CN113269346A - Virtual power plant interval optimization method and system considering flexible load participation - Google Patents

Virtual power plant interval optimization method and system considering flexible load participation Download PDF

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CN113269346A
CN113269346A CN202110335273.1A CN202110335273A CN113269346A CN 113269346 A CN113269346 A CN 113269346A CN 202110335273 A CN202110335273 A CN 202110335273A CN 113269346 A CN113269346 A CN 113269346A
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韩帅
孙乐平
郭小璇
卢健斌
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Electric Power Research Institute of Guangxi Power Grid Co Ltd
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Abstract

The invention discloses a virtual power plant interval optimization method and system considering flexible load participation, wherein the method comprises the following steps: performing mathematical modeling on each component unit of the virtual power plant to obtain the operating characteristics of each component unit; constructing an objective function of the virtual power plant based on the operating characteristics of each component unit, and determining constraint conditions of the objective function; and performing simulation analysis on the objective function by combining a non-dominated sorting genetic algorithm and an interval number theory to obtain an optimization decision scheme of the virtual power plant. In the embodiment of the invention, uncertainty factors existing in the operation process of each component unit in the virtual power plant can be avoided by combining the interval number theory, and the optimal scheduling precision of the virtual power plant is improved.

Description

Virtual power plant interval optimization method and system considering flexible load participation
Technical Field
The invention relates to the technical field of electric power, in particular to a virtual power plant interval optimization method and system considering flexible load participation.
Background
With the development of economic society of China, the problems of energy shortage and environmental pollution are increasingly prominent, the development of renewable energy becomes a necessary way for China to deal with the increasingly severe energy environmental problems, and distributed renewable energy represented by wind energy and solar energy plays an important role in energy patterns. But the distributed renewable energy is limited by the characteristics of small capacity, intermittence, dispersity and the like, and is difficult to independently join the electric power market to participate in operation, so that the virtual power plant can play a positive promoting role. The virtual power plant can use a more flexible software framework to realize the mutually coordinated optimized operation among a plurality of distributed renewable energy sources, and simultaneously, the grid-connected mode of each distributed renewable energy source is not changed, however, the optimized scheduling mode proposed aiming at the stable operation of the virtual power plant still has the defects: uncertainty factors brought by distributed renewable energy sources, power loads and the like in the virtual power plant in the operation process are not considered, so that obvious deviation exists in the optimized scheduling result.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a virtual power plant interval optimization method and system considering flexible load participation.
In order to solve the above problems, the present invention provides a virtual power plant interval optimization method considering flexible load participation, the method including:
performing mathematical modeling on each component unit of the virtual power plant to obtain the operating characteristics of each component unit;
constructing an objective function of the virtual power plant based on the operating characteristics of each component unit, and determining constraint conditions of the objective function;
and performing simulation analysis on the objective function by combining a non-dominated sorting genetic algorithm and an interval number theory to obtain an optimization decision scheme of the virtual power plant.
Optionally, performing mathematical modeling on each component unit of the virtual power plant, and acquiring the operating characteristics of each component unit includes:
performing mathematical modeling on a distributed power supply unit in a virtual power plant to obtain the operating characteristics of the distributed power supply unit;
performing mathematical modeling on a flexible load unit in the virtual power plant to obtain the operating characteristics of the flexible load unit;
and performing mathematical modeling on the energy storage unit in the virtual power plant to obtain the output power characteristic of the energy storage unit.
Optionally, performing mathematical modeling on the distributed power supply unit inside the virtual power plant, and acquiring the operating characteristics of the distributed power supply unit includes:
based on that the distributed power supply unit comprises a wind generating set and a photovoltaic generating set, determining that the output power characteristic of the wind generating set is as follows:
Figure BDA0002997260150000021
and determining the output power characteristic of the photovoltaic generator set as:
Figure BDA0002997260150000022
wherein, PWPPIs the actual output power, P, of the wind turbineratedV is the current actual wind speed and v is the rated output of the wind generating setinCutting into wind speed, v, for the unitoutCutting out wind speed v for the unitratedAt rated wind speed, PPVIs the actual output power, P, of the photovoltaic generator setSTCFor the maximum output power of the photovoltaic generator set under standard test conditions, GINCAs actual solar radiation intensity, GSTCFor the intensity of solar radiation under standard test conditions,k is the power temperature coefficient, TeIs the actual temperature, T, of the batteryrThe rated temperature of the battery.
Optionally, the flexible load unit in the virtual power plant is subjected to mathematical modeling, and the operation characteristics of the flexible load unit are acquired by the method including:
determining the charging power characteristic of the electric vehicle as follows based on that the flexible load unit comprises the electric vehicle and an air conditioner:
Figure BDA0002997260150000031
and determining the periodic variation characteristic of the air conditioner as follows:
Figure BDA0002997260150000032
wherein, PEV(t) is the actual charging power, P, of the electric vehiclerIs rated power of the electric automobile, t is current charging time, t1To start the charging time, t2For end of charge time, Ti(T) is the indoor temperature of the air conditioner at time T, To(t) is the outdoor temperature of the air conditioner at the time t, C is the specific heat capacity of the air conditioner, R is the thermal resistance of the air conditioner, P is the cooling power or the heating power of the air conditioner under the operation condition, ON represents that the air conditioner is in the operation state, and OFF represents that the air conditioner is in the OFF state.
Optionally, the output power characteristic of the energy storage unit is as follows:
PESS＝uessdisPessdis-uesschrPesschr
wherein, PESSIs the actual output power of the energy storage unit uessdisIs a discharge state variable, P, of the energy storage unitessdisIs the discharge power of the energy storage unit uesschrIs a state of charge variable, P, of the energy storage unitesschrCharging power for the energy storage unit。
Optionally, the objective function of the virtual power plant is:
Figure BDA0002997260150000033
wherein F is an objective function of the virtual power plant, min (F)ss) Max (f) as a function of the thermal comfort optima of the virtual power plantsy) As a function of the operating yield optimum of the virtual power plant, TsFor the set temperature of the air conditioner, CWPPFor the operational benefits of the wind turbine, CPVFor the operating yield of the photovoltaic generator set, CESSFor the operating gain of the energy storage unit, CLOADAnd the operation income after the load demand response.
Optionally, the constraint condition of the objective function is:
Figure BDA0002997260150000041
wherein, PDWContract electric quantity, P, signed for the virtual power plant and the power gridLOADFor the total output of the flexible load unit,
Figure BDA0002997260150000042
and the upper limit value of the power transmitted to the power grid by the virtual power plant.
Optionally, the simulation analysis of the objective function by combining the non-dominated sorting genetic algorithm and the interval number theory includes:
based on the interval number theory, limiting a plurality of related parameters related to the target function one by one in a dynamic interval;
and performing optimization solution on the processed objective function by using a non-dominated sorting genetic algorithm to obtain the optimal scheduling information of the virtual power plant.
In addition, an embodiment of the present invention further provides a virtual power plant interval optimization system considering flexible load participation, where the system includes:
the operation characteristic acquisition module is used for performing mathematical modeling on each component unit of the virtual power plant to acquire the operation characteristics of each component unit;
the target function determining module is used for constructing a target function of the virtual power plant based on the operation characteristics of each component unit and determining the constraint condition of the target function;
and the decision information output module is used for carrying out simulation analysis on the target function by combining a non-dominated sorting genetic algorithm and an interval number theory to obtain an optimized decision scheme of the virtual power plant.
In addition, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the virtual plant interval optimization method considering flexible load participation described in any one of the above.
In the embodiment of the invention, the operation characteristics of each component unit in the virtual power plant are utilized to construct the objective function, and the interval limitation is carried out on uncertain factors existing in the objective function by combining the interval number theory, so that the optimal scheduling precision of the virtual power plant is effectively improved, the system simulation result can provide a guiding function for the actual system operation, and the stable safety and the operation economy of the virtual power plant are ensured.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a virtual power plant interval optimization method considering flexible load participation in an embodiment of the present invention;
FIG. 2 is a graph of the temperature-power cycle variation characteristic of air conditioner operation in an embodiment of the present invention;
FIG. 3 is a schematic structural composition diagram of a virtual power plant interval optimization system considering flexible load participation in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Examples
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating a virtual power plant interval optimization method considering flexible load participation according to an embodiment of the present invention.
As shown in fig. 1, a virtual power plant interval optimization method considering flexible load participation includes the following steps:
s101, performing mathematical modeling on each component unit of the virtual power plant to obtain the operating characteristics of each component unit;
the specific implementation process comprises the following steps:
(1) performing mathematical modeling on a distributed power supply unit in a virtual power plant to obtain the operating characteristics of the distributed power supply unit;
further, based on that the distributed power supply unit includes a wind generating set and a photovoltaic generating set having energy complementarity, that is, the wind generating set generates a large amount of power at night in winter, and the photovoltaic generating set generates a large amount of power at day in summer, it is determined that the output power characteristic of the wind generating set is as follows:
Figure BDA0002997260150000061
and determining the output power characteristic of the photovoltaic generator set as:
Figure BDA0002997260150000062
wherein, PWPPIs the actual output power, P, of the wind turbineratedV is the current actual wind speed and v is the rated output of the wind generating setinCutting into wind speed, v, for the unitoutCutting out wind speed v for the unitratedAt rated wind speed, PPVIs the actual output power, P, of the photovoltaic generator setSTCFor the maximum output power of the photovoltaic generator set under standard test conditions, GINCAs actual solar radiation intensity, GSTCFor the intensity of solar radiation under standard test conditions, k is the power temperature coefficient, TeIs the actual temperature, T, of the batteryrThe rated temperature of the battery.
(2) Performing mathematical modeling on a flexible load unit in the virtual power plant to obtain the operating characteristics of the flexible load unit, wherein the flexible load unit comprises an electric automobile and an air conditioner;
firstly, by analyzing the charging demand and the charging behavior of the electric vehicle, the charging power characteristic of the electric vehicle can be determined by using a monte carlo simulation algorithm as follows:
Figure BDA0002997260150000063
then, according to the air conditioner operation temperature-power cycle variation characteristic diagram shown in fig. 2, when the operation temperature of the air conditioner is equal to the air conditioner on-limit temperature value θ+Down to air conditioner off boundary temperature value theta-When the air conditioner is in operation for a duration time TonThe internal power value is maintained at the rated power PACAnd when the operation temperature of the air conditioner is determined by the air conditioner off boundary temperature value theta-Rising to the air conditioner starting boundary temperature value theta+When the air conditioner is closed for a duration time ToffThe inner power value remains zero; in summary, the periodic variation characteristic of the air conditioner can be determined by using a first-order differential model as follows:
Figure BDA0002997260150000071
wherein, PEV(t) is the actual charging power, P, of the electric vehiclerIs rated power of the electric automobile, t is current charging time, t1To start the charging time, t2For end of charge time, Ti(T) is the indoor temperature of the air conditioner at time T, To(t) is the outdoor temperature of the air conditioner at the moment t, C is the specific heat capacity of the air conditioner, R is the thermal resistance of the air conditioner, P is the cooling power or the heating power of the air conditioner under the operation condition, specifically according to the actual application requirement, ON represents that the air conditioner is in the operation state, and OFF represents that the air conditioner is in the OFF state.
(3) Performing mathematical modeling on the energy storage unit in the virtual power plant, and acquiring the output power characteristic of the energy storage unit as follows:
PESS＝uessdisPessdis-uesschrPesschr
wherein, PESSIs the actual output power of the energy storage unit uessdisIs the discharge state variable (and u) of the energy storage unit essdis0 or uessdis＝1)，PessdisIs the discharge power of the energy storage unit uesschrIs the state of charge variable (and u) of the energy storage unit esschr0 or uesschr＝1)，PesschrAnd charging power for the energy storage unit.
In practical application, the distributed power supply unit is mainly used for meeting the power consumption requirement of the flexible load unit, and the energy storage unit is mainly used for smoothing the output power of the whole virtual power plant based on the self charge-discharge characteristic so as to improve the power generation grid-merging amount of the whole virtual power grid.
S102, constructing an objective function of the virtual power plant based on the operation characteristics of each component unit, and determining constraint conditions of the objective function;
the specific implementation process comprises the following steps:
(1) according to the operation characteristics of each component unit obtained in step S101, constructing an objective function of the virtual power plant as follows:
Figure BDA0002997260150000072
in the formula:
Figure BDA0002997260150000081
wherein F is an objective function of the virtual power plant, min (F)ss) Max (f) as a function of the thermal comfort optima of the virtual power plantsy) As a function of the operating yield optimum of the virtual power plant, TsFor the set temperature of the air conditioner, CWPPFor the operating yield of the wind turbine, cWPPIs the unit power cost of the wind turbine generator system, cPVIs the unit power cost of the photovoltaic generator set, CPVFor the operating yield of the photovoltaic generator set, CESSFor the operating yield of the energy storage unit, cessdisIs the discharge price of the energy storage unit, cesschrCharging price for the energy storage unit, CLOADFor operating benefits after load demand response, clThe price is the electricity price before the load demand response, the delta c is the price change value before and after the load demand response, the L is the demand before the load demand response, and the L' is the demand after the load demand response;
(2) considering the power balance problem and the energy storage charging and discharging regularity problem of the whole virtual power plant, determining the constraint condition of the objective function as follows:
Figure BDA0002997260150000082
wherein, PDWContract electric quantity, P, signed for the virtual power plant and the power gridLOADFor the total output of the flexible load unit,
Figure BDA0002997260150000083
and the upper limit value of the power transmitted to the power grid by the virtual power plant.
S103, performing simulation analysis on the objective function by combining a non-dominated sorting genetic algorithm and an interval number theory to obtain an optimization decision scheme of the virtual power plant.
The specific implementation process comprises the following steps:
(1) based on the interval number theory, the plurality of relevant parameters related to the objective function are subjected to dynamic interval limiting processing one by one, and the following steps are further shown: obtaining the actual output power P of the wind generating set through historical operation dataWPPUpper and lower limits of, actual output power P of the photovoltaic generator setPVUpper and lower limits of, total output P of said flexible load unitLOADUpper and lower limits of and the indoor temperature T of the air conditioneriThe upper and lower limits of (t);
(2) performing optimization solution on the processed objective function by using a non-dominated sorting genetic algorithm to obtain the optimal scheduling information of the virtual power plant, wherein the optimal scheduling information is represented as: when the objective function F is in the thermal comfort optimum function min (F)ss) And operation yield optimization function max (f)sy) When the switching occurs, for any switching moment, the class of electric loads with smaller scheduling power variation are extracted from the flexible load unit and used as demand targets for scheduling and supplying.
In the embodiment of the invention, the operation characteristics of each component unit in the virtual power plant are utilized to construct the objective function, and the interval limitation is carried out on uncertain factors existing in the objective function by combining the interval number theory, so that the optimal scheduling precision of the virtual power plant is effectively improved, the system simulation result can provide a guiding function for the actual system operation, and the stable safety and the operation economy of the virtual power plant are ensured.
Examples
Referring to fig. 3, fig. 3 is a schematic structural composition diagram of a virtual power plant interval optimization system considering flexible load participation in an embodiment of the present invention.
As shown in fig. 3, a virtual plant interval optimization system considering flexible load participation, the system comprises the following:
an operation characteristic obtaining module 201, configured to perform mathematical modeling on each component unit of the virtual power plant, and obtain operation characteristics of each component unit;
the specific implementation process comprises the following steps:
(1) performing mathematical modeling on a distributed power supply unit in a virtual power plant to obtain the operating characteristics of the distributed power supply unit;
further, based on that the distributed power supply unit includes a wind generating set and a photovoltaic generating set having energy complementarity, that is, the wind generating set generates a large amount of power at night in winter, and the photovoltaic generating set generates a large amount of power at day in summer, it is determined that the output power characteristic of the wind generating set is as follows:
Figure BDA0002997260150000091
and determining the output power characteristic of the photovoltaic generator set as:
Figure BDA0002997260150000092
wherein, PWPPIs the actual output power, P, of the wind turbineratedV is the current actual wind speed and v is the rated output of the wind generating setinCutting into wind speed, v, for the unitoutCutting out wind speed v for the unitratedAt rated wind speed, PPVIs the actual output power, P, of the photovoltaic generator setSTCFor the maximum output power of the photovoltaic generator set under standard test conditions, GINCAs actual solar radiation intensity, GSTCFor the intensity of solar radiation under standard test conditions, k is the power temperature coefficient, TeIs the actual temperature of the battery，TrThe rated temperature of the battery.
(2) Performing mathematical modeling on a flexible load unit in the virtual power plant to obtain the operating characteristics of the flexible load unit, wherein the flexible load unit comprises an electric automobile and an air conditioner;
firstly, by analyzing the charging demand and the charging behavior of the electric vehicle, the charging power characteristic of the electric vehicle can be determined by using a monte carlo simulation algorithm as follows:
Figure BDA0002997260150000101
then, according to the air conditioner operation temperature-power cycle variation characteristic diagram shown in fig. 2, when the operation temperature of the air conditioner is equal to the air conditioner on-limit temperature value θ+Down to air conditioner off boundary temperature value theta-When the air conditioner is in operation for a duration time TonThe internal power value is maintained at the rated power PACAnd when the operation temperature of the air conditioner is determined by the air conditioner off boundary temperature value theta-Rising to the air conditioner starting boundary temperature value theta+When the air conditioner is closed for a duration time ToffThe inner power value remains zero; in summary, the periodic variation characteristic of the air conditioner can be determined by using a first-order differential model as follows:
Figure BDA0002997260150000102
wherein, PEV(t) is the actual charging power, P, of the electric vehiclerIs rated power of the electric automobile, t is current charging time, t1To start the charging time, t2For end of charge time, Ti(T) is the indoor temperature of the air conditioner at time T, To(t) is the outdoor temperature of the air conditioner at the moment t, C is the specific heat capacity of the air conditioner, R is the thermal resistance of the air conditioner, P is the refrigerating power or the heating power of the air conditioner under the operation condition, which is specifically determined by the actual application requirement, and ON represents thatThe air conditioner is in an operating state, and OFF indicates that the air conditioner is in an OFF state.
(3) Performing mathematical modeling on the energy storage unit in the virtual power plant, and acquiring the output power characteristic of the energy storage unit as follows:
PESS＝uessdisPessdis-uesschrPesschr
wherein, PESSIs the actual output power of the energy storage unit uessdisIs the discharge state variable (and u) of the energy storage unit essdis0 or uessdis＝1)，PessdisIs the discharge power of the energy storage unit uesschrIs the state of charge variable (and u) of the energy storage unit esschr0 or uesschr＝1)，PesschrAnd charging power for the energy storage unit.
In practical application, the distributed power supply unit is mainly used for meeting the power consumption requirement of the flexible load unit, and the energy storage unit is mainly used for smoothing the output power of the whole virtual power plant based on the self charge-discharge characteristic so as to improve the power generation grid-merging amount of the whole virtual power grid.
An objective function determination module 202, configured to construct an objective function of the virtual power plant based on the operation characteristics of the respective component units, and determine a constraint condition of the objective function at the same time;
the specific implementation process comprises the following steps:
(1) according to the operation characteristics of each component unit acquired by the operation characteristic acquisition module 201, constructing an objective function of the virtual power plant as follows:
Figure BDA0002997260150000111
in the formula:
Figure BDA0002997260150000112
wherein F is of the virtual power plantObjective function, min (f)ss) Max (f) as a function of the thermal comfort optima of the virtual power plantsy) As a function of the operating yield optimum of the virtual power plant, TsFor the set temperature of the air conditioner, CWPPFor the operating yield of the wind turbine, cWPPIs the unit power cost of the wind turbine generator system, cPVIs the unit power cost of the photovoltaic generator set, CPVFor the operating yield of the photovoltaic generator set, CESSFor the operating yield of the energy storage unit, cessdisIs the discharge price of the energy storage unit, cesschrCharging price for the energy storage unit, CLOADFor the operation income after the load demand response, cl is the electricity price before the load demand response, Δ c is the price change value before and after the load demand response, L is the demand before the load demand response, and L' is the demand after the load demand response;
(2) considering the power balance problem and the energy storage charging and discharging regularity problem of the whole virtual power plant, determining the constraint condition of the objective function as follows:
Figure BDA0002997260150000121
wherein, PDWContract electric quantity, P, signed for the virtual power plant and the power gridLOADFor the total output of the flexible load unit,
Figure BDA0002997260150000122
and the upper limit value of the power transmitted to the power grid by the virtual power plant.
And the decision information output module 203 is used for performing simulation analysis on the target function by combining a non-dominated sorting genetic algorithm and an interval number theory to obtain an optimized decision scheme of the virtual power plant.
The specific implementation process comprises the following steps:
(1) based on the interval number theory, the plurality of relevant parameters related to the objective function are subjected to dynamic interval limiting processing one by one, and the following steps are further shown: passing through historyThe operation data obtains the actual output power P of the wind generating setWPPUpper and lower limits of, actual output power P of the photovoltaic generator setPVUpper and lower limits of, total output P of said flexible load unitLOADUpper and lower limits of and the indoor temperature T of the air conditioneriThe upper and lower limits of (t);
(2) performing optimization solution on the processed objective function by using a non-dominated sorting genetic algorithm to obtain the optimal scheduling information of the virtual power plant, wherein the optimal scheduling information is represented as: when the objective function F is in the thermal comfort optimum function min (F)ss) And operation yield optimization function max (f)sy) When the power supply is switched, for any switching moment, the class of electric loads with smaller scheduling power variation are extracted from the flexible load unit and used as demand targets for scheduling and supplying.
In the embodiment of the invention, the operation characteristics of each component unit in the virtual power plant are utilized to construct the objective function, and the interval limitation is carried out on uncertain factors existing in the objective function by combining the interval number theory, so that the optimal scheduling precision of the virtual power plant is effectively improved, the system simulation result can provide a guiding function for the actual system operation, and the stable safety and the operation economy of the virtual power plant are ensured.
The embodiment of the invention provides a computer-readable storage medium, wherein an executable computer program is stored on the computer-readable storage medium, and when the program is executed by a processor, the virtual power plant interval optimization method considering flexible load participation provided by the embodiment is realized. The computer-readable storage medium includes, but is not limited to, any type of disk including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks, ROMs (Read-Only memories), RAMs (Random AcceSS memories), EPROMs (EraSable Programmable Read-Only memories), EEPROMs (Electrically EraSable Programmable Read-Only memories), flash memories, magnetic cards, or optical cards. That is, a storage device includes any medium that stores or transmits information in a form readable by a device (e.g., a computer, a mobile phone, etc.), and may be a read-only memory, a magnetic or optical disk, or the like.
The virtual power plant interval optimization method and system considering flexible load participation provided by the embodiment of the invention are introduced in detail, a specific embodiment is adopted in the method to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims (10)

1. A virtual power plant interval optimization method considering flexible load participation is characterized by comprising the following steps:
performing mathematical modeling on each component unit of the virtual power plant to obtain the operating characteristics of each component unit;
constructing an objective function of the virtual power plant based on the operating characteristics of each component unit, and determining constraint conditions of the objective function;
and performing simulation analysis on the objective function by combining a non-dominated sorting genetic algorithm and an interval number theory to obtain an optimization decision scheme of the virtual power plant.
2. The virtual power plant interval optimization method considering flexible load participation according to claim 1, wherein the mathematical modeling is performed on each component unit of the virtual power plant, and the obtaining the operation characteristics of each component unit comprises:
performing mathematical modeling on a distributed power supply unit in a virtual power plant to obtain the operating characteristics of the distributed power supply unit;
performing mathematical modeling on a flexible load unit in the virtual power plant to obtain the operating characteristics of the flexible load unit;
and performing mathematical modeling on the energy storage unit in the virtual power plant to obtain the output power characteristic of the energy storage unit.
3. The virtual power plant interval optimization method considering flexible load participation according to claim 2, wherein the mathematically modeling distributed power units inside a virtual power plant, and the obtaining the operating characteristics of the distributed power units comprises:
based on that the distributed power supply unit comprises a wind generating set and a photovoltaic generating set, determining that the output power characteristic of the wind generating set is as follows:
Figure FDA0002997260140000021
and determining the output power characteristic of the photovoltaic generator set as:
Figure FDA0002997260140000022
wherein, PWPPIs the actual output power, P, of the wind turbineratedV is the current actual wind speed and v is the rated output of the wind generating setinCutting into wind speed, v, for the unitoutCutting out wind speed v for the unitratedAt rated wind speed, PPVIs the actual output power, P, of the photovoltaic generator setSTCFor the maximum output power of the photovoltaic generator set under standard test conditions, GINCAs actual solar radiation intensity, GSTCFor the intensity of solar radiation under standard test conditions, k is the power temperature coefficient, TeIs the actual temperature, T, of the batteryrThe rated temperature of the battery.
4. The virtual power plant interval optimization method considering flexible load participation according to claim 3, wherein the performing mathematical modeling on the flexible load units inside the virtual power plant and the obtaining the operating characteristics of the flexible load units comprises:
determining the charging power characteristic of the electric vehicle as follows based on that the flexible load unit comprises the electric vehicle and an air conditioner:
Figure FDA0002997260140000023
and determining the periodic variation characteristic of the air conditioner as follows:
Figure FDA0002997260140000024
wherein, PEV(t) is the actual charging power, P, of the electric vehiclerIs rated power of the electric automobile, t is current charging time, t1To start the charging time, t2For end of charge time, Ti(T) is the indoor temperature of the air conditioner at time T, To(t) is the outdoor temperature of the air conditioner at the time t, C is the specific heat capacity of the air conditioner, R is the thermal resistance of the air conditioner, P is the cooling power or the heating power of the air conditioner under the operation condition, ON represents that the air conditioner is in the operation state, and OFF represents that the air conditioner is in the OFF state.
5. The virtual power plant interval optimization method considering flexible load participation according to claim 4, wherein the output power characteristics of the energy storage unit are as follows:
PESS＝uessdisPessdis-uesschrPesschr
wherein, PESSIs the actual output power of the energy storage unit uessdisIs a discharge state variable, P, of the energy storage unitessdisIs the discharge power of the energy storage unit uesschrIs a state of charge variable, P, of the energy storage unitesschrAnd charging power for the energy storage unit.
6. The method for virtual plant interval optimization considering flexible load participation according to claim 5, characterized in that the objective function of the virtual plant is:
Figure FDA0002997260140000031
wherein F is an objective function of the virtual power plant, min (F)ss) Max (f) as a function of the thermal comfort optima of the virtual power plantsy) As a function of the operating yield optimum of the virtual power plant, TsFor the set temperature of the air conditioner, CWPPFor the operational benefits of the wind turbine, CPVFor the operating yield of the photovoltaic generator set, CESSFor the operating gain of the energy storage unit, CLOADAnd the operation income after the load demand response.
7. The virtual power plant interval optimization method considering flexible load participation according to claim 6, wherein the constraint conditions of the objective function are as follows:
Figure FDA0002997260140000032
wherein, PDWContract electric quantity, P, signed for the virtual power plant and the power gridLOADFor the total output of the flexible load unit,
Figure FDA0002997260140000033
and the upper limit value of the power transmitted to the power grid by the virtual power plant.
8. The virtual power plant interval optimization method considering flexible load participation as claimed in claim 7, wherein said performing simulation analysis on the objective function in combination with non-dominated sorting genetic algorithm and interval number theory comprises:
based on the interval number theory, limiting a plurality of related parameters related to the target function one by one in a dynamic interval;
and performing optimization solution on the processed objective function by using a non-dominated sorting genetic algorithm to obtain the optimal scheduling information of the virtual power plant.
9. A virtual plant interval optimization system considering flexible load participation, the system comprising:
the operation characteristic acquisition module is used for performing mathematical modeling on each component unit of the virtual power plant to acquire the operation characteristics of each component unit;
the target function determining module is used for constructing a target function of the virtual power plant based on the operation characteristics of each component unit and determining the constraint condition of the target function;
and the decision information output module is used for carrying out simulation analysis on the target function by combining a non-dominated sorting genetic algorithm and an interval number theory to obtain an optimized decision scheme of the virtual power plant.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a virtual plant interval optimization method taking flexible load participation into account as claimed in any one of claims 1 to 8.
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