KR102549096B1 - Power generation control system capable of reducing peak load power generated by using fossile fuels for carbon neutrality - Google Patents

Abstract

A power generation control system that controls the generation of peak power for carbon neutrality includes a base power generator, a peak power generator that generates peak power using fossil fuels, a wind power generator, a solar power generator, an energy storage system, a power supply control systems, and a server.

Description

Power generation control system that can reduce peak power generated by using fossil fuels for carbon neutrality

The present invention relates to a technology capable of reducing peak power generated using fossil fuels, and in particular, a power generation control system capable of reducing peak power by an increase in renewable energy for carbon neutrality, a method thereof, and performing the method It is about an artificial intelligence computer program that can do.

Carbon neutrality is when a company, country, or individual generates greenhouse gases such as carbon dioxide (CO2) in order to minimize their contribution to global warming, but at the same time remove or replace the same amount of greenhouse gases to reduce net greenhouse gas emissions. It means making it 0 (zero). This can reduce greenhouse gas emissions and mitigate global warming.

In order to realize carbon neutrality, it is necessary to achieve zero-emission by minimizing the emission of greenhouse gases and using technologies that remove other emitted greenhouse gases from the atmosphere. For example, in order to achieve carbon neutrality, electricity must be generated using renewable energy sources such as sunlight, hydropower, and wind power.

Renewable energy refers to energy generated using natural energy sources. These natural sources of energy include solar, wind, hydropower, geothermal, or the ocean. Energy such as electricity or heat is produced using these natural energy sources.

Renewable energy is environmentally friendly, and solar, wind, hydropower, geothermal, or ocean is sustainable as an energy source, and can provide most of the energy required for sustainable energy supply. Through this, environmental pollution and climate change caused by the use of fossil fuels can be reduced.

However, renewable energy can be expensive to install and maintain compared to conventional fossil fuels, and there are limitations to connecting to the electrical grid.

The power demand of the power grid changes every moment, but the generation capacity consumed even when the power demand is minimal is called base load. A base-load power plant is a power plant that generates base load power corresponding to a base load. Examples of base load power plants include nuclear power plants and thermal power plants.

Peak load refers to the maximum power demand occurring in a specific time period in the power supply system. In general, it occurs in the morning and evening hours when electricity usage is high, and at this time, peak load has a great influence on power supply and demand. A peak-load power plant is a power plant that uses fossil fuels to generate peak-load power corresponding to a peak load. Examples of peak load power plants include a power plant using liquefied natural gas (LNG) or a power plant using diesel.

Peak loads vary according to several factors, including temperature, season, and level of economic activity. For example, in summer, power demand increases rapidly due to the use of air conditioners, etc., at which time a peak load occurs. In addition, peak loads may appear higher in economically active regions or time zones.

Registered Patent Publication: Registration No. 10-1795589 (Announced on November 09, 2017) Registered Patent Publication: Registration No. 10-1400482 (Announced on May 28, 2014) Registered Patent Publication: Registration No. 10-2117672 (Announced on June 3, 2020)

A technical problem to be achieved by the present invention is a power generation control system capable of reducing the amount of peak power by an increase in renewable energy using big data on power consumption and big data on weather for carbon neutrality, a method thereof, and performing the method It is to provide an artificial intelligence computer program that can do.

A power generation control system capable of reducing peak power generated using fossil fuel for carbon neutrality according to an embodiment of the present invention includes a base power generator including a base power generator generating base power, and using fossil fuel A peak power generator including a peak power generator generating peak power and a first control device controlling operation of the peak power generator, a wind power generator generating first renewable power using wind, and operation of the wind power generator A wind power generator including a second control device and a first group of sensors for controlling the wind power generator, a photovoltaic generator for generating second renewable power using sunlight, and a third control for controlling the operation of the photovoltaic generator. A photovoltaic device including a device and a second group of sensors, an energy storage system storing renewable power corresponding to a sum of the first renewable power and the second renewable power, and the base power and the peak power , and a power supply control system for supplying the renewable power to electricity customers, and a server, wherein the server generates power use big data by analyzing past power use data used by the electricity customers, and Monitors the current consumption power to calculate the current total power consumption, monitors the base power, the peak power, and the renewable power to calculate the current total generated power corresponding to the sum of them, and the current total produced Calculating a power difference between power and the current total power consumption, correcting the power difference using the power use big data, and controlling the wind power generator to increase the renewable power within the range of the corrected power difference Controls the second control device, controls the third control device that controls the photovoltaic generator, and controls the first control device to reduce the peak power by the increased regenerative power.

The server generates first weather big data by analyzing past weather information of a first region where the wind power generator is installed, and analyzes past weather information of a second region where the photovoltaic generator is installed to generate second weather big data. generates, receives first weather information collected from the first group of sensors, receives second weather information collected from the second group of sensors, and uses the first weather big data to A first analysis result is generated by analyzing the first weather information, a first weight value is generated using the first analysis result, and a second analysis is performed by analyzing the second weather information using the second weather big data. A result is generated, a second weight value is generated using the second analysis result, and a first weight value is generated using the first weight value, the first renewable power, the lowest design efficiency of the wind power generator, and a first correction value. The amount of increase in renewable power is predicted, the amount of increase in renewable power is predicted using the second weight, the second renewable power, the design minimum efficiency of the photovoltaic device, and a second correction value, and the first renewable power is predicted. A peak power control signal for predicting a final renewable power increase based on the increase amount and the second renewable power increase amount, and reducing only by the final renewable power increase amount among the peak power when the final renewable power increase amount is smaller than the power difference generated and further transmitted to the first control device.

The power generation control system and method according to an embodiment of the present invention has an effect of reducing the amount of peak power by an increase in renewable energy by using power use big data and meteorological big data for carbon neutrality.

A detailed description of each drawing is provided in order to more fully understand the drawings cited in the detailed description of the present invention.
1 is a block diagram of a power generation control system capable of reducing peak power using renewable energy for carbon neutrality according to an embodiment of the present invention.
FIG. 2 is a flowchart illustrating an embodiment of a method of operating the power generation control system shown in FIG. 1 .
FIG. 3 is a flowchart illustrating an embodiment of a method for calculating weights by the server of FIG. 1 .
FIG. 4 is a flowchart illustrating another embodiment of a method for calculating weights by the server of FIG. 1 .
FIG. 5 is a flowchart illustrating a method of controlling a wind power generator and a solar power generator by the power generation control system shown in FIG. 1 .
FIG. 6 is a flowchart illustrating another embodiment of a method of operating the power generation control system shown in FIG. 1 .
7 is a flowchart illustrating a method in which the server of FIG. 1 predicts an increase in power of a wind power generator and an increase in power of a photovoltaic generator.

Power generation is usually expressed in units of megawatts (MW) or gigawatts (GW), and also in units of hours. For example, if one megawatt of power is produced in one hour, it is expressed as 1 MW/h or 1 MWh.

The base load corresponds to the generation capacity generated by the base load generator (this is referred to as 'base power'), and the peak load corresponds to the additional moment-to-moment generating capacity generated by the peak power generator (this is referred to as 'peak power'). referred to as).

1 is a block diagram of a power generation control system capable of reducing peak power using renewable energy for carbon neutrality according to an embodiment of the present invention.

Referring to FIG. 1, the power generation control system 100 includes a base power generator 110, a peak power generator 200, a first renewable power generator 300, a second renewable power generator 400, an energy It includes a storage system 500, a power supply control system 600, and a server 700, and may further include a weather information providing device 800 and/or a mobile communication device 900 according to embodiments. For example, the generation control system 100 that reduces peak power can be used as an indicator for evaluating a company's non-financial factors with ESG (an abbreviation of Environment, (Social) Social, and Governance) there is.

The base power generator 110, also referred to as a base power plant, includes a base power generator 112 that generates base load power (BLP) through a first power cable PWC1. It is supplied to the power supply control system 600. For example, the base power plant may be a nuclear power plant, a coal-fired power plant, or a hydroelectric power plant, but is not limited thereto.

The peak power generator 200, also called a peak power plant, includes a peak power generator 210 using fossil fuel as fuel, a first control device 220, a first communication device 230, and a display device 240. include For example, a peak power plant may be a gas turbine power plant, or a liquefied natural gas (LNG) plant.

The peak power generator 210 generates peak load power (PLP) using fossil fuel under the control of the first control device 220, and converts the peak load power (PLP) to the second power cable (PWC2). ) is supplied to the power supply control system 600.

The first control device 220 controls the operation of the peak power generator 210 to control (eg, increase or decrease) the generation of the peak power (PLP) under the control of the server 700.

The first communication device 230 is a general term for communication devices that perform communication with the server 700 and/or communication with the mobile communication device 900 . For example, when the first communication device 230 and the server 700 can communicate through a wired communication network, the first communication device 230 is a wired communication device, and the first communication device 230 and the mobile communication device When 900 can communicate through a wireless communication network, the first communication device 230 is a wireless communication device.

The display device 240 displays information related to generation of the peak power (PLP) according to the control of the first control device 220 .

For convenience of explanation, the first renewable power generator 300 is a wind power generator that generates first renewable electricity (eg, wind power) using a renewable energy source (eg, wind), It is assumed that the second renewable power generating device 400 is a photovoltaic generating device that generates second renewable electric power (eg, solar power) using a renewable energy source (eg, sunlight).

However, according to embodiments, the first renewable power generation device 300 generates at least one of renewable energy sources (eg, solar energy, wind energy, hydroelectric energy, geothermal energy, biogas energy, and ocean energy). It is sufficient if the generator generates power using one, and the second renewable power generator 400 generates power using at least one of the renewable energy sources.

The wind turbine generator 300 includes a first group of sensors 310, a wind generator 320, wind turbine blades 330, a second control device 340, and a second communication device 350. .

The first group of sensors 310 provides meteorological information (eg, hourly, daily, monthly, and/or yearly wind speed, wind direction, Information on temperature, humidity, atmospheric pressure, etc.) can be collected or collected in real time, and the first group of sensors 310 is a sensor for measuring wind speed, a sensor for measuring wind direction, a sensor for measuring temperature, It includes a sensor that measures humidity, and a sensor that measures atmospheric pressure.

For example, weather information measured by each of the sensors 310 of the first group (eg, every 30 minutes, every 1 hour, etc.) or whenever a special weather report is issued For example, wind speed information, wind direction information, temperature information, humidity information, and atmospheric pressure information) are moved to and from the server 700 in real time through the second communication device 350 under the control of the second control device 340. Provided as a communication device (900).

The wind power generator 320 generates first regenerative power REP1 according to the rotation of the wind turbine blades 330, and transmits the first regenerative power REP1 through the third power cable PWC3 to the power supply control system ( 600) is supplied.

The second control device 340 controls (for example, increases or decreases) the generation of the first renewable power REP1 according to the control of the server 700 by the wind generator 320 and/or the wind turbine. Controls the operation of the blades 330.

According to embodiments, the second control device 340 measures the maximum rotational speed (Mspeed) of the wind turbine blades 330 and the current rotational speed (Cspeed) of the wind turbine blades 330, and these (Mspeed and Cspeed) may be transmitted to the server communication device 710 through the second communication device 350. The artificial intelligence (AI) computer program 730 may calculate the first correction value CalV1 according to Equation 1 using the maximum rotation speed Mspeed and the current rotation speed Cspeed.

The second communication device 350 is a general term for communication devices that perform communication with the server 700 and/or communication with the mobile communication device 900 . For example, when the second communication device 350 and the server 700 can communicate through a wired communication network, the second communication device 350 is a wired communication device, and the second communication device 350 and the mobile communication device When 900 can communicate through a wireless communication network, the second communication device 350 is a wireless communication device.

The photovoltaic device 400 includes a second group of sensors 410, a photovoltaic generator 420, at least one photovoltaic module 430, a third control device 440, and a third communication device 450. ).

For example, each of the communication devices 230, 350, 450, and 710 may be a communication device using a Bluetooth specification version 5.0 or higher protocol or a communication device using a low-power wide-area network (LPWAN). It is not limited.

The second group of sensors 410 are sensors capable of collecting weather information about the second area (or second place) where the photovoltaic module 430 is installed or in real time, and the second group of sensors ( 410) includes a sensor for measuring wind speed, a sensor for measuring wind direction, a sensor for measuring temperature, a sensor for measuring humidity, and a sensor for measuring atmospheric pressure. According to embodiments, the first region and the second region may mean the same region or different regions.

For example, weather information measured by each of the second group of sensors 410 every time period (eg, 30-minute period, 1-hour period, etc.) or whenever a weather warning is issued (e.g., For example, wind speed information, wind direction information, temperature information, humidity information, and atmospheric pressure information) are transferred to the server 700 and/or in real time through the third communication device 450 under the control of the third control device 440. Provided as a communication device (900).

The photovoltaic generator 420 and/or the photovoltaic module 430 generates second renewable power REP2 according to the operation of the photovoltaic module 430, and converts the second renewable power REP2 into a fourth power cable ( PWC4) is supplied to the power supply control system 600.

The third control device 440 controls (eg, increases or decreases) the generation of the second renewable power REP2 according to the control of the server 700, the solar generator 420 and/or the sun. The operation of the optical module 430 is controlled.

For example, the third control device 440 calculates the total number of solar cells included in the solar module 430 (Tcell) and the total number of failed solar cells among the solar cells (Mcell), These (Tcell and Mcell) are transmitted to the server communication device 710 through the third communication device 450 . The artificial intelligence computer program 730 receives the total number of solar cells (Tcell) and the total number of failed solar cells (Mcell) included in the solar module 430 through the server communication device 710 and formulates 2 can be used to calculate the second correction value CalV2.

The third communication device 450 is a generic term for communication devices that perform communication with the server 700 and/or communication with the mobile communication device 900 . For example, when the third communication device 450 and the server 700 can communicate through a wired communication network, the third communication device 450 is a wired communication device, and the third communication device 450 and the mobile communication device When 900 can communicate through a wireless communication network, the third communication device 450 is a wireless communication device.

The energy storage system 500 is renewable power corresponding to the sum of the first renewable power REP1 generated by the wind power generator 300 and the second renewable power REP2 generated by the photovoltaic generator 400. (REP) is stored, and the regenerative power (REP) is supplied to the power supply control system 600 through the fifth power cable PWC5. The energy storage system 500 may include batteries for storing renewable power (REP).

The power supply control system 600 may supply base power (BLP), peak power (PLP), and renewable power (REP) to each of the electricity consumers included in the power grid 650 . For example, the power supply control system 600 may be a smart grid, and the smart grid is an advanced power grid that efficiently manages power supplied to each of the electricity consumers through digitalization and automation. The smart grid aims to increase power efficiency and minimize power loss by monitoring and optimizing power demand, production, and transmission.

The server 700 monitors base power (BLP), peak power (PLP), first renewable power (REP1), second renewable power (REP2), and renewable power (REP), peak power (PLP) Perform functions to be described with reference to Figs. can do.

The server 700 includes a server communication device 710, a server processor 720 that executes an artificial intelligence computer program 730 for collecting and analyzing data according to an artificial intelligence algorithm, and a data storage device 740. do. The data storage device 740 may also be referred to as a database.

According to an embodiment, the artificial intelligence computer program 730 for controlling the operation of the generation control system 100 for controlling the generation of peak power (PLP) for carbon neutrality is a storage medium (eg, server processor 720) stored in a data storage device or data storage device 740 accessible by the server processor 720 .

The weather information providing device 800 is a generic term for a computing device (including a server) that provides weather information, and provides past weather information (WIi, i = 1 or 2) of areas including a first area and a second area. It is provided to the server communication device 710 of the server 700. The server communication device 710 provides the weather information WI1 and WI2 to the artificial intelligence computer program 730.

The mobile communication device 900 refers to a manager's mobile communication device (eg, a smart phone, a PDA, or a laptop computer) that manages each of the power generation devices 200, 300, and 400.

The mobile communication device 900 includes a wireless transceiver 910, a processor 920, a memory device 930, a display device 940, and an input device 950.

The wireless transceiver 910 connects each communication device 230, 350, and 450 and/or server communication device 710 of each power generation device 200, 300, and 400 and information ('signal' or 'data'). It performs the function of an interface or modem for exchanging (also referred to as

Processor 920 runs a mobile application 922 that processes information.

The memory device 930 is a data storage device that stores information to be processed by the mobile application 922 or information processed by the mobile application 922 .

The display device 940 is a display device that displays information to be processed by the mobile application 922 or information processed by the mobile application 922 .

The input device 950 is a general term for devices that input information to be input into the mobile application 922 .

For example, when the mobile communication device 900 approaches the range in which it can communicate with the first communication device 230 (for example, a manager who owns the mobile communication device 900 can communicate with the first communication device 230). When approaching), the first control device 220 automatically determines that the mobile communication device 900 is close to the range in which it can communicate with the first communication device 230, and generates peak power (PLP). Control history of the first control device 220 performed under the control of the server 700 and related information (eg, generation amount of peak power (PLP) per hour, carbon dioxide emission that fluctuates according to the generation amount, etc.) (eg For example, a control history for reducing peak power (PLP), etc.) may be automatically transmitted to the wireless transceiver 910 through the first communication device 230 .

Accordingly, the mobile application 922 receives the information related to the generation of the peak power (PLP) and the control history of the first control device 220 performed under the control of the server 700 and stores them in the memory device 930. It can be displayed on the display device 940 .

As another example, when the mobile communication device 900 approaches the range in which it can communicate with the second communication device 350 (for example, a manager who owns the mobile communication device 900 moves the second communication device 350). close to), the second control device 340 automatically determines that the mobile communication device 900 is close to the range in which it can communicate with the second communication device 350, and supplies the first regeneration power REP1 to Information related to generation (eg, generation amount of first regenerative power REP1 per time), control history of the second control device 340 performed under the control of the server 700 (eg, first regenerative power REP1) (Control history for increasing REP1, etc.), and/or meteorological information measured by the first group of sensors 310 is automatically transmitted to the wireless transceiver 910 through the second communication device 350. can

Accordingly, the mobile application 922 may provide information related to generation of the first regenerative power REP1 , a control history of the second control device 340 performed under the control of the server 700, and/or a first group of sensors. Weather information measured by the field 310 may be received, stored in the memory device 930 , and displayed on the display device 940 .

As another example, when the mobile communication device 900 approaches the range in which it can communicate with the third communication device 450 (for example, a manager holding the mobile communication device 900 ), the third control device 440 automatically determines that the mobile communication device 900 is close to the range in which it can communicate with the third communication device 450, and the second regeneration power REP2 Information related to generation (eg, generation amount of second regenerative power REP2 per hour), control history of the third control device 440 performed under the control of the server 700 (eg, second regenerative power REP2) Control history for increasing power (REP2, etc.), and/or meteorological information measured by the second group of sensors 410 are automatically transmitted to the wireless transceiver 910 through the third communication device 450. can transmit

Accordingly, the mobile application 922 provides information related to generation of the second regenerative power REP2, a control history of the third control device 440 performed under the control of the server 700, and/or a second group of sensors. Weather information measured by the field 410 may be received, stored in the memory device 930 , and displayed on the display device 940 .

A manager who actually checks each power generator 200, 300, and 400 in the vicinity of each power generator 200, 300, and 400 (here, the manager is located far from the server 700 and the server 700) Each power generation device (200, 300, and 400) by the server 700 without knowing. Even if the amount of power generation is automatically increased or decreased, since the mobile application 922 can automatically and in real time receive the information described above through the corresponding communication devices 230, 350, and 450, the manager Among the power generators 200, 300, and 400 to be managed, which power generator is controlled by the server 700 automatically and in real time from the corresponding communication device 230, 350, and 450 can be checked with

FIG. 2 is a flowchart illustrating an embodiment of a method of operating the power generation control system shown in FIG. 1 .

Referring to FIGS. 1 and 2 , the artificial intelligence computer program 730 provides power for a certain period in the past (eg, 1 year or 2 years or more) supplied to electricity consumers through the power supply control system 600. The power use big data BDT1 is generated by analyzing the use data, and the power use big data BDT1 is stored in the database 740 (S110).

For example, past power usage data refers to data including records of usage, charge, usage time, usage location, purpose of usage, power quality, power reserve ratio, and the like.

Usage records the amount of electricity (Wh and/or kWh) used by each electricity consumer, and the usage is mainly recorded hourly, daily, monthly, and yearly. The usage amount may include the total amount of usage used by electricity consumers.

The charge includes the charge for the electricity used by each electricity consumer.

The usage time includes the time each electric consumer used the electric power, and is mainly recorded hourly.

The location of use records the location where each electricity consumer uses power, and generally records the building, each floor within the building, and each location.

The purpose of use records the purpose of each electric consumer's use of power, and records the purpose of using electrical appliances such as, for example, lighting, air conditioners, refrigerators, and TVs.

Power quality records the quality of power used by each electricity customer, and includes information such as voltage, current, frequency, and waveform.

The power reserve ratio is an indicator representing the ratio of actual power demand to the total capacity of power generation facilities in operation in a power plant in preparation for a situation where the power supply supplied through the power grid unexpectedly and rapidly increases, and is recorded in percentage units. For example, the power reserve ratio is recorded in units of minutes or units of hours.

For example, the artificial intelligence computer program 730 collects power consumption data for a certain period in the past, refines missing values or errors if necessary, performs a data collection and refinement process to improve data quality, and analyzes the collected data. Data pre-processing to easily perform pre-processing tasks (including, for example, transforming data by methods such as normalization, scaling, and/or encoding), extracting characteristics of data to be used for power consumption big data (BDT1) A feature extraction process that transforms into a form that can be analyzed (for example, using techniques such as statistical analysis, dimensionality reduction, clustering, and pattern recognition), and a modeling process that analyzes the extracted feature data to develop a predictive model (for this purpose, a machine training, using algorithms such as deep learning, regression, and/or classification), model evaluation and enhancement processes that evaluate the performance of the model and, where necessary, improve the model (for this purpose cross-validation, error matrix, and ROC (Receiver Operating Characteristic), and analysis results to derive insights that can improve the power system by interpreting the analysis results.

In particular, examples of methods for extracting characteristics of data are as follows.

1. Statistical analysis: Calculate the basic statistics of the data to understand the characteristics of the data. For example, the average, median, standard deviation, variance, minimum value, and/or maximum value of the data may be calculated to determine the distribution, central tendency, and/or variance of the data.

2. Dimensionality Reduction: Data characteristics are extracted by reducing the dimensionality of the data. For example, a technique such as PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), or t-SNE (t-distributed Stochastic Neighbor Embedding) is used.

3. Clustering: By grouping data into clusters, the characteristics of each cluster are identified. For example, techniques such as K-means, hierarchical clustering, and DBSCAN are used.

4. Pattern Recognition: Recognize patterns in data to identify characteristics of data. For example, a technique such as a deep neural network (DNN), a convolutional neural network (CNN), or a recurrent neural network (RNN) is used.

5. Natural language processing: Extracts information such as keywords, context, and subject from natural language data and identifies the characteristics of the data. For example, using NLP (Natural Language Processing) techniques.

For example, the artificial intelligence computer program 730 may include a machine learning algorithm that analyzes data to learn patterns and makes predictions, a deep learning algorithm that analyzes data and learns patterns using an artificial neural network, and/or a human language. You can use natural language processing algorithms that allow machines to understand and process them.

Examples of machine learning algorithms include decision trees, random forests, support vector machines (SVMs), and neural networks.

Examples of deep learning algorithms include Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM).

Examples of natural language processing algorithms include word embedding, topic modeling, and document summarization.

The artificial intelligence computer program 730 predicts the power reserve ratio for a time interval defined from the present time to a certain future time using the power use big data (BDT1) (S112). For example, the time interval may mean a unit of minutes or units of time, but may be changed. Hereinafter, for convenience of explanation, it is assumed that the time interval is 1 hour.

For example, the first power reserve ratio at 2:00 pm on August 10 of a specific year included in the power use big data BDT1 is different from the second power reserve ratio at 3:00 pm on August 10.

For example, when the present is 2:20 pm on August 10, 2023, the artificial intelligence computer program 730 calculates the first power reserve ratio at 2:00 pm on August 10, 2022 included in the power consumption big data (BDT1). (eg, 22%) and the second power reserve ratio (eg, 18%) at 3 pm on August 10, 2022 (eg, arithmetic mean of the first power reserve ratio and the second power reserve ratio) , or the minimum value (or maximum value) of the first power reserve ratio and the second power reserve ratio, etc.) to predict the power reserve ratio at 3:20 pm on August 10, 2023 (S112). For convenience of description, it is assumed that the predicted power reserve ratio is 20%.

According to embodiments, the artificial intelligence computer program 730 calculates the arithmetic average of the power reserve ratios at 2:00 pm on August 10, 2020 to 2022, included in the power use big data (BDT1), as the first power reserve ratio. And, the arithmetic average of the power reserve rates at 3:00 pm on August 10, 2022 in each of 2020 to 2022 can be calculated as the second power reserve rate.

The artificial intelligence computer program 730 connected to the power supply control device 600 through the server communication device 710 monitors the current power consumption consumed by power consumers and calculates the current total power consumption (eg, 1000 MW) Do (S114).

According to embodiments, the artificial intelligence computer program 730 may directly monitor the current total power consumption (eg, 1000 MW), and the current total power consumption calculated by the power supply control device 600 (eg, For example, 1000 MW) may be received through the server communication device 710 and current total power consumption (eg, 1000 MW) may be indirectly monitored.

The artificial intelligence computer program 730 generates base power (BLP, eg 800 MW), peak power (PLP, eg 300 MW), and renewable power (REP, eg 200 MW, where a first renewable power ( It is assumed that REP1) is 120 MW and the second renewable power (REP1) is 80 MW.) is monitored to calculate the current total generated power (e.g., 1300 MW) corresponding to the sum of these (BLP, PLP, and REP) (S116).

The artificial intelligence computer program 730, based on the current total power consumption (eg, 1000 MW) and the predicted power reserve ratio (eg, 20%), during the time interval (eg, August 10, 2023) Estimate the total power requirement (e.g., 1000MW+1000MW*0.2=1200MW) to be consumed by electricity consumers during an hour (defined as 2:20 PM on Sunday to 3:20 PM on August 10, 2023) Do (S118).

The artificial intelligence computer program 730 calculates a power difference (eg, 100MW) between the current total generated power (eg, 1300MW) and the predicted total required power (eg, 1200MW) (S120).

The artificial intelligence computer program 730 generates a peak power generation control signal using a power difference (eg, 100 MW) and a weight, and transmits the peak power generation control signal to the first control device through the communication devices 710 and 230. It is transmitted to (220) (S122).

The first control device 220 controls the peak power generator 210 to reduce generation of the peak power (PLP) in response to the peak power generation control signal (S124).

The artificial intelligence computer program 730 calculates how much carbon (or greenhouse gas) emissions corresponding to the decrease in peak power (PLP) are reduced by using fossil fuel (S126).

For example, in step S122, the artificial intelligence computer program 730 calculates the peak power reduction amount corresponding to the product of the power difference (eg, 100 MW) and the weight, and calculates the peak power reduction amount as much as the peak power PLP. Generates a peak generation control signal to reduce generation. At this time, the weight is greater than 0 and less than 1.

FIG. 3 is a flowchart illustrating an embodiment of a method for calculating weights by the server of FIG. 1 .

Referring to FIGS. 1 to 3 , a wind turbine generator 300 installed in a first region includes a first group of sensors 310 each measuring wind speed, wind direction, temperature, humidity, and atmospheric pressure.

The artificial intelligence computer program 730 analyzes the past weather information (WIi, i=1) of the first area provided from the weather information providing device 800 to generate the first weather big data BDT2 and store it in the database 700. Save (S210).

The artificial intelligence computer program 730 receives current first weather information (eg, first wind speed information, first wind direction information, first temperature information, first humidity information, and First atmospheric pressure information) is received (S212).

For example, the first weather information collected by the first group of sensors 310 is transmitted to the server communication device 710 through the second communication device 350 under the control of the second control device 340. and the first weather information is provided to the artificial intelligence computer program 730 (S212).

The artificial intelligence computer program 730 uses the first weather big data BDT2 to provide current first weather information (eg, first wind speed information, first wind direction information, first temperature information, first humidity information, and first atmospheric pressure information) to generate a first analysis result (S214).

For example, the first weather big data (BDT2) is wind speed information, wind direction information, temperature information, and humidity for the first region at 2:00 pm on August 10, 2023 and at 3:00 pm on August 10, 2023, respectively. information, and atmospheric pressure information, the artificial intelligence computer program 730 calculates average wind speed information by using weather information at 2:00 pm on August 10, 2023 and 3:00 pm on August 10, 2023, respectively. Average wind direction information, average temperature information, average humidity information, and average atmospheric pressure information are calculated.

The artificial intelligence computer program 730 uses the first wind speed information and the average wind speed information to perform a wind speed ratio (for example, a ratio of a larger value to a smaller value between a value included in the first wind speed information and a value included in the average wind speed information) , The wind direction ratio (eg, the value included in the first wind direction information (eg, 22.5 degrees clockwise from the north) and the value included in the average wind direction information (eg, 22.5 degrees clockwise from the north) using the first wind direction information and the average wind direction information For example, the ratio of the largest value to the smallest value among 45) in a clockwise direction from the north), and the temperature ratio (for example, the value included in the first temperature information and the average temperature information) using the first temperature information and the average temperature information The ratio of the larger value to the smaller value among the included values), the humidity ratio using the first humidity information and the average humidity information (for example, the ratio of the larger value between the value included in the first humidity information and the value included in the average humidity information) the ratio of small values), and the atmospheric pressure ratio (for example, the ratio of the large value to the small value between the value included in the first atmospheric pressure information and the value included in the average atmospheric pressure information) using the first atmospheric pressure information and the average atmospheric pressure information Calculate.

The artificial intelligence computer program 730 calculates the first analysis result (eg, wind speed ratio, wind direction ratio, temperature ratio, humidity ratio, and atmospheric pressure ratio) as an arithmetic average (standard deviation or variance instead of the arithmetic mean according to embodiments). Calculate) to generate a first weight (S216). For convenience of description, it is assumed that the first weight is 0.6.

Again, referring to step S122, the artificial intelligence computer program 730 determines the peak power reduction amount (eg, 100MW) corresponding to the product of the power difference (eg, 100MW) and the weight (eg, 0.6). *0.6 = 60 MW) is calculated, and a peak power generation control signal is generated to reduce the generation of the peak power (PLP) by the peak power reduction amount (eg, 60 MW) (S122).

Under the control of the first control device 220 operating according to the peak power generation control signal, the peak power generator 210 generates peak power (eg, 240 MW) using fossil fuel. That is, the peak power generator 210 that used to generate 300 MW of peak power (PLP) generates 240 MW of peak power (PLP) under the control of the first control device 220 (S124). Accordingly, carbon emissions corresponding to 60 MW are reduced (S126).

Even if the peak power generator 210 generates a peak power (PLP) of 240 MW, the base power (BLP) is 800 MW, the renewable power (REP) is 200 MW and the sum of these (BLP, PLP, and REP) is 1240 MW, so The sum of these (BLP, PLP, and REP) (eg, 1240 MW) is greater than the total required power (eg, 1200 MW).

Referring back to FIGS. 1 to 3 , the photovoltaic device 400 installed in the second region includes a second group of sensors 410 each measuring wind speed, wind direction, temperature, humidity, and atmospheric pressure. .

The artificial intelligence computer program 730 analyzes the past weather information (WIi, i = 2) of the second region provided from the weather information providing device 800 to generate the second weather big data BDT3 and stores it in the database 700. Save (S210).

The artificial intelligence computer program 730 receives current second weather information (eg, second wind speed information, second wind direction information, second temperature information, second humidity information, and Second atmospheric pressure information) is received (S212). For example, the second weather information collected by the second group of sensors 410 is transmitted to the server communication device 710 through the third communication device 450 under the control of the third control device 440. and the second weather information is provided to the artificial intelligence computer program 730 (S212).

The artificial intelligence computer program 730 uses the second weather big data BDT3 to provide current second weather information (eg, second wind speed information, second wind direction information, second temperature information, second humidity information, and second atmospheric pressure information) to generate a second analysis result (S214).

For example, the second weather big data (BDT3) is wind speed information, wind direction information, temperature information, and humidity for the second region at 2:00 pm on August 10, 2022 and at 3:00 pm on August 10, 2022, respectively. information, and atmospheric pressure information, the artificial intelligence computer program 730 calculates average wind speed information and average wind speed information using weather information at 2:00 pm on August 10, 2022 and at 3:00 pm on August 10, 2022, respectively. Wind direction information, average temperature information, average humidity information, and average atmospheric pressure information are calculated.

The artificial intelligence computer program 730 uses the second wind speed information and the average wind speed information to perform a wind speed ratio (for example, a ratio of a larger value to a smaller value between a value included in the second wind speed information and a value included in the average wind speed information) , Using the second wind direction information and the average wind direction information, the wind direction ratio (eg, the value included in the second wind direction information (eg, 45 degrees clockwise from the north) and the value included in the average wind direction information (eg, For example, the ratio of the larger value to the smaller value among 67.5) in a clockwise direction from the north), and the temperature ratio (for example, the value included in the second temperature information and the average temperature information) using the second temperature information and the average temperature information The ratio of the larger value to the smaller value among the included values), the humidity ratio using the second humidity information and the average humidity information (for example, the larger value between the value included in the second humidity information and the value included in the average humidity information) the ratio of small values), and the atmospheric pressure ratio (for example, the ratio of the larger value to the smaller value between the value included in the second atmospheric pressure information and the value included in the average atmospheric pressure information) using the second atmospheric pressure information and the average atmospheric pressure information Calculate.

The artificial intelligence computer program 730 averages the second analysis result (eg, wind speed ratio, wind direction ratio, temperature ratio, humidity ratio, and atmospheric pressure ratio for the second region) (according to embodiments, instead of the arithmetic mean) Standard deviation or variance is calculated) to generate a second weight (S216). For convenience of explanation, it is assumed that the second weight is 0.4.

Again, referring to step S122, the artificial intelligence computer program 730 determines the peak power reduction amount (eg, 100MW) corresponding to the product of the power difference (eg, 100MW) and the weight (eg, 0.4). *0.4 = 40 MW) is calculated, and a peak power generation control signal is generated to reduce the generation of peak power (PLP) by the peak power reduction amount (eg, 40 MW).

Under the control of the first control device 220 operating according to the peak power generation control signal, the peak power generator 210 generates peak power (eg, 260 MW) using fossil fuel. That is, the peak power generator 210 that used to generate 300 MW of peak power (PLP) generates 260 MW of peak power (PLP) under the control of the first control device 220 (S124). Accordingly, carbon emissions corresponding to 40 MW are reduced (S126).

Even if the peak power generator 210 generates a peak power (PLP) of 260 MW, the base power (BLP) is 800 MW, the renewable power (REP) is 200 MW and the sum of these (BLP, PLP, and REP) is 1260 MW, The sum of these (BLP, PLP, and REP) (eg, 1260 MW) is greater than the total required power (eg, 1200 MW).

FIG. 4 is a flowchart illustrating another embodiment of a method for calculating weights by the server of FIG. 1 .

Referring to FIGS. 1 to 4 , the process of generating the first analysis result is as described in FIG. 3 .

The photovoltaic device 400 installed in the second region includes a second group of sensors 410 that each measure wind speed, wind direction, temperature, humidity, and atmospheric pressure.

The artificial intelligence computer program 730 analyzes the past weather information WI2 of the second area provided from the weather information providing device 800 to generate second weather big data BDT3 and stores it in the database 700 (S310). ).

The artificial intelligence computer program 730 receives current second weather information (eg, second wind speed information, second wind direction information, second temperature information, second humidity information, and Second atmospheric pressure information) is received (S312). For example, the second weather information collected by the second group of sensors 410 is transmitted to the server communication device 710 through the third communication device 450 under the control of the third control device 440. and the second weather information is provided to the artificial intelligence computer program 730 (S312).

The artificial intelligence computer program 730 uses the second weather big data BDT3 to provide current second weather information (eg, second wind speed information, second wind direction information, second temperature information, second humidity information, and second atmospheric pressure information) to generate a second analysis result (S314).

For example, the second weather big data (BDT3) is wind speed information, wind direction information, temperature information, and humidity for the second region at 2:00 pm on August 10, 2022 and at 3:00 pm on August 10, 2022, respectively. information, and atmospheric pressure information, the artificial intelligence computer program 730 calculates average wind speed information and average wind speed information using weather information at 2:00 pm on August 10, 2022 and at 3:00 pm on August 10, 2022, respectively. Wind direction information, average temperature information, average humidity information, and average atmospheric pressure information are calculated.

Here, the average wind speed information is the wind speed value for the first region (or second region) at 2:00 pm on August 10, 2022 and the first region (or second region) at 3:00 pm on August 10, 2022 It is the arithmetic average of the wind speed values for, and the average wind direction information is the wind direction value for the first region (or second region) at 2:00 pm on August 10, 2022 and the first region at 3:00 pm on August 10, 2022 ( Or the arithmetic mean of the wind direction values for the second region), and the average temperature information is the temperature value for the first region (or region 2) at 2:00 pm on August 10, 2022 and the temperature value at 3:00 pm on August 10, 2022 It is the arithmetic average of the temperature values for the first area (or second area) of the city, and the average humidity information is the humidity value for the first area (or second area) at 2:00 pm on August 10, 2022 and the 2022 year It is the arithmetic average of the humidity values for the first area (or second area) at 3:00 pm on August 10, and the average atmospheric pressure information is in the first area (or second area) at 2:00 pm on August 10, 2022. It is the arithmetic average of the atmospheric pressure value for the first area (or the second area) at 3:00 pm on August 10, 2022.

The artificial intelligence computer program 730 uses the second wind speed information and the average wind speed information to perform a wind speed ratio (for example, a ratio of a larger value to a smaller value between a value included in the second wind speed information and a value included in the average wind speed information) , Using the second wind direction information and the average wind direction information, the wind direction ratio (eg, the value included in the second wind direction information (eg, 45 degrees clockwise from the north) and the value included in the average wind direction information (eg, For example, the ratio of the larger value to the smaller value among 67.5) in a clockwise direction from the north), and the temperature ratio (for example, the value included in the second temperature information and the average temperature information) using the second temperature information and the average temperature information The ratio of the larger value to the smaller value among the included values), the humidity ratio using the second humidity information and the average humidity information (for example, the larger value between the value included in the second humidity information and the value included in the average humidity information) the ratio of small values), and the atmospheric pressure ratio (for example, the ratio of the larger value to the smaller value between the value included in the second atmospheric pressure information and the value included in the average atmospheric pressure information) using the second atmospheric pressure information and the average atmospheric pressure information Calculate.

The artificial intelligence computer program 730 calculates the first weight value using the first analysis result (eg, the wind speed ratio, the wind direction ratio, the temperature ratio, the humidity ratio, and the atmospheric pressure ratio of the first region), and the second analysis result. Final weights are generated by arithmetic averaging of second weights calculated using the results (eg, wind speed ratio, wind direction ratio, temperature ratio, humidity ratio, and atmospheric pressure ratio of the second region) (S316). For convenience of description, it is assumed that the final weight is 0.5.

Referring back to step S122, the artificial intelligence computer program 730 determines the peak power reduction amount (eg, 50MW) corresponding to the product of the power difference (eg, 100MW) and the final weight (eg, 0.5). ) is calculated, and a peak power generation control signal is generated to reduce the generation of the peak power (PLP) by the peak power reduction amount (eg, 50 MW).

Under the control of the first control device 220 operating according to the peak power generation control signal, the peak power generator 210 generates peak power (eg, 250 MW) using fossil fuel. That is, the peak power generator 210, which was generated with 300 MW of peak power (PLP), generates 250 MW of peak power (PLP) under the control of the first control device 220. Thus, carbon emissions equivalent to 50 MW are reduced.

Even if the peak power generator 210 generates a peak power (PLP) of 250 MW, the base power (BLP) is 800 MW, the renewable power (REP) is 200 MW and the sum of these (BLP, PLP, and REP) is 1250 MW, The sum of these (BLP, PLP, and REP) (eg, 1250 MW) is greater than the total required power (eg, 1200 MW).

FIG. 5 is a flowchart illustrating a method of controlling a wind power generator and a solar power generator by the power generation control system shown in FIG. 1 .

1 to 5, the artificial intelligence computer program 730 generates a wind power generation control signal using a power difference (eg, 100 MW) and a first weight (eg, 0.6) to control a second control signal. It is transmitted to the device 340 (S410A).

For example, the artificial intelligence computer program 730 uses a first weight corresponding to the first analysis result (eg, wind speed ratio, wind direction ratio, temperature ratio, humidity ratio, and atmospheric pressure ratio of the first region) A wind power generation control signal is generated by predicting future wind speed, wind direction, temperature, humidity, and atmospheric pressure for the first region and transmitted to the second control device 340 .

Accordingly, the second control device 340 adjusts the angle and rotational speed of each of the wind turbine blades 330 to increase the generation of the first renewable power REP1 according to the wind power generation control signal to generate the first renewable power (REP1). REP1) is increased (S412A).

Simultaneously or in parallel with step S410A, the artificial intelligence computer program 730 generates a photovoltaic power generation control signal using the power difference (eg, 100MW) and the second weight (eg, 0.4), It is transmitted to the third control device 440 (S410B).

For example, the artificial intelligence computer program 730 uses a second weight value corresponding to the second analysis result (eg, wind speed ratio, wind direction ratio, temperature ratio, humidity ratio, and atmospheric pressure ratio of the second region) A wind power generation control signal is generated by predicting future wind speed, wind direction, temperature, humidity, and atmospheric pressure for the second region and transmitted to the second control device 340 . Accordingly, the third control device 440 adjusts the angle and height of the solar module 430 to increase the generation of the second renewable power REP2 according to the photovoltaic power generation control signal to generate the second renewable power REP2. Increases the generation of (S412B).

For example, the first weather information actually measured by the first group of sensors 310 reflects (or measures) only the actual weather information of the first region and reflects future weather information of the first region ( or prediction), the angle and rotational speed of the turbine blades 330 cannot be adjusted in advance.

However, the artificial intelligence computer program 730 analyzes the first weather information using the second big data BDT2, generates a first analysis result corresponding to the analysis result, and generates a first analysis result corresponding to the first analysis result. Since a wind power generation control signal capable of adjusting the angle and rotational speed of each of the turbine blades 330 in advance can be generated using one weight, the second control device 340 responds to the wind power generation control signal to generate the first wind power generation control signal. In order to increase regenerative power, the angle and rotational speed of the turbine blades 330 may be preemptively adjusted.

In addition, the second weather information actually measured by the second group of sensors 410 reflects (or measures) only the actual weather information of the second region and reflects (or predicts) future weather information of the second region. ), so the angle and height of the solar module 430 cannot be adjusted in advance.

However, the artificial intelligence computer program 730 analyzes the second weather information using the third big data (BDT3), generates a second analysis result corresponding to the analysis result, and weights corresponding to the second analysis result. The angle (eg, at least one of an angle according to latitude, an angle according to a season, and an angle according to the time of the sun) and a height (eg, a height according to the shape of the terrain) of the photovoltaic module 430 using , And at least one of height according to the wind speed) can generate a photovoltaic power generation control signal capable of adjusting in advance, so that the third control device 440 increases the second renewable power in response to the photovoltaic power generation control signal. For this purpose, the angle and rotational speed of the solar module 430 may be preemptively adjusted.

FIG. 6 is a flowchart illustrating another embodiment of a method of operating the power generation control system shown in FIG. 1 .

Referring to FIGS. 1 to 6 , the artificial intelligence computer program 730 analyzes past power use data used by electricity customers to generate power use big data (BDT1) and stores it in the database 740 (S510).

The artificial intelligence computer program 730 monitors power consumption currently consumed by power consumers and calculates current total power consumption (eg, 1000 MW) (S512).

The artificial intelligence computer program 730 monitors base power (BLP, eg 800 MW), peak power (PLP, eg 300 MW), and renewable power (REP, eg 200 MW) to determine these ( BLP, PLP< and REP) to calculate the current total produced power (eg, 1300MW) (S514).

The artificial intelligence computer program 730 calculates a power difference (eg, 300MW) between the current total power generation (eg, 1300MW) and the current total power consumption (eg, 1000MW) (S516).

The artificial intelligence computer program 730 corrects the power difference (eg, 300MW) using the power use big data (BDT1) (S518).

For example, the artificial intelligence computer program 730 corresponds to the current time (2:00 pm on August 10, 2023) and the past time (eg, August 10, 2022) included in the power usage big data (BDT1). First total power consumption (eg, 1020 MW) at 1:00 PM), second total power consumption (eg, 2:00 PM on August 10, 2022) at the same time as the current time (eg, 2:00 PM on August 10, 2022) eg, 980 MW)), and the arithmetic mean value (eg, 1020 MW) of the third total power consumption (eg, 1060 MW)) at a future time (eg, 3:00 pm on August 10, 2022) Calculate , and calculate the difference (eg, 280MW) between the current total generated power (eg, 1300MW) and the average value (eg, 1020MW), and calculate the power difference (eg, S516). For example, 300 MW) is corrected with an arithmetic average value (eg, 280 MW) (S518).

In the above description, an embodiment of calculating an arithmetic average value of total power consumption for three different times is shown, but the artificial intelligence computer program 730 calculates the total consumption for at least two or more times compared to the current time. Any one of the minimum value, maximum value, average value, median value, standard deviation, and variance of the powers may be calculated, and the power difference calculated in step S516 may be corrected using the calculation result (S518).

Artificial intelligence computer program 730 controls wind generator 320 and/or wind turbine blades 330 to increase renewable power (REP) within a calibrated power differential (eg, 280 MW). Controls the second control device 340 and controls the third control device 440 that controls the photovoltaic generator 420 and/or the solar module 430, and generates peak power (PLP) as much as the increased renewable power. The first control device 220 may be controlled to decrease (S520).

Control for each of the control devices 220, 340, and 440 may be controlled simultaneously or in parallel.

7 is a flowchart illustrating a method in which the server of FIG. 1 predicts an increase in power of a wind power generator and an increase in power of a photovoltaic generator.

1 to 7, the artificial intelligence computer program 730 analyzes the past weather information WI1 of the first area where the wind power generator 300 is installed, generates first weather big data BDT2, and generates a database. It is stored in 740 (S610).

The artificial intelligence computer program 730 analyzes the past weather information WI2 of the second area where the photovoltaic device 400 is installed, generates second weather big data BDT3, and stores it in the database 740 (S610). ).

The artificial intelligence computer program 730 receives first weather information collected from the first group of sensors 310 (S614) and receives second weather information collected from the second group of sensors 410. (S616).

The artificial intelligence computer program 730 analyzes the first weather information for the first region using the first weather big data (BDT2) to generate a first analysis result (S618), and generates the second weather big data (BDT2) A second analysis result is generated by analyzing the second weather information of the second region using (S620). The process of generating the first analysis result and/or the first weight is the same as the process described in step S214, and the process of generating the second analysis result and/or the second weight is the process described in step S3124. is the same as

As described above, the artificial intelligence computer program 730 generates a first weight value by arithmetic averaging the first analysis results (eg, wind speed ratio, wind direction ratio, temperature ratio, humidity ratio, and atmospheric pressure ratio), and describes For the sake of convenience, it is assumed that the first weight is 0.6.

The artificial intelligence computer program 730 predicts the first renewable power increase based on the first weight generated according to the first analysis result, the first renewable power, the design minimum efficiency of the wind power generator 320, and the first correction value. Do (S622). Here, the first correction value CalV1 may be calculated using Equation 1.

[Equation 1]

For example, Mspeed represents the maximum rotational speed of the wind turbine blades 330, and Cspeed represents the current rotational speed of the wind turbine blades 330.

Depending on the embodiment, for example, when Mspeed is 20 rpm and Cspeed is 18 rpm, the artificial intelligence computer program 730 determines the first weight value (eg, 0.6) generated according to the first analysis result, the first reproduction A first renewable power increase (eg, 0.1) based on the product of power (eg, 120 MW), the lowest design efficiency (eg, 45%) of the wind power generator 320, and a first correction value (eg, 0.1). For example, 0.6*120*0.45*0.1=3.24MW) is calculated (S622).

As described above, the artificial intelligence computer program 730 generates a second weight value by arithmetic averaging the second analysis results (eg, wind speed ratio, wind direction ratio, temperature ratio, humidity ratio, and atmospheric pressure ratio), and describes For the sake of convenience, it is assumed that the second weight is 0.4.

The artificial intelligence computer program 730 calculates the second renewable power increase based on the second weight, the second renewable power, the design minimum efficiency of the solar generator 320, and the second correction value generated according to the second analysis result. Predict (S624). Here, the second correction value CalV2 may be calculated using Equation 2.

[Equation 2]

Here, Tcell represents the total number of solar cells included in the solar module 430, and Mcell represents the total number of failed solar cells among the solar cells.

According to an embodiment, when the total number of solar cells in Tcell is 600 and the total number of failed solar cells is 60, the artificial intelligence computer program 730 calculates the second weight value (eg, Tcell) generated according to the second analysis result. For example, 0.4), the second renewable power (eg, 80 MW), the design minimum efficiency of the photovoltaic generator 320 (eg, 8%), and the second correction value (eg, 0.1) Based on the product, the second renewable power increase amount (eg, 0.4*80*0.08*0.1 = 0.256MW) is predicted (S624).

After steps S622 and 624 are performed, the artificial intelligence computer program 730 calculates the sum (eg, 0.256 MW) of the first renewable power increment (eg, 3.24 MW) and the second renewable power increment (eg, 0.25 MW). For example, 3.24 + 0.256 = 3.495 MW) is predicted as the final renewable power increase (S626).

When the final renewable power increase (eg 3.496 MW) is smaller than the corrected power difference (eg 280 MW) in step S518, the artificial intelligence computer program 730 calculates the peak power (PLP, eg, 30 MW), a peak power control signal for reducing only the final renewable power increase amount (eg, 3.495 MW) is generated and transmitted to the first control device 220 (S628).

In addition, the artificial intelligence computer program 730 generates a wind power generation control signal for increasing the first renewable power REP1 by the first renewable power increase amount (eg, 3.24MW) to the second control device 340. Transmit (S682).

In addition, the artificial intelligence computer program 730 generates a photovoltaic power generation control signal for increasing the second renewable power REP2 by the amount of the second renewable power increase (eg, 0.256 MW), and the third control device 440 It is transmitted to (S682).

An embodiment in which the artificial intelligence computer program 730 can predict the sum of the first and second regenerative power increments as the final regenerative power increase has been described, but according to the embodiments, the artificial intelligence computer program 730 is the first regenerative power An arithmetic mean of the power increase amount and the second renewable power increase amount may be predicted as the final renewable power increase amount, and a smaller value between the first renewable power increase amount and the second renewable power increase amount may be predicted as the final renewable power increase amount.

The artificial intelligence computer program 730 that can reduce peak power by using renewable energy for carbon neutrality is the steps described with reference to FIG. 2 (S110 to S126) and the steps described with reference to FIG. 3 (S210). ~ S216), steps described with reference to FIG. 4 (S210 to S316), steps described with reference to FIG. 5 (S410A and S412A, and S410B and S412B), steps described with reference to FIG. 6 (S510) ~ S520), and/or steps S610 to S628 described with reference to FIG. 7 may be performed. Numbers in parentheses in this specification are illustrated for convenience of description.

Although the present invention has been described with reference to the embodiments shown in the drawings, this is only exemplary, and those skilled in the art will understand that various modifications and equivalent other embodiments are possible therefrom. Therefore, the true technical scope of protection of the present invention will have to be determined by the technical spirit of the pointed claims.

100: power generation control system
110: base power generator, base power power plant
200: peak power generator, peak power plant
210: peak power generator
220: first control device
300: first renewable power generation device, wind power generation device
310: first group of sensors
320: wind generator
330: wind turbine blades
340: second control device
350: second communication device
400: second renewable power generation device, solar power generation device
410: second group of sensors
420: solar generator
430: solar module
440: third control device
450: third communication device
500: energy storage system
600: power supply control system
700: server
710: server communication device
720: server processor
730: artificial intelligence computer program
740: data storage, database
800: Weather information providing device
900: mobile communication device

Claims (4)

delete a base power generator including a base power generator generating base power;
a peak power generator including a peak power generator that generates peak power using fossil fuel and a first control device that controls an operation of the peak power generator;
A wind power generator including a wind power generator generating first renewable power using wind, a second control device controlling operation of the wind power generator, and a first group of sensors;
a photovoltaic generator including a photovoltaic generator that generates second renewable power using sunlight, a third control device that controls operation of the photovoltaic generator, and a second group of sensors;
an energy storage system that stores renewable power corresponding to the sum of the first renewable power and the second renewable power;
a power supply control system supplying the base power, the peak power, and the renewable power to electricity customers; and
including the server;
The server,
Analyzing past power use data used by the electricity consumers to generate power use big data;
Calculate current total power consumption by monitoring power consumption currently consumed by the electricity consumers;
Monitoring the base power, the peak power, and the renewable power to calculate a current total generated power corresponding to the sum of them;
Calculate a power difference between the current total produced power and the current total power consumption;
Correcting the power difference using the power use big data;
Control the second control device for controlling the wind power generator and the third control device for controlling the photovoltaic generator to increase the regeneration power within the range of the corrected power difference, and the peak power by the increased regeneration power Controlling the first control device using a peak power control signal to reduce
First weather big data is generated by analyzing past weather information of a first region where the wind power generator is installed;
Analyzing past weather information of a second area where the photovoltaic power generation device is installed to generate second weather big data;
receiving first weather information collected from the first group of sensors;
receiving second weather information collected from the second group of sensors;
The first weather information is analyzed using the first weather big data to generate a first analysis result, and a first weight value is generated using the first analysis result;
The second weather information is analyzed using the second weather big data to generate a second analysis result, and a second weight value is generated using the second analysis result;
Predicting a first renewable power increase using the first weight, the first renewable power, the lowest design efficiency of the wind power generator, and a first correction value,
Predicting an increase in second renewable power using the second weight, the second renewable power, the lowest design efficiency of the photovoltaic device, and a second correction value;
Predicting a final renewable power increase based on the first renewable power increase and the second renewable power increase,
When the final renewable power increment is smaller than the corrected power difference, the peak power control signal for reducing the peak power by only the final renewable power increment is generated and transmitted to the first control device Fossil fuel for carbon neutrality A power generation control system capable of reducing the peak power generated by using The method of claim 2, wherein the first correction value (CalV1) is predicted using Equation 1 below,
[Equation 1]

Here, Mspeed represents the maximum rotational speed of the wind turbine blades included in the wind turbine generator, and Cspeed represents the current rotational speed of the wind turbine blades. For carbon neutrality, which can reduce peak power generated using fossil fuels power generation control system. The method of claim 2, wherein the second correction value (CalV2) is predicted using Equation 2 below,
[Equation 2]

Here, Tcell represents the total number of photovoltaic cells included in the photovoltaic device, and Mcell represents the total number of failed photovoltaic cells among the photovoltaic cells Peak power generated using fossil fuel for carbon neutrality A power generation control system that can reduce

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