CN114493222A - Wind power plant energy storage power station multi-market participation strategy considering output prediction and price - Google Patents

Wind power plant energy storage power station multi-market participation strategy considering output prediction and price Download PDF

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CN114493222A
CN114493222A CN202210065291.7A CN202210065291A CN114493222A CN 114493222 A CN114493222 A CN 114493222A CN 202210065291 A CN202210065291 A CN 202210065291A CN 114493222 A CN114493222 A CN 114493222A
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energy storage
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wind
price
power
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齐放
王淼
郑灏
石家太
邓体喆
陈甜甜
程师
张帅
赵星
孙飒爽
颜宇飞
刘友波
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CGN Wind Energy Ltd
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    • G06Q10/06375—Prediction of business process outcome or impact based on a proposed change
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    • 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
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    • 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
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    • 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
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • 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
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    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract

The invention discloses a multi-market participation strategy of a wind power plant energy storage power station considering output prediction and price, and belongs to the technical field of power systems, the invention provides the multi-market participation strategy of the wind power plant energy storage power station considering double uncertainty of the output prediction and the price, expands an energy storage application scene to medium and long-term markets, day-ahead markets and real-time markets, establishes an economic model of the wind power plant energy storage system facing to various markets, constructs an optimized model by taking the maximum income of the wind power storage system participating in the multi-market as a target and the requirement of fan output and energy storage charge-discharge power satisfaction as constraints, optimizes the model based on a random opportunity constraint planning theory, converts uncertainty in the opportunity constraint into certainty, solves the uncertainty in an MATLAB environment by using CPLEX solver to obtain a wind power plant energy storage operation strategy meeting different market requirements, thereby improving the overall economic benefit of the operation of the wind power plant energy storage power station, the aim of maximizing the overall economic benefit of the wind power plant energy storage power station is achieved.

Description

Wind power plant energy storage power station multi-market participation strategy considering output prediction and price
Technical Field
The invention relates to the technical field of power systems, in particular to a multi-market participation strategy of a wind power plant energy storage power station in consideration of output prediction and price.
Background
The power supply structure of China will be deeply changed, and a novel power system taking clean energy as a main body can be constructed. The energy storage is used as an important technology for promoting the energy structure to accelerate the transformation, the flexible charging and discharging control capability can effectively relieve the uncertain fluctuation problem of wind power, the wind abandon phenomenon is reduced, and the wind power plant matched energy storage facility is enabled to become a new development trend. In order to further promote the collaborative development of clean energy power generation and energy storage, the country frequently develops the matching energy storage benefit policy of the wind power plant, most provinces configure energy storage encouraging attitudes for new energy power generation stations, and few provinces (such as Liaoning, Shandong and the like) consider preferentially constructing new energy power generation projects equipped with energy storage facilities or require that the new energy stations must be equipped with certain energy storage facilities, so that the country has a great incentive effect on the development of a wind power plant energy storage system. The arrangement of energy storage facilities in the wind power plant is a general phenomenon in the future, so that the research on the energy storage system of the wind power plant has great significance in the field of new energy power generation.
As the electric power market in China is still in the development stage, the medium-and-long-term market trading is mainly used at present, and the day-ahead and real-time electric power spot trading market is actively developed. However, after the wind power generation is connected to the power grid, the wind power output has randomness and volatility, the real-time electricity price also has volatility, and how to design a proper wind storage system operation mode in the power market to reduce the instability of wind power grid connection and improve the economic benefit of the wind power plant energy storage power station participating in multiple markets is still a key point and a difficult point existing at the present stage.
Disclosure of Invention
In view of the technical defects, the invention provides a multi-market participation strategy of the wind power plant energy storage power station, which considers the output prediction and the price.
In order to achieve the purpose, the technical scheme of the invention is as follows:
the wind power plant energy storage power station multi-market participation strategy considering output prediction and price comprises the following steps:
step 1: considering the randomness and the volatility of wind power, and establishing a fan output prediction error probability distribution function based on a multi-scenario method;
step 2: considering uncertainty of the electric power spot price, describing the electric power spot price by adopting a first-order autoregressive model, and simultaneously establishing an operation income model and an operation constraint condition of the wind farm energy storage power station participating in multi-market;
and step 3: based on a random opportunity constraint planning theory, establishing a target revenue function and a target constraint function according to the real-time spot price of electric power, an operation revenue model and operation constraint conditions of a wind field energy storage power station participating in multi-market;
and 4, step 4: and changing the target revenue function and the target constraint function into a mixed integer linear programming problem, and solving the problem by using a CPLEX solver in an MATLAB environment to obtain a wind power plant energy storage operation strategy meeting different market requirements.
Preferably, in step 1, the fan output prediction error probability distribution function formula is as follows:
Figure BDA0003476671600000021
in the formula, alpha and beta are both fan output prediction error probability distribution parameters, alpha and beta are both larger than 0, and delta P is a sample value of the fan output prediction error.
Preferably, the sample value of the fan output prediction error probability distribution function is obtained by the following steps:
step a: dividing a fan output prediction error probability distribution function into m equal probability intervals;
step b: using the midpoint of each probability interval or the random number in the interval as a sampling point;
step c: and carrying out inverse transformation on the corresponding sampling points according to an inverse transformation formula of the fan output prediction error probability distribution function to obtain corresponding prediction error sample values.
Step d: and adding the prediction error sample value and the ultra-short-term prediction power of the fan output to obtain a plurality of actual fan output samples.
Preferably, in the step d, a synchronous back-substitution subtraction method is adopted to perform sampling scene subtraction on the multiple actual fan output samples until the number of scenes meets the reduction requirement, and the number of fan output samples required to be reduced in the invention is 6.
Preferably, in step 2, the time series formula for describing the electric power spot price by using the first-order autoregressive model is as follows:
Figure BDA0003476671600000022
wherein q (t) and q (t-1) are the electric power spot market prices at time t and time t-1, respectively; phi (t) is the historical average value of the electric power spot price in the t period;
Figure BDA0003476671600000023
the autoregressive coefficient is the electric power spot price; epsilon1Is a normally distributed random number with an expected value of 0 and a variance of σ2。
Preferably, in step 2, the operating yield model of the wind farm energy storage power station participating in the multi-market is as follows:
max Es＝Em+Ed+Er-Ep-Es
in the formula EmEarnings for the wind storage system to participate in the medium and long-term market; edEarnings obtained for the wind storage system to participate in the market in the day ahead; erEarnings obtained for the wind storage system to participate in the real-time market; epThe deviation assessment cost is taken; esWhich is the cost of energy storage.
Preferably, in the step 2, the operation constraint conditions of the wind farm energy storage power station participating in the multi-market include the following contents:
the fan operation constraint is as follows:
Figure BDA0003476671600000031
wherein
Figure BDA0003476671600000032
The rated power of the fan is set for the scene S in the time period t.
The energy storage operation constraints are as follows:
Figure BDA0003476671600000033
Figure BDA0003476671600000034
Figure BDA0003476671600000035
wherein:
Figure BDA0003476671600000036
and
Figure BDA0003476671600000037
respectively the minimum discharge power and the maximum discharge power of the stored energy;
Figure BDA0003476671600000038
and
Figure BDA0003476671600000039
respectively the minimum charging power and the maximum charging power of the stored energy;
Figure BDA00034766716000000310
and
Figure BDA00034766716000000311
in the form of a binary auxiliary variable,
Figure BDA00034766716000000312
in order to be in a charging state,
Figure BDA00034766716000000313
is in a discharge state.
Preferably, in step 3, the target revenue function in step S4 and the constraint function in step S5 are represented in a random chance constraint planning form as follows:
Figure BDA00034766716000000314
in the formula: alpha is the confidence of the objective function; pr { } is a probabilistic operator; u (t) is a control variable for the period t; ξ (t) is a t-period random variable.
Figure BDA00034766716000000315
The formula shows that the income of the wind storage system participating in the multi-market is not less than that of the wind storage system under the premise of considering random variables for the target value corresponding to the income function
Figure BDA00034766716000000316
The probability of (a) is greater than α; c (t) is a relevant parameter in the constraint condition of the t period; es(u (t), ξ (t), c (t)) as a revenue function for time period t; gt(u (t), c (t)) determinism constraints for period t.
The invention has the beneficial effects that:
1. aiming at the problem of improving the economic benefit of the wind power plant energy storage system, the invention researches a multi-market-oriented wind power plant energy storage system operation strategy, expands the energy storage application scene to multi-market including medium and long term, day ahead and real time, and can fully explore the application value of energy storage.
2. The uncertainty of wind power output and the uncertainty of the price of the power market are fully considered, the problem of the operation strategy of the wind power plant energy storage power station participating in the medium-long-term market and the spot market under the condition of system uncertainty is deeply excavated, a reasonable suggestion is provided for the operation strategy of the wind power plant energy storage power station participating in the multi-market, and unnecessary risks and losses are reduced.
3. The method comprises the steps of establishing an economic model of the wind power plant energy storage system facing multiple types of markets, constructing an optimization model by taking the maximum income of the wind power plant energy storage system participating in the multiple markets as a target and taking the maximum demand of the wind power plant energy storage system meeting requirements for output of a fan and energy storage charge and discharge power as constraints, optimizing a model based on a random chance constraint planning theory, considering operating parameters of energy storage, enhancing feasibility of configuring energy storage equipment in the wind power plant, and having the characteristics of science, reasonability and strong practicability.
Drawings
FIG. 1 is a flow chart of a wind farm energy storage power station multi-market participation strategy considering double uncertainties of output prediction and price;
FIG. 2 is a flow chart of solving a fan output prediction error probability distribution function sample value by a multi-market participation strategy of a wind power plant energy storage power station considering double uncertainties of output prediction and price.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the accompanying drawings, and other advantages and effects of the present invention will be readily apparent to those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and its several details are capable of modifications and variations in various respects, all without departing from the spirit of the invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in FIG. 1, the multi-market participation strategy of the wind power plant energy storage power station considering output prediction and price comprises the following steps:
step 1: considering the randomness and the volatility of wind power, and establishing a fan output prediction error probability distribution function based on a multi-scenario method;
step 2: considering uncertainty of the electric power spot price, describing the electric power spot price by adopting a first-order autoregressive model, and simultaneously establishing an operation income model and an operation constraint condition of the wind farm energy storage power station participating in multi-market;
and step 3: based on a random opportunity constraint planning theory, establishing a target revenue function and a target constraint function according to the real-time spot price of electric power, an operation revenue model and operation constraint conditions of a wind field energy storage power station participating in multi-market;
and 4, step 4: and changing the target revenue function and the target constraint function into a mixed integer linear programming problem, and solving the problem by using a CPLEX solver in an MATLAB environment to obtain a wind power plant energy storage operation strategy meeting different market requirements.
Furthermore, in step 1, the fan output prediction error probability distribution function formula is as follows:
Figure BDA0003476671600000041
in the formula, alpha and beta are both fan output prediction error probability distribution parameters, alpha and beta are both larger than 0, and delta P is a sample value of the fan output prediction error.
The value of the historical data can be determined through analysis.
As shown in fig. 2, further, the sample value of the fan output prediction error probability distribution function is obtained by the following steps:
step a: dividing a fan output prediction error probability distribution function into m equal probability intervals;
step b: using the midpoint of each probability interval or the random number in the interval as a sampling point;
step c: and carrying out inverse transformation on the corresponding sampling points according to an inverse transformation formula of the fan output prediction error probability distribution function to obtain corresponding prediction error sample values.
Step d: and adding the prediction error sample value and the ultra-short-term prediction power of the fan output to obtain a plurality of actual fan output samples.
Furthermore, in the step d, the sampling scene reduction is performed on the multiple actual fan output samples by adopting a synchronous back substitution reduction method until the number of scenes meets the reduction requirement, and the number of the fan output samples required to be reduced in the invention is 6.
The scene generated by sampling usually comprises scenes similar to other scenes, and in order to improve the calculation efficiency and reduce the calculation complexity, the scene reduction is carried out on the sample clustering by adopting a synchronous back substitution reduction method, and the specific steps are as follows:
a) assuming that the co-sampling produces M scenes, the target sample scene number is M'. Assume an initial probability of each scene as
Figure BDA0003476671600000051
b) For each sample xi(t) finding the distance d from its sampleijNearest sample and calculate the probability distance pdj。
min d(xi(t),xj(t))＝min||xi(t)-xj(t)||2
t＝1,2,…,N；xi(t),xj(t)∈D；i≠j
Pdi＝pimin d(xi,xj)
In the formula: n is an optimized step length; d is the original scene set to be cut down.
c) Elimination of all samples PdiThe sample corresponding to the minimum value in and updating the sample corresponding to xiProbability of sample being closest to sample, i.e.
Pj＝Pi+Pj
Where j is the number of scenes cut.
d) And repeating the steps b and c until the scene number reaches the reduction requirement.
Furthermore, in step 2, the time-series formula for describing the electric power spot price by using the first-order autoregressive model is as follows:
Figure BDA0003476671600000052
wherein q (t) and q (t-1) are the electric power spot market prices at time t and time t-1, respectively; phi (t) is the historical average value of the electric power spot price in the t period;
Figure BDA0003476671600000053
the autoregressive coefficient is the electric power spot price; epsilon1Is a normally distributed random number with an expected value of 0 and a variance of σ2。
Furthermore, in step 2, the operating yield model of the wind field energy storage power station participating in the multi-market is as follows:
max Es＝Em+Ed+Er-Ep-Es
in the formula EmEarnings for the wind storage system to participate in the medium and long-term market; edEarnings obtained for the wind storage system to participate in the market in the day ahead; erEarnings obtained for the wind storage system to participate in the real-time market; epThe deviation assessment cost is taken; esWhich is the cost of energy storage.
The mathematical formula for the medium and long term market benefits is as follows:
Figure BDA0003476671600000061
in the formula: lambda [ alpha ]mThe price of electricity is medium and long term; p is a radical of formulam,tDecomposing the electric quantity for a medium and long term at the time t; Δ t is the transaction time scale.
The wind storage system is considered as a price receiver because the wind storage system occupies a small market share and has negligible control and influence capacity on the whole market electricity price. The electric quantity of the day-ahead electricity price settlement when the wind storage system participates in the day-ahead market is the difference between the clear electric quantity and the medium-long term decomposed electric quantity of the day-ahead market. In general, the market gives priority to giving priority to consumption of new energy, but the electric quantity declared by some new energy day before can be reduced at the moment when the power generation of some new energy is sufficient and the supply of the whole network is larger than the demand. So introducing the clean cut coefficient, the revenue of the wind storage system participating in the market at the day-ahead can be expressed as:
Figure BDA0003476671600000062
in the formula: lambdad,tThe day-ahead electricity price at the time t; p is a radical ofd,tReporting the electric quantity of the system in the market day before; deltad,tAnd (3) reducing the coefficient for clearing at the moment t, wherein the value is determined by the supply and demand consumption relation of the whole network at the corresponding moment, and the value range is 0-1.
The electric quantity settled by the wind storage system in the real-time market is the difference between the actual on-line electric quantity and the daily output clear electric quantity. In fact, the grid power is composed of the ultra-short term prediction value and the ultra-short term prediction error, and the ultra-short term prediction error can be determined by step S1. Meanwhile, settlement of the real-time market needs physical delivery, so that charging and discharging intervention of the energy storage system needs to be considered, and the income of the wind storage system participating in the real-time market can be expressed as follows:
Figure BDA0003476671600000063
ps,t＝(pw,t-pch,t+pdis,t)
Figure BDA0003476671600000064
in the formula: p is a radical ofs,tThe actual network access power of the system at the moment t; lambda [ alpha ]r,tThe real-time electricity price at the moment t; p is a radical ofch,tFor charging energy stored at time tElectrical power; p is a radical ofdis,tThe charging power for storing energy at the moment t; p is a radical ofw,tThe actual power generation power of the fan at the moment t;
Figure BDA0003476671600000065
ultra-short-term power prediction is carried out on the fan at the moment t;
Figure BDA0003476671600000066
and predicting an error value, also called a random quantity, of the fan in the ultra-short term at the moment t.
When the system participates in the real-time market, the electric quantity is declared according to the ultra-short-term predicted power, the real-time output clear electric quantity of the system is obtained through market output clear, and finally the real-time output clear electric quantity is issued to a power generation plan through an AGC after scheduling safety check. If the deviation between the actual network access electric quantity of the system and the power generation plan issued by the AGC is too large, deviation checking cost is generated. Because the reduction generated by the dispatching safety check is completely uncontrollable and unpredictable, and the difference between the real-time output power curve and the power generation curve issued by the AGC is usually smaller in the actual operation, the real-time output power curve is approximately considered to be approximately equal to the scheduled power generation amount of the AGC. Further, the AGC planned power generation can be approximately represented by the ultra-short term predicted power x output reduction coefficient, and then the deviation assessment cost can be represented as:
Figure BDA0003476671600000071
wherein 0.05 and 0.25 represent that the deviation amount between the actual generated output and the AGC issuing plan exceeds + 5% to-25% of the planned value at the moment to execute deviation check, and lambda ispAnd punishing prices are checked for deviation.
The energy storage comprises the economic total cost including the primary investment construction cost and the secondary operation maintenance cost, then the total charge and discharge capacity in the life limit of the energy storage is calculated according to the rated capacity and the life cycle of the energy storage and the attenuation loss generated by considering the actual operation, finally the electricity compensation cost of the energy storage is calculated according to the total cost and the total discharge capacity, and then the cost of the energy storage operation can be approximately expressed as:
Figure BDA0003476671600000072
in the formula: phi is acThe cost is compensated for the stored energy.
Furthermore, in step 2, the operation constraint conditions of the wind farm energy storage power station participating in the multi-market include the following: the fan operation constraint is as follows:
Figure BDA0003476671600000073
wherein
Figure BDA0003476671600000074
And (5) the rated power of the fan at the time t for the scene S.
The energy storage operation constraints are as follows:
Figure BDA0003476671600000075
Figure BDA0003476671600000076
Figure BDA0003476671600000077
wherein:
Figure BDA0003476671600000078
and
Figure BDA0003476671600000079
respectively the minimum discharge power and the maximum discharge power of the stored energy;
Figure BDA00034766716000000710
and
Figure BDA00034766716000000711
respectively the minimum charging power and the maximum charging power of the stored energy;
Figure BDA00034766716000000712
and
Figure BDA00034766716000000713
in the form of a binary auxiliary variable,
Figure BDA00034766716000000714
in order to be in a charging state,
Figure BDA00034766716000000715
is in a discharge state.
In addition, the charge/discharge value at the moment of energy storage t cannot exceed the maximum power limit, i.e. the energy storage state constraint needs to be met:
Figure BDA00034766716000000716
Emin≤Es(t)≤Emax
wherein Es(t) storing energy stored in the energy storage at time t for scene s; etac、ηdA charge-discharge efficiency value for energy storage; the delta t is the length of the time interval between two adjacent time points; eminAnd EmaxRespectively representing the upper and lower limits of the stored energy.
Further, in step 3, the target revenue function in step S4 and the constraint function in step S5 are represented in the form of random chance constraint planning, which is represented as follows:
Figure BDA0003476671600000081
in the formula: alpha is the confidence of the objective function; pr { } is a probabilistic operator; u (t) is a control variable for the period t; ξ (t) is a t-period random variable.
Figure BDA0003476671600000082
The formula shows that the income of the wind storage system participating in the multi-market is not less than that of the wind storage system under the premise of considering random variables for the target value corresponding to the income function
Figure BDA0003476671600000083
The probability of (a) is greater than α; c (t) is a relevant parameter in the constraint condition of the t period; es(u (t), ξ (t), c (t)) as a revenue function for time period t; gt(u (t), c (t)) a time period certainty constraint for t.
The specific forms of u (t) and ξ (t) are represented as follows:
Figure BDA0003476671600000084
the optimization control problem utilizes probabilistic formal constraints to deal with the problem of random variables in the model, and when the random variables of the inequality in the opportunistic constraints can be separated from the polynomial, the conversion of the opportunistic constraints from uncertainty form to determination of similarity can be realized. And the error modeling of the wind power prediction power can be represented by beta distribution, the electricity price of the real-time market is represented by an autoregressive model, the conversion from model uncertain constraint to a determined form is met, a target revenue function and a target constraint function are changed into a mixed integer linear programming problem, a CPLEX solver is used for solving in an MATLAB environment, and the multi-market parameter and optimal strategy of the wind power plant energy storage power station is obtained.

Claims (8)

1. The wind power plant energy storage power station multi-market participation strategy considering output prediction and price is characterized by comprising the following steps of:
step 1: considering the randomness and the volatility of wind power, and establishing a fan output prediction error probability distribution function based on a multi-scenario method;
step 2: considering uncertainty of the electric power spot price, describing the electric power spot price by adopting a first-order autoregressive model, and simultaneously establishing an operation income model and an operation constraint condition of the wind field energy storage power station participating in multi-market;
and step 3: based on a random opportunity constraint planning theory, establishing a target profit function and a target constraint function according to the real-time spot price of electric power, an operation profit model and operation constraint conditions of a wind field energy storage power station participating in multi-market;
and 4, step 4: and changing the target revenue function and the target constraint function into a mixed integer linear programming problem, and solving the problem by using a CPLEX solver in an MATLAB environment to obtain a wind power plant energy storage operation strategy meeting different market requirements.
2. The wind farm energy storage power station multi-market participation strategy considering the output prediction and the price according to claim 1, wherein in the step 1, a fan output prediction error probability distribution function formula is as follows:
Figure FDA0003476671590000011
in the formula, alpha and beta are both fan output prediction error probability distribution parameters, alpha and beta are both larger than 0, and delta P is a sample value of the fan output prediction error.
3. The wind farm energy storage power station multi-market participation strategy considering contribution prediction and price of claim 2, wherein the sample values of the fan contribution prediction error probability distribution function are obtained by:
step a: dividing a fan output prediction error probability distribution function into m equal probability intervals;
step b: using the midpoint of each probability interval or the random number in the interval as a sampling point;
step c: and carrying out inverse transformation on the corresponding sampling points according to an inverse transformation formula of the fan output prediction error probability distribution function to obtain corresponding prediction error sample values.
Step d: and adding the prediction error sample value and the ultra-short-term prediction power of the fan output to obtain a plurality of actual fan output samples.
4. The wind farm energy storage plant multi-market participation strategy considering output prediction and price according to claim 3, wherein in step d, sampling scene reduction is performed on a plurality of fan actual output samples by adopting a synchronous back-substitution reduction method until the number of scenes reaches a preset reduction threshold value.
5. The wind farm energy storage plant multi-market participation strategy considering the contribution prediction and the price according to claim 1, wherein in the step 2, a time series formula for describing the electric power spot price by adopting a first-order autoregressive model is as follows:
Figure FDA0003476671590000012
wherein q (t) and q (t-1) are the electric power spot market prices at time t and time t-1, respectively; phi (t) is the historical average value of the electric power spot price in the t period;
Figure FDA0003476671590000021
the autoregressive coefficient is the electric power spot price; epsilon1Is a normally distributed random number with an expected value of 0 and a variance of σ2。
6. The wind farm energy storage power station multi-market participation strategy considering the output prediction and the price according to claim 1, wherein in the step 2, the operation yield model of the wind farm energy storage power station participating in the multi-market is as follows:
maxEs＝Em+Ed+Er-Ep-Es
in the formula EmEarnings for the wind storage system to participate in the medium and long-term market; edEarnings obtained for the wind storage system to participate in the market in the day ahead; erEarnings obtained for the wind storage system to participate in the real-time market; epThe deviation assessment cost is taken; esWhich is the cost of energy storage.
7. The wind farm energy storage power station multi-market participation strategy considering the contribution prediction and the price according to claim 1, wherein in the step 2, the operation constraint conditions of the wind farm energy storage power station participating in the multi-market comprise the following contents:
the fan operation constraint is as follows:
Figure FDA0003476671590000022
wherein
Figure FDA0003476671590000023
For scenario S the rated power of the fan during time period t,
Figure FDA0003476671590000024
the actual output of the fan at the time t is shown as the scene S.
The energy storage operation constraints are as follows:
Figure FDA0003476671590000025
Figure FDA0003476671590000026
Figure FDA0003476671590000027
wherein:
Figure FDA0003476671590000028
and
Figure FDA0003476671590000029
respectively the minimum discharge power and the maximum discharge power of the stored energy;
Figure FDA00034766715900000210
and
Figure FDA00034766715900000211
respectively the minimum charging power and the maximum charging power of the stored energy;
Figure FDA00034766715900000212
and
Figure FDA00034766715900000213
in the form of a binary auxiliary variable,
Figure FDA00034766715900000214
in order to be in a charging state,
Figure FDA00034766715900000215
is in a discharge state.
8. The wind farm energy storage plant multi-market participation strategy considering contribution prediction and price according to claim 1, wherein in the step 3, the target revenue function in the step S4 and the constraint function in the step S5 are expressed in a random chance constraint planning form as follows:
Figure FDA00034766715900000216
in the formula: alpha is the confidence of the objective function; pr { } is a probabilistic operator; u (t) is a control variable for the period t; ξ (t) is a t-period random variable.
Figure FDA00034766715900000217
A target value corresponding to the revenue function; c (t) is a relevant parameter in the constraint condition of the t period; es(u (t), ξ (t), c (t)) as a revenue function for time period t; gt(u (t), c (t)) a time period certainty constraint for t.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115276099A (en) * 2022-08-26 2022-11-01 中国华能集团清洁能源技术研究院有限公司 Wind power plant energy storage system flexible control method and device based on artificial intelligence technology
CN115659595A (en) * 2022-09-26 2023-01-31 中国华能集团清洁能源技术研究院有限公司 Energy storage control method and device of new energy station based on artificial intelligence
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115276099A (en) * 2022-08-26 2022-11-01 中国华能集团清洁能源技术研究院有限公司 Wind power plant energy storage system flexible control method and device based on artificial intelligence technology
CN115659595A (en) * 2022-09-26 2023-01-31 中国华能集团清洁能源技术研究院有限公司 Energy storage control method and device of new energy station based on artificial intelligence
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