CN110689286A - Optimal contract electric quantity decision method for wind-fire bundling power plant in medium-and-long-term electric power market - Google Patents

Optimal contract electric quantity decision method for wind-fire bundling power plant in medium-and-long-term electric power market Download PDF

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CN110689286A
CN110689286A CN201911107812.5A CN201911107812A CN110689286A CN 110689286 A CN110689286 A CN 110689286A CN 201911107812 A CN201911107812 A CN 201911107812A CN 110689286 A CN110689286 A CN 110689286A
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power
wind
electric quantity
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CN110689286B (en
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朱明辉
姜正庭
郁翔
王建学
罗继峰
周磊
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Xian Jiaotong University
Electric Power Research Institute of State Grid Shaanxi Electric Power Co Ltd
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Xian Jiaotong University
Electric Power Research Institute of State Grid Shaanxi Electric Power Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G06Q10/06375—Prediction of business process outcome or impact based on a proposed change
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects

Abstract

The invention discloses an optimal contract electric quantity decision method for a wind-fire bundling power plant in a medium-long term electric power market, which is used for obtaining basic technical parameters of a wind-fire bundling system, system operation constraint condition data and historical trading price; establishing an optimization target by taking the benefit of the maximized wind-fire bundling power plant as an objective function; constructing a system operation constraint condition, and establishing a maximum benefit model; inputting the obtained system basic technical data, system operation constraint condition data and historical transaction prices into a constructed optimization model, and solving to obtain optimized wind power bidding electric quantity and thermal power bidding electric quantity; and feeding back the obtained optimal contract electric quantity calculation result to the power plant, and making a decision on the electric quantity bidding of the wind-fire bundling power plant. The optimal contract electric quantity and the historical average transaction price obtained by the invention are used for signing a medium-term and long-term power contract with a user or a power selling company so as to obtain the maximum benefit.

Description

Optimal contract electric quantity decision method for wind-fire bundling power plant in medium-and-long-term electric power market
Technical Field
The invention belongs to the technical field of power supply, and particularly relates to an optimal contract electric quantity decision method for a wind-fire bundling power plant in a medium-long term electric power market.
Background
In a new period, a power producer no longer only uses fossil energy as his main production energy, and new energy such as wind power photovoltaic also participates. The vigorous development of wind energy resources has important significance for meeting the energy demand of rapid development, realizing the aims of energy conservation and emission reduction and treating atmospheric pollution. However, due to the uncertainty and intermittency of wind power generation, supplying power to the receiving end system by wind power generation alone causes problems of difficult peak shaving and unstable voltage. In order to fully utilize the advantages of wind resources and reduce the influence of output power fluctuation of a pure wind power base on a receiving end system, the new energy and conventional thermal power are combined to generate electricity, namely, a peak regulation power supply with a corresponding scale is constructed in a matching way of a centralized wind power base, and the purpose of the peak regulation power supply is to improve the utilization rate of wind and provide a reserve for wind power by utilizing the regulation characteristic of the thermal power.
The wind-fire bundling operation mode is an application mode of carrying out combined power generation on wind power and thermal power and carrying out power transmission through a power transmission channel. The mode can meet the requirements of trans-regional power transmission and strong power grid construction, and simultaneously, the utilization and development of renewable energy sources such as wind power and the like are increased. The method is very suitable for the energy development characteristics that energy is reversely distributed and the coal production area and the wind energy enrichment area are overlapped.
There are many studies on wind-fire bundling mode. Some of the wind-fire bundling configuration schemes are considered, and the optimal wind-fire capacity configuration under different operation modes of bundling AC (alternating current) and DC (direct current) outward conveying from wind-fire is provided, and the wind-fire combined outward conveying operation problem is also considered. Specifically, for example, some methods analyze the optimal wind-fire capacity configuration in different operation modes of wind-fire bundling and outward delivery; some methods are useful for selecting a proper transmission capacity and a proper wind-fire bundling configuration scheme to economically and stably operate the power grid, and a planning method is provided; some proposed methods for evaluating transmission capacity between regions; some proposed theoretical calculation methods for proper capacity of wind-fire bundling; some wind-fire combined delivery systems perform random production simulation. However, the research on participation of the wind-fire bundling and delivery system in the power market is not many, and the degree of marketization of power will gradually increase in the future. Therefore, for wind-fire bundled power plants, it is very important to conduct their bidding strategy research to provide decision reference for the power market participants.
Disclosure of Invention
The invention aims to solve the technical problem of providing an optimal contract electric quantity decision method of a wind-fire bundling power plant in a medium-long term electric power market aiming at the defects in the prior art, and can provide valuable opinions for the power plant to participate in the medium-long term market in a new situation based on the principle of optimal benefit of the wind-fire bundling power plant.
The invention adopts the following technical scheme:
the optimal contract electric quantity decision method for the wind-fire bundling power plant in the medium-and-long-term electric power market comprises the following steps:
s1, obtaining basic technical parameters of the wind-fire bundling system, system operation constraint condition data and historical transaction prices;
s2, establishing an optimization target by taking the benefit of the maximized wind-fire bundling power plant as an objective function; constructing a system operation constraint condition, and establishing a maximum benefit model;
s3, inputting the system basic technical data, the system operation constraint condition data and the historical trading price obtained in the step S1 into the optimization model constructed in the step S2, and solving to obtain optimized wind power bidding electric quantity and thermal power bidding electric quantity;
and S4, feeding back the optimal contract electric quantity calculation result obtained in the step S3 to the power plant, and making a decision on the electric quantity bidding of the wind-fire bundling power plant.
Specifically, in step S1, the system basic technical data includes coal consumption coefficients a, b, c of the thermal power generating unit, maximum output of the wind power generating unit, predicted output of one year of wind power, power generation cost of a wind power unit, grid electricity price of wind power, electricity and coal price per month and delivery power demand; and the system operation constraint condition data comprises upper and lower limit values of the output of each generator set.
Specifically, in step S2, the objective function is:
Figure BDA0002271843720000031
wherein λ ismA unit electricity transaction price of m months; emTotal transaction electricity for m months; n is a radical ofmIs the month included each year; dmDays included in month m; cT,mCoal value of month m; t is the number of hours per day; Δ t is the time interval; n is a radical ofgThe number of thermal power generating units;
Figure BDA0002271843720000032
represents the output of the ith thermal power unit in the mth month
Figure BDA0002271843720000033
Corresponding coal consumption; c. CwThe unit power generation cost of wind power;
Figure BDA0002271843720000034
the wind power output is the wind power output in the mth time period of the mth month; w is a abandoned wind loss coefficient and is taken as the wind power on-grid electricity price of each month;
Figure BDA0002271843720000035
the abandoning power of the wind power is the abandoning power of the wind power in the mth time period of the mth month.
Specifically, in step S2, the system operation constraint conditions include a unit output constraint, an electric quantity balance constraint, a wind curtailment power balance constraint, a consumption rate constraint, a total electric quantity balance and a bundling ratio constraint; the unit output constraint comprises thermal power unit output upper and lower limit constraints.
Further, the thermal power generating unit output upper and lower limits are constrained as follows:
Figure BDA0002271843720000036
wherein N ismIs the month included each year; t is the number of hours per day; n is a radical ofgThe number of thermal power generating units;
Figure BDA0002271843720000037
the wind power output is the wind power output in the mth time period of the mth month;the minimum generated power of the ith thermal power generating unit,
Figure BDA0002271843720000039
the maximum generated power of the ith thermal power generating unit,
Figure BDA00022718437200000310
the output power of the thermal power generating unit in the t period of the ith unit in the mth month,
Figure BDA00022718437200000311
and predicting the maximum output for the wind power in the mth time period of the mth month.
Further, the power balance constraint is:
Figure BDA0002271843720000041
wherein m is 1,2, …, NmT is the number of hours per day; em,wThe wind power of the mth month is used for bidding the electric quantity; em,thBidding electric quantity for the thermal power of the mth month; dmDays included in month m; n is a radical ofgThe number of thermal power generating units; at is the time interval at which the time interval,
Figure BDA0002271843720000042
and outputting the power of the thermal power generating unit in the t period of the ith unit in the mth month.
Further, the curtailment wind power balance constraint is as follows:
Figure BDA0002271843720000043
wherein the content of the first and second substances,
Figure BDA0002271843720000044
predicting the maximum output for the wind power in the mth time period of the mth month,
Figure BDA0002271843720000045
the wind power output is the wind power output in the mth time period of the mth month.
Further, the consumption rate constraint is:
Figure BDA0002271843720000046
wherein m is 1,2, …, NmEta is the consumption rate required to be met by the wind power plant, and T is the number of hours per day; Δ t is the time interval;
Figure BDA0002271843720000047
the wind power output of the mth time period of the mth month,
Figure BDA0002271843720000048
and predicting the maximum output for the wind power in the mth time period of the mth month.
Further, the total electric quantity balance and bundling proportion constraint are as follows:
Figure BDA0002271843720000049
wherein m is 1,2, …,12, EmTotal transaction capacity of m months, Em,wFor bidding for the m-th month wind powerm,thBidding electric quantity, beta, for the thermal power of the mth monthmIs the proportion of the generated energy of wind power and thermal power.
Compared with the prior art, the invention has at least the following beneficial effects:
according to the optimal contract electric quantity decision method of the wind-fire bundling power plant in the long-term electric power market, firstly, factors needing to be considered in modeling of a bidding method are analyzed, and then according to the power generation cost of the wind-fire bundling power plant, an electric quantity bidding method model is established to analyze the influence of different contract electric quantities and different power plant bundling proportions on the benefit of the wind-fire bundling power plant; finally, simulation analysis is carried out on the monthly market, and results show that the method can effectively improve bidding efficiency of the wind-fire bundling power plant and provide value for participating in medium-long-term market.
Further, the system parameters and the historical data obtained in step S1 are the basis for making the optimal contract power decision.
Further, the optimal contract electric quantity can be reasonably determined according to the historical data and the system parameters through the objective function of the step S2, so that the wind-fire bundling system can maximize the electricity selling benefit of the wind-fire bundling system.
Further, the system operation constraint conditions in the step S2 ensure that the medium-and-long-term power contract signed by the wind-fire bundling power plant can be practically applied.
Furthermore, the purpose of the constraint setting of the upper and lower limits of the thermal power output of the thermal power generating unit is to ensure that the thermal power output of each time period can be within the power generation range of the thermal power generating unit after the electric quantity of the medium-long term electric power contract is decomposed, and ensure that the thermal power generating unit normally operates to complete the electric power contract requirement.
Further, the purpose of the power balance constraint setting is to ensure that the sum of the power generation amount of each time interval of each month can be equal to the signed contract power amount.
Furthermore, the purpose of the abandoned wind power balance setting is to obtain abandoned wind power quantity, so that the power plant can punish the abandoned wind power quantity according to the wind power consumption requirement of the power plant in the objective function.
Furthermore, the purpose of the consumption rate constraint setting is to guarantee the wind power consumption requirement of the wind power plant.
Furthermore, the purpose of setting the total electric quantity balance and the bundling proportion constraint is to ensure that the sum of the electric energy generated by the wind power and the thermal power is equal to the jointly signed contract electric quantity, and the bundling proportion constraint stipulates the electric energy generated ratio requirement of the wind power and the thermal power.
In conclusion, the invention establishes the secondary planning model for maximizing the benefit of the wind-fire bundling power plant by considering factors such as historical transaction price, thermal power cost, wind power punishment and the like. The optimal contract electric quantity suitable for signing by the wind fire bundling system can be obtained by solving the model. In practical application, the wind-fire bundling system can sign medium-long term power contract with the user or the power selling company according to the optimal contract power and the historical average transaction price obtained by the invention so as to obtain the maximum benefit.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The traditional wind-fire bundling researches are various, but the research angles are different, the optimal wind-fire capacity configuration under different operation modes of wind-fire bundling outgoing is considered, a planning method for a wind-fire bundling power plant is provided, and a theoretical calculation method for the optimal capacity of the wind-fire bundling power plant is also provided. The above researches are all in consideration of the planning problem of the wind-fire bundling power plant, and the research results of the wind-fire bundling power plant participating in the electric power market transaction are not many, so that the decision method provided by the invention is necessary for the wind-fire bundling power plant to participate in the medium-and long-term electric power market.
The invention provides an optimal contract electric quantity decision method for a wind-fire bundling power plant in a medium-long term electric power market, which can provide valuable opinions for the power plant to participate in the medium-long term market in a new situation based on the principle of optimal benefit of the wind-fire bundling power plant. The research on the participation of wind-fire bundled power plants in the power market is not much, and the degree of marketization of the power market will be gradually deepened in the future. Therefore, for wind-fire bundled power plants, it is very important to conduct research on bidding methods thereof to provide decision references for participants in the power market.
Referring to fig. 1, the method for determining the optimal contract electric quantity of the wind-fire bundled power plant in the medium-and long-term electric power market of the invention includes the following steps:
s1, obtaining basic technical parameters of the system, system operation constraint condition data and historical trading prices from related departments;
basic technical data of the system: coal consumption coefficients a, b and c of the thermal power generating unit, maximum output of the wind power generating unit, predicted output of one year of wind power, power generation cost of a wind power unit, power price of wind power on grid, price of electricity and coal in each month and delivery power demand.
System operating constraint data: and (4) the output upper and lower limit values of each generator set.
S2, constructing a maximum benefit model;
s201, establishing an optimization target by taking the benefit of the maximized wind-fire bundling power plant as an objective function;
Figure BDA0002271843720000071
wherein λ ismA unit electricity transaction price of m months; emTotal transaction electricity for m months; n is a radical ofmIs the month contained in each year, 12 is taken; dmDays included in month m; cT,mCoal value of month m; t is the number of hours per day, and 24 is taken; Δ t is the time interval; n is a radical ofgThe number of thermal power generating units;
Figure BDA0002271843720000072
represents the output of the ith thermal power unit in the mth monthCorresponding coal consumption; c. CwThe unit power generation cost of wind power;
Figure BDA0002271843720000074
the wind power output is the wind power output in the mth time period of the mth month; w is a abandoned wind loss coefficient and is taken as the wind power on-grid electricity price of each month;
Figure BDA0002271843720000075
the abandoning power of the wind power is the abandoning power of the wind power in the mth time period of the mth month.
S202, constructing system operation constraint conditions including unit output constraint, electric quantity balance constraint, consumption rate constraint and wind-fire bundling electric quantity proportion constraint; the unit output constraint comprises the thermal power unit output upper and lower limit constraints;
and (3) balancing the total electric quantity and binding the proportion constraint:
Figure BDA0002271843720000076
wherein m is 1,2, …,12, Em,wFor bidding for the m-th month wind powerm,wFor bidding for the m-th month wind powerm,thAnd bidding electric quantity for the thermal power of the mth month.
Thermal power coal consumption balance constraint:
Figure BDA0002271843720000077
wherein, ai、bi、ciIs the coal consumption coefficient of the unit,
Figure BDA0002271843720000078
represents the output of the ith thermal power unit in the mth monthCorresponding coal consumption.
And (3) abandoned wind power balance constraint:
Figure BDA0002271843720000081
wherein the content of the first and second substances,predicting the maximum output for the wind power in the mth time period of the mth month,
Figure BDA0002271843720000083
the wind power output is the wind power output in the mth time period of the mth month.
And electric quantity balance constraint:
Figure BDA0002271843720000084
wherein m is 1,2, …, Nm，Em,wFor bidding for the m-th month wind powerm,thBidding for thermal power of month mAn amount of electricity; dmDays included in month m; n is a radical ofgThe number of thermal power generating units; at is the time interval at which the time interval,
Figure BDA0002271843720000085
and outputting the power of the thermal power generating unit in the t period of the ith unit in the mth month.
Wind power thermal power output constraint:
wherein N ismIs the month included each year; t is the number of hours per day; n is a radical ofgThe number of thermal power generating units;
Figure BDA0002271843720000087
the wind power output is the wind power output in the mth time period of the mth month;
Figure BDA0002271843720000088
the minimum generated power of the ith thermal power generating unit,
Figure BDA0002271843720000089
the maximum generated power of the ith thermal power generating unit,
Figure BDA00022718437200000810
the output power of the thermal power generating unit in the t period of the ith unit in the mth month,
Figure BDA00022718437200000811
and predicting the maximum output for the wind power in the mth time period of the mth month.
The consumption rate satisfies the constraint:
Figure BDA00022718437200000812
wherein m is 1,2, …, NmEta is the consumption rate required to be met by the wind power plant, and T is the number of hours per day; Δ t is the time interval;the wind power output of the mth time period of the mth month,
Figure BDA00022718437200000814
and predicting the maximum output for the wind power in the mth time period of the mth month.
S3, inputting the system basic technical data, the system operation constraint condition data and the historical trading price obtained in the step S1 into the optimization model constructed in the step S2, and solving to obtain the optimized contract electric quantity Em,wAnd Em,th；
In mathematic calculation software Matlab, the required data and mathematic model are written into an optimization solver CPLEX, and the solver is called to solve to obtain the optimized contract electric quantity Em,wAnd Em,th。
And S4, feeding back the calculation result of the optimal contract electric quantity to the power plant, and further providing reference for bidding the electric quantity of the wind-fire bundling power plant.
In practical application, the wind-fire bundling power plant can sign medium-long term power contracts with users or power selling companies according to the optimal contract electric quantity and the historical trading price average value obtained by the decision method, and the final trading is completed after the medium-long term power contracts are reported to a power trading center.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In practical application, a market trading mode is that a thermal power plant represents a wind-fire bundling system to trade with a large user or an electricity selling company, the bundling proportion also determines the total power generation amount of the system in the mode, so different bundling proportions can obtain different benefits, and the wind-fire bundling proportion of each month is different due to seasonal variation of wind power. According to the invention, the thermal power plant can sign the medium-term and long-term electric power contract with the trading object according to the optimal contract electric quantity and the historical trading price average value obtained by the decision method, and meanwhile, the invention also considers the bundling proportion of the wind-fire bundling system, so that the generated energy of the wind-fire bundling system can meet the contract electric quantity requirement under the specified proportion and the maximum benefit can be obtained. After the medium-and-long-term electric power contract is signed, the medium-and-long-term electric power contract can be reported to the electric power transaction center to complete the medium-and-long-term electric power transaction in the year.
In the medium and long-term market, for the direct transaction mode of the wind-fire bundling power plant, the thermal power plant is responsible for achieving transaction with large users. After the contract is signed, the thermal power plant and the wind farm negotiate about the minimum bundling ratio to determine the active power generation of the unit. In this mode, the bundling ratio of the thermal power plant or the wind power plant is determined according to the total power generation amount, so that the wind power plant can obtain different benefits under different bundling ratios. However, the electric quantity bundling proportion of the wind power plant and the thermal power plant should change along with the month, the wind power generation is influenced by the seasons and the climate, for example, the wind in winter is small, the power generation output of the wind power is insufficient, then the thermal power should generate more electric quantity, at the moment, the wind-fire bundling proportion is small, and the bundling proportion is not changed, so that the electric quantity meeting the requirement cannot be jointly generated. The optimal contract electric quantity calculation method based on the wind-fire bundling power plant cost fully considers the influence of the wind-fire-electricity bundling proportion and the market electric power trading price on the bidding of the electric quantity in each month, and the obtained optimal contract electric quantity has good guiding significance on the wind-fire bundling power plant.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (9)

1. The optimal contract electric quantity decision method for the wind-fire bundling power plant in the medium-and-long-term electric power market is characterized by comprising the following steps of:
s1, obtaining basic technical parameters of the wind-fire bundling system, system operation constraint condition data and historical transaction prices;
s2, establishing an optimization target by taking the benefit of the maximized wind-fire bundling power plant as an objective function; constructing a system operation constraint condition, and establishing a maximum benefit model;
s3, inputting the system basic technical data, the system operation constraint condition data and the historical trading price obtained in the step S1 into the optimization model constructed in the step S2, and solving to obtain optimized wind power bidding electric quantity and thermal power bidding electric quantity;
and S4, feeding back the optimal contract electric quantity calculation result obtained in the step S3 to the power plant, and making a decision on the electric quantity bidding of the wind-fire bundling power plant.
2. The optimal contract electric quantity decision method for the wind-fire bundling power plant under the medium-and-long-term electric power market according to claim 1, characterized in that in step S1, the system basic technical data comprise coal consumption coefficients a, b, c of the thermal power generating unit, the maximum output of the wind power generating unit, the predicted output of one year of wind power, the power generation cost of the wind power unit, the price of the electricity and coal on the wind power grid, the price of the electricity and coal in each month and the delivery power demand; and the system operation constraint condition data comprises upper and lower limit values of the output of each generator set.
3. The method for deciding on the optimal contract power of the wind-fire bundling power plant under the medium-and-long-term electric power market according to claim 1, wherein in step S2, the objective function is:
wherein λ ismA unit electricity transaction price of m months; emTotal transaction power of m monthsAn amount; n is a radical ofmIs the month included each year; dmDays included in month m; cT,mCoal value of month m; t is the number of hours per day; Δ t is the time interval; n is a radical ofgThe number of thermal power generating units;
Figure FDA0002271843710000012
represents the output of the ith thermal power unit in the mth month
Figure FDA0002271843710000021
Corresponding coal consumption; c. CwThe unit power generation cost of wind power;
Figure FDA0002271843710000022
the wind power output is the wind power output in the mth time period of the mth month; w is a abandoned wind loss coefficient and is taken as the wind power on-grid electricity price of each month;
Figure FDA0002271843710000023
the abandoning power of the wind power is the abandoning power of the wind power in the mth time period of the mth month.
4. The optimal contract electric quantity decision method of the wind-fire bundling power plant under the medium-and-long-term electric power market according to claim 1, characterized in that in step S2, the system operation constraint conditions include unit output constraint, electric quantity balance constraint, abandoned wind power balance constraint, consumption rate constraint, total electric quantity balance and bundling ratio constraint; the unit output constraint comprises thermal power unit output upper and lower limit constraints.
5. The optimal contract electric quantity decision method for the wind-fire bundling power plant in the medium-and-long-term electric power market according to claim 4, characterized in that the upper and lower output limits of the thermal power generating unit are constrained as follows:
Figure FDA0002271843710000024
wherein N ismIs the month included each year; t is the number of hours per day; n is a radical ofgFor thermal power generating unitsThe number of (2);
Figure FDA0002271843710000025
the wind power output is the wind power output in the mth time period of the mth month;
Figure FDA0002271843710000026
the minimum generated power of the ith thermal power generating unit,
Figure FDA0002271843710000027
the maximum generated power of the ith thermal power generating unit,
Figure FDA0002271843710000028
the output power of the thermal power generating unit in the t period of the ith unit in the mth month,
Figure FDA0002271843710000029
and predicting the maximum output for the wind power in the mth time period of the mth month.
6. The optimal contract power decision method of the wind-fire bundling power plant under the medium-and-long-term electric power market according to claim 4, characterized in that the power balance constraint is as follows:
wherein m is 1,2, …, Nm，Em,wFor bidding for the m-th month wind powerm,thBidding electric quantity for the thermal power of the mth month; dmDays included in month m; n is a radical ofgThe number of thermal power generating units; at is the time interval at which the time interval,
Figure FDA00022718437100000211
the wind power output of the mth time period of the mth month,
Figure FDA0002271843710000031
thermal power generating unit output for t time period of ith unit in m monthAnd (4) power.
7. The optimal contract electric quantity decision method of the wind-fire bundling power plant under the medium-and-long-term electric power market is characterized in that the abandoned wind power balance constraint is as follows:
wherein the content of the first and second substances,
Figure FDA0002271843710000033
predicting the maximum output for the wind power in the mth time period of the mth month,
Figure FDA0002271843710000034
the wind power output is the wind power output in the mth time period of the mth month.
8. The optimal contract power decision method of the wind-fire bundling power plant under the medium-and-long-term electric power market according to claim 4, characterized in that the consumption rate constraint is as follows:
wherein m is 1,2, …, NmEta is the consumption rate required to be met by the wind power plant, and T is the number of hours per day; Δ t is the time interval;
Figure FDA0002271843710000036
the wind power output of the mth time period of the mth month,
Figure FDA0002271843710000037
and predicting the maximum output for the wind power in the mth time period of the mth month.
9. The optimal contract electric quantity decision method of the wind-fire bundling power plant under the medium-and-long-term electric power market is characterized in that the total electric quantity balance and bundling proportion constraint is as follows:
Figure FDA0002271843710000038
wherein m is 1,2, …,12, EmTotal transaction capacity of m months, Em,wFor bidding for the m-th month wind powerm,thBidding electric quantity, beta, for the thermal power of the mth monthmIs the proportion of the generated energy of wind power and thermal power.
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