CN113792953A - Virtual power plant optimal scheduling method and system - Google Patents

Virtual power plant optimal scheduling method and system Download PDF

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CN113792953A
CN113792953A CN202110763812.1A CN202110763812A CN113792953A CN 113792953 A CN113792953 A CN 113792953A CN 202110763812 A CN202110763812 A CN 202110763812A CN 113792953 A CN113792953 A CN 113792953A
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scene
power plant
virtual power
probability
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CN113792953B (en
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陈良亮
宋杰
张卫国
杨凤坤
顾琳琳
郑红娟
周材
李奕杰
吴建斌
齐慧文
张翔宇
许振波
任端
王子成
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State Grid Corp of China SGCC
NARI Group Corp
Nari Technology Co Ltd
NARI Nanjing Control System Co Ltd
State Grid Electric Power Research Institute
Beijing State Grid Purui UHV Transmission Technology Co Ltd
Economic and Technological Research Institute of State Grid Shanxi Electric Power Co Ltd
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State Grid Corp of China SGCC
NARI Group Corp
Nari Technology Co Ltd
NARI Nanjing Control System Co Ltd
State Grid Electric Power Research Institute
Beijing State Grid Purui UHV Transmission Technology Co Ltd
Economic and Technological Research Institute of State Grid Shanxi Electric Power Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses a virtual power plant optimal scheduling method and a system, comprising the following steps: forecasting the power scene and probability of the wind power and the photovoltaic power generation in the next day according to historical information of wind speed, temperature and illumination radiation intensity of the area where the renewable distributed energy sources in the virtual power plant are located; counting the number of electric automobiles in the virtual power plant, the capacity of an energy storage system, the number of cogeneration units and the number of flexible loads participating in excitation type demand response; inputting the data into a pre-constructed optimized scheduling model which meets the electric load demand and the heat load demand of a user and aims at maximizing the income of a virtual power plant; and solving the optimized scheduling model to obtain an optimized scheduling scheme of the virtual power plant. The advantages are that: by participating the flexible load in the excitation type demand response of the virtual power plant, the peak clipping and valley filling functions of distributed energy sources, an energy storage system, an electric automobile and the flexible load in the virtual power plant are fully exerted, and the pollutant discharge amount of the thermoelectric interconnected unit is reduced.

Description

Virtual power plant optimal scheduling method and system
Technical Field
The invention relates to a virtual power plant optimal scheduling method and system, and belongs to the technical field of power system optimal scheduling.
Background
In the face of environmental issues associated with conventional power generation, distributed generation using solar, wind and fuel cells as energy sources has gradually become recognized as a reliable, clean way of generating electricity to meet future energy needs. And along with the gradual increase of the permeability of the new energy power generation in the power grid, the uncertainty and the volatility of the new energy power generation bring great interference to a power system, and the phenomena of wind abandonment and light abandonment are serious due to insufficient absorption capacity in the region. In 2019, a ubiquitous power internet of things construction concept is proposed by national grid companies, and the friendly grid-connected level of distributed new energy is improved and clean energy consumption is promoted mainly by means of virtual power plant construction, multi-energy complementation and other measures.
The virtual power plant organically combines a generator set, an energy storage system, distributed energy and a controllable flexible load in a certain area together, and combines the generator set, the energy storage system, the distributed energy and the controllable flexible load into a whole to participate in the operation of a power grid on the premise of meeting the power load in the area through advanced control, metering and communication technologies.
The virtual power plant comprises various distributed energy sources, an energy storage system, a cogeneration unit, an electric vehicle and a flexible load in an area, obtains profits by regulating and controlling the virtual power plant, and carries out economic compensation on the participation excitation type demand response load. Therefore, how to balance the unbalance between the distributed energy output and the load demand in the virtual power plant, integrate the distributed energy output and the user-side flexible load actively participate in the excitation type demand response, improve the economic benefit of the virtual power plant, reduce the pollutant emission of the virtual power plant and is an important problem to be solved by the optimized scheduling of the virtual power plant.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a virtual power plant optimal scheduling method and system.
In order to solve the technical problem, the invention provides a virtual power plant optimal scheduling method, which comprises the following steps:
forecasting the power scene and probability of the wind power and the photovoltaic power generation in the next day according to historical information of wind speed, temperature and illumination radiation intensity of the area where the renewable distributed energy sources in the virtual power plant are located;
counting the number of electric automobiles in the virtual power plant, the capacity of an energy storage system, the number of cogeneration units and the number of flexible loads participating in excitation type demand response;
inputting the next day wind power, the power scene and the probability generated by photovoltaic power generation, the number of electric vehicles in the virtual power plant, the capacity of an energy storage system, the number of cogeneration units and the number of flexible loads participating in incentive type demand response to a pre-constructed optimized scheduling model meeting the electric load demand and the heat load demand of a user and aiming at maximizing the benefit of the virtual power plant;
and solving the optimized scheduling model to obtain the hourly output condition of each power generation asset in the virtual power plant, and determining the optimized scheduling scheme of the virtual power plant according to the output condition.
Further, the process of predicting the wind power of the next day, the power scene generated by photovoltaic power generation and the probability according to the historical information of the wind speed, the temperature and the illumination radiation intensity of the area where the renewable distributed energy sources in the virtual power plant are located comprises the following steps:
the log-likelihood function of the edge distribution function is established by the following formula,
Figure BDA0003150049800000021
in the formula: l (delta) is a log-likelihood function, f (x)tδ) is an edge probability density function, xtDelta is an edge probability density function parameter, and T is the total hours of a day;
obtaining f (x) by maximizing equation (1)tδ) edge distribution function
Figure BDA0003150049800000022
And use
Figure BDA0003150049800000023
Calculating edge probability density function estimation parameter
Figure BDA0003150049800000024
Method for solving Copula function estimation parameter by using maximization formula (2)
Figure BDA0003150049800000025
Figure BDA0003150049800000026
In the formula: c is a Copula probability density function, theta represents a Copula function parameter, and the Copula function parameter is substituted into the Student-T Copula to generate a wind speed and illumination intensity scene set according to the solved Copula function estimation parameter;
reducing the number of wind speed and illumination intensity scenes generated by the Student-T Copula by utilizing a synchronous back-substitution technology to obtain a reduced wind speed and illumination intensity scene set;
obtaining predicted wind power and photovoltaic power output scenes of the next day according to the reduced wind speed and illumination scene set by using the following formula;
Figure BDA0003150049800000031
PPV,t＝ηPVSPVεt
in the formula: gWPP,tGenerating power of the wind turbine generator set at the moment t; v. oftThe predicted wind speed at time t; v. ofinAnd voutThe cut-in wind speed and the cut-out wind speed are adopted; v. ofrRated wind speed; grRated output power; pPV,tThe output of the photovoltaic generator set at the moment t is obtained; etaPVPhotovoltaic conversion efficiency; sPVIs the photovoltaic area; epsilontTo predict photovoltaic radiation intensity.
Further, the process of reducing the number of scenes by using the synchronous back-substitution technique includes:
s1, setting the reduced wind speed and illumination scene set as C, setting the iteration number k as 1, and setting the scene set in the iteration process as Ck＝C；
S2, determining the probability distance between any two scenes in the reduced wind speed and illumination scene set, wherein the probability distance formula is as follows
Figure BDA0003150049800000032
In the formula: ║, ║2Is a norm expression; i, j ∈ CkAnd i is not equal to j,
Figure BDA0003150049800000033
corresponding to any two scenes, the corresponding probability is pii、πj；
S3, finding the scene closest to the scene i, and determining the minimum distance between the scene and other scenes as
Figure BDA0003150049800000034
Then finding a scene matched with the minimum distance J;
s4, setting the minimum distance of each sceneMultiplying the probability with the corresponding probability to obtain a scene matched with the minimum value determined in the previous step, and then obtaining the scene according to the minimum value
Figure BDA0003150049800000035
Determining a scene C1,ZC1＝minZi,i∈Ck；
S5, eliminating scene C1At the same time, the scene C1The probability of (2) is shifted to the closest probability, and the remaining scenes are set to Ck+1＝Ck-C1；
S6, recording the number of the remaining scenes, if the number of the remaining scenes meets the calculation requirement, continuing the next step, if not, returning to the step S2;
s7, reserving the set Ck+1And the corresponding probability.
Further, the incentive-type demand-responsive flexible loads include a transferable load and an interruptible load;
wherein the model of transferable load is
Figure BDA0003150049800000041
In the formula:
Figure BDA0003150049800000042
loads before and after the load transfer of the d-level load at the time t are respectively; p (t, t ') is the amount of load transferred from time t to time t';
when the transferable load on the user side participates in the demand response of the virtual power plant, the virtual power plant carries out economic compensation on the transferable load, and the economic compensation cost expression of the transferable load is
Figure BDA0003150049800000043
In the formula: sigma1、σ2Compensation of base compensation costs and user comfort for transferable load compensation costsPaying the cost; t ismaxIs the maximum transfer time interval; cDR1Total compensation costs for transferable loads;
interruptible load for directly reducing total load, mathematical model of interruptible load and compensation cost are expressed by
Figure BDA0003150049800000044
Figure BDA0003150049800000045
In the formula:
Figure BDA0003150049800000046
P′load,d,trespectively the total load before and after the d-level load interruption load; cDR2,dCompensating costs for the interrupted load; pDR,d,tThe d-th load interruption amount;
Figure BDA0003150049800000047
compensating the electricity price for the interruption amount of the d-th class load; n isdIndicating the power load level.
Further, the expression of the optimized scheduling model is as follows:
Figure BDA0003150049800000048
in the formula: pi (omega) is the probability of the scene omega; w is the total scene number; f is the net income of the virtual power plant;
Figure BDA0003150049800000051
the profit of the virtual power plant for supplying power to the total load under the scene omega is gained;
Figure BDA0003150049800000052
the heat sale income of the virtual power plant under the scene omega is obtained;
Figure BDA0003150049800000053
generating and heating cost for the cogeneration unit;
Figure BDA0003150049800000054
the cost of environment protection for the operation of a virtual power plant is lowered;
Figure BDA0003150049800000055
the running cost of the electric automobile is reduced;
Figure BDA0003150049800000056
the operating cost of the energy storage system;
Figure BDA0003150049800000057
the output cost of the electric boiler is saved;
Figure BDA0003150049800000058
penalty cost for deviations from the contribution plan;
Figure BDA0003150049800000059
respectively compensating cost for transferring load and compensating cost for interrupting load;
the constraint conditions for optimizing the scheduling model comprise: the system comprises an electric power balance constraint, a thermal power balance constraint, a distributed power supply output constraint, a combined heat and power generation system output and climbing constraint, an energy storage system constraint, an electric vehicle charge state constraint and an excitation type demand response constraint.
A virtual power plant optimal scheduling system, comprising:
the prediction module is used for predicting the wind power of the next day, the power scene generated by photovoltaic power generation and the probability according to historical information of wind speed, temperature and illumination radiation intensity of the area where the renewable distributed energy sources in the virtual power plant are located;
the counting module is used for counting the number of electric automobiles in the virtual power plant, the capacity of an energy storage system, the number of cogeneration units and the number of flexible loads participating in excitation type demand response;
the input module is used for inputting the next day wind power, the power scene and the probability generated by photovoltaic power generation, the number of electric vehicles in the virtual power plant, the capacity of an energy storage system, the number of cogeneration units and the number of flexible loads participating in incentive type demand response to a pre-constructed optimized scheduling model meeting the electric load demand and the heat load demand of a user and aiming at maximizing the benefit of the virtual power plant;
and the solving module is used for solving the optimized scheduling model to obtain the hourly output condition of each power generation asset in the virtual power plant, and determining the optimized scheduling scheme of the virtual power plant according to the output condition.
Further, the prediction module comprises:
a first calculation module for establishing a log-likelihood function of the edge distribution function by,
Figure BDA00031500498000000510
in the formula: l (delta) is a log-likelihood function, f (x)tδ) is an edge probability density function, xtDelta is an edge probability density function parameter, and T is the total hours of a day;
a second calculation module for obtaining f (x) by maximizing the formula (1)tδ) edge distribution function
Figure BDA0003150049800000061
And use
Figure BDA0003150049800000062
Calculating edge probability density function estimation parameter
Figure BDA0003150049800000063
A third calculation module for calculating Copula function estimation parameters by using the maximization formula (2)
Figure BDA0003150049800000064
Figure BDA0003150049800000065
In the formula: c is a Copula probability density function, theta represents a Copula function parameter, and the Copula function parameter is substituted into the Student-T Copula to generate a wind speed and illumination intensity scene set according to the solved Copula function estimation parameter;
the reduction module is used for reducing the number of wind speed and illumination intensity scenes generated by the Student-T Copula by utilizing a synchronous back-substitution technology to obtain a reduced wind speed and illumination intensity scene set;
the fourth calculation module is used for obtaining predicted next-day wind power and photovoltaic power output scenes according to the reduced wind speed and illumination scene sets by using the following formula;
Figure BDA0003150049800000066
PPV,t＝ηPVSPVεt
in the formula: gWPP,tGenerating power of the wind turbine generator set at the moment t; v. oftThe predicted wind speed at time t; v. ofinAnd voutThe cut-in wind speed and the cut-out wind speed are adopted; v. ofrRated wind speed; grRated output power; pPV,tThe output of the photovoltaic generator set at the moment t is obtained; etaPVPhotovoltaic conversion efficiency; sPVIs the photovoltaic area; epsilontTo predict photovoltaic radiation intensity.
Further, the reduction module comprises:
a setting module, configured to set the reduced wind speed and illumination scene set as C, where the iteration number k is 1, and the scene set in the iteration process is set as Ck＝C；
A first determining module for determining the probability distance between any two scenes in the reduced wind speed and illumination scene set, wherein the probability distance formula is as follows
Figure BDA0003150049800000067
In the formula: ║, ║2Is a norm expression; i, j ∈ CkAnd i is not equal to j,
Figure BDA0003150049800000068
corresponding to any two scenes, the corresponding probability is pii、πj；
A second determining module, configured to find a scene closest to the scene i, and determine that a minimum distance between the scene and another scene is
Figure BDA0003150049800000071
Then finding a scene matched with the minimum distance J;
a third determining module, configured to multiply the minimum distance of each scene by the corresponding probability to obtain a scene matched with the minimum value determined in the previous step, and then obtain a probability value according to the minimum distance
Figure BDA0003150049800000072
Determining a scene C1,
Figure BDA0003150049800000073
A cancellation module for canceling scene C1At the same time, the scene C1The probability of (2) is shifted to the closest probability, and the remaining scenes are set to Ck+1＝Ck-C1；
The recording module is used for recording the number of the remaining scenes, if the number of the remaining scenes meets the calculation requirement, continuing the next step, and if the number of the remaining scenes does not meet the calculation requirement, sequentially passing through the first determining module, the second determining module, the third determining module and the eliminating module to re-determine the remaining scenes;
a reservation module for reserving the set Ck+1And the corresponding probability.
Further, the statistic module comprises:
a transferable load calculation module for calculating a transferable load:
Figure BDA0003150049800000074
in the formula:
Figure BDA0003150049800000075
loads before and after the load transfer of the d-level load at the time t are respectively; p (t, t ') is the amount of load transferred from time t to time t';
when the transferable load on the user side participates in the demand response of the virtual power plant, the virtual power plant carries out economic compensation on the transferable load, and the economic compensation cost expression of the transferable load is
Figure BDA0003150049800000076
In the formula: sigma1、σ2A base compensation cost and a user comfort compensation cost for a transferable load compensation cost; t ismaxIs the maximum transfer time interval; cDR1Total compensation costs for transferable loads;
a transferable load calculation module for calculating interruptible loads and compensation costs for the interruptible loads:
Figure BDA0003150049800000081
Figure BDA0003150049800000082
in the formula:
Figure BDA0003150049800000083
P′load,d,trespectively the total load before and after the d-level load interruption load; cDR2,dCompensating costs for the interrupted load; pDR,d,tThe d-th load interruption amount;
Figure BDA0003150049800000084
compensating the electricity price for the interruption amount of the d-th class load; n isdIndicating the power load level.
Further, the expression of the optimized scheduling model is as follows:
Figure BDA0003150049800000085
in the formula: pi (omega) is the probability of the scene omega; w is the total scene number; f is the net income of the virtual power plant;
Figure BDA0003150049800000086
the profit of the virtual power plant for supplying power to the total load under the scene omega is gained;
Figure BDA0003150049800000087
the heat sale income of the virtual power plant under the scene omega is obtained;
Figure BDA0003150049800000088
generating and heating cost for the cogeneration unit;
Figure BDA0003150049800000089
the cost of environment protection for the operation of a virtual power plant is lowered;
Figure BDA00031500498000000810
the running cost of the electric automobile is reduced;
Figure BDA00031500498000000811
the operating cost of the energy storage system;
Figure BDA00031500498000000812
the output cost of the electric boiler is saved;
Figure BDA00031500498000000813
penalty cost for deviations from the contribution plan;
Figure BDA00031500498000000814
respectively compensating costs and center for transferring loadOff-load compensation cost;
the constraint conditions for optimizing the scheduling model comprise: the system comprises an electric power balance constraint, a thermal power balance constraint, a distributed power supply output constraint, a combined heat and power generation system output and climbing constraint, an energy storage system constraint, an electric vehicle charge state constraint and an excitation type demand response constraint.
The invention achieves the following beneficial effects:
according to the invention, the flexible load participates in the excitation type demand response of the virtual power plant, the peak clipping and valley filling functions of distributed energy sources, an energy storage system, an electric automobile and the flexible load in the virtual power plant are fully exerted, the load curve is smoothed, the overall operation economy of the virtual power plant is improved, and the pollutant discharge amount of a thermoelectric interconnection unit is reduced.
Drawings
FIG. 1 is a flow chart of a virtual power plant optimization scheduling method in an embodiment of the present invention;
FIG. 2 is a virtual power plant overall framework in an embodiment of the invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1-2, the present invention provides a virtual power plant optimal scheduling method, and the implementation process includes the following detailed steps:
step 1, forecasting the wind power of the next day, the power scene and the probability generated by photovoltaic power generation according to historical information of wind speed, temperature and illumination radiation intensity of an area where renewable distributed energy sources in a virtual power plant are located;
on the premise of knowing the historical wind speed and the illumination intensity, judging that the historical wind speed and the illumination intensity have upper and lower tail dependence at different times;
the log-likelihood function of the edge distribution function is established by the following formula,
Figure BDA0003150049800000091
in the formula: l (delta) is a log-likelihood function, f (x)tδ) is an edge probability density function, xtAnd delta is an edge probability density function parameter for the wind speed/illumination intensity at the moment T, and T is expressed as the total hours of a day.
Obtaining f (x) by maximizing the above formulat) Edge distribution function F (x)t) And can utilize
Figure BDA0003150049800000092
Calculating edge probability density function estimation parameter
Figure BDA0003150049800000093
Method for solving Copula function estimation parameter by using maximized formula
Figure BDA0003150049800000094
Figure BDA0003150049800000095
In the formula: c is a Copula probability density function, and theta represents a Copula function parameter. And substituting the obtained Copula function parameters into the Student-T Copula to generate a scene set of wind speed and illumination intensity.
Obtaining a wind power and photovoltaic power output scene according to the wind speed and the illumination scene generated in the previous step by using the following formula;
Figure BDA0003150049800000096
PPV,t＝ηPVSPVεt
in the formula: gWPP,tGenerating power for the wind generating set at the moment t; v. oftThe predicted wind speed at time t; v. ofinAnd voutThe cut-in wind speed and the cut-out wind speed are adopted; v. ofrRated wind speed; grRated output power; pPV,tThe output of the photovoltaic generator set at the moment t is obtained; etaPVPhotovoltaic conversion efficiency; sPVIs the photovoltaic area; epsilon is the predicted photovoltaic radiation intensity;
because the data volume of the scenes generated by the Student-T Copula function is huge, the similarity between the scenes is very high, and the calculation speed is influenced, the near-field scenes are combined more effectively, the calculation speed is accelerated, and the number of the scenes is reduced by utilizing a synchronous back substitution technology.
Step 2, counting the number of electric automobiles in the virtual power plant, the capacity of an energy storage system, the number of cogeneration units and the number of flexible loads participating in excitation type demand response;
in a virtual power plant, an electric automobile is used as a mobile energy storage, electric energy can be obtained from a power Grid by means of a charging pile, and meanwhile, the electric energy can be transmitted to the power Grid under an excitation mechanism, so that V2G (Vehicle-to-Grid) interaction between the electric automobile and the power Grid is realized, and the functions of stabilizing uncertainty and volatility of distributed power generation are achieved. The state of charge model of the electric vehicle is expressed as
Figure BDA0003150049800000101
In the formula: SOCtThe state of charge of the electric vehicle at the moment t; pEV,tThe equivalent output value of the electric automobile is greater than 0 to represent charging, less than 0 to represent discharging, and equal to 0 to represent an off-grid state; cEVThe battery capacity of the electric automobile; lambda is the unit mileage power consumption of the electric automobile; and L is the driving mileage of the electric automobile.
The flexible load comprises a transferable load and an interruptible load, both of which can participate in demand response scheduling, and the transferable load and the interruptible load are transferred and interrupted according to supply and demand conditions, so that the controllability degree is higher.
The transferable load can change the load of the use period of the transferable load under the condition of ensuring that the total load quantity in the whole scheduling cycle is not changed. When the transferable load on the user side participates in the demand response, the virtual power plant needs to carry out economic compensation on the transferable load. The compensation cost is not only related to the size of the load transfer amount, but also related to the time interval of load transfer, because the larger the transferable load scheduling interval before and after optimization is, the more the electricity utilization comfort of the user is seriously influenced. The transferable load model and the compensation costs are expressed as follows:
Figure BDA0003150049800000111
Figure BDA0003150049800000112
in the formula (I), the compound is shown in the specification,
Figure BDA0003150049800000113
loads before and after the load transfer of the d-level load at the time t are respectively; p (t, t ') is the amount of load transferred from time t to time t'; sigma1、σ2A base compensation cost and a user comfort compensation cost for a transferable load compensation cost; t ismaxIs the maximum transfer time interval; cDR1The cost is compensated for the transferable load.
Unlike transferable loads that do not change the total amount of load, interruptible loads can directly curtail the total amount of load. The mathematical model of interruptible load and the compensation cost are respectively
Figure BDA0003150049800000114
Figure BDA0003150049800000115
In the formula (I), the compound is shown in the specification,
Figure BDA0003150049800000116
P′load，d，trespectively the total load before and after the d-level load interruption load; cDR2,dCompensating costs for the interrupted load; pDR,d,tThe d-th load interruption amount;
Figure BDA0003150049800000117
compensating the electricity price for the interruption of the class d load, ndIndicating the power load level.
Step 3, on the premise of meeting the electric load demand and the heat load demand of a user, establishing an optimization model by taking the maximized virtual power plant income as a target, and performing economic optimization scheduling on the virtual power plant;
3.1 virtual Power plant Whole framework
The virtual power plant aggregates cogeneration units, wind power, photovoltaic, an energy storage system, electric vehicles and participates in electric market transaction at the power generation side, and subscribes a bilateral transaction agreement with electric vehicle users, primary users and secondary users, as shown in figure 2, wherein the primary users consist of industrial and commercial loads with large electric quantity demands, the power consumption peak is relatively dispersed and the power price is high, the secondary users consist of resident loads with small electric quantity demands, and the power consumption peak is concentrated and the power price is low. The primary users and the secondary users are both composed of traditional loads and flexible loads, the traditional loads do not participate in scheduling of the virtual power plant and must be met preferentially, and the flexible loads can participate in incentive type demand response to adjust own power consumption demands.
3.2 virtual Power plant scheduling objective function
The virtual power plant scheduling objective function is maximum in overall profit, the objective function is mainly divided into total income and operation cost, and the objective function is expressed as follows:
Figure BDA0003150049800000121
in the formula: f is the net income of the virtual power plant; pi (omega) is the probability of the scene omega; w is the total scene number;
Figure BDA0003150049800000122
the profit of the virtual power plant for supplying power to the total load under the scene omega is gained;
Figure BDA0003150049800000123
the heat sale income of the virtual power plant under the scene omega is obtained;
Figure BDA0003150049800000124
generating and heating cost for the cogeneration unit;
Figure BDA0003150049800000125
the cost of environment protection for the operation of a virtual power plant is lowered;
Figure BDA0003150049800000126
the running cost of the electric automobile is reduced;
Figure BDA0003150049800000127
the operating cost of the energy storage system;
Figure BDA0003150049800000128
the output cost of the electric boiler is saved;
Figure BDA0003150049800000129
penalty cost for deviations from the contribution plan; cDR1Total compensation costs for transferable loads; cDR2The costs are compensated for the interrupted load.
3.3 virtual Power plant scheduling constraints
(1) Electric power balance constraint
Figure BDA00031500498000001210
In the formula: pCHPThe power output is provided for the cogeneration unit; p'WPP、P′PVWind power and photovoltaic output are respectively;
Figure BDA00031500498000001211
Figure BDA00031500498000001212
respectively discharging and charging power for the electric automobile; pE1，dis、PE1，chrRespectively discharging and charging power for the energy storage system; p'load1、P′load2The first-level user electrical load requirements and the second-level user electrical load requirements are respectively met; pebFor electricity to heat equipment consumptionPower;
(2) thermal equilibrium constraint
HCHP+Heb+HE2，dis＝Hload+HE2，chr
In the formula: hCHPThe heat power output of the cogeneration unit is provided; hebOutputting thermal power for the electric-to-heat equipment; hE2，chr、HE2，disRespectively charging and discharging heat for the heat storage tank; hloadIs the user's heat load demand.
(3) Electric vehicle restraint
PEV,t|≤PEV,max
SOCmin≤SOCt≤SOCmax
SOCout,t≥SOCdemand
In the formula: pEV,maxThe maximum charge and discharge power of the electric automobile; SOCmin、SOCmaxThe minimum and maximum charge states of the electric vehicle are obtained; SOCdemandThe expected state of charge of the owner of the electric automobile.
(4) Transferable load constraint
Transferable load constraint
Figure BDA0003150049800000131
Figure BDA0003150049800000132
In the formula:
Figure BDA0003150049800000133
the maximum allowable load transfer amount is t time;
Figure BDA0003150049800000134
the maximum allowable load transfer is at time t.
Transferable load transfer time constraints
p(t,t′)＝0 t′∈T1
p(t′,t)＝0 t∈T2
In the formula: t is1、T2Respectively, the transfer-out time is not allowed and the transfer-in time is not allowed.
Load total invariant constraint before and after transfer
Figure BDA0003150049800000135
(5) Interruptible load constraints
PDR,d,min,t≤PDR,d,t≤PDR,d,max,t
PDR,d,t-1+PDR,d,t≤PDR,d,max
In the formula: pDR,d,min,tIs the minimum value of the interrupt load power; pDR,d,max,tMaximum value of interrupt load power; pDR,d,maxThe maximum response power of the controllable load in the d-th load continuous time.
And 4, solving the virtual power plant optimized scheduling model to obtain the optimized scheduling scheme of the virtual power plant.
Correspondingly, the invention also provides a virtual power plant optimal scheduling system, which comprises:
the prediction module is used for predicting the wind power of the next day, the power scene generated by photovoltaic power generation and the probability according to historical information of wind speed, temperature and illumination radiation intensity of the area where the renewable distributed energy sources in the virtual power plant are located;
the counting module is used for counting the number of electric automobiles in the virtual power plant, the capacity of an energy storage system, the number of cogeneration units and the number of flexible loads participating in excitation type demand response;
the input module is used for inputting the next day wind power, the power scene and the probability generated by photovoltaic power generation, the number of electric vehicles in the virtual power plant, the capacity of an energy storage system, the number of cogeneration units and the number of flexible loads participating in incentive type demand response to a pre-constructed optimized scheduling model meeting the electric load demand and the heat load demand of a user and aiming at maximizing the benefit of the virtual power plant;
and the solving module is used for solving the optimized scheduling model to obtain the hourly output condition of each power generation asset in the virtual power plant, and determining the optimized scheduling scheme of the virtual power plant according to the output condition.
Further, the prediction module comprises:
a first calculation module for establishing a log-likelihood function of the edge distribution function by,
Figure BDA0003150049800000141
in the formula: l (delta) is a log-likelihood function, f (x)tδ) is an edge probability density function, xtDelta is an edge probability density function parameter, and T is the total hours of a day;
a second calculation module for obtaining f (x) by maximizing the formula (1)tδ) edge distribution function
Figure BDA0003150049800000142
And use
Figure BDA0003150049800000143
Calculating edge probability density function estimation parameter
Figure BDA0003150049800000144
A third calculation module for calculating Copula function estimation parameters by using the maximization formula (2)
Figure BDA0003150049800000145
Figure BDA0003150049800000146
In the formula: c is a Copula probability density function, theta represents a Copula function parameter, and the Copula function parameter is substituted into the Student-T Copula to generate a wind speed and illumination intensity scene set according to the solved Copula function estimation parameter;
the reduction module is used for reducing the number of wind speed and illumination intensity scenes generated by the Student-T Copula by utilizing a synchronous back-substitution technology to obtain a reduced wind speed and illumination intensity scene set;
the fourth calculation module is used for obtaining predicted next-day wind power and photovoltaic power output scenes according to the reduced wind speed and illumination scene sets by using the following formula;
Figure BDA0003150049800000151
PPV,t＝ηPVSPVεt
in the formula: gWPP,tGenerating power of the wind turbine generator set at the moment t; v. oftThe predicted wind speed at time t; v. ofinAnd voutThe cut-in wind speed and the cut-out wind speed are adopted; v. ofrRated wind speed; grRated output power; pPV,tThe output of the photovoltaic generator set at the moment t is obtained; etaPVPhotovoltaic conversion efficiency; sPVIs the photovoltaic area; epsilontTo predict photovoltaic radiation intensity.
Further, the reduction module comprises:
a setting module, configured to set the reduced wind speed and illumination scene set as C, where the iteration number k is 1, and the scene set in the iteration process is set as Ck＝C；
A first determining module for determining the probability distance between any two scenes in the reduced wind speed and illumination scene set, wherein the probability distance formula is as follows
Figure BDA0003150049800000152
In the formula: ║, ║2Is a norm expression; i, j ∈ CkAnd i is not equal to j,
Figure BDA0003150049800000153
corresponding to any two scenes and corresponding outlinesThe ratio is respectively pii、πj；
A second determining module, configured to find a scene closest to the scene i, and determine that a minimum distance between the scene and another scene is
Figure BDA0003150049800000154
Then finding a scene matched with the minimum distance J;
a third determining module, configured to multiply the minimum distance of each scene by the corresponding probability to obtain a scene matched with the minimum value determined in the previous step, and then obtain a probability value according to the minimum distance
Figure BDA0003150049800000155
Determining a scene C1,
Figure BDA0003150049800000161
A cancellation module for canceling scene C1At the same time, the scene C1The probability of (2) is shifted to the closest probability, and the remaining scenes are set to Ck+1＝Ck-C1；
The recording module is used for recording the number of the remaining scenes, if the number of the remaining scenes meets the calculation requirement, continuing the next step, and if the number of the remaining scenes does not meet the calculation requirement, sequentially passing through the first determining module, the second determining module, the third determining module and the eliminating module to re-determine the remaining scenes;
a reservation module for reserving the set Ck+1And the corresponding probability.
Further, the statistic module comprises:
a transferable load calculation module for calculating a transferable load:
Figure BDA0003150049800000162
in the formula:
Figure BDA0003150049800000163
loads before and after the load transfer of the d-level load at the time t are respectively; p (t, t ') is the amount of load transferred from time t to time t';
when the transferable load on the user side participates in the demand response of the virtual power plant, the virtual power plant carries out economic compensation on the transferable load, and the economic compensation cost expression of the transferable load is
Figure BDA0003150049800000164
In the formula: sigma1、σ2A base compensation cost and a user comfort compensation cost for a transferable load compensation cost; t ismaxIs the maximum transfer time interval; cDR1Total compensation costs for transferable loads;
a transferable load calculation module for calculating interruptible loads and compensation costs for the interruptible loads:
Figure BDA0003150049800000165
Figure BDA0003150049800000166
in the formula:
Figure BDA0003150049800000167
P′load,d,trespectively the total load before and after the d-level load interruption load; cDR2,dCompensating costs for the interrupted load; pDR,d,tThe d-th load interruption amount;
Figure BDA0003150049800000171
compensating the electricity price for the interruption amount of the d-th class load; n isdIndicating the power load level.
Further, the expression of the optimized scheduling model is as follows:
Figure BDA0003150049800000172
in the formula: pi (omega) is the probability of the scene omega; w is the total scene number; f is the net income of the virtual power plant;
Figure BDA0003150049800000173
the profit of the virtual power plant for supplying power to the total load under the scene omega is gained;
Figure BDA0003150049800000174
the heat sale income of the virtual power plant under the scene omega is obtained;
Figure BDA0003150049800000175
generating and heating cost for the cogeneration unit;
Figure BDA0003150049800000176
the cost of environment protection for the operation of a virtual power plant is lowered;
Figure BDA0003150049800000177
the running cost of the electric automobile is reduced;
Figure BDA0003150049800000178
the operating cost of the energy storage system;
Figure BDA0003150049800000179
the output cost of the electric boiler is saved;
Figure BDA00031500498000001710
penalty cost for deviations from the contribution plan;
Figure BDA00031500498000001711
respectively compensating cost for transferring load and compensating cost for interrupting load;
the constraint conditions for optimizing the scheduling model comprise: the system comprises an electric power balance constraint, a thermal power balance constraint, a distributed power supply output constraint, a combined heat and power generation system output and climbing constraint, an energy storage system constraint, an electric vehicle charge state constraint and an excitation type demand response constraint.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (10)

1. A virtual power plant optimal scheduling method is characterized by comprising the following steps:
forecasting the power scene and probability of the wind power and the photovoltaic power generation in the next day according to historical information of wind speed, temperature and illumination radiation intensity of the area where the renewable distributed energy sources in the virtual power plant are located;
counting the number of electric automobiles in the virtual power plant, the capacity of an energy storage system, the number of cogeneration units and the number of flexible loads participating in excitation type demand response;
inputting the next day wind power, the power scene and the probability generated by photovoltaic power generation, the number of electric vehicles in the virtual power plant, the capacity of an energy storage system, the number of cogeneration units and the number of flexible loads participating in incentive type demand response to a pre-constructed optimized scheduling model meeting the electric load demand and the heat load demand of a user and aiming at maximizing the benefit of the virtual power plant;
and solving the optimized scheduling model to obtain the hourly output condition of each power generation asset in the virtual power plant, and determining the optimized scheduling scheme of the virtual power plant according to the output condition.
2. The optimal scheduling method for the virtual power plant according to claim 1, wherein the process of predicting the wind power of the next day, the power scene generated by photovoltaic power generation and the probability according to the historical information of the wind speed, the temperature and the illumination radiation intensity of the area where the renewable distributed energy sources in the virtual power plant are located comprises the following steps:
the log-likelihood function of the edge distribution function is established by the following formula,
Figure FDA0003150049790000011
in the formula: l (delta) is a log-likelihood function, f (x)tδ) is an edge probability density function, xtDelta is an edge probability density function parameter, and T is the total hours of a day;
obtaining f (x) by maximizing equation (1)tδ) edge distribution function
Figure FDA0003150049790000012
And use
Figure FDA0003150049790000013
Calculating edge probability density function estimation parameter
Figure FDA0003150049790000014
Method for solving Copula function estimation parameter by using maximization formula (2)
Figure FDA0003150049790000015
Figure FDA0003150049790000016
In the formula: c is a Copula probability density function, theta represents a Copula function parameter, and the Copula function parameter is substituted into the Student-T Copula to generate a wind speed and illumination intensity scene set according to the solved Copula function estimation parameter;
reducing the number of wind speed and illumination intensity scenes generated by the Student-T Copula by utilizing a synchronous back-substitution technology to obtain a reduced wind speed and illumination intensity scene set;
obtaining predicted wind power and photovoltaic power output scenes of the next day according to the reduced wind speed and illumination scene set by using the following formula;
Figure FDA0003150049790000021
PPV,t＝ηPVSPVεt
in the formula: gWPP,tGenerating power of the wind turbine generator set at the moment t; v. oftThe predicted wind speed at time t; v. ofinAnd voutThe cut-in wind speed and the cut-out wind speed are adopted; v. ofrRated wind speed; grRated output power; pPV,tThe output of the photovoltaic generator set at the moment t is obtained; etaPVPhotovoltaic conversion efficiency; sPVIs the photovoltaic area; epsilontTo predict photovoltaic radiation intensity.
3. The virtual power plant optimized scheduling method of claim 2, wherein the process of reducing the number of scenes using a synchronous back-substitution technique comprises:
s1, setting the reduced wind speed and illumination scene set as C, setting the iteration number k as 1, and setting the scene set in the iteration process as Ck＝C；
S2, determining the probability distance between any two scenes in the reduced wind speed and illumination scene set, wherein the probability distance formula is as follows
Figure FDA0003150049790000022
In the formula: ║, ║2Is a norm expression; i, j ∈ CkAnd i is not equal to j,
Figure FDA0003150049790000023
corresponding to any two scenes, the corresponding probability is pii、πj；
S3, finding the scene closest to the scene i, and determining the minimum distance between the scene and other scenes as
Figure FDA0003150049790000024
Then finding a scene matched with the minimum distance J;
s4, dividing each sceneMultiplying the minimum distance with the corresponding probability to obtain a scene matched with the minimum value determined in the previous step, and then obtaining the scene according to the minimum distance and the corresponding probability
Figure FDA0003150049790000031
Determining a scene C1,
Figure FDA0003150049790000032
S5, eliminating scene C1At the same time, the scene C1The probability of (2) is shifted to the closest probability, and the remaining scenes are set to Ck+1＝Ck-C1；
S6, recording the number of the remaining scenes, if the number of the remaining scenes meets the calculation requirement, continuing the next step, if not, returning to the step S2;
s7, reserving the set Ck+1And the corresponding probability.
4. The virtual power plant optimal scheduling method of claim 3, wherein the incentive type demand response flexible load comprises a transferable load and an interruptible load;
wherein the model of transferable load is
Figure FDA0003150049790000033
In the formula:
Figure FDA0003150049790000034
loads before and after the load transfer of the d-level load at the time t are respectively; p (t, t ') is the amount of load transferred from time t to time t';
when the transferable load on the user side participates in the demand response of the virtual power plant, the virtual power plant carries out economic compensation on the transferable load, and the economic compensation cost expression of the transferable load is
Figure FDA0003150049790000035
In the formula: sigma1、σ2A base compensation cost and a user comfort compensation cost for a transferable load compensation cost; t ismaxIs the maximum transfer time interval; cDR1Total compensation costs for transferable loads;
interruptible load for directly reducing total load, mathematical model of interruptible load and compensation cost are expressed by
Figure FDA0003150049790000036
Figure FDA0003150049790000041
In the formula:
Figure FDA0003150049790000042
P′load,d,trespectively the total load before and after the d-level load interruption load; cDR2,dCompensating costs for the interrupted load; pDR,d,tThe d-th load interruption amount;
Figure FDA0003150049790000043
compensating the electricity price for the interruption amount of the d-th class load; n isdIndicating the power load level.
5. The virtual power plant optimal scheduling method of claim 4, wherein the optimal scheduling model has an expression:
Figure FDA0003150049790000044
in the formula: pi (omega) is the probability of the scene omega; w is the total scene number; f is a virtualNet revenue from the power plant;
Figure FDA0003150049790000045
the profit of the virtual power plant for supplying power to the total load under the scene omega is gained;
Figure FDA0003150049790000046
for heat sales revenue of virtual power plant under scene omega
Figure FDA0003150049790000047
Generating and heating cost for the cogeneration unit;
Figure FDA0003150049790000048
the cost of environment protection for the operation of a virtual power plant is lowered;
Figure FDA0003150049790000049
the running cost of the electric automobile is reduced;
Figure FDA00031500497900000410
the operating cost of the energy storage system;
Figure FDA00031500497900000411
the output cost of the electric boiler is saved;
Figure FDA00031500497900000412
penalty cost for deviations from the contribution plan;
Figure FDA00031500497900000413
respectively compensating cost for transferring load and compensating cost for interrupting load;
the constraint conditions for optimizing the scheduling model comprise: the system comprises an electric power balance constraint, a thermal power balance constraint, a distributed power supply output constraint, a combined heat and power generation system output and climbing constraint, an energy storage system constraint, an electric vehicle charge state constraint and an excitation type demand response constraint.
6. A virtual power plant optimal scheduling system, comprising:
the prediction module is used for predicting the wind power of the next day, the power scene generated by photovoltaic power generation and the probability according to historical information of wind speed, temperature and illumination radiation intensity of the area where the renewable distributed energy sources in the virtual power plant are located;
the counting module is used for counting the number of electric automobiles in the virtual power plant, the capacity of an energy storage system, the number of cogeneration units and the number of flexible loads participating in excitation type demand response;
the input module is used for inputting the next day wind power, the power scene and the probability generated by photovoltaic power generation, the number of electric vehicles in the virtual power plant, the capacity of an energy storage system, the number of cogeneration units and the number of flexible loads participating in incentive type demand response to a pre-constructed optimized scheduling model meeting the electric load demand and the heat load demand of a user and aiming at maximizing the benefit of the virtual power plant;
and the solving module is used for solving the optimized scheduling model to obtain the hourly output condition of each power generation asset in the virtual power plant, and determining the optimized scheduling scheme of the virtual power plant according to the output condition.
7. The virtual power plant optimized scheduling system of claim 6, wherein the prediction module comprises:
a first calculation module for establishing a log-likelihood function of the edge distribution function by,
Figure FDA0003150049790000051
in the formula: l (delta) is a log-likelihood function, f (x)tδ) is an edge probability density function, xtDelta is an edge probability density function parameter, and T is the total hours of a day;
a second calculation module for obtaining f (x) by maximizing the formula (1)tδ) edge distribution function
Figure FDA0003150049790000052
And use
Figure FDA0003150049790000053
Calculating edge probability density function estimation parameter
Figure FDA0003150049790000054
A third calculation module for calculating Copula function estimation parameters by using the maximization formula (2)
Figure FDA0003150049790000055
Figure FDA0003150049790000056
In the formula: c is a Copula probability density function, theta represents a Copula function parameter, and the Copula function parameter is substituted into the Student-T Copula to generate a wind speed and illumination intensity scene set according to the solved Copula function estimation parameter;
the reduction module is used for reducing the number of wind speed and illumination intensity scenes generated by the Student-T Copula by utilizing a synchronous back-substitution technology to obtain a reduced wind speed and illumination intensity scene set;
the fourth calculation module is used for obtaining predicted next-day wind power and photovoltaic power output scenes according to the reduced wind speed and illumination scene sets by using the following formula;
Figure FDA0003150049790000057
PPV,t＝ηPVSPVεt
in the formula: gWPP,tGenerating power of the wind turbine generator set at the moment t; v. oftThe predicted wind speed at time t; v. ofinAnd voutThe cut-in wind speed and the cut-out wind speed are adopted; v. ofrRated wind speed; grRated output power; pPV,tThe output of the photovoltaic generator set at the moment t is obtained; etaPVPhotovoltaic conversion efficiency; sPVIs the photovoltaic area; epsilontTo predict photovoltaic radiation intensity.
8. The virtual power plant optimized scheduling system of claim 7, wherein the curtailment module comprises:
a setting module, configured to set the reduced wind speed and illumination scene set as C, where the iteration number k is 1, and the scene set in the iteration process is set as Ck＝C；
A first determining module for determining the probability distance between any two scenes in the reduced wind speed and illumination scene set, wherein the probability distance formula is as follows
Figure FDA0003150049790000061
In the formula: ║, ║2Is a norm expression; i, j ∈ CkAnd i is not equal to j,
Figure FDA0003150049790000062
corresponding to any two scenes, the corresponding probability is pii、πj；
A second determining module, configured to find a scene closest to the scene i, and determine that a minimum distance between the scene and another scene is
Figure FDA0003150049790000063
Then finding a scene matched with the minimum distance J;
a third determining module, configured to multiply the minimum distance of each scene by the corresponding probability to obtain a scene matched with the minimum value determined in the previous step, and then obtain a probability value according to the minimum distance
Figure FDA0003150049790000064
Determining a scene C1,
Figure FDA0003150049790000065
A cancellation module for canceling scene C1At the same time, the scene C1The probability of (2) is shifted to the closest probability, and the remaining scenes are set to Ck+1＝Ck-C1；
The recording module is used for recording the number of the remaining scenes, if the number of the remaining scenes meets the calculation requirement, continuing the next step, and if the number of the remaining scenes does not meet the calculation requirement, sequentially passing through the first determining module, the second determining module, the third determining module and the eliminating module to re-determine the remaining scenes;
a reservation module for reserving the set Ck+1And the corresponding probability.
9. The virtual power plant optimized scheduling system of claim 8, wherein the statistics module comprises:
a transferable load calculation module for calculating a transferable load:
Figure FDA0003150049790000071
in the formula:
Figure FDA0003150049790000072
loads before and after the load transfer of the d-level load at the time t are respectively; p (t, t ') is the amount of load transferred from time t to time t';
when the transferable load on the user side participates in the demand response of the virtual power plant, the virtual power plant carries out economic compensation on the transferable load, and the economic compensation cost expression of the transferable load is
Figure FDA0003150049790000073
In the formula: sigma1、σ2To be able to transfer loadThe basic compensation cost of the compensation cost and the user comfort compensation cost; t ismaxIs the maximum transfer time interval; cDR1Total compensation costs for transferable loads;
a transferable load calculation module for calculating interruptible loads and compensation costs for the interruptible loads:
Figure FDA0003150049790000074
Figure FDA0003150049790000075
in the formula:
Figure FDA0003150049790000076
P′load,d,trespectively the total load before and after the d-level load interruption load; cDR2,dCompensating costs for the interrupted load; pDR,d,tThe d-th load interruption amount;
Figure FDA0003150049790000077
compensating the electricity price for the interruption amount of the d-th class load; n isdIndicating the power load level.
10. The virtual power plant optimal scheduling system of claim 9, wherein the optimal scheduling model has an expression:
Figure FDA0003150049790000078
in the formula: pi (omega) is the probability of the scene omega; w is the total scene number; f is the net income of the virtual power plant;
Figure FDA0003150049790000079
the profit of the virtual power plant for supplying power to the total load under the scene omega is gained;
Figure FDA00031500497900000710
for heat sales revenue of virtual power plant under scene omega
Figure FDA00031500497900000711
Generating and heating cost for the cogeneration unit;
Figure FDA00031500497900000712
the cost of environment protection for the operation of a virtual power plant is lowered;
Figure FDA00031500497900000713
the running cost of the electric automobile is reduced;
Figure FDA00031500497900000714
the operating cost of the energy storage system;
Figure FDA00031500497900000715
the output cost of the electric boiler is saved;
Figure FDA00031500497900000716
penalty cost for deviations from the contribution plan;
Figure FDA00031500497900000717
respectively compensating cost for transferring load and compensating cost for interrupting load;
the constraint conditions for optimizing the scheduling model comprise: the system comprises an electric power balance constraint, a thermal power balance constraint, a distributed power supply output constraint, a combined heat and power generation system output and climbing constraint, an energy storage system constraint, an electric vehicle charge state constraint and an excitation type demand response constraint.
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CN116031886A (en) * 2023-01-30 2023-04-28 天津大学 Method for controlling two-stage flexible climbing capacity of heat pump virtual power plant day-day before day
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CN115358519A (en) * 2022-07-13 2022-11-18 上海嘉柒智能科技有限公司 Virtual power plant optimal scheduling method and device
CN116031886A (en) * 2023-01-30 2023-04-28 天津大学 Method for controlling two-stage flexible climbing capacity of heat pump virtual power plant day-day before day
CN116031886B (en) * 2023-01-30 2024-02-13 天津大学 Method for controlling two-stage flexible climbing capacity of heat pump virtual power plant day-day before day
CN116031954A (en) * 2023-02-21 2023-04-28 国网浙江省电力有限公司金华供电公司 Virtual power plant optimal scheduling method under fuzzy opportunity constraint
CN116562575A (en) * 2023-05-16 2023-08-08 中国电力工程顾问集团有限公司 Optimized scheduling method of comprehensive energy system
CN116562575B (en) * 2023-05-16 2023-10-31 中国电力工程顾问集团有限公司 Optimized scheduling method of comprehensive energy system
CN117094453A (en) * 2023-10-20 2023-11-21 国网安徽省电力有限公司合肥供电公司 Scheduling optimization system and method for virtual power plant
CN117094453B (en) * 2023-10-20 2024-01-30 国网安徽省电力有限公司合肥供电公司 Scheduling optimization system and method for virtual power plant
CN117791627A (en) * 2024-02-26 2024-03-29 国网山东省电力公司东营供电公司 Flexible load dynamic aggregation method and system considering uncertainty of virtual power plant

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