WO2019242657A1 - 微电网重构方法、装置、微电网保护控制中心和存储介质 - Google Patents
微电网重构方法、装置、微电网保护控制中心和存储介质 Download PDFInfo
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- WO2019242657A1 WO2019242657A1 PCT/CN2019/091951 CN2019091951W WO2019242657A1 WO 2019242657 A1 WO2019242657 A1 WO 2019242657A1 CN 2019091951 W CN2019091951 W CN 2019091951W WO 2019242657 A1 WO2019242657 A1 WO 2019242657A1
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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
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/048—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators using a predictor
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
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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
- H02J3/001—Methods to deal with contingencies, e.g. abnormalities, faults or failures
- H02J3/0012—Contingency detection
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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
- H02J4/00—Circuit arrangements for mains or distribution networks not specified as ac or dc
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/28—The renewable source being wind energy
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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
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/10—The network having a local or delimited stationary reach
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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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- 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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
Definitions
- the invention relates to the field of power control, in particular to a microgrid reconstruction method, a microgrid reconstruction device, a microgrid protection control center, and a storage medium.
- microgrids are widely distributed in current power systems. It is urgent to proceed from the safe and efficient operation of the microgrid, to realize flexible protection and control of the microgrid, and to improve the automation and intelligent level of system operation.
- the present invention has been made in view of the above problems, so as to provide a microgrid reconstruction solution that at least partially solves the above problems.
- a microgrid reconstruction method including:
- a current control scheme of the microgrid is determined according to a current analysis result, and the microgrid is reconstructed according to the current control scheme.
- the analyzing the operating state of the microgrid based on the stored operating data and corresponding time stamp information in the database includes:
- the determining a current control scheme of the microgrid according to a current analysis result includes:
- the current control scheme is determined according to the accident analysis result.
- the current control scheme is determined according to a current state analysis result and a future state prediction result.
- the determining the current control scheme according to the accident analysis result includes:
- the determining the current control scheme according to a current state analysis result and a future state prediction result includes:
- the method further includes:
- the switch of the protection device is controlled to be closed.
- the method further includes: issuing an alarm message for a situation in which an accident analysis result indicates that the microgrid has a real fault.
- the microgrid includes power generation equipment, power electronic converters, controllable loads and / or protection devices,
- the determining a current control scheme of the microgrid according to a current analysis result, and reconstructing the microgrid according to the current control scheme includes:
- determining the switching state of the protection device according to an operating state and operating parameters of the power generation equipment, the power electronic converter, and / or the controllable load includes:
- the switching state of the protection device is determined according to the retrieval result.
- the determining a current control scheme of the microgrid according to a current analysis result includes:
- the current control scheme of the microgrid is determined based on the constraints of the microgrid, where the total operating cost of the system is equal to that calculated according to the current analysis result Sum of system commission costs and system dispatch costs.
- determining the current control scheme of the microgrid according to the current analysis result includes: using an artificial intelligence model to determine the current control scheme of the microgrid according to the current analysis result.
- the method further includes:
- a microgrid reconstruction device including:
- a data acquisition unit configured to monitor and collect current operation data of the microgrid in real time, and store the collected current operation data and corresponding time stamp information in a database
- a state analysis unit configured to perform an operation state analysis on the microgrid based on the operation data stored in the database and corresponding time stamp information;
- An optimization decision unit is configured to determine a current control scheme of the microgrid according to a current analysis result, and reconstruct the microgrid according to the current control scheme.
- a microgrid protection control center which includes a processor and a memory, where the computer program instructions are stored in the memory, and the computer program instructions are used to execute the foregoing when the processor is run Microgrid reconstruction method.
- a storage medium is further provided, and program instructions are stored on the storage medium, and the program instructions are used to execute the foregoing microgrid reconstruction method when running.
- the technical scheme of the invention optimizes the system operation of the micro-grid and improves the reliability of power supply. By making the entire microgrid flexible and automatic reconfiguration operation, the intelligence, safety and economy of the microgrid operation are improved.
- Figure 1 shows a schematic diagram of a power distribution network according to an embodiment of the invention
- FIG. 2 shows a schematic flowchart of a microgrid reconstruction method according to an embodiment of the present invention
- FIG. 3 shows a schematic diagram of an AC / DC hybrid power grid according to an embodiment of the present invention
- FIG. 4 shows a schematic flowchart of a microgrid reconstruction method according to another embodiment of the present invention.
- FIG. 5 shows a partial schematic diagram of a microgrid according to an embodiment of the present invention
- FIG. 6 shows a partial schematic diagram of a microgrid according to another embodiment of the present invention.
- FIG. 7 shows a partial schematic diagram of a microgrid according to yet another embodiment of the present invention.
- FIG. 8 shows a schematic diagram of a microgrid reconstruction device according to an embodiment of the present invention.
- FIG. 9 shows a schematic block diagram of a microgrid protection control center according to an embodiment of the present invention.
- Microgrids are small power generation and distribution systems. Microgrids can include a variety of primary and / or secondary equipment such as power generation equipment, energy storage equipment, protection devices, compensation equipment, loads, and power electronic converters.
- Primary equipment refers to all equipment directly related to the power generation-transmission-distribution function, such as power generation equipment, circuit breakers, current / voltage transformers, transformers, arresters and disconnectors.
- Secondary equipment refers to equipment related to protection and control, such as protection relays, communication equipment, control switches, indicators and measuring instruments.
- FIG. 1 shows a schematic diagram of a power distribution network according to an embodiment of the present invention.
- the distribution network includes at least one microgrid.
- the microgrid is shown in the dashed box in Figure 1.
- the microgrid includes power generation equipment 3, energy storage equipment 4, protection devices 5, data acquisition equipment 6, and other equipment 2 such as reactive power compensation equipment.
- the power generation device 3 may be any device capable of providing electrical energy, such as photovoltaic, wind and other new energy power generation devices.
- the energy storage device 4 is, for example, an energy storage battery.
- the protection device 5 may be a power protection device such as a relay protector. It can be understood that the protection device 5 can be connected to at least one other device. The switching operation of the protection device 5 can ensure the safety of these connected devices.
- the data acquisition device 6 may also be connected to at least one other device, which may be used to collect analog, digital, and protection action data of these downstream devices.
- the microgrid also includes a microgrid protection control center 7.
- microgrid protection control center 7 Any electrical equipment including the aforementioned power generation equipment 3, energy storage equipment 4, protection device 5, data acquisition equipment 6, and other equipment 2 is connected to the microgrid protection control center 7 through a microgrid communication network 8.
- the microgrid protection control center 7 is used to protect and control the microgrid by controlling various devices in the microgrid.
- the power distribution network can also include equipment such as a substation 18, a collection control device 19, a communication gateway 10, and a transmission and distribution network protection control center 1.
- the transmission and distribution network protection control center 1 is used to perform operations on various devices in the distribution network as needed to protect and control the distribution network.
- the substation 18 in the distribution network communicates with the transmission and distribution network protection control center 1 through the communication gateway 10.
- the various analog, digital, and protection actions in the distribution network collected by the acquisition control device 19 are transmitted to the distribution network protection control center 1 through the substation 18, and the control is completed under the control of the transmission and distribution network protection control center 1. Protection and control of distribution networks.
- the microgrid protection control center 7 may be connected to the distribution network communication network 9 through a communication gateway 10.
- the distribution network communication network 9 is connected to the distribution network and the transmission and distribution network protection control center 1 of the transmission network via a communication gateway 10. Therefore, the transmission and distribution network protection control center 1 can perform real-time information interaction with the microgrid protection control center 7 via the communication gateway 10 and the distribution network communication network 9 and control the microgrid protection control center 7 to correspondingly protect and control the microgrid operating. In other words, the microgrid protection control center 7 can accept the unified dispatching of the transmission and distribution network protection control center 1.
- the microgrid protection control center 7 is compatible with the IEC 61850 communication protocol through signal conversion of the communication gateway 10, and the microgrid protection control center 7 performs external communication with the distribution network communication network 9 in the GOOSE manner.
- the microgrid protection control center 7 can also control the operation of the entire microgrid through a predetermined optimized protection control strategy.
- the optimization protection control strategy includes, for example, a power flow optimization strategy, a network topology adjustment strategy, a load and switching strategy of a power generation device, a protection device setting value update strategy, and the like.
- the micro-grid communication network 8 and the distribution network communication network 9 include, but are not limited to, a wired communication network such as a fiber-optic communication network and various wireless communication networks.
- the communication gateway 10 can realize the interconnection between the micro-grid and the power distribution network, which can be realized by any existing or future related technologies developed.
- the communication gateway 10 is compatible with the IEC61850 communication protocol of the power grid and supports the GOOSE communication method.
- the microgrid protection control center 7 can be used to perform automatic power control on the operation of the microgrid.
- the microgrid protection control center 7 can be used to realize the automatic reconfiguration of the microgrid.
- the power transmission and distribution network protection control center 1 may also perform automatic power control on the operation of the microgrid.
- FIG. 2 shows a schematic flowchart of a microgrid reconstruction method 200 according to an embodiment of the present invention. As shown in FIG. 2, the microgrid reconstruction method 200 includes the following steps.
- step S210 the current operation data of the microgrid is monitored and collected in real time.
- various primary and / or secondary devices may be included in a microgrid. It can monitor and collect the current operating data of each device in the microgrid in real time. For example, you can monitor the power flow distribution in the microgrid, monitor and collect the output voltage, output current, output power, etc. of the power generation equipment, the energy storage status, input / output voltage, input / output current, input / output power, etc. , The switching operation status of the relay protection device, the equipment / line parameter information in the microgrid, such as voltage, current, power angle, frequency, heat generation, etc., parameters such as the voltage, current, compensation power, and harmonic suppression of the compensation device .
- the output power of the power generation equipment 3, the energy storage of the energy storage equipment 4, the switching quantity of the protection device 5 to indicate the switching state thereof, and the communication equipment can be collected Voltage, current and temperature of the load.
- the data collection operation can be performed at a certain frequency.
- the data collection frequency can be controlled at the level of minutes (min) or seconds (s) to reduce the calculation and communication load brought by the data collection processing.
- the frequency of data collection and processing can be set higher, such as milliseconds (ms) or higher.
- the above-mentioned data collection operation frequency will be different according to the actual operation of the system, without exact limitation.
- step S220 the collected current running data and corresponding time stamp information are stored in a database.
- the time stamp information of the operation data includes information of the exact time when the operation data was collected.
- the collected operating data and its corresponding time stamp information can be stored in a database.
- the historical data of the microgrid stored in the database is helpful for analyzing the operating status of the microgrid, and it is convenient for users to check the archives, which improves the user experience and system operation efficiency.
- Step S230 Perform a running status analysis on the micro-grid based on the running data stored in the database and the corresponding time stamp information.
- step S230 may include performing a current state estimation on the micro-grid based on the current operating data. Based on the current operating data, determine whether the microgrid is abnormal. It can be understood that here is a rough estimation of the state of the microgrid, and the estimation result may be inaccurate. For example, microgrids may simply cause fluctuations due to noise interference, rather than a real failure.
- step S230 may further include performing an accident analysis on the microgrid based on the stored operation data and corresponding time stamp information in the database.
- Accident analysis is used to determine if a real fault has occurred in the microgrid.
- a decision tree can be used to implement accident analysis.
- step S230 may further include analyzing the current state and predicting the future state of the microgrid based on the operating data stored in the database and the corresponding time tag information.
- an artificial neural network is used for current state analysis and future state prediction.
- the artificial neural network may be obtained by training.
- the current status analysis is used to accurately determine the current operating status of the microgrid.
- Future state prediction is used to predict the future operating state of the microgrid.
- Step S240 Determine the current control scheme of the microgrid according to the current analysis result, and reconstruct the microgrid according to the current control scheme.
- the current analysis results may include micro-grid accident information, current operating status information, and / or future operating status information.
- the corresponding control scheme of the microgrid can be determined for different analysis results, and the microgrid can be automatically reconstructed according to the determined control scheme.
- connection structure between the power devices in the microgrid and / or the operation mode of the power devices can be adjusted accordingly according to the current analysis results.
- the switching state of the protection device connected to the part in the microgrid may be changed.
- Local devices that have short-circuit faults can be disconnected from the microgrid.
- this part can also be connected to other lines or nodes through the switch of the protection device.
- the network topology of the microgrid has changed. Other devices in the microgrid are protected.
- the microgrid Compared with the power distribution network, the microgrid has more specific and complete supporting power generation equipment, energy storage equipment, and loads, which ensures the smooth implementation of the above technical solutions.
- the above microgrid reconstruction method 200 reconstructs the microgrid based on the microgrid operation data, ensuring that the microgrid operates more safely and efficiently.
- the above micro-grid may be an AC grid, a DC grid, or an AC-DC hybrid grid.
- the AC / DC hybrid power grid has both an AC power grid and a DC power grid.
- FIG. 3 illustrates an AC / DC hybrid power grid according to an embodiment of the present invention.
- the AC / DC hybrid power grid includes an AC network shown in FIG. 3, a DC network shown in FIG. 3, and a communication network shown by dotted lines.
- the AC / DC hybrid grid includes a microgrid protection control center.
- the microgrid protection control center is respectively connected to the AC network and the DC network through a communication network, and reconstructs the AC-DC hybrid power grid based on current operating data collected from the AC network and / or the DC network.
- the micro-grid may include a DC bus and a DC network, an AC bus and an AC network, and have the function of bidirectional energy flow in an AC-DC hybrid system.
- the above microgrid can be transformed, extended and expanded.
- the DC system and / or the AC system can be transformed, expanded, and extended to a new DC or AC bus, circuit network structure, and connection method, respectively.
- the above-mentioned microgrid reconstruction method 200 further includes: storing state data and operation data of the microgrid, such as the current analysis result, the current control scheme executed, and / or the two corresponding time stamp information, respectively, in a database .
- the time stamp information of the analysis result includes information of the time when the analysis result was obtained.
- the time stamp information of the control scheme includes information that determines the time of the control scheme.
- the above steps S230 and S240 are performed once every specific time period. According to this time period, the correspondence between the operating data, the analysis results and the control scheme can be determined.
- the operation data corresponding to the analysis result may be determined according to the operation data and the time labeling information of the analysis result and the execution cycle of the above step S230.
- the analysis result corresponding to the control scheme can be determined, and then the operation data corresponding to the control scheme can be determined.
- a large amount of status data and operation data of the microgrid are stored in the database, which can be convenient for users to check the files later and make or adjust control strategies. This improves the user experience and system operation efficiency.
- FIG. 4 shows a schematic flowchart of a microgrid reconstruction method 400 according to another embodiment of the present invention. Steps S410 and S420 in the microgrid reconstruction method 400 are similar to steps S210 and S220 in the microgrid reconstruction method 200. For the sake of brevity, details are not described herein again.
- the microgrid reconstruction method 400 includes the following steps.
- step S431 it is initially determined whether there is an abnormality in the microgrid according to the current operation data.
- the micro-grid can be initially judged based on whether the current operating data collected in real time exceeds the normal limit range.
- it can be initially judged whether there is an abnormality in the microgrid according to the fault information reported by the protection device such as a relay protector.
- the protection device such as a relay protector.
- step S431 only a preliminary judgment is made on the operating state of the microgrid. The preliminary judgment result may be incorrect. For example, in some cases, signal fluctuations may have occurred in the microgrid. This signal fluctuation may be caused by a short-term disturbance. Even if no operation is performed, the microgrid may gradually return to the normal state. However, the signal fluctuation may cause the microgrid to be judged as abnormal.
- step S432 if there is an abnormality in the microgrid, go to step S432; if there is no abnormality in the microgrid, go to step S435.
- step S432 the operation data of the related equipment at the location where the abnormality occurs is retrieved from the operation data stored in the database.
- the location of the abnormality may be taken as the center, and the specific equipment number interval or the specific geographic distance interval may be used as a boundary to determine the related equipment of the abnormality location.
- the current operating data and previous operating data of the related equipment can be retrieved from the database to provide complete information about the exception.
- Step S433 Perform an accident analysis based on the retrieved operating data and corresponding time stamp information to determine whether there is a real fault in the microgrid.
- the preliminary judgment result in step S431 may not be correct.
- this step S433 a more comprehensive analysis of the abnormal situation is performed based on the retrieved data related to the occurrence of the abnormality, so as to draw the exact conclusion whether the microgrid has a real fault.
- accident analysis can be implemented using decision trees. Decision tree is a mathematical model based on a tree structure. After the evaluation judgment function of the accident analysis is assigned to each leaf node of the decision tree and stored, in this step, the decision tree is automatically searched and the final result of the accident analysis is determined.
- step S434 and step S441 may be performed.
- step S434 an alarm message is issued for a situation where the result of the accident analysis indicates that the microgrid has a real fault.
- Alarm messages can be issued through sound, images, and alarm devices such as alarm lights. As a result, staff can be notified in a timely manner to prevent the accident from causing greater losses. It can be understood that step S434 does not have to exist.
- step S441 the current control scheme may be determined according to the analysis result of the accident.
- an optimization model can be used to determine the control scheme. It can be understood that according to the needs of different microgrids, one or more optimization models may exist.
- multiple optimization models are provided for determining a current control scheme based on the results of an accident analysis.
- a decision tree or table can be established for the results of the accident analysis and optimization.
- Table 1 shows a decision table according to an embodiment of the present invention.
- step S441 first, a first optimization model is determined according to an accident analysis result.
- the optimization model can take the shortest restoration time or the lowest restoration cost as the optimization goal.
- the first optimization model is used to determine the current control scheme based on the results of the accident analysis.
- the accident analysis result is input as an input amount into the first optimization model, so that each component of the current control scheme is output by the first optimization model.
- the first optimization model may be based on an artificial neural network model (ANN) and a Monte Carlo tree search model (MCTS).
- a Monte Carlo tree search model can be used to search the tree structure.
- Artificial neural networks can be used as evaluation functions set at the nodes and leaves of the tree structure. The artificial neural network model is used to evaluate the searched targets according to the results of the accident analysis to determine the optimal control scheme as the current control scheme. While ensuring that the search target is the current optimal control scheme, the search time is shortened and the model calculation efficiency is improved.
- step S443 the microgrid is reconstructed according to the current control scheme determined in step S441.
- Corresponding instructions can be generated according to the control scheme.
- the generated instructions are issued to various devices in the micro-grid to change the operating status and / or connection mode of the devices in the micro-grid.
- steps S451 and S452 may be performed.
- step S451 for the case where the accident analysis result indicates that there is no real fault in the microgrid, it is determined whether the protection device in the microgrid has performed an incorrect operation. In step S452, if the protection device in the micro-grid has been operated incorrectly, the switch of the protection device is controlled to be closed.
- step S431 it is initially determined that there is an abnormality in the micro-grid.
- This abnormality may cause the protection device in the microgrid to malfunction. For example, a switch in a protective device has tripped by mistake.
- step S451 it is determined whether the protection device in the microgrid has performed a wrong operation due to the abnormality judged in step S431.
- step S433 it is determined that there is no real fault in the microgrid. Therefore, in step S452, the switch of the protection device that has been operated by mistake is controlled to be closed again, so that the microgrid continues to operate normally. It can be understood that, in the case that it is determined in step S451 that the protection device in the microgrid has not been operated incorrectly, this round of microgrid reconstruction operation can be directly ended.
- step S435 current state analysis and future state prediction are performed based on the running data stored in the database and the corresponding time label information.
- the operation data stored in the database includes current operation data and previous operation data
- the database can also store a mathematical model for analyzing the current state and a mathematical model for predicting the future state.
- the mathematical model used to analyze the current state can be implemented with an artificial neural network.
- the mathematical model used to make future state predictions can be implemented with a long-term and short-term memory optimization model (LSMT).
- LSMT long-term and short-term memory optimization model
- the long-term and short-term memory optimization model can use memory cells to store and output information, thereby ensuring the accuracy of predictions, and thus the rationality of reconstruction.
- a mathematical model for analyzing the current state and / or a mathematical model for predicting the future state can be selected to analyze the current operating state of the microgrid in detail, and to analyze the future operating state of the microgrid. Rationalize predictions.
- step S442 the current control scheme is determined according to the current state analysis result and the future state prediction result.
- the active content, reactive content, harmonic content of the load in the microgrid, and the new energy supply of power generation equipment can be perceptively predicted.
- an optimization model can also be used to determine the control scheme. It can be understood that according to the needs of different microgrids, one or more optimization models may exist. Alternatively, the optimization model may be based on an artificial neural network model (ANN) and a Monte Carlo tree search model (MCTS). A Monte Carlo tree search model can be used to search the tree structure. The artificial neural network model is used to evaluate the searched targets based on the analysis results of the current state. While ensuring that the search target is the current optimal control scheme, it shortens the search time and improves the model calculation efficiency.
- ANN artificial neural network model
- MCTS Monte Carlo tree search model
- a plurality of optimization models for determining a current control scheme based on a current state analysis result and a future state prediction result are provided.
- a second optimization model is determined according to a current state analysis result and a future state prediction result.
- the optimization model can take the optimal power flow or the lowest operating cost as the optimization goal.
- the second optimization model is used to determine the current control scheme based on the current state analysis results and future state prediction results.
- the current state analysis results and future state prediction results are input as input quantities into the second optimization model, so that each component of the current control scheme is output by the second optimization model.
- step S443 is also performed. Step S443 has been described in the foregoing, and for the sake of brevity, it will not be repeated here.
- microgrid reconstruction method 400 shown in FIG. 4 is only an example, and does not constitute a limitation on the present invention.
- steps S451 and S452 may not exist.
- the protection device itself is highly reliable and is not prone to misoperation. In this case, these two steps may not exist.
- step S434 may be absent as described above. Step S434 may be performed simultaneously with step S441 or after step S441.
- the operating state analysis is performed for the fault state and the normal operating state of the microgrid, and then the optimal solution for the microgrid reconfiguration operation is determined based on the analysis results. Therefore, the electrical connection and operation mode of the microgrid can be adjusted according to the optimal scheme, so that the microgrid can perform active intelligent reconstruction under both fault and non-fault conditions.
- the economic operation of the micro-grid is optimized and the reliability of power supply is improved.
- the system configuration and network structure can be actively adjusted in both system fault and non-fault states to improve system power supply reliability and operating efficiency.
- the above-mentioned intelligent reconstruction scheme also has high flexibility, plasticity and security.
- the microgrid reconstruction methods 200 and 400 include the steps of determining the current control scheme of the microgrid according to the current analysis results, for example, step S240 in the microgrid reconstruction method 200, steps S441 in the microgrid reconstruction method 400 and Step S442.
- a detailed implementation of determining a current control scheme of a microgrid according to a current analysis result according to an embodiment of the present invention is described in detail below.
- the minimum total system operation cost of the microgrid is used as the optimization objective function, and the current control scheme of the microgrid is determined based on the constraints of the microgrid.
- the total operating cost of the system is equal to the sum of the system commissioning cost and the system scheduling cost calculated according to the current analysis result.
- the optimization goal can be to minimize the operating cost of the microgrid, that is, to minimize the total operating cost of the microgrid system as the objective function.
- the total operating cost of the microgrid system can be expressed by the sum of the system commissioning cost and the system dispatching cost, as shown in the following formula.
- the equipment state commission X includes the operation mode X g of the main power generation equipment, the operation mode X r of the backup power generation equipment, and the operation mode X es of the energy storage equipment.
- the main power generation equipment can be any form of generator, such as a wind turbine or a permanent magnet synchronous generator, and its working mode only includes a power generation mode.
- the operation modes of the main power generation equipment may include a power generation mode and a deactivation mode.
- the backup power generation equipment may be a device such as a power electronic converter that has other operating modes in addition to the power generation mode.
- the power electronic converter can also be in shutdown mode.
- the working mode of the energy storage device may include a charging mode and a discharging mode.
- the continuous variable Y represents the equipment scheduling amount of the micro-grid, and may include the power generation amount P g of the main power generation equipment, the power generation amount P r of the backup power generation equipment, the charge / discharge amount P es of the energy storage equipment, and the power consumption of the controllable load P ⁇ and the amount of energy exchange Pex with the external power grid.
- the quantities P ex can all be active power.
- the above-mentioned device state commissioning amount X may represent different network structures composed of devices in the micro-grid
- the above-mentioned scheduling amount Y may represent operating parameters of each device in the micro-grid under the structure represented by the device state commissioning amount X. Therefore, the control scheme of the microgrid can be expressed by the equipment state commission amount X and the scheduling amount Y.
- the specific value of each control amount in the two items of the equipment state commission amount X and the scheduling amount Y is the amount to be solved.
- f (X) and g (Y) can take different mathematical functions.
- f (X) may be the sum of the products of each element in X and its respective commission cost factor.
- g (Y) may be the sum of the products of each element in Y and its respective scheduling cost coefficient.
- I represents the set of all main power generation equipment in the microgrid
- J represents the set of all backup power generation equipment in the microgrid
- K represents a set of all energy storage devices in the microgrid
- D represents the set of all controllable loads in the microgrid
- L represents all of the microgrids
- C gx represents the commission cost coefficient matrix of the main power generation equipment, Is the transpose matrix of C gx .
- C rx represents the commission cost coefficient matrix of backup power generation equipment, Is the transpose matrix of C rx .
- C esx represents the commission cost matrix of energy storage equipment, Is the transpose matrix of C esx .
- Is the start / stop commission cost factor for the main power generation facility labeled i Is the commission cost factor for the backup power generation facility labeled j, Is the commission cost factor for the energy storage device labeled k.
- C g represents the scheduling cost coefficient matrix of the main power generation equipment.
- C r represents the scheduling cost coefficient matrix of backup power generation equipment.
- C es represents the scheduling cost coefficient matrix of the energy storage equipment.
- C ⁇ represents the scheduling cost coefficient matrix of the controllable load.
- Represents the scheduling cost coefficient of the main power generation equipment labeled i Represents the scheduling cost coefficient of the backup power generation equipment labeled j
- C ex represents the scheduling cost coefficient of energy exchange between the microgrid and the external grid.
- the above steps of determining the current control scheme by using the optimization model may be performed using ⁇ T as a calculation time interval. Every time ⁇ T is performed, the optimization model is calculated.
- the count variable t takes 0 as the initial value and accumulates 1 each time. When the accumulated count reaches ⁇ , t is reset to 0 and then counted again.
- x g (i, t) represents the operation mode (enable / stop, etc.) of the main power generation equipment labeled i in the t-th calculation cycle
- x r (j, t) represents the backup power generation equipment labeled j in the The operation modes (generation mode and APF mode) to be calculated for t calculation cycles
- x es (k, t) represents the operation mode (charge / discharge, etc.) of the energy storage device labeled k to be calculated in the tth calculation cycle.
- P g (i, t) represents the active power generation amount of the main power generation equipment labeled i in the t-th calculation cycle
- P r (j, t) represents the backup power generation equipment labeled j in the t-th calculation cycle
- P es (k, t) represents the active power charging / discharging capacity of the energy storage device labeled k in the t-th calculation cycle
- P ⁇ (d, t) indicates the label d
- the controllable load is the active load to be calculated in the t-th calculation cycle.
- P ex (t) represents the amount of active power exchange with the external power grid to be calculated in the t-th calculation cycle.
- microgrids there may be many corresponding constraints. Microgrid obeying these constraints can guarantee its normal operation. Taking the AC grid part of the microgrid shown in FIG. 3 as an example, the following constraints may exist.
- the state value constraint of the equipment state commission X The operation mode x g (i, t) of the main power generation equipment, the operation mode x r (j, t) of the backup power generation equipment, and the operation mode x es (k, t) of the energy storage equipment satisfy the following constraints:
- P load (t) is the total active power consumption of the uncontrollable load in the t-th calculation cycle.
- the value of P load (t) can be used to determine the future status based on the current operating data, previous operating data, and corresponding time stamp information. Obtained by prediction.
- the harmonic content and reactive power content in the microgrid need to be controlled within a certain range at a specific node u.
- a APF, j represents the rated compensation capacity of the backup power generation equipment j in the APF mode, It is the harmonic compensation capacity provided by node u and other compensation methods.
- Reactive power compensation capacity provided at node u and other compensation methods Is the maximum compensation capacity allowed at node u.
- Is the relaxation variable in the harmonic content constraint Is the relaxation variable in the reactive content constraint.
- H u is the harmonic content at node u
- Q u is the amount of reactive power at node u.
- Both Hu and Q u can be obtained by performing future state prediction based on current operating data, previous operating data, and corresponding time-labeled information.
- Relaxation constant with The positive and negative values of can be set as constants or obtained by performing state analysis based on the current operating data and corresponding time-labeled information.
- the number of reconstructions N g, i , N r, j performed by the main power generation equipment i and the backup power generation equipment j within a certain time interval T may be limited to a certain range.
- the number of charge / discharge reconstructions N es, k performed by the energy storage device k within a certain time interval T may be limited to a certain range.
- Constraint of active power generation of main power generation equipment It is expected that the active power from the main power generation equipment in the microgrid is within a certain range. constant Lower limit of active power from main power generation equipment i, constant The upper limit of the active power emitted by the main power generation equipment i.
- Constraint of generating capacity that backup power generation equipment can use in the t-th cycle S r (j, t) represents the power generation capacity that the backup power generation equipment j itself can use in the t-th cycle. S r (j, t) needs to ensure to provide sufficient power output for the backup unit within the time interval of ⁇ T within [t, t + 1], ie Such as / or S r (j, t) ⁇ P r (j, t) ⁇ ⁇ T. Indicates the lower limit of the generation capacity that the backup power generation equipment j itself can use, The upper limit of the power generation capacity that the backup power generation equipment j itself can use.
- the constant ⁇ r, j is a relaxation variable in the power generation capacity constraint of the backup power generation equipment j.
- the value of S r (j, t) can be obtained by performing future state prediction based on the current running data, previous running data, and corresponding time label information. with The value of can be set to a constant or obtained by performing future state prediction based on the current operating data, previous operating data, and corresponding time-labeled information.
- the positive and negative values of the relaxation constant ⁇ r, j can be set as constants or obtained by performing state analysis based on the current running data and corresponding time label information.
- Constraints on the rate of change of the main power generation equipment It is expected that the rate of change in the amount of electricity generated by the main power generation equipment within the microgrid is limited to a certain range.
- constant Lower limit of the rate of change of the main power generation equipment i constant
- ⁇ represents the rate of change of the active power emitted or absorbed by the electrical equipment within ⁇ T.
- the power generation rate change rate ⁇ of the backup power generation equipment of the microgrid is limited to a certain range.
- Cap represents the rated capacity of electrical equipment
- Cap g (i, t) is the rated capacity of main power generating equipment i
- Cap es, k is the rated capacity of energy storage equipment k
- ⁇ represents the capacity coefficient of electrical equipment
- the constant ⁇ g, i is the reserve capacity coefficient of the main power generation equipment i
- the constant ⁇ es, k is the reserve capacity coefficient of the energy storage equipment k.
- controllable load scheduling amount is limited to a certain range. If the scheduling function of the controllable load is activated, the state quantity s d is taken as 1 and in other cases s d is taken as 0. The value of s d can be determined by analyzing the operating status of the microgrid based on the operating data stored in the database and the corresponding time labeling information. constant Is the controllable load d, the lower limit of the dispatchable active power when the dispatch function is activated; constant For the controllable load d, the upper limit of the dispatchable active power when the dispatch function is activated.
- constant Is the controllable load d the lower limit of the dispatchable reactive power when the dispatch function is activated, constant For the controllable load d, the upper limit of the schedulable reactive power when the scheduling function is activated.
- constant Is the controllable load d the lower limit of the dispatchable active power when the dispatch function is not activated; constant For the controllable load d, the upper limit of the dispatchable active power when the dispatch function is not activated.
- constant Is the controllable load d the lower limit of the dispatchable reactive power when the dispatch function is not activated, constant For the controllable load d, the upper limit of the schedulable reactive power when the scheduling function is not activated.
- constraint condition may not include: a restriction on the number of reconstructions.
- the above-mentioned determination of the current control scheme of the microgrid based on the constraints of the microgrid based on the minimum total operating cost of the system can be implemented using any optimization model used, such as, but not limited to, a mixed integer optimization (MIO) algorithm model.
- MIO mixed integer optimization
- an artificial intelligence model can also be used to determine the current control scheme of the microgrid based on the current analysis results.
- Artificial intelligence models such as artificial neural network (ANN) models combined with tree search models. Artificial intelligence models can be obtained by training with large amounts of data. The tree search model establishes the target search structure. The artificial neural network comprehensively evaluates the search target and then searches for the final target. Artificial intelligence models have the ability to learn. Through training of a large amount of data, the artificial intelligence model can determine the current suitable control scheme of the microgrid according to the current analysis results. The use of artificial intelligence models to obtain optimized control schemes can effectively utilize the computing power of the system and significantly improve the quality of the control schemes.
- ANN artificial neural network
- the analysis results and control schemes of the microgrid and their corresponding time stamp information can be stored in the database.
- the aforementioned artificial intelligence model may be trained based on previously stored time-labeled information in the database, previous analysis results, and previous control schemes.
- the operation status analysis results of the microgrid are first input into an artificial intelligence model and other optimization decision models (such as the aforementioned mixed integer optimization algorithm model) capable of determining a control scheme based on the status analysis results, so as to obtain respective results from the two models. Determined control plan. Then, the two control schemes obtained are compared. Adjust the parameters of the artificial intelligence model according to the deviation distance between the two control schemes. Optionally, the parameters of the artificial intelligence model are adjusted only for cases where the deviation distance between the two control schemes exceeds or is less than a certain threshold.
- other optimization decision models such as the aforementioned mixed integer optimization algorithm model
- the above training method can be implemented during the operation of the microgrid, which effectively uses the operating data of the microgrid, and the obtained artificial intelligence model is more suitable for the microgrid, further improving the quality of the control scheme determined by the artificial intelligence model .
- the above microgrid may include power generation equipment, power electronic converters, controllable loads and / or protection devices.
- the microgrid can also include other power equipment such as energy storage equipment and transformers.
- the microgrid can be an AC / DC hybrid grid. Referring again to FIG. 3, in the AC-DC hybrid power grid, power electronic converters are respectively connected to the AC network and the DC network in the micro-grid to achieve bidirectional energy flow.
- the power electronic converter may include a bidirectional DC / DC converter and a bidirectional DC / AC converter.
- the foregoing step S240 determines a current control scheme of the microgrid according to a current analysis result, and reconstructing the microgrid according to the current control scheme includes at least one of the following steps.
- the power generation equipment, the power electronic converter and / or the controllable load are respectively controlled to change the operating state and / or operating parameters accordingly. For example, if it is determined that the operation state of a power electronic converter in the microgrid should be changed from the power generation mode to the active filtering mode according to the current analysis result, a corresponding control operation may be performed on the power electronic converter.
- the switching state of the protective device is determined according to the operating state and operating parameters of the power generation equipment, power electronic converter and / or controllable load, and the protective device is controlled to perform corresponding switching operations according to the switching state of the protective device to change the topology of the microgrid. .
- Fig. 5 shows a partial schematic diagram of a microgrid according to an embodiment of the invention.
- the upper side in FIG. 5 is the power generation side, and the lower side is the load side.
- DG indicates that the power electronic converter works in the power generation mode.
- APF means that the power electronic converter works in active filtering mode.
- Dark rectangles represent relay protection devices, which can have different switching states. When the relay protection device is in a state (for example, on), the power electronic converter in the shaded rectangular frame is in a power generation mode. When the relay protection device is in another state (for example, off), the power electronic converter is in an active filtering mode. Therefore, by changing the switching state of the relay protection device, the topology of the microgrid shown in FIG. 5 is changed.
- FIG. 6 shows a partial schematic diagram of a microgrid according to another embodiment of the present invention.
- the power electronic converter is connected to the AC / DC power grid through an electrical switch controlled by a relay protection device.
- the electrical switch can be a single-pole multi-throw switch and other equipment, which can adjust the switching state and change the operating state of the microgrid. It can be understood that when the microgrid protection control center controls the electrical switches to connect different paths through the relay protection device, the power electronic converter is connected to different electrical connection points in the AC-DC grid. As a result, the topology of the microgrid has also changed.
- the power electronic converter can flexibly work in a power generation mode, an APF mode, and a shutdown mode.
- Power electronic converters can be used as backup power generation equipment in microgrids to adjust according to operating needs. They can be used as a supplement to traditional power generation equipment as the main power generation equipment to jointly support microgrid operation. As a result, microgrid operations are made more flexible, smarter, and more automated.
- FIG. 7 shows a partial schematic diagram of a microgrid according to yet another embodiment of the present invention.
- the switching state of the protection device can be stored in the relevant data table in the database.
- state 1 indicates that the switch of the protection device is closed
- state 0 indicates that the switch of the protection device is open.
- protection device shown in FIG. 7 can have protection interlocking and long jump functions to react and isolate faults in the microgrid faster.
- FIG. 8 shows a schematic diagram of a microgrid reconstruction device according to an embodiment of the present invention.
- the microgrid reconstruction device communicates with electrical equipment including the power generation equipment, energy storage equipment, and protection devices in the microgrid through a communication network.
- the microgrid reconstruction device can connect various electrical devices through communication gateways in a variety of communication modes such as fiber optics and wireless.
- the microgrid reconstruction device includes a data acquisition unit, a state analysis unit, and an optimization decision unit.
- the data acquisition unit is shown as "SCADA" in Figure 8.
- the data acquisition unit is used to monitor and collect the current running data of the microgrid in real time, and store the collected current running data and corresponding time stamp information in a database.
- the data collection unit is configured to perform steps S210 and S220 in the above microgrid reconstruction method 200, and may also be used to perform steps S410 and S420 in the above microgrid reconstruction method 400.
- the relevant operating data such as the operating conditions of the equipment and the system power flow are collected and processed.
- operating data such as the amount of switching action, analog quantity, and digital quantity of each electrical device in the microgrid is monitored in real time.
- the state analysis unit is configured to perform an operation state analysis on the microgrid based on the operation data stored in the database and the corresponding time label information. That is, the state analysis unit is configured to execute step S230 in the microgrid reconstruction method 200 described above. The state analysis unit may be further configured to execute steps S431 to S435 and step S451 in the microgrid reconstruction method 400 described above.
- the state analysis unit may further include a perception prediction module, a state analysis module, and an accident analysis module.
- the perceptual prediction module is configured to perform step S433 in the microgrid reconstruction method 400 described above.
- the state analysis module and the accident analysis module are configured to execute step S435 in the microgrid reconstruction method 400 described above.
- the optimization decision unit is configured to determine a current control scheme of the microgrid according to a current analysis result, and reconstruct the microgrid according to the current control scheme. That is, the optimization decision unit is configured to execute step S240 in the microgrid reconstruction method 200 described above. The optimization decision unit may be further configured to execute steps S441 to S443 in the microgrid reconstruction method 400 described above.
- FIG. 9 shows a schematic block diagram of a microgrid protection control center 900 according to an embodiment of the present invention.
- the microgrid protection control center 900 includes an input device 910, a storage device (ie, a memory) 920, and a processor 930.
- the input device 910 is configured to receive a user instruction and the like.
- the storage device 920 stores computer program instructions for implementing corresponding steps in the microgrid reconstruction method according to an embodiment of the present invention.
- the processor 930 is configured to run computer program instructions stored in the storage device 920 to perform corresponding steps of the microgrid reconstruction method according to an embodiment of the present invention.
- the computer program instructions are used by the processor 930 to execute the following steps: monitoring and collecting current running data of the microgrid in real time; marking the collected current running data and corresponding time
- the information is stored in a database; the operating status analysis of the microgrid is performed based on the stored operating data and corresponding time stamp information in the database; and the current control scheme of the microgrid is determined according to the current analysis result, and according to the The current control scheme reconstructs the microgrid.
- a storage medium is also provided, and program instructions are stored on the storage medium, and the program instructions are used to execute the microgrid reconfiguration of the embodiment of the present invention when the program instructions are run by a computer or a processor.
- the corresponding steps of the method are configured and used to implement the corresponding units or modules in the microgrid reconstruction device according to the embodiment of the present invention.
- the storage medium may include, for example, a storage part of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disk read-only memory (CD-ROM), USB memory, or any combination of the above storage media.
- the program instructions when executed by a computer or processor, may cause the computer or processor to implement each unit or module of the microgrid reconstruction device according to an embodiment of the present invention, and may execute the unit or module according to the embodiment of the present invention.
- Microgrid reconstruction method when executed by a computer or processor, may cause the computer or processor to implement each unit or module of the microgrid reconstruction device according to an embodiment of the present invention, and may execute the unit or module according to the embodiment of the present invention.
- Microgrid reconstruction method when executed by a computer or processor, may cause the computer or processor to implement each unit or module of the microgrid reconstruction device according to an embodiment of the present invention, and may execute the unit or module according to the embodiment of the present invention.
- the program instructions are used to perform the following steps when running: monitoring and collecting current operating data of the microgrid in real time; storing the collected current operating data and corresponding time stamp information in a database; Analyze the operating status of the microgrid based on the stored operating data and corresponding time stamp information in the database; and determine the current control scheme of the microgrid according to the current analysis result, and reconstruct it according to the current control scheme The microgrid.
- the disclosed device and method may be implemented in other ways.
- the device embodiments described above are only schematic.
- the division of the unit is only a logical function division.
- multiple units or components may be combined or It can be integrated into another device, or some features can be ignored or not implemented.
- the bi-directional power electronic converter is only a functional illustration. In actual implementation, multiple power electronic converters can be combined and implemented in a certain way.
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Abstract
本发明实施例提供一种微电网重构方法、装置、微电网保护控制中心和存储介质,该方法包括:实时监控并采集所述微电网的当前运行数据;将所采集的当前运行数据和对应的时间标注信息存储到数据库中;基于所述数据库中已存储的运行数据和对应的时间标注信息对所述微电网进行运行状态分析;以及根据当前分析结果确定所述微电网的当前控制方案,并且根据所述当前控制方案重构所述微电网。上述技术方案实现了对微电网的灵活保护和控制,提升了系统运行的自动化和智能化水平。
Description
本发明涉及电力控制领域,特别是一种微电网重构方法、微电网重构装置、微电网保护控制中心和存储介质。
随着电力系统的快速发展,对传统电力系统的全面智能化运行提出了越来越高的要求。电网的保护控制系统的性能已经有了较大提升,但其主要针对广域电网。
目前的电力系统中,广泛地分布了大量的微电网。迫切需要从微电网的安全高效运行出发,实现对微电网的灵活保护和控制,提升系统运行的自动化和智能化水平。
发明内容
鉴于上述问题提出了本发明,以便提供一种至少部分地解决上述问题的微电网重构方案。
根据本发明一个方面,提供了一种微电网重构方法,包括:
实时监控并采集所述微电网的当前运行数据;
将所采集的当前运行数据和对应的时间标注信息存储到数据库中;
基于所述数据库中已存储的运行数据和对应的时间标注信息对所述微电网进行运行状态分析;以及
根据当前分析结果确定所述微电网的当前控制方案,并且根据所述当前控制方案重构所述微电网。
示例性地,所述基于所述数据库中已存储的运行数据和对应的时间标注信息对所述微电网进行运行状态分析包括:
基于所述当前运行数据初判所述微电网是否存在异常;
对于所述微电网存在异常的情况,
在所述当前运行数据和先前运行数据中检索异常发生位置的相 关设备的运行数据,
基于所检索到的运行数据和对应的时间标注信息进行事故分析;以及
对于所述微电网不存在异常的情况,基于所述当前运行数据、所述先前运行数据和对应的时间标注信息进行当前状态解析和未来状态预测;
所述根据当前分析结果确定所述微电网的当前控制方案包括:
对于事故分析结果表示所述微电网存在真实故障的情况,根据所述事故分析结果确定所述当前控制方案;以及
对于所述微电网不存在异常的情况,根据当前状态解析结果和未来状态预测结果确定所述当前控制方案。
示例性地,所述根据所述事故分析结果确定所述当前控制方案包括:
根据所述事故分析结果确定第一优化模型;
利用所述第一优化模型基于所述事故分析结果确定所述当前控制方案;
所述根据当前状态解析结果和未来状态预测结果确定所述当前控制方案包括:
根据所述当前状态解析结果和所述未来状态预测结果确定第二优化模型;
利用所述第二优化模型基于所述当前状态解析结果和所述未来状态预测结果确定所述当前控制方案。
示例性地,所述方法还包括:
对于所述事故分析结果表示所述微电网不存在真实故障的情况,确定所述微电网中的保护装置是否进行了误操作;
对于所述微电网中的保护装置进行了误操作的情况,控制所述保护装置的开关闭合。
示例性地,所述方法还包括:对于事故分析结果表示所述微电网存在真实故障的情况,发出警报信息。
示例性地,所述微电网包括发电设备、电力电子变换器、可控负载和/或保护装置,
所述根据当前分析结果确定所述微电网的当前控制方案,并且根据所述当前控制方案重构所述微电网包括:
根据所述当前分析结果确定所述发电设备、所述电力电子变换器和/或所述可控负载的运行状态和运行参数,并且根据所述发电设备、所述电力电子变换器和/或所述可控负载的运行状态和运行参数控制所述发电设备、所述电力电子变换器和/或所述可控负载相应地改变运行状态和/或运行参数;和/或
根据所述发电设备、所述电力电子变换器和/或所述可控负载的运行状态和运行参数确定所述保护装置的开关状态,并且根据所述保护装置的开关状态控制所述保护装置执行相应的开关操作,以改变所述微电网的拓扑结构。
示例性地,所述根据所述发电设备、所述电力电子变换器和/或所述可控负载的运行状态和运行参数确定所述保护装置的开关状态包括:
基于所述发电设备、所述电力电子变换器和/或所述可控负载的运行状态和运行参数检索所述微电网的保护装置状态决策表;以及
根据检索结果确定所述保护装置的开关状态。
示例性地,所述根据当前分析结果确定所述微电网的当前控制方案包括:
以所述微电网的系统总运行费用最小为目标函数,基于所述微电网的约束条件,确定所述微电网的当前控制方案,其中所述系统总运行费用等于根据所述当前分析结果计算的系统委任费用和系统调度费用的加和。
示例性地,所述根据当前分析结果确定所述微电网的当前控制方案包括:利用人工智能模型,根据所述当前分析结果确定所述微电网的当前控制方案。
示例性地,所述方法还包括:
将所述当前分析结果和对应的时间标注信息存储在所述数据库中;
将所述当前控制方案和对应的时间标注信息存储在所述数据库中;
基于所述数据库中存储的时间标注信息、先前分析结果和先前控制方案,训练所述人工智能模型。
根据本发明另一方面,还提供了一种微电网重构装置,包括:
数据采集单元,用于实时监控并采集所述微电网的当前运行数据,并将所采集的当前运行数据和对应的时间标注信息存储到数据库中;
状态分析单元,用于基于所述数据库中已存储的运行数据和对应的时间标注信息对所述微电网进行运行状态分析;以及
优化决策单元,用于根据当前分析结果确定所述微电网的当前控制方案,并且根据所述当前控制方案重构所述微电网。
根据本发明再一方面,还提供了一种微电网保护控制中心,包括处理器和 存储器,所述存储器中存储有计算机程序指令,所述计算机程序指令被所述处理器运行时用于执行上述微电网重构方法。
根据本发明又一方面,还提供了一种存储介质,在所述存储介质上存储了程序指令,所述程序指令在运行时用于执行上述微电网重构方法。
本发明的技术方案优化了微电网的系统运行并提高了供电可靠性。通过使整个微电网灵活、自动地进行重构运行,提高了微电网运行的智能性、安全性和经济性。
以下结合附图和具体实施例对本发明进行详细描述,但不作为对本发明的限定。
以下附图用来提供对本发明实施例的进一步理解,并且构成说明书的一部分,与本发明实施例一起用于解释本发明,并不构成对本发明的限制。在附图中,相同的参考标号通常代表相同部件或步骤。
图1示出了根据本发明一个实施例的配电网的示意图;
图2示出了根据本发明一个实施例的微电网重构方法的示意性流程图;
图3示出了根据本发明一个实施例的交直流混合电网的示意图;
图4示出了根据本发明另一个实施例的微电网重构方法的示意性流程图;
图5示出了根据本发明的一个实施例的微电网的局部示意图;
图6示出了根据本发明的另一个实施例的微电网的局部示意图;
图7示出了根据本发明的又一个实施例的微电网的局部示意图;
图8示出了根据本发明一个实施例的微电网重构装置的示意图;以及
图9示出了根据本发明一个实施例的微电网保护控制中心的示意性框图。
为了使得本发明的目的、技术方案和优点更为明显,下面将参照附图详细描述根据本发明的示例实施例。显然,所描述的实施例仅仅是本发明的一部分实施例,而不是本发明的全部实施例,应理解,本发明不受这里描述的示例实施例的限制。基于本发明中描述的本发明实施例,本领域技术人员在没有付出创造性劳动的情况下所得到的所有其它实施例都应落入本发明的保护范围之内。
根据本发明的实施例,提供了一种对微电网主动智能重构的控制方法。微 电网是小型发配电系统。微电网可以包括例如发电设备、储能设备、保护装置、补偿设备、负载以及电力电子变换器等等多种一次和/或二次设备。一次设备指直接与发电-输电-配电功能有关的所有设备,如发电设备、断路器、电流/电压互感器、变压器、避雷器以及隔离开关等。二次设备是指与保护和控制有关的设备,如保护继电器、通信设备、控制开关、指示灯以及测量仪表等。通过该微电网重构方法,可以对微电网中的一次和/或二次设备的工作模式和/或运行状态进行调整,由此重构该微电网。
为了更清楚地说明本发明的实施例,图1示出了根据本发明一个实施例的配电网的示意图。该配电网中包括至少一个微电网。该微电网如图1中虚线框内部分所示。
参见图1,该微电网包括发电设备3、储能设备4、保护装置5、数据采集设备6及诸如无功补偿设备的其他设备2等。该发电设备3可以任何能够提供电能的设备,诸如光伏、风力等新能源发电设备。储能设备4例如储能电池等。保护装置5可以是诸如继电保护器等的电力保护装置。可以理解保护装置5可以连接至少一个其他设备。通过保护装置5的开关操作可以确保这些所连接的设备的安全。数据采集设备6也可以连接至少一个其他设备,其可以用于采集这些下游设备的模拟量、数字量和保护动作量等数据。该微电网还包括微电网保护控制中心7。包括前述发电设备3、储能设备4、保护装置5、数据采集设备6及其他设备2等的任意电气设备均通过微电网通信网络8与所述微电网保护控制中心7连接。该微电网保护控制中心7用于通过控制微电网中的各种设备来保护和控制微电网。
配电网除了包括上述微电网,还可包括变电站18、采集控制设备19以及通信网关10以及输配电网保护控制中心1等设备。输配电网保护控制中心1用于根据需要对配电网中的各个设备执行操作,以保护和控制配电网。配电网中的变电站18通过通信网关10与输配电网保护控制中心1通信。采集控制设备19采集的配电网中的各种模拟量、数字量和保护动作量等通过变电站18上传输配电网保护控制中心1,并在输配电网保护控制中心1的控制下完成对配电网的保护与控制。
所述微电网保护控制中心7可以通过通信网关10与配电网通信网络9连接。配电网通信网络9经由通信网关10与配电网、输电网的输配电网保护控制中心1连接。由此,输配电网保护控制中心1能够经由通信网关10和配电网通 信网络9与微电网保护控制中心7进行实时信息交互,并控制微电网保护控制中心7对微电网进行相应保护和控制操作。换言之,微电网保护控制中心7可以接受输配电网保护控制中心1的统一调度。
在一个示例中,所述微电网保护控制中心7通过通信网关10的信号转换兼容IEC61850通信协议,并且所述微电网保护控制中心7以GOOSE方式与配电网通信网络9进行外部通信。
微电网保护控制中心7还可以通过既定的优化保护控制策略对整个微电网的运行进行控制。所述优化保护控制策略包括如潮流优化策略、网络拓扑调整策略、负载和发电设备的投切策略、保护装置的设定值更新策略等。
微电网通信网络8和配电网通信网络9包含但不限于诸如光纤通信网络的有线通信网络以及各种无线通信网络等。通信网关10可实现微电网与配电网之间的互连,其可通过任何现有或未来研发的相关技术实现。可选地,通信网关10兼容电网的IEC61850通信协议并支持GOOSE通信方式。
如上所述,微电网保护控制中心7可用于对微电网的运行进行自动电力控制。可以利用微电网保护控制中心7实现微电网的自动重构。替代地,因为微电网保护控制中心7与输配电网保护控制中心1连接并受其调度和控制,也可以由输配电网保护控制中心1对微电网的运行进行自动电力控制。
图2示出了根据本发明一个实施例的微电网重构方法200的示意性流程图。如图2所示,该微电网重构方法200包括以下步骤。
步骤S210,实时监控和采集微电网的当前运行数据。
如前所述,微电网中可能包括各种一次和/或二次设备。可以实时监控并采集微电网中的各个设备的当前运行数据。例如,可以监控微电网中的潮流分布,监控并采集发电设备的输出电压、输出电流、输出功率等,储能设备的能量存储状态、输入/输出电压、输入/输出电流、输入/输出功率等,继电保护装置的开关动作状态,微电网中的设备/线路参数信息,例如电压、电流、功角、频率、发热量等,补偿装置的电压、电流、补偿功率和谐波抑制量等参量。
具体地,以图1中所示的微电网为例,可以采集其中的发电设备3的输出功率、储能设备4的储能量、保护装置5的用于表示其开关状态的开关量以及通信设备的负载的电压、电流和温度等。
可以理解,可以以一定的频率来进行数据采集操作,例如:数据采集频率可控制在分钟(min)或秒(s)级别,以减少数据采集处理带来的计算和通信 负担。而对于微电网中的发电设备、电力电子变换器、储能设备、继电器等电气设备,为维持其自身正常运行,数据采集和处理频率可以设置更高,例如毫秒(ms)级别或更高。上述数据采集操作频率根据实际系统的运行的差异会不同,而不做确切限制。
步骤S220,将所采集的当前运行数据和对应的时间标注信息存储在数据库中。运行数据的时间标注信息包括采集该运行数据的确切时间的信息。在自动控制微电网重构的整个过程中,可以将所采集的运行数据和其对应的时间标注信息都存储到数据库中。数据库中所存储的微电网的历史数据有助于对微电网进行运行状态分析,而且便于用户查档,提高了用户体验和系统运行效率。
步骤S230,基于数据库中已存储的运行数据和对应的时间标注信息对微电网进行运行状态分析。
示例性地,该步骤S230可以包括基于当前运行数据对微电网进行当前状态估计。基于当前运行数据初判微电网是否存在异常。可以理解,在这里是对微电网的状态进行粗略估计,其估计结果可能是不准确的。比如,微电网可能仅仅是由于噪声干扰的原因导致波动,而并非发生了真实故障。
示例性地,该步骤S230还可以包括基于数据库中已存储的运行数据和对应的时间标注信息对微电网进行事故分析。事故分析用于确定微电网是否发生了真实故障。可选地,可以利用决策树来实现事故分析。
示例性地,该步骤S230还可以包括基于数据库中已存储的运行数据和对应的时间标注信息对微电网进行当前状态解析和未来状态预测。可选地,利用人工神经网络来进行当前状态解析和未来状态预测。该人工神经网络可以是经训练获得的。当前状态解析用于准确确定微电网的当前运行状态。未来状态预测用于预测微电网的未来运行状态。
步骤S240,根据当前分析结果确定所述微电网的当前控制方案,并且根据所述当前控制方案重构所述微电网。当前分析结果可以包括微电网的事故信息、当前运行状态信息和/或未来运行状态信息等。可以针对不同的分析结果确定微电网的对应控制方案,并根据所确定的控制方案自动重构微电网。
例如,在此步骤中,可以根据当前分析结果相应地调整微电网中的电力设备之间的连接结构和/或电力设备的运行模式。生成并将相应的调整指令通过微电网中的通信网络下发到电力设备,以重构微电网。
具体例如,对于当前分析结果表示微电网的局部可能要发生短路故障的情 况,可以改变微电网中的与该局部连接的保护装置的开关状态。由此发生短路故障的局部设备可以从微电网中断开。可选地,该局部还可以通过保护装置的开关连接到其它线路或节点。微电网的网络拓扑结构改变了。微电网中的其他设备得到了保护。
与配电网相比,微电网具有更明确和完整配套的发电设备、储能设备、负载,这保证了上述技术方案的顺利实现。上述微电网重构方法200基于微电网的运行数据来重构微电网,确保了微电网更安全且高效地运行。
上述微电网可以是交流电网、直流电网或交直流混合电网。交直流混合电网同时具有交流电网和直流电网。图3示出了根据本发明一个实施例的交直流混合电网。如图3所示,交直流混合电网包括图3中上方的交流网络、图3中下方的直流网络、以虚线示出的通信网络。该交直流混合电网中包括微电网保护控制中心。所述微电网保护控制中心通过通信网络分别与所述交流网络和所述直流网络连接,并且基于从交流网络和/或直流网络采集的当前运行数据重构该交直流混合电网。
应该理解到,微电网可以包括直流母线和直流网络、交流母线和交流网络,并具备交直流混合系统双向能量流动的功能。在此基础上可对上述微电网进行变换、延伸和拓展。例如,可对直流系统和/或交流系统分别进行变换、拓展和延伸出新的直流或交流母线、电路网络结构和连接方式。
可选地,上述微电网重构方法200还包括:将当前分析结果、所执行的当前控制方案和/或前述两项各自对应的时间标注信息等微电网的状态数据和操作数据存储在数据库中。
分析结果的时间标注信息包括获得该分析结果的时间的信息。控制方案的时间标注信息包括确定该控制方案的时间的信息。在一个实施例中,上述步骤S230和步骤S240每隔特定时间周期执行一次。根据该时间周期可以确定运行数据、分析结果和控制方案之间的对应关系。具体地,对于任一个分析结果来说,根据运行数据和分析结果的时间标注信息以及上述步骤S230的执行周期,可以确定该分析结果所对应的运行数据。对于任一个控制方案来说,根据分析结果的时间标注信息和步骤S240的执行时间,可以确定该控制方案所对应的分析结果,进而确定该控制方案所对应的运行数据。
数据库中存储微电网的大量状态数据和操作数据,可以便于用户以后查档,做出或调整控制策略。由此,提高了用户体验和系统运行效率。
图4示出了根据本发明另一个实施例的微电网重构方法400的示意性流程图。微电网重构方法400中的步骤S410和步骤S420和微电网重构方法200中的步骤S210和步骤S220类似,为了简洁,在此不再赘述。
在微电网重构方法400中,具体包括以下步骤。
步骤S431,根据当前运行数据初判微电网是否存在异常。例如,可以根据实时采集的当前运行数据是否超出正常限定范围来初判微电网是否存在异常。又例如,可以根据诸如继电保护器的保护装置上报的故障信息初判微电网是否存在异常。可以理解,在步骤S431中,仅仅对微电网的运行状态进行初判。该初判结果可能是不正确的。例如,在一些情况中,可能微电网中发生了信号波动。该信号波动可能是短暂的扰动所致,即使不进行任何操作,微电网可能也会逐渐恢复正常状态。但是该信号波动可能导致微电网被初判为发生了异常。
在该实施例中,对于微电网存在异常的情况,转步骤S432;对于微电网不存在异常的情况,转步骤S435。
步骤S432,在数据库中已存储的运行数据中检索异常发生位置的相关设备的运行数据。例如,可以以异常发生位置为中心,以特定设备个数间隔或者特定地理距离间隔为界限,确定异常发生位置的相关设备。在此步骤中,可以在数据库中检索相关设备的当前运行数据和先前运行数据,以提供关于该异常的完备信息。
步骤S433,基于所检索到的运行数据和对应的时间标注信息进行事故分析,以确定微电网是否存在真实故障。如前所述步骤S431中的初判结果可能不是正确的。在此步骤S433中,基于所检索到的、与异常发生有关的数据对异常情况进行更全面的分析,以此得出微电网是否存在真实故障的确切结论。可以理解,事故分析可以利用决策树来实现。决策树是以树形结构为基础建立的数学模型。在决策树的各个叶子节点上赋予事故分析的评估判断函数并存储后,在此步骤中自动搜索决策树并确定事故分析的最终结果。
可选地,如果通过步骤S433确定微电网确实发生了故障,即步骤S433的事故分析结果表示微电网存在真实故障,可以执行步骤S434和步骤S441。
在步骤S434中,对于事故分析该结果表示微电网存在真实故障的情况,发出警报信息。可以通过声音、图像以及诸如警报灯等警报装置发出警报信息。由此,可以及时通知工作人员进行处理,避免事故造成更大损失。可以理解,该步骤S434并非必须存在的。
在步骤S441中,可以根据事故分析结果确定当前控制方案。在此步骤中,可以利用优化模型来确定控制方案。可以理解,根据不同微电网的需要,可以存在一个或多个优化模型。
在一个示例中,提供了多个用于根据事故分析结果确定当前控制方案的优化模型。可以建立事故分析结果与优化模型的决策树或决策表。表1示出了根据本发明一个实施例的决策表。
表1决策表
事故分析结果 | 结果1 | 结果2 | …… | 结果n |
优化模型 | 模型1 | 模型2 | …… | 模型n |
在步骤S441中,首先,根据事故分析结果确定第一优化模型。根据需要,该优化模型可以以供电恢复时间最短或恢复费用最低为优化目标。然后,利用该第一优化模型基于事故分析结果确定当前控制方案。将事故分析结果作为输入量输入到第一优化模型中,以由第一优化模型输出当前控制方案的各个分量。
在一个示例中,第一优化模型可以基于人工神经网络模型(ANN)和蒙特卡洛树搜索模型(MCTS)。可以利用蒙特卡洛树搜索模型进行树形结构的搜索。人工神经网络可以用作在树形结构的节点和叶子处设置的评估函数。利用人工神经网络模型根据事故分析结果对搜索到的目标进行评估,以确定最优的控制方案作为当前控制方案。在保证搜索目标为当前最优的控制方案同时,缩短搜索时间并提高模型计算效率。
在步骤S443中,根据步骤S441所确定的当前控制方案重构微电网。可以根据控制方案生成对应的指令。将所生成的指令下发到微电网中的各个设备,以改变微电网中的设备的运行状态和/或连接方式。
可选地,如果通过步骤S433确定微电网未发生故障,即步骤S433的事故分析结果表示微电网不存在真实故障,可以执行步骤S451和步骤S452。
在步骤S451中,对于事故分析结果表示微电网不存在真实故障的情况,确定微电网中的保护装置是否进行了误操作。在步骤S452中,对于微电网中的保护装置进行了误操作的情况,控制所述保护装置的开关闭合。
在前述步骤S431中,初判了微电网存在异常。该异常可能导致微电网中的保护装置发生了误操作。例如保护装置中的开关发生了误跳闸。在步骤S451中,判断微电网中的保护装置是否由于步骤S431所初判的异常而进行了误操作。在前述步骤S433中,确定微电网并不存在真实故障。故此,在步骤S452中, 控制进行了误操作的保护装置的开关重新合闸,以使微电网继续正常运行。可以理解,对于在步骤S451中确定微电网中的保护装置未进行误操作的情况,此轮微电网重构操作可以直接结束。
如前所述,对于步骤S431确定微电网不存在异常的情况,转步骤S435。在步骤S435中,基于数据库中存储的运行数据和对应的时间标注信息进行当前状态解析和未来状态预测。可以理解数据库中存储的运行数据包括当前运行数据和先前运行数据,并且数据库还可以存储用于进行当前状态解析的数学模型、用于进行未来状态预测的数学模型。可选地,用于进行当前状态解析的数学模型可以用人工神经网络来实现。用于进行未来状态预测的数学模型可以用长短期记忆优化模型(LSMT)来实现。长短期记忆优化模型能够使用记忆细胞来存储和输出信息,由此,保证了预测的准确性,进而确保了重构的合理性。基于这些运行数据,可以选取用于进行当前状态解析的数学模型和/或用于进行未来状态预测的数学模型,对微电网的当前运行状态进行详细地解析,并对微电网未来的运行状态进行合理化预测。
在步骤S442,根据当前状态解析结果和未来状态预测结果确定当前控制方案。在一个示例中,可以对微电网中的负载的有功含量、无功含量、谐波含量,以及发电设备的新能源供给进行感知预测。
在此步骤中,也可以利用优化模型来确定控制方案。可以理解,根据不同微电网的需要,可以存在一个或多个优化模型。可选地,该优化模型可以基于人工神经网络模型(ANN)和蒙特卡洛树搜索模型(MCTS)。可以利用蒙特卡洛树搜索模型进行树形结构的搜索。人工神经网络模型用于根据当前状态分析结果对搜索到的目标进行评估,在保证搜索目标为当前最优的控制方案同时,缩短搜索时间并提高模型计算效率。
在一个示例中,提供了多个用于根据当前状态分析结果和未来状态预测结果确定当前控制方案的优化模型。在步骤S442中,首先,根据当前状态分析结果和未来状态预测结果确定第二优化模型。根据需要,该优化模型可以以潮流最优或运行费用最低为优化目标。然后,利用该第二优化模型基于当前状态分析结果和未来状态预测结果确定当前控制方案。将当前状态分析结果和未来状态预测结果作为输入量输入到第二优化模型中,以由第二优化模型输出当前控制方案的各个分量。
与步骤S441类似地,在步骤S442之后,也执行步骤S443。步骤S443已 经在前面进行描述,为了简洁,在此不再赘述。
本领域普通技术人员可以理解,图4所示的微电网重构方法400仅为示例,而不构成对本发明的限制。例如其中的步骤S451和步骤S452可以不存在。在有些微电网中,保护装置本身可靠性较强,不易发生误操作。在此情况下,这两个步骤可以是不存在的。又例如,如前所述步骤S434可以是不存在的。步骤S434也可以与步骤S441同时执行或者在步骤S441之后执行。
上述微电网重构方法400中,针对微电网的故障状态和正常运行状态分别进行运行状态分析,然后基于分析结果确定微电网重构运行的最优方案。由此,可以按照该最优方案调整微电网的电气连接和运行模式,使所述微电网在故障和非故障情况下均能进行主动智能重构。优化了微电网的经济运行,提高了供电可靠性。在系统故障和非故障状态均能对系统配置和网络结构进行主动调整,提高系统的供电可靠性和运行效率。上述智能重构方案还具有高度的灵活性、可塑性和安全性。
在微电网重构方法200和400中,包括根据当前分析结果确定微电网的当前控制方案的步骤,例如微电网重构方法200中的步骤S240、微电网冲重构方法400中的步骤S441和步骤S442。下面详细描述根据本发明一个实施例的根据当前分析结果确定微电网的当前控制方案的具体实现。在该实施例中,以微电网的系统总运行费用最小为优化目标函数,基于微电网的约束条件,确定微电网的当前控制方案。其中该系统总运行费用等于根据所述当前分析结果计算的系统委任费用和系统调度费用的加和。
可以以微电网运行成本最低为优化目标,即以微电网的系统总运行费用最小作为目标函数。微电网的系统总运行费用可以用系统委任费用和系统调度费用的加和来表示,如以下公式所示。
C(X,Y)=f(X)+g(Y);其中,X=[X
g,X
r,X
es],Y=[P
g,P
r,P
es,P
γ,P
ex]。
C(X,Y)表示最小目标函数,f(X)表示系统委任费用,其是整数变量X的函数,g(Y)表示系统调度费用,其是连续变量Y的函数。整数变量X表示微电网中的设备状态委任量。该设备状态委任量X包括主力发电设备的运行模式X
g、后备发电设备的运行模式X
r和储能设备的运行模式X
es。主力发电设备可以是任何形式的发电机,例如风力发电机、永磁同步发电机,其工作模式仅包括发电模式。主力发电设备的运行模式可以包括发电模式和停用模式。后备发电设备可以是诸如电力电子变换器等除了发电模式还有其他工作模式的设备。电力 电子变换器有多种不同的工作模式,例如发电模式、有源滤波模式(APF)。当然,电力电子变换器也可以处于停机模式。储能设备的工作模式可以包括充电模式和放电模式。连续变量Y表示微电网的设备调度量,可以包括主力发电设备的发电量P
g、后备发电设备的发电量P
r、储能设备的充电/放电量P
es、可控负载的耗电量P
γ以及与外部电网的能量交换量P
ex等。在此示例中,主力发电设备的发电量P
g、后备发电设备的发电量P
r、储能设备的充电/放电量P
es、可控负载的耗电量P
γ以及与外部电网的能量交换量P
ex可以都是有功功率。
上述设备状态委任量X可以表示微电网中的设备所组成的不同网络结构,上述调度量Y可以表示在设备状态委任量X所表示的结构下,微电网中的各个设备的运行参数。由此,微电网的控制方案可以用设备状态委任量X和调度量Y来表示。设备状态委任量X和调度量Y这二项中的各个控制量的具体数值是待求解量。
根据不同的微电网的实际运行要求和优化目标,f(X)和g(Y)可取不同的数学函数。在该示例中,f(X)可以是X中的各个元素与其各自的委任费用系数的乘积的加和。g(Y)可以是Y中的各个元素与其各自的调度费用系数的乘积的加和。
其中,I表示微电网中所有主力发电设备的集合,i是I中的元素,可表示为I={1,2,3…,i,…};J表示微电网中所有后备发电设备的集合,j是J中的元素,即J={1,2,3…,j,…};K表示微电网中所有储能设备的集合,k是K中的元素,即K={1,2,3…,k,…};D表示微电网中所有可控负载的集合,d是D中的元素,即D={1,2,3…,d,…};L表示微电网中所有连接线路的集合,l是L中的元素,即L={1,2,3…,l,…}。
C
gx表示主力发电设备的委任费用系数矩阵,
是C
gx的转置矩阵。C
rx表示后备发电设备的委任费用系数矩阵,
是C
rx的转置矩阵。C
esx表示储能设备的委任费用系数矩阵,
是C
esx的转置矩阵。
是标号为i的主力发电设备的启/停委任费用系数,
是标号为j的后备发电设备的委任费用系数,
是标号为k的储能设备的委任费用系数。
C
g表示主力发电设备的调度费用系数矩阵。C
r表示后备发电设备的调度费用系数矩阵。C
es表示储能设备的调度费用系数矩阵。C
γ表示可控负载的调度费用系数矩阵。
表示标号为i的主力发电设备的调度费用系数,
表示标号为j的后备发电设备的调度费用系数,
表示标号为k的储能设备的调度费用系数,
表示标号为d的可控负载的调度费用系数,C
ex表示微电网与外部电网进行能量交换的调度费用系数。
可以以ΔT为计算时间间隔,执行上述利用优化模型确定当前控制方案的步骤。每经过ΔT进行一次优化模型计算,计数变量t以0为初始值,每次累加1。当累加计数到Γ时将t重置为0,然后重新计数。
x
g(i,t)表示标号为i的主力发电设备在第t个计算周期待求的运行模式(启用/停止等),x
r(j,t)表示标号为j的后备发电设备在第t个计算周期待求的运行模式(发电模式和APF模式),x
es(k,t)表示标号为k的储能设备在第t个计算周期待求的运行模式(充电/放电等)。
P
g(i,t)表示标号为i的主力发电设备在第t个计算周期待求的有功功率发电量,P
r(j,t)表示标号为j的后备发电设备在第t个计算周期待求的有功功率发电量,P
es(k,t)表示标号为k的储能设备在第t个计算周期待求的有功功率充电/放电电量,P
γ(d,t)表示标号为d的可控负载在第t个计算周期待求的有功负荷,P
ex(t)表示在第t个计算周期待求的与外部电网的有功功率交换量。
上述最小目标函数保证了微电网的运行成本最小。
对于微电网来说,其中可能存在很多相应的约束条件。微电网服从这些约束条件能够保证其正常运行。以图3所示的微电网的交流电网部分为例,可存在以下约束条件。
1.设备状态委任量X的状态取值约束。主力发电设备的运行模式x
g(i,t)、后备发电设备的运行模式x
r(j,t)和储能设备的运行模式x
es(k,t)满足以下约束条件:
2.微电网中有功功率供需平衡约束。微电网中整体有功发电量和外部电网 的有功电能交换与负载的有功消耗要供需平衡。
其中,P
load(t)为第t个计算周期不可控负载的有功功率消耗总量,P
load(t)的取值可以通过基于当前运行数据、先前运行数据和对应的时间标注信息进行未来状态预测而获得。
3.电网中谐波含量、无功含量的约束。
后备发电设备接入到微电网中的网络节点,这些网络节点构成节点集合U,u是U={1,2,3…,u,…}中的元素。U中共有节点个数为μ,μ=card(U)。因此后备发电设备的标识集合J也可表示为J={J
1,J
2,…J
u,…,J
μ},J中的元素J
u表示在节点u处所接入的后备发电设备的集合。微电网中的谐波含量和无功含量在特定节点u处需要控制在一定范围内。
其中,A
APF,j表示后备发电设备j在APF模式下的额定补偿容量,
为节点u处,其他补偿方式提供的谐波补偿容量。
为节点u处,其他补偿方式提供的无功补偿容量。
为节点u处,允许提供的最大补偿容量。
是谐波含量约束中的松弛变量,
是无功含量约束中的松弛变量。H
u是在节点u处的谐波量,Q
u是在节点u处的无功量。H
u和Q
u均可以通过基于当前运行数据、先前运行数据和对应的时间标注信息进行未来状态预测而获得。松弛常变量
和
的正负取值可设定为常数,或通过基于当前运行数据和对应的时间标注信息进行状态解析而获得。
4.重构次数约束。主力发电设备i、后备发电设备j在一定时间间隔T内执行的重构次数N
g,i、N
r,j可以限制在一定范围内。常数
为主力发电设备i的重构次数上限,常数
为后备发电设备j的重构次数上限。
6.与外部电网有功能量交换约束。期望微电网与外部电网的能量交换在合理的一定范围内。如果微电网处于并网运行状态,则状态变量s
ex用1表示,如果微电网处于孤岛运行状态,则s
ex用0表示。常数
为与外部电网有功交换量的下限,常数
为与外部电网有功交换量的上限。
9.后备发电设备在第t个周期自身可使用的发电容量约束。S
r(j,t)表示后备发电设备j在第t个周期自身可使用的发电容量。S
r(j,t)需保证在[t,t+1]内以ΔT的时间间隔为后备单元提供足够的功率输出,即
如/或S
r(j,t)≥P
r(j,t)·ΔT。
表示后备发电设备j自身可使用的发电容量下限,
为后备发电设备j自身可使用的发电容量上限。常数ε
r,j为后备发电设备j发电容量约束中的松弛变量。其中S
r(j,t)的取值可以通过基于当前运行数据、先前运行数据和对应的时间标注信息进行未来状态预测而获得。
和
的取值可设定为常数或通过基于当前运行数据、先前运行数据和对应的时间标注信息进行未来状态预测而获得。松弛常变量ε
r,j的正负取值可设定为常数,或通过基于当前运行数据和对应的时间标注信息进行状态解析而获得。
10.主力发电设备发电量变化率约束。期望微电网内部主力发电设备的发电量变化率限制在一定范围内。常数
为主力发电设备i发电量变化率的下限,常数
为主力发电设备i发电量变化率的上限。τ表示电气设备在ΔT内,发出或吸收有功功率的变化率。
12.储能设备充放电变化率约束。期望微电网中的储能设备的充放电量变化率限制在一定范围内。
为储能设备k在第t个计算周期的充放电量变化率的下限,
为储能设备k在第t个计算周期的发电量变化率的上限。
和
的设定值,可设定为常数或由状态预测单元决定。
13.储能设备的充电/放电量约束。期望微电网中的储能设备k的充放电量P
es(k,t)控制在一定范围内。常数
为储能设备k放电量的下限,常数
为储能设备k放电量的上限。常数
为储能设备k充电量的下限,常数
为储能设备k充电量的上限。
14.储能设备的充放电状态约束。在[t,t+1]内以ΔT为计算时间间隔,储能设备的充电/放电总电量为
和/或η
k·P
es(k,t)·ΔT。常数η
k表示储能设备k的充电/放电效率,常数Cap
es,k表示储能设备k的额定容量。SOC(k,t)表示储能设备k在第t个计算周期的充电/放电程度。常数
为储能设备k充放电状态的下限,常数
为储能设备k充放电状态的上限。
15.微电网中总储备容量约束。为应对微电网系统中突发紧急状况,期望主力发电设备具备一定的储备容量。Cap表示电气设备的额定容量,Cap
g(i,t)是主力发电设备i的额定容量,Cap
es,k是储能设备k的额定容量。ρ表示电气设备的容量系数,常数ρ
g,i为主力发电设备i的储备容量系数,常数ρ
es,k为储能设备k的储备容量系数。
16.功角约束。各线路的传输功率和功角差θ
l要在一定范围内。
17.可控负载的功率消耗约束。期望可控负载的调度量限制在一定范围内。如果可控负载的调度功能被激活则状态量s
d取1,其他情况s
d取0。s
d的取值可以通过基于数据库中已存储的运行数据和对应的时间标注信息对微电网进行运行状态分析来确定。常数
为可控负载d,在调度功能被激活时的可调度有功功率的下限;常数
为可控负载d,在调度功能被激活时的可调度有功功率的上限。常数
为可控负载d,在调度功能被激活时的可调度无功功率的下限,常数
为可控负载d,在调度功能被激活时的可调度无功功率的上限。常数
为可控负载d,在调度功能未激活时的可调度有功功率的下限;常数
为可控负载d,在调度功能未激活时的可调度有功功率的上限。常数
为可控负载d,在调度功能未激活时的可调度无功功率的下限,常数
为可控负载d,在调度功能未激活时的可调度无功功率的上限。
根据上面的描述,本领域普通技术人员可以理解上述约束条件的具体含义。上述约束条件仅为举例,并不构成对本发明的技术方案的限制。例如,约束条件可以不包括:重构次数约束。
上述以系统总运行费用最小为目标函数、基于微电网的约束条件确定微电网的当前控制方案可以利用任何使用的优化模型来实现,例如但并不限于混合整数优化(MIO)算法模型。
替代地,还可以利用人工智能模型根据当前分析结果确定微电网的当前控制方案。人工智能模型例如人工神经网络(ANN)模型结合树搜索模型。人工智能模型可以是利用大量数据训练获得的。树搜索模型建立目标搜索结构。人工神经网络对搜索目标进行综合评估进而搜索得到最终目标。人工智能模型具有学习能力。通过大量数据的训练,人工智能模型能够根据当前分析结果确定微电网的当前适宜的控制方案。利用人工智能模型来获得优化控制方案能够有效利用系统的计算能力,显著提高控制方案的质量。
如前所述,可以将微电网的分析结果和控制方案与其对应的时间标注信息存储在数据库中。可以基于数据库中先前存储的时间标注信息、先前分析结果和先前控制方案训练上述人工智能模型。
在一个示例中,首先将微电网的运行状态分析结果分别输入人工智能模型和其他能够基于状态分析结果确定控制方案的优化决策模型(例如前述混合整 数优化算法模型),以自两个模型获得各自所确定的控制方案。然后,比较所获得的两个控制方案。根据两个控制方案之间的偏差距离,调整人工智能模型的参数。可选地,仅对于两个控制方案之间的偏差距离超过或小于一定阈值的情况,调整人工智能模型的参数。
上述训练方法可以在微电网运行的过程中实现,有效利用了微电网的运行数据,并且所获得的人工智能模型更适合于该微电网,进一步提高了利用人工智能模型所确定的控制方案的质量。
如前所述,上述微电网中可以包括发电设备、电力电子变换器、可控负载和/或保护装置。当然,微电网中还可以包括储能设备、变压器等其他电力设备。如前所述,该微电网可以是交直流混合电网。再次参考图3,在交直流混合电网中,电力电子变换器分别与微电网中的交流网络和直流网络连接,以实现双向能量流动。电力电子变换器可以包括双向DC/DC变换器和双向DC/AC变换器。
可选地,前述步骤S240根据当前分析结果确定所述微电网的当前控制方案,并且根据所述当前控制方案重构所述微电网包括以下步骤中的至少一个。
根据当前分析结果确定微电网中的发电设备、电力电子变换器和/或可控负载的运行状态和运行参数,并且根据发电设备、电力电子变换器和/或可控负载的运行状态和运行参数分别控制发电设备、电力电子变换器和/或可控负载相应地改变运行状态和/或运行参数。例如,根据当前分析结果确定微电网中的某电力电子变换器的运行状态应该由发电模式改变为有源滤波模式,则可以对该电力电子变换器执行相应的控制操作。
根据发电设备、电力电子变换器和/或可控负载的运行状态和运行参数确定保护装置的开关状态,并且根据保护装置的开关状态控制保护装置执行相应的开关操作,以改变微电网的拓扑结构。
图5示出了根据本发明的一个实施例的微电网的局部示意图。图5中上方所示是发电侧,下方所示是负载侧。DG表示电力电子变换器工作在发电模式。APF表示电力电子变换器工作在有源滤波模式。深色矩形表示继电保护装置,其可以有不同的开关状态。当继电保护装置处于一个状态(例如打开)时,有阴影的矩形框内的电力电子变换器处于发电模式。当继电保护装置处于另一个状态(例如关闭)时,该电力电子变换器处于有源滤波模式。由此,通过改变继电保护装置的开关状态,改变了图5所示微电网的拓扑结构。
图6示出了根据本发明的另一个实施例的微电网的局部示意图。图6中电力电子变换器通过继电保护装置控制的电气开关连接到交直流电网。该电气开关可以是单刀多掷开关等设备,其能够进行投切状态调整,使微电网运行状态做出改变。可以理解,当微电网保护控制中心通过继电保护装置控制电气开关连接不同的通路时,该电力电子变换器即连接到了交直流电网中的不同的电气连接点。由此,微电网的拓扑结构也发生了改变。
在本发明的上述实施例中,电力电子变换器可灵活工作在发电模式、APF模式和停机模式。电力电子变换器可作为微电网中的后备发电设备根据运行需求进行调整,其可以用作作为主力发电设备的传统发电设备的补充,共同支撑微电网运行。由此,使得微电网运行更灵活、更智能、更自动化。
图7示出了根据本发明的又一个实施例的微电网的局部示意图。微电网的一次/二次设备与微电网通信网络之间设置有n个保护装置。对应如下表2所示,保护装置的开关状态可以存储在数据库中的相关数据表中。表2中,用状态1表示保护装置的开关闭合,状态0表示保护装置的开关打开。
表二保护装置的开关的状态表
保护装置1 | 保护装置2 | 保护装置3 | …… | 保护装置n | |
状态 | 0 | 1 | 1 | …… | 0 |
可以理解图7中所示的保护装置可以有保护互锁和远跳功能,以更快地对微电网中的故障进行反应并隔离。
根据本发明的另一方面,提供了一种微电网重构装置。图8示出了根据本发明一个实施例的微电网重构装置的示意图。如图8所示,微电网重构装置通过通信网络与微电网中包括发电设备、储能设备、保护装置等电气设备进行连通。微电网重构装置可以通过通信网关以光纤、无线等多种通信方式连接各个电气设备。微电网重构装置中包括数据采集单元、状态分析单元和优化决策单元。
数据采集单元如图8中“SCADA”所示。数据采集单元用于实时监控并采集微电网的当前运行数据,并将所采集的当前运行数据和对应的时间标注信息存储到数据库中。换言之,数据采集单元用于执行上述微电网重构方法200中的步骤S210和步骤S220,还可以用于执行上述微电网重构方法400中的步骤S410和步骤S420。通过对微电网中的电气设备进行实时的监控,对各设备的 运行情况和系统潮流等相关运行数据进行采集处理。由此,实时监测微电网中各电气设备的开关动作量、模拟量、数字量等运行数据。
状态分析单元用于基于所述数据库中已存储的运行数据和对应的时间标注信息对所述微电网进行运行状态分析。即状态分析单元用于执行上述微电网重构方法200中的步骤S230。状态解析单元还可以用于执行上述微电网重构方法400中的步骤S431至步骤S435以及步骤S451。
具体地,状态分析单元可以进一步包括感知预测模块、状态解析模块和事故分析模块。感知预测模块用于执行上述微电网重构方法400中的步骤S433。状态解析模块和事故分析模块用于执行上述微电网重构方法400中的步骤S435。
优化决策单元用于根据当前分析结果确定所述微电网的当前控制方案,并且根据所述当前控制方案重构所述微电网。即优化决策单元用于执行上述微电网重构方法200中的步骤S240。优化决策单元还可以用于执行上述微电网重构方法400中的步骤S441至步骤S443。
本领域普通技术人员通过阅读上文关于微电网重构方法的详细描述,能够理解上述微电网重构装置的结构、实现以及优点,因此这里不再赘述。
图9示出了根据本发明一个实施例的微电网保护控制中心900的示意性框图。微电网保护控制中心900包括输入装置910、存储装置(即存储器)920、以及处理器930。
所述输入装置910用于接收用户指令等。
所述存储装置920存储用于实现根据本发明实施例的微电网重构方法中的相应步骤的计算机程序指令。
所述处理器930用于运行所述存储装置920中存储的计算机程序指令,以执行根据本发明实施例的微电网重构方法的相应步骤。
在一个实施例中,所述计算机程序指令被所述处理器930运行时用于执行以下步骤:实时监控并采集所述微电网的当前运行数据;将所采集的当前运行数据和对应的时间标注信息存储到数据库中;基于所述数据库中已存储的运行数据和对应的时间标注信息对所述微电网进行运行状态分析;以及根据当前分析结果确定所述微电网的当前控制方案,并且根据所述当前控制方案重构所述微电网。
此外,根据本发明实施例,还提供了一种存储介质,在所述存储介质上存储了程序指令,在所述程序指令被计算机或处理器运行时用于执行本发明实施 例的微电网重构方法的相应步骤,并且用于实现根据本发明实施例的微电网重构装置中的相应单元或模块。所述存储介质例如可以包括平板电脑的存储部件、个人计算机的硬盘、只读存储器(ROM)、可擦除可编程只读存储器(EPROM)、便携式紧致盘只读存储器(CD-ROM)、USB存储器、或者上述存储介质的任意组合。
在一个实施例中,所述程序指令在被计算机或处理器运行时可以使得计算机或处理器实现根据本发明实施例的微电网重构装置的各个单元或模块,并可以执行根据本发明实施例的微电网重构方法。
在一个实施例中,所述程序指令在运行时用于执行以下步骤:实时监控并采集所述微电网的当前运行数据;将所采集的当前运行数据和对应的时间标注信息存储到数据库中;基于所述数据库中已存储的运行数据和对应的时间标注信息对所述微电网进行运行状态分析;以及根据当前分析结果确定所述微电网的当前控制方案,并且根据所述当前控制方案重构所述微电网。
尽管这里已经参考附图描述了示例实施例,应理解上述示例实施例仅仅是示例性的,并且不意图将本发明的范围限制于此。本领域普通技术人员可以在其中进行各种改变和修改,而不偏离本发明的范围和精神。所有这些改变和修改意在被包括在所附权利要求所要求的本发明的范围之内。
在本申请所提供的几个实施例中,应该理解到,所揭露的设备和方法,可以通过其它的方式实现。例如,以上所描述的设备实施例仅仅是示意性的,例如,所述单元的划分,仅仅为一种逻辑功能划分,实际实现时可以有另外的划分方式,例如多个单元或组件可以结合或者可以集成到另一个设备,或一些特征可以忽略,或不执行,例如所述双向电力电子变换器,仅仅是一种功能示意性的,实际实现时可以多个电力电子变换器组合并实现某种功能。
在此处所提供的说明书中,说明了大量具体细节。然而,能够理解,本发明的实施例可以在没有这些具体细节的情况下实践。在一些实例中,并未详细示出公知的方法、结构和技术,以便不模糊对本说明书的理解。
类似地,应当理解,为了精简本发明并帮助理解各个发明方面中的一个或多个,在对本发明的示例性实施例的描述中,本发明的各个特征有时被一起分组到单个实施例、图、或者对其的描述中。然而,并不应将该本发明的方法解释成反映如下意图:即所要求保护的本发明要求比在每个权利要求中所明确记载的特征更多的特征。更确切地说,如相应的权利要求书所反映的那样,其发 明点在于可以用少于某个公开的单个实施例的所有特征的特征来解决相应的技术问题。因此,遵循具体实施方式的权利要求书由此明确地并入该具体实施方式,其中每个权利要求本身都作为本发明的单独实施例。
本领域的技术人员可以理解,除了特征之间相互排斥之外,可以采用任何组合对本说明书(包括伴随的权利要求、摘要和附图)中公开的所有特征以及如此公开的任何方法或者设备的所有过程或单元进行组合。除非另外明确陈述,本说明书(包括伴随的权利要求、摘要和附图)中公开的每个特征可以由提供相同、等同或相似目的的替代特征来代替。
此外,本领域的技术人员能够理解,尽管在此所述的一些实施例包括其它实施例中所包括的某些特征而不是其它特征,但是不同实施例的特征的组合意味着处于本发明的范围之内并且形成不同的实施例。例如,在权利要求书中,所要求保护的实施例的任意之一都可以以任意的组合方式来使用。
以上所述,仅为本发明的具体实施方式或对具体实施方式的说明,本发明的保护范围并不局限于此,任何熟悉本技术领域的技术人员在本发明揭露的技术范围内,可轻易想到变化或替换,都应涵盖在本发明的保护范围之内。本发明的保护范围应以权利要求的保护范围为准。
Claims (13)
- 一种微电网重构方法,其特征在于,包括:实时监控并采集所述微电网的当前运行数据;将所采集的当前运行数据和对应的时间标注信息存储到数据库中;基于所述数据库中已存储的运行数据和对应的时间标注信息对所述微电网进行运行状态分析;以及根据当前分析结果确定所述微电网的当前控制方案,并且根据所述当前控制方案重构所述微电网。
- 如权利要求1所述的微电网重构方法,其特征在于,所述基于所述数据库中已存储的运行数据和对应的时间标注信息对所述微电网进行运行状态分析包括:基于所述当前运行数据初判所述微电网是否存在异常;对于所述微电网存在异常的情况,在所述当前运行数据和先前运行数据中检索异常发生位置的相关设备的运行数据,基于所检索到的运行数据和对应的时间标注信息进行事故分析;以及对于所述微电网不存在异常的情况,基于所述当前运行数据、所述先前运行数据和对应的时间标注信息进行当前状态解析和未来状态预测;所述根据当前分析结果确定所述微电网的当前控制方案包括:对于事故分析结果表示所述微电网存在真实故障的情况,根据所述事故分析结果确定所述当前控制方案;以及对于所述微电网不存在异常的情况,根据当前状态解析结果和未来状态预测结果确定所述当前控制方案。
- 如权利要求2所述的微电网重构方法,其特征在于,所述根据所述事故分析结果确定所述当前控制方案包括:根据所述事故分析结果确定第一优化模型;利用所述第一优化模型基于所述事故分析结果确定所述当前控制方案;所述根据当前状态解析结果和未来状态预测结果确定所述当前控制方案包括:根据所述当前状态解析结果和所述未来状态预测结果确定第二优化模型;利用所述第二优化模型基于所述当前状态解析结果和所述未来状态预测结果确定所述当前控制方案。
- 如权利要求2或3所述的微电网重构方法,其特征在于,所述方法还包括:对于所述事故分析结果表示所述微电网不存在真实故障的情况,确定所述微电网中的保护装置是否进行了误操作;对于所述微电网中的保护装置进行了误操作的情况,控制所述保护装置的开关闭合。
- 如权利要求2或3所述的微电网重构方法,其特征在于,所述方法还包括:对于事故分析结果表示所述微电网存在真实故障的情况,发出警报信息。
- 如权利要求1至3任一项所述的微电网重构方法,其特征在于,所述微电网包括发电设备、电力电子变换器、可控负载和/或保护装置,所述根据当前分析结果确定所述微电网的当前控制方案,并且根据所述当前控制方案重构所述微电网包括:根据所述当前分析结果确定所述发电设备、所述电力电子变换器和/或所述可控负载的运行状态和运行参数,并且根据所述发电设备、所述电力电子变换器和/或所述可控负载的运行状态和运行参数控制所述发电设备、所述电力电子变换器和/或所述可控负载相应地改变运行状态和/或运行参数;和/或根据所述发电设备、所述电力电子变换器和/或所述可控负载的运行状态和运行参数确定所述保护装置的开关状态,并且根据所述保护装置的开关状态控制所述保护装置执行相应的开关操作,以改变所述微电网的拓扑结构。
- 如权利要求6所述的微电网重构方法,其特征在于,所述根据所述发电设备、所述电力电子变换器和/或所述可控负载的运行状态和运行参数确定所述保护装置的开关状态包括:基于所述发电设备、所述电力电子变换器和/或所述可控负载的运行状态和运行参数检索所述微电网的保护装置状态决策表;以及根据检索结果确定所述保护装置的开关状态。
- 如权利要求1至3任一项所述的微电网重构方法,其特征在于,所述根据当前分析结果确定所述微电网的当前控制方案包括:以所述微电网的系统总运行费用最小为目标函数,基于所述微电网的约束条件,确定所述微电网的当前控制方案,其中所述系统总运行费用等于根据所述当前分析结果计算的系统委任费用和系统调度费用的加和。
- 如权利要求1至3任一项所述的微电网重构方法,其特征在于,所述根据当前分析结果确定所述微电网的当前控制方案包括:利用人工智能模型,根据所述当前分析结果确定所述微电网的当前控制方案。
- 如权利要求9所述的微电网重构方法,其特征在于,还包括:将所述当前分析结果和对应的时间标注信息存储在所述数据库中;将所述当前控制方案和对应的时间标注信息存储在所述数据库中;基于所述数据库中存储的时间标注信息、先前分析结果和先前控制方案,训练所述人工智能模型。
- 一种微电网重构装置,其特征在于,包括:数据采集单元,用于实时监控并采集所述微电网的当前运行数据,并将所采集的当前运行数据和对应的时间标注信息存储到数据库中;状态分析单元,用于基于所述数据库中已存储的运行数据和对应的时间标注信息对所述微电网进行运行状态分析;以及优化决策单元,用于根据当前分析结果确定所述微电网的当前控制方案,并且根据所述当前控制方案重构所述微电网。
- 一种微电网保护控制中心,包括处理器和存储器,其特征在于,所述存储器中存储有计算机程序指令,所述计算机程序指令被所述处理器运行时用于执行如权利要求1至10任一项所述的微电网重构方法。
- 一种存储介质,在所述存储介质上存储了程序指令,其特征在于,所述程序指令在运行时用于执行如权利要求1至10任一项所述的微电网重构方法。
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