CN116799828A - Energy storage multi-time scale capacity configuration method for flexible interconnection power distribution network - Google Patents
Energy storage multi-time scale capacity configuration method for flexible interconnection power distribution network Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/04—Power grid distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
Abstract
According to the energy storage multi-time scale capacity configuration method for the flexible interconnected power distribution network, after typical data of the flexible interconnected power distribution network connected with new energy sources in a preset historical period are obtained, multi-scenario demands of the flexible interconnected power distribution network under different time scales and output control strategies of an energy storage system in the flexible interconnected power distribution network in response to the multi-scenario demands can be determined according to the typical data, so that a time sequence simulation model can be built based on the multi-scenario demands and the output control strategies, the time sequence simulation model is utilized to simulate charge and discharge power and capacity consumption of the energy storage system after the energy storage system responds to the multi-scenario demands according to the output control strategies under different pre-configured energy storage capacity schemes, then an objective function and corresponding constraint conditions with optimal economical efficiency as targets are determined, and after the objective function is optimally solved under the constraint conditions, the optimal configuration capacity with the maximum energy storage net value of the energy storage system can be determined.
Description
Technical Field
The application relates to the technical field of energy storage configuration planning of flexible interconnection power distribution networks, in particular to an energy storage multi-time scale capacity configuration method for a flexible interconnection power distribution network.
Background
At present, a novel power system taking new energy as a main body is constructed, so that the novel power system is not only a necessary requirement for energy power transformation, but also a key way for realizing a 'double-carbon' target. With the massive access of distributed power sources in distribution networks, the intermittence and variability of high-permeability renewable energy sources bring new challenges to the operation of flexible interconnected distribution networks. Therefore, a stable and efficient new energy grid-connected method is sought, and the problem to be solved is urgent. The energy storage system has the characteristics of energy time shifting, quick response, flexible arrangement and the like, and along with the rapid development of energy storage technology, the novel structure of new energy and energy storage is widely applied and developed. The coupling of the energy storage and the new energy can not only improve the new energy consumption level, but also provide auxiliary services such as power prediction compensation and frequency modulation, thereby reducing the negative influence of the new energy grid connection on the flexible interconnection power distribution network and improving the new energy grid connection friendliness and economy.
However, on the one hand, the new energy access scale is continuously enlarged, the flexible interconnection power distribution network system has multiple requirements on renewable energy grid connection and active support, and the existing energy storage system cannot meet the multiple requirements on renewable energy grid connection and active support of the flexible interconnection power distribution network system; on the other hand, the existing energy storage system has high energy storage cost and small profit, and how to optimally configure the capacity of the energy storage system on the basis of meeting the multiple requirements of the flexible interconnection power distribution network system on renewable energy grid connection and active support so as to reduce the energy storage cost and improve the profit is also a problem to be solved urgently.
Disclosure of Invention
The application aims to at least solve one of the technical defects, and particularly the technical defect that the energy storage system in the prior art cannot meet multiple requirements of the flexible interconnection power distribution network system on renewable energy grid connection and active support, and cannot optimally configure the capacity of the energy storage system on the basis of meeting the multiple requirements of the flexible interconnection power distribution network system on renewable energy grid connection and active support.
The application provides an energy storage multi-time scale capacity configuration method for a flexible interconnection power distribution network, which comprises the following steps:
acquiring typical data of a flexible interconnection power distribution network accessed with new energy in a preset historical period, and determining multi-scene requirements of the flexible interconnection power distribution network under different time scales according to the typical data, and an output control strategy when an energy storage system in the flexible interconnection power distribution network responds to the multi-scene requirements;
building a time sequence simulation model based on the multi-scenario demands and the output control strategy, and simulating charge and discharge power and capacity consumption of the energy storage system after responding to the multi-scenario demands according to the output control strategy under different pre-configured energy storage capacity schemes by utilizing the time sequence simulation model;
Determining an objective function with optimal economical efficiency as a target and corresponding constraint conditions, and carrying out optimization solution on the objective function based on charge and discharge power and capacity consumption of the energy storage system under different energy storage capacity schemes under the constraint conditions to obtain a solution result of the objective function under different energy storage capacity schemes;
and determining the optimal configuration capacity for maximizing the net gain value of the energy storage system according to the solving result of the objective function under different energy storage capacity schemes.
Optionally, the typical data comprise rated frequency, real-time frequency, frequency modulation coefficient, inertia coefficient of the flexible interconnection power distribution network, rated power, predicted power and actual output power of new energy grid connection, and grid connection peak shaving lines and grid connection valley filling lines of system nodes; the multi-scene requirements comprise a frequency modulation requirement, a power prediction compensation requirement and a new energy consumption requirement;
the determining the multi-scenario requirement of the flexible interconnection power distribution network under different time scales according to the typical data comprises the following steps:
determining the frequency modulation requirements of the flexible interconnection power distribution network under different time scales according to the rated frequency, the real-time frequency, the frequency modulation coefficient, the inertia coefficient and the rated power of the new energy grid connection;
Determining power prediction compensation requirements of the flexible interconnection power distribution network under different time scales according to the predicted power and the actual output power of the new energy grid connection in the flexible interconnection power distribution network;
and determining new energy consumption requirements of the flexible interconnection power distribution network under different time scales according to the grid-connected peak cut lines and the grid-connected valley fill lines of the system nodes in the flexible interconnection power distribution network.
Optionally, the energy storage system is of a double-battery structure, and the charge and discharge states of each group of batteries in the double-battery structure are different, and when any group of batteries is fully charged or fully discharged, the charge and discharge states of the two groups of batteries are switched;
the determining an output control strategy when the energy storage system in the flexible interconnection power distribution network responds to the multi-scenario demand comprises the following steps:
acquiring an actual new energy output value of the flexible interconnection power distribution network under the new energy consumption requirement, and comparing the actual new energy output value with a preset peak clipping and valley filling line of the energy storage system;
if the actual output value of the new energy is outside the preset peak clipping and valley filling line, acquiring the real-time frequency of the flexible interconnection power distribution network under the frequency modulation requirement, and comparing the real-time frequency with a preset frequency regulation dead zone of the energy storage system;
If the real-time frequency is not in the preset frequency adjustment dead zone, starting the energy storage system to participate in primary frequency modulation of the flexible interconnection power distribution network;
if the real-time frequency is in the preset frequency adjustment dead zone, new energy is consumed for the flexible interconnection power distribution network by utilizing the energy storage system;
if the actual output value of the new energy is between the preset peak clipping and valley filling lines and the real-time frequency is not in the preset frequency adjustment dead zone, starting the energy storage system to participate in primary frequency modulation of the flexible interconnection power distribution network;
if the actual output value of the new energy is between the preset peak clipping and valley filling lines and the real-time frequency is in the preset frequency adjustment dead zone, acquiring the actual output power of the flexible interconnection power distribution network, which is in the new energy grid connection under the power prediction compensation requirement, and comparing the actual output power with a preset power prediction error zone of the energy storage system;
if the actual output power is outside the upper limit and the lower limit of the preset power prediction error band, performing power prediction compensation on the flexible interconnection power distribution network by using the energy storage system;
if the actual output power is within the upper limit and the lower limit of the preset power prediction error band, determining the state of charge unbalance of the energy storage system at the current moment, and comparing the state of charge unbalance with a preset unbalance range;
If the state of charge unbalance is outside the upper limit and the lower limit of the preset unbalance range, controlling the energy storage system to perform self-adaptive charging and discharging;
if the state of charge imbalance is within the upper and lower limits of the preset imbalance range, the energy storage system is not required to be controlled to output at the current moment.
Optionally, a calculation formula of the state of charge imbalance of the energy storage system at the current moment is:
A(t)＝2×S soc (t)-(S socmax +S socmin )
wherein A (t) represents the charge state unbalance degree of the energy storage system at the t moment, S SOC (t) represents the state of charge of the energy storage system at time t, S soc,min Is the lower limit of the charge state of the energy storage system, S soc,max Is the upper limit of the state of charge of the energy storage system;
output P of energy storage system during self-adaptive charge and discharge bess The method comprises the following steps:
P bess ＝A(t)×Er
wherein Er is the rated capacity of the battery in the energy storage system.
Optionally, the simulating the charge and discharge power and the capacity consumption of the energy storage system after responding to the multi-scenario demand according to the output control strategy under different pre-configured energy storage capacity schemes by using the time sequence simulation model includes:
inputting different pre-configured energy storage capacity schemes into the time sequence simulation model, and configuring related parameters of the flexible interconnection power distribution network under the multi-scene requirement and simulation periods corresponding to the different energy storage capacity schemes in the time sequence simulation model;
And for each energy storage capacity scheme, simulating the charge and discharge power and capacity consumption of the energy storage system in a corresponding simulation period by using the time sequence simulation model, wherein the charge and discharge power and the capacity consumption are responded according to the output control strategy after the multi-scene requirement.
Optionally, the formula of the objective function targeting the economic optimization is as follows:
f 1 ＝max(S x +S y +S f -C bess )
wherein S is x S, participating in new energy consumption of the energy storage system y Benefits of participating in power forecast compensation for energy storage system, S f C, for the energy storage system to participate in the income of frequency modulation bess For the cost of the energy storage battery, the energy storage system participates in the income S of new energy consumption x Comprises two parts, one part is peak clipping and valley filling income S x1 Another part is the sale income S x2 The calculation formulas are respectively as follows:
S x1 ＝K b Q xian
S x2 ＝S dianjia ×Q binwang
wherein K is b Compensation coefficient of unit electric quantity, Q xian For storing the electric quantity participating in peak clipping and valley filling, S dianjia Grid-connected electricity price for unit energy, Q binwang The energy is stored to participate in peak clipping electric quantity.
Optionally, the reservoirRevenue S capable of systematically participating in power prediction compensation y Comprises two parts, wherein one part is automatic power control service compensation, the other part is selling electricity income, the calculation formula of the automatic power control service compensation R is as follows,
R＝Ks×D×[ln(K pd )+1]×YAPC
wherein YAPC is an automatic power control regulation performance compensation standard, K pd The performance index is the adjustment performance index of the unit on the same day, and D is the adjustment depth.
Optionally, the energy storage system participates in the revenue S of frequency modulation f Comprises two parts, one part is frequency modulation mileage compensation S f1 Another part is frequency modulation capacity compensation S f2 The calculation formulas are respectively as follows:
wherein N is the total transaction time period number of the day, D i,t For the tuning mileage of the fm unit i during the transaction period t,for the comprehensive frequency modulation performance index of the frequency modulation unit i in the transaction period t, B t Price is paid for the frequency modulated mileage during the transaction period t,for the tuning factor of the FM unit i, C i,t For the winning capacity of the frequency modulation unit i in the trade period t, B Cp Price is compensated for frequency modulation capacity.
Optionally, cost C of the energy storage battery bess Comprises three parts, namely the device cost C of the energy storage battery bsys_p Cost of operation and maintenance C yw Cost of life loss C loss The calculation formulas are respectively as follows:
C yw ＝c pyw C bsys_p
wherein C is bsys_p For the cost of the energy storage battery, C E The cost coefficient of the unit capacity of the energy storage battery is t is the configuration duration of the energy storage battery, eta b For power conversion efficiency, C P Power cost coefficient per unit of energy storage battery, P rat I is the rated power of the energy storage battery, i is the discount rate, and N is the service cycle; c (C) yw Maintenance cost for operation of energy storage cell c pyw Operation maintenance coefficient for unit investment cost of energy storage battery, N BE The maximum cycle life of the stored energy provided for the manufacturer of the stored energy battery; c (C) loss For the life loss cost of the energy storage battery, n is the total charge and discharge times in the whole life cycle of the energy storage battery, C s,k The life loss cost for the kth charge-discharge cycle.
Optionally, the constraint condition includes charge and discharge power of the energy storage system at different moments and electric quantity of a battery in the energy storage system;
the formula of the constraint condition corresponding to the objective function with the optimal economical efficiency as the target is as follows:
the relation between the charge and discharge power of the energy storage system and the electric quantity of the battery in the energy storage system is as follows:
wherein E is BAT,n For the electric quantity of the battery in the energy storage system at the nth moment E BAT,0 For the electric quantity of the battery in the energy storage system at the initial moment, P BAT,n The charging and discharging power of the energy storage system;
the state of charge of the battery in the energy storage system is expressed as follows:
wherein S is SOC,n The state of charge of the battery in the energy storage system at time n,the rated capacity of a battery in the energy storage system;
the electric quantity constraint of the battery in the energy storage system is as follows:
S soc,min ≤S SOC,n ≤S soc,max
wherein S is soc,min Is the lower limit of the charge state of the battery, S soc,max Is the upper limit of the state of charge of the battery;
the charge and discharge power constraint of the energy storage system is as follows:
Wherein P is pcs Is the maximum charge and discharge power of the energy storage system.
From the above technical solutions, the embodiment of the present application has the following advantages:
the application provides an energy storage multi-time scale capacity configuration method for a flexible interconnected power distribution network, when typical data of the flexible interconnected power distribution network accessed with new energy in a preset historical period is obtained, the multi-scenario demands of the flexible interconnected power distribution network under different time scales and the output control strategy of an energy storage system in the flexible interconnected power distribution network responding to the multi-scenario demands can be determined according to the typical data, so that a time sequence simulation model can be built based on the multi-scenario demands and the output control strategy, and the time sequence simulation model is utilized to simulate the charge and discharge power and the capacity consumption of the energy storage system after responding to the multi-scenario demands according to the output control strategy under different pre-configured energy storage capacity schemes, then, the application can determine an objective function and corresponding constraint condition aiming at optimal economical efficiency, based on the charge and discharge power and capacity consumption of the energy storage system under different energy storage capacity schemes, the objective function is optimized and solved, so as to obtain the solving result of the objective function under different energy storage capacity schemes, and finally, the optimal configuration capacity for maximizing the net gain value of the energy storage system can be determined according to the solving result of the objective function under different energy storage capacity schemes, so that the existing energy storage system can meet the multiple requirements of the flexible interconnection power distribution network system on renewable energy grid connection and active support, and the energy storage multifunctional and multi-time-scale capacity configuration model can be built based on the time sequence simulation model on the basis of the multi-scene requirement and the output control strategy, and the energy storage capacity is optimized and configured with the economical optimization as the target, thereby not only improving the integral economical efficiency of the energy storage equipment, the energy storage system is ensured to have the capability of adapting to complex scenes, and the return on investment can be ensured under the condition that the energy storage cost is still high.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the application, and that other drawings can be obtained from these drawings without inventive faculty for a person skilled in the art.
Fig. 1 is a schematic flow chart of an energy storage multi-time scale capacity configuration method for a flexible interconnection power distribution network, which is provided by the embodiment of the application;
fig. 2 is a topology structure diagram of a flexible interconnection network containing dual-battery energy storage according to an embodiment of the present application;
fig. 3 is a graph showing net benefit versus different energy storage capacity configurations of an energy storage system in a flexible interconnection power distribution network according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
At present, on one hand, the new energy access scale is continuously enlarged, the flexible interconnection power distribution network system has multiple requirements on renewable energy grid connection and active support, and the existing energy storage system cannot meet the multiple requirements on renewable energy grid connection and active support of the flexible interconnection power distribution network system; on the other hand, the existing energy storage system has high energy storage cost and small profit, and how to optimally configure the capacity of the energy storage system on the basis of meeting the multiple requirements of the flexible interconnection power distribution network system on renewable energy grid connection and active support so as to reduce the energy storage cost and improve the profit is also a problem to be solved urgently.
Based on the above, the application provides the following technical scheme, and the technical scheme is specifically shown in the following:
in one embodiment, as shown in fig. 1, fig. 1 is a schematic flow chart of an energy storage multi-time scale capacity configuration method for a flexible interconnection power distribution network according to an embodiment of the present application; the application provides an energy storage multi-time scale capacity configuration method for a flexible interconnection power distribution network, which can comprise the following steps:
s110: typical data of the flexible interconnection power distribution network accessed with new energy in a preset historical period are obtained, and multi-scenario requirements of the flexible interconnection power distribution network under different time scales and output control strategies when an energy storage system in the flexible interconnection power distribution network responds to the multi-scenario requirements are determined according to the typical data.
In the step, when the capacity of the energy storage system in the flexible interconnection power distribution network is optimally configured to meet multiple requirements of the flexible interconnection power distribution network system on renewable energy grid connection and active support, typical data of the flexible interconnection power distribution network connected with new energy in a preset historical period can be obtained first, then the multi-scene requirements of the flexible interconnection power distribution network under different time scales are determined according to the typical data, and an output control strategy when the energy storage system responds to the multi-scene requirements is provided.
It can be understood that in order to perform optimal configuration on the capacity of the energy storage system, the application can firstly determine the multi-functional multi-scenario requirement of the flexible interconnection power distribution network, then determine the output control strategy of the energy storage system when responding to the multi-functional multi-scenario requirement, thus constructing a corresponding time sequence simulation model, and simulate the output condition of the energy storage system under different energy storage capacity schemes through the time sequence simulation model, thereby determining the optimal configuration capacity.
Based on the method, the typical data of the flexible interconnection power distribution network accessed with the new energy in the past year or two years can be acquired and analyzed, so that the multi-scene requirements of the flexible interconnection power distribution network under different time scales can be determined. For example, the application can acquire annual frequency data, output data of new energy stations, daily forecast data, load peak clipping and valley filling data and the like of the flexible interconnected power distribution network system, and calculate multi-scene demands of the flexible interconnected power distribution network, such as frequency modulation demands, power forecast compensation demands, new energy consumption demands and the like.
Then, in order to build a time sequence simulation model to simulate the output of the energy storage system in response to the multi-scenario demands, the application can formulate an output control strategy of the energy storage system in response to the multi-scenario demands according to the multi-scenario demands of the flexible interconnection power distribution network under different time scales, wherein the output control strategy can be a control strategy of the energy storage system in response to any one of the demands, or a control strategy of the energy storage system in response to the multi-scenario demands, and the control strategy can be that the energy storage system participates in primary frequency modulation, the energy storage system performs new energy consumption, the energy storage system performs power prediction compensation, or the energy storage system performs self-adaptive charge and discharge, and the like, and the application can be specifically set according to actual scenario demands without limitation.
S120: and building a time sequence simulation model based on the multi-scenario demands and the output control strategy, and simulating charge and discharge power and capacity consumption of the energy storage system after responding to the multi-scenario demands according to the output control strategy under different pre-configured energy storage capacity schemes by utilizing the time sequence simulation model.
In the step, after determining the multi-scenario requirements of the flexible interconnection power distribution network under different time scales and the output control strategies when the energy storage system in the flexible interconnection power distribution network responds to the multi-scenario requirements through the S110, the application can build a time sequence simulation model according to the multi-scenario requirements and the corresponding output control strategies, the input of the time sequence simulation model can be related parameters of the flexible interconnection power distribution network under different scenario requirements and preconfigured different energy storage capacity schemes, and the time sequence simulation model can simulate the charge and discharge power and capacity consumption of the energy storage system under different energy storage capacity schemes according to the output control strategies after responding to the multi-scenario requirements by utilizing the preconfigured output control strategies of the energy storage system, so that the optimal configuration capacity can be selected according to the charge and discharge power and the capacity consumption of the energy storage system under different energy storage capacity schemes.
S130: determining an objective function with optimal economical efficiency as a target and corresponding constraint conditions, and carrying out optimization solution on the objective function based on charge and discharge power and capacity consumption of the energy storage system under different energy storage capacity schemes under the constraint conditions to obtain a solution result of the objective function under the different energy storage capacity schemes.
In the step, when determining the optimal configuration capacity for maximizing the energy storage net gain value of the energy storage system, an objective function and corresponding constraint conditions with optimal economical efficiency as targets can be constructed first, then the charge and discharge power and capacity consumption obtained after the energy storage system responds to the multi-scenario demands according to the output control strategy under different energy storage capacity schemes are simulated by the time sequence simulation model and input into the objective function, and the objective function is optimally solved under the constraint conditions, so that the solving result of the objective function under different energy storage capacity schemes can be obtained, and the solving result shows the energy storage net gain value of the energy storage system under different energy storage capacity schemes, so that the optimal configuration capacity for maximizing the energy storage net gain value of the energy storage system can be determined by solving the objective function.
Specifically, when determining the objective function and the corresponding constraint condition which aim at the optimal economical efficiency, the application not only needs to consider the income situation when the energy storage system participates in different scene demands of the flexible interconnection power distribution network, but also needs to consider the loss situation, cost and the like of the energy storage system, and also needs to consider the charge and discharge power of the energy storage system, the electric quantity of a battery in the energy storage system and the like at different moments, and constructs the objective function and the corresponding constraint condition which aim at the optimal economical efficiency under the circumstance, so that after the charge and discharge power and the capacity consumption of the energy storage system are obtained, the total life cycle cost and auxiliary frequency modulation income under the current energy storage capacity can be calculated through the objective function and the constraint condition, and the net income value of the energy storage under the current combination is output.
Further, after the time sequence simulation model is built, an objective function and corresponding constraint conditions which aim at optimizing economy are determined, after the time sequence simulation model is utilized to simulate charge and discharge power and capacity consumption obtained after the energy storage system responds to multi-scene demands according to an output control strategy under different energy storage capacity schemes, the objective function is utilized to solve the net energy storage benefit value of the energy storage system; the method can also determine the objective function and the corresponding constraint condition which aim at the optimal economical efficiency when the time sequence simulation model is built or before the time sequence simulation model is built, so that the objective function and the corresponding constraint condition can be added into the time sequence simulation model when the time sequence simulation model is built, and the final time sequence simulation model can directly output the charge and discharge power and the capacity consumption of the energy storage system after the energy storage system responds to the multi-scene requirements under different energy storage capacity schemes.
S140: and determining the optimal configuration capacity for maximizing the net gain value of the energy storage system according to the solving result of the objective function under different energy storage capacity schemes.
In this step, after obtaining the solving result of the objective function under different energy storage capacity schemes through S130, the present application may determine the optimal configuration capacity that maximizes the net gain value of energy storage of the energy storage system according to the solving result of the objective function under different energy storage capacity schemes.
Specifically, after the solving result under different energy storage capacity schemes is obtained, the energy storage net benefit value of the energy storage system under the different energy storage capacity schemes can be determined, then the energy storage net benefit value of the energy storage system under each energy storage capacity scheme can be compared, the energy storage capacity scheme with the largest energy storage net benefit value of the energy storage system can be determined, and the energy storage capacity scheme is used as the optimal configuration capacity.
In the above embodiment, after typical data of the flexible interconnection power distribution network connected with new energy in a preset history period is obtained, the multi-scenario requirements of the flexible interconnection power distribution network under different time scales and the output control strategy of the energy storage system in the flexible interconnection power distribution network in response to the multi-scenario requirements can be determined according to the typical data, so that a time sequence simulation model can be built based on the multi-scenario requirements and the output control strategy, and the time sequence simulation model is utilized to simulate the charge and discharge power and the capacity consumption of the energy storage system after the energy storage system responds to the multi-scenario requirements according to the output control strategy under different pre-configured energy storage capacity schemes, then, the application can determine an objective function and corresponding constraint conditions with optimal economical efficiency as targets, and based on the charge and discharge power and the capacity consumption of the energy storage system under different energy storage capacity schemes under the constraint conditions, the objective function is optimized and solved, and then the solving result of the objective function under different energy storage capacity schemes is obtained, finally, the optimal configuration capacity which enables the energy storage net gain value of the energy storage system to be maximum can be determined according to the solving result of the objective function under the different energy storage capacity schemes, so that the existing energy storage system can meet the multiple requirements of the flexible interconnection power distribution network system on renewable energy grid connection and active support, the capacity configuration model with the functions of energy storage and multiple time scales can be built based on the time sequence simulation model on the basis of the multiple scene requirements and the output control strategy, and the energy storage capacity is optimally configured with the economical optimization as a target, thereby not only improving the overall economical efficiency of the energy storage device and ensuring the capability of the energy storage system to adapt to complex scenes, and the return on investment can be ensured under the condition that the energy storage cost is still higher.
In one embodiment, the typical data includes rated frequency, real-time frequency, frequency modulation coefficient, inertia coefficient of the flexible interconnection power distribution network, rated power, predicted power and actual output power of new energy grid connection, and grid connection peak shaving lines and grid connection valley filling lines of system nodes; the multi-scenario demands include a frequency modulation demand, a power prediction compensation demand, and a new energy consumption demand.
In S110, determining, according to the typical data, a multi-scenario requirement of the flexible interconnection power distribution network under different time scales may include:
s1110: and determining the frequency modulation requirements of the flexible interconnection power distribution network under different time scales according to the rated frequency, the real-time frequency, the frequency modulation coefficient, the inertia coefficient and the rated power of the new energy grid connection.
S1111: and determining the power prediction compensation requirement of the flexible interconnection power distribution network under different time scales according to the predicted power and the actual output power of the new energy grid connection in the flexible interconnection power distribution network.
S1112: and determining new energy consumption requirements of the flexible interconnection power distribution network under different time scales according to the grid-connected peak cut lines and the grid-connected valley fill lines of the system nodes in the flexible interconnection power distribution network.
In this embodiment, when determining the multi-scenario requirement of the flexible interconnection power distribution network under different time scales, different data in the typical data may be selected according to different scenario requirements for calculation.
Specifically, in calculating the frequency modulation requirements P of the flexible interconnection power distribution network at different time scales f In this case, the rated frequency f of the flexible interconnection power distribution network can be used N Real-time frequency f pll Frequency modulation factor K f Inertia coefficient T j Rated power P of new energy grid connection N The calculation is carried out according to the following specific calculation formula:
in-process flexible interconnection power distribution network computingPower prediction compensation demand P at different time scales yu When the method is used, the predicted power P of the new energy grid connection in the flexible interconnection power distribution network can be used yuce And the actual output power P shiji The calculation is carried out according to the following specific calculation formula:
P yu ＝P shiji -P yuce
new energy consumption requirement P of flexible interconnection power distribution network under different time scales is calculated xiaona When the system node is in the flexible interconnection power distribution network, the grid-connected peak cutting line P of the system node can be used shangxian And grid-connected valley filling line P xiaona The calculation is carried out according to the following specific calculation formula:
the multi-scenario requirement of the flexible interconnection power distribution network under different time scales can be calculated through the calculation formula, and then the output control strategy of the energy storage system in response to the multi-scenario requirement can be determined according to the multi-scenario requirement.
In one embodiment, as shown in fig. 2, fig. 2 is a topology structure diagram of a flexible interconnection network containing dual-battery energy storage according to an embodiment of the present application; the energy storage system is of a double-battery structure, the charging and discharging states of each group of batteries in the double-battery structure are different, and when any group of batteries is fully charged or fully discharged, the charging and discharging states of the two groups of batteries are switched.
In S110, determining an output control policy of the energy storage system in the flexible interconnection power distribution network in response to the multi-scenario demand may include:
s1113: and acquiring an actual new energy output value of the flexible interconnection power distribution network under the new energy consumption requirement, and comparing the actual new energy output value with a preset peak clipping and valley filling line of the energy storage system.
S1114: and if the actual output value of the new energy is outside the preset peak clipping and valley filling line, acquiring the real-time frequency of the flexible interconnection power distribution network under the frequency modulation requirement, and comparing the real-time frequency with a preset frequency regulation dead zone of the energy storage system.
S1115: and if the real-time frequency is not in the preset frequency adjustment dead zone, starting the energy storage system to participate in primary frequency modulation of the flexible interconnection power distribution network.
S1116: and if the real-time frequency is in the preset frequency adjustment dead zone, utilizing the energy storage system to perform new energy consumption on the flexible interconnection power distribution network.
S1117: and if the actual output value of the new energy is between the preset peak clipping and valley filling lines and the real-time frequency is not in the preset frequency adjustment dead zone, starting the energy storage system to participate in primary frequency modulation of the flexible interconnection power distribution network.
S1118: and if the actual output value of the new energy is between the preset peak clipping and valley filling lines and the real-time frequency is in the preset frequency adjustment dead zone, acquiring the actual output power of the flexible interconnection power distribution network, which is in the new energy grid connection under the power prediction compensation requirement, and comparing the actual output power with a preset power prediction error zone of the energy storage system.
S1119: and if the actual output power is outside the upper limit and the lower limit of the preset power prediction error band, performing power prediction compensation on the flexible interconnection power distribution network by using the energy storage system.
S1120: and if the actual output power is within the upper limit and the lower limit of the preset power prediction error band, determining the state of charge unbalance of the energy storage system at the current moment, and comparing the state of charge unbalance with a preset unbalance range.
S1121: and if the state of charge unbalance is outside the upper limit and the lower limit of the preset unbalance range, controlling the energy storage system to perform self-adaptive charging and discharging.
S1122: if the state of charge imbalance is within the upper and lower limits of the preset imbalance range, the energy storage system is not required to be controlled to output at the current moment.
In this embodiment, as shown in fig. 2, the energy storage system in the flexible interconnection power distribution network of the present application may have a dual-battery structure, and in order to avoid frequent switching of the batteries, the energy storage system may be divided into two groups of parts with different charge and discharge states, and when any group of batteries is fully charged or fully discharged, the charge and discharge states of the two groups of batteries are switched. Thus, the functions of frequency modulation, power prediction compensation, new energy consumption and the like can be realized, the multiple requirements of the flexible Internet on multiple time scales are further realized, and the utilization rate of the energy storage equipment is improved; meanwhile, the control strategy and the charge unbalance degree of the energy storage battery are considered, the charge state can be automatically recovered after the energy storage battery participates in various scenes, and the charge state is kept in an intermediate state as far as possible, so that the service life of energy storage is prolonged, and the multiple requirements of the flexible interconnection power distribution network are responded better; and the designed double-battery structure can also prevent the battery from frequently switching the charge and discharge states, thereby further prolonging the service life of the battery.
On the basis, the application designs an output control strategy of the energy storage system, and the output control strategy can assist the energy storage system to respond to the multi-scene demands of the flexible interconnection power distribution network.
Specifically, the application can preset corresponding adjusting parameters according to a plurality of scenes of the flexible interconnection power distribution network, such as a preset frequency adjusting dead zone, a preset power prediction error zone, a preset peak clipping and valley filling line and the like; then, according to the actual operation requirement of the flexible interconnection power distribution network, the actual output value of the new energy is divided into two working conditions, wherein the first working condition is that the actual output value of the new energy is outside peak clipping and valley filling lines, and the second working condition is that the actual output value of the new energy is between the peak clipping and valley filling lines; in the first working condition, when the real-time frequency of the system is not in the preset frequency adjustment dead zone range, the energy storage system starts to participate in primary frequency modulation, and when the real-time frequency of the system is in the preset frequency adjustment dead zone range, the energy storage system absorbs new energy; in the second working condition, when the real-time frequency of the system is not in the preset frequency adjustment dead zone range, the energy storage system starts to participate in primary frequency modulation, when the real-time frequency of the system is in the preset frequency adjustment dead zone range, power prediction compensation judgment is carried out, namely, if the actual grid-connected power of the new energy source is out of the upper limit and the lower limit of a preset power prediction error zone, the energy storage system carries out power prediction compensation, and if the actual grid-connected power of the new energy source is in the upper limit and the lower limit of the preset power prediction error zone, the state of charge imbalance of the energy storage system at the current moment is calculated; if the state of charge of the energy storage system is unbalanced, the energy storage system performs self-adaptive charging and discharging, and if the state of charge of the energy storage system is balanced, the energy storage system does not need to perform output control at the current moment, and the next time cycle is performed. Thus, the output control strategy of the energy storage system can be realized.
In one embodiment, the calculation formula of the state of charge imbalance of the energy storage system at the current moment is:
A(t)＝2×S soc (t)-(S socmax +S socmin )
wherein A (t) represents the charge state unbalance degree of the energy storage system at the t moment, S SOC (t) represents the state of charge of the energy storage system at time t, S soc,min Is the lower limit of the charge state of the energy storage system, S soc,max Is the upper limit of the state of charge of the energy storage system;
output P of energy storage system during self-adaptive charge and discharge bess The method comprises the following steps:
P bess ＝A(t)×Er
wherein Er is the rated capacity of the battery in the energy storage system.
In one embodiment, in S120, using the time sequence simulation model, simulating charge and discharge power and capacity consumption of the energy storage system after responding to the multi-scenario demand according to the output control strategy under different pre-configured energy storage capacity schemes may include:
s121: inputting different pre-configured energy storage capacity schemes into the time sequence simulation model, and configuring relevant parameters of the flexible interconnection power distribution network under the multi-scene requirement and simulation periods corresponding to the different energy storage capacity schemes in the time sequence simulation model.
S122: and for each energy storage capacity scheme, simulating the charge and discharge power and capacity consumption of the energy storage system in a corresponding simulation period by using the time sequence simulation model, wherein the charge and discharge power and the capacity consumption are responded according to the output control strategy after the multi-scene requirement.
In this embodiment, when the time sequence simulation model simulates the energy storage system to respond to the multi-scenario demands, the time sequence simulation model can input the pre-configured different energy storage capacity schemes into the time sequence simulation model, and configures relevant parameters of the flexible interconnection power distribution network under the multi-scenario demands and simulation periods corresponding to the different energy storage capacity schemes in the time sequence simulation model, so that for each energy storage capacity scheme, the time sequence simulation model can be utilized to simulate the charge and discharge power and capacity consumption of the energy storage system after responding to the multi-scenario demands in the corresponding simulation period according to the output control strategy.
Specifically, when the time sequence simulation model is built, the time sequence simulation model can be built by combining an objective function and constraint conditions, so that when the time sequence simulation model is utilized to simulate the output of an energy storage system responding to multi-scene requirements, real-time frequency data, energy storage full life cycle cost data, auxiliary income data, rated power and other data of a flexible interconnection power distribution network can be input into the time sequence simulation model, then parameters of the time sequence simulation model are initialized, the upper limit and the lower limit of the charge state of an energy storage battery are set according to the current energy storage capacity scheme, the charge-discharge conversion efficiency is achieved, the upper boundary and the lower boundary of the energy storage power and reasonable time length are selected, the step length is set, the rated power of an energy storage system converter and the rated capacity of the battery are set, the simulation time T is set, the simulation starting time T is set to be 1, and the initial energy storage electric quantity of the energy storage battery pack is set. After the parameters are initialized, the time sequence simulation model can simulate the output of the energy storage system in response to multiple scenes, the charge and discharge power and the real-time capacity of the battery in the energy storage system at the current moment are updated, and then whether the steps are finished at the current moment is judged; if the operation is completed, let t=t+1, calculate the capacity loss of the energy storage battery, update the rated capacity of the energy storage battery; if not, continuing to finish the steps; then judging whether the running simulation period is finished; if the circulation of the operation simulation period is not completed, continuously simulating the output of the energy storage system in response to multiple scenes through the time sequence simulation model, and updating the charge and discharge power and the real-time capacity of the battery in the energy storage system at the current moment until all the operation is completed; if the circulation of the operation simulation period is completed, the total life period cost and auxiliary frequency modulation gain under the current energy storage capacity can be calculated, and then after the energy storage net gain value under the current combination is output, the parameters of the next energy storage capacity scheme are continuously configured, and finally the optimal configuration capacity corresponding to the maximum energy storage net gain is output.
In a specific implementation manner, the application can select typical data of a flexible interconnection power distribution network connected with new energy for analysis in one year, and related economic and technical parameters are shown in table 1:
table 1 shows the index of the example parameters
Next, the present application can perform capacity configuration as follows:
step 1: leading in new energy related data; determining relevant calculation parameters of the energy storage system; importing actual output data, daily forecast data, real-time frequency data and peak-valley data of new energy all year round; importing energy storage system full life cycle cost economic parameters, error band upper and lower limits and the like;
step 2: initializing parameters; setting upper and lower limits of the charge state of the energy storage battery; selecting the power configuration boundary of the energy storage converter as 12MW-24MW and the step length as 3MW; the boundary of the energy storage configuration time length is 0.5h-2h, and the step length is set to be 0.5 h;
step 3: setting rated power and rated capacity of a battery of a converter in the energy storage system. Setting simulation time T; performing initialization setting of simulation running time, and setting t=1;
step 4: calculating the available charge and discharge power of energy storage at the moment t; calculating the charge and discharge power of the stored energy according to a multi-scene cooperative operation strategy;
step 5: updating the capacity of the current period of the battery pack;
Step 7: judging whether the steps complete a charge-discharge operation simulation cycle, enabling t=t+1, calculating the capacity loss of the energy storage battery, and continuously executing the step 4;
step 8: judging whether the running simulation period is ended; if the simulation period is not completed circularly, continuing the step 4 until all the simulation periods are completed; if the cycle is completed, calculating the total life cycle cost and all benefits under the current energy storage capacity; outputting the net gain value of energy storage under the current combination; continuing the step 2;
step 9: judging whether all schemes are traversed, if yes, continuing to execute the step 2; and outputting the energy storage optimal configuration capacity corresponding to the maximum net benefit after the completion.
The optimal configuration capacity with the maximum energy storage net benefit value of the energy storage system can be obtained through calculation through the steps.
In one embodiment, the formula of the objective function targeting the economic optimization is as follows:
f 1 ＝max(S x +S y +S f -C bess )
wherein S is x S, participating in new energy consumption of the energy storage system y Benefits of participating in power forecast compensation for energy storage system, S f C, for the energy storage system to participate in the income of frequency modulation bess For the cost of the energy storage battery, the energy storage system participates in the income S of new energy consumption x Comprises two parts, one part is peak clipping and valley filling income S x1 Another part is the sale income S x2 The calculation formulas are respectively as follows:
S x1 ＝K b Q xian
S x2 ＝S dianjia ×Q binwang
wherein K is b Compensation coefficient of unit electric quantity, Q xian For storing the electric quantity participating in peak clipping and valley filling, S dianjia Grid-connected electricity price for unit energy, Q binwang The energy is stored to participate in peak clipping electric quantity.
Optionally, the energy storage system participates in the power prediction compensation of the revenue S y Comprises two parts, wherein one part is automatic power control service compensation, the other part is selling electricity income, the calculation formula of the automatic power control service compensation R is as follows,
R＝Ks×D×[ln(K pd )+1]×YAPC
wherein YAPC is an automatic power control regulation performance compensation standard, K pd The performance index of the unit is adjusted on the same day, and D is the adjustment depthDegree.
In this embodiment, after an objective function and a corresponding constraint condition with economy as targets are established, the objective function and the constraint condition are used as a part of a time sequence simulation model, so that the time sequence simulation model can directly calculate the net gain value of energy storage under each energy storage capacity scheme according to the objective function and the constraint condition.
Schematically, as shown in fig. 3, fig. 3 is a graph of comparing net gains of an energy storage system in a flexible interconnection power distribution network provided by the embodiment of the present application under different energy storage capacity configuration schemes, where the net gain corresponding to the scheme number 12 in fig. 3 is the highest, the economical efficiency is the best, and at this time, the energy storage capacity is 18MWh, and the charge and discharge power is 9MW. Further, as shown in table 2, comparing and analyzing the profitability of the energy storage in the single scene and the multi-functional scene, it can be seen that the comprehensive profit of the multi-functional energy storage system is significantly improved compared with the profit of the single-functional energy storage system.
Table 2 Multi-functional scenario and Single-functional scenario revenue comparison graph
In one embodiment, the energy storage system participates in the revenue S of frequency modulation f Comprises two parts, one part is frequency modulation mileage compensation S f1 Another part is frequency modulation capacity compensation S f2 The calculation formulas are respectively as follows:
/>
wherein N is the total transaction time period number of the day, D i,t For the tuning mileage of the fm unit i during the transaction period t,for the comprehensive frequency modulation performance index of the frequency modulation unit i in the transaction period t, B t Price is paid for the frequency modulated mileage during the transaction period t,for the tuning factor of the FM unit i, C i,t For the winning capacity of the frequency modulation unit i in the trade period t, B Cp Price is compensated for frequency modulation capacity.
In one embodiment, the cost C of the energy storage cell bess Comprises three parts, namely the device cost C of the energy storage battery bsys_p Cost of operation and maintenance C yw Cost of life loss C loss The calculation formulas are respectively as follows:
C yw ＝c pyw C bsys_p
wherein C is bsys_p For the cost of the energy storage battery, C E The cost coefficient of the unit capacity of the energy storage battery is t is the configuration duration of the energy storage battery, eta b For power conversion efficiency, C P Power cost coefficient per unit of energy storage battery, P rat I is the rated power of the energy storage battery, i is the discount rate, and N is the service cycle; c (C) yw Maintenance cost for operation of energy storage cell c pyw Operation maintenance coefficient for unit investment cost of energy storage battery, N BE The maximum cycle life of the stored energy provided for the manufacturer of the stored energy battery; c (C) loss For the life loss cost of the energy storage battery, n is the total charge and discharge times in the whole life cycle of the energy storage battery, C s,k The life loss cost for the kth charge-discharge cycle.
In one embodiment, the constraints include charge and discharge power of the energy storage system at different times, and the charge of the battery in the energy storage system.
The formula of the constraint condition corresponding to the objective function with the optimal economical efficiency as the target is as follows:
the relation between the charge and discharge power of the energy storage system and the electric quantity of the battery in the energy storage system is as follows:
wherein E is BAT,n For the electric quantity of the battery in the energy storage system at the nth moment E BAT,0 For the electric quantity of the battery in the energy storage system at the initial moment, P BAT,n The charging and discharging power of the energy storage system;
the state of charge of the battery in the energy storage system is expressed as follows:
wherein S is SOC,n The state of charge of the battery in the energy storage system at time n,the rated capacity of a battery in the energy storage system;
the electric quantity constraint of the battery in the energy storage system is as follows:
S soc,min ≤S SOC,n ≤S soc,max
wherein S is soc,min Is the lower limit of the charge state of the battery, S soc,max Is the upper limit of the state of charge of the battery;
the charge and discharge power constraint of the energy storage system is as follows:
wherein P is pcs Is the maximum charge and discharge power of the energy storage system.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the present specification, each embodiment is described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and may be combined according to needs, and the same similar parts may be referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The energy storage multi-time scale capacity configuration method for the flexible interconnection power distribution network is characterized by comprising the following steps of:
acquiring typical data of a flexible interconnection power distribution network accessed with new energy in a preset historical period, and determining multi-scene requirements of the flexible interconnection power distribution network under different time scales according to the typical data, and an output control strategy when an energy storage system in the flexible interconnection power distribution network responds to the multi-scene requirements;
building a time sequence simulation model based on the multi-scenario demands and the output control strategy, and simulating charge and discharge power and capacity consumption of the energy storage system after responding to the multi-scenario demands according to the output control strategy under different pre-configured energy storage capacity schemes by utilizing the time sequence simulation model;
Determining an objective function with optimal economical efficiency as a target and corresponding constraint conditions, and carrying out optimization solution on the objective function based on charge and discharge power and capacity consumption of the energy storage system under different energy storage capacity schemes under the constraint conditions to obtain a solution result of the objective function under different energy storage capacity schemes;
and determining the optimal configuration capacity for maximizing the net gain value of the energy storage system according to the solving result of the objective function under different energy storage capacity schemes.
2. The flexible interconnection power distribution network-oriented energy storage multi-time scale capacity configuration method according to claim 1, wherein the typical data comprise rated frequency, real-time frequency, frequency modulation coefficient, inertia coefficient of the flexible interconnection power distribution network, rated power, predicted power and actual output power of new energy grid connection, grid connection peak shaving lines and grid connection valley filling lines of system nodes; the multi-scene requirements comprise a frequency modulation requirement, a power prediction compensation requirement and a new energy consumption requirement;
the determining the multi-scenario requirement of the flexible interconnection power distribution network under different time scales according to the typical data comprises the following steps:
Determining the frequency modulation requirements of the flexible interconnection power distribution network under different time scales according to the rated frequency, the real-time frequency, the frequency modulation coefficient, the inertia coefficient and the rated power of the new energy grid connection;
determining power prediction compensation requirements of the flexible interconnection power distribution network under different time scales according to the predicted power and the actual output power of the new energy grid connection in the flexible interconnection power distribution network;
and determining new energy consumption requirements of the flexible interconnection power distribution network under different time scales according to the grid-connected peak cut lines and the grid-connected valley fill lines of the system nodes in the flexible interconnection power distribution network.
3. The energy storage multi-time scale capacity configuration method for the flexible interconnection distribution network according to claim 2, wherein the energy storage system is of a double-battery structure, the charge and discharge states of each group of batteries in the double-battery structure are different, and when any group of batteries is fully charged or fully discharged, the charge and discharge states of the two groups of batteries are switched;
the determining an output control strategy when the energy storage system in the flexible interconnection power distribution network responds to the multi-scenario demand comprises the following steps:
acquiring an actual new energy output value of the flexible interconnection power distribution network under the new energy consumption requirement, and comparing the actual new energy output value with a preset peak clipping and valley filling line of the energy storage system;
If the actual output value of the new energy is outside the preset peak clipping and valley filling line, acquiring the real-time frequency of the flexible interconnection power distribution network under the frequency modulation requirement, and comparing the real-time frequency with a preset frequency regulation dead zone of the energy storage system;
if the real-time frequency is not in the preset frequency adjustment dead zone, starting the energy storage system to participate in primary frequency modulation of the flexible interconnection power distribution network;
if the real-time frequency is in the preset frequency adjustment dead zone, new energy is consumed for the flexible interconnection power distribution network by utilizing the energy storage system;
if the actual output value of the new energy is between the preset peak clipping and valley filling lines and the real-time frequency is not in the preset frequency adjustment dead zone, starting the energy storage system to participate in primary frequency modulation of the flexible interconnection power distribution network;
if the actual output value of the new energy is between the preset peak clipping and valley filling lines and the real-time frequency is in the preset frequency adjustment dead zone, acquiring the actual output power of the flexible interconnection power distribution network, which is in the new energy grid connection under the power prediction compensation requirement, and comparing the actual output power with a preset power prediction error zone of the energy storage system;
If the actual output power is outside the upper limit and the lower limit of the preset power prediction error band, performing power prediction compensation on the flexible interconnection power distribution network by using the energy storage system;
if the actual output power is within the upper limit and the lower limit of the preset power prediction error band, determining the state of charge unbalance of the energy storage system at the current moment, and comparing the state of charge unbalance with a preset unbalance range;
if the state of charge unbalance is outside the upper limit and the lower limit of the preset unbalance range, controlling the energy storage system to perform self-adaptive charging and discharging;
if the state of charge imbalance is within the upper and lower limits of the preset imbalance range, the energy storage system is not required to be controlled to output at the current moment.
4. The energy storage multi-time scale capacity configuration method for the flexible interconnection power distribution network according to claim 3, wherein the calculation formula of the state of charge imbalance of the energy storage system at the current moment is:
A(t)＝2×S soc (t)-(S socmax +S socmin )
wherein A (t) represents the charge state unbalance degree of the energy storage system at the t moment, S SOC (t) represents the state of charge of the energy storage system at time t, S soc,min Is the lower limit of the charge state of the energy storage system, S soc,max Is the upper limit of the state of charge of the energy storage system;
output P of energy storage system during self-adaptive charge and discharge bess The method comprises the following steps:
P bess ＝A(t)×Er
wherein Er is the rated capacity of the battery in the energy storage system.
5. The method for configuring energy storage multi-time scale capacity for a flexible interconnection distribution network according to claim 1 or 3, wherein the simulating the charge and discharge power and capacity consumption of the energy storage system under different pre-configured energy storage capacity schemes according to the output control strategy by using the time sequence simulation model, comprises:
inputting different pre-configured energy storage capacity schemes into the time sequence simulation model, and configuring related parameters of the flexible interconnection power distribution network under the multi-scene requirement and simulation periods corresponding to the different energy storage capacity schemes in the time sequence simulation model;
and for each energy storage capacity scheme, simulating the charge and discharge power and capacity consumption of the energy storage system in a corresponding simulation period by using the time sequence simulation model, wherein the charge and discharge power and the capacity consumption are responded according to the output control strategy after the multi-scene requirement.
6. The flexible interconnection power distribution network-oriented energy storage multi-time scale capacity configuration method according to claim 1, wherein the formula of the objective function targeting the economical optimization is as follows:
f 1 ＝max(S x +S y +S f -C bess )
Wherein S is x S, participating in new energy consumption of the energy storage system y Benefits of participating in power forecast compensation for energy storage system, S f C, for the energy storage system to participate in the income of frequency modulation bess For the cost of the energy storage battery, the energy storage system participates in the income S of new energy consumption x Comprises two parts, one part is peak clipping and valley filling income S x1 Another part is the sale income S x2 The calculation formulas are respectively as follows:
S x1 ＝K b Q xian
S x2 ＝S dianjia ×Q binwang
wherein K is b Compensation coefficient of unit electric quantity, Q xian For storing the electric quantity participating in peak clipping and valley filling, S dianjia Grid-connected electricity price for unit energy, Q binwang The energy is stored to participate in peak clipping electric quantity.
7. The flexible-interconnect-power-distribution-network-oriented energy storage multi-time-scale capacity configuration method according to claim 6, wherein the energy storage system participates in power prediction compensation of the revenue S y Comprises two parts, wherein one part is automatic power control service compensation, the other part is selling electricity income, the calculation formula of the automatic power control service compensation R is as follows,
R＝Ks×D×[ln(K pd )+1]×YAPC
wherein YAPC is an automatic power control regulation performance compensation standard, K pd The performance index is the adjustment performance index of the unit on the same day, and D is the adjustment depth.
8. The flexible-interconnection-power-distribution-network-oriented energy storage multi-time-scale capacity configuration method according to claim 6, wherein the energy storage system participates in frequency modulation of income S f Comprises two parts, one part is frequency modulation mileage compensation S f1 Another part is frequency modulation capacity compensation S f2 The calculation formulas are respectively as follows:
wherein N is the total transaction time period number of the day, D i,t For the tuning mileage of the fm unit i during the transaction period t,for the comprehensive frequency modulation performance index of the frequency modulation unit i in the transaction period t, B t Price +.f. for FM mileage during transaction period t>For the tuning factor of the FM unit i, C i,t For the winning capacity of the frequency modulation unit i in the trade period t, B Cp Price is compensated for frequency modulation capacity.
9. The flexible-interconnect-power-distribution-network-oriented energy storage multi-time-scale capacity configuration method of claim 6, wherein the cost C of the energy storage battery bess Comprises three parts, namely the device cost C of the energy storage battery bsys_p Cost of operation and maintenance C yw Cost of life loss C loss The calculation formulas are respectively as follows:
C yw ＝c pyw C bsys_p
wherein C is bsys_p For the cost of the energy storage battery, C E The cost coefficient of the unit capacity of the energy storage battery is t is the configuration duration of the energy storage battery, eta b For power conversion efficiency, C P Power cost coefficient per unit of energy storage battery, P rat I is the rated power of the energy storage battery, i is the discount rate, and N is the service cycle; c (C) yw Maintenance cost for operation of energy storage cell c pyw Operation maintenance coefficient for unit investment cost of energy storage battery, N BE The maximum cycle life of the stored energy provided for the manufacturer of the stored energy battery; c (C) loss For the life loss cost of the energy storage battery, n is the total charge and discharge times in the whole life cycle of the energy storage battery, C s,k The life loss cost for the kth charge-discharge cycle.
10. The flexible-interconnect-power-distribution-network-oriented energy storage multi-time-scale capacity configuration method according to claim 1 or 6, wherein the constraint conditions include charge and discharge power of an energy storage system at different times and electric quantity of a battery in the energy storage system;
the formula of the constraint condition corresponding to the objective function with the optimal economical efficiency as the target is as follows:
the relation between the charge and discharge power of the energy storage system and the electric quantity of the battery in the energy storage system is as follows:
wherein E is BAT,n For the electric quantity of the battery in the energy storage system at the nth moment E BAT,0 For the electric quantity of the battery in the energy storage system at the initial moment, P BAT,n The charging and discharging power of the energy storage system;
the state of charge of the battery in the energy storage system is expressed as follows:
wherein S is SOC,n The state of charge of the battery in the energy storage system at time n,the rated capacity of a battery in the energy storage system;
the electric quantity constraint of the battery in the energy storage system is as follows:
S soc,min ≤S SOC,n ≤S soc,max
Wherein S is soc,min Is the lower limit of the charge state of the battery, S soc,max Is the upper limit of the state of charge of the battery;
the charge and discharge power constraint of the energy storage system is as follows:
wherein P is pcs Is the maximum charge and discharge power of the energy storage system.
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| CN117154781A (en) * | 2023-10-31 | 2023-12-01 | 国网山西省电力公司电力科学研究院 | Energy storage frequency modulation capacity configuration method and device and computer readable storage medium |
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| CN117117874A (en) * | 2023-10-23 | 2023-11-24 | 广东电网有限责任公司佛山供电局 | Control method, device, equipment and medium of distributed power grid system |
| CN117117874B (en) * | 2023-10-23 | 2024-03-05 | 广东电网有限责任公司佛山供电局 | Control method, device, equipment and medium of distributed power grid system |
| CN117154781A (en) * | 2023-10-31 | 2023-12-01 | 国网山西省电力公司电力科学研究院 | Energy storage frequency modulation capacity configuration method and device and computer readable storage medium |
| CN117154781B (en) * | 2023-10-31 | 2024-01-23 | 国网山西省电力公司电力科学研究院 | Energy storage frequency modulation capacity configuration method and device and computer readable storage medium |
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