CN111934359A - Equipment scheduling management method and system of comprehensive energy system - Google Patents
Equipment scheduling management method and system of comprehensive energy system Download PDFInfo
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- CN111934359A CN111934359A CN202010622361.5A CN202010622361A CN111934359A CN 111934359 A CN111934359 A CN 111934359A CN 202010622361 A CN202010622361 A CN 202010622361A CN 111934359 A CN111934359 A CN 111934359A
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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
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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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
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
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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
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected 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
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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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 provides a device scheduling management method and a device scheduling management system for an integrated energy system, wherein the device scheduling management method comprises the following steps: acquiring current predicted values of load demand and energy supply duration of various energy devices of the comprehensive energy system; inputting the current predicted value into a pre-constructed equipment scheduling management digital twin model DT3 to obtain the energy inflow, outflow and equipment capacity of various energy equipment; controlling the starting, stopping and output of various energy devices of the comprehensive energy system based on the energy inflow, outflow and device capacity of the various energy devices; the equipment scheduling management digital twin model DT3 is constructed by considering the coupling relationship among various energy source flows of the comprehensive energy system, input quantity constraint, power flow constraint and equipment operation scheduling cost constraint.
Description
Technical Field
The invention belongs to the technical field of control of an integrated energy system, and particularly relates to a method and a system for equipment scheduling management of the integrated energy system.
Background
With the development of social economy and the progress of technology, energy revolution and reserve by taking energy interconnection as a characteristic, a comprehensive energy system becomes one of important development trends of future energy field revolution, wherein a power network is taken as a core backbone, and a plurality of energy coupling complementary interconnection becomes one of main expression forms of the comprehensive energy system.
At present, due to the complexity of equipment in the integrated energy system, the equipment scheduling management of the integrated energy system has the following problems: firstly, only independent scheduling management is carried out on various energy devices in the comprehensive energy system, centralized scheduling management cannot be carried out on the various energy devices, and the flexibility of system energy scheduling is not high; secondly, the existing comprehensive energy system scheduling management mainly depends on scheduling personnel to pre-judge the load condition of the comprehensive energy system and issue an actual operation instruction to the main energy subsystem according to experience, and the operation is influenced by human factors, lacks stability and cannot perform real-time scheduling management on comprehensive energy equipment; therefore, how to solve the above problems is a problem to be urgently solved by those skilled in the art.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a device scheduling management method of an integrated energy system, which comprises the following steps:
acquiring current predicted values of load demand and energy supply duration of various energy devices of the comprehensive energy system;
inputting the current predicted value into a pre-constructed equipment scheduling management digital twin model DT3Obtaining the energy inflow, outflow and equipment capacity of various energy equipment;
controlling the starting, stopping and output of various energy devices of the comprehensive energy system based on the energy inflow, outflow and device capacity of the various energy devices;
the device scheduling management digital twin model DT3The system is constructed by considering the coupling relation among various energy source flows of the comprehensive energy source system, input quantity constraint, power flow constraint and equipment operation scheduling cost constraint.
Preferably, obtaining the current predicted values of the load demand and the energy supply duration of various energy devices of the integrated energy system includes:
acquiring current historical load data of various energy devices of the comprehensive energy system;
and inputting the current historical load data into a pre-constructed neural network model to obtain the current predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system.
Preferably, the device scheduling management digital twin model DT3The method comprises the following steps:
constructing a digital twin model DT for equipment scheduling management in a debugging stage by taking the minimum running cost of the comprehensive energy system as an objective function and considering the coupling relation among various energy source flows of the comprehensive energy system1；
Digital twin model DT (differential transformation) for equipment scheduling management based on debugging phase1Constructing a digital twin model DT for equipment scheduling management in an operation phase by considering input quantity constraint and power flow constraint of a comprehensive energy system2；
Managing a digital twin model DT based on said operational phase equipment scheduling2Constructing a device scheduling management digital twin model DT in consideration of the constraint of the operation scheduling cost of the integrated energy system device3(ii) a The operation scheduling cost constraint of the comprehensive energy system equipment is constructed by considering the scheduling response condition of each type of equipment.
Preferably, the digital twin model DT is managed based on said commissioning phase device schedule1Constructing a digital twin model DT for equipment scheduling management in an operation phase by considering input quantity constraint and power flow constraint of a comprehensive energy system2The method comprises the following steps:
digital twin model DT (differential transformation) for equipment scheduling management based on debugging phase1Considering the input quantity constraint of the comprehensive energy system, constructing a digital twin model DT for equipment scheduling management in the debugging stage1’；
Digital twin model DT (differential transformation) for equipment scheduling management based on debugging phase1', considering the flow constraint of the comprehensive energy system, constructing the digital twin model DT for equipment scheduling management in the operation stage2。
Preferably, the construction of the constraint of the input quantity of the integrated energy system comprises the following steps:
calculating the load demand and the debugging stage predicted value of the energy supply duration of various energy equipment of the comprehensive energy system based on the historical load data of the various energy equipment debugging stages of the comprehensive energy system;
inputting the predicted value of the debugging stage into a digital twin of equipment scheduling management of the debugging stageRaw model DT1Obtaining the energy inflow, outflow and equipment capacity of various energy equipment at the primary debugging stage;
debugging and controlling the output and the start and stop of various energy devices of the comprehensive energy system based on the energy inflow, outflow and device capacity of the various energy devices after one-time debugging;
determining the maximum value and the minimum value of the energy input quantity of the comprehensive energy system based on the operation data of multiple times of one-time debugging;
constructing an integrated energy system input quantity constraint based on the maximum value and the minimum value of the integrated energy system energy input quantity;
the debugging operation data comprises the energy inflow, the outflow and the equipment capacity of various energy equipment during the debugging period, and the actual load demand and the energy supply duration of various energy equipment of the comprehensive energy system.
Preferably, the construction of the tidal current constraint of the integrated energy system comprises the following steps:
inputting the predicted value of the debugging stage into a digital twin model DT for equipment scheduling management in the debugging stage1', obtaining the energy inflow, outflow and equipment capacity of various energy equipment in the secondary debugging stage;
debugging and controlling the output and the start and stop of various energy devices of the comprehensive energy system based on the energy inflow, outflow and device capacity of the various energy devices after secondary debugging;
determining the maximum value and the minimum value of the tidal current of the comprehensive energy system based on the operation data of multiple times of secondary debugging;
and constructing a comprehensive energy system power flow constraint based on the maximum value and the minimum value of the comprehensive energy system power flow.
Preferably, the management digital twin model DT is based on said operation phase equipment scheduling2Constructing a device scheduling management digital twin model DT in consideration of the constraint of the operation scheduling cost of the integrated energy system device3The method comprises the following steps:
a1, calculating the load demand and the operation stage predicted value of the energy supply duration of various energy equipment of the comprehensive energy system based on the historical load data of the various energy equipment of the comprehensive energy system in the operation stage;
a2 inputting the operation phase predicted value into operation phase equipment scheduling management digital twin model DT2Obtaining the energy inflow, outflow and equipment capacity of various energy equipment in the operation stage;
a3 controls the output and start and stop of various energy devices of the comprehensive energy system based on the energy inflow, outflow and device capacity of the various energy devices after operation;
a4 determining the operation scheduling cost constraint of the integrated energy system equipment based on the operation data after multiple operations, and adding the operation scheduling cost constraint of the integrated energy system equipment to the operation phase equipment scheduling management digital twin model DT2Obtaining the digital twin model DT of equipment scheduling management in the operation stage2’；
A5 judging the device scheduling management digital twin model DT2Whether the regulation and control result meets the predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system in the operation stage is judged: if yes, the equipment scheduling management digital twin model DT2' i.e. a digital twin model DT for equipment scheduling management3(ii) a If not, executing the steps A1-A5 until the device scheduling management digital twin model DT2The regulation and control result of the method meets the operation stage predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system;
the operation data comprises the energy inflow, outflow and equipment capacity of various energy equipment during operation, the dispatching response condition of various types of equipment, and the actual load demand and energy supply duration of various energy equipment of the comprehensive energy system.
Preferably, the debugging phase device scheduling management digital twin model DT1The following were used:
in the formula (I), the compound is shown in the specification,
representing a function for running optimum for debugging the integrated energy system in consideration of mutual coupling of energy sources, wherein PAs the input of energy-like flow, LIs the output of energy-like flow, RvIs the equipment capacity of the similar energy, and N is the total energy type; l is an output matrix of the comprehensive energy system, P is an input matrix of the comprehensive energy system, and C is a coupling coefficient matrix of the comprehensive energy, and represents the coupling relation among all energy forms; f. of2(P,Rv) C is the operation and maintenance cost constraint of the comprehensive energy system equipment, and c is the upper limit value of the operation and maintenance cost of various comprehensive energy equipment; e ()inαFor injecting power into the energy-like network node alpha, E ()outαFor power of the egress class energy network node alpha, neIs the total number of the energy-like network nodes.
Preferably, the debugging phase device scheduling management digital twin model DT1' the following:
in the formula, Pmax≤P≤PminThe total energy input amount of the system is restricted in the operation process of the comprehensive energy system, P is the total energy input amount of the comprehensive energy systemmaxIs the upper limit of the total energy input of the integrated energy system, PminIs the lower limit of the total energy input of the comprehensive energy system.
Preferably, the operation phase equipment scheduling management digital twin model DT2The following were used:
in the formula, Fmin≤F≤FmaxFor the network flow constraint of the integrated energy system, F is the network flow of the integrated energy system, FmaxUpper limit of network power flow for integrated energy system, FminUnder the network tide of the comprehensive energy systemAnd (4) limiting.
Preferably, the operation phase equipment scheduling management digital twin model DT2' the following:
in the formula (f)3(Etr,Etp,Est) C is the operation scheduling cost constraint of the integrated energy system equipment considering the scheduling response condition of each type of equipment, wherein EtrCost of equipment operating schedule for conducted equipment in similar energy under different energy execution conditions, EtpCost of equipment operation scheduling cost and E for energy conversion equipment in similar energy under different energy execution conditionsstAnd (4) cost for equipment operation scheduling of energy storage equipment in similar energy under different energy execution conditions.
Based on the same conception, the invention also provides an equipment scheduling management system of the comprehensive energy system, which comprises the following components:
the execution scene prediction module is used for acquiring the current predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system;
a digital twin simulation module for inputting the current predicted value into a pre-constructed equipment scheduling management digital twin model DT3Obtaining the energy inflow, outflow and equipment capacity of various energy equipment;
the equipment scheduling module is used for controlling the starting, the stopping and the output of various energy equipment of the comprehensive energy system based on the energy inflow, the outflow and the equipment capacity of the various energy equipment;
the device scheduling management digital twin model DT3The system is constructed by considering the coupling relation among various energy source flows of the comprehensive energy source system, input quantity constraint, power flow constraint and equipment operation scheduling cost constraint.
Preferably, the scene prediction module is executed, and includes:
the data acquisition unit is used for acquiring current historical load data of various energy devices of the comprehensive energy system;
and the neural network computing unit is used for inputting the current historical load data into a pre-constructed neural network model to obtain the current predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system.
Preferably, the system further comprises a model building module, the model building module comprising:
a debugging model building unit for building a digital twin model DT for equipment scheduling management in a debugging stage by taking the minimum running cost of the comprehensive energy system as an objective function and considering the coupling relation among various energy source flows of the comprehensive energy system1；
An operation model construction unit for managing a digital twin model DT based on said commissioning phase device scheduling1Constructing a digital twin model DT for equipment scheduling management in an operation phase by considering input quantity constraint and power flow constraint of a comprehensive energy system2；
An operation model revision unit for managing the digital twin model DT based on the operation phase device schedule2Constructing a device scheduling management digital twin model DT in consideration of the constraint of the operation scheduling cost of the integrated energy system device3(ii) a The operation scheduling cost constraint of the comprehensive energy system equipment is constructed by considering the scheduling response condition of each type of equipment.
Preferably, the operation model revision unit includes:
the revision subunit 1 is used for calculating the predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system in the operation stage based on the historical load data of the various energy equipment of the comprehensive energy system in the operation stage;
a revision subunit 2 for inputting the operation phase predicted value into an operation phase equipment scheduling management digital twin model DT2Obtaining the energy inflow, outflow and equipment capacity of various energy equipment in the operation stage;
the revising subunit 3 is used for controlling the output and the start and stop of various energy devices of the comprehensive energy system by the debugging subunit based on the energy inflow, the energy outflow and the device capacity of the various energy devices after operation;
a revising subunit 4, configured to determine an operation scheduling cost constraint of the integrated energy system device based on the operation data after the multiple operations, and add the operation scheduling cost constraint of the integrated energy system device to the operation phase device scheduling management digital twin model DT2Obtaining the digital twin model DT of equipment scheduling management in the operation stage2’；
A revision subunit 5 for judging the device scheduling management digital twin model DT2Whether the regulation and control result meets the predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system in the operation stage is judged: if yes, the equipment scheduling management digital twin model DT2' i.e. a digital twin model DT for equipment scheduling management3(ii) a If not, executing the functions of the step revision subunit 1-revision subunit 5 until the device scheduling management digital twin model DT2The regulation and control result of the method meets the operation stage predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system;
the operation data comprises the energy inflow, outflow and equipment capacity of various energy equipment during operation, the dispatching response condition of various types of equipment, and the actual load demand and energy supply duration of various energy equipment of the comprehensive energy system.
Compared with the closest prior art, the invention has the following beneficial effects:
the invention provides a device scheduling management method and a device scheduling management system for an integrated energy system, wherein the device scheduling management method comprises the following steps: acquiring current predicted values of load demand and energy supply duration of various energy devices of the comprehensive energy system; inputting the current predicted value into a pre-constructed equipment scheduling management digital twin model DT3 to obtain the energy inflow, outflow and equipment capacity of various energy equipment; controlling the starting, stopping and output of various energy devices of the comprehensive energy system based on the energy inflow, outflow and device capacity of the various energy devices; the equipment scheduling management digital twin model DT3 is constructed by considering the coupling relationship among various energy source flows of the comprehensive energy system, input quantity constraint, power flow constraint and equipment operation scheduling cost constraint;
meanwhile, the dispatching management process is modeled, the model is self-corrected according to the actual operation effect, the automatic dispatching management of the comprehensive energy system is realized, the real-time performance and the accuracy of the dispatching management of the comprehensive energy system are improved, and the safe and reliable operation of the comprehensive energy system is realized.
Drawings
Fig. 1 is a schematic diagram illustrating an apparatus scheduling management method of an integrated energy system according to the present invention;
FIG. 2 is a schematic diagram of an equipment scheduling management system of an integrated energy system according to the present invention;
fig. 3 is a flow chart of constructing a device scheduling management digital twin model according to an embodiment of the present invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
Example 1:
the embodiment of the invention discloses a device scheduling management method of an integrated energy system, which is shown in figure 1 and comprises the following steps:
s1, acquiring the current predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system;
s2 inputting the current predicted value into a pre-constructed equipment scheduling management digital twin model DT3Obtaining the energy inflow, outflow and equipment capacity of various energy equipment;
s3, controlling the starting, stopping and output of various energy devices of the comprehensive energy system based on the energy inflow, outflow and device capacity of the various energy devices;
the device scheduling management digital twin model DT3And constructing by considering the coupling relation among various energy source flows of the comprehensive energy system, input quantity constraint and power flow constraint and equipment operation scheduling cost constraint.
Before carrying out scheduling management on the comprehensive energy equipment, an equipment scheduling management digital twin model DT needs to be constructed in advance3The specific process of construction is shown in fig. 3, and specifically includes:
step A1, collecting data of energy equipment related to each energy form in the comprehensive energy system, wherein the data of each type of energy equipment comprises the data of electric energy equipment related to: such as voltage U, current I, line impedance Z, etc.; natural gas related data: such as upstream and downstream pressure P of the gas network pipelinegGas temperature T in gas pipe networkgThe friction coefficient f of the air network pipeline and the like; heating and cooling related equipment data: such as heat medium flow rate q and heat medium temperature TqAnd the like.
Step A2 is to construct a digital twin virtual model DT of the debugging stage of the integrated energy system corresponding to the integrated energy system by taking the minimum running cost of the integrated energy system as an objective function and considering the coupling relation among the energy flows of the integrated energy system1；
In particular, the digital twin virtual model DT of the debugging phase1As follows:
in the formula (I), the compound is shown in the specification,
representing the input P of energy streams taking into account the mutual coupling of the energy sourcesOutput LAnd the capacity R of each type of energy equipmentvThe comprehensive energy system debugging and running cost optimal function represents each energy type, and N represents the total energy type; in constraint condition f2(P,Rv) C represents the equipment operation and maintenance cost constraint in the comprehensive energy system, the cost constraint is constructed by considering various factors such as equipment capacity requirement, energy input quantity and the like, and c represents the equipment operation and maintenance cost in the comprehensive energy systemThe allowed value.
The coupling relationship between the energy flows of the integrated energy system can be expressed as:
L＝CP
in the formula (I), the compound is shown in the specification,
l is an output matrix of the comprehensive energy system, L1、L2…LNOutputting various types of energy of the comprehensive energy system;
p is the input matrix of the integrated energy system, P1、P2…PgInputting various types of energy in the comprehensive energy system;
c is a coupling coefficient matrix representing the coupling relation among the energy forms, eta is the conversion efficiency of the energy conversion device among the energy forms, etamnRepresenting the conversion efficiency between the mth energy source and the nth energy source;
rho is the distribution coefficient of each energy source to be proportionally distributed to different energy sources for transmission or conversion, and rhomnRepresenting the proportionality coefficient of the m-th energy source to the n-th energy source.
Wherein the energy exchange constraint under the type II energy form is as follows:
in the formula (I), the compound is shown in the specification,
E()inαrepresenting the energy injected into the type of energy network node alpha;
E()outαrepresenting the energy flowing out of this type of energy network node a;
nrepresenting the total number of nodes of the energy network of the first type.
Step A3, a neural network model is built, and the BP neural network is utilized to input historical load data (including energy consumption time interval and load size under the corresponding energy consumption time interval) of the comprehensive energy system to predict different types of energy negative finally needed in the future under each energy execution sceneLoad demand fxAnd a time period t of energy supplyg: the energy execution scene comprises various forms of energy transfer, load reduction and the like among various energy forms;
different types of energy load demand f finally needed in the future under each energy execution scenexAnd a time period t of energy supplygInputting the digital twin virtual model DT of the integrated energy system debugging stage1Obtaining the energy inflow, outflow and equipment capacity of various energy equipment at the primary debugging stage;
based on the energy inflow, outflow and equipment capacity of various energy equipment in the primary debugging stage, the start-up and output conditions of the relevant energy equipment are debugged and adjusted to meet the predicted energy execution scene.
Specifically, the construction of the neural network model comprises the following steps:
(1) initializing each parameter in BP neural network, and initializing learning sample number N in BP neural networktestNumber of predicted samples Npred(ii) a Number of nodes N of input layer, hidden layer and output layerin、Nhi、Nout(ii) a Weights ω from input layer to hidden layer and from hidden layer to output layerih、ωho(ii) a Threshold b between input layer and hidden layer, and between output layer and hidden layerh、bo. Inputting historical load data of the comprehensive energy system, normalizing the data, and setting the maximum training times E of the networkmaxLearning rate plrTarget error E0。
(2) Carrying out BP neural network sample training, and solving the output of each hidden layer and each output layer forwards;
wherein the hidden layer output is:
in the formula (I), the compound is shown in the specification,
logsig is the excitation function of the neural network;
x is a historical load data matrix of the comprehensive energy system;
ωih、bhand weights and thresholds from each node of the input layer to each node of the hidden layer are respectively set, i represents the ith node in the input layer, and h represents the ith node in the hidden layer.
The output of the output layer is:
in the formula (I), the compound is shown in the specification,
ωho、borespectively are the weight and the threshold value between each node of the output layer and each node of the hidden layer;
h denotes the h-th node in the hidden layer and o denotes the o-th node in the output layer.
(3) And calculating the deviation between the output of the output layer and the expected output, which is specifically represented as:
in the formula, OnetTo output the result as desired, OoutAnd outputting the result for the output layer.
When the error is larger than the target error, the weight omega between the output layer and the hidden layer is adjustedhoAnd a threshold value boAnd (5) correcting:
ωho t+1＝ωho t+plrOhi(Onet-Oout)
in the formula, t and t +1 represent iteration times; omegaho t、bo tRepresents the weight and threshold, omega, between the output layer and the hidden layer at the t-th iterationho t+1、bo t+1Representing the result after correcting the weight and the threshold between the output layer and the hidden layer after the t +1 th iteration;
for between input layer and hidden layerWeight value omega ofihAnd a threshold value bhAnd (5) correcting:
bh t+1＝bh t+plr(Onet-Oout)
in the formula, t and t +1 represent the number of iterations, ωih t、bh tRepresents the weight and threshold, ω, between the input layer and the hidden layer at the t-th iterationih t+1、bh t+1And representing the result after the weight and the threshold between the input layer and the hidden layer are modified after the t +1 th iteration.
(4) Repeating the step 2-3, carrying out iterative operation updating on the threshold and the weight, and solving the optimal weight and the optimal threshold; the weight value is adjusted between layer neurons, and the threshold value is adjusted in the neurons.
(5) Judging whether the algorithm iteration is finished, and when the error E is smaller than the target error, finishing the training of the BP neural network to obtain energy execution data, namely the energy load demand prediction quantity f of different typesxAnd a time period t of energy supplygAnd predicting the numerical value.
Step A4, determining the input quantity constraint of the integrated energy system based on the operation data (including the energy inflow, outflow and equipment capacity of various energy equipment in one debugging stage and the actual load demand and energy supply duration of various energy equipment in the integrated energy system) of multiple times of one debugging, and further correcting the digital twin virtual model DT in the debugging stage1Obtaining DT1', particularly, DT1The expression is as follows:
in the formula, Pmin≤P≤PmaxRepresenting system input quantity constraint in the debugging and running process of the integrated energy system, mainly coming from the debugging stage to the integrated energy systemAnd device operational constraints associated data.
Step A5 inputting the predicted value of the debugging phase into the digital twin model DT of equipment scheduling management in the debugging phase1', obtaining the energy inflow, outflow and equipment capacity of various energy equipment in the secondary debugging stage;
debugging and controlling the output and the start and stop of various energy devices of the comprehensive energy system based on the energy inflow, outflow and device capacity of the various energy devices after secondary debugging;
determining the tidal current constraint of the comprehensive energy system based on the operation data of multiple secondary debugging, and further based on the digital twin virtual model DT of the comprehensive energy system in the debugging stage1' construction of device scheduling management digital twin virtual model DT of integrated energy system operation phase2，DT2The expression is as follows:
in the formula, Fmin≤F≤FmaxAnd representing the system network flow constraint in the operation process of the integrated energy system.
Step A6 manages a digital twin virtual model DT according to the device scheduling of the operating phases2Inputting relevant data of energy execution prediction results in the operation stage, and controlling the output and start and stop of relevant energy equipment in the comprehensive energy system in the operation stage;
step A7, according to the energy execution data and start-stop and output condition data generated in the operation stage, determining the operation scheduling cost constraint of the comprehensive energy system equipment (constructed by considering the scheduling response condition of each type of equipment) and updating the equipment scheduling management digital twin virtual model DT in the operation stage2Obtaining the digital twin virtual model DT for equipment scheduling management in the operation stage2', particularly, DT2The expression is as follows:
in the formula (f)3(Etr,Etp,Est) C is the scheduling operation cost of the comprehensive energy system equipment considering the scheduling response condition of each type of equipment, and comprises various factors such as the proportion of equipment participating in each type of comprehensive energy scheduling, the required cost and the like, wherein the equipment types in the comprehensive energy system comprise: energy conduction equipment, energy conversion equipment and energy storage equipment;
the integrated energy system device scheduling operation cost considering the scheduling response condition of each type of device can be expressed as:
in the formula, Etr、Etp、EstRespectively representing the equipment start-stop scheduling cost of different energy transmission equipment, energy conversion equipment and energy storage equipment under different energy execution conditions; i isetr、Ietp、IestRespectively representing the equipment response degrees of the energy conduction equipment, the energy conversion equipment and the energy storage equipment under different energy execution conditions; sgn(Δ x) is a signal function that characterizes the scheduled execution class of the device, where Δ x ═ fx-fgRepresenting the difference between the demand and supply of a certain type of energy;
sgnthe value of the (Δ x) function is
sgnWhen (Δ x) ═ 0, the device does not participate in scheduling;
sgn(Δ x) +1, the device participates in energy transfer;
sgnwhen (Δ x) — 1, the device participates in load shedding.
Step A8, judging the integrated energy iterative scheduling digital twin virtual model DT2' if the demand of the predicted comprehensive energy execution scene is met, if the demand of the predicted comprehensive energy execution scene is metThe scenes are given by DT2' construction of final comprehensive energy equipment scheduling model DT3(ii) a If the predicted comprehensive energy execution scene is not satisfied, repeating the steps A7 to DT2' satisfying the expression requirement, and finally outputting the comprehensive energy scheduling model DT3。
And the digital twin virtual models are updated in an iterative manner, so that the expression precision of the digital twin is improved, and finally the dispatching management of the comprehensive energy system equipment is realized.
Example 2:
the embodiment of the invention discloses an equipment scheduling management system of a comprehensive energy system, which is shown in figure 2 and comprises the following components:
the execution scene prediction module is used for acquiring the current predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system;
a digital twin simulation module for inputting the current predicted value into a pre-constructed equipment scheduling management digital twin model DT3Obtaining the energy inflow, outflow and equipment capacity of various energy equipment;
the equipment scheduling module is used for controlling the starting, the stopping and the output of various energy equipment of the comprehensive energy system based on the energy inflow, the outflow and the equipment capacity of the various energy equipment;
the device scheduling management digital twin model DT3And constructing by considering the coupling relation among various energy source flows of the comprehensive energy system, input quantity constraint and power flow constraint and equipment operation scheduling cost constraint.
Preferably, the scene prediction module is executed, and includes:
the data acquisition unit is used for acquiring current historical load data of various energy devices of the comprehensive energy system;
and the neural network computing unit is used for inputting the current historical load data into a pre-constructed neural network model to obtain the current predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system.
Preferably, the system further comprises a model building module, the model building module comprising:
a debugging model building unit for building a digital twin model DT for equipment scheduling management in a debugging stage by taking the minimum running cost of the comprehensive energy system as an objective function and considering the coupling relation among various energy source flows of the comprehensive energy system1；
An operation model construction unit for managing a digital twin model DT based on said commissioning phase device scheduling1Constructing a digital twin model DT for equipment scheduling management in an operation phase by considering input quantity constraint and power flow constraint of a comprehensive energy system2；
An operation model revision unit for managing the digital twin model DT based on the operation phase device schedule2Constructing a device scheduling management digital twin model DT in consideration of the constraint of the operation scheduling cost of the integrated energy system device3(ii) a The operation scheduling cost constraint of the comprehensive energy system equipment is constructed by considering the scheduling response condition of each type of equipment.
Preferably, the operation model revision unit includes:
the revision subunit 1 is used for calculating the predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system in the operation stage based on the historical load data of the various energy equipment of the comprehensive energy system in the operation stage;
a revision subunit 2 for inputting the operation phase predicted value into an operation phase equipment scheduling management digital twin model DT2Obtaining the energy inflow, outflow and equipment capacity of various energy equipment in the operation stage;
the revising subunit 3 is used for controlling the output and the start and stop of various energy devices of the comprehensive energy system by the debugging subunit based on the energy inflow, the energy outflow and the device capacity of the various energy devices after operation;
a revising subunit 4, configured to determine an operation scheduling cost constraint of the integrated energy system device based on the operation data after the multiple operations, and add the operation scheduling cost constraint of the integrated energy system device to the operation phase device scheduling management digital twin model DT2Obtaining the device scheduling management number in the operation stageTwin model DT2’；
A revision subunit 5 for judging the device scheduling management digital twin model DT2Whether the regulation and control result meets the predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system in the operation stage is judged: if yes, the equipment scheduling management digital twin model DT2' i.e. a digital twin model DT for equipment scheduling management3(ii) a If not, executing the functions of the step revision subunit 1-revision subunit 5 until the device scheduling management digital twin model DT2The regulation and control result of the method meets the operation stage predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system;
the operation data comprises the energy inflow, outflow and equipment capacity of various energy equipment during operation, the dispatching response condition of various types of equipment, and the actual load demand and energy supply duration of various energy equipment of the comprehensive energy system.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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 application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present application and not for limiting the scope of protection thereof, and although the present application is described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that after reading the present application, they can make various changes, modifications or equivalents to the specific embodiments of the application, but these changes, modifications or equivalents are all within the scope of protection of the claims to be filed.
Claims (15)
1. An equipment scheduling management method of an integrated energy system is characterized by comprising the following steps:
acquiring current predicted values of load demand and energy supply duration of various energy devices of the comprehensive energy system;
inputting the current predicted value into a pre-constructed equipment scheduling management digital twin model DT3Obtaining the energy inflow, outflow and equipment capacity of various energy equipment;
controlling the starting, stopping and output of various energy devices of the comprehensive energy system based on the energy inflow, outflow and device capacity of the various energy devices;
the device scheduling management digital twin model DT3The system is constructed by considering the coupling relation among various energy source flows of the comprehensive energy source system, input quantity constraint, power flow constraint and equipment operation scheduling cost constraint.
2. The method according to claim 1, wherein the obtaining the current predicted values of the load demand and the energy supply duration of each type of energy equipment of the integrated energy system comprises:
acquiring current historical load data of various energy devices of the comprehensive energy system;
and inputting the current historical load data into a pre-constructed neural network model to obtain the current predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system.
3. The method of claim 1, wherein the device scheduling management digital twin model (DT)3The method comprises the following steps:
constructing a digital twin model DT for equipment scheduling management in a debugging stage by taking the minimum running cost of the comprehensive energy system as an objective function and considering the coupling relation among various energy source flows of the comprehensive energy system1；
Digital twin model DT (differential transformation) for equipment scheduling management based on debugging phase1Constructing a digital twin model DT for equipment scheduling management in an operation phase by considering input quantity constraint and power flow constraint of a comprehensive energy system2；
Managing a digital twin model DT based on said operational phase equipment scheduling2Constructing a device scheduling management digital twin model DT in consideration of the constraint of the operation scheduling cost of the integrated energy system device3(ii) a The operation scheduling cost constraint of the comprehensive energy system equipment is constructed by considering the scheduling response condition of each type of equipment.
4. The method of claim 3, wherein the step of removing comprises removing the substrate from the substrateIn that said managing a digital twin model DT based on said commissioning phase device schedule1Constructing a digital twin model DT for equipment scheduling management in an operation phase by considering input quantity constraint and power flow constraint of a comprehensive energy system2The method comprises the following steps:
digital twin model DT (differential transformation) for equipment scheduling management based on debugging phase1Considering the input quantity constraint of the comprehensive energy system, constructing a digital twin model DT for equipment scheduling management in the debugging stage1’；
Digital twin model DT (differential transformation) for equipment scheduling management based on debugging phase1', considering the flow constraint of the comprehensive energy system, constructing the digital twin model DT for equipment scheduling management in the operation stage2。
5. The method of claim 4, wherein the building of the integrated energy system input constraints comprises:
calculating the load demand and the debugging stage predicted value of the energy supply duration of various energy equipment of the comprehensive energy system based on the historical load data of the various energy equipment debugging stages of the comprehensive energy system;
inputting the predicted value of the debugging stage into a digital twin model DT for equipment scheduling management in the debugging stage1Obtaining the energy inflow, outflow and equipment capacity of various energy equipment at the primary debugging stage;
debugging and controlling the output and the start and stop of various energy devices of the comprehensive energy system based on the energy inflow, outflow and device capacity of the various energy devices after one-time debugging;
determining the maximum value and the minimum value of the energy input quantity of the comprehensive energy system based on the operation data of multiple times of one-time debugging;
constructing an integrated energy system input quantity constraint based on the maximum value and the minimum value of the integrated energy system energy input quantity;
the debugging operation data comprises the energy inflow, the outflow and the equipment capacity of various energy equipment during the debugging period, and the actual load demand and the energy supply duration of various energy equipment of the comprehensive energy system.
6. The method of claim 5, wherein the constructing of the integrated energy system flow constraint comprises:
inputting the predicted value of the debugging stage into a digital twin model DT for equipment scheduling management in the debugging stage1', obtaining the energy inflow, outflow and equipment capacity of various energy equipment in the secondary debugging stage;
debugging and controlling the output and the start and stop of various energy devices of the comprehensive energy system based on the energy inflow, outflow and device capacity of the various energy devices after secondary debugging;
determining the maximum value and the minimum value of the tidal current of the comprehensive energy system based on the operation data of multiple times of secondary debugging;
and constructing a comprehensive energy system power flow constraint based on the maximum value and the minimum value of the comprehensive energy system power flow.
7. The method of claim 4, wherein managing a digital twin model DT based on the operational phase device schedule2Constructing a device scheduling management digital twin model DT in consideration of the constraint of the operation scheduling cost of the integrated energy system device3The method comprises the following steps:
a1, calculating the load demand and the operation stage predicted value of the energy supply duration of various energy equipment of the comprehensive energy system based on the historical load data of the various energy equipment of the comprehensive energy system in the operation stage;
a2 inputting the operation phase predicted value into operation phase equipment scheduling management digital twin model DT2Obtaining the energy inflow, outflow and equipment capacity of various energy equipment in the operation stage;
a3 controls the output and start and stop of various energy devices of the comprehensive energy system based on the energy inflow, outflow and device capacity of the various energy devices after operation;
a4 determining the operation scheduling cost constraint of the integrated energy system device based on the operation data after multiple operations, and adding the operation scheduling cost constraint of the integrated energy system device to the operationDigital twin model DT for scheduling and managing phased equipment2Obtaining the digital twin model DT of equipment scheduling management in the operation stage2’；
A5 judging the device scheduling management digital twin model DT2Whether the regulation and control result meets the predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system in the operation stage is judged: if yes, the equipment scheduling management digital twin model DT2' i.e. a digital twin model DT for equipment scheduling management3(ii) a If not, executing the steps A1-A5 until the device scheduling management digital twin model DT2The regulation and control result of the method meets the operation stage predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system;
the operation data comprises the energy inflow, outflow and equipment capacity of various energy equipment during operation, the dispatching response condition of various types of equipment, and the actual load demand and energy supply duration of various energy equipment of the comprehensive energy system.
8. The method of claim 7, wherein the commissioning phase device schedule management digital twin model (DT)1The following were used:
DT1:
in the formula (I), the compound is shown in the specification,
representing a function for running optimum for debugging the integrated energy system in consideration of mutual coupling of energy sources, wherein PAs the input of energy-like flow, LIs the output of energy-like flow, RvIs the equipment capacity of the similar energy, and N is the total energy type; l is an output matrix of the comprehensive energy system, P is an input matrix of the comprehensive energy system, and C is a coupling coefficient matrix of the comprehensive energy, and represents the coupling relation among all energy forms; f. of2(P,Rv) C is not more than c for the operation and maintenance of the comprehensive energy system equipmentC, cost constraint, wherein the value c is an upper limit value of the operation and maintenance cost of various comprehensive energy equipment; e ()inαFor injecting power into the energy-like network node alpha, E ()outαFor power of the egress class energy network node alpha, neIs the total number of the energy-like network nodes.
9. The method of claim 8, wherein the commissioning phase device schedule management digital twin model (DT)1' the following:
DT1':
in the formula, Pmax≤P≤PminThe total energy input amount of the system is restricted in the operation process of the comprehensive energy system, P is the total energy input amount of the comprehensive energy systemmaxIs the upper limit of the total energy input of the integrated energy system, PminIs the lower limit of the total energy input of the comprehensive energy system.
10. The method of claim 9, wherein the operational phase device schedule management digital twin model DT2The following were used:
DT2:
in the formula, Fmin≤F≤FmaxFor the network flow constraint of the integrated energy system, F is the network flow of the integrated energy system, FmaxUpper limit of network power flow for integrated energy system, FminThe network trend lower limit of the comprehensive energy system.
11. The method of claim 10, wherein the operational phase device schedule management digital twin model DT2' the following:
DT2’:
in the formula (f)3(Etr,Etp,Est) C is the operation scheduling cost constraint of the integrated energy system equipment considering the scheduling response condition of each type of equipment, wherein EtrCost of equipment operating schedule for conducted equipment in similar energy under different energy execution conditions, EtpCost of equipment operation scheduling cost and E for energy conversion equipment in similar energy under different energy execution conditionsstAnd (4) cost for equipment operation scheduling of energy storage equipment in similar energy under different energy execution conditions.
12. An equipment scheduling management system for an integrated energy system, comprising:
the execution scene prediction module is used for acquiring the current predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system;
a digital twin simulation module for inputting the current predicted value into a pre-constructed equipment scheduling management digital twin model DT3Obtaining the energy inflow, outflow and equipment capacity of various energy equipment;
the equipment scheduling module is used for controlling the starting, the stopping and the output of various energy equipment of the comprehensive energy system based on the energy inflow, the outflow and the equipment capacity of the various energy equipment;
the device scheduling management digital twin model DT3The system is constructed by considering the coupling relation among various energy source flows of the comprehensive energy source system, input quantity constraint, power flow constraint and equipment operation scheduling cost constraint.
13. The system of claim 12, wherein the executing the scene prediction module comprises:
the data acquisition unit is used for acquiring current historical load data of various energy devices of the comprehensive energy system;
and the neural network computing unit is used for inputting the current historical load data into a pre-constructed neural network model to obtain the current predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system.
14. The system of claim 12, further comprising a model building module, the model building module comprising:
a debugging model building unit for building a digital twin model DT for equipment scheduling management in a debugging stage by taking the minimum running cost of the comprehensive energy system as an objective function and considering the coupling relation among various energy source flows of the comprehensive energy system1；
An operation model construction unit for managing a digital twin model DT based on said commissioning phase device scheduling1Constructing a digital twin model DT for equipment scheduling management in an operation phase by considering input quantity constraint and power flow constraint of a comprehensive energy system2；
An operation model revision unit for managing the digital twin model DT based on the operation phase device schedule2Constructing a device scheduling management digital twin model DT in consideration of the constraint of the operation scheduling cost of the integrated energy system device3(ii) a The operation scheduling cost constraint of the comprehensive energy system equipment is constructed by considering the scheduling response condition of each type of equipment.
15. The system of claim 14, wherein the operational model revision unit comprises:
the revision subunit 1 is used for calculating the predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system in the operation stage based on the historical load data of the various energy equipment of the comprehensive energy system in the operation stage;
a revision subunit 2 for inputting the operation phase predicted value into an operation phase equipment scheduling management digital twin model DT2Obtaining the energy inflow, outflow and equipment capacity of various energy equipment in the operation stage;
the revising subunit 3 is used for controlling the output and the start and stop of various energy devices of the comprehensive energy system by the debugging subunit based on the energy inflow, the energy outflow and the device capacity of the various energy devices after operation;
a revising subunit 4, configured to determine an operation scheduling cost constraint of the integrated energy system device based on the operation data after the multiple operations, and add the operation scheduling cost constraint of the integrated energy system device to the operation phase device scheduling management digital twin model DT2Obtaining the digital twin model DT of equipment scheduling management in the operation stage2’；
A revision subunit 5 for judging the device scheduling management digital twin model DT2Whether the regulation and control result meets the predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system in the operation stage is judged: if yes, the equipment scheduling management digital twin model DT2' i.e. a digital twin model DT for equipment scheduling management3(ii) a If not, executing the functions of the step revision subunit 1-revision subunit 5 until the device scheduling management digital twin model DT2The regulation and control result of the method meets the operation stage predicted values of the load demand and the energy supply duration of various energy equipment of the comprehensive energy system;
the operation data comprises the energy inflow, outflow and equipment capacity of various energy equipment during operation, the dispatching response condition of various types of equipment, and the actual load demand and energy supply duration of various energy equipment of the comprehensive energy system.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114897447A (en) * | 2022-07-12 | 2022-08-12 | 北京智芯微电子科技有限公司 | Comprehensive energy cooperative control method and system |
| CN116226263A (en) * | 2023-01-03 | 2023-06-06 | 大唐可再生能源试验研究院有限公司 | Renewable energy source visual intelligent pipe control method and system |
| CN116719861A (en) * | 2023-06-27 | 2023-09-08 | 哈尔滨源芯智能科技发展有限公司 | Multi-source data interaction management system and method based on big data |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114897447A (en) * | 2022-07-12 | 2022-08-12 | 北京智芯微电子科技有限公司 | Comprehensive energy cooperative control method and system |
| CN116226263A (en) * | 2023-01-03 | 2023-06-06 | 大唐可再生能源试验研究院有限公司 | Renewable energy source visual intelligent pipe control method and system |
| CN116719861A (en) * | 2023-06-27 | 2023-09-08 | 哈尔滨源芯智能科技发展有限公司 | Multi-source data interaction management system and method based on big data |
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