CN111092450A - Energy storage capacity configuration method based on cost performance analysis - Google Patents

Energy storage capacity configuration method based on cost performance analysis Download PDF

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CN111092450A
CN111092450A CN201911285224.0A CN201911285224A CN111092450A CN 111092450 A CN111092450 A CN 111092450A CN 201911285224 A CN201911285224 A CN 201911285224A CN 111092450 A CN111092450 A CN 111092450A
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energy storage
storage capacity
constraint
empire
cost
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高倩
高忠旭
陈其
包伟涔
施海峰
顾华东
全心雨
周池
袁国珍
周一鸣
李仕杰
钱启宇
俞威
刘至甚
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Haining Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract

The invention discloses a method for configuring energy storage capacity based on cost performance analysis, which comprises the following steps: step S1: establishing an energy storage capacity configuration model by combining an operation strategy and charge-discharge characteristics of battery energy storage; step S2: solving the model of the step S1 through an empire competition algorithm; step S3: performing energy storage capacity configuration according to the optimal solution of the empire competition algorithm; the establishment of the energy storage capacity configuration model comprises the following steps: step S11: setting an objective function; step S12: a constraint is set. The method uses a Monte Carlo simulation method to evaluate the reliability and the power shortage of the network, establishes an energy storage capacity configuration model taking the energy storage configuration cost performance as an objective function, and uses an empire competition algorithm to solve the model to obtain an energy storage capacity configuration combination with high cost performance, thereby improving the reliability of the network and reducing the cost of energy storage capacity configuration.

Description

Energy storage capacity configuration method based on cost performance analysis
Technical Field
The invention relates to the technical field of power grid energy storage, in particular to a method for configuring energy storage capacity based on cost performance analysis.
Background
In recent years, a large number of distributed photovoltaics are accessed to a power grid in a large-scale and high-permeability mode, and certain influence is brought to the reliable operation of the power grid, because the fluctuation of the distributed photovoltaic output and the fluctuation of the load are different greatly in normal operation, the situation that the load supply and demand are insufficient or the photovoltaic output surplus needs to be transmitted to the upper-level power grid can occur, and the reliable operation of the power grid can be influenced in both situations. The energy storage equipment is connected to the power grid, can be used as a load, is charged in the load valley, and can also be used as a power supply, and can supply power to the load in time when the photovoltaic output is insufficient, so that the influence of photovoltaic output fluctuation on load supply and demand is effectively inhibited, the photovoltaic permeability is improved, the influence of photovoltaic access on the safe operation of the power grid is reduced, and the power supply reliability is also improved.
Distributed photovoltaic power generation has gained more and more extensive attention and application in recent years, and by 8 months in 2017, the photovoltaic installation capacity of a hill power distribution network has reached 199.8MW, photovoltaic projects are widely distributed in the range of a whole hill new area, the access voltage grades cover 380V, 10kV and 20kV, and the situation of high-permeability distributed photovoltaic, multi-point and multi-voltage grades and various forms of access is presented. With the large-scale distributed photovoltaic access to the regional power distribution network, the fluctuation of the photovoltaic power supply is not matched with the load characteristics, and the stable operation of the power grid is influenced. The energy storage system is configured, so that fluctuation of photovoltaic output power can be stabilized, photovoltaic permeability and utilization rate are improved, and the effect is more obvious under the condition of a high-permeability photovoltaic power distribution network.
There are many methods for configuring the capacity of the energy storage system, and the methods are mainly divided into three categories: the method aims at optimizing indexes, minimizing energy storage capacity and minimizing system cost. At present, the development of energy storage technology tends to be mature, but the application of large-scale energy storage systems in various industries is still less, and the main problem is that the cost for configuring the large-scale energy storage systems at the present stage is higher, so that the energy storage devices are configured for improving the network reliability, and the benefit and the cost need to be comprehensively considered.
For example, a method and a system for allocating energy storage capacity to meet the requirement of frequency modulation are disclosed in the chinese patent document, which is disclosed in the publication No.: CN108092291A, filing date thereof: 29 th of 05 month in 2018, acquiring energy storage capacity parameters which meet the primary frequency modulation requirement of the power system and are participated by the energy storage device from the power system instruction; calculating the energy storage capacity meeting the primary frequency modulation requirement of the power system according to the energy storage capacity parameter meeting the primary frequency modulation requirement of the power system; calculating the energy storage capacity meeting the secondary frequency modulation requirement of the power system on the basis of the primary frequency modulation requirement energy storage capacity; the minimum energy storage capacity meeting the primary frequency modulation requirement energy storage capacity and the secondary frequency modulation requirement energy storage capacity of the power system is the energy storage capacity meeting the frequency modulation requirement of the power system. The application acquires the primary frequency modulation required energy storage capacity from the power system and calculates the secondary frequency modulation required energy storage capacity on the basis, but the application still has the problems of high cost of a storage system and low comprehensive income.
Disclosure of Invention
The invention mainly solves the problems of high energy storage capacity configuration cost and low comprehensive profit cost performance in the prior art; the energy storage capacity configuration method based on the cost performance analysis is provided, the energy storage capacity configuration cost is reduced, the energy storage cost performance is improved, and the reliability of a power grid network is improved.
The technical problem of the invention is mainly solved by the following technical scheme: a method for configuring energy storage capacity based on cost performance analysis comprises the following steps: step S1: establishing an energy storage capacity configuration model by combining an operation strategy and charge-discharge characteristics of battery energy storage; step S2: solving the model of the step S1 through an empire competition algorithm; step S3: and configuring the energy storage capacity according to the optimal solution of the empire competition algorithm. The capacity configuration meets the requirements of an electric power system by establishing an energy storage capacity configuration model, the energy storage capacity is configured according to the optimal solution of the empire state competitive algorithm, the cost is reduced, the energy storage cost performance is improved, and the reliability of a power grid network is improved by the energy storage capacity configuration.
Preferably, in step S1, the building of the energy storage capacity configuration model includes the following steps: step S11: setting an objective function; step S12: a constraint is set.
Preferably, the objective function is:
Figure BDA0002317787730000021
wherein, XjFor configuring vectors, X, for energy storagej＝{x1、x2…xnRepresents the j energy storage configuration scheme,
Figure BDA0002317787730000022
is a vector XjThe corresponding energy storage cost performance is improved,
Figure BDA0002317787730000023
according to a vector XjThe income brought by the stored energy is configured,
Figure BDA0002317787730000024
is a vector XjCorresponding energy storage investment cost.
Preferably, the constraint conditions include a voltage constraint, a current constraint, an energy storage capacity constraint and a charge point state constraint, and the voltage constraint is as follows:
Umin≤Ui≤Umax(2)
wherein, UiFor the voltage, U, switched on at the i-th nodeminMinimum value of voltage allowed for i-th node, UmaxThe maximum voltage allowed for the ith node;
the current constraints are:
Ii≤Iimax(3)
wherein, IiIs the current of the ith line, IimaxThe maximum value of the current allowed by the ith line;
the energy storage capacity constraint is:
0≤Pi≤Pimax(4)
wherein, PiFor the allowed access capacity, P, of the ith energy-storing access pointimaxThe maximum allowable access capacity of the ith energy storage access point is obtained;
the charge point state constraint is as follows:
Figure BDA0002317787730000031
wherein, PSOCIs the state of the charge point of the energy storage device,
Figure BDA0002317787730000032
is the minimum value of the charge point state of the energy storage device,
Figure BDA0002317787730000033
is the maximum value of the state of the charge point of the energy storage device.
Preferably, in step S2, the solving of the energy storage capacity configuration model by using the empire algorithm includes the following steps: step S21: evaluating the initial net rack without the energy storage device by a sequential Monte Carlo simulation method to obtain the maximum value and the minimum value of the power shortage amount and the power shortage capacity; step S22: initializing a country population according to the result of the step S21; step S23: calculating the momentum of each country according to the target function, selecting part of countries as empires, and distributing other countries as colonial areas to the empires; step S24: carrying out a colonial assimilation process, and updating the positions of each empire country and a colonial area; step S25: carrying out colonial competition, and carrying out imperial division when the imperial meets the division condition; step S26: and judging whether the result is converged according to the constraint condition, if not, returning to the step S22, and if so, outputting a unique empire as an optimal solution.
Preferably, the objective function is an energy storage configuration cost performance function, and the calculation formula of the energy storage configuration cost performance function is as follows:
Figure BDA0002317787730000034
wherein η is the cost performance of energy storage configuration, B is the income brought after energy storage configuration, and C is the investment cost of energy storage.
Preferably, the calculation formula of the profit B brought by the energy storage configuration is as follows:
B＝(W0-WESS)·Q＝ΔW·Q (7)
Figure BDA0002317787730000035
wherein, W0In the absence of electricity before allocation for energy storage, WESSIn order to configure the power shortage after energy storage, Q is GDP generated by unit electric quantity, and N is the power consumption of the whole society.
Preferably, the calculation formula of the energy storage investment cost C is:
C＝CIN+MESS(9)
wherein, CINFor energy storage at one time, MESSWhich is the maintenance cost of the energy storage device.
The invention has the beneficial effects that: the reliability and the power shortage amount of the network are evaluated by using a Monte Carlo simulation method, an energy storage capacity configuration model taking the energy storage configuration cost performance as a target function is established, the model is solved by using an empire competition algorithm, and an energy storage capacity configuration combination with high cost performance is obtained, so that the reliability of the network is improved, and the cost of energy storage capacity configuration is reduced.
Drawings
Fig. 1 is a flowchart of a method for configuring energy storage capacity according to the first embodiment.
Fig. 2 is a flow chart of an empire competition algorithm according to the first embodiment.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
The first embodiment is as follows: a method for configuring energy storage capacity based on cost-performance analysis, as shown in fig. 1, includes the following steps: step S1: establishing an energy storage capacity configuration model by combining an operation strategy and charge-discharge characteristics of battery energy storage; the establishment of the energy storage capacity configuration model comprises the following steps: step S11: setting an objective function; the objective function is:
Figure BDA0002317787730000041
wherein, XjFor configuring vectors, X, for energy storagej＝{x1、x2…xnRepresents the j energy storage configuration scheme,
Figure BDA0002317787730000042
is a vector XjThe corresponding energy storage cost performance is improved,
Figure BDA0002317787730000043
according to a vector XjConfiguration storageThe benefits that can be brought later are,
Figure BDA0002317787730000044
is a vector XjCorresponding energy storage investment cost.
Step S12: setting constraint conditions; the constraint conditions comprise voltage constraint, current constraint, energy storage capacity constraint and charge point state constraint, and the voltage constraint is as follows:
Umin≤Ui≤Umax(2)
wherein, UiFor the voltage, U, switched on at the i-th nodeminMinimum value of voltage allowed for i-th node, UmaxThe maximum voltage allowed for the ith node;
the current constraints are:
Ii≤Iimax(3)
wherein, IiIs the current of the ith line, IimaxThe maximum value of the current allowed by the ith line;
the energy storage capacity constraint is:
0≤Pi≤Pimax(4)
wherein, PiFor the allowed access capacity, P, of the ith energy-storing access pointimaxThe maximum allowable access capacity of the ith energy storage access point is obtained;
the charged-particle state constraints are:
Figure BDA0002317787730000045
wherein, PsocIs the state of the charge point of the energy storage device,
Figure BDA0002317787730000046
is the minimum value of the charge point state of the energy storage device,
Figure BDA0002317787730000047
the method comprises the following steps that the maximum value of the charge point state of the energy storage device is obtained, an objective function is an energy storage configuration cost performance function, and the calculation formula of the energy storage configuration cost performance function is as follows:
Figure BDA0002317787730000051
η is energy storage configuration cost performance, B is the income brought after the energy storage configuration, C is the energy storage investment cost, and the calculation formula of the income B brought after the energy storage configuration is:
B＝(W0-WESS)·Q＝ΔW·Q (7)
Figure BDA0002317787730000052
wherein, W0In the absence of electricity before allocation for energy storage, WESSIn order to configure the power shortage after energy storage, Q is GDP generated by unit electric quantity, N is the power consumption of the whole society, and the calculation formula of the energy storage investment cost C is as follows:
C＝CIN+MESS(9)
wherein, CINFor energy storage at one time, MESSWhich is the maintenance cost of the energy storage device.
As shown in fig. 2, step S2: solving the model of the step S1 through an empire competition algorithm; the method comprises the following steps: step S21: evaluating the initial net rack without the energy storage device by a sequential Monte Carlo simulation method to obtain the maximum value and the minimum value of the power shortage amount and the power shortage capacity; step S22: initializing a country population according to the result of the step S21; step S23: calculating the momentum of each country according to the target function, selecting part of countries as empires, and distributing other countries as colonial areas to the empires; according to the objective function, each country is:
country＝Xi＝{x1,x2…xn} (10)
xithe capacity of the ith energy storage access point is represented, and the national potential is represented as:
Figure BDA0002317787730000053
the normalized forces in the ith country are:
Figure BDA0002317787730000054
according to the magnitude of the standardized potential force of each country, the N with the maximum potential force is determinedimpOne country acts as the empire country, and the remaining countries are randomly assigned to the empire country as colonial sites.
The method includes the steps of S24, performing a colonial assimilation process, updating the position of each empire country and colonial area, simulating the assimilation process by moving the colonial area to the empire country to which the colonial area belongs, wherein the moving distance of the colonial area to the empire country is represented by x-U (0, β xd), wherein β > 1, d represents the distance between the colonial area and the empire country, and a random variable x obeys uniform distribution on (0, β xd).
Step S25: carrying out colonial competition, and carrying out imperial division when the imperial meets the division condition; the overall strength of an empire:
Figure RE-GDA0002390353480000061
wherein f iskIs the objective function value of empire k; σ is a weight coefficient:
Figure RE-GDA0002390353480000062
the average value of the objective function of the colonial area is occupied by empire state k. Selecting a plurality of breeding grounds with small potential from the empire with the weakest total potential, and distributing the breeding grounds to other N nations according to a certain probabilityimp1 empire, the probability of occupation of the kth empire is:
Figure RE-GDA0002390353480000063
when an empire country has a potential equivalent to that of another colonial country, other than the strongest empire country, this can result in the division of the empire. Specifically comprising the following stepsThe following steps: step 31: the strongest potential empire is selected and recorded as Empbest(ii) a Step S32: emp in the strongest empirebestThe breeding place with the strongest strength is selected to be Colbest，EmpbestThe empire nationality of China is denoted impold(ii) a Step S33: calculating ColbestAnd impoldThe potential force difference between, and whether the distance meets a predetermined threshold:
Figure RE-GDA0002390353480000064
wherein, 0 < t1If the search range is less than 1, searchspace is a search range, a global range can be searched by adopting the maximum distance of a fixed space, and the search range can also be dynamically determined according to the force range of the empire; step S34: dividing empire state meeting the above conditions, and dividing ColbestThe empire State, as the New empire State, is denoted impnewAccording to impnewAnd impoldRandomly distributing the remaining colonial areas; step S35: death of empire; the powerful empire has the colonial areas of other empires, so that the powerful and weak colonial areas are reduced, when the empire loses all colonial areas, the empire goes out, and finally only one empire is left, and all the colonial areas are moved to the position of the empire's primary country.
Step S26: and judging whether the result is converged according to the constraint condition, if not, returning to the step S22, and if so, outputting a unique empire as an optimal solution.
Step S3: and configuring the energy storage capacity according to the optimal solution of the empire competition algorithm.
In specific application, when a network normally operates, a main network power supply is unchanged, when photovoltaic output power is lower than supplied load power, electric energy supply and demand in the network can be balanced by load reduction, and at the moment, power failure load can be reduced by configuring energy storage with certain capacity; when the photovoltaic output power is higher than the supplied load power, namely the electric energy generated by the photovoltaic can not be consumed on the spot and needs to be sent back to the upper-level power grid, this will affect the grid structure and operation stability of the superior grid, by configuring a certain amount of stored energy, the surplus electric quantity of partial photovoltaic power generation can be consumed, the influence on the superior power grid is reduced, and when the network fails, part of the load needs to be transferred to the interconnection power supply, the conditions of the interconnection power supply and the load fluctuation are considered, during the period from switching operation to fault recovery, two situations also occur when the network is in normal operation, namely, only partial load of the load to be transferred recovers the power supply, and the energy storage with certain capacity can be configured, the power failure time of partial load can be reduced, the power shortage amount of the network is reduced, the reliability of the network is improved, and the existence of some invalid contacts can be reduced.
The method disclosed by the invention evaluates the reliability and the power shortage of the network by using a Monte Carlo simulation method, simultaneously considers the influence of photovoltaic output fluctuation on the operation and the load supply of the power grid during normal operation, establishes an energy storage capacity configuration model taking the energy storage configuration cost performance as an objective function, and solves the model by using an empire and state competition algorithm to obtain an energy storage capacity configuration combination with high cost performance, so that the reliability of the network is improved, the influence of the photovoltaic output fluctuation on the operation and the load supply of the power grid is reduced, and the energy storage capacity configuration cost is reduced by using the energy storage configuration cost performance function.
The above-described embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the spirit of the invention as set forth in the claims.

Claims (8)

1. A method for configuring energy storage capacity based on cost performance analysis is characterized by comprising the following steps:
step S1: establishing an energy storage capacity configuration model by combining an operation strategy and charge-discharge characteristics of battery energy storage;
step S2: solving the model of the step S1 through an empire competition algorithm;
step S3: and configuring the energy storage capacity according to the optimal solution of the empire competition algorithm.
2. The method for energy storage capacity configuration based on cost-performance analysis according to claim 1, wherein in the step S1, the building of the energy storage capacity configuration model includes the following steps:
step S11: setting an objective function;
step S12: a constraint is set.
3. The method of claim 2, wherein the objective function is:
Figure FDA0002317787720000011
wherein, XjFor configuring vectors, X, for energy storagej＝{x1、x2…xnRepresents the j energy storage configuration scheme,
Figure FDA0002317787720000012
is a vector XjThe corresponding energy storage cost performance is improved,
Figure FDA0002317787720000013
according to a vector XjThe income brought by the stored energy is configured,
Figure FDA0002317787720000014
is a vector XjCorresponding energy storage investment cost.
4. The method of claim 3, wherein the constraint conditions include a voltage constraint, a current constraint, a storage capacity constraint, and a charge point state constraint, and the voltage constraint is:
Umin≤Ui≤Umax(2)
wherein, UiFor the voltage, U, switched on at the i-th nodeminMinimum value of voltage allowed for i-th node, UmaxVoltage allowed for ith nodeA maximum value;
the current constraints are:
Ii≤Iimax(3)
wherein, IiIs the current of the ith line, IimaxThe maximum value of the current allowed by the ith line;
the energy storage capacity constraint is:
0≤Pi≤Pimax(4)
wherein, PiFor the allowed access capacity, P, of the ith energy-storing access pointimaxThe maximum allowable access capacity of the ith energy storage access point is obtained;
the charge point state constraint is as follows:
Figure FDA0002317787720000015
wherein, PsocIs the state of the charge point of the energy storage device,
Figure FDA0002317787720000016
is the minimum value of the charge point state of the energy storage device,
Figure FDA0002317787720000017
is the maximum value of the state of charge point of the energy storage device.
5. The method for energy storage capacity allocation based on cost/performance analysis of claim 4, wherein the solving of the energy storage capacity allocation model by the empire algorithm in step S2 comprises the following steps:
step S21: evaluating the initial net rack without the energy storage device by a sequential Monte Carlo simulation method to obtain the maximum value and the minimum value of the power shortage amount and the power shortage capacity;
step S22: initializing a country population according to the result of the step S21;
step S23: calculating the momentum of each country according to the target function, selecting part of countries as empires, and distributing other countries as colonial areas to the empires;
step S24: carrying out a colonial assimilation process, and updating the positions of each empire country and a colonial area;
step S25: carrying out colonial competition, and carrying out imperial division when the imperial meets the division condition;
step S26: and judging whether the result is converged according to the constraint condition, if not, returning to the step S22, and if so, outputting a unique empire as an optimal solution.
6. The method according to claim 5, wherein the objective function is an energy storage configuration cost performance function, and the calculation formula of the energy storage configuration cost performance function is:
Figure FDA0002317787720000021
wherein η is the cost performance of energy storage configuration, B is the income brought after energy storage configuration, and C is the investment cost of energy storage.
7. The method according to claim 6, wherein the calculation formula of the profit B after the energy storage configuration is as follows:
B＝(W0-WESS)·Q＝ΔW·Q (7)
Figure FDA0002317787720000022
wherein, W0In the absence of electricity before allocation for energy storage, WESSIn order to configure the power shortage after energy storage, Q is GDP generated by unit electric quantity, and N is the power consumption of the whole society.
8. The method for energy storage capacity allocation based on cost-performance analysis according to claim 6, wherein the energy storage investment cost C is calculated by the following formula:
C＝CIN+MESS(9)
wherein, CINFor energy storage at one time, MESSWhich is the maintenance cost of the energy storage device.
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