CN112561123A - Electric power quotation strategy determination method, income distribution method and device thereof - Google Patents

Electric power quotation strategy determination method, income distribution method and device thereof Download PDF

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CN112561123A
CN112561123A CN202011277582.XA CN202011277582A CN112561123A CN 112561123 A CN112561123 A CN 112561123A CN 202011277582 A CN202011277582 A CN 202011277582A CN 112561123 A CN112561123 A CN 112561123A
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魏超
焦晓峰
刘永江
张沈彬
张爱军
张利慧
韩义
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Inner Mongolia Electric Power Research Institute of Inner Mongolia Power Group Co Ltd
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Abstract

The invention discloses a method for determining a alliance market quotation strategy of a wind power and combined heat and power generation unit, which comprises the following steps: constructing a day-ahead and real-time two-stage random optimization model according to the double coupling relation between day-ahead operation and real-time operation of the wind power and cogeneration unit; determining the maximum expected income of the union of the wind power and cogeneration units according to the two-stage random optimization model; and determining the alliance market quotation strategy according to the maximum expected income. The method for determining the alliance market quotation strategy of the wind power and cogeneration unit disclosed by the invention optimizes the day-ahead operation and real-time operation stages by constructing a two-stage random optimization model, integrates uncertainty factors of wind power output, can quickly and effectively determine the maximum expected income of the alliance of the wind power and cogeneration unit by using the model, and provides a basis for the alliance market quotation strategy of the wind power and cogeneration unit.

Description

Electric power quotation strategy determination method, income distribution method and device thereof
Technical Field
The invention relates to the field of optimized scheduling of power systems, in particular to a method for joint participation of wind power cogeneration units in a market quotation strategy and income distribution.
Background
In recent years, under the excitation of national relevant policies, wind power in China develops rapidly. The uncertainty and weak regulation capability of the output of the fluctuating power supply requires a power system to provide more flexible regulation service for high-proportion access. At present, the active regulation capacity of renewable energy sources represented by wind power at the source side in an energy grid is increasingly deteriorated, so that the grid economy faces a lot of difficulties, and large-scale wind abandon is caused.
In this environment, it is a trend to switch wind power operation from a protective full-scale grid up to as competitive in the market as conventional power supplies. Compared with other market main bodies, new energy such as wind power easily appears unbalanced in real-time operation due to self-output uncertainty and volatility, so that large real-time balance cost is caused, the competitiveness of the wind power in the market can be weakened, and the wind power cannot be fully absorbed.
Therefore, the research on the operation mode of wind power participating in the market is an urgent problem to be solved in China for developing new energy, promoting new energy consumption and promoting market construction.
Disclosure of Invention
In view of the above problems, the present invention has been made to provide a quotation strategy determination method and a profit sharing method for a federated market of wind power and cogeneration sets and an apparatus thereof that overcome or at least partially solve the above problems.
In a first aspect, an embodiment of the present invention provides a method for determining a market quotation strategy of a wind power and cogeneration unit alliance, including the following steps:
constructing a day-ahead and real-time two-stage random optimization model according to the double coupling relation between day-ahead operation and real-time operation of the wind power and cogeneration unit;
determining the maximum expected income of the union of the wind power and cogeneration units according to the two-stage random optimization model;
and determining the alliance market quotation strategy according to the maximum expected income.
In one embodiment, constructing the two-stage stochastic optimization model comprises:
and constructing an objective function of the union expected income of the wind power and combined heat and power generation units and a constraint condition of the objective function according to the electricity selling income, the real-time unbalanced settlement income, the heat supply income and the fuel cost of the combined heat and power generation units of the union of the wind power and combined heat and power generation units in the market at the day time.
In one embodiment, the objective function is:
Figure BDA0002779631630000021
wherein R iswCHPMaximum expected revenue for a federation of wind power and cogeneration units;
Figure BDA0002779631630000022
the electricity selling income of the alliance of the wind power and the cogeneration unit in the market at the day before is calculated by the formula 2:
Figure BDA0002779631630000023
wherein λ isDA,tIn order to achieve the current price of the electricity in the market,
Figure BDA0002779631630000024
and
Figure BDA0002779631630000025
the day-ahead generating power of the wind power generation unit and the cogeneration unit is respectively;
Figure BDA0002779631630000026
for unbalanced income settlement of the union of the wind power generation unit and the cogeneration unit in real-time operation, the calculation formula is as follows:
Figure BDA0002779631630000027
where ρ issThe scene probability is calculated based on wind power output generated by the wind power prediction result and historical price information and corresponding market price scene,
Figure BDA0002779631630000028
respectively positive and negative real-time unbalanced prices;
Figure BDA0002779631630000029
respectively the positive and negative real-time unbalanced power of the alliance;
RHfor heat supply income, the calculation formula is as follows:
Figure BDA0002779631630000031
wherein,
Figure BDA0002779631630000032
and λHRespectively heat load demand and heat supply price;
CHfor the fuel cost of the cogeneration unit, the calculation formula is as follows:
Figure BDA0002779631630000033
Figure BDA0002779631630000034
the day-ahead generated power of the cogeneration unit, and a, b and c are fuel cost coefficients of the cogeneration unit.
In one embodiment, the objective function constraints include: power balance constraint, wind power quotation constraint and constraint of a cogeneration unit:
the power balance constraint is:
in equation 3
Figure BDA0002779631630000035
And
Figure BDA0002779631630000036
the following formulas 6 to 8 need to be satisfied:
Figure BDA0002779631630000037
wherein,
Figure BDA0002779631630000038
respectively the real-time power generation power of the wind power generation unit and the cogeneration unit in the S scene,
Figure BDA0002779631630000039
respectively the day-ahead generating power of the wind power generation unit and the cogeneration unit,
Figure BDA00027796316300000310
Figure BDA00027796316300000311
wherein, ys,tFor binary variables of unbalanced power states, M1 and M2 are sufficiently largeA positive number of;
the wind power quotation constraint is as follows:
in equation 2
Figure BDA00027796316300000312
The following equation:
Figure BDA00027796316300000313
wherein,
Figure BDA00027796316300000314
the output limit value of the wind power quotient is obtained;
the constraints of the cogeneration unit are:
in equation 4
Figure BDA00027796316300000315
The following equations 10 and 13 are satisfied at the same time:
Figure BDA0002779631630000041
wherein,
Figure BDA0002779631630000042
which is the thermal power of the cogeneration unit at the day before, is calculated by formula 11,
Figure BDA0002779631630000043
and
Figure BDA0002779631630000044
the input power and the output power of the heat storage tank before the day are respectively obtained by calculation of a formula 12;
Figure BDA0002779631630000045
wherein eta isCHPThe heat-electricity ratio of the cogeneration unit;
Figure BDA0002779631630000046
wherein,
Figure BDA0002779631630000047
for energy of day-ahead heat storage tanks, Tt tesAnd Tt envThe temperature of the heat storage medium in the heat storage tank and the ambient temperature rt tesIs internal resistance of the heat storage tank;
Figure BDA0002779631630000048
wherein,
Figure BDA0002779631630000049
is the real-time thermal power of the cogeneration unit and is calculated by the formula 14,
Figure BDA00027796316300000410
and
Figure BDA00027796316300000411
the input power and the output power of the real-time heat storage tank under the S scene are respectively obtained by calculation according to a formula 15;
Figure BDA00027796316300000412
Figure BDA00027796316300000413
wherein,
Figure BDA00027796316300000414
is real-time heat storage tank energy.
In one embodiment, determining a maximum expected revenue for a federation of wind power and combined heat and power generation units based on the two-stage stochastic optimization model, determining the federation market quotation strategy based on the maximum expected revenue, comprises:
determining according to the formulas 2-15 in the two-stage random optimization model
Figure BDA00027796316300000415
RH、CHObtaining R output by the two-stage stochastic optimization modelwCHPAs the maximum expected revenue for a federation of wind power and cogeneration units;
and determining the alliance market quotation strategy of the wind power and combined heat and power generation unit according to the maximum expected income.
In a second aspect, an embodiment of the present invention provides a revenue allocation method for a alliance market of wind power and cogeneration units, including the following steps:
determining the alliance market quotation strategy of the wind power and cogeneration unit by adopting the method;
and generating a revenue distribution method based on the alliance market quotation strategy.
In one embodiment, a revenue distribution method, comprising:
respectively determining an independent wind power generation unit, an independent cogeneration unit and the alliance market quotation strategies corresponding to the wind power generation unit and the cogeneration unit based on the alliance market quotation strategies;
removing the related variables of the target function cogeneration set and the related conditions of the cogeneration set in the constraint conditions, and determining the alliance market quotation strategy of the independent wind power;
removing the wind power related variables in the objective function and the wind power related conditions in the constraint conditions, and determining the alliance market quotation strategy of the independent cogeneration unit;
determining a alliance market quotation strategy of the wind power and cogeneration unit;
and determining the income distribution method according to the three alliance market quotation strategies.
In one embodiment, based on the sharley value method and the independent wind power, the independent cogeneration units, and the alliance market quotation strategy to which the wind and cogeneration units correspond, the allocation of revenue for the wind and cogeneration units can be determined by equation 16 below,
Figure BDA0002779631630000051
wherein R iswpMaximum expected revenue, R, for the alliance market of independent wind powerCHPFor maximum expected revenue in the alliance market of independent cogeneration units, RwCHPFor maximum expected revenue in the alliance market of wind power and cogeneration units, Xwp、XCHPRespectively the earnings divided by the wind power generation unit and the cogeneration unit.
In a third aspect, the present invention provides a quotation strategy determination device for a alliance market of wind power and cogeneration units, comprising:
the method comprises the steps that a two-stage random optimization model module is constructed and used for constructing a day-ahead and real-time two-stage random optimization model according to the double coupling relation between day-ahead operation and real-time operation of the wind power and cogeneration unit;
the maximum expected profit determining module is used for determining the maximum expected profit of the union of the wind power and the cogeneration unit according to the two-stage random optimization model;
and the alliance market quotation strategy determining module is used for determining the alliance market quotation strategy according to the maximum expected income.
In a fourth aspect, the present invention provides a revenue sharing apparatus for a alliance market of wind power and cogeneration units, comprising:
the system comprises a determining alliance market quotation strategy module, a determining alliance market quotation strategy module and a determining alliance market quotation strategy module, wherein the determining alliance market quotation strategy module is used for respectively determining an independent wind power generation unit, an independent cogeneration unit and the alliance market quotation strategies corresponding to the wind power generation unit and the cogeneration unit;
and the revenue generation distribution method module is used for generating a revenue distribution method based on the independent wind power generation and independent cogeneration units and the alliance market quotation strategies corresponding to the wind power generation and cogeneration units.
In a fifth aspect, the present invention provides a computer readable storage medium, which when executed by a processor implements the above-mentioned alliance market quotation strategy determination method for a wind and combined heat and power generation unit and the revenue distribution method for an alliance market for a wind and combined heat and power generation unit that can implement the above-mentioned alliance market for a wind and combined heat and power generation unit.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
the embodiment of the invention provides a method for determining a market quotation strategy of a union of wind power and cogeneration units, which optimizes the day-ahead operation and real-time operation stages by constructing a two-stage random optimization model, integrates uncertainty factors of wind power output, can quickly and effectively determine the maximum expected income of the union of the wind power and cogeneration units by using the model, and provides a basis for the market quotation strategy of the union of the wind power and cogeneration units. The method can reduce the uncertainty of wind power output, improve the self-prediction precision, and reduce the imbalance between real-time and future values by cooperating with a market main body with flexible adjustment capability.
The embodiment of the invention provides a revenue distribution method for an alliance market of wind power and cogeneration units, coordinated control of a wind power heating system taking cogeneration as a core is beneficial to wind power consumption, resources are effectively optimized and configured, and the overall flexibility of the wind power and cogeneration units can be obviously improved by configuring a heat storage device. The method provided by the embodiment of the invention fully mobilizes the regulation capacity of multiple links of source-network-load-storage, reasonably quantifies the service value of the method, fully optimizes the resources and can realize the maximization of the alliance income; for the overall operation of the system, the system balance cost is reduced, and the new energy consumption capability and the operation economy of the system are improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a method for determining a market quotation strategy of a wind power and cogeneration unit union according to an embodiment of the invention;
fig. 2 is a schematic diagram of a quotation strategy determination device for a alliance market of wind power and cogeneration units in an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
In order to solve the problems that the active regulation capability of the source side in the energy grid is increasingly deteriorated to cause the grid economy to face a lot of difficulties due to the uncertainty and the weak regulation capability of the wind power fluctuation power output, the invention is explained by using a plurality of specific embodiments below.
Example one
As shown in fig. 1, a method for determining a alliance market quotation strategy of a wind power and cogeneration unit according to an embodiment of the present invention includes the following steps:
s101, constructing a day-ahead and real-time two-stage random optimization model according to a double coupling relation between day-ahead operation and real-time operation of a wind power and cogeneration unit;
s102, determining the maximum expected income of the union of the wind power generation unit and the cogeneration unit according to the two-stage random optimization model;
s103, determining the alliance market quotation strategy according to the maximum expected income.
By constructing a two-stage random optimization model of day-ahead operation and real-time operation of the wind power and hot spot cogeneration units, the maximum expected profit of the union of the wind power and the cogeneration units can be determined, thereby determining the union market quotation strategy of the wind power and the cogeneration units. The two-stage random optimization model provided by the embodiment of the invention optimizes the day-ahead operation stage and the real-time operation stage, integrates uncertainty factors of wind power output, can quickly and effectively determine the maximum expected income of the union of the wind power and cogeneration units by using the model, and provides a basis for the market quotation strategy of the union of the wind power and cogeneration units.
In one embodiment, constructing a two-stage stochastic optimization model comprises:
and constructing an objective function of the union expected income of the wind power and combined heat and power generation units and a constraint condition of the objective function according to the electricity selling income, the real-time unbalanced settlement income, the heat supply income and the fuel cost of the combined heat and power generation units of the union of the wind power and combined heat and power generation units in the market at the day time.
The construction of the two-stage stochastic optimization model comprises the following steps: according to the electricity selling income, real-time unbalanced settlement income, heat supply income and the fuel cost of the cogeneration units of the union of the wind power and the cogeneration units in the market at present, an objective function of the union expected income of the wind power and the cogeneration units is constructed, and the maximum expected income of the union of the wind power and the cogeneration units is obtained by determining the maximum value of the objective function. Wherein, the maximum value of the objective function needs to be determined by the set constraint condition.
The embodiment of the invention adopts the parameters related to the maximum expected income of the alliance of four wind power and cogeneration units: electricity sales revenue in the day-ahead market for a federation of wind and combined heat and power units
Figure BDA0002779631630000081
Real-time unbalanced settlement revenue
Figure BDA0002779631630000082
Heat supply income RHAnd fuel cost C of cogeneration unitHThe target function is formed together, and the appointed conditions required to be met by the four parameters can be set simultaneously, so that the maximum value of the target function, namely the maximum expected income of the alliance of the wind power and cogeneration units, can be obtained by the target function, and the market quotation strategy of the alliance can be determined accordingly.
The embodiment of the invention aims to maximize the income of the wind power and cogeneration unit, and constructs an objective function by the income before the day, the real-time unbalanced settlement income, the heat supply income and the fuel cost of the cogeneration unit.
In one embodiment, the value of the objective function is calculated by the following equation 1:
Figure BDA0002779631630000091
wherein R iswCHPMaximum expected revenue for a federation of wind power and cogeneration units;
Figure BDA0002779631630000092
selling electricity revenue in the day-ahead market for a consortium of wind and combined heat and power units, the value of which is measured by
The formula 2 is calculated as follows:
Figure BDA0002779631630000093
wherein λ isDA,tIn order to achieve the current price of the electricity in the market,
Figure BDA0002779631630000094
and
Figure BDA0002779631630000095
the day-ahead generating power of the wind power generation unit and the cogeneration unit is respectively;
Figure BDA0002779631630000096
the value of the unbalanced settlement income of the union of the wind power generation unit and the cogeneration unit in real-time operation is calculated by the following formula 3:
Figure BDA0002779631630000097
where ρ issThe scene probability is calculated based on wind power output generated by the wind power prediction result and historical price information and corresponding market price scene,
Figure BDA0002779631630000098
respectively positive and negative real-time unbalanced prices and positive and negative real-time unbalanced power of the alliance;
RHfor the heating income, the value is calculated by the following formula 4:
Figure BDA0002779631630000099
wherein,
Figure BDA00027796316300000910
and λHRespectively heat load demand and heat supply price;
CHthe fuel cost of the cogeneration unit is calculated by the following equation 5:
Figure BDA00027796316300000911
Figure BDA00027796316300000912
the day-ahead generated power of the cogeneration unit, and a, b and c are fuel cost coefficients of the cogeneration unit.
In one embodiment, according to the objective function, constraint conditions are set, and the constraint conditions may include a power balance constraint, a wind power quotation constraint and a constraint of a cogeneration unit:
and power balance constraint:
in equation 3
Figure BDA0002779631630000101
And
Figure BDA0002779631630000102
the following formulas 6 to 8 need to be satisfied:
Figure BDA0002779631630000103
wherein,
Figure BDA0002779631630000104
respectively the real-time power generation power of the wind power generation unit and the cogeneration unit in the S scene,
Figure BDA0002779631630000105
and
Figure BDA0002779631630000106
respectively the day-ahead generating power of the wind power generation unit and the cogeneration unit,
Figure BDA0002779631630000107
Figure BDA0002779631630000108
wherein, ys,tBeing a binary variable of the unbalanced power state, M1 and M2 are sufficiently large positive numbers;
wind power quotation constraint:
in equation 2
Figure BDA0002779631630000109
The following equation:
Figure BDA00027796316300001010
wherein,
Figure BDA00027796316300001011
the output limit value of the wind power quotient is obtained;
constraint of cogeneration units:
in equation 4
Figure BDA00027796316300001012
The following equations 10 and 13 are satisfied at the same time:
Figure BDA00027796316300001013
wherein,
Figure BDA00027796316300001014
which is the thermal power of the cogeneration unit at the day before, is calculated by formula 11,
Figure BDA00027796316300001015
and
Figure BDA00027796316300001016
the input power and the output power of the heat storage tank before the day are respectively obtained by calculation of a formula 12;
Figure BDA00027796316300001017
wherein eta isCHPThe heat-electricity ratio of the cogeneration unit;
Figure BDA0002779631630000111
wherein,
Figure BDA0002779631630000112
for energy of day-ahead heat storage tanks, Tt tesAnd Tt envThe temperature of the heat storage medium in the heat storage tank and the ambient temperature rt tesIs internal resistance of the heat storage tank;
Figure BDA0002779631630000113
wherein,
Figure BDA0002779631630000114
is the real-time thermal power of the cogeneration unit and is calculated by the formula 14,
Figure BDA0002779631630000115
and
Figure BDA0002779631630000116
the input power and the output power of the real-time heat storage tank under the S scene are respectively obtained by calculation according to a formula 15;
Figure BDA0002779631630000117
Figure BDA0002779631630000118
wherein,
Figure BDA0002779631630000119
is real-time heat storage tank energy.
In one embodiment, determining a maximum expected revenue for a federation of wind power and combined heat and power generation units based on a two-stage stochastic optimization model, the determining the federation market quotation strategy based on the maximum expected revenue, comprises:
according to formulas 2-15 in the two-stage random optimization model, determining
Figure BDA00027796316300001110
RH、CHObtaining R output by the two-stage stochastic optimization modelwCHPAs the maximum expected revenue for a federation of wind power and cogeneration units;
and determining the alliance market quotation strategy of the wind power and cogeneration unit according to the maximum expected income.
The objective function in the embodiment of the invention can be flexibly selected and customized according to the actual scheduling cost, the constraint condition can be added and deleted according to the actual requirement, and the expandability is strong. According to the embodiment of the invention, the uncertainty of wind power output can be reduced, the self prediction precision is improved, and the imbalance between the real-time output and the future value is reduced by cooperating with the market main body with the flexible adjustment capability.
Example two
The embodiment of the invention provides a income distribution method for a alliance market of wind power and combined heat and power generation units, which comprises the following steps:
determining the alliance market quotation strategy of the wind power and cogeneration unit by adopting the method of the embodiment;
and generating a revenue distribution method based on the alliance market quotation strategy.
According to the method for determining the alliance market quotation strategy of the wind power and cogeneration unit, the alliance market quotation strategy of the wind power and cogeneration unit is determined, and the income distribution method of the alliance market of the wind power and cogeneration unit is generated based on the alliance market quotation strategy of the wind power and cogeneration unit.
In one embodiment, a revenue distribution method, comprising:
respectively determining independent wind power generation units, independent cogeneration units and alliance market quotation strategies corresponding to the wind power generation units and the cogeneration units based on the alliance market quotation strategies;
removing the relevant variables of the target function cogeneration unit and the relevant conditions of the cogeneration unit in the constraint conditions, and determining the alliance market quotation strategy of the independent wind power;
removing wind power related variables in the objective function and wind power related conditions in the constraint conditions, and determining an alliance market quotation strategy of the independent cogeneration unit;
and determining the alliance market quotation strategy of the wind power and cogeneration set.
In this embodiment, based on the above alliance market quotation strategy, the alliance market quotation strategy of the independent wind power is determined by removing the related variables of the target function cogeneration unit and the related conditions of the cogeneration unit in the constraint conditions, so as to obtain the maximum expected profit R of the alliance market of the independent wind powerwp. Similarly, wind power related variables in the objective function and wind power related conditions in the constraint conditions are removed, and the alliance market quotation strategy of the independent cogeneration unit is determined, so that the maximum expected profit R of the alliance market of the independent cogeneration unit can be obtainedCHP. Moreover, the maximum expected income R of the alliance market of the wind power and cogeneration units can be obtainedwCHP. According to Rwp、RCHP、RwCHPA revenue allocation method may be determined.
In one embodiment, the revenue allocation for the wind and cogeneration machine is determined based on a sharey value method and the federation market quotation policies for the independent wind and independent cogeneration units, and the wind and cogeneration units.
In an embodiment of the invention, a sharey value method is selected for determining said allocation of revenue for the wind power and cogeneration machine. R obtained in the abovewp、RCHP、RwCHPSubstituted into the following equation 16:
Figure BDA0002779631630000131
wherein, Xwp、XCHPRespectively the earnings divided by the wind power generation unit and the cogeneration unit.
Thus, an allocation method that maximizes the revenue of the alliance market of wind power and cogeneration units can be determined.
The embodiment of the invention is not limited to the Shapley value method as the income distribution method for determining the alliance market of the wind power and cogeneration set, and other methods can be selected as the income distribution method for the alliance market of the wind power and cogeneration set.
By using the method provided by the embodiment of the invention, the coordinated control of the wind power heating system with the heat and power combination as the core is beneficial to wind power consumption, market optimization configuration resources are effectively utilized, the market competitiveness of wind power is improved, the overall flexibility of the alliance can be obviously improved by configuring the heat storage device, and the wind power and the heat and power cogeneration unit containing heat storage form an effective alliance to participate in market operation. The method provided by the embodiment of the invention fully mobilizes the regulation capacity of multiple links of source-network-load-storage, reasonably embodies the service value of the method, fully utilizes the market optimization configuration resources, can realize the maximization of alliance income, and provides an effective solution for the wind power and cogeneration units to participate in the market; for the overall operation of the system, the system balance cost is reduced, and the new energy consumption capability and the operation economy of the system are improved.
EXAMPLE III
As shown in fig. 2, a third embodiment of the present invention provides a quotation strategy determination device for a alliance market of wind power and cogeneration units, including:
a two-stage random optimization model module 201 is constructed and used for constructing a day-ahead and real-time two-stage random optimization model according to the double coupling relation between day-ahead operation and real-time operation of the wind power and cogeneration unit;
the maximum expected profit determining module is used for determining the maximum expected profit of the union of the wind power and the cogeneration unit according to the two-stage random optimization model;
and a determine the alliance market quotation strategy module 202, configured to determine the alliance market quotation strategy according to the maximum expected revenue.
And a revenue allocation method determining module 203, configured to determine the revenue allocation method according to the three alliance market quotation strategy modules.
Example four
The invention provides a income distribution device of a alliance market of wind power and cogeneration units, which comprises:
the system comprises a determining alliance market quotation strategy module, a determining alliance market quotation strategy module and a determining alliance market quotation strategy module, wherein the determining alliance market quotation strategy module is used for respectively determining an independent wind power generation unit, an independent cogeneration unit and the alliance market quotation strategies corresponding to the wind power generation unit and the cogeneration unit;
and the revenue generation distribution method module is used for generating a revenue distribution method based on the independent wind power generation and independent cogeneration units and the alliance market quotation strategies corresponding to the wind power generation and cogeneration units.
Embodiments of the present invention also provide a computer-readable storage medium, where the instructions, when executed by a processor, implement the above-mentioned method for determining a price quotation strategy for a federated market of wind power and cogeneration units and a method for allocating revenue that can implement the above-mentioned federated market of wind power and cogeneration units.
It will be apparent to those skilled in the art that embodiments of the present invention may be provided as methods and apparatus therefor, and that various changes and modifications may be made by those skilled in the art without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. A method for determining a alliance market quotation strategy of a wind power and combined heat and power generation unit is characterized by comprising the following steps:
constructing a day-ahead and real-time two-stage random optimization model according to the double coupling relation between day-ahead operation and real-time operation of the wind power and cogeneration unit;
determining the maximum expected income of the union of the wind power and cogeneration units according to the two-stage random optimization model;
and determining the alliance market quotation strategy according to the maximum expected income.
2. The method of claim 1, wherein constructing the two-stage stochastic optimization model comprises:
and constructing an objective function of the union expected income of the wind power and combined heat and power generation units and a constraint condition of the objective function according to the electricity selling income, the real-time unbalanced settlement income, the heat supply income and the fuel cost of the combined heat and power generation units of the union of the wind power and combined heat and power generation units in the market at the day time.
3. The method of claim 2, wherein the objective function is:
Figure FDA0002779631620000011
wherein R iswCHPMaximum expected revenue for a federation of wind power and cogeneration units;
Figure FDA0002779631620000012
the electricity selling income of the alliance of the wind power and the cogeneration unit in the market at the day before is calculated by the formula 2:
Figure FDA0002779631620000013
wherein λ isDA,tIn order to achieve the current price of the electricity in the market,
Figure FDA0002779631620000014
and
Figure FDA0002779631620000015
the day-ahead generating power of the wind power generation unit and the cogeneration unit is respectively;
Figure FDA0002779631620000016
for unbalanced settlement income of the union of the wind power generation unit and the cogeneration unit in real-time operation, the method is calculated according to the following formula 3:
Figure FDA0002779631620000021
where ρ issThe scene probability is calculated based on wind power output generated by the wind power prediction result and historical price information and corresponding market price scene,
Figure FDA0002779631620000022
respectively positive and negative real-time unbalanced prices;
Figure FDA0002779631620000023
respectively the positive and negative real-time unbalanced power of the alliance;
RHfor heat input, the following formula 4 is used for calculation:
Figure FDA0002779631620000024
wherein,
Figure FDA0002779631620000025
and λHRespectively heat load demand and heat supply price;
CHthe fuel cost for the cogeneration unit is calculated according to the following equation 5:
Figure FDA0002779631620000026
Figure FDA0002779631620000027
the day-ahead generated power of the cogeneration unit, and a, b and c are fuel cost coefficients of the cogeneration unit.
4. The method of claim 2, wherein the objective function constraints comprise: power balance constraint, wind power quotation constraint and constraint of a cogeneration unit:
the power balance constraint is:
in said formula 3
Figure FDA0002779631620000028
And
Figure FDA0002779631620000029
the following formulas 6 to 8 need to be satisfied:
Figure FDA00027796316200000210
wherein,
Figure FDA00027796316200000211
respectively the real-time power generation power of the wind power generation unit and the cogeneration unit in the S scene,
Figure FDA00027796316200000212
and
Figure FDA00027796316200000213
respectively the day-ahead generating power of the wind power generation unit and the cogeneration unit,
Figure FDA00027796316200000214
Figure FDA00027796316200000215
wherein, ys,tBeing a binary variable of the unbalanced power state, M1 and M2 are sufficiently large positive numbers;
the wind power quotation constraint is as follows:
in equation 2
Figure FDA0002779631620000031
The following formula 9 is satisfied:
Figure FDA0002779631620000032
wherein,
Figure FDA0002779631620000033
the output limit value of the wind power quotient is obtained;
the constraints of the cogeneration unit are:
in equation 4
Figure FDA0002779631620000034
The following equations 10 and 13 are satisfied at the same time:
Figure FDA0002779631620000035
wherein,
Figure FDA0002779631620000036
which is the thermal power of the cogeneration unit at the day before, is calculated by formula 11,
Figure FDA0002779631620000037
and
Figure FDA0002779631620000038
the input power and the output power of the heat storage tank before the day are respectively obtained by calculation of a formula 12;
Figure FDA0002779631620000039
wherein eta isCHPThe heat-electricity ratio of the cogeneration unit;
Figure FDA00027796316200000310
wherein,
Figure FDA00027796316200000311
for energy of day-ahead heat storage tanks, Tt tesAnd Tt envThe temperature of the heat storage medium in the heat storage tank and the ambient temperature rt tesIs internal resistance of the heat storage tank;
Figure FDA00027796316200000312
wherein,
Figure FDA00027796316200000313
is the real-time thermal power of the cogeneration unit and is calculated by the formula 14,
Figure FDA00027796316200000314
and
Figure FDA00027796316200000315
the input power and the output power of the real-time heat storage tank under the S scene are respectively obtained by calculation according to a formula 15;
Figure FDA00027796316200000316
Figure FDA00027796316200000317
wherein,
Figure FDA00027796316200000318
is real-time heat storage tank energy.
5. The method of claim 4, wherein determining a maximum expected revenue for a federation of wind power and combined heat and power generation units based on the two-stage stochastic optimization model, and determining the federation market quotation strategy based on the maximum expected revenue comprises:
determining according to the formulas 2-15 in the two-stage random optimization model
Figure FDA0002779631620000041
RH、CHObtaining R output by the two-stage stochastic optimization modelwCHPAs the maximum expected revenue for a federation of wind power and cogeneration units;
and determining the alliance market quotation strategy of the wind power and combined heat and power generation unit according to the maximum expected income.
6. A income distribution method for the alliance market of wind power and cogeneration units is characterized by comprising the following steps:
determining a alliance market quotation strategy of a combined power and heat and power generation unit by adopting the method of any one of claims 1-5;
and generating a revenue distribution method based on the alliance market quotation strategy.
7. The method of claim 6, wherein generating a revenue distribution method based on the federation market quotation policy comprises:
respectively determining an independent wind power generation unit, an independent cogeneration unit and the alliance market quotation strategies corresponding to the wind power generation unit and the cogeneration unit based on the alliance market quotation strategies;
removing the related variables of the target function cogeneration set and the related conditions of the cogeneration set in the constraint conditions, and determining the alliance market quotation strategy of the independent wind power;
removing the wind power related variables in the objective function and the wind power related conditions in the constraint conditions, and determining the alliance market quotation strategy of the independent cogeneration unit;
determining a alliance market quotation strategy of the wind power and cogeneration unit;
and determining the income distribution method according to the three alliance market quotation strategies.
8. The method of claim 7, wherein the allocation of revenue for the wind and the cogeneration machine is determined by the following equation 16 based on a Shapley value method and the alliance market quotation strategy for the independent wind power, the independent cogeneration unit, and the wind and cogeneration unit,
Figure FDA0002779631620000051
wherein R iswpMaximum expected revenue, R, for the alliance market of independent wind powerCHPFor maximum expected revenue in the alliance market of independent cogeneration units, RwCHPFor maximum expected revenue in the alliance market of wind power and cogeneration units, Xwp、XCHPRespectively the earnings divided by the wind power generation unit and the cogeneration unit.
9. A quotation strategy determination device for a federated market of wind power and cogeneration units, comprising:
the method comprises the steps that a two-stage random optimization model module is constructed and used for constructing a day-ahead and real-time two-stage random optimization model according to the double coupling relation between day-ahead operation and real-time operation of the wind power and cogeneration unit;
the maximum expected profit determining module is used for determining the maximum expected profit of the union of the wind power and the cogeneration unit according to the two-stage random optimization model;
and the alliance market quotation strategy determining module is used for determining the alliance market quotation strategy according to the maximum expected income.
10. A revenue sharing apparatus for a federated market of wind power and cogeneration units, comprising:
the system comprises a determining alliance market quotation strategy module, a determining alliance market quotation strategy module and a determining alliance market quotation strategy module, wherein the determining alliance market quotation strategy module is used for respectively determining an independent wind power generation unit, an independent cogeneration unit and the alliance market quotation strategies corresponding to the wind power generation unit and the cogeneration unit;
and the revenue generation distribution method module is used for generating a revenue distribution method based on the independent wind power generation and independent cogeneration units and the alliance market quotation strategies corresponding to the wind power generation and cogeneration units.
CN202011277582.XA 2020-11-16 2020-11-16 Electric power quotation strategy determination method, income distribution method and device thereof Pending CN112561123A (en)

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