CN112968450A - Energy storage system benefit evaluation method for energy storage participating in frequency modulation - Google Patents
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Abstract
The invention discloses a benefit evaluation method of an energy storage system for energy storage to participate in frequency modulation, which comprises the following steps: constructing a constraint condition for a battery in the energy storage system to participate in frequency modulation service; establishing an energy storage system benefit evaluation model taking energy storage benefit maximization as an optimization target according to constraint conditions; obtaining a typical daily scene of each year according to the historical market price and frequency modulation signal data, solving a benefit evaluation model according to the typical daily scene, and obtaining an optimal energy storage capacity allocation strategy corresponding to the year; and evaluating the energy storage benefit value of the energy storage system according to the optimal energy storage capacity allocation strategy. The energy storage system benefit evaluation model established by the invention not only considers a frequency modulation profit mechanism model, but also considers the internal influence of battery attenuation on energy storage benefit by introducing an energy storage attenuation penalty term, so that the benefit evaluation model is more comprehensive and accurate; a large amount of frequency modulation signals and market price data are subjected to typical daily scene extraction through a clustering method, so that the data input amount is reduced, and the solving efficiency of the evaluation model is improved.
Description
Technical Field
The invention relates to the technical field of energy storage systems, in particular to a benefit evaluation method of an energy storage system for energy storage to participate in frequency modulation.
Background
With the rapid development of renewable energy sources with randomness and the like and energy storage technology, the application of energy storage in the future energy Internet is very wide. However, the development of large-scale energy storage systems is hampered by the relatively high investment costs and uncertainty of the profitability. Therefore, it is necessary to research an economic benefit evaluation method for more comprehensively and accurately evaluating energy storage in the energy market.
However, the currently available research still has deficiencies:
(1) the benefits and losses in the energy storage system are not considered comprehensively;
the current research introduces related research of an energy storage benefit model considering auxiliary services compared with the less battery cycle life attenuation. In order to more comprehensively and accurately evaluate the cost benefit of the energy storage system, the benefit compensation of the frequency modulation market considers the capacity of the frequency modulation market, and also considers the frequency modulation effect and the actual frequency modulation mileage; in addition to considering the impact of constraints on the maximum tunable capacity of the battery, the energy storage degradation should also consider the potential loss of benefit due to degradation in life and even failure.
(2) A data processing mode for long-term planning;
in the existing research, the service life is optimized and calculated only through the operation of a battery in a typical day, so that the evaluation result is possibly not accurate enough, and massive data which can be as long as many years are directly used as benefit evaluation model input, so that the model solving time is very long.
Disclosure of Invention
The invention aims to provide an energy storage system benefit evaluation method for energy storage to participate in frequency modulation, so as to solve the technical problems that the benefits and losses in an energy storage system are not fully considered and the solving efficiency is low in the technology.
The purpose of the invention can be realized by the following technical scheme:
a benefit evaluation method for an energy storage system with energy storage participating in frequency modulation comprises the following steps:
constructing a constraint condition for a battery in the energy storage system to participate in frequency modulation service;
establishing an energy storage system benefit evaluation model taking energy storage benefit maximization as an optimization target according to the constraint conditions;
obtaining typical daily scenes of each year according to historical market prices and frequency modulation signal data, and solving the benefit evaluation model according to the typical daily scenes to obtain an optimal energy storage capacity allocation strategy corresponding to each year;
and evaluating the energy storage benefit value of the energy storage system according to the optimal energy storage capacity allocation strategy.
Optionally, the evaluating the energy storage benefit value of the energy storage system according to the optimal energy storage capacity allocation strategy includes:
calculating to obtain the maximum capacity of the battery in the next year according to the optimal energy storage capacity distribution strategy;
and obtaining the expected energy storage benefit and the battery health state of the corresponding year according to the maximum capacity of the battery, wherein when the battery health state is lower than a preset threshold value, the battery fails, and the sum of the expected energy storage benefits of all the years before the battery fails is used as the energy storage benefit value of the energy storage system.
Optionally, the optimization goal of the benefit evaluation model is:
wherein 1 isTLine vector, A ', representing all components as 1'tTo account for the gain matrix of the frequency modulation gain, t is hours, τ is the estimated battery life, xtFor decision vectors, CostinvesFor the investment cost of the energy storage system, Meg is the attenuation penalty factor of the battery, XtIs the amount of cycle decay of the cell.
Optionally, energy storage system investment CostinvesThe method comprises the following steps:
Costinvest＝CeEmax+CpPmax；
wherein, Ce、CpCost factor for investment in energy storage systems, Emax、PmaxThe maximum capacity and the maximum power of the battery are respectively.
Optionally, the decision vector xtThe method comprises the following steps:
wherein, ctIs the amount of charge of the battery, dtIs the amount of discharge of the battery,
in order to devote capacity to the frequency up modulation service,
capacity for putting down frequency modulation service, StIs the state variable of the battery.
Optionally, the frequency modulation benefit includes:
frequency modulated capacity gain RcapAnd frequency-modulated mileage yield Rperf(ii) a Wherein the frequency modulation capacity gain RcapIn relation to the projected FM capacity, the FM mileage yield RperfRelated to frequency modulated mileage.
Optionally, frequency modulated capacity gain RcapComprises the following steps:
Rcap＝pcaprtScore；
wherein p iscapRevenue unit price, r, for providing FM capacity for FM markettAnd Score is the frequency modulation capacity input within t hours and is the index of the frequency modulation performance.
Optionally, frequency modulated mileage revenue RperfComprises the following steps:
Rperf＝pperfMScore；
wherein p isperfThe price of the income unit of the actual frequency modulation effect is, and M is the frequency modulation mileage.
Optionally, a benefit matrix A 'taking into account frequency modulation benefits'tThe method comprises the following steps:
wherein, LMPtFor the revenue trading 1MWh of energy in the energy market at time t,
and
representing the ratio of the amount of real-time up-down frequency modulation service to the capacity put into the frequency modulation market,
and
respectively the ratio of the up-and-down frequency-modulation mileage to the inputted frequency-modulation capacity, copIn order to obtain the operation and maintenance cost coefficient,
and
respectively, frequency modulation capacity gains of the upper frequency modulation and the lower frequency modulation.
Optionally, deriving a typical daily scene from historical market prices and fm signal data comprises:
acquiring annual historical market price and frequency modulation signal data, and clustering by using a K-means clustering algorithm to obtain price curves and frequency modulation signals of a plurality of typical days.
The invention provides a benefit evaluation method of an energy storage system for energy storage to participate in frequency modulation, which comprises the following steps: constructing a constraint condition for a battery in the energy storage system to participate in frequency modulation service; establishing an energy storage system benefit evaluation model taking energy storage benefit maximization as an optimization target according to the constraint conditions; obtaining typical daily scenes of each year according to historical market prices and frequency modulation signal data, and solving the benefit evaluation model according to the typical daily scenes to obtain an optimal energy storage capacity allocation strategy corresponding to each year; and evaluating the energy storage benefit value of the energy storage system according to the optimal energy storage capacity allocation strategy.
Based on the technical scheme, the invention has the beneficial effects that: the energy storage system benefit evaluation model established by the invention jointly considers the battery attenuation and the frequency modulation benefit in the energy storage system, not only considers the frequency modulation benefit mechanism model, but also considers the internal influence of the battery attenuation on the energy storage benefit by introducing the energy storage attenuation penalty term, so that the energy storage system benefit evaluation model is more comprehensive and accurate; the typical scene is obtained through clustering, so that the efficiency is improved, and a large amount of frequency modulation signals and market price data are subjected to typical daily scene extraction through a k-means clustering method, so that the data input amount is reduced to a great extent, and the solving efficiency of the evaluation model is greatly improved.
Drawings
FIG. 1 is a schematic flow chart of a method for evaluating benefits of an energy storage system for energy storage and frequency modulation according to the present invention;
FIG. 2 is a schematic diagram of an embodiment of a method for evaluating benefits of an energy storage system for participating in frequency modulation;
FIG. 3 is a schematic diagram of frequency-modulated mileage of an energy storage system benefit evaluation method for energy storage participating in frequency modulation according to the present invention;
FIG. 4 is a flowchart illustrating an external attenuation solution of the method for evaluating the benefits of an energy storage system in which energy storage participates in frequency modulation according to the present invention;
fig. 5 is a flowchart of an internal attenuation algorithm of the energy storage system benefit evaluation method for energy storage participating in frequency modulation according to the present invention.
Detailed Description
The embodiment of the invention provides an energy storage system benefit evaluation method for energy storage to participate in frequency modulation, and aims to solve the technical problems that the benefits and losses in an energy storage system are not fully considered and the solving efficiency is low in the technology.
To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present invention are shown in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
At present, China is actively promoting market reformation of electric power trading, and has practical significance for planning and cost benefit evaluation of energy storage construction by taking main benefits of third-party energy storage investors as targets in consideration of energy storage as income modes such as frequency modulation standby and the like under the background of an energy market. And the combination of a large amount of historical data in the model makes the cost-benefit assessment more referential.
Past studies have generally not considered the additional losses that result from the effects of charge and discharge behavior on the decay in battery cycle life. Some papers have studied the relationship between battery life and operating cycle parameters, have performed energy storage planning and operating optimization considering life decay, have studied energy storage planning in energy market and auxiliary service market, but there are few related studies that battery cycle life introduces an energy market bidding model considering auxiliary service, and decay models are mostly applied to other optimization occasions but not suitable for them. Except for a specific cycle life model, the rain flow counting method is generally used for obtaining cycle parameters and calculating the battery life in different application occasions, but the method is mostly used for static evaluation under a fixed operation strategy, and dynamic strategy optimization is inconvenient to use for commercial optimization solution. Some scholars do some work in conjunction with ancillary services and life decay, but are more focused on battery control in the service than how much service should be provided at once. In consideration of research aspects of an energy storage system combining fast frequency modulation auxiliary service and optimal bidding strategy and benefit evaluation of battery life in an energy market, the prior art simplifies battery life calculation through a decomposition method, establishes a simple nonlinear battery life attenuation model, can avoid a rain flow counting method and introduce an auxiliary service optimization problem, and meanwhile, carries out linearization mathematical simplification on a nonlinear and complex external attenuation model based on the rain flow counting method through a data driving method based on output characteristics such as SOC (system on chip) and the like, and can obtain a better simplified approximate result. And an objective function of an external attenuation model is introduced into the internal attenuation planning through an attenuation factor, and as a result, value recovery can be realized through prolonging the service life.
With the adoption of the relative law promulgated by the FERC, the benefit compensation of the frequency modulation market also considers the frequency modulation effect and the actual frequency modulation mileage besides the capacity put into the frequency modulation market, the PJM, the CASIO and the like in the United states all introduce corresponding mechanisms as market learning references, China also starts to research on pricing mechanisms, but the research combining the pricing mechanisms with energy storage benefit evaluation is less.
Through the analysis, how to comprehensively consider the influence brought by the battery attenuation when the energy storage system is subjected to combined optimization is known, so that better benefit and more accurate evaluation result are obtained, and the method has important significance for planning the energy storage system.
Referring to fig. 1, an embodiment of the present invention provides a benefit evaluation method for an energy storage system that participates in frequency modulation, including:
constructing a constraint condition for a battery in the energy storage system to participate in frequency modulation service;
establishing an energy storage system benefit evaluation model taking energy storage benefit maximization as an optimization target according to the constraint conditions;
obtaining typical daily scenes of each year according to historical market prices and frequency modulation signal data, and solving the benefit evaluation model according to the typical daily scenes to obtain an optimal energy storage capacity allocation strategy corresponding to each year;
and evaluating the energy storage benefit value of the energy storage system according to the optimal energy storage capacity allocation strategy.
Referring to fig. 2, in the embodiment of the present invention, an energy storage benefit evaluation model in the power market considering the auxiliary service is adopted, a typical scene is obtained by k-means clustering Based on market price and frequency modulation signal data pre-measured by a large amount of historical data and is used as an input, and a lithium ion battery attenuation model Based on semi-empirical analysis and a frequency modulation benefit mechanism (PBR) Based on an actual frequency modulation effect are combined to perform optimization, so that an optimal energy storage capacity allocation strategy can be obtained, the benefit of an energy storage system is evaluated, and the calculation efficiency is improved.
(1) Battery decay model
The embodiment of the invention adopts a semi-empirical analysis attenuation model of a lithium ion battery (see B.xu, A.Oudalov, A.Ulblig, G.Andersson, and D.Kirschen, "Modeling of lithium-ion battery degradation for cell life assessment," IEEE trans.Smart Grid, vol.9, No.2, pp.1131-1140, Mar.2018), is a model combining Arrhenius relationship (arrhenius relationship), solid electrolyte phase interface film formation (SEI film formation) theory and experimental observation data, and can be suitable for different types of lithium ion batteries and different energy storage operation scenes.
For each cycle of discharge of the battery (indexed by i), the cyclic decay stress factor is calculated as equation (1) for calculating the subsequent battery decay function:
DOD in the above formulai、SOCi、CRiThe parameters are circulation parameters which respectively correspond to the depth, the charge state and the current multiplying power of the ith circulation discharge。
The above cycle parameters in common in each cycle can be determined from the SOC curves obtained for a given cell behaviour by means of the rain flow counting method RCA. The parameters k and the like in the above formula can be obtained by fitting test data given by a battery manufacturer, and the reference values are shown in table 1.
TABLE 1 lithium ion Battery decay function parameters
The decay function of the cell is derived from the decay stress factor over each cycle as follows:
in equation (2), the first part of the equation to the right corresponds to the cyclic decay, and the latter part corresponds to the time-dependent natural life decay only of the battery, LnIs the total number of cycles in the nth year.
The maximum battery capacity in the next year (n +1 th year) is calculated as follows:
in the formula (3), the first exponential term on the right side of the equation corresponds to the fast decay caused by the establishment of the solid electrolyte interface, the second exponential term represents the slow decay caused by the ion loss, and the parameter r1And r2In the table 1, r1＝5.75×10-2，r2＝121，degiAs a function of the decay of the battery at year i,
is the original maximum capacity of the battery.
SOH of battery in year i (i is more than or equal to 1 and less than or equal to n)iDefined as the ratio of the maximum capacity of the battery in the ith year to the original maximum capacity of the battery:
when the battery capacity decays to SOHiWhen the battery life is 0.75, the battery is considered to be dead, and the battery life can be obtained.
(2) Benefit evaluation model
Linear programming LP (linear programming) (see, e.g., fog B, Yu n. improved magnetic storage estimation through estimation reduction. ieee Trans Smart Grid,2017) is used to evaluate the benefits of energy storage systems in frequency modulation and energy markets, where LP is composed of an objective function represented by equation (9) and a constraint condition represented by equation (12).
The projected range of the LP is the estimated battery life, denoted as τ, which is the highest possible estimate of the true life of the battery, which can then be discarded after a portion beyond the actual life cycle.
The available battery capacity in LP on an hour scale, τ hours, is divided into a set of profitable behaviors in different markets, namely the charge ctDischarge quantity dtCapacity to put into FM-up service
Capacity for putting down frequency modulation service
These values are all non-negative. Node marginal price LMPtTo trade a profit for 1MWh of energy in the energy market at time t,
and (4) providing the benefit of putting 1MWh into up and down frequency modulation service for the battery at the time t.
The gains in the FM market are
The income of the energy market comprises two parts, namely a charging and discharging LM in which the stored energy is directly put into the energy market within hoursPt(dt-ct) The second part is to obtain extra income in the energy market for the frequency modulation service
It is to be noted that u/d represents u or d, and, for example,
to represent
Or
Similarly.
Wherein the content of the first and second substances,
and
representing the ratio of the amount of up and down frequency modulation performed in real time to the capacity put into the frequency modulation market, calculated as the average of the frequency modulated signal, i.e. the frequency modulated signal
Wherein f ist,kRepresenting a frequency modulated signal. Regulating deviceThe frequency signal is [ -1,1 [ ]]The value within the interval, when it is regular, is a frequency up-regulation signal, the complex value represents a down-regulation signal, i.e.:
signal 2s in the market is updated once, so k is 1,2, 3.. 1800 in hours.
The operation and maintenance cost is related to the battery behavior and is written as
Corresponding to the negative revenue portions, c, represented by the third row of the revenue matrix A in equation (11)opIs the operation and maintenance cost coefficient.
The energy storage system investment cost is related to the maximum capacity and power of the battery and can be recorded as:
Costinvest＝CeEmax+CpPmax (8)
Ce、Cprepresenting the energy storage system investment cost factor, Emax、PmaxCorresponding to the maximum capacity and maximum power of the battery, respectively.
Therefore, the embodiment can establish an optimization model with the maximum benefit as the target, and the objective function of the optimization model is as follows:
wherein x istIs a decision vector expressed asBattery decision-making behavior plus battery state variable St，StRepresents the state of charge (SoC) of the battery at the beginning of hour t; c. CtIs the amount of charge of the battery, dtIs the amount of discharge of the battery,
in order to devote capacity to the frequency up modulation service,
to the capacity devoted to the down-modulation service.
AtIs a matrix of the revenues,
the constraint condition of the benefit evaluation model is shown as the formula (12):
the constraint condition has 9 expressions which are respectively marked as constraint 1 to constraint 9; constraint 1 is the constraint of the battery state of charge SOC, γ in the first term on the right of the equation in constraint 1 is the self-discharge rate of the battery, the second term is the energy change caused by the charging and discharging actions of the battery, and the third term represents the resistance loss varying with the total output power. The resistance loss ρ is related to the cycle efficiency κ of the battery as follows:
constraints 2,3,4 represent limits on the capacity of the battery; emaxIs the maximum state of charge of the battery, the capacity constraint ensures that physical limitations are not violated even when the capacity is fully put into the fm market.
Constraints 5, 6, 7, 8 represent the output power limit of the battery; pmaxIs the maximum power output of the battery.
Constraint condition 9 indicates charge amount ctDischarge quantity dtCapacity to put into FM-up service
Capacity for putting down frequency modulation service
These values are all non-negative.
Under the PBR mechanism (frequency modulation benefit mechanism based on frequency modulation effect), the benefit of the frequency modulation service is divided into two parts, the first part is the frequency modulation capacity benefit R related to the input frequency modulation capacitycapThe other part is frequency modulation performance/mileage profit R related to the frequency modulation mileageperfAs shown in formulas (14) to (16):
Rcap＝pcaprtScore (14)
Rperf＝pperfMScore (15)
Rreg＝Rcap+Rperf (16)
wherein p iscap，pperfThe unit price of the income for providing the frequency modulation capacity and the unit price of the income for providing the actual frequency modulation effect ($/MW) are put into the frequency modulation market. r istThe volume of the frequency modulation is the volume of the frequency modulation input within t hours, M is the frequency modulation mileage and represents the amount of the actually provided up-modulation and down-modulation output, and Score is a frequency modulation performance index and reflects the corresponding accuracy and rapidity of frequency modulation resources to frequency modulation signals. In the invention, the frequency modulation performance index can be taken as 1, and the frequency modulation service can be assumed to be capable of accurately keeping up with the frequency modulation signal.
The frequency-modulated mileage is defined as the actually provided frequency-modulated output, as shown by the sum of all dashed lines with double arrows in fig. 3, i.e. the sum of the absolute values of the differences of the frequency-modulated signals at adjacent times, where M is used in the following equationtAnd (4) showing.
Frequency-modulated mileage M in the tth hour according to the above definitiontThe calculation formula is as follows
Adding the above-mentioned R to the objective function of the model (expressed by equation (9))perfAnd partially obtaining an energy storage benefit evaluation model considering a PBR mechanism, wherein an objective function of the model is shown as a formula (20):
wherein the content of the first and second substances,
the other constraints are not changed.
Dividing the LP into N sections by taking T hours as a calculation period, optimizing each section in sequence to realize the attenuation of the battery, obtaining an SOC curve in the hour when each iteration is finished, obtaining a more accurate SOC curve with 2s as intervals according to a frequency modulation signal updated by 2s, obtaining a cycle parameter output by taking the SOC curve as an input value through a rain flow counting method, and finally calculating the battery attenuation of the battery on each section according to the battery attenuation model in the previous chapter. From this calculation, a new EmaxThe value is input into the constraint of the next segment. This will include the non-linear term EmaxThe planning of (2) is converted into piecewise linear optimization, and each segment can be optimized and calculated by using MATLAB software to call a Cplex optimization solver through Yalmip.
The algorithm runs in the above N iterations so that NT τ, since the cell attenuation calculation is outside the optimization results of each segment LP, this process is referred to as external attenuation in this embodiment. The solving flow is shown in fig. 4.
The solution period T for each segment represents a trade-off between the optimality and the accuracy of the attenuation calculation. Higher values of T will yield better decision variables, but lower values of T may yield more accurate EmaxThe value is obtained. Because EmaxThe change over time is relatively slow (3% per year), and when T exceeds 2 months, the output of the LP can converge. So in this context, T is chosen to be one year with an expected lifetime τ of 15 years (N15, T8760 hours).
Since the model relies on long-term predictions of market prices, which are however quite unstable in practice, the result of LP planning is a reference estimate of the actual benefit of the energy storage system.
(3) Benefit assessment model considering internal attenuation
In the prior art (fog B, Yu n. improved rain storage estimation through estimation reduction, ieee Trans Smart Grid,2017), it is proved that, according to output characteristics such as SOC and DOD, the original nonlinear complex attenuation function based on the rain flow counting method is heuristically simplified by mathematical methods such as luber integral reconstruction, and is changed into an approximate linear attenuation function directly corresponding to the battery behavior decision variable
Wherein the content of the first and second substances,
Xt＝bTxt (23)
wherein, a2＝1×10-5,pz＝0.4×10-6,a1＝2R×fDOD(2R),
Respectively the variance of the up and down frequency-modulated signals in hours,
therefore, the attenuation function deg skips the problem that the attenuation function is changed into a value directly calculated by decision variables (namely, the attenuation function is converted into data-to-data) by considering a physical mechanism, and the complicated calculation that the circulation parameters are calculated by a fixed battery behavior through a rain flow counting method and then substituted into a semi-analytical attenuation model is avoided. EmaxSince the deg part is greatly simplified, the model is significantly simplified, and the solving flow chart becomes as shown in fig. 5.
In this embodiment, by introducing a suitable battery attenuation penalty factor Meg, a linear attenuation term can be considered as an objective function of the energy storage benefit assessment LP model, as shown in equation (27):
wherein 1 isTLine vector, A ', representing all components as 1'tTo account for the gain matrix of the frequency modulation gain, t is hours, τ is the estimated battery life, xtFor decision vectors, CostinvesFor the investment cost of the energy storage system, Meg is the attenuation penalty factor of the battery, XtIs the amount of cycle decay of the cell.
The embodiment adds a penalty function to the objective function to penalize the battery attenuation too high in the optimization calculation of one year, so as to prolong the service life and realize the value recovery. Corresponding to the energy storage efficiency evaluation model considering only the external attenuation (Meg ≠ 0), the case after introducing Meg ≠ 0 is called the internal attenuation.
1 in the objective function shown in formula (27) represents a 3-dimensional column vector with all components being 1, and T at the upper right corner is a matrix transpose, and together, is a 1x3 matrix (3-dimensional row vector) with all values being 1; at is a 3x5 matrix; x is the number oftSetting as a 5x1 matrix; the objective function thus finally obtains the total energy storage benefit value of the energy storage system.
And the constraint condition after considering the internal attenuation is correspondingly increased by one item Xt，
Xt＝bTxt； (28)
It is worth noting that the introduction of the decay penalty factor Meg in this embodiment is actually equivalent to the introduction of a trade-off: when Meg is too large, the linear programming of each segment will sacrifice too much benefit for prolonging the total life span of the battery, and what is more, it will destroy the cyclic parameter characteristics for obtaining the linear decay function model deg; and if Meg is too small, the result of the new optimization scheme is close to the result of the original optimization scheme. For the sake of distinction, in the present embodiment, such attenuation is referred to as internal attenuation (i.e., Meg ≠ 0), and the original attenuation is referred to as external attenuation (i.e., Meg ≠ 0). Due to the cycle degradation of the battery being in the order of 10-6And the unit of the target benefit function should be on the order of 1$ so the Meg should be on the order of 106Book, bookExample after several tests, Meg 6X 10 was selected6。
(4) Data clustering
In one embodiment, a large number of historical market price and chirp data sets are obtained from the PJM official network, with market price chirps being updated every 2s and market prices being updated every hour.
In consideration of the calculation time, it is considered to adopt a clustering method to improve the efficiency. K-means clustering is a very simple, fast and typical method. The method comprises the steps that scene clustering is carried out every year through a K-means clustering method to obtain price curves and frequency modulation signals of 4 typical days, benefits of each year are represented by profits under corresponding typical day scenes s, Ps represent the probability of occurrence of the scenes s and are weight factors of each scene, probability Ps weighted averaging is carried out on each scene benefit to approximately represent the situation of one year, and therefore, the original data of 15 years is simplified into data of 60 (15 multiplied by 4) days to carry out optimization calculation. That is, the expression (9) becomes at the time of actual calculation
In the formula, S represents typical day scenes, and the number of S typical day scenes, and the number of clustering scenes is 4 in this embodiment, that is, S is 1,2,3, 4. PsThe index of the remaining band s represents the value corresponding to a typical scene, as is the probability of scene s occurring in one year.
In this embodiment, the actual life of the battery is estimated to be as high as possible to 15 years, and then the LP model is divided into 15 segments according to 1 year as one calculation cycle, so that each segment can be optimized and solved through MATLAB to obtain a decision vector xt(i.e. the optimal energy storage capacity allocation strategy corresponding to the year), so that the maximum capacity E of the battery in the next year can be calculatedmaxWill constrain E in the conditionmaxAnd updating and then carrying out optimization solution of the next section, and obtaining the expected energy storage benefit (objective function) value of each year after 15 sections are sequentially calculated. The state of health SOH of the battery can be regulated from EmaxIt is calculated that when the SOH of a year is below the threshold of 0.75Indicating a battery failure, after which the energy storage system actually no longer benefits, so the total energy storage benefit value is the sum of all energy storage benefits before the battery failure year.
The method for evaluating the benefit of the energy storage system with energy storage participating in frequency modulation, provided by the embodiment, overcomes the defect of battery attenuation in the energy storage system on the consideration of benefit evaluation; energy storage is taken into consideration in the energy storage benefit evaluation model as the benefit of frequency modulation calling, and not only is the frequency modulation capacity benefit taken into consideration, but also the frequency modulation mileage benefit is taken into consideration; a typical scene is obtained from a large amount of data by using a clustering method and is used as data input, so that the calculation speed can be greatly improved.
The energy storage system benefit evaluation method for energy storage to participate in frequency modulation provided by the embodiment makes up for the deficiencies of the existing research on the aspects of considering and processing methods for battery attenuation and frequency modulation benefits in an energy storage system and the calculation efficiency, and specifically includes the following two major aspects:
(1) in the established energy storage system benefit evaluation model, battery attenuation and frequency modulation benefit in the energy storage system are jointly considered; the frequency modulation gain mechanism model is considered, and the internal influence of the battery attenuation on the energy storage benefit is considered by introducing the energy storage attenuation penalty term, so that the energy storage system benefit evaluation model is more comprehensive and accurate;
(2) the typical scene is obtained through clustering, so that the efficiency is improved, and a large amount of frequency modulation signals and market price data are subjected to typical daily scene extraction through a k-means clustering method, so that the data input amount is reduced to a great extent, and the solving efficiency of the evaluation model is greatly improved.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A benefit evaluation method for an energy storage system with energy storage participating in frequency modulation is characterized by comprising the following steps:
constructing a constraint condition for a battery in the energy storage system to participate in frequency modulation service;
establishing an energy storage system benefit evaluation model taking energy storage benefit maximization as an optimization target according to the constraint conditions;
obtaining typical daily scenes of each year according to historical market prices and frequency modulation signal data, and solving the benefit evaluation model according to the typical daily scenes to obtain an optimal energy storage capacity allocation strategy corresponding to the year;
and evaluating the energy storage benefit value of the energy storage system according to the optimal energy storage capacity allocation strategy.
2. The energy storage system benefit evaluation method for energy storage participation in frequency modulation according to claim 1, wherein evaluating the energy storage benefit value of the energy storage system according to the optimal energy storage capacity allocation strategy comprises:
calculating to obtain the maximum capacity of the battery in the next year according to the optimal energy storage capacity distribution strategy;
and obtaining the expected energy storage benefit and the battery health state of the corresponding year according to the maximum capacity of the battery, wherein when the battery health state is lower than a preset threshold value, the battery fails, and the sum of the expected energy storage benefits of all the years before the battery fails is used as the energy storage benefit value of the energy storage system.
3. The energy storage system benefit evaluation method for energy storage participation in frequency modulation according to claim 2, wherein the optimization objective of the benefit evaluation model is as follows:
wherein 1 isTLine vector, A ', representing all components as 1'tTo account for the gain matrix of the frequency modulation gain, t is hours, τ is the estimated battery life, xtFor decision vectors, CostinvesFor the investment cost of the energy storage system, Meg is the attenuation penalty factor of the battery, XtIs the amount of cycle decay of the cell.
4. The method for evaluating the benefits of an energy storage system participating in frequency modulation according to claim 3, wherein the investment Cost of the energy storage systeminvesThe method comprises the following steps:
Costinvest＝CeEmax+CpPmax；
wherein, Ce、CpCost factor for investment in energy storage systems, Emax、PmaxThe maximum capacity and the maximum power of the battery are respectively.
5. The method according to claim 4, wherein the decision vector x is a decision vectortThe method comprises the following steps:
wherein, ctIs the amount of charge of the battery, dtIs the amount of discharge of the battery,
in order to devote capacity to the frequency up modulation service,
capacity for putting down frequency modulation service, StIn the shape of a batteryAnd (4) state variables.
6. The method of claim 5, wherein the frequency modulation benefit comprises:
frequency modulated capacity gain RcapAnd frequency-modulated mileage yield Rperf(ii) a Wherein the frequency modulation capacity gain RcapIn relation to the projected FM capacity, the FM mileage yield RperfRelated to frequency modulated mileage.
7. The method of claim 6, wherein the FM capacity gain R iscapComprises the following steps:
Rcap＝pcaprtScore；
wherein p iscapRevenue unit price, r, for providing FM capacity for FM markettAnd Score is the frequency modulation capacity input within t hours and is the index of the frequency modulation performance.
8. The method of claim 7, wherein the frequency modulation mileage profit R isperfComprises the following steps:
Rperf＝pperfMScore：
wherein p isperfThe price of the income unit of the actual frequency modulation effect is, and M is the frequency modulation mileage.
9. The method of claim 8, wherein a benefit matrix A 'of frequency modulation benefits is taken into account'tThe method comprises the following steps:
wherein, LMPtFor the revenue trading 1MWh of energy in the energy market at time t,
and
represents the ratio of the volume of up and down frequency modulation service at the time t (real time) to the volume of the frequency modulation market,
and
respectively the ratio of the up-and-down frequency-modulation mileage to the inputted frequency-modulation capacity, copIn order to obtain the operation and maintenance cost coefficient,
and
respectively, frequency modulation capacity gains of the upper frequency modulation and the lower frequency modulation.
10. The method of claim 9, wherein deriving a typical daily scenario from historical market prices and frequency modulated signal data comprises:
acquiring annual historical market price and frequency modulation signal data, and clustering by using a K-means clustering algorithm to obtain a plurality of typical target price curves and frequency modulation signals.
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