CN114925892A - Water-electricity-to-gas combined medium-and-long-term wind-water-fire generating capacity double-layer planning method - Google Patents

Water-electricity-to-gas combined medium-and-long-term wind-water-fire generating capacity double-layer planning method Download PDF

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CN114925892A
CN114925892A CN202210493535.1A CN202210493535A CN114925892A CN 114925892 A CN114925892 A CN 114925892A CN 202210493535 A CN202210493535 A CN 202210493535A CN 114925892 A CN114925892 A CN 114925892A
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张小兵
钟浩
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China Three Gorges University CTGU
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Abstract

The invention relates to a water-electricity-to-gas combined medium-and-long-term wind-water-fire power generation capacity double-layer planning method, which comprises the following steps: forecasting medium and long term wind power, hydropower output and load electric quantity, and establishing a random scene set for wind power uncertainty; establishing a medium and long term electric energy market electricity purchasing model by taking the minimum electricity purchasing cost of an electricity purchasing company as a target; establishing a wind, water and thermal power generation optimal configuration model based on a contract transfer mechanism with the maximum income of a power generator as a target; and solving the double-layer planning model to obtain the optimal power-to-gas capacity and the optimal power generation scheme of each power generator. The invention utilizes the upper-layer medium-and-long-term electric energy market electricity purchasing model of the double-layer planning to realize the minimum electricity purchasing cost of the electric energy market; the lower layer of the double-layer planning is based on a wind, water and fire power generation optimal configuration model of a contract transfer mechanism, optimal electricity-to-gas capacity configuration is obtained through solution, the contract electric quantity of each power generator improves the flexibility of water and electricity regulation, the wind power consumption proportion in a medium-term and a long-term is improved, and electricity-to-gas equipment and energy optimal configuration are realized.

Description

Water-electricity-to-gas combined medium-and-long-term wind-water-fire generating capacity double-layer planning method
Technical Field
The invention belongs to the field of new energy optimization control, and particularly relates to a water-electricity-to-gas combined medium-and-long-term wind-water-fire generated energy double-layer planning method.
Background
In order to improve the flexibility of water and electricity regulation and improve the consumption proportion of wind power in a medium-long term, wind, fire and water power generators sign medium-long term contracts with a power grid. The actual output of the wind power has obvious volatility and randomness, so that the normal-bid electric quantity of the wind power in the medium-term and long-term market is lower. The mature electricity-to-gas technology can realize the conversion of the electric energy to the hydrogen and the natural gas. The electric conversion gas has the characteristics of energy transfer and large-scale energy storage, can be widely applied to an electric power system, has the positive effects of wind and water consumption and can provide services such as peak regulation and frequency modulation. However, the investment cost of converting electricity into gas is high, and if the electricity into gas is used as an investment operation main body alone, the problems of site selection, large construction investment, difficult cost recovery and the like can be met. Water and electricity are high-quality adjusting energy sources and can provide adjusting services for the system. But the water is generated in a large amount due to the participation of the water-saving device in the adjustment, so that the generated energy is less, the economic benefit and the competitiveness are reduced, and the participation system has low adjustment initiative.
Disclosure of Invention
The technical problem of the invention is that wind power has volatility and randomness, so that wind power generators cannot fulfill medium and long-term contracts, even abandon wind and the like, and how to improve the regulation flexibility of a power grid system and improve the proportion of clean energy becomes a problem to be solved urgently on the premise of ensuring the safe and stable operation of a power system.
The invention aims to solve the problems and provides a water-electricity-to-gas combined medium-and-long-term wind-water-fire power generation capacity double-layer planning method, wherein a wind power quality segmentation idea is adopted, and wind power output prediction is divided into a determined part and an uncertain part; the electric conversion gas and the water and electricity are introduced for combined regulation, on one hand, the electric conversion gas is configured in the hydropower station, so that the problem of site selection of the electric conversion gas is solved, on the other hand, the electric conversion gas can be sold by converting redundant abandoned water into gas, direct energy sale benefit is brought to the water and electricity, and the initiative of the water and electricity participating in system regulation can be improved; and a medium-and-long-term contract transfer mechanism is introduced, and medium-and-long-term power generation contracts can be transferred to wind power by hydroelectric power and thermal power. The upper layer of the double-layer planning is a medium and long-term electric energy market electricity purchasing model, and the upper layer planning enables the electricity purchasing cost of the electric energy market to be minimum; the lower layer is a wind, water and fire power generation optimal configuration model based on a contract transfer mechanism, and the lower layer of the double-layer planning promotes the contract transfer of hydropower to wind power generation by utilizing a hydropower and electricity-to-gas combined regulation mode, improves the wind power consumption proportion and optimizes the electricity-to-gas capacity configuration.
The technical scheme of the invention is a water-electricity-to-gas combined medium-and-long-term wind-water-fire power generation capacity double-layer planning method, which comprises the following steps:
step 1: forecasting medium and long term wind power, hydropower output and load electric quantity, and establishing a random scene set for wind power uncertainty;
and 2, step: establishing a medium and long term electric energy market electricity purchasing model by taking the minimum electricity purchasing cost of an electric power market as a target;
and step 3: the specific process of converting hydrogen and natural gas (methane) by electric energy is realized by converting electricity into gas;
and 4, step 4: establishing a wind, water and fire power generation optimal configuration model based on a contract transfer mechanism with the maximum income of a power generator as a target;
step 4.1: establishing an objective function and a constraint condition of a wind-water-heat power generation optimal configuration model;
step 4.2: respectively determining the gain functions of wind power, hydropower and thermal power generators;
and 5: and solving the double-layer planning model to obtain the optimal electricity-to-gas capacity and the optimal power generation scheme of each power generator.
In the step 1, a wind power quality segmentation idea is adopted to divide the generated energy predicted by wind power into a determined part and an uncertain part; the method comprises the steps of obtaining a wind power probability density curve by using a nonparametric kernel density estimation method, establishing a random simulation scene for an uncertain part of wind power, reducing distance scenes by using improved k-means clustering, representing scenes with similar probability distances by using a typical scene, and forming a typical scene set with probability values.
Preferably, the objective function of the medium and long term electric energy market purchasing model is as follows:
Figure BDA0003632734130000021
in the formula, f represents the medium and long term electricity purchasing cost in the power market, and T is the total number of medium and long term time periods; n is the number of thermal power generating units; m is the number of wind power plants;
Figure BDA0003632734130000022
contract price for the hydropower provider for time period t;
Figure BDA0003632734130000023
contract price for time period t of wind farm m;
Figure BDA0003632734130000024
the contract price of the thermal power generating unit n in the t time period;
Figure BDA0003632734130000025
contract electric quantity for the hydropower provider in the time period t;
Figure BDA0003632734130000026
the contract electric quantity of the wind power plant m in the t period;
Figure BDA0003632734130000027
and (4) contract electric quantity of the thermal power generating unit n in the time period t.
Further, the constraint conditions of the electricity purchasing model of the medium and long-term electric energy market comprise:
1) quotation constraints
Figure BDA0003632734130000028
Figure BDA0003632734130000029
Figure BDA00036327341300000210
In the formula
Figure BDA00036327341300000211
Minimum and maximum quotation limits of the generator are respectively set;
2) bid amount constraint in power generator business
Figure BDA00036327341300000212
Figure BDA00036327341300000213
Figure BDA00036327341300000214
In the formula
Figure BDA00036327341300000215
Predicting power output for hydroelectric power;
Figure BDA00036327341300000216
maximum output of the determined part of the power generation prediction for the wind farm m;
Figure BDA00036327341300000217
respectively declaring minimum and maximum electric quantity for the thermal power generating unit n;
3) power balance constraint
Figure BDA0003632734130000031
In the formula P l,t An amount of power is predicted for the load.
Preferably, the objective function of the wind-water-fire power generation optimization configuration model is as follows:
maxS＝max(S1+S2-S3) (9)
wherein S is the income of a wind power generator; s1 is the total income of the wind power generator with double-layer planning; s2 wind power obtains the environmental cost reduced by the transfer of the thermal power contract; s3 is the deviation penalty generated by wind power.
Total yield of wind power generator:
Figure BDA0003632734130000032
Figure BDA0003632734130000033
in the formula
Figure BDA0003632734130000034
Contract electric quantity of t time period of the wind power station m;
Figure BDA0003632734130000035
transferring the electric quantity for the contract accepted by the wind power station m;
Figure BDA0003632734130000036
a contract price for a t time period declared for the wind farm m;
Figure BDA0003632734130000037
contract transferred electricity prices for the wind farm m in a time period t; c W The cost of wind power;
Figure BDA0003632734130000038
the power transferred for the contract of the hydropower generation business;
Figure BDA0003632734130000039
and (4) the amount of electricity transferred by the thermal power generating unit n contract.
Environmental cost reduction after contract transfer by thermal power generation merchants:
Figure BDA00036327341300000310
Figure BDA00036327341300000311
Figure BDA00036327341300000312
Figure BDA00036327341300000313
Figure BDA00036327341300000314
in the formula
Figure BDA00036327341300000315
The environmental cost of the thermal power generator before the contract transfer;
Figure BDA00036327341300000316
the thermal power generator environmental cost after the contract transfer is achieved; xi shape n The unit generating coal consumption of the thermal power generating unit n is calculated;
Figure BDA00036327341300000317
the output electric quantity of the thermal power generating unit n before the contract transfer is obtained;
Figure BDA00036327341300000318
the output electric quantity of the thermal power generating unit n after the contract transfer is obtained;
Figure BDA00036327341300000319
the fuel consumption of the thermal power generating unit n before the contract transfer;
Figure BDA00036327341300000320
the fuel consumption of the thermal power generating unit n after the contract transfer; mu.s s 、μ c Are each SO 2 、CO 2 Cost factor of gas emissions;
Figure BDA0003632734130000041
Figure BDA0003632734130000042
respectively SO of thermal power generating unit n 2 、CO 2 Gas emission coefficient.
Preferably, the deviation penalty function for the wind power generator is as follows:
Figure BDA0003632734130000043
Figure BDA0003632734130000044
in the formula [ theta ] W A wind power deviation penalty coefficient;
Figure BDA0003632734130000045
deviation electric quantity is contracted for the wind power generation quotient; v is the number of wind power scenes;
Figure BDA0003632734130000046
transferring the electric quantity for the contract accepted by the wind power station m;
Figure BDA0003632734130000047
and (4) the predicted output of the wind power plant m.
The constraint conditions of the wind-water-fire power generation optimal configuration model are as follows:
(1) contract transfer demand balancing constraints:
Figure BDA0003632734130000048
in the formula,. DELTA.P t T Contract transfer demand for time period t;
Figure BDA0003632734130000049
the contract transfers the electric quantity in the t time period for the hydropower generation business;
Figure BDA00036327341300000410
the method comprises the steps of (1) transferring electric quantity for a contract of a thermal power generating unit n in a given time period t;
(2) and (3) transfer of the contract of the power generator with the winning bid amount constraint:
Figure BDA00036327341300000411
Figure BDA00036327341300000412
in the formula
Figure BDA00036327341300000413
The method comprises the steps of providing contractually transferable electric quantity for a thermal power generating unit n in a t period;
Figure BDA00036327341300000414
the method comprises the steps of providing contracted electric quantity for hydropower suppliers in a time period t;
Figure BDA00036327341300000415
transferring electric quantity for a contract of a thermal power generating unit n in a t-time period;
Figure BDA00036327341300000416
the contract of the bidding time period t for the hydropower generation business transfers the electric quantity;
(3) power generator contract transfer initiative constraint:
1) profit before and after thermal power participation contract transfer
Figure BDA00036327341300000417
Figure BDA0003632734130000051
In the formula
Figure BDA0003632734130000052
Profit for thermal power generation businessmen to participate in contract transfer;
Figure BDA0003632734130000053
the method comprises the steps of (1) transferring electric quantity for a contract of a thermal power generating unit n in a given time period t;
Figure BDA0003632734130000054
the contract transfer price of the thermal power generating unit n in the t time period is obtained;
Figure BDA0003632734130000055
profit for thermal power generation providers before participating in contract transfer;
Figure BDA0003632734130000056
the transferred profit is the profit of the thermal power generation business; c G The thermal power generation cost;
2) profit before and after contract transfer of hydropower participation
Figure BDA0003632734130000057
Figure BDA0003632734130000058
Figure BDA0003632734130000059
In the formula
Figure BDA00036327341300000510
Profits for hydropower generators to participate in contract transfer;
Figure BDA00036327341300000511
the contract transfers the electric quantity in the t time period for the hydropower generation business;
Figure BDA00036327341300000512
the contract of the hydropower provider at the time of t is transferred with the electricity price;
Figure BDA00036327341300000513
the income of selling gas for changing electricity into gas; eta P2G The efficiency of converting electricity into natural gas;
Figure BDA00036327341300000514
converting the electricity into the actual running power for t time period;
Figure BDA00036327341300000515
natural gas price for time period t;
Figure BDA00036327341300000516
the profit of the hydropower generator before participating in the contract transfer is generated;
Figure BDA00036327341300000517
the profit of the hydropower generator after participating in the contract transfer is realized; c H The cost of hydroelectric power generation;
3) profit before and after wind power participation in contract transfer
Figure BDA00036327341300000518
In the formula
Figure BDA0003632734130000061
Profit for the wind power generator before participating in contract transfer;
Figure BDA0003632734130000062
the profit of the wind power generator after participating in the contract transfer is obtained; c W The cost of wind power generation;
(4) and (4) quotation constraint:
Figure BDA0003632734130000063
Figure BDA0003632734130000064
in the formula
Figure BDA0003632734130000065
Respectively limiting the minimum price and the maximum price of the hydroelectric power generator;
Figure BDA0003632734130000066
respectively limiting the minimum and maximum quotation of the thermal power generator;
(5) unifying the electricity price constraint:
Figure BDA0003632734130000067
(6) electric-to-gas operation strategy constraints:
Figure BDA0003632734130000068
Figure BDA0003632734130000069
in the formula (I), the compound is shown in the specification,
Figure BDA00036327341300000610
theoretical water and electricity abandoning quantity for t time after the hydropower participates in the contract transfer;
Figure BDA00036327341300000611
the predicted electric quantity is the predicted electric quantity of the hydropower t period;
Figure BDA00036327341300000612
actual operating power for converting electricity into gas; c P2G Is the electric-to-gas capacity;
(7) electric-to-gas operation constraint:
Figure BDA00036327341300000613
compared with the prior art, the invention has the beneficial effects that:
(1) the method adopts a double-layer planning method, aims at minimizing the electricity purchasing cost of the electric energy market, constructs an upper-layer medium-and-long-term electric energy market electricity purchasing model, aims at maximizing the income of a wind power generator, constructs a lower-layer wind, water and fire electricity generation optimal configuration model based on a contract transfer mechanism, and solves to obtain optimal electricity-to-gas capacity configuration, and each generator contracts electric quantity improves the water and electricity regulation flexibility, improves the medium-and-long-term consumption and occupation ratio of wind power, and realizes the optimal configuration of electricity-to-gas equipment and energy;
(2) the method considers the volatility and the randomness of the wind power output, describes the wind power output, the hydroelectric power output and the load electric quantity as random variables based on predicted values, adopts a wind power quality segmentation idea to divide the wind power into a determined part and an uncertain part, adopts a Monte Carlo sampling method to construct a multi-scene for the uncertain part, and then utilizes a k-means clustering algorithm to reduce the scene reduction, thereby reducing the calculated amount related to the scene of the uncertain part of the wind power and improving the overall efficiency;
(3) the invention adopts the modified IEEE30 node system to simulate, and solves the model by using MATPOWER and CPLEX solver on an MATLAB platform, thereby overcoming the defect that the optimization method is easy to fall into local optimal solution and improving the optimization performance.
Drawings
The invention is further illustrated by the following figures and examples.
Fig. 1 is a schematic diagram of a two-layer scheme according to an embodiment of the present invention.
Fig. 2 is a schematic flow diagram of a random simulation scene reduction of a wind power uncertain portion in an embodiment of the present invention.
Fig. 3 is a schematic flow chart of solving an optimal solution by double-layer planning according to an embodiment of the present invention.
Detailed Description
As shown in fig. 1, the double-layer planning method for medium-and-long-term wind, water and fire power generation capacity by combining water, electricity and gas comprises the following steps:
step 1: according to the data of runoff water inflow and wind power field wind speed of the hydropower station, the wind power output P of the medium and long term is predicted W ＝[P w,1 ,P w,2 ,...,P w,T ]Hydroelectric power P H ＝[P h,1 ,P h,2 ,...,P h,T ]And the load capacity P L ＝[P l,1 ,P l,2 ,...,P l,T ]In which P is w,i 1,2, wherein T represents the predicted output of the wind farm in the ith month of the wind farm in a time period T; p is h,i 1,2, wherein T represents the predicted output of the hydropower station in the ith month in the time period T; p l,i 1,2, wherein T represents the predicted electric quantity of the load in the ith month in a time period T, and T represents a medium-long time period; in the examples T is 12 months; the method comprises the steps of obtaining a wind power probability density curve by adopting a wind power quality segmentation idea and utilizing a non-parameter kernel density estimation method, and dividing wind power into a determined part and an uncertain part; the uncertain part of the wind power adopts a normal distribution function, a random simulation scene is established by using a Monte Carlo method, then distance scene reduction is adopted by using improved k-means clustering, scenes with similar probability distances are represented by a typical scene, and a typical scene set with probability values is formed.
As shown in fig. 2, the wind power output is segmented according to the wind power quality concept, and the specific process of establishing a scene set for the uncertain part of wind power is as follows:
(1) calculating the probability density of wind power by using a nonparametric kernel density estimation method;
non-parametric kernel density estimation function
Figure BDA0003632734130000071
The following were used:
Figure BDA0003632734130000072
wherein h is the window width; x is the number of i The ith sample data of the wind power is obtained; n is the number of samples; x represents a wind power output time sequence;
(2) for wind power probability density function
Figure BDA0003632734130000073
Performing inverse integration to obtain an inverse function G (x) of the probability distribution function;
(3) setting the confidence coefficient of wind power as m ═ beta t -α t Wherein beta is t ∈(0.5,1]，a t ∈(0,0.5]，α t Representing a wind power confidence level; beta is a beta t Representing a wind power confidence level;
will beta t 、a t Substituting the wind power into a probability density inverse function G (x) to obtain a wind power up-and-down fluctuation area;
the fluctuation region function is as follows:
Figure BDA0003632734130000081
Figure BDA0003632734130000082
in the formula (I), the compound is shown in the specification,
Figure BDA0003632734130000083
the lower bound of the wind power fluctuation region is defined;
Figure BDA0003632734130000084
the wind power fluctuation area is an upper bound; p W,t Predicting output for wind power;
(4) dividing the wind power output into a determined part and an uncertain part according to the upper and lower bounds of the fluctuation region obtained in the step (3);
(5) the specific process of scene reduction by using the Monte Carlo method and the improved k-means clustering algorithm is as follows:
1) setting sampling scene sample D and reduced sample number F d ；
2) Inputting wind power uncertain part data and setting probability distribution;
3) adopting Monte Carlo sampling to obtain D scene samples;
4) judgment of F d Whether or not it is less than D, if F d If the value is less than D, executing the step 5), otherwise, ending;
5) for each scene gamma i Calculating the residual scene gamma j And finding the minimum probability distance KD value min { KD (gamma) } from the probability distance KD between the two i ,γ j )}；
6) Each scene gamma i Corresponding minimum probability distance KD value multiplied by fieldScene probability lambda (gamma) i ) Finding the minimum value in the D scenes;
7) the scene corresponding to the minimum value is recorded as scene gamma m And scene gamma n Satisfies lambda (. gamma.) m )≤λ(γ n )；
8) Will scene gamma m 、γ n Cut down by combining two into one and update scene probability lambda (gamma) n )＝λ(γ m )+λ(γ n )；
9) Updating the value D, wherein D is D-1;
10) output F d And finishing the number of scenes and the probability of the scenes.
Step 2: the wind power determination part and hydropower and thermal power participate in medium and long-term electric energy market bidding and clearing, an upper layer model is established by taking the minimum electricity purchasing cost of an electric power market as a target, and the target function is as follows:
Figure BDA0003632734130000085
in the formula, T is the total number of the medium and long-term time periods; n is the number of thermal power generating units; m is the number of wind power plants;
Figure BDA0003632734130000086
contract price declared for the time period t of the upper layer of the hydropower station;
Figure BDA0003632734130000087
contract price declared for the t time period of the upper layer of the wind power m;
Figure BDA0003632734130000088
contract price declared for the upper t period of the thermal power n;
Figure BDA0003632734130000091
the contract electric quantity is winning bid in the time period t of the upper layer of the hydropower station;
Figure BDA0003632734130000092
contract electric quantity winning in the t time period of the upper layer of the wind power m;
Figure BDA0003632734130000093
contract electric quantity for bidding in the upper t period of the thermal power n;
the constraints are as follows:
(1) and (4) quotation constraint:
Figure BDA0003632734130000094
Figure BDA0003632734130000095
Figure BDA0003632734130000096
in the formula (I), the compound is shown in the specification,
Figure BDA0003632734130000097
minimum and maximum quotation limits for the generator;
(2) and (3) bidding electric quantity restriction in a power generator:
Figure BDA0003632734130000098
Figure BDA0003632734130000099
Figure BDA00036327341300000910
in the formula (I), the compound is shown in the specification,
Figure BDA00036327341300000911
predicting power output for hydropower;
Figure BDA00036327341300000912
determining partial maximum output for the wind power m;
Figure BDA00036327341300000913
reporting minimum and maximum electric quantities for the thermal power n;
(3) and power balance constraint:
Figure BDA00036327341300000914
in the formula, P l,t Predicting an electrical quantity for the load;
and step 3: the electric gas conversion technology can realize the conversion of electric energy to hydrogen and natural gas (methane), and the specific process is as follows:
the electricity-to-gas technology is divided into two types of electricity-to-hydrogen and electricity-to-methane, wherein the electricity-to-hydrogen utilizes the principle that water is electrolyzed to generate hydrogen and oxygen, and the chemical equation is as follows:
Figure BDA00036327341300000915
the hydrogen produced by electrolysis of water by electric conversion can be directly used, but usually takes the form of electrolytic methane due to the difficulty of hydrogen storage and transport. Natural gas (methane) has a higher specific energy density than hydrogen and can be directly injected into existing natural gas networks for sale. The electric conversion of methane is that on the basis of hydrogen electrolysis, carbon dioxide and hydrogen react to generate methane under high-temperature and high-pressure environment, and the chemical equation is as follows:
CO 2 +4H 2 →CH 4 +2H 2 O (13)
wherein, the energy conversion efficiency of electricity-to-hydrogen is about 75-80%, and the comprehensive energy conversion efficiency of the complete chemical reaction of electricity-to-methane is about 45-60%.
And 4, step 4: the cost of each generator constitutes:
(1) cost of thermal power
The cost of the thermal power generating unit is mainly the coal consumption operation cost, so the coal consumption operation cost curve is as follows:
Figure BDA0003632734130000101
in the formula, C G The coal consumption cost of the thermal power generating unit n is obtained;
Figure BDA0003632734130000102
the output electric quantity of the thermal power generating unit n is obtained; a. and b and c are consumption coefficients of the thermal power generating unit n.
After the thermal power generator carries out contract transfer transaction, the thermal power generator undertakes the following losses:
Figure BDA0003632734130000103
in the formula, C G,hyzr Peak regulation loss is transferred for the contract of the thermal power generator; theta hyzr The loss factor is transferred to the contract.
C G ＝C G,mhyx +C G,hyzr (16)
(2) Cost of water, electricity and gas conversion
The cost of hydroelectric power generators comes mainly from the operating cost of the hydropower itself and the investment cost and operating cost of converting electricity into gas, namely:
Figure BDA0003632734130000104
Figure BDA0003632734130000105
Figure BDA0003632734130000106
Figure BDA0003632734130000107
in the formula, C H The total cost to the hydroelectric power plant;
Figure BDA0003632734130000108
the operating cost of hydropower;
Figure BDA0003632734130000109
investment cost for electric gas conversion equipment;
Figure BDA00036327341300001010
the operating cost of the electric gas conversion equipment; mu.s H,t Is a hydropower running cost coefficient; c P2G Capacity of the electric gas conversion equipment; gamma is the unit investment cost coefficient of electricity to gas; e is annual interest rate; l is the service life of the electric gas conversion equipment; e.g. of the type P2G The operation cost of the annual unit investment cost of the electric gas conversion equipment is 2 percent;
Figure BDA00036327341300001011
the actual operating power of the electric gas conversion equipment is obtained.
(3) Cost of wind power
C W ＝C yxcb +C bc +C cf (21)
Figure BDA00036327341300001012
Figure BDA0003632734130000111
In the formula, C W The total cost of wind power; c yxcb The operating cost of the wind power; c bc Compensation cost for wind power (balance cost); c cf Penalizing cost for deviation of wind power;
Figure BDA0003632734130000112
the running cost coefficient of the wind power is obtained; v is the number of scenes with uncertain wind power generation; theta.theta. v And the probability of the wind power uncertainty scene is shown.
And 5: the uncertain part of wind power and hydropower and thermal power participate in medium and long term contract transfer, the maximum profit of a wind power generator is a target, a lower-layer wind, hydropower and thermal power generation optimal configuration model based on a contract transfer mechanism is established, and a target function is as follows:
maxS＝max(S1+S2-S3) (24)
wherein S is the income of a wind power generator; s1 is the total income of the wind power generator with double-layer planning; s2, wind power obtains the environmental cost reduced by the transfer of the thermal power contract; s3 is a deviation penalty generated by wind power.
Wind power double-layer grid-connected income function:
Figure BDA0003632734130000113
Figure BDA0003632734130000114
in the formula (I), the compound is shown in the specification,
Figure BDA0003632734130000115
bidding contract electric quantity for the upper layer of the wind power;
Figure BDA0003632734130000116
receiving contract transfer electric quantity for wind power;
Figure BDA0003632734130000117
the electricity price is regulated for the upper layer;
Figure BDA0003632734130000118
transferring electricity prices for lower level contracts;
Figure BDA0003632734130000119
the contract for water and electricity is given out to transfer the electric quantity;
Figure BDA00036327341300001110
giving a contract for transferring electricity for thermal power; c W The cost of wind power;
the wind power obtains an environmental cost function with reduced transfer of thermal power contracts:
Figure BDA00036327341300001111
Figure BDA00036327341300001112
Figure BDA00036327341300001113
Figure BDA00036327341300001114
Figure BDA00036327341300001115
in the formula (I), the compound is shown in the specification,
Figure BDA00036327341300001116
the cost of the upper environment of the thermal power is high;
Figure BDA00036327341300001117
the environmental cost after the thermal power lower layer contract is transferred is calculated; xi n The unit power generation coal consumption of the thermal power n;
Figure BDA00036327341300001118
the output power of the upper layer thermal power n;
Figure BDA00036327341300001119
the transferred electric quantity is the lower thermal power n contract;
Figure BDA00036327341300001120
the fuel consumption of the upper layer ignition power n;
Figure BDA0003632734130000121
for lower layer firing power nThe amount of fuel consumed; mu.s s And mu c Are each SO 2 And CO 2 Cost factors corresponding to gas emissions;
Figure BDA0003632734130000122
and
Figure BDA0003632734130000123
SO of fossil power n respectively 2 And CO 2 A gas emission coefficient;
wind power generation deviation penalty function:
Figure BDA0003632734130000124
Figure BDA0003632734130000125
in the formula, theta W Punishment coefficients of wind power deviation;
Figure BDA0003632734130000126
deviation electric quantity is contracted for the wind power generation quotient;
the constraints are as follows:
(1) contract transfer demand balance constraints:
Figure BDA0003632734130000127
in the formula,. DELTA.P t T A demand for contract transfer;
(2) and (3) winning electricity quantity constraint in contract transfer of power generators:
Figure BDA0003632734130000128
Figure BDA0003632734130000129
in the formula (I), the compound is shown in the specification,
Figure BDA00036327341300001210
the maximum value of the electric quantity can be transferred for the thermal power in a contract way;
Figure BDA00036327341300001211
the maximum value of the electric quantity can be transferred for the hydropower contract;
(3) power generator contract transfer initiative constraint:
1) profit before and after thermal power participation contract transfer:
Figure BDA00036327341300001212
Figure BDA00036327341300001213
in the formula (I), the compound is shown in the specification,
Figure BDA00036327341300001214
compensation obtained by participating in contract transfer for thermal power generators;
Figure BDA00036327341300001215
the contract for giving a lead for the thermal power n transfers electric quantity;
Figure BDA00036327341300001216
assigning prices for the underlying contracts;
Figure BDA00036327341300001217
profit for the upper layer of the thermal power;
Figure BDA00036327341300001218
profit after the thermal power participates in the contract transfer;
2) profit before and after the hydropower participation contract transfer:
Figure BDA00036327341300001219
Figure BDA0003632734130000131
Figure BDA0003632734130000132
in the formula (I), the compound is shown in the specification,
Figure BDA0003632734130000133
compensation obtained for the participation of hydropower in the contract transfer;
Figure BDA0003632734130000134
transferring the electricity for the contract of the water and electricity yield;
Figure BDA0003632734130000135
transferring electricity prices for contracts;
Figure BDA0003632734130000136
changing electricity into gas selling income; h is P2G Efficiency for converting electricity into gas into natural gas;
Figure BDA0003632734130000137
converting electricity into actual operating power;
Figure BDA0003632734130000138
is the natural gas price;
Figure BDA0003632734130000139
profit for the upper layer of water and electricity;
Figure BDA00036327341300001310
the profit after the participation of hydropower in the contract transfer;
(3) profit before and after the wind power participates in contract transfer:
Figure BDA00036327341300001311
in the formula (I), the compound is shown in the specification,
Figure BDA00036327341300001312
profits for the upper layer of wind power;
Figure BDA00036327341300001313
the profit after the wind power participates in the contract transfer;
(4) and (4) quotation constraint:
Figure BDA00036327341300001314
Figure BDA00036327341300001315
in the formula (I), the compound is shown in the specification,
Figure BDA00036327341300001316
the minimum and maximum quotation limits of hydropower t time periods are respectively;
Figure BDA00036327341300001317
respectively the minimum and maximum quotation limits of the thermal power t period;
(5) unifying the electricity price constraint:
the invention adopts the direct current trend mode for optimization, so the unified electricity price of the contract transferred electricity price is as follows
Figure BDA00036327341300001318
(6) Electric-to-gas operation strategy constraints:
Figure BDA00036327341300001319
Figure BDA0003632734130000141
in the formula (I), the compound is shown in the specification,
Figure BDA0003632734130000142
transferring the water and electricity abandoning amount for the water and electricity contract;
Figure BDA0003632734130000143
predicting the electric quantity for the water and electricity;
Figure BDA0003632734130000144
converting electricity into actual operating power;
(7) electric-to-gas operation constraint:
Figure BDA0003632734130000145
in the formula, C P2G Is the electric to gas capacity;
and 5: as shown in fig. 3, the model is solved using MATPOWER and CPLEX solver on MATLAB platform. The upper layer uses SMARTMARKET toolboxes in MATPOWER to solve the contract electric quantity and the contract price of each power generator, and the lower layer optimizes the contract electric quantity of each power generator through a CPLEX solver under the contract electric quantity and the contract price of each power generator transmitted by the upper layer to obtain the profit of each power generator, and obtains the optimal capacity allocation of electricity-to-gas when the profit of water and electricity is maximum.
In the method, a wind power quality segmentation idea is adopted, and data of the wind power determined part and the uncertain part are obtained according to wind power predicted electric quantity data, as shown in fig. 2. The wind power determination part and the hydroelectric power and the thermal power jointly participate in bidding and clearing of the upper-layer medium and long-term electric energy market to obtain the electricity quantity and the electricity price of the bid-winning contract of each power generator. And on the basis of the upper-layer result, the uncertain part of the wind power, hydropower and thermal power are subjected to lower-layer contract transfer transaction to obtain contract transfer electric quantity, contract transfer electric price and optimal configuration of electric-to-gas capacity. Through double-layer optimization, secondary planning is carried out on each energy source on a medium-long term scale, the wind power digestion capacity is improved, and the water and electricity regulation flexibility is improved.
The above-mentioned embodiments further describe the object and technical solution of the present invention in detail. The above description is only exemplary of the present invention, and is not intended to limit the scope of the present invention. It should be noted that those skilled in the art should, without making any creative effort, make equivalent changes, substitutions and improvements within the scope of the present invention.

Claims (9)

1. The wind, water and electricity-to-gas combined medium and long term wind, water and fire power generation capacity double-layer planning method is characterized in that the upper layer of the double-layer planning is a medium and long term electric energy market electricity purchasing model, and the lower layer is a wind, water and fire power generation optimization configuration model based on a contract transfer mechanism; the lower layer of the double-layer plan promotes the power generation contract transfer of hydropower to wind power by using a mode of jointly adjusting hydropower and electricity to gas, and improves the wind power consumption proportion;
the method comprises the following steps:
step 1: forecasting medium and long term wind power, hydroelectric output and load electric quantity, and establishing a scene set for wind power uncertainty;
and 2, step: establishing a medium and long-term electric energy market electricity purchasing model by taking the minimum electricity purchasing cost of an electric power market as a target;
and step 3: establishing a wind, water and fire power generation optimal configuration model based on a contract transfer mechanism with the maximum income of a power generator as a target;
step 3.1: establishing an objective function and a constraint condition of a wind-water-heat power generation optimal configuration model;
step 3.2: respectively determining the income functions of wind power, hydropower and thermal power generators;
and 4, step 4: and solving the double-layer planning model to obtain the optimal electricity-to-gas capacity and the optimal power generation scheme of each power generator.
2. The medium-and-long-term wind, water and fire power generation double-layer planning method is characterized in that in the step 1, a wind power quality segmentation idea is adopted to divide the power generation amount predicted by wind power into a determined part and an uncertain part; the method comprises the steps of obtaining a wind power probability density curve by using a nonparametric kernel density estimation method, establishing a random simulation scene for an uncertain part of wind power, reducing distance scenes by using improved k-means clustering, representing scenes with similar probability distances by using a typical scene, and forming a typical scene set with probability values.
3. The medium-and-long-term wind-water-fire power generation capacity double-layer planning method according to claim 2, characterized in that an objective function of a medium-and-long-term electric energy market electricity purchasing model is as follows:
Figure FDA0003632734120000011
in the formula, f represents the medium and long term electricity purchasing cost in the power market, and T is the total number of medium and long term time periods; n is the number of thermal power generating units; m is the number of wind power plants;
Figure FDA0003632734120000012
contract price for the hydropower generator for time period t;
Figure FDA0003632734120000013
contract price for time period t of wind farm m;
Figure FDA0003632734120000014
the contract price of the thermal power generating unit n in the t period is obtained;
Figure FDA0003632734120000015
contract electric quantity for the hydropower provider in the time period t;
Figure FDA0003632734120000016
the contract electric quantity of the wind power plant m in the t period;
Figure FDA0003632734120000017
and (4) the contract electric quantity of the thermal power generating unit n in the period t.
4. The medium-long term wind, water and fire power generation capacity double-layer planning method according to claim 3, wherein the constraint conditions of the medium-long term electric energy market purchasing power model comprise:
1) quotation constraints
Figure FDA0003632734120000018
Figure FDA0003632734120000019
Figure FDA0003632734120000021
In the formula
Figure FDA0003632734120000022
Respectively the minimum and maximum quotation limits of the generator;
2) bid amount constraint in power generator
Figure FDA0003632734120000023
Figure FDA0003632734120000024
Figure FDA0003632734120000025
In the formula
Figure FDA0003632734120000026
Predicting power output for hydroelectric power;
Figure FDA0003632734120000027
maximum output of the determined part of the power generation prediction for the wind farm m;
Figure FDA0003632734120000028
respectively declaring minimum and maximum electric quantity for the thermal power generating unit n;
3) power balance constraint
Figure FDA0003632734120000029
In the formula P l,t An amount of power is predicted for the load.
5. The medium-long term wind-water-fire power generation capacity double-layer planning method according to claim 3, wherein an objective function of a wind-water-fire power generation optimization configuration model is as follows:
maxS＝max(S1+S2-S3)(9)
wherein S is the income of a wind power generator; s1 is the total income of the wind power generator with double-layer planning; s2, wind power obtains the environmental cost reduced by the transfer of the thermal power contract; s3 is the deviation penalty generated by wind power.
6. The medium-and-long-term wind, water, fire and power generation double-layer planning method according to claim 5, wherein the total income of a wind power generator is as follows:
Figure FDA00036327341200000210
Figure FDA00036327341200000211
in the formula
Figure FDA00036327341200000212
For wind farmsContract electric quantity of t period of m;
Figure FDA00036327341200000213
transferring the electric quantity for the contract accepted by the wind power station m;
Figure FDA00036327341200000214
contract price of t period declared for wind farm m;
Figure FDA00036327341200000215
contract transferred electricity price for the wind power station m in the t period; c W The cost of wind power;
Figure FDA00036327341200000216
the electric quantity transferred for the contract of the hydropower generation business;
Figure FDA00036327341200000217
and (4) the amount of electricity transferred by the thermal power generating unit n contract.
7. The medium-and-long-term wind, water, fire and power generation capacity double-layer planning method is characterized in that the environmental cost reduced after the contract of a thermal power generator is transferred is as follows:
Figure FDA0003632734120000031
Figure FDA0003632734120000032
Figure FDA0003632734120000033
Figure FDA0003632734120000034
Figure FDA0003632734120000035
in the formula
Figure FDA0003632734120000036
The environmental cost of the thermal power generator before the contract transfer;
Figure FDA0003632734120000037
the thermal power generator environmental cost after the contract transfer is achieved; xi shape n The unit generating coal consumption of the thermal power generating unit n is obtained;
Figure FDA0003632734120000038
the output electric quantity of the thermal power generating unit n before the contract transfer is obtained;
Figure FDA0003632734120000039
the output electric quantity of the thermal power generating unit n after the contract transfer is obtained;
Figure FDA00036327341200000310
the fuel consumption of the thermal power generating unit n before the contract transfer;
Figure FDA00036327341200000311
the fuel consumption of the thermal power generating unit n after the contract transfer; mu.s s 、μ c Are each SO 2 、CO 2 Cost factor of gas emissions;
Figure FDA00036327341200000312
Figure FDA00036327341200000313
respectively SO of thermal power generating unit n 2 、CO 2 Gas emission coefficient.
8. The medium-long term wind, water, fire and power generation double-layer planning method according to claim 5, wherein a deviation penalty function of a wind power generator:
Figure FDA00036327341200000314
Figure FDA00036327341200000315
in the formula theta W A wind power deviation penalty coefficient;
Figure FDA00036327341200000316
deviation electric quantity is contracted for the wind power generation quotient;
Figure FDA00036327341200000317
transferring the electric quantity for the contract accepted by the wind power station m;
Figure FDA00036327341200000318
and (4) the predicted output of the wind farm m.
9. The medium-and-long-term wind-water-fire power generation capacity double-layer planning method according to claim 5, wherein the constraint conditions of the wind-water-fire power generation optimization configuration model are as follows:
(1) contract transfer demand balancing constraints:
Figure FDA00036327341200000319
in the formula,. DELTA.P t T Contract transfer requirements for a time period t;
Figure FDA00036327341200000320
the contract of the hydropower generator in the t time period is assigned with electric quantity;
Figure FDA00036327341200000321
the method comprises the steps of (1) transferring electric quantity for a contract of a thermal power generating unit n in a given time period t;
(2) and (3) winning electricity quantity constraint in contract transfer of power generators:
Figure FDA0003632734120000041
Figure FDA0003632734120000042
in the formula
Figure FDA0003632734120000043
The method comprises the steps of providing contractually transferable electric quantity for a thermal power generating unit n in a t time period;
Figure FDA0003632734120000044
the method comprises the steps of providing the electric quantity which can be transferred by a hydropower generator in a contract in a time period t;
Figure FDA0003632734120000045
transferring electric quantity for contracts of the thermal power generating unit n in the t time period;
Figure FDA0003632734120000046
the contract at the time t for the hydropower supplier pays the transfer of the electric quantity;
(3) power generator contract transfer initiative constraint:
1) profit before and after thermal power participation contract transfer
Figure FDA0003632734120000047
Figure FDA0003632734120000048
In the formula
Figure FDA0003632734120000049
Profit for thermal power generation providers to participate in contract transfer;
Figure FDA00036327341200000410
the method comprises the steps of (1) transferring electric quantity for a contract of a thermal power generating unit n in a given time period t;
Figure FDA00036327341200000411
the contract transfer price of the thermal power generating unit n in the t period is given;
Figure FDA00036327341200000412
profit before the thermal power generator participates in the contract transfer;
Figure FDA00036327341200000413
the transferred profit is the profit of the thermal power generation business; c G The thermal power generation cost is reduced;
2) profit before and after contract transfer of hydropower participation
Figure FDA00036327341200000414
Figure FDA00036327341200000415
Figure FDA00036327341200000416
In the formula
Figure FDA00036327341200000417
Involving contract transfer for hydroelectric power generation tradersProfit;
Figure FDA00036327341200000418
the contract of the hydropower generator in the t time period is assigned with electric quantity;
Figure FDA0003632734120000051
the contract of the hydropower generation business in the time period t transfers electricity price;
Figure FDA0003632734120000052
the income of selling gas for changing electricity into gas; h is P2G The efficiency of converting electricity into gas;
Figure FDA0003632734120000053
converting electricity into actual operation power for a period of t;
Figure FDA0003632734120000054
natural gas price for time period t;
Figure FDA0003632734120000055
profits are generated before the hydropower generators participate in the contract transfer;
Figure FDA0003632734120000056
the profit of the hydropower generator after participating in the contract transfer is realized; c H The cost of hydroelectric power generation;
3) profit before and after participation of wind power in contract transfer
Figure FDA0003632734120000057
In the formula
Figure FDA0003632734120000058
The profit before the wind power generator participates in the contract transfer is obtained;
Figure FDA0003632734120000059
the profit of the wind power generator after participating in the contract transfer is obtained; c W The cost of wind power generation;
(4) and (4) quotation constraint:
Figure FDA00036327341200000510
Figure FDA00036327341200000511
in the formula
Figure FDA00036327341200000512
Respectively limiting the minimum price and the maximum price of the hydropower generator;
Figure FDA00036327341200000513
respectively limiting the minimum and maximum quotation of the thermal power generator;
(5) electric-to-gas operation strategy constraints:
Figure FDA00036327341200000514
Figure FDA00036327341200000515
in the formula (I), the compound is shown in the specification,
Figure FDA00036327341200000516
theoretical water and electricity abandoning quantity for t time after the hydropower participates in the contract transfer;
Figure FDA00036327341200000517
the predicted electric quantity is the predicted electric quantity of the hydropower t period;
Figure FDA00036327341200000518
actual operating power for converting electricity into gas; c P2G Is the electric to gas capacity;
(6) electric-to-gas operation constraint:
Figure FDA00036327341200000519
CN202210493535.1A 2022-05-07 2022-05-07 Water-electricity-to-gas combined medium-and-long-term wind-water-fire generating capacity double-layer planning method Pending CN114925892A (en)

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* Cited by examiner, † Cited by third party
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115776110A (en) * 2022-10-28 2023-03-10 国网黑龙江省电力有限公司 Generating capacity prediction model, electricity purchasing optimization model and electricity purchasing optimization model system
CN115776110B (en) * 2022-10-28 2023-10-03 国网黑龙江省电力有限公司 Electricity generation prediction model, electricity purchasing optimization model and electricity purchasing optimization model system

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