power grid node at t time; P load,i (t) represents the active power of the load on the i-th power grid node at t time; Q load,i (t) represents the reactive power of the load on the i-th power grid node at t time; Y load,i (t) represents the actual obtained reactive power of the power load on the i-th power grid node at t time;

[0020] Set the regional power distribution network operation constraints;

[0021] The optimal power flow calculation method is used to complete power flow optimization, and a plurality of technical indexes describing the operation state of the distribution network are calculated to form a distribution network operation state index set γ = {targe r | r = 1, 2,..., R}.

[0022] Wherein: targe r represents the rth index value in the distribution network operation state index set, and R is the number of technical indexes describing the operation state of the distribution network.

[0023] Optionally, the regional distribution network operation constraint specifically includes:

[0024] The active power and reactive power balance constraints of the power system are as follows:

[0025]

[0026] Wherein: X E,i (t) represents the actual output active power of the energy storage system of the i th power grid node at t time; X new,i (t) represents the actual output active power of the new energy power station of the i th power grid node at t time; P i,j (t) represents the active power output from the i th power grid node to the j th power grid node; I i,j (t) represents the current square output from the i th power grid node to the j th power grid node; R i,j represents the resistance between the power grid node j and the power grid node i; Y E,i (t) represents the actual output reactive power of the energy storage system of the i th power grid node at t time; Y new,i (t) represents the actual output reactive power of the new energy power station of the i th power grid node at t time; Q i,j (t) represents the reactive power output from the i th power grid node to the j th power grid node; X i,j represents the reactance between the power grid node j and the power grid node i;

[0027] The node voltage and line current constraint is as follows:

[0028]

[0029] Wherein: U i (t) represents the square of the voltage of the i th power grid node at t time; U j (t) represents the square of the voltage of the j th power grid node at t time; represents the square of the lower limit value of the voltage of the power grid node; U i U represents the square of the upper limit value of the voltage of the power grid node; I maxrepresents the maximum passing current of the branch;

[0030] New energy power output constraint, the formula is as follows:

[0031]

[0032] Wherein: P new,i (t) represents the maximum output active power of the new energy power station at the i-th power grid node at t time; S new,i represents the installed capacity of the new energy power station at the i-th power grid node;

[0033] Energy storage system energy balance constraint, the formula is as follows:

[0034]

[0035] Wherein: C E,i (t+Δt) represents the capacity state of the energy storage system at the i-th power grid node at t+Δt time; Δt represents the time interval; C E,i (t) represents the capacity state of the energy storage system at the i-th power grid node at t time; represents the charging efficiency of the energy storage system at the i-th power grid node; represents the discharging efficiency of the energy storage system at the i-th power grid node; represents the lower limit value of the capacity state of the energy storage system at the i-th power grid node; represents the upper limit value of the capacity state of the energy storage system at the i-th power grid node; C E,i (1) represents the initial capacity of the energy storage system at the i-th power grid node within a typical day; C E,i (N t ) represents the final capacity of the energy storage system at the i-th power grid node within a typical day; N t represents that there are N t time points in a typical day.

[0036] Optionally, the step of obtaining the tie switch state of the flexible loop device formed by the individual in the population according to the swarm intelligence algorithm specifically comprises:

[0037] Selecting a swarm intelligence optimization algorithm with discrete characteristics of optimization variables;

[0038] Setting the serial number, number of individuals, individual binary code string and maximum iteration number of individuals in the population, and defining the tie switch state of the flexible loop device connected to the flexible loop device as a controllable optimization control variable;

[0039] Obtaining the tie switch state of the flexible loop device formed by the individual in the population.

[0040] Optionally, the step of constructing a typical day operation model of the power distribution network and performing optimization according to the power distribution network information, the day-ahead prediction values of the new energy power stations and the power consumption loads, and the contact switch state of the individual formed flexible loop device obtained in step S3, and the step of forming the latest power distribution network operation state index set according to the optimized typical day operation model of the power distribution network, specifically comprises:

[0041] According to the power distribution network information, the day-ahead prediction values of the new energy power stations and the power consumption loads, and the contact switch state of the individual formed flexible loop device obtained in step S3, the increase of the number of power grid branches and the change of the power grid form are analyzed, and a typical day operation model of the power distribution network is constructed.

[0042] A restriction condition for the capacity of the flexible loop device is added in the typical day operation model of the power distribution network, and the formula is as follows:

[0043]

[0044] Wherein, U a (t) represents the voltage at node a of the power grid at time t; U b (t) represents the voltage at node b of the power grid at time t; I a,b (t) represents the branch current between node a and node b of the power grid at time t; represents the rated capacity of the flexible loop between node a and node b of the power grid;

[0045] After the typical day operation model of the power distribution network is optimized by the optimal power flow calculation method, the technical index describing the operation state of the power distribution network is calculated, and the latest power distribution network operation state index set is formed

[0046] Wherein: represents the rth index value in the latest power distribution network operation state index set of the nth individual in the gth generation.

[0047] Optionally, the step of calculating the individual fitness value according to the power distribution network operation state index set in step S2 and the latest power distribution network operation state index set in step S4, specifically comprises:

[0048] The encoding bits of the power distribution network operation state index set, the latest power distribution network operation state index set and the individual binary code string are considered to calculate the fitness value F (g) (n) of the nth individual in the gth generation, and the formula is as follows:

[0049]

[0050] Wherein: represents the sum of the encoding bits in the binary code string of the nth individual in the gth iteration, and ω ra weight coefficient of an rth index value in the set of index values of the set of operation state indexes of the power distribution network;

[0051] The individual with the highest fitness value is the optimal individual.

[0052] Optionally, the step of obtaining the tie switch state of the access flexible loop device represented by the optimal individual under the maximum number of iterations according to the swarm intelligence algorithm in step S3 and the fitness value of the individual obtained in step S5, and forming the day-ahead optimal active power adjustment control plan of the power distribution network, specifically comprises the following steps:

[0053] S6.1. After the gth iteration, it is judged whether the individual serial number is less than the number of individuals, if yes, the individual serial number is increased by 1 and the step S4 is returned; if no, the optimal individual after the gth iteration is recorded and the step S6.2 is entered;

[0054] S6.2. It is judged whether the number of iterations is less than the maximum number of iterations, if yes, the number of iterations is increased by 1, the individual serial number is set to 1 and the step S6.3 is entered; if no, the step S6.4 is entered;

[0055] S6.3. The iteration update formula of the swarm intelligence algorithm in step S3 is obtained, and all N individuals in the gth iteration population are updated using the iteration update formula, a new population consisting of N individuals of the gth generation is formed, and the step S4 is returned;

[0056] S6.4. The tie switch state of the access flexible loop device represented by the optimal individual under the maximum number of iterations is obtained, and the day-ahead optimal active power adjustment control plan of the power distribution network is formed.

[0057] In a second aspect, the present application provides a day-ahead active power adjustment control device of a power distribution network, comprising:

[0058] an information acquisition module, configured to acquire power distribution network information, and day-ahead prediction values of each new energy power station and power load;

[0059] a first operation state index set module, configured to calculate technical indexes describing the operation state of the power distribution network when all flexible loop devices and tie switches in the power distribution network are disconnected, and form a set of power distribution network operation state indexes, according to the power distribution network information and the day-ahead prediction values of each new energy power station and power load, with the maximum load power consumption guarantee as the target;

[0060] a swarm intelligence algorithm module, configured to obtain the tie switch state of the flexible loop device formed by the individual in the population according to the swarm intelligence algorithm;

[0061] The second operation state index set module is configured to construct a typical day operation model of the power distribution network and perform optimization according to the power distribution network information and the day-ahead predicted values of the new energy power stations and the power consumption loads and the contact switch state of the flexible loop device formed by the individual, and form a latest power distribution network operation state index set according to the optimized typical day operation model of the power distribution network.

[0062] The individual fitness value calculation module is configured to calculate an individual fitness value according to the power distribution network operation state index set and the latest power distribution network operation state index set.

[0063] The power control plan module is configured to obtain the contact switch state of the flexible loop device represented by the optimal individual under the maximum number of iterations according to the swarm intelligence algorithm and the individual fitness value, and form a day-ahead optimal active power control plan of the power distribution network.

[0064] In a third aspect, the present application provides an electronic device comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the power distribution network day-ahead active power control method.

[0065] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores at least one instruction, and the at least one instruction is executed by a processor to implement the power distribution network day-ahead active power control method.

[0066] Compared with the prior art, the present application has the following beneficial effects:

[0067] The present application provides a power distribution network day-ahead active power control method, device, equipment and medium, and the method comprises:

[0068] S1. Obtain power distribution network information, and day-ahead predicted values of new energy power stations and power consumption loads;

[0069] S2. According to the power distribution network information and the day-ahead predicted values of the new energy power stations and the power consumption loads, calculate technical indexes describing the operation state of the power distribution network when all the flexible loop devices and the contact switches in the power distribution network are disconnected, form a power distribution network operation state index set, and take the maximum load power consumption guarantee as the target;

[0070] S3. Obtain the contact switch state of the flexible loop device formed by the individual in the population according to the swarm intelligence algorithm;

[0071] S4. According to the power distribution network information and the day-ahead predicted values of the new energy power stations and the power consumption loads, consider the contact switch state of the flexible loop device formed by the individual obtained in step S3, construct a typical day operation model of the power distribution network and perform optimization, and form a latest power distribution network operation state index set according to the optimized typical day operation model of the power distribution network.

[0072] S5. Calculate the individual fitness value according to the power distribution network operation state index set in step S2 and the latest power distribution network operation state index set in step S4;

[0073] S6. Obtain the contact switch state of the access flexible loop device represented by the optimal individual under the maximum iteration number according to the swarm intelligence algorithm in step S3 and the individual fitness value obtained in step S5, and form the day-ahead optimal active power control plan of the power distribution network.

[0074] The method provided in the application optimizes the contact switch state of the flexible loop device in the power distribution network based on the day-ahead prediction value of each new energy power station and the power load, provides the power distribution network operation constraint, and then optimizes and calculates the power distribution network index, so as to guide the active control opening and closing state update of the flexible loop device; by obtaining the day-ahead optimal active power control plan, the resource utilization efficiency of the power distribution network is improved, and resource waste is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0075] The accompanying drawings, which form a part of the present application, are used to provide further understanding of the present application, and the illustrative embodiments of the present application and their description serve the purpose of explaining the present application. The present application is not limited by the accompanying drawings.

[0076] Figure 1 A flow chart of the day-ahead active power control method of the power distribution network according to the application;

[0077] Figure 2 A structure diagram of the day-ahead active power control device of the power distribution network according to the application;

[0078] Figure 3 A structure block diagram of the electronic equipment according to the application;

[0079] Figure 4 An IEEE33 node power distribution network structure diagram in the embodiment of the application. DETAILED DESCRIPTION

[0080] The application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments. It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other without conflict.

[0081] The following detailed description is exemplary description, which aims to provide further detailed description of the application. Unless otherwise specified, all technical terms used in the application have the same meaning as that understood by the general technical personnel in the field to which the application belongs. The terms used in the application are only used to describe the specific embodiments, and are not intended to limit the exemplary embodiments according to the application.

[0082] Embodiment 1

[0083] In Figure 4The shown IEEE33 node power distribution network structure is taken as an example, Figure 4 The indicated distributed power supply, 3 photovoltaic power stations, 2 energy storage systems and the load user access position condition of new energy power station are indicated, the new energy power station can be output active and reactive power regulation, the flexible loop device can realize two power network node connection in active power regulation, the flexible loop device auxiliary tie switch is disconnected in not active power regulation, and the two power network nodes are not connected.

[0084] Please refer to Figure 1 The present application provides a kind of power distribution network day before active power regulation control method, specifically including the following steps:

[0085] S1. obtain power distribution network information, and the day before prediction value of each new energy power station and electric load;

[0086] The power distribution network information is power distribution network network structure and multiple power supply, flexible loop controller and load information;Specifically include: grid line parameters, voltage grade, system capacity, flexible loop device and tie switch access position, transfer capacity, distributed power supply, new energy system, the access position and installed capacity of energy storage system, energy storage system capacity state, charging efficiency and discharging efficiency, the output active power, reactive power, current, resistance and reactance between power grid node, power grid node quantity, load active power on power grid node, load reactive power on power grid node, active power actually obtained by electric load on power grid node, the active power actually obtained by electric load on power grid node, etc.

[0087] The day before prediction value of each new energy power station and electric load is obtained by the following steps:

[0088] Obtain the historical data information of new energy power station and the historical data information of each power grid node user load;

[0089] The prediction model based on machine learning or regression model is established, and the active power and reactive power demand of each new energy power station output active power and electric load in the second day are predicted;So as to obtain the day before prediction value of each new energy power station and electric load.

[0090] S2. according to power distribution network information and the day before prediction value of each new energy power station and electric load, with maximum load power supply guarantee as target, calculate the technical index describing the operation state of power distribution network when all flexible loop devices and tie switches in power distribution network are disconnected, form power distribution network operation state index set;

[0091] The optimization variables of the optimal power flow calculation method of the selected regional distribution network include: the actual output active power and reactive power variables of the new energy power station, which are continuous variables; the output active power and reactive power variables of the energy storage system, which are continuous variables; the voltage of each power grid node on the power grid and the current on each power grid branch; and the actual obtained active power and reactive power of the power load, which are continuous variables.

[0092] To maximize the power system load power, it is desired to reduce the amount of load shedding, and the optimization objective function of the distribution network for maximizing the load power is defined, and the formula is as follows:

[0093]

[0094] Wherein: J represents the objective function; Node represents the number of nodes of the power grid; X load,i (t) represents the actual obtained active power of the power load on the i th power grid node at t time; P load,i (t) represents the active power of the load on the i th power grid node at t time; Q load,i (t) represents the reactive power of the load on the i th power grid node at t time; Y load,i (t) represents the actual obtained reactive power of the power load on the i th power grid node at t time.

[0095] The operation constraints of the regional distribution network are set, which specifically include:

[0096] The active power and reactive power balance constraints of the power system are as follows:

[0097]

[0098] Wherein: X E,i (t) represents the actual output active power of the energy storage system of the i th power grid node at t time; X new,i (t) represents the actual output active power of the new energy power station of the i th power grid node at t time; P i,j (t) represents the active power output from the i th power grid node to the j th power grid node; I i,j (t) represents the current square output from the i th power grid node to the j th power grid node; R i,j represents the resistance between the power grid node j and the power grid node i; Y E,i (t) represents the actual output reactive power of the energy storage system of the i th power grid node at t time; Y new,i (t) represents the actual output reactive power of the new energy power station of the i th power grid node at t time; Q i,j (t) represents the reactive power output from the i th power grid node to the j th power grid node; Xi,j This represents the reactance between power grid node j and power grid node i;

[0099] The constraints on node voltage and line current are given by the following formula:

[0100]

[0101] Among them: U i (t) represents the square of the voltage at node i of the power grid at time t; U j (t) represents the square of the voltage at node j of the power grid at time t; This represents the square of the lower limit of the voltage at a power grid node. I represents the square of the upper limit of the voltage at a power grid node; max Indicates the maximum current flowing through the branch;

[0102] The power output constraint of new energy sources is given by the following formula:

[0103]

[0104] Where: P new,i (t) represents the predicted maximum active power output of the renewable energy power plant at time t for the i-th power grid node; S new,i This represents the installed capacity of the new energy power station at the i-th power grid node;

[0105] The energy balance constraint of an energy storage system is given by the following formula:

[0106]

[0107] Where: C E,i (t+Δt) represents the capacity state of the energy storage system at time t+Δt on the i-th power grid node; Δt represents the time interval; C E,i (t) represents the capacity status of the energy storage system at time t on the i-th power grid node; This represents the charging efficiency of the energy storage system at the i-th power grid node; This represents the discharge efficiency of the energy storage system at the i-th power grid node; This represents the lower limit of the capacity status of the energy storage system at the i-th power grid node; C represents the upper limit of the capacity state of the energy storage system on the i-th power grid node; E,i (1) represents the initial capacity of the energy storage system on the i-th power grid node within a typical day; C E,i (N t ) represents the final capacity of the energy storage system on the i-th power grid node within a typical day; N t This indicates that there are a total of N in a typical day. t Each point in time.

[0108] The power flow optimization is completed by using an optimal power flow calculation method such as an interior point method, and active power, reactive power output of the distributed power supply, new energy power station and energy storage system and information such as node voltage and branch current of each power grid frame of the distribution network when all flexible loop devices and tie switches of the distribution network are disconnected are calculated, so that various technical indexes describing the operation state of the distribution network such as new energy consumption, maximum voltage deviation and load power shortage rate are calculated, and a set of operation state indexes γ = {target r | r = 1, 2,..., R} is formed.

[0109] target r r represents the rth index value in the set of operation state indexes of the distribution network, and R is the number of technical indexes describing the operation state of the distribution network.

[0110] S3. The state of the tie switch of the flexible loop device formed by the individual in the population is obtained according to the swarm intelligence algorithm.

[0111] The swarm intelligence optimization algorithm with discrete characteristics of the optimization variable is selected, parameters of the swarm intelligence optimization algorithm are set, the serial number, the number of individuals, the binary code string of the individual and the maximum number of iterations of the individual in the population are initialized, the state of the tie switch of the flexible loop device is defined as the controllable control amount of the optimization, the control amount is 0, which means that the tie switch of the flexible loop device is in the off state, and the control amount is 1, which means that the tie switch of the flexible loop device is in the closed state, so that the state of the tie switch of the flexible loop device formed by the individual in the population is obtained.

[0112] Taking the genetic algorithm as an example, the number of individuals in the population is set as N, the selection rate p s , the crossover rate p c , the mutation rate p m , the maximum number of iterations G and the like are set, the number of tie switches of the flexible loop device connected to the distribution network is z b , the number of time points in a typical day is N t , the iteration number g is set as 1 and the individual serial number n is set as 1, the binary code string of the N individuals in the gth generation is initialized, the length of the binary code string is z b ×N t , and the code bit of the individual corresponding to the state of the u th tie switch of the flexible loop device at the N t time points in a typical day is represented as [(u-1)×N t +1, u×N t ], and the code bit can only be 0 or 1.

[0113] S4. According to the power distribution network information and the day-ahead forecast values of each new energy power station and power load, considering the tie switch state of the flexible loop device formed by the individual obtained in step S3, a typical day operation model of the power distribution network is constructed and optimized, and the latest power distribution network operation state index set is formed according to the optimized typical day operation model of the power distribution network;

[0114] According to the power distribution network information and the day-ahead forecast values of each new energy power station and power load, considering the tie switch state of the flexible loop device formed by the individual obtained in step S3, the increase of the number of power grid branches and the change of the power grid form are analyzed, and a typical day operation model of the power distribution network is constructed;

[0115] A restriction condition for the capacity of the flexible loop device is added in the typical day operation model of the power distribution network, and the formula is as follows:

[0116]

[0117] Wherein: U a (t) represents the voltage at node a of the power grid at time t; U b (t) represents the voltage at node b of the power grid at time t; I a,b (t) represents the branch current between node a and node b of the power grid at time t; represents the rated capacity of the flexible loop between node a and node b of the power grid;

[0118] After optimizing the typical day operation model of the power distribution network by using the optimal power flow calculation method, the active power and reactive power output of the distributed power supply, the new energy power station and the energy storage system, and the voltage and branch current of each power grid node of the power distribution network are calculated, so as to calculate various technical indexes for describing the operation state of the power distribution network, such as new energy consumption, maximum voltage deviation and load power shortage rate, thereby forming the latest power distribution network operation state index set

[0119] Wherein: represents the rth index value in the latest power distribution network operation state index set of the nth individual in the gth generation.

[0120] S5. According to the power distribution network operation state index set in step S2 and the latest power distribution network operation state index set in step S4, the individual fitness value is calculated;

[0121] Considering the power distribution network operation state index set, the latest power distribution network operation state index set and the case that the coding bit in the individual binary coding string is 1, the fitness value F (g) (n) of the nth individual in the gth generation is calculated.

[0122]

[0123] Wherein: ω represents the sum of each coding bit in the binary coding string of the nth individual in the gth iteration, ω r ω is a weight coefficient of the rth index value in the set of set power grid operation state indexes, which is set according to the importance of each index value, and the higher the weight coefficient, the higher the importance;

[0124] The individual with the highest individual fitness value is the optimal individual.

[0125] S6. According to the swarm intelligence algorithm in step S3 and the individual fitness value obtained in step S5, the optimal individual represented by the contact switch state of the access flexible loop device under the maximum iteration number is obtained, and a day-ahead optimal active power adjustment control plan of the power grid is formed; specifically including the following steps:

[0126] S6.1. After the gth iteration, it is judged whether the individual number n is less than the individual number N, if yes, the individual number n is increased by 1 and the step S4 is returned; if no, the optimal individual after the gth iteration is recorded and step S6.2 is entered;

[0127] S6.2. It is judged whether the iteration number g is less than the maximum iteration number G, if yes, the iteration number g is increased by 1, the individual number n is set to 1 and step S6.3 is entered; if no, step S6.4 is entered;

[0128] S6.3. The iteration update formula of the swarm intelligence algorithm in step S3 is obtained, and all N individuals in the gth iteration population are updated using the iteration update formula, a new population consisting of N individuals in the gth generation is formed, and step S4 is returned; taking the genetic optimization algorithm as an example, the binary coding string of all N individuals in the gth generation population is updated by using selection, crossover, mutation, and reinsertion operations, a new population consisting of N individuals in the gth generation is formed, and step S4 is returned;

[0129] S6.4. The optimal individual under the maximum iteration number G is obtained, and the contact switch state of the access flexible loop device is represented, and a day-ahead optimal active power adjustment control plan of the power grid is formed.

[0130] Embodiment 2

[0131] As shown in the IEEE33 node power grid structure, Figure 4 Figure 4 The distributed power supply, three photovoltaic power stations, two energy storage systems and the load user access position are indicated in the IEEE33 node power grid structure, the new energy power station can perform output active and reactive power adjustment, the flexible loop device can realize the connection of two power network nodes during active power adjustment, and the flexible loop device auxiliary contact switch is disconnected when the active power adjustment is not performed, and the two power network nodes are not connected.

[0132] Please refer to Figure 2 ​As shown, the application provides a power distribution network day-ahead active power control device, comprising:

[0133] An information acquisition module is configured to acquire power distribution network information and day-ahead predicted values of each new energy power station and power load.

[0134] The power distribution network information includes power distribution network structure and various power sources, flexible loop closing controllers and load information; specifically including: power grid line parameters, voltage levels, system capacity, flexible loop closing device and tie switch access position, transfer capacity, distributed power source, new energy system, energy storage system access position and installed capacity, energy storage system capacity state, charging efficiency and discharging efficiency, output active power, reactive power, current, resistance and reactance between power grid node, power grid node quantity, load active power on the power grid node, load reactive power on the power grid node, actual active power obtained by the power load on the power grid node, actual reactive power obtained by the power load on the power grid node, etc.

[0135] The day-ahead predicted values of each new energy power station and power load are obtained by the following steps:

[0136] Acquire historical data information of new energy power stations and historical data information of each power grid node user load.

[0137] Based on the prediction model established by machine learning or regression model, the output active power of each new energy power station and the active power and reactive power demand of power load are predicted; thereby obtaining the day-ahead predicted values of each new energy power station and power load.

[0138] A first operating state index set module is configured to calculate technical indexes describing the operating state of the power distribution network when all flexible loop closing devices and tie switches in the power distribution network are disconnected, based on the power distribution network information and the day-ahead predicted values of each new energy power station and power load, with the maximum load power consumption guarantee as the target, to form a power distribution network operating state index set.

[0139] The optimization variables of the optimal power flow calculation method of the regional power distribution network include: new energy power station actual output active power and reactive power variables, whose values are continuous variables; energy storage system output active power and reactive power variables, whose values are continuous variables; voltage of each power grid node on the power network and current on each power network branch; actual active power and reactive power obtained by the power load, whose values are continuous variables.

[0140] In order to guarantee the power load of the power system as much as possible, the amount of load shedding is expected to be reduced, and the optimization objective function of the power distribution network for guaranteeing the power load to the greatest extent is defined, and the formula is as follows:

[0141]

[0142] wherein: J represents the objective function; Node represents the number of nodes of the power grid framework; X load,i (t) represents the actual active power obtained by the power load on the i th node of the power grid framework at time t; P load,i (t) represents the active power of the load on the i th node of the power grid framework at time t; Q load,i (t) represents the reactive power of the load on the i th node of the power grid framework at time t; Y load,i (t) represents the actual reactive power obtained by the power load on the i th node of the power grid framework at time t.

[0143] The regional power distribution network operation constraints are set, specifically including:

[0144] The active power and reactive power balance constraints of the power system are as follows:

[0145]

[0146] wherein: X E,i (t) represents the actual output active power of the energy storage system of the i th node of the power grid framework at time t; X new,i (t) represents the actual output active power of the new energy power station of the i th node of the power grid framework at time t; P i,j (t) represents the active power output from the i th node of the power grid framework to the j th node of the power grid framework; I i,j (t) represents the current square output from the i th node of the power grid framework to the j th node of the power grid framework; R i,j represents the resistance between the power grid node j and the power grid node i; Y E,i (t) represents the actual output reactive power of the energy storage system of the i th node of the power grid framework at time t; Y new,i (t) represents the actual output reactive power of the new energy power station of the i th node of the power grid framework at time t; Q i,j (t) represents the reactive power output from the i th node of the power grid framework to the j th node of the power grid framework; X i,j represents the reactance between the power grid node j and the power grid node i;

[0147] The node voltage and line current constraints are as follows:

[0148]

[0149] wherein: U i (t) represents the square of the voltage of the i th node of the power grid framework at time t; U j (t) represents the square of the voltage of the j th node of the power grid framework at time t; represents the square of the lower limit value of the voltage of the power grid node; represents the square of the upper limit value of the voltage of the power grid frame node; I max represents the maximum passing current of the branch;

[0150] New energy power output constraint, the formula is as follows:

[0151]

[0152] Wherein: P new,i (t) represents the maximum output active power of the new energy power station at the i-th power grid frame node at t time; S new,i represents the installed capacity of the new energy power station at the i-th power grid frame node;

[0153] Energy storage system energy balance constraint, the formula is as follows:

[0154]

[0155] Wherein: C E,i (t+Δt) represents the capacity state of the energy storage system at t+Δt time at the i-th power grid frame node; Δt represents the time interval; C E,i (t) represents the capacity state of the energy storage system at t time at the i-th power grid frame node; represents the charging efficiency of the energy storage system at the i-th power grid frame node; represents the discharging efficiency of the energy storage system at the i-th power grid frame node; represents the lower limit value of the capacity state of the energy storage system at the i-th power grid frame node; represents the upper limit value of the capacity state of the energy storage system at the i-th power grid frame node; C E,i (1) represents the initial capacity of the energy storage system at the i-th power grid frame node within a typical day; C E,i (N t ) represents the final capacity of the energy storage system at the i-th power grid frame node within a typical day; N t represents that there are N t time points in a typical day.

[0156] Using optimal power flow calculation method such as interior point method, the power flow optimization is completed, and the active power, reactive power output of distributed power supply, new energy power station and energy storage system and the voltage of each power grid frame node and the current of branch in distribution network when all flexible loop devices and tie switches are disconnected are calculated, so as to calculate various technical indexes for describing the operation state of distribution network, such as new energy consumption, voltage maximum deviation, load power shortage rate, etc., to form the distribution network operation state index set γ={targe r |r=1,2,...,R};

[0157] Wherein: targe rRth represents the rth index value in the power distribution network operation state index set, and R represents the number of technical indexes describing the operation state of the power distribution network.

[0158] A swarm intelligence algorithm module is configured to obtain the tie switch state of the flexible loop device formed by the individual in the population according to a swarm intelligence algorithm.

[0159] The swarm intelligence optimization algorithm with discrete characteristics is selected, the parameters of the swarm intelligence optimization algorithm are set, the serial number, the number of individuals, the binary code string of the individual and the maximum number of iterations of the individual in the population are initialized, the tie switch state of the flexible loop device accessed is defined as a controllable optimization control variable, the control variable is 0, indicating that the tie switch of the flexible loop device accessed is in an off state, and the control variable is 1, indicating that the tie switch of the flexible loop device accessed is in a closed state; and thus the tie switch state of the flexible loop device formed by the individual in the population is obtained.

[0160] Taking a genetic algorithm as an example, the number of individuals in the population is set to N, the selection rate p s , the crossover rate p c , the mutation rate p m , the maximum number of iterations G, the number of tie switches of the flexible loop device accessed by the power distribution network is z b , the number of time points in a typical day is N t , the iteration number g is set to 1 and the individual serial number n is set to 1, the binary code string of the N individuals in the gth generation is initialized, the length of the binary code string is z b ×N t , the individual code bit corresponding to the state of the u th tie switch of the flexible loop device accessed in N t time points in a typical day is represented as [(u-1)×N t +1,u×N t ], and the code bit can only be 0 or 1.

[0161] The second operation state index set module is configured to consider the tie switch state of the flexible loop device formed by the individual, construct a power distribution network typical day operation model and optimize the power distribution network typical day operation model according to the power distribution network information and the day-ahead prediction values of each new energy power station and power load, and form the latest power distribution network operation state index set according to the optimized power distribution network typical day operation model.

[0162] According to the power distribution network information and the day-ahead prediction values of each new energy power station and power load, considering the tie switch state of the flexible loop device formed by the individual obtained by the swarm intelligence algorithm module, the increase of the number of power grid branches and the change of the power grid form are analyzed, and a power distribution network typical day operation model is constructed.

[0163] A restriction condition for the capacity of the flexible loop device is added in the power distribution network typical day operation model, and the formula is as follows:

[0164]

[0165] wherein: U a (t) represents the voltage at the power grid node a at time t; U b (t) represents the voltage at the power grid node b at time t; I a,b (t) represents the branch current between the power grid node a and the power grid node b at time t; represents the rated capacity of the flexible loop closing device between the power grid node a and the power grid node b;

[0166] After optimizing the typical day operation model of the distribution network by using the optimal power flow calculation method, the active power and reactive power outputs of the distributed power supply, the new energy power station and the energy storage system, and the voltage and branch current of each power grid node of the distribution network are calculated, so as to calculate various technical indexes for describing the operation state of the distribution network, such as new energy consumption, maximum voltage deviation and load power shortage rate, thereby forming the latest distribution network operation state index set

[0167] wherein: represents the rth index value in the latest distribution network operation state index set of the nth individual in the gth generation.

[0168] The individual fitness value calculation module is configured to calculate the individual fitness value according to the distribution network operation state index set and the latest distribution network operation state index set.

[0169] The individual fitness value F (n) of the nth individual in the gth generation is calculated considering the distribution network operation state index set, the latest distribution network operation state index set and the case that the coding bit in the binary coding string is 1. (g)

[0170]

[0171] wherein: represents the sum of each coding bit in the binary coding string of the nth individual in the gth iteration, ω r is a weight coefficient of the rth index value in the distribution network operation state index set, which is set according to the importance of each index value, and the higher the weight coefficient, the higher the importance.

[0172] The individual with the highest individual fitness value is the optimal individual.

[0173] The power control plan module is configured to obtain the contact switch state of the flexible loop closing device represented by the optimal individual under the maximum iteration number according to the swarm intelligence algorithm and the individual fitness value, so as to form the day-ahead optimal active power control plan of the distribution network, and specifically includes the following steps:

[0174] ​1) after the gth iteration, judging whether the individual number n is less than the individual number N, if yes, adding 1 to the individual number n and returning to the second running state index set module, if no, recording the optimal individual after the gth iteration and entering step 2);

[0175] 2) judging whether the iteration number g is less than the maximum iteration number G, if yes, adding 1 to the iteration number g, setting the individual number n to 1 and entering step 3); if no, entering step 4);

[0176] 3) obtaining the iteration update formula of the swarm intelligence algorithm in the swarm intelligence algorithm module, updating all N individuals in the gth iteration population by using the iteration update formula, forming a new population consisting of N individuals in the gth generation and returning to the second running state index set module; taking the genetic optimization algorithm as an example, using selection, crossover, mutation, reinsertion and other operations to update the binary code string of all N individuals in the gth generation population, forming a new population consisting of N individuals in the gth generation and returning to the second running state index set module;

[0177] 4) obtaining the contact switch state of the access flexible closed loop device represented by the optimal individual under the maximum iteration number G, forming a power distribution network day-ahead active power control plan.

[0178] Embodiment 3

[0179] Please refer to Figure 3 The electronic device 100 comprises a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and capable of running on the at least one processor 102, and at least one communication bus 104.

[0180] The memory 101 can be used to store the computer program 103, and the processor 102 can realize the steps of the power distribution network day-ahead active power control method of embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device.

[0181] The at least one processor 102 can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The processor 102 can be a microprocessor or the processor 102 can also be any conventional processor, etc., which is a control center of the electronic device 100, and connects various parts of the entire electronic device 100 through various interfaces and lines.

[0182] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a power grid day-ahead active power adjustment control method, and the processor 102 can execute the plurality of instructions to implement:

[0183] S1. Obtain power grid information, and day-ahead predicted values of each new energy power station and power load;

[0184] S2. According to the power grid information and the day-ahead predicted values of each new energy power station and power load, and taking maximum load power consumption guarantee as a target, calculate technical indicators describing the operating state of the power grid when all flexible loop devices and tie switches in the power grid are disconnected, to form a power grid operating state indicator set;

[0185] S3. Obtain the tie switch state of the flexible loop device formed by the individual according to the swarm intelligence algorithm;

[0186] S4. According to the power grid information and the day-ahead predicted values of each new energy power station and power load, and considering the tie switch state of the flexible loop device formed by the individual obtained in step S3, construct a typical day operating model of the power grid and optimize it, and form a latest power grid operating state indicator set according to the optimized typical day operating model of the power grid;

[0187] S5. Calculate the individual fitness value according to the power grid operating state indicator set in step S2 and the latest power grid operating state indicator set in step S4;

[0188] S6. Obtain the tie switch state of the access flexible loop device represented by the optimal individual under the maximum number of iterations according to the swarm intelligence algorithm in step S3 and the individual fitness value obtained in step S5, and form a power grid day-ahead optimal active power adjustment control plan.

[0189] Embodiment 4

[0190] The modules / units integrated in the electronic device 100, if implemented in the form of software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of each method embodiment described above can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms. The computer-readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, and read-only memory (ROM).

[0191] Those skilled in the art will understand that embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0192] The present application is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The means for implementing each flow or multiple flows and / or blocks Figure 1 The means for implementing each flow or multiple flows and / or blocks

[0193] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The means for implementing each flow or multiple flows and / or blocks Figure 1the function specified in the one or more blocks.

[0194] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operation steps are performed on the computer or other programmable data processing devices to generate computer-implemented processes, so that the instructions executed on the computer or other programmable devices provide processes for implementing the flowcharts Figure 1 one or more flows and / or blocks Figure 1 the steps of the function specified in the one or more blocks.

[0195] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, but not to limit it, although the above embodiments of the present application have been described in detail, those skilled in the art should understand: the specific embodiments of the present application can be modified or replaced by the same, without departing from the spirit and scope of the present application, any modification or equivalent replacement, which should be covered in the protection scope of the claims of the present application.

Claims

1. A day-ahead active power dispatching control method for a power distribution network, characterized in that, The method comprises the following steps: S1. Obtain power distribution network information, and day-ahead prediction values of each new energy power station and power consumption load; S2. According to the power distribution network information and the day-ahead prediction values of each new energy power station and power consumption load, calculate technical indicators describing the operation state of the power distribution network when all flexible loop devices and tie switches in the power distribution network are disconnected, with the maximum load power consumption guarantee as the target, to form a power distribution network operation state indicator set; S3. Obtain the tie switch state of the flexible loop device formed by each individual in the population according to the swarm intelligence algorithm; S4. According to the power distribution network information and the day-ahead prediction values of each new energy power station and power consumption load, consider the tie switch state of the flexible loop device formed by each individual obtained in step S3, construct a typical day operation model of the power distribution network and perform optimization, and form a latest power distribution network operation state indicator set according to the optimized typical day operation model of the power distribution network; S5. Calculate the individual fitness value according to the power distribution network operation state indicator set in step S2 and the latest power distribution network operation state indicator set in step S4; specifically including: The formula for calculating the fitness value of the individual in the generation is as follows: g The formula for calculating the fitness value of the individual in the generation is as follows: n The formula for calculating the fitness value of the individual in the generation is as follows: The formula for calculating the fitness value of the individual in the generation is as follows: wherein: denotes the sum of the coding bits in the binary coding string of the gth individual in the gth iteration, n is the weight coefficient of the rth index value in the set of power distribution network operating state indexes, r denotes the rth index value in the set of power distribution network operating state indexes, and R is the number of technical indexes describing the power distribution network operating state; denotes the rth index value in the set of power distribution network operating state indexes of the th individual in the th generation, g n r denotes the rth index value in the set of power distribution network operating state indexes of the th individual in the th generation.​​​​ The individual with the highest individual fitness value is the optimal individual; S6. Obtain the tie switch state of the flexible loop device represented by the optimal individual under the maximum number of iterations according to the swarm intelligence algorithm in step S3 and the individual fitness value obtained in step S5, and form a day-ahead optimal active power control plan of the power distribution network.

2. The power grid day-ahead active power dispatching control method according to claim 1, characterized in that, The step of calculating the technical indicators describing the operation state of the power distribution network when all flexible loop devices and tie switches in the power distribution network are disconnected, with the maximum load power consumption guarantee as the target, to form a power distribution network operation state indicator set according to the power distribution network information and the day-ahead prediction values of each new energy power station and power consumption load, specifically includes: The power distribution network information includes: energy storage system capacity state, energy storage system charging efficiency, energy storage system discharging efficiency, number of power grid nodes, output active power, reactive power, current, resistance and reactance between power grid nodes, installed capacity of new energy power station; The day-ahead prediction value of the new energy power station includes: day-ahead predicted output active power of the new energy power station; The optimization variables for selecting the optimal power flow calculation method of the power distribution network include: actual output active power and reactive power of the new energy power station, output active power and reactive power of the energy storage system, voltage of each power grid node on the power grid, current on each power grid branch, actual obtained active power and reactive power of the power consumption load; Define the optimization objective function of the power distribution network for the maximum load power consumption guarantee, and the formula is as follows: Where: J represents the objective function; Node represents the number of nodes in the power grid; express t Time of the first i The actual active power obtained by the electrical load at each node of the power grid; This represents the active power of the load at the i-th node of the power grid at time t; This represents the reactive power of the load at the i-th node of the power grid at time t; express t Time of the first i The actual reactive power obtained by the electrical load at each node of the power grid; Set the regional power distribution network operation constraint; The optimal power flow calculation method is used to complete power flow optimization, and various technical indexes describing the operation state of the distribution network are calculated to form a distribution network operation state index set ; wherein: denotes the rth index value in the set of distribution network operating state indices, and R is the number of technical indices describing the distribution network operating state.

3. The power grid day-ahead active power dispatching control method according to claim 2, characterized in that, The regional power distribution network operation constraint specifically includes: Active power and reactive power balance constraint of the power system, and the formula is as follows: wherein: represents the actual output active power of the energy storage system of the i th power grid framework node at the t th moment; represents the actual output active power of the new energy power station of the i th power grid framework node at the t th moment; represents the active power output from the i th power grid framework node to the j th power grid framework node; represents the current square output from the i th power grid framework node to the j th power grid framework node; represents the resistance between the j th power grid framework node and the i th power grid framework node; represents the actual output reactive power of the energy storage system of the i th power grid framework node at the t th moment; represents the actual output reactive power of the new energy power station of the i th power grid framework node at the t th moment; represents the reactive power output from the i th power grid framework node to the j th power grid framework node; represents the reactance between the j th power grid framework node and the i th power grid framework node; Node voltage and line current constraint, and the formula is as follows: wherein: represents the square of the voltage at the t moment of time at the power grid node i voltage; represents the square of the voltage at the t moment of time at the power grid node j voltage; represents the square of the lower voltage limit of the power grid node voltage; represents the square of the upper voltage limit of the power grid node voltage; represents the maximum passing current of the branch; New energy power output constraint, and the formula is as follows: wherein: represents the maximum output active power of the new energy power station at the time moment predicted by the day-ahead prediction of the i th power grid framework node; t th power grid framework node; represents the installed capacity of the new energy power station of the i th power grid framework node; Energy storage system energy balance constraint, and the formula is as follows: in: Indicates the first i Energy storage system at each power grid node The capacity status at any given moment; Indicates a time interval; Indicates the first i Energy storage system at each power grid node t The capacity status at any given moment; Indicates the first i Charging efficiency of energy storage systems on individual power grid nodes; Indicates the first i Discharge efficiency of energy storage systems at individual power grid nodes; Indicates the first i The lower limit of the capacity status of energy storage systems on each power grid node; Indicates the first i The upper limit of the capacity status of energy storage systems on each power grid node; Indicates the first i The initial capacity of an energy storage system on a power grid node in a typical day; Indicates the first i The final capacity of an energy storage system on a power grid node in a typical day; This indicates a typical day with a total of Each point in time.

4. The power grid day-ahead active power dispatching control method according to claim 2, characterized in that, The step of obtaining the tie switch state of the flexible loop device formed by each individual in the population according to the swarm intelligence algorithm specifically includes: Select a swarm intelligence optimization algorithm with discrete characteristics of optimization variables; Set the serial number of individuals in the population, the number of individuals, the binary code string of individuals and the maximum number of iterations, define the contact switch state of the flexible loop-in device as a controllable optimization control variable; Obtain the contact switch state of the flexible loop-in device formed by the individuals in the population.

5. The power grid day-ahead active power dispatching control method according to claim 4, characterized in that, According to the power distribution network information and the day-ahead forecast values of each new energy power station and power load, considering the contact switch state of the flexible loop-in device formed by the individuals obtained in step S3, a typical day operation model of the power distribution network is constructed and optimized, and the latest power distribution network operation state index set is formed according to the optimized typical day operation model of the power distribution network. According to the power distribution network information and the day-ahead forecast values of each new energy power station and power load, considering the contact switch state of the flexible loop-in device formed by the individuals, the increase of the number of power grid branches and the change of the power grid form are analyzed, and a typical day operation model of the power distribution network is constructed; In the typical day operation model of the power distribution network, a restriction condition for the capacity of the flexible loop-in device is added, and the formula is as follows: wherein: Vb(t) denotes the voltage at the power grid node b at time t; t Vb(t) denotes the voltage at the power grid node b at time t; a Vb(t) denotes the voltage at the power grid node b at time t; Vb(t) denotes the voltage at the power grid node b at time t; t Vb(t) denotes the voltage at the power grid node b at time t; Vb(t) denotes the voltage at the power grid node b at time t; t Vb(t) denotes the voltage at the power grid node b at time t; a Vb(t) denotes the voltage at the power grid node b at time t; b Vb(t) denotes the voltage at the power grid node b at time t; Vb(t) denotes the voltage at the power grid node b at time t; a Vb(t) denotes the voltage at the power grid node b at time t; b Vb(t) denotes the voltage at the power grid node b at time t; The optimal power flow calculation method is used to optimize a typical day operation model of a distribution network, technical indexes describing the operation state of the distribution network are calculated, and a latest operation state index set of the distribution network is formed ; wherein: represents the g th indicator value in the latest power distribution network operation state indicator set of the n th individual in the r th generation.

6. The power grid day-ahead active power dispatching control method according to claim 1, characterized in that, According to the group intelligent algorithm in step S3 and the individual fitness value obtained in step S5, the contact switch state of the flexible loop-in device represented by the optimal individual under the maximum number of iterations is obtained, and the day-ahead optimal active power control plan of the power distribution network is formed. S6.

1. After the gth iteration, determine whether the individual serial number is less than the number of individuals, if yes, add 1 to the individual serial number and return to step S4; if not, record the optimal individual after the gth iteration and enter step S6.2; S6.

2. Determine whether the iteration number is less than the maximum iteration number, if yes, add 1 to the iteration number, set the individual serial number to 1 and enter step S6.3; if not, enter step S6.4; S6.

3. Obtain the iteration update formula of the group intelligent algorithm in step S3, update all N individuals in the gth iteration population using the iteration update formula, form a new population consisting of N individuals in the gth generation, and return to step S4; S6.

4. Obtain the contact switch state of the flexible loop-in device represented by the optimal individual under the maximum number of iterations, and form the day-ahead optimal active power control plan of the power distribution network.

7. A power distribution network day-ahead active power dispatching control device, characterized in that, It comprises: An information acquisition module is used to acquire power distribution network information and day-ahead forecast values of each new energy power station and power load; A first operation state index set module is used to calculate technical indicators describing the operation state of the power distribution network when all flexible loop-in devices and contact switches in the power distribution network are disconnected, form a power distribution network operation state index set, according to the power distribution network information and the day-ahead forecast values of each new energy power station and power load, and take the maximum load power consumption guarantee as the target; A group intelligent algorithm module is used to obtain the contact switch state of the flexible loop-in device formed by the individuals in the population according to the group intelligent algorithm; A second operation state index set module is used to construct and optimize a typical day operation model of the power distribution network according to the power distribution network information and the day-ahead forecast values of each new energy power station and power load, considering the contact switch state of the flexible loop-in device formed by the individuals, and form the latest power distribution network operation state index set according to the optimized typical day operation model of the power distribution network. An individual fitness value calculation module is configured to calculate an individual fitness value according to the power distribution network operation state index set and the latest power distribution network operation state index set; Specifically comprising: The formula for calculating the fitness value of the individual in the generation is as follows: g The formula for calculating the fitness value of the individual in the generation is as follows: n The formula for calculating the fitness value of the individual in the generation is as follows: The formula for calculating the fitness value of the individual in the generation is as follows: in: In the g-th iteration, the first... n The sum of each encoded bit in the binary encoded string of each individual. The first in the set of distribution network operation status indicators r Weighting coefficients for each indicator value; The individual with the highest individual fitness value is the optimal individual; A power adjustment control plan module is configured to obtain, according to the swarm intelligence algorithm and the individual fitness value, a state of a tie-in switch of the flexible loop closing device represented by the optimal individual under the maximum number of iterations, and form a power distribution network day-ahead optimal active power adjustment control plan.

8. An electronic device, comprising: The computer readable storage medium stores at least one instruction, and the at least one instruction is executed by the processor to implement the power distribution network day-ahead active power adjustment control method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores at least one instruction, and the at least one instruction is executed by the processor to implement the power distribution network day-ahead active power adjustment control method according to any one of claims 1 to 6.

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