CN113627991A - Bidding method and system for demand response aggregators in frequency modulation market environment - Google Patents

Bidding method and system for demand response aggregators in frequency modulation market environment Download PDF

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CN113627991A
CN113627991A CN202110967995.9A CN202110967995A CN113627991A CN 113627991 A CN113627991 A CN 113627991A CN 202110967995 A CN202110967995 A CN 202110967995A CN 113627991 A CN113627991 A CN 113627991A
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李扬
刘鑫
林雪杉
郭吉群
史云鹏
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Southeast University
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Abstract

The invention discloses a bidding method and a bidding system of a demand response aggregator in a frequency modulation market environment, wherein the bidding method comprises the following steps: s1, establishing an operation frame of the demand response aggregator and a bidding model in the frequency modulation market based on the trading rules of the frequency modulation market; s2, constructing a risk measurement index of the coupling model and the bidding model of the uncertainty factor based on the copula function; s3, establishing a dynamic optimization method and system of a demand response aggregator bidding model on the basis of considering load offset by establishing a time-varying demand response cost measuring model, wherein the bidding system comprises: the device comprises an information communication module, a data input module, a simulation calculation module and a data storage module. The invention establishes a bidding model aiming at maximizing the benefits of DRA, provides a time-varying demand response cost measuring and calculating method based on the response potential analysis of demand side resources, dynamically optimizes the bidding model of DRA on the basis of considering load deviation and provides reference for bidding decision of DRA participating in frequency modulation market.

Description

Bidding method and system for demand response aggregators in frequency modulation market environment
Technical Field
The invention belongs to the field of power demand response, and particularly relates to a bidding method and a bidding system for demand response aggregators in a frequency modulation market environment.
Background
With the continuous development of power technology, the penetration rate of intermittent renewable energy sources in the traditional power distribution network is continuously increased, so that the frequency fluctuation of a power system is increased, and the demand of the power auxiliary service market for the frequency regulation capability is increased.
Compared with a newly-added traditional frequency modulation unit, the adoption of the demand response participation system frequency modulation is generally considered to be a clean and effective solution. Demand side resources are characterized by large scale, distributed layout, and it is impractical for all resources to be individually managed by market scheduling, usually with Demand Response Aggregators (DRAs) organizing flexible loads to participate in market trading as a whole.
However, there is a great uncertainty about the price and the frequency modulation demand of other frequency modulation markets, so that the bidding decision of the DRA needs to bear a great risk, and the economic benefit of the DRA is seriously threatened. Therefore, how the DRA aggregates and manages flexible resources to participate in market bidding is an important issue affecting the economics of the DRA.
In order to solve the problems, a bidding method and a bidding system for demand response aggregators in a frequency modulation market environment are designed.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a bidding method and a bidding system of a demand response aggregator in a frequency modulation market environment, aiming at risks caused by a plurality of uncertainty factors in an electric power market to DRA economic benefits, based on copula functions (connection functions), an uncertainty model of mutual correlation between market prices and frequency modulation demands is constructed, and corresponding copula-CVaR models are used as market risk measurement indexes to quantify decision risks of DRAs, so that a bidding model aiming at maximizing benefits of DRAs is established, based on response potential analysis of demand side resources, a time-varying demand response cost measuring and calculating method is provided, the bidding model of DRAs is dynamically optimized on the basis of considering load offset, and references are provided for decision making of DRAs participating in frequency modulation market bidding.
The purpose of the invention can be realized by the following technical scheme:
a bidding method of demand response aggregators in a frequency modulation market environment comprises the following steps:
s1, establishing an operation frame of the demand response aggregator and a bidding model in the frequency modulation market based on the trading rules of the frequency modulation market;
s2, constructing a risk measurement index of the coupling model and the bidding model of the uncertainty factor based on the copula function;
s3, establishing a dynamic optimization method and system of the demand response aggregator bidding model on the basis of considering load deviation by establishing a time-varying demand response cost measuring and calculating model.
Further, in S1, in the fm market environment, the demand response aggregator integrates the commercial users, the residential users, and the electric vehicles to participate in bidding in the fm market, and controls the load type of the user to be a demand response behavior of the corresponding user to be composed of a fixed load, a transferable load, and an interruptible load, so as to meet the requirements of the two services of up-modulation and down-modulation.
The trading process of the demand response aggregator participating in the fm market may be further divided into a day-ahead stage, a real-time stage, and a settlement stage.
In the previous stage, the demand response aggregator acquires a charging plan of the electric automobile, calculates the bidding capacity of the next day frequency modulation market according to the predicted user load curve and information issued by the market, and uses the bidding capacity as a price receiver to ensure that the user participates in market bidding in a certain bargain mode by only reporting and not making a price.
In the real-time stage, a market manager issues a frequency modulation capacity clearing result and distributes frequency modulation mileage according to the capacity proportion, a demand response aggregator determines a demand response scheme according to the winning and winning result of the frequency modulation capacity and the frequency modulation mileage demand, and if the demand response cannot complete corresponding frequency modulation service, the demand response aggregator receives punishment.
And in the settlement stage, acquiring frequency modulation income from the market and issuing compensation of demand response to the user.
The net profit of the demand response aggregator is made up of four parts-frequency modulated capacity profit, mileage profit, demand response cost and opportunity cost.
The calculation formula of the demand response cost is as follows:
CDR＝μ(aM2+bM)+(1-μ)(λmM) ①
in formula I, CDRMu is the proportion of the measurement value in the compensation cost of demand response, a and b are non-negative measurement value coefficients, M is the frequency modulation mileage of demand response, and lambda is the compensation cost of demand responsemIs the frequency modulation mileage price in the market.
The revenue function for a DRA is:
π(R,M,λc,λm,λLMP)＝saλcR+saλmM-μ(aM2+bM)-(1-μ)(λmM)-slλLMPM ②
Figure BDA0003224865430000031
formula II and formula III, R is the competitive bidding decision of the frequency modulation capacity of DRA, lambdacFor the price of the modulated capacity in the market, lambdaLMPMarginal price of electricity for nodes in the market, saIs the frequency modulation performance index of DRA, slIs a Boolean variable, λΝΒΤIs the net test price in the market.
Further, in S2, the demand response aggregator faces a plurality of uncertainty factors when participating in the market bidding, so that the bidding decision of the demand response aggregator faces a risk of a lower profit than cost, and a copula-CVaR model is constructed as a risk measure index of the demand response aggregator bidding decision model in consideration of the coupling characteristics of the two uncertainty factors of the market price and the frequency modulation demand in the frequency modulation market.
Further, CVaR is a conditional mean value of the investment portfolio loss over a VaR, and the specific formula is as follows:
Figure BDA0003224865430000041
in the formula V, x is decision variable, y is random variableCVaR,βIs the value of CVaR, VVaR,βFor the value of VaR, β gives the confidence, f (x, y) is the loss function, and ρ (y) is the probability density function of y.
Due to VCVaR,β(x) The analytical formula (2) is difficult to find, and the expression of CVaR can be expressed as:
VCVaR,β＝minα∈DG(α,x) ⑥
Figure BDA0003224865430000042
in the formula (c) and the formula (c), α is a critical value of the loss function f (x, y), and D is a feasible domain set of α.
The simplified DRA revenue function is:
π(R,M,λ)＝sakcλR+μsakmλM-μ(aM2+bM)-slkLMPλM ⑧
in the formula (r), λ is the virtual price, kcTime-varying parameters, k, for frequency-modulated capacity pricesmTime-varying parameters, k, for frequency-modulated mileage pricesLMPThe time-varying parameter is the marginal electricity price of the market node.
In combination with the distribution of prices and demands, the copula-CVaR model for DRA bidding decision is:
Figure BDA0003224865430000043
in the formula ninthly, deltaαIs the value of copula-CVaR, lambdamaxH (λ, M) is the probability density function, which is the maximum value of the virtual price.
Selecting a binary normal copula function to randomly simulate the relevance of price and demand in the frequency modulation market, wherein the specific function expression is as follows:
Figure BDA0003224865430000044
in equation r, ρ is the correlation coefficient, Φ-1(. cndot.) is the inverse of the standard normal distribution function.
Let the discrete scene set generated by the Copula function be S ═ Mω,λω,ω＝1,2,…,NωAnd recording the value of the omega scene probability as qωIntroducing non-negative auxiliary variables may represent the discrete model of copula-CVaR as:
Figure BDA0003224865430000051
formula (II)
Figure BDA0003224865430000058
In, NωFor the number of discrete scenes, zωAre auxiliary variables.
Will deltaαAnd adding the coefficient into an objective function in a form of multiplying a risk preference coefficient L, wherein the bidding decision model for maximizing the profit of the DRA based on copula-CVaR in the frequency modulation market is as follows:
Figure BDA0003224865430000052
formula (II)
Figure BDA0003224865430000057
In the middle, T is the total number of competitive bidding time intervals of the frequency modulation market in one day, kc,tThe factor for the price of the fm capacity at time t,
Figure BDA0003224865430000053
for the virtual price at time t in the omega scenario,
Figure BDA0003224865430000054
for the frequency modulation capacity of DRA at t moment under omega sceneQuantitative bidding, km,tIs the coefficient of the frequency modulation mileage price at the moment t,
Figure BDA0003224865430000055
competitive bidding for the frequency-modulated mileage of the DRA at the t moment under the omega scene,
Figure BDA0003224865430000056
is a Boolean variable, k, at time t in the omega sceneLMP,tAnd the coefficient of the marginal electricity price of the node at the time t.
Furthermore, the demand response aggregator makes different compensation standards based on the magnitude of the user response potential, and provides a demand response cost measurement model which gives consideration to the real-time values of demand responses on the market side and the demand side.
Considering that the phenomenon of power offset of transferable loads in the process of demand response is considered, the load base line of a demand response aggregator also dynamically changes along with response behaviors, and the bidding strategy obtained by adopting a fixed base line has larger errors, so that a multi-iteration load base line calculation method is provided, the running condition of flexible resources is updated in real time, the bidding strategy of the demand response aggregator is dynamically optimized, and the influence of the load offset on the economic benefit is relieved.
A bidding system of a bidding method of a demand response aggregator in a frequency modulation market environment comprises: the device comprises an information communication module, a data input module, a simulation calculation module and a data storage module.
The information communication module and the data input module are used for acquiring market information of a frequency modulation market, operation data of user side resources and a regulation and control instruction of a load aggregator.
And the simulation calculation module is used for calculating a bidding model of the demand response aggregator in the frequency modulation market environment according to the operation data, the regulation and control instruction and the copula-CVaR risk measurement method of the demand response aggregator to obtain a bidding scheme of the demand response aggregator.
The data storage module is used for storing historical response data of the user side resources participating in the frequency modulation market and helping demand response aggregators to analyze and evaluate the frequency modulation potential of the users when the demand response aggregators participate in market trading next time.
Further, the simulation computation module includes: the system comprises a scene generation unit, a risk measurement unit and a dynamic optimization unit, wherein the scene generation unit is used for simulating and generating a typical scene of the power market and predicting market information of a demand response aggregator participating in bidding; the risk measurement unit is used for calculating the condition risk value of the demand response aggregator participating in market bidding, quantitatively analyzing the risk of the bidding strategy of the demand response aggregator and the dynamic optimization unit, and considering the phenomenon of load deviation, updating the running state and the demand response potential of the user in real time and dynamically optimizing the bidding strategy of the demand response aggregator.
Further, the demand response aggregator bidding system operation basic data includes: the load baseline, the real-time power, the response speed and the upper and lower limits of the response power of the user, the frequency modulation capacity demand of the frequency modulation market, the frequency modulation mileage demand, the frequency modulation capacity price, the frequency modulation mileage price, the real-time electricity price of the electricity market, the aggregation capability of the demand response aggregator, the adjustment capability and the risk measurement.
The invention has the beneficial effects that:
compared with the prior art, the competitive bidding method and system of the demand response aggregator in the frequency modulation market environment provided by the invention have the advantages that the existing research only considers the participation of user side resources in peak clipping and valley filling in the power demand response field, only analyzes the influence of a single factor on the demand response operation income, is rarely involved in the research of the competitive bidding strategy of DRA participation in the frequency modulation market under multiple uncertain factors, and does not quantitatively research the risk measurement of the competitive bidding strategy in the aspect of numerical value; the invention can effectively measure the risk of the DRA during bidding, and provides a dynamic optimization method for DRA bidding decision on the basis of considering the load offset caused by bidirectional frequency modulation, thereby ensuring that the DRA selects a bidding strategy more accurately.
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In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is an overall method flow diagram of an embodiment of the present invention;
FIG. 2 is a block diagram of an embodiment of the present invention for DRA participation in frequency modulation market operations;
FIG. 3 is a schematic illustration of load baseline shift for an embodiment of the present invention;
fig. 4 is a flow chart of dynamic optimization of DRA bidding decision in accordance with an embodiment of the present invention;
fig. 5 is a block diagram of a DRA bidding system according to 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.
As shown in fig. 1, a bidding method for demand response aggregators in a frequency modulation market environment includes the following steps:
s1, establishing an operation frame of the demand response aggregator and a bidding model in the frequency modulation market based on the trading rules of the frequency modulation market;
as shown in fig. 2, the fm market includes four major factors: frequency modulation capacity, frequency modulation mileage, market price of electricity, and opportunity cost. In the frequency modulation market environment, the operation of DRA participates in bidding in the frequency modulation market as a whole by integrating commercial users, residential users and Electric Vehicles (EV), and controls the load type of a user to be composed of fixed load, transferable load and interruptible load according to the demand response behavior of the corresponding user, so that the demands of two services of frequency modulation up and frequency modulation down can be met. It should be noted that the present invention does not consider the case where EV discharging provides a frequency up-modulation service, due to the higher cost of EV battery depletion.
The process of participating in the frequency modulation market by the DRA can be further divided into a day-ahead stage, a real-time stage and a settlement stage.
In the stage before the day, the DRA acquires a charging plan of the EV, calculates the bidding capacity of the next day frequency modulation market according to a predicted user load curve and information issued by the market, and takes the bidding capacity as a price receiver to participate in market bidding in a mode of '0 quotation' (only reporting the quantity and not quotation, and ensuring certain bargain);
in the real-time stage, a market manager issues a frequency modulation capacity clearing result and distributes frequency modulation mileage according to the capacity proportion, DRA determines a DR (demand response) scheme according to the winning and winning result of the frequency modulation capacity and the frequency modulation mileage demand, and if DR cannot complete corresponding frequency modulation service, the DR is punished;
in the settlement stage, fm revenue is obtained from the market and compensation for DR is delivered to the user.
DRA participates in the frequency modulation market, and can only obtain frequency modulation capacity compensation and frequency modulation mileage compensation, but can not obtain opportunity cost compensation. Considering that resources in the DRA that do not participate in the frequency modulation market can participate in energy market trading, opportunity costs still need to be considered in a bidding decision model of the DRA, and the DRA can obtain compensation from the energy market if and only if the node marginal price is greater than the net test price.
Thus, the net profit for the DRA consists of four parts, frequency modulation capacity profit, mileage profit, demand response cost, and opportunity cost, respectively. Different demand response cost models affect the operational revenue of the DRA, the DRA and the demand-side resource are in a symbiotic relationship, and in order to maintain the stability of the relationship for a long time, the DRA needs to enable users to feel feedback from the market revenue.
Therefore, considering that the compensation cost of the demand response is composed of two parts, namely a measured value and a market value, the calculation formula of the demand response cost is as follows:
CDR＝μ(aM2+bM)+(1-μ)(λmM) ①
in formula I, CDRMu is the proportion of the estimation value in the compensation cost of the demand response, a and b are non-negative estimation value coefficients, and M is the modulation of the demand responseFrequency mileage, λmIs the frequency modulation mileage price in the market.
The revenue function for a DRA is:
π(R,M,λc,λm,λLMP)＝saλcR+saλmM-μ(aM2+bM)-(1-μ)(λmM)-slλLMPM ②
Figure BDA0003224865430000091
formula II and formula III, R is the competitive bidding decision of the frequency modulation capacity of DRA, lambdacFor the price of the modulated capacity in the market, lambdaLMPMarginal price of electricity for nodes in the market, saIs the frequency modulation performance index of DRA, slIs a Boolean variable, λΝΒΤIs the net test price in the market.
S2, constructing a risk measurement index of the coupling model and the bidding model of the uncertainty factor based on the copula function;
the coupling relationship between random variables can be modeled by copula function and has no limit to edge distribution. Any form of distribution can be constructed by selecting a particular copula function, thereby generating a particular joint distribution function of a plurality of random variables. The method for modeling the joint distribution by utilizing the copula function and the edge distribution of each random variable can be obtained through the Sklar theorem. Taking a two-dimensional random variable as an example, the relationship between the probability density function and the copula function is:
f(x1,x2)＝C(F1(x1),F2(x2))f1(x1)f2(x2) ④
in the formula (iv), x1And x2Are two random variables, F1(x1) And F2(x2) Are respectively x1And x2Edge distribution function of f1(x1) And f2(x2) Are respectively x1And x2C (-) is a copula function.
Conditional risk value (CVaR) is a risk management technique commonly used in the current financial field, and is generally used as a risk measurement method, CVaR refers to a conditional mean value of investment portfolio loss exceeding a given risk value (VaR), and its specific formula is as follows:
Figure BDA0003224865430000092
in the formula V, x is decision variable, y is random variableCVaR,βIs the value of CVaR, VVaR,βFor the value of VaR, β gives the confidence, f (x, y) is the loss function, and ρ (y) is the probability density function of y.
Due to VCVaR,β(x) The analytical formula (2) is difficult to find, and the expression of CVaR can be expressed as:
VCVaR,β＝minα∈D G(α,x) ⑥
Figure BDA0003224865430000101
in the formula (c) and the formula (c), α is a critical value of the loss function f (x, y), and D is a feasible domain set of α.
Because there are multiple market price variables in the revenue function, the correlation between all prices and the frequency modulated mileage demand cannot be modeled. Therefore, a virtual price is set, then based on the historical electricity price data of the market, the linear relation between various types of electricity prices and the virtual electricity prices is approximately fitted, and corresponding time-varying parameters are obtained. Further, the simplified DRA revenue function is:
π(R,M,λ)＝sakcλR+μsakmλM-μ(aM2+bM)-slkLMPλM ⑧
in the formula (r), λ is the virtual price, kcTime-varying parameters, k, for frequency-modulated capacity pricesmTime-varying parameters, k, for frequency-modulated mileage pricesLMPThe time-varying parameter is the marginal electricity price of the market node.
In combination with the distribution of prices and demands, the copula-CVaR model for DRA bidding decision is:
Figure BDA0003224865430000102
in the formula ninthly, deltaαIs the value of copula-CVaR, lambdamaxH (λ, M) is the probability density function, which is the maximum value of the virtual price.
Since the analytic expression of the probability density function h (λ, M) is difficult to obtain, the estimation of the integral in the formula ninthly is usually obtained by a historical simulation method or a random simulation method. The normal copula function is suitable for the problem that tail parts of variables are not related, and has been widely applied to various fields, and in extreme cases, the relevance between the price of a frequency modulation market and the requirement of frequency modulation mileage is not large. Then the binary normal copula function is selected to randomly simulate the relevance of price and demand in the frequency modulation market, and the specific function expression is as follows:
Figure BDA0003224865430000111
in equation r, ρ is the correlation coefficient, Φ-1(. cndot.) is the inverse of the standard normal distribution function.
Let the discrete scene set generated by the Copula function be S ═ Mω,λω,ω＝1,2,…,NωAnd recording the value of the omega scene probability as qω. Introducing non-negative auxiliary variables can represent the discrete model of copula-CVaR as:
Figure BDA0003224865430000112
formula (II)
Figure BDA00032248654300001113
In, NωFor the number of discrete scenes, zωAre auxiliary variables.
Will deltaαBy multiplication by a risk preference coefficient LAnd adding the form into an objective function, and then the bidding decision model for maximizing the profit of the DRA based on copula-CVaR in the frequency modulation market is as follows:
Figure BDA0003224865430000113
formula (II)
Figure BDA0003224865430000114
In the middle, T is the total number of competitive bidding time intervals of the frequency modulation market in one day, kc,tThe factor for the price of the fm capacity at time t,
Figure BDA0003224865430000115
for the virtual price at time t in the omega scenario,
Figure BDA0003224865430000116
competitive bidding for the frequency modulation capacity of DRA at t moment under omega scene, km,tIs the coefficient of the frequency modulation mileage price at the moment t,
Figure BDA0003224865430000117
competitive bidding for the frequency-modulated mileage of the DRA at the t moment under the omega scene,
Figure BDA0003224865430000118
is a Boolean variable, k, at time t in the omega sceneLMP,tAnd the coefficient of the marginal electricity price of the node at the time t.
The constraints for the auxiliary variables are:
Figure BDA0003224865430000119
Figure BDA00032248654300001110
the constraint conditions of the frequency modulation capacity are as follows:
Figure BDA00032248654300001111
formula (II)
Figure BDA00032248654300001112
In, PDR,max,tThe maximum power of the DRA demand response at time t.
S3, establishing a dynamic optimization method and system of the demand response aggregator bidding model on the basis of considering load deviation by establishing a time-varying demand response cost measuring and calculating model.
In the process of participation of DRA in bidding, not only the uncertainty factor of the market side needs to be considered, but also the operation condition of the resource on the demand side needs to be mastered as much as possible, so that the bidding strategy can be reasonably formulated, and the frequency modulation signal is responded. The users aggregated by the DRA include business users, residential users, and EV users of various types. The load characteristics and the response potentials of different users are greatly different, so that the influence of the demand response behaviors in different periods on each type of user is also different. In order to enable the bidding strategy to consider the real-time operation condition of the DRA and transmit the real-time demand response cost of the user to the market side through the DRA, different compensation standards are formulated according to the magnitude of the demand response potential of the user at different time periods, and settlement is carried out in a demand response time-sharing price mode.
The frequency modulation potential of the user consists of fixed demand response potential and dynamic demand response potential obtained by real-time calculation according to the load rate, when the dynamic demand response potential is calculated, the load rates of all time points on the user side are averagely divided into Q intervals Iq (Q is 1, 2, … and Q) from 0 to 100%, and the difference value between the load rate and the average load rate at each moment is respectively solved to serve as the dynamic real-time demand response potential of the user. The method for measuring and calculating the demand response up-frequency modulation potential and the demand response down-frequency modulation potential respectively comprises the following steps:
Figure BDA0003224865430000121
Figure BDA0003224865430000122
formula (II)
Figure BDA0003224865430000123
And formula
Figure BDA0003224865430000124
In (1),
Figure BDA0003224865430000125
for the up-modulation potential of class d users at time t,
Figure BDA0003224865430000126
for the down-tuning potential of class d users at time t,
Figure BDA0003224865430000127
for a fixed demand response potential for class d users to participate in up-tuning at time t,
Figure BDA0003224865430000128
fixed demand response potential, lrat, for frequency modulation with class d users participating at time td,tFor the real-time load rate of class d users at time t,
Figure BDA0003224865430000129
the average load rate of the qth interval in which the real-time load rate of the class d user is located and the previous interval,
Figure BDA00032248654300001210
and the average load rate of the qth interval where the real-time load rate of the class d user is located and the next interval.
The average load rate is calculated by the following method:
Figure BDA0003224865430000131
formula (II)
Figure BDA0003224865430000132
In, NqThe number of the load rates counted in the q-th interval is shown.
Setting time-varying compensation coefficients with different sizes through different DR potential levels of users, and then calculating the demand response cost by the following method:
Figure BDA0003224865430000133
formula (II)
Figure BDA0003224865430000134
In, CDR,tCompensating costs, k, for demand response of DRA at time td,tTo compensate for the coefficient, PDR,d,tAnd responding to the power for the demand of the class d user at the time t.
Thus, the bidding decision model of DRA in fm market can be expressed as:
Figure BDA0003224865430000135
the constraints for the auxiliary variables are:
Figure BDA0003224865430000136
Figure BDA0003224865430000137
the constraint conditions of the frequency modulation capacity are as follows:
Figure BDA0003224865430000138
Figure BDA0003224865430000139
formula (II)
Figure BDA00032248654300001310
And formula
Figure BDA00032248654300001311
In, Pbase,d,tThe baseline load power at time t for class d users.
The constraint conditions of the frequency modulation mileage are as follows:
Figure BDA00032248654300001312
formula (II)
Figure BDA00032248654300001313
In (1),
Figure BDA00032248654300001314
and the required response power of the d-th class user at the moment t under the omega scene.
The constraint conditions of the user load baseline are as follows:
Figure BDA0003224865430000141
Figure BDA0003224865430000142
Figure BDA0003224865430000143
formula (II)
Figure BDA0003224865430000144
Formula (II)
Figure BDA0003224865430000145
And formula
Figure BDA0003224865430000146
In (1),
Figure BDA0003224865430000147
real-time load power at T moment of class d user under omega sceneupFor the time interval in which the DRA participates in the up-modulation, TdownFor the time period when the DRA participates in the down-modulation,
Figure BDA0003224865430000148
the minimum load of the class d user at the moment t in the omega scene,
Figure BDA0003224865430000149
the maximum load of the class d user at the moment t under the omega scene.
The constraint condition of the user side demand response action is as follows:
Figure BDA00032248654300001410
Figure BDA00032248654300001411
the constraint on the EV response action is:
Figure BDA00032248654300001412
Figure BDA00032248654300001413
formula (II)
Figure BDA00032248654300001414
And formula
Figure BDA00032248654300001415
In (1),
Figure BDA00032248654300001416
t time in omega sceneAt the moment of the battery capacity of the EV,
Figure BDA00032248654300001417
the minimum value of the battery capacity of the EV at the t moment under the omega scene,
Figure BDA00032248654300001418
the maximum value of the EV battery capacity at the t moment under the omega scene.
Considering that the DRA participating in the frequency modulation market transaction can correct the bidding capacity according to the condition of internal resources before the market is cleared, the optimal bidding strategy of the DRA should be dynamically updated in real time. And in the market settlement link, the difference value between the actual load curve and the fixed load base line is used as the frequency modulation mileage of the DRA. And in the process of demand response, the phenomenon that the transferable load generates power offset exists, and the load baseline of the DRA also dynamically changes along with the response behavior. As shown in fig. 3, when the DRA completes the first frequency tuning, the load transfer phenomenon occurs to cause the actual load baseline to be tuned up, the response power measured during the second frequency tuning is the shadow area of the dotted line, and the actual response power is the shadow area of the solid line, the frequency tuning effect of the DRA is not accurately recorded, and the benefit is damaged. If the DRA performs down frequency modulation after finishing up frequency modulation, the actual response power of the DRA is a solid line shadow area, and the measured response power is a dotted line shadow area, which exceeds the actual size, and the DRA obtains the income which does not belong to the DRA. And vice versa. Therefore, the bidding strategy obtained by adopting the fixed base line has larger error, so a multi-iteration load base line calculation method is adopted to update the operation condition of flexible resources in the DRA in real time, dynamically optimize the bidding strategy of the DRA and relieve the influence of load deviation on the economic benefit of the DRA.
Considering that the phenomenon that the real load baseline is larger or smaller than the original load baseline possibly exists in the load shifting process, two relaxation variables are set for the phenomenon: the load offset caused by the upper frequency modulation and the load offset caused by the lower frequency modulation. For constraint formula in M2
Figure BDA0003224865430000151
And formula
Figure BDA0003224865430000152
Updating:
Figure BDA0003224865430000153
Figure BDA0003224865430000154
Figure BDA0003224865430000155
formula (II)
Figure BDA0003224865430000156
Formula (II)
Figure BDA0003224865430000157
And formula
Figure BDA0003224865430000158
In the above-mentioned formula, k is an iteration index,
Figure BDA0003224865430000159
the load offset caused by frequency modulation at the t moment of the class d user in the omega scene,
Figure BDA00032248654300001510
the load offset is caused by frequency modulation at the moment t of the class d user in the omega scene.
The dynamic optimization process of DRA bidding decision is shown in fig. 4, and includes the following steps:
s1, setting k to 1, and obtaining an initial load baseline from the user-side history data and the EV charging schedule
Figure BDA00032248654300001511
S2, obtaining a typical discrete scene set by adopting a copula function according to market side historical data;
s3, base line according to load
Figure BDA00032248654300001512
Solving the dynamic optimization model to obtain a bidding strategy of the DRA, and determining the frequency modulation mileage requirement according to the bidding result
Figure BDA00032248654300001513
S4, passing formula
Figure BDA00032248654300001514
Determining response plans for various types of loads
Figure BDA00032248654300001515
S5, calculating
Figure BDA00032248654300001516
And
Figure BDA00032248654300001517
obtaining a new load baseline
Figure BDA00032248654300001518
S6, checking whether the requirements are met
Figure BDA00032248654300001519
Wherein epsilondIf meeting, outputting the latest bidding decision R (k) and the response plan of various loads
Figure BDA0003224865430000161
Otherwise, let k be k +1, return to S3, and recalculate the bidding strategy according to the updated baseline.
The invention also provides a bidding system of DRA in frequency modulation market environment, which comprises: the device comprises an information communication module, a data input module, a simulation calculation module and a data storage module.
The information communication module and the data input module are used for acquiring market information of a frequency modulation market, operation data of user side resources and a regulation and control instruction of a load aggregator.
And the simulation calculation module is used for calculating a bidding model of the demand response aggregator in the frequency modulation market environment according to the operation data, the regulation and control instruction and the copula-CVaR risk measurement method of the demand response aggregator to obtain a bidding scheme of the demand response aggregator.
The simulation calculation module comprises: the system comprises a scene generation unit, a risk measurement unit and a dynamic optimization unit, wherein the scene generation unit is used for simulating and generating a typical scene of the power market and predicting market information of a demand response aggregator participating in bidding; the risk measurement unit is used for calculating the condition risk value of the demand response aggregator participating in market bidding, quantitatively analyzing the risk of the bidding strategy of the demand response aggregator and the dynamic optimization unit, and considering the phenomenon of load deviation, updating the running state and the demand response potential of the user in real time and dynamically optimizing the bidding strategy of the demand response aggregator.
The data storage module is used for storing historical response data of the user side resources participating in the frequency modulation market and helping demand response aggregators to analyze and evaluate the frequency modulation potential of the users when the demand response aggregators participate in market trading next time.
The operation basic data of the demand response aggregator bidding system comprises the following steps: the load baseline, the real-time power, the response speed and the upper and lower limits of the response power of the user, the frequency modulation capacity demand of the frequency modulation market, the frequency modulation mileage demand, the frequency modulation capacity price, the frequency modulation mileage price, the real-time electricity price of the electricity market, the aggregation capability of the demand response aggregator, the adjustment capability and the risk measurement.
An example of a specific structural block diagram of a bidding system of DRA in a frequency-modulated market environment is shown in fig. 5, and includes: a computer device 12, the components of the computer device 12 including: the system comprises a display 24, an external device 14, a network adapter 20, a processing unit 16, a system memory 28, an I/O interface 22 and a bus 18, wherein the network adapter 20 is an information communication module and a data input module of the system, the processing unit 16 is an emulation calculation module of the system, and the system memory 28 and the bus 18 are data storage modules of the system.
The processing unit 16 is provided with at least one, and the system memory 28 includes a RAM (random Access memory) 30, a cache 32, a storage system 34, a ROM40, and a memory chip 42.
The processing unit 16 runs the program stored in the system memory 28, thereby implementing the bidding method for DRA in the frequency modulation market environment provided by the embodiment of the present invention.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.

Claims (9)

1. A bidding method of a demand response aggregator in a frequency modulation market environment is characterized by comprising the following steps:
s1, establishing an operation frame of the demand response aggregator and a bidding model in the frequency modulation market based on the trading rules of the frequency modulation market;
s2, constructing a risk measurement index of the coupling model and the bidding model of the uncertainty factor based on the copula function;
s3, establishing a dynamic optimization method and system of the demand response aggregator bidding model on the basis of considering load deviation by establishing a time-varying demand response cost measuring and calculating model.
2. The bidding method of demand response aggregator according to claim 1, wherein in said S1, in fm market environment, the demand response aggregator' S operation participates in bidding in fm market by integrating industry commercial users, residential users and electric vehicles as a whole, and controls the load type of the demand response behavior user of the corresponding user to be composed of fixed load, transferable load and interruptible load, able to satisfy the demand of both up-fm and down-fm services;
the trading process of the demand response aggregator participating in the frequency modulation market can be further divided into a day-ahead stage, a real-time stage and a settlement stage;
in the previous stage, a demand response aggregator acquires a charging plan of the electric automobile, calculates the bidding capacity of the next day frequency modulation market according to a predicted user load curve and information issued by the market, and takes the bidding capacity as a price receiver to participate in market bidding in a mode of only reporting volume and not offering price and ensuring certain bargaining;
in the real-time stage, a market manager issues a frequency modulation capacity clearing result and distributes frequency modulation mileage according to the capacity proportion, a demand response aggregator determines a demand response scheme according to a winning result of the frequency modulation capacity and the frequency modulation mileage demand, and if the demand response cannot complete corresponding frequency modulation service, the demand response aggregator receives punishment;
and in the settlement stage, acquiring frequency modulation income from the market and issuing compensation of demand response to the user.
3. The bidding method of demand response aggregator in fm market environment according to claim 2, wherein net profit of said demand response aggregator is comprised of four parts-fm capacity profit, mileage profit, demand response cost and opportunity cost;
the calculation formula of the demand response cost is as follows:
CDR＝μ(aM2+bM)+(1-μ)(λmM) ①
in formula I, CDRMu is the proportion of the measurement value in the compensation cost of demand response, a and b are non-negative measurement value coefficients, M is the frequency modulation mileage of demand response, and lambda is the compensation cost of demand responsemThe frequency-modulated mileage price in the market;
the revenue function for a DRA is:
π(R,M,λc,λm,λLMP)＝saλcR+saλmM-μ(aM2+bM)-(1-μ)(λmM)-slλLMPM ②
Figure FDA0003224865420000021
formula II and formula III, R is the competitive bidding decision of the frequency modulation capacity of DRA, lambdacFor the price of the modulated capacity in the market, lambdaLMPMarginal price of electricity for nodes in the market, saIs the frequency modulation performance index of DRA, slIs a Boolean variable, λΝΒΤIs the net test price in the market.
4. The method as claimed in claim 1, wherein in S2, the demand response aggregator faces a plurality of uncertainty factors when participating in market bidding, causing its bidding decision to face a risk of earning lower than cost, and a copula-CVaR model is constructed as a risk measure index of the demand response aggregator bidding decision model by considering the coupling characteristics of two uncertainty factors of market price and demand for frequency modulation in the frequency modulation market.
5. The method as claimed in claim 2, wherein CVaR is a conditional mean value of investment portfolio loss over a VaR, and the specific formula is as follows:
Figure FDA0003224865420000022
in the formula V, x is decision variable, y is random variableCVaR,βIs the value of CVaR, VVaR,βThe value of VaR, β given confidence, f (x, y) being the loss function, ρ (y) being the probability density function of y;
due to VCVaR,β(x) The analytical formula (2) is difficult to find, and the expression of CVaR can be expressed as:
VCVaR,β＝minα∈DG(α,x) ⑥
Figure FDA0003224865420000031
in the formula (sixthly) and the formula (seventhly), alpha is a critical value of a loss function f (x, y), and D is a feasible domain set of alpha;
the simplified DRA revenue function is:
π(R,M,λ)＝sakcλR+μsakmλM-μ(aM2+bM)-slkLMPλM ⑧
in the formula (r), λ is the virtual price, kcTime-varying parameters, k, for frequency-modulated capacity pricesmTime-varying parameters, k, for frequency-modulated mileage pricesLMPTime-varying parameters of marginal electricity price of the market node;
in combination with the distribution of prices and demands, the copula-CVaR model for DRA bidding decision is:
Figure FDA0003224865420000032
in the formula ninthly, deltaαIs the value of copula-CVaR, lambdamaxH (λ, M) is the probability density function;
selecting a binary normal copula function to randomly simulate the relevance of price and demand in the frequency modulation market, wherein the specific function expression is as follows:
Figure FDA0003224865420000033
in equation r, ρ is the correlation coefficient, Φ-1(. h) is the inverse of a standard normal distribution function;
let the discrete scene set generated by the Copula function be S ═ Mω,λω,ω＝1,2,…,NωAnd recording the value of the omega scene probability as qωIntroducing non-negative auxiliary variables may represent the discrete model of copula-CVaR as:
Figure FDA0003224865420000041
formula (II)
Figure FDA0003224865420000042
In, NωFor the number of discrete scenes, zωIs an auxiliary variable;
will deltaαAnd adding the coefficient into an objective function in a form of multiplying a risk preference coefficient L, wherein the bidding decision model for maximizing the profit of the DRA based on copula-CVaR in the frequency modulation market is as follows:
Figure FDA0003224865420000043
formula (II)
Figure FDA0003224865420000044
In the middle, T is the total number of competitive bidding time intervals of the frequency modulation market in one day, kc,tThe factor for the price of the fm capacity at time t,
Figure FDA0003224865420000045
for the virtual price at time t in the omega scenario,
Figure FDA0003224865420000046
competitive bidding for the frequency modulation capacity of DRA at t moment under omega scene, km,tIs the coefficient of the frequency modulation mileage price at the moment t,
Figure FDA0003224865420000047
competitive bidding for the frequency-modulated mileage of the DRA at the t moment under the omega scene,
Figure FDA0003224865420000048
is a Boolean variable, k, at time t in the omega sceneLMP,tAnd the coefficient of the marginal electricity price of the node at the time t.
6. The bidding method of demand response aggregator in fm market environment according to claim 1, wherein in S3, the demand response aggregator makes different compensation criteria based on magnitude of user response potential, and provides a demand response cost calculation model that considers real-time value of demand response at both market side and demand side;
considering that the phenomenon of power offset of transferable loads in the process of demand response is considered, the load base line of a demand response aggregator also dynamically changes along with response behaviors, and the bidding strategy obtained by adopting a fixed base line has larger errors, so that a multi-iteration load base line calculation method is provided, the running condition of flexible resources is updated in real time, the bidding strategy of the demand response aggregator is dynamically optimized, and the influence of the load offset on the economic benefit is relieved.
7. A bidding system based on the bidding method of demand response aggregator in FM market environment as claimed in any one of claims 1-6, comprising: the system comprises an information communication module, a data input module, a simulation calculation module and a data storage module;
the information communication module and the data input module are used for acquiring market information of a frequency modulation market, operation data of user side resources and a regulation and control instruction of a load aggregator;
the simulation calculation module is used for calculating a bidding model of the demand response aggregator in the frequency modulation market environment according to the operation data, the regulation and control instruction and a copula-CVaR risk measurement method of the demand response aggregator to obtain a bidding scheme of the demand response aggregator;
the data storage module is used for storing historical response data of the user side resources participating in the frequency modulation market and helping demand response aggregators to analyze and evaluate the frequency modulation potential of the users when the demand response aggregators participate in market trading next time.
8. The bidding system of the bidding method of demand response aggregator in fm market environment according to claim 7, wherein said simulation calculating module comprises: the system comprises a scene generation unit, a risk measurement unit and a dynamic optimization unit, wherein the scene generation unit is used for simulating and generating a typical scene of the power market and predicting market information of a demand response aggregator participating in bidding; the risk measurement unit is used for calculating the condition risk value of the demand response aggregator participating in market bidding, quantitatively analyzing the risk of the bidding strategy of the demand response aggregator and the dynamic optimization unit, and considering the phenomenon of load deviation, updating the running state and the demand response potential of the user in real time and dynamically optimizing the bidding strategy of the demand response aggregator.
9. The bidding system of the bid method of the demand response aggregator in the fm market environment of claim 7, wherein said demand response aggregator bidding system operating basic data comprises: the load baseline, the real-time power, the response speed and the upper and lower limits of the response power of the user, the frequency modulation capacity demand of the frequency modulation market, the frequency modulation mileage demand, the frequency modulation capacity price, the frequency modulation mileage price, the real-time electricity price of the electricity market, the aggregation capability of the demand response aggregator, the adjustment capability and the risk measurement.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115036920A (en) * 2022-07-05 2022-09-09 东南大学 Capacity bidding method for mixed energy storage participating in frequency modulation auxiliary service market

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115036920A (en) * 2022-07-05 2022-09-09 东南大学 Capacity bidding method for mixed energy storage participating in frequency modulation auxiliary service market

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