CN114549067A - Virtual power plant optimal day-ahead bidding strategy considering demand response and frequency modulation performance change - Google Patents

Virtual power plant optimal day-ahead bidding strategy considering demand response and frequency modulation performance change Download PDF

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CN114549067A
CN114549067A CN202210137118.3A CN202210137118A CN114549067A CN 114549067 A CN114549067 A CN 114549067A CN 202210137118 A CN202210137118 A CN 202210137118A CN 114549067 A CN114549067 A CN 114549067A
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frequency modulation
power plant
air conditioner
capacity
electric automobile
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孙丰杰
撖奥洋
朱晓东
苗骁健
孙宏宇
菅学辉
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QINGDAO POWER SUPPLY Co OF STATE GRID SHANDONG ELECTRIC POWER Co
State Grid Corp of China SGCC
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State Grid Corp of China SGCC
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Abstract

An optimal day-ahead bidding method of a virtual power plant considering demand response and frequency modulation performance change comprises the steps of constructing an organization structure and an operation mechanism of the virtual power plant, modeling of demand response of distributed resources based on price, modeling of secondary frequency modulation performance index functions of the virtual power plant and day-ahead bidding constraint models of the virtual power plant. Aiming at the problems of uncertain output of wind power and the requirement that a fan replaces a conventional unit to increase secondary frequency modulation of a power grid, the invention encourages the wind power and generalized energy storage to form a virtual power plant in a benefit-driven mode and participates in an energy-frequency modulation combined market in the form of the virtual power plant, thereby not only improving the hours and total frequency modulation capacity of the virtual power plant participating in the frequency modulation market, but also improving the overall frequency modulation performance of the virtual power plant and providing a higher-quality frequency modulation service for a system.

Description

Virtual power plant optimal day-ahead bidding strategy considering demand response and frequency modulation performance change
Technical Field
The invention relates to the field of power markets, in particular to the fields related to demand response and virtual power plants, and particularly relates to an optimal day-ahead bidding strategy of a virtual power plant, which considers demand response and frequency modulation performance change.
Background
Wind power is one of main new energy, and is rapidly developed, and the total installed capacity of the accumulated grid-connected wind power accounts for 12.8 percent of the total installed capacity of the whole country by the end of 2020. However, the uncertainty of wind power output and the replacement of a conventional unit by a fan increase the secondary frequency modulation requirement of the power grid, and bring adverse effects on the safe and stable operation of the power grid. Therefore, when wind power participates in the electric energy market, the wind power also needs to participate in the frequency modulation auxiliary market, and the frequency modulation pressure of a power grid is reduced. Frequency modulation refers to secondary frequency modulation, and the invention does not relate to primary frequency modulation.
The electric power marketization transaction plays an active role in improving the quality of electric energy and promoting the consumption of renewable energy electric power. At present, partial literature is used for researching participation of wind power in a power market, and methods such as a demand response trading market, peak load regulation trading, supply and demand interaction market mechanism and the like are introduced to promote wind power consumption. The development of energy storage technology and the reduction of energy storage cost enable an energy storage system to be connected into a power network to provide services for the power system. Wind power can participate in system frequency modulation by controlling the speed and the pitch angle of the rotor, and the fast response of energy storage can make up the uncertainty of the wind power, improve the frequency modulation performance of the wind power and increase the frequency modulation benefit. Except for the traditional energy storage battery, the electric automobile, the air conditioner cluster and other generalized energy storage can also serve as an energy storage unit to provide service for wind power without adding a new energy storage unit. However, energy storage resources such as electric vehicles and air conditioner clusters are generally distributed widely, have small capacity and have certain uncertainty, and are not suitable to participate in the power market as separate energy storage resources.
The virtual power plant may aggregate multiple distributed resources together to participate in the electricity market as a whole. At present, relevant mechanism policies of virtual power plants participating in the power market are promulgated by part of provincial and municipal regions in China. Certain research has been carried out at home and abroad aiming at virtual power plants containing wind power. Through research, CHEN W and the like propose an optimal bidding strategy of a virtual power plant combined wind farm in a frequency modulation auxiliary service market by 'locking gain-based configuration of virtual power plant in frequency modulation auxiliary service life' published in IEEE Transactions on Smart Grid; wang 26203et al, "Game model that wind power and electric vehicles form a virtual power plant to participate in the power market," published in Power System Automation, prove that more profits can be obtained when a wind power supplier and an electric vehicle aggregator participate in the power market in a virtual power plant cooperation mode. However, none of the above documents considers the influence of the variation of the fm performance index on the bidding strategy. Meanwhile, most documents do not consider the application of generalized energy storage in a virtual power plant.
At present, no explanation or report of the similar technology of the invention is found, and similar data at home and abroad are not collected.
Disclosure of Invention
Based on the background, the invention provides a bidding strategy for a wind-generalized energy storage-containing virtual power plant to participate in an energy-frequency modulation combined market, wherein the bidding strategy takes the influence of frequency modulation performance indexes into consideration. And modeling the response of the electric automobile and the air conditioner cluster to the compensation electricity price by adopting the Stevens law, and constructing a functional relation between the frequency modulation performance and the fan bid frequency modulation capacity, the reserved frequency modulation capacity and the generalized energy storage frequency modulation capacity. And with the maximum income of the wind power plant as an optimization target, formulating a bidding strategy of the virtual power plant in the market in the day ahead according to the trading rule of the power market. And finally, reflecting the influence of the frequency modulation performance on the bidding strategy through an example, and verifying the economy and the effectiveness of the proposed strategy.
The technical solution of the invention is as follows:
a virtual power plant optimal day-ahead bidding strategy considering demand response and frequency modulation performance change mainly comprises five parts: constructing an organization structure and an operation mechanism of a virtual power plant containing wind power and generalized energy storage; modeling the response of the electric automobile and the air conditioner cluster to the compensation electricity price by adopting the Stevens law; analyzing factors influencing the frequency modulation performance of the virtual power plant, and modeling a secondary frequency modulation performance index function of the virtual power plant; based on the internal composition and operation mode of the virtual power plant, providing a target function and constraint conditions of the virtual power plant for bidding day before; based on the proposed objective function and constraint conditions, the uncertainty of wind power output is considered by introducing robust optimization, the obtained nonlinear mixed integer programming is solved, and the optimal day-ahead bidding decision of the virtual power plant considering demand response and frequency modulation performance change is obtained, and the method specifically comprises the following steps:
1. virtual power plant organization structure and operation mechanism
Organization structure: the virtual power plant provided by the invention integrates three types of distributed resources of a fan, an electric automobile and an air conditioner cluster. The fans serve as main components of the virtual power plant, and the electric automobile and the air conditioner cluster serve as flexibly-schedulable fast-response resources to cooperate with the fans to receive scheduling and participate in the power market together, as shown in fig. 1. The whole virtual power plant externally presents 'power generation performance', and distributed loads in the virtual power plant are all provided with required energy by wind power.
The operation mechanism is as follows: in the day-ahead stage, the wind power, the electric automobile and the air conditioner cluster respectively report the day-ahead predicted power and the available power in each period to the virtual power plant service center. And according to the information reported by the wind power and the generalized energy storage and the forecast of the clearing price of the power market on a typical day, the virtual power plant service center makes an optimal bidding strategy and reports the capacity of the power market. And the dispatching center formulates an energy base point and a frequency modulation capacity of the virtual power plant on the next day according to the declared capacity of the virtual power plant. In the real-time stage, the wind power, the electric automobile and the air conditioner cluster together provide an energy base point and a frequency modulation capacity required by the system, and if the real-time energy base point or the real-time frequency modulation capacity does not meet the requirement of the system, the default capacity is punished, and the specific flow is shown in fig. 2.
2. Stevens law-based demand response model
a) Electric automobile response model
In order to attract the electric automobile to participate in the dispatching operation of the virtual power plant, make up the uncertainty of wind power output and improve the wind power frequency modulation performance, the invention provides an electric automobile charging and discharging induction mechanism based on time-of-use electricity price and a variable subsidy mechanism for encouraging the electric automobile to participate in the frequency modulation service. And the mechanism for participating in frequency modulation subsidy schedules the proper electric automobile to participate in frequency modulation by taking 15min as a time period by changing the subsidy electricity price of the electric automobile participating in frequency modulation. For the electric automobile which does not participate in the frequency modulation service, the inherent charging and discharging habit of the electric automobile is changed by changing the price of electricity by utilizing a charging and discharging induction mechanism, and the electric automobile is guided to be charged and discharged in a specific time period. The method takes Stevens's law as a pricing basis to construct a response model of the electric automobile to frequency modulation service and charging and discharging behaviors.
Assuming a compensation price of electricity of caWhen the electric automobile starts to receive the dispatching of the virtual power plant, the electric automobile participates in frequency modulation; compensating electricity price of cbWhen all the electric automobiles participate in frequency modulation, the frequency modulation capacity of the electric automobiles is the total capacity of grid connection, and then the response rate of the electric automobiles to the frequency modulation service
Figure BDA0003505321130000031
Expressed as:
Figure BDA0003505321130000032
in the formula:
Figure BDA0003505321130000033
for the frequency modulation compensation of the electricity price of the electric automobile,
Figure BDA0003505321130000034
is the frequency modulation response coefficient of the electric automobile,
Figure BDA0003505321130000035
is the frequency modulation sensory index of the electric automobile.
T-time-interval electric automobile frequency modulation capacity
Figure BDA0003505321130000036
Comprises the following steps:
Figure BDA0003505321130000037
in the formula:
Figure BDA0003505321130000038
responsibility of T-time electric automobile participating in frequency modulation, PEV,tThe total capacity of the electric automobile which can participate in frequency modulation in the t period;
for the electric automobile which does not participate in frequency modulation, a time-of-use electricity price mode is adopted to induce the electric automobile to change charging and discharging behaviors, the maximum income can be obtained, and the response degree of the part of electric automobiles to charging and discharging also meets the Stevens law:
Figure BDA0003505321130000039
Figure BDA00035053211300000310
in the formula: c. CchCharging price for electric vehicles, RchTo charge responsivity, cdisDischarge electricity price for electric vehicles, RdisAs discharge responsivity, cc0And cc1Lowest electricity price and highest electricity price, k, for electric vehicles to participate in charging respectivelychResponse coefficient of charging for electric vehicle, nchSensory index for charging electric vehicles, cd0And cd1Respectively being electric steamLowest and highest electricity prices, k, at which the vehicle participates in the dischargedisIs the discharge response coefficient of the electric vehicle, ndisIs the electric automobile discharge sensory index.
b) Air conditioner cluster response model
Assuming that the number of air conditioning clusters participating in the virtual power plant is constant, the capacity of the air conditioning clusters to modulate frequency is determined by the responsiveness of the user, i.e.
Figure BDA0003505321130000041
In the formula:
Figure BDA0003505321130000042
the frequency modulation capacity of the ACLs for the t period,
Figure BDA0003505321130000043
responsivity of ACLs participating in frequency modulation, P, for period tACL,tIs the operating power of ACLs for the t period.
Figure BDA0003505321130000044
In the formula (I), the compound is shown in the specification,
Figure BDA0003505321130000045
subsidizing the electricity price for the air conditioner agreeing to participate in the frequency modulation service,
Figure BDA0003505321130000046
is the frequency modulation response coefficient of the air conditioner cluster,
Figure BDA0003505321130000047
sensory index of frequency modulation for air conditioner cluster, cmAnd cnThe lowest electricity price and the highest electricity price of the air conditioner cluster participating in frequency modulation are respectively.
After the air conditioner starts to operate, the air conditioner firstly enters a working state until the room temperature reaches the fluctuation lower limit of the preset temperature, if the virtual power plant dispatching is not accepted, the air conditioner keeps the room temperature fluctuating up and down within the preset temperature range according to an automatic start-stop operation mode. If the subsidy electricity price reaches the psychological expectation of the user, the air conditioner participates in the frequency modulation service and receives the scheduling of the virtual power plant, the air conditioner is started and stopped according to the requirement of the virtual power plant, and the room temperature is still kept within the fluctuation allowable range. After the frequency modulation service is finished, the air conditioner firstly enters a working state until the room temperature reaches the lower limit of the allowable range, and then the air conditioner operates according to an automatic start-stop mode.
The automatic start-stop period of the air conditioner cluster is generally less than half an hour, and when the temperature difference between the indoor temperature and the outdoor temperature is too large, the start-stop period can be even less than 15 min. At this time, in order to ensure the comfort of the user, no matter how much the time-of-use electricity price is, the air conditioner cluster must enter an automatic start-stop period. Therefore, no charge-discharge induction mechanism will be introduced for air conditioning clusters.
3. Frequency modulation performance index prediction function
In actual scheduling, the wind turbine firstly meets the medium scalar quantity of the energy market, and the wind turbine operates by taking the medium scalar quantity as a power base point. Because the fan also participates in bidding in the frequency modulation market, the fan also needs to adjust output according to an AGC instruction issued by the dispatching center in the bidding range of the frequency modulation capacity in each trading period. When the real-time reserved frequency modulation capacity is smaller than the medium-frequency modulation capacity, the condition that the real-time output of the fan cannot meet the AGC signal is very likely to occur. Therefore, the frequency modulation performance comprehensive index S and the day-ahead frequency modulation projection quantity
Figure BDA0003505321130000048
Real-time reservation of frequency modulation capacity
Figure BDA0003505321130000049
It is related.
When the output of the fan is insufficient, the frequency modulation performance of the fan is greatly reduced and even lower than the PJM market requirement and is not accepted[20]. The electric automobile and the air conditioner cluster integrated in the virtual power plant can provide the frequency modulation requirement required by the dispatching center through charging and discharging, changing the running state and the like, and the frequency modulation deviation is reduced. Meanwhile, the quick response capability of the electric automobile and the air conditioner cluster enables the two generalized energy storages to be capable of compensating for windThe frequency modulation deviation caused by insufficient machine regulation rate further improves the frequency modulation performance of the fan. Therefore, the comprehensive index S of the frequency modulation performance is the frequency modulation capacity of the electric automobile
Figure BDA0003505321130000051
Air conditioner cluster frequency modulation capacity
Figure BDA0003505321130000052
The influence of (c).
In summary, the frequency modulation performance index S is affected by the above four variables, and the frequency modulation performance index estimation function should be a quaternary function, which can be recorded as
Figure BDA0003505321130000053
As S is used as a parameter to be applied to the final optimization model, the function is fitted in a polynomial form for simple operation, and the expression form of the function can be obtained through fitting specific data.
4. Objective function and constraint condition of virtual power plant day-ahead bid
a) Objective function
The invention aims to encourage wind power to participate in the frequency modulation market, combine the wind power with generalized energy storage, fully exert the potential of distributed generalized energy storage and provide high-performance frequency modulation service for a power grid. Aiming at maximizing the income of the wind power plant:
C＝Cen+Cre+CEV+CACL-CPUN (7)
the objective function comprises five parts which are respectively energy market income CenFrequency modulation market income CreElectric automobile electricity selling income CEVAir conditioner cluster electricity selling income CACLAnd a penalty cost CPUN。
Energy market profit CenDetermined by the bidding strategy and the energy clearing price:
Figure BDA0003505321130000054
in the formula:
Figure BDA0003505321130000055
the energy clearing price is given for the period i,
Figure BDA0003505321130000056
bid capacity for the i slot energy market.
Frequency modulated market revenue CreDivided into capacity gains Cre,fcAnd adjusting mileage earnings Cre,fp：
Figure BDA0003505321130000057
In the formula:
Figure BDA0003505321130000058
the clearing price is given for the frequency modulation capacity in the period i,
Figure BDA0003505321130000059
for i-time period frequency-modulated mileage clearing price, SiIs the i-time period frequency modulation performance index, lambda is the mileage benefit factor,
Figure BDA00035053211300000510
market bid capacity is modulated for period i.
The virtual power plant is used as a price acceptor of the power market, the declared capacity can be used as a bid, and the punishment cost C is introduced to prevent the virtual power plant from intentionally giving false reports to disturb the market orderPUN：
Figure BDA00035053211300000511
Figure BDA00035053211300000512
Figure BDA0003505321130000061
In the formula: rhoen(. and ρ)reThe penalty functions corresponding to the energy market and the frequency modulation market respectively,
Figure BDA0003505321130000062
for the i-slot real-time energy base point,
Figure BDA0003505321130000063
real-time frequency modulation capacity for the i time period.
The electric automobile is a consumer of wind power and a provider of frequency modulation service, and the electric automobile sells electricity income CEVElectricity selling profit obtained from wind power to electric automobile electricity selling
Figure BDA0003505321130000064
And cost of frequency modulation patch
Figure BDA0003505321130000065
The two parts are as follows:
Figure BDA0003505321130000066
in the formula: c. Cch,t、cdis,tTime-of-use electricity price, P, of charging and discharging electric vehicle respectively in t periodch,t、Pdis,tThe power of charging and discharging the electric automobile in the period t respectively,
Figure BDA0003505321130000067
for the t time interval, the electric automobile participates in the compensation electricity price of frequency modulation,
Figure BDA0003505321130000068
the frequency modulation capacity of the electric automobile is t time.
Air conditioner cluster electricity selling income CACL：
Figure BDA0003505321130000069
In the formula:
Figure BDA00035053211300000610
for the electricity selling profit obtained by selling electricity from the wind power to the air conditioner cluster,
Figure BDA00035053211300000611
subsidizing the frequency modulation cost for the air-conditioning cluster, cACLThe electricity price of the wind power is supplied,
Figure BDA00035053211300000612
for the power consumption of the part of the air conditioner cluster not participating in the frequency modulation service,
Figure BDA00035053211300000613
and supplementing the electricity price for the air conditioner cluster frequency modulation in the t time period.
b) Constraint conditions
1) Capacity constraints
The day-ahead bidding amount of the virtual power plant is constrained by wind power predicted output, electric vehicle dispatching output and air conditioner cluster dispatching output:
Figure BDA00035053211300000614
Figure BDA00035053211300000615
in the formula:
Figure BDA00035053211300000616
predicting power, P, for wind power i period day aheadEV,iFor the electric vehicle to output maximum power outwards in the period i, PACL,iAnd in order to ensure that the air conditioner cluster outputs maximum power outwards in the period i, delta t is a time interval of the period t, and delta i is a time interval of the period i.
The frequency modulation capacity and the charge-discharge power of the electric automobile are constrained by the maximum available power of the electric automobile:
Figure BDA0003505321130000071
the frequency modulation capacity of the air conditioner cluster is constrained by the maximum power of the air conditioner cluster:
Figure BDA0003505321130000072
the frequency modulation bidding capacity is restricted by the wind power rated capacity and the energy market bidding capacity:
Figure BDA0003505321130000073
Figure BDA0003505321130000074
Figure BDA0003505321130000075
2) electric vehicle state of charge constraint
The SOC in a certain period is constrained by the SOC in the last period and the energy change in the current period:
SOCk,t＝SOCk,t-1+ΔSOCk,t (22)
Figure BDA0003505321130000076
in the formula: SOCk,tIs the average state of charge, Δ SOC, of a class k electric vehicle at a time tk,tAnd sigma is the charge and discharge efficiency of the electric automobile.
Deep charge and discharge can shorten battery life, and SOC is restrained by battery residual energy:
SOCmin≤SOCk,t≤SOCmax (24)
in the formula:SOCminand SOCmaxRespectively, the lower upper limit of the allowable state of charge of the electric vehicle battery.
The final state of the optimization period is constrained by the initial state:
SOCk,0＝SOCk,95+ΔSOCk,96＝SOCk,96 (25)
3) air conditioning cluster room temperature constraints
The indoor temperature should be kept within the allowable range of the set temperature fluctuation:
Figure BDA0003505321130000077
in the formula:
Figure BDA0003505321130000078
the temperature is a preset upper limit and a preset lower limit of indoor temperature fluctuation.
5. Virtual power plant day-ahead bidding robust optimization model and solution
In the objective function, the decision variables are day-ahead energy market casting scalar quantity, day-ahead frequency modulation market casting scalar quantity, next day electric vehicle frequency modulation compensation electricity price, next day electric vehicle charging electricity price, next day electric vehicle discharging electricity price and next day air conditioner cluster frequency modulation compensation electricity price
Figure BDA0003505321130000081
The uncertain variable is wind power real-time output and actual market clearing price
Figure BDA0003505321130000082
Figure BDA0003505321130000083
According to historical statistical data, the real-time output of the fan is generally smaller than the power prediction in the day-ahead, the deviation is generally within 15%, and the real-time output fluctuation of the wind power is supposed to be between-15% and + 5%]Within the range, i.e.
Figure BDA0003505321130000084
And assuming that the fluctuation range of the market clearing price is + -10%. According to the method, robust optimization is carried out according to the power prediction in the day-ahead and the clearing price in the typical day, an optimal bidding strategy is obtained, and the actual profit of the wind power is calculated according to the real-time output of the wind power.
The proposed model is a nonlinear mixed integer programming, which is solved by using YALMIP + GUROBI under MATLAB R2021 environment.
Compared with the prior art, the invention has the following characteristics:
1. providing an optimal day-ahead bidding strategy of the virtual power plant considering demand response and frequency modulation performance change;
2. the virtual power plant structure comprises a fan, an electric vehicle, an air conditioner cluster and other generalized energy storage, and the provided operation mechanism is original, so that the virtual power plant can fully utilize the potential value of distributed generalized energy storage and is beneficial to the management and control of distributed resources;
3. modeling is carried out on demand response based on Stevens law, the Stevens law is a law reflecting the relation between stimulation intensity and sensory quantity, and the law is utilized to be more suitable for the psychological change of users to which distributed resources belong, so that the proposed strategy is more practical;
4. the invention utilizes the frequency modulation performance index estimation function to carry out dynamic estimation on the frequency modulation performance index, and the change encourages a virtual power plant to participate in frequency modulation service, relieves the frequency modulation pressure and provides higher-quality frequency modulation service.
Drawings
FIG. 1 is a diagram of a virtual power plant organization according to the present invention;
FIG. 2 is a schematic diagram of a virtual power plant operation mechanism according to the present invention;
FIG. 3 is a graph of frequency modulation performance combination indicator fit function versus performance indicator dynamic estimates in an embodiment of the present invention;
FIG. 4 is a diagram of bid results according to the prior art in accordance with an embodiment of the present invention;
FIG. 5 is a diagram of bid results according to the bidding strategies of the present invention in one embodiment of the present invention.
Detailed Description
The following examples illustrate the invention in detail: the present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, and the present invention is further described with reference to the embodiments and the accompanying drawings. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.
A virtual power plant optimal day-ahead bidding strategy considering demand response and frequency modulation performance change mainly comprises five parts: constructing an organization structure and an operation mechanism of a virtual power plant containing wind power and generalized energy storage; modeling the response of the electric automobile and the air conditioner cluster to the compensation electricity price by adopting the Stevens law; analyzing factors influencing the frequency modulation performance of the virtual power plant, and modeling a secondary frequency modulation performance index function of the virtual power plant; based on the internal composition and operation mode of the virtual power plant, providing a target function and constraint conditions of the virtual power plant for bidding day before; based on the proposed objective function and constraint conditions, the uncertainty of wind power output is considered by introducing robust optimization, the obtained nonlinear mixed integer programming is solved, and the optimal day-ahead bidding decision of the virtual power plant considering demand response and frequency modulation performance change is obtained, and the method specifically comprises the following steps:
1. virtual power plant organization structure and operation mechanism
Organization structure: the virtual power plant provided by the invention integrates three types of distributed resources of a fan, an electric automobile and an air conditioner cluster. The fans serve as main components of the virtual power plant, and the electric automobile and the air conditioner cluster serve as flexibly-schedulable fast-response resources to cooperate with the fans to receive scheduling and participate in the power market together, as shown in fig. 1. The whole virtual power plant externally presents 'power generation performance', and distributed loads in the virtual power plant are all provided with required energy by wind power.
The operation mechanism is as follows: the invention refers to the operation mechanism of the American PJM electric power market, the energy market and the frequency modulation auxiliary market are combined to be cleared, and the trading stage is divided into a day-ahead market and a day-in real-time market. In the day-ahead market, a generator submits bidding information in a time scale of 1 hour, enters a market clearing stage after the bidding is stopped, and a PJM determines the unit combination of each hour, the day-ahead node marginal price and the requirement for meeting day-ahead plan standby according to the unit combination analysis, and publishes the day-ahead generation plan and the node marginal cost of each hour. The real-time market is a supplement to the day-ahead market, providing market members with an opportunity to adjust the dispatch plan. The real-time market is cleared every 5min or 15min according to the real-time node marginal electricity price of the actual power grid operation condition.
The frequency modulation market adopts a performance-based income mechanism, and the settlement price of the frequency modulation market is the sum of the settlement price of the capacity part and the settlement price of the performance index part. The higher the performance index is, the better the quality of the frequency modulation service provided by the frequency modulation resource is represented, and the higher the gain obtained by the frequency modulation resource is. In the real-time market, PJM clears the fm price every 5min, and the fm prices every 5min for all 1h will be averaged to calculate the fm price per hour.
In the day-ahead stage, the wind power, the electric automobile and the air conditioner cluster respectively report the day-ahead predicted power and the available power in each period to the virtual power plant service center. And according to the information reported by the wind power and the generalized energy storage and the forecast of the clearing price of the power market on a typical day, the virtual power plant service center makes an optimal bidding strategy and reports the capacity of the power market. And the dispatching center formulates an energy base point and a frequency modulation capacity of the virtual power plant on the next day according to the declared capacity of the virtual power plant. In the real-time stage, the wind power, the electric vehicle and the air conditioner cluster jointly provide an energy base point and a frequency modulation capacity required by the system, and if the real-time energy base point or the real-time frequency modulation capacity does not meet the requirement of the system, the lack capacity is punished, and the specific flow is shown in fig. 2.
Because the capacities of wind power and generalized energy storage are both small, the system is only used as a price receiver of an electric power market, only the capacity is submitted in a bidding stage, and the submitted capacity can be adopted. The invention mainly focuses on the bidding strategy of the virtual power plant in the market in the day ahead, and determines the bidding capacity of the virtual power plant in the energy market and the frequency modulation market by taking the maximum total income as the target. Meanwhile, accurate prediction is achieved as far as possible for exciting wind power quotient, errors between bid amount and actual output in the market at the day before are reduced, and deviation punishment cost is introduced to restrict the false report behavior of participants.
2. Stevens law-based demand response model
a) Electric automobile response model
According to the method, the charging and discharging behaviors of the electric automobile group in the operation process of the virtual power plant are researched, if the charging and discharging behaviors of each electric automobile are considered, the solving process is extremely complicated and is unnecessary. Therefore, the electric vehicles in the jurisdiction range of the virtual power plant are clustered by adopting a k-means clustering algorithm, and the grid connection and disconnection time of the electric vehicles and the state of charge (SOC) during grid connection are selected as characteristic parameters. In order to facilitate subsequent calculation, the number of clusters of the data set is set to be between [2 and 10], the index size of Calinski-Harabasz (CH) under each cluster number is calculated respectively, and the cluster number when CH is maximum is taken as the k value of the cluster.
After clustering is finished, the geometric centers of all the categories are selected as behavior rules of all the electric vehicles in the categories, namely, all the electric vehicles in all the categories are assumed to run according to grid-connection and off-grid time represented by the geometric centers and SOC during grid connection.
In order to attract the electric automobile to participate in the dispatching operation of the virtual power plant, make up the uncertainty of wind power output and improve the wind power frequency modulation performance, the invention provides an electric automobile charging and discharging induction mechanism based on time-of-use electricity price and a variable subsidy mechanism for encouraging the electric automobile to participate in the frequency modulation service. And the mechanism for participating in frequency modulation subsidy schedules the proper electric automobile to participate in frequency modulation by taking 15min as a time period by changing the subsidy electricity price of the electric automobile participating in frequency modulation. For the electric automobile which does not participate in the frequency modulation service, the inherent charging and discharging habit of the electric automobile is changed by changing the price of electricity by utilizing a charging and discharging induction mechanism, and the electric automobile is guided to be charged and discharged in a specific time period. The method takes Stevens's law as a pricing basis to construct a response model of the electric automobile to frequency modulation service and charging and discharging behaviors.
Assuming a compensation price of electricity of caWhen the electric automobile starts to receive the dispatching of the virtual power plant, the electric automobile participates in frequency modulation; compensation electricity price of cbWhen all the electric automobiles participate in frequency modulation, the frequency modulation capacity of the electric automobiles is the total capacity of grid connection, and then the response rate of the electric automobiles to the frequency modulation service
Figure BDA0003505321130000101
Expressed as:
Figure BDA0003505321130000102
in the formula:
Figure BDA0003505321130000103
for the frequency modulation compensation of the electricity price of the electric automobile,
Figure BDA0003505321130000104
is the frequency modulation response coefficient of the electric automobile,
Figure BDA0003505321130000105
is the frequency modulation sensory index of the electric automobile.
T-time-interval electric automobile frequency modulation capacity
Figure BDA0003505321130000106
Comprises the following steps:
Figure BDA0003505321130000111
in the formula:
Figure BDA0003505321130000112
responsibility of T-time electric automobile participating in frequency modulation, PEV,tThe total capacity of the electric automobile which can participate in frequency modulation in the t period;
for the electric automobile which does not participate in frequency modulation, a time-of-use electricity price mode is adopted to induce the electric automobile to change charging and discharging behaviors, the maximum income can be obtained, and the response degree of the part of electric automobiles to charging and discharging also meets the Stevens law:
Figure BDA0003505321130000113
Figure BDA0003505321130000114
in the formula: c. CchCharging price for electric vehicles, RchTo charge responsivity, cdisDischarge electricity price for electric vehicles, RdisAs discharge responsivity, cc0And cc1Lowest electricity price and highest electricity price, k, for electric vehicles to participate in charging respectivelychResponse coefficient of charging for electric vehicle, nchSensory index for charging electric vehicles, cd0And cd1The lowest electricity price and the highest electricity price, k, of the electric automobile participating in discharging respectivelydisIs the discharge response coefficient of the electric automobile, ndisIs the electric automobile discharge sensory index.
b) Air conditioner cluster response model
The air conditioner load is modeled by a method based on second-order equivalent thermal parameters, and the air conditioner generally runs near a preset temperature in an automatic starting and stopping mode. The air conditioner is started, and when the indoor temperature reaches the preset temperature, the air conditioner automatically stops working and enters an idle state. Because there is a temperature difference between the indoor and outdoor, the indoor temperature will gradually approach the outdoor temperature with the passage of time. When the difference between the indoor temperature and the preset temperature exceeds the allowable range, the air conditioner is automatically started to enter a working state, so that the indoor temperature is always kept near the preset temperature. The change rule of the indoor temperature along with the time in the starting and stopping state of the air conditioner is as follows:
Tin,t+1＝Tout-(Tout-Tin,t)e-Δt/RC s＝0 (5)
Figure BDA0003505321130000115
in the formula: s represents the operation state of the air conditioner, when s is 0, the air conditioner is in idle state, when s is 1, the air conditioner is in working state, T isin,tIs the indoor temperature in the period of T, Tin,t+1Is the room temperature in the period of T +1, ToutAssuming that the outdoor temperature is kept constant, delta t is the time interval of each time interval, namely 15min, R is equivalent thermal resistance, C is equivalent thermal capacity, eta is air conditioner energy efficiency ratio, P is0The rated power of the air conditioner is A, and the coefficient of heat conductivity is A.
Similar to the electric vehicle response model, assuming that the number of air conditioning clusters participating in the virtual power plant is constant, the frequency modulation capacity of the air conditioning clusters is determined by the responsiveness of the user, i.e.
Figure BDA0003505321130000121
In the formula:
Figure BDA0003505321130000122
for the frequency modulation capacity of the ACLs for the t period,
Figure BDA00035053211300001211
responsivity of ACLs participating in frequency modulation, P, for period tACL,tIs the operating power of ACLs for the t period.
Figure BDA0003505321130000123
In the formula (I), the compound is shown in the specification,
Figure BDA0003505321130000124
subsidizing the electricity price for the air conditioner agreeing to participate in the frequency modulation service,
Figure BDA0003505321130000125
is the frequency modulation response coefficient of the air conditioner cluster,
Figure BDA0003505321130000126
is a sensory index of the air conditioner cluster frequency modulation, cmAnd cnThe lowest electricity price and the highest electricity price of the air conditioner cluster participating in frequency modulation are respectively.
After the air conditioner starts to operate, the air conditioner firstly enters a working state until the room temperature reaches the fluctuation lower limit of the preset temperature, if the virtual power plant dispatching is not accepted, the air conditioner keeps the room temperature fluctuating up and down within the preset temperature range according to an automatic start-stop operation mode. If the subsidy electricity price reaches the psychological expectation of the user, the air conditioner participates in the frequency modulation service and receives the scheduling of the virtual power plant, the air conditioner is started and stopped according to the requirement of the virtual power plant, and the room temperature is still kept within the fluctuation allowable range. After the frequency modulation service is finished, the air conditioner firstly enters a working state until the room temperature reaches the lower limit of the allowable range, and then the air conditioner operates according to an automatic start-stop mode.
The automatic start-stop period of the air conditioner cluster is generally less than half an hour, and when the temperature difference between the indoor temperature and the outdoor temperature is too large, the start-stop period can be even less than 15 min. At this time, in order to ensure the comfort of the user, no matter how much the time-of-use electricity price is, the air conditioner cluster must enter an automatic start-stop period. Therefore, no charge-discharge induction mechanism will be introduced for air conditioning clusters.
3. Frequency modulation performance index prediction function
In actual scheduling, the wind turbine firstly meets the medium scalar quantity of the energy market, and the wind turbine operates by taking the medium scalar quantity as a power base point. Because the fan also participates in bidding in the frequency modulation market, the fan also needs to adjust output according to an AGC instruction issued by the dispatching center in the bidding range of the frequency modulation capacity in each trading period. When the real-time reserved frequency modulation capacity is smaller than the medium-frequency modulation capacity, the condition that the real-time output of the fan cannot meet the AGC signal is very likely to occur. Therefore, the frequency modulation performance comprehensive index S and the day-ahead frequency modulation projection quantity
Figure BDA0003505321130000127
Real-time reservation of frequency modulation capacity
Figure BDA0003505321130000128
It is related.
When the fanWhen the output is insufficient, the frequency modulation performance of the fan is greatly reduced and even lower than the PJM market requirement and is not accepted[20]. The electric automobile and the air conditioner cluster integrated in the virtual power plant can provide the frequency modulation requirement required by the dispatching center through charging and discharging, changing the running state and the like, and the frequency modulation deviation is reduced. Meanwhile, due to the quick response capability of the electric automobile and the air conditioner cluster, the frequency modulation deviation caused by insufficient fan regulation rate can be made up by the two generalized energy storages, and the frequency modulation performance of the fan is further improved. Therefore, the comprehensive index S of the frequency modulation performance is the frequency modulation capacity of the electric automobile
Figure BDA0003505321130000129
Air conditioner cluster frequency modulation capacity
Figure BDA00035053211300001210
The influence of (c).
In summary, the frequency modulation performance index S is affected by the above four variables, and the frequency modulation performance index estimation function should be a quaternary function, which can be recorded as
Figure BDA0003505321130000131
As S is used as a parameter to be applied to the final optimization model, the function is fitted in a polynomial form for simple operation, and the expression form of the function can be obtained through fitting specific data.
Should contain a similar to
Figure BDA0003505321130000132
And
Figure BDA0003505321130000133
meanwhile, in order to reduce the non-linearity degree of the estimation function and facilitate the solution of the optimization model, a polynomial is adopted for fitting, and finally the function with the best fitting effect is obtained
Figure BDA0003505321130000134
The function has 10 terms in total, an
Figure BDA0003505321130000135
And
Figure BDA0003505321130000136
there are 4 items in total, the remaining items being all 2 times and below, as shown below:
Figure BDA0003505321130000137
the goodness of fit is 0.966, the fitting effect is good, the average deviation of the estimated data and the experimental data of the fitting function is 1.36%, the error is small, the engineering requirement is met, the function can be used for optimizing the model, and the fitting effect graph is shown in fig. 3.
4. Objective function and constraint condition of virtual power plant day-ahead bid
a) Objective function
The invention aims to encourage wind power to participate in the frequency modulation market, combine the wind power with generalized energy storage, fully exert the potential of distributed generalized energy storage and provide high-performance frequency modulation service for a power grid. Aiming at maximizing the income of the wind power plant:
C＝Cen+Cre+CEV+CACL-CPUN (10)
the objective function comprises five parts which are respectively energy market income CenFrequency modulation market income CreElectric automobile electricity selling income CEVAir conditioner cluster electricity selling income CACLAnd a penalty cost CPUN。
Energy market profit CenDetermined by the bidding strategy and the energy clearing price:
Figure BDA0003505321130000138
in the formula:
Figure BDA0003505321130000139
the energy clearing price is given for the period i,
Figure BDA00035053211300001310
capacity is bid for the i slot energy market.
Frequency modulated market revenue CreDivided into capacity gains Cre,fcAnd adjusting mileage earnings Cre,fp：
Figure BDA00035053211300001311
In the formula:
Figure BDA00035053211300001312
the clearing price is given for the frequency modulation capacity in the period i,
Figure BDA00035053211300001313
for i-time period frequency-modulated mileage clearing price, SiIs the i-time period frequency modulation performance index, lambda is the mileage benefit factor,
Figure BDA00035053211300001314
market bid capacity is modulated for period i.
The virtual power plant is used as a price acceptor of the power market, the declared capacity can be used as a bid, and the punishment cost C is introduced to prevent the virtual power plant from intentionally giving false reports to disturb the market orderPUN：
Figure BDA0003505321130000141
Figure BDA0003505321130000142
Figure BDA0003505321130000143
In the formula: rhoen(. and ρ)reThe penalty functions corresponding to the energy market and the frequency modulation market respectively,
Figure BDA0003505321130000144
for the i-slot real-time energy base point,
Figure BDA0003505321130000145
real-time frequency modulation capacity for the i time period.
The electric automobile is a consumer of wind power and a provider of frequency modulation service, and the electric automobile sells electricity income CEVElectricity selling profit obtained from wind power to electric automobile electricity selling
Figure BDA0003505321130000146
And cost of frequency modulation patch
Figure BDA0003505321130000147
The two parts are as follows:
Figure BDA0003505321130000148
in the formula: c. Cch,t、cdis,tTime-of-use electricity price, P, of charging and discharging electric vehicle respectively in t periodch,t、Pdis,tThe power of charging and discharging the electric automobile in the period t respectively,
Figure BDA0003505321130000149
for the t time interval, the electric automobile participates in the compensation electricity price of frequency modulation,
Figure BDA00035053211300001410
the frequency modulation capacity of the electric automobile is t time.
Air conditioner cluster electricity selling income CACL：
Figure BDA00035053211300001411
In the formula:
Figure BDA00035053211300001412
for wind-powered electricity generation to air conditioner clusterThe profit of the electricity sold is obtained by selling the electricity,
Figure BDA00035053211300001413
subsidizing the frequency modulation cost for the air-conditioning cluster, cACLThe electricity price of the wind power is supplied,
Figure BDA00035053211300001414
for the power consumption of the part of the air conditioner cluster not participating in the frequency modulation service,
Figure BDA00035053211300001415
and supplementing the electricity price for the air conditioner cluster frequency modulation in the t time period.
b) Constraint conditions
1) Capacity constraints
The day-ahead bidding amount of the virtual power plant is constrained by wind power predicted output, electric vehicle dispatching output and air conditioner cluster dispatching output:
Figure BDA00035053211300001416
Figure BDA00035053211300001417
in the formula:
Figure BDA0003505321130000151
predicting power, P, for wind power i period day aheadEV,iFor the electric vehicle to output maximum power outwards in the period i, PACLAnd i is the maximum output power of the air conditioner cluster in the period i, delta t is the time interval of the period t, and delta i is the time interval of the period i.
The frequency modulation capacity and the charge-discharge power of the electric automobile are constrained by the maximum available power of the electric automobile:
Figure BDA0003505321130000152
the air conditioner cluster frequency modulation capacity is constrained by the maximum power of the air conditioner cluster:
Figure BDA0003505321130000153
the frequency modulation bid capacity is restricted by the wind power rated capacity and the energy market bid capacity:
Figure BDA0003505321130000154
Figure BDA0003505321130000155
Figure BDA0003505321130000156
2) electric vehicle state of charge constraint
The SOC in a certain period is constrained by the SOC in the last period and the energy change in the current period:
SOCk,t＝SOCk,t-1+ΔSOCk,t (25)
Figure BDA0003505321130000157
in the formula: SOCk,tIs the average state of charge, Δ SOC, of a class k electric vehicle at a time tk,tAnd sigma is the charge and discharge efficiency of the electric automobile.
Deep charge and discharge can shorten battery life, and SOC is restrained by battery residual energy:
SOCmin≤SOCk,t≤SOCmax (27)
in the formula: SOCminAnd SOCmaxRespectively, the lower upper limit of the allowable state of charge of the electric vehicle battery.
The final state of the optimization period is constrained by the initial state:
SOCk,0＝SOCk,95+ΔSOCk,96＝SOCk,96 (28)
3) air conditioning cluster room temperature constraints
The indoor temperature should be kept within the allowable range of the set temperature fluctuation:
Figure BDA0003505321130000158
in the formula:
Figure BDA0003505321130000161
the preset upper and lower limits of the indoor temperature fluctuation are set.
5. Virtual power plant day-ahead bidding robust optimization model and solution
In the objective function, the decision variables are day-ahead energy market casting scalar quantity, day-ahead frequency modulation market casting scalar quantity, next day electric vehicle frequency modulation compensation electricity price, next day electric vehicle charging electricity price, next day electric vehicle discharging electricity price and next day air conditioner cluster frequency modulation compensation electricity price
Figure BDA0003505321130000162
The uncertain variable is wind power real-time output and actual market clearing price
Figure BDA0003505321130000163
Figure BDA0003505321130000164
According to historical statistical data, the real-time output of the fan is generally smaller than the power prediction in the day-ahead, the deviation is generally within 15%, and the real-time output fluctuation of the wind power is supposed to be between-15% and + 5%]Within the range, i.e.
Figure BDA0003505321130000165
And assuming that the fluctuation range of the market clearing price is + -10%. The invention carries out robust optimization according to the forecast of the day-ahead power and the clearing price of the typical day, obtains the optimal bidding strategy and outputs the power in real time according to the wind powerAnd calculating the actual profit of the wind power.
The proposed model is a nonlinear mixed integer programming, which is solved by using YALMIP + GUROBI under MATLAB R2021 environment.
The above technical solution of the present invention is further described below with reference to a specific example.
The specific example comprises a wind power plant with rated installed capacity of 200MW, 200 electric automobiles with the same model number and 300 air conditioner clusters with the same model number. The wind power plant is assumed to be tuned up and down to 20% of rated capacity respectively. Due to the limitation of the climbing rate of the fan and the avoidance of climbing events, the standard regulation rate of the fan is 3%/min of the rated capacity. The control error of the fan meets the normal distribution with the mean value of 0 and the variance of 0.000013, and the response delay time allowed by the unit is 1 min.
Acquiring the day-ahead predicted power and the day-ahead real-time output of a fan in a certain area of 9 months and 1 days in 2021 from a PJM official network, and converting the acquired data into the day-ahead predicted power and the real-time output of a wind power plant with the rated capacity of 200MW according to the installed capacity of the area; and obtains the corresponding energy-frequency modulation market price for the post-calculation of the profit.
This specific example designed 2 scenarios for comparison tests: in a scene 1, a wind power plant independently participates in a power market, and a frequency modulation performance comprehensive index is calculated according to a historical frequency modulation condition and is a constant; and in the scene 2, the wind power, the electric automobile and the air conditioner cluster jointly form a virtual power plant, the virtual power plant participates in the power market, and the comprehensive index of the frequency modulation performance is predicted according to the estimation function obtained above and is variable.
The wind farm bidding strategy in scenario 1 is shown in fig. 4. Under the scene, the time of the wind power plant participating in the frequency modulation market is 15 hours, the total bidding capacity of the energy market is 1693MW, and the total bidding capacity of the frequency modulation market is 980 MW. At the moment, the comprehensive index of the wind power frequency modulation performance is 0.82. Market revenues as shown in table 1, most of the revenues are derived from the energy market.
The virtual power plant bidding strategy under the scene 2 is shown in FIG. 5. Under the scene, the time of the virtual power plant participating in the frequency modulation market is 20 hours, the total bidding capacity of the energy market is 1569MW, and the total bidding capacity of the frequency modulation market is 1312 MW. At this time, as the frequency modulation performance can be roughly estimated and controlled, the virtual power plant can participate in the frequency modulation market more, and more frequency modulation services are provided for the system. At the moment, the wind power plant can more actively encourage the generalized energy storage to participate in frequency modulation for obtaining the maximum profit, and the generalized energy storage is more fully utilized. Due to the increase of the energy storage frequency modulation capacity, the average frequency modulation performance index is increased to 0.96, and the frequency modulation performance is greatly improved. The wind farm gains are shown in table 1, and compared with the scene 1, the energy market gains are reduced by 7.70%, the frequency modulation market gains are improved by 48.45%, and the total net wind power gains are improved by 10.16%. The wind power plant income is mainly increased from a frequency modulation market, and the improvement of the frequency modulation performance leads to that wind power can obtain more economic benefits from the frequency modulation market, so that the frequency modulation capacity is increased, and the wind power plant income and the frequency modulation capacity are mutually promoted, so that the frequency modulation income is greatly increased.
TABLE 1 wind power yield comparison under different scenarios
Figure BDA0003505321130000171

Claims (6)

1. A virtual power plant optimal day-ahead bidding method considering demand response and frequency modulation performance change is characterized by comprising the following steps:
constructing an organization structure and an operation mechanism of a virtual power plant containing wind power and generalized energy storage;
modeling the response of the electric automobile and the air conditioner cluster to the compensation electricity price by adopting the Stevens law;
analyzing factors influencing the frequency modulation performance of the virtual power plant, and modeling a secondary frequency modulation performance index function of the virtual power plant;
based on the internal composition and operation mode of the virtual power plant, providing a target function and constraint conditions of the virtual power plant for bidding day before;
based on the proposed objective function and constraint conditions, the uncertainty of wind power output is considered by introducing robust optimization, and the obtained nonlinear mixed integer programming is solved to obtain the optimal day-ahead bidding decision of the virtual power plant considering demand response and frequency modulation performance change.
2. The method of claim 1, wherein the virtual power plant organization structure comprises: the system comprises three types of distributed resources including a fan, an electric automobile and an air conditioner cluster, wherein the fan is used as a main component of a virtual power plant, and the electric automobile and the air conditioner cluster are used as fast-response resources which can be flexibly scheduled to cooperate with the fan to receive scheduling and participate in the power market together; the virtual power plant integrally presents power generation performance to the outside, and the internal distributed load is provided with required energy by wind power.
The operation mechanism is that in the day-ahead stage: the method comprises the following steps that wind power, an electric automobile and an air conditioner cluster respectively report the predicted power in the day and the available power in each period to a virtual power plant service center; according to the information reported by wind power and generalized energy storage and the forecast of the clearing price of the power market on a typical day, the virtual power plant service center makes an optimal bidding strategy and reports the capacity of the power market; the scheduling center formulates an energy base point and a frequency modulation capacity of the virtual power plant on the next day according to the declared capacity of the virtual power plant; in the real-time phase: the wind power, the electric automobile and the air conditioner cluster jointly provide an energy base point and frequency modulation capacity required by the system, and if the real-time energy base point or the real-time frequency modulation capacity does not meet the requirement of the system, the lack capacity is punished.
3. The method for optimal day-ahead bidding of a virtual power plant in consideration of demand response and frequency modulation performance variation according to claim 1, wherein modeling the response to the compensation electricity prices comprises:
a) electric automobile response model
Assuming a compensation price of electricity of caWhen the electric automobile starts to receive the dispatching of the virtual power plant, the electric automobile participates in frequency modulation; compensating electricity price of cbWhen all the electric automobiles participate in frequency modulation, the frequency modulation capacity of the electric automobiles is the total capacity of grid connection, and the electric automobiles are exchangedResponse rate of frequency service
Figure FDA0003505321120000011
Expressed as:
Figure FDA0003505321120000012
in the formula:
Figure FDA0003505321120000021
for the frequency modulation compensation of the electricity price of the electric automobile,
Figure FDA0003505321120000022
is the frequency modulation response coefficient of the electric automobile,
Figure FDA0003505321120000023
the sensory index of the frequency modulation of the electric automobile is obtained;
t-time-interval electric automobile frequency modulation capacity
Figure FDA0003505321120000024
Comprises the following steps:
Figure FDA0003505321120000025
in the formula:
Figure FDA0003505321120000026
responsibility of t-time electric automobile participating in frequency modulation, PEV,tThe total capacity of the electric automobile which can participate in frequency modulation in the t period;
for the electric automobile which does not participate in frequency modulation, a time-of-use electricity price mode is adopted to induce the electric automobile to change charging and discharging behaviors, income can be obtained to the maximum extent, and the response degree of the part of electric automobiles to charging and discharging also meets the Stevens law:
Figure FDA0003505321120000027
Figure FDA0003505321120000028
in the formula: c. CchCharging price for electric vehicles, RchTo charge responsivity, cdisDischarge electricity price for electric vehicles, RdisAs discharge responsivity, cc0And cc1Lowest electricity price and highest electricity price, k, for electric vehicles to participate in charging respectivelychResponse coefficient of charging for electric vehicle, nchSensory index for charging electric vehicles, cd0And cd1The lowest electricity price and the highest electricity price, k, of the electric automobile participating in discharging respectivelydisIs the discharge response coefficient of the electric automobile, ndisIs the electric automobile discharge sensory index;
b) air conditioner cluster response model
Assuming that the number of air conditioning clusters participating in the virtual power plant is constant, the capacity of the air conditioning clusters to modulate frequency is determined by the responsiveness of the user, i.e.
Figure FDA0003505321120000029
In the formula:
Figure FDA00035053211200000210
for the frequency modulation capacity of the ACLs for the t period,
Figure FDA00035053211200000211
responsivity of ACLs participating in frequency modulation, P, for period tACL,tThe operating power of the ACLs in the t period;
Figure FDA00035053211200000212
in the formula (I), the compound is shown in the specification,
Figure FDA00035053211200000213
subsidizing the electricity price for the air conditioner agreeing to participate in the frequency modulation service,
Figure FDA00035053211200000214
is the frequency modulation response coefficient of the air conditioner cluster,
Figure FDA00035053211200000215
is a sensory index of the air conditioner cluster frequency modulation, cmAnd cnRespectively the lowest electricity price and the highest electricity price of the air conditioner cluster participating in frequency modulation;
after the air conditioner starts to operate, the air conditioner firstly enters a working state until the room temperature reaches the lower fluctuation limit of the preset temperature, if the virtual power plant dispatching is not accepted, the air conditioner keeps the room temperature fluctuating up and down within the preset temperature range according to an automatic start-stop operation mode; if the subsidy electricity price reaches the psychological expectation of a user, the air conditioner participates in frequency modulation service and receives the dispatching of the virtual power plant, the air conditioner is started and stopped according to the requirement of the virtual power plant, and the room temperature is still kept within the fluctuation allowable range;
after the frequency modulation service is finished, the air conditioner firstly enters a working state until the room temperature reaches the lower limit of an allowable range, and then the air conditioner operates according to an automatic start-stop mode;
the automatic start-stop period of the air conditioner cluster is generally less than half an hour, and when the temperature difference between the indoor temperature and the outdoor temperature is too large, the start-stop period can be even less than 15 min. At the moment, in order to ensure the comfort of the user, no matter how much the time-of-use electricity price is, the air conditioner cluster must enter an automatic start-stop period; no charge-discharge induction mechanism will be introduced for the air conditioning cluster.
4. The method of claim 1, wherein modeling a virtual plant secondary performance indicator function comprises: the frequency modulation performance index S influenced by the four variables is recorded as
Figure FDA0003505321120000031
Figure FDA0003505321120000032
A scalar quantity is projected for the day-ahead frequency modulation,
Figure FDA0003505321120000033
in order to reserve the frequency modulation capacity in real time,
Figure FDA0003505321120000034
the frequency modulation capacity of the electric automobile is improved,
Figure FDA0003505321120000035
and the capacity is the air conditioning cluster frequency modulation capacity.
5. The method of claim 1, wherein the objective functions and constraints of the virtual plant's day-ahead bid include:
a) objective function
Aiming at maximizing the income of the wind power plant:
C＝Cen+Cre+CEV+CACL-CPUN (7)
in the formula: cenFor energy market revenue, CreFor frequency modulation market revenue, CEVSelling electric power for electric automobile and obtaining CACLSelling electricity income and C for air conditioner clusterPUNPenalty cost;
wherein, energy market profit CenDetermined by the bidding strategy and the energy clearing price:
Figure FDA0003505321120000036
in the formula:
Figure FDA0003505321120000037
the energy clearing price is given for the period i,
Figure FDA0003505321120000038
bidding capacity for the i slot energy market;
frequency modulated market revenue CreDivided into capacity gains Cre,fcAnd adjusting mileage earnings Cre,fp：
Figure FDA0003505321120000039
In the formula:
Figure FDA00035053211200000310
the price is cleared for the frequency modulation capacity in the period i,
Figure FDA00035053211200000311
for i-time period frequency-modulated mileage clearing price, SiIs the i-time period frequency modulation performance index, lambda is the mileage benefit factor,
Figure FDA00035053211200000312
adjusting the bid capacity for the market for the period i;
the virtual power plant is used as a price acceptor of the power market, the declared capacity can be used as a bid, and the punishment cost C is introduced to prevent the virtual power plant from intentionally giving false reports to disturb the market orderPUN：
Figure FDA0003505321120000041
Figure FDA0003505321120000042
Figure FDA0003505321120000043
In the formula: rhoen(. and ρ)reThe penalty functions corresponding to the energy market and the frequency modulation market respectively,
Figure FDA0003505321120000044
for the i-slot real-time energy base point,
Figure FDA0003505321120000045
real-time frequency modulation capacity for the period i;
the electric automobile is a consumer of wind power and a provider of frequency modulation service, and the electric automobile sells electricity income CEVElectricity selling profit obtained from wind power to electric automobile electricity selling
Figure FDA0003505321120000046
And cost of frequency modulation patch
Figure FDA0003505321120000047
The two parts are as follows:
Figure FDA0003505321120000048
in the formula: c. Cch,t、cdis,tTime-of-use electricity price, P, of charging and discharging electric vehicle respectively in t periodch,t、Pdis,tThe power of charging and discharging the electric automobile in the period t respectively,
Figure FDA0003505321120000049
for the t time interval, the electric automobile participates in the compensation electricity price of frequency modulation,
Figure FDA00035053211200000410
the frequency modulation capacity of the electric automobile is t time.
Air conditioner cluster electricity selling income CACL：
Figure FDA00035053211200000411
In the formula:
Figure FDA00035053211200000412
for the electricity selling profit obtained by selling electricity from the wind power to the air conditioner cluster,
Figure FDA00035053211200000413
subsidizing the frequency modulation cost for the air-conditioning cluster, cACLThe electricity price of the wind power is supplied,
Figure FDA00035053211200000414
for the power consumption of the part of the air conditioner cluster not participating in the frequency modulation service,
Figure FDA00035053211200000415
supplementing the electricity price for the air conditioner cluster frequency modulation in the t time period;
b) constraint conditions
1) Capacity constraints
The day-ahead bidding amount of the virtual power plant is constrained by wind power predicted output, electric vehicle dispatching output and air conditioner cluster dispatching output:
Figure FDA00035053211200000416
Figure FDA0003505321120000051
in the formula:
Figure FDA0003505321120000052
predicting power, P, for wind power i period day aheadEV,iFor the electric vehicle to output maximum power outwards in the period i, PACLI is the maximum output power of the air conditioner cluster in the period i, delta t is the time interval of the period t, delta i is the time interval of the period i,
the frequency modulation capacity and the charge-discharge power of the electric automobile are constrained by the maximum available power of the electric automobile:
Figure FDA0003505321120000053
the air conditioner cluster frequency modulation capacity is constrained by the maximum power of the air conditioner cluster:
Figure FDA0003505321120000054
the frequency modulation bidding capacity is restricted by the wind power rated capacity and the energy market bidding capacity:
Figure FDA0003505321120000055
Figure FDA0003505321120000056
Figure FDA0003505321120000057
2) electric vehicle state of charge constraint
The SOC of a certain period is constrained by the SOC of the last period and the energy change of the current period:
SOCk,t＝SOCk,t-1+ΔSOCk,t (22)
Figure FDA0003505321120000058
in the formula: SOCk,tIs the average state of charge, Δ SOC, of a class k electric vehicle at a time tk,tThe variation of the SOC within the t period is shown, and sigma is the charge-discharge efficiency of the electric automobile;
deep charge and discharge can shorten battery life, and SOC is restrained by battery residual energy:
SOCmin≤SOCk,t≤SOCmax (24)
in the formula: SOCminAnd SOCmaxRespectively the lower upper limit of the allowable state of charge of the battery of the electric automobile;
the final state of the optimization period is constrained by the initial state:
SOCk,0＝SOCk,95+ΔSOCk,96＝SOCk,96 (25)
3) air conditioning cluster room temperature constraints
The indoor temperature should be kept within the allowable range of the set temperature fluctuation:
Figure FDA0003505321120000061
in the formula:
Figure FDA0003505321120000062
the preset upper and lower limits of the indoor temperature fluctuation are set.
6. The method for optimal day-ahead bidding of a virtual power plant in consideration of demand response and frequency modulation performance variation according to claim 1, comprising: establishing a robust optimization model of a day-ahead bidding strategy, and solving the nonlinear mixed integer model by using yalcip + gurobi, wherein the method specifically comprises the following steps:
in the objective function, the decision variables are day-ahead energy market casting scalar quantity, day-ahead frequency modulation market casting scalar quantity, next day electric vehicle frequency modulation compensation electricity price, next day electric vehicle charging electricity price, next day electric vehicle discharging electricity price and next day air conditioner cluster frequency modulation compensation electricity price
Figure FDA0003505321120000063
The uncertain variable is wind power real-time output and actual market clearing price
Figure FDA0003505321120000064
Figure FDA0003505321120000065
According to historical statistical data, the real-time output of the fan is generally smaller than the power prediction in the day-ahead, the deviation is generally within 15%, and the real-time output fluctuation of the wind power is supposed to be between-15% and + 5%]Within the range of, i.e.
Figure FDA0003505321120000066
And supposing that the fluctuation range of the market clearing price is +/-10%, carrying out robust optimization according to the day-ahead power prediction and the clearing price on a typical day, obtaining an optimal bidding strategy, and calculating the actual profit of the wind power according to the real-time output of the wind power.
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CN114865662A (en) * 2022-07-06 2022-08-05 华中科技大学 Thermoelectric unit power output control method and system for frequency modulation market
CN115345389A (en) * 2022-10-19 2022-11-15 广东电网有限责任公司佛山供电局 Multi-time scale optimization scheduling method for virtual power plant by cluster
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114865662A (en) * 2022-07-06 2022-08-05 华中科技大学 Thermoelectric unit power output control method and system for frequency modulation market
CN115345389A (en) * 2022-10-19 2022-11-15 广东电网有限责任公司佛山供电局 Multi-time scale optimization scheduling method for virtual power plant by cluster
CN116823332A (en) * 2023-06-29 2023-09-29 广东电网有限责任公司广州供电局 Quantitative analysis system for virtual power plant operation benefits considering distributed resources
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CN117808565A (en) * 2024-02-29 2024-04-02 国网上海市电力公司 Virtual power plant multi-time bidding method considering green evidence and carbon transaction
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