KR102691608B1 - Digital twin-based convergence village-level microgrid operation system and method - Google Patents

Abstract

The digital twin-based convergence village-level microgrid operation system and method according to the embodiment can improve energy use efficiency and stability by analyzing the power patterns of power generation sources and loads in villages or complexes of different types with different peak times. In addition, through the embodiment, it is possible to share power operation and surplus power by type of demonstration site according to peak load and energy saving analysis, thereby reducing power purchase costs and linking power operation between demonstration complexes by type. In addition, through examples, we provide a plan to enhance the operation of the microgrid through digital twins, such as improving energy utilization and contributing to power grid stabilization through internal power operation of small-scale demonstration sites and energy sharing between complexes of each type.

Description

Digital twin-based convergence village-level microgrid operation system and method {DIGITAL TWIN-BASED CONVERGENCE VILLAGE-LEVEL MICROGRID OPERATION SYSTEM AND METHOD}

This disclosure relates to a microgrid operation system and method, and specifically, to a digital twin-based convergence village-level microgrid operation system and method.

Unless otherwise indicated herein, the material described in this section is not prior art to the claims of this application, and is not admitted to be prior art by inclusion in this section.

Microgrid is a power supply and management system that represents a distributed group of power networks and sources. Microgrids generate electricity by utilizing multiple energy sources such as solar panels, wind turbines, fuel cells, and diesel generators.

In addition, microgrids use energy storage systems (ESS), batteries, supercapacitor storage systems, etc. to adjust the mismatch between power production and consumption and store energy. Additionally, microgrids allow energy production and consumption to be managed at a local level. It enables independent operation unconnected to the power network and aims to improve the stability and sustainability of the power network by providing innovative methods of distributed power generation, storage and management.

However, microgrids do not have monitoring facilities installed, so it is difficult to determine the status of power generation and whether there is a breakdown. In addition, there is no standardized model for the operation and management of new and renewable energy facilities according to the various installation environments, energy consumption, and production patterns of microgrids. In addition, support measures are needed to establish a microgrid base to establish a distributed energy production and consumption system at the regional level. In order to move toward a distributed energy system, it is necessary to improve independent and autonomous power grid operation and management capabilities that take into account the unique characteristics of each village. To this end, it is necessary to supply power generation facilities such as AMI and solar power, taking into account the unique characteristics of villages such as rural areas and islands, while discovering the optimal power grid construction plan for each region. In addition, there is a growing demand for the development and operation of a prosumer-based open power platform through demonstration of direct power transactions between power producers, consumers, and service providers within the village.

1. Korean Patent Publication No. 10-2020-0109196 (2020.09.22) 2. Korean Patent Registration No. 10-1793149 (2017.10.27)

The digital twin-based convergence village-level microgrid operation system and method according to the embodiment implements a digital twin through modeling of energy sources and loads, and analyzes real-time power generation and load based on actual data through real-time digital twin data monitoring. and analyze peak load analysis and energy self-sufficiency rate.

In addition, the digital twin-based convergence village-level microgrid operation system and method according to the embodiment derives the energy savings rate, energy independence rate, and energy sharing rate through optimal peak load reduction through power analysis of each type of demonstration complex.

In addition, the digital twin-based convergence village-level microgrid operation system and method according to the embodiment generates standard references for peak load reduction, energy sharing rate, and energy independence rate for real-time control, and establishes a demonstration complex through the standard reference. Allows control of microgrid.

The digital twin-based convergence village-level microgrid operating system according to the embodiment collects power data for each type of demonstration complex, extracts energy sources and loads from the collected power data, and models the extracted energy sources and loads to construct the demonstration complex. a data server that creates a digital twin of; A digital twin that predicts the power generation and load of the demonstration complex based on the modeling results of the energy source and load of the demonstration complex by type, and analyzes real-time monitoring data based on actual measurement data for each demonstration complex; Real-time monitoring data includes power generation, load, and peak load, and digital twin; Simulates the analysis results of real-time monitoring data, calculates the energy savings rate, energy independence rate, and energy sharing rate through peak load reduction based on the simulation results, creates a standard reference for real-time control, and generates a standard reference according to the generated standard reference. Control the demonstration complex.

The digital twin-based convergence village-level microgrid operation system and method as described above can improve energy use efficiency and stability by analyzing the power patterns of power generation sources and loads in villages or complexes of different types with different peak times.

In addition, through the embodiment, it is possible to share power operation and surplus power by type of demonstration site according to peak load and energy saving analysis, thereby reducing power purchase costs and linking power operation between demonstration complexes by type.

In addition, through examples, we provide a plan to enhance the operation of the microgrid through digital twins, such as improving energy utilization and contributing to power grid stabilization through internal power operation of small-scale demonstration sites and energy sharing between complexes of each type.

The effects of the present invention are not limited to the effects described above, and should be understood to include all effects that can be inferred from the configuration of the invention described in the detailed description or claims of the present invention.

Figure 1 is a diagram showing a digital twin-based convergence village-level microgrid operation system according to an embodiment.
Figure 2 is a diagram showing the configuration of a data server according to an embodiment
Figure 3 is a diagram showing the data processing configuration of a digital twin according to an embodiment
Figure 4 is a diagram showing the real-time simulation data processing process of the digital twin according to an embodiment.
Figure 5 is a diagram showing a microgrid control process through linking virtual simulation data of a digital twin according to an embodiment.
Figure 6 is a diagram illustrating the functions provided by the digital twin-based convergence village-level microgrid operating system according to an embodiment.

Hereinafter, embodiments disclosed in the present specification will be described in detail with reference to the attached drawings. However, identical or similar components will be assigned the same reference numerals regardless of reference numerals, and duplicate descriptions thereof will be omitted. The suffixes “module” and “part” for components used in the following description are given or used interchangeably only for the ease of preparing the specification, and do not have distinct meanings or roles in themselves. Additionally, in describing the embodiments disclosed in this specification, if it is determined that detailed descriptions of related known technologies may obscure the gist of the embodiments disclosed in this specification, the detailed descriptions will be omitted. In addition, the attached drawings are only for easy understanding of the embodiments disclosed in this specification, and the technical idea disclosed in this specification is not limited by the attached drawings, and all changes included in the spirit and technical scope of the present invention are not limited. , should be understood to include equivalents or substitutes.

Terms containing ordinal numbers, such as first, second, etc., may be used to describe various components, but the components are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another.

When a component is said to be "connected" or "connected" to another component, it is understood that it may be directly connected to or connected to the other component, but that other components may exist in between. It should be. On the other hand, when it is mentioned that a component is “directly connected” or “directly connected” to another component, it should be understood that there are no other components in between.

In this application, terms such as “comprise” or “have” are intended to designate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are not intended to indicate the presence of one or more other features. It should be understood that this does not exclude in advance the possibility of the existence or addition of elements, numbers, steps, operations, components, parts, or combinations thereof.

In this specification, 'part' includes a unit realized by hardware, a unit realized by software, and a unit realized using both. Additionally, one unit may be realized using two or more pieces of hardware, and two or more units may be realized using one piece of hardware.

In this specification, some of the operations or functions described as being performed by a terminal, apparatus, or device may instead be performed on a server connected to the terminal, apparatus, or device. Likewise, some of the operations or functions described as being performed by the server may also be performed in a terminal, apparatus, or device connected to the server.

Hereinafter, the present invention will be described in detail with reference to the attached drawings.

Figure 1 is a diagram showing a digital twin-based convergence village-level microgrid operation system according to an embodiment.

Referring to Figure 1, the digital twin-based convergence village-level microgrid operating system according to the embodiment includes a demonstration complex (1, 2, 3), a data server (100), a digital twin (200), and a monitoring system (300). It may be configured to include. In the embodiment, the demonstration complexes 1, 2, and 3 are sites where convergence-type village-level microgrids are installed, and each demonstration complex includes an energy source and load. In the embodiment, the demonstration complex may be classified according to type, and the types of the demonstration complex include, but are not limited to, production factories, tourist complexes, residential complexes, and leisure complexes.

In the embodiment, the demonstration complexes 1, 2, and 3 for each type collect power data including actual power generation and load usage through sensors and PMD, and transmit it to the data server 100.

The data server 100 collects power data including energy sources and loads from each demonstration complex (1, 2, and 3) and creates a digital twin 200 of the demonstration complex microgrid through the power data. In the embodiment, the data server 100 implements a digital twin by modeling each energy source and load included in the power data.

In the embodiment, the digital twin 200 is a system that identically simulates power data including energy sources and loads of microgrids installed in each demonstration complex. In the embodiment, the digital twin 200 performs real-time power generation, load analysis, peak load analysis, and energy self-sufficiency analysis based on actual measured data. In an embodiment, the digital twin 200 transmits modeling data to the monitoring system 300, and the monitoring system 300 monitors the modeling data and analysis data transmitted from the digital twin 200.

Figure 2 is a diagram showing the configuration of a data server according to an embodiment.

Referring to FIG. 2, the data server 100 according to the embodiment may be configured to include a collection unit 110 and a modeling unit 240. The term 'part' used in this specification should be construed to include software, hardware, or a combination thereof, depending on the context in which the term is used. For example, software may be machine language, firmware, embedded code, and application software. As another example, hardware may be a circuit, processor, computer, integrated circuit, integrated circuit core, sensor, Micro-Electro-Mechanical System (MEMS), passive device, or a combination thereof.

The collection unit 110 collects power data for each type of demonstration complex and extracts energy sources and loads from the collected power data. Afterwards, the modeling unit 240 creates a digital twin by modeling the extracted energy source and load for each type of demonstration complex, and calculates the power generation and load of the demonstration complex based on the modeling results of the energy source and load performed on the generated digital twin. predict

In the embodiment, the collection unit 110 collects data about the energy source and load of the demonstration complex. In an embodiment, an energy source is a system that generates power and may include solar power, wind power, a fuel cell, a power grid, etc., and an energy load is a system that consumes power and may include buildings, facilities, etc. In embodiments, data regarding energy sources and loads may include power usage, energy generation, power demand patterns, operating hours, etc.

The processing unit 120 preprocesses the collected data on the energy source and load of the demonstration complex. In an embodiment, the preprocessing process may include processes such as noise removal, outlier removal, and missing value processing. Additionally, the processing unit 120 may normalize data, remove outliers, or adjust the scale of data through data preprocessing.

Figure 3 is a diagram showing the data processing configuration of a digital twin according to an embodiment.

Referring to FIG. 3, the digital twin 200 according to the embodiment may be configured to include an analysis unit 210, a generation unit 220, and a control unit 230.

In an embodiment, the digital twin 200 simulates energy production and use scenarios in the demonstration complex and supports interaction with real-time data. Additionally, in the embodiment, the digital twin 200 interacts with real-time data to reflect the current state and simulates interactions with external factors.

In the embodiment, the digital twin 200 is a virtual model that digitally simulates an actual demonstration complex. Digital twins simulate the operation of energy sources, energy conversion processes, and power production and use in real time. Additionally, in the embodiment, the digital twin 200 reflects external factors in the current microgrid state of the demonstration complex through real-time data integration. For example, the digital twin 200 may reflect external factors such as sensor data, environmental conditions, weather information, power rates, etc. in the simulation.

Additionally, in embodiments, digital twin 200 may simulate and test various scenarios. For example, the use of different energy sources, changes in power consumption patterns, and peak load management can be tested. Additionally, the digital twin 200 can predict and optimize energy production and use. For example, it can determine optimal charging and discharging times for energy storage devices or optimize energy sharing systems. Additionally, in embodiments, the digital twin 200 can predict and optimize energy production and use. Twin supports monitoring and control of the actual complex. For example, it tracks the current status of the demonstration complex in real time and adjusts energy production and use to maximize energy efficiency and manage peak load.

The analysis unit 210 monitors the digital twin 200 and analyzes real-time monitoring data based on actual measurement data for each demonstration complex. In an embodiment, monitoring data may include power generation, load, peak load, etc. Through monitoring data analysis, the analysis unit 210 determines the real-time power generation amount, load change pattern, and peak load occurrence time of each energy source.

In the embodiment, the analysis unit 210 analyzes the power usage patterns of power generation sources and loads in each type of village or complex with different peak times. To this end, the analysis unit 220 collects data related to power generation sources and loads in each village or complex. Data related to power generation sources and loads may include power generation from a power plant, power demand from the power grid, power consumption patterns in a town or complex, weather data, and daily or seasonal data. Afterwards, the analysis unit 210 separates data related to power generation sources and loads by time period. For example, the analysis unit 210 identifies different time zones such as peak hours, off-peak hours, weekends, and weekdays, and identifies peak load generation times. Specifically, the analysis unit 220 identifies the peak load generation time period in the demonstration complex for each type. The peak load occurs during the time when electricity demand is highest, which often occurs in the morning or evening. Thereafter, the analysis unit 210 analyzes the power generation source and load data patterns during peak load generation times and off-peak times. Analysis of power generation source and load data patterns during peak load generation times and off-peak times is to understand how power generation sources and loads change during peak times and how energy production and use patterns differ. Thereafter, the analysis unit 220 predicts the performance of the power generation source using weather data such as solar radiation and wind speed and estimates the amount of power generation during the peak load generation time. Additionally, the analysis unit 220 analyzes the correlation between weather and power generation. Additionally, the analysis unit 220 generates an efficient power management strategy for peak load generation times based on the analysis results. For example, the analysis unit 220 considers ways to reduce the peak load by adjusting the operation of the power generation source or managing the load during peak load occurrence times.

The generation unit 220 simulates the analysis results of the analysis unit 210, calculates the energy saving rate, energy self-sufficiency rate, and energy sharing rate through peak load reduction based on the power analysis results of the demonstration complex, and calculates the energy saving rate, energy independence rate, and energy sharing rate based on the calculated data. creates a standard reference for real-time control.

For example, the generator 220 divides the reduced peak load that calculates at least one of the energy savings rate, energy independence rate, and energy sharing rate as the maximum among the reduced peak loads into the size of the optimal peak load extracted as the optimal peak load, A standard reference can be created based on the savings time, size of optimal peak load reduction, and changes in energy savings rate, energy independence rate, and energy sharing rate due to optimal peak load reduction.

To this end, the generator 220 collects and analyzes data including peak load, energy production and use patterns, and performance of the energy storage system in the microgrid system. Afterwards, loads that can be reduced among the peak loads are identified. This means optimizing energy use to reduce peak loads. Thereafter, the generator 220 measures the energy consumption reduction rate due to the reduced peak load. This can be expressed as the difference between the energy saved and the initial peak load. In addition, we calculate the energy self-sufficiency rate, which is the ability of the microgrid system to generate energy on its own, and determine how much the reduced peak load changed the energy self-sufficiency rate. Additionally, the energy sharing ratio is calculated, which indicates the degree to which the microgrid system shares energy with other systems or the grid. In an embodiment, the generator 220 identifies the optimal peak load size by maximizing at least one of the energy savings rate, energy independence rate, and energy sharing rate. Additionally, the generator 220 calculates how long the peak load can be reduced and what effect the reduced peak load has on the system. Thereafter, the generator 220 calculates the amount of peak load reduction when the optimal peak load is applied. In an embodiment, the generator 220 may generate a reference for the control and operation of the microgrid system based on all calculated information, thereby defining a method for optimizing peak load management, energy storage and production, energy sharing, and self-reliance. Let it happen. For example, baseline references may include baseline figures and methods for distributing power during peak power usage, when to start up renewable energy systems and energy storage systems, when to connect and disconnect from the grid, and the baseline values for determining grid connection and disconnection. You can.

In the embodiment, the generator 220 generates various scenarios to reduce peak load for simulation of analysis results. For example, the generator 220 may change the peak load time to a time when the amount of power generation is maximum, or may generate a simulation scenario by adjusting the driving time of each load constituting the peak load. Afterwards, the results of each peak load reduction scenario are analyzed through simulation to calculate the energy savings rate. Energy saving rate represents the amount of power consumption reduction due to reduction of peak load. Additionally, the generator 220 calculates the energy self-sufficiency rate according to the simulation results. In the embodiment, the energy self-sufficiency rate is the amount of energy generated by the demonstration complex itself. Additionally, in the embodiment, the generator 220 calculates the energy production amount of the demonstration complex by type based on simulation and analysis results. Additionally, the generator 220 calculates the energy sharing ratio. Energy sharing refers to sharing or trading energy between demonstration complexes. In an embodiment, the generator 220 evaluates the energy sharing scenario through simulation and determines how much energy can be shared through energy trading with other complexes.

Afterwards, the generation unit 220 generates a standard reference for real-time control based on analysis results and data obtained through simulation. Baseline references take into account energy production and use patterns, peak load times, and energy consumption reduction goals. In embodiments, the baseline reference may be used to control the system via the digital twin during actual operation.

For example, the standard reference sets standard values for charging and discharging control of energy sources for each demonstration complex. Specifically, the standard reference may include the standard value of the peak load discharging the stored power for each demonstration complex, the standard value of the stored power for each demonstration complex that receives power from other demonstration complexes, etc. In the embodiment, the standard value may be set differently depending on the amount of power used, peak load, and stored power for each type of demonstration complex.

In addition, the generator 220 generates a power use schedule according to the power use pattern of each demonstration complex and analyzes the energy savings rate according to each generated power use schedule.

To this end, the generator 220 first collects data on power use patterns in each demonstration complex. This data may include daily or hourly power usage, weekly or seasonal power consumption patterns, peak load times, etc. Thereafter, the generator 220 generates a power use schedule based on the power use pattern of each demonstration complex. This schedule indicates how to increase or decrease power usage during specific times and may include strategies to minimize power usage or reduce peak loads, for example. Thereafter, the generator 220 analyzes power usage by applying the generated power usage schedule to simulation. These simulations show how power usage schedules can be implemented in the real world and provide predictive information on power usage. Additionally, the generator 220 calculates the energy saving rate according to each power use schedule based on the simulation results. This can indicate how much energy can be saved by executing a power usage schedule. Thereafter, the generator 220 evaluates the calculated energy saving rate and analyzes the effect of the generated power use schedule. Additionally, the generator 220 adjusts or optimizes the power use schedule based on the analysis results.

The control unit 230 controls the demonstration complex. For example, the control unit 230 monitors the demonstration complex in real time using the digital twin 200 and adjusts energy production and use of the demonstration complex based on the standard reference. At this time, peak load management and energy sharing can be supported.

In the embodiment, the control unit 230 connects power operation between demonstration complexes according to the amount of surplus power for each demonstration complex according to peak load and energy saving analysis. For example, the control unit 230 may extract a demonstration complex requiring connection whose energy self-sufficiency rate is below a certain level, and control the extracted demonstration complex requiring connection by linking it with a demonstration complex capable of supplying power whose amount of surplus power exceeds a certain level. Specifically, the control unit 230 can identify the power usage and load patterns of the demonstration complex capable of supplying power, and transfer power to the energy source of the demonstration complex requiring linkage during a time period when power use and load are within the standard range. In addition, according to the peak load generation pattern of the demonstration complex requiring connection, surplus power can be supplied to the demonstration complex requiring connection a certain time before the peak load occurs.

In addition, in the embodiment, the control unit 230 extracts a demonstration complex capable of selling power among the demonstration complexes capable of supplying power, where surplus power exceeds a certain percentage of the peak load of the corresponding demonstration complex. Afterwards, in the case of demonstration complexes capable of selling electricity, surplus power with different charges depending on the power supply time and amount of power supplied can be sold to demonstration complexes requiring linkage. For example, the control unit 230 receives bids from the demonstration complex that requires linking the billing of electricity to be sold in the demonstration complex capable of selling electricity, and wins the bid on one of the bids, through an auction method to link the demonstration complex with the ability to sell electricity. Allows for linking.

In an embodiment, the control unit 230 may set the unit rate of power to be sold in the power sales demonstration complex differently depending on the hourly load of the electricity sales capability demonstration complex. For example, the control unit 230 can calculate the cost of power sold to a demonstration complex requiring connection by setting the unit price of power to be sold in a demonstration complex capable of selling power to increase as the hourly load increases. This is to reflect the value of electricity sold during times of high load, such as summer, and to improve the profits of demonstration complexes capable of selling electricity.

In addition, the control unit 230 performs a process to improve the energy self-sufficiency rate according to the energy self-sufficiency rate of the demonstration complex requiring surplus power connection.

In the embodiment, the control unit 230 sets a demonstration complex with an energy self-sufficiency rate below a certain level as a demonstration complex that requires connection to surplus power. Afterwards, if the energy self-sufficiency rate of the demonstration complex requiring surplus power connection is included in the first range, which is the lowest range (e.g., 10 percent to 25 percent), an energy source additional facility plan for the demonstration complex requiring surplus power connection is created. In addition, the control unit 230 determines the load pattern and the peak load generation time when the energy self-sufficiency rate of the demonstration complex requiring connection to surplus power is included in the second range, which is the middle range (e.g., 25 percent to 40 percent). , the peak load can be lowered by adjusting the operation schedule of the facility corresponding to each load. In addition, the control unit 230 determines the load pattern and determines the energy source and load use schedule when the energy self-sufficiency rate of the demonstration complex requiring connection to surplus power is included in the second range, which is the middle range (e.g., 40 percent to 60 percent). is adjusted to reduce the amount of power supplied from the power supply demonstration complex.

In addition, the control unit 230 monitors the power generation source, storage device, load, and grid status within the microgrid installed in the demonstration complex through digital twin, and collects sensor and meter data from the demonstration complex in real time. Through this, abnormal situations in the demonstration complex are predicted and the microgrid is controlled through a process that responds to the predicted abnormal situations. For example, the control unit 230 predicts power supply interruption due to external causes such as weather, grid failure, equipment failure, or internal problems as an abnormal situation, and when power supply interruption is predicted, the microgrid itself maintains power or operates in an emergency situation. Make sure to utilize the power generation source.

In addition, the control unit 230 provides an extra power source or a smart switching system in preparation for this, as if one of the power generation sources in the microgrid fails, the entire system may be affected. For example, when an abnormal situation in which the storage device of the microgrid is broken is predicted, the control unit 230 operates a backup system for the storage device failure situation. This is to prevent problems with energy storage and supply, and the backup system for failure situations may include storing and receiving power from other storage devices or linking with other microgrids.

Additionally, when the load within the microgrid shows a change that exceeds a certain value, the control unit 230 stops supplying energy and determines the cause of the load change.

In addition, in the embodiment, the control unit 230 monitors the performance of the power generation source of the microgrid and, when the performance of the power source deteriorates due to weather conditions, natural disasters, or environmental changes, resolves the cause of the deterioration in power source performance. Report to the administrator terminal. For example, when dust or snow accumulates in the solar power generation system or when the wind turbine is exposed to strong winds, the control unit 230 reports this to the manager terminal for resolution, thereby maintaining the performance of the power generation source.

The modeling unit 240 uses the collected data on energy sources and loads to model the energy sources and loads of the demonstration complex. In the embodiment, the modeling unit 240 models by considering the performance characteristics of the energy source, energy conversion efficiency, energy production and use patterns, and characteristics of facilities and equipment.

For example, the modeling unit 240 models the performance characteristics of the energy source, energy conversion efficiency, energy production and use patterns of the demonstration complex, and characteristics of buildings, equipment, and facilities within the demonstration complex. Specifically, the prediction unit 121 models the performance characteristics of each energy source, such as solar panels, wind turbines, and fuel cells. In an embodiment, the modeling unit 240 may model the performance characteristics of each energy source by calculating energy production, efficiency, volatility, and capacity.

Additionally, the modeling unit 240 models energy conversion efficiency to consider the efficiency of the process of converting energy produced from an energy source into a power grid or usable form.

Additionally, the modeling unit 240 models the energy production and use patterns of the demonstration complex. Modeling the energy production and use patterns of the demonstration complex may include daily, weekly, or seasonal energy production and consumption patterns, hourly energy requirements, peak load times, and energy rate structures. Additionally, the modeling unit 240 models the characteristics of buildings, equipment, and facilities within the demonstration complex. Specifically, the modeling unit 240 can model characteristics of buildings, equipment, and facilities within the demonstration complex, such as building size, heat emissions, power requirements, efficiency of devices and facilities, heating and cooling systems, and lighting. Specifically, if there is a special load facility including an electric vehicle charging facility in the demonstration complex, the modeling unit 240 models the characteristics of the special load facility and interacts with the energy source that supplies energy to the special load facility. Taking into account, the entire energy system is modeled.

Afterwards, the modeling unit 240 verifies the model with actual data collected from the demonstration complex and adjusts it according to the verification results. Afterwards, the modeling unit 240 implements a digital twin based on the modeling information.

Below, we will sequentially explain the digital twin-based convergence village-level microgrid operation method. The operation (function) of the digital twin-based convergence-type village-level microgrid operation method according to the embodiment is essentially the same as the function of the digital twin-based convergence-type village-level microgrid operation system, so the description overlaps with Figures 1 to 3. should be omitted.

Figure 4 is a diagram showing a real-time simulation data processing process of a digital twin according to an embodiment.

Referring to Figure 4, in step S410, the energy source and load of the demonstration complex by type are modeled in the digital twin, and in step S420, the power generation and load of the demonstration complex are predicted based on the modeling results. Afterwards, in step S430, real-time monitoring data including power generation, load, and peak load based on actual measurement data for each demonstration complex is analyzed. In step S430, pit load generation time period analysis, power generation and load analysis can be performed. In step S440, simulation is performed according to modeling data, power generation, load forecast data, and monitoring data analysis results, and in step S450, energy sharing rate analysis, energy self-sufficiency rate analysis, and peak load reduction rate analysis are performed according to the simulation results.

Figure 5 is a diagram showing a microgrid control process through linking virtual simulation data of a digital twin according to an embodiment.

Referring to Figure 5, in step S460, the energy saving rate, energy independence rate, and energy sharing rate through peak load reduction are calculated based on simulation results based on modeling data, power generation, load forecast data, and monitoring data analysis results, and based on this, creates a standard reference for real-time control. In step S470, microgrid control is simulated using the standard reference, and in step S480, the demonstration complex is controlled according to the generated standard reference.

Figure 6 is a diagram for explaining the functions provided by the digital twin-based convergence village-level microgrid operating system according to an embodiment.

Referring to FIG. 6, the digital twin-based convergence village-level microgrid operation system and method according to the embodiment generates a standard reference for peak load reduction amount, energy sharing rate, and energy independence rate for real-time control, and the standard reference It is possible to control the microgrid of the demonstration complex with a digital twin. The digital twin-based convergence village-level microgrid operation system and method as described above can improve energy use efficiency and stability by analyzing the power patterns of power generation sources and loads in villages or complexes of different types with different peak times. In addition, the embodiment allows sharing of power operation and surplus power by type of demonstration site according to peak load and energy saving analysis, thereby reducing power purchase costs and linking power operation between demonstration complexes by type.

In addition, through examples, we provide a plan to enhance the operation of the microgrid through digital twins, such as improving energy utilization and contributing to power grid stabilization through internal power operation of small-scale demonstration sites and energy sharing between complexes of each type.

The disclosed content is merely an example and can be modified and implemented in various ways by those skilled in the art without departing from the gist of the claims, so the scope of protection of the disclosed content is limited to the above-mentioned specific scope. It is not limited to the examples.

Claims (7)

In the digital twin-based convergence village-level microgrid operation system,
A data server that collects power data by type of demonstration complex, extracts energy sources and loads from the collected power data, models the extracted energy sources and loads, and creates a digital twin of the demonstration complex;
A digital twin that predicts the power generation and load of the demonstration complex based on modeling results of energy sources and loads of each type of demonstration complex, and analyzes real-time monitoring data based on actual measurement data for each demonstration complex; Including,
The real-time monitoring data includes power generation, load, and peak load,
The digital twin; silver
Simulate the analysis results of the real-time monitoring data, calculate the energy saving rate, energy independence rate, and energy sharing rate through peak load reduction based on the simulation results, generate a standard reference for real-time control, and according to the generated standard reference. Control the demonstration complex,
The digital twin; silver
A digital twin-based convergence village-level microgrid operation system characterized by analyzing the power patterns of power generation sources and loads in villages or demonstration complexes of different types with different peak load generation times.
delete The method of claim 1, wherein the digital twin; silver
A digital twin-based convergence village-level microgrid operation system characterized by linking power operation between demonstration complexes according to the amount of surplus power for each demonstration complex according to peak load and energy saving analysis.
The method of claim 3, wherein the digital twin; silver
A digital twin-based convergence-type village unit characterized by extracting demonstration complexes requiring linkage whose energy self-sufficiency rate is below a certain level, and controlling the extracted demonstration complexes requiring linkage in connection with demonstration complexes whose surplus power amount exceeds a certain level. Microgrid operating system.
The method of claim 1, wherein the digital twin; silver
A digital twin-based convergence village-level microgrid operation system that generates a power use schedule according to the power use pattern and analyzes the energy saving rate according to each generated power use schedule.
According to claim 1, the type of demonstration complex is
A digital twin-based convergence village-level microgrid operation system that includes a production factory, a tourist complex, a residential complex, and a leisure complex.
In the digital twin-based convergence village-level microgrid operation method,
(A) collecting power data by type of demonstration complex from a data server, extracting energy sources and loads from the collected power data, and modeling the extracted energy sources and loads to create a digital twin of the demonstration complex;
(B) predicting the power generation and load of the demonstration complex based on the modeling results of the energy source and load of the demonstration complex by type in the digital twin, and analyzing real-time monitoring data based on actual measurement data for each demonstration complex;
(C) Simulating the analysis results of the real-time monitoring data in the digital twin, calculating the energy savings rate, energy independence rate, and energy sharing rate through peak load reduction based on the simulation results, and generating a standard reference for real-time control. ; and
(D) controlling the demonstration complex according to the standard reference generated from the digital twin; includes
The real-time monitoring data includes power generation, load, and peak load.
Step (B) above; Is
A digital twin-based convergence village-level microgrid operation method characterized by analyzing the power patterns of power generation sources and loads in villages or demonstration complexes of different types with different peak load generation times.