CN117767433A - Real-time county energy internet scheduling method and system based on digital twin - Google Patents
Real-time county energy internet scheduling method and system based on digital twin Download PDFInfo
- Publication number
- CN117767433A CN117767433A CN202311766003.1A CN202311766003A CN117767433A CN 117767433 A CN117767433 A CN 117767433A CN 202311766003 A CN202311766003 A CN 202311766003A CN 117767433 A CN117767433 A CN 117767433A
- Authority
- CN
- China
- Prior art keywords
- county
- energy internet
- power
- model
- scheduling
- 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.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 91
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 39
- 230000006870 function Effects 0.000 claims description 39
- 230000008569 process Effects 0.000 claims description 25
- 238000004590 computer program Methods 0.000 claims description 22
- 238000005457 optimization Methods 0.000 claims description 17
- 238000010248 power generation Methods 0.000 claims description 12
- 238000003860 storage Methods 0.000 claims description 10
- 239000013598 vector Substances 0.000 claims description 7
- 239000013256 coordination polymer Substances 0.000 claims description 6
- 238000009825 accumulation Methods 0.000 claims description 5
- 239000003949 liquefied natural gas Substances 0.000 claims description 4
- CLOMYZFHNHFSIQ-UHFFFAOYSA-N clonixin Chemical compound CC1=C(Cl)C=CC=C1NC1=NC=CC=C1C(O)=O CLOMYZFHNHFSIQ-UHFFFAOYSA-N 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 9
- YHXISWVBGDMDLQ-UHFFFAOYSA-N moclobemide Chemical compound C1=CC(Cl)=CC=C1C(=O)NCCN1CCOCC1 YHXISWVBGDMDLQ-UHFFFAOYSA-N 0.000 description 9
- 238000009826 distribution Methods 0.000 description 7
- 230000001105 regulatory effect Effects 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000013461 design Methods 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 125000001145 hydrido group Chemical group *[H] 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000007704 transition Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000003795 chemical substances by application Substances 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
A real-time county energy Internet scheduling method and system based on digital twinning, the method includes the following steps: constraint is carried out on a pre-established county energy internet scheduling model to obtain an actual working condition scheduling model; the method comprises the steps of establishing an objective function, wherein the total power deviation between frequency modulation instruction output and actual power output is minimum; and solving the actual working condition scheduling model according to the objective function by combining the autoregressive comprehensive moving average model and the balance optimizer algorithm to obtain the optimal real-time county energy Internet scheduling scheme. When the main power grid receives random disturbance, transmitting a total regulation command from the main power grid to the county energy Internet scheduling model; the county energy internet scheduling model solves the optimal real-time county energy internet scheduling scheme and issues the total adjustment command to each frequency modulation unit. The present invention considers the current and future time control intervals, and the optimal cooperative control scheme can be obtained efficiently by utilizing the autoregressive integrated moving balance optimizer algorithm.
Description
Technical Field
The invention belongs to the field of power grid energy scheduling, and particularly relates to a digital twinning-based real-time county energy internet scheduling method and system.
Background
With the development of power electronics technology and the breakthrough of battery technology, renewable energy sources such as Wind Farms (WF) and photovoltaic power systems (PV) are connected to the county energy internet. In order to fully utilize the rapid adjustment performance of wind power plants and photovoltaics, it is important to coordinate the distribution between a main power grid and the county energy Internet. As more and more distributed energy enters the grid, optimization of county energy internet scheduling (county energy internet dispatch, CEID) is difficult and time consuming. With advances in electronics and software, digital twin agents can be considered as digital counterparts to the optimization, control, and monitoring processes of real-world power grids. Digital twinning is a good tool for integrating reality and virtualization, and it can manage intelligence more effectively. Currently, there have been some studies to combine the grid with digital twinning technology. However, conventionally, these scheduling distribution studies have focused mainly on the current and past time intervals, and ignored the disturbances of the next time control interval, which negatively affects the optimization results.
Disclosure of Invention
The invention aims to solve the problems in the prior art, and provides a real-time county energy Internet scheduling method and system based on digital twinning, which can predict data of a plurality of time intervals in a future sequence according to the difference of historical data by utilizing a historical sequence, effectively and efficiently acquire a real-time county energy Internet scheduling scheme based on digital twinning, and better coordinate power instruction distribution of a plurality of adjustment resources and optimization intervals between current and future time control based on digital twinning.
In order to achieve the above purpose, the present invention has the following technical scheme:
in a first aspect, a digital twinning-based real-time county energy internet scheduling method is provided, which includes the following steps:
constraint is carried out on a pre-established county energy internet scheduling model to obtain an actual working condition scheduling model;
the method comprises the steps of establishing an objective function, wherein the total power deviation between frequency modulation instruction output and actual power output is minimum;
and solving the actual working condition scheduling model according to the objective function by combining the autoregressive comprehensive moving average model and the balance optimizer algorithm to obtain the optimal real-time county energy Internet scheduling scheme.
As a preferred scheme, the following operations are performed through a county energy internet scheduling model: (1) When the main power grid receives random disturbance, transmitting a total regulation command from the main power grid to the county energy Internet scheduling model; (2) The county energy internet scheduling model solves the optimal real-time county energy internet scheduling scheme and issues the total adjustment command to each frequency modulation unit.
In the step of constraining the pre-established county energy internet scheduling model, the county energy internet scheduling model takes coal-fired units, hydroelectric units, liquefied natural gas, wind power units and photovoltaics as scheduling objects, and the set constraint conditions comprise adjustment direction consistency constraint, power balance constraint, adjustment capacity constraint and power generation adjustment constraint.
As a preferable mode, the consistency constraint of the adjustment direction means that in the kth control section, the adjustment direction of the unit power command is consistent with the direction of the adjustment command, and the expression is as follows:
in the method, in the process of the invention,is the input power command, delta P, received by the ith county energy Internet scheduling model unit at the kth control interval m (k) A signal representing an energy internet dispatch model from a main grid to a county;
The power balance constraint refers to that in the kth control interval, the accumulation of power adjustment input commands received by all county energy internet scheduling model units is equal to an adjustment command issued by a total power main power network, and the expression is as follows:
and the capacity regulation constraint means that in the kth control interval, the power regulation input instructions received by all county energy Internet scheduling model units do not exceed the minimum and maximum capacities of the corresponding county energy Internet scheduling model units, and the expression is as follows:
in the method, in the process of the invention,and->The minimum capacity and the maximum capacity of the i-th county energy Internet scheduling model unit are respectively;
the power generation regulation constraint is expressed as follows:
in the method, in the process of the invention,the output power commands received by the i county energy Internet scheduling model unit at the k-1 control intervals are respectively; deltaT represents the time length of a control interval, deltaP i And the maximum slope of the i-th county energy Internet scheduling model unit is represented.
As a preferred solution, in the step of establishing an objective function to achieve minimum total power deviation between the frequency modulation command output and the actual power output, the objective function considers the expected power output of the next adjacent control interval as follows:
Wherein DeltaM m (k+1) is in the (k+1) th control regionInter-predicted total power command, whileIs the optimal power output at the (k+1) th control interval according to the predicted total power command; ΔP m (k) A signal representing an energy internet dispatch model from a main grid to a county; />Is the output power command received by the i-th county energy internet scheduling model unit at the k+1th control interval.
As a preferred scheme, according to the objective function, combining an autoregressive comprehensive moving average model and a balance optimizer algorithm, solving an actual working condition scheduling model, and in the step of obtaining an optimal real-time county energy internet scheduling scheme, predicting an adjustment command of a next adjacent control interval according to a historical adjustment command by using the autoregressive comprehensive moving average model; and optimizing the current and next predicted scheduling schemes according to the total power deviation between the current control interval and the next adjacent control interval, and optimizing the scheduling scheme between the two adjacent control intervals by utilizing a balance optimizer algorithm to obtain the optimal real-time county energy Internet scheduling scheme.
As a preferred embodiment, the step of predicting the adjustment command of the next adjacent control interval from the historical adjustment command using an autoregressive integrated moving average model is given by:
ε k ＝ΔP m (k)-ΔP m (k-1)
Wherein ε k Representing the sequence difference of the kth time interval; ΔM m (k+1) is the predicted total power command at the (k+1) th control interval; ΔP m (k) Representing energy interconnection from a main grid to countyA signal of a network scheduling model; alpha i For the corresponding i-order sequence lag coefficient, beta i And the hysteresis coefficient is the corresponding i-order sequence difference.
As a preferred solution, the step of optimizing the scheduling solution between two adjacent control intervals by using a balance optimizer algorithm to obtain an optimal real-time county energy internet scheduling solution, where the specific operation of the balance optimizer algorithm includes:
parameter initialization: initializing a volume V, maximum iteration times T and an optimal solution set S 0 Initial solution N 0 ；
Cluster initialization: firstly, setting the lower limit and the upper limit of all optimization variables as the lower limit and the upper limit adjustment capability of all county energy Internet scheduling model units respectively, and then, in a solution space P 0 In initializing clusters Generating an initialization solution according to the corresponding capacity, the expression is as follows:
in the method, in the process of the invention,representing an ith dimension of the initialization solution; ΔP m (k) A signal representing an energy internet dispatch model from a main grid to a county; />And->The minimum capacity and the maximum capacity of the i-th county energy Internet scheduling model unit are respectively;
The fitness is calculated as follows:
wherein f (X) j ) Represents the objective function value of the jth cluster, F (X j ) Representing the fitness value of the jth cluster,and->Minimum and maximum adjustment capacities of the b-th county energy Internet scheduling model unit are respectively;
selecting optimal individuals from the five currently optimal candidate solutions to form a balance state pool as follows:
X eq,pool ＝{X eq,1 ,X eq,2 ,X eq,3 ,X eq,4 ,X eq,ave }
wherein X is eq,1 ,X eq,2 ,X eq,3 ,X eq,4 The best four solutions found by the current iteration are respectively cut off; x is X eq,ave Representing the average state of the four solutions; the probabilities of the five candidate solutions being selected are the same;
calculating an exponential term coefficient F, wherein the expression is as follows:
F＝a*sign(r-0.5)[e -λ -1]
wherein a is a weight constant coefficient of global search; sign is a sign function; r and lambda represent random number vectors, the dimension is consistent with the dimension of the optimized space, and each element value is a random number from 0 to 1;
the mass generation coefficient G is calculated with the expression:
G＝G CP (X eq -λX)
wherein G is CP Rate of generationControlling the parameter vector; x represents the newly generated current solution; x is X eq Represents the best solution currently found by the algorithm, r i The dimension is consistent with the dimension of the optimized space, and each element value is a random number from 0 to 1; e, e 0 A random number in the range of 0 to 1;
the current solution is calculated, and the expression is as follows:
X＝X ep +(X 0 -X ep )F+G(1-F)/λV
Wherein X is the newly generated current solution; x is X 0 The solution obtained in the last iteration is obtained; x is X eq For the best solution currently found.
In a second aspect, a digital twinning-based real-time county energy internet dispatching system is provided, which comprises:
the model constraint module is used for constraining a pre-established county energy internet scheduling model to obtain an actual working condition scheduling model;
the objective function building module is used for building an objective function by realizing that the total power deviation between the frequency modulation instruction output and the actual power output is minimum;
and the scheme solving module is used for solving the actual working condition scheduling model according to the objective function by combining the autoregressive comprehensive moving average model and the balance optimizer algorithm to obtain the optimal real-time county energy Internet scheduling scheme.
As a preferable scheme, the constraint conditions set by the model constraint module comprise adjustment direction consistency constraint, power balance constraint, adjustment capacity constraint and power generation adjustment constraint;
the consistency constraint of the adjusting direction means that in the kth control interval, the adjusting direction of the unit power command is consistent with the direction of the adjusting command, and the expression is as follows:
in the method, in the process of the invention,is the input power command, delta P, received by the ith county energy Internet scheduling model unit at the kth control interval m (k) A signal representing an energy internet dispatch model from a main grid to a county;
the power balance constraint refers to that in the kth control interval, the accumulation of power adjustment input commands received by all county energy internet scheduling model units is equal to an adjustment command issued by a total power main power network, and the expression is as follows:
and the capacity regulation constraint means that in the kth control interval, the power regulation input instructions received by all county energy Internet scheduling model units do not exceed the minimum and maximum capacities of the corresponding county energy Internet scheduling model units, and the expression is as follows:
in the method, in the process of the invention,and->The minimum capacity and the maximum capacity of the i-th county energy Internet scheduling model unit are respectively;
the power generation regulation constraint is expressed as follows:
in the method, in the process of the invention,the output power commands received by the i county energy Internet scheduling model unit at the k-1 control intervals are respectively; deltaT represents the time length of a control interval, deltaR i And the maximum slope of the i-th county energy Internet scheduling model unit is represented.
As a preferred solution, the objective function established by the objective function establishing module considers the expected power output of the next adjacent control interval as follows:
Wherein DeltaM m (k+1) is the predicted total power command at the (k+1) th control interval, whileIs the optimal power output at the (k+1) th control interval according to the predicted total power command; ΔP m (k) A signal representing an energy internet dispatch model from a main grid to a county; />Is the output power command received by the i-th county energy internet scheduling model unit at the k+1th control interval.
As a preferable scheme, the scheme solving module predicts the adjustment command of the next adjacent control interval according to the historical adjustment command by utilizing an autoregressive comprehensive moving average model; and optimizing the current and next predicted scheduling schemes according to the total power deviation between the current control interval and the next adjacent control interval, and optimizing the scheduling scheme between the two adjacent control intervals by utilizing a balance optimizer algorithm to obtain the optimal real-time county energy Internet scheduling scheme.
In a third aspect, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the digital twinning-based real-time county energy internet scheduling method when executing the computer program.
In a fourth aspect, a computer readable storage medium is provided, where the computer readable storage medium stores a computer program, where the computer program when executed by a processor implements the digital twinning-based real-time county energy internet scheduling method.
Compared with the prior art, the first aspect of the invention has at least the following beneficial effects:
in order to better coordinate the distribution of power instructions of a plurality of adjustment resources based on a digital twin technology, the invention combines an autoregressive integrated moving average model (autoregressive integrated moving average model, ARIMA) and a balance optimizer algorithm (equilibrium optimizer, EO), and specifically, firstly, the invention adopts the autoregressive integrated moving average model to extract the characteristics of historical data such as a frequency modulation unit, a load and the like, further rapidly predicts the load trend of a main power grid, and dynamically predicts and evaluates the power instructions of the next control period. The method mainly comprises two processes, namely, real-time prediction and evaluation of the system frequency modulation instruction under the dynamic working condition and power instruction distribution of the county energy Internet scheduling model. Then, in order to cooperate with the prediction of the real-time frequency modulation instruction of the main power grid and the optimization of the power scheduling instruction, the invention provides an autoregressive integrated moving-based balance optimizer algorithm (ARMA-EO) so as to rapidly and effectively acquire the real-time optimal frequency modulation scheme of the county energy Internet scheduling model. The autoregressive comprehensive moving average model provided by the invention has the advantages of high calculation speed, few parameters, simple prediction mechanism and the like, and can be used for predicting the future frequency modulation demand of the main power grid according to the difference of historical operation data mainly by utilizing the historical operation data. The balance optimizer algorithm based on autoregressive comprehensive movement provided by the invention considers the response speed of various frequency modulation resources and the frequency modulation instruction requirement of the main power grid, can acquire a more efficient cooperative control scheme, and further improves the stability of the regional power grid. According to the invention, according to the objective function, the autoregressive comprehensive moving average model and the balance optimizer algorithm are combined to solve the actual working condition scheduling model, so that the characteristics of historical operation data can be effectively extracted, the power adjustment instruction of the next control period in the future can be rapidly predicted, the real-time prediction of the county energy Internet scheduling main power grid operation state is facilitated, and meanwhile, better reference can be provided for solving the optimal cooperative control scheme of the regional power grid. Based on the prediction of the running state transition of the main power grid, the self-regression integrated movement-based balance optimizer algorithm provided by the invention can effectively consider the current and future time control intervals, and can efficiently acquire the optimal cooperative control scheme through the self-regression integrated movement-based balance optimizer algorithm.
It will be appreciated that the advantages of the second to fourth aspects may be found in the relevant description of the first aspect and are not repeated here.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required for the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, 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 schematic diagram of a pre-established county energy Internet scheduling model in an embodiment of the invention;
FIG. 2 is a schematic diagram of steps performed by a balance optimizer algorithm based on autoregressive integrated movement according to an embodiment of the present invention;
FIG. 3 is a time series diagram of an embodiment of the present invention utilizing an autoregressive moving average model to predict next adjacent control interval adjustment commands based on historical adjustment commands.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system configurations, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
In addition, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
The embodiment of the invention discloses a digital twinning-based real-time county energy Internet scheduling (county energy internet dispatch, CEID) method, which is used for effectively and efficiently acquiring a real-time county energy Internet scheduling scheme and mainly comprises the following steps of:
(1) And constructing a county energy Internet scheduling model frame. Mainly comprises two parts: the method comprises the steps of firstly, a main power grid prediction model based on an autoregressive integrated moving average model (autoregressive integrated moving average model, ARIMA), and secondly, a regional power grid cooperative control model based on a balance optimizer algorithm (equilibrium optimizer, EO);
(2) In the power distribution process, in order to ensure the stable operation of the system, constraints are added to a county energy Internet scheduling model;
(3) Constructing a CEID system framework of an autoregressive integrated moving-based balance optimizer algorithm (ARIMA-EO);
(4) Constructing an objective function aiming at realizing the minimum total power deviation between the frequency modulation instruction output and the actual power output;
(5) And solving the CEID system model by adopting an ARIMA-EO method to obtain the optimal dispatching scheme.
Referring to fig. 1, in one possible implementation, the CEID system mainly includes two operations. The first operation is to transmit a total regulation command from the main grid to the CEID when the main grid receives a random disturbance. The second operation is that the CEID issues the overall directives to the individual frequency modulation units according to an optimal algorithm of a specific rule. The CEID considers coal-fired units, hydro units, liquefied natural gas, wind units and photovoltaics as dispatch objects, and in addition, to simplify the model, the power transmission loss and the trend of reactive power are ignored, mainly focusing on two processes. The first step is the prediction of the running state of the main power grid under load disturbance, and the second step is the power instruction distribution of the CEID system.
In one possible implementation, some constraints are placed on the CEID, by:
in CEID, some constraints need to be considered in order to make the model as close to the actual conditions as possible. These constraints mainly include regulatory direction compliance constraints, power balance constraints, regulatory capacity constraints, and power Generation Regulatory Constraints (GRC), as follows:
1) Consistency constraint of adjustment direction: in order to fully utilize the regulating unit, in the kth control interval, the regulating direction of the unit power command should be completely consistent with the direction of the regulating command:
Is the input power command, ΔP, received by the ith CEID unit at the kth control interval m (k) Representing a signal from the main grid to the CEID.
2) Power balance constraint: in order to ensure an optimal scheduling scheme meeting the adjustment requirements of the main network, in the kth control interval, the summation of the power adjustment input commands received by all CEID units should be exactly equal to the adjustment command issued by the total power main network:
3) Adjusting capacity constraints: in order to ensure that the optimal scheduling scheme meets the actual running condition of the CEID units, in the kth control interval, the power adjustment input instructions received by all the CEID units should exceed the minimum and maximum capacities of the CEID units:
in the method, in the process of the invention,and->The minimum capacity and the maximum capacity of the i-th county energy Internet scheduling model unit are respectively;
4) GRC: among the different types of CEID units, the dynamic response model of renewable energy sources (such as photovoltaic power plants and wind power generation units) has lower delay and faster tuning performance than without GRC, whereas the dynamic response model of traditional units (such as coal-fired units, hydro units and liquefied natural gas units) should take GRC into account due to poor tuning performance. If the GRC and the power limiter are considered, the actual output of the CEID unit can be calculated as follows:
In the method, in the process of the invention,is the output power command received by the ith CEID unit at the kth control interval, Δt represents the length of time of one control interval (typically about 4s or 1 s), Δr i Is the maximum slope of the i-th cell.
In one possible implementation, a CEID system framework based on an autoregressive integrated moving balance optimizer algorithm (ARIMA-EO) is constructed as follows:
since the CEID should take into account the adjustment instructions of the last control interval and the next adjacent control interval, the framework of the auto-regressive integrated moving-based equilibrium optimizer algorithm (ARIMA-EO) should comprise two steps, as follows:
1) Adjustment command prediction: the next adjacent control interval is predicted from the historical adjustment command as shown in fig. 2. The prediction method is realized by ARIMA, and can help to quickly acquire the running state transition of the main power grid. And a relatively accurate power command in the next step will facilitate the next optimization process.
2) And (3) optimizing a scheduling scheme: and optimizing the current and next predicted scheduling schemes according to the total power deviation of the current control interval and the next adjacent control interval. And then adopting EO to optimize the dispatching optimization between the two control intervals.
In the real dynamic optimization of the CEID system power scheduling, the controller creates a series of power commands to balance the power disturbance, then the proposed ARIMA can help predict the power command of the next control interval for subsequent optimization, and finally the proposed EO optimization two-step optimization is adopted to obtain the optimal power scheduling scheme of the CEID system.
In one possible implementation, the method of establishing the objective function is as follows:
according to the prediction method, the algorithm can optimize more adjacent control intervals to improve the performance of dynamic optimization scheduling. Since the dynamic tuning performance of the CEID unit is of major concern, the optimization objective is to minimize the power deviation between the chirped output and the actual power chirped output. The calculation of the objective function should take into account the expected power output of the next adjacent control interval as follows:
wherein DeltaM m (k+1) is the predicted total power command at the (k+1) th control interval, whileIs the optimal power output at the (k+1) th control interval according to the predicted total power command.
In one possible implementation, the ARIMA-EO method is used to solve the CEID system model to obtain the best optimal scheduling scheme, and the method is as follows:
(1) ARIMA design
The conventional algorithm mainly focuses on the scheduling of the current time interval, but ignores the influence of the next adjacent control interval, so that the dynamic optimization effect is poor. In order to maintain consistency in the next adjacent control interval, it is critical to predict the total power command for the next control interval from historical operating data. Referring to fig. 3, the arima model is one of the time series autoregressive movement models that uses historical sequences to predict data for several time intervals in future sequences based on differences in the historical data. Typically, ARIMA contains three parts, which are a mixture of autoregressions, moving averages, and ARIMA.
ARIMA (p, D, q) represents the level difference D times, p is the level lag operator, and q is the level difference lag operator.
The time sequence of the total adjustment command for time interval k is given by
Y k+1 ＝α 0 +α 1 X k +α 2 X k-1 +…α p X k-p+1 +ε k -β 1 ε k-1 -β 2 ε k-2 -…-β q ε k-q (7)
Y k Is the predicted total power command, ε, for the (k+1) th control interval k Representing the number of steps of the kth time interval, X k Is the history sequence of the kth time interval, alpha i Is the corresponding i-order sequence lag coefficient, beta i Is the corresponding i-order sequence differential hysteresis coefficient.
In the solution incorporated in the invention, the time sequence of the total adjustment command for the time interval k is given by
ε k Represents the kth time intervalSequence differences of the partitions.
(2) EO design
Typically, EO consists essentially of 7 operations, as follows:
1) Parameter initialization: initializing a volume V, maximum iteration times T and an optimal solution set S 0 Initial solution N 0 。
2) Cluster initialization: first, the lower limit and the upper limit of all optimization variables are set as the lower limit and the upper limit adjustment capability of all CEID units, respectively. Then, in the solution space P 0 In initializing clustersGenerating an initialization solution according to the corresponding capacity, the expression is as follows:
representing the ith dimension of the initialization solution.
3) Calculating the fitness: to ensure that the optimal solution satisfies (1) the power balance constraint, the variable dim may be considered a known quantity when calculating the fitness value. In particular, the value of the balancing unit violates the capacity constraint adjustment in equation (3), and should be considered as a penalty term.
Thus, the fitness value may be calculated as follows:
wherein f (X) j ) Represents the objective function value of the jth cluster, F (X j ) Representing the fitness value of the jth cluster,and->The minimum and maximum adjustment capacities of the b-th unit, respectively.
4) Constructing a balance state pool: in order to improve the global searching capability of the algorithm and avoid sinking into low-quality local optimal solutions, an optimal individual is selected from five currently optimal candidate solutions, and an equilibrium state pool formed by the candidate solutions is as follows:
X eq,pool ＝{X eq,1 ,X eq,2 ,X eq,3 ,X eq,4 ,X eq,ave } (12)
Wherein X is eq,1 ,X eq,2 ,X eq,3 ,X eq,4 The best four solutions found by the current iteration are respectively cut off; x is X eq,ave Representing the average state of the four solutions. The probabilities of the five candidate solutions being selected are the same, all being 0.2.
5) Design exponential term coefficient F: for better balancing the local search and the global search of the algorithm, the method is designed according to the following exponential term coefficients:
wherein a is a weight constant coefficient of global search; sign is a sign function; r and lambda each represent a random number vector, the dimensions of which are consistent with the dimensions of the optimized space, and each element value is a random number from 0 to 1.
6) Calculating a quality generation coefficient G: to enhance the local optimizing capability of the algorithm, the mass generation rate is designed as follows:
G＝G CP (X eq -λX) (14)
wherein G is CP Generating a rate control parameter vector; x represents the newly generated current solution; x is X eq Represents the best solution currently found by the algorithm, r i Is a random number vector whose dimension is equal to that ofOptimizing space dimension consistency, wherein each element value is a random number from 0 to 1; r is (r) 0 Random numbers in the range of 0 to 1.
7) Calculating a current solution: based on the equilibrium pool state and the resulting exponential term coefficients and quality generation coefficients, the local and global search capabilities of the algorithm are improved, and the individual solutions may be updated as follows:
X＝X ep +(X 0 -X ep )F+G(1-F)/λV (16)
x represents the newly generated current solution; x is X 0 Representing the solution obtained in the last iteration; x is X eq Representing the best solution currently found by the algorithm.
The invention also provides a real-time county energy internet dispatching system based on digital twinning, which comprises:
the model constraint module is used for constraining a pre-established county energy internet scheduling model to obtain an actual working condition scheduling model;
the objective function building module is used for building an objective function by realizing that the total power deviation between the frequency modulation instruction output and the actual power output is minimum;
and the scheme solving module is used for solving the actual working condition scheduling model according to the objective function by combining the autoregressive comprehensive moving average model and the balance optimizer algorithm to obtain the optimal real-time county energy Internet scheduling scheme.
In one possible implementation, the constraint conditions set by the model constraint module include an adjustment direction consistency constraint, a power balance constraint, an adjustment capacity constraint, and a power generation adjustment constraint;
the consistency constraint of the adjusting direction means that in the kth control interval, the adjusting direction of the unit power command is consistent with the direction of the adjusting command, and the expression is as follows:
in the method, in the process of the invention,is the input power command, delta P, received by the ith county energy Internet scheduling model unit at the kth control interval m (k) A signal representing an energy internet dispatch model from a main grid to a county;
the power balance constraint refers to that in the kth control interval, the accumulation of power adjustment input commands received by all county energy internet scheduling model units is equal to an adjustment command issued by a total power main power network, and the expression is as follows:
and the capacity regulation constraint means that in the kth control interval, the power regulation input instructions received by all county energy Internet scheduling model units do not exceed the minimum and maximum capacities of the corresponding county energy Internet scheduling model units, and the expression is as follows:
in the method, in the process of the invention,and->The minimum capacity and the maximum capacity of the i-th county energy Internet scheduling model unit are respectively;
the power generation regulation constraint is expressed as follows:
in the method, in the process of the invention,the output power commands received by the i county energy Internet scheduling model unit at the k-1 control intervals are respectively; deltaT represents the time length of a control interval, deltaR i And the maximum slope of the i-th county energy Internet scheduling model unit is represented.
In one possible implementation, the objective function established by the objective function establishment module considers the expected power output of the next adjacent control interval as follows:
Wherein DeltaM m (k+1) is the predicted total power command at the (k+1) th control interval, whileIs the optimal power output at the (k+1) th control interval according to the predicted total power command; ΔP m (k) A signal representing an energy internet dispatch model from a main grid to a county; />Is the output power command received by the i-th county energy internet scheduling model unit at the k+1th control interval.
In one possible implementation, the solution module predicts the adjustment command of the next adjacent control interval according to the historical adjustment command using an autoregressive integrated moving average model; and optimizing the current and next predicted scheduling schemes according to the total power deviation between the current control interval and the next adjacent control interval, and optimizing the scheduling scheme between the two adjacent control intervals by utilizing a balance optimizer algorithm to obtain the optimal real-time county energy Internet scheduling scheme.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the real-time county energy internet scheduling method based on digital twinning when executing the computer program.
Another embodiment of the present invention further provides a computer readable storage medium, where a computer program is stored, where the computer program when executed by a processor implements the digital twin-based real-time county energy internet scheduling method.
The computer program comprises computer program code which may be in source code form, object code form, executable file or in some intermediate form, etc. The computer readable storage medium may include: any entity or device, medium, usb disk, removable hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier wave signals, telecommunications signals, software distribution media, and the like capable of carrying the computer program code. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals. For convenience of description, the foregoing disclosure shows only those parts relevant to the embodiments of the present invention, and specific technical details are not disclosed, but reference is made to the method parts of the embodiments of the present invention. The computer readable storage medium is non-transitory and can be stored in a storage device formed by various electronic devices, and can implement the execution procedure described in the method according to the embodiment of the present invention.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flowchart and/or block of the flowchart illustrations and/or block diagrams, and combinations of flowcharts and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (14)
1. A real-time county energy internet scheduling method based on digital twinning is characterized by comprising the following steps:
constraint is carried out on a pre-established county energy internet scheduling model to obtain an actual working condition scheduling model;
the method comprises the steps of establishing an objective function, wherein the total power deviation between frequency modulation instruction output and actual power output is minimum;
and solving the actual working condition scheduling model according to the objective function by combining the autoregressive comprehensive moving average model and the balance optimizer algorithm to obtain the optimal real-time county energy Internet scheduling scheme.
2. The digital twinning-based real-time county energy internet scheduling method according to claim 1, wherein the following operations are performed through a county energy internet scheduling model: (1) When the main power grid receives random disturbance, transmitting a total regulation command from the main power grid to the county energy Internet scheduling model; (2) The county energy internet scheduling model solves the optimal real-time county energy internet scheduling scheme and issues the total adjustment command to each frequency modulation unit.
3. The digital twinning-based real-time county energy internet dispatching method according to claim 1, wherein in the step of constraining the county energy internet dispatching model established in advance, the county energy internet dispatching model considers a coal-fired unit, a hydraulic unit, liquefied natural gas, a wind turbine unit and photovoltaics as dispatching objects, and the set constraint conditions comprise a regulation direction consistency constraint, a power balance constraint, a regulation capacity constraint and a power generation regulation constraint.
4. The digital twinning-based real-time county energy internet scheduling method according to claim 3, wherein the method comprises the following steps:
the consistency constraint of the adjusting direction means that in the kth control interval, the adjusting direction of the unit power command is consistent with the direction of the adjusting command, and the expression is as follows:
in the method, in the process of the invention,is the input power command, delta P, received by the ith county energy Internet scheduling model unit at the kth control interval m (k) A signal representing an energy internet dispatch model from a main grid to a county;
the power balance constraint refers to that in the kth control interval, the accumulation of power adjustment input commands received by all county energy internet scheduling model units is equal to an adjustment command issued by a total power main power network, and the expression is as follows:
and the capacity regulation constraint means that in the kth control interval, the power regulation input instructions received by all county energy Internet scheduling model units do not exceed the minimum and maximum capacities of the corresponding county energy Internet scheduling model units, and the expression is as follows:
in the method, in the process of the invention,and->The minimum capacity and the maximum capacity of the i-th county energy Internet scheduling model unit are respectively;
the power generation regulation constraint is expressed as follows:
in the method, in the process of the invention, The output power commands received by the i county energy Internet scheduling model unit at the k-1 control intervals are respectively; deltaT represents the time length of a control interval, deltaT i And the maximum slope of the i-th county energy Internet scheduling model unit is represented.
5. The digital twinning-based real-time county energy internet scheduling method according to claim 1, wherein the method comprises the following steps: in the step of establishing an objective function to achieve minimum total power deviation between the frequency modulation command output and the actual power output, the objective function considers the expected power output of the next adjacent control interval as follows:
wherein DeltaM m (k+1) is the predicted total power command at the (k+1) th control interval, whileIs the optimal power output at the (k+1) th control interval according to the predicted total power command; ΔP m (k) A signal representing an energy internet dispatch model from a main grid to a county; />Is the energy source of the ith countyThe internet scheduling model unit receives the output power command at the k+1th control interval.
6. The digital twinning-based real-time county energy internet scheduling method according to claim 1, wherein the method comprises the following steps: according to the objective function, combining an autoregressive comprehensive moving average model and a balance optimizer algorithm, solving an actual working condition scheduling model, and predicting an adjustment command of a next adjacent control interval according to a historical adjustment command by utilizing the autoregressive comprehensive moving average model in the step of obtaining an optimal real-time county energy Internet scheduling scheme; and optimizing the current and next predicted scheduling schemes according to the total power deviation between the current control interval and the next adjacent control interval, and optimizing the scheduling scheme between the two adjacent control intervals by utilizing a balance optimizer algorithm to obtain the optimal real-time county energy Internet scheduling scheme.
7. The digital twinning-based real-time county energy internet scheduling method according to claim 6, wherein the method comprises the following steps: the step of predicting the adjustment command of the next adjacent control interval according to the historical adjustment command by using the autoregressive integrated moving average model, wherein the time sequence of the total adjustment command of the autoregressive integrated moving average model for the time interval k is given by the following formula:
ε k ＝ΔP m (k)-ΔP m (k-1)
wherein ε k Representing the sequence difference of the kth time interval; ΔM m (k+1) is the predicted total power command at the (k+1) th control interval; ΔP m (k) A signal representing an energy internet dispatch model from a main grid to a county; alpha i For the corresponding i-order sequence lag coefficient, beta i And the hysteresis coefficient is the corresponding i-order sequence difference.
8. The digital twinning-based real-time county energy internet scheduling method according to claim 6, wherein the method comprises the following steps: the step of optimizing the scheduling scheme between two adjacent control intervals by utilizing a balance optimizer algorithm to obtain the optimal real-time county energy Internet scheduling scheme comprises the following specific operations:
parameter initialization: initializing a volume V, maximum iteration times T and an optimal solution set S 0 Initial solution N 0 ；
Cluster initialization: firstly, setting the lower limit and the upper limit of all optimization variables as the lower limit and the upper limit adjustment capability of all county energy Internet scheduling model units respectively, and then, in a solution space P 0 In initializing clusters Generating an initialization solution according to the corresponding capacity, the expression is as follows:
in the method, in the process of the invention,representing an ith dimension of the initialization solution; ΔP m (k) A signal representing an energy internet dispatch model from a main grid to a county; />And->The minimum capacity and the maximum capacity of the i-th county energy Internet scheduling model unit are respectively;
the fitness is calculated as follows:
wherein f (X) j ) Represents the objective function value of the jth cluster, F (X j ) Representing the fitness value of the jth cluster,andminimum and maximum adjustment capacities of the b-th county energy Internet scheduling model unit are respectively;
selecting optimal individuals from the five currently optimal candidate solutions to form a balance state pool as follows:
X eq,pool ＝{X eq,1 ,X eq,2 ,X eq,3 ,X eq,4 ,X eq,ave }
wherein X is eq,1 ,X eq,2 ,X eq,3 ,X eq,4 The best four solutions found by the current iteration are respectively cut off; x is X eq,ave Representing the average state of the four solutions; the probabilities of the five candidate solutions being selected are the same;
calculating an exponential term coefficient F, wherein the expression is as follows:
F＝a*sign(r-0.5)[e -λt -1]
wherein a is a weight constant coefficient of global search; sign is a sign function; r and lambda represent random number vectors, the dimension is consistent with the dimension of the optimized space, and each element value is a random number from 0 to 1;
The mass generation coefficient G is calculated with the expression:
G＝G CP (X eq -λX)
in the method, in the process of the invention,G CP generating a rate control parameter vector; x represents the newly generated current solution; x is X eq Represents the best solution currently found by the algorithm, r i The dimension is consistent with the dimension of the optimized space, and each element value is a random number from 0 to 1; r is (r) 0 A random number in the range of 0 to 1;
the current solution is calculated, and the expression is as follows:
X＝X ep +(X 0 -X ep )F+G(1-F)/λV
wherein X is the newly generated current solution; x is X 0 The solution obtained in the last iteration is obtained; x is X eq For the best solution currently found.
9. A digital twinning-based real-time county energy internet scheduling system, comprising:
the model constraint module is used for constraining a pre-established county energy internet scheduling model to obtain an actual working condition scheduling model;
the objective function building module is used for building an objective function by realizing that the total power deviation between the frequency modulation instruction output and the actual power output is minimum;
and the scheme solving module is used for solving the actual working condition scheduling model according to the objective function by combining the autoregressive comprehensive moving average model and the balance optimizer algorithm to obtain the optimal real-time county energy Internet scheduling scheme.
10. The digital twinning-based real-time county energy internet dispatch system of claim 9, wherein: the constraint conditions set by the model constraint module comprise adjustment direction consistency constraint, power balance constraint, adjustment capacity constraint and power generation adjustment constraint;
The consistency constraint of the adjusting direction means that in the kth control interval, the adjusting direction of the unit power command is consistent with the direction of the adjusting command, and the expression is as follows:
in the method, in the process of the invention,is the input power command, delta P, received by the ith county energy Internet scheduling model unit at the kth control interval m (k) A signal representing an energy internet dispatch model from a main grid to a county;
the power balance constraint refers to that in the kth control interval, the accumulation of power adjustment input commands received by all county energy internet scheduling model units is equal to an adjustment command issued by a total power main power network, and the expression is as follows:
and the capacity regulation constraint means that in the kth control interval, the power regulation input instructions received by all county energy Internet scheduling model units do not exceed the minimum and maximum capacities of the corresponding county energy Internet scheduling model units, and the expression is as follows:
in the method, in the process of the invention,and->The minimum capacity and the maximum capacity of the i-th county energy Internet scheduling model unit are respectively;
the power generation regulation constraint is expressed as follows:
in the method, in the process of the invention,the output power commands received by the i county energy Internet scheduling model unit at the k-1 control intervals are respectively; deltaT represents the time length of a control interval, deltaR i And the maximum slope of the i-th county energy Internet scheduling model unit is represented.
11. The digital twinning-based real-time county energy internet dispatch system of claim 9, wherein: the objective function established by the objective function establishing module considers the expected power output of the next adjacent control interval, and the expression is as follows:
wherein DeltaM m (k+1) is the predicted total power command at the (k+1) th control interval, whileIs the optimal power output at the (k+1) th control interval according to the predicted total power command; ΔP m (k) A signal representing an energy internet dispatch model from a main grid to a county; />Is the output power command received by the i-th county energy internet scheduling model unit at the k+1th control interval.
12. The digital twinning-based real-time county energy internet dispatch system of claim 9, wherein: the scheme solving module predicts the adjusting command of the next adjacent control interval according to the historical adjusting command by utilizing an autoregressive comprehensive moving average model; and optimizing the current and next predicted scheduling schemes according to the total power deviation between the current control interval and the next adjacent control interval, and optimizing the scheduling scheme between the two adjacent control intervals by utilizing a balance optimizer algorithm to obtain the optimal real-time county energy Internet scheduling scheme.
13. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized by: the processor executes the computer program to implement the digital twinning-based real-time county energy internet scheduling method according to any one of claims 1 to 8.
14. A computer-readable storage medium storing a computer program, characterized in that: the computer program, when executed by a processor, implements a digital twinning-based real-time county energy internet scheduling method according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311766003.1A CN117767433A (en) | 2023-12-20 | 2023-12-20 | Real-time county energy internet scheduling method and system based on digital twin |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311766003.1A CN117767433A (en) | 2023-12-20 | 2023-12-20 | Real-time county energy internet scheduling method and system based on digital twin |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117767433A true CN117767433A (en) | 2024-03-26 |
Family
ID=90321497
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311766003.1A Pending CN117767433A (en) | 2023-12-20 | 2023-12-20 | Real-time county energy internet scheduling method and system based on digital twin |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117767433A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117972365A (en) * | 2024-04-01 | 2024-05-03 | 四川省公路规划勘察设计研究院有限公司 | Tunnel bottom pile pressure bearing monitoring system and monitoring method |
-
2023
- 2023-12-20 CN CN202311766003.1A patent/CN117767433A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117972365A (en) * | 2024-04-01 | 2024-05-03 | 四川省公路规划勘察设计研究院有限公司 | Tunnel bottom pile pressure bearing monitoring system and monitoring method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108365608B (en) | Uncertain optimization scheduling method and system for regional energy Internet | |
| CN107039977B (en) | Robust scheduling uncertainty set construction method for power system | |
| CN117767433A (en) | Real-time county energy internet scheduling method and system based on digital twin | |
| CN109886446B (en) | Dynamic economic dispatching method of electric power system based on improved chaotic particle swarm algorithm | |
| CN112186811A (en) | AGC unit dynamic optimization method based on deep reinforcement learning | |
| CN113988481B (en) | Wind power prediction method based on dynamic matrix prediction control | |
| CN111917111B (en) | Method, system, equipment and storage medium for online evaluation of distributed photovoltaic power supply acceptance capacity of power distribution network | |
| CN115425668A (en) | Energy storage capacity optimal configuration method based on power system time sequence production simulation | |
| CN117674197B (en) | Frequency adjustment method, storage medium and equipment using virtual power plant active support | |
| CN115345380A (en) | New energy consumption electric power scheduling method based on artificial intelligence | |
| CN115796501A (en) | Multi-level and multi-target cooperative scheduling method and system for cascade hydropower station group | |
| CN115795992A (en) | Park energy Internet online scheduling method based on virtual deduction of operation situation | |
| CN109711612A (en) | Wind power generation power prediction method and device for optimizing echo state network | |
| CN113363976A (en) | Scene graph-based mid-term optimized scheduling method for wind, light and water complementary power generation system | |
| CN112531735A (en) | Power distribution method and device of automatic power generation control system based on machine learning | |
| CN110061510B (en) | Rapid solving method and system for time period decoupling safety constraint economic dispatch | |
| CN111798060A (en) | Power instruction optimal distribution method based on unit climbing rate estimation | |
| CN115115276A (en) | Virtual power plant scheduling method and system considering uncertainty and privacy protection | |
| CN116054179A (en) | Event-triggering-based reactive power preference control system and method for power system | |
| CN115833240A (en) | Capacity allocation optimization method of wind-light-water complementary system considering cascade hydropower station connection | |
| CN114298429A (en) | Power distribution network scheme aided decision-making method, system, device and storage medium | |
| CN114301062A (en) | Distributed energy optimization treatment device | |
| JP6950805B1 (en) | Power distribution device, power distribution method, and program | |
| CN114204613A (en) | Reactive compensation method and system for offshore wind farm access power system | |
| Yang et al. | Offshore Wind Power Prediction Based on Variational Mode Decomposition and Long Short Term Memory Networks |
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 |