CN112001528A - Optimal bidding method and system for wind storage combined participation in energy-frequency modulation market - Google Patents

Optimal bidding method and system for wind storage combined participation in energy-frequency modulation market Download PDF

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CN112001528A
CN112001528A CN202010754922.7A CN202010754922A CN112001528A CN 112001528 A CN112001528 A CN 112001528A CN 202010754922 A CN202010754922 A CN 202010754922A CN 112001528 A CN112001528 A CN 112001528A
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frequency modulation
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刘鑫
王智伟
褚云龙
韩华玲
徐海超
李征
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Northwest Branch Of State Grid Power Grid Co
State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
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Abstract

The invention discloses an optimal bidding method and system for wind storage combined participation in an energy-frequency modulation market, wherein the bidding method comprises the following steps: acquiring the predicted output and energy storage capacity of the wind power plant in each transaction period and the clearing price of an energy market and a frequency modulation market; the predicted output and the energy storage capacity of the wind power plant in each transaction period and the clearing prices of the energy market and the frequency modulation market are brought into a pre-constructed electric power market bidding model for calculation to obtain the capacity of the wind power and/or the energy storage participating in each scene; the scene comprises a wind power participating energy market, an energy storage participating energy market, a wind storage jointly participating frequency modulation market and an energy storage participating frequency modulation market; the power market bidding model is constructed by taking optimal income when wind power and/or energy storage participate in various scenes as a target. The invention effectively improves the frequency modulation performance of wind power and reduces the loss cost of energy storage to a certain extent.

Description

Optimal bidding method and system for wind storage combined participation in energy-frequency modulation market
Technical Field
The invention relates to the field of power markets, in particular to an optimal bidding method and system for wind storage combined participation in an energy-frequency modulation market.
Background
With the increasing penetration of renewable energy sources, the demand of power systems for frequency modulation services is increasing. In a power system with high renewable energy permeability, renewable energy such as wind power tends to bear the frequency modulation service of part of conventional generator sets. When wind power participates in frequency modulation market trading, the output uncertainty of the wind power can reduce the frequency modulation service quality of the wind power, and the stored energy has huge potential in the frequency modulation market due to the good frequency modulation precision and the fast response speed, so that the defect of wind power frequency modulation can be well overcome by the stored energy. Therefore, the virtual power plant formed by wind power and electricity combined energy storage has certain research and development significance for providing power generation and frequency modulation services for the power market. At present, after wind power and energy storage jointly participate in frequency modulation service, the condition that frequency modulation capacity and declared capacity are inconsistent exists, and an energy storage battery faces accelerated aging risk in the frequency modulation process of frequent charging and discharging, so that the service life of the energy storage battery is shortened.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides an optimal bidding method for wind storage combined participation in an energy-frequency modulation market, which comprises the following steps:
acquiring the predicted output and energy storage capacity of the wind power plant in each transaction period and the clearing price of an energy market and a frequency modulation market;
the predicted output and the energy storage capacity of the wind power plant in each transaction period and the clearing prices of the energy market and the frequency modulation market are brought into a pre-constructed electric power market bidding model for calculation to obtain the capacity of the wind power and/or the energy storage participating in each scene;
the scene comprises wind power participating in an energy market, energy storage participating in the energy market, wind storage jointly participating in a frequency modulation market and energy storage participating in the frequency modulation market;
the power market bidding model is constructed by taking optimal income when wind power and/or energy storage participate in various scenes as a target.
Preferably, the construction of the electricity market bidding model includes:
constructing an optimal objective function of expected profit with the optimal expected profit as a target based on the current income of wind power participating in the energy market, the current income of energy storage participating in the energy market, the current income of wind storage jointly participating in the frequency modulation market, the current income of energy storage participating in the frequency modulation market independently and the historical daily loss cost of energy storage;
obtaining the relation between the energy clearing price, the frequency modulation mileage clearing price and the frequency modulation capacity clearing price and the day-ahead bidding capacity based on the expected income optimal objective function and the constraint condition constructed for the expected income optimal objective function;
constructing a total income optimal objective function by taking the total income optimal as a target based on net income of wind power and energy storage participating in an energy market, wind storage combined participating in frequency modulation capacity income, wind storage combined participating in frequency modulation mileage income, day-ahead income of energy storage independently participating in the frequency modulation market, day-in income of energy storage independently participating in the frequency modulation market, penalty cost and loss cost of energy storage, and day-ahead bid capacity;
and obtaining the relation among the energy clearing price, the frequency modulation mileage clearing price, the frequency modulation capacity clearing price, the capacity of wind power participating in the energy market, the capacity of energy storage participating in the energy market, the capacity of wind storage participating in the frequency modulation market jointly, the capacity of energy storage participating in wind storage joint frequency modulation and the capacity of energy storage participating in the frequency modulation market independently on the basis of the total income optimal objective function and the constraint condition constructed for the total income optimal objective function.
Preferably, the expected profit optimization objective function is as follows:
Figure BDA0002610625300000021
in the formula:
Figure BDA0002610625300000022
the day-ahead clearing price of the power market at the time t;
Figure BDA0002610625300000023
the day-ahead clearing capacity for storing energy at the time t and participating in an energy market;
Figure BDA0002610625300000024
the day-ahead output capacity of the wind power participating in the energy market at the time t; delta t is the time scale of the supernatant day ahead;
Figure BDA0002610625300000025
the capacity of wind storage for taking part in frequency modulation day ahead is provided; rho is a frequency modulation performance index;
Figure BDA0002610625300000026
clearing price for day-ahead capacity of the frequency modulation market at the time t; eta is a mileage income factor;
Figure BDA0002610625300000027
clearing the day-ahead mileage of the frequency modulation market at the time t;
Figure BDA0002610625300000028
the capacity of wind storage for taking part in frequency modulation day ahead is provided; hinvestInvestment cost for energy storage; n is a radical of100％The maximum cycle number under a complete discharge period;
Figure BDA0002610625300000031
the historical average daily loss cost under a complete discharge period; hour is a set containing 24 whole-point moments.
Preferably, the total profit optimization objective function is as follows:
Figure BDA0002610625300000032
in the formula:
Figure BDA0002610625300000033
(ii) a net gain for wind power and stored energy to participate in the energy market;
Figure BDA0002610625300000034
capacity revenue for jointly participating in frequency modulation market for wind storage;
Figure BDA0002610625300000035
the mileage income for jointly participating in the frequency modulation market for wind storage;
Figure BDA0002610625300000036
capacity gain for independently participating in a frequency modulation market for energy storage;
Figure BDA0002610625300000037
mileage earnings for independently participating in frequency modulation markets for energy storage;
Figure BDA0002610625300000038
penalty cost at time t;
Figure BDA0002610625300000039
to storeThe cost of energy loss.
Preferably, the penalty cost
Figure BDA00026106253000000310
As shown in the following formula:
Figure BDA00026106253000000311
in the formula:
Figure BDA00026106253000000312
the penalty factor of the frequency modulation market is given;
Figure BDA00026106253000000313
the capacity of the supernatant fluid before the day for storing energy at the moment t and independently participating in frequency modulation;
Figure BDA00026106253000000314
and (4) storing energy for the time tau and independently participating in the real-time clearing capacity of frequency modulation.
Preferably, the cost of loss of stored energy is
Figure BDA00026106253000000315
As shown in the following formula:
Figure BDA00026106253000000316
in the formula:
Figure BDA00026106253000000317
the capacity cost per unit time of energy storage;
Figure BDA00026106253000000318
the energy storage cost at the time of tau;
wherein the capacity cost per unit time of the stored energy
Figure BDA00026106253000000319
As shown in the following formulaThe following steps:
Figure BDA00026106253000000320
in the formula: h iscapacityCost per capacity for stored energy; cbatteryRated output power for stored energy; r is the discount rate; t isfloatFloat charge life for energy storage;
energy storage cost at the time of τ
Figure BDA00026106253000000321
As shown in the following formula:
Figure BDA0002610625300000041
in the formula: hinvestInvestment cost for energy storage; n is a radical of100％The maximum cycle number under a complete discharge period;
Figure BDA0002610625300000042
is [ k, k + Δ k [ ]]The time period is the number of cycles at a cycle depth of one full discharge cycle.
Preferably, the number of cycles at a cycle depth of one full discharge period is
Figure BDA0002610625300000043
Calculated as follows:
Figure BDA0002610625300000044
in the formula:
Figure BDA0002610625300000045
the number of daily cycles at the depth of discharge d; d is the circulation depth; k is a radical ofpIs a constant;
wherein the cycle depth d is calculated according to the following formula:
Figure BDA0002610625300000046
in the formula: dkThe energy storage cycle depth in two adjacent control moments is set;
Figure BDA0002610625300000047
the charge-discharge state of the energy storage battery at the moment of k + 1;
Figure BDA0002610625300000048
the charge-discharge state of the energy storage battery at the moment k; xi is the charging efficiency of the battery;
Figure BDA0002610625300000049
the charging and discharging power of the battery at the moment k is equal to the power of the stored energy participating in the energy market at the moment k
Figure BDA00026106253000000410
Power stored at time k and participating in frequency modulation market
Figure BDA00026106253000000411
And wherein
Figure BDA00026106253000000412
Capacity of energy storage participating in wind storage combined frequency modulation at moment k
Figure BDA00026106253000000413
Capacity of real-time discharge of stored energy at time τ
Figure BDA00026106253000000414
And (6) determining.
Preferably, the energy storage solely participates in the capacity gain of the frequency modulation market
Figure BDA00026106253000000415
Calculated as follows:
Figure BDA00026106253000000416
in the formula: rho is a frequency modulation performance index value;
Figure BDA00026106253000000417
clearing price for day-ahead capacity of the frequency modulation market at the time t;
Figure BDA00026106253000000418
storing energy for tau moment and independently participating in the daily clearing capacity of frequency modulation;
Figure BDA00026106253000000419
the capacity of the supernatant fluid before the day for storing energy at the moment t and independently participating in frequency modulation; delta t is the time scale of the supernatant day ahead; Δ τ is the real-time clearance scale.
Preferably, the stored energy solely participates in the mileage income of the frequency modulation market
Figure BDA00026106253000000420
Calculated as follows:
Figure BDA00026106253000000421
in the formula: rho is a frequency modulation performance index value; eta is a mileage income factor;
Figure BDA0002610625300000051
clearing the mileage of the frequency modulation market at the moment t;
Figure BDA0002610625300000052
storing energy for tau moment and independently participating in real-time clearing capacity of frequency modulation;
Figure BDA0002610625300000053
and (4) the day-ahead clear capacity for storing energy at the time t and independently participating in frequency modulation.
Preferably, the wind storage is jointly participated in the capacity gain of the frequency modulation market
Figure BDA0002610625300000054
Calculated as follows:
Figure BDA0002610625300000055
in the formula: rho is a frequency modulation performance index value;
Figure BDA0002610625300000056
clearing price for day-ahead capacity of the frequency modulation market at the time t;
Figure BDA0002610625300000057
the capacity of real-time clearing of wind storage jointly participating in the frequency modulation market at the time tau is obtained;
Figure BDA0002610625300000058
and the wind storage capacity jointly participates in frequency modulation at the moment t.
Preferably, the wind power storage jointly participates in the mileage income of the frequency modulation market
Figure BDA0002610625300000059
Calculated as follows:
Figure BDA00026106253000000510
in the formula: eta is a mileage income factor;
Figure BDA00026106253000000511
and clearing the day-ahead mileage of the frequency modulation market at the moment t.
Preferably, after obtaining the total profit optimal objective function and the constraint condition constructed for the total profit optimal objective function, the method further includes:
updating the total income optimal objective function and uncertainty variables in constraint conditions constructed for the total income optimal objective function by adopting a robust optimization model;
the uncertain variables comprise an energy market clearing price, a frequency modulation capacity clearing price, a frequency modulation mileage clearing price and wind power real-time output.
Preferably, the updated total profit optimization objective function is as follows:
Figure BDA0002610625300000061
in the formula:
Figure BDA0002610625300000062
a predicted value of the daily clearing price of the electric power market at the time t;
Figure BDA0002610625300000063
the benefit of the battery at the moment t is obtained;
Figure BDA0002610625300000064
the income of wind power at the time t is obtained;
Figure BDA0002610625300000065
penalty cost at time t;
Figure BDA0002610625300000066
clearing a predicted value of the price for the energy market;
Figure BDA0002610625300000067
clearing price predicted value for frequency modulation capacity;
Figure BDA0002610625300000068
capacity revenue for jointly participating in frequency modulation market for wind storage;
Figure BDA0002610625300000069
capacity gain for independently participating in a frequency modulation market for energy storage;
Figure BDA00026106253000000610
penalty cost at time t;
Figure BDA00026106253000000611
predicting the daily capacity clearing price of the frequency modulation market at the time t;
Figure BDA00026106253000000612
the predicted value of the clearing price is obtained for the day-ahead mileage of the frequency modulation market at the time t;
Figure BDA00026106253000000613
the mileage income for jointly participating in the frequency modulation market for wind storage;
Figure BDA00026106253000000614
mileage earnings for independently participating in frequency modulation markets for energy storage;
Figure BDA00026106253000000615
is [ k, k + Δ k [ ]]The cycle times of the time period under the condition that the cycle depth is a complete discharge cycle are predicted;
Figure BDA00026106253000000616
the energy storage cost at the time of tau;
Figure BDA00026106253000000617
the capacity cost per unit time of energy storage; z is a radical of1Obtaining a first type of optimization parameter corresponding to the clearing price for the energy market; lambda1A second type of optimization parameter corresponding to the clearing price is obtained for the energy market;
Figure BDA00026106253000000618
a third type of optimization parameter corresponding to clearing price for the energy market; z is a radical of2First-class optimization parameters corresponding to the clearing price of the frequency modulation capacity; lambda2Second type optimization parameters corresponding to the clearing price of the frequency modulation capacity;
Figure BDA00026106253000000619
a third type of optimization parameter corresponding to the clearing price of the frequency modulation capacity; z is a radical of3Obtaining a first type of optimization parameters corresponding to the clearing price for the frequency modulation mileage; lambda3Obtaining a second type of optimization parameters corresponding to the clearing price for the frequency modulation mileage;
Figure BDA00026106253000000620
obtaining a third type of optimization parameters corresponding to the clearing price for the frequency modulation mileage; hour is a set containing 24 whole-point moments.
Preferably, the updated constraint is as follows:
Figure BDA0002610625300000071
in the formula: lambda [ alpha ]1Is a first even multiplier; lambda [ alpha ]2Is a second even multiplier; lambda [ alpha ]3Is a third dual multiplier; lambda [ alpha ]4Is a fourth dual multiplier, ΛwindThe conservative parameter is the real-time output of wind power; y is1A fourth type of optimization parameter corresponding to clearing price for the energy market; y is2A fourth type of optimization parameter corresponding to the clearing price of the frequency modulation capacity; y is3A fourth type of optimization parameter corresponding to the frequency modulation mileage clearing price;
Figure BDA0002610625300000072
the capacity of real-time clearing of wind storage jointly participating in the frequency modulation market at the time tau is obtained;
Figure BDA0002610625300000073
the wind power participates in the real-time clearing capacity of the energy market at the time tau;
Figure BDA0002610625300000074
storing energy for tau moment and participating in real-time clearing capacity of an energy market;
Figure BDA0002610625300000075
the wind power participates in upward frequency modulation at the time tau to obtain the real-time clearing capacity;
Figure BDA0002610625300000076
and the output of the wind power plant at the moment tau.
Based on the same inventive concept, the invention also provides an optimal bidding system for wind storage combined participation in an energy-frequency modulation market, which comprises the following steps:
the acquisition module is used for acquiring the predicted output and the energy storage capacity of the wind power plant in each transaction period and clearing prices of an energy market and a frequency modulation market;
the result module is used for substituting the predicted output and the energy storage capacity of the wind power plant in each transaction period and the clearing prices of the energy market and the frequency modulation market into a pre-constructed electric power market bidding model for calculation to obtain the capacity of each scene where the wind power and/or the energy storage participate;
the scene comprises wind power participating in an energy market, energy storage participating in the energy market, wind storage jointly participating in a frequency modulation market and energy storage participating in the frequency modulation market;
the power market bidding model is constructed by taking optimal income when wind power and/or energy storage participate in various scenes as a target.
Preferably, the system further comprises a module for constructing a power market bidding model, specifically configured to:
constructing an optimal objective function of expected profit with the optimal expected profit as a target based on the current income of wind power participating in the energy market, the current income of energy storage participating in the energy market, the current income of wind storage jointly participating in the frequency modulation market, the current income of energy storage participating in the frequency modulation market independently and the historical daily loss cost of energy storage;
obtaining the relation between the energy clearing price, the frequency modulation mileage clearing price and the frequency modulation capacity clearing price and the day-ahead bidding capacity based on the expected income optimal objective function and the constraint condition constructed for the expected income optimal objective function;
constructing a total income optimal objective function by taking the total income optimal as a target based on net income of wind power and energy storage participating in an energy market, wind storage combined participating in frequency modulation capacity income, wind storage combined participating in frequency modulation mileage income, day-ahead income of energy storage independently participating in the frequency modulation market, day-in income of energy storage independently participating in the frequency modulation market, penalty cost and loss cost of energy storage, and day-ahead bid capacity;
and obtaining the relation among the energy clearing price, the frequency modulation mileage clearing price, the frequency modulation capacity clearing price, the capacity of wind power participating in the energy market, the capacity of energy storage participating in the energy market, the capacity of wind storage participating in the frequency modulation market jointly, the capacity of energy storage participating in wind storage joint frequency modulation and the capacity of energy storage participating in the frequency modulation market independently on the basis of the total income optimal objective function and the constraint condition constructed for the total income optimal objective function.
Compared with the prior art, the invention has the beneficial effects that:
according to the technical scheme provided by the invention, the predicted output and energy storage capacity of the wind power plant in each transaction period and the clearing price of an energy market and a frequency modulation market are obtained; the predicted output and the energy storage capacity of the wind power plant in each transaction period and the clearing prices of the energy market and the frequency modulation market are brought into a pre-constructed electric power market bidding model for calculation to obtain the capacity of the wind power and/or the energy storage participating in each scene; the method comprises the steps that a power market bidding model is constructed by taking optimal income when wind power and/or energy storage participates in each scene as a target, and the scenes that the wind power participates in an energy market, the energy storage participates in the energy market, the wind storage jointly participates in a frequency modulation market and the energy storage participates in the frequency modulation market are considered; the capacity of the wind power and the stored energy which are flexible under the condition of the maximum economic benefit and participate in the frequency modulation market and the energy market effectively improves the frequency modulation performance of the wind power and reduces the loss cost of the stored energy.
The optimal scheme provided by the invention fully considers the advantages and disadvantages of the wind power and the energy storage resources, introduces penalty cost and energy storage loss cost to embody the influence of the characteristics of the two resources on the profit, and effectively improves the frequency modulation performance of the wind power and reduces the loss cost of the energy storage under the condition of not influencing the service life of an energy storage battery according to a power market bidding model.
Drawings
FIG. 1 is a flow chart of a method for optimal bidding of wind storage combined participation in an energy-frequency modulation market according to the present invention;
FIG. 2 is a schematic diagram of a model of the wind power storage union participating in the power market in the embodiment of the invention;
FIG. 3 is a flow chart of an optimal bidding strategy for wind storage joint participation in an energy-frequency modulation market based on penalty and energy storage loss cost according to an embodiment of the present invention;
FIG. 4 is a graph of historical data of electric power market transactions and a historical solar output of certain wind power in an embodiment of the invention;
FIG. 5 shows the real-time capacity (Λ) of wind park participating in the electricity market alone according to an embodiment of the present inventionwind＝5％,Λ1＝Λ2＝Λ35%) graph;
FIG. 6 shows the real-time capacity (Λ) of the wind power generation and storage combined participation in the power market in the embodiment of the present inventionwind＝5％,Λ1＝Λ2＝Λ35%) graph;
FIG. 7 shows the real-time capacity (Λ) of the wind power generation and storage combined participation in the power market in the embodiment of the present inventionwind＝10％,Λ1＝Λ2＝Λ35%) graph.
Detailed Description
For a better understanding of the present invention, reference is made to the following description taken in conjunction with the accompanying drawings and examples.
Example 1: the embodiment of the invention provides a bidding method for wind storage combined participation in an energy market and a frequency modulation market, which comprises a profit model for wind storage participation in an electric power market under four scenes. The advantages and disadvantages of the two resources are fully considered, the penalty cost and the loss cost are introduced to enable the influence of the characteristics of the two resources on the benefits to be specific, and corresponding objective functions and constraint conditions are set according to a bidding model. And optimizing the real-time capacity and the net income, and finally verifying the effectiveness and the correctness of the model. As a combined bidding scheme, the scheme effectively improves the frequency modulation performance of wind power and reduces the loss cost of energy storage to a certain extent under the condition of not influencing the service life of the energy storage battery.
As shown in fig. 1, an optimal bidding method for jointly participating in an energy-frequency modulation market by wind power storage provided by an embodiment of the present invention includes:
s1, acquiring the predicted output and energy storage capacity of the wind power plant in each transaction period and clearing prices of an energy market and a frequency modulation market;
s2, the predicted output and the energy storage capacity of the wind power plant in each transaction period and the clearing prices of the energy market and the frequency modulation market are brought into a pre-constructed electric power market bidding model for calculation to obtain the capacity of the wind power and/or the energy storage participating in each scene;
the scene comprises wind power participating in an energy market, energy storage participating in the energy market, wind storage jointly participating in a frequency modulation market and energy storage participating in the frequency modulation market;
the power market bidding model is constructed by taking optimal income when wind power and/or energy storage participate in various scenes as a target.
As shown in fig. 2, a model of wind storage federation participation in the electricity market is presented.
As shown in fig. 3, in the embodiment of the present invention, a wind storage joint participation optimal bidding strategy flow of an energy-frequency modulation market based on penalty and energy storage loss cost includes:
(1) analyzing an energy-frequency modulation service market mechanism, wherein the energy-frequency modulation service market mechanism comprises a bidding mechanism and a market running mechanism of an energy market and a frequency modulation service market, applying the mechanism to cost analysis and income analysis by combining different time scales of day ahead and real time according to the proposed punishment mechanism, and adopting a forward bidding mode of firstly using the energy market and then using the frequency modulation market;
(2) providing a wind storage combined participation electric power market profit model considering energy storage loss cost and deviation penalty cost, wherein the wind storage combined participation electric power market profit model comprises a wind power and energy storage participation electric power market profit model, a wind storage combined participation frequency modulation market profit model, an energy storage independent participation frequency modulation market profit model, an energy storage participation electric power market cost model and a wind storage combined participation electric power market model;
(3) and establishing a bidding model based on the optimal economic benefits, establishing an optimal model of expected benefits, and obtaining real-time clearing capacity and total benefits through total benefit optimization. A robust optimization model is established, uncertainty of wind power output and market price fluctuation are fully considered, the fluctuation degree of uncertain parameters is adjusted through conservative degree parameters, and bidding strategies under different conservative degrees can be effectively obtained.
(4) And (3) carrying out parameter setting on related equipment such as energy storage equipment, verifying a bidding model of the wind storage joint participating in the power market, comparing the bidding model with a bidding model of the wind storage independent participating in the power market, and analyzing the real-time capacity difference between the two resources participating in the energy market and the frequency modulation market under the two methods to obtain the net benefits of the two resources under the two scenes.
The inventor considers that if the actual power generation amount of the wind power is smaller than the declared capacity, a certain increase, namely punishment cost, is brought to the power generation cost of the wind power; if the real-time power generation amount of the wind power is the over-generation, the power price of the over-generation part is lower than the clearing price within the time of [ t, t + delta t ], and the condition that the frequency modulation capacity is not consistent with the declared capacity exists in the frequency modulation service market. Considering that the occupation ratio of new energy in the power system is increased year by year, the uncertainty of the new energy also brings great influence to the power grid, the actual condition of the energy storage battery is fully considered in the profit analysis, and the bias punishment cost and the battery loss cost are introduced into the bidding model to enable the profit to be accurate. The method comprises the following specific steps:
step 1: building a profit model for wind power and stored energy to participate in an energy market
Wind power and stored energy both participate in the trading of the energy market independently, so the income models of the wind power and the stored energy in the energy market are the same. Profit of resource at time t
Figure BDA0002610625300000111
Including the earnings of the day ahead and the earnings of the real-time earnings:
Figure BDA0002610625300000112
in formula (1):
Figure BDA0002610625300000113
the day-ahead clearing price for the electricity market; delta t is the time scale of the supernatant day ahead;
Figure BDA0002610625300000114
the real-time clearing capacity of the resources at the time tau is obtained. The first term is the day ahead earnings and the second term is the real time earnings.
Introducing a bias penalty cost in the energy market for incentivizing supplier offeringsThe generating capacity of the declared capacity is met. Penalty cost at time t
Figure BDA0002610625300000115
The sum of penalty costs for each delta tau cycle. Namely:
Figure BDA0002610625300000116
Figure BDA0002610625300000117
finally, the net gain of participation in the energy market at time t
Figure BDA0002610625300000118
By profit
Figure BDA0002610625300000119
Deduct penalty cost
Figure BDA00026106253000001110
Obtaining delta tau as the time scale of real-time clearing;
Figure BDA00026106253000001111
a multiple penalty factor for the frequency modulated market;
Figure BDA00026106253000001112
the penalty factor is the undergeneration penalty factor of the frequency modulation market.
Figure BDA00026106253000001113
The penalty cost is considered when a profit model of the wind power and the stored energy participating in the energy market is constructed, the frequency modulation performance of the wind power is improved, and the profit of the wind power and the stored energy participating in the power market is accurately expressed.
Step 2: model for establishing wind storage joint participation frequency modulation market income
The energy storage and the wind power are complementary in the frequency modulation capacity and the frequency modulation accuracy. Because the time period of AGC frequency modulation is much shorter than the time interval of real-time clear, the control period of AGC is set to be delta k (the period is 5s), and k is the time node of AGC frequency modulation. When the wind power is not enough to meet the frequency modulation instruction of the AGC, the energy storage fills up the part of the margin capacity. In the model, the key parameter is the capacity of the wind storage two resources participating in frequency modulation. Assuming that the stored energy can meet the frequency modulation capacity of insufficient wind power, the calculation is as follows:
Figure BDA0002610625300000121
in the formula (5), the reaction mixture is,
Figure BDA0002610625300000122
the capacity of the battery participating in wind storage combined frequency modulation at the time k given by a control instruction;
Figure BDA0002610625300000123
a control instruction at the time of k, if an upward frequency modulation instruction is given by the AGC, the control instruction is
Figure BDA0002610625300000124
And-1 when the frequency modulation command is downwards;
Figure BDA0002610625300000125
the capacity of the supernatant is the capacity of the supernatant participating in frequency modulation at the moment t;
Figure BDA0002610625300000126
and
Figure BDA0002610625300000127
and the wind power participates in the real-time upward and downward clearing capacity of frequency modulation at the moment tau respectively.
Finally obtaining the income of the virtual power plant participating in frequency modulation through the following formula:
Figure BDA0002610625300000128
Figure BDA0002610625300000129
wherein,
Figure BDA00026106253000001210
and
Figure BDA00026106253000001211
respectively the day-ahead capacity clearing price and the day-ahead mileage clearing price of the frequency modulation market at the time t;
Figure BDA00026106253000001212
and
Figure BDA00026106253000001213
respectively representing real-time output capacity and day-ahead output capacity of the virtual power plant participating in frequency modulation; eta is the mileage revenue factor. Equation (6) is the yield to solve for frequency modulation capacity, while equation (7) is the yield to solve for frequency modulation mileage. The frequency modulation performance index rho is different for different models. When wind power independently participates in frequency modulation, the wind power belongs to A signal resources, rho is approximately equal to 1, and when energy storage independently participates in a frequency modulation market, the frequency modulation performance index is rho is approximately equal to 3. In the invention, the wind storage combined frequency modulation has a slow response speed on the frequency modulation capacity, but has high accuracy, so that the performance index of the wind storage combination is rho & lt 1-3 & gt.
And step 3: building a profit model for energy storage to participate in frequency modulation market independently
Figure BDA00026106253000001214
Figure BDA00026106253000001215
In the formula:
Figure BDA00026106253000001216
and
Figure BDA00026106253000001217
the capacity of the stored energy participating in frequency modulation before and in the day is respectively the clear capacity, the frequency modulation performance index is far higher than the performance index of the signal resource A and is about 2-4 times of the signal resource A (in the invention, rho is taken to be 3). If the real-time output capacity of the stored energy is smaller than the day-ahead output capacity, a penalty cost needs to be deducted, otherwise, the penalty cost is not needed.
Figure BDA0002610625300000131
As with wind storage joint participation in frequency modulation, in addition to penalty costs, there are also loss costs of stored energy, as will be described in the cost model of stored energy participation in the electricity market.
If the income of the energy storage participating in the frequency modulation market independently is not considered, the income of the energy storage is reduced to a certain extent and is not consistent with the actual situation, so that the scene of the energy storage participating in the frequency modulation market independently is considered, on one hand, the resource participating scene can be flexibly combined, the value of the energy storage is exerted to the maximum extent, and on the other hand, the income of the energy storage participating in the frequency modulation is obtained.
And 4, step 4: establishing cost model for energy storage participating in electric power market
The capacity cost of the energy storage unit time means the depreciation cost of apportioning the capacity investment cost of the energy storage to each real-time clearing period. The following calculation formula can be obtained by the workload algorithm.
Figure BDA0002610625300000132
In the formula (11), hcapacityCost per capacity for stored energy; cbatteryRated output power for stored energy; r is the discount rate; t isfloatThe float charge life for energy storage, which refers to the life of the energy storage battery in normal operation, generally depends on the material and corrosion cost of the battery, and can be considered as a constant.
Definition of
Figure BDA0002610625300000133
The mileage cost of energy storage depends on the degradation of battery life caused by the electrical depth d of the energy storage cycle. Converting daily cycle times under different cycle depths into cycle times under 100% cycle depths
Figure BDA0002610625300000134
Figure BDA0002610625300000135
In formula (12): k is a radical ofpThe constant is usually provided by a battery manufacturer, and is a constant of 0.8-2.1;
Figure BDA0002610625300000136
the number of daily cycles at depth of discharge d. The cost of the battery loss is related to the charge-discharge state of the battery, since the cycle depth d depends on the difference between the charge-discharge states at two adjacent control moments.
Figure BDA0002610625300000137
In the formula
Figure BDA0002610625300000138
And the charge-discharge state of the energy storage battery at the moment k is shown. The difference between the charge and discharge states of the two control periods can be determined by the capacity of the energy storage participating in the upward and downward frequency modulation at the moment k, as shown in formula (14):
Figure BDA0002610625300000141
where ξ is the charging efficiency of the battery;
Figure BDA0002610625300000142
for the charging and discharging power of the battery at time k,
Figure BDA0002610625300000143
if the voltage is larger than zero, the battery is in a charging state, otherwise, the battery is in a discharging state. The charging and discharging power of the battery is influenced by the energy market and the frequency modulation market. Therefore, the charge and discharge power
Figure BDA0002610625300000144
Equivalent to the power of energy storage participating in energy market at the moment k
Figure BDA0002610625300000145
Power stored at time k and participating in frequency modulation market
Figure BDA0002610625300000146
Sum of
Figure BDA0002610625300000147
Capacity of energy storage participating in wind storage combined frequency modulation at moment k
Figure BDA0002610625300000148
Capacity of real-time discharge of stored energy at time τ
Figure BDA0002610625300000149
Determining, as shown in equation (15):
Figure BDA00026106253000001410
Figure BDA00026106253000001411
from the above equation, the cycle depth d of two adjacent control times can be determinedk. For Δ k period, the battery is cycled by a depth dkThe cycle is only once, and the state of the battery exists in only one state (charge or discharge), so the cycle number thereof is half of the actual number. From formula (12) [ k, k + Δ k]Depth of circulation within a time period100% d of the number of cycles
Figure BDA00026106253000001412
Finally, the energy storage cost at the time of tau is obtained by the formula (17)
Figure BDA00026106253000001413
Figure BDA00026106253000001414
In formula (17): hinvestInvestment cost for energy storage; n is a radical of100％The maximum number of cycles at 100% d, where 100% d is one complete discharge cycle.
Finally, the capacity cost and the mileage cost of the stored energy are added to obtain the loss cost of the stored energy
Figure BDA00026106253000001416
Figure BDA00026106253000001415
In constructing the energy storage cost model, the inventors found that the loss cost of a battery can be divided into two aspects: capacity cost and mileage cost, capacity cost depends on the construction cost of the stored energy, and mileage cost depends on the operational loss life of the battery. The larger the capacity of the stored energy, the greater the cost of its losses. On the other hand, the frequency modulation mileage determines the circulating power depth of the energy storage battery. The greater the depth of charge and discharge, the faster the battery life is lost and the higher the cost of the loss. If only the operation loss cost of the battery is considered, but the inherent loss of the energy storage is not considered, namely the early investment cost is shared by the cost of each scheduling period, the bidding benefit of obtaining the energy storage under the existing condition is too optimistic due to neglecting the capacity cost, and a certain deviation exists between the capacity cost and the actual income, so that the battery loss cost is more accurate from two aspects when an energy storage cost model is constructed.
In the established electric power market bidding model, wind energy and stored energy firstly participate in energy market trading, namely, the real-time clearing capacity of the energy market is firstly obtained, and then the wind energy and the stored energy participate in the frequency modulation market. Energy storage participating in the frequency modulation market can be divided into two parts: and the independent participation and the joint wind power participation. The capacity of the energy storage participating in wind storage combined frequency modulation can be obtained by an equation (5), and the capacity of the energy storage participating in an energy market and the capacity of the energy storage participating in a frequency modulation line independently are obtained by a bidding model based on optimal economic operation. Similarly, wind power firstly participates in energy market trading, and the residual capacity and the stored energy jointly participate in the frequency modulation market.
The bidding model of the wind power and the stored energy participating in the energy market and the frequency modulation market is essentially a model for optimizing economic benefits. In this bidding model, there are two types of components to optimize, one being expected revenue and the other being total revenue. The expected revenue determines the day-ahead bid capacity, which is the input variable for optimizing the total revenue, and the specific steps are as follows:
and 5: establishing an optimization model of expected revenue
The objective function for expected revenue optimization is:
Figure BDA0002610625300000151
in formula (19): the first term is the day-ahead clearing income of the wind power and the stored energy which respectively participate in the energy market, the second term is the day-ahead clearing income of the wind power and the stored energy which jointly participate in the frequency modulation market, and the third term is the day-ahead income of the stored energy which independently participate in the frequency modulation market; the final cost deducted is the historical daily loss cost of the energy storage battery,
Figure BDA0002610625300000152
is the historical average daily loss cost at 100% d. Hour is a set containing 24 whole-point moments.
The constraint conditions must meet capacity constraints, including energy storage capacity constraints and wind power capacity constraints:
Figure BDA0002610625300000153
Figure BDA0002610625300000154
Figure BDA0002610625300000155
in the formula: cwindThe rated output power of the wind power is obtained.
Step 6: establishing an optimization model of total revenue
The total profit is considered to be the sum of wind power and net profit of stored energy, which is equal to the sum of profit of two resources participating in the power market minus penalty cost and loss cost of stored energy.
Figure BDA0002610625300000161
The constraint function of the total gain optimization model also needs to satisfy the capacity constraint. In addition to the constraints on the day-ahead output capacity by equations (20), (21) and (22), the real-time output capacity and the upward and downward frequency modulation capacity also need to be limited. Equation (24) (25) is a limit on the sum of the capacities of the fm market and the energy market for wind power participation at time τ.
Figure BDA0002610625300000162
Figure BDA0002610625300000163
The wind power has limited climbing capability, and the maximum upward and downward climbing capability is limited to be less than beta% of the rated power of the wind power:
Figure BDA0002610625300000164
the sum of the capacity of the stored energy participating in the energy market and the capacity participating in the upward and downward direction must remain within its rated power, where k e [ τ, τ + Δ τ ]:
Figure BDA0002610625300000165
Figure BDA0002610625300000166
the energy storage battery needs to ensure a certain residual capacity (gamma% of the frequency modulation capacity of the day), so as to perform frequency modulation upwards and downwards. The constraint condition ensures that the energy storage battery can compensate the part of wind power storage combination with the real-time frequency modulation capacity lower than the daily output capacity under the condition that the capacity of wind power participating in frequency modulation is too low due to external factors.
Figure BDA0002610625300000167
In the formula, Cbattery,storedFor a given capacity of stored energy, k ∈ [ tau, tau + Δ tau]。
The objective function of the power market bidding model is divided into two parts, wherein the first part is used for solving day-ahead bidding capacity, and the second part is used for solving real-time bidding capacity. Compared with the method that optimization is carried out by putting the day-ahead bids and the real-time bids together, the method is more accurate and more suitable for practical application, because in the actual bidding process of the electric power market, the day-ahead bid amount needs to be submitted one day in advance, and the exact value of the real-time bid capacity cannot be known at the time of submission. In addition, if the day-ahead bid amount and the actual bid amount are optimized together, software can avoid deviation in the optimization process through a corresponding algorithm, so that the two types of bid amounts are very close to each other, the advantages of the algorithm cannot be embodied, and therefore the method for optimizing the day-ahead and real-time bid amounts separately solves the influence of the problem on the algorithm result.
And 8: after the electric power market bidding model is built, the robust optimization model is built, uncertainty of wind power output and market price fluctuation are fully considered, the fluctuation degree of uncertain parameters in the electric power market bidding model is adjusted through the conservative parameter, and bidding strategies under different conservative degrees can be effectively obtained.
In the total profit optimization model, the value of the decision variable and the optimal profit have volatility due to the existence of the uncertain variable, and in order to reduce the influence of the fluctuation of the uncertain variable on the optimization result, the objective functions of the decision variable and the optimal profit are set to be max-min as shown in the formula (30). And solving the expected income model by adopting an improved robust optimization algorithm, converting the double-layer max-min model into a single-layer linear robust optimization model to quantify the influence of the uncertain variables, and providing the use of the robust optimization algorithm.
The linear programming function can be converted into a robust equivalent form as shown in (30) for all the linear programming functions containing uncertainty variables:
Figure BDA0002610625300000171
s.t.l≤x≤u (31)
wherein c is a parameter matrix independent of the uncertain variable; a is a parameter matrix of uncertainty variables; a isiFor the ith column vector to correspond to some uncertainty variable,
Figure BDA0002610625300000172
is a predicted value; x is a decision variable matrix.
Introducing an integer variable Λ to adjust the balance between the optimality of the solution to the robust optimization model result and the system robustness, equation (30) changes to the following equation:
Figure BDA0002610625300000173
in the formula: j is a set of uncertainty variables; s is a set of J, representing a set of uncertainty variable expectation values.
The above model is converted to an equivalent mixed integer linear optimization model (NP-hard) using the dual principle, as follows:
Figure BDA0002610625300000181
in formula (33): z, pi、yiTo optimize the variables; the value range of lambda is lambda epsilon [0, | J-]When Λ is 0, the model is equivalent to a linear programming model composed of equations (30) to (31) without considering uncertainty factors; when Λ ═ J |, the model is the most robust, meaning that the solution found is the most conservative and the solution is the most optimal. Setting the value of Λ reasonably can effectively adjust the balance between the optimality of the solution and the robustness of the system.
In the total profit optimization model, the decision variables in the objective function of the total profit are set as
Figure BDA0002610625300000182
Similar to the prediction revenue model, the input variables also include the real-time price of the market and the real-time output of wind power. The accuracy requirements for the input variables are high in the real-time market, so the uncertainty needs to be considered. Definition of
Figure BDA0002610625300000183
For sets of uncertain variables, including energy market clearing price
Figure BDA0002610625300000184
Frequency modulation capacity clearing price
Figure BDA0002610625300000185
Frequency-modulated mileage price
Figure BDA0002610625300000186
Real-time output with wind power
Figure BDA0002610625300000187
Assuming that the range of the uncertain variable is a predicted value which is symmetrical up and down, the constraint shown in the formula (34) is satisfied:
Figure BDA0002610625300000188
in the formula:
Figure BDA0002610625300000189
the predicted value of the uncertain variable is delta c, and the floating quantity of the predicted value is delta c.
And updating the total yield optimization model according to the robust optimization model, and updating the objective function and the constraint condition into the formulas (35) - (36).
Figure BDA0002610625300000191
In the formula:
Figure BDA0002610625300000192
a predicted value of the daily clearing price of the electric power market at the time t;
Figure BDA0002610625300000193
the benefit of the battery at the moment t is obtained;
Figure BDA0002610625300000194
the income of wind power at the time t is obtained;
Figure BDA0002610625300000195
penalty cost at time t;
Figure BDA0002610625300000196
clearing a predicted value of the price for the energy market;
Figure BDA0002610625300000197
clearing price predicted value for frequency modulation capacity;
Figure BDA0002610625300000198
capacity revenue for jointly participating in frequency modulation market for wind storage;
Figure BDA0002610625300000199
capacity gain for independently participating in a frequency modulation market for energy storage;
Figure BDA00026106253000001910
penalty cost at time t;
Figure BDA00026106253000001911
predicting the daily capacity clearing price of the frequency modulation market at the time t;
Figure BDA00026106253000001912
the predicted value of the clearing price is obtained for the day-ahead mileage of the frequency modulation market at the time t;
Figure BDA00026106253000001913
the mileage income for jointly participating in the frequency modulation market for wind storage;
Figure BDA00026106253000001914
mileage earnings for independently participating in frequency modulation markets for energy storage;
Figure BDA00026106253000001915
is [ k, k + Δ k [ ]]The cycle times of the time period under the condition that the cycle depth is a complete discharge cycle are predicted;
Figure BDA00026106253000001916
the energy storage cost at the time of tau;
Figure BDA00026106253000001917
the capacity cost per unit time of energy storage; z is a radical of1Obtaining a first type of optimization parameter corresponding to the clearing price for the energy market; lambda1A second type of optimization parameter corresponding to the clearing price is obtained for the energy market;
Figure BDA00026106253000001918
a third type of optimization parameter corresponding to clearing price for the energy market; z is a radical of2First-class optimization parameters corresponding to the clearing price of the frequency modulation capacity; lambda2For discharging the frequency-modulated capacitySecond type optimization parameters corresponding to the price;
Figure BDA00026106253000001919
a third type of optimization parameter corresponding to the clearing price of the frequency modulation capacity; z is a radical of3Obtaining a first type of optimization parameters corresponding to the clearing price for the frequency modulation mileage; lambda3Obtaining a second type of optimization parameters corresponding to the clearing price for the frequency modulation mileage;
Figure BDA00026106253000001920
obtaining a third type of optimization parameters corresponding to the clearing price for the frequency modulation mileage; hour is a set containing 24 whole-point moments.
The constraints of the objective function (35) are:
Figure BDA0002610625300000201
in the formula: lambda [ alpha ]1Is a first even multiplier; lambda [ alpha ]2Is a second even multiplier; lambda [ alpha ]3Is a third dual multiplier; lambda [ alpha ]4Is a fourth dual multiplier, ΛwindThe conservative parameter is the real-time output of wind power; y is1A fourth type of optimization parameter corresponding to clearing price for the energy market; y is2A fourth type of optimization parameter corresponding to the clearing price of the frequency modulation capacity; y is3A fourth type of optimization parameter corresponding to the frequency modulation mileage clearing price;
Figure BDA0002610625300000202
the capacity of real-time clearing of wind storage jointly participating in the frequency modulation market at the time tau is obtained;
Figure BDA0002610625300000203
the wind power participates in the real-time clearing capacity of the energy market at the time tau;
Figure BDA0002610625300000204
storing energy for tau moment and participating in real-time clearing capacity of an energy market;
Figure BDA0002610625300000205
the wind power participates in upward frequency modulation at the time tau to obtain the real-time clearing capacity;
Figure BDA0002610625300000206
and the output of the wind power plant at the moment tau. Equations (20) - (22), (25) - (29) need not be updated.
Due to wind power output, each market price has uncertainty, the uncertainty random change can be well described by using a robust optimization model, and the conservative parameter can be used for adjusting and respectively adjusting the fluctuation range of each uncertain variable according to the condition of a trading day.
And step 9: and (3) carrying out parameter setting on related equipment such as energy storage equipment, verifying a bidding model of the wind storage joint participating in the power market, comparing the bidding model with a bidding model of the wind storage independent participating in the power market, and analyzing the real-time capacity difference between the two resources participating in the energy market and the frequency modulation market under the two methods to obtain the net benefits of the two resources under the two scenes.
Firstly, a bidding scene is established, and parameters of related equipment such as energy storage and the like are set. The energy storage power station is a 30MW sodium-sulfur battery composition, kp1.3, and the specific parameters are shown in table 1;
TABLE 1 energy storage station basic parameter settings consisting of lithium batteries
Figure BDA0002610625300000211
As shown in FIG. 4, the wind power station adopts the actual power generation data of a 200MW wind power station in a power grid of a certain area, the PJM transaction actual data of 2017 is used as the power market data, and in order to eliminate the interference of other factors, the dates of the wind power generation data and the transaction data are taken from the same day.
The experiment is mainly carried out in two scenes; scene 1: wind storage is independently involved in electric power market transaction; scene 2: and the wind storage participates in the electric power market transaction under multiple operation modes. The optimization target of the scene 1 is obtained by the optimal sum of the economic benefits of energy storage and wind power; and scenario 2 is optimized through the joint bidding model proposed by the present embodiment. Because uncertainty of wind power output and market price is considered in the optimization process, the optimization result of the wind storage combined bidding model under different conservative parameters in simulation is obtained. FIG. 5 is a real-time declared capacity of wind storage participating in the electricity market alone, divided into 96 time periods; FIG. 6 is a graph of real-time clearing capacity of each resource participating in the power market in the wind-storage union; fig. 7 shows real-time declared capacity of each resource participating in the power market in the wind power storage combination (10%) under different wind power conservation degree parameters.
And (4) according to the real-time capacity in the two cases, the net income in the trading day is finally obtained through the net income calculation method in the steps 1-8. Table 2 compares net profit values of wind power and stored energy when the wind storage jointly participates in the electric power market and the wind storage independently participates in the electric power market.
TABLE 2 Net returns from wind storage to participating in the electric power market under different scenarios
Figure BDA0002610625300000212
Comparing the data of fig. 5 and 6, the following conclusions can be drawn:
it can be seen from fig. 5 and 6 that the sum of the real-time wind power capacities participating in the energy market and the frequency modulation market changes along with the wind power solar output curve in fig. 4. In a time period with higher frequency modulation price (sum of mileage price and capacity price), the ratio of the wind power/wind storage combined frequency modulation capacity is higher; in order to reduce the larger punishment cost caused by undergeneration, the bidding model can slightly improve the capacity of wind power independently participating in frequency modulation so as to avoid the situation, the accuracy of wind storage combined frequency modulation is higher, and the punishment cost is reduced without adopting a multi-generation means. In the time periods from 1 point to 2 points, from 6 points to 8 points and from 19 points to 21 points, the capacity of the wind power alone participating in frequency modulation in fig. 5 is slightly larger than the capacity of the wind power storage combined participating in frequency modulation in fig. 6, and is about 10%. When the frequency modulation price is lower, the bidding model is better than the energy market, and the declared capacity of the wind power participating in the frequency modulation market is further reduced so as to reduce the capacity of excessive investment in the frequency modulation market and inhibit the output fluctuation of the frequency modulation market, thereby reducing the punishment cost. In the time periods from 3 to 6 and from 15 to 17, the capacity of wind power alone participating in frequency modulation in fig. 5 is far smaller than the capacity of wind power storage combined participating in frequency modulation in fig. 6.
Secondly, due to the constraint of the formula (29), a part of capacity must be reserved for energy storage to prevent the large scale of wind power generation; when the wind storage independently participates in the power market, the reserve capacity does not need to be reserved, so that the capacities of the energy storage participating energy market and the frequency modulation market in each time period are increased to a certain extent compared with the situation that the wind storage jointly participates in the power market. Furthermore, by comparing the capacities of the energy storage of fig. 5 and 6 to participate in the electricity market, it is concluded that: under the same frequency modulation capability, the cycle times and the cycle depth of the stored energy in the wind storage combined power market are far smaller than those of the wind storage singly participating in the power market, namely, the stored energy can provide more frequency modulation capacity under the same 100% d under the current simulation, and further the loss cost under unit power is reduced.
Setting different conservation degrees also influences the declared capacity of the energy-frequency modulation market in which the wind storage participates. Higher conservation means less economical optimization results. Comparing fig. 6 with fig. 7, we can see that: as the wind power conservative parameter is increased, the robust optimization model can reduce declaration capacity containing wind power resources in each market so as to reduce punishment cost caused by wind power output fluctuation. In the whole time period, the wind power participation energy market and declaration capacity is reduced along with the rise of the wind power conservation degree parameter, and is about 4%; the sum of the declaration capacity of the energy storage participating in the power market is reduced to make up the influence of the increase of the wind power conservative parameter on the declaration capacity of the wind storage participating in the frequency modulation market, and the reduction is about 5%; the declared capacity of wind storage participating in the power market is almost unchanged.
The net revenue results for the wind storage pool participation in the electricity market are shown in table 2. Analysis of table 2 leads to the following conclusions:
under the bidding model provided by the invention, the total income increase mainly comes from the frequency modulation market, which is about 12.8%. Due to the fact that the resource accuracy of the energy storage and wind power combination is high, the bidding model is better in preference to the frequency modulation market in the program optimization process, and the occupation ratio of the energy market is obviously reduced.
And secondly, under the condition of wind storage combination, the net income of participating in frequency modulation is increased by 12.3%, and the income of participating in an energy market is reduced by 6.7%, so that the bidding model of the wind storage combination participating in the power market effectively improves the frequency modulation performance of wind power and the income of participating in the power market, and reduces the loss cost of energy storage to a certain extent.
A virtual power plant formed by wind power and electricity combined energy storage provides power generation and frequency modulation services for a power market. The method provides a real-time bidding scheme containing wind power and energy storage under different time scales of a day-ahead market and a real-time market, maximizes the income of the wind power and energy storage participating in the power market by utilizing the complementary characteristics of the two schemes, and establishes income models under different scenes. In addition, the actual situation is fully considered in the profit analysis, and bias penalty cost and battery loss cost are introduced into the bidding model to refine the profit. And finally, based on an economically optimal objective function, establishing a wind storage combined bidding model to obtain the net benefits of wind storage participating in the power market under different scenes.
In the embodiment of the invention, the characteristics of energy storage and wind power which are complementary in two aspects of frequency modulation capacity and frequency modulation accuracy are combined, a yield model including punishment cost is analyzed under four scenes that wind power/energy storage participates in energy market transaction, wind storage jointly participates in frequency modulation market transaction and energy storage independently participates in frequency modulation market transaction, and then loss cost caused by the participation of energy storage in a power market is analyzed; and then, respectively establishing an expected income and total income bidding model of the wind-storage combination based on an economically optimal objective function, wherein the expected income determines the day-ahead bidding capacity which is an input variable for optimizing the total income, and the real-time clearing capacity and the total income are obtained through the total income optimization. Complementary advantages of wind power and battery energy storage in frequency modulation precision and durability are fully exerted and used as a reference basis for energy suppliers in time throwing.
In order to consider deviation of the bidding capacity caused by the uncertain variables, a robust optimization model with conservation degree is established, different conservation degrees can be set according to the condition of the trading day to simulate actual wind power and actual market price, and more reasonable bidding capacity is obtained.
Example 2: based on the same inventive concept, the embodiment of the invention also provides an optimal bidding system for wind storage combined participation in an energy-frequency modulation market, which comprises the following steps:
the acquisition module is used for acquiring the predicted output and the energy storage capacity of the wind power plant in each transaction period and clearing prices of an energy market and a frequency modulation market;
the result module is used for substituting the predicted output and the energy storage capacity of the wind power plant in each transaction period and the clearing prices of the energy market and the frequency modulation market into a pre-constructed electric power market bidding model for calculation to obtain the capacity of each scene where the wind power and/or the energy storage participate;
the scene comprises wind power participating in an energy market, energy storage participating in the energy market, wind storage jointly participating in a frequency modulation market and energy storage participating in the frequency modulation market;
the power market bidding model is constructed by taking optimal income when wind power and/or energy storage participate in various scenes as a target.
In an embodiment, the system further includes a module for constructing a power market bidding model, specifically configured to:
constructing an optimal objective function of expected profit with the optimal expected profit as a target based on the current income of wind power participating in the energy market, the current income of energy storage participating in the energy market, the current income of wind storage jointly participating in the frequency modulation market, the current income of energy storage participating in the frequency modulation market independently and the historical daily loss cost of energy storage;
obtaining the relation between the energy clearing price, the frequency modulation mileage clearing price and the frequency modulation capacity clearing price and the day-ahead bidding capacity based on the expected income optimal objective function and the constraint condition constructed for the expected income optimal objective function;
constructing a total income optimal objective function by taking the total income optimal as a target based on net income of wind power and energy storage participating in an energy market, wind storage combined participating in frequency modulation capacity income, wind storage combined participating in frequency modulation mileage income, day-ahead income of energy storage independently participating in the frequency modulation market, day-in income of energy storage independently participating in the frequency modulation market, penalty cost and loss cost of energy storage, and day-ahead bid capacity;
and obtaining the relation among the energy clearing price, the frequency modulation mileage clearing price, the frequency modulation capacity clearing price, the capacity of wind power participating in the energy market, the capacity of energy storage participating in the energy market, the capacity of wind storage participating in the frequency modulation market jointly, the capacity of energy storage participating in wind storage joint frequency modulation and the capacity of energy storage participating in the frequency modulation market independently on the basis of the total income optimal objective function and the constraint condition constructed for the total income optimal objective function.
In an embodiment, the expected revenue optimizing objective function is as follows:
Figure BDA0002610625300000241
in the formula:
Figure BDA0002610625300000242
the day-ahead clearing price of the power market at the time t;
Figure BDA0002610625300000243
the day-ahead clearing capacity for storing energy at the time t and participating in an energy market;
Figure BDA0002610625300000244
the day-ahead output capacity of the wind power participating in the energy market at the time t; delta t is the time scale of the supernatant day ahead;
Figure BDA0002610625300000245
the capacity of wind storage for taking part in frequency modulation day ahead is provided; rho is a frequency modulation performance index;
Figure BDA0002610625300000246
clearing price for day-ahead capacity of the frequency modulation market at the time t; eta is a mileage income factor;
Figure BDA0002610625300000247
clearing the day-ahead mileage of the frequency modulation market at the time t;
Figure BDA0002610625300000248
the capacity of wind storage for taking part in frequency modulation day ahead is provided; hinvestInvestment cost for energy storage; n is a radical of100％The maximum cycle number under a complete discharge period;
Figure BDA0002610625300000249
the historical average daily loss cost under a complete discharge period; hour is a set containing 24 whole-point moments.
In an embodiment, the total profit optimization objective function is as follows:
Figure BDA00026106253000002410
in the formula:
Figure BDA0002610625300000251
(ii) a net gain for wind power and stored energy to participate in the energy market;
Figure BDA0002610625300000252
capacity revenue for jointly participating in frequency modulation market for wind storage;
Figure BDA0002610625300000253
the mileage income for jointly participating in the frequency modulation market for wind storage;
Figure BDA0002610625300000254
capacity gain for independently participating in a frequency modulation market for energy storage;
Figure BDA0002610625300000255
mileage earnings for independently participating in frequency modulation markets for energy storage;
Figure BDA0002610625300000256
penalty cost at time t;
Figure BDA0002610625300000257
the cost of energy loss.
In an embodiment, the penalty isBook (I)
Figure BDA0002610625300000258
As shown in the following formula:
Figure BDA0002610625300000259
in the formula:
Figure BDA00026106253000002510
the penalty factor of the frequency modulation market is given;
Figure BDA00026106253000002511
the capacity of the supernatant fluid before the day for storing energy at the moment t and independently participating in frequency modulation;
Figure BDA00026106253000002512
and (4) storing energy for the time tau and independently participating in the real-time clearing capacity of frequency modulation.
In an embodiment, the loss cost of stored energy
Figure BDA00026106253000002513
As shown in the following formula:
Figure BDA00026106253000002514
in the formula:
Figure BDA00026106253000002515
the capacity cost per unit time of energy storage;
Figure BDA00026106253000002516
the energy storage cost at the time of tau;
wherein the capacity cost per unit time of the stored energy
Figure BDA00026106253000002517
As shown in the following formula:
Figure BDA00026106253000002518
in the formula: h iscapacityCost per capacity for stored energy; cbatteryRated output power for stored energy; r is the discount rate; t isfloatFloat charge life for energy storage;
energy storage cost at the time of τ
Figure BDA00026106253000002519
As shown in the following formula:
Figure BDA00026106253000002520
in the formula: hinvestInvestment cost for energy storage; n is a radical of100％The maximum cycle number under a complete discharge period;
Figure BDA00026106253000002521
is [ k, k + Δ k [ ]]The time period is the number of cycles at a cycle depth of one full discharge cycle.
In one embodiment, the number of cycles at a cycle depth of one full discharge cycle is described
Figure BDA00026106253000002522
Calculated as follows:
Figure BDA0002610625300000261
in the formula:
Figure BDA0002610625300000262
the number of daily cycles at the depth of discharge d; d is the circulation depth; k is a radical ofpIs a constant;
wherein the cycle depth d is calculated according to the following formula:
Figure BDA0002610625300000263
in the formula: dkThe energy storage cycle depth in two adjacent control moments is set;
Figure BDA0002610625300000264
the charge-discharge state of the energy storage battery at the moment k; xi is the charging efficiency of the battery;
Figure BDA0002610625300000265
the charging and discharging power of the battery at the moment k is equal to the power of the stored energy participating in the energy market at the moment k
Figure BDA0002610625300000266
Power stored at time k and participating in frequency modulation market
Figure BDA0002610625300000267
And wherein
Figure BDA0002610625300000268
Capacity of energy storage participating in wind storage combined frequency modulation at moment k
Figure BDA0002610625300000269
Capacity of real-time discharge of stored energy at time τ
Figure BDA00026106253000002610
And (6) determining.
In an embodiment, the capacity gain of the frequency modulation market is solely participated by the energy storage
Figure BDA00026106253000002611
Calculated as follows:
Figure BDA00026106253000002612
in the formula: rho is a frequency modulation performance index value;
Figure BDA00026106253000002613
clearing price for day-ahead capacity of the frequency modulation market at the time t;
Figure BDA00026106253000002614
storing energy for tau moment and independently participating in the daily clearing capacity of frequency modulation;
Figure BDA00026106253000002615
the capacity of the supernatant fluid before the day for storing energy at the moment t and independently participating in frequency modulation; delta t is the time scale of the supernatant day ahead; Δ τ is the time scale of real-time ejection.
In an embodiment, the mileage revenue of the frequency modulated market is solely participated by the stored energy
Figure BDA00026106253000002616
Calculated as follows:
Figure BDA00026106253000002617
in the formula: rho is a frequency modulation performance index value; eta is a mileage income factor;
Figure BDA00026106253000002618
clearing the mileage of the frequency modulation market at the moment t;
Figure BDA00026106253000002619
storing energy for tau moment and independently participating in real-time clearing capacity of frequency modulation;
Figure BDA00026106253000002620
and (4) the day-ahead clear capacity for storing energy at the time t and independently participating in frequency modulation.
In an embodiment, the wind storage is jointly participated in capacity gain of frequency modulation market
Figure BDA00026106253000002621
Calculated as follows:
Figure BDA0002610625300000271
in the formula: rho is a frequency modulation performance index value;
Figure BDA0002610625300000272
clearing price for day-ahead capacity of the frequency modulation market at the time t;
Figure BDA0002610625300000273
the capacity of real-time clearing of wind storage jointly participating in the frequency modulation market at the time tau is obtained;
Figure BDA0002610625300000274
and the wind storage capacity jointly participates in frequency modulation at the moment t.
In an embodiment, the wind storage is jointly participated in the mileage income of the frequency modulation market
Figure BDA0002610625300000275
Calculated as follows:
Figure BDA0002610625300000276
in the formula: eta is a mileage income factor;
Figure BDA0002610625300000277
and clearing the day-ahead mileage of the frequency modulation market at the moment t.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.

Claims (16)

1. An optimal bidding method for wind storage combined participation in an energy-frequency modulation market is characterized by comprising the following steps:
acquiring the predicted output and energy storage capacity of the wind power plant in each transaction period and the clearing price of an energy market and a frequency modulation market;
the predicted output and the energy storage capacity of the wind power plant in each transaction period and the clearing prices of the energy market and the frequency modulation market are brought into a pre-constructed electric power market bidding model for calculation to obtain the capacity of the wind power and/or the energy storage participating in each scene;
the scene comprises wind power participating in an energy market, energy storage participating in the energy market, wind storage jointly participating in a frequency modulation market and energy storage participating in the frequency modulation market;
the power market bidding model is constructed by taking optimal income when wind power and/or energy storage participate in various scenes as a target.
2. The method of claim 1, wherein the construction of the electric power market bidding model comprises:
constructing an optimal objective function of expected profit with the optimal expected profit as a target based on the current income of wind power participating in the energy market, the current income of energy storage participating in the energy market, the current income of wind storage jointly participating in the frequency modulation market, the current income of energy storage participating in the frequency modulation market independently and the historical daily loss cost of energy storage;
obtaining the relation between the energy clearing price, the frequency modulation mileage clearing price and the frequency modulation capacity clearing price and the day-ahead bidding capacity based on the expected income optimal objective function and the constraint condition constructed for the expected income optimal objective function;
constructing a total income optimal objective function by taking the total income optimal as a target based on net income of wind power and energy storage participating in an energy market, wind storage combined participating in frequency modulation capacity income, wind storage combined participating in frequency modulation mileage income, day-ahead income of energy storage independently participating in the frequency modulation market, day-in income of energy storage independently participating in the frequency modulation market, penalty cost and loss cost of energy storage, and day-ahead bid capacity;
and obtaining the relation among the energy clearing price, the frequency modulation mileage clearing price, the frequency modulation capacity clearing price, the capacity of wind power participating in the energy market, the capacity of energy storage participating in the energy market, the capacity of wind storage participating in the frequency modulation market jointly, the capacity of energy storage participating in wind storage joint frequency modulation and the capacity of energy storage participating in the frequency modulation market independently on the basis of the total income optimal objective function and the constraint condition constructed for the total income optimal objective function.
3. The method of claim 2, wherein the expected revenue optimization objective function is expressed as:
Figure FDA0002610625290000021
in the formula:
Figure FDA0002610625290000022
the day-ahead clearing price of the power market at the time t;
Figure FDA0002610625290000023
the day-ahead clearing capacity for storing energy at the time t and participating in an energy market;
Figure FDA0002610625290000024
the day-ahead output capacity of the wind power participating in the energy market at the time t; delta t is the time scale of the supernatant day ahead;
Figure FDA0002610625290000025
the capacity of wind storage for taking part in frequency modulation day ahead is provided; rho is a frequency modulation performance index;
Figure FDA0002610625290000026
clearing price for day-ahead capacity of the frequency modulation market at the time t; eta is a mileage income factor;
Figure FDA0002610625290000027
clearing the day-ahead mileage of the frequency modulation market at the time t;
Figure FDA0002610625290000028
the capacity of wind storage for taking part in frequency modulation day ahead is provided; hinvestInvestment cost for energy storage; n is a radical of100％The maximum cycle number under a complete discharge period;
Figure FDA0002610625290000029
the historical average daily loss cost under a complete discharge period; hour is a set containing 24 whole-point moments.
4. The method of claim 2, wherein the total gain optimization objective function is expressed as:
Figure FDA00026106252900000210
in the formula:
Figure FDA00026106252900000211
(ii) a net gain for wind power and stored energy to participate in the energy market;
Figure FDA00026106252900000212
capacity revenue for jointly participating in frequency modulation market for wind storage;
Figure FDA00026106252900000213
the mileage income for jointly participating in the frequency modulation market for wind storage;
Figure FDA00026106252900000214
capacity gain for independently participating in a frequency modulation market for energy storage;
Figure FDA00026106252900000215
mileage earnings for independently participating in frequency modulation markets for energy storage;
Figure FDA00026106252900000216
penalty cost at time t;
Figure FDA00026106252900000217
the cost of energy loss.
5. The method of claim 4, in which the penalty cost
Figure FDA00026106252900000218
As shown in the following formula:
Figure FDA00026106252900000219
in the formula:
Figure FDA0002610625290000031
the penalty factor of the frequency modulation market is given;
Figure FDA0002610625290000032
the capacity of the supernatant fluid before the day for storing energy at the moment t and independently participating in frequency modulation;
Figure FDA0002610625290000033
and (4) storing energy for the time tau and independently participating in the real-time clearing capacity of frequency modulation.
6. The method of claim 4, wherein the cost of the depletion of stored energy is
Figure FDA0002610625290000034
As shown in the following formula:
Figure FDA0002610625290000035
in the formula:
Figure FDA0002610625290000036
the capacity cost per unit time of energy storage;
Figure FDA0002610625290000037
the energy storage cost at the time of tau;
wherein the capacity cost per unit time of the stored energy
Figure FDA0002610625290000038
As shown in the following formula:
Figure FDA0002610625290000039
in the formula: h iscapacityCost per capacity for stored energy; cbatteryRated output power for stored energy; r is the discount rate; t isfloatFloat charge life for energy storage;
energy storage cost at the time of τ
Figure FDA00026106252900000310
As shown in the following formula:
Figure FDA00026106252900000311
in the formula: hinvestInvestment cost for energy storage; n is a radical of100％The maximum cycle number under a complete discharge period;
Figure FDA00026106252900000312
is [ k, k + Δ k [ ]]The time period is the number of cycles at a cycle depth of one full discharge cycle.
7. The method of claim 6, wherein the number of cycles at a cycle depth of one full discharge cycle
Figure FDA00026106252900000313
Calculated as follows:
Figure FDA00026106252900000314
in the formula:
Figure FDA00026106252900000315
the number of daily cycles at the depth of discharge d; d is the circulation depth; k is a radical ofpIs a constant;
wherein the cycle depth d is calculated according to the following formula:
Figure FDA00026106252900000316
in the formula: dkThe energy storage cycle depth in two adjacent control moments is set;
Figure FDA00026106252900000317
the charge-discharge state of the energy storage battery at the moment k; xi is the charging efficiency of the battery;
Figure FDA00026106252900000318
the charging and discharging power of the battery at the moment k is equal to the power of the stored energy participating in the energy market at the moment k
Figure FDA0002610625290000041
Power stored at time k and participating in frequency modulation market
Figure FDA0002610625290000042
And wherein
Figure FDA0002610625290000043
Capacity of energy storage participating in wind storage combined frequency modulation at moment k
Figure FDA0002610625290000044
Capacity of real-time discharge of stored energy at time τ
Figure FDA0002610625290000045
And (6) determining.
8. The method of claim 4, wherein the stored energy is solely involved in capacity revenue for FM market
Figure FDA0002610625290000046
Calculated as follows:
Figure FDA0002610625290000047
in the formula: rho is a frequency modulation performance index value;
Figure FDA0002610625290000048
clearing price for day-ahead capacity of the frequency modulation market at the time t;
Figure FDA0002610625290000049
storing energy for tau moment and independently participating in the daily clearing capacity of frequency modulation;
Figure FDA00026106252900000410
the capacity of the supernatant fluid before the day for storing energy at the moment t and independently participating in frequency modulation; delta t is the time scale of the supernatant day ahead; Δ τ is the time scale of real-time ejection.
9. The method of claim 4, wherein the stored energy is solely participating in mileage revenue for frequency modulated markets
Figure FDA00026106252900000411
Calculated as follows:
Figure FDA00026106252900000412
in the formula: rho is a frequency modulation performance index value; eta is a mileage income factor;
Figure FDA00026106252900000413
clearing the mileage of the frequency modulation market at the moment t;
Figure FDA00026106252900000414
storing energy for tau moment and independently participating in real-time clearing capacity of frequency modulation;
Figure FDA00026106252900000415
and (4) the day-ahead clear capacity for storing energy at the time t and independently participating in frequency modulation.
10. The method of claim 4, wherein the wind reservoir is jointly engaged in capacity revenue for FM market
Figure FDA00026106252900000416
Calculated as follows:
Figure FDA00026106252900000417
in the formula: rho is a frequency modulation performance index value;
Figure FDA00026106252900000418
clearing price for day-ahead capacity of the frequency modulation market at the time t;
Figure FDA00026106252900000419
the capacity of real-time clearing of wind storage jointly participating in the frequency modulation market at the time tau is obtained;
Figure FDA00026106252900000420
and (4) wind storage and wind storage jointly participate in the day-ahead clear capacity of frequency modulation at the time t.
11. The method of claim 10, wherein the wind reservoir is jointly engaged in mileage revenue for frequency modulated markets
Figure FDA00026106252900000421
Calculated as follows:
Figure FDA0002610625290000051
in the formula: eta is a mileage income factor;
Figure FDA0002610625290000052
and clearing the day-ahead mileage of the frequency modulation market at the moment t.
12. The method of claim 2, wherein obtaining the total gain optimization objective function and the constraints constructed for the total gain optimization objective function further comprises:
updating the total income optimal objective function and uncertainty variables in constraint conditions constructed for the total income optimal objective function by adopting a robust optimization model;
the uncertain variables comprise an energy market clearing price, a frequency modulation capacity clearing price, a frequency modulation mileage clearing price and wind power real-time output.
13. The method of claim 12, wherein the updated total earning optimization objective function is expressed by:
Figure FDA0002610625290000053
in the formula:
Figure FDA0002610625290000054
a predicted value of the daily clearing price of the electric power market at the time t;
Figure FDA0002610625290000055
for the collection of the battery at time tBenefiting;
Figure FDA0002610625290000056
the income of wind power at the time t is obtained;
Figure FDA0002610625290000057
penalty cost at time t;
Figure FDA0002610625290000058
clearing a predicted value of the price for the energy market;
Figure FDA0002610625290000059
clearing price predicted value for frequency modulation capacity;
Figure FDA00026106252900000510
capacity revenue for jointly participating in frequency modulation market for wind storage;
Figure FDA00026106252900000511
capacity gain for independently participating in a frequency modulation market for energy storage;
Figure FDA00026106252900000512
penalty cost at time t;
Figure FDA00026106252900000513
predicting the daily capacity clearing price of the frequency modulation market at the time t;
Figure FDA00026106252900000514
the predicted value of the clearing price is obtained for the day-ahead mileage of the frequency modulation market at the time t;
Figure FDA00026106252900000515
the mileage income for jointly participating in the frequency modulation market for wind storage;
Figure FDA00026106252900000516
for storing energy singlyMileage earnings of participating in frequency modulation markets only;
Figure FDA0002610625290000061
is [ k, k + Δ k [ ]]The cycle times of the time period under the condition that the cycle depth is a complete discharge cycle are predicted;
Figure FDA0002610625290000062
the energy storage cost at the time of tau;
Figure FDA0002610625290000063
the capacity cost per unit time of energy storage; z is a radical of1Obtaining a first type of optimization parameter corresponding to the clearing price for the energy market; lambda1A second type of optimization parameter corresponding to the clearing price is obtained for the energy market;
Figure FDA0002610625290000064
a third type of optimization parameter corresponding to clearing price for the energy market; z is a radical of2First-class optimization parameters corresponding to the clearing price of the frequency modulation capacity; lambda2Second type optimization parameters corresponding to the clearing price of the frequency modulation capacity;
Figure FDA0002610625290000065
a third type of optimization parameter corresponding to the clearing price of the frequency modulation capacity; z is a radical of3Obtaining a first type of optimization parameters corresponding to the clearing price for the frequency modulation mileage; lambda3Obtaining a second type of optimization parameters corresponding to the clearing price for the frequency modulation mileage;
Figure FDA0002610625290000066
obtaining a third type of optimization parameters corresponding to the clearing price for the frequency modulation mileage; hour is a set containing 24 whole-point moments.
14. The method of claim 13, wherein the updated constraint is expressed by:
Figure FDA0002610625290000067
in the formula: lambda [ alpha ]1Is a first even multiplier; lambda [ alpha ]2Is a second even multiplier; lambda [ alpha ]3Is a third dual multiplier; lambda [ alpha ]4Is a fourth dual multiplier, ΛwindThe conservative parameter is the real-time output of wind power; y is1A fourth type of optimization parameter corresponding to clearing price for the energy market; y is2A fourth type of optimization parameter corresponding to the clearing price of the frequency modulation capacity; y is3A fourth type of optimization parameter corresponding to the frequency modulation mileage clearing price;
Figure FDA0002610625290000068
the capacity of real-time clearing of wind storage jointly participating in the frequency modulation market at the time tau is obtained;
Figure FDA0002610625290000069
the wind power participates in the real-time clearing capacity of the energy market at the time tau;
Figure FDA00026106252900000610
storing energy for tau moment and participating in real-time clearing capacity of an energy market;
Figure FDA00026106252900000611
the wind power participates in upward frequency modulation at the time tau to obtain the real-time clearing capacity;
Figure FDA00026106252900000612
and the output of the wind power plant at the moment tau.
15. A wind storage combined optimal bidding system for participating in an energy-frequency modulation market is characterized by comprising:
the acquisition module is used for acquiring the predicted output and the energy storage capacity of the wind power plant in each transaction period and clearing prices of an energy market and a frequency modulation market;
the result module is used for substituting the predicted output and the energy storage capacity of the wind power plant in each transaction period and the clearing prices of the energy market and the frequency modulation market into a pre-constructed electric power market bidding model for calculation to obtain the capacity of each scene where the wind power and/or the energy storage participate;
the scene comprises wind power participating in an energy market, energy storage participating in the energy market, wind storage jointly participating in a frequency modulation market and energy storage participating in the frequency modulation market;
the power market bidding model is constructed by taking optimal income when wind power and/or energy storage participate in various scenes as a target.
16. The system of claim 15, further comprising a build power market bidding model module specifically configured to:
constructing an optimal objective function of expected profit with the optimal expected profit as a target based on the current income of wind power participating in the energy market, the current income of energy storage participating in the energy market, the current income of wind storage jointly participating in the frequency modulation market, the current income of energy storage participating in the frequency modulation market independently and the historical daily loss cost of energy storage;
obtaining the relation between the energy clearing price, the frequency modulation mileage clearing price and the frequency modulation capacity clearing price and the day-ahead bidding capacity based on the expected income optimal objective function and the constraint condition constructed for the expected income optimal objective function;
constructing a total income optimal objective function by taking the total income optimal as a target based on net income of wind power and energy storage participating in an energy market, wind storage combined participating in frequency modulation capacity income, wind storage combined participating in frequency modulation mileage income, day-ahead income of energy storage independently participating in the frequency modulation market, day-in income of energy storage independently participating in the frequency modulation market, penalty cost and loss cost of energy storage, and day-ahead bid capacity;
and obtaining the relation among the energy clearing price, the frequency modulation mileage clearing price, the frequency modulation capacity clearing price, the capacity of wind power participating in the energy market, the capacity of energy storage participating in the energy market, the capacity of wind storage participating in the frequency modulation market jointly, the capacity of energy storage participating in wind storage joint frequency modulation and the capacity of energy storage participating in the frequency modulation market independently on the basis of the total income optimal objective function and the constraint condition constructed for the total income optimal objective function.
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