CN115545579B - Aggregation intelligent control method and system for user flexible resources - Google Patents

Aggregation intelligent control method and system for user flexible resources Download PDF

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CN115545579B
CN115545579B CN202211524029.0A CN202211524029A CN115545579B CN 115545579 B CN115545579 B CN 115545579B CN 202211524029 A CN202211524029 A CN 202211524029A CN 115545579 B CN115545579 B CN 115545579B
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CN115545579A (en
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鲍卫东
顾春云
骆小明
陈英俊
吴佳佳
鲍宁
郑艳
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Jinhua Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Yiwu Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The invention relates to the technical field of power scheduling, and particularly discloses an aggregation intelligent control method and system for user flexible resources. According to the method, a credible and adjustable potential prediction model of the user and the aggregation user is constructed by responding to the reliability evaluation indexes, methods and results; calculating the adjustable potential value of each aggregation user based on the credible adjustable potential prediction model to generate an adjusting time sequence table; formulating flexible load resource scheduling strategy and scheme, through terminal accuse equipment, realize accurate regulation and control, the dynamic change of trusteeship and regulation potentiality of the flexible resource of user side has fully been considered, can effectively converge wide area, distributed, the flexible load resource of low capacity, possess nimble regulating power, the uncertainty of flexible resource regulation has been solved, realize virtual power plant to the optimal utilization and the accurate management and control of user flexible load resource, promote and promote novel electric power system flexibility and electric power guarantee ability, can assist the novel electric power system and construct.

Description

Aggregation intelligent control method and system for user flexible resources
Technical Field
The invention belongs to the technical field of power dispatching, and particularly relates to an aggregation intelligent control method and system for user flexible resources.
Background
The condition that the load increase speed of the electric load in China is higher than the electric quantity increase speed easily occurs, the problems of large peak-valley difference, short peak load duration, large regional difference and the like are solved, and a local and staged electric power tension state occurs. With the access of a large amount of distributed energy, the problems of difficult system peak regulation and the like are gradually highlighted, and the requirements of safe, stable and economic operation of a power grid are difficult to meet only by a traditional thinking mode of adjusting by power generation resources. The demand side gradually becomes the focus of attention and research in the field of electric power systems, and through the change of the demand side, flexible loads are used as schedulable resources to participate in system cooperative control, so that the contradiction between supply and demand can be improved, and the operation efficiency of the electric power system is improved.
The power distribution network contains a large amount of flexible load resources, the scheduling mode is flexible after flexible load aggregation, the regulation and control potential is huge, the power distribution network has the capability of participating in power grid regulation, aggregation scheduling is carried out through a reasonable mode, intermittent energy fluctuation can be stabilized, and the system peak-valley difference can be reduced. In recent years, flexible load has become an important point of academic research, and scheduling and adjustment of flexible load are one of important means for alleviating contradiction between supply and demand. The flexible adjustment capability of the flexible load changes the history of unidirectional and passive adjustment of the original load and changes the characteristics of rigidity, uncertainty and the like of load parameters.
In the prior art, a virtual power plant is constructed, so that power can be supplied to a power grid, surplus power of the power grid can be consumed, and auxiliary services such as frequency modulation and peak regulation are provided for the power grid through source-load friendly interaction. The virtual power plant provides an effective way for solving the problems of new energy consumption and low-carbon energy transformation. However, the existing virtual power plant control method is difficult to effectively control the terminal load of the virtual power plant.
Disclosure of Invention
An object of the embodiments of the present invention is to provide a method and a system for intelligent control of aggregation of user flexible resources, which aim to solve the problems set forth in the background art.
In order to achieve the above purpose, the embodiments of the present invention provide the following technical solutions:
an aggregation intelligent control method for user flexible resources specifically comprises the following steps:
based on the energy utilization genes, the flexible resources at the user side are hierarchically aggregated to a polymerization layer;
constructing a multivariate user credibility adjustable potential prediction model of the residents and commercial users at the user side by combining with the user response reliability evaluation indexes, methods and results, and constructing a multivariate user aggregation credibility adjustable potential prediction model of the aggregation users by combining with the aggregation user response reliability evaluation indexes, methods and results;
calculating credibility adjustable potential values of different users and aggregated users by using the multivariate user credibility adjustable potential prediction model and the multivariate user aggregation credibility adjustable potential prediction model, sequencing according to the credibility adjustable potential values to generate an adjusting time sequence table, and guiding the formulation of a scheduling strategy and a scheme of a flexible load according to the adjusting time sequence table;
according to adjust the time sequence table, through terminal intelligent control equipment, gather the power consumption information and accept the scheduling signal, user flexible load resource in the regulation and control resource pond.
As a further limitation of the technical scheme of the embodiment of the invention, the energy utilization genes comprise an invokable starting moment, an adjustable time interval length, a user participation rate, a user response degree and a credible adjustable potential prediction value.
As a further limitation of the technical solution of the embodiment of the present invention, the calculation process of the user response reliability evaluation index is as follows:
setting participation status of single user demand response
Figure 322720DEST_PATH_IMAGE001
Comprises the following steps: />
Figure 895652DEST_PATH_IMAGE002
，
Wherein the content of the first and second substances,
Figure 347493DEST_PATH_IMAGE003
responding to the state for the user;
engagement rate of response
Figure 273861DEST_PATH_IMAGE004
Comprises the following steps: />
Figure 353813DEST_PATH_IMAGE005
Wherein n represents the total number of users;
degree of user response
Figure 832067DEST_PATH_IMAGE006
Comprises the following steps: />
Figure 630259DEST_PATH_IMAGE007
，
Wherein the content of the first and second substances,
Figure 235684DEST_PATH_IMAGE008
for the capacity of the user participating in the response>
Figure 435721DEST_PATH_IMAGE009
Is the total capacity of the user group.
As a further limitation of the technical solution of the embodiment of the present invention, the calculation process of aggregating the user response reliability evaluation index is as follows:
user k's power consumption state at time t
Figure 102455DEST_PATH_IMAGE010
Comprises the following steps: />
Figure 387943DEST_PATH_IMAGE011
；
Simultaneous rate of aggregated users
Figure 531480DEST_PATH_IMAGE012
Comprises the following steps: />
Figure 586023DEST_PATH_IMAGE013
Wherein, K represents the total number of users of the aggregation user;
aggregating user response degrees
Figure 671660DEST_PATH_IMAGE014
Comprises the following steps: />
Figure 444444DEST_PATH_IMAGE015
Wherein, i represents the total number of aggregated users,
Figure 126092DEST_PATH_IMAGE016
、/>
Figure 300721DEST_PATH_IMAGE017
、/>
Figure 291680DEST_PATH_IMAGE018
respectively representing the participation rate, the response degree and the power of the aggregation user.
As a further limitation of the technical solution of the embodiment of the present invention, the user response reliability evaluation method includes:
the sustained load curve of the user is
Figure 551760DEST_PATH_IMAGE019
In which>
Figure 37099DEST_PATH_IMAGE020
For on-line rate of station 1 device, based on a predetermined criterion>
Figure 66235DEST_PATH_IMAGE021
The offline rate of the 1 st equipment;
total capacity of total responsibilities of p devices held by user
Figure 493674DEST_PATH_IMAGE022
Comprises the following steps:
Figure 975471DEST_PATH_IMAGE023
wherein the total responsibilities of the p devices held by the user are ≥>
Figure 264501DEST_PATH_IMAGE024
The maximum equivalent load is->
Figure 148143DEST_PATH_IMAGE025
；
Actual responsibilities of usersValue of
Figure 12063DEST_PATH_IMAGE026
：/>
Figure 856522DEST_PATH_IMAGE027
Wherein is present>
Figure 808298DEST_PATH_IMAGE028
The area enclosed by the initial equivalent continuous load of the user and the continuous load curve after the indexes such as the online rate of the equipment are considered.
Trustworthy response level of user
Figure 936660DEST_PATH_IMAGE029
Comprises the following steps: />
Figure 987792DEST_PATH_IMAGE030
。
As a further limitation of the technical solution of the embodiment of the present invention, the aggregate user response reliability is evaluated as:
aggregated user sustained load profile
Figure 444182DEST_PATH_IMAGE031
Comprises the following steps:
Figure 324282DEST_PATH_IMAGE032
；
aggregating user equivalent continuous response load curves
Figure 916937DEST_PATH_IMAGE033
Comprises the following steps:
Figure 873392DEST_PATH_IMAGE034
in which>
Figure 817077DEST_PATH_IMAGE035
For consideration of a continuous load profile after the qth aggregated user engagement rate, a decision is made as to whether the load profile is constant or not>
Figure 766447DEST_PATH_IMAGE036
For the qth aggregated user engagement rate, < >>
Figure 354555DEST_PATH_IMAGE037
A response potential reduction resulting from non-engagement with responses for the qth aggregated user;
total response potential
Figure 75386DEST_PATH_IMAGE038
Comprises the following steps: />
Figure 636860DEST_PATH_IMAGE039
，
Actual potential value of respondability
Figure 734129DEST_PATH_IMAGE040
Comprises the following steps: />
Figure 442322DEST_PATH_IMAGE041
Wherein, in the step (A),
Figure 599634DEST_PATH_IMAGE042
；
aggregating trustworthy response levels of users
Figure 642546DEST_PATH_IMAGE043
Comprises the following steps: />
Figure 543505DEST_PATH_IMAGE044
。
An aggregation intelligent control system for user flexible resources, the system comprises a hierarchical aggregation unit, a prediction model construction unit, an adjustment time sequence table generation unit and a flexible resource regulation and control unit, wherein:
the hierarchical aggregation unit is used for hierarchically aggregating the user-side flexible resources to an aggregation layer based on the energy utilization genes;
the prediction model construction unit is used for constructing a multivariate user credible adjustable potential prediction model of residents and commercial users at the user side by combining with the user response reliability evaluation indexes, methods and results, and constructing a multivariate user aggregation credible adjustable potential prediction model of aggregation users by combining with the aggregation user response reliability evaluation indexes, methods and results;
the adjusting time sequence table generating unit is used for calculating the credibility adjustable potential values of different users and aggregation users by using the multivariate user credibility adjustable potential prediction model and the multivariate user aggregation credibility adjustable potential prediction model, sequencing according to the credibility adjustable potential values, generating an adjusting time sequence table, and guiding the formulation of a scheduling strategy and a scheme of a flexible load according to the adjusting time sequence table;
and the flexible resource regulation and control unit is used for regulating the time sequence table, acquiring the power utilization information and receiving the regulation signal through the terminal intelligent control equipment, and regulating and controlling the flexible load resources of the user in the resource pool.
Compared with the prior art, the invention has the beneficial effects that:
according to the embodiment of the invention, a credible and adjustable potential prediction model of the user and the aggregation user is constructed by responding to the reliability evaluation index, method and result; calculating the adjustable potential value of each aggregation user based on the credible adjustable potential prediction model to generate an adjusting time sequence table; formulating flexible load resource scheduling strategy and scheme, through terminal accuse equipment, realize accurate regulation and control, the dynamic change of trusteeship and regulation potentiality of the flexible resource of user side has fully been considered, can effectively converge wide area, distributed, the flexible load resource of low capacity, possess nimble regulating power, the uncertainty of flexible resource regulation has been solved, realize virtual power plant to the optimal utilization and the accurate management and control of user flexible load resource, promote and promote novel electric power system flexibility and electric power guarantee ability, can assist the novel electric power system and construct.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention.
Fig. 1 shows a flow chart of a method provided by an embodiment of the invention.
Fig. 2 shows an application architecture diagram of a system provided by an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
It can be understood that in the prior art, by constructing the virtual power plant, not only can power be supplied to the power grid, but also surplus power of the power grid can be consumed, and auxiliary services such as frequency modulation and peak shaving are provided for the power grid through source-load friendly interaction. The virtual power plant provides an effective way for solving the problems of new energy consumption and low-carbon energy transformation. However, the existing virtual power plant control method is difficult to effectively control the terminal load of the virtual power plant.
In order to solve the problems, the embodiment of the invention constructs a credible adjustable potential prediction model of users and aggregated users by responding to reliability evaluation indexes, methods and results; calculating the adjustable potential value of each aggregation user based on the credible adjustable potential prediction model to generate an adjusting time sequence table; formulating flexible load resource scheduling strategy and scheme, through terminal accuse equipment, realize accurate regulation and control, the dynamic change of trusteeship and regulation potentiality of the flexible resource of user side has fully been considered, can effectively converge wide area, distributed, the flexible load resource of low capacity, possess nimble regulating power, the uncertainty of flexible resource regulation has been solved, realize virtual power plant to the optimal utilization and the accurate management and control of user flexible load resource, promote and promote novel electric power system flexibility and electric power guarantee ability, can assist the novel electric power system and construct.
Fig. 1 shows a flow chart of a method provided by an embodiment of the invention.
Specifically, the method for intelligent control of aggregation of user flexible resources specifically comprises the following steps:
and S101, hierarchically polymerizing the user-side flexible resources to a polymerization layer based on the energy utilization genes.
In the embodiment of the invention, the adjustable starting time, the adjustable time interval length, the user participation rate, the user response degree and the credible adjustable potential predicted value of the user flexible load resource are taken as the energy using genes, and the flexible load resource is hierarchically and hierarchically aggregated to the aggregation layer based on the energy using genes. Specifically, the aggregator signs a resource hosting protocol with the user according to the flexible load resource cluster.
It can be understood that the flexible load resource refers to an electric power flexible load, which can be actively involved in the operation control of the power grid, can perform energy interaction with the power grid, and has a flexible characteristic. Load compliance appears to be flexible over a period of time. The scheduling and adjustment of flexible loads is one of the important means for relieving the contradiction between supply and demand. The flexible adjustment capability of the flexible load changes the history of unidirectional and passive load adjustment, and changes the characteristics of rigidity, uncertainty and the like of load parameters. In addition, the access of the electric automobile and the distributed power supply enables the load to have a certain power supply function. Electrical flexible loads belong to the regulated loads, for example: air conditioners, electric vehicles, and the like.
And S102, constructing a multivariate user credibility adjustable potential prediction model of the residents and commercial users at the user side by combining the user response reliability evaluation indexes, the methods and the results, and constructing a multivariate user aggregation credibility adjustable potential prediction model of the aggregation users by combining the aggregation user response reliability evaluation indexes, the methods and the results.
It can be understood that the user response refers to a response condition of a certain user group, the user participation rate reflects a proportion of the user participation response, the user response degree represents a response execution degree of the user, and the definition of the reliability index for measuring the response load can be performed according to the reliability index of the traditional demand response, specifically:
setting participation status of single user demand response
Figure 840626DEST_PATH_IMAGE045
Comprises the following steps: />
Figure 168839DEST_PATH_IMAGE046
In, is greater than or equal to>
Figure 433467DEST_PATH_IMAGE045
Calculating the user participation rate for the user response state, wherein the user response participation rate indicates that the probability that the response of the user meets the response requirement is ≥>
Figure 138118DEST_PATH_IMAGE047
Wherein n represents the total number of users, a demand response contract signed by the user side and the power grid specifies that demand response is lightened, but actual response electric quantity is influenced by uncertain factors and has certain randomness, in order to evaluate the demand response reliability under the influence of uncertain factors, the response degree of the demand response is defined, as shown in the following formula, the ratio of the capacity of the user participating in the response to the total controllable capacity of the user is the user response degree ≥>
Figure 555324DEST_PATH_IMAGE048
Wherein, in the step (A),
Figure 54438DEST_PATH_IMAGE049
for the capacity of the user participating in the response>
Figure 71942DEST_PATH_IMAGE050
Total capacity for the user group;
it can be understood that the aggregated user response refers to a response condition of a certain aggregated user group, the response concurrence rate represents an electricity utilization condition of the aggregated user in a certain period, and the aggregated response degree represents a response execution degree of the aggregated user, specifically: describing the degree of mutual overlapping power utilization of the users at the same time by using the concept of response simultaneous rate, wherein the simultaneous rate of the aggregated users represents the proportion of the users in the power utilization state at the time t in the whole users, and introducing variables
Figure 190070DEST_PATH_IMAGE051
To characterize the electricity consumption state of a user, withThe power utilization state of the user k at the moment t is
Figure 320837DEST_PATH_IMAGE052
In, is greater than or equal to>
Figure 990853DEST_PATH_IMAGE053
A time of 0 indicates that at time t the user k is in an unpowered state, i.e. < >>
Figure 230074DEST_PATH_IMAGE054
The time indicates that no electric quantity is consumed by the user k at the moment t, and the simultaneous rate of the aggregated users is defined as
Figure 745368DEST_PATH_IMAGE055
Wherein K represents the total number of users of the aggregation user, and the response degree of the aggregation user is
Figure 137167DEST_PATH_IMAGE056
Wherein i represents the total number of aggregated users, and ` H `>
Figure 978084DEST_PATH_IMAGE057
、/>
Figure 314387DEST_PATH_IMAGE058
、/>
Figure 23586DEST_PATH_IMAGE059
Represents the participation rate, the response degree and the power of the aggregated user respectively>
Figure 535470DEST_PATH_IMAGE060
Representing the degree of response of the aggregated user;
it can be understood that, for the user response reliability evaluation method, it is assumed that the number of devices which can respond owned by the user is p, and the online rate of the ith device is p
Figure 281709DEST_PATH_IMAGE061
According to the historical data of the user in a certain period, the expected response of the user is carried outThe loads are ranked with a response potential of £ greater than or equal to £ based on the online rate of the 1 st responding device>
Figure 105309DEST_PATH_IMAGE062
When the device is offline, its continuous load curve can be used>
Figure 618198DEST_PATH_IMAGE063
Equivalent to a continuous load curve shifted to the right by the load of C, the continuous load curve of the aggregated user becomes, considering the online situation of the plant: />
Figure 843643DEST_PATH_IMAGE064
Wherein is present>
Figure 901729DEST_PATH_IMAGE065
For the on-line rate of the 1 st device, <' >>
Figure 478204DEST_PATH_IMAGE066
Sequentially considering the online rates and the failure rates of p devices as the offline rate of the 1 st device, continuously changing the continuous load curve of the user, considering the online rate of the ith device, and obtaining the continuous load curve
Figure 794785DEST_PATH_IMAGE067
The total responsibilities of the p devices held by the user are @>
Figure 750102DEST_PATH_IMAGE068
Maximum equivalent load is->
Figure 369303DEST_PATH_IMAGE069
，/>
Figure 286269DEST_PATH_IMAGE070
The method is characterized in that the method is the area enclosed by the initial equivalent continuous load of a user and a continuous load curve considering indexes such as the online rate of equipment and the like, and the physical meaning of the method is that the user can respond to the load, and the reduction amount of the response potential is greater than or equal to the device offline>
Figure 16327DEST_PATH_IMAGE071
Total available potential total capacity for p devices held by a user->
Figure 91731DEST_PATH_IMAGE072
Is composed of
Figure 881832DEST_PATH_IMAGE073
When the convolution operation of all the devices which can respond to the user is finished, the corrected actual response potential value of the user can be calculated and then is based on the value of the expected response potential>
Figure 291954DEST_PATH_IMAGE074
Is->
Figure 560124DEST_PATH_IMAGE075
The response degree expectation value of the user->
Figure 755613DEST_PATH_IMAGE076
According to the corrected actual response potential value of the user, the credible response degree of the user can be obtained as
Figure 106829DEST_PATH_IMAGE077
；
It can be understood that, for the user aggregation response reliability evaluation method, a continuous load curve is obtained according to the historical load data of the aggregation users, and the continuous load curve is corrected by considering the concurrence rate and the participation rate of the aggregation users. Let the number of aggregated users be q, the concurrency rate be s, and the participation rate of user j be
Figure 754979DEST_PATH_IMAGE078
The calculation steps of the aggregation user continuous load curve obtained based on the user continuous load curve calculation method are as follows: step one, considering that the first aggregation user has p pieces of responsive equipment in common, and the 1 st piece of responsive equipmentOn-line rate of the device is->
Figure 826840DEST_PATH_IMAGE079
Response potential is { (R) }>
Figure 126104DEST_PATH_IMAGE080
When the equipment is in off-line state, its continuous load curve is available
Figure 258008DEST_PATH_IMAGE081
Representing the load corresponding to the sustained load curve shifted by C to the right, the sustained load curve for the aggregated users becomes, taking into account the on-line condition of the plant
Figure 393454DEST_PATH_IMAGE082
(ii) a And step two, sequentially considering the online rates of p devices of the first aggregation user, continuously changing the continuous load curve of the aggregation user, considering the online rate of the ith device, and judging whether the continuous load curve is based on ^ 4>
Figure 3427DEST_PATH_IMAGE083
(ii) a Step three, when the online rates of p devices are considered, the equivalent load curve is
Figure 422776DEST_PATH_IMAGE084
In the formula (I), wherein,
Figure 335368DEST_PATH_IMAGE085
for consideration of a continuous load profile after a first aggregate user engagement rate>
Figure 348323DEST_PATH_IMAGE086
For the first aggregate probability that the user does not participate in the response, based on the number of active users>
Figure 152200DEST_PATH_IMAGE087
A response potential reduction amount caused by not participating in a response for the first aggregated user;step four, repeating the above processes, finally considering the participation rate of the qth aggregation user, and correcting the continuous load curve according to the following formula to obtain the equivalent continuous response load curve of the aggregation user as
Figure 176788DEST_PATH_IMAGE088
In the formula (I), wherein,
Figure 306287DEST_PATH_IMAGE089
for consideration of the continuous load curve after the qth aggregated user engagement rate, a @>
Figure 275380DEST_PATH_IMAGE090
For the qth aggregated user engagement rate, < >>
Figure 102522DEST_PATH_IMAGE091
The response potential reduction resulting from not participating in a response for the qth aggregated user. The total available response potential of all users in an area is ≥ n>
Figure 106250DEST_PATH_IMAGE092
Maximum equivalent load is->
Figure 875491DEST_PATH_IMAGE093
，/>
Figure 207247DEST_PATH_IMAGE094
The area enclosed by the initial equivalent continuous load of the user response potential and the continuous load curve after the indexes such as the user concurrency rate, the participation rate and the like are aggregated for the area, and the physical meaning is that the reduction amount of the response potential caused by the offline of the equipment of the aggregate user response load.
Figure 962713DEST_PATH_IMAGE095
When the simultaneous rate and the participation rate of q aggregated users are taken into account, the continuous load curve is ≥>
Figure 555369DEST_PATH_IMAGE096
Has a total response potential of ≥>
Figure 501371DEST_PATH_IMAGE097
，
Figure 445056DEST_PATH_IMAGE098
Because the response concurrency rate, the participation rate and the response degree of the aggregated user are influenced, the response potential of the equipment which can respond to the aggregated user cannot reach the value of the expected response potential, and after the convolution operation of all the equipment which can respond to the user is finished, the corrected actual response potential value of the user can be calculated and then is/is judged>
Figure 614001DEST_PATH_IMAGE099
Wherein: />
Figure 326742DEST_PATH_IMAGE100
Suppose that the total response degree expectation of the grid company to the aggregated user is ≥>
Figure 437786DEST_PATH_IMAGE101
Because the aggregated user is not online or refuses to participate at the response moment, the response potential of the user is reduced, the actual response degree of the aggregated user cannot reach the theoretical value according to the corrected actual response potential value of the aggregated user, and the credible response degree of the aggregated user can be obtained as
Figure 868767DEST_PATH_IMAGE102
；
It can be understood that for the construction of the multi-user credible adjustable potential prediction model, the influence factors of the multi-user credible adjustable potential are complex, and the adjustment potential which can be finally achieved is calculated by comprehensively considering factors such as electricity price, an excitation mechanism, equipment failure rate, communication equipment reliability, user response willingness and the like. For the resident users, the responsivedevices of the resident users generally comprise air conditioners, water heaters, refrigerators, electric vehicles and the like according to different device types, and the maximum adjustable potential calculation formula of the resident users is as follows:
Figure 841403DEST_PATH_IMAGE103
the minimum adjustable physical potential calculation formula is as follows:
Figure 408650DEST_PATH_IMAGE104
based on the response reliability evaluation method of the user, after iterative computation, the credible response degree of the multi-user can be obtained>
Figure 425017DEST_PATH_IMAGE105
Combining the tunable potential to calculate the credible tunable potential of the user as
Figure 343294DEST_PATH_IMAGE106
Then the confidence interval of the regulatory potential at the confidence level L is
Figure 854041DEST_PATH_IMAGE107
. In particular, the method comprises the following steps of,
Figure 806953DEST_PATH_IMAGE108
；
Figure 994221DEST_PATH_IMAGE109
in which>
Figure 134215DEST_PATH_IMAGE110
For the most reliable regulation potential (reliably adjustable potential), based on the measured value>
Figure 714232DEST_PATH_IMAGE111
Is the upper limit of the credible and adjustable potential,
Figure 256072DEST_PATH_IMAGE112
for the lower limit of the credible adjustable potential, for commercial users, the adjustable equipment is generally a central air conditioner, an electric automobile and the like, and the credible adjustable potential is calculatedThe mode is the same as that of a resident user;
it can be understood that, for the construction of the prediction model of the credible and adjustable potential aggregated by the multiple users, the respondent devices of the residential users generally comprise an air conditioner, a water heater, a refrigerator, an electric vehicle and the like according to different device types, and the calculation formula of the adjustable potential aggregated by the residential users according to the adjustable potential of the residential users is
Figure 489607DEST_PATH_IMAGE113
The minimum adjustable potential physical calculation formula is
Figure 507111DEST_PATH_IMAGE114
According to the response reliability evaluation method, after iterative computation, the credible response range of the resident user aggregation can be obtained>
Figure 218715DEST_PATH_IMAGE115
Calculating aggregate credibility adjustable potential by combining adjustable potentials
Figure 21586DEST_PATH_IMAGE116
Having found the maximum physical regulation potential and the most reliable regulation potential for aggregating the responsibilities of the residential users, the confidence interval of the regulation potential at the confidence level L is then
Figure 816235DEST_PATH_IMAGE117
In particular, the amount of the surfactant is,
Figure 930822DEST_PATH_IMAGE118
in the formula (I), the compound is shown in the specification,
Figure 587062DEST_PATH_IMAGE119
for the most reliable regulation potential (reliably adjustable potential), based on the measured value>
Figure 103494DEST_PATH_IMAGE120
Is a trusted adjustable upper potential limit, based on the measured potential value>
Figure 69045DEST_PATH_IMAGE121
For commercial users, the tunable devices are generally central air conditioners, electric vehicles and the like, and the calculation mode of the converged trusted tunable potential and the confidence interval is the same as that of residential users.
Step S103, calculating credible adjustable potential values of different users and aggregated users by using the multivariate user credible adjustable potential prediction model and the multivariate user aggregated credible adjustable potential prediction model, sequencing according to the credible adjustable potential values, generating an adjusting time sequence table, and guiding the formulation of a scheduling strategy and a scheme of the flexible load according to the adjusting time sequence table.
And step S104, acquiring power utilization information and receiving a scheduling signal through the terminal intelligent control equipment according to the adjusting time sequence table, and regulating and controlling the user flexible load resource in the resource pool.
Further, fig. 2 shows an application architecture diagram of the system provided in the embodiment of the present invention.
In another preferred embodiment, the present invention provides an aggregation intelligent control system for user flexible resources, including:
and a hierarchical aggregation unit 101 for hierarchical and hierarchical aggregation of the user-side flexible resources to the aggregation layer based on the energy-using genes.
The prediction model construction unit 102 is configured to construct a multivariate user credibility adjustable potential prediction model of the user-side residents and the commercial users in combination with the user response reliability evaluation indexes, methods and results, and construct a multivariate user aggregate credibility adjustable potential prediction model of the aggregate users in combination with the aggregate user response reliability evaluation indexes, methods and results.
And the adjusting time sequence table generating unit 103 is configured to calculate the credibility adjustable potential values of different users and aggregated users by using the multivariate user credibility adjustable potential prediction model and the multivariate user aggregation credibility adjustable potential prediction model, perform sorting according to the credibility adjustable potential values, generate an adjusting time sequence table, and guide the formulation of a scheduling strategy and a scheme of a flexible load according to the adjusting time sequence table.
And the flexible resource regulation and control unit 104 is used for collecting power utilization information and receiving a regulation signal through the terminal intelligent control equipment according to the regulation time sequence table, and regulating and controlling the flexible load resources of the user in the resource pool.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in various embodiments may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (2)

1. A method for intelligent control of aggregation of user flexible resources is characterized by specifically comprising the following steps:
based on the energy utilization genes, the flexible resources at the user side are hierarchically aggregated to a polymerization layer;
constructing a multivariate user credibility adjustable potential prediction model of the residents and commercial users at the user side by combining with the user response reliability evaluation indexes, methods and results, and constructing a multivariate user aggregation credibility adjustable potential prediction model of the aggregation users by combining with the aggregation user response reliability evaluation indexes, methods and results;
calculating credible adjustable potential values of different users and aggregated users by using the multivariate user credible adjustable potential prediction model and the multivariate user aggregated credible adjustable potential prediction model, sequencing according to the credible adjustable potential values to generate an adjusted time sequence table, and guiding the formulation of a scheduling strategy and a scheme of a flexible load according to the adjusted time sequence table;
according to the adjusting time sequence table, through the tail end intelligent control equipment, collecting power utilization information and receiving an adjusting signal, and adjusting and controlling user flexible load resources in a resource pool;
the energy using genes comprise an adjustable starting moment, an adjustable time interval length, a user participation rate, a user response degree and a credible adjustable potential prediction value;
the calculation process of the user response reliability evaluation index comprises the following steps:
participation state D with single user demand response i Comprises the following steps:
Figure FDA0004053922970000011
wherein D is i Responding to the state for the user;
participation rate PR of response user Comprises the following steps:
Figure FDA0004053922970000012
wherein n represents the total number of users;
degree of user response r user Comprises the following steps:
Figure FDA0004053922970000013
wherein, P user-response Capacity of users to participate in the response, P user-all Total capacity for the user group;
the calculation process of the aggregate user response reliability evaluation index is as follows:
the power utilization state U of the user k at the moment t K Comprises the following steps:
Figure FDA0004053922970000021
the simultaneous rate AURR (t) of aggregated users is:
Figure FDA0004053922970000022
wherein K represents the total number of users of the aggregation user;
aggregating user response degree r AU Comprises the following steps:
Figure FDA0004053922970000023
wherein i denotes the aggregate user total, PR user，i 、r user，i 、P i Respectively representing the participation rate, the response degree and the power of the aggregation user;
the user response reliability evaluation method comprises the following steps:
the continuous load curve of the user is f (1) (x)＝m 1 f (0) (x)+(1-m 1 )f (0) (x-C 1 ) Wherein m is 1 Is the on-line rate of the 1 st equipment, 1-m 1 The offline rate of the 1 st equipment;
total capacity Q of total responsibilities of p devices held by user user Comprises the following steps:
Figure FDA0004053922970000024
wherein, the total responsivity potential of p devices held by the user is C s Maximum equivalent load of P L +C s ；
Actual responsiveness value Q of user real ：Q real ＝Q user -Q 1 Wherein Q is 1 The area enclosed by the initial equivalent continuous load of the user and the continuous load curve after the indexes such as the online rate of the equipment are considered.
Credible response degree r of user user-cre Comprises the following steps:
Figure FDA0004053922970000025
the aggregate user response reliability is evaluated as:
aggregated user sustained load profile
Figure FDA0004053922970000026
Comprises the following steps:
Figure FDA0004053922970000031
aggregating user equivalent continuous response load curve f (q) (x) Comprises the following steps:
Figure FDA0004053922970000032
wherein it is present>
Figure FDA0004053922970000033
To take into account the persistent load curve after the qth aggregate user engagement rate, r q For the qth aggregate user engagement rate, C uq A response potential reduction resulting from non-engagement with responses for the qth aggregated user;
total response potential Q AU Comprises the following steps:
Figure FDA0004053922970000034
actual responsibilities Q real Comprises the following steps: q real ＝Q AU -Q 1 Wherein, in the step (A),
Figure FDA0004053922970000035
aggregating trustworthy response levels r of users AU-cre Comprises the following steps:
Figure FDA0004053922970000036
specifically, the influence factors of the credible and adjustable potential of the multiple users are complex, the factors of electricity price, an excitation mechanism, equipment failure rate, reliability of communication equipment and response willingness of the users are comprehensively considered, the finally achieved adjustment potential is calculated, and different users and aggregated users have different credible and adjustable potential values.
2. The utility model provides a system is controlled to polymerization intelligence for user's flexible resource which characterized in that, the system includes hierarchical polymerization unit, prediction model construction unit, adjusts time sequence table generation unit and flexible resource regulation and control unit, wherein:
the hierarchical polymerization unit is used for hierarchically polymerizing the user-side flexible resources to a polymerization layer based on the energy utilization genes;
the prediction model construction unit is used for constructing a multivariate user credible adjustable potential prediction model of residents and commercial users at the user side by combining with the user response reliability evaluation indexes, methods and results, and constructing a multivariate user aggregation credible adjustable potential prediction model of aggregation users by combining with the aggregation user response reliability evaluation indexes, methods and results;
the adjusting time sequence table generating unit is used for calculating the credible adjustable potential values of different users and aggregated users by using the multivariate user credible adjustable potential prediction model and the multivariate user aggregated credible adjustable potential prediction model, sequencing according to the credible adjustable potential values, generating an adjusting time sequence table, and guiding the formulation of a scheduling strategy and a scheme of a flexible load according to the adjusting time sequence table;
the flexible resource regulating and controlling unit is used for acquiring power utilization information and receiving a regulation signal through the tail end intelligent control equipment according to the regulation time sequence table, and regulating and controlling the flexible load resources of the user in the resource pool;
the energy using genes comprise an adjustable starting moment, an adjustable time interval length, a user participation rate, a user response degree and a credible adjustable potential prediction value;
the calculation process of the user response reliability evaluation index comprises the following steps:
participation state D with single user demand response i Comprises the following steps:
Figure FDA0004053922970000041
wherein D is i Responding to the state for the user;
participation rate PR of response user Comprises the following steps:
Figure FDA0004053922970000042
wherein n represents the total number of users;
degree of user response r user Comprises the following steps:
Figure FDA0004053922970000043
wherein, P user-response Capacity of users to participate in the response, P user-all Total capacity for the user group;
the calculation process of the aggregate user response reliability evaluation index is as follows:
user k's power consumption state U at time t K Comprises the following steps:
Figure FDA0004053922970000044
the user aggregation concurrency rate AURR (t) is:
Figure FDA0004053922970000045
wherein K represents the total number of users of the aggregation user;
aggregating user response degree r AU Comprises the following steps:
Figure FDA0004053922970000051
wherein i denotes the aggregate user total, PR user，i 、r user，i 、P i Respectively representing the participation rate, the response degree and the power of the aggregation user;
the user response reliability evaluation method comprises the following steps:
the continuous load curve of the user is f (1) (x)＝m 1 f (0) (x)+(1-m 1 )f (0) (x-C 1 ) Wherein m is 1 Is the on-line rate of the 1 st equipment, 1-m 1 The offline rate of the 1 st equipment;
total capacity Q of total responsibilities of p devices held by user user Comprises the following steps:
Figure FDA0004053922970000052
wherein, the total responsivity potential of p devices held by the user is C s Maximum equivalent load of P L +C s ；/>
Actual responsiveness value Q of user real ：Q real ＝Q user -Q 1 Wherein Q is 1 The area enclosed by the initial equivalent continuous load of the user and the continuous load curve after the indexes such as the online rate of the equipment are considered.
Confidence response degree r of user user-cre Comprises the following steps:
Figure FDA0004053922970000053
the aggregate user response reliability assessment is:
aggregated user sustained load profile
Figure FDA0004053922970000054
Comprises the following steps:
Figure FDA0004053922970000055
aggregating user equivalent continuous response load curve f (q) (x) Comprises the following steps:
Figure FDA0004053922970000056
wherein +>
Figure FDA0004053922970000057
To take into account the persistent load curve after the qth aggregate user engagement rate, r q For the qth aggregate user engagement rate, C uq A response potential reduction amount caused by non-participation in a response for the qth aggregated user;
total response potential Q AU Comprises the following steps:
Figure FDA0004053922970000061
actual potential response value Q real Comprises the following steps: q real ＝Q AU -Q 1 Wherein, in the step (A),
Figure FDA0004053922970000062
aggregating trustworthy response levels r of users AU-cre Comprises the following steps:
Figure FDA0004053922970000063
specifically, the influence factors of the credible and adjustable potential of the multiple users are complex, the factors of electricity price, an excitation mechanism, equipment failure rate, reliability of communication equipment and user response will are comprehensively considered, the finally achieved adjustment potential is calculated, and different users and aggregated users have different credible and adjustable potential values.
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