CN112380694A - Power distribution network optimization planning method based on differential reliability requirements - Google Patents

Power distribution network optimization planning method based on differential reliability requirements Download PDF

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CN112380694A
CN112380694A CN202011267156.8A CN202011267156A CN112380694A CN 112380694 A CN112380694 A CN 112380694A CN 202011267156 A CN202011267156 A CN 202011267156A CN 112380694 A CN112380694 A CN 112380694A
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李鹏
李俊杰
邓嘉明
姜世公
杨卫红
王子轩
王云飞
范须露
柯贤杨
王第成
杨赫
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
North China Electric Power University
State Grid Economic and Technological Research Institute
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State Grid Tianjin Electric Power Co Ltd
North China Electric Power University
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Abstract

A power distribution network optimization planning method based on differential reliability requirements comprises the following steps: constructing an upper-layer planning model which takes the optimal coordination of economy and safety as a target function and enables the voltage fluctuation of each node of the system to be less than or equal to 5% of the rated voltage level; establishing a lower-layer planning model by taking the differential reliability index and the operation and maintenance cost as multi-objective functions; respectively establishing the following models based on differential reliability requirements: the method comprises the following steps of providing an investment cost model of a fan, a photovoltaic and energy storage device of the power distribution network, an operation cost model of the fan, the photovoltaic and the energy storage device of the power distribution network, an interruptible load compensation cost model, a power purchase cost model of a superior power grid of the power distribution network, an active management cost model of the fan, the photovoltaic and the energy storage device of the power distribution network and a line operation loss cost model of the power distribution network, and providing corresponding constraint conditions; and solving the model by using a chaotic self-adaptive particle swarm optimization algorithm. The method and the device can scientifically quantify the influence of the construction and the transformation of the power distribution network on the reliability of the terminal user.

Description

Power distribution network optimization planning method based on differential reliability requirements
Technical Field
The invention relates to an optimization planning method for a power distribution network. In particular to a power distribution network optimization planning method based on differentiation reliability requirements.
Background
At present, the China power industry gradually tends to be marketized, and different terminal users can provide electric energy quality and reliability requirements suitable for the terminal users to power supply enterprises according to the actual requirements of the terminal users on the electric energy quality, so that the safety and the reliability of the power utilization of the terminal users are guaranteed. Along with the rapid development of distributed energy, energy storage equipment and a plurality of controllable loads, the construction mode and the operation mode of the power distribution network are more flexible and diversified, and the capacity of meeting the power supply reliability differentiation requirements of different areas and different types of terminal users is continuously improved. The distribution network based on the differentiated reliability requirements of the terminal users can improve the flexibility and reliability of power supply on the whole, optimize distribution in a power resource network, promote multi-source complementation, improve the utilization rate of distributed power supplies, and simultaneously can make differentiated electricity prices according to the user requirements, optimize investment cost and realize the maximization of economic benefits on the premise of meeting the differentiated reliability requirements of the users.
The power distribution network based on the differentiated reliability requirements of the terminal users is different from the traditional power distribution network, after the distributed power supply and the energy storage equipment are connected, the controllable load is managed, the supporting effect of resources such as source, load and storage on the reliability of the power distribution network can be effectively improved, the power distribution network can be used as a small-sized system to independently operate, and the power distribution network can also be connected with an external large power grid in a grid mode. The operation process can be managed and controlled by self, and meanwhile, the anti-interference capability of the power grid to the outside is effectively improved. However, the distribution network based on the differentiated reliability requirements of the terminal users has different reliability indexes which need to be considered when aiming at different types of users in actual operation, which increases the difficulty of planning and optimizing. Therefore, discussion and research are carried out on different types of users, and a reasonable and effective power distribution network optimization planning method is found.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a power distribution network optimization planning method based on differential reliability requirements, which can meet different types of loads such as time type, electric quantity type, frequency type and the like.
The technical scheme adopted by the invention is as follows: a power distribution network optimization planning method based on differential reliability requirements comprises the following steps:
1) determining the installation position and the capacity of a distributed power supply consisting of a fan, a photovoltaic device and an energy storage device in a power distribution network from the two aspects of economy and safety, enabling the voltage fluctuation of each node of the system to be within an allowable range, and constructing an upper-layer planning model which takes the optimum coordination of economy and safety as a target function and enables the voltage fluctuation of each node of the system to be less than or equal to 5% of a rated voltage grade;
2) aiming at the differentiated reliability requirements of different types of terminal users, establishing a lower-layer planning model by taking differentiated reliability indexes and operation and maintenance cost as multi-objective functions;
3) on the basis of the upper-layer planning model and the lower-layer planning model, the following models based on differential reliability requirements are respectively established: the method comprises the following steps of providing an investment cost model of a fan, a photovoltaic and energy storage device of the power distribution network, an operation cost model of the fan, the photovoltaic and the energy storage device of the power distribution network, an interruptible load compensation cost model, a power purchase cost model of a superior power grid of the power distribution network, an active management cost model of the fan, the photovoltaic and the energy storage device of the power distribution network and a line operation loss cost model of the power distribution network, and providing corresponding constraint conditions;
4) Solving an upper-layer planning model, a lower-layer planning model, an investment cost model, a fan of the power distribution network, an operation cost model of photovoltaic and energy storage equipment, an interruptible load compensation cost model, a power purchase cost model of the power distribution network from a higher-level power grid, an active management cost model of the fan of the power distribution network, the photovoltaic and energy storage equipment and a line operation loss cost model of the power distribution network by using a chaotic self-adaptive particle swarm optimization algorithm, and finally obtaining a power distribution network optimization planning scheme considering differentiated reliability requirements of terminal users.
According to the power distribution network optimization planning method based on the differential reliability requirements, a double-layer planning model with the optimal coordination among economy, reliability and safety as a target is established, the planning model is established on the upper layer with the optimal coordination among economy and safety, the differential reliability requirements of various loads are considered on the lower layer, the reliability optimization model based on the differential requirements for power supply capacity, power consumption cost, power quality and the like is established, and the double-layer planning model completely expresses possible running states of source, load and storage and differential reliability requirement results. In addition, when the lower-layer optimization model is established, the method is very practical in terms of wind and light real-time output, and considering that the adjustable capacity of the system is influenced by the energy storage equipment accessed to the power distribution network and controllable load resources. The invention adopts a chaos search-based adaptive particle swarm algorithm to process the optimization problem of multiple targets. Through chaotic search and self-adaptive variation of the algorithm, the particles are searched in a given range, the accuracy of the algorithm can be effectively improved, and the convergence rate of calculation is accelerated. According to the invention, the influence of power distribution network construction and reconstruction on the reliability of the terminal user can be scientifically quantized, the power distribution network planning technical principle facing the differentiated reliability requirements of the terminal user is determined, the planning design of the power distribution network and the formulation of the construction and reconstruction scheme can be effectively guided, and the efficiency benefit level of the power distribution network planning and construction can be improved.
Drawings
FIG. 1 is a block diagram of a power distribution network optimization planning method based on differential reliability requirements according to the present invention;
FIG. 2 is a flow chart of an adaptive variation particle swarm algorithm based on chaotic search;
fig. 3 is a process of performing DG and energy storage device location determination optimization convergence on an IEEE33 node standard test system by using a chaos search-based adaptive variation particle swarm algorithm.
Detailed Description
The following describes in detail a power distribution network optimization planning method based on differentiated reliability requirements according to the present invention with reference to embodiments and drawings.
The invention discloses a power distribution network optimization planning method based on differential reliability requirements, which comprises the following steps of:
1) determining the installation position and the capacity of a distributed power supply consisting of a fan, a photovoltaic device and an energy storage device in a power distribution network from the two aspects of economy and safety, enabling the voltage fluctuation of each node of the system to be within an allowable range, and constructing an upper-layer planning model which takes the optimum coordination of economy and safety as a target function and enables the voltage fluctuation of each node of the system to be less than or equal to 5% of a rated voltage grade; the objective function is specifically as follows:
minF1＝ω1(1-f1)+ω2f2
f1＝CI+COM+Cen+CAM+Closs+Cload
Figure BDA0002776429160000031
wherein, F1To the value of the objective function, minF 1Is the minimum of the objective function, f1For economic cost, f2As a safety measure, ω1、ω2Is a weight factor of a multi-objective function, and1+ω2＝1，CIfor reduced investment costs of distributed power, COMFor operating the distributed power supply, CenFor electricity purchase charge, CAMFor active charge management, ClossFor line running loss cost, CloadIn order to compensate for the costs of interruptible loads,
Figure BDA0002776429160000032
representing the sum of the real-time power of the load nodes under the condition that all the constraint conditions are met; piRepresenting the real-time power value of the load node i; m represents the total number of the load nodes; alpha is alphaiIs a grade factor of the load node i, 0 < alphai≤1，αiThe safety influence of the load node i on the power grid is reflected, and the larger the value is, the more important the load is.
2) Classifying the reliability requirements of the terminal users into time type, frequency type and electric quantity type, corresponding to the reliability indexes of the time type, the frequency type and the electric quantity type, adopting different planning construction and operation and maintenance schemes in different client areas, and establishing a lower-layer planning model by taking the differentiation reliability indexes and the operation and maintenance cost as multi-target functions according to the differentiation reliability requirements of different types of terminal users;
the time type preferentially ensures reliable power supply of the customer in the power utilization period, investment can be properly reduced according to customer requirements during planning, a power distribution network with proper redundancy is customized, and operation and maintenance strength preferentially responds in the power utilization period of the customer.
The frequency type is very sensitive to power supply interruption, and a customer is recommended to be additionally provided with an emergency power supply on the basis that an external network meets n-1. The uninterrupted power mode is used as far as possible during maintenance, and the power failure of a client caused by the power grid problem of a power supply enterprise is avoided.
The electric quantity type and the load are relatively concentrated, the design standard of an external power grid is properly improved during planning, and the overload condition is avoided.
The lower layer planning model is as follows:
minF2＝λA+μB+γC
wherein, F2To the value of the objective function, minF2Is the minimum value of the objective function; a is a system voltage stability reliability index corresponding to the electric quantity type load, B is a power supply rate reliability index corresponding to the frequency type load, C is a voltage deviation reliability index corresponding to the time type load, and A, B, C form a differential reliability index; λ, μ, and γ are weighting factors corresponding to the electric quantity type load, the frequency type load, and the time type load, respectively, and λ + μ + γ is 1. Wherein:
(2.1) the system voltage stability reliability index A is that in the electric quantity type user area, the voltage fluctuation of each load node is as small as possible, so that a line two-end voltage deviation value A connecting the load nodes can be led outijIt is also as small as possible, and the specific formula is shown as follows:
Figure BDA0002776429160000033
Wherein i and j are respectively connected with two ends of the circuitTwo load nodes, AijIs the voltage deviation value of two ends of the line, RijIs a line resistance value, XijIs the line reactance value, PjIs the active power of the load node j, QjIs the reactive power of load node j. U shapeiThe load node i is connected with the load node j, and the load node j is connected with the load node i.
Taking the voltage deviation value A of two ends of the circuitijThe maximum value is defined as a system voltage stability reliability index A of the whole power distribution network:
A＝max(Aij)
the smaller the system voltage stability reliability index A is, the higher the voltage stability level of the power distribution network is;
(2.2) the power supply rate reliability index B has a specific formula shown as follows:
LLPS＝Pload(t)-Pin(t)
Figure BDA0002776429160000041
wherein, Pload(t)、Pin(t) load power and grid inflow power, L, respectively, at time tLPS(t) is the value of the load power minus the grid inflow power at time t, and B (t) is the power supply rate at time t;
and taking the minimum value of the power supply B (t) at the moment t as a power supply rate reliability index B of the whole power distribution network:
B＝min(B(t))
the smaller the power supply rate reliability index B is, the higher the power supply rate of the power distribution network is;
(2.3) the Voltage deviation reliability index C
C＝maxUi-minUj
Wherein, maxUi、minUjThe maximum voltage value and the minimum voltage value of a load node i and a load node j of the power distribution network are respectively;
The smaller the voltage deviation reliability index C value, the smaller the voltage deviation.
3) On the basis of the upper-layer planning model and the lower-layer planning model, the following models based on differential reliability requirements are respectively established: the method comprises the following steps of providing an investment cost model of a fan, a photovoltaic and energy storage device of the power distribution network, an operation cost model of the fan, the photovoltaic and the energy storage device of the power distribution network, an interruptible load compensation cost model, a power purchase cost model of a superior power grid of the power distribution network, an active management cost model of the fan, the photovoltaic and the energy storage device of the power distribution network and a line operation loss cost model of the power distribution network, and providing corresponding constraint conditions; wherein the content of the first and second substances,
(3.1) the model is specifically as follows:
(3.1.1) investment cost model C of fan, photovoltaic and energy storage equipment of power distribution networkI
Figure BDA0002776429160000042
Wherein n is the number of load points of the nodes of the power distribution network,
Figure BDA0002776429160000043
is divided into rated capacities of a fan, a photovoltaic device and an energy storage device which are arranged at a load node i,
Figure BDA0002776429160000044
respectively fixing investment cost for unit capacity of a fan, a photovoltaic device and energy storage equipment which are arranged at a load node i;
(3.1.2) running cost model C of fan, photovoltaic and energy storage equipment of power distribution networkOM
Figure BDA0002776429160000045
Wherein n is the number of load nodes of the power distribution network,
Figure BDA0002776429160000046
the operation and maintenance costs P of unit electric quantity generated by a fan, a photovoltaic device and an energy storage device which are respectively arranged at a load node i WTG,i,h、PPVG,i,h、PC,i,hAre respectively anActive power output eta of fan, photovoltaic and energy storage equipment arranged at load node i at typical day hWTG,i、ηPVG,i、ηC,iRespectively the wind energy utilization rate, the solar energy utilization rate and the inverter conversion rate of a fan, a photovoltaic device and an energy storage device which are arranged at a load node i;
(3.1.3) interruptible load Compensation cost model Cload
Figure BDA0002776429160000051
Wherein: pload,j,hActive power, ξ, representing interruptible load node j being interrupted at typical time of day hjIs a compensation unit price, N, of an interrupted unit load signed in advanceloadThe number of interruptible load nodes;
(3.1.4) Power purchase cost model C of power distribution network from higher-level power griden
Figure BDA0002776429160000052
Wherein n is the number of load nodes of the power distribution network, P∑LTo predict the total active power of the load at a typical time of day h, P∑WTG,i,h、P∑PVG,i,h、P∑C,i,h、P∑load,j,hThe total active power output, xi, of the fan, the photovoltaic, the energy storage device and the interruptible load at the typical day h momentpnFor a unit of electricity purchase cost of the load, NloadFor the number of interruptible load nodes, ηWTG,i、ηPVG,i、ηC,iRespectively the wind energy utilization rate, the solar energy utilization rate and the inverter conversion rate of a fan, a photovoltaic device and an energy storage device which are arranged at a load node i;
(3.1.5) active management cost model C of fan, photovoltaic and energy storage equipment of power distribution networkAM
Figure BDA0002776429160000053
Wherein n is the number of load nodes of the power distribution network,
Figure BDA0002776429160000054
Respectively the active management cost of the unit generated energy of the fan, the photovoltaic and the energy storage equipment which are arranged at the load node i; pWTG,i,h、PPVG,i,h、PC,i,hActive power output, eta, of a fan, a photovoltaic device and an energy storage device which are respectively arranged at a load node i at a typical day h momentWTG,i、ηPVG,i、ηC,iRespectively the wind energy utilization rate, the solar energy utilization rate and the inverter conversion rate of a fan, a photovoltaic device and an energy storage device which are arranged at a load node i;
(3.1.6) line operating loss cost model C of distribution networkloss
Figure BDA0002776429160000055
Wherein, Ploss,line,hThe active power of the line lost at a typical day h, eta is the loss cost of unit active power, and L is the total number of lines of the power distribution network.
(3.2) the corresponding constraints comprising: power balance constraints, node voltage constraints, branch power constraints, distributed power supply capacity constraints, charge and discharge power constraints, charge constraints, dischargeable capacity constraints, and interruptible load interruption constraints. The method comprises the following specific steps:
(3.2.1) Power balance constraints
Figure BDA0002776429160000056
Wherein, PLiFor the active power of the load node i, QLiIs the active power of the load node i, PDGiActive power, Q, of distributed power supply for load node iDGiThe reactive power of the distributed power supply of the load node i, n is the number of nodes of the power distribution network, UiIs the voltage value of the load node i, U jAs a load nodeVoltage value of j, GijIs the line conductance value, BijFor line susceptance value, deltaijIs the difference in voltage angle between load node i and load node j.
(3.2.2) node Voltage constraints
Uimin≤Ui≤Uimax
Wherein, UiIs the voltage value of the load node i, Uimin、UimaxThe minimum value and the maximum value of the voltage of the load node i are respectively.
(3.2.3) Branch Power constraint
Pl≤Pl.max
Wherein, PlFor transmission power of the line, Pl.maxTo allow the maximum transmission power to flow through the line.
(3.2.4) distributed Power Capacity constraints
PDGmin≤PDG≤PDGmax
Wherein, PDGActive power injected for distributed power supply, PDGmin、PDGmaxThe minimum value and the maximum value of active power injected by the distributed power supply are respectively.
(3.2.5) Charge/discharge Power constraint
Pcmax-ESS≤P(t)≤Pdmax-ESS
Wherein P (t) is the operating power of the energy storage device at time t, Pcmax-ESS、Pdmax-ESSRespectively the maximum charging power and the maximum discharging power of the energy storage device.
(3.2.6) Charge constraint
In order to avoid the influence of the overcharge and the overdischarge of the battery on the service life of the battery, the state of charge is limited within a certain range, and the storage battery cannot completely discharge the electric quantity and cannot be fully charged.
Smin-ESS≤SESS(t)≤Smax-ESS
Wherein S isESS(t) is the charge of the accumulator at time t, Smin-ESS、Smax-ESSThe minimum charge capacity and the maximum charge capacity of the storage battery are respectively.
(3.2.7) dischargeable quantity constraint
In order to make the obtained solution conform to the reality and avoid the situation that the discharge capacity in the continuous discharge time period is larger than the residual electric quantity before discharge, the dischargeable capacity in the continuous discharge time period has certain constraint.
SESS0≤SESS(t)
Wherein S isESS(t) is the charge of the accumulator at time t, SESS0Is the residual capacity of the storage battery before discharging.
(3.2.8) interrupt quantity constraint of interruptible load
Figure BDA0002776429160000061
Wherein, Pj,s,DSMFor interruptible load j to interrupt the real power at time s,
Figure BDA0002776429160000062
the maximum active power allowed to be interrupted at time s for interruptible load j.
4) Solving an upper-layer planning model, a lower-layer planning model, an investment cost model, a fan of the power distribution network, an operation cost model of photovoltaic and energy storage equipment, an interruptible load compensation cost model, a power purchase cost model of the power distribution network from a higher-level power grid, an active management cost model of the fan of the power distribution network, the photovoltaic and energy storage equipment and a line operation loss cost model of the power distribution network by using a chaotic self-adaptive particle swarm optimization algorithm, and finally obtaining a power distribution network optimization planning scheme considering differentiated reliability requirements of terminal users.
Finally, in order to verify the correctness and feasibility of the power distribution network optimization planning method based on the differential reliability requirements, according to the flow diagram shown in fig. 1, the adaptive variant particle swarm algorithm flow diagram based on the chaotic search shown in fig. 2 is used for carrying out location selection and volume determination on the IEEE33 node standard test system under the condition that the positions and capacities of the distributed power supply and the energy storage device are uncertain, and the operation parameters are shown in table 1. The effectiveness of the planning method is analyzed and compared by obtaining an optimal solution and the difference of reliability indexes before and after the DG and the energy storage equipment are installed through a self-adaptive variation particle swarm algorithm based on chaotic search.
The initial particle number of the upper layer of the algorithm is set as 100, the iteration number is set as 50, the initial particle number of the lower layer is set as 80, the iteration number is set as 50, and the DG addressing and sizing results of the IEEE33 node standard test system are shown in Table 2.
TABLE 1 simulation example parameters
Figure BDA0002776429160000071
TABLE 2 chaos search based adaptive variant particle swarm algorithm DG locating and sizing result
Figure BDA0002776429160000072
Table 3 shows the cost and the comprehensive cost of the DG locating and sizing result of the IEEE33 node standard test system; the differentiation reliability indexes of the three types of loads are shown in table 4. The data of table 4 are observed and analyzed, and reliability indexes required by various loads before and after DG installation can be improved to different degrees.
TABLE 3 cost per item and the cost of integration for the different solutions
Figure BDA0002776429160000081
TABLE 4 results of various indexes of different schemes
Figure BDA0002776429160000082
TABLE 5 different scheme System minimum Voltage
Figure BDA0002776429160000083
Table 3 transversely compares six costs, such as the allocation investment and operation maintenance cost, the line loss cost, the electricity shortage and electricity purchasing cost, of the DG and the energy storage device with the selected location and the fixed volume, wherein the DG and the energy storage device are installed, only the DG is installed, only the energy storage device is installed, and the scheme one, the scheme two and the scheme three in the table respectively correspond to the scheme one, the scheme two and the scheme three in the table, and the comparison shows that: the DG and the energy storage equipment are installed more advantageously, wherein the network loss cost and the interruptible load compensation cost are lower than those of the other two schemes, although the configuration operation cost is high, the annual comprehensive cost is only 402.198 ten thousand yuan and is less than 406.47 thousand yuan due to a series of economic benefits brought by high permeability, and a more ideal result is obtained; and 4, comparing the reliability indexes of the three types of loads, and comparing to obtain that each index of the first scheme is superior to that of the other two schemes. Comparing the load node voltages of the table 5, the amplitude of various load voltages of the power distribution network can reach within 5% after the DG is installed, and the national power quality requirement is met; the lowest voltage value of the scheme II is 0.9499, the lowest voltage value of the scheme III is 0.9454, and the lowest voltage value of the scheme III does not meet the lowest voltage requirement of 0.95, so that the operating economy of the power distribution network is improved to a great extent by the scheme I.
The self-adaptive variation particle swarm algorithm based on the chaotic search also has better convergence and optimization capability when solving the problem of power distribution network optimization planning based on the differentiation reliability requirement, and as can be seen from the graph 3, the algorithm is stable from the moment that the adaptability value is monotonically decreased from iteration to about the 6 th generation, so that the algorithm is continuously self-adjusted in the optimization process to avoid the optimization process from falling into local optimization, and a more accurate adaptability value is obtained before iteration is performed for 50 times, so that the algorithm has excellent convergence and rapid and stable optimization capability.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (6)

1. A power distribution network optimization planning method based on differential reliability requirements is characterized by comprising the following steps:
1) determining the installation position and the capacity of a distributed power supply consisting of a fan, a photovoltaic device and an energy storage device in a power distribution network from the two aspects of economy and safety, enabling the voltage fluctuation of each node of the system to be within an allowable range, and constructing an upper-layer planning model which takes the optimum coordination of economy and safety as a target function and enables the voltage fluctuation of each node of the system to be less than or equal to 5% of a rated voltage grade;
2) Aiming at the differentiated reliability requirements of different types of terminal users, establishing a lower-layer planning model by taking differentiated reliability indexes and operation and maintenance cost as multi-objective functions;
3) on the basis of the upper-layer planning model and the lower-layer planning model, the following models based on differential reliability requirements are respectively established: the method comprises the following steps of providing an investment cost model of a fan, a photovoltaic and energy storage device of the power distribution network, an operation cost model of the fan, the photovoltaic and the energy storage device of the power distribution network, an interruptible load compensation cost model, a power purchase cost model of a superior power grid of the power distribution network, an active management cost model of the fan, the photovoltaic and the energy storage device of the power distribution network and a line operation loss cost model of the power distribution network, and providing corresponding constraint conditions;
4) solving an upper-layer planning model, a lower-layer planning model, an investment cost model, a fan of the power distribution network, an operation cost model of photovoltaic and energy storage equipment, an interruptible load compensation cost model, a power purchase cost model of the power distribution network from a higher-level power grid, an active management cost model of the fan of the power distribution network, the photovoltaic and energy storage equipment and a line operation loss cost model of the power distribution network by using a chaotic self-adaptive particle swarm optimization algorithm, and finally obtaining a power distribution network optimization planning scheme considering differentiated reliability requirements of terminal users.
2. The power distribution network optimization planning method based on differentiation reliability requirements according to claim 1, wherein the objective function in step 1) is specifically:
min F1＝ω1(1-f1)+ω2f2
f1＝CI+COM+Cen+CAM+Closs+Cload
Figure FDA0002776429150000011
wherein, F1Is the value of the objective function, min F1Is the minimum of the objective function, f1For economic cost, f2As a safety measure, ω1、ω2Is a weight factor of a multi-objective function, and1+ω2＝1，CIfor reduced investment costs of distributed power, COMOperating costs of distributed power supplies, CenFor electricity purchase charge, CAMFor active charge management, ClossFor line running loss cost, CloadIn order to compensate for the costs of interruptible loads,
Figure FDA0002776429150000012
representing the sum of the real-time power of the load nodes under the condition that all the constraint conditions are met; piRepresenting the real-time power value of the load node i; m represents the total number of the load nodes; alpha is alphaiIs a grade factor of the load node i, 0 < alphai≤1，αiThe safety influence of the load node i on the power grid is reflected, and the larger the value is, the more important the load is.
3. The power distribution network optimization planning method based on differential reliability requirements according to claim 1, wherein the lower layer planning model in the step 2) is:
min F2＝λA+μB+γC
wherein, F2Is the value of the objective function, min F2Is the minimum value of the objective function; a is the system voltage stability and reliability index corresponding to the electric quantity type load, B is the frequency type The power supply rate reliability index corresponding to the load, C is the voltage deviation reliability index corresponding to the time type load, and A, B, C form a differentiation reliability index; λ, μ, and γ are weighting factors corresponding to the electric quantity type load, the frequency type load, and the time type load, respectively, and λ + μ + γ is 1.
4. The method according to claim 3, wherein the power distribution network optimization planning method based on the differential reliability requirements is characterized in that
(2.1) the system voltage stability reliability index A has a specific formula shown as the following formula:
Figure FDA0002776429150000021
wherein i and j are two load nodes connected to two ends of the line respectively, AijIs the voltage deviation value of two ends of the line, RijIs a line resistance value, XijIs the line reactance value, PjIs the active power of the load node j, QjBeing reactive power of load node j, UiThe load node i is a load node j, and the load node i is a load node;
taking the voltage deviation value A of two ends of the circuitijThe maximum value is defined as a system voltage stability reliability index A of the whole power distribution network:
A＝max(Aij)
the smaller the system voltage stability reliability index A is, the higher the voltage stability level of the power distribution network is;
(2.2) the power supply rate reliability index B is shown as the following formula:
LLPS(t)＝Pload(t)-Pin(t)
Figure FDA0002776429150000022
Wherein, Pload(t)、Pin(t) load work at time tRate and grid inflow power, LLPS(t) is the value of the load power minus the grid inflow power at time t, and B (t) is the power supply rate at time t;
and taking the minimum value of the power supply B (t) at the moment t as a power supply rate reliability index B of the whole power distribution network:
B＝min(B(t))
the smaller the power supply rate reliability index B is, the higher the power supply rate of the power distribution network is;
(2.3) Voltage deviation reliability index C
C＝max Ui-min Uj
Therein, max Ui、min UjThe maximum voltage value and the minimum voltage value of a load node i and a load node j of the power distribution network are respectively;
the smaller the voltage deviation reliability index C value, the smaller the voltage deviation.
5. The method according to claim 1, wherein the model in step 3) is specifically:
(3.1) investment cost model C of fan, photovoltaic and energy storage equipment of power distribution networkI
Figure FDA0002776429150000031
Wherein n is the number of load points of the nodes of the power distribution network,
Figure FDA0002776429150000032
is divided into rated capacities of a fan, a photovoltaic device and an energy storage device which are arranged at a load node i,
Figure FDA0002776429150000033
respectively fixing investment cost for unit capacity of a fan, a photovoltaic device and energy storage equipment which are arranged at a load node i;
(3.2) running cost model C of fan, photovoltaic and energy storage equipment of power distribution network OM
Figure FDA0002776429150000034
Wherein n is the number of load nodes of the power distribution network,
Figure FDA0002776429150000035
the operation and maintenance costs P of unit electric quantity generated by a fan, a photovoltaic device and an energy storage device which are respectively arranged at a load node iWTG,i,h、PPVG,i,h、PC,i,hActive power output, eta, of a fan, a photovoltaic device and an energy storage device which are respectively arranged at a load node i at a typical day h momentWTG,i、ηPVG,i、ηC,iRespectively the wind energy utilization rate, the solar energy utilization rate and the inverter conversion rate of a fan, a photovoltaic device and an energy storage device which are arranged at a load node i;
(3.3) interruptible load Compensation cost model Cload
Figure FDA0002776429150000036
Wherein: pload,j,hActive power, ξ, representing interruptible load node j being interrupted at typical time of day hjIs a compensation unit price, N, of an interrupted unit load signed in advanceloadThe number of interruptible load nodes;
(3.4) Power purchase cost model C of power distribution network from higher-level power griden
Figure FDA0002776429150000037
Wherein n is the number of load nodes of the power distribution network, P∑LTo predict the total active power of the load at a typical time of day h, P∑WTG,i,h、P∑PVG,i,h、P∑C,i,h、P∑load,j,hThe total active output of a fan, a photovoltaic device, an energy storage device and an interruptible load at a typical day h moment，ξpnFor a unit of electricity purchase cost of the load, NloadFor the number of interruptible load nodes, ηWTG,i、ηPVG,i、ηC,iRespectively the wind energy utilization rate, the solar energy utilization rate and the inverter conversion rate of a fan, a photovoltaic device and an energy storage device which are arranged at a load node i;
(3.5) active management cost model C of fan, photovoltaic and energy storage equipment of power distribution networkAM
Figure FDA0002776429150000038
Wherein n is the number of load nodes of the power distribution network,
Figure FDA0002776429150000039
respectively the active management cost P of the unit generated energy of the fan, the photovoltaic and the energy storage equipment which are arranged at a load node iWTG,i,h、PPVG,i,h、PC,i,hActive power output, eta, of a fan, a photovoltaic device and an energy storage device which are respectively arranged at a load node i at a typical day h momentWTG,i、ηPVG,i、ηC,iRespectively the wind energy utilization rate, the solar energy utilization rate and the inverter conversion rate of a fan, a photovoltaic device and an energy storage device which are arranged at a load node i;
(3.6) line operating loss cost model C of distribution networkloss
Figure FDA0002776429150000041
Wherein, Ploss,line,hThe active power of the line lost at a typical day h is shown, eta is the loss cost of unit active power, and L is the total number of lines of the power distribution network.
6. The method according to claim 1, wherein the corresponding constraint conditions in step 3) include: power balance constraints, node voltage constraints, branch power constraints, distributed power supply capacity constraints, charge and discharge power constraints, charge constraints, dischargeable capacity constraints, and interruptible load interruption constraints.
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