CN110633854A - Full life cycle optimization planning method considering energy storage battery multiple segmented services - Google Patents
Full life cycle optimization planning method considering energy storage battery multiple segmented services Download PDFInfo
- Publication number
- CN110633854A CN110633854A CN201910870910.8A CN201910870910A CN110633854A CN 110633854 A CN110633854 A CN 110633854A CN 201910870910 A CN201910870910 A CN 201910870910A CN 110633854 A CN110633854 A CN 110633854A
- Authority
- CN
- China
- Prior art keywords
- energy storage
- storage battery
- life
- battery system
- service
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004146 energy storage Methods 0.000 title claims abstract description 243
- 238000005457 optimization Methods 0.000 title claims abstract description 40
- 238000000034 method Methods 0.000 title claims abstract description 36
- 230000008901 benefit Effects 0.000 claims abstract description 21
- 230000008569 process Effects 0.000 claims description 9
- 230000005611 electricity Effects 0.000 claims description 8
- 238000012423 maintenance Methods 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000012546 transfer Methods 0.000 claims description 5
- BTCSSZJGUNDROE-UHFFFAOYSA-N gamma-aminobutyric acid Chemical compound NCCCC(O)=O BTCSSZJGUNDROE-UHFFFAOYSA-N 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 2
- 238000007599 discharging Methods 0.000 description 10
- 238000010586 diagram Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 8
- 230000006872 improvement Effects 0.000 description 6
- 230000032683 aging Effects 0.000 description 4
- 238000004064 recycling Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000010835 comparative analysis Methods 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 238000003912 environmental pollution Methods 0.000 description 2
- 230000014509 gene expression Effects 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 208000012599 Blepharophimosis-intellectual disability syndrome Diseases 0.000 description 1
- 241000341910 Vesta Species 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000002238 attenuated effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000006731 degradation reaction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000005562 fading Methods 0.000 description 1
- 238000009396 hybridization Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000012886 linear function Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 210000000352 storage cell Anatomy 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/30—Administration of product recycling or disposal
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
- Y02W90/00—Enabling technologies or technologies with a potential or indirect contribution to greenhouse gas [GHG] emissions mitigation
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Theoretical Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Sustainable Development (AREA)
- Life Sciences & Earth Sciences (AREA)
- Educational Administration (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a full life cycle optimization planning method considering the multiple segmented services of an energy storage battery, which comprises the following steps: s1, modeling a battery life cycle, dividing the battery life cycle into two life stages, sequentially using the two life stages in an auxiliary service market and a real-time energy market, and constructing a profit model of the energy storage battery in different power markets; s2, constructing a multi-segment service full-life-cycle optimization planning model of the energy storage battery system according to the constructed battery life-cycle model and constraint conditions by taking the maximum total income of the energy storage battery system as a target and taking the capacity distribution proportion of the batteries in different life stages as optimization variables, and calculating the service life and the total cost/benefit of the batteries in different life stages; and S3, solving the planning model by using a differential evolution algorithm to obtain a planning scheme of the energy storage battery system. The invention is helpful for investors to distribute energy of the energy storage battery to different markets strategically so as to maximize economic benefits of the investors and guarantee the effective service life of the battery.
Description
Technical Field
The invention relates to the technical field of power system planning, in particular to a full life cycle optimization planning method considering multiple segmented services of an energy storage battery.
Background
The continuous promotion of the innovation of the power System and the opening of the retail power market provide a brand new opportunity for the development and application of a Battery Energy Storage System (BESS). The energy storage battery is a power supply with flexible time scale, has the advantages of high response speed, high control precision and the like, and can effectively meet the requirement of system optimization operation. The energy storage battery system can provide various services to support the power grid, but the return on investment, the operation conditions and the influence on the service life of the energy storage battery system are greatly different among different services. The service life of the energy storage battery is an important factor for analyzing the return on investment, and is different from the relatively fixed service life of a conventional unit, and the service life of the energy storage battery with limited cycle times is closely related to the factors such as the working environment, the discharge depth, the charge and discharge times and the like.
Nowadays, energy storage batteries are widely used in frequency modulation auxiliary service markets, real-time energy markets and the like to obtain economic benefits. The energy storage battery can maintain the system frequency stability by participating in the frequency modulation market, and obtain greater economic benefit in a short time. However, considering the dynamic operating characteristics and frequency modulation requirements of the system, high-frequency cyclic charge and discharge can accelerate battery aging and shorten the normal service life of the battery; when the energy storage battery system is applied to a real-time energy market to provide peak clipping and valley filling services, the energy storage system can obtain profits through a strategy of high power generation and low power storage, and at the moment, the battery is low in use frequency but large in charge-discharge depth, so that the demand on the capacity of the battery is high. Considering that the cost of the energy storage battery system is still high at the present stage, it is difficult to recover the cost within the life cycle of the battery only by using the peak-to-valley difference price arbitrage as the only profit point, so the economic benefit of the mode still needs to be improved.
In addition, the energy storage battery can also obtain multiple benefits by participating in the energy and auxiliary service joint market. However, the high-frequency deep charge and discharge behavior causes the performance of the battery to decline rapidly, which affects the accurate response capability of the battery, accelerates the aging process of the battery, and shortens the normal service life of the battery seriously. Because the energy storage battery fails in advance, the replacement cycle of the energy storage battery is shortened, the actual construction cost and the recovery cost are higher, and the economic loss and the environmental pollution are further caused.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the problems in the prior art and economy, the invention provides the energy storage battery multi-section service full life cycle optimization planning method which can maximize the total income of an energy storage battery system, simultaneously ensure that the energy storage battery has a certain service life, and has simple implementation method and flexible application.
In order to solve the problems, the technical scheme provided by the invention is as follows:
a full life cycle optimization planning method considering energy storage battery multiple segmentation service comprises the following steps:
step S1: constructing a profit model of the energy storage battery system;
modeling the life cycle of the energy storage battery, and dividing the energy storage battery into two life stages: in the first life stage, the energy storage battery system is applied to an auxiliary service market to participate in system frequency adjustment; and in the second life stage, the energy storage battery is switched into a real-time energy market to assist in realizing load transfer service. Constructing a profit model of the energy storage battery system in different power markets;
step S2: constructing an energy storage battery system full life cycle multi-segment service optimization planning model;
the method comprises the steps of taking the maximum total income of an energy storage battery system as a target, taking the battery capacity distribution proportion of different life stages as an optimization variable, calculating the service life and the total cost/benefit of energy storage batteries of different life stages according to a constructed energy storage battery life cycle model and constraint conditions, and constructing a full life cycle multi-segment service optimization planning model of the energy storage battery system;
step S3: forming a planning scheme of the energy storage battery system;
and solving the full-life-cycle multi-segment service optimization planning model of the energy storage battery system by using a differential evolution algorithm to obtain a planning scheme of the energy storage battery system.
As a further improvement of the invention: in step S1, the life cycle of the energy storage battery is quantitatively evaluated: and calculating the available total effective throughput of the energy storage battery according to the technical parameters of the energy storage battery, converting the discharge process of each discharge event of the energy storage battery into effective ampere-hours under the rated discharge depth, and when the accumulated effective ampere-hours reach the total effective throughput of the energy storage battery, considering that the energy storage battery reaches the service life and performing scrapping treatment.
As a further improvement of the invention: in step S1, to evaluate the impact of the capacity allocation schemes of different energy storage battery systems on the investment profit, the service lives of the energy storage battery systems in different life stages are calculated according to the following formula:
in the formula,
equivalent conversion service life of the energy storage battery in the first and second life stages of the energy storage battery system i, gammaR、Γ′RRated throughput of battery in first and second life stages, gammaeff、Γ′effThe accumulated effective ampere-hours of the battery in the first and second life stages respectively.
As a further improvement of the present invention, in step S1, the profit model of the energy storage battery system in different power markets is calculated according to the following formula:
in the formula, Profit1For the first life-stage benefit of the energy storage battery system, CreserveTo assist the price per unit of capacity of the service market,
for the first life stage rated capacity, Profit, of the energy storage battery system i2For the second life stage benefit, λ, of the energy storage battery systempeak、λoffpeakRespectively represent the peak and the valley electricity prices,
the charge/discharge amount of the energy storage battery system i in the h period in the zeta th scene is shown.
As a further improvement of the invention: in step S2, the energy storage battery full-life-cycle optimization planning model with the goal of maximizing the return of investors is calculated according to the following formula:
F＝max(F1+F2-Cinv)
wherein F is the total yield of the energy storage battery system in the whole life cycle, F1Earnings for a first life stage of the energy storage battery system; f2For the total gain of the second life stage of the energy storage battery system, CinvIs the total investment cost. Copm、Cage、CpenaltyRespectively the operation and maintenance cost, the life loss cost and the penalty cost of uncompensated electric quantity in the first life stage. C'opm、C′ageRespectively the operation maintenance cost, the life loss cost, C of the second life stageEAnd d is the unit capacity cost and the current rate of the energy storage battery system respectively. ξ and ζ are respectively the scene indexes of the real-time energy market and the auxiliary service market, and S, S' is the total scene number of the real-time energy market and the auxiliary service market.
As a further improvement of the invention: the full life cycle optimization planning model comprises total investment constraint, energy storage battery system first life stage operation constraint, energy storage battery system second life stage operation constraint and energy storage battery system life constraint.
As a further improvement of the invention: the process of solving the model based on the differential evolution algorithm in step S3 includes:
step S301: determining algorithm control parameters and a fitness function according to the energy storage battery system planning model;
step S302: initializing a power system, a power grid bus, load, renewable energy output, system frequency and market electricity price data, and establishing an energy storage battery system full life cycle optimization planning model with the maximum total profit of the energy storage battery system as a target;
step S303: compared with the prior art, the method has the advantages that the iterative computation of the differential evolution algorithm model is used for obtaining the energy storage battery full life cycle optimization planning model scheme, and the method has the following steps:
1) the invention considers the full life cycle optimization planning of the energy storage battery system multiple segmented service, and particularly, the energy storage battery system is successively applied to an auxiliary service market to provide a frequency adjustment service as a first life stage. After a certain service period, considering the decline characteristic of the battery capacity, calculating the residual cycle life and the residual effective throughput of the battery, and applying the residual cycle life and the residual effective throughput to the real-time energy market again to provide peak clipping and valley filling service as a second life stage of the battery.
2) The invention takes the location and capacity fixing scheme of the energy storage battery system and the battery capacity distribution proportion in different life stages as the programmable resources, and balances the programmable resources in the system according to the service income, the operation and maintenance cost, the life loss cost, the punishment cost of uncompensated electric quantity and the total investment cost of the energy storage battery system in different life stages, so that the planning result of the energy storage battery system can obtain reasonable compromise between the system operation economy and the service life of the energy storage battery.
3) According to the invention, the capacity distribution proportion of the batteries in different life stages is taken as a constraint condition, and the efficiency of the energy storage battery system is fully utilized, the environmental pollution is reduced, and the pressure of the battery recycling market is relieved by adjusting the capacity distribution proportion of the energy storage batteries in different life stages according to different operation mechanisms and business modes of an auxiliary service market and a real-time energy market on the premise of considering the operation characteristics and the market boundary conditions of the batteries.
Drawings
Fig. 1 is a schematic flow chart of an implementation of the energy storage battery full-life-cycle multi-segment service optimization planning method according to this embodiment.
Fig. 2 is a schematic diagram of the relationship between the actual cycle life and the depth of discharge of the energy storage battery in this embodiment.
Fig. 3 is a schematic diagram of a full-life-cycle multi-segment service optimization planning framework of the energy storage battery system according to the embodiment.
Fig. 4 is a schematic diagram of the power-frequency characteristic of the energy storage battery system in the embodiment.
Fig. 5 is a schematic diagram of a model solving process based on a differential evolution algorithm in the embodiment.
Fig. 6 is a schematic diagram of the IEEE33 node system wiring utilized in the present embodiment.
Fig. 7 is a diagram illustrating frequency curves of four seasons used in this embodiment.
Fig. 8 is a schematic diagram in a typical scenario in the present embodiment, where (a) is an hour wind speed, (b) is a load, and (c) is a power rate schematic diagram.
Detailed Description
The invention is further described below with reference to the drawings and specific preferred embodiments of the description, without thereby limiting the scope of protection of the invention.
As shown in fig. 1, a full life cycle optimization planning method considering multiple segmentation services of an energy storage battery according to the present invention includes:
step S1: constructing a profit model of the energy storage battery system;
modeling the life cycle of the energy storage battery, and dividing the energy storage battery into two life stages: the energy storage battery system is applied to the auxiliary service market in the first life stage to participate in system frequency adjustment; the second life stage: and the energy storage battery is transferred to a real-time energy market to assist in realizing load transfer service. Constructing a profit model of the energy storage battery system in different power markets;
step S2: constructing an energy storage battery system full life cycle multi-segment service optimization planning model;
the method comprises the steps of taking the maximum total income of an energy storage battery system as a target, taking the battery capacity distribution proportion of different life stages as an optimization variable, calculating the service life and the total cost/benefit of energy storage batteries of different life stages according to a constructed energy storage battery life cycle model and constraint conditions, and constructing a full life cycle multi-segment service optimization planning model of the energy storage battery system;
step S3: and forming a planning scheme of the energy storage battery system.
And solving the full-life-cycle multi-segment service optimization planning model of the energy storage battery system by using a differential evolution algorithm to obtain a planning scheme of the energy storage battery system.
In a specific application example, in step S1, the life cycle of the energy storage battery is quantitatively evaluated: and calculating the total available effective throughput in the whole life cycle of the battery according to the technical parameters of the energy storage battery, and converting the discharge process of each discharge event of the energy storage battery into effective ampere-hours under the rated discharge depth.
The rated throughput of the energy storage battery is:
Γrated＝LRDRCR (1)
in the formula, gammaratedRating the throughput, L, for the batteryRFor the number of cycles at the rated depth of discharge and rated discharge current, DRTo a nominal depth of discharge, CRThe rated capacity of the battery.
In this embodiment, an energy storage battery life attenuation fitting model is established according to the relationship between the energy storage battery cycle life and the depth of discharge, the relationship between the energy storage battery actual cycle life and the depth of discharge is shown in fig. 2, and the calculation formula is as follows:
in the formula, LB(dB) For practical recycling times, LRFor the number of cycles at the rated depth of discharge and rated discharge current, DRTo a nominal depth of discharge, DATo the actual depth of discharge, u0、u1And e is a natural logarithm base number.
In the embodiment, the influence of the discharge depth and the discharge rate on the service life of the energy storage battery is comprehensively considered, and the equivalent ampere-hour consumed by the energy storage battery in single discharge can be calculated by a formula (3); if n discharging events are contained in the scheduling time period T, the actual service life of the energy storage battery is calculated by the formula (4).
In the formula, LtimeFor the actual service life of the battery, deffEffective ampere-hours converted for a single discharge event, CAIs the actual capacity of the battery, dactΓ for the actual discharge ampere-hourseffTo accumulate effective ampere-hours, DATo the actual depth of discharge, u0、u1And e is a natural logarithm base number.
In a specific application example, in step S1, the frequency control strategy of the energy storage battery system in the first life stage is: and (3) simulating droop control of the traditional synchronous generator set, namely calculating the power requirement of the energy storage battery participating in primary frequency modulation service according to the frequency deviation of the power grid and the self power-frequency characteristic of the battery. In order to avoid unnecessary frequent actions of the energy storage battery, the energy storage battery system is regulated not to provide frequency modulation service within a primary frequency modulation dead zone range. In addition, since the energy storage battery system has power constraint, the power-frequency characteristic curve is a piecewise linear function, as shown in fig. 4. Assuming that the state of charge of each energy storage battery is a given value at the beginning of each day, during the operation, a charge/discharge command is issued according to the frequency change of the system, and the specific strategy of the power control can be expressed as follows:
wherein σ is a battery power-frequency characteristic coefficient,
for the charge/discharge amount of the energy storage battery system i in the time t in the ξ scenario,for the rated power of an energy storage battery system i, delta f and delta P are respectively a system frequency deviation value and an active power increment, fratedAnd the grid frequency reference value is obtained.
In a specific application example, a profit model of the energy storage battery system participating in the auxiliary service market for providing the frequency adjustment service refers to a service mechanism of a European power market, a power grid company compensates investors according to rated electric quantity provided by the energy storage battery participating in the frequency modulation market, and profits of the investors are 'fixed' capacity profits. In addition, considering the frequency modulation demand which cannot be completely met due to the limitation of the energy storage capacity/power of the battery in the actual operation process, punishment is carried out according to the difference between the actual frequency modulation electric quantity provided by the energy storage battery and the frequency modulation demand electric quantity. The frequency modulation compensation gain can be calculated as:
in the formula, Profit1For the first life-stage benefit of the energy storage battery system, CreserveTo assist the price per unit of capacity of the service market,
the capacity is rated for the first life of the energy storage battery system i.
In a specific application example, when the energy storage battery system is in the second life stage, the energy storage battery participates in the real-time energy market to provide the load transfer service. The working state of the battery is divided into three types of charging, discharging and idling according to the peak-valley electricity price difference value, and the battery is supposed to be discharged in the peak electricity price period, charged in the valley time period and not to act in the idle time period. The profit when the energy storage battery system participates in the real-time energy market to provide the load transfer service can be calculated as follows:
in the formula, Profit2For the second life stage benefit, λ, of the energy storage battery systempeak、λoffpeakRespectively represent the peak and the valley electricity prices,
the charge/discharge amount of the energy storage battery system i in the h period in the zeta th scene is shown.
In this embodiment, the total revenue function of the energy storage battery system in the first life stage includes four parts, i.e., frequency modulation compensation revenue, operation and maintenance cost, life loss cost, and penalty cost of uncompensated electric quantity, and the total revenue function and other cost expressions are respectively as shown in formulas (9) to (12):
Cage＝(Cinv/ΓR)×∑ξ∑tΓeff(t) (11)
in formulae (9) to (12), CO,BESSFor annual operating and maintenance costs of energy storage batteries, gammaRRating the throughput for the batteryeff(t) is the number of active amp-hours consumed by the battery at discharge event time t under the first life cycle,
the deviation between the reserve power required for maintaining the system frequency stability and the actual consumed/supplied power in the first life stage is represented by rho which is a unit penalty coefficient, xi which corresponds to a scene index of an auxiliary service market, S which is the total scene number of the auxiliary service market, T which is a first life stage time interval index, T which is a total scheduling time interval of the auxiliary service market, y which is a year index of a first life stage of the energy storage battery, and delta T which is a first life stage power instruction time interval.
In this embodiment, the total gain function of the energy storage battery system in the second life stage includes three parts, namely peak clipping and valley filling gains, operation and maintenance costs, and life loss costs, and the total gain function and other cost expressions are shown in equations (13) to (15):
C′age＝(Cinv/Γ′R)×∑ζ∑hΓ′eff(h) (15)
in formulae (13) to (15), Γ'eff(h) And the effective ampere-hour number consumed by the battery of the discharge event at H moment in the second life stage is S' and H respectively representing the total scene number of the real-time energy market and the total scheduling time period of the real-time energy market, H represents the time period index of the second life stage, and zeta is the scene index of the real-time energy market.
In this embodiment, the total investment cost of the energy storage battery system is directly related to the service life of the battery, and the total investment cost of the energy storage battery system can be calculated by the following formula:
in the formula, CinvIn order to account for the total investment costs,
for the rated capacity, C, of the first life stage of the energy storage battery system iED is the cost per unit capacity, the current rate and Y of the energy storage battery system respectively1st、Y2ndRespectively corresponding to the service life of the energy storage battery system in the first life stage and the second life stage.
In this embodiment, a model for optimizing and planning the full life cycle of the energy storage battery with the goal of maximizing the income of the investor is provided, which specifically includes:
F＝max(F1+F2-Cinv) (17)
wherein F is the total yield of the energy storage battery system, F1For the total yield of the first life phase of the energy storage battery system, F2For the total gain of the second life stage of the energy storage battery system, CinvIs the total investment cost.
In this embodiment, when constructing the energy storage battery system full life cycle optimization planning model, the control target power system needs to consider one or more of total investment constraints, energy storage battery system first life stage operation constraints, energy storage battery system second life stage operation constraints, and energy storage battery system life constraints:
firstly, total investment constraint;
Cinv≤CAP (18)
xBESS∈Φ (19)
formula (18) is the Total investment constraint, CinvCAP is the planning budget investment for the total investment cost; formula (19) is the site selection constraint, xBESSAnd F is the installation position of the energy storage battery system, and phi is a system node set.
Operation constraint of the energy storage battery system in the first life stage;
equation (20) is the charging and discharging power constraint of the energy storage battery, equation (21) is the charge state of the energy storage battery, and equation (22) is the relationship between the capacity and the output power of the energy storage battery in the front and back time periods. Wherein,
is the charge/discharge amount of the battery energy storage system i in the time t in the xi scene,
for the state of charge of the energy storage battery system i in the ξ -th scenario during the t period,
for the energy stored by the energy storage battery system i during the time period t in the ξ -th scenario,
energy, eta, stored in the energy storage battery system at the t time period and the t-1 time period in the xi scene respectivelych、ηdisThe charging efficiency and the discharging efficiency of the energy storage battery system are respectively, delta t is the power instruction time interval of the first life stage, and fifteen seconds are taken in the invention.
Operation constraint of the energy storage battery system in the second life stage;
in a second life phase, the battery is used to provide load shifting services, the main considered system constraints include line current constraints (23), node voltage constraints (24), grid power balance constraints (25), transmission line constraints (26) and reverse power flow constraints (27):
in the formula,
for line current flowing between nodes I, j during period h in the zeta th scenario, ImaxFor the maximum line current that the line will allow to pass,node voltage, v, for period h node i in the zeta th scenariomax、vminRespectively are the upper limit value and the lower limit value of the node voltage,
for the amount of charge/discharge of the battery energy storage system i during period h in the ζ -th scenario,
respectively the j active power output, the system power purchasing, the total system load and the network loss of the wind turbine generator in the h time period in the zeta th scene, wherein N is the number of the wind turbine generators,
for the flow through line l during period h in the zeta th scenario, fl max、fl minRespectively an upper limit value and a lower limit value of the power flow of the line l, ft ζFor the power flow flowing through the feeder line connected with the main network and the microgrid in the period t in the zeta th scene,
the feeder flow upper limit value.
The operation constraint of the energy storage battery system in the second life stage is as follows:
equation (28) represents the charging and discharging power constraint of the energy storage battery, equation (29) represents the state of charge of the energy storage battery, and equation (30) represents the relationship between the capacity and the output power of the energy storage battery in the previous and subsequent periods. Wherein,
for the amount of charge/discharge of the battery energy storage system i during period h in the ζ -th scenario,
for the state of charge of the energy storage battery system i during period h in the ζ -th scenario,
for the energy stored by the energy storage battery system i in the zeta th scene during the h period, considering the phenomenon of the fading and degradation of the rated capacity of the battery after a period of service time,
for the capacity rating of the energy storage battery system i in the second life stage,
respectively storing energy, eta, of the energy storage battery system in the h time period and the h-1 time period in the zeta th scenech、ηdisThe charging efficiency and the discharging efficiency of the energy storage battery system are respectively, delta h is the power instruction time interval of the first life stage, and one hour is taken in the invention.
Fourthly, restricting the service life of the energy storage battery system;
the discharging process of each discharging event of the energy storage battery can be converted into effective ampere-hours, and the effective throughput of the energy storage battery under different operating conditions can be estimated by summarizing all discharging events of the energy storage battery in the service life. In order to evaluate the influence of different capacity distribution schemes of the energy storage battery system on the investment income, the invention considers the different capacity distribution constraint conditions of the energy storage battery system as follows:
in the formula,
respectively converting the equivalent service life N of the energy storage battery in the first and second life stages of the energy storage battery system iBESSThe total age is planned for the project,
the service lives of the energy storage battery system i in the first life stage and the second life stage respectively,
rated capacities, D, of the first and second life stages of the energy storage battery system i, respectivelyRTo a nominal depth of discharge, LRFor rated cycle life, gammaR、Γ′RRated throughput of battery in first and second life stages, gammaeff、Γ′effRespectively the accumulated effective ampere-hour of the battery in the first and second life stages, deffThe effective ampere-hour number converted for a single discharge event, alpha is the rated capacity distribution ratio of the first and second life stages of the energy storage battery, 0<α<1, S, S' are the total scene numbers of the auxiliary service market and the real-time energy market respectively, ξ and ζ correspond to the operation scene indexes of the auxiliary service market and the real-time energy market respectively, T, H are the total scheduling time periods of the auxiliary service market and the real-time energy market respectively, and t and h respectively represent the first life stage time period index and the second life stage time period index.
In a specific application example, the differential evolution algorithm is used for solving the energy storage battery system full-life-cycle optimization planning model in the step S3, which is beneficial to quickly solving and obtaining an accurate and feasible planning method.
Referring to fig. 5, the process of solving the model based on the differential evolution algorithm in this embodiment includes:
step S301: determining algorithm control parameters and a fitness function according to the energy storage battery system planning model, wherein the control parameters comprise a population size NPMaximum number of iterations kmaxScaling factor F and hybridization probability CR。
Step S302: initializing data such as a power system, a power grid bus, load, renewable energy output, system frequency, market electricity price and the like, and generating a series of initial schemes for energy storage battery system position site selection and capacity allocation ratio in different life stages, wherein each initial scheme is an individual of a population.
Step S303: and respectively calculating the first and second life stage gains and the battery service life of the energy storage battery system in each initial scheme according to the energy storage battery gain functions (7) - (15), the system operation constraint conditions (18) - (30) and the battery service life constraints (31) - (34), calculating a target value according to the formulas (16) - (17), and substituting all individual results in the population into the fitness function to obtain the fitness value of each individual, namely the total gain of the energy storage battery system.
Step S304: and (3) carrying out variation and cross operation on the population individuals according to the fitness to obtain a new generation population, and carrying out migration operation according to a fixed algebraic interval, wherein the iteration number k is k + 1.
Step S305: judging whether a termination condition is reached or the number reaches the maximum iteration number, if so, terminating the evolution, and outputting the obtained optimal individual as an optimal solution; if not, the process returns to step S303.
Taking a specific application as an example, the optimization planning method is verified, a 33-node power distribution system shown in fig. 6 is adopted, the rated active power and reactive power levels of loads and feeder data are known, two wind farms are configured at No. 19 and No. 32, 4 and 3 Vestas V52-850kW wind turbines are respectively installed, and the cut-in speed, the cut-out speed and the rated rotating speed are respectively 4, 17 and 25 m/s. The price per unit capacity of the energy storage battery of the auxiliary service market is regulated to be $ 120/kilowatt/year, the penalty coefficient of uncompensated electric quantity is 20 $ 20/kilowatt, and system frequency data of spring, summer, autumn and winter seasons of a BMRS system in the UK are selected and converted into typical day data as shown in figure 7. Fig. 8 is a schematic diagram of a typical day in four seasons of spring, summer, autumn and winter in the present embodiment, in which (a) is an hour wind speed, (b) is a load and (c) is a power price. The maximum iteration number of the differential evolution algorithm solver is set to be 100, and the condition for stopping the calculation is that 10 iterations are reached within 100 iterations-4And (4) precision.
In this embodiment, in order to verify the effectiveness of the proposed energy storage battery full-life cycle optimization planning method, with the goal that the total profit of the investor is the maximum, the following 3 planning schemes are set for comparative analysis:
1) scheme 1: providing only load shifting services;
2) scheme 2: providing only frequency adjustment services;
3) scheme 3: provided is a multi-segment service optimization planning method.
In addition, in this example it is specified that the energy storage battery system is fully charged and discharged only once a day during the second life phase (2: 00-7:00 am charging, 17:00-22:00 pm discharging), and table 3 lists the detailed planning scheme and cost comparison.
TABLE 3 planning solutions and Total cost comparisons
In scheme I, the service life of the energy storage battery is the longest, namely 12.54 years, but the return on investment is the lowest, and the economic feasibility is not realized. In scheme II, the aging of the battery is accelerated by high-frequency charge and discharge, the expected service life of the energy storage battery is shortest and is only 4.35 years, but the return on investment is highest, and although the energy storage battery can obtain higher benefits in a short period by participating in the frequency regulation service, the aging process of the battery is accelerated by frequent charge/discharge. In case III, the service life of a single energy storage cell is slightly shorter than case I, but significantly longer than case II, and the overall yield is increased by nearly 53% compared to case I, II. In summary, the planning method can balance the maximization of the income of investors and the guarantee of the effective service life of the energy storage battery.
In this embodiment, in order to evaluate the influence of different capacity allocation schemes on the economic benefit and the service life of the energy storage battery, different capacity allocation ratios are considered as constraint conditions, comparative analysis is performed, and the calculation result is shown in table 4.
TABLE 4 comparison of planning costs for different capacity allocation fractions
Alpha represents the capacity distribution proportion of the energy storage battery in the first life stage and the second life stage, the larger the value of alpha is, the more the capacity of the energy storage battery is distributed to the first life stage, and the smaller the residual capacity which can be used in the second life stage is. From the calculation results we can find that: 1) in most cases, applying energy storage batteries sequentially to multiple power markets may yield higher profits than providing a single service; 2) the energy storage battery is firstly applied to a frequency adjustment market, and when the residual capacity is attenuated to 78% of the original rated capacity, the energy storage battery is applied to a real-time energy market again, so that the maximum benefit can be obtained; 3) the energy storage battery after being used excessively is not suitable for the real-time energy market, and when the residual capacity of the energy storage battery is reduced to 60%, the energy storage battery needs to be scrapped.
Through the experiment, the embodiment can find that the energy storage battery full life cycle planning model can be effectively solved by utilizing the differential evolution algorithm, the obtained energy storage battery optimization planning scheme can maximize the economic benefit of an investor and ensure the effective service life of the battery, and meanwhile, the economic loss and the environmental influence caused by battery recycling are effectively reduced.
The foregoing is considered as illustrative of the preferred embodiments of the invention and is not to be construed as limiting the invention in any way. Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical spirit of the present invention should fall within the protection scope of the technical scheme of the present invention, unless the technical spirit of the present invention departs from the content of the technical scheme of the present invention.
Claims (7)
1. A full life cycle optimization planning method considering the multiple segmentation services of an energy storage battery is characterized by comprising the following steps:
step S1: constructing a full life cycle model of the energy storage battery system;
modeling the life cycle of the energy storage battery, and dividing the energy storage battery into two life stages: in the first life stage, the energy storage battery system is applied to an auxiliary service market to participate in system frequency adjustment; and in the second life stage, the energy storage battery is switched into a real-time energy market to assist in realizing load transfer service. Constructing a profit model of the energy storage battery system in different power markets;
step S2: constructing an energy storage battery system full life cycle multi-segment service optimization planning model;
the method comprises the steps of taking the maximum total income of an energy storage battery system as a target, taking the battery capacity distribution proportion of different life stages as an optimization variable, calculating the service life and the total cost/benefit of energy storage batteries of different life stages according to a constructed energy storage battery life cycle model and constraint conditions, and constructing a full life cycle multi-segment service optimization planning model of the energy storage battery system;
step S3: forming a planning scheme of the energy storage battery system;
and solving the full-life-cycle multi-segment service optimization planning model of the energy storage battery system by using a differential evolution algorithm to obtain a planning scheme of the energy storage battery system.
2. The method for optimizing and planning the full life cycle of the multi-segment service of the energy storage battery according to claim 1, wherein in step S2, the service life of the energy storage battery system in each life stage is calculated according to the following formula:
in the formula,
equivalent conversion service life of the energy storage battery in the first and second life stages of the energy storage battery system i, gammaR、Γ′RRated throughput of battery in first and second life stages, gammaeff、Γ′effThe accumulated effective ampere-hours of the battery in the first and second life stages respectively.
3. The method for full-life-cycle optimal planning considering the multi-segment service of the energy storage battery as claimed in claim 1, wherein in step S1, the profit models of the energy storage battery system in different power markets are calculated according to the following formula:
in the formula, Profit1For the first life-stage benefit of the energy storage battery system, CreserveTo assist the price per unit of capacity of the service market,
for the first life stage rated capacity, Profit, of the energy storage battery system i2For the second life stage benefit, λ, of the energy storage battery systempeak、λoffpeakRespectively represent the peak and the valley electricity prices,the charge/discharge amount of the energy storage battery system i in the h period in the zeta th scene is shown.
4. The method for full-life-cycle optimal planning considering the multi-segment service of the energy storage battery as claimed in claim 1, wherein the total revenue function of the system is calculated according to the following formula:
F＝max(F1+F2-Cinv)
wherein F is the total yield of the energy storage battery system in the whole life cycle, F1Earnings for a first life stage of the energy storage battery system; f2For the total gain of the second life stage of the energy storage battery system, CinvIs the total investment cost. Copm、Cage、CpenaltyRespectively the operation and maintenance cost, the life loss cost and the penalty cost of uncompensated electric quantity in the first life stage. C'opm、C′ageRespectively the operation maintenance cost, the life loss cost, C of the second life stageED is divided intoThe cost per unit capacity and the rate of the current application of the energy storage battery system are different. ξ and ζ are respectively the scene indexes of the real-time energy market and the auxiliary service market, and S, S' is the total scene number of the real-time energy market and the auxiliary service market.
5. The method according to claim 1, wherein the full-life-cycle optimization planning model comprises a total investment constraint, an energy storage battery system first life-stage operation constraint, an energy storage battery system second life-stage operation constraint, and an energy storage battery system life constraint.
6. The method for optimizing and planning the full life cycle of the multi-segment service of the energy storage battery according to claim 1, wherein the method for planning the energy storage battery system in step S3 is an energy storage battery system scheme, and includes a location and volume determination scheme of the energy storage battery and a battery capacity distribution ratio of the energy storage battery at different life stages, and one or more planning resources are adjusted to improve the total profit of investors and ensure the service life of the energy storage battery.
7. The method for full-life-cycle optimization planning considering energy storage battery multi-segment service according to any one of claims 3 to 6, wherein the process of solving the model based on the differential evolution algorithm in step S3 includes:
step S301: determining algorithm control parameters and a fitness function according to the energy storage battery system planning model;
step S302: initializing a power system, a power grid bus, load, renewable energy output, system frequency and market electricity price data, and establishing an energy storage battery system full life cycle optimization planning model with the maximum total profit of the energy storage battery system as a target;
step S303: and obtaining the scheme of the energy storage battery full life cycle optimization planning model through iterative calculation of the differential evolution algorithm model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910870910.8A CN110633854A (en) | 2019-09-16 | 2019-09-16 | Full life cycle optimization planning method considering energy storage battery multiple segmented services |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910870910.8A CN110633854A (en) | 2019-09-16 | 2019-09-16 | Full life cycle optimization planning method considering energy storage battery multiple segmented services |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110633854A true CN110633854A (en) | 2019-12-31 |
Family
ID=68972650
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910870910.8A Pending CN110633854A (en) | 2019-09-16 | 2019-09-16 | Full life cycle optimization planning method considering energy storage battery multiple segmented services |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110633854A (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112036735A (en) * | 2020-08-28 | 2020-12-04 | 北方工业大学 | Energy storage capacity planning method and system for energy storage system of photovoltaic power station |
| CN112688318A (en) * | 2020-12-21 | 2021-04-20 | 国网浙江省电力有限公司电力科学研究院 | Method for constructing new energy station internal frequency modulation instruction decomposition model |
| CN112769150A (en) * | 2021-02-04 | 2021-05-07 | 湖北省电力勘测设计院有限公司 | Power distribution method, system and medium of hybrid energy storage device for frequency modulation |
| CN113361781A (en) * | 2021-06-09 | 2021-09-07 | 广东电网有限责任公司 | Power grid investment scale optimization method, system, equipment and storage medium |
| CN113554272A (en) * | 2021-06-18 | 2021-10-26 | 中国电建集团华东勘测设计研究院有限公司 | Full life cycle supplement method for electrochemical energy storage power station |
| CN113610380A (en) * | 2021-08-02 | 2021-11-05 | 上海电气集团股份有限公司 | Multi-energy complementary energy planning method |
| CN114118575A (en) * | 2021-11-25 | 2022-03-01 | 西安西电电力系统有限公司 | Method and device for determining depth of discharge of electrochemical energy storage system and computer equipment |
| CN114912722A (en) * | 2022-07-18 | 2022-08-16 | 广东电网有限责任公司佛山供电局 | Operation arrangement method and device of battery energy storage device and electronic equipment |
| CN115423388A (en) * | 2022-11-04 | 2022-12-02 | 国网浙江省电力有限公司金华供电公司 | Dynamic capacity distribution method for independent energy storage station at power grid side |
| CN115545588A (en) * | 2022-12-06 | 2022-12-30 | 北京大学 | Fixed energy storage system site selection determining method and device, electronic equipment and storage medium |
| CN117526387A (en) * | 2023-12-29 | 2024-02-06 | 太原理工大学 | Optimal power distribution network energy storage locating and sizing method considering energy storage capacity attenuation |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20130035758A (en) * | 2011-09-30 | 2013-04-09 | 한국전력공사 | Device and method for managing power storage |
| CN104953674A (en) * | 2015-07-10 | 2015-09-30 | 北京交通大学 | Charge-discharge control system and method capable of prolonging service life of energy-storage battery |
| US20160042369A1 (en) * | 2014-08-08 | 2016-02-11 | Nec Laboratories America, Inc. | Dynamic co-optimization management for grid scale energy storage system (gsess) market participation |
| CN109755946A (en) * | 2017-11-07 | 2019-05-14 | 通力股份公司 | Energy storage management system |
-
2019
- 2019-09-16 CN CN201910870910.8A patent/CN110633854A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20130035758A (en) * | 2011-09-30 | 2013-04-09 | 한국전력공사 | Device and method for managing power storage |
| US20160042369A1 (en) * | 2014-08-08 | 2016-02-11 | Nec Laboratories America, Inc. | Dynamic co-optimization management for grid scale energy storage system (gsess) market participation |
| CN104953674A (en) * | 2015-07-10 | 2015-09-30 | 北京交通大学 | Charge-discharge control system and method capable of prolonging service life of energy-storage battery |
| CN109755946A (en) * | 2017-11-07 | 2019-05-14 | 通力股份公司 | Energy storage management system |
Non-Patent Citations (3)
| Title |
|---|
| 向育鹏等: "基于全寿命周期成本的配电网蓄电池储能系统的优化配置", 《电网技术》 * |
| 周洁等: "计及循环寿命的用户侧储能调频经济性研究", 《重庆理工大学学报（自然科学）》 * |
| 朱寰等: "储能多功能模式运行优化研究", 《供用电》 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112036735A (en) * | 2020-08-28 | 2020-12-04 | 北方工业大学 | Energy storage capacity planning method and system for energy storage system of photovoltaic power station |
| CN112036735B (en) * | 2020-08-28 | 2023-11-03 | 北方工业大学 | Energy storage capacity planning method and system for energy storage system of photovoltaic power station |
| CN112688318A (en) * | 2020-12-21 | 2021-04-20 | 国网浙江省电力有限公司电力科学研究院 | Method for constructing new energy station internal frequency modulation instruction decomposition model |
| CN112769150A (en) * | 2021-02-04 | 2021-05-07 | 湖北省电力勘测设计院有限公司 | Power distribution method, system and medium of hybrid energy storage device for frequency modulation |
| CN112769150B (en) * | 2021-02-04 | 2022-07-08 | 湖北省电力勘测设计院有限公司 | Power distribution method, system and medium of hybrid energy storage device for frequency modulation |
| CN113361781A (en) * | 2021-06-09 | 2021-09-07 | 广东电网有限责任公司 | Power grid investment scale optimization method, system, equipment and storage medium |
| CN113554272B (en) * | 2021-06-18 | 2023-02-14 | 中国电建集团华东勘测设计研究院有限公司 | Full life cycle supplement method for electrochemical energy storage power station |
| CN113554272A (en) * | 2021-06-18 | 2021-10-26 | 中国电建集团华东勘测设计研究院有限公司 | Full life cycle supplement method for electrochemical energy storage power station |
| CN113610380A (en) * | 2021-08-02 | 2021-11-05 | 上海电气集团股份有限公司 | Multi-energy complementary energy planning method |
| CN114118575A (en) * | 2021-11-25 | 2022-03-01 | 西安西电电力系统有限公司 | Method and device for determining depth of discharge of electrochemical energy storage system and computer equipment |
| CN114912722A (en) * | 2022-07-18 | 2022-08-16 | 广东电网有限责任公司佛山供电局 | Operation arrangement method and device of battery energy storage device and electronic equipment |
| CN115423388A (en) * | 2022-11-04 | 2022-12-02 | 国网浙江省电力有限公司金华供电公司 | Dynamic capacity distribution method for independent energy storage station at power grid side |
| CN115545588A (en) * | 2022-12-06 | 2022-12-30 | 北京大学 | Fixed energy storage system site selection determining method and device, electronic equipment and storage medium |
| CN117526387A (en) * | 2023-12-29 | 2024-02-06 | 太原理工大学 | Optimal power distribution network energy storage locating and sizing method considering energy storage capacity attenuation |
| CN117526387B (en) * | 2023-12-29 | 2024-03-22 | 太原理工大学 | Optimal power distribution network energy storage locating and sizing method considering energy storage capacity attenuation |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110633854A (en) | Full life cycle optimization planning method considering energy storage battery multiple segmented services | |
| CN108960510B (en) | Virtual power plant optimization trading strategy device based on two-stage random planning | |
| CN108667052B (en) | Multi-type energy storage system planning configuration method and system for virtual power plant optimized operation | |
| CN109325608B (en) | Distributed power supply optimal configuration method considering energy storage and considering photovoltaic randomness | |
| CN110119886B (en) | Active distribution network dynamic planning method | |
| CN107464010B (en) | Virtual power plant capacity optimal configuration method | |
| CN110310173B (en) | Electric quantity distribution method for renewable energy to participate in medium-and-long-term electric power transaction | |
| CN111697578B (en) | Multi-target energy storage-containing regional power grid operation control method | |
| CN105846423A (en) | Method for photovoltaic microgrid energy storage multi-target capacity configuration by taking demand response into consideration | |
| CN109146320B (en) | Virtual power plant optimal scheduling method considering power distribution network safety | |
| CN105005872A (en) | Capacity configuration method for peak-load-shifting energy storage system | |
| CN111244985B (en) | Distributed energy storage sequence optimization configuration method based on node comprehensive sensitivity coefficient | |
| CN110245794B (en) | Flexibility-considered double-layer optimization method for central fire storage capacity in multi-energy convergence | |
| CN114529100A (en) | Energy storage optimal configuration method and system for wind and light absorption of regional power grid | |
| CN115510677A (en) | Wind power plant generating capacity evaluation method and system | |
| CN110021932B (en) | Bilateral participation peak shaving auxiliary service capacity selection and economic model construction method | |
| CN111047093A (en) | Optimal operation configuration method for typical quick charging station of electric automobile | |
| CN114465226A (en) | Method for establishing multi-level standby acquisition joint optimization model of power system | |
| CN112510690B (en) | Optimal scheduling method and system considering wind-fire-storage combination and demand response reward and punishment | |
| CN117333058A (en) | Energy storage comprehensive evaluation method based on typical application scene | |
| CN117273490A (en) | Random planning method for multi-new-energy station co-construction shared energy storage | |
| CN112633675A (en) | Energy scheduling method, device and equipment and computer readable storage medium | |
| CN116979619A (en) | Micro-grid energy storage configuration method and device, computer equipment and storage medium | |
| CN117114281A (en) | Determination method of flexible resource multi-stage planning scheme | |
| CN114039351B (en) | Energy storage capacity configuration method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191231 |
|
| RJ01 | Rejection of invention patent application after publication |