CN111210144A - Power generation risk management method and system for electric power spot market - Google Patents

Power generation risk management method and system for electric power spot market Download PDF

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CN111210144A
CN111210144A CN202010008105.7A CN202010008105A CN111210144A CN 111210144 A CN111210144 A CN 111210144A CN 202010008105 A CN202010008105 A CN 202010008105A CN 111210144 A CN111210144 A CN 111210144A
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price
bidding
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周保荣
黎小林
赵文猛
洪潮
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CSG Electric Power Research Institute
China Southern Power Grid Co Ltd
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China Southern Power Grid Co Ltd
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Abstract

The invention discloses a power generation risk management method and a power generation risk management system for a power spot market, wherein the method comprises the steps of obtaining N price scenes based on a Monte Carlo simulation method; establishing a power plant profit model according to bidding data of the generator sets participating in bidding and the constraint conditions of the generator sets, wherein the bidding data comprises quotation data and income data of the generator sets; constructing a risk model taking the generator sets in the N price scenes as bidding decision makers; constructing a multi-objective optimization model according to the power plant profit model, the risk model and the unit constraint conditions, and solving the multi-objective optimization model to obtain an optimal solution set; and constructing the optimal day-ahead quotation data of the power generation unit under different expected benefits by using different optimal solutions selected from the optimal solution set. According to the embodiment of the invention, the risk and the income of the electric power spot market can be taken into consideration, and the optimal quotation data of the generator set is obtained, so that the accuracy of risk management is improved, and the operation of a power generation enterprise is facilitated.

Description

Power generation risk management method and system for electric power spot market
Technical Field
The invention relates to the technical field of electric power, in particular to a power generation risk management method and system for an electric power spot market.
Background
With the appearance of simulation trial run starting of the inner Mongolia electric power multilateral trade spot market, the first 8 electric power spot market construction test points determined by the State energy agency and the State development reform Commission all enter the trial run stage, and the electric power market construction in China makes another important breakthrough. The trading patterns and rules of the electric power spot market are very different from those of the current electric power wholesale market based on physical delivery. The trade period of the existing market is divided into annual and monthly, the electric energy market of the electric power existing market is divided into medium-long term (annual and monthly), day-ahead and real-time markets, and the trade modes of different trade periods are different. The electric quantity of medium and long term transaction is decomposed to the day ahead through a decomposition curve, and a production plan of a running day is not determined. The node electricity price of the spot market is updated once per hour, and the marginal blocking cost is introduced on the basis of the marginal electric energy cost. The changes increase factors and constraints which need to be considered when the power generation enterprise quotes the price, increase the difficulty of designing and implementing the trading strategy, and reduce the probability of winning bid and the space of profit. If the supply and demand of the market cannot be accurately estimated and the quotation strategy is made according to the supply and demand, the profit of the enterprise is compressed, and even more electricity generation and more money loss can occur.
Under the background of accelerating and promoting the construction of the electric power spot market, power generation enterprises adopt the most favorable market game strategy to carry out power generation quotation according to the self power generation capacity and the understanding of the market through the self reasonable quotation behavior. However, in the present stage, power generation enterprise risk management in the power market environment is mainly performed qualitative analysis and response in terms of market policy risk, market competition risk, spot market risk, fuel market risk, and the like. Market policy risk refers to a rapidly decreasing proportion of fixed contracts and policies on fixed contract bidding that compromise the benefits of the generator. Power generation enterprises should further develop retail markets and increase retail contract quantity so as to lock power generation profit in response to the gradually reduced fixed contract proportion; the market competition risk means that the electricity price may change greatly after the power market is released in the situation that the total supply and demand of the power is larger than the demand at present. The market competitiveness of the units with high power generation cost is reduced, and the utilization hour difference between various types of units is further expanded.
At present, part of domestic areas present a vicious competition situation in the aspects of direct supply and alternative power generation of large users, and if the market is released, the competition is further possibly aggravated; the spot market risk means that the electricity price changes in each time period according to the market supply and demand conditions and the quotation of each power generator, and the electricity price level is difficult to predict; fuel costs are the largest component of coal-fired power generation costs. The domestic coal price fluctuates sharply according to market conditions, the fuel cost cannot be predicted accurately, and great risks are brought to power generation enterprises to achieve profit targets. Therefore, in the current generation enterprise risk management, qualitative analysis is mainly performed from a plurality of macroscopic angles, but the business related to the actual production plan, such as the current goods declaration volume curve of the generation enterprise, is not intuitively and quantitatively analyzed, so that the risk and the income are difficult to be considered at the same time.
Disclosure of Invention
The invention provides a power generation risk management method and system facing to a power spot market, which are used for solving the technical problem that the existing power generation risk management cannot analyze the business related to an actual production plan.
In order to solve the technical problem, an embodiment of the present invention provides a power generation risk management method for a power spot market, including:
obtaining N price scenes based on a Monte Carlo simulation method, wherein N is an integer greater than 1;
establishing a power plant profit model according to bidding data of the generator sets participating in bidding and the constraint conditions of the generator sets, wherein the bidding data comprises quotation data and income data of the generator sets;
constructing a risk model taking the generator sets in the N price scenes as bidding decision makers;
constructing a multi-objective optimization model according to the power plant profit model, the risk model and the unit constraint conditions, and solving the multi-objective optimization model to obtain an optimal solution set;
and constructing the optimal day-ahead quotation data of the power generation unit under different expected benefits by using a plurality of different optimal solutions selected from the optimal solution set.
As a preferred scheme, the obtaining N price scenes based on the monte carlo simulation method specifically includes:
carrying out Monte Carlo simulation on day-ahead node electricity price data obtained by prediction in advance based on a Monte Carlo simulation method to generate M price scenes, wherein M is an integer larger than N;
and carrying out scene reduction on the M price scenes to obtain N price scenes.
As a preferred scheme, the power plant profit model is established according to bidding data of the generator sets participating in bidding and the constraint conditions of the generator sets, and specifically comprises the following steps:
obtaining bidding data of the generator set participating in bidding through a preset objective function;
establishing a power plant profit model according to the bidding data and the unit constraint conditions;
wherein the preset objective function comprises:
the maximum profit calculation formula of the generator set is as follows:
Figure BDA0002354173850000031
Figure BDA0002354173850000032
Figure BDA0002354173850000033
the quotation calculation formula of the generator set is as follows:
Figure BDA0002354173850000034
kmin≤ki≤kmax
wherein the number of the generator sets participating in bidding is I, k is more than 0 and less than or equal to I, I is more than 0 and less than or equal to I, InckFor the benefit of the kth power plant, piFor the ith generator set, PiIs the winning bid amount of the ith generating set, FiFor the generating cost of the ith generating set, Pi,sThe winning bid amount of the ith power generation set in the s section is obtained; k is a radical ofminIndicates the lower limit of the quote, kmaxIndicating an upper limit for the bid.
Preferably, the constraint condition includes:
a) the adjustable output constraint conditions of the generator set comprise output upper limit constraint and output lower limit constraint of the generator set, and satisfy the formula:
Figure BDA0002354173850000041
wherein the content of the first and second substances,
Figure BDA0002354173850000042
the upper limit of the output of the unit is set;P ithe lower limit of the output of the unit;
b) the unit climbing constraint condition comprises the rising output rate constraint and the falling output rate constraint of the generator unit and meets the formula:
Figure BDA0002354173850000043
wherein the content of the first and second substances,
Figure BDA0002354173850000044
representing the uphill limit of the ith unit in a time period, iΔPrepresenting the lower climbing limit of the ith unit in a period of time;
c) the unit electric quantity constraint condition specifies the interval of the generating electric quantity of the generator set, and satisfies the formula:
Figure BDA0002354173850000045
wherein, T0For the time of each of the time periods,
Figure BDA0002354173850000046
is the maximum power generation capacity of the ith unit,
Figure BDA0002354173850000047
the minimum generated electricity quantity of the ith unit;
d) the non-decreasing constraint condition of the quotation curve meets the formula:
(ρs-ρs′)(qs-qs′)≥0，0≤s≤5
where ρ issFor the unit date declaring the price of electricity, rho, declared by each segment in the quotation curvesIs ` psElectricity price at the previous moment, qsElectric quantity, q, declared for each section in a unit date declaration quotation curves' is qsThe amount of power at the previous moment.
Preferably, in the risk model for constructing the generator set as the bid decision maker in the N price scenarios, the risk model is:
Figure BDA0002354173850000048
0≤risk(s)≤M
Figure BDA0002354173850000049
wherein z is0For expected benefits, risk is the risk in s-price scenario, M is the acceptable risk, fsFor revenue in s-price scenario, risk in s-price scenario is risk(s), risk in all price scenarios is Ra, R0To a set maximum risk value, epsilonsIs the probability value in the s price scenario.
As an optimal scheme, in the process of constructing a multi-objective optimization model according to the power plant profit model, the risk model and the unit constraint conditions, and obtaining an optimal solution set by solving the multi-objective optimization model, the multi-objective optimization model is as follows:
min[fi(x)]i＝1,2,...,Nob
Figure BDA0002354173850000051
wherein x is a decision variable, pjRepresenting the jth inequality constraint, qwDenotes the w-th equality constraint, XkDenotes xkSolution space of, NobNumber of representing objective function, NieqNumber of inequality constraints representing the objective function, NeqNumber of equality constraints representing the objective function, NbRepresenting the number of upper and lower bounds of the objective function.
As a preferred scheme, the step of obtaining an optimal solution set by solving the multi-objective optimization model specifically includes:
converting the multi-objective optimization model according to a generalized specification normal constraint method to obtain a plurality of types of single-objective problems;
solving the multi-class single-target problem by using a firefly algorithm, and searching to obtain all pareto solution sets;
and obtaining the optimal solution set in all the pareto solution sets through a hyperplane decision method.
The embodiment of the invention also provides a power generation risk management system facing the electric power spot market, which comprises the following steps:
the system comprises a scene generation module, a price analysis module and a price analysis module, wherein the scene generation module is used for obtaining N price scenes based on a Monte Carlo simulation method, wherein N is an integer greater than 1;
the profit model building module is used for building a profit model of the power plant according to bidding data of the generator set participating in bidding and the constraint conditions of the generator set, wherein the bidding data comprises quoted price data and income data of the generator set;
the risk model building module is used for building risk models of the generator sets in the N price scenes as bidding decision makers;
the optimization model building module is used for building a multi-objective optimization model according to the power plant profit model, the risk model and the unit constraint conditions, and obtaining an optimal solution set by solving the multi-objective optimization model;
and the result construction module is used for constructing the optimal day-ahead quotation data of the power generation unit under different expected benefits by utilizing a plurality of different optimal solutions selected from the optimal solution set.
As a preferred scheme, the scene generation module is configured to:
carrying out Monte Carlo simulation on day-ahead node electricity price data obtained by prediction in advance based on a Monte Carlo simulation method to generate M price scenes, wherein M is an integer larger than N;
and carrying out scene reduction on the M price scenes to obtain N price scenes.
Preferably, the profit model building module is configured to:
obtaining bidding data of the generator set participating in bidding through a preset objective function;
establishing a power plant profit model according to the bidding data and the unit constraint conditions;
wherein the preset objective function comprises:
the maximum profit calculation formula of the generator set is as follows:
Figure BDA0002354173850000061
Figure BDA0002354173850000062
Figure BDA0002354173850000063
the quotation calculation formula of the generator set is as follows:
Figure BDA0002354173850000064
kmin≤ki≤kmax
wherein the number of the generator sets participating in bidding is I, k is more than 0 and less than or equal to I, I is more than 0 and less than or equal to I, InckFor the benefit of the kth power plant, piFor the ith generator set, PiIs the winning bid amount of the ith generating set, FiFor the generating cost of the ith generating set, Pi,sThe winning bid amount of the ith power generation set in the s section is obtained; k is a radical ofminIndicates the lower limit of the quote, kmaxIndicating an upper limit for the bid.
Preferably, the constraint condition includes:
a) the adjustable output constraint conditions of the generator set comprise output upper limit constraint and output lower limit constraint of the generator set, and satisfy the formula:
Figure BDA0002354173850000071
wherein the content of the first and second substances,
Figure BDA0002354173850000072
the upper limit of the output of the unit is set;P ithe lower limit of the output of the unit;
b) the unit climbing constraint condition comprises the rising output rate constraint and the falling output rate constraint of the generator unit and meets the formula:
Figure BDA0002354173850000073
wherein the content of the first and second substances,
Figure BDA0002354173850000074
representing the uphill limit of the ith unit in a time period, iΔPrepresenting the lower climbing limit of the ith unit in a period of time;
c) the unit electric quantity constraint condition specifies the interval of the generating electric quantity of the generator set, and satisfies the formula:
Figure BDA0002354173850000075
wherein, T0For the time of each of the time periods,
Figure BDA0002354173850000076
is the maximum power generation capacity of the ith unit,
Figure BDA0002354173850000077
the minimum generated electricity quantity of the ith unit;
d) the non-decreasing constraint condition of the quotation curve meets the formula:
(ρs-ρs′)(qs-qs′)≥0，0≤s≤5
where ρ issFor the unit date declaring the price of electricity, rho, declared by each segment in the quotation curvesIs ` psElectricity price at the previous moment, qsElectric quantity, q, declared for each section in a unit date declaration quotation curves' is qsThe amount of power at the previous moment.
Preferably, in the risk model for constructing the generator set as the bid decision maker in the N price scenarios, the risk model is:
Figure BDA0002354173850000078
0≤risk(s)≤M
Figure BDA0002354173850000079
wherein z is0For expected benefits, risk is the risk in s-price scenario, M is the acceptable risk, fsFor revenue in s-price scenario, risk in s-price scenario is risk(s), risk in all price scenarios is Ra, R0To a set maximum risk value, epsilonsIs the probability value in the s price scenario.
As an optimal scheme, in the process of constructing a multi-objective optimization model according to the power plant profit model, the risk model and the unit constraint conditions, and obtaining an optimal solution set by solving the multi-objective optimization model, the multi-objective optimization model is as follows:
min[fi(x)]i＝1,2,...,Nob
Figure BDA0002354173850000081
wherein x is a decision variable, pjRepresenting the jth inequality constraint, qwDenotes the w-th equality constraint, XkDenotes xkSolution space of, NobNumber of representing objective function, NieqNumber of inequality constraints representing the objective function, NeqNumber of equality constraints representing the objective function, NbRepresenting the number of upper and lower bounds of the objective function.
As a preferred solution, the optimization model building module is configured to:
converting the multi-objective optimization model according to a generalized specification normal constraint method to obtain a plurality of types of single-objective problems;
solving the multi-class single-target problem by using a firefly algorithm, and searching to obtain all pareto solution sets;
and obtaining the optimal solution set in all the pareto solution sets through a hyperplane decision method.
To sum up, the embodiment of the invention provides a power generation risk management method and system for the electric power spot market, and any embodiment of the method has the following beneficial effects: obtaining N price scenes based on a Monte Carlo simulation method; establishing a power plant profit model according to bidding data of the generator sets participating in bidding and the constraint conditions of the generator sets, wherein the bidding data comprises quotation data and income data of the generator sets; constructing a risk model taking the generator sets in the N price scenes as bidding decision makers; constructing a multi-objective optimization model according to the power plant profit model, the risk model and the unit constraint conditions, and solving the multi-objective optimization model to obtain an optimal solution set; and constructing the optimal day-ahead quotation data of the power generation unit under different expected benefits by using a plurality of different optimal solutions selected from the optimal solution set. Firstly, generating different price scenes and ineffective reduction scenes by utilizing a Monte Carlo method based on predicted price data by considering risk and income; and then constructing a minimized risk and benefit maximization model under different price scenes, and finally solving the established dual-objective optimization model to obtain an optimal day-ahead quotation curve of the generator set, so that a basis is provided for day-ahead bidding of a power generation side in a spot market environment, and further, matching between risk and income is realized, and thus, the accuracy of risk management can be improved by obtaining the optimal quotation data of the generator set, and the operation of a power generation enterprise is facilitated.
Drawings
FIG. 1 is a flow chart of a power generation risk management method for the electricity spot market in an embodiment of the invention;
FIG. 2 is a scene tree diagram of random prices at four times in an embodiment of the invention;
FIG. 3 is a tree diagram of random scenes for 500 scenes in an embodiment of the present invention;
FIG. 4 is a pareto plot of risk versus benefit in an embodiment of the present invention;
FIG. 5 is a graph of an optimal quote in an embodiment of the present invention;
FIG. 6 is another optimal quote graph in an embodiment of the present invention;
fig. 7 is a graph of yet another optimal quote in an embodiment of the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Referring to fig. 1, a preferred embodiment of the present invention provides a power generation risk management method for a power spot market, which is suitable for power generation side bidding in which the power spot market considers both risk and income, and mainly includes the following steps:
s1, obtaining N price scenes based on a Monte Carlo simulation method, wherein N is an integer greater than 1;
in this embodiment, a monte carlo method is used, and different price scenes and ineffective reduction scenes are generated based on predicted price data, so as to obtain N price scenes, and the method specifically includes the following steps:
s11, carrying out Monte Carlo simulation on the day-ahead node electricity price data obtained by prediction in advance based on a Monte Carlo simulation method to generate M price scenes, wherein M is an integer larger than N;
and S12, performing scene reduction on the M price scenes to obtain N price scenes.
As an exemplary method for generating price scenes, as shown in fig. 2, 10 ten thousand monte carlo simulations are performed based on the predicted day-ahead node electricity price data to obtain 100000 price scenes, and then scene reduction technology gadget scenes, that is, ineffective scenes are reduced, and the number of simplified scenes is set to N.
Assuming that there are 4 generator sets in a power plant, 500 random candidate scenes are screened out by a scene reduction method for optimal scheduling in 10 ten thousand scenes, as shown in fig. 3, where fig. 3 is a random scene tree in 500 price scenes.
S2, establishing a power plant profit model according to bidding data of the generator set participating in bidding and the constraint conditions of the generator set, wherein the bidding data comprises quotation data and income data of the generator set;
the method specifically comprises the following steps:
s21, obtaining bidding data of the generator set participating in bidding through a preset objective function;
s22, establishing a power plant profit model according to the bidding data and the unit constraint conditions;
assuming that I generator sets participate in bidding in the power plant, the generator set k is used as a bidding decision maker, and the maximum self-income of the whole bidding period is realized by determining the proper multi-section bidding price and the corresponding output in the bidding period. Wherein the preset objective function comprises:
the maximum profit mathematical model of the generator set is as follows:
Figure BDA0002354173850000101
Figure BDA0002354173850000102
Figure BDA0002354173850000103
the quotation mode of the generator set is as follows:
Figure BDA0002354173850000104
kmin≤ki≤kmax(5)
wherein the number of the generator sets participating in bidding is I, k is more than 0 and less than or equal to I, I is more than 0 and less than or equal to I, InckFor the benefit of the kth power plant, piFor the ith generator set, PiIs the winning bid amount of the ith generating set, FiFor the generating cost of the ith generating set, Pi,sThe winning bid amount of the ith power generation set in the s section is obtained; k is a radical ofminTo representLower limit of price, kmaxIndicating an upper limit for the bid.
In this embodiment, the constraint conditions include:
a) the adjustable output constraint conditions of the generator set comprise output upper limit constraint and output lower limit constraint of the generator set, and satisfy the formula:
Figure BDA0002354173850000111
wherein the content of the first and second substances,
Figure BDA0002354173850000112
the upper limit of the output of the unit is set; piThe lower limit of the output of the unit;
b) the unit climbing constraint condition comprises rising output rate constraint and falling output rate constraint of a generator set, wherein each time interval can be independently set, and the unit plan time interval output at the beginning of the day and the previous time interval plan at the end of the day or the actual output can be linked, and the formula is required to be met:
Figure BDA0002354173850000113
wherein the content of the first and second substances,
Figure BDA0002354173850000114
representing the uphill limit of the ith unit in a time period, iΔPrepresenting the lower climbing limit of the ith unit in a period of time;
c) the unit electric quantity constraint condition can specify the interval of the generating electric quantity of the generator set, and the formula is satisfied:
Figure BDA0002354173850000115
wherein, T0For the time of each of the time periods,
Figure BDA0002354173850000116
is the maximum power generation capacity of the ith unit,
Figure BDA0002354173850000117
the minimum generated electricity quantity of the ith unit;
d) the non-decreasing constraint condition of the quotation curve takes the Guangdong electric power spot market rule as an example, the maximum quotation curve of five sections before the day of the unit is declared, and the quotation must be greater than or equal to the quotation of the previous moment along with the increment of the electric quantity, and the formula needs to be satisfied:
(ρs-ρs′)(qs-qs′)≥0，0≤s≤5 (9)
where ρ issFor the unit date declaring the price of electricity, rho, declared by each segment in the quotation curvesIs ` psElectricity price at the previous moment, qsElectric quantity, q, declared for each section in a unit date declaration quotation curves' is qsThe amount of power at the previous moment.
Assuming that the maximum and minimum capacity limits, the hill climbing limits, and the cost factors for 4 units of a power plant are shown in table 1 below:
TABLE 1 stochastic parameters of the Generator sets
Machine set a b c Pmax Pmin Prd Pru
G1 0.02 2 0 100 10 50 50
G2 0.0175 1.75 0 80 5 35 35
G3 0.0625 1 0 85 10 40 40
G4 0.00994 3.25 0 90 8 35 35
S3, constructing a risk model with the generator sets in the N price scenes as bidding decision makers;
the risk model is:
Figure BDA0002354173850000121
Figure BDA0002354173850000122
wherein z is0For expected benefits, risk is the risk in s-price scenario, M is the acceptable risk, fsFor revenue in s-price scenario, risk in s-price scenario is risk(s), risk in all price scenarios is Ra, R0To a set maximum risk value, epsilonsIs the probability value in the s price scenario.
S4, constructing a multi-objective optimization model according to the power plant profit model, the risk model and the unit constraint conditions, and solving the multi-objective optimization model to obtain an optimal solution set;
in this embodiment, the multi-objective problem of the multi-objective optimization model can be written into a standard Pareto multi-objective optimization form by comprehensively considering the two objective functions and the three types of constraint conditions in steps S2 and S3:
Figure BDA0002354173850000123
wherein x is a decision variable, pjRepresenting the jth inequality constraint, qwDenotes the w-th equality constraint, XkDenotes xkSolution space of, NobNumber of representing objective function, NieqNumber of inequality constraints representing the objective function, NeqNumber of equality constraints representing the objective function, NbRepresenting the number of upper and lower bounds of the objective function.
As a preferred scheme, the step of obtaining an optimal solution set by solving the multi-objective optimization model specifically includes:
s41, converting the multi-objective optimization model according to a generalized specification normal constraint method to obtain multiple types of single-objective problems;
s42, solving the multi-class single-target problem by using a firefly algorithm, and searching to obtain all pareto solution sets;
s43, obtaining the optimal solution set in all the pareto solution sets through a hyperplane decision method.
The expected income and risk dual-target optimization problem of the generator set is solved by adopting a reinforced firefly algorithm based on a generalized specification normal constraint method, the problem is converted into a plurality of types of single-target problems by the generalized specification normal constraint method, the plurality of types of single-target problems are solved according to the reinforced firefly algorithm, all pareto solution sets are found, and finally the best compromise solution is found by a hyperplane decision method. As shown in fig. 4, a pareto curve chart (feasibility optimization scheme) of risk and benefit is shown, and compared with common dual-objective optimization algorithms such as MGSOA, MOEA, NSGAII and the like, the optimization energy performance of the enhanced firefly algorithm of the generalized specification normal constraint method is optimal, and then a reasonable and optimal risk and benefit scheme can be selected for the power plant.
And S5, constructing the optimal day-ahead quotation data of the generator set under different expected benefits by using a plurality of different optimal solutions selected from the optimal solution set.
In the embodiment, three different optimal solution schemes are selected from the step S43 to construct a quotation curve, and the adopted constraint conditions, expected benefits and risks remain unchanged from the upper part; the optimal quote curves at the expected profit are 2040.2564$, 2375.5885$, 2560.2637$ respectively, as shown in fig. 5-7.
In summary, according to the power generation risk management method provided by the invention, by considering both risk and income, different price scenes and ineffective scenes are generated by using a monte carlo method based on predicted price data; and then constructing a minimized risk and benefit maximization model under different price scenes, and finally solving the established dual-objective optimization model to obtain an optimal day-ahead quotation curve of the generator set, so that a basis is provided for day-ahead bidding of a power generation side in a spot market environment, and further, matching between risk and income is realized, and thus, the accuracy of risk management can be improved by obtaining the optimal quotation data of the generator set, and the operation of a power generation enterprise is facilitated.
In order to adapt to the power generation risk management method, an embodiment of the present invention further provides a power generation risk management system for an electric power spot market, including:
the system comprises a scene generation module, a price analysis module and a price analysis module, wherein the scene generation module is used for obtaining N price scenes based on a Monte Carlo simulation method, wherein N is an integer greater than 1;
the profit model building module is used for building a profit model of the power plant according to bidding data of the generator set participating in bidding and the constraint conditions of the generator set, wherein the bidding data comprises quoted price data and income data of the generator set;
the risk model building module is used for building risk models of the generator sets in the N price scenes as bidding decision makers;
the optimization model building module is used for building a multi-objective optimization model according to the power plant profit model, the risk model and the unit constraint conditions, and obtaining an optimal solution set by solving the multi-objective optimization model;
and the result construction module is used for constructing the optimal day-ahead quotation data of the power generation unit under different expected benefits by utilizing a plurality of different optimal solutions selected from the optimal solution set.
As a preferred scheme, the scene generation module is configured to:
carrying out Monte Carlo simulation on day-ahead node electricity price data obtained by prediction in advance based on a Monte Carlo simulation method to generate M price scenes, wherein M is an integer larger than N;
and carrying out scene reduction on the M price scenes to obtain N price scenes.
Preferably, the profit model building module is configured to:
obtaining bidding data of the generator set participating in bidding through a preset objective function;
establishing a power plant profit model according to the bidding data and the unit constraint conditions;
wherein the preset objective function comprises:
the maximum profit calculation formula of the generator set is as follows:
Figure BDA0002354173850000141
Figure BDA0002354173850000151
Figure BDA0002354173850000152
the quotation calculation formula of the generator set is as follows:
Figure BDA0002354173850000153
kmin≤ki≤kmax
wherein the number of the generator sets participating in bidding is I, k is more than 0 and less than or equal to I, I is more than 0 and less than or equal to I, InckFor the benefit of the kth power plant, piFor the ith generator set, PiIs the winning bid amount of the ith generating set, FiFor the generating cost of the ith generating set, Pi,sThe winning bid amount of the ith power generation set in the s section is obtained; k is a radical ofminIndicates the lower limit of the quote, kmaxIndicating an upper limit for the bid.
The constraint conditions include:
a) the adjustable output constraint conditions of the generator set comprise output upper limit constraint and output lower limit constraint of the generator set, and satisfy the formula:
Figure BDA0002354173850000154
wherein the content of the first and second substances,
Figure BDA0002354173850000155
the upper limit of the output of the unit is set; piThe lower limit of the output of the unit;
b) the unit climbing constraint condition comprises the rising output rate constraint and the falling output rate constraint of the generator unit and meets the formula:
Figure BDA0002354173850000156
wherein the content of the first and second substances,
Figure BDA0002354173850000157
representing the uphill limit of the ith unit in a time period, iΔPrepresenting the lower climbing limit of the ith unit in a period of time;
c) the unit electric quantity constraint condition specifies the interval of the generating electric quantity of the generator set, and satisfies the formula:
Figure BDA0002354173850000158
wherein, T0For the time of each of the time periods,
Figure BDA0002354173850000159
is the maximum power generation capacity of the ith unit,
Figure BDA00023541738500001510
the minimum generated electricity quantity of the ith unit;
d) the non-decreasing constraint condition of the quotation curve meets the formula:
(ρs-ρs′)(qs-qs′)≥0，0≤s≤5
where ρ issFor the unit date declaring the price of electricity, rho, declared by each segment in the quotation curvesIs ` psElectricity price at the previous moment, qsElectric quantity, q, declared for each section in a unit date declaration quotation curves' is qsThe amount of power at the previous moment.
In the risk model for constructing the generator set as the bidding decision maker in the N price scenarios, the risk model is:
Figure BDA0002354173850000161
0≤risk(s)≤M
Figure BDA0002354173850000162
wherein z is0For expected benefits, risk is the risk in s-price scenario, M is the acceptable risk, fsFor revenue in s-price scenario, risk in s-price scenario is risk(s), risk in all price scenarios is Ra, R0To a set maximum risk value, epsilonsIs the probability value in the s price scenario.
Constructing a multi-objective optimization model according to the power plant profit model, the risk model and the unit constraint conditions, and solving the multi-objective optimization model to obtain an optimal solution set, wherein the multi-objective optimization model is as follows:
min[fi(x)]i＝1,2,...,Nob
Figure BDA0002354173850000163
wherein x is a decision variable, pjRepresenting the jth inequality constraint, qwDenotes the w-th equality constraint, XkDenotes xkSolution space of, NobNumber of representing objective function, NieqNumber of inequality constraints representing the objective function, NeqNumber of equality constraints representing the objective function, NbRepresenting the number of upper and lower bounds of the objective function.
As a preferred solution, the optimization model building module is configured to:
converting the multi-objective optimization model according to a generalized specification normal constraint method to obtain a plurality of types of single-objective problems;
solving the multi-class single-target problem by using a firefly algorithm, and searching to obtain all pareto solution sets;
and obtaining the optimal solution set in all the pareto solution sets through a hyperplane decision method.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.

Claims (10)

1. A power generation risk management method for a power spot market is characterized by comprising the following steps:
obtaining N price scenes based on a Monte Carlo simulation method, wherein N is an integer greater than 1;
establishing a power plant profit model according to bidding data of the generator sets participating in bidding and the constraint conditions of the generator sets, wherein the bidding data comprises quotation data and income data of the generator sets;
constructing a risk model taking the generator sets in the N price scenes as bidding decision makers;
constructing a multi-objective optimization model according to the power plant profit model, the risk model and the unit constraint conditions, and solving the multi-objective optimization model to obtain an optimal solution set;
and constructing the optimal day-ahead quotation data of the power generation unit under different expected benefits by using a plurality of different optimal solutions selected from the optimal solution set.
2. The power generation risk management method according to claim 1, wherein the N price scenarios are obtained based on a monte carlo simulation method, specifically:
carrying out Monte Carlo simulation on day-ahead node electricity price data obtained by prediction in advance based on a Monte Carlo simulation method to generate M price scenes, wherein M is an integer larger than N;
and carrying out scene reduction on the M price scenes to obtain N price scenes.
3. The power generation risk management method according to claim 1, wherein a power plant profit model is established according to bidding data of the generator sets participating in bidding and the unit constraint conditions, specifically:
obtaining bidding data of the generator set participating in bidding through a preset objective function;
establishing a power plant profit model according to the bidding data and the unit constraint conditions;
wherein the preset objective function comprises:
the maximum profit calculation formula of the generator set is as follows:
Figure FDA0002354173840000011
Figure FDA0002354173840000012
Figure FDA0002354173840000021
the quotation calculation formula of the generator set is as follows:
Figure FDA0002354173840000022
kmin≤ki≤kmax
wherein the number of the generator sets participating in bidding is I, k is more than 0 and less than or equal to I, I is more than 0 and less than or equal to I, InckFor the benefit of the kth power plant, piFor the ith generator set, PiIs the winning bid amount of the ith generating set, FiFor the generating cost of the ith generating set, Pi,sThe winning bid amount of the ith power generation set in the s section is obtained; k is a radical ofminIndicates the lower limit of the quote, kmaxIndicating an upper limit for the bid.
4. The power generation risk management method of claim 3, wherein the constraints comprise:
a) the adjustable output constraint conditions of the generator set comprise output upper limit constraint and output lower limit constraint of the generator set, and satisfy the formula:
Figure FDA0002354173840000023
wherein the content of the first and second substances,
Figure FDA0002354173840000024
the upper limit of the output of the unit is set;P ithe lower limit of the output of the unit;
b) the unit climbing constraint condition comprises the rising output rate constraint and the falling output rate constraint of the generator unit and meets the formula:
Figure FDA0002354173840000025
wherein the content of the first and second substances,
Figure FDA0002354173840000026
representing the uphill limit of the ith unit in a time period, iΔPrepresenting the lower climbing limit of the ith unit in a period of time;
c) the unit electric quantity constraint condition specifies the interval of the generating electric quantity of the generator set, and satisfies the formula:
Figure FDA0002354173840000027
wherein, T0For the time of each of the time periods,
Figure FDA0002354173840000028
is the maximum power generation capacity of the ith unit,
Figure FDA0002354173840000029
the minimum generated electricity quantity of the ith unit;
d) the non-decreasing constraint condition of the quotation curve meets the formula:
(ρs-ρs′)(qs-qs′)≥0，0≤s≤5
where ρ issFor the unit date declaring the price of electricity, rho, declared by each segment in the quotation curvesIs ` psElectricity price at the previous moment, qsElectric quantity, q, declared for each section in a unit date declaration quotation curves' is qsThe amount of power at the previous moment.
5. The power generation risk management method of claim 1, wherein in constructing the risk model with the generator set as a bidding decision maker in the N price scenarios, the risk model is:
Figure FDA0002354173840000031
0≤risk(s)≤M
Figure FDA0002354173840000032
wherein z is0For expected benefits, risk is the risk in s-price scenario, M is the acceptable risk, fsFor revenue in s-price scenario, risk in s-price scenario is risk(s), risk in all price scenarios is Ra, R0To a set maximum risk value, epsilonsIs the probability value in the s price scenario.
6. The power generation risk management method of claim 5, wherein in the constructing a multi-objective optimization model based on the power plant profit model, the risk model, and the unit constraints and obtaining an optimal solution set by solving the multi-objective optimization model, the multi-objective optimization model is:
min[fi(x)]i＝1,2,...,Nob
Figure FDA0002354173840000033
wherein x is a decision variable, pjRepresenting the jth inequality constraint, qwDenotes the w-th equality constraint, XkDenotes xkSolution space of, NobNumber of representing objective function, NieqNumber of inequality constraints representing the objective function, NeqNumber of equality constraints representing the objective function, NbRepresenting the number of upper and lower bounds of the objective function.
7. The power generation risk management method according to claim 1 or 6, wherein the step of obtaining an optimal solution set by solving the multiobjective optimization model comprises:
converting the multi-objective optimization model according to a generalized specification normal constraint method to obtain a plurality of types of single-objective problems;
solving the multi-class single-target problem by using a firefly algorithm, and searching to obtain all pareto solution sets;
and obtaining the optimal solution set in all the pareto solution sets through a hyperplane decision method.
8. A power generation risk management system for an electricity spot market, comprising:
the system comprises a scene generation module, a price analysis module and a price analysis module, wherein the scene generation module is used for obtaining N price scenes based on a Monte Carlo simulation method, wherein N is an integer greater than 1;
the profit model building module is used for building a profit model of the power plant according to bidding data of the generator set participating in bidding and the constraint conditions of the generator set, wherein the bidding data comprises quoted price data and income data of the generator set;
the risk model building module is used for building risk models of the generator sets in the N price scenes as bidding decision makers;
the optimization model building module is used for building a multi-objective optimization model according to the power plant profit model, the risk model and the unit constraint conditions, and obtaining an optimal solution set by solving the multi-objective optimization model;
and the result construction module is used for constructing the optimal day-ahead quotation data of the power generation unit under different expected benefits by utilizing a plurality of different optimal solutions selected from the optimal solution set.
9. The power generation risk management system of claim 8, wherein the scenario generation module is to:
carrying out Monte Carlo simulation on day-ahead node electricity price data obtained by prediction in advance based on a Monte Carlo simulation method to generate M price scenes, wherein M is an integer larger than N;
and carrying out scene reduction on the M price scenes to obtain N price scenes.
10. The power generation risk management system of claim 8, wherein the profit model building module is to:
obtaining bidding data of the generator set participating in bidding through a preset objective function;
establishing a power plant profit model according to the bidding data and the unit constraint conditions;
wherein the preset objective function comprises:
the maximum profit calculation formula of the generator set is as follows:
Figure FDA0002354173840000051
Figure FDA0002354173840000052
Figure FDA0002354173840000053
the quotation calculation formula of the generator set is as follows:
Figure FDA0002354173840000054
kmin≤ki≤kmax
wherein the number of the generator sets participating in bidding is I, k is more than 0 and less than or equal to I, I is more than 0 and less than or equal to I, InckFor the benefit of the kth power plant, piFor the ith generator set, PiIs the winning bid amount of the ith generating set, FiFor the generating cost of the ith generating set, Pi,sThe winning bid amount of the ith power generation set in the s section is obtained; k is a radical ofminIndicates the lower limit of the quote, kmaxIndicating an upper limit for the bid.
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