CN109799708B - Virtual power plant flexibility aggregation method based on maximum embedded cube - Google Patents

Virtual power plant flexibility aggregation method based on maximum embedded cube Download PDF

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CN109799708B
CN109799708B CN201910092126.9A CN201910092126A CN109799708B CN 109799708 B CN109799708 B CN 109799708B CN 201910092126 A CN201910092126 A CN 201910092126A CN 109799708 B CN109799708 B CN 109799708B
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parameters
energy storage
power plant
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CN109799708A (en
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陆秋瑜
杨银国
吴文传
李博
栗子豪
朱誉
夏天
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Tsinghua University
Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd
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Tsinghua University
Electric Power Dispatch Control Center of Guangdong Power Grid Co Ltd
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Abstract

The invention collects the heat parameters of the house heating information by a polymerization controller, and establishes a model according to the parameters; simultaneously collecting predicted power parameters and energy storage parameters of the distributed photovoltaic power supply through the aggregation controller in the first step, and establishing a model according to the parameters; and generating a polymerization power constraint space by adopting a maximum embedded cube method according to the house heating information thermal parameter, the distributed photovoltaic power supply predicted power parameter and the energy storage parameter data obtained in the first step and the second step, and obtaining a virtual power plant model with aggregated scheduling flexibility by solving the maximum embedded cube. After the aggregation controller collects the thermal parameters of the house heating information, the parameters of the distributed photovoltaic power supply and the parameters of the energy storage battery, all the parameters are collected, and under the constraint condition of the embedded cube, the virtual power plant model obtained by calculation is carried out inside, and the aggregated virtual power plant model is only uploaded to the power distribution network dispatching center, so that the communication data volume is greatly reduced.

Description

Virtual power plant flexibility aggregation method based on maximum embedded cube
Technical Field
The invention belongs to the technical field of operation and control of power systems, and particularly relates to a virtual power plant flexibility aggregation method based on a maximum embedded cube.
Background
In order to control air pollution, the project of changing coal into electricity in North China and south China is rapidly developed, and at present, 38 cities in nine provinces are covered. The electric heat conversion efficiency of the air source heat pump can reach more than 2.5, so the energy utilization efficiency is far higher than that of a direct electric heating mode. Air source heat pumps have become the mainstream way of heating from coal to electricity. Meanwhile, distributed photovoltaic power sources and energy storage are widely connected to a power distribution network, a traditional passive power distribution network gradually transits to an active power distribution network with user-power grid interaction, and typical household power users gradually become household power-generation users. In this case, the user not only consumes electric energy but also produces his own energy according to local resources and demands.
With the high integration of distributed power generation and load side response control strategies, family users are willing to participate in the day-ahead scheduling of the power distribution network. However, how to manage a huge number of individual household users with low controllable power to participate in power distribution to directly and individually establish a connection relationship between a distribution network and loads generates a huge data information flow, and may cause the distribution network to generate a large data load. Air source heat pumps, distributed photovoltaic power sources, energy storage devices and other devices have conditions for participating in management and control of active power distribution networks, and related control schemes and profit settlement methods have been widely concerned and researched in recent years.
In fact, it is difficult for the distribution network dispatching center to directly establish a connection with a single user load, after the distribution network dispatching center establishes a connection with a single user load, if the base number of the user load is too large, data collection needs to be performed for each user load, and the data also includes specific data of devices such as an air source heat pump, a distributed photovoltaic power supply and an energy storage device, so that the data volume is too large, and a clear access mechanism exists when a load side participates in the day-ahead dispatching of the distribution network. Generally, the aggregation controller is responsible for collecting controllable resources in a certain area of a power distribution network, making an agreement with users holding equipment assets, and aggregating the resources into a virtual power plant through a certain control strategy. The aggregation controller obtains benefits by participating in power distribution network scheduling, and the users are rewarded by fulfilling a protocol. The main problems of the traditional polymerization controller in practical application are: collected single equipment information is still directly transmitted to a power distribution network dispatching center without being processed, the transmitted data volume is overlarge, and the power distribution network is difficult to process.
Disclosure of Invention
Aiming at the problems, the invention relates to a virtual power plant flexibility polymerization method based on a maximum embedded cube, which comprises the following steps:
the method comprises the following steps: collecting the house heating information thermal parameters by a polymerization controller, and establishing a model according to the house heating information thermal parameters;
step two: simultaneously collecting the predicted power parameters and the energy storage parameters of the distributed photovoltaic power supply through the aggregation controller in the first step, and establishing a model according to the predicted power parameters and the energy storage parameters of the distributed photovoltaic power supply;
step three: and generating a polymerization power constraint space by adopting a maximum embedded cube method according to the house heating information thermal parameter, the distributed photovoltaic power supply predicted power parameter and the energy storage parameter data obtained in the first step and the second step, and obtaining a virtual power plant model with aggregated scheduling flexibility by solving the maximum embedded cube.
Further, the virtual power plant model comprises a house heating information thermal parameter model in the step one, a parameter model of the distributed photovoltaic power supply and a parameter model of the energy storage battery in the step two.
Further, the house heating information thermal parameter model comprises a house indoor temperature, a water tank water temperature and an air source heat pump parameter model.
Further, the thermodynamic data of the room indoor temperature and the water tank water temperature are constrained in the formula:
Figure BDA0001963560680000021
Figure BDA0001963560680000022
in the above formula, the first and second carbon atoms are,
Figure BDA0001963560680000023
is the thermal power of the heat pump of the ith room in the T periodout(t) is the outdoor air temperature for the t-th period,
Figure BDA0001963560680000031
is the indoor air temperature of the ith room for the t-th period,
Figure BDA0001963560680000032
the water temperature of the water tank in the t period of the ith room,
Figure BDA0001963560680000033
is the equivalent thermal resistance and thermal capacity parameters of the water tank of the ith room,
Figure BDA0001963560680000034
equivalent thermal resistance and thermal capacity parameters of the ith room; Δ t is the length of time per period;
Figure BDA0001963560680000035
the coefficient of heat dissipation efficiency of the water tank of the ith room;
determining
Figure BDA0001963560680000036
And
Figure BDA0001963560680000037
and obtaining the following expression:
Figure BDA0001963560680000038
Figure BDA0001963560680000039
Figure BDA00019635606800000310
Figure BDA00019635606800000311
in the above formula, wherein,
Figure BDA00019635606800000312
respectively the initial temperature of the indoor temperature of the house and the water temperature of the water tank,
Figure BDA00019635606800000313
the upper and lower limits of the indoor temperature;
Figure BDA00019635606800000314
the upper and lower limits of the water tank temperature.
Further, the data of the electric heating model of the air source heat pump is constrained in the formula:
Figure BDA00019635606800000315
Figure BDA00019635606800000316
Figure BDA00019635606800000317
in the above formula, the first and second carbon atoms are,
Figure BDA00019635606800000318
is the electric power of the heat pump of the ith room during the t-th period,
Figure BDA00019635606800000319
is the rated electric power of the heat pump of the ith room,
Figure BDA00019635606800000320
the electric heat conversion efficiency of the heat pump of the ith room in the t period
Figure BDA00019635606800000321
Is its linear coefficient of electrothermal efficiency.
Further, the distributed photovoltaic power model data is constrained into the formula:
Figure BDA00019635606800000322
in the above formula, the first and second carbon atoms are,
Figure BDA00019635606800000323
is the electric power of the distributed photovoltaic power supply of the ith room during the t period,
Figure BDA00019635606800000324
and t is 1,2, …, H, wherein H is the number of scheduling time periods in one day.
Further, the energy storage model data is constrained to the formula:
Figure BDA00019635606800000325
Figure BDA0001963560680000041
Figure BDA0001963560680000042
Figure BDA0001963560680000043
in the above formula, the first and second carbon atoms are,
Figure BDA0001963560680000044
in order to store the electric quantity of the battery,
Figure BDA0001963560680000045
storing the charging power of the ith time interval for the ith room,
Figure BDA0001963560680000046
for the discharge power of the ith room for the stored energy t period,
Figure BDA0001963560680000047
energy storage capacity, eta, of ith room for energy storage t periodi,chFor charging efficiency, ηi,disTo discharge efficiency;
Figure BDA0001963560680000048
for the upper and lower limits of the stored energy charging power,
Figure BDA0001963560680000049
the energy storage discharge power is the upper and lower limits of the energy storage discharge power,
Figure BDA00019635606800000410
the energy storage capacity is the upper and lower limits of the energy storage capacity.
Further, the virtual power plant model is solved as follows:
Figure BDA00019635606800000411
Figure BDA00019635606800000412
Figure BDA00019635606800000413
Figure BDA00019635606800000414
in the above formula, the first and second carbon atoms are,
Figure BDA00019635606800000415
the side length of the cube;
ΩVPPis a constraint space defined by a constraint-space;
Figure BDA00019635606800000416
representing a side length of
Figure BDA00019635606800000417
A set of cube vertices of (a);
Figure BDA00019635606800000418
Figure BDA00019635606800000419
is a vertex of the cube and is provided with a plurality of vertexes,
Figure BDA00019635606800000420
representing a real space with dimension H;
Figure BDA00019635606800000421
wherein
Figure BDA00019635606800000422
The virtual power plant aggregate power at time t — 1,2, …, H, respectively.
Further, the needleDerived thereby in distributed photovoltaic light sources
Figure BDA00019635606800000423
And
Figure BDA00019635606800000424
the maximum power and the minimum power of the virtual power plant are respectively:
Figure BDA00019635606800000425
Figure BDA00019635606800000426
the virtual power plant model obtained finally is as follows:
Figure BDA0001963560680000051
wherein p isVPPAnd (t) is the power of the virtual power plant in the t-th period.
According to the invention, after the heat parameters of the house heating information, the parameters of the distributed photovoltaic power supply and the parameters of the energy storage battery are collected through the aggregation controller, all the parameters are collected and then are operated inside to obtain the virtual power plant model under the constraint condition of the embedded cube, and the aggregated virtual power plant model is only uploaded to the power distribution network dispatching center, so that the communication data volume is greatly reduced, and the use of the power distribution network dispatching center is facilitated.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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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, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 shows a detailed flow diagram of the present technical solution.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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 some, but not all, embodiments of the present invention. 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.
A virtual power plant flexibility polymerization method based on maximum embedded cubes comprises the following steps:
the method comprises the following steps: collecting the house heating information thermal parameters by a polymerization controller, and establishing a model according to the house heating information thermal parameters;
step two: simultaneously collecting the predicted power parameters and the energy storage parameters of the distributed photovoltaic power supply through the aggregation controller in the first step, and establishing a model according to the predicted power parameters and the energy storage parameters of the distributed photovoltaic power supply;
step three: and generating a polymerization power constraint space by adopting a maximum embedded cube method according to the house heating information thermal parameter, the distributed photovoltaic power supply predicted power parameter and the energy storage parameter data obtained in the first step and the second step, and obtaining a virtual power plant model with aggregated scheduling flexibility by solving the maximum embedded cube.
The aggregation controller in the present invention is only used as a data collection method, and is not limited to only one collection method, i.e., the aggregation controller.
The method comprises the following steps: before establishing a virtual power plant model, disassembling all components of the power plant model, and acquiring heating information of houses in a jurisdiction area;
exemplarily, 1) a scheduling day is selected, which is only for convenience of establishing data of a complete day, 5 minutes before the scheduling day, namely 23: and 55, when the dispatching day is finished, collecting all change data of the whole day in the whole dispatching day, and collecting a house heating parameter model, a distributed photovoltaic power supply parameter model and an energy storage parameter model of the jurisdiction region by the aggregation controller.
The house heating parameter model comprises a room temperature change parameter model, an electric heating model of an air source heat pump and a parameter model of water temperature of a water tank, and is obtained by performing constraint calculation on all aspects of the house heating parameter model in a distributed mode.
2) Setting thermodynamic data of the room indoor temperature and the water tank water temperature to be restricted in the formula:
Figure BDA0001963560680000061
Figure BDA0001963560680000062
wherein the content of the first and second substances,
Figure BDA0001963560680000063
is the thermal power of the heat pump of the ith room in the T periodout(t) is the outdoor air temperature for the t-th period,
Figure BDA0001963560680000064
is the indoor air temperature of the ith room for the t-th period,
Figure BDA0001963560680000065
the water temperature of the water tank in the t period of the ith room,
Figure BDA0001963560680000071
for water tanks in the ith room, etcThe parameters of effective heat resistance and heat capacity,
Figure BDA0001963560680000072
equivalent thermal resistance and thermal capacity parameters of the ith room are obtained by an actual measurement mode; delta t is the time length of each time interval, generally from 15 minutes to 1 hour, and is taken as 1 hour in the technical scheme;
Figure BDA0001963560680000073
obtaining the heat dissipation efficiency coefficient of the water tank of the ith room through actual measurement;
from equation (1), assuming that the t-th time period is set as the 1 st time period, the thermal power of the 1 st time period of the heat pump in the i-th room is added to the thermal power of the 1 st time period of the water tank in the i-th room, that is, the average value of the thermal power of the 1 st time period of the water tank in the i-th room.
The heat dissipation power of the room of the ith room in the 1 st time period to the outside of the room is added to the heat dissipation power of the water tank of the ith room in the 1 st time period to the indoor by the equation (2), that is, the average value of the heat dissipation power of the ith room in the 1 st time period to the indoor is obtained by the equation (1).
Determining
Figure BDA0001963560680000074
And
Figure BDA0001963560680000075
and obtaining the following expression:
Figure BDA0001963560680000076
Figure BDA0001963560680000077
Figure BDA0001963560680000078
Figure BDA0001963560680000079
it is exemplary, among others, that,
Figure BDA00019635606800000710
respectively the initial temperature of the indoor temperature of the house and the water temperature of the water tank,
Figure BDA00019635606800000711
the upper limit and the lower limit of the indoor temperature are 24 ℃ and 18 ℃ respectively;
Figure BDA00019635606800000712
the upper limit and the lower limit of the water tank temperature are set to be 60 ℃ and the lower limit is set to be 50 ℃;
it can be derived from the expressions (3) and (6), the indoor air temperature in the 0 th time interval of the ith room and the water tank water temperature in the 0 th time interval of the ith room are set as the initial temperatures of the room indoor temperature and the water tank water temperature when the detection is started, and along with the change of time, the indoor temperature and the water tank temperature are always kept within a certain range and cannot exceed the upper and lower limits of the indoor temperature and the water tank temperature.
As can be seen from the equation (5), the maximum temperature of the indoor temperature does not exceed 24 ℃ and the minimum temperature does not fall below 18 ℃ in the t-th time period of the ith room, so that the indoor temperature can be always in a relatively comfortable interval.
As can be known from the formula (6), the highest temperature of the water tank temperature does not exceed 60 ℃ in the t-th time period of the ith room, and the lowest temperature of the water tank temperature does not fall below 50 ℃, so that the condition that the temperature inside the house is changed too much due to too much temperature change of the water tank can be avoided, and further the heat change condition of the indoor temperature is influenced.
Exemplarily, 3) setting the electric heating model data of the air source heat pump to be constrained in the formula:
Figure BDA0001963560680000081
Figure BDA0001963560680000082
Figure BDA0001963560680000083
in an exemplary manner, the first and second electrodes are,
Figure BDA0001963560680000084
is the electric power of the heat pump of the ith room during the t-th period,
Figure BDA0001963560680000085
the rated electric power of the heat pump of the ith room is obtained by looking at the name plate of the equipment,
Figure BDA0001963560680000086
the electric heat conversion efficiency of the heat pump of the ith room in the t period, wherein the heat pump is assumed to be
Figure BDA0001963560680000087
And T period outdoor temperature Tout(t) is in a linear relationship as shown in formula (9), wherein
Figure BDA0001963560680000088
For its linear electrothermal efficiency coefficient, the coefficient is obtained by looking up the device specification parameters or by experimental tests, and thus not for
Figure BDA0001963560680000089
The result of (a) has an influence.
Of an air source heat pump represented by the formula (7)
Figure BDA00019635606800000810
The maximum electric power can only reach the rated electric power of the air source heat pump, so the electric power of the air source heat pump only can be operated between 0 and the rated power, and the operation power of the heat pump cannot exceed the rated electric power of the heat pump due to the operation limit of the heat pump device。
The air heat source pump operating by itself as shown in formulas (8) and (9)
Figure BDA00019635606800000811
Condition and
Figure BDA00019635606800000812
in connection with
Figure BDA00019635606800000813
And Tout(t) again, there is a linear relationship, illustratively, but the change in outdoor temperature is between 89.2 ℃ and 50 ℃, i.e.
Figure BDA00019635606800000814
Tends to a steady state and, under certain conditions,
Figure BDA00019635606800000815
will be engaged with
Figure BDA00019635606800000816
An equal condition occurs when the outdoor temperature Tout(t) the condition of 0 ℃ occurs,
Figure BDA00019635606800000817
for a fixed linear electrical heating efficiency parameter,
Figure BDA00019635606800000818
in a constant condition, i.e.
Figure BDA00019635606800000819
Also at a fixed, constant operating power.
Step two: exemplarily, 4) setting the distributed photovoltaic power model data to be constrained in the formula:
Figure BDA00019635606800000820
in an exemplary manner, the first and second electrodes are,
Figure BDA0001963560680000091
is the electric power of the distributed photovoltaic power supply of the ith room during the t period,
Figure BDA0001963560680000092
the maximum predicted electric power of the distributed photovoltaic power supply of the ith room in the t period is 1,2, …, H, where H is the number of scheduling periods in a day, and H is set to 24 in the application technical solution.
The H is set to be 24, so that the data counting stage is mainly convenient to be used at each time period of 24H in one day, and the specific change information of house heating, air source heat pumps and distributed photovoltaic power supply models in the jurisdiction area at different time periods can be obtained along with the change of time according to the change of the outside air temperature of 24H in one day.
Exemplarily, 5) setting the energy storage model data to be constrained in the formula:
Figure BDA0001963560680000093
Figure BDA0001963560680000094
Figure BDA0001963560680000095
Figure BDA0001963560680000096
wherein the content of the first and second substances,
Figure BDA0001963560680000097
in order to store the electric quantity of the battery,
Figure BDA0001963560680000098
is the ith roomThe charging power of the stored energy t-th period,
Figure BDA0001963560680000099
for the discharge power of the ith room for the stored energy t period,
Figure BDA00019635606800000910
energy storage capacity, eta, of ith room for energy storage t periodi,chFor charging efficiency, ηi,disThe discharge efficiency is obtained.
In an exemplary manner, the first and second electrodes are,
Figure BDA00019635606800000911
for the upper and lower limits of the stored energy charging power,
Figure BDA00019635606800000912
the energy storage discharge power is the upper and lower limits of the energy storage discharge power,
Figure BDA00019635606800000913
the energy storage capacity is the upper and lower limits of the energy storage capacity.
Represented by formula (11) to formula (13): the energy storage in the ith room is charged, the energy storage is discharged and the energy storage electric quantity is within the upper and lower limit range of restraint, exceeds the upper limit, will produce the loss to the energy storage model in the room, and then influences energy storage efficiency, if be less than the lower limit, the work efficiency of the energy storage model in the room can be very slow to the undulant situation of energy storage circuit appears.
Represented by formula (14): in the scheduling day, the energy storage capacity of the ith room in the energy storage t period
Figure BDA00019635606800000914
I.e. the initial energy storage battery charge
Figure BDA00019635606800000915
Adding the charging electric quantity and the discharging electric quantity of the energy storage battery in the ith room from 0 to the t time period; thereby obtaining the specific amount of stored energy during the t-th time period.
Step three: exemplary, 6) set the virtual plant model to solve as follows:
Figure BDA0001963560680000101
Figure BDA0001963560680000102
Figure BDA0001963560680000103
Figure BDA0001963560680000104
in the above formula, maximize represents the maximum value;
subject to represents an optimization constraint;
Figure BDA0001963560680000105
the side length of the cube;
ΩVPPis a constraint space defined by a constraint-space;
Figure BDA0001963560680000106
representing a side length of
Figure BDA0001963560680000107
A set of cube vertices of (a);
Figure BDA0001963560680000108
Figure BDA0001963560680000109
is a vertex of the cube and is provided with a plurality of vertexes,
Figure BDA00019635606800001010
representing a real space with dimension H;
Figure BDA00019635606800001011
wherein
Figure BDA00019635606800001012
The virtual power plant aggregate power respectively represents the time t-1, 2, …, H;
the meaning of the model shows that finding the maximum cluster power scheduling range that can be performed at any time of the scheduling day corresponds to the constraint space Ω of the time period H of one dayVPPFinding out the embedded cube with the longest side length.
Represented by formula (15): the vertex after the scheduling day in the virtual power plant model is selected to the maximum extent that the vertex is positioned at the side length of
Figure BDA00019635606800001013
The cube of (a) is on the side.
Represented by formula (16): the side length of the power plant model is in the constraint-limited constraint space, and
Figure BDA00019635606800001014
i.e. the side length of the power plant model.
Represented by formula (17): within a scheduling day, a vertex set field consisting of 24 time periods
Figure BDA00019635606800001015
And the vertices of the 24 time periods all have a side length of
Figure BDA00019635606800001016
Inside the set of cube vertices.
Represented by formula (18): in the ith time period in the ith room, the power of the distributed light source is added with the energy storage discharge power and the power of the air source heat pump minus the energy storage charging power, so that the power of a time period is obtained, and the power of all the time periods in a scheduling day is added.
Exemplary, 7) derived from step 4)
Figure BDA0001963560680000111
And
Figure BDA0001963560680000112
the maximum power and the minimum power of the virtual power plant are respectively:
Figure BDA0001963560680000113
Figure BDA0001963560680000114
the virtual power plant model obtained finally is as follows:
Figure BDA0001963560680000115
wherein p isVPPAnd (t) is the power of the virtual power plant in the t-th period.
Represented by formula (19) to formula (21): obtained in step 4)
Figure BDA0001963560680000116
And
Figure BDA0001963560680000117
obtaining a virtual power plant model of the ith room in the formula (15) to the formula (18), and determining the virtual power plant p after obtaining the maximum value and the minimum value of the ith roomVPP(t) power at the t-th period.
The invention provides a virtual power plant flexibility aggregation method based on a maximum embedded cube, which is characterized in that an aggregation power constraint space is generated by utilizing the maximum embedded cube method, a virtual power plant model after scheduling flexibility aggregation is obtained by solving the maximum internal cube of the virtual power plant model, after a aggregation controller collects the thermal parameters of house heating information, the parameters of a distributed photovoltaic power supply and the parameters of an energy storage battery, the virtual power plant model obtained by calculation is internally carried out under the constraint condition of the internal cube after all the parameters are collected, and only the aggregated virtual power plant model is uploaded to a power distribution network scheduling center, so that the communication data volume is greatly reduced, and the power distribution network scheduling center is convenient to use.
Although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (8)

1. A virtual power plant flexibility polymerization method based on maximum embedded cubes is characterized by comprising the following steps:
the method comprises the following steps: collecting the house heating information thermal parameters by a polymerization controller, and establishing a model according to the house heating information thermal parameters;
step two: simultaneously collecting the predicted power parameters and the energy storage parameters of the distributed photovoltaic power supply through the aggregation controller in the first step, and establishing a model according to the predicted power parameters and the energy storage parameters of the distributed photovoltaic power supply;
step three: generating a polymerization power constraint space by adopting a maximum embedded cube method according to the house heating information thermal parameter, the distributed photovoltaic power supply predicted power parameter and the energy storage parameter data obtained in the first step and the second step, and obtaining a virtual power plant model with aggregated scheduling flexibility by solving the maximum embedded cube;
the virtual power plant model is solved as follows:
maximize:
Figure FDA0003159024830000011
subject to:
Figure FDA0003159024830000012
Figure FDA0003159024830000013
Figure FDA0003159024830000014
in the above formula, the first and second carbon atoms are,
Figure FDA0003159024830000015
the side length of the cube; omegaVPPIs a constraint space defined by a constraint-space;
Figure FDA0003159024830000016
representing a side length of
Figure FDA0003159024830000017
A set of cube vertices of (a);
Figure FDA0003159024830000018
Figure FDA0003159024830000019
is a vertex of the cube and is provided with a plurality of vertexes,
Figure FDA00031590248300000110
representing a real space with dimension H;
Figure FDA00031590248300000111
wherein
Figure FDA00031590248300000112
The virtual power plant aggregate power at time t — 1,2, …, H, respectively.
2. The maximum embedded cube based virtual power plant flexibility aggregation method according to claim 1, wherein the virtual power plant model comprises a thermal parametric model for determining the house heating information in the first step, a parametric model for the distributed photovoltaic power supply and a parametric model for the energy storage battery in the second step.
3. The maximum embedded cube based virtual power plant flexibility aggregation method according to claim 2, wherein the house heating information thermal parameter model comprises a parametric model of house room temperature, water tank water temperature, and air source heat pumps.
4. The maximum embedded cube based virtual power plant flexibility aggregation method in claim 3, where the house room temperature and tank water temperature thermodynamic data are constrained into the formula:
Figure FDA0003159024830000021
Figure FDA0003159024830000022
in the above formula, the first and second carbon atoms are,
Figure FDA0003159024830000023
is the thermal power of the heat pump of the ith room in the T periodout(T) outdoor air temperature in T-th period, Ti air(T) is the room air temperature, T, of the ith room during the T-th time periodi W(t) is the tank water temperature of the ith room during the t period,
Figure FDA0003159024830000024
is the equivalent thermal resistance and thermal capacity parameters of the water tank of the ith room,
Figure FDA0003159024830000025
Figure FDA0003159024830000026
equivalent thermal resistance and thermal capacity parameters of the ith room; Δ t is the length of time per period;
Figure FDA0003159024830000027
the coefficient of heat dissipation efficiency of the water tank of the ith room;
determination of Ti air(T) and Ti W(t) and obtaining the following expression:
Figure FDA0003159024830000028
Figure FDA0003159024830000029
Figure FDA00031590248300000210
Figure FDA00031590248300000211
in the above formula, wherein,
Figure FDA00031590248300000212
respectively the initial temperature of the indoor temperature of the house and the water temperature of the water tank,
Figure FDA00031590248300000213
the upper and lower limits of the indoor temperature;
Figure FDA00031590248300000214
the upper and lower limits of the water tank temperature.
5. The maximum cube-embedded based virtual power plant flexibility aggregation method according to claim 3, wherein the electrothermal model data of the air source heat pump is constrained to the formula:
Figure FDA00031590248300000215
Figure FDA00031590248300000216
Figure FDA00031590248300000217
in the above formula, the first and second carbon atoms are,
Figure FDA00031590248300000218
electric power of the heat pump of the ith room during the t period, Pi HPIs the rated electric power of the heat pump of the ith room,
Figure FDA0003159024830000031
the electric heat conversion efficiency of the heat pump of the ith room in the t period
Figure FDA0003159024830000032
Figure FDA0003159024830000033
Is its linear coefficient of electrothermal efficiency.
6. The virtual power plant flexibility aggregation method based on maximum embedded cubes of claim 2, wherein the distributed photovoltaic power model data is constrained into the formula:
Figure FDA0003159024830000034
in the above formula, the first and second carbon atoms are,
Figure FDA0003159024830000035
is the electric power of the distributed photovoltaic power supply of the ith room during the t period,
Figure FDA0003159024830000036
and t is 1,2, …, H, wherein H is the number of scheduling time periods in one day.
7. The maximum embedded cube based virtual power plant flexibility aggregation method in claim 2, wherein the energy storage model data is constrained into the formula:
Figure FDA0003159024830000037
Figure FDA0003159024830000038
Figure FDA0003159024830000039
Figure FDA00031590248300000310
in the above formula, the first and second carbon atoms are,
Figure FDA00031590248300000311
in order to store the electric quantity of the battery,
Figure FDA00031590248300000312
is the ith roomThe charging power of the stored energy t-th period,
Figure FDA00031590248300000313
for the discharge power of the ith room for the stored energy t period,
Figure FDA00031590248300000314
energy storage capacity, eta, of ith room for energy storage t periodi,chFor charging efficiency, ηi,disTo discharge efficiency;
Figure FDA00031590248300000315
for the upper and lower limits of the stored energy charging power,
Figure FDA00031590248300000316
the energy storage discharge power is the upper and lower limits of the energy storage discharge power,
Figure FDA00031590248300000317
the energy storage capacity is the upper and lower limits of the energy storage capacity.
8. The method for maximum embedded cube based virtual plant flexibility aggregation according to claim 1, characterized by the derived maximum embedded cube for distributed photovoltaic light sources
Figure FDA00031590248300000318
And
Figure FDA00031590248300000319
the maximum power and the minimum power of the virtual power plant are respectively:
Figure FDA00031590248300000320
Figure FDA0003159024830000041
the virtual power plant model obtained finally is as follows:
Figure FDA0003159024830000042
wherein p isVPPAnd (t) is the power of the virtual power plant in the t-th period.
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