CN112464418B - Universal digital twin body construction method for distributed energy resources - Google Patents

Universal digital twin body construction method for distributed energy resources Download PDF

Info

Publication number
CN112464418B
CN112464418B CN202011281424.1A CN202011281424A CN112464418B CN 112464418 B CN112464418 B CN 112464418B CN 202011281424 A CN202011281424 A CN 202011281424A CN 112464418 B CN112464418 B CN 112464418B
Authority
CN
China
Prior art keywords
der
power
model
state
transient
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202011281424.1A
Other languages
Chinese (zh)
Other versions
CN112464418A (en
Inventor
吴清
李志勇
庞松岭
方连航
钟准
何光宇
原启涛
唐春梅
李川江
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanghai Qianguan Energy Saving Technology Co ltd
Hainan Electric Power School Hainan Electric Power Technical School
Shanghai Jiaotong University
Original Assignee
Shanghai Qianguan Energy Saving Technology Co ltd
Hainan Electric Power School Hainan Electric Power Technical School
Shanghai Jiaotong University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shanghai Qianguan Energy Saving Technology Co ltd, Hainan Electric Power School Hainan Electric Power Technical School, Shanghai Jiaotong University filed Critical Shanghai Qianguan Energy Saving Technology Co ltd
Priority to CN202011281424.1A priority Critical patent/CN112464418B/en
Publication of CN112464418A publication Critical patent/CN112464418A/en
Application granted granted Critical
Publication of CN112464418B publication Critical patent/CN112464418B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00—Details relating to CAD techniques
    • G06F2111/02—CAD in a network environment, e.g. collaborative CAD or distributed simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • General Engineering & Computer Science (AREA)
  • Computational Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Software Systems (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Computer Hardware Design (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Physics (AREA)
  • Computing Systems (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Algebra (AREA)
  • Probability & Statistics with Applications (AREA)
  • Power Engineering (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention relates to the technical field of electrical engineering and automation thereof, and provides a universal digital twin body construction method of distributed energy resources; the method has the advantages that common DER can be modeled, the operating rule of the DER can be accurately simulated, the DER can be predicted for a long time by utilizing big data, and the functions of short-term prediction of the power of the DER, state identification of the DER, abnormal state monitoring, power consumption data compression, operation simulation and the like can be realized by utilizing steady-state model parameters because the steady-state parameters of the DER reflect the power change characteristics inside the steady state of the DER; the transient model parameters of the DER reflect the operation mode of the DER and the use habit of a user, and the functions of long-term prediction of the DER power, analysis of the electricity consumption behavior of the user and the like can be realized by utilizing the parameters.

Description

Universal digital twin body construction method for distributed energy resources
Technical Field
The invention relates to the technical field of electrical engineering and automation thereof, in particular to a universal digital twin body construction method of distributed energy resources.
Background
Adaptive frequency control (adaptive control of frequency) refers to secondary frequency adjustment that aims to optimize certain operating conditions of the power system. The self-adaptive frequency control system can automatically adjust the structure or parameters (such as automatically changing the gain of each feedback quantity) according to the change of the operation condition so as to enable the self-adaptive frequency control system to operate under the optimal working condition.
The existing adaptive frequency control method mainly has the following defects: (1) Only the operation characteristics of Distributed Energy Resources (DERs) are considered, and the behavior habits of users are not considered; (2) Only modeling is carried out for a plurality of specific distributed energy resources, and a general model construction method applicable to various distributed resources with mass, complex, multiple and heterogeneous characteristics is lacking.
Based on the existence of the problems, the scheme establishes a universal digital twin body construction method of the distributed energy resource.
Disclosure of Invention
The invention aims to provide a universal digital twin body construction method of distributed energy resources, which can be used for pre-testing massive and complex DERs before grid connection, and parallel testing and prediction of the grid-connected DERs, and is beneficial to mining the most economical or safer operation mode of the DERs through efficient simulation of the DERs, so that the overall operation efficiency of a power grid is improved.
In order to achieve the above purpose, the present invention provides the following technical solutions: the method for constructing the universal digital twin body of the distributed energy resource is characterized by comprising the following steps of:
step one: constructing a general steady-state model of the DER digital twin;
under relatively steady operating conditions and normal environmental conditions, the DER operating power tends to be constant or slowly varying. The DER operating power will vary over time, with variations arising from two aspects: the influence of the DER internal operation mode, such as the gradual decrease of the power after the refrigerator is started, the stable operation of the power of the water heater in a heating state, the gradual attenuation of the charging power of the battery when the battery is nearly full, and the like, has obvious rules and trends, and can be expressed by a reasonable analysis model, so that the changes can be regarded as the rule components of the DER power; on the other hand, as the DER is connected with the power grid, the power of the DER can be influenced by the relevant factors of the power quality such as the voltage, the frequency, the harmonic wave and the like of the power grid to generate fluctuation, and the DER can also be influenced by the design factors of a power supply circuit of the DER to generate power fluctuation. In addition, due to the absolute nature of errors, the measured DER power fluctuation also contains measurement errors of measurement equipment, the change rule of the type of power is not obvious, and a rule method is difficult to find and describe the power, so that the factors need to be quantized through a probability model, and the fluctuation of the type of power can be regarded as noise components of the DER power. Thus, for the power curve P (t) of DER, it can be written as:
P(t)＝S(t)+e(t) (1)
where S (t) is a regular component of power and e (t) is a noise component of power.
Based on the power decomposition concept, the patent defines DER steady-state power as the superposition result of regular component and noise component. The two components are formed differently in reason and performance characteristics, and thus should be modeled differently.
The variation form of the regular component of the DER power is various, and the patent uniformly models the DER power into the following general analytic models of the regular component.
(1) Linear rule model
The linear law model appears in both steady state and transient state of DER, and the expression of the DER linear law model is as follows:
S(t)＝k·t+b (2)
where k is a slope of a straight line, and represents the amount of power change per unit time, and b is the state initial power.
(2) Exponential law model
Exponential regularity usually occurs in the steady state of the DER, such as in the battery charging process of mobile phones, tablets, etc. electronic devices. The exponential power generally increases or decreases in an exponential decay trend, and the expression of the exponential model is as follows:
S(t)＝a+be -t/t (3)
where a is the state final power, t is the decay time constant, and b is the power decay amount. Depending on the nature of the DER, the decay time constant t differs significantly.
(3) Fourier rule model
Since the completeness of the Fourier series can be used for expressing any complex continuous function, any rule can be expressed by using infinite Fourier type theoretically. However, because an excessively high number of stages increases the complexity of the model and has the possibility of overfitting, the special purpose is to express DER steady state with more complex process by using the Fourier series of not more than 5 stages. The expression of the fourier rule model is as follows:
in most current studies, DER is generally considered to have a finite number of states, in each of which the power of the DER remains constant, i.e., the linear law model described above. However, to account for power fluctuations and sampling errors, researchers typically consider the power of each state of the DER to be subject to a Gaussian distribution. However, since the steady state power of DER tends to vary in most cases, this would lead to large errors in the power model if the steady state power were considered to be Gaussian. On the contrary, based on the power decomposition model of the patent, if regular components of DER power are separated, noise component variation lacks regularity and has Gaussian distribution characteristics, so that the DER power can be modeled by Gaussian distribution, error surge under the condition that the power state is simply considered to be subjected to Gaussian distribution is avoided, and digital twin accurate mirror image mapping is facilitated.
The expression of the noise component model for the DER steady state power is as follows:
e(t)～N(m,s 2 ) (5)
it should be noted that since the regular component is a regression model of the DER power samples, the noise is the deviation of the regression model, and in general, the mean value m of the noise should be close to zero.
Step two: construction of a general transient model of DER digital twins
DER rapidly transitions from one steady state through some intermediate transition state process to a new steady state process. The operation of the DER is alternating between steady state and transient, typically the transient is of short duration, and the DER is in steady state operation for the majority of the time. Transient therefore primarily refers to transitions between steady states of DER, and encompasses transition state processes therebetween. In some cases, transient states experienced by the DER are very transient and may not be sampled at the second sampling frequency, at which point the DER may be approximated as a direct transition from one steady state to another.
Because the steady state is the basic state of DER operation and producing result of use, its duration is long, and the condition is also important than transient state process, so this patent will focus on the transition law of research transient state and steady state, agrees with: the "transients" described below are each simplified to refer to "transitions between steady states". For example, for a DER with N steady states, the maximum number of transients is 2 N-1 And each.
Transient probability matrix t= [ T ] ij ,1≤i,j≤N]Transition probabilities of the DER turning to various steady states during transients are depicted. Unlike the transition matrix in the conventional HMM, in the present transient model, the diagonal elements of the transient probability matrix T are all 0, i.e., the case of the self-transition of DER to self-steady state (i.e., no transition) is not considered, because the premise that the transient is T has already occurred.
Transient probability t ij The probability of the DER transitioning to steady state j when it is in steady state i and a transient is required to occur. Since DER must be transient, each line of T corresponds to a certain event, the sum of the probabilities is 1, then there is
If a transient condition occurs for a certain DER in the steady state, it is the end of the current steady state, and thus the transient condition of this DER can be described from the point of view of the steady state duration. The steady state duration variation will remain within a certain range, taking into account the DER operating regularity and the stationarity of the operating mode. This patent proposes a time-long transient probability matrix T l To describe the duration probability distribution of the DER transient transition. Modeling the transient process with Gaussian distribution, and a time-long transient probability matrix T l The mathematical expression of (2) is:
in the method, in the process of the invention,is DER firstDuration probability distribution of i steady-state, S o Is a roll-out steady state set.
The patent proposes a time-instant transient probability matrix T t To describe transient (steady state transition) temporal distribution of DER, its mathematical expression is:
T t ＝[G(θ ij ),1≤i,j≤N∧i∈S i ] (7)
wherein G (θ) ij ) Is a moment Gaussian mixture model for moment type i-to-j-transition from moment type i to moment type j, theta ij Is a model parameter S i Is a set of all transfer-in steady states, similar S o Is a roll-out steady state set.
T t Element G (θ) ij ) The distribution is periodic over a day, describing the probability distribution of DER switching from steady state i to steady state j over a period of day. By T t The usage habits of the user for certain DERs may be described, but the GMM is periodic in a day, which can only reflect the user's time preference for using the DER during the day, and cannot reflect the number and frequency of DERs used by the user during the day. This patent proposes a frequency-type transient probability matrix T f The mathematical expression is used for describing the frequency times of transient states generated by using DER in one day of a user:
in the method, in the process of the invention,is a gaussian distribution of the frequency of transitions from i steady state to j steady state within a day of DER.
T l 、T t And T f Can jointly characterize certain DER transfer characteristics, T l The duration probability distribution, T, of DER transient transitions can be described t The probability of the occurrence of a certain behavior of the user is measured in the time scale of the day, T f Reflecting how frequently such behavior repeatedly occurs.
The steady-state model parameters reflect the power change condition and the corresponding physical action in the DER steady-state, the time scale of the research is small, and the analysis model is taken as the main part, so that the DER power can be accurately depicted; the transient model parameters reflect the behavior rules of the user and the DER, the research time scale is large, the probability model is taken as the main part, the probability of the DER transient event can be inferred, the two parts of the model are mutually influenced and alternately act, and the operation rules of the DER are jointly described.
Compared with the prior art, the invention has the beneficial effects that: the invention can model the common DER, accurately simulate the operation rule of the DER, and can utilize big data to predict the DER for a long time, and as the steady-state parameters of the DER reflect the power change characteristics in the steady state of the DER, the functions of short-term prediction of the power of the DER, state identification of the DER, abnormal state monitoring, power consumption data compression, operation simulation and the like can be realized by utilizing the steady-state model parameters; the transient model parameters of the DER reflect the operation mode of the DER and the use habit of a user, and the functions of long-term prediction of the DER power, analysis of the electricity consumption behavior of the user and the like can be realized by utilizing the parameters.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a diagram of a DER universal digital twin phantom in accordance with the present invention;
FIG. 2 is a typical operating curve of a water dispenser according to an embodiment of the present invention;
FIG. 3 is a distribution of the steady state duration of the water dispenser on in an embodiment of the invention;
FIG. 4 is a graph showing transient probability distribution of a water dispenser at a steady-state time of shut-down/standby in an embodiment of the invention;
FIG. 5 is a graph showing transient probability distribution of a steady state frequency of water dispenser shutdown/standby in an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides a technical solution: the method for constructing the universal digital twin body of the distributed energy resource is characterized by comprising the following steps of:
step one: constructing a general steady-state model of the DER digital twin;
step two: and constructing a general transient model of the DER digital twin body.
In the first step, the steady-state model construction process is specifically as follows:
the power curve P (t) for the DER is determined, which can be written as:
P(t)＝S(t)+e(t) (1)
wherein S (t) is a regular component of power, and e (t) is a noise component of power;
based on the power decomposition idea, DER steady-state power is defined as the superposition result of regular components and noise components, and the formation reasons and performance characteristics of the two components are different, so that the two components should be modeled differently
The modeling of the regular components of the DER steady-state power is realized, and the regular components of the DER steady-state power are uniformly modeled into an analytical model of the following three general regular components due to various variation forms of the regular components of the DER steady-state power;
a. linear rule model
The linear law model appears in both steady state and transient state of DER, and the expression of the DER linear law model is as follows:
S(t)＝k·t+b (2)
wherein k is a linear slope, represents the power variation in unit time, and b is the state initial power;
b. exponential law model
Exponential regularity usually occurs in the steady state of DER, such as the charging process of batteries of mobile phones, tablets and other electronic devices; the exponential power generally increases or decreases in an exponential decay trend, and the expression of the exponential model is as follows:
S(t)＝a+be -t/t (3)
wherein a is the final power of the state, t is the decay time constant, and b is the power attenuation; according to different DER characteristics, the decay time constants t of the DER are obviously different;
c. fourier rule model
The completeness of the Fourier series can be used for expressing any complex continuous function, so that any rule can be expressed by using infinite Fourier type theoretically; the expression of the fourier rule model is as follows:
however, the fourier series of the model is not more than 5 stages, since an excessively high number of stages increases the complexity of the model and the possibility of overfitting.
Wherein, regarding modeling of noise components of the DER steady-state power, modeling of noise components of the DER steady-state power is performed using gaussian distribution, and an expression of the noise component model is as follows:
e(t)～N(m,s 2 ) (5)
since the regular component is a regression model of the DER power samples, the noise is the deviation of the regression model, and in general, the mean value m of the noise should be close to zero.
In the second step, the transient model construction process specifically comprises the following steps:
put forward a time-long transient probability matrix T l To describe a duration probability distribution of the DER transient transition; by height ofThe transient process is modeled by the S distribution, and a time-long transient probability matrix T l The mathematical expression of (2) is:
in the method, in the process of the invention,is the duration probability distribution of the ith steady state of DER, S o Is a roll-out steady state set.
The time-of-day transient probability matrix T is proposed t To describe the transient time distribution of DER, its mathematical expression is:
T t ＝[G(θ ij ),1≤i,j≤N∧i∈S i ] (7)
wherein G (θ) ij ) Is a moment Gaussian mixture model for moment type i-to-j-transition from moment type i to moment type j, theta ij Is a model parameter S i Is a set of all transfer-in steady states, similar S o Is a roll-out steady state set.
Frequency-type transient probability matrix T is proposed f The mathematical expression is used for describing the frequency times of transient states generated by using DER in one day of a user:
in the method, in the process of the invention,is a gaussian distribution of the frequency of transitions from i steady state to j steady state within the DER day;
T l 、T t and T f Can jointly characterize certain DER transfer characteristics, T l The duration probability distribution, T, of DER transient transitions can be described t The probability of the occurrence of a certain behavior of the user is measured in the time scale of the day, T f Reflecting the frequent course of such behavior recurrenceDegree.
Working principle: taking the operation power of the water dispenser as an example, the specific implementation mode of the patent is described.
A typical operating curve of a water dispenser is shown in fig. 2. The steady state parameters of the water dispenser model obtained using the operation history data are shown in table 1. The transient state of the water dispenser belongs to step-type abrupt change, so that the transient state process is very short and negligible.
Table 1 steady state parameters for water dispenser model
Since there are only two steady states, the transient probability matrix T of the television is
The closing/standby steady state of the water dispenser is a transfer-in steady state, and the opening steady state is a transfer-out steady state. Figures 3-5 show the parameters between steady states for each steady state.
As can be seen from the parameters between the water dispenser stable states, the user can use the water dispenser for 3-4 times from day to night, and the average heating time of the water dispenser is 3-4 minutes.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.

Claims (1)

1. The method for constructing the universal digital twin body of the distributed energy resource is characterized by comprising the following steps of:
step one: constructing a general steady-state model of the DER digital twin;
step two: constructing a general transient model of the DER digital twin body;
in the first step, the steady-state model construction process is specifically as follows:
the power curve P (t) for the DER is determined, which can be written as:
P(t)＝S(t)+e(t) (1)
wherein S (t) is a regular component of power, and e (t) is a noise component of power;
based on the power decomposition idea, DER steady-state power is defined as the superposition result of regular components and noise components, and the formation reasons and the performance characteristics of the two components are different, so that the two components should be modeled differently;
regarding modeling of the regular components of the DER steady-state power, as the variation forms of the regular components of the DER steady-state power are various, the DER steady-state power is uniformly modeled into an analytical model of the following three general regular components;
a. linear rule model
The linear law model appears in both steady state and transient state of DER, and the expression of the DER linear law model is as follows:
S(t)＝k·t+b （2）
wherein k is a linear slope and represents the power variation in unit time; b is the state initial power;
b. exponential law model
The exponential power increases or decreases in an exponential decay trend, and the expression of the exponential model is as follows:
S(t)＝a+be -t/τ (3)
wherein a is the final power of the state, tau is the decay time constant, and b is the power attenuation; the decay time constant tau is obviously different according to the different characteristics of DER;
c. fourier rule model
The completeness of the Fourier series can be used for expressing any complex continuous function, so that any rule can be expressed by using infinite Fourier type theoretically; the expression of the fourier rule model is as follows:
however, the Fourier series of the model is not more than 5 stages because the complexity of the model is increased and the possibility of overfitting exists due to the excessively high series;
modeling of noise components of DER steady-state power with Gaussian distribution, the expression of the noise component model is as follows:
e(t)～N(m,s 2 ) (5)
since the regular component is a regression model of the DER power sample, the noise is the deviation of the regression model, and the mean value m of the noise should be close to zero;
in the second step, the transient model construction process specifically comprises the following steps:
put forward a time-long transient probability matrix T l To describe a duration probability distribution of the DER transient transition; modeling the transient process with Gaussian distribution, and a time-long transient probability matrix T l The mathematical expression of (2) is:
in the method, in the process of the invention,is the duration probability distribution of the ith steady state of DER, S o Is a roll-out steady state set;
in the second step, the transient model construction process specifically comprises the following steps:
the time-of-day transient probability matrix T is proposed t To describe the transient time distribution of DER, its mathematical expression is:
T t ＝[G(θ ij ),1≤i,j≤N∧i∈S i ] (7)
wherein G (θ) ij ) Is a moment Gaussian mixture model for moment type i-to-j-transition from moment type i to moment type j, theta ij Is a model parameter S i Is a set of all transfer-in steady states, similar S o Is a roll-out steady state set;
in the second step, the transient model construction process specifically comprises the following steps:
frequency-type transient probability matrix T is proposed f The mathematical expression is used for describing the frequency times of transient states generated by using DER in one day of a user:
in the method, in the process of the invention,is a gaussian distribution of the frequency of transitions from i steady state to j steady state within the DER day;
T l 、T t and T f Can jointly characterize certain DER transfer characteristics, T l The duration probability distribution, T, of DER transient transitions can be described t The probability of the occurrence of a certain behavior of the user is measured in the time scale of the day, T f Reflecting how frequently such behavior repeatedly occurs.
CN202011281424.1A 2020-11-17 2020-11-17 Universal digital twin body construction method for distributed energy resources Active CN112464418B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011281424.1A CN112464418B (en) 2020-11-17 2020-11-17 Universal digital twin body construction method for distributed energy resources

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011281424.1A CN112464418B (en) 2020-11-17 2020-11-17 Universal digital twin body construction method for distributed energy resources

Publications (2)

Publication Number Publication Date
CN112464418A CN112464418A (en) 2021-03-09
CN112464418B true CN112464418B (en) 2023-07-28

Family

ID=74836361

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011281424.1A Active CN112464418B (en) 2020-11-17 2020-11-17 Universal digital twin body construction method for distributed energy resources

Country Status (1)

Country Link
CN (1) CN112464418B (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113505490B (en) * 2021-07-22 2024-05-24 国网宁夏电力有限公司电力科学研究院 Digital twin body parameter correction method and device for power system based on GMM
CN113675863B (en) * 2021-08-23 2024-02-06 南京理工大学 Digital twin-based micro-grid frequency secondary cooperative control method
CN114124529B (en) * 2021-11-23 2024-03-29 国网山东省电力公司电力科学研究院 Method and device for encrypting/decrypting grid-connected test data, and data processing method and device
CN115859700B (en) * 2023-03-02 2023-05-05 国网湖北省电力有限公司电力科学研究院 Power grid modeling method based on digital twin technology

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016082556A1 (en) * 2014-11-26 2016-06-02 国家电网公司 Gis isolation switch high frequency arc resistance model modeling method
WO2018131174A1 (en) * 2017-01-13 2018-07-19 Nec Corporation Microgrid power management system and method of managing
US10452032B1 (en) * 2016-09-08 2019-10-22 PXiSE Energy Solutions, LLC Optimizing power contribution of distributed energy resources for real time power demand scheduling
EP3671374A1 (en) * 2018-12-21 2020-06-24 ABB Schweiz AG Method and system for determining system settings for an industrial system
KR20200126122A (en) * 2019-04-29 2020-11-06 한국전력공사 Distributed scheduling method in dynamic virtual plants and apparatus thereof

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2016082556A1 (en) * 2014-11-26 2016-06-02 国家电网公司 Gis isolation switch high frequency arc resistance model modeling method
US10452032B1 (en) * 2016-09-08 2019-10-22 PXiSE Energy Solutions, LLC Optimizing power contribution of distributed energy resources for real time power demand scheduling
WO2018131174A1 (en) * 2017-01-13 2018-07-19 Nec Corporation Microgrid power management system and method of managing
EP3671374A1 (en) * 2018-12-21 2020-06-24 ABB Schweiz AG Method and system for determining system settings for an industrial system
KR20200126122A (en) * 2019-04-29 2020-11-06 한국전력공사 Distributed scheduling method in dynamic virtual plants and apparatus thereof

Non-Patent Citations (6)

* Cited by examiner, † Cited by third party
Title
Distributed energy resource system optimisation using mixed integer linear programming;Akomeno Omu 等;《Energy Policy》;20130705;249-266 *
IoT-Based Digital Twin for Energy Cyber-Physical Systems: Design and Implementation;Ahmed Saad 等;《energies》;20200912;1-19 *
分布式能源资源的通用数字孪生体构建与模拟方法;原启涛 等;《可再生能源》;20220630;第40卷(第6期);822-829 *
微电网能量管理策略研究;陈亚红;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20160315;C042-1275 *
微网系统的运行优化与能量管理研究;张颖媛;《中国博士学位论文全文数据库 工程科技Ⅱ辑》;20110915;第75页第3段 *
考虑污染物排放约束的传统电厂与虚拟电厂间环境经济调度模型;刘然 等;《电力建设》;20170930;第38卷(第9期);第53页摘要 *

Also Published As

Publication number Publication date
CN112464418A (en) 2021-03-09

Similar Documents

Publication Publication Date Title
CN112464418B (en) Universal digital twin body construction method for distributed energy resources
CN115114878B (en) Method and device for online prediction of battery life of energy storage power station and storage medium
CN112433907B (en) Method and device for processing host operation parameter data of uninterruptible power supply and electronic device
CN110850315A (en) Method and device for estimating state of charge of battery
CN116960941A (en) Power climbing event prediction method, device, equipment and storage medium
Chen et al. Imbalance correction method based on ratio of loss function values for transient stability assessment
Xu et al. Short-term wind speed prediction based on GRU
CN118136906A (en) Control system and method for all-vanadium liquid flow energy storage
CN114371416A (en) Method for predicting service life of fuel cell
Cao et al. Multi-kernel support vector regression optimization model and indirect health factor extraction strategy for the accurate lithium-ion battery remaining useful life prediction
CN109061509A (en) A kind of battery capacity remaining value evaluation method
CN114565172B (en) Battery remaining life prediction model construction and battery remaining life prediction method
CN115395613A (en) Battery charging management method and device, electronic equipment and storage medium
Huang et al. SOC prediction of Lithium battery based on fuzzy information granulation and support vector regression
CN114976115A (en) Low-temperature purging method and device for fuel cell engine
Zhang et al. Wind farm wind power prediction method based on CEEMDAN and DE optimized DNN neural network
Xiong et al. A Definition and Prediction Method for Wind Power Low Output Events
Zhai et al. Short-term wind speed prediction based on quadratic decomposition improved particle swarm optimization extreme learning machine
Xu et al. Short-term time series data prediction of power consumption based on deep neural network
Chen et al. Short-term power load forecasting based on CEEMDAN-CNN-LSTM hybrid modeling
Pindaro Controlling lithium-ion batteries through reinforcement learning
Huang et al. Evaluation index of battery pack of energy storage station based on RB recession mechanism
Zhao et al. Short-term Power Load Forecasting Based on Dual Decomposition and LSTM
Zhou et al. Life prediction method of battery energy storage system in frequency modulation application
Ma et al. Prediction of Lithium-Ion Battery Capacity based on The ARMA Method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant