CN115423161A - Digital twin-based multi-energy coupling optimization scheduling method and system - Google Patents

Digital twin-based multi-energy coupling optimization scheduling method and system Download PDF

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CN115423161A
CN115423161A CN202210999936.4A CN202210999936A CN115423161A CN 115423161 A CN115423161 A CN 115423161A CN 202210999936 A CN202210999936 A CN 202210999936A CN 115423161 A CN115423161 A CN 115423161A
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energy
power
digital twin
cost function
twin
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于芃
邢家维
孙树敏
李勇
程艳
王玥娇
李笋
王士柏
王楠
关逸飞
张兴友
周光奇
刘奕元
赵帅
王彦卓
常万拯
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Shandong Electric Power Co Ltd
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Shandong Electric Power Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/088—Non-supervised learning, e.g. competitive learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • 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
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • 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
    • 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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • 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
    • 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
    • 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]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/30—The power source being a fuel cell
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention belongs to the technical field of energy optimization scheduling, and provides a digital twin-based multi-energy coupling optimization scheduling method and system. The method comprises the steps of obtaining historical operation data, equipment constraint conditions and initial states of the multi-energy-flow comprehensive energy system, and predicting similar daily load and power generation data based on a deep neural network; correcting the coefficient of the virtual space twin model corresponding to the multi-energy flow comprehensive energy system according to the predicted similar daily load and the predicted power generation data; and performing multi-objective optimization decision based on the maintenance cost function, the transaction cost function and the environmental protection cost function according to the electricity price and the equipment constraint condition of each time period to obtain an optimal solution set, and finally determining the charge and discharge power of the energy storage device, the output power of each unit and the power of a connecting line.

Description

Digital twin-based multi-energy coupling optimization scheduling method and system
Technical Field
The invention belongs to the technical field of energy optimization scheduling, and particularly relates to a digital twin-based multi-energy coupling optimization scheduling method and system.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The traditional mode of mutually independent operation of electricity, heat and gas cannot meet the current energy development requirement, and a multi-energy flow system for realizing open interconnection of various energy sources is an important development direction of the current energy industry. The multi-energy flow system reduces the consumption and waste of energy sources, improves the comprehensive utilization efficiency of the energy sources, reduces the energy consumption cost and improves the economical efficiency and reliability of energy supply through the cascade development and intelligent utilization management of various energy sources.
The multi-energy flow system can effectively improve energy efficiency but also increases the complexity of the energy system. The multi-energy flow system is composed of a plurality of energy flow subsystems, and various energies are tightly coupled, so that the complexity of analysis is increased. The prior art establishes a theoretical framework and a technical practice route of a multi-energy microgrid distributed control system. In order to increase the utilization of the integrated energy system and in order to increase the utilization of the integrated energy system, a longitudinal optimization is carried out to achieve a lateral coupling of the electric-cold-hot-gas energy, taking into account the uncertainty of the source load. In the prior art, an efficient integrated energy system modeling method is applied to optimization modeling of electricity-cooling and hot-gas multi-energy flows, so that not only can the system optimization of integrated energy be improved, but also the planning and analysis of integrated energy innovation can be realized.
The inventor finds that most of the existing researches are limited to simple micro-grids or combined cooling heating and power systems, and few researches considering the optimal configuration of hydrogen energy flow in a multi-energy-flow comprehensive energy system are carried out; the source end and the energy storage end are not integrally adjusted; and the installed proportion and the utilization rate of renewable energy in the traditional multi-stream comprehensive energy system are still low.
Disclosure of Invention
In order to solve the technical problems existing in the background technology, the invention provides a digital twin-based multi-energy coupling optimization scheduling method and system, which predict and optimize future behavior trends according to corresponding information, and in the simulation operation process, the coefficient of a simulation operation model is adjusted by utilizing the difference between the simulation operation and a physical entity, so that the synchronization of the digital twin and the physical entity is realized.
In order to achieve the purpose, the invention adopts the following technical scheme:
the first aspect of the invention provides a digital twin-based multi-energy coupling optimization scheduling method, which comprises the following steps:
acquiring historical operating data, equipment constraint conditions and initial states of the multi-energy flow comprehensive energy system, and predicting similar daily loads and power generation data based on a deep neural network;
correcting the coefficient of the virtual space twin model corresponding to the multi-energy flow comprehensive energy system according to the predicted similar daily load and the predicted power generation data;
and performing multi-objective optimization decision based on a maintenance cost function, a transaction cost function and an environmental protection cost function according to the electricity price and the equipment constraint conditions of each time interval to obtain an optimal solution set, and finally determining the charge and discharge power of the energy storage device, the output power of each unit and the power of a connecting line.
A second aspect of the present invention provides a digital twin-based multi-energy coupling optimized scheduling system, comprising:
the data prediction module is used for acquiring historical operating data, equipment constraint conditions and initial states of the multi-energy flow comprehensive energy system and predicting similar daily loads and power generation data based on a deep neural network;
the coefficient correction module is used for correcting the coefficient of the virtual space twin model corresponding to the multi-energy flow comprehensive energy system according to the predicted similar daily load and the predicted power generation data;
and the target optimization module is used for carrying out multi-target optimization decision based on the maintenance cost function, the transaction cost function and the environmental protection cost function according to the electricity price and the equipment constraint condition of each time interval to obtain an optimal solution set, and finally determining the charge and discharge power of the energy storage device, the output power of each unit and the power of the connecting line.
A third aspect of the present invention provides a computer readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the digital twin based multi-energy coupling optimized scheduling method as described above.
A fourth aspect of the present invention provides a computer device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the steps of the digital twin-based multi-energy coupling optimization scheduling method as described above.
Compared with the prior art, the invention has the beneficial effects that:
based on digital twin drive, a multi-energy flow comprehensive energy system simulation modeling and optimization control strategy is provided, a mirror image cooperative interaction mechanism containing a physical digital space is established, a multi-energy flow virtual entity containing electric gas hydrogen is established, a multi-target optimization scheduling strategy based on digital twin drive is provided according to equipment state information distribution and deep neural network power prediction, the source end and the energy storage end are integrally adjusted, and the installed proportion and the utilization rate of renewable energy in the multi-flow comprehensive energy system are improved; thereby reasonably planning and utilizing energy, reducing energy consumption and improving economic benefit.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a diagram of generation of a countermeasure network architecture based on deep convolution according to an embodiment of the present invention;
FIG. 2 is a diagram of an integrated energy system operating scenario according to an embodiment of the present invention;
FIG. 3 is a flowchart of the training of a countermeasure network based on deep convolution generation according to an embodiment of the present invention;
FIG. 4 is a sample comparison of a cold load scenario of an embodiment of the present invention and a typical real scenario;
FIG. 5 is a sample comparison of a thermal load scenario and a typical real scenario for an embodiment of the present invention;
FIG. 6 is a sample comparison of a new energy contribution generation scenario and a typical real scenario for an embodiment of the present invention;
FIG. 7 is a sample comparison of a temperature scene and a typical real scene in accordance with an embodiment of the present invention;
FIG. 8 is a flow chart of multi-objective optimization decision making using the NSGA-II algorithm in the embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
The embodiment provides a digital twin-based multi-energy coupling optimization scheduling method, which specifically comprises the following steps:
step 1: historical operating data of the multi-energy flow comprehensive energy system, equipment constraint conditions and initial states are obtained, and similar daily loads and power generation data are predicted based on the deep neural network.
The equipment constraint comprises network power flow constraint, energy storage charging and discharging constraint and equipment maximum output constraint.
In the specific implementation process of the step 1, the input of the deep neural network comprises meteorological information such as illumination conditions, humidity, temperature, wind speed and the like, and similar daily load and power generation prediction data are output through a plurality of hidden layers.
In some embodiments, the deep neural network may be a deep convolutional neural network or other existing deep neural network model, and those skilled in the art can specifically select the deep neural network according to the actual situation, and will not be described in detail here.
Step 2: and correcting the coefficient of the virtual space twin model corresponding to the multi-energy flow comprehensive energy system according to the predicted similar daily load and the predicted power generation data.
The key of the digital twin lies in that high-fidelity virtual entities of physical entities are constructed based on virtual space to simulate the behaviors in the real world, and the future behavior trend is predicted and optimized according to corresponding information. In the process of simulation operation, the coefficient of the simulation operation model is adjusted by utilizing the difference between the simulation operation and the physical entity, so that the synchronization of the digital twin and the physical entity is realized. The digital twin can also be fed back to the physical entity to realize the optimized operation of the physical entity, as shown in fig. 1.
The virtual space test entity generates panoramic information of equipment state, network frame topological structure, line parameters, electricity, heat, gas, hydrogen energy flow and the like based on the digital twin body, the optimization result of the simulation is input into the control equipment for testing, and the actual running state of the equipment can also be fed back into the twin body for improvement and optimization.
As shown in fig. 2, the virtual space twin body is built by integrating different networks such as Electricity, heat, gas, hydrogen, and traffic through various energy conversion devices and communication devices, and the system is connected with an electric grid (EN) and a Natural Gas grid (NGN). The Photovoltaic (PV), fan (Wind Turbine, WT), combined Heating and Power (CHP), electric Hydrogen production device (P2H), hydrogen Storage Tank (HT), fuel Cell (FC), thermal energy Storage device (TT), storage Battery (SB), and other devices are included inside.
And step 3: and performing multi-objective optimization decision based on a maintenance cost function, a transaction cost function and an environmental protection cost function according to the electricity price and the equipment constraint conditions of each time interval to obtain an optimal solution set, and finally determining the charge and discharge power of the energy storage device, the output power of each unit and the power of a connecting line.
A power flow model in the integrated energy system is constructed by using a traditional alternating current power flow model. In this embodiment, a node power balance equation is used as a basis, a newton-raphson algorithm is used, a jacobian matrix is constructed to perform iterative solution, and a state quantity of each node is calculated, so that a power flow distribution of a power network is obtained, and a calculation formula is given:
Figure BDA0003807086340000061
in the formula, P i And Q i Representing the injected active and reactive power at node i respectively,
Figure BDA0003807086340000062
representing the node voltage, and Y is the node admittance matrix. Re and Im represent the real and imaginary parts, and the asterisks indicate the conjugation.
The heat supply network structure is similar to a radial structure of a power distribution network and is divided into a hydraulic model and a thermal model. The hydraulic model state quantity to be solved mainly comprises pipeline flow and pressure loss h f ：
Figure BDA0003807086340000063
In the formula, A represents a node-branch incidence matrix; b represents a branch circular correlation matrix, and m represents the branch flow of each pipeline; m is q Is the flow through a heat source or load node; k is the pipeline drag coefficient matrix.
The thermodynamic model describes the heat balance behavior of the heating system at the nodes and in the heating pipeline
Figure BDA0003807086340000064
In the formula, T start And T end Respectively representing the temperature of the head and tail ends of the pipeline, T a Is at the temperature of the surroundings and is, α is a proportionality coefficient related to a pipeline parameter; l is the length of the pipeline; t is in And T cut Representing the temperature of the heating medium, m, respectively, injected into and discharged from a node in And m out Respectively representing the flow of the heating medium injected into and flowed out of a certain node; p h The thermal power required by the load or the thermal power provided by the heat source; c p Specific heat capacity of heat medium, T s Supplying water temperature to the node; t is o Is the node thermal mass exit temperature.
The natural gas system meets kirchhoff's law, and the pipeline model flow equation without considering the compressor is as follows:
Figure BDA0003807086340000071
wherein, F bd Is the natural gas flow rate of the pipeline bd; k is a radical of bd Is a parameter of the pipeline; s bd Is a parameter indicative of the direction of flow of the natural gas; p is a radical of b And p d The pressures at node b and node d, respectively.
When the comprehensive energy system operates economically, the maintenance cost of the distributed energy, the transaction cost with a power grid, a natural gas grid and a load and the emission of CO need to be considered 2 、SO 2 And NO x Etc. environmental protection costs.
A maintenance cost function:
Figure BDA0003807086340000072
wherein, c i,t The operation and maintenance cost coefficient of the distributed energy i at the time t; p i,t Is the output power of the micro source i at time t;
energy cost function:
f 2 ＝c buy,t P buy,t +c gas,t G buy,t -c sell,t P sell,t (6)
wherein, c buy,t And c sell,t Is the electricity purchase price and the electricity sale price at the moment t; p buy,t And P sell,t The electricity purchasing and selling quantity of the comprehensive energy system is realized; c. C gas,t Is the price of purchasing natural gas at time t; g buy,t Is the amount of natural gas purchased.
Environmental protection cost function:
Figure BDA0003807086340000073
wherein, c i,k Is a type of contaminant (NO) x 、SO 2 Or CO 2 ) Number of (2), λ i,k Is the unit treatment cost of the kth class of contaminants; p i,t Is the emission coefficient of the pollutants.
In the optimization process, panoramic information such as load, wind energy, historical photovoltaic power generation data, meteorological data and the like is input into a database management module. The initial state and constraints of each device are set to create a virtual image of the integrated energy system. And then, predicting load and wind power generation data based on a deep neural network algorithm. And modifying the twin model according to the physical model and the date of similarity. And then, performing multi-objective optimization decision by adopting an NSGA-II algorithm according to the electricity price and the constraint conditions of each time interval. Finally, as shown in fig. 8, based on the obtained optimal Pareto solution set, the charge and discharge power of the energy storage device, the output power of each cell, and the power of the tie line are determined.
To verify the effectiveness of the method provided by the embodiment, calculation analysis is performed by taking an actual industrial park in a certain area as an example. The comprehensive energy system comprises electric, heat, gas and hydrogen loads, a typical day is selected for carrying out 24h optimized scheduling, the power of the neural network is preliminarily predicted according to meteorological data of the environment where the equipment is located, and the twin database is synchronously updated. In addition, the actual value and the predicted value of the solar power generation power under similar conditions are compared through a similar day weather retrieval method, and the final predicted value of the digital twin is obtained after error algorithm correction. Typical daily load curves and wind-light maximum output predicted from historical data are shown in fig. 3 and 4.
The optimization method provided by the embodiment is adopted to solve the system, and the obtained hydrogen energy, electric energy and heat energy balance optimization result is shown in fig. 5-7. The hydrogen production of the P2H equipment provides hydrogen demand in the park, and the hydrogen storage tanks cooperate to improve the wind and light utilization rate and reduce the operation cost. As shown in fig. 5, the fuel cell has low energy conversion efficiency and is therefore used only at the late peak stage. And the P2H equipment is in a working state in the rest time, so that the load requirement and the hydrogen storage tank requirement are met. Meanwhile, the wind-powered electricity generation peak load can be used as a transferable electric load to realize a peak regulation function, and redundant electric energy of wind power and photovoltaic is consumed. In contrast, the battery has a more flexible scheduling strategy for smoothing out some short-term load fluctuations during the day, as shown in fig. 6, since it has a higher energy conversion efficiency than the hydrogen storage facility, although its environmental pollution costs are increased. The CHP unit needs to ensure the balance of heat energy and electric energy at the same time, proper output is selected, the vacant part of heat energy can be complemented by a gas boiler and heat storage energy, the waste of the heat energy is avoided as much as possible, and therefore the integral optimization of the system is realized. According to the calculation result, the economic cost of the comprehensive energy system before and after optimization is reduced by 2137 yuan, the utilization rate of wind and light energy is improved by more than 40% through reasonable planning of hydrogen energy storage and storage batteries, and the waste of heat energy is effectively reduced through heat energy storage.
Example two
The embodiment provides a digital twin-based multi-energy coupling optimization scheduling system, which comprises:
the data prediction module is used for acquiring historical operating data, equipment constraint conditions and initial states of the multi-energy flow comprehensive energy system and predicting similar daily loads and power generation data based on a deep neural network;
the coefficient correction module is used for correcting the coefficient of the virtual space twin model corresponding to the multi-energy flow comprehensive energy system according to the predicted similar daily load and the predicted power generation data;
and the target optimization module is used for carrying out multi-target optimization decision based on the maintenance cost function, the transaction cost function and the environmental protection cost function according to the electricity price and the equipment constraint condition of each time interval to obtain an optimal solution set, and finally determining the charge and discharge power of the energy storage device, the output power of each unit and the power of the connecting line.
It should be noted that, each module in the present embodiment corresponds to each step in the first embodiment one to one, and the specific implementation process is the same, which is not described herein again.
EXAMPLE III
The present embodiment provides a computer readable storage medium having stored thereon a computer program which, when being executed by a processor, implements the steps of the digital twin based multi-energy coupling optimized scheduling method as described above.
Example four
The present embodiment provides a computer device, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the program to implement the steps in the digital twin-based multi-energy coupling optimization scheduling method as described above.
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 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.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A digital twin-based multi-energy coupling optimal scheduling method is characterized by comprising the following steps:
acquiring historical operating data, equipment constraint conditions and initial states of the multi-energy flow comprehensive energy system, and predicting similar daily loads and power generation data based on a deep neural network;
correcting the coefficient of the virtual space twin model corresponding to the multi-energy flow comprehensive energy system according to the predicted similar daily load and the predicted power generation data;
and performing multi-objective optimization decision based on a maintenance cost function, a transaction cost function and an environmental protection cost function according to the electricity price and the equipment constraint conditions of each time interval to obtain an optimal solution set, and finally determining the charge and discharge power of the energy storage device, the output power of each unit and the power of a connecting line.
2. The digital twin based multi-energy coupling optimized scheduling method of claim 1, wherein the virtual space twin model connects the multi-energy flow integrated energy system with the power network and the natural gas grid through various energy conversion devices and communication devices.
3. The digital twin-based multi-energy coupling optimization scheduling method of claim 1, wherein an NSGA-II algorithm is adopted to perform multi-objective optimization decision to obtain an optimal Pareto solution set.
4. The digital twin based multi-energy coupling optimized scheduling method of claim 1, wherein the device constraints include network power flow constraints, energy storage charge and discharge constraints, and device maximum output constraints.
5. A digital twin based multi-energy coupling optimized dispatch system, comprising:
the data prediction module is used for acquiring historical operating data, equipment constraint conditions and initial states of the multi-energy flow comprehensive energy system and predicting similar daily loads and power generation data based on a deep neural network;
the coefficient correction module is used for correcting the coefficient of the virtual space twin model corresponding to the multi-energy flow comprehensive energy system according to the predicted similar daily load and the predicted power generation data;
and the target optimization module is used for carrying out multi-target optimization decision based on the maintenance cost function, the transaction cost function and the environmental protection cost function according to the electricity price and the equipment constraint condition of each time interval to obtain an optimal solution set, and finally determining the charging and discharging power of the energy storage device, the output power of each unit and the power of a connecting line.
6. The digital twin based multi-energy coupling optimized dispatch system of claim 5, wherein the virtual space twin model connects the multi-energy flow integrated energy system with the power network and the natural gas grid through various energy conversion devices and communication devices.
7. The digital twin-based multi-energy coupling optimization scheduling system of claim 5, wherein an NSGA-II algorithm is used to perform multi-objective optimization decision to obtain an optimal Pareto solution set.
8. The digital twin based multi-energy coupling optimized dispatch system of claim 5, wherein the device constraints include network power flow constraints, energy storage charge-discharge constraints, and device maximum output constraints.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for optimized scheduling of a digital twin based multi-energy coupling according to any of the claims 1-4.
10. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor when executing the program carries out the steps of the method of digitally twin based multi-energy coupling optimized scheduling according to any of the claims 1-4.
CN202210999936.4A 2022-08-19 2022-08-19 Digital twin-based multi-energy coupling optimization scheduling method and system Pending CN115423161A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115660226A (en) * 2022-12-13 2023-01-31 国网冀北电力有限公司 Power load prediction model construction method and construction device based on digital twins
CN115660232A (en) * 2022-12-26 2023-01-31 尚特杰电力科技有限公司 Ultra-short-term prediction method, device and system for wind power
CN116109335A (en) * 2023-04-10 2023-05-12 国网浙江省电力有限公司 New energy dynamic electricity price data management system based on digital twin

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115660226A (en) * 2022-12-13 2023-01-31 国网冀北电力有限公司 Power load prediction model construction method and construction device based on digital twins
CN115660232A (en) * 2022-12-26 2023-01-31 尚特杰电力科技有限公司 Ultra-short-term prediction method, device and system for wind power
CN116109335A (en) * 2023-04-10 2023-05-12 国网浙江省电力有限公司 New energy dynamic electricity price data management system based on digital twin
CN116109335B (en) * 2023-04-10 2023-09-08 国网浙江省电力有限公司 New energy dynamic electricity price data management system based on digital twin

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