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