CN112039066B

CN112039066B - New energy consumption capacity optimization method and device applied to power distribution network

Info

Publication number: CN112039066B
Application number: CN202010903889.XA
Authority: CN
Inventors: 陈岩; 李征; 靳伟; 李剑锋; 陈秦超; 王光远; 李泽卿; 王浩; 贾清泉; 王珺; 孙玲玲; 王宁; 陶涛; 王伟; 于辉; 王云改; 张瑞峰; 范彦伟; 田非; 陈晓军
Original assignee: State Grid Corp of China SGCC; Yanshan University; State Grid Hebei Electric Power Co Ltd; Xingtai Power Supply Co of State Grid Hebei Electric Power Co Ltd; Ja Solar Co Ltd
Current assignee: State Grid Corp of China SGCC; Yanshan University; State Grid Hebei Electric Power Co Ltd; Xingtai Power Supply Co of State Grid Hebei Electric Power Co Ltd; Jingao Solar Co Ltd
Priority date: 2020-09-01
Filing date: 2020-09-01
Publication date: 2022-05-03
Anticipated expiration: 2040-09-01
Also published as: CN112039066A

Abstract

The application discloses new energy consumption ability optimization method applied to a power distribution network, which comprises the following steps: acquiring data of a power distribution network; according to the power distribution network data, constructing a new energy consumption capacity maximum objective function, a power distribution network autonomous operation index constraint condition, a power distribution network operation constraint condition and a power distribution network controllable resource calling constraint condition; taking the power distribution network autonomous operation index constraint condition, the power distribution network operation constraint condition and the power distribution network controllable resource calling constraint condition as constraint conditions in a genetic algorithm, and solving an optimal solution for the maximum target function of the new energy consumption capacity based on the genetic algorithm and power distribution network related parameters, wherein the power distribution network related parameters are influence parameters related to the new energy consumption capacity in the power distribution network; and configuring the power distribution network based on the related parameters of the power distribution network when the optimal solution is obtained. The method provided by the application is beneficial to more accurately calculating the maximum consumption capacity of the power distribution network to the new energy and carrying out optimal configuration.

Description

New energy consumption capacity optimization method and device applied to power distribution network

Technical Field

The application relates to the technical field of power distribution network planning, in particular to a new energy consumption capacity optimization method and device applied to a power distribution network.

Background

With the increasing severity of energy problems and environmental problems, new energy sources such as wind and light become important means for promoting energy transformation development in various countries in the world. At present, new energy in China is changed from supplementary energy to large-scale replacement, a large amount of distributed wind and light new energy is gushed into a power distribution network, but the new energy can cause the problem of power disturbance, and the existing power distribution network cannot directly realize the consumption of all new energy and needs to be reasonably planned.

In the prior art, a wind power plant and a photoelectric field are often arranged in an area with large load and strong local power transmission capacity, so that the consumption level of a power distribution network on new energy is improved. The prior art has the defect that scientific calculation and global planning are not carried out, so that the maximum consumption capacity of a power distribution network on new energy is not obtained.

Disclosure of Invention

The application provides a new energy consumption capability optimization method and device applied to a power distribution network, scientific calculation and global planning can be carried out on the new energy consumption capability of the power distribution network, and the maximum consumption capability of the power distribution network to new energy can be obtained.

In order to achieve the above technical effect, a first aspect of the present application provides a new energy consumption capability optimization method applied to a power distribution network, where the new energy consumption capability optimization method includes:

acquiring data of a power distribution network;

according to the power distribution network data, constructing a new energy consumption capacity maximum objective function, a power distribution network autonomous operation index constraint condition, a power distribution network operation constraint condition and a power distribution network controllable resource calling constraint condition;

taking the power distribution network autonomous operation index constraint condition, the power distribution network operation constraint condition and the power distribution network controllable resource calling constraint condition as constraint conditions in a genetic algorithm, and solving an optimal solution for the maximum target function of the new energy consumption capacity based on the genetic algorithm and power distribution network related parameters, wherein the power distribution network related parameters are influence parameters related to the new energy consumption capacity in the power distribution network;

and configuring the power distribution network based on the related parameters of the power distribution network when the optimal solution is obtained.

Optionally, the solving an optimal solution for the maximum objective function of the new energy absorption capacity based on the genetic algorithm and the relevant parameters of the power distribution network includes:

step 1: establishing a population and generating an initial population individual based on the power distribution network related parameters, setting the iteration number to be 1, initializing an optimal target value and corresponding target power distribution network related parameters, wherein the optimal target value is the value of the target function with the maximum new energy consumption capacity, and the target power distribution network related parameters are the values of the power distribution network related parameters when the optimal target value is obtained;

step 2: judging whether the numerical value of the population individual meets the constraint condition of the power distribution network autonomous operation index;

and step 3: when the numerical value of the population individual does not meet the constraint condition of the distribution network autonomous operation index, adjusting a distribution network absorption scheme based on the autonomous operation index limit value, so that the adjusted numerical value of the population individual meets the constraint condition of the distribution network autonomous operation index;

and 4, step 4: when the numerical value of the population individual meets the constraint condition of the power distribution network autonomous operation index, judging whether the numerical value of the population individual meets the constraint condition of the power distribution network operation and the constraint condition of the power distribution network controllable resource calling;

and 5: when the numerical value of the population individual does not meet the operation constraint condition of the power distribution network or the controllable resource calling constraint condition of the power distribution network, crossing and varying the population individual based on a genetic algorithm, and updating the numerical value of the population individual so that the updated numerical value of the population individual meets the operation constraint condition of the power distribution network and the controllable resource calling constraint condition of the power distribution network;

step 6: when the numerical value of the population individual meets the operation constraint condition of the power distribution network and the controllable resource calling constraint condition of the power distribution network, calculating the value of a new energy absorption capacity maximum objective function and judging whether the value of the new energy absorption capacity maximum objective function is larger than an optimal target value or not, when the value of the new energy absorption capacity maximum objective function is larger than the optimal target value, updating the optimal target value to the value of the new energy absorption capacity maximum objective function, and updating the relevant parameters of the target power distribution network to the values of the corresponding power distribution network relevant parameters when the value of the new energy absorption capacity maximum objective function is obtained, wherein the value of the new energy absorption capacity maximum objective function is equal to the sum of the numerical values of the various population individuals;

and 7: judging whether the iteration times are greater than a preset termination iteration time, when the iteration times are not greater than the termination iteration times, performing crossing and variation on the population individuals based on a genetic algorithm, updating the numerical values of the population individuals, and returning to the step 2 after increasing the iteration times by 1;

and step 8: and when the iteration times are greater than the termination iteration times, outputting the optimal target value and the corresponding target power distribution network related parameters.

Optionally, the power distribution network data comprises power distribution network structure parameters, new energy power generation prediction data, load prediction data, energy storage data, demand side response data, a reconfiguration switch action frequency constraint limit value and an autonomous operation index limit value;

the construction of the maximum target function of the new energy consumption capacity, the power distribution network autonomous operation index constraint condition, the power distribution network operation constraint condition and the power distribution network controllable resource calling constraint condition according to the power distribution network data specifically comprises the following steps:

constructing a new energy consumption capacity maximum objective function according to the new energy power generation prediction data;

according to the self-discipline operation index limit value, a power distribution network self-discipline operation index constraint condition is constructed;

constructing a power distribution network operation constraint condition according to the power distribution network structure parameters, the new energy power generation prediction data and the load prediction data;

and constructing a power distribution network controllable resource calling constraint condition according to the power distribution network structure parameters, the reconstruction switch action frequency constraint limit value, the energy storage data and the demand side response data.

Optionally, the distribution network autonomous operation index limit includes: the method comprises the steps of receiving power peak-valley limit value, receiving power change rate limit value and power distribution network reserve capacity adjustable margin;

the constructing of the power distribution network autonomous operation index constraint condition according to the autonomous operation index limit specifically includes: and constructing a power distribution network autonomous operation index constraint condition according to the received power peak-valley limit value, the received power change rate limit value and the power distribution network reserve capacity adjustable margin.

This application second aspect provides a new forms of energy consumption ability optimizing arrangement for distribution network, and above-mentioned new forms of energy consumption ability optimizing arrangement includes:

the data acquisition module is used for acquiring the data of the power distribution network;

the data processing module is used for constructing a new energy consumption capacity maximum target function, a distribution network autonomous operation index constraint condition, a distribution network operation constraint condition and a distribution network controllable resource calling constraint condition according to the distribution network data;

the calculation module is used for taking the power distribution network autonomous operation index constraint condition, the power distribution network operation constraint condition and the power distribution network controllable resource calling constraint condition as constraint conditions in a genetic algorithm, and solving an optimal solution for the maximum target function of the new energy consumption capacity based on the genetic algorithm and power distribution network related parameters, wherein the power distribution network related parameters are influence parameters related to the new energy consumption capacity in the power distribution network;

and the configuration module is used for configuring the power distribution network based on the related parameters of the power distribution network when the optimal solution is obtained.

Optionally, the calculating module is specifically configured to execute the following steps:

and 8: and when the iteration times are greater than the termination iteration times, outputting the optimal target value and the corresponding target power distribution network related parameters.

Optionally, the power distribution network data include power distribution network structure parameters, new energy power generation prediction data, load prediction data, energy storage data, demand side response data, a reconfiguration switch action frequency constraint limit value and an autonomous operation index limit value;

the data processing module is specifically configured to: constructing a new energy consumption capacity maximum objective function according to the new energy power generation prediction data;

the data processing module is specifically configured to: and constructing a power distribution network autonomous operation index constraint condition according to the received power peak-valley limit value, the received power change rate limit value and the power distribution network reserve capacity adjustable margin.

A third aspect of the present application provides an electronic device, including a memory and a processor, where the memory stores a software program, and the processor implements the steps of the new energy consumption capability optimization method when executing the software program.

A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, where the computer program is adapted to, when executed by a processor, perform the steps of the new energy absorption capacity optimization method.

Compared with the prior art, the method has the advantages that the genetic algorithm is introduced into the new energy consumption capability optimization method, the power distribution network autonomous index constraint condition, the power distribution network operation constraint condition and the power distribution network controllable resource calling constraint condition are used as the constraint conditions in the genetic algorithm, and the new energy consumption capability of the power distribution network is scientifically calculated and globally planned, so that the maximum consumption capability of the power distribution network on new energy can be more accurately calculated and optimized.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.

Fig. 1 is a flowchart of a new energy maximum consumption capability calculation method according to an embodiment of the present application;

fig. 2 is a flowchart of a specific manner of solving an optimal solution for a maximum objective function of new energy absorption capacity according to an embodiment of the present application;

FIG. 3 is a flowchart illustrating an embodiment of step 206 shown in FIG. 2;

fig. 4 is a schematic diagram of a new energy maximum consumption capability calculation apparatus according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail. It will be understood that the term "comprising" when used in this specification and the appended claims indicates the presence of the stated features but does not preclude the presence or addition of one or more other features.

The technical solutions in the embodiments of the present application are clearly and completely described below with reference to the drawings of the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

A first aspect of an embodiment of the present application provides a new energy consumption capability optimization method applied to a power distribution network, as shown in fig. 1, where the new energy consumption capability optimization method includes:

step 101, acquiring power distribution network data.

And 102, constructing a new energy consumption capacity maximum target function, a distribution network autonomous operation index constraint condition, a distribution network operation constraint condition and a distribution network controllable resource calling constraint condition according to the distribution network data.

Optionally, the power distribution network data comprises power distribution network structure parameters, new energy power generation prediction data, load prediction data, energy storage data, demand side response data, a reconfiguration switch action frequency constraint limit value and an autonomous operation index limit value; the construction of the maximum target function of the new energy consumption capacity, the power distribution network autonomous operation index constraint condition, the power distribution network operation constraint condition and the power distribution network controllable resource calling constraint condition according to the power distribution network data specifically comprises the following steps: constructing a new energy consumption capacity maximum objective function according to the new energy power generation prediction data; according to the self-discipline operation index limit value, a power distribution network self-discipline operation index constraint condition is constructed; constructing a power distribution network operation constraint condition according to the power distribution network structure parameters, the new energy power generation prediction data and the load prediction data; and constructing a power distribution network controllable resource calling constraint condition according to the power distribution network structure parameters, the reconstruction switch action frequency constraint limit value, the energy storage data and the demand side response data.

the constructing of the power distribution network autonomous operation index constraint condition according to the autonomous operation index limit specifically includes: and constructing a constraint condition of the power distribution network autonomous operation index according to the received power peak-valley limit value, the received power change rate limit value and the adjustable margin of the reserve capacity of the power distribution network.

In this embodiment, the maximum objective function of the new energy absorption capacity is shown in formula (1):

in the formula (1), S_G,iEstablishing node access capacity for each new energy in the power distribution network; omega_GAnd constructing a node collection for the new energy of the power distribution network.

In this embodiment, the new energy accessed by the power distribution network includes photovoltaic power generation and wind power generation, and therefore, the access capacity of each new energy construction node is as shown in formula (2):

S_G,i＝S_PV,i+S_W,i (2)

in the formula (2), S_PV,iAnd S_W,iThe photovoltaic power generation capacity and the wind power generation capacity which are respectively accessed to the node i.

Let the power receiving power of the distribution network at time t be P_G(t), in this embodiment, the distribution network autonomous operation index conditions include a received power peak-valley limit constraint shown in formula (3), a received power change rate constraint shown in formula (4), and a distribution network spare capacity adjustable margin constraint shown in formula (5):

in the formula (3), the first and second groups,

and

the peak-to-valley upper limit value and the lower limit value of the received power of the power distribution network are respectively.

In the formula (4), Δ P_G(t) represents the rate of change of the received power at time t;

and

respectively an upper limit value and a lower limit value of the change rate of the received power of the power distribution network.

Equation (5)) In, P_AN(t) the adjustable reserve capacity margin of the power distribution network at the moment t; p_ESS,AN(t) reserving power for energy storage charging and discharging at the moment t; p is_DR,AN(t) calling reserved power for Demand side Response (DR) at time t; h is_G(t) the response speed of the power distribution network at the moment t; sigma is the response rate limit value of the power distribution network; optionally, the σ may be preset to be 0.5% to 3%.

In this embodiment, the power distribution network operation constraint conditions include a power distribution network power flow constraint condition considering network reconfiguration as shown in the following formulas (6) and (7), a power distribution network access capacity constraint condition as shown in the following formula (8), a power distribution network power balance constraint condition as shown in the following formula (9), and a power distribution network operation safety constraint condition as shown in the following formula (10).

In the formula (6), A_ij(t) is the connection status of the branch i-j (representing the branch from node i to node j) at time t, A_ij(t) ═ 1 indicates that branch i-j at time t is connected, A_ij(t) ═ 0 indicates that branch i-j is open at time t; n (i) is a node set connected with the node i in the power distribution network; p_ij(t) is the active power flowing through branch i-j at time t; q_L,i(t) is reactive load at a node i at time t; q_ij(t) is the reactive power flowing through branch i-j at time t; p_ESS.c、P_ESS.disRespectively single-point energy storage charging and discharging power; p_TL.d,iAnd P_TL.r,iRespectively carrying out controllable load transfer in and out of active power for a node i considering network reconstruction; p_L,i(t) is the active load of node i at time t, P'_W,i(t) and P'_PV,i(t) respectively connecting the wind power and the photovoltaic power generation active power of the power distribution network after the power distribution network reconfiguration switch acts at the moment t; q_ij(t) is the reactive power flowing through branch i-j at time t; q_TL.d,iAnd Q_TL.r,iRespectively taking the controllable load of a node i of the network reconstruction into consideration and shifting reactive power in and out; q_DG,i(t) is the reactive power generated by new energy such as wind, light and the like accessed to the node i at the time tA sum of powers; p_IL,i(t) and Q_IL,iAnd (t) the active power and the reactive power of the interruptible load accessed by the node i at the moment t respectively. In the above equation (6), the right side of the equation represents the total active outflow power and the total reactive outflow power of all branches connected to the node i at time t, respectively.

Adding the above-mentioned P_ij(t) and Q_ijThe expressions of (t) are respectively subjected to imaginary part and real part separation and simplification, as shown in formula (7):

in the formula (7), G_ij、B_ijAnd theta_ijRespectively are the mutual conductance, mutual susceptance and voltage phase angle difference between nodes i and j, U is voltage, theta_i(t) and θ_j(t) is the phase of the voltages at node i and node j, respectively, at time t. And combining the formula (6) and the formula (7) to obtain the power flow constraint condition of the power distribution network considering the network reconstruction.

In this embodiment, the capacity of the single node of the power distribution network accessing photovoltaic power generation and wind power generation needs to satisfy the formula (8):

in the formula (8), S_W,maxAnd S_PV,maxWind power and photovoltaic power generation capacity upper limits which are accessed by single points respectively; omega_WAnd Ω_PVRespectively a wind power and photovoltaic power generation access node collection.

In this embodiment, the operation data of the distribution network every 1 hour in 24 hours in the whole day period is used as a research object, and the received power before and after the regulation of the distribution network satisfies the formula (9):

in formula (9), P_D(t) is regulationA receive forward power; p_W(t) and P_PV(t) the output power of wind power and photovoltaic at the moment t respectively; p_L(t) load power at time t; p_DNR(t) reconstructing power of the power distribution network; p_ESS(t) storing energy and charging and discharging power at the moment t; p_DR(t) calling Power for time t DR, including interruptible load calling Power P_IL(t) and transferable load calling Power P_TL(t)；P_S(t) is the sum of the power values of the wind curtailment and the light curtailment, i.e. P_S(t)＝P_S,W(t)+P_S,PV(t); wherein t ∈ [1, 24 ]]。

Optionally, the power distribution network operation safety constraint conditions include node voltage and line ampacity operation index constraints in each time period:

in the formula (10), U_j(t) is the voltage amplitude of node j; u shape_j,minAnd U_j,maxMinimum and maximum limits for node voltage, respectively; i is_ij(t) and I_ij.maxThe current amplitude and the maximum limit of the branch i-j are respectively.

In this embodiment, the constraint conditions for invoking the controllable resources of the power distribution network include a constraint condition for reconfiguring the number of switching operations as shown in the following formula (11), an energy storage operation constraint as shown in the following formula (12), and a constraint condition for controlling loads as shown in the following formulas (13) and (14).

Frequent change of the network reconfiguration switch state in the day can affect the stable operation and the power quality of the power distribution network, and the switching times can also affect the service life of the reconfiguration switch, in this embodiment, the reconfiguration switch action time constraint condition is shown as formula (11):

in the formula (11), the reaction mixture,

to reconstruct the sum of the number of movements of the switch in a day, Δ Z_maxIs the limit for the number of reconfiguration switch actions in a day. Optionally,. DELTA.Z_maxThe value of (d) may be preset to 5 to 10.

In this embodiment, the energy storage operation constraint conditions include a charge/discharge power limit constraint, a remaining capacity limit constraint, a charge/discharge power balance constraint, and an energy storage time sequence operation constraint, as shown in formula (12):

in the formula (12), k and k_maxThe number of charge and discharge times and the limit value thereof; p_ESS(t) is the energy storage charge and discharge power; p_ESS,c.maxIs the maximum allowed charging power; p_ESS,d.maxIs the maximum allowable discharge power; s_SOC.maxAnd S_SOC.minThe upper and lower limit values of the residual electric quantity level; psi is the energy storage charge-discharge efficiency.

In this embodiment, the controllable load constraint condition includes an interruptible load constraint and a transferable load constraint. The interruptible load constraints include an interrupt capacity constraint, an interrupt number constraint and a continuous interrupt time constraint, as shown in equation (13):

in the formula (13), Δ P_IL,h(t) and P_IL,maxRespectively serving as an h node interrupt load power and a maximum allowable interrupt power at the time t; n is_IL,hAnd n_IL,h,maxRespectively the interruption times of the h node interruption load and the maximum allowable interruption times; t is_IL,h(t)、T_IL,h,maxAnd T_IL,h,minThe upper limit and the lower limit of the interruptible load interruption time and the allowable interruption time of the h node at the time t are respectively.

The transferable load constraints include transferable capacity constraints, transfer capacity balance constraints, and transfer times constraints, as shown in equation (14):

in formula (14), P_TL,d,maxThe maximum allowable power transfer for the transferable load at the moment t; n is_TL,mAnd n_TL,m,maxRespectively the transferable load transfer times of the m node and the maximum allowable transfer times.

And 103, taking the power distribution network autonomous operation index constraint condition, the power distribution network operation constraint condition and the power distribution network controllable resource calling constraint condition as constraint conditions in a genetic algorithm, and solving an optimal solution for the maximum target function of the new energy consumption capacity based on the genetic algorithm and relevant parameters of the power distribution network.

Wherein, the relevant parameters of the power distribution network are influence parameters related to the consumption capacity of the new energy in the power distribution network.

And 104, configuring the power distribution network based on the power distribution network related parameters when the optimal solution is obtained.

step 1031: establishing a population and generating an initial population individual based on the power distribution network related parameters, setting the iteration number to be 1, initializing an optimal target value and corresponding target power distribution network related parameters, wherein the optimal target value is the value of the target function with the maximum new energy consumption capacity, and the target power distribution network related parameters are the values of the power distribution network related parameters when the optimal target value is obtained;

step 1032: judging whether the numerical value of the population individual meets the constraint condition of the power distribution network autonomous operation index;

step 1033: when the numerical value of the population individual does not meet the constraint condition of the distribution network autonomous operation index, adjusting a distribution network absorption scheme based on the autonomous operation index limit value, so that the adjusted numerical value of the population individual meets the constraint condition of the distribution network autonomous operation index;

step 1034: when the numerical value of the population individual meets the constraint condition of the power distribution network autonomous operation index, judging whether the numerical value of the population individual meets the constraint condition of the power distribution network operation and the constraint condition of the power distribution network controllable resource calling;

step 1035: when the numerical value of the population individual does not meet the operation constraint condition of the power distribution network or the controllable resource calling constraint condition of the power distribution network, crossing and varying the population individual based on a genetic algorithm, and updating the numerical value of the population individual so that the updated numerical value of the population individual meets the operation constraint condition of the power distribution network and the controllable resource calling constraint condition of the power distribution network;

step 1036: when the numerical value of the population individual meets the operation constraint condition of the power distribution network and the controllable resource calling constraint condition of the power distribution network, calculating the value of a new energy absorption capacity maximum objective function and judging whether the value of the new energy absorption capacity maximum objective function is larger than an optimal target value or not, when the value of the new energy absorption capacity maximum objective function is larger than the optimal target value, updating the optimal target value to the value of the new energy absorption capacity maximum objective function, and updating the relevant parameters of the target power distribution network to the values of the corresponding power distribution network relevant parameters when the value of the new energy absorption capacity maximum objective function is obtained, wherein the value of the new energy absorption capacity maximum objective function is equal to the sum of the numerical values of the various population individuals;

step 1037: judging whether the iteration times are larger than a preset termination iteration time, when the iteration times are not larger than the termination iteration times, crossing and varying the population individuals based on a genetic algorithm, updating the numerical values of the population individuals, and returning to the step 1032 after increasing the iteration times by 1;

step 1038: and when the iteration times are greater than the termination iteration times, outputting the optimal target value and the corresponding target power distribution network related parameters.

Optionally, an operation cycle may be set, so that the power distribution network configured based on the target power distribution network related parameter corresponding to the optimal target value meets the power distribution network configuration requirement in the operation cycle.

In this embodiment, 365 days of a year is taken as an overall operation cycle, and 24 hours of a day is taken as a unit operation cycle, that is, the power distribution network configured based on the target power distribution network related parameters corresponding to the optimal target values should meet the power distribution network configuration requirements corresponding to 24 moments of each day of 365 days of a year.

As shown in fig. 2, a specific way for solving the optimal solution of the maximum objective function of the new energy absorption capacity based on a genetic algorithm and relevant parameters of the power distribution network, which takes 365 days of a year as an overall operation cycle and 24 hours of a day as a unit operation cycle, provided by the embodiment of the present application, includes:

step 201, initializing a genetic algorithm, establishing a population and initializing individual population based on relevant parameters of the power distribution network, setting the iteration number n to be 1, and initializing an optimal target value and corresponding relevant parameters of the target power distribution network; in this embodiment, the initial value of the optimal target value is set to be 0, and the corresponding target distribution network related parameters are all 0.

Optionally, the population individuals include active power and reactive power of each node of the power distribution network, power accessed to wind power generation, power accessed to photovoltaic power generation, power of an interruptible load, power of a transferable load, charge and discharge power of the energy storage device, and node voltage. And carrying out binary coding on the population individuals, wherein the code of the individual related to the node i is 1, and the code of the individual unrelated to the node i is 0. The initialization of population individuals is specifically performed by the MATLAB genetic algorithm toolkit.

Step 202, obtaining a termination iteration number N, where the termination iteration number N is an upper limit value of an iteration number of the genetic algorithm, and may be input during calculation or preset.

And step 203, making a variable T of the overall operation period equal to 1, wherein T is used for indicating each day in the overall operation period.

In step 204, let the unit operation cycle variable t be 1, where t is used to indicate each time in the unit operation cycle.

Step 205, judge the populationWhether the individual value meets the constraint condition of the self-disciplined operation index of the power distribution network or not. In this embodiment, step 205 is specifically: calculating the receiving power P of the distribution network based on the numerical values of the various groups and the formula (9)_D(t) determining the received power P of the distribution network_D(t) whether the power distribution network autonomous operation index constraint conditions shown in the above formula (3), formula (4) and formula (5) are satisfied. When the judgment result is negative, executing step 206; when the result of the determination is yes, step 207 is performed.

And step 206, adjusting the power distribution network consumption scheme based on the autonomous operation index limit value, enabling the adjusted numerical value of the population to meet the power distribution network autonomous operation index constraint condition, and going to step 207.

In this embodiment, a specific process of adjusting the power distribution network consumption scheme in step 206 is shown in fig. 3: firstly, the received power P of the power distribution network at the time t is obtained_D(t) then, the received power P at the time t is determined_D(t) whether the peak-to-valley power of the distribution network is exceeded

And lower limit value

The limit of (2). When P is present_D(t) is greater than

Then, energy storage discharge is carried out; when P is present_D(t) is less than

And then, energy storage charging is carried out. Then, the receiving power change rate Δ P at time t is obtained_G(t); wherein, Δ P_G(t)＝P_G(t)-P_G(t-1). Determining the received power change rate Δ P at time t_G(t) whether or not the upper limit value of the rate of change of the received power of the distribution network is exceeded

And a lower limit value

The limit of (2). When Δ P_G(t) is greater than

Then, energy storage discharge is carried out; when Δ P_G(t) is less than

And then, energy storage charging is carried out. Then a first regulated powered power P 'at time t is obtained'_D(t), wherein, P'_D(t)＝P_L(t)-P_W(t)-P_PV(t)-P_ESS(t)，P_L(t) load power at time t, P_W(t) and P_PV(t) wind power and photovoltaic output power at time t, P_ESSAnd (t) is the energy storage charging and discharging power at the time t. Further, first regulated received power P 'at time t is judged'_D(t) whether the peak-to-valley power of the distribution network is exceeded

And lower limit value

The limit of (2). When P'_D(t) is greater than

Then, calling interruptible load and transferable load; when P'_D(t) is less than

The transferable load is invoked. Then, a second regulated received power P' at time t is obtained_D(t) wherein, P ″)_D(t)＝P_L(t)-P_W(t)-P_PV(t)-P_ESS(t)-P_DR(t)，P_DR(t) calling Power for time t DR, including interruptible load calling Power P_IL(t) and transferable load calling Power P_TL(t) of (d). Determining a second adjusted received power P ″, at time t_D(t) isWhether the peak-to-valley power is less than the lower limit value of the received power of the power distribution network

When P ″)_D(t) is less than

And when the wind and light abandoning operation is performed.

Step 207, judging whether the variable t of the unit operation period is greater than or equal to 24, thereby judging whether each moment in the unit operation period is traversed; if the judgment result is negative, go to step 208; when the judgment result is yes, step 209 is executed.

In step 208, let t equal to t +1, and return to step 205.

Step 209, obtaining the relevant parameters of the distribution network in each time interval, i.e. the numerical values of various groups and individuals.

Step 210, determining whether the variable T of the overall operation cycle is greater than or equal to 365, thereby determining whether each day of the overall operation cycle is facilitated; when the judgment result is no, go to step 211; when the determination result is yes, step 212 is performed.

In step 211, let T be T +1, and return to step 204.

Step 212, further determining whether the numerical value of the population individual satisfies the power distribution network operation constraint condition and the power distribution network callable resource constraint condition, if not, executing step 216, and if so, executing step 213.

Step 213, calculating a value of the new energy consumption capacity maximum objective function, and judging whether the value of the new energy consumption capacity maximum objective function is larger than the optimal target value; and when the value of the new energy consumption capacity maximum objective function is larger than the optimal target value, updating the optimal target value to the value of the new energy consumption capacity maximum objective function, and updating the target power distribution network related parameters to the values of the corresponding power distribution network related parameters when the value of the new energy consumption capacity maximum objective function is obtained.

Step 214, determining whether the iteration number N is greater than or equal to the termination iteration number N, and if the determination result is no, executing step 215; when the judgment result is yes, step 217 is performed.

In step 215, let the iteration number n be n +1, and go to step 216.

Step 216, selecting, crossing and mutating population individuals based on a genetic algorithm, and returning to step 203; in the process of selecting the population individuals, the individuals with high population fitness (namely the population individuals with large corresponding values) are preferentially reserved, when the population individuals are crossed, the crossing rate is set to be 70%, and when the population individuals are changed, the variation probability is set to be 0.5%.

And step 217, outputting the optimal target value and the corresponding target power distribution network related parameters, and ending the process.

As can be seen from the above, the method for optimizing the new energy consumption capacity of the power distribution network, which is provided by the embodiment of the application, acquires power distribution network data, constructs a maximum target function of new energy consumption capacity according to the power distribution network data, constructs a constraint condition of an autonomous operation index of the power distribution network, an operation constraint condition of the power distribution network, and a constraint condition of a controllable resource call of the power distribution network as constraint conditions in a genetic algorithm, finds an optimal solution for the maximum target function of new energy consumption capacity based on the genetic algorithm and related parameters of the power distribution network, and configures the power distribution network based on the related parameters of the power distribution network when the optimal solution is found. Compared with the prior art, the method introduces the genetic algorithm into the new energy consumption capability optimization method, and takes the power distribution network self-discipline index constraint condition, the power distribution network operation constraint condition and the power distribution network controllable resource calling constraint condition as the constraint conditions in the genetic algorithm, so that the new energy consumption capability of the power distribution network is scientifically calculated and globally planned, and the maximum consumption capability of the power distribution network to new energy can be more accurately calculated and optimized.

In a second aspect of the embodiments of the present application, there is provided a new energy consumption capability optimizing device applied to a power distribution network, as shown in fig. 4, the new energy consumption capability optimizing device includes:

the data obtaining module 401 is configured to obtain power distribution network data.

And the data processing module 402 is configured to construct a maximum target function of new energy consumption capacity, a constraint condition of power distribution network autonomous operation indexes, a constraint condition of power distribution network operation, and a constraint condition of power distribution network controllable resource calling according to the power distribution network data. Optionally, the power distribution network data includes power distribution network structure parameters, new energy power generation prediction data, load prediction data, energy storage data, demand side response data, a reconfiguration switch action number constraint limit value, and an autonomous operation index limit value. The data processing module 402 is specifically configured to: constructing a new energy consumption capacity maximum objective function according to the new energy power generation prediction data; according to the self-discipline operation index limit value, a power distribution network self-discipline operation index constraint condition is constructed; constructing a power distribution network operation constraint condition according to the power distribution network structure parameters, the new energy power generation prediction data and the load prediction data; and constructing a power distribution network controllable resource calling constraint condition according to the power distribution network structure parameters, the reconstruction switch action frequency constraint limit value, the energy storage data and the demand side response data.

Optionally, the distribution network autonomous operation index limit includes: the method comprises the steps of receiving power peak-valley limit value, receiving power change rate limit value and power distribution network reserve capacity adjustable margin; the data processing module 402 is specifically configured to: and constructing a power distribution network autonomous operation index constraint condition according to the received power peak-valley limit value, the received power change rate limit value and the power distribution network reserve capacity adjustable margin.

In this embodiment, the maximum objective function of the new energy absorption capacity is shown in formula (15):

in the formula (15), S_G,iEstablishing node access capacity for each new energy in the power distribution network; omega_GAnd constructing a node collection for the new energy of the power distribution network.

In this embodiment, the new energy accessed by the power distribution network includes photovoltaic power generation and wind power generation, and therefore, the access capacity of each new energy construction node is as shown in formula (16):

S_G,i＝S_PV,i+S_W,i (16)

in the formula (16), S_PV,iAnd S_W,iThe photovoltaic power generation capacity and the wind power generation capacity which are respectively accessed to the node i.

Let the power receiving power of the distribution network at time t be P_G(t), in this embodiment, the distribution network autonomous operation index conditions include a received power peak-valley limit constraint shown in formula (17), a received power change rate constraint shown in formula (18), and a distribution network spare capacity adjustable margin constraint shown in formula (19):

in the formula (17), the reaction is carried out,

and

the peak-valley upper limit value and the lower limit value of the received power of the power distribution network are respectively.

In the formula (18), Δ P_G(t) represents the rate of change of the received power at time t;

and

In the formula (19), P_AN(t) Adjustable Standby Capacity for distribution network at time tMargin; p_ESS,AN(t) reserving power for energy storage charging and discharging at the moment t; p_DR,AN(t) invoking a reserved power for the DR at time t; h is_G(t) the response speed of the power distribution network at the moment t; sigma is the response rate limit value of the power distribution network; optionally, the σ may be preset to be 0.5% to 3%.

In this embodiment, the power distribution network operation constraint conditions include a power distribution network power flow constraint condition considering network reconfiguration as shown in the following formulas (20) and (21), a power distribution network access capacity constraint condition as shown in the following formula (22), a power distribution network power balance constraint condition as shown in the following formula (23), and a power distribution network operation safety constraint condition as shown in the following formula (24).

In the formula (20), A_ij(t) is the connection status of the branch i-j (representing the branch from node i to node j) at time t, A_ij(t) ═ 1 indicates that branch i-j at time t is connected, A_ij(t) ═ 0 indicates that branch i-j is open at time t; n (i) is a node set connected with the node i in the power distribution network; p is_ij(t) is the active power flowing through branch i-j at time t; q_L,i(t) is reactive load at a node i at time t; q_ij(t) is the reactive power flowing through branch i-j at time t; p_ESS.c、P_ESS.disRespectively single-point energy storage charging and discharging power; p_TL.d,iAnd P_TL.r,iRespectively carrying out controllable load transfer in and transfer out of active power for a node i considering network reconstruction; p_L,i(t) is the active load of node i at time t, P'_W,i(t) and P'_PV,i(t) respectively connecting the wind power and the photovoltaic power generation active power of the power distribution network after the power distribution network reconfiguration switch acts at the moment t; q_ij(t) is the reactive power flowing through branch i-j at time t; q_TL.d,iAnd Q_TL.r,iRespectively moving in and out reactive power for controllable load of a node i considering network reconstruction; q_DG,i(t) is the sum of the reactive power generated by the new energy such as wind, light and the like accessed to the node i at the moment t; p_IL,i(t) and Q_IL,i(t) availability of node i for access at time tActive and reactive power of the load is cut off.

In the above equation (20), the right side of the equation represents the total active outflow power and the total reactive outflow power of all branches connected to the node i at time t, respectively.

Adding the above-mentioned P_ij(t) and Q_ijThe expressions of (t) are respectively subjected to imaginary part and real part separation and simplification, as shown in formula (21):

in the formula (21), G_ij、B_ijAnd theta_ijRespectively are the mutual conductance, mutual susceptance and voltage phase angle difference between nodes i and j, U is voltage, theta_i(t) and θ_j(t) is the phase of the voltages at node i and node j, respectively, at time t. And combining the formula (20) and the formula (21) to obtain the power flow constraint condition of the power distribution network considering the network reconstruction.

In this embodiment, the capacity of the single node of the power distribution network accessing photovoltaic power generation and wind power generation needs to satisfy the formula (22):

in the formula (8), S_W,maxAnd S_PV,maxWind power and photovoltaic power generation capacity upper limits which are accessed by single points respectively; omega_WAnd Ω_PVRespectively wind power and photovoltaic power generation access node collection.

In this embodiment, the operation data of the distribution network every 1 hour in 24 hours of the whole day is taken as a research object, and the received power before and after the regulation of the distribution network satisfies the formula (23):

P_D(t)＝P_L(t)-P_W(t)-P_PV(t)

P_D(t)-P_G(t)＝P_DNR(t)+P_ESS(t)+P_DR(t)+P_S(t) (23)

in the formula (23), P_D(t) power received before regulation; p_W(t) and P_PV(t) the output power of wind power and photovoltaic at the moment t respectively; p_L(t) load power at time t; p_DNR(t) reconstructing power of the power distribution network; p_ESS(t) storing energy and charging and discharging power at the moment t; p_DR(t) calling Power for time t DR, including interruptible load calling Power P_IL(t) and transferable load calling Power P_TL(t)；P_S(t) is the sum of the power values of the wind curtailment and the light curtailment, i.e. P_S(t)＝P_S,W(t)+P_S,PV(t); wherein t ∈ [1, 24 ]]。

in the formula (24), U_j(t) is the voltage amplitude of node j; u shape_j,minAnd U_j,maxMinimum and maximum limits for node voltage, respectively; i is_ij(t) and I_ij.maxThe current amplitude and the maximum limit of the branch i-j are respectively.

In this embodiment, the constraint conditions for invoking the controllable resource of the power distribution network include a constraint condition for the number of times of the reconfiguration switch operation as shown in the following formula (25), an energy storage operation constraint as shown in the following formula (26), and a constraint condition for the controllable load as shown in the following formulas (27) and (28).

In this embodiment, the constraint condition of the reconfiguration switch action times is shown in formula (25):

in the formula (25), the first and second groups,

to reconstruct the sum of the number of movements of the switch in a day, Δ Z_maxIs the limit for the number of reconfiguration switch actions in a day. Optionally, Δ Z_maxThe value of (d) may be preset to 5 to 10.

In this embodiment, the energy storage operation constraint conditions include a charge/discharge power limit constraint, a remaining capacity limit constraint, a charge/discharge power balance constraint, and an energy storage time sequence operation constraint, as shown in formula (26):

in the formula (26), k and k_maxThe number of charge and discharge times and the limit value thereof; p_ESS(t) is the energy storage charge and discharge power; p_ESS,c.maxIs the maximum allowed charging power; p_ESS,d.maxIs the maximum allowable discharge power; s_SOC.maxAnd S_SOC.minThe upper and lower limit values of the residual electric quantity level; psi is the energy storage charge-discharge efficiency.

In this embodiment, the controllable load constraint condition includes an interruptible load constraint and a transferable load constraint. The interruptible load constraints include an interrupt capacity constraint, an interrupt number constraint and a continuous interrupt time constraint, as shown in equation (27):

in the formula (27), Δ P_IL,h(t) and P_IL,maxRespectively serving as an h node interrupt load power and a maximum allowable interrupt power at the time t; n is_IL,hAnd n_IL,h,maxRespectively the interruption times of the h node interruption load and the maximum allowable interruption times; t is_IL,h(t)、T_IL,h,maxAnd T_IL,h,minRespectively at t time h node interruptible load interruption time and allowable interruption timeUpper and lower limits of (1).

The transferable load constraints include transferable capacity constraints, transfer capacity balance constraints, and transfer times constraints, as shown in equation (28):

in the formula (28), P_TL,d,maxThe maximum allowable power transfer for the transferable load at the moment t; n is_TL,mAnd n_TL,m,maxRespectively the transferable load transfer times of the m node and the maximum allowable transfer times.

A calculation module 403, configured to use the power distribution network autonomous operation index constraint condition, the power distribution network operation constraint condition, and the power distribution network controllable resource invocation constraint condition as constraint conditions in a genetic algorithm, and solve an optimal solution for the maximum target function of the new energy absorption capacity based on the genetic algorithm and power distribution network related parameters, where the power distribution network related parameters are influence parameters related to new energy absorption capacity in a power distribution network;

and a configuration module 404, configured to perform configuration of the power distribution network based on the power distribution network related parameters when the optimal solution is obtained.

Optionally, the calculating module 403 is specifically configured to execute the following steps:

In this embodiment, the steps executed by the calculating module 403 are as shown in fig. 2, and details refer to corresponding descriptions of the method for calculating the maximum new energy consumption capability of the power distribution network provided in the first aspect of the embodiment of the present application, which are not described herein again.

As can be seen from the above, the new energy consumption capability optimizing device applied to the power distribution network provided by the embodiment of the application acquires data of the power distribution network through the data acquisition module 401; constructing a new energy consumption capacity maximum objective function according to the power distribution network data through the data processing module 402, and constructing a power distribution network autonomous operation index constraint condition, a power distribution network operation constraint condition and a power distribution network controllable resource calling constraint condition as constraint conditions in a genetic algorithm; solving an optimal solution for the maximum target function of the new energy consumption capacity based on the genetic algorithm and the relevant parameters of the power distribution network through a calculation module 403; the configuration module 404 configures the power distribution network based on the relevant parameters of the power distribution network when the optimal solution is obtained. Compared with the prior art, the method and the device introduce the genetic algorithm, take the power distribution network self-discipline index constraint condition, the power distribution network operation constraint condition and the power distribution network controllable resource calling constraint condition as the constraint conditions in the genetic algorithm, and scientifically calculate and globally plan the new energy consumption capability of the power distribution network, so that the method and the device are beneficial to more accurately calculating the maximum consumption capability of the power distribution network to new energy and carrying out optimal configuration.

In a third aspect of the embodiments of the present application, as shown in fig. 5, an electronic device is provided, and includes a memory 501 and a processor 502, where the memory 501 stores a computer program, and the processor 502 executes the computer program to implement the steps of the method for optimizing new energy consumption capability applied to a power distribution network, provided by the first aspect of the embodiments of the present application.

A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method for optimizing new energy consumption capability applied to a power distribution network, provided by the first aspect of the embodiments of the present application.

It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. It is obvious to those skilled in the art that, for convenience and simplicity of description, the foregoing functional units and modules are merely illustrated in terms of division, and in practical applications, the foregoing functions may be distributed as needed by different functional units and modules. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

Those of ordinary skill in the art would appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

The integrated modules described above, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. All or part of the flow in the method of the embodiments may be realized by a computer program, which may be stored in a computer-readable storage medium and executed by a processor, to instruct related hardware to implement the steps of the embodiments of the methods. The computer readable medium may include: any entity or device capable of carrying the above-mentioned computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signal, telecommunication signal, software distribution medium, etc. It should be noted that the contents contained in the computer-readable storage medium can be increased or decreased as required by legislation and patent practice in the jurisdiction.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; as will be appreciated by those of ordinary skill in the art; the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present application, and they should be construed as being included therein.

Claims

1. A new energy consumption capacity optimization method applied to a power distribution network is characterized by comprising the following steps:

acquiring power distribution network data, wherein the power distribution network data comprises power distribution network structure parameters, new energy power generation prediction data, load prediction data, energy storage data, demand side response data, a reconfiguration switch action frequency constraint limit value and an autonomous operation index limit value;

constructing a new energy consumption capacity maximum objective function, a distribution network autonomous operation index constraint condition, a distribution network operation constraint condition and a distribution network controllable resource calling constraint condition according to the distribution network data; constructing a new energy consumption capacity maximum objective function according to the new energy power generation prediction data; according to the self-discipline operation index limit value, a power distribution network self-discipline operation index constraint condition is constructed; constructing a power distribution network operation constraint condition according to the power distribution network structure parameters, the new energy power generation prediction data and the load prediction data; constructing a power distribution network controllable resource calling constraint condition according to the power distribution network structure parameters, the reconfiguration switch action frequency constraint limit, the energy storage data and the demand side response data;

configuring the power distribution network based on the related parameters of the power distribution network when the optimal solution is obtained;

the solving of the optimal solution of the maximum objective function of the new energy consumption capacity based on the genetic algorithm and the relevant parameters of the power distribution network comprises the following steps:

step 2: judging whether the numerical value of the population individual meets the power distribution network autonomous operation index constraint condition or not;

and 5: when the numerical value of the population individual does not meet the power distribution network operation constraint condition or the power distribution network controllable resource calling constraint condition, crossing and varying the population individual based on a genetic algorithm, and updating the numerical value of the population individual so that the updated numerical value of the population individual meets the power distribution network operation constraint condition and the power distribution network controllable resource calling constraint condition;

step 6: when the numerical value of the population individual meets the operation constraint condition of the power distribution network and the controllable resource calling constraint condition of the power distribution network, calculating the value of a new energy consumption capacity maximum target function and judging whether the value of the new energy consumption capacity maximum target function is larger than an optimal target value or not, when the value of the new energy consumption capacity maximum target function is larger than the optimal target value, updating the optimal target value to the value of the new energy consumption capacity maximum target function, and updating the relevant parameters of the target power distribution network to the values of the corresponding power distribution network relevant parameters when the value of the new energy consumption capacity maximum target function is obtained, wherein the value of the new energy consumption capacity maximum target function is equal to the sum of the numerical values of various population individuals;

and 7: judging whether the iteration times are larger than a preset termination iteration time or not, when the iteration times are not larger than the termination iteration times, crossing and varying the population individuals based on a genetic algorithm, updating the numerical values of the population individuals, and returning to the step 2 after increasing the iteration times by 1;

2. The new energy consumption capability optimization method of claim 1, wherein the autonomous operation index limit comprises: the method comprises the steps of receiving power peak-valley limit value, receiving power change rate limit value and power distribution network reserve capacity adjustable margin;

the step of constructing the power distribution network autonomous operation index constraint condition according to the autonomous operation index limit specifically comprises the following steps: and constructing a power distribution network autonomous operation index constraint condition according to the received power peak-valley limit value, the received power change rate limit value and the power distribution network reserve capacity adjustable margin.

3. The utility model provides a new forms of energy consumption ability optimizing device for distribution network which characterized in that, new forms of energy consumption ability optimizing device includes:

the data acquisition module is used for acquiring power distribution network data, and the power distribution network data comprises power distribution network structure parameters, new energy power generation prediction data, load prediction data, energy storage data, demand side response data, a reconfiguration switch action frequency constraint limit value and an autonomous operation index limit value;

the data processing module is used for constructing a new energy consumption capacity maximum target function, a distribution network autonomous operation index constraint condition, a distribution network operation constraint condition and a distribution network controllable resource calling constraint condition according to the distribution network data; constructing a new energy consumption capacity maximum objective function according to the new energy power generation prediction data; according to the self-discipline operation index limit value, a power distribution network self-discipline operation index constraint condition is constructed; constructing a power distribution network operation constraint condition according to the power distribution network structure parameters, the new energy power generation prediction data and the load prediction data; constructing a power distribution network controllable resource calling constraint condition according to the power distribution network structure parameters, the reconfiguration switch action frequency constraint limit, the energy storage data and the demand side response data;

the computing module is specifically configured to perform: step 1: establishing a population and generating an initial population individual based on the power distribution network related parameters, setting the iteration number to be 1, initializing an optimal target value and corresponding target power distribution network related parameters, wherein the optimal target value is the value of the target function with the maximum new energy consumption capacity, and the target power distribution network related parameters are the values of the power distribution network related parameters when the optimal target value is obtained; step 2: judging whether the numerical value of the population individual meets the power distribution network autonomous operation index constraint condition or not; and step 3: when the numerical value of the population individual does not meet the constraint condition of the distribution network autonomous operation index, adjusting a distribution network consumption scheme based on the autonomous operation index limit value, so that the adjusted numerical value of the population individual meets the constraint condition of the distribution network autonomous operation index; and 4, step 4: when the numerical value of the population individual meets the constraint condition of the power distribution network autonomous operation index, judging whether the numerical value of the population individual meets the constraint condition of the power distribution network operation and the constraint condition of the power distribution network controllable resource calling;

and 5: when the numerical value of the population individual does not meet the power distribution network operation constraint condition or the power distribution network controllable resource calling constraint condition, crossing and varying the population individual based on a genetic algorithm, and updating the numerical value of the population individual so that the updated numerical value of the population individual meets the power distribution network operation constraint condition and the power distribution network controllable resource calling constraint condition; step 6: when the numerical value of the population individual meets the operation constraint condition of the power distribution network and the controllable resource calling constraint condition of the power distribution network, calculating the value of a new energy consumption capacity maximum target function and judging whether the value of the new energy consumption capacity maximum target function is larger than an optimal target value or not, when the value of the new energy consumption capacity maximum target function is larger than the optimal target value, updating the optimal target value to the value of the new energy consumption capacity maximum target function, and updating the relevant parameters of the target power distribution network to the values of the corresponding power distribution network relevant parameters when the value of the new energy consumption capacity maximum target function is obtained, wherein the value of the new energy consumption capacity maximum target function is equal to the sum of the numerical values of various population individuals; and 7: judging whether the iteration times are larger than a preset termination iteration time or not, when the iteration times are not larger than the termination iteration times, crossing and varying the population individuals based on a genetic algorithm, updating the numerical values of the population individuals, and returning to the step 2 after increasing the iteration times by 1; and 8: when the iteration times are larger than the iteration termination times, outputting the optimal target value and corresponding target power distribution network related parameters;

4. The new energy consumption capability optimization device of claim 3, wherein the autonomous operation index limit comprises: the method comprises the steps of receiving power peak-valley limit value, receiving power change rate limit value and distribution network reserve capacity adjustable margin;

5. An electronic device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 2 when executing the computer program.

6. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 2.