Digital twin-based converged village-level microgrid operation system and method

Abstract

A digital twin-based converged village-level microgrid operation system and method according to an embodiment analyze power patterns of power generation sources and loads in type-specific villages or complexes having different peak time zones, thereby enabling energy use efficiency and stability to be improved. In addition, through an embodiment, power operation and surplus power for each type of empirical site according to a peak load and energy saving analysis can be shared, and thus power purchase costs can be reduced and a power operation between type-specific empirical complexes can be linked. In addition, through an embodiment, provided are digital twin-based microgrid operation advancement methods such as those for energy utilization improvement and power grid stabilization contribution by using an internal power operation of a small-scale empirical site and energy sharing between type-specific complexes.

Description

Digital Twin-Based Integrated Village-Level Microgrid Operation System and Method

[0001] The present disclosure relates to a microgrid operation system and method, and more specifically, to a digital twin-based convergence village-level microgrid operation system and method.

[0002] Unless otherwise indicated herein, the materials described in this section are not prior art to the claims of this application, and their inclusion in this section is not intended to be admitted as prior art.

[0003] A microgrid is a power supply and management system that represents a distributed group of power networks and sources. Microgrids generate power using multiple energy sources, such as solar panels, wind turbines, fuel cells, and diesel generators.

[0004] Furthermore, microgrids utilize energy storage systems (ESS), batteries, and supercapacitor storage systems to balance the imbalance between power generation and consumption and store energy. Furthermore, microgrids enable local management of energy production and consumption. This enables independent operation, disconnected from the power grid, and provides innovative methods for distributed power generation, storage, and management, contributing to improved power network stability and sustainability.

[0005] However, microgrids lack monitoring facilities, making it difficult to monitor power generation status and identify malfunctions. Furthermore, there is a lack of a standardized model for the operation and management of renewable energy facilities based on the diverse installation environments, energy consumption, and production patterns of microgrids. Furthermore, support measures are needed to establish a microgrid infrastructure for regional distributed energy production and consumption systems. To advance to a distributed energy system, it is necessary to enhance the independent and autonomous power grid operation and management capabilities of each village, taking into account the unique characteristics of each village. To achieve this, it is necessary to expand power generation facilities such as AMI and solar power, taking into account the unique characteristics of rural and island communities, while also identifying optimal power grid construction methods for each region. Furthermore, through demonstrations of direct power transactions between power producers, consumers, and service providers within villages, demand is growing for the development and operation of a prosumer-based open power platform.

[0006] 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, analyzes real-time power generation and load based on actual data through real-time digital twin data monitoring, and analyzes peak load and energy self-sufficiency rate.

[0007] In addition, the digital twin-based convergence village-level microgrid operation system and method according to the embodiment derives the energy saving rate, energy self-sufficiency rate, and energy sharing rate through optimal peak load reduction through power analysis of each type of demonstration complex.

[0008] In addition, 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 self-sufficiency rate for real-time control, and enables the control of the microgrid of the demonstration complex through the standard reference.

[0009] A digital twin-based convergence village-level microgrid operation system according to an embodiment includes: 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 the modeling results of the energy sources and loads of the demonstration complex by type, and analyzes real-time monitoring data based on actual data for each demonstration complex; wherein the real-time monitoring data includes power generation, load, and peak load; and the digital twin simulates the analysis results of the real-time monitoring data, and calculates an energy saving rate, an energy self-sufficiency rate, and an energy sharing rate through peak load reduction based on the simulation results, creates a reference reference for real-time control, and controls the demonstration complex according to the created reference reference.

[0010] The digital twin-based convergence village-level microgrid operation system and method described above can improve energy efficiency and stability by analyzing the power patterns of power sources and loads of villages or complexes of different types with different peak times.

[0011] In addition, by enabling power operation and surplus power sharing by type of demonstration site based on peak load and energy saving analysis through examples, power purchase costs can be reduced and power operation between demonstration complexes by type can be linked.

[0012] Furthermore, through examples, we provide a plan to enhance the operation of microgrids through digital twins, such as improving energy utilization rates and contributing to power grid stabilization through internal power operation of small-scale demonstration sites and energy sharing between complexes of different types.

[0013] 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 detailed description of the present invention or the composition of the invention described in the claims.

[0014] Figure 1 is a drawing showing a digital twin-based convergence village-unit microgrid operation system according to an embodiment.

[0015] Figure 2 is a diagram showing the configuration of a data server according to an embodiment.

[0016] Figure 3 is a diagram showing a data processing configuration of a digital twin according to an embodiment.

[0017] Figure 4 is a diagram showing the real-time simulation data processing process of a digital twin according to an embodiment.

[0018] Figure 5 is a diagram showing a microgrid control process through virtual simulation data linkage of a digital twin according to an embodiment.

[0019] Figure 6 is a drawing for explaining the functions provided by the digital twin-based convergence village-unit microgrid operation system according to an embodiment.

[0020] Hereinafter, the embodiments disclosed in this specification will be described in detail with reference to the attached drawings. Regardless of the drawing numbers, identical or similar components will be given the same reference numbers, and redundant descriptions thereof will be omitted. The suffixes "module" and "part" used for components in the following description are assigned or used interchangeably only for the convenience of writing the specification, and do not in themselves have distinct meanings or roles. In addition, when describing the embodiments disclosed in this specification, if it is determined that a specific description of a related known technology may obscure the gist of the embodiments disclosed in this specification, a detailed description thereof will be omitted. In addition, the attached drawings are only intended to facilitate easy understanding of the embodiments disclosed in this specification, and the technical ideas disclosed in this specification are not limited by the attached drawings, and should be understood to include all modifications, equivalents, and substitutes included in the spirit and technical scope of the present invention.

[0021] Terms that include ordinal numbers, such as first, second, etc., may be used to describe various components, but the components are not limited by these terms. These terms are used solely to distinguish one component from another.

[0022] When a component is referred to as being "connected" or "connected" to another component, it should be understood that it may be directly connected or connected to that other component, but that there may be other components intervening. Conversely, when a component is referred to as being "directly connected" or "connected" to another component, it should be understood that there are no other components intervening.

[0023] In this application, terms such as “include” or “have” are intended to specify the presence of a feature, number, step, operation, component, part or combination thereof described in the specification, but should be understood not to exclude in advance the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts or combinations thereof.

[0024] In this specification, the term "unit" includes a unit realized by hardware, a unit realized by software, and a unit realized using both. Furthermore, a single unit may be realized using two or more pieces of hardware, and two or more units may be realized by a single piece of hardware.

[0025] Some of the operations or functions described herein as being performed by a terminal, apparatus, or device may instead be performed by a server connected to the terminal, apparatus, or device. Similarly, some of the operations or functions described herein as being performed by a server may also be performed by a terminal, apparatus, or device connected to the server.

[0026] Hereinafter, the present invention will be described in detail with reference to the attached drawings.

[0027] Figure 1 is a diagram illustrating a digital twin-based convergence village-unit microgrid operation system according to an embodiment.

[0028] Referring to FIG. 1, a digital twin-based convergence village-unit microgrid operating system according to an embodiment may be configured to include demonstration complexes (1, 2, 3), a data server (100), a digital twin (200), and a monitoring system (300). In the embodiment, the demonstration complexes (1, 2, 3) are sites where a convergence village-unit microgrid is installed, and each demonstration complex includes an energy source and a load. In the embodiment, the demonstration complexes may be classified according to type, and the types of the demonstration complexes include, but are not limited to, a production plant, a tourism complex, a residential complex, and a leisure complex.

[0029] In the embodiment, the type-specific demonstration complexes (1, 2, 3) collect power data including actual power generation and load usage through sensors and PMDs and transmit them to the data server (100).

[0030] The data server (100) collects power data including energy sources and loads from each demonstration complex (1, 2, 3) and creates a digital twin (200) of the demonstration complex microgrid using the power data. In the embodiment, the data server (100) models each energy source and load included in the power data to implement a digital twin.

[0031] 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 rate analysis based on actual data. In the 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).

[0032] Fig. 2 is a diagram showing the configuration of a data server according to an embodiment.

[0033] Referring to FIG. 2, a data server (100) according to an embodiment may be configured to include a collection unit (110) and a modeling unit (240). The term 'unit' used in this specification should be interpreted 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, a processor, a computer, an integrated circuit, an integrated circuit core, a sensor, a MEMS (Micro-Electro-Mechanical System), a passive device, or a combination thereof.

[0034] The collection unit (110) collects power data by type of demonstration complex and extracts energy sources and loads from the collected power data. Thereafter, the modeling unit (240) models the extracted energy sources and loads by type of demonstration complex to create a digital twin, and predicts the power generation and load of the demonstration complex based on the results of modeling the energy sources and loads performed on the created digital twin.

[0035] In an embodiment, the collection unit (110) collects data regarding the energy sources and loads of the demonstration complex. In an embodiment, energy sources may include systems that generate power, such as solar power, wind power, fuel cells, and power grids, while energy loads may include systems that consume power, such as buildings and facilities. In an embodiment, data regarding energy sources and loads may include power usage, energy generation, power demand patterns, operating hours, and the like.

[0036] The processing unit (120) preprocesses data regarding the energy sources and loads of the collected demonstration complex. In an embodiment, the preprocessing process may include processes such as noise removal, outlier removal, and missing value processing. Furthermore, the processing unit (120) may perform data normalization, outlier removal, and data scaling through data preprocessing.

[0037] Fig. 3 is a diagram showing a data processing configuration of a digital twin according to an embodiment.

[0038] Referring to FIG. 3, a digital twin (200) according to an embodiment may be configured to include an analysis unit (210), a generation unit (220), and a control unit (230).

[0039] In this embodiment, the digital twin (200) simulates the energy production and usage scenarios of the demonstration complex and supports interaction with real-time data. Furthermore, in this embodiment, the digital twin (200) interacts with real-time data to reflect the current state and simulate interactions with external factors.

[0040] In this embodiment, the digital twin (200) is a virtual model that digitally simulates an actual demonstration complex. The digital twin simulates the operation of energy sources, energy conversion processes, power production, and usage in real time. Furthermore, in this embodiment, the digital twin (200) reflects external factors into the current microgrid status of the demonstration complex through real-time data integration. For example, the digital twin (200) can incorporate external factors such as sensor data, environmental conditions, weather information, and power rates into the simulation.

[0041] In addition, in the embodiment, the digital twin (200) can simulate and test various scenarios. For example, it can test the use of different energy sources, changes in power consumption patterns, peak load management, etc. In addition, the digital twin (200) can predict and optimize energy production and use. For example, it can determine the optimal charging and discharging times of energy storage devices or optimize energy sharing systems. In the embodiment, the digital twin also supports monitoring and control of an actual complex. For example, it can track the current status of a demonstration complex in real time and adjust energy production and use to maximize energy efficiency and manage peak loads.

[0042] The analysis unit (210) monitors the digital twin (200) and analyzes real-time monitoring data based on actual data for each demonstration complex. In an embodiment, the monitoring data may include power generation, load, peak load, etc. Through monitoring data analysis, the analysis unit (210) identifies real-time power generation, load change patterns, and peak load occurrence time periods for each energy source.

[0043] In the embodiment, the analysis unit (210) analyzes the power consumption patterns of power generation sources and loads in villages or complexes of different types during different peak hours. To this end, the analysis unit (220) collects data related to power generation sources and loads from each village or complex. The 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 of the village or complex, weather data, and daily or seasonal data. The analysis unit (210) then separates the data related to power generation sources and loads by time period. For example, the analysis unit (210) identifies different time periods, such as peak hours, off-peak hours, weekends, and weekdays, and identifies peak load times. Specifically, the analysis unit (220) identifies peak load times in each type of demonstration complex. Peak load times are the times when power demand is highest, often occurring in the morning or evening. The analysis unit (210) then analyzes the data patterns of power generation sources and loads during peak and off-peak hours. Analysis of power generation and load data patterns during peak and off-peak periods aims to determine how power generation and load change during peak periods, as well as how energy production and usage patterns differ. The analysis unit (220) then uses weather data, such as solar irradiance and wind speed, to predict power generation performance and estimate power generation during peak periods. Furthermore, the analysis unit (220) analyzes the correlation between weather and power generation. Furthermore, based on the analysis results, the analysis unit (220) generates an efficient power management strategy for peak periods. For example, the analysis unit (220) considers methods for adjusting power generation operation or managing load during peak periods to reduce peak load.

[0044] The generation unit (220) simulates the analysis results of the analysis unit (210), calculates the energy saving rate through peak load reduction, energy self-sufficiency rate, and energy sharing rate based on the power analysis results of the demonstration complex, and creates a standard reference for real-time control based on the calculated data.

[0045] For example, the generation unit (220) can generate a reference according to the size of the optimal peak load extracted as the optimal peak load, the reduced peak load that produces at least one of the energy saving rate, the energy self-sufficiency rate, and the energy sharing rate as the maximum among the reduced peak loads, the reduction time, the reduction size of the optimal peak load, and the change in the energy saving rate, the energy self-sufficiency rate, and the energy sharing rate due to the optimal peak load reduction.

[0046] To this end, the generation unit (220) collects and analyzes data from the microgrid system, including peak load, energy production and usage patterns, and the performance of the energy storage system. Subsequently, the generation unit (220) identifies loads that can be reduced among the peak loads. This means optimizing energy usage to reduce peak load. Subsequently, the generation unit (220) measures the energy consumption reduction rate due to the reduced peak load. This can be expressed as the difference between the reduced energy and the initial peak load. Furthermore, the generation unit calculates the energy self-sufficiency rate, which is the ability of the microgrid system to generate energy on its own, and determines how much the reduced peak load changes the energy self-sufficiency rate. Furthermore, the generation unit calculates the energy sharing rate, which indicates the degree to which the microgrid system shares energy with other systems or the grid. In one embodiment, the generation unit (220) identifies the optimal peak load size by maximizing at least one of the energy savings rate, energy self-sufficiency rate, and energy sharing rate. Furthermore, the generation unit (220) calculates how long the peak load can be reduced and what impact the reduced peak load has on the system. Subsequently, the generation unit (220) calculates the peak load reduction amount when the optimal peak load is applied. In one embodiment, the generator (220) generates a control and operation reference for 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-sufficiency. For example, the reference reference may include reference values ​​and methods for distributing power during peak power usage, reference values ​​for determining the operating timing of renewable energy systems and energy storage systems, grid connection and disconnection timing, and grid connection and disconnection.

[0047] In an embodiment, the generation unit (220) generates various scenarios for reducing peak load for analysis result simulation. For example, the generation unit (220) may change the peak load time to the time when power generation is maximum or adjust the operating time of each load constituting the peak load to generate a simulation scenario. Thereafter, the results of each peak load reduction scenario are analyzed through simulation to calculate an energy savings rate. The energy savings rate represents the amount of power consumption reduction due to peak load reduction. In addition, the generation unit (220) calculates an energy self-sufficiency rate based on the simulation results. In an embodiment, the energy self-sufficiency rate is the amount of energy generated by the demonstration complex itself. In an embodiment, the generation unit (220) calculates the energy production amount of each demonstration complex based on the simulation and analysis results. In addition, the generation unit (220) calculates an energy sharing rate. Energy sharing refers to sharing or trading energy between demonstration complexes. In an embodiment, the generation unit (220) evaluates energy sharing scenarios through simulation and determines how much energy can be shared through energy trading with other complexes.

[0048] Afterwards, the generation unit (220) generates a reference for real-time control based on the analysis results and data obtained through simulation. The reference reference considers energy production and usage patterns, peak load times, energy consumption reduction targets, etc. In an embodiment, the reference reference can be used to control the system via a digital twin during actual operation.

[0049] For example, the standard reference sets a reference value for controlling the charging and discharging of energy sources for each demonstration complex. Specifically, the standard reference may include a reference value for the peak load at which stored power is discharged for each demonstration complex, a reference value for stored power for each demonstration complex receiving power from another demonstration complex, etc. In an embodiment, the reference value may be set differently depending on the power consumption, peak load, and stored power for each type of demonstration complex.

[0050] In addition, the generation unit (220) generates a power usage schedule according to the power usage pattern of each demonstration complex and analyzes the energy saving rate according to each generated power usage schedule.

[0051] To this end, the generation unit (220) first collects data on power usage patterns in each demonstration complex. This data may include daily or hourly power usage, weekly or seasonal power consumption patterns, peak load times, etc. Next, the generation unit (220) generates a power usage schedule based on the power usage patterns of each demonstration complex. This schedule indicates how to increase or decrease power usage at specific times, and may include strategies for minimizing power usage or reducing peak loads, for example. Next, the generation unit (220) applies the generated power usage schedule to a simulation to analyze power usage. This simulation shows how the power usage schedule can be implemented in the real world and provides prediction information on power usage. Furthermore, the generation unit (220) calculates an energy savings rate for each power usage schedule based on the simulation results. This can indicate how much energy can be saved by executing the power usage schedule. Thereafter, the generation unit (220) evaluates the calculated energy savings rate and analyzes the effectiveness of the generated power usage schedule. Furthermore, the generation unit (220) adjusts or optimizes the power usage schedule based on the analysis results.

[0052] The control unit (230) controls the demonstration complex. For example, the control unit (230) monitors the demonstration complex in real time using a digital twin (200) and regulates the demonstration complex's energy production and use based on a reference standard. This can support peak load management and energy sharing.

[0053] In an embodiment, the control unit (230) links power operations between demonstration complexes according to the surplus power of each demonstration complex based on peak load and energy saving analysis. For example, the control unit (230) can extract demonstration complexes requiring linkage whose energy self-sufficiency rate is below a certain level and control the extracted demonstration complexes requiring linkage by linking them with demonstration complexes capable of supplying power whose surplus power exceeds a certain level. Specifically, the control unit (230) can identify the power usage and load patterns of demonstration complexes capable of supplying power and transfer power as an energy source to demonstration complexes requiring linkage during time periods when power usage and load are within a standard range. In addition, the control unit can supply surplus power to demonstration complexes requiring linkage a certain time before the peak load occurs according to the peak load occurrence pattern of the demonstration complex requiring linkage.

[0054] In addition, in the embodiment, the control unit (230) extracts a demonstration complex capable of selling electricity among the demonstration complexes capable of supplying electricity, wherein the surplus electricity exceeds a certain percentage of the peak load of the demonstration complex. Thereafter, in the case of the demonstration complex capable of selling electricity, the surplus electricity can be sold to the demonstration complex requiring linkage at different rates depending on the power supply time and amount of electricity supplied. For example, the control unit (230) allows the demonstration complex capable of selling electricity to be linked with the demonstration complex requiring linkage through an auction method in which the demonstration complex requiring linkage bids on the rates for electricity to be sold by the demonstration complex capable of selling electricity from the demonstration complex requiring linkage and the winning bid is selected at one of the rates submitted.

[0055] In an embodiment, the control unit (230) may set the unit price for electricity to be sold in a power sales demonstration complex differently depending on the hourly load of the power sales demonstration complex. For example, the control unit (230) may set the unit price for electricity to be sold in a power sales demonstration complex to increase as the hourly load increases, thereby calculating the cost of electricity sold to a connected demonstration complex. This is to reflect the value of electricity sold during periods of high load, such as summer, and to improve the profits of the power sales demonstration complex.

[0056] 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 linkage.

[0057] 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 requiring surplus power linkage. Thereafter, if the energy self-sufficiency rate of the demonstration complex requiring surplus power linkage falls within the first range (e.g., 10 percent to 25 percent), which is the lowest range, the control unit (230) generates an energy source additional installation plan for the demonstration complex requiring surplus power linkage. In addition, if the energy self-sufficiency rate of the demonstration complex requiring surplus power linkage falls within the second range (e.g., 25 percent to 40 percent), which is an intermediate range, the control unit (230) identifies the load pattern and the peak load occurrence time zone, and adjusts the operation schedule of the facilities corresponding to each load to reduce the peak load. In addition, when the energy self-sufficiency rate of the surplus power linkage demonstration complex is within the second range (e.g., 40 percent to 60 percent), the control unit (230) identifies the load pattern and adjusts the energy source and load usage schedule to reduce the amount of power supplied from the power supply demonstration complex.

[0058] Additionally, the control unit (230) monitors the power sources, storage devices, loads, and grid status within the microgrid installed in the demonstration complex via a digital twin, and collects sensor and meter data from the demonstration complex in real time. Through this, it predicts abnormal situations in the demonstration complex and controls the microgrid through a process that responds to the predicted abnormal situations. For example, the control unit (230) predicts power outages due to external causes such as weather, grid failures, or equipment failures, or internal problems, as abnormal situations. If a power outage is predicted, the control unit automatically maintains power in the microgrid or utilizes emergency power sources.

[0059] Additionally, the control unit (230) provides a redundant power source or a smart switching system to prepare for the event of a failure in one of the power sources within the microgrid, which could impact the entire system. For example, if an abnormal situation in which a storage device within the microgrid fails is predicted, the control unit (230) activates a backup system for the storage device failure. This is to prevent problems with energy storage and supply, and the backup system for the failure situation may include storing and receiving power from another storage device or connecting to another microgrid.

[0060] Additionally, if the load within the microgrid fluctuates beyond a certain value, the control unit (230) stops supplying energy and identifies the cause of the load fluctuation.

[0061] Additionally, in the embodiment, the control unit (230) monitors the performance of the power generation source of the microgrid, and if the performance of the power generation source is degraded due to weather conditions, natural disasters, or environmental changes, the control unit (230) reports to the manager terminal to resolve the cause of the degraded power generation source performance. For example, if dust or snow accumulates on a solar power generation system or a wind turbine is exposed to strong winds, the control unit (230) reports to the manager terminal to resolve the issue, thereby maintaining the performance of the power generation source.

[0062] The modeling unit (240) models the energy sources and loads of the demonstration complex using the collected data on energy sources and loads. In the embodiment, the modeling unit (240) models by considering the performance characteristics of the energy sources, energy conversion efficiency, energy production and use patterns, and the characteristics of facilities and equipment.

[0063] For example, the modeling unit (240) models the performance characteristics of energy sources, 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) can model the performance characteristics of each energy source by calculating energy production, efficiency, variability, and capacity.

[0064] In addition, the modeling unit (240) models energy conversion efficiency so that the efficiency of the process of converting energy produced from an energy source into a power grid or usable form can be considered.

[0065] In addition, the modeling unit (240) models the energy production and usage patterns of the demonstration complex. Modeling the energy production and usage patterns of the demonstration complex may include daily, weekly, or seasonal energy production and consumption patterns, hourly energy demand, peak load hours, and energy rate structures. In addition, the modeling unit (240) models the characteristics of buildings, equipment, and facilities within the demonstration complex. Specifically, the modeling unit (240) may model the characteristics of buildings, equipment, and facilities within the demonstration complex, such as building size, heat emission, power demand, efficiency of devices and equipment, heating and cooling systems, and lighting. Specifically, if there are special load facilities, including electric vehicle charging facilities, within the demonstration complex, the modeling unit (240) models the characteristics of the special load facilities, and models the entire energy system by considering the interaction with the energy source that supplies energy to the special load facilities.

[0066] Thereafter, the modeling unit (240) verifies the model using actual data collected from the demonstration complex and adjusts it based on the verification results. Thereafter, the modeling unit (240) implements a digital twin based on the modeling information.

[0067] Below, the method for operating a digital twin-based, convergent, village-level microgrid is sequentially described. The operation (function) of the digital twin-based, convergent, village-level microgrid operating method according to the embodiment is essentially the same as the function of the digital twin-based, convergent, village-level microgrid operating system, so any description overlapping with that of Figures 1 to 3 will be omitted.

[0068] Figure 4 is a diagram showing a real-time simulation data processing process of a digital twin according to an embodiment.

[0069] Referring to Fig. 4, in step S410, the energy source and load of each type of demonstration complex 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. Thereafter, in step S430, real-time monitoring data including power generation, load, and peak load based on actual data for each demonstration complex are analyzed. In step S430, analysis of the time zone of pit load occurrence, power generation, and load can be performed. In step S440, a simulation is performed based on the modeling data, power generation, load prediction 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 based on the simulation results.

[0070] Fig. 5 is a diagram illustrating a microgrid control process through virtual simulation data linkage of a digital twin according to an embodiment.

[0071] Referring to Figure 5, in step S460, energy savings rates through peak load reduction, energy self-sufficiency rates, and energy sharing rates are calculated based on simulation results from analysis of modeling data, power generation, load prediction data, and monitoring data. Based on these results, a reference reference for real-time control is generated. In step S470, microgrid control is simulated using the reference reference, and in step S480, the demonstration complex is controlled based on the generated reference reference.

[0072] Figure 6 is a drawing for explaining the functions provided by a digital twin-based convergence village-unit microgrid operation system according to an embodiment.

[0073] Referring to Fig. 6, a digital twin-based convergence village-level microgrid operation system and method according to an embodiment generates a standard reference for peak load reduction amount, energy sharing rate, and energy self-sufficiency rate for real-time control, and enables the control of the microgrid of a demonstration complex with a digital twin through the standard reference. The digital twin-based convergence village-level microgrid operation system and method as described above can improve energy utilization efficiency and stability by analyzing the power patterns of power sources and loads of villages or complexes by type with different peak hours. In addition, through the embodiment, it is possible to reduce power purchase costs and link power operations between demonstration complexes by type by enabling power operation by type of demonstration site and sharing of surplus power according to peak load and energy saving analysis.

[0074] Furthermore, through examples, we provide a plan to enhance the operation of microgrids 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 different types.

[0075] The disclosed content is merely an example, and various modifications and implementations can be made by a person skilled in the art without departing from the gist of the claims claimed in the patent, so the scope of protection of the disclosed content is not limited to the specific embodiments described above.

Claims

1. In a 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, and models the extracted energy sources and loads to create a digital twin of the demonstration complex; A digital twin that predicts the power generation and load of a demonstration complex based on the modeling results of the energy source and load of each demonstration complex by type, and analyzes real-time monitoring data based on actual data for each demonstration complex; The above real-time monitoring data includes power generation, load, and peak load. The above digital twin; The analysis results of the above real-time monitoring data are simulated, and based on the simulation results, the energy saving rate through peak load reduction, energy self-sufficiency rate, and energy sharing rate are calculated, and a standard reference for real-time control is created. A digital twin-based convergence village-level microgrid operation system that controls the demonstration complex based on the generated reference standard.

2. In the first paragraph, the digital twin; A digital twin-based convergence-type village-level microgrid operation system characterized by analyzing the power patterns of power sources and loads of villages or demonstration complexes of different types with different peak load occurrence times.

3. In the first paragraph, the digital twin; A digital twin-based convergence-type village-level microgrid operation system that links power operations between demonstration complexes according to the surplus power of each demonstration complex based on peak load and energy saving analysis.

4. In the third paragraph, the digital twin; A digital twin-based convergence-type village-unit microgrid operation system characterized by extracting demonstration complexes requiring connection whose energy self-sufficiency rate is below a certain level and controlling the extracted demonstration complexes requiring connection by linking them with demonstration complexes whose surplus power exceeds a certain level.

5. In the first paragraph, the digital twin; A digital twin-based convergence village-level microgrid operation system characterized by generating a power usage schedule based on a power usage pattern and analyzing the energy saving rate for each generated power usage schedule.

6. In paragraph 1, the type of the demonstration complex is A digital twin-based integrated village-level microgrid operation system featuring a production plant, tourist complex, residential complex, and leisure complex.

7. In the operation method of a digital twin-based convergence village-level microgrid, (A) A step of 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) A step of predicting the power generation and load of a demonstration complex based on the modeling results of the energy source and load of each type of demonstration complex in a digital twin, and analyzing real-time monitoring data based on actual data for each demonstration complex; (C) A step of simulating the analysis results of the real-time monitoring data in the digital twin, calculating the energy saving rate, energy self-sufficiency rate, and energy sharing rate through peak load reduction based on the simulation results, and creating a standard reference for real-time control; and (D) a step of controlling the demonstration complex according to a reference standard generated from the digital twin; including; The above real-time monitoring data is a digital twin-based integrated village-level microgrid operation method including power generation, load, and peak load.

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