WO2017159982A1 - Système et procédé d'exploitation de micro-réseau - Google Patents

Système et procédé d'exploitation de micro-réseau Download PDF

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Publication number
WO2017159982A1
WO2017159982A1 PCT/KR2017/001063 KR2017001063W WO2017159982A1 WO 2017159982 A1 WO2017159982 A1 WO 2017159982A1 KR 2017001063 W KR2017001063 W KR 2017001063W WO 2017159982 A1 WO2017159982 A1 WO 2017159982A1
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data
time
prediction
operation plan
diesel
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PCT/KR2017/001063
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English (en)
Korean (ko)
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류광렬
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부산대학교 산학협력단
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Publication of WO2017159982A1 publication Critical patent/WO2017159982A1/fr

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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P80/00—Climate change mitigation technologies for sector-wide applications
    • Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
    • Y02P80/14—District level solutions, i.e. local energy networks

Definitions

  • the present invention relates to microgrid operating systems and methods.
  • Microgrid refers to the application of smart grid, a next-generation intelligent power grid that optimizes energy efficiency by integrating information technology (IT) into the existing grid.
  • microgrid refers to a local power supply system centered on a distributed power source independent from the existing wide area power system.
  • Independent microgrids provide power through diesel and renewable generation.
  • the generation amount of the diesel generator according to the time zone is determined based on the generation amount prediction information for the renewable power, the prediction amount information on the load characteristics information of the energy storage device and the characteristics and operating state information of the diesel generator, Maintenance cost can be reduced.
  • the problem to be solved by the present invention is to provide a microgrid operating system that can minimize the operating cost, and prevent blackout.
  • Another object of the present invention is to provide a microgrid operating method that can minimize the operating cost and prevent blackout.
  • the microgrid operating system for solving the above problems is an information providing unit for receiving raw data including operating status data of diesel power generation, prediction of diesel power generation using the furnace data
  • a prediction unit for generating data
  • an operation plan optimization unit for establishing an optimal operation plan for diesel power generation using the prediction data, and collecting and transmitting the operation status data to the information providing unit, and the operation status data and the optimal operation.
  • a monitoring unit for transmitting a plan to a user, wherein the prediction unit includes a training data collection module for generating training data using the raw data, an algorithm providing module for providing and updating a prediction model using the training data, and Example of generating prediction data through raw data and the prediction model A module.
  • the training data collection module may generate training data using raw data of a first time point
  • the prediction module may generate training data using raw data of a second time point later than the first time point
  • the second time point may be a current time point.
  • the raw data may include at least one of weather forecast data, real-time weather data, and real-time power consumption data.
  • the weather forecast data may include at least one of wind speed forecast data and insolation forecast data
  • the real time weather data may include at least one of real time wind speed data and real time insolation data
  • the operation plan optimization unit includes a population including a plurality of candidate operation plans, a genetic operator that performs generation replacement by genetically calculating the plurality of candidate operation plans, and selects an optimal operation plan.
  • the generation change may genetically compute the plurality of candidate operating plans to add a new candidate operating plan to the population, simulate each candidate operating plan to derive an evaluation value, and according to the evaluation value, some candidate operating plans.
  • removing from the population, and selecting the optimal operation plan may include selecting a candidate operation plan having the highest evaluation value as the optimal operation plan.
  • the genetic operation may include at least one of a crossover operation and a mutation operation.
  • the evaluation value may be the sum of the total diesel generation cost and the power shortage loss cost in consideration of blackout.
  • control unit may further include a power generation unit that is controlled by the optimal operation plan and transmits operation status data to the monitoring unit.
  • the power generation unit may include a diesel generator, an energy storage system (ESS), and a power transmission system.
  • ESS energy storage system
  • the information providing unit receives the furnace data including the operation status data of the diesel power generation, the prediction unit using the furnace data to predict the diesel power generation Generate data, the operation plan optimizer generates a plurality of candidate operation plans of diesel power generation, the operation plan optimizer evaluates each of the plurality of candidate operation plans, derives an evaluation value, and exceeds a preset number or time limit If not, the operation plan optimizer updates the candidate operation plan according to the evaluation value, derives an updated evaluation value of the candidate operation plan, and if the preset number or time limit is exceeded, the operation plan.
  • the optimizer uses the evaluation value to optimize the operating system among the candidate operation plans. The selection and involves the parts of the control according to the power operating plan optimization in addition to the optimal operation program.
  • the deriving of the evaluation value may include generating power generation amount by unit time in the candidate operation plan, determining whether blackout occurs according to the generation amount by unit time, and calculating total cost according to generation amount and blackout occurrence by unit time. It may include.
  • Determining whether the blackout occurs may include determining a charge / discharge state of the energy storage system.
  • the generating of the prediction data may include learning data collection module collecting the raw data to generate training data, an algorithm providing module providing and updating a prediction model using the training data, and the prediction module is configured to generate the raw data. It may include generating prediction data through the prediction model.
  • Selecting the optimal operating plan may include selecting the optimal operating plan again at predetermined intervals.
  • the microgrid operating system and method according to some embodiments of the present invention can accurately predict the auxiliary diesel generation amount corresponding to the fluctuation range of renewable power generation, which is the main energy source of the independent microgrid.
  • the fluctuation range of power generation is very large according to weather conditions such as weather, so if conservatively reducing the expected generation amount of renewable energy, diesel generation will be excessive and operation cost will increase, and the expectation of renewable energy If the power generation is excessively caught, it may cause a blackout phenomenon in which the power supply is cut off because the load power cannot be handled.
  • the microgrid operating system and method according to some embodiments of the present invention accurately predict the expected generation of renewable energy and precisely specify diesel generation, thereby minimizing the operating cost of the standalone microgrid while preventing blackout. can do.
  • FIG. 1 is a block diagram illustrating a microgrid operating system according to some embodiments of the present invention.
  • FIG. 2 is a block diagram illustrating in detail the predictor of FIG. 1.
  • FIG. 3 is a block diagram illustrating in detail the operation plan optimizer of FIG. 1.
  • FIG. 4 is a diagram illustrating a solution representation of a candidate operating plan of the microgrid operating system according to some embodiments of the present invention.
  • FIG. 5 is a diagram for describing repetition of selecting an optimal operation plan of a microgrid operating system according to some embodiments of the present disclosure.
  • FIG. 6 is a flowchart illustrating a microgrid operating method according to some embodiments of the present invention.
  • FIG. 7 is a flowchart for explaining in detail the step of evaluating the candidate operation plan of FIG.
  • first, second, etc. are used to describe various elements, components and / or sections, these elements, components and / or sections are of course not limited by these terms. These terms are only used to distinguish one element, component or section from another element, component or section. Therefore, the first device, the first component, or the first section mentioned below may be a second device, a second component, or a second section within the technical spirit of the present invention.
  • spatially relative terms below “, “ beneath “, “ lower”, “ above “, “ upper” It may be used to easily describe the correlation of a device or components with other devices or components. Spatially relative terms are to be understood as including terms in different directions of the device in use or operation in addition to the directions shown in the figures. For example, when flipping a device shown in the figure, a device described as “below or beneath” of another device may be placed “above” of another device. Thus, the exemplary term “below” can encompass both an orientation of above and below. The device may be oriented in other directions as well, in which case spatially relative terms may be interpreted according to orientation.
  • FIG. 1 is a block diagram illustrating a microgrid operating system according to some embodiments of the present invention
  • FIG. 2 is a block diagram illustrating in detail the predictor of FIG. 1.
  • 3 is a block diagram illustrating in detail the operation plan optimization unit of FIG. 1
  • FIG. 4 is a diagram illustrating a solution representation of a candidate operation plan of a microgrid operating system according to some embodiments of the present disclosure.
  • FIG. 5 is a diagram for describing repetition of selecting an optimal operation plan of a microgrid operating system according to some embodiments of the present disclosure.
  • a microgrid operating system may include an information providing unit 100, a prediction unit 200, an operation plan optimization unit 300, a power generation unit 400, and a monitoring unit 500. ).
  • the information provider 100 may receive raw data.
  • the information provider 100 may receive the raw data and transmit it to the predictor 200.
  • the raw data may include at least one of weather forecast data, real-time weather data, and real-time power consumption data.
  • the weather forecast data may include wind speed forecast data and solar radiation forecast data.
  • the real-time weather data may include real-time wind speed data and real-time solar radiation data.
  • the raw data may also include operational status data.
  • the operation status data may be transmitted from the monitoring unit 500 to the information providing unit 100. However, it is not limited thereto. That is, the operation status data may be transmitted directly from the power generation unit 400 without passing through the monitoring unit 500.
  • the operation status data may include a diesel generator status data, an energy storage system (ESS) status data and a transmission status data.
  • ESS energy storage system
  • the raw data may include past history data and data of the present time. That is, the raw data may include past weather change history data and past time slot power consumption history data.
  • the information provider 100 may process the raw data into a format required by the predictor 200 and transmit the raw data to the predictor 200.
  • the present invention is not limited thereto, and the raw data may be transmitted to the predicting unit 200 as it is, and the raw data may be processed by the predicting unit 200 in a required format.
  • the information provider 100 may update the raw data at predetermined intervals or in real time. That is, the raw data may be updated using new data at the present time.
  • the prediction unit 200 may receive the raw data from the information providing unit 100.
  • the prediction unit 200 may generate prediction data using the raw data.
  • the prediction unit 200 may transmit the prediction data to the operation plan optimization unit 300.
  • the prediction data may include prediction weather data and prediction power consumption data.
  • the prediction data may be continuously updated at predetermined intervals or in real time.
  • the operation plan optimizer 300 may receive the prediction data from the predictor 200.
  • the operation plan optimizer 300 may establish an optimal operation plan of diesel power using the prediction data.
  • the optimal operation plan may be an operation plan of the diesel generator 410, the energy storage system (ESS) 420, and the power transmission system 430. That is, the power generation unit 400 may be controlled according to the optimum operation plan.
  • the generator 400 may actually produce power.
  • the generator 400 may include a diesel generator 410, an energy storage system 420, and a power transmission system 430.
  • the generation unit 400 may generate operation status data.
  • the operation status data of the power generation unit 400 may be operation status data of the diesel generator 410, the energy storage system 420, and the power transmission system 430, respectively. That is, the operation status data may include a diesel generator status data, energy storage system status data and transmission system status data.
  • the operation status data may be transmitted to the monitoring unit 500. Alternatively, the operation status data may be directly transmitted to the information providing unit 100 without passing through the monitoring unit 500.
  • the diesel generator 410 may generate electric power using diesel.
  • the diesel generator 410 may be used as an energy source to supplement renewable energy with a large fluctuation in a microgrid system that mainly uses renewable energy.
  • the diesel generator 410 may transmit the diesel generator status data to the monitoring unit 500.
  • the diesel generator 410 may be controlled by the optimum operation plan of the operation plan optimizer 300.
  • the energy storage system 420 may be a device for storing generated power. If the energy stored in the energy storage system 420 is less than the power consumption, the microgrid system may black out. The energy storage system 420 may transmit the energy storage system status data to the monitoring unit 500. The energy storage system 420 may be controlled by the optimal operation plan of the operation plan optimizer 300.
  • the power transmission system 430 may be a system for transmitting generated energy or power to a place where electric power is required.
  • the power transmission system 430 may transmit the power transmission system status data to the monitoring unit 500.
  • the power transmission system 430 may be controlled by the optimum operation plan of the operation plan optimizer 300.
  • the monitoring unit 500 may receive operation status data from the power generation unit 400.
  • the monitoring unit 500 may provide the user 10 with the operation status data and the optimal operation plan.
  • the monitoring unit 500 may transmit the operation status data to the information providing unit 100.
  • the monitoring unit 500 may provide the operation status data and the optimum operation plan to the user in the form of an electronic signal. However, it is not limited thereto.
  • the prediction unit 200 may include a data processing module 210, a learning data collection module 220, an algorithm providing module 230, a prediction module 240, and a generator emulator 250. .
  • the data processing module 210 may process raw data.
  • the data processing module 210 may later process the raw data into a format required by the prediction module 240 and the algorithm providing module 230.
  • the data processing module 210 may process data of a current time point among raw data and send the processed data to the prediction module 240.
  • the data processing module 210 may process historical data of past time points among raw data and transmit the processed historical data to the learning data collection module 220.
  • the training data collection module 220 may generate training data using the raw data.
  • the training data may be provided to the algorithm providing module 230 and used to update the prediction model of the algorithm.
  • the learning data may be generated using past history data among raw data.
  • the training data may be generated by selecting only data suitable for model training among raw data.
  • the algorithm providing module 230 may receive the training data from the training data collection module 220.
  • the algorithm providing module 230 may apply a machine learning algorithm based on the training data to form a prediction model and update it.
  • the machine learning algorithm may be a k-Nearest Neighbor algorithm. However, it is not limited thereto.
  • the algorithm providing module 230 may update the prediction model and provide it to the prediction module 240.
  • the prediction module 240 may generate the prediction data using the processed raw data provided by the data processing module 210 and the prediction model updated and provided by the algorithm providing module 230.
  • the prediction data may include wind speed / insolation amount prediction data and power consumption amount prediction data.
  • the power consumption prediction data may be transmitted to the operation plan optimizer 300.
  • the generator emulator 250 may be an emulator of a wind generator and a solar generator.
  • the generator emulator 250 may generate wind / solar power generation forecast data using the wind speed / insolation amount prediction data. That is, the generator emulator 250 may predict the actual generation amount of renewable energy of the wind power generator and the solar generator using the data of the wind speed and the solar radiation amount.
  • the generator emulator 250 may transmit the wind / solar power generation forecast data to the operation plan optimizer 300.
  • the operation plan optimizer 300 may receive wind / solar power generation forecast data, power consumption forecast data, energy storage system status data, and diesel generator status data.
  • the operation plan optimizer 300 may include a population 310 and a genetic operator 320.
  • the population 310 may include a plurality of candidate operation plans 311a and 311b therein.
  • the candidate operation plans 311a and 311b inside the population 310 may include the amount of power generated by the diesel generator 410 per unit time.
  • Candidate operating plans 311a and 311b may be arbitrarily generated initially.
  • Genetic operator 320 may evaluate candidate operational plans 311a and 311b in population 310. That is, the genetic operator 320 may derive evaluation values of the candidate operation plans 311a and 311b in the population 310. The evaluation value is determined in consideration of the operating cost and black out of the diesel generator 410. The said evaluation value is explained in full detail later.
  • the genetic operator 320 may use the evaluation value to alternate generations of candidate operation plans within the population 310 through genetic operations.
  • the genetic operation may include at least one of crossover and mutation.
  • the breeding means generating new candidate operation plans 311a and 311b by intersecting the symbol strings of the two candidate operation plans 311a and 311b.
  • new candidate operation plans 311a and 311b may be generated by crossing not only two but also three or more candidate operation plans 311a and 311b. That is, it may be a method of forming a new child whose parents are the candidate candidate plans 311a and 311b.
  • a simulated binary crossover (SBX) operation may be performed.
  • SBX simulated binary crossover
  • the mutation refers to a method in which at least a part of symbol strings of one candidate operation plan 311a or 311b is changed in order or arbitrarily to generate another candidate operation plan 311a or 311b.
  • This mutation operation can prevent the entire candidate operating plan 311a, 311b from falling into one regional optimal solution by crossover.
  • a PM (Polynomial Mutation) operation may be performed. However, it is not limited thereto.
  • the genetic operator 320 may derive an evaluation value by evaluating the newly formed candidate operation plans 311a and 311b. At this time, based on the evaluation value, the candidate operation plan 311b whose evaluation value is greater than or equal to a certain criterion is maintained in the population 310, and the candidate operation plan 311a whose evaluation value is lower than or equal to the specific criterion is removed from the population 310. can do. In this way, updating the group of candidate operation plans 311a and 311b having a high evaluation value through the genetic operation of the genetic calculator 320 is called generation replacement.
  • the genetic operator 320 may repeat the generation replacement several times. That is, the generation replacement can be completed when the preset number of times is met or the preset time limit is exceeded.
  • candidate operation plans 311a and 311b having the highest evaluation values may be selected as optimal operation plans. That is, the operation plan optimization unit 300 may select the candidate operation plans 311a and 311b having the highest evaluation values as the optimal operation plan.
  • the candidate operation plans 311a and 311b may be represented by the solution representation of FIG. 4.
  • the solution representation of FIG. 4 shows the power generation plan until h minutes after the diesel generator 410.
  • the candidate operation plans 311a and 311b may be normalized values of ⁇ 1 to 1 based on the maximum allowable value per minute of the change in output per minute for each diesel generator 410.
  • D t, 1 For example, if the value of D t, 1 is 0.5, it means to increase 50KW when the allowable power change per minute of each diesel generator is 100KW.
  • the genetic operator 320 may evaluate the candidate operation plans 311a and 311b. At this time, the evaluation value may be the same as Equation 1.
  • C is an evaluation value
  • ⁇ may be an additional loss factor for preventing blackout.
  • C DG is the total diesel generation cost
  • P DG, i, j is the generation amount of the j-th unit of the i-th diesel generator over time
  • N is the total number of generators
  • H is the planning time.
  • a, b and c are real coefficients.
  • LC DG is the power shortage loss cost
  • L DG, i, j is the required generation amount by the j-th unit of the i-th diesel generator.
  • N is the total number of generators and H is the planning time.
  • L DG, i, j may be a value obtained by subtracting the P DG, i, j in the energy consumption prediction data.
  • the genetic operator 320 may derive the evaluation value C by using Equations 1 to 3 above. Accordingly, an optimal operation plan with the highest evaluation value C can be selected.
  • the microgrid system may newly select and apply an optimum operation plan at a predetermined time (t minutes) even when an optimal operation plan is selected. That is, the operation planning period (P1 ⁇ P4) of diesel power generation can be newly started every t minutes. This is because it is established based on the estimated value of the power supply and demand, in order to reduce the risk of blackout or dump load of the diesel generator 410 when the difference between the actual power supply and demand is large. That is, by selecting a new optimal operation plan every predetermined time (t minutes), it is possible to cope with the unexpected imbalance of power supply and demand.
  • the microgrid system may use an evaluation value, such as Equation 4, which is more precise than the above-described embodiment.
  • C G (G i ) is the cost of diesel generating per unit time
  • N is the present time point
  • ⁇ is an additional loss factor to prevent blackout
  • C P (G i ) is the present time point.
  • GAVG is the average cost of diesel generation over a predetermined time period according to the existing diesel generation plan.
  • the last term in equation 1 Can be added to calculate the evaluation value C.
  • the last term may be to add a blackout penalty in the future, unlike a blackout penalty in the current state.
  • the average diesel power generation for the four hours may be obtained as G AVG .
  • G AVG is 400 KW
  • M in Equation 4 may be a time point at this time.
  • FIGS. 1 to 7 Portions that overlap with the description of the microgrid operating system described above are briefly or omitted.
  • FIG. 6 is a flowchart illustrating a microgrid operating method according to some embodiments of the present invention
  • FIG. 7 is a flowchart illustrating a detailed step of evaluating a candidate operation plan of FIG. 6.
  • the population 310 may include a plurality of candidate operation plans 311a and 311b therein.
  • the candidate operation plans 311a and 311b inside the population 310 may include the amount of power generated by the diesel generator 410 per unit time.
  • Candidate operating plans 311a and 311b may be arbitrarily generated initially.
  • Genetic operator 320 may evaluate candidate operational plans 311a and 311b in population 310. That is, the genetic operator 320 may derive evaluation values of the candidate operation plans 311a and 311b in the population 310. The evaluation value is determined in consideration of the operating cost and black out of the diesel generator 410.
  • step S200 of evaluating the candidate operation plan may be described in detail.
  • the oil field calculator 320 receives the operation status data of the diesel generator / energy storage system and the power supply and demand prediction value (S210).
  • the genetic operator 320 receives candidate operation plans 311a and 311b from the population 310 (S220).
  • the oilfield calculator 320 determines the diesel generation amount, the energy storage system charge / discharge amount, and the blackout occurrence at each time point from the candidate operation plans 311a and 311b (S230).
  • the diesel generation amount for each time point is expressed in the candidate operation plans 311a and 311b as shown in FIG.
  • the energy storage system charge / discharge amount is expressed as in Equation 5.
  • P ESS is the amount of charge / discharge of the energy storage system, and if greater than zero, the discharge from the energy storage system may be charged to the energy storage system.
  • L is power demand
  • P W is wind power generation
  • P PV is solar power generation.
  • P DG is diesel generation.
  • ESS t + 1 is the energy storage system charge at the time t + 1
  • ESS t is the energy storage system charge at the time t.
  • P ESS is the charge / discharge amount of the energy storage system.
  • blackout occurs when ESS t +1 is less than 0, and dump load occurs when ESS t +1 exceeds ESS MAX .
  • an operation cost is calculated according to the amount of diesel generation and blackout occurring at each time point in the candidate operation plan (S240).
  • Equation 1 This may be calculated with reference to Equation 1 or Equation 4 above.
  • ⁇ in Equations 1 and 4 may vary depending on whether blackout occurs.
  • the candidate operation plan is updated based on the evaluation (S400).
  • the update may mean generation replacement according to genetic operation. That is, the genetic operator 320 may alternate generations of candidate operation plans in the population 310 through genetic calculation using the evaluation value.
  • the genetic operation may include at least one of crossover and mutation.
  • the genetic operator 320 may derive an evaluation value by evaluating the newly formed candidate operation plans 311a and 311b. At this time, based on the evaluation value, the candidate operation plan 311b whose evaluation value is greater than or equal to a certain criterion is maintained in the population 310, and the candidate operation plan 311a whose evaluation value is lower than or equal to the specific criterion is removed from the population 310. can do. In this way, updating the group of candidate operation plans 311a and 311b having a high evaluation value through the genetic operation of the genetic calculator 320 is called generation replacement.
  • an optimal operation plan is selected using the evaluation value among candidate operation plans (S500).
  • the genetic operator 320 may repeat the generation replacement several times. That is, the generation replacement can be completed when the preset number of times is met or the preset time limit is exceeded.
  • candidate operation plans 311a and 311b having the highest evaluation values may be selected as optimal operation plans. That is, the operation plan optimization unit 300 may select the candidate operation plans 311a and 311b having the highest evaluation values as the optimal operation plan.

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Abstract

La présente invention concerne un système et un procédé d'exploitation de micro-réseau. Le système d'exploitation de micro-réseau selon la présente invention comprend : une unité de fourniture d'informations destinée à recevoir des données brutes contenant des données à propos de l'état de fonctionnement actuel de la génération d'énergie diesel; une unité de prédiction destinée à créer des données de prédiction de la génération d'énergie diesel en utilisant les données brutes; une unité d'optimisation de plan d'exploitation destinée à établir un plan d'exploitation optimal pour la génération d'énergie diesel en utilisant les données de prédiction; et une unité de surveillance destinée à collecter les données sur l'état de fonctionnement actuel, les transmettre à l'unité de fourniture d'informations et transmettre les données à propos de l'état de fonctionnement actuel et du plan d'exploitation optimal à un utilisateur. L'unité de prédiction comprend un module de collecte de données d'apprentissage destiné à créer des données d'apprentissage en utilisant les données brutes, un module de fourniture d'algorithme destiné à fournir et à mettre à jour un modèle de prédiction en utilisant les données d'apprentissage et un module de prédiction destiné à créer les données de prédiction par le biais des données brutes et du modèle de prédiction.
PCT/KR2017/001063 2016-03-14 2017-02-01 Système et procédé d'exploitation de micro-réseau WO2017159982A1 (fr)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107834603A (zh) * 2017-11-29 2018-03-23 上海电气风电集团有限公司 一种风电场机组网络化实时控制方法和系统

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR102194002B1 (ko) * 2018-08-02 2020-12-22 한국에너지기술연구원 최적 물리 학습모델과 기계학습을 이용한 에너지 운영관리 시스템
KR102274076B1 (ko) * 2019-05-14 2021-07-08 효성중공업 주식회사 독립형 마이크로그리드 및 그 엔진 발전 제어 방법
KR20240062361A (ko) 2022-10-31 2024-05-09 (주)헤리트 신재생 에너지 기반 마을 단위 마이크로그리드 운영 시스템

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005130550A (ja) * 2003-10-21 2005-05-19 Nippon Telegr & Teleph Corp <Ntt> 分散型エネルギーシステム運転計画作成装置および作成方法
JP2011130584A (ja) * 2009-12-17 2011-06-30 Fuji Electric Systems Co Ltd 発電計画作成方法および発電計画作成システム
KR20130020857A (ko) * 2011-08-18 2013-03-04 주식회사 포스코아이씨티 신재생에너지 관리시스템 및 방법
KR20150062188A (ko) * 2013-11-28 2015-06-08 한국전기연구원 독립형 마이크로 그리드의 발전기 출력 제어 방법 및 시스템

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005130550A (ja) * 2003-10-21 2005-05-19 Nippon Telegr & Teleph Corp <Ntt> 分散型エネルギーシステム運転計画作成装置および作成方法
JP2011130584A (ja) * 2009-12-17 2011-06-30 Fuji Electric Systems Co Ltd 発電計画作成方法および発電計画作成システム
KR20130020857A (ko) * 2011-08-18 2013-03-04 주식회사 포스코아이씨티 신재생에너지 관리시스템 및 방법
KR20150062188A (ko) * 2013-11-28 2015-06-08 한국전기연구원 독립형 마이크로 그리드의 발전기 출력 제어 방법 및 시스템

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
MOON, DAE-SUN ET AL.: "Development of Wireless Remote Monitoring System for Small Wind Turbine System", JOURNAL OF KOREAN INSTITUTE OF INTELLIGENT SYSTEMS, vol. 22, no. 4, August 2012 (2012-08-01), pages 460 - 467, XP055421505 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107834603A (zh) * 2017-11-29 2018-03-23 上海电气风电集团有限公司 一种风电场机组网络化实时控制方法和系统

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