KR-20260067038-A - Simulator system for simulating flexible resource combination and distribution system situations

KR20260067038AKR 20260067038 AKR20260067038 AKR 20260067038AKR-20260067038-A

Abstract

A simulator system for simulating flexible resource combinations and distribution system conditions is disclosed. The simulator system for simulating flexible resource combinations and distribution system conditions according to an embodiment of the present invention comprises: a flexible resource unit representing data related to a plurality of flexible resources that produce power; a distribution system unit representing data related to a distribution system that supplies power produced through a plurality of flexible resources constituting the flexible resource unit; a combination setting unit that arranges the data related to flexible resources in the distribution system of the distribution system unit based on data input from a combination input unit; a situation simulation unit that simulates power conditions through the distribution system by operating the flexible resources constituting the flexible resources based on the arrangement form of the flexible resources arranged from the combination setting unit and data input from the combination input unit; and a screen output unit that visually outputs data regarding the flexible resource unit, the distribution system unit, the combination setting unit, and the situation simulation unit, and visually outputs the state of the flexible resources arranged in the distribution system based on data input through the combination input unit. The gist of the configuration is to include a combination input unit that accepts data input by a user and transmits it to a flexible resource unit, a power distribution system unit, a combination setting unit, a situation simulation unit, and a screen output unit.

Inventors

하태진
오홍근
박난선
최진혁

Assignees

주식회사 비온시이노베이터

Dates

Publication Date: 20260512
Application Date: 20241105

Claims (5)

Flexible resource section (110) representing multiple flexible resource-related data that produce power; A distribution system unit (120) representing distribution system-related data that supplies power produced through a plurality of flexible resources constituting the above flexible resource unit (110); A combination setting unit (130) that places flexible resource-related data into the distribution system of the distribution system unit (120) based on data input from the combination input unit (160); A situation simulation unit (140) that simulates power conditions through a power distribution system by operating flexible resources that constitute flexible resources based on the arrangement form of flexible resources arranged from the above combination setting unit (130) and data input from the combination input unit (160); A screen output unit (150) that visually outputs data regarding the flexible resource unit (110), distribution system unit (120), combination setting unit (130), and situation simulation unit (140), and visually outputs the state of the flexible resource placed in the distribution system based on the data input through the combination input unit (160); and A combination input unit (160) that accepts data entered by a user and transmits it to a flexible resource unit (110), a power distribution system unit (120), a combination setting unit (130), a situation simulation unit (140), and a screen output unit (150); A simulator system for simulating flexible resource combinations and distribution system conditions, characterized by including
In paragraph 1, The above combination input unit (160) is, Data entered by the user is accepted, and the flexible resource selected by the user is placed on the distribution system-related data of the distribution system unit (120) using a drag-and-drop method, and the placement-related data is transmitted in real time to the combination setting unit (130) and the screen output unit (150). The distribution system-related setting parameters of the distribution system unit (120) and setting parameters corresponding to each flexible resource are received from the user and transmitted in real time to the combination setting unit (130) and the screen output unit (150). A simulator system for simulating flexible resource combination and distribution system conditions, characterized by storing data transmitted to the combination setting unit (130) and screen output unit (150) internally, and retrieving stored data selected by the user and transmitting it to the combination setting unit (130) and screen output unit (150).
In paragraph 2, The above situation simulation unit (140) is, Based on the arrangement of flexible resources arranged from the combination setting unit (130) and data input from the combination input unit (160), the power generation, power consumption, charging status, and discharging status of each flexible resource are simulated, and at the same time, the simulation result value is transmitted to the screen output unit (150) in real time. Based on the simulation result value above, the power trend of the power flowing through the power distribution system of the power distribution system unit (120) is processed into a graph form and transmitted to the screen output unit (150). A simulator system for simulating flexible resource combinations and distribution system conditions, characterized by internally storing the simulation result values and processed graph-related data, and allowing the user to view past progress history.
In paragraph 3, The above situation simulation unit (140) is, Based on the data input through the above combination input unit (160), the operation and output of the flexible resource are controlled, and the grid flexibility is derived based on the power data generated through the flexible resource. It receives weather-related data and observed meteorological data from the surrounding environment where flexible resources are installed in real time to derive the power output of renewable energy-related flexible resources and reflects this in the simulation in real time, and It accepts data related to the charge/discharge control of Energy Storage Systems (ESS), Electric Vehicle (EV) charge/discharge control, and SOC upper/lower limit limit operation characteristics in real time and reflects them in the simulation in real time, Information on distribution system line utilization rate and NWAs resource estimated potential amount through simulation is provided to the user through the screen output unit (150), and A simulator system for simulating flexible resource combinations and distribution system conditions, characterized by calculating the amount of flexible resources used through the difference between the amount of renewable energy generated and the load power relative to the total power of the system and providing this to the user through a screen output unit (150).
In paragraph 4, The above situation simulation unit (140) is, A simulation is performed using a learning algorithm that incorporates a time series forecasting model for each flexible resource item, and The time series forecasting model by flexible resource item is, A simulator system for simulating flexible resource combinations and distribution system conditions, characterized by being designed by a time series sampling method based on the selection of a time range for real-time power generation prediction, placing perceptrons in each dense layer based on a Hidden Layer structure, selecting loss and optimization functions for a time series analysis model, and deriving result values based on future power generation prediction results based on past data by utilizing a constructed time series prediction model for each data item.

Description

Simulator system for simulating flexible resource combination and distribution system situations The present invention relates to a simulator system, and more specifically, to a simulator system for simulating flexible resource combinations and distribution system conditions. As the proportion of renewable energy generation facilities has increased rapidly over the past few years, the ratio of distributed power sources connected to the distribution system has been rising sharply. Due to the nature of renewable energy, power generation fluctuates irregularly depending on weather, time, and season, posing a significant risk of hindering the stable operation of the power grid. In particular, highly variable renewable energy sources such as solar and wind power cause unpredictable changes, making it difficult to balance power supply and demand. (See Fig. 1) Under these circumstances, existing facilities in the distribution system have limitations in responding to the variability of rapidly changing renewable energy. Conventional distribution systems were designed to be optimized for large-scale, centralized power generation methods, and the system primarily involved producing electricity at large power plants and supplying it to users through transmission and distribution networks. However, as small-scale, numerous distributed renewable energy resources are introduced, this approach is causing problems with network operation and management becoming overloaded. Furthermore, as renewable energy increases rapidly, problems arise where the distribution system becomes overloaded during specific times or in specific regions. For example, during daytime hours, the concentration of solar power generation can lead to power supply exceeding the capacity of the distribution network. Consequently, the system is unable to fully accommodate the electricity produced by renewable energy facilities, often resulting in power losses or the need to limit the output of these facilities. These issues act as factors hindering the expansion of renewable energy adoption and its efficient utilization. Existing countermeasures include adding power storage devices to the distribution system or expanding facility capacity. However, these methods entail high costs and are practically difficult to apply to every section of the distribution system. Furthermore, power storage devices require high-capacity batteries, and their economic efficiency is diminished because maintenance and replacement costs increase as capacity grows. Consequently, the existing approach relying on the expansion of distribution network facilities is gradually reaching its limits. Therefore, a new approach is required to overcome the limitations of existing distribution systems and respond flexibly. Utilizing a simulator system capable of simulating flexible resource combinations and distribution system conditions enables the effective integration of various renewable energy resources and distributed power sources, thereby maximizing the operational efficiency of the distribution system. This will be of great help in establishing distribution system operation strategies that balance power supply and demand and manage the real-time variability of renewable energy. Figure 1 is a schematic diagram showing a paradigm shift in a power distribution system according to conventional technology. FIG. 2 is a block diagram showing a simulator system for simulating flexible resource combinations and distribution system conditions according to an embodiment of the present invention. FIG. 3 is a schematic diagram showing a simulator system for simulating flexible resource combinations and distribution system conditions according to an embodiment of the present invention. FIG. 4 is a flowchart illustrating the process of designing, operating, evaluating, and developing activation policy proposals for non-expansion investment alternatives (NWAs) to increase the limit capacity of distribution network connections using a simulator system for simulating flexible resource combinations and distribution system conditions according to an embodiment of the present invention. Figure 5 is a table showing an example of solar power plant power data specifications required for collecting AI learning data for renewable energy power plant operation considering grid impact and economic feasibility. Figure 6 is a table showing an example of public weather data specifications required for collecting AI learning data for the operation of renewable energy power plants considering grid impact and economic feasibility. Figure 7 is a schematic diagram showing an example of an ESS operation algorithm design for distribution system stabilization to design an optimal operation algorithm considering system impact and economic feasibility. Figure 8 is a table showing examples of equipment aging and life analysis data for the design of equipment aging/life analysis evaluation data items for the design of data ite