CN112183825A - Electric power market simulation method and system for introducing futures and electronic equipment - Google Patents

Electric power market simulation method and system for introducing futures and electronic equipment Download PDF

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CN112183825A
CN112183825A CN202010964225.4A CN202010964225A CN112183825A CN 112183825 A CN112183825 A CN 112183825A CN 202010964225 A CN202010964225 A CN 202010964225A CN 112183825 A CN112183825 A CN 112183825A
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辜炜德
冷媛
陈政
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Energy Development Research Institute of China Southern Power Grid Co Ltd
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Abstract

The invention provides a simulation method, a simulation system and electronic equipment for introducing futures in an electric power market, wherein the method comprises the following steps: constructing a simulated power market environment according to the SCED power scheduling model and the optimal quotation model of the power generation side; designing a monthly-based electric power futures contract according to a standard electric power futures contract, and introducing the monthly-based electric power futures contract into the simulation electric power market environment; and obtaining a simulation result by the monthly base electric power futures contract according to a preset rule. The method analyzes the influence of the introduced electric power futures on spot market price by a market simulation method, and effectively analyzes the influence of the introduced electric power futures on electricity price and generating set income.

Description

Electric power market simulation method and system for introducing futures and electronic equipment
Technical Field
The present invention relates to the field of power technologies, and in particular, to a method and a system for simulating a power market by introducing futures, and an electronic device.
Background
With the deepening of market-oriented reformation of the electric power system, the construction of the electric power spot market in China is gradually promoted, but the market-oriented operation also brings the risk of fluctuation of the electricity price for the participants of the electric power market. With the gradual improvement of the construction price mechanism of the electric power spot market, the risk brought to the participants of the electric power market by the fluctuation of the electric power spot price is gradually highlighted. As an important risk management tool, electric futures are widely used in each of the large and mature electric markets. The electric power market in China is in the initial stage of construction, and the introduction of electric power futures will certainly bring influence on the current spot market.
The electric power futures are an important electric power financial derivative, trade in the mode of futures contracts, and are designed, formulated and organized and settled by futures exchange in a unified way. Since the first power futures contract in the world was introduced at the northern european power exchange in 1995, power futures have played an important role in the smoothing of power production and power prices in various countries and have provided a means of risk management for power market participants. When the opportunity is mature, the trade of the electric power futures and off-site electric power financial derivatives is explored and developed, and medium and long-term electric price benchmarks and spot market risk control tools are provided for power generation enterprises, power selling enterprises and users. At present, the middle-term and long-term electric power trading in China is organized and managed by an electric power trading center, and main trading varieties are only traded between power generation enterprises and large users or power selling companies, so that the middle-term and long-term electric power trading is essentially different from widely flowing financial risk-avoiding products. However, with the gradual advance of the reformation of electric market in China, the demand of market main bodies on financial derivatives such as electric futures and the like will be gradually highlighted. In addition, the introduction of electric power futures will also have a positive impact on price fluctuations in the electric power spot market.
Therefore, it is very important to research a power spot market simulation method to quantitatively analyze the influence of the introduced power futures on the spot market price.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method, a system and an electronic device for simulating a power market introducing futures, so as to obtain base charge price changes before and after introducing the power futures, and further analyze the influence of the power futures on the spot market.
A first aspect of an embodiment of the present invention provides a method for simulating an electric power market in which futures are introduced, including:
constructing a simulated power market environment according to the SCED power scheduling model and the optimal quotation model of the power generation side;
designing a monthly-based electric power futures contract according to a standard electric power futures contract, and introducing the monthly-based electric power futures contract into the simulation electric power market environment;
and obtaining a simulation result by the monthly base electric power futures contract according to a preset rule.
Further, the step of constructing the simulated power market environment according to the SCED power scheduling model and the optimal price quotation model at the power generation side specifically includes:
and constructing an SCED power scheduling model according to the power generation cost and the total scheduling cost, and constructing an optimal quotation model of the power generation side according to the power generation cost and the market power load.
Further, the step of constructing the SCED power scheduling model according to the power generation cost and the total scheduling cost includes:
under the safety constraint condition, an SCED power scheduling model is constructed according to the following objective function:
Figure BDA0002681256950000021
in the formula, PGIs a m-dimensional vector and
Figure BDA0002681256950000022
wherein
Figure BDA0002681256950000023
Is the generated power of the ith generator set;
Figure BDA0002681256950000024
is the power generation cost of the ith generator set; f is the total dispatch cost for the power system.
Further, the SCED power scheduling model satisfies a generating power constraint of the generator set, a transmission power constraint of the transmission line, a voltage constraint of the node, and a system stable operation constraint, wherein,
the generated power constraint of the ith genset (i ═ 1,2 …, m) is expressed as:
Figure BDA0002681256950000025
Figure BDA0002681256950000026
in the formula
Figure BDA0002681256950000031
And
Figure BDA0002681256950000032
respectively the minimum and the maximum active power,
Figure BDA0002681256950000033
and
Figure BDA0002681256950000034
minimum and maximum reactive power, respectively;
the transmission power constraint for the ith transmission line (i ═ 1,2 …, l) is expressed as:
Figure BDA0002681256950000035
in the formula
Figure BDA0002681256950000036
Is the transmission power of the transmission line;
Figure BDA0002681256950000037
and
Figure BDA0002681256950000038
minimum and maximum power transmission power, respectively;
the voltage constraint for the ith node (i ═ 1,2 …, n) is expressed as:
Vimin≤Vi≤Vimax
in the formula ViIs the node voltage; viminAnd VimaxMaximum and minimum voltage that can be tolerated, respectively;
the constraint on the stable operation of the system is expressed as follows from Kirchoff's Law:
Figure BDA0002681256950000039
Figure BDA00026812569500000310
in the formula (I), the compound is shown in the specification,
Figure BDA00026812569500000311
and
Figure BDA00026812569500000312
mismatch of active and reactive power for the ith node;
Figure BDA00026812569500000313
and
Figure BDA00026812569500000314
respectively the active load and the reactive load of the node corresponding to the ith generator set; y isijIs the line admittance from the ith node to the jth node; thetaiIs the voltage phase angle of the ith node;ijis line admittance YijThe phase angle of (c).
Further, the step of constructing the optimal quotation model of the power generation side according to the power generation cost and the market power load comprises the following steps: constructing the optimal quotation strategy model according to the following linear bidding function:
Ei(Pi)＝αi+βiPi
in the formula, PiIs the active power of the generator set; alpha is alphaiAnd betaiIf not, the quotation coefficient is the corresponding quotation coefficient of the generator set;
wherein the quoting coefficient of the ith generator set ( i 1, 2.., m) is obtained by solving the following optimization problem,
Figure BDA00026812569500000315
the constraint conditions are as follows:
Figure BDA0002681256950000041
Figure BDA0002681256950000042
Figure BDA0002681256950000043
wherein S is the price of the electricity for market settlement;
Figure BDA0002681256950000044
is the active power of the ith generator; ciThe power generation cost of the ith generator set; d is the expectation of the total power demand of market users;
Figure BDA0002681256950000045
and
Figure BDA0002681256950000046
is the maximum and minimum active power of the jth generator set;
and the quotation coefficient α of the opponent generator set j (j ≠ i) of the ith generator set (i ═ 1, 2.., m)jAnd betajObeying a joint normal distribution, represented as:
Figure BDA0002681256950000047
in the formula, ρjIs alphajAnd betajThe correlation coefficient of (a);
Figure BDA0002681256950000048
and
Figure BDA0002681256950000049
are each alphajMean, standard deviation and beta ofjMean, standard deviation of.
Further, the step of designing a monthly base electric power futures contract according to the standard electric power futures contract comprises:
setting contract units, delivery periods, contract sizes of each hand, contract electricity prices, contract period settlement prices and contract delivery modes of the electric futures; the contract unit of the electric futures is a preset contract unit, the delivery period is all time of the next month, the contract electricity price is the monthly average base charge electricity price of the same month as the previous year and the delivery period, the closing period settlement price is the arithmetic average of the daily average base charge spot electricity prices of the contract months, and the contract delivery mode is cash delivery;
determining a monthly average base charge price, a contract value and a contract number of each free party, and determining a quotation strategy, a settlement electricity price, a free party income and a multi-party income according to the monthly average base charge price and the contract number;
wherein the monthly-averaged charge value is represented as:
E(Ss)t＝r(St，s′t)×S′s
in the formula, StIs whenA set of daily average base charge prices t days before the month; stIs the set of daily average base charge prices t days before the same month of the previous year; r(s)t，S′t) Is StAnd S'tPearson's correlation coefficient; s'sThe monthly average base charge spot price of the same month as the previous year and the delivery period;
contract value of each free hand on day t of trading period
Figure BDA0002681256950000051
Expressed as:
Figure BDA0002681256950000052
in the formula ItMarket information of the t day of trading period, and is at S's＝SFUnder the conditions of (a) under (b),
the value of the power futures contract on each hand on the tth day of the trade is expressed as:
Figure BDA0002681256950000053
number x of contract hands traded on the t day of trading by the ith generator setitExpressed as:
Figure BDA0002681256950000054
in the formula, piAnd q isiNon-negative, which is the slope and intercept of the futures demand curve of the generator set;
Figure BDA0002681256950000055
represents a rounding down operation;
the objective function of the ith generator set for making the quotation strategy every day is represented as:
Figure BDA0002681256950000056
in the formula, XiIs the total number of hands of futures contracts signed by the generator set; e (S)s) The expected value of the settlement electricity price of the generator group is obtained;
on day t of the delivery month, the expected value of the set of departure units to settle the electricity prices is expressed as:
Figure BDA0002681256950000057
in the formula, SiIs the node base charge price on day i of the delivery month;
free income B after settlement of contract of each handshortExpressed as:
Bshort＝(SF-SS)×m
in the formula, SsIs the settlement price; sFIs a contract price; m is the size of each contract; multi-party profit BlongIs equal to-Bshort。
Further, the step of obtaining the simulation result by the monthly base electric charge capacity futures contract according to a predetermined rule includes:
and simulating the change data of the average electricity price of the market, the average monthly basic charge price and the total income of each generator set after the electric power futures are introduced, and carrying out quantitative analysis on the change data of the average electricity price of the market, the average monthly basic charge price and the total income of each generator set when the electric power futures are not introduced.
A second aspect of an embodiment of the present invention provides a system for electric power market simulation of futures introduction, the system including:
the simulation environment construction module is used for constructing a simulation electric power market environment according to the SCED electric power scheduling model and the optimal quotation model at the power generation side;
the electric power futures contract design module is used for designing a monthly basic electric charge power futures contract according to a standard electric power futures contract and introducing the monthly basic electric charge power futures contract into the simulation electric power market environment;
and the simulation module is used for obtaining a simulation result according to the monthly base electric charge future contract according to a preset rule.
A third aspect of embodiments of the present invention provides an electronic device, comprising at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores a program of instructions executable by the at least one processor to enable the at least one processor to perform the method described above.
A fourth aspect of an embodiment of the present invention provides a computer program product for use in a futures-introduced power market simulation system, the computer program product comprising the functional modules described above.
Compared with the prior art, the invention has the beneficial effects that: the method simulates the electric power market based on the optimal quotation strategy of security constraint economic dispatching and power generation side based on the incomplete adversary information, designs a monthly-based electric power futures contract according to the electric power futures contract which is listed on the market in the world, analyzes the influence of the introduction of the electric power futures on the spot market price by a market simulation method, and effectively analyzes the influence of the introduction of the electric power futures on the electricity price and the power generation group profit.
Drawings
Fig. 1 is a flowchart of a method for simulating an electric power market by introducing futures according to an embodiment of the present invention;
FIG. 2 is a flowchart of step S200 in FIG. 1;
FIG. 3 is a schematic diagram of an IEEE 14-bus power grid system simulated by an SCED power scheduling model according to an embodiment of the present invention;
FIG. 4 illustrates some parameters of each generator set in a simulation environment according to an embodiment of the present invention;
FIG. 5 is a graph illustrating the electrical load factor of each node used in the simulation environment in an embodiment of the present invention;
fig. 6 is a diagram illustrating a trend analysis of market average electricity prices for cases where power futures are introduced and power futures are not introduced in simulation result analysis according to an embodiment of the present invention;
fig. 7 is a monthly average base charge price analysis diagram of node 1 in simulation under two conditions of introducing power futures and not introducing power futures in simulation result analysis according to the embodiment of the present invention;
fig. 8 is an analysis diagram illustrating a ratio of a change in the monthly average base charge price of each No. 1 month node after futures contracts are introduced in simulation result analysis in the embodiment of the present invention, in comparison with a ratio when futures are not introduced, if the change is regular, price increase is represented, and if the change is negative, price decrease is represented;
fig. 9 is a diagram of the total profit analysis of each generator set under both power futures introduced and power futures not introduced according to the simulation result analysis in the embodiment of the present invention.
Fig. 10 is a block diagram of a power market simulation system for introducing futures according to an embodiment of the present invention;
FIG. 11 is a diagram of an embodiment of an electronic device according to the embodiment of the 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.
The terms "first" and "second" in the description and claims of the present invention and the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Referring to fig. 1, an embodiment of the present invention provides a method for simulating an electric power market by introducing futures, including the steps of:
s100, establishing a simulated power market environment according to the SCED power scheduling model and the optimal quotation model of the power generation side.
The method and the device for generating the electricity comprise the steps of constructing an SCED (Security Constrained Economic Dispatch) power scheduling model according to the electricity generation cost and the total scheduling cost, and constructing an Optimal quotation (Optimal binary recommendations of Imperfect Information amplitude composite Generator) model on the electricity generation side according to the electricity generation cost and the market electricity load. And taking the SCED power scheduling model and the optimal quotation model of the power generation side based on the adversary incomplete information as the optimal quotation model of the power generation side.
The construction process of the two models is described in detail below.
The SCED model is a power scheduling model. Under the safety constraint condition, the SCED performs power scheduling at the minimum economic cost to meet the power demand of the market. For a power market with m units, n nodes and l lines, the SCED model has an objective function of
Figure BDA0002681256950000081
In the formula, PGIs a m-dimensional vector and
Figure BDA0002681256950000082
wherein
Figure BDA0002681256950000083
Is the generated power of the ith generator set;
Figure BDA0002681256950000084
is the power generation cost of the ith generator set; f is the total dispatch cost for the power system. The SCED needs to satisfy the generated power constraint of a generator set, the transmission power constraint of a transmission line, the voltage constraint of a node and a systemAnd (5) stable operation constraint.
The generated power constraint of the ith generator set (i ═ 1,2 …, m) can be expressed as
Figure BDA0002681256950000085
Figure BDA0002681256950000086
In the formula
Figure BDA0002681256950000091
And
Figure BDA0002681256950000092
respectively the minimum and the maximum active power,
Figure BDA0002681256950000093
and
Figure BDA0002681256950000094
minimum and maximum reactive power, respectively.
The transmission power constraint of the ith transmission line (i ═ 1, 2.., l) can be expressed as
Figure BDA0002681256950000095
In the formula
Figure BDA0002681256950000096
Is the transmission power of the transmission line;
Figure BDA0002681256950000097
and
Figure BDA0002681256950000098
respectively minimum and maximum power transmitted.
The voltage constraint of the ith node (i ═ 1, 2.., n) can be expressed as
Vimin≤Vi≤Vimax
In the formula ViIs the node voltage; viminAnd VimaxRespectively, a maximum and a minimum voltage that can be tolerated.
According to Kirchoff's Law, the constraint of stable operation of the system can be expressed as,
Figure BDA0002681256950000099
Figure BDA00026812569500000910
in the formula (I), the compound is shown in the specification,
Figure BDA00026812569500000911
and
Figure BDA00026812569500000912
mismatch of active and reactive power for the ith node;
Figure BDA00026812569500000913
and
Figure BDA00026812569500000914
respectively the active load and the reactive load of the node corresponding to the ith generator set; y isijIs the line admittance from the ith node to the jth node; thetaiIs the voltage phase angle of the ith node;ijis line admittance YijThe phase angle of (c).
The marginal electricity price of the SCED model describes the cost increased by increasing the output of the unit node, and the essence of the marginal electricity price is the shadow price of the power system when the power system operates according to the economic dispatching model, and the shadow price can be obtained by the first-order KKT condition of the optimization problem in the SCED model. The SCED optimization problem is converted to an equivalent lagrangian function,
Figure BDA00026812569500000915
constrain the corresponding Lagrangian multiplier for the equation in the formula,
Figure BDA00026812569500000916
And
Figure BDA00026812569500000917
corresponding lagrange multipliers are constrained for two inequalities. The first order condition of the Lagrangian function corresponding to the SCED optimization problem can be obtained
Figure BDA00026812569500000918
In the formula
Figure BDA00026812569500000919
Is the optimal transmit power vector;
Figure BDA00026812569500000920
namely the node marginal electricity price LMP of the ith node.
When the generator set bids, the bidding strategy of the generator set is generally adjusted according to factors such as market power load, power generation cost, bidding strategies of opponent enterprises and the like, so that benefit maximization of the enterprises is realized. The embodiment of the invention adopts an optimal quotation strategy model based on the incomplete information of the opponents. The model is a power generation side optimal quotation model, and provides a method for making respective optimal quotation strategies according to the expectation of market power demand when a bidding generator set in a power market is based on the incomplete information of an opponent.
The optimal bidding strategy model based on the incomplete information of the opponents assumes that the generator set needs to provide a linear bidding function to the power exchange, which can be expressed as
Ei(Pi)＝αi+βiPi
In the formula, PiIs the active power of the generator set; alpha is alphaiAnd betaiAnd if not, the quotation coefficient is the corresponding quotation coefficient of the generator set. Generator set needs to be analyzed to determine alphaiAnd betaiTo maximize their own benefits. Meanwhile, the model assumes that the generator sets consider that the market has uniform settlement of electricity prices, namely, the electricity price difference between different nodes is ignored, and optimization is only carried out under the condition of considering the active power generation power constraint of each generator set.
Thus, for an electricity market with m units, to maximize its own profit, the quoting factor for the ith unit (i-1, 2.., m) can be obtained by solving the following optimization problem,
Figure BDA0002681256950000101
the constraint condition is that,
Figure BDA0002681256950000102
Figure BDA0002681256950000103
Figure BDA0002681256950000104
wherein S is the price of the electricity for market settlement;
Figure BDA0002681256950000105
is the active power of the ith generator; ciThe power generation cost of the ith generator set; d is the expectation of the total power demand of market users;
Figure BDA0002681256950000106
and
Figure BDA0002681256950000107
is the maximum and minimum active power of the jth genset. Assuming that the market rule is a single-side price quoted for the generator set, the market power demand is a completely inelastic demand, and the total power demand of market users is expectedD does not change with the change in the market price S.
However, since the electricity market employs a bid with a dark bid, the quotation coefficients for the other generator sets are unknown for the ith generator set, i.e., in the above formula
Figure BDA0002681256950000111
Is unknown. The generator set can predict the quotation coefficient of the mobile unit according to the historical quotation data of the mobile unit. An optimal quotation strategy model based on incomplete adversary information provides that the ith generator set (i ═ 1, 2.., m) can be regarded as a quotation coefficient alpha of an adversary generator set j (j ≠ i)jAnd betajSubject to a joint normal distribution, which can be expressed as,
Figure BDA0002681256950000112
in the formula, ρjIs alphajAnd betajThe correlation coefficient of (a);
Figure BDA0002681256950000113
and
Figure BDA0002681256950000114
are each alphajMean, standard deviation and beta ofjMean, standard deviation of.
In this embodiment, the optimal quotation strategy model based on the incomplete adversary information has two solving methods, namely a monte carlo method and an optimization method, and the solving processes of the two solving methods are not described in detail in the embodiment of the present invention. In the simulation method of the embodiment of the invention, the bidding strategy is determined by assuming that the daily average power consumption of each generator set on the same day of the year is the expectation of the market power consumption demand, and a method for simulating the bidding behavior of each generator set to predict the opponents is selected by the Monte Carlo method.
Based on the SCED power scheduling model and the optimal price quoting model of the power generation side, the embodiment of the invention constructs a simulated power market environment which is very similar to the real power market environment and can be effectively used as the power market environment after power futures are introduced subsequently.
Step S200, designing a monthly base electric charging capacity futures contract according to the standard electric power futures contract, and introducing the monthly base electric charging capacity futures contract into the simulation electric power market environment.
The embodiment of the invention designs a monthly-based electric power futures contract according to a mainstream standard electric power futures contract in the current actual electric power futures market, and as shown in fig. 2, the method comprises the following steps:
s201, setting contract units, delivery periods, contract sizes of each hand, contract electricity prices, contract period settlement prices and contract delivery modes of the monthly base electric power futures contracts; the contract unit of the electric futures is a preset contract unit, the delivery period is all time of the next month, the contract electricity price is the monthly average base charge electricity price of the same month as the previous year and the delivery period, the closing period settlement price is the arithmetic average value of the daily average base charge spot electricity price of the contract month, and the contract delivery mode is cash delivery.
S202, determining the monthly average base charge price, the contract value and the contract number of each free party, and determining a quotation strategy, a settlement electricity price, the free party income and the multi-party income according to the monthly average base charge price and the contract number.
The above-described process is specifically described below.
Assuming that each month is 30 days, the power futures contract unit in the monthly-based electric power futures contract designed by the embodiment of the present invention is 0.1 MW; the delivery period is 1 month, 0: 00 to next month 30 day 24: 00; the contract size of each hand is 0.1MW multiplied by 30 days multiplied by 24 hours which is 72 MWh; the contract electricity price is the average monthly base charge price of the previous year and the same month of the delivery period; the contract Settlement Price (setting Price) is an arithmetic average of daily average base charge spot electricity prices of contract months; the contract delivery mode is cash delivery; the exchange allows the generator set and the electricity retailer to operate at 1 month, 1 day, 0: 00 to 12 month of the year, 31 day 24: 00 freely sign any number of the contracts; there is no contract transaction fee.
Therefore, the free party who has settled the contract is received by each handYi BshortCan be expressed as
Bshort＝(SF-SS)×m
In the formula, SsIs the settlement price; sFIs a contract price; m is the size of each contract; multi-party profit BlongIs equal to-Bshort. In the actual futures market, the price of each manual electric futures contract is generally matched by the price quoted by both the air and the multiple parties, but theoretically, the value of each manual electric futures contract in the trading period reflects the expected value of the market for the contract income, so the value of each manual air contract on the t day of the trading period
Figure BDA0002681256950000121
Can be expressed as a number of times,
Figure BDA0002681256950000122
in the formula ItMarket information for the t-th day of the trade period.
In the simulation method of the embodiment of the invention, it is assumed that the generator set in the market only signs a contract of the empty party (Short Side), and the electric power retailer only signs a contract of the multi-party (Long Side); on the tth day of each month (i.e., tth day of trading), the market predicts the relationship between the average charge price of the next month and the average charge price of the same month of the previous year by using the observable correlation between the average charge price of the current month and the average charge price of the same month of the previous year, i.e., the market's expectation for the average charge price of the next month is that on tth day of trading 24: 00 may be expressed as a number 00 of,
E(Ss)t＝r(St，s′t)×S′s
in the formula, StIs a set of daily average base charge prices t days before the current month; stIs the set of daily average base charge prices t days before the same month of the previous year; r (S)t，S′t) Is StAnd S'tPearson's correlation coefficient; s'sThe monthly average base charge spot price of the same month as the previous year and the delivery period. Due to the contract price of the above-mentioned electric power futures contractThe grid is also the base charge price of the same month as the delivery period in the previous year, namely S's＝SFThe value of the free hand of the above-described power futures contract on day t of the trade day may be expressed as,
Figure BDA0002681256950000131
meanwhile, we assume that each generator set is only on 24: 00, one transaction of futures contracts is carried out, and the futures contract demand curve of each generator set is a linear demand curve. Therefore, the number x of contract hands traded by the ith generator set on the t day of the trading dayitCan be represented as
Figure BDA0002681256950000132
In the formula, piAnd q isiNon-negative, which is the slope and intercept of the futures demand curve of the generator set;
Figure BDA0002681256950000133
indicating a rounding down operation.
When electric power futures are not introduced, in order to maximize benefits of the generator sets, each generator set needs to solve an optimization problem under the constraint conditions; however, after the electric power futures are introduced, since the quotation strategy made by the generator set will affect the market electricity price, and the benefit of the futures contract is related to the market electricity price, the generator set needs to consider not only the power generation cost, but also the benefit or loss caused by the electric power futures contract. After the introduction of the power futures, therefore, the objective function of the ith generator set to develop the pricing strategy on a daily basis may be re-expressed as,
Figure BDA0002681256950000134
in the formula, XiIs the total number of hands of futures contracts signed by the generator set; e (S)s) Is thatThe generator set settles the expected value of the electricity price. Assuming that the generator set is an expected value of the settlement price according to the arithmetic mean value of the daily average node base charge price and the tomorrow market price generated in the delivery month, namely the expected value of the generator set to the settlement price on the tth day of the delivery month can be expressed as
Figure BDA0002681256950000141
In the formula, SiIs the node-based charge rate on day i of the delivery month.
And step S300, obtaining a simulation result according to the monthly charge capacity futures contract according to a preset rule.
The embodiment of the invention simulates the change data of the average electricity price of the market, the average monthly basis charge electricity price and the total income of each generator set after the electric power futures are introduced, and carries out quantitative analysis on the change data of the average electricity price of the market, the average monthly basis charge electricity price and the total income of each generator set when the electric power futures are not introduced. After the electric power futures contract is introduced, various parameters of each generator set, the electric load coefficient of each used node and other change data can be effectively checked in the simulation environment, and the following quantitative analysis can be carried out: 1) a trend of change in market average electricity prices under both cases of introduction of power futures and without introduction of power futures; 2) the monthly average base charge price changes under the two conditions of introducing power futures and not introducing power futures; 3) the proportion of the monthly average base charge price of the specific node at the specific time of each month after the futures contract is introduced is changed compared with the proportion when the futures are not introduced; 4) the total revenue for each generator set in both cases of introducing power futures and not introducing power futures.
In order to demonstrate the above analysis in detail, the embodiment of the present invention provides a simulation analysis result in a power grid system, which is specifically as follows: FIG. 3 is a diagram of a power grid system of IEEE 14-bus simulated by a safety-constrained economic dispatch model according to an embodiment of the present invention. In the power grid system, fig. 4 is a partial parameter of each generator set in the simulation environment in the embodiment of the present invention; FIG. 5 is a graph illustrating the electrical load factor of each node used in the simulation environment in an embodiment of the present invention.
Based on the power grid system, the embodiment of the invention can analyze the change trend of the average power price of the market under the two situations of introducing the power futures and not introducing the power futures according to the simulation result, and fig. 6 is the analysis of the change trend of the average power price of the market under the two situations of introducing the power futures and not introducing the power futures analyzed by the simulation result in the embodiment of the invention. However, whether the electric power futures are introduced or not, the average electricity price of the market is always positively correlated with the average power consumption.
Fig. 7 is a monthly average base charge price analysis of node 1 in simulation under two conditions of introducing power futures and not introducing power futures according to simulation result analysis in the embodiment of the present invention, which can find that: on one hand, the introduction of the electric power futures can obviously reduce the electricity price of a month with higher node average power consumption, namely, reduce the fluctuation of the electricity price; on the other hand, in months with higher average power consumption, the reduction rate of the power price is larger, namely the price reduction space is positively correlated with the average power consumption of the node, but the reduction amplitude of the power price is irrelevant to the average power consumption of the node.
Fig. 8 is a proportion of a change in the monthly average base charge price of node No. 1 in each month after introduction of futures contracts, which is analyzed according to simulation results in the embodiment of the present invention, compared with a proportion when futures are not introduced, a price increase is represented if the proportion is regular, and a price decrease is represented if the proportion is negative. Through simulation, the embodiment of the invention can effectively determine the proportion change of the monthly average base charge price after the futures contract is introduced.
Fig. 9 is a total profit analysis of each generator set under two conditions of introducing power futures and not introducing power futures according to simulation result analysis in the embodiment of the present invention, and the result shows: although the contract for futures may be held flat against spot prices for months of higher electric power, the profitability of the generator set does not change significantly, both with and without the introduction of electric power futures, since the generator set already receives a portion of the profitability when making futures contracts.
In summary, in the electric power market simulation method for introducing futures provided in the embodiment of the present invention, the safety constraint economic scheduling model is adopted to simulate the scheduling of the electric power market, and the node marginal electricity price is used as the clearing price of the spot market. And an optimal quotation strategy model based on the incomplete adversary information is adopted, and a Monte Carlo method is selected to simulate each generator set to predict the adversary bidding behavior so as to generate quotation strategies of each generator set. And a monthly base electric power futures contract is designed according to the electric power futures contract which is on the market globally. Finally, the average node electricity price, the average electricity power, the monthly average base charge price and the change trend of the total income of each generator set before and after the introduction of the electric power futures are obtained, and the influence of the introduction of the electric power futures on the spot market price is effectively analyzed.
An embodiment of the present invention further provides a system for simulating an electric power market by introducing futures, as shown in fig. 10, where the system includes:
the simulation environment construction module 10 is used for constructing a simulation power market environment according to the SCED power scheduling model and the optimal price quoting model of the power generation side;
a power futures contract design module 20, configured to design a monthly base electric power futures contract according to a standard power futures contract, and introduce the monthly base electric power futures contract in the simulated power market environment;
and the simulation module 30 is configured to obtain a simulation result according to the monthly basis electric charge future contract according to a predetermined rule.
It should be noted that the electric power market simulation system for introducing futures and the electric power market simulation method for introducing futures provided in the above embodiments are based on the same inventive concept. Therefore, the steps of each specific embodiment in the electric power market simulation method for introducing futures may be executed by corresponding functional modules, and the specific functions in the functional modules may also have corresponding method steps in the electric power market simulation system for introducing futures, which are not described herein again.
In summary, the electric power market simulation system and the electric power market simulation method for introducing futures provided by the embodiments of the present invention provide a set of complete simulation electric power market environment building process and futures contract design standard for the problem that whether the introduction of electric power futures affects the spot market price, and the simulation process is the simulation display of futures contracts introduced into the electric power market in a computer, and is intuitive and accurate. And different simulation electric power market environments and different futures contracts can be set up according to different electric power environments and analysis requirements.
Fig. 11 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application, where the electronic device includes: one or more processors 801 and memory 802. One is taken as an example in fig. 11. The processor 801 and the memory 802 may be connected by a bus or other means, and fig. 11 illustrates an example of connection by a bus.
Memory 802 is provided as a non-transitory computer-readable storage medium that can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the base leveling design system in the embodiments of the present invention. The processor 801 executes various functional applications and data processing of the server by running nonvolatile software programs, instructions and modules stored in the memory 802, so as to implement the base leveling design system in the above method embodiments.
The memory 802 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the stored data area may store data created from use of the base leveling design system, and the like. Further, the memory 802 may include high speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, memory 802 optionally includes memory located remotely from processor 801, which may be connected to a base leveling design system via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device can execute the system or the method provided by the embodiment of the application, and has corresponding functional modules and beneficial effects for executing the system or the method. For technical details that are not described in detail in the present embodiment, reference may be made to the system or method provided in the embodiments of the present application.
Moreover, the above-described system embodiments are only illustrative, wherein the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a general hardware platform, and certainly can also be implemented by hardware. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A method for electric power market simulation of futures import, comprising:
constructing a simulated power market environment according to the SCED power scheduling model and the optimal quotation model of the power generation side;
designing a monthly-based electric power futures contract according to a standard electric power futures contract, and introducing the monthly-based electric power futures contract into the simulation electric power market environment;
and obtaining a simulation result by the monthly base electric power futures contract according to a preset rule.
2. The futures-introduced power market simulation method according to claim 1, wherein the step of constructing a simulated power market environment according to the SCED power scheduling model and the optimal price-quote model at the power generation side specifically comprises:
and constructing an SCED power scheduling model according to the power generation cost and the total scheduling cost, and constructing an optimal quotation model of the power generation side according to the power generation cost and the market power load.
3. The futures-introduced power market simulation method according to claim 2, wherein the step of building the SCED power scheduling model according to the generation cost and the total scheduling cost comprises:
under the safety constraint condition, an SCED power scheduling model is constructed according to the following objective function:
Figure FDA0002681256940000011
in the formula, PGIs a m-dimensional vector and
Figure FDA0002681256940000012
wherein
Figure FDA0002681256940000013
Is the generated power of the ith generator set;
Figure FDA0002681256940000014
is the power generation cost of the ith generator set; f is the total dispatch cost for the power system.
4. The futures-introduced power market simulation method according to claim 3, wherein the SCED power scheduling model satisfies a generating power constraint of a generator set, a transmission power constraint of a transmission line, a voltage constraint of a node, and a system steady operation constraint,
the generated power constraint of the ith genset (i ═ 1, 2.., m) is expressed as:
Figure FDA0002681256940000015
Figure FDA0002681256940000016
in the formula
Figure FDA0002681256940000017
And
Figure FDA0002681256940000018
respectively the minimum and the maximum active power,
Figure FDA0002681256940000019
and
Figure FDA00026812569400000110
minimum and maximum reactive power, respectively;
the transmission power constraint of the ith transmission line (i ═ 1, 2.., l) is expressed as:
Figure FDA0002681256940000021
in the formula
Figure FDA0002681256940000022
Is the transmission power of the transmission line;
Figure FDA0002681256940000023
and
Figure FDA0002681256940000024
minimum and maximum power transmission power, respectively;
the voltage constraint of the ith node (i ═ 1, 2.., n) is expressed as:
Vi min≤Vi≤Vi max
in the formula ViIs the node voltage; vi minAnd Vi maxMaximum and minimum voltage that can be tolerated, respectively;
according to kirchhoff's Law, the system steady operation constraint is expressed as:
Figure FDA0002681256940000025
Figure FDA0002681256940000026
in the formula (I), the compound is shown in the specification,
Figure FDA0002681256940000027
and
Figure FDA0002681256940000028
mismatch of active and reactive power for the ith node;
Figure FDA0002681256940000029
and
Figure FDA00026812569400000210
respectively the active load and the reactive load of the node corresponding to the ith generator set; y isijIs the line admittance from the ith node to the jth node; thetaiIs the voltage phase angle of the ith node;ijis line admittance YijThe phase angle of (c).
5. The futures-introduced electric power market simulation method according to claim 2, wherein the step of constructing a power generation-side optimal offer model according to power generation cost and market electric load comprises: constructing the optimal quotation strategy model according to the following linear bidding function:
Ei(Pi)＝αi+βiPi
in the formula, PiIs the active power of the generator set; alpha is alphaiAnd betaiIf not, the quotation coefficient is the corresponding quotation coefficient of the generator set;
wherein the quoting coefficient of the ith generator set (i 1, 2.., m) is obtained by solving the following optimization problem,
Figure FDA00026812569400000211
the constraint conditions are as follows:
Figure FDA0002681256940000031
Figure FDA0002681256940000032
Figure FDA0002681256940000033
wherein S is the price of the electricity for market settlement;
Figure FDA0002681256940000034
is the active power of the ith generator; ciThe power generation cost of the ith generator set; d is the expectation of the total power demand of market users;
Figure FDA0002681256940000035
and
Figure FDA0002681256940000036
is the maximum and minimum active power of the jth generator set;
and the quotation coefficient α of the opponent generator set j (j ≠ i) of the ith generator set (i ═ 1, 2.., m)jAnd betajObeying a joint normal distribution, represented as:
Figure FDA0002681256940000037
in the formula, ρjIs alphajAnd betajThe correlation coefficient of (a);
Figure FDA0002681256940000038
and
Figure FDA0002681256940000039
are each alphajMean, standard deviation and beta ofjMean, standard deviation of.
6. The method for electric power market simulation of futures introductions according to claim 1, wherein the step of designing a monthly-based electric power futures contract according to standard electric power futures contracts comprises:
setting contract units, delivery periods, contract sizes of each hand, contract electricity prices, contract period settlement prices and contract delivery modes of the electric futures; the contract unit of the electric futures is a preset contract unit, the delivery period is all time of the next month, the contract electricity price is the monthly average base charge electricity price of the same month as the previous year and the delivery period, the closing period settlement price is the arithmetic average of the daily average base charge spot electricity prices of the contract months, and the contract delivery mode is cash delivery;
determining a monthly average base charge price, a contract value and a contract number of each free party, and determining a quotation strategy, a settlement electricity price, a free party income and a multi-party income according to the monthly average base charge price and the contract number;
wherein the monthly-averaged charge value is represented as:
E(Ss)t＝r(St，s′t)×S′s
in the formula, StIs a set of daily average base charge prices t days before the current month; s'tIs the set of daily average base charge prices t days before the same month of the previous year; r (S)t，S′t) Is StAnd S'tPearson's correlation coefficient; s'sThe monthly average base charge spot price of the same month as the previous year and the delivery period;
contract value of each free hand on day t of trading period
Figure FDA0002681256940000041
Expressed as:
Figure FDA0002681256940000042
in the formula ItMarket information of the t day of trading period, and is at S's＝SFUnder the conditions of (a) under (b),
the value of the power futures contract on each hand on the tth day of the trade is expressed as:
Figure FDA0002681256940000043
number x of contract hands traded on the t day of trading by the ith generator setitExpressed as:
Figure FDA0002681256940000044
in the formula, piAnd q isiNon-negative, which is the slope and intercept of the futures demand curve of the generator set;
Figure FDA0002681256940000045
represents a rounding down operation;
the objective function of the ith generator set for making the quotation strategy every day is represented as:
Figure FDA0002681256940000046
in the formula, XiIs the total number of hands of futures contracts signed by the generator set; e (S)s) The expected value of the settlement electricity price of the generator group is obtained;
on day t of the delivery month, the expected value of the set of departure units to settle the electricity prices is expressed as:
Figure FDA0002681256940000047
in the formula, SiIs the node base charge price on day i of the delivery month;
free income B after settlement of contract of each handshortExpressed as:
Bshort＝(SF-SS)×m
in the formula, SsIs the settlement price; sFIs a contract price; m is the size of each contract; multi-party profit BlongIs equal to-Bshort。
7. The method for electric power market simulation of futures brought in according to claim 1, wherein the step of obtaining the simulation result from the monthly base electric power futures contract according to a predetermined rule comprises:
and simulating the change data of the average electricity price of the market, the average monthly basic charge price and the total income of each generator set after the electric power futures are introduced, and carrying out quantitative analysis on the change data of the average electricity price of the market, the average monthly basic charge price and the total income of each generator set when the electric power futures are not introduced.
8. A futures-introduced power market simulation system, the system comprising:
the simulation environment construction module is used for constructing a simulation electric power market environment according to the SCED electric power scheduling model and the optimal quotation model at the power generation side;
the electric power futures contract design module is used for designing a monthly basic electric charge power futures contract according to a standard electric power futures contract and introducing the monthly basic electric charge power futures contract into the simulation electric power market environment;
and the simulation module is used for obtaining a simulation result according to the monthly base electric charge future contract according to a preset rule.
9. An electronic device comprising at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores a program of instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7.
10. A computer program product for use in a futures-introduced power market simulation system, characterized in that the computer program product comprises the functional modules of claim 8.
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