Apparatus and method for managing voltage of hybrid distribution network

Abstract

Disclosed in the present disclosure are an apparatus and method for managing voltage of a hybrid distribution network. According to an aspect of the present disclosure, provided is a method for managing voltage of a hybrid distribution network, the method comprising: receiving monitoring information related to a hybrid distribution network; carrying out probabilistic power flow calculation on the basis of the received monitoring information; determining, on the basis of a result of the probabilistic power flow calculation, whether a voltage violation occurs in the distribution network; on the basis of whether the voltage violation occurs, selectively determining whether the maximum voltage or the minimum voltage in the distribution network violates a voltage management range; and on the basis of whether the maximum voltage or the minimum voltage in the distribution network violates the voltage management range, selectively deriving a droop gain by using an optimization model.

Description

Hybrid distribution network voltage management device and method

[0001] The present disclosure relates to a hybrid distribution network voltage management device and method.

[0002] The following description merely provides background information related to the present embodiment and does not constitute prior art.

[0003] AC power distribution systems face challenges such as the expansion of high-voltage DC loads, increased losses during DC-AC conversion, saturation of distribution network connection capacity, and growing power grid uncertainty based on the intermittent nature of renewable energy generation sources. To address these issues, the introduction of AC / DC hybrid distribution networks has been proposed.

[0004] AC / DC hybrid distribution networks offer advantages over conventional AC networks, such as expanded connection capacity, improved line utilization, and reduced DC-AC conversion losses. Additionally, AC / DC hybrid networks can optimize distribution network operations through active power flow control. However, as voltage fluctuations intensify due to the increase in renewable energy and output uncertainty, grid voltage management technology has become essential.

[0005] Conventional droop control methods are inefficient because they are configured in a simplistic manner that does not adequately reflect grid conditions. In particular, methods configured by assuming excessively worst-case scenarios have limitations, making practical application and optimization difficult.

[0006] Therefore, to improve the inefficiency of droop control, a method is required to probabilistically analyze the uncertainty of renewable energy output and derive voltage sensitivity by performing power flow calculations that include the influence of droop gain.

[0007] The main purpose of this disclosure is to provide a method for efficiently managing grid voltage by deriving an optimal droop gain, taking into account the uncertainty and prediction error of renewable energy output.

[0008] The problems that the present invention aims to solve are not limited to those mentioned above, and other unmentioned problems will be clearly understood by a person skilled in the art from the description below.

[0009] According to one aspect of the present disclosure, a method for managing the voltage of a hybrid distribution network is provided, comprising receiving monitoring information related to the hybrid distribution network, performing a probabilistic power flow calculation based on the received monitoring information, determining whether there is a voltage violation within the distribution network based on the result of the probabilistic power flow calculation, selectively determining whether a maximum voltage or a minimum voltage within the distribution network violates a voltage management range based on whether a voltage violation occurs, and selectively deriving a droop gain using an optimization model based on whether a maximum voltage or a minimum voltage within the distribution network violates a voltage management range.

[0010] According to another aspect of the present disclosure, an apparatus is provided comprising at least one memory; and at least one processor, wherein the at least one processor receives monitoring information related to a hybrid distribution network by executing instructions, performs a probabilistic power flow calculation based on the received monitoring information, determines whether there is a voltage violation within the distribution network based on the result of the probabilistic power flow calculation, selectively determines whether the maximum voltage or minimum voltage of the distribution network violates the voltage management range based on whether a voltage violation occurs, and selectively derives a droop gain using an optimization model based on whether the maximum voltage or minimum voltage within the distribution network violates the voltage management range.

[0011] According to one embodiment of the present disclosure, by utilizing probabilistic analysis and a droop gain optimization model, it is possible to efficiently perform voltage management of a hybrid distribution network.

[0012] The effects of the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description below.

[0013] FIG. 1 is a drawing illustrating an exemplary hybrid distribution network voltage management system according to one embodiment of the present disclosure.

[0014] FIG. 2 is a block diagram illustrating the configuration of a hybrid distribution network voltage management device according to one embodiment of the present disclosure.

[0015] FIG. 3 is a flowchart for explaining the operation of a hybrid distribution network voltage management device according to one embodiment of the present disclosure.

[0016] FIG. 4 is a flowchart for explaining the process of determining voltage violation according to one embodiment of the present disclosure.

[0017] FIG. 5 is a drawing for explaining a hybrid distribution network to which a hybrid distribution network voltage management device according to one embodiment of the present disclosure is applied.

[0018] Figures 6a and 6b are drawings showing droop curves.

[0019] FIGS. 7a and 7b are drawings for explaining the probability distribution of system voltage due to variations in droop gain according to one embodiment of the present disclosure.

[0020] FIG. 8 is a block diagram schematically illustrating an exemplary computing device that can be used to implement a method or device according to the present disclosure.

[0021] Some embodiments of the present disclosure are described in detail below with reference to exemplary drawings. It should be noted that in assigning reference numerals to the components of each drawing, the same components are given the same reference numeral whenever possible, even if they are shown in different drawings. Furthermore, in describing the present disclosure, if it is determined that a detailed description of related known components or functions could obscure the essence of the present disclosure, such detailed description is omitted.

[0022] In describing the components of the embodiments according to the present disclosure, symbols such as first, second, i), ii), a), b), etc., may be used. These symbols are intended only to distinguish the components from other components, and the essence, order, or sequence of the components is not limited by the symbols. When a part in the specification is described as 'comprising' or 'having' a component, this means that, unless explicitly stated otherwise, it does not exclude other components but may include additional components.

[0023] The detailed description set forth below, together with the accompanying drawings, is intended to describe exemplary embodiments of the present disclosure and is not intended to represent the only embodiment in which the present disclosure can be practiced.

[0024] FIG. 1 is a drawing illustrating an exemplary hybrid distribution network voltage management system according to one embodiment of the present disclosure.

[0025] Referring to FIG. 1, a hybrid distribution network voltage management system according to one embodiment of the present disclosure may include all or part of a hybrid distribution network voltage management device (100), a server (110), and a network (120).

[0026] A hybrid distribution network voltage management device (100) is a device capable of performing a method for managing the voltage of a hybrid distribution network according to an embodiment of the present disclosure, and may be implemented as hardware such as an electronic device capable of executing a program, software executed by a processor, or a combination thereof.

[0027] According to one embodiment, when the hybrid distribution network voltage management device (100) is implemented in hardware, it may include a general PC such as a general desktop or laptop, and may include mobile terminals such as a smartphone, tablet PC, PDA (Personal Digital Assistants), and mobile communication terminal. However, this is merely an example, and any electronic device capable of communicating with the server (110) may be broadly interpreted without being limited to its name or type. The hybrid distribution network voltage management device (100) may receive monitoring information regarding voltage violations occurring from the hybrid distribution network and the distribution network from the server (110). Additionally, the hybrid distribution network voltage management device (100) may be implemented as an application capable of performing a method for managing the voltage of the hybrid distribution network according to an embodiment of the present disclosure.

[0028] The hybrid distribution network voltage management device (100) is connected to a server (110) to receive monitoring information regarding the hybrid distribution network and can determine and transmit a droop gain based on stochastic analysis within the hybrid distribution network.

[0029] The server (110) can be connected to the hybrid distribution network voltage management device (100) via a network (120). Here, the server (110) may be an Energy Management System (EMS) or a Voltage Management System (VMS). An Energy Management System or a Voltage Management System may refer to a system that utilizes computers and information and communication technology to automatically monitor and store line operation information, such as voltage and current, of switches for distribution lines scattered over long distances in real time, and, if the monitoring results are analyzed and found to be abnormal, allows for remote operation. The server (110) may refer to a computer system that receives work execution requests from clients or other servers and derives and provides work results, or it may refer to computer software (server program) installed for such a computer system. In addition to the aforementioned server program, the server (110) should be understood as a broad concept that includes a series of application programs operating on the server (110) and, in some cases, various databases built internally or externally. Here, the server (110) may refer to a collection of data in which data, such as information or materials, is structured and managed for use by a server or other device, and may also refer to a storage medium that stores such a collection of data. The server (110) may include multiple databases classified according to the data structuring method, management method, type, etc. In some cases, the server (110) may include a Database Management System (DBMS), which is software that enables the addition, modification, deletion, etc. of information or materials.

[0030] The network (120) is a network that connects the hybrid distribution network voltage management device (100) and the server (110), and may be a closed network such as a LAN (Local Area Network) or WAN (Wide Area Network), but may also be an open network such as the Internet. Here, the Internet refers to a global open computer network structure that provides the TCP / IP protocol and various services existing in the upper layer, namely HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), SNMP (Simple Network Management Protocol), NFS (Network File Service), and NIS (Network Information Service).

[0031] FIG. 2 is a block diagram illustrating the configuration of a hybrid distribution network voltage management device (100) according to one embodiment of the present disclosure.

[0032] A hybrid distribution network voltage management device (100) according to one embodiment of the present disclosure may include, in whole or in part, a data collection unit (102), a probabilistic power flow calculation unit (104), a voltage state analysis unit (106), and a control unit (108). The components shown in FIG. 2 represent functionally distinct elements, and at least one component may be implemented in a form that is integrated with one another in an actual physical environment.

[0033] The data collection unit (102) can receive monitoring information related to voltage violations occurring from the hybrid distribution network. For example, the data collection unit (102) can receive the initial set point, status data, and voltage fluctuation data of each component (e.g., transformer, inverter, converter, etc.) from an energy management system or a voltage management system. The data collection unit (102) can collect monitoring information such as the variability of renewable energy sources, changes in load, and the status of an Energy Storage System (ESS) to provide basic data for probabilistic analysis. Probabilistic analysis is a methodology that evaluates the operation or state of a system by considering uncertainty and statistical fluctuation. Probabilistic analysis is used to analyze the variability and reliability of results based on the probability distribution of various input variables, rather than simply assuming average values ​​or specific fixed scenarios. For example, assuming the output of a renewable energy source follows a normal distribution, various scenarios can be generated based on the mean and standard deviation of the output to analyze voltage fluctuations in the system. The analysis results are expressed as the frequency or probability of voltage deviations from the acceptable range (e.g., ±5%) and can be used to determine droop gain optimization.

[0034] The probabilistic power flow calculation unit (104) performs a probabilistic power flow calculation based on data provided by the data collection unit (102). In the calculation process, a probability distribution of the system voltage is derived by reflecting the uncertainty of the output. For example, the probabilistic power flow calculation unit (104) probabilistically analyzes the voltage variability of the system by reflecting the prediction error of the output and / or load change of renewable energy sources within the hybrid power distribution. Additionally, the probabilistic power flow calculation unit (104) can set a droop control target within the voltage fluctuation interval (e.g., a 95% or 99% confidence interval). The probabilistic power flow calculation is performed by considering the voltage constraints of the system and the capacity constraints for each device.

[0035] The voltage state analysis unit (106) analyzes the results derived from the probabilistic power flow calculation unit (104) to determine whether there is a voltage violation within the distribution network. Here, a voltage violation may occur when the output of a load or distributed power source, etc., exceeds the expected operating range. The voltage state analysis unit (106) determines whether the voltage exceeds the voltage management range based on the range of the maximum or minimum voltage of the system (e.g., a 99% confidence interval set based on ±2.576σ) and derives an optimization plan to maintain it within the set range.

[0036] The control unit (108) dynamically determines the optimal operating point and droop gain of each device within the hybrid distribution network based on the results analyzed by the voltage state analysis unit (106). To determine the droop gain, the control unit (108) may use a droop gain optimization model. The droop gain optimization model may include an objective function and constraints. Specifically, the control unit (108) reflects the constraints of each device (e.g., SOC of the ESS, capacity limit of the device). The control unit (108) may derive the optimal droop gain based on the power flow calculation results by utilizing voltage sensitivity analysis. The control unit (108) may be configured to simultaneously achieve voltage stability and load sharing efficiency within the distribution network through the setting and adjustment of the droop gain. For example, the magnitude of the droop gain may have a direct effect on changes in the voltage and output of the distribution network. If the droop gain is set high, the voltage change becomes small, but the output change becomes large, which can reduce the stability of the system. If the droop gain is set low, the output change becomes small, but the voltage change may become large. Therefore, the control unit (108) is designed to derive a droop gain that maintains a balance between stability and control performance.

[0037] FIG. 3 is a flowchart for explaining the operation of a hybrid distribution network voltage management device (100) according to one embodiment of the present disclosure.

[0038] The data collection unit (102) can receive initial setting values, status data, and voltage fluctuation data of each component (e.g., transformer, inverter, converter, etc.) from an energy management system or a voltage management system (S302). The data collection unit (102) can collect monitoring information to provide basic data for probabilistic analysis. The monitoring information includes the variability of renewable energy sources, changes in load, and the status of an Energy Storage System (ESS).

[0039] The probabilistic power flow calculation unit (104) performs a probabilistic power flow calculation based on data provided by the data collection unit (102) (S304). The probabilistic power flow calculation unit (104) can probabilistically analyze the voltage variability of the system and derive a probability distribution of the voltage distribution based on uncertainty.

[0040] The voltage state analysis unit (106) analyzes the results derived from the probabilistic power flow calculation unit (104) to determine whether there is a voltage violation within the distribution network (S306). Here, a voltage violation may occur when the output of a load or distributed power source, etc., exceeds the expected operating range. If no voltage violation occurs within the distribution network (S306-NO), the voltage state analysis unit (106) determines whether the voltage exceeds the voltage management range based on the maximum or minimum voltage range of the system (e.g., a 99% confidence interval set based on ±2.576σ) (S308).

[0041] When the voltage management range is exceeded (S308-YES), the control unit (108) dynamically determines the optimal operating point and droop gain of each device within the hybrid distribution network based on the results analyzed by the voltage state analysis unit (106) (S310). To determine the droop gain, the control unit (108) uses a droop gain optimization model. When a voltage violation occurs within the distribution network (S306-YES), the control unit (108) dynamically determines the optimal operating point and droop gain of each device within the hybrid distribution network based on the results analyzed by the voltage state analysis unit (106) (S310).

[0042] FIG. 4 is a flowchart for explaining the process of determining voltage violation according to one embodiment of the present disclosure.

[0043] The voltage state analysis unit (106) calculates the probabilistic voltage distribution of each bus in the distribution network (S402). Here, the probabilistic voltage distribution refers to the probabilistic power flow (PPF) calculated based on the predicted output value of the DG and the load and the error of the prediction. The probabilistic power flow may follow a normal distribution.

[0044] The voltage state analysis unit (106) calculates the voltage probability distribution of each bus (S404). The voltage state analysis unit (106) can calculate the average voltage and standard deviation for the voltage of each bus. Here, the voltage probability distribution of each bus can be calculated using the voltage sensitivity matrix and the standard deviation. The droop gain optimization model can be derived from the voltage sensitivity matrix of each bus in the distribution network based on whether the droop gain varies. The voltage sensitivity matrix of each bus can be derived based on whether the droop gain varies. The droop gain optimization model can be constructed by reflecting the voltage sensitivity matrix of each bus. Power flow within the hybrid distribution network can be analyzed. Power flow analysis can be derived by applying the Newton-Raphson method. Power mismatch between a given power value and a calculated power value can be analyzed using power flow analysis. The sensitivity matrix can be derived by inversely transforming the Jacobian matrix.

[0045] The voltage state analysis unit (106) calculates the probability of voltage violation for each bus (S406). The voltage state analysis unit (106) can calculate the probability that the voltage of each bus deviates from the allowable range based on the upper limit and lower limit of the allowable voltage. Here, the allowable range refers to the range from the lower limit to the upper limit of the allowable voltage.

[0046] The voltage state analysis unit (106) identifies a critical bus (S408). The voltage state analysis unit (106) can calculate the probability of exceeding the allowable range for all buses within the distribution network. Based on the calculated probability, the bus with the highest probability of voltage violation can be identified. Here, the bus with the highest probability of voltage violation means the bus with the highest probability of exceeding the allowable range. The identified bus with the highest probability of voltage violation can be identified as a critical bus. For example, identification as a critical bus can be determined based on the entire network of the distribution network. As another example, identification as a critical bus can be determined independently in the AC system and DC system of the distribution network, respectively. The voltage state analysis unit (106) determines whether the voltage has been violated based on the identified critical bus (S410). If no voltage violation is determined in the identified critical bus (S410-Yes), a droop gain can be determined using an optimization model (S310). If a voltage violation is determined at the identified critical busbar (S410-No), it can be determined whether the maximum or minimum voltage within the distribution network has violated the voltage management range (S308). Here, the voltage management range refers to the range from the lower limit to the upper limit of the allowable voltage.

[0047] FIG. 5 is a drawing for explaining a hybrid distribution network (500) to which a hybrid distribution network voltage management device (100) according to one embodiment of the present disclosure is applied.

[0048] Referring to FIG. 5, the hybrid distribution network (500) may include AC distribution lines (lines connected to 34 to 50 and lines connected to 81 to 85), DC distribution lines (lines connected to 1 to 33 and lines connected to 51 to 80), AC loads, DC loads, Solar Photovoltaic (PV) plants, Energy Storage Systems (ESS1 to ESS4), grid-connected converters, Soft Open Points (SOPs), and AC grids.

[0049] Voltage violations may occur when the output of a load or distributed power source, etc., is outside the expected operating range. For example, voltage violations may occur in specific sections within a DC distribution line (e.g., sections connected from 15 to 17 or sections connected from 63 to 71, etc.).

[0050] The control unit (108) dynamically determines the optimal operating point and droop gain of each device within the hybrid distribution network based on the results analyzed by the voltage state analysis unit (106). To determine the droop gain, the control unit (108) uses a droop gain optimization model. The droop gain optimization model may include an objective function and constraints.

[0051] The objective function of the Droop Gain optimization model aims to minimize the sum of voltage fluctuations within the distribution network and output losses per device. That is, the objective function (F) of the optimization model can be defined as Equation 1.

[0052] [Mathematical Formula 1]

[0053]

[0054] In mathematical formula 1, and is a weight that takes into account the voltage limit and capacity limit for each device. That is, is the weight of the reactive power, and is the weight of the active power, and is the reactive power droop gain, and is the active power drub gain.

[0055] and It can be defined as in mathematical formula 2 and mathematical formula 3.

[0056] [Mathematical Formula 2]

[0057] ,

[0058] is a constant that balances the importance of DC distribution lines and AC distribution lines.

[0059] [Mathematical Formula 3]

[0060] ,

[0061] In mathematical formulas 2 and 3, is a voltage margin-based importance factor. is the output margin-based adjustment margin factor. Here, the voltage margin-based importance factor refers to a factor that quantitatively reflects how close the voltage is to the allowable limit. The output margin-based adjustment margin factor refers to a factor indicating how much margin remains in the current output compared to the rated capacity.

[0062] and It can be defined as in mathematical formula 4 and mathematical formula 5.

[0063] [Mathematical Formula 4]

[0064] ,

[0065] [Mathematical Formula 5]

[0066] ,

[0067] is a voltage limit proximity-based importance factor. is the output margin-based adjustability coefficient. and The final weight using the product of It can produce.

[0068] The constraints of the droop gain optimization model may include a first constraint regarding voltage limiting, a second constraint regarding current limiting, a third constraint regarding voltage standard deviation reduction, a fourth constraint regarding limiting droop gain variation in AC distribution lines, and a fifth constraint regarding limiting droop gain variation in DC distribution lines.

[0069] The first constraint can be defined as shown in Equation 6 below.

[0070] [Mathematical Formula 6]

[0071] ,

[0072] The first constraint is the node voltage of the system ( This means that ) must be within a specific allowable range. Here, means the lowest allowable voltage, and This refers to the maximum allowable voltage. By preventing the voltage from becoming excessively low or high, stable system operation can be ensured.

[0073] The second constraint can be defined as shown in Equation 7 below.

[0074] [Mathematical Formula 7]

[0075] ,

[0076] The second constraint is the current flowing from each branch of the distribution network ( This means that ) must be within the allowable current range. Here, This is the allowable branch current.

[0077] The third constraint can be defined as shown in Equation 8 below.

[0078] [Mathematical Formula 8]

[0079]

[0080] The third constraint means that the actual variation of the standard deviation of the adjustment result must be greater than or equal to the target variation of the set standard deviation. is the actual amount of variation in the standard deviation when the droop gain is adjusted. ε is the target variation amount of the voltage standard deviation. Here, the target variation amount can be calculated based on the voltage violation probability. The voltage violation probability can primarily be defined by statistical criteria, and constraints can be set to prevent excessive variation in order to maintain system stability. For example, if the allowable voltage violation probability is set to 1%, this means that there is a 99% probability that the voltage exists within a range of approximately ±2.579 σ based on a normal distribution, and it means limiting the probability that the system voltage deviates from this range to 1% or less. The third constraint refers to a condition that evaluates whether droop gain adjustment is sufficiently effective using sensitivity analysis.

[0081] The formula for how the voltage standard deviation changes based on sensitivity when the droop gain is varied can be defined as Equation 9 below.

[0082] [Mathematical Formula 9]

[0083]

[0084] is the droop gain fluctuation amount. Using Equation 9 According to can calculate.

[0085] The fourth constraint can be defined as shown in Equation 10 below.

[0086] [Mathematical Formula 10]

[0087] ,

[0088] The fourth constraint means that the droop gain variation in AC distribution lines must be within 1 / 1.3 of the existing value.

[0089] The fifth constraint can be defined as shown in the following mathematical formula 11.

[0090] [Mathematical Formula 11]

[0091] ,

[0092] The fifth constraint means that the droop gain variation in DC distribution lines must be within 1 / 1.3 of the existing value.

[0093] The objective function of the Droop Gain optimization model must consider the above constraints to ensure voltage stability and load sharing efficiency.

[0094] Figures 6a and 6b are diagrams showing droop curves. Figure 6a is in an AC distribution line - Represents the droop curve. Voltage ( Reactive power based on the change of ) It represents the change of ). Is - It is the reciprocal of Drupgain. The value of can be defined as shown in the following mathematical formula 12.

[0095] [Mathematical Formula 12]

[0096] ,

[0097] Fig. 6b is in a DC distribution line - Represents the droop curve. Voltage ( Based on the change in ), active power ( It represents the change of ). Is - It is the reciprocal of Drupgain. The value of can be defined as in the following mathematical formula 13.

[0098] [Mathematical Formula 13]

[0099] ,

[0100] FIGS. 7a and 7b are drawings for explaining the probability distribution of system voltage due to variations in droop gain according to one embodiment of the present disclosure.

[0101] FIG. 7a illustrates how a droop gain variation according to one embodiment of the present disclosure affects the probability density (PDF) of the system voltage. The x-axis represents voltage. The y-axis represents the probability density (PDF) of the system voltage. The solid line represents the voltage distribution after droop gain adjustment. The dotted line represents the voltage distribution before droop gain adjustment. It shows how the adjustment of the droop gain improves the statistical stability of the voltage.

[0102] FIG. 7b illustrates how a droop gain variation according to one embodiment of the present disclosure affects the probability distribution function (CDF) of the system voltage. The x-axis represents voltage. The y-axis represents the cumulative distribution function (CDF) of the system voltage. The solid line represents the voltage distribution after droop gain adjustment. The dotted line represents the voltage distribution before droop gain adjustment. It shows how the adjustment of the droop gain improves the statistical stability of the voltage.

[0103] FIG. 8 is a block diagram schematically illustrating an exemplary computing device that can be used to implement a method or device according to the present disclosure.

[0104] The computing device (80) may include some or all of memory (800), a processor (820), storage (840), an input / output interface (860), and a communication interface (880). The computing device (80) may be a stationary computing device such as a desktop computer or server, as well as a mobile computing device such as a laptop computer or smartphone. The computing device (80) may include any specialized hardware accelerator capable of processing operations on an artificial intelligence model in an efficient manner. For example, the computing device (80) may include a graphic processing unit (GPU), a tensor processing unit (TPU), or a neural processing unit (NPU).

[0105] Memory (800) may store a program that enables the processor (820) to perform a method or operation according to various embodiments of the present disclosure. For example, the program may include a plurality of instructions executable by the processor (820), and the aforementioned method or operation may be performed by executing the plurality of instructions by the processor (820). Memory (800) may be a single memory or multiple memories. In this case, information required to perform a method or operation according to various embodiments of the present disclosure may be stored in a single memory or divided and stored in multiple memories. If memory (800) is composed of multiple memories, the multiple memories may be physically separated. Memory (800) may include at least one of volatile memory and non-volatile memory. Volatile memory includes Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), etc., and non-volatile memory includes flash memory, etc.

[0106] The processor (820) may include at least one core capable of executing at least one instruction. The processor (820) may execute instructions stored in memory (800). The processor (820) may be a single processor or multiple processors.

[0107] Storage (840) retains stored data even if the power supplied to the computing device (80) is cut off. For example, storage (840) may include non-volatile memory and may include storage media such as magnetic tape, optical disc, or magnetic disc. A program stored in storage (840) may be loaded into memory (800) before being executed by the processor (820). Storage (840) may store a file written in a programming language, and a program generated from the file by a compiler, etc., may be loaded into memory (800). Storage (840) may store data to be processed by the processor (820) and / or data processed by the processor (820).

[0108] The input / output interface (860) may provide an interface with input devices such as a keyboard, mouse, etc. and / or output devices such as a display device, printer, etc. The user may trigger the execution of a program by the processor (820) through the input device and / or check the processing results of the processor (820) through the output device.

[0109] The communication interface (880) can provide access to an external network. The computing device (80) can communicate with other devices through the communication interface (880).

[0110] Each component of the device or method according to the present invention may be implemented in hardware or software, or in a combination of hardware and software. Additionally, the function of each component may be implemented in software, and a microprocessor may be implemented to execute the function of the software corresponding to each component.

[0111] At least some of the components described in the exemplary embodiments of the present disclosure may be implemented as hardware elements including at least one of a Digital Signal Processor (DSP), a processor, a controller, an Application-Specific IC (ASIC), a programmable logic device (such as an FPGA), or other electronic devices, or a combination thereof. Additionally, at least some of the functions or processes described in the exemplary embodiments may be implemented in software, and the software may be stored on a recording medium. At least some of the components, functions, and processes described in the exemplary embodiments of the present disclosure may be implemented as a combination of hardware and software.

[0112] The method according to exemplary embodiments of the present disclosure may be written as a program executable on a computer and may also be implemented on various recording media such as magnetic storage media, optical reading media, and digital storage media.

[0113] Implementations of the various technologies described herein may be implemented as digital electronic circuits, or as computer hardware, firmware, software, or combinations thereof. Implementations may be implemented as computer program products, i.e., computer programs tangibly embodied in information carriers, such as machine-readable storage devices (computer-readable media) or radio signals, for processing by the operation of data processing devices, e.g., programmable processors, computers, or multiple computers, or for controlling such operation. Computer programs such as the computer program(s) described above may be written in any form of programming language, including compiled or interpreted languages, and may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. Computer programs may be deployed to be processed on one computer or multiple computers at one site, or distributed across multiple sites and interconnected by a communication network.

[0114] Processors suitable for processing computer programs include, for example, both general-purpose and special-purpose microprocessors, and any one or more processors of any type of digital computer. Generally, the processor will receive instructions and data from read-only memory or random access memory, or both. The elements of the computer may include at least one processor that executes instructions and one or more memory devices that store instructions and data. Generally, the computer may include one or more mass storage devices that store data, for example, magnetic, magneto-optical disks, or optical disks, or may be combined to receive data from these, transmit data to these, or both. Information carriers suitable for embodying computer program instructions and data include, for example, semiconductor memory devices, magnetic media such as hard disks, floppy disks, and magnetic tapes; optical media such as CD-ROMs (Compact Disk Read Only Memory) and DVDs (Digital Video Disks); magneto-optical media such as floptical disks; ROMs (Read Only Memory); RAMs (Random Access Memory); flash memory; EPROMs (Erasable Programmable ROM); EEPROMs (Electrically Erasable Programmable ROM); etc. Processors and memory may be supplemented by or included in special-purpose logic circuit organizations.

[0115] The processor may execute an operating system and software applications executed on said operating system. Additionally, the processor device may access, store, manipulate, process, and generate data in response to the execution of software. For ease of understanding, the processor device may be described as being used as a single unit; however, those skilled in the art will understand that the processor device may include multiple processing elements and / or multiple types of processing elements. For example, the processor device may include multiple processors or one processor and one controller. Other processing configurations, such as a parallel processor, are also possible.

[0116] Additionally, non-transitory computer-readable media may be any available medium accessible by a computer and may include both computer storage media and transmission media.

[0117] Although this specification contains details of a number of specific embodiments, they should not be understood as limiting the scope of any invention or claimables, but rather as descriptions of features that may be characteristic of a specific embodiment of a specific invention. Specific features described in this specification in the context of individual embodiments may be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented in multiple embodiments individually or in any appropriate sub-combination. Furthermore, while features may operate in a specific combination and be described as initially claimed, one or more features from the claimed combination may be excluded from the combination in some cases, and the claimed combination may be changed to a sub-combination or a variation of the sub-combination.

[0118] Likewise, although operations are depicted in the drawings in a specific order, this should not be understood as requiring that such operations be performed in that specific or sequential order depicted to obtain a desirable result, or that all depicted operations must be performed. In certain cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of the various device components of the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and devices can generally be integrated together into a single software product or packaged into multiple software products.

[0119] Meanwhile, the embodiments of the present invention disclosed in this specification and drawings are merely specific examples provided to aid understanding and are not intended to limit the scope of the present invention. It is obvious to those skilled in the art that other variations based on the technical concept of the present invention are possible in addition to the embodiments disclosed herein.

[0120] The scope of protection of this embodiment shall be interpreted by the claims below, and all technical ideas within an equivalent scope shall be interpreted as being included within the scope of rights of this embodiment.

[0121]

[0122] CROSS-REFERENCE TO RELATED APPLICATION

[0123] This patent application claims priority to Korean patent application No. 10-2025-0001328, filed on January 6, 2025, the entirety of which is incorporated herein by reference.

Claims

1. Regarding the method of managing the voltage of a hybrid distribution network, Receive monitoring information related to the above hybrid distribution network, and Based on the received monitoring information, perform probabilistic power flow calculations, and Based on the results of the above probabilistic power flow calculation, determine whether there is a voltage violation within the distribution network, and Based on whether the above voltage violation occurs, selectively determine whether the maximum voltage or minimum voltage within the distribution network violates the voltage management range, and A method for selectively deriving droop gain using an optimization model based on whether the maximum voltage or minimum voltage within the above distribution network violates the voltage management range.

2. In Paragraph 1, Based on the determination that the above voltage violation has occurred, the above droop gain is derived using the above optimization model, and Based on the determination that the above voltage violation did not occur, determine whether the maximum voltage or minimum voltage within the distribution network violates the voltage management range, and A method for deriving the droop gain using the optimization model based on the determination that the maximum voltage or minimum voltage within the distribution network has violated the voltage management range.

3. In Paragraph 1, The above probabilistic power flow calculation is, A method for probabilistically analyzing voltage variability by reflecting the prediction error of output or load change of renewable energy sources within the above distribution network.

4. In Paragraph 1, The result of the above probabilistic power flow calculation is, A method that is a probability distribution of a voltage distribution based on uncertainty.

5. In Paragraph 1, The determination of whether there is a voltage violation within the aforementioned distribution network is, Calculate the probabilistic voltage distribution of each bus of the above hybrid distribution network, and Based on the probabilistic voltage distribution of each of the above buses, the voltage probability distribution is calculated, and Based on the above voltage probability distribution, calculate the voltage violation probability of each busbar, and It is configured to identify the bus with the highest probability of voltage violation among each of the above buses as the critical bus, and The identification of the bus with the highest probability of voltage violation for each of the above buses as the critical bus is, A method for evaluating the voltage sensitivity of each bus using a voltage sensitivity matrix.

6. In Paragraph 5, The evaluation of the voltage sensitivity of each of the above busbars is, A method for determining based on the change in the standard deviation of the voltage of the busbar according to the above droop gain.

7. In Paragraph 5, The identification of the bus with the highest probability of voltage violation for each of the above buses as the critical bus is, A method for independently identifying the critical busbar in each of the AC distribution line and the DC distribution line of the above hybrid distribution network.

8. In Paragraph 5, The above optimization model is, Based on whether the above droop gain varies, the sensitivity matrix of the voltage of each busbar is derived, and A method using the above sensitivity matrix.

9. In Paragraph 1, The above optimization model is, A method comprising at least one of an objective function and a constraint.

10. In Paragraph 9, The above constraints are, A method comprising at least one first constraint regarding voltage limiting, a second constraint regarding current limiting, a third constraint regarding voltage standard deviation reduction, a fourth constraint regarding limiting droop gain variation of an AC distribution line, and a fifth constraint regarding limiting droop gain variation of a DC distribution line.

11. At least one memory; and Includes at least one processor, The above at least one processor executes instructions, Receive monitoring information related to the hybrid distribution network, and Based on the received monitoring information, perform probabilistic power flow calculations, and Based on the results of the above probabilistic power flow calculation, determine whether there is a voltage violation within the distribution network, and Based on whether the above voltage violation occurs, selectively determine whether the maximum voltage or minimum voltage of the distribution network violates the voltage management range, and A device that selectively derives droop gain using an optimization model based on whether the maximum voltage or minimum voltage within the above distribution network violates the voltage management range.

12. In Paragraph 11, The above at least one processor executes instructions, Based on the determination that the above voltage violation has occurred, the above droop gain is derived using the above optimization model, and Based on the determination that the above voltage violation did not occur, determine whether the maximum voltage or minimum voltage within the distribution network violates the voltage management range, and A device for deriving the droop gain using the optimization model based on the determination that the maximum voltage or minimum voltage within the distribution network has violated the voltage management range.

13. In Paragraph 11, The above at least one processor executes the above instructions, The performance of the above probabilistic power flow calculation is, A device that probabilistically analyzes voltage variability by reflecting the prediction error of output or load change of renewable energy sources within the above distribution network.

14. In Paragraph 11, The above at least one processor executes the above instructions, The result of the above probabilistic power flow calculation is, A device that is a probability distribution of a voltage distribution based on uncertainty.

15. In Paragraph 11, The above at least one processor executes the above instructions, The determination of whether there is a voltage violation within the aforementioned distribution network is, Calculate the probabilistic voltage distribution of each bus of the above hybrid distribution network, and Based on the probabilistic voltage distribution of each of the above buses, the voltage probability distribution is calculated, and Based on the above voltage probability distribution, calculate the voltage violation probability of each busbar, and It is configured to identify the bus with the highest probability of voltage violation for each of the above buses as the critical bus, and The identification of the bus with the highest probability of voltage violation for each of the above buses as the critical bus is, A device that evaluates the voltage sensitivity of each bus using a voltage sensitivity matrix.

16. In Paragraph 15, The above at least one processor executes the above instructions, The evaluation of the voltage sensitivity of each of the above busbars is, A device that determines based on the change in the standard deviation of the voltage of the busbar according to the above droop gain.

17. In Paragraph 15, The above at least one processor executes the above instructions, The identification of the bus with the highest probability of voltage violation for each of the above buses as the critical bus is, A device for independently identifying the critical busbar in each of the AC distribution line and the DC distribution line of the above hybrid distribution network.

18. In Paragraph 15, The above at least one processor executes the above instructions, The above optimization model is, Based on whether the above droop gain varies, the sensitivity matrix of the voltage of each busbar is derived, and A device utilizing the above sensitivity matrix.

19. In Paragraph 11, The above at least one processor executes the above instructions, The above optimization model is, A device comprising at least one of an objective function and a constraint.

20. In Paragraph 19, The above at least one processor executes the above instructions, The above constraints are, A device comprising at least one first constraint regarding voltage limiting, a second constraint regarding current limiting, a third constraint regarding voltage standard deviation reduction, a fourth constraint regarding limiting droop gain variation in AC distribution lines, and a fifth constraint regarding limiting droop gain variation in DC distribution lines.

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