CN109190792B

CN109190792B - Method and system for determining configuration of distributed power supply in power distribution network

Info

Publication number: CN109190792B
Application number: CN201810834925.4A
Authority: CN
Inventors: 李蓓; 靳文涛; 牛萌; 郁正纲; 苏粟; 房凯; 李建林; 孙海霞; 张晓晴; 董静然; 胡勇; 光鸿伟; 岳付昌
Original assignee: State Grid Corp of China SGCC; China Electric Power Research Institute Co Ltd CEPRI; Lianyungang Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
Current assignee: State Grid Corp of China SGCC; China Electric Power Research Institute Co Ltd CEPRI; Lianyungang Power Supply Co of State Grid Jiangsu Electric Power Co Ltd
Priority date: 2018-07-26
Filing date: 2018-07-26
Publication date: 2022-05-24
Anticipated expiration: 2038-07-26
Also published as: CN109190792A

Abstract

The invention provides a method and a system for determining configuration and evaluation of distributed power sources in a power distribution network, wherein the method and the system comprise the following steps: establishing an index system for evaluating the power supply reliability of the power distribution network accessed to the distributed power supply and a model of each index, establishing an economic model of the power distribution network accessed to the distributed power supply according to the network loss and the power failure cost of the power distribution network and the investment cost of the distributed power supply, taking the power supply reliability index model and the economic model as a multi-target planning model of the power distribution network accessed to the distributed power supply, and obtaining the optimal configuration of the distributed power supply in the power distribution network by utilizing a genetic algorithm. The method and the system realize the simultaneous optimization of the type, the position and the access capacity of the distributed power supply, and determine the improvement of the optimal configuration of the access distributed power supply in the power distribution network on the power supply reliability and the economical efficiency of the power distribution network by comparing and analyzing the power supply reliability and the economical efficiency of the power distribution network without the addition of the distributed power supply and after the addition of the distributed power supply.

Description

Method and system for determining configuration of distributed power supply in power distribution network

Technical Field

The present invention relates to the field of power distribution network planning configuration, and more particularly, to a method and system for determining a configuration of a distributed power source in a power distribution network.

Background

The power distribution network is an intermediate link for connecting a high-voltage transmission network of an electric power system with users, the power distribution network has multiple voltage levels, a complex network structure and various equipment types, and therefore the safety risk factors of the power distribution network are relatively more. In order to ensure reliable and efficient operation of the distribution network, planning is required. Traditional power distribution network planning is a process of modifying an existing power distribution network to meet the optimization of a certain aspect or certain aspects on the basis of the analysis of the conditions of the power distribution network. The power distribution network planning generally includes load prediction during planning, system network loss calculation, voltage level analysis, power supply reliability analysis, and the like. In recent years, with the rapid development of Distributed Generation (DG), more new factors are considered when planning a distribution network, and the planning content and method of the conventional distribution network are challenged. The distributed power supply has the characteristics of small scale, environmental friendliness and high energy utilization efficiency, and the distributed power supply can be connected into a power distribution network to adjust an energy structure and reduce environmental pressure, and can solve the problem of difficult power supply in remote areas due to the independent power generation characteristic and improve the overall power supply reliability of the power distribution network, so that the large-scale application of the distributed power supply has great benefits to the society and the power distribution system. But the access of distributed power sources also impacts the planning of traditional distribution grids. The planning content of the power distribution network becomes more complex due to the access of the distributed power sources, and firstly, the objective function, constraint conditions and the like of a planning model are changed after the distributed power sources are accessed to the power distribution network; secondly, along with the complexity of the planning model, the difficulty of solving the planning problem is increased.

In summary, there is an urgent need for a planning for a distribution network with access to distributed power sources to improve the reliability and economy of the power supply of said distribution network.

Disclosure of Invention

In order to solve the technical problems that a power distribution network accessed with a distributed power supply lacks corresponding planning and solving and reliability and economy verification in the prior art, the invention provides a method for determining the configuration of the distributed power supply in the power distribution network, which comprises the following steps:

establishing an output model of a distributed power supply accessed in a power distribution network, wherein the distributed power supply comprises a wind power generation system and a photovoltaic power generation system;

establishing an index system for evaluating the power supply reliability of a power distribution network accessed to a distributed power supply, wherein indexes in the index system comprise 1 index power distribution network load point annual average power failure time relevant to evaluation of the economy of the power distribution network and a plurality of indexes irrelevant to evaluation of the economy of the power distribution network;

according to an output model of a distributed power supply accessed to the power distribution network, establishing a model of indexes irrelevant to the evaluation of the economy of the power distribution network;

according to the output model of the distributed power supply of the power distribution network, the fault model and the fault restoration model of the elements in the power distribution network, determining the annual average power failure time of each load point of the power distribution network by using a time sequence Monte Carlo method;

determining the power failure cost of the power distribution network according to the annual average power failure time of each load point of the power distribution network;

the method comprises the steps that an economical model of operation of the power distribution network connected to the distributed power supply is established according to investment cost, network loss cost and power failure cost of the distributed power supply of the power distribution network by taking annual operation cost of the power distribution network as a general target;

and taking the model for evaluating indexes irrelevant to evaluation economy in the power supply reliability of the power distribution network accessed to the distributed power supply and the economy model as a multi-target planning model of the power distribution network accessed to the distributed power supply, determining the optimal configuration of the distributed power supply accessed to the power distribution network by using a genetic algorithm under the set constraint condition, and carrying out comparative analysis on the power supply reliability and economy of the power distribution network which is not accessed to the distributed power supply and the power distribution network which is accessed to the distributed power supply.

Further, the establishing of the output model of the distributed power source accessed in the power distribution network includes:

establishing a wind speed model which obeys Weibull distribution according to actual wind speed data of the area where the wind power generation system to be accessed to the power distribution network is located, and establishing an output model P of the wind power generation system according to the wind speed model_WTG(v) The calculation formula is as follows;

in the formula, v_ci，v_cr，v_coRespectively cut-in wind speed, rated wind speed and cut-out wind speed, p_rRated output power of a fan of the wind power generation system when the wind speed is less than v_ciWhen the fan is not operated; when the wind speed is v_ciAnd v_crIn the meantime, the output of the fan is increased along with the increase of the wind speed, and the output of the fan is approximately in a primary curve; when the wind speed is greater than v_crAnd is less than v_coThe output of the fan is a rated value; when the wind speed is greater than v_coWhen the fan is in use, the fan stops working for safety;

establishing an illumination intensity model which obeys beta distribution according to actual illumination intensity data of the region where the photovoltaic power generation system to be accessed to the power distribution network is located, and establishing an output model P of the photovoltaic power generation system according to the illumination intensity model_PVGThe calculation formula is as follows:

wherein S is the intensity of light, S_rFor the rated value of the illumination intensity of the photovoltaic power generation system, when the illumination intensity is greater than 0 and less than the rated value, the photovoltaic output is increased along with the increase of the light intensity and is approximately in a primary curve; when the illumination intensity is larger than the rated value, the photovoltaic output reaches the rated output and does not increase along with the increase of the light intensity.

Further, the establishing of the index system for evaluating the power supply reliability of the power distribution network accessed to the distributed power supply means that the annual power generation rate, the annual output interruption rate, the fluctuation rate, the island year average power supply time, the island year average power supply capacity, the island year average power shortage capacity and the distribution network load point annual average power outage time of the distributed power supply are used as indexes for evaluating the power supply reliability of the power distribution network accessed to the distributed power supply, wherein the annual average power outage time of the distribution network load point is an index related to the evaluation of the power distribution network economy, and the annual power generation rate, the annual output interruption rate, the fluctuation rate, the island year average power supply time, the island year average power supply capacity and the island year average power shortage capacity of the distributed power supply are indexes unrelated to the evaluation of the power distribution network economy.

Further, the establishing of the model of the index irrelevant to the evaluation of the economy of the power distribution network according to the output model of the distributed power supply accessed to the power distribution network comprises the following steps:

the annual generation rate index GRDG of the distributed power supply reflects the proportion of the actual generated energy of the distributed power supply to the rated generated energy of the distributed power supply, and the unit is% as follows:

wherein P (t) is the output value of the distributed power supply at the t moment, P_DGNIs rated output power, T, of the distributed power supply₁Total time for distributed power operation;

the annual output discontinuity rate index PIPDG of the distributed power supply reflects the discontinuity degree of the output of the distributed power supply, and the index value has great influence on the power supply reliability of the island, and the unit is% as follows:

in the formula, T₂Represents the operation age of the distributed power supply, and Σ t { p (t) ═ 0} represents a set of times at which the distributed power supply has an output of 0 within the operation age;

the distributed power supply fluctuation rate index PFDG obtains the whole T through the difference calculation of the previous moment and the next moment₃The fluctuation condition of the output power of the distributed power supply in a time period can effectively reflect the fluctuation change of the output power of the distributed power supply, and the unit is as follows:

wherein p (t) is the output power of the distributed power supply at the time t, and p (t +1) is the output power of the distributed power supply at the time t + 1;

the island year average power supply time APSTI reflects the year average power failure condition of a main network and reflects the year average power supply duration of an island when the main network fails, and the calculation formula is as follows:

in the above formula, YEAR is the simulation YEAR, i is the load point, P_L，iIs the power demand of load point i, P_DGFor the output power of the distributed power supply, N is the frequency of normal power supply of the island, R_jIs an islanding zone in the jth islanding state, t_jThe power supply time when the power is normally supplied to the jth island;

the average power supply capacity AESI in the island reflects the power supply capacity of the island in the aspect of power support, and the calculation formula is as follows:

in the formula, YEAR is simulation age, N is the number of times of island normal power supply in YEAR, E_jThe power supply electric quantity R for the jth island in normal power supply_jIs an islanding zone in the jth islanding state, P_L，iPower of the ith load point in island, t_jThe power supply time when the jth island is normally powered;

the average power shortage AENSI in the island reflects the power supply capacity of the island in the aspect of power shortage, and the calculation formula is as follows:

wherein YEAR is the simulation YEAR, N_lossNumber of insufficient power supply for island E_loss，jThe power supply shortage R during the jth island power failure_jIs an islanding zone in the jth islanding state, P_L，iPower of the ith load point in island, t_loss，jAnd the power failure time of the j th island is shown.

Further, the determining the annual average power failure time of each load point of the power distribution network by using the time sequence Monte Carlo method according to the output model of the distributed power supply of the power distribution network, the fault model of the element in the power distribution network and the fault restoration model comprises the following steps:

step 1, setting the initial time t of the annual average power failure time of each load point of a power distribution network accessed to a distributed power supply to be 0;

step 2, generating (0, 1) random numbers for all elements of the power distribution network, and solving the normal working time TTF of each element according to a fault model of the elements, wherein the formula of the fault model is as follows:

F(t)＝1-e^-λt

wherein F (t) is the failure probability of the element, λ is the failure rate of the element over time, λ is a constant;

and 3, taking the element with the minimum TTF as a fault element, generating (0, 1) random numbers for the fault element, and calculating the repair time TTR of the fault element according to a fault repair model, wherein the formula of the fault repair model is as follows:

F_D(t)＝1-e^-μt

in the formula, F_D(t) is the probability corresponding to the element repair time, μ is the repair rate of the element, and μ is a constant;

step 4, determining a load point with power failure due to the influence of a fault element, judging whether the power failure load point can be recovered from the power supply of the main network circuit breaker, turning to step 5 when the power failure load point cannot be recovered from the power supply of the main network circuit breaker, and turning to step 9 when the power failure load point can be recovered from the power supply of the main network circuit breaker;

step 5, determining the output and the load power of the distributed power supply by combining the type and the installation capacity of the distributed power supply installed at the power failure load point and an output model of the power distribution network accessed to the distributed power supply, turning to step 6 when the output of the distributed power supply is greater than the load power, and turning to step 8 when the output of the distributed power supply is not greater than the load power;

step 6, adding 1 to the number of the faults of the load point, wherein the power failure time is the time for the distributed power supply to be put into operation;

step 7, accumulating the TTF and TTR of each element to a calculation time t, and turning to the step 2 when t is smaller than a set value, and turning to the step 10 when t is not smaller than the set value;

step 8, adding 1 to the number of the load point faults, wherein the power failure time is the repair time of the fault element, and turning to step 10;

step 9, adding 1 to the number of the faults of the load point, wherein the power failure time is the operation time of the breaker;

and step 10, outputting the power failure times and the power failure time of the whole calculation time of each load point of the power distribution network accessed to the distributed power supply, and averaging to obtain the annual average power failure time of each load point.

Further, the power failure cost of the power distribution network is determined according to the annual average power failure time of each load point of the power distribution network, and the calculation formula is as follows:

where n is the number of distribution network load nodes, t_iIs the annual outage time of the ith load point, P_iIs the load power of the i-th load point, c_eIs the unit electricity price.

Further, the establishing of the economic model of the operation of the power distribution network connected to the distributed power supply according to the investment cost, the network loss cost and the power failure cost of the distributed power supply of the power distribution network by taking the annual operation cost of the power distribution network as a general target comprises the following steps:

calculating investment cost C of distributed power supply_DGNamely, the total annual cost of the nodes to be installed in the power distribution network for building the distributed power supply, the calculation formula is as follows:

where r is the annual cost coefficient, m is the operational age of the distributed power plan, N_DGNumber of nodes to be installed for distributed power supply, C_iFor the unit investment cost, S, of various distributed power supplies_DG,iIs the installation capacity of the distributed power supply;

the distributed power supply is regarded as a load node of the power distribution network, and the network loss cost C of the power distribution network is calculated by using a forward-backward substitution method_lossThe calculation formula is：

Wherein t is the number of branches of the distribution network, P_loss，iIs the network loss, τ, of the ith branch_maxIs the annual maximum load loss hours of the ith branch, c_eIs the unit electricity price;

the method comprises the following steps of taking annual operation cost of the power distribution network as a general target, establishing an economic model of operation of the power distribution network accessed to the distributed power supply according to investment cost, network loss cost and reliability cost of the distributed power supply of the power distribution network, wherein a calculation formula is as follows:

minZ_cost＝C_DG+C_loss+C_D

in the formula, Z_costIs the annual integrated cost, C, of the distribution network after the distributed power supply is connected_DGIs the average annual investment cost of the distributed power supply, C_lossIs the average annual network loss charge of the distribution network after the distributed power supply is connected, C_DThe annual power failure cost of the power distribution network after the distributed power supply is connected.

Further, the step of taking the model for evaluating the index irrelevant to the evaluation economy in the reliability of the power supply of the power distribution network accessed to the distributed power supply and the economy model as a multi-objective planning model of the power distribution network accessed to the distributed power supply, determining the optimal configuration of the distributed power supply accessed to the power distribution network by using a genetic algorithm under the set constraint condition, and performing comparative analysis on the reliability and the economy of the power supply of the power distribution network which is not accessed to the distributed power supply and is accessed to the distributed power supply comprises the following steps:

taking a model for evaluating indexes irrelevant to evaluation economy in power supply reliability of a power distribution network accessed to the distributed power supply and an economy model as a multi-target planning model of the power distribution network accessed to the distributed power supply, and setting two constraint conditions of equality constraint and inequality constraint for enabling the power distribution network accessed to the distributed power supply to normally operate, wherein the equality constraint is power flow constraint of the power distribution network, and the inequality constraint comprises node voltage constraint, line current constraint, line transmission power constraint and distributed power supply quantity constraint of the power distribution network;

the distributed power source to be accessed in the power distribution network is coded in an integer mode, the chromosome coding length of a genetic algorithm is set to be two sections of the type of the distributed power source and the installation position and the capacity of the distributed power source, and then the optimal configuration of the distributed power source to be accessed in the power distribution network is determined, wherein:

the type code of the distributed power supply is randint (1, n, [0,1]), 0 or 1 can be defined by self to represent a photovoltaic or a fan, and n is the number of the distributed power supply to be installed;

the installation position and capacity code of the distributed power supply is randint (1, d, [1, n ]), wherein d is:

d＝floor(PDGmax/DGmin)

in the formula, PDGmax is the power corresponding to the upper limit of the installation capacity of the distributed power supply allowed by the power distribution network, and DGmin is the minimum installation capacity of the distributed power supply;

the chromosomes encode:

x＝[randint(1，n，[0，1])randint(1，d，[1，n])]；

according to the determined optimal configuration of the distributed power source to be accessed in the power distribution network, comparing and analyzing the power supply reliability and economy of the power distribution network without adding the distributed power source and after adding the distributed power source, and determining the improvement of the optimal configuration of the power distribution network accessed to the distributed power source in the power distribution network on the power supply reliability and economy of the power distribution network.

According to another aspect of the invention, there is provided a system for determining the configuration of a distributed power source in a power distribution network, the system comprising:

the output model unit is used for establishing an output model of a distributed power supply accessed in the power distribution network, wherein the distributed power supply comprises a wind power generation system and a photovoltaic power generation system;

the index determining unit is used for establishing an index system for evaluating the power supply reliability of the power distribution network connected with the distributed power supply, wherein indexes in the index system comprise 1 index power distribution network load point annual average power failure time relevant to the evaluation of the economy of the power distribution network and a plurality of indexes irrelevant to the evaluation of the economy of the power distribution network;

the index model unit is used for establishing a model of indexes irrelevant to the evaluation of the economy of the power distribution network according to an output model of a distributed power supply accessed to the power distribution network;

the power failure time unit is used for determining the annual average power failure time of each load point of the power distribution network by applying a time sequence Monte Carlo method according to the output model of the distributed power supply of the power distribution network, the fault model and the fault restoration model of elements in the power distribution network;

the power failure cost unit is used for determining the power failure cost of the power distribution network according to the annual average power failure time of each load point of the power distribution network;

the economic model unit is used for establishing an economic model of the operation of the power distribution network accessed to the distributed power supply according to the investment cost, the network loss cost and the power failure cost of the distributed power supply of the power distribution network by taking the annual operation cost of the power distribution network as a general target;

and the configuration unit is used for taking the model for evaluating the indexes irrelevant to the evaluation economy in the power supply reliability of the power distribution network accessed to the distributed power supply and the economy model as a multi-target planning model of the power distribution network accessed to the distributed power supply, determining the optimal configuration of the distributed power supply accessed to the power distribution network by using a genetic algorithm under the set constraint condition, and carrying out comparative analysis on the power supply reliability and the economy of the power distribution network which is not accessed to the distributed power supply and is accessed to the distributed power supply.

Further, the output model unit includes:

the first output model unit is used for establishing a wind speed model which follows Weibull distribution according to actual wind speed data of the region where the wind power generation system to be accessed to the power distribution network is located, and establishing an output model P of the wind power generation system according to the wind speed model_WTG(v) The calculation formula is as follows;

in the formula, v_ci，v_cr，v_coRespectively cut-in wind speed and rated wind speedAnd cut-out wind speed, p_rRated output power of a fan of the wind power generation system when the wind speed is less than v_ciWhen the fan is not running; when the wind speed is v_ciAnd v_crIn the meantime, the output of the fan is increased along with the increase of the wind speed, and the output of the fan is approximately in a primary curve; when the wind speed is greater than v_crAnd is less than v_coThe output of the fan is a rated value; when the wind speed is greater than v_coWhen the fan is in use, the fan stops working for safety;

a second output model unit for establishing an illumination intensity model complying with beta distribution according to actual illumination intensity data of the region where the photovoltaic power generation system to be accessed to the power distribution network is located, and establishing an output model P of the photovoltaic power generation system according to the illumination intensity model_PVGThe calculation formula is as follows:

wherein S is the intensity of light, S_rWhen the illumination intensity is greater than 0 and less than the rated value, the output of the photovoltaic is increased along with the increase of the light intensity and is approximately in a primary curve; when the illumination intensity is larger than the rated value, the photovoltaic output reaches the rated output and does not increase along with the increase of the light intensity.

Further, the index determination unit takes the annual power generation rate, the annual output interruption rate, the fluctuation rate, the island year average power supply time, the island year average power supply quantity, the island year average power shortage quantity and the distribution network load point annual average power failure time of the distributed power supply as indexes for evaluating the power supply reliability of the distribution network connected to the distributed power supply, wherein the annual average power failure time of the distribution network load point is an index related to the evaluation of the economy of the distribution network, and the annual power generation rate, the annual output interruption rate, the fluctuation rate, the island year average power supply time, the island year average power supply quantity and the island year average power shortage quantity of the distributed power supply are indexes unrelated to the evaluation of the economy of the distribution network.

Further, the index model unit includes:

the first model unit is used for calculating a GRDG (grid-distributed generation rate) index, the GRDG reflects the proportion of the actual generation capacity of the distributed power supply to the rated generation capacity of the distributed power supply, and the calculation formula is as follows in unit:

wherein P (t) is the output value of the distributed power supply at the t moment, P_DGNIs rated output power, T, of the distributed power supply₁Is the total time the distributed power supply is running;

the second model unit is used for calculating a distributed generator annual output discontinuity rate index PIPDG, the PIPDG reflects the discontinuity degree of the distributed generator output, and the index value has a great influence on the power supply reliability of the island, and the unit is% as follows:

in the formula, T₂Represents the operation age of the distributed power supply, and Σ t { p (t) ═ 0} represents the set of times at which the distributed power supply outputs 0 within the operation age;

a third model unit for calculating a PFDG (pulse-to-grid ratio) index, wherein the PFDG obtains the whole T through the difference calculation of the previous moment and the next moment₃The fluctuation condition of the distributed power output in a period of time can effectively reflect the fluctuation change of the output power of the distributed power, and the unit is as follows:

the fourth model unit is used for calculating island annual average power supply time APSTI, the APSTI reflects the annual average power failure condition of the main network and reflects the annual average power supply duration of the island when the main network fails, and the calculation formula is as follows:

in the above formula, YEAR is the simulation YEAR, i is the load point, P_L，iIs the power demand of load point i, P_DGFor the output power of the distributed power supply, N is the frequency of normal power supply of the island, R_jIs an islanding zone in the jth islanding state, t_jThe power supply time when the jth island is normally powered;

a fifth model unit, configured to calculate an average island year power supply capacity AESI, where the average island year power supply capacity AESI reflects a power supply capacity of an island in terms of capacity support, and a calculation formula is as follows:

a sixth model unit, configured to calculate an average island-year-shortage power amount AENSI, where the average island-year-shortage power amount AENSI reflects a power supply capability of an island in terms of power shortage, and the calculation formula is as follows:

in the formula, YEAR is the simulation YEAR, N_lossNumber of insufficient power supply for island E_loss，jThe power supply shortage R during the jth island power failure_jIs an islanding zone in the jth islanding state, P_L，iPower of the ith load point in island, t_loss，jAnd the power failure time of the j th island is shown.

Further, the determining, by the power outage time unit, the annual average power outage time of each load point of the power distribution network by using a time sequence monte carlo method according to the output model of the distributed power supply of the power distribution network, the fault model and the fault restoration model of the elements in the power distribution network includes:

F(t)＝1-e^-λt

F_D(t)＝1-e^-μt

step 4, determining a load point with power failure due to the influence of a fault element, judging whether the power failure load point can be recovered from the main network circuit breaker, turning to step 5 when the power failure load point cannot be recovered from the main network circuit breaker, and turning to step 9 when the power failure load point can be recovered from the main network circuit breaker;

Further, the power failure cost unit determines the power failure cost of the power distribution network according to the annual average power failure time of each load point of the power distribution network, and the calculation formula is as follows:

where n is the number of distribution network load nodes, t_iIs the annual power outage time of the ith load point, P_iIs the load power of the i-th load point, c_eIs the unit electricity price.

Further, the economic model unit includes:

a first cost unit for calculating an investment cost C of the distributed power supply_DGNamely, the total annual cost of the distributed power supply built by the nodes to be installed in the power distribution network is calculated according to the formula:

where r is the annual cost coefficient, m is the operational age of the distributed power plan, N_DGNumber of nodes to be installed for distributed power supply, C_iIs divided into various categoriesUnit investment cost of distributed power supply, S_DG，iIs the installation capacity of the distributed power supply;

a second cost unit for calculating the network loss cost C of the power distribution network by using a push-back substitution method by regarding the distributed power supply as a load node of the power distribution network_lossThe calculation formula is as follows:

wherein t is the number of branches of the distribution network, P_lpss，iIs the network loss, τ, of the ith branch_maxIs the annual maximum load loss hours of the ith branch, c_eIs the unit electricity price;

the model determining unit is used for establishing an economic model of the operation of the power distribution network accessed to the distributed power supply according to the investment cost, the network loss cost and the reliability cost of the distributed power supply of the power distribution network by taking the annual operation cost of the power distribution network as a general target, and the calculation formula is as follows:

minZ_cost＝C_DG+C_loss+C_D

Further, the configuration unit includes:

the condition setting unit is used for taking a model for evaluating indexes irrelevant to evaluation economy in power supply reliability of the power distribution network accessed to the distributed power supply and an economy model as a multi-target planning model of the power distribution network accessed to the distributed power supply, and setting two constraint conditions of equality constraint and inequality constraint for enabling the power distribution network accessed to the distributed power supply to normally operate, wherein the equality constraint is power flow constraint of the power distribution network, and the inequality constraint comprises node voltage constraint, line current constraint, line transmission power constraint and distributed power supply quantity constraint of the power distribution network;

the configuration determining unit is used for encoding the distributed power source to be accessed in the power distribution network in an integer mode, setting the chromosome encoding length of the genetic algorithm as the type of the distributed power source and the installation position and capacity of the distributed power source, and then determining the optimal configuration of the distributed power source to be accessed in the power distribution network, wherein:

the type of the distributed power supply is coded as randin (1, n, [0,1]), 0 or 1 can be specified by self to represent a photovoltaic or a fan, and n is the number of the distributed power supply to be installed;

the distributed power source installation position and capacity code is randint (1, d, [1, n ]), where d is:

d＝floor(PDGmax/DGmin)

the chromosomes encode:

x＝[randint(1，n，[0，1])randint(1，d，[1，n])]。

and the configuration verification unit is used for comparing and analyzing the power supply reliability and economy of the distribution network without adding the distributed power supply and after adding the distributed power supply according to the determined optimal configuration of the distributed power supply to be accessed in the distribution network, and determining the improvement of the optimal configuration of the distributed power supply accessed in the distribution network on the power supply reliability and economy of the distribution network.

The method and the system for determining the configuration and the evaluation of the distributed power supply in the power distribution network provided by the technical scheme of the invention carry out the optimized configuration of the distributed power supply of the power distribution network under the condition of considering the power supply reliability and the economical efficiency of the power distribution network, establish a power output model of the distributed power supply from the influences of the access of the distributed power supply to the power distribution network on the network loss of system branches, the node voltage and the reliability of load points, establish an index system for evaluating the power supply reliability of the power distribution network accessed to the distributed power supply and a model of each index, carry out the annual power failure time of the load points on the power distribution network accessed to the distributed power supply by using a time sequence Monte Carlo method, carry out the network loss calculation on the power distribution network accessed to the distributed power supply by using a forward-backward substitution method, and establish an economical model of the power distribution network accessed to the distributed power supply according to the network loss, the power failure cost and the investment cost of the distributed power supply, and taking a model and an economic model for evaluating indexes irrelevant to evaluation economy in the power supply reliability of the power distribution network accessed to the distributed power supply as a multi-objective planning model of the power distribution network accessed to the distributed power supply, performing model solution by using a genetic algorithm, realizing simultaneous optimization of the type, the position and the access capacity of the distributed power supply, comparing and analyzing the power supply reliability and the economic performance of the power distribution network without adding the distributed power supply and after adding the distributed power supply according to the determined optimal configuration of the distributed power supply to be accessed in the power distribution network, and determining the optimal configuration of the power distribution network accessed to the distributed power supply to improve the power supply reliability and the economic performance of the power distribution network. The method and the device have the following advantages:

1. a new index and index calculation method for power distribution network power supply reliability evaluation after the distributed power supply is accessed is provided, and the influence of the distributed power supply on the power distribution network power supply reliability after the distributed power supply is accessed is quantized.

2. The chromosome coding is different from the traditional distributed power supply planning coding, not only considers the installation capacity of the distributed power supply, but also considers the installation type of the distributed power supply. The chromosome generated by the coding mode can clearly see the type and the installation capacity of the distributed power supply installed by each node.

3. The model and the economic model for evaluating indexes irrelevant to evaluation economy in the power supply reliability of the power distribution network accessed to the distributed power supply are used as the multi-target planning model of the power distribution network accessed to the distributed power supply, and the optimal configuration of the multi-target planning model is obtained by utilizing a genetic algorithm, so that the power supply reliability and the economic performance of the power distribution network accessed to the distributed power supply are obviously improved.

Drawings

A more complete understanding of exemplary embodiments of the present invention may be had by reference to the following drawings in which:

FIG. 1 is a flow diagram of a method of determining a configuration of a distributed power source in a power distribution network in accordance with a preferred embodiment of the present invention;

FIG. 2 is a schematic diagram of a wind speed probability density curve obeying a Weibull distribution in accordance with a preferred embodiment of the present invention;

FIG. 3 is a diagram illustrating a probability density curve of illumination intensity obeying a beta distribution according to a preferred embodiment of the present invention;

FIG. 4 is a schematic flow chart illustrating a method for determining an average annual outage time at each load point of a power distribution network accessing a distributed power source using a time-sequential Monte Carlo method according to a preferred embodiment of the present invention;

fig. 5 is a structural diagram of the photovoltaic power generation system connected to the tail ends of four main feeders of the actual topology of the power distribution network in the north of the wing according to the preferred embodiment of the invention.

Fig. 6 is a schematic diagram of a system for determining the configuration of a distributed power source in a power distribution network according to a preferred embodiment of the present invention.

Detailed Description

The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.

Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.

Fig. 1 is a flow chart of a method of determining a configuration of a distributed power source in a power distribution network according to a preferred embodiment of the present invention. As shown in fig. 1, a method 100 of determining a configuration of a distributed power source in a power distribution network according to the present invention begins at step 101.

In step 101, an output model of a distributed power source connected to a power distribution network is established, wherein the distributed power source comprises a wind power generation system and a photovoltaic power generation system.

Preferably, the establishing of the output model of the distributed power source connected to the power distribution network includes:

and establishing a wind speed model which obeys Weibull distribution according to the actual wind speed data of the region where the wind power generation system to be accessed to the power distribution network is located.

In the preferred embodiment, wind speed modeling is performed by using actual wind speed data in a region in the north of the wing and subject to Weibull distribution, and the probability density function is as follows:

where v denotes the wind speed, k, c are the shape and scale parameters of the Weibull distribution of the two parameters, respectively, and σ_windIs the standard deviation of the wind speed, E_windIs the average of the wind speeds.

FIG. 2 is a schematic diagram of a wind speed probability density curve obeying a Weibull distribution according to a preferred embodiment of the present invention. FIG. 2 is a wind speed probability density curve diagram when the average value of the actually measured wind speed in the area north of Ji is 2.21m/s and the standard deviation is 1.45 m/s.

Establishing an output model P of the wind power generation system according to the Weibull distribution-obeying wind speed model_WTG(v) The calculation formula of (2) is as follows:

in the formula, v_ci，v_cr，v_coRespectively cut-in wind speed, rated wind speed and cut-out wind speed, p_rRated output power of a fan of the wind power generation system when the wind speed is less than v_ciWhen the fan is not running; when the wind speed is v_ciAnd v_crIn the meantime, the output of the fan is increased along with the increase of the wind speed, and the output of the fan is approximately in a primary curve; when the wind speed is greater than v_crAnd is smallIn v_coThe output of the fan is a rated value; when the wind speed is greater than v_coIn time, the fan stops working for safety.

And establishing an illumination intensity model which obeys the beta distribution according to the actual illumination intensity data of the region where the photovoltaic power generation system to be accessed to the power distribution network is located.

In the preferred embodiment, the illumination intensity modeling complying with the beta distribution is performed by using the actual light intensity data in the area in north of the wing, and the probability density function is as follows:

wherein S represents the intensity of light, S_maxRepresenting the maximum value of the illumination intensity, alpha and beta are two shape parameters of the beta distribution respectively,

is a Gamma function, μ is the mean value of the intensity of illumination, and σ is the standard deviation of the intensity of illumination.

Fig. 3 is a diagram illustrating a probability density curve of illumination intensity obeying a beta distribution according to a preferred embodiment of the present invention. FIG. 3 shows that the average value of the actually measured light intensity in the north of the wing is 170.40w/m²Standard deviation of 248.48w/m²The illumination intensity probability density graph of (a).

Establishing an output model P of the photovoltaic power generation system according to the illumination intensity model obeying the beta distribution_PVGThe calculation formula is as follows:

In step 102, an index system for evaluating the power supply reliability of the power distribution network connected to the distributed power supply is established, wherein indexes in the index system comprise 1 index power distribution network load point annual average power failure time relevant to evaluation of the power distribution network economy and a plurality of indexes irrelevant to evaluation of the power distribution network economy.

Preferably, the establishing of the index system for evaluating the power supply reliability of the power distribution network accessed to the distributed power supply means that the annual power generation rate, the annual output interruption rate, the fluctuation rate, the island year average power supply time, the island year average power supply capacity, the island year average power shortage capacity and the distribution network load point annual average power outage time of the distributed power supply are used as indexes for evaluating the power supply reliability of the power distribution network accessed to the distributed power supply, wherein the annual average power outage time of the distribution network load point is an index related to the evaluation of the power distribution network economy, and the annual power generation rate, the annual output interruption rate, the fluctuation rate, the island year average power supply time, the island year average power supply capacity and the island year average power shortage capacity of the distributed power supply are indexes unrelated to the evaluation of the power distribution network economy.

In step 103, a model of indexes irrelevant to the evaluation of the economy of the power distribution network is established according to the output model of the distributed power supply accessed to the power distribution network.

Preferably, the establishing of the model of the index irrelevant to the evaluation of the economy of the power distribution network according to the output model of the distributed power supply accessed to the power distribution network includes:

And step 104, determining the annual average power failure time of each load point of the power distribution network by using a time sequence Monte Carlo method according to the output model of the distributed power supply of the power distribution network, the fault model and the fault restoration model of the elements in the power distribution network.

Fig. 4 is a schematic flow chart illustrating a process for determining an average annual outage time at each load point of a power distribution network connected to a distributed power source by using a time sequence monte carlo method according to a preferred embodiment of the present invention. As shown in fig. 4, the step of determining the annual average outage time of each load point of the power distribution network by using the time sequence monte carlo method according to the output model of the distributed power supply of the power distribution network, the fault model of the element in the power distribution network and the fault repair model starts from step 401.

In step 401, an initial time t of calculating an average annual outage time for each load point of a distribution network connected to a distributed power supply is set to 0.

In step 402, random numbers (0, 1) are generated for all elements of the distribution network, and the normal operating time TTF of each element is determined according to the element failure probability distribution function.

The fault probability distribution of elements in the power distribution network presents an exponential distribution trend along with time, and a fault model is as follows:

F(t)＝1-e^-λt

where f (t) is the failure probability of the element and λ is the failure rate of the element over time, where the failure rate of the element is constant.

In step 403, the element with the smallest TTF is taken as the failed element, a (0, 1) random number is generated for the failed element, and the repair time TTR of the failed element is calculated according to the fault repair model.

The repair time of the element is similar to the fault probability distribution of the element and also follows exponential distribution, and the fault repair model is as follows:

F_D(t)＝1-e^-μt

in the formula, F_D(t) is a probability corresponding to the element repair time, and μ is an element repair rate, where the element repair rate is constant.

In step 404, determining a load point with power failure due to the influence of the fault element, judging whether the power failure load point can be recovered from the power supply of the main network circuit breaker, turning to step 405 when the power failure load point cannot be recovered from the power supply of the main network circuit breaker, and turning to step 409 when the power failure load point can be recovered from the power supply of the main network circuit breaker;

in step 405, determining the output and load power of the distributed power supply by combining the type and the installation capacity of the distributed power supply installed at the power failure load point and an output model of the power distribution network accessed to the distributed power supply, and turning to step 406 when the output of the distributed power supply is greater than the load power, and turning to step 408 when the output of the distributed power supply is not greater than the load power;

in step 406, adding 1 to the number of faults of the load point, wherein the power failure time is the time for the distributed power supply to be put into operation;

in step 407, adding TTF and TTR of each element to a calculation time t, and going to step 402 when t is less than a set value, and going to step 410 when t is not less than the set value;

in step 8, adding 1 to the number of load point faults, wherein the power failure time is the repair time of the fault element, and turning to step 410;

in step 409, adding 1 to the number of load point faults, wherein the power failure time is the operation time of the breaker;

in step 410, the power failure times and the power failure time of the whole calculation time of each load point of the power distribution network accessed to the distributed power supply are output, and the average power failure time per year of each load point is obtained by averaging.

And after the distributed power sources are accessed at different positions in the actual topology of the power distribution network to be accessed to the distributed power sources, determining the influence of the annual average power failure time of each load point on the power supply reliability of the power distribution network according to the process. The positions of accessing distributed power sources in the actual topology of the power distribution network are divided into two situations: the first case is that the distributed power supplies are connected to different positions of the same feeder line, and the second case is that the distributed power supplies are connected to different feeder line ends. The annual average power failure time of each load point when the distributed power supply is connected to different positions is calculated based on actual topology and power distribution network data, the calculated data of different conditions are compared, the tail end of a photovoltaic power generation system connected to a feeder line can be obtained, the annual average power failure time of each load point is the minimum, and the reliability of a power distribution network is improved most obviously.

And after the distributed power supplies with different capacities are accessed in the actual topology of the power distribution network to be accessed to the distributed power supplies, determining the annual average power failure time of each load point of the power distribution network according to the process.

Fig. 5 is a structural diagram of the photovoltaic power generation system connected to the tail ends of four main feeders of the actual topology of the power distribution network in the north of the wing according to the preferred embodiment of the invention. As shown in fig. 5, after the terminals of the four main feeders in the main topology of the distribution network are connected to the photovoltaic power generation system, the permeability of the connected photovoltaic power generation system is changed, the annual average power failure time of each load point is calculated respectively, and the comparison of the calculation results shows that the inflection point of the photovoltaic access permeability is 10% and the saturation point is 30%, that is, as the access permeability is gradually increased, the annual average power failure time of each load point is smaller, and the power supply reliability is gradually increased; when the photovoltaic permeability is less than 10%, the power supply reliability is improved quickly; when the access permeability is more than 30%, the power supply reliability is improved slowly. The inflection point of the permeability of the fan is 30%, and the saturation point is 50%.

In step 104, the power failure cost of the power distribution network is determined according to the annual average power failure time of each load point of the power distribution network.

Preferably, the power failure cost of the power distribution network is determined according to the annual average power failure time of each load point of the power distribution network, and the calculation formula is as follows:

In step 106, an economic model of the operation of the power distribution network connected to the distributed power supply is established according to the investment cost, the network loss cost and the power failure cost of the distributed power supply of the power distribution network, with the annual operation cost of the power distribution network as a general target.

Preferably, the establishing an economic model of the operation of the power distribution network accessing the distributed power supply according to the investment cost, the network loss cost and the power failure cost of the distributed power supply of the power distribution network by taking the annual operation cost of the power distribution network as a total target comprises:

calculating investment cost C of distributed power supply_DGNamely, the total annual cost of the distributed power supply built by the nodes to be installed in the power distribution network is calculated according to the formula:

where r is the annual cost coefficient, m is the operational age of the distributed power plan, N_DGNumber of nodes to be installed for distributed power supply, C_iFor the unit investment cost, S, of various distributed power supplies_DG，iIs the installation capacity of the distributed power supply;

the distributed power supply is regarded as a load node of the power distribution network, and the network loss cost C of the power distribution network is calculated by using a forward-backward substitution method_lossThe calculation formula is as follows:

minZ_cost＝C_DG+C_loss+C_D

And calculating the economy of the distributed power supply accessed to different positions of the power distribution network according to the economy model. 300kW photovoltaic is respectively connected to the head, the middle and the tail ends of four main feeders in the main topology of the power distribution network in the North region shown in FIG. 5, the annual power failure cost is obtained by using the calculation method in the step 105, the network loss is calculated by using a push-back substitution method, the economy of the distributed power supply connected to different positions of the power distribution network is calculated according to the economy model, the economy of the distributed power supply connected to the tail end of the feeder is larger than that of the connected feeder, and the economy of the connected feeder is larger than that of the connected feeder.

And calculating the economy of the distributed power supplies with different capacities accessed to the power distribution network according to the economy model. 100kW, 200kW and 300kW photovoltaics are respectively connected to the tail ends of four main feeders in a main topology of a power distribution network in a certain area in the North area shown in figure 5, the respective economy is calculated according to an economy model of the power distribution network connected to the distributed power sources, and the calculation results are compared to obtain that the investment of the distributed power sources is increased along with the increase of the capacity of the distributed power sources, but the reliability cost and the network loss cost of the power distribution network are continuously reduced. Thus, the capacity of the distributed power supply needs to be planned to optimize the economy of the power distribution network.

In step 107, the model for evaluating the indexes irrelevant to the evaluation economy in the reliability of the power supply of the power distribution network accessed to the distributed power supply and the economy model are used as a multi-target planning model of the power distribution network accessed to the distributed power supply, the optimal configuration of the distributed power supply accessed to the power distribution network is determined by using a genetic algorithm under the set constraint condition, and the reliability and the economy of the power supply of the power distribution network which is not accessed to the distributed power supply and the reliability and the economy of the power supply accessed to the distributed power supply are compared and analyzed.

Preferably, the step of determining the optimal configuration of the distributed power sources accessed to the power distribution network by using a genetic algorithm under the set constraint condition by using the operation economy model of the power distribution network accessed to the distributed power sources as the multi-target planning model of the power distribution network accessed to the distributed power sources, and the step of performing comparative analysis on the power supply reliability and economy after the power distribution network is not accessed to the distributed power sources and the power distribution network is accessed to the distributed power sources comprises the steps of:

the method comprises the following steps of taking an operation economy model of a power distribution network accessed with the distributed power supply as a multi-target planning model of the power distribution network accessed with the distributed power supply, and setting two constraint conditions of equality constraint and inequality constraint for enabling the power distribution network accessed with the distributed power supply to normally operate, wherein the equality constraint is power flow constraint of the power distribution network, and the inequality constraint comprises node voltage constraint, line current constraint, line transmission power constraint and distributed power supply quantity constraint of the power distribution network, and specifically comprises the following steps:

the voltage amplitudes, G, of nodes i and j, respectively_ijAnd B_ijAre respectively node admittance matricesReal and imaginary parts of, delta_ijIs the phase angle difference between node i and node j;

the node voltage constraints of the power distribution network are as follows:

0.93U_N≤U_i≤1.07U_N

in the formula of U_NIs the node rated voltage, U_iIs the magnitude of the node voltage;

the line current constraints are:

I_l≤I_l，max

in the formula I_lIs the value of the current flowing through the line l, I_l，maxThe maximum current allowed through line i.

The line transmission power constraint is:

0≤S_ij≤S_ij，max

in the formula, S_ijIs the power transmitted by line ij, S_ij，maxIs the nominal power that line ij allows transmission.

The distributed power capacity constraints are:

0≤P_DG，i≤P_DG，imax

0≤S_∑DG≤S_max

in the formula P_DG，iIs the distributed power capacity, P, installed at the ith node in the distribution network_DG，imaxIs the maximum capacity, S, allowed by node i to access the distributed power supply_∑DGIs the total amount of capacity of the distribution network to install the distributed power supply, S_maxIs the upper limit of capacity of the distribution network that allows installation of distributed power sources.

d＝floor(PDGmax/DGmin)＝floor(P_load×20％/DGmin)

in the formula, PDGmax is the power corresponding to the upper limit of the installation capacity of the distributed power supply allowed by the power distribution network, DGmin is the minimum installation capacity of the distributed power supply, and P is the minimum installation capacity of the distributed power supply_loadThe power corresponding to the total capacity of the load;

the chromosomes encode:

x＝[randint(1，n，[0，1])randint(1，d，[1，n])]

when in use

x＝[randint(1，n，[0，1])randint(1，d，[1，n])]＝

10110010000133125561287910, and if 0 is fan and 1 is photovoltaic, the result is 1, 3, 4, 7, 12 node installation photovoltaic, 2, 5, 6, 8, 9, 10, 11 node installation fan; wherein, 1, 2, 6, 7, 8, 9, 10 and 12 nodes are provided with distributed power supplies with DGmin capacity, and 3 and 5 nodes are provided with distributed power supplies with 2-point DGmin capacity.

In the preferred embodiment, for the power distribution network in a certain region in north of Ji, the reliability and the economy of the unaccessed distributed power supply are calculated according to the traditional method, the reliability and the economy of the accessed distributed power supply are calculated according to the method provided by the invention, the reliability and the economy are respectively analyzed and compared, the power supply reliability and the network loss of the power distribution network after the distributed power supply is accessed are improved, the investment in the aspects of the network loss and the power supply reliability of the traditional power distribution network is reduced, and the running economy of the power distribution network is improved.

Fig. 6 is a schematic structural diagram of a system for determining a configuration of a distributed power source in a power distribution network according to a preferred embodiment of the present invention, and as shown in fig. 6, a system 600 for determining a configuration of a distributed power source in a power distribution network according to a preferred embodiment of the present invention includes:

the output model unit 601 is used for establishing an output model of a distributed power source accessed in a power distribution network, wherein the distributed power source comprises a wind power generation system and a photovoltaic power generation system;

the index determining unit 602 is configured to establish an index system for evaluating power supply reliability of a power distribution network connected to a distributed power supply, where indexes in the index system include 1 index power distribution network load point annual average power failure time related to evaluation of power distribution network economy and a plurality of indexes unrelated to evaluation of power distribution network economy;

the index model unit 603 is used for establishing a model of indexes irrelevant to the evaluation of the economy of the power distribution network according to an output model of a distributed power supply accessed to the power distribution network;

a power failure time unit 604, configured to determine an average power failure time per year at each load point of the power distribution network by using a time sequence monte carlo method according to an output model of the distributed power supply of the power distribution network, a fault model and a fault restoration model of an element in the power distribution network;

a power outage cost unit 605, configured to determine a power outage cost of the power distribution network according to an annual average power outage time of each load point of the power distribution network;

the economic model unit 606 is used for establishing an economic model of the operation of the power distribution network accessed to the distributed power supply according to the investment cost, the network loss cost and the power failure cost of the distributed power supply of the power distribution network by taking the annual operation cost of the power distribution network as a general target;

the configuration unit 607 is configured to use the model for evaluating the index irrelevant to the evaluation economy in the reliability of power supply to the distribution network accessed to the distributed power supply and the economy model as a multi-objective planning model for the distribution network accessed to the distributed power supply, determine the optimal configuration for accessing the distributed power supply to the distribution network by using a genetic algorithm under a set constraint condition, and perform comparative analysis on the reliability and economy of power supply after the distribution network is not accessed to the distributed power supply and the distribution network is accessed to the distributed power supply.

Preferably, the output model unit 601 includes:

first output forceA model unit 611, configured to establish a wind speed model complying with weibull distribution according to actual wind speed data of an area where the wind power generation system to be connected to the power distribution network is located, and establish an output model P of the wind power generation system according to the wind speed model_WTG(v) The calculation formula is as follows;

in the formula, v_ci，v_cr，v_coRespectively cut-in wind speed, rated wind speed and cut-out wind speed, p_rRated output power of a fan of the wind power generation system when the wind speed is less than v_ciWhen the fan is not running; when the wind speed is v_ciAnd v_crIn the meantime, the output of the fan is increased along with the increase of the wind speed, and the output of the fan is approximately in a primary curve; when the wind speed is greater than v_crAnd is less than v_coThe output of the fan is a rated value; when the wind speed is greater than v_coWhen the fan is in use, the fan stops working for safety;

a second output model unit 612, configured to establish an illumination intensity model complying with beta distribution according to actual illumination intensity data of an area where the photovoltaic power generation system to be connected to the power distribution network is located, and establish an output model P of the photovoltaic power generation system according to the illumination intensity model_PVGThe calculation formula is as follows:

Preferably, the index determining unit 602 uses an annual generation rate, an annual outages interruption rate, a fluctuation rate, an average annual power supply time of an island, an average annual power supply capacity of the island, an average annual power shortage amount of the island, and an average annual power outage time of a distribution network load point as indexes for evaluating power supply reliability of the distribution network connected to the distributed power supply, where the annual average annual power outage time of the distribution network load point is an index related to evaluation of power distribution network economy, and the annual generation rate, the annual outages interruption rate, the fluctuation rate, the average annual power supply time of the island, the average annual power supply capacity of the island, the average annual power shortage amount of the island are indexes unrelated to evaluation of power distribution network economy.

Preferably, the index model unit 603 includes:

the first model unit 631 is configured to calculate a distributed power supply annual generation rate index GRDG, where the GRDG reflects a ratio of an actual power generation amount of the distributed power supply to a rated power generation amount of the distributed power supply, and a calculation formula is as follows in unit:

a second model unit 632, configured to calculate a pdg (distributed generator power generation annual output discontinuity rate index), where the pdg reflects a discontinuity degree of the distributed generator power generation, and the index value has a large influence on power supply reliability of an island, and a calculation formula is as follows in unit:

a third model unit 633 for calculating a distributed power supply fluctuation rate index PFDG, where the PFDG obtains the whole T through a difference calculation between a previous time and a next time₃Fluctuation situation of distributed power supply output in time periodThe fluctuation change of the output power of the distributed power supply can be effectively reflected, and the unit is as follows:

the fourth model unit 634 is configured to calculate an island average annual power supply time APSTI, where the APSTI reflects an average annual power outage condition of the main network and reflects an average annual power supply time of the island when the main network fails, and a calculation formula is as follows:

a fifth model unit 635, configured to calculate an average island year supply power amount AESI, where the average island year supply power amount AESI reflects a power supply capacity of an island in terms of power support, and the calculation formula is as follows:

a sixth model unit 636, configured to calculate an average island-year-shortage power amount AENSI, where the average island-year-shortage power amount AENSI reflects a power supply capability of an island in terms of a power shortage, and the calculation formula is as follows:

Preferably, the determining, by the power outage time unit 604, the annual average power outage time of each load point of the power distribution network by using a time sequence monte carlo method according to the output model of the distributed power supply of the power distribution network, the fault model and the fault restoration model of the elements in the power distribution network includes:

F(t)＝1-e^-λt

F_D(t)＝1-e^-μt

Preferably, the power outage cost unit 605 determines the power outage cost of the power distribution network according to the annual average power outage time of each load point of the power distribution network, and the calculation formula is as follows:

Preferably, the economic model unit 606 includes:

a first cost unit 661 for calculating an investment cost C of the distributed power supply_DGNamely, the total annual cost of the distributed power supply built by the nodes to be installed in the power distribution network is calculated according to the formula:

a second cost unit 662 for calculating the network loss charge C of the power distribution network by using a push-back substitution method by regarding the distributed power source as a load node of the power distribution network_lossThe calculation formula is as follows:

the model determining unit 663 is configured to establish an economic model of operation of the power distribution network accessing the distributed power sources according to investment cost, network loss cost, and reliability cost of the distributed power sources of the power distribution network, with an annual operation cost of the power distribution network as a total target, and a calculation formula of the model determining unit is as follows:

minZ_cost＝C_DG+C_loss+C_D

in the formula, Z_cpstIs the annual integrated cost, C, of the distribution network after the distributed power supply is connected_DGIs the average annual investment cost of the distributed power supply, C_lossIs the average annual network loss charge of the distribution network after the distributed power supply is connected, C_DIs the annual power failure of the power distribution network after the distributed power supply is connectedThe method is as follows.

Preferably, the configuration unit 607 includes:

the condition setting unit 671 is used for taking a model for evaluating indexes irrelevant to evaluation economy in power supply reliability of the power distribution network accessed to the distributed power supply and an economic model as a multi-target planning model of the power distribution network accessed to the distributed power supply, and setting two constraint conditions of equality constraint and inequality constraint for enabling the power distribution network accessed to the distributed power supply to normally run, wherein the equality constraint is a power flow constraint of the power distribution network, and the inequality constraint comprises a node voltage constraint, a line current constraint, a line transmission power constraint and a distributed power supply quantity constraint of the power distribution network;

a configuration determining unit 672, configured to encode the distributed power sources to be accessed in the power distribution network in an integer manner, and determine the optimal configuration of the distributed power sources to be accessed in the power distribution network after setting the chromosome encoding length of the genetic algorithm as the type of the distributed power sources and the installation position and capacity of the distributed power sources, where:

d＝floor(PDGmax/DGmin)

the chromosomes encode:

x＝[randint(1，n，[0，1])randint(1，d，[1，n])]；

the configuration verification unit 673 is configured to compare and analyze power supply reliability and economy of the distribution network after the distribution network is not added to the distribution network and after the distribution network is added to the distribution network according to the determined optimal configuration of the distributed power source to be accessed in the distribution network, and determine improvement of the optimal configuration of the distribution network to the power supply reliability and economy of the distribution network.

The invention has been described with reference to a few embodiments. However, other embodiments of the invention than the one disclosed above are equally possible within the scope of the invention, as would be apparent to a person skilled in the art from the appended patent claims.

Generally, all terms used in the claims are to be interpreted according to their ordinary meaning in the technical field, unless explicitly defined otherwise herein. All references to "a/an/the [ device, component, etc ]" are to be interpreted openly as referring to at least one instance of said device, component, etc., unless explicitly stated otherwise. The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated.

Claims

1. A method of determining a configuration of a distributed power source in a power distribution network, the method comprising:

2. The method of claim 1, wherein the establishing a model of the output of the distributed power sources connected to the power distribution network comprises:

establishing an illumination intensity model complying with beta distribution according to actual illumination intensity data of the region where the photovoltaic power generation system to be accessed into the power distribution network is located, and establishing an output model P of the photovoltaic power generation system according to the illumination intensity model_PVGThe calculation formula is as follows:

3. The method according to claim 1, wherein the establishing of the index system for evaluating the power supply reliability of the power distribution network connected to the distributed power supply means that the annual power generation rate, the annual power output interruption rate, the fluctuation rate, the island annual average power supply time, the island annual average power supply capacity, the island annual average power shortage capacity and the distribution network load point annual average power outage time of the distributed power supply are used as indexes for evaluating the power supply reliability of the power distribution network connected to the distributed power supply, wherein the distribution network load point annual average power outage time is an index related to evaluation of the power distribution network economy, and the annual power generation rate, the annual power output interruption rate, the fluctuation rate, the island annual average power supply time, the island annual average power supply capacity and the island annual average power shortage capacity are indexes unrelated to evaluation of the power distribution network economy.

4. The method of claim 3, wherein modeling indicators that are not relevant to assessing power distribution grid economics based on the power output model of the distributed power sources connected to the power distribution grid comprises:

island annual average power supply time APSTI reflects the annual average power failure condition of a main network and reflects the annual average power supply time of an island when the main network fails, and the calculation formula is as follows:

in the above formula, YEAR is the simulation YEAR, i is the load point, P_L,iIs the power demand of load point i, P_DGFor the output power of the distributed power supply, N is the frequency of normal power supply of the island, R_jDividing the island into regions for the jth island state, t_jIs the jth islandPower supply time in normal power supply;

in the formula, YEAR is simulation age, N is the number of times of island normal power supply in YEAR, E_jThe power supply electric quantity R for the jth island in normal power supply_jIs an islanding zone in the jth islanding state, P_L,iPower of the ith load point in island, t_jThe power supply time when the jth island is normally powered;

in the formula, YEAR is the simulation YEAR, N_lossNumber of insufficient power supply for island E_loss,jThe power supply shortage R during the jth island power failure_jIs an islanding division region in the j time islanding state, P_L,iPower of the ith load point in island, t_loss,jAnd the power failure time of the j th island is shown.

5. The method of claim 1, wherein determining the annual average blackout time for each load point of the power distribution network using a time series monte carlo method based on the output model of the distributed power sources of the power distribution network, the fault model and the fault remediation model of the components in the power distribution network comprises:

F(t)＝1-e^-λt

F_D(t)＝1-e^-μt

6. The method of claim 1, wherein the power outage cost of the power distribution network is determined according to the annual average power outage time of each load point of the power distribution network, and is calculated by the formula:

7. The method of claim 6, wherein the step of establishing an economic model of operation of the power distribution network to access the distributed power sources based on investment costs, network loss costs, and blackout costs of the distributed power sources of the power distribution network, the annual operation costs of the power distribution network being a total target, comprises:

where r is the annual cost coefficient, m is the operational age of the distributed power plan, N_DGNumber of nodes to be installed for distributed power supply, C_iFor the unit investment cost, S, of various distributed power supplies_DG,iIs the installed capacity of the distributed power supply;

wherein t is the number of branches of the distribution network, P_loss,iIs the network loss, τ, of the ith branch_maxIs the annual maximum load loss hours of the ith branch, c_eIs the unit electricity price;

minZ_cost＝C_DG+C_loss+C_D

8. The method according to claim 1, wherein the step of taking the model for evaluating the index irrelevant to the evaluation economy in the reliability of the power supply of the power distribution network accessed to the distributed power supply and the economy model as a multi-objective planning model for the power distribution network accessed to the distributed power supply, the step of determining the optimal configuration of the distributed power supply accessed to the power distribution network by using a genetic algorithm under the set constraint condition, and the step of performing comparative analysis on the reliability and the economy of the power supply of the power distribution network after the distributed power supply is not accessed to the distributed power supply and after the distributed power supply is accessed to the power distribution network comprises the steps of:

d＝floor(PDGmax/DGmin)

the chromosomes encode:

x＝[randint(1,n,[0,1])randint(1,d,[1,n])]；

9. A system for determining a configuration of a distributed power source in a power distribution network, the system comprising:

10. The system of claim 9, wherein the output model unit comprises:

the first output model unit is used for establishing a wind speed model which obeys Weibull distribution according to actual wind speed data of the area where the wind power generation system to be accessed into the power distribution network is located, and establishing an output model P of the wind power generation system according to the wind speed model_WTG(v) The calculation formula is as follows;

11. The system according to claim 9, wherein the index determination unit uses an annual generation rate, an annual outage rate, a fluctuation rate, an average island power supply time, an average island power supply capacity, an average island power shortage capacity, and an average distribution network load point annual outage time as indexes for evaluating the power supply reliability of the distribution network connected to the distributed power supply, wherein the average distribution network load point annual outage time is an index related to evaluating the power distribution network economy, and the annual generation rate, the annual outage rate, the fluctuation rate, the average island power supply time, the average island power supply capacity, and the average island power shortage capacity are indexes unrelated to evaluating the power distribution network economy.

12. The system of claim 11, wherein the metric model unit comprises:

a third model unit for calculating a PFDG, wherein the PFDG obtains the whole T through the difference calculation between the previous time and the next time₃The fluctuation condition of the output power of the distributed power supply in a time period can effectively reflect the fluctuation change of the output power of the distributed power supply, and the unit is as follows:

in the above formula, YEAR is the simulation YEAR, i is the load point, P_L,iIs the power demand of load point i, P_DGFor the output power of the distributed power supply, N is the frequency of normal power supply of the island, R_jIs an islanding zone in the jth islanding state, t_jThe power supply time when the jth island is normally powered;

a fifth model unit, configured to calculate an average island annual energy supply capacity AESI, where the average island annual energy supply capacity AESI reflects an island energy supply capacity in terms of energy support, and a calculation formula is as follows:

in the formula, YEAR is simulation age, N is the number of times of island normal power supply in YEAR, E_jThe power supply electric quantity R when the j time isolated island is normally supplied with power_jIs an islanding division region in the j time islanding state, P_L,iPower of the ith load point in island, t_jThe power supply time when the jth island is normally powered;

13. The system of claim 9, wherein the blackout time unit determines the annual average blackout time per load point of the power distribution network using a time series monte carlo method according to an output model of the distributed power sources of the power distribution network, a fault model of elements in the power distribution network, and a fault remediation model, and comprises:

F(t)＝1-e^-λt

F_D(t)＝1-e^-μt

14. The system of claim 9, wherein the blackout cost unit determines the blackout cost of the distribution network according to the annual average blackout time of each load point of the distribution network, and the calculation formula is as follows:

15. The system of claim 14, wherein the economic model unit comprises:

minZ_cost＝C_DG+C_loss+C_D

in the formula, Z_costIs the annual integrated cost of the distribution network after the distributed power supply is connected, C_DGIs the average annual investment cost of the distributed power supply, C_lossIs the average annual network loss charge of the distribution network after the distributed power supply is connected, C_DIs connected toAnnual outage cost of the power distribution network after entering the distributed power supply.

16. The system of claim 9, wherein the configuration unit comprises:

d＝floor(PDGmax/DGmin)

the chromosomes encode:

x＝[randint(1,n,[0,1])randint(1,d,[1,n])]；