WO2020041903A1 - Système de distribution d'énergie de nano-micro réseau électrique - Google Patents

Système de distribution d'énergie de nano-micro réseau électrique Download PDF

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Publication number
WO2020041903A1
WO2020041903A1 PCT/CA2019/051224 CA2019051224W WO2020041903A1 WO 2020041903 A1 WO2020041903 A1 WO 2020041903A1 CA 2019051224 W CA2019051224 W CA 2019051224W WO 2020041903 A1 WO2020041903 A1 WO 2020041903A1
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WO
WIPO (PCT)
Prior art keywords
power
grid
subsystem according
subsystem
observer
Prior art date
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Ceased
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PCT/CA2019/051224
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English (en)
Inventor
Majid Pahlevaninezhad
Dawood Shekari BEYRAGH
Iman ASKARIANABAYANEH
Seyedali Seif KASHANI
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UTI LP
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UTI LP
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Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; 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
    • H02J3/28—Arrangements for balancing of the load in networks by storage of energy
    • H02J3/32—Arrangements for balancing of the load in networks by storage of energy using batteries or super capacitors with converting means
    • H02J3/322—Arrangements for balancing of the load in networks by storage of energy using batteries or super capacitors with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; 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
    • H02J3/28—Arrangements for balancing of the load in networks by storage of energy
    • H02J3/32—Arrangements for balancing of the load in networks by storage of energy using batteries or super capacitors with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; 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 feeding a single network from two or more generators or sources in parallel; Arrangements for feeding already energised networks from additional generators or sources in parallel
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; 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 feeding a single network from two or more generators or sources in parallel; Arrangements for feeding already energised networks from additional generators or sources in parallel
    • H02J3/46—Controlling the sharing of generated power between the generators, sources or networks
    • H02J3/466—Scheduling or selectively controlling the operation of the generators or sources, e.g. connecting or disconnecting generators to meet a demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2101/00—Supply or distribution of decentralised, dispersed or local electric power generation
    • H02J2101/10—Dispersed power generation using fossil fuels, e.g. diesel generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2101/00—Supply or distribution of decentralised, dispersed or local electric power generation
    • H02J2101/20—Dispersed power generation using renewable energy sources
    • H02J2101/22—Solar energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2101/00—Supply or distribution of decentralised, dispersed or local electric power generation
    • H02J2101/20—Dispersed power generation using renewable energy sources
    • H02J2101/28—Wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2101/00—Supply or distribution of decentralised, dispersed or local electric power generation
    • H02J2101/40—Hybrid power plants, i.e. a plurality of different generation technologies being operated at one power plant
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2105/00—Networks for supplying or distributing electric power characterised by their spatial reach or by the load
    • H02J2105/10—Local stationary networks having a local or delimited stationary reach

Definitions

  • the present invention relates to systems, methods, and devices relating to power distribution in nano/micro -grids. More specifically, the present invention relates to managing the power flow both within a nano/micro-grid and between the nano/micro -grid and other systems or devices.
  • Micro-grids and nano-grids are the main components of a DPGS.
  • the current power system provides AC power due to its advantages for long distance power transmission (i.e., transformers can be used to adjust optimal voltage levels).
  • transformers can be used to adjust optimal voltage levels.
  • the development of nano/micro -grid technology has been mostly confined to AC nano/micro -grids.
  • the number of local nano/micro -grids increases the need for long distance power transmission will significantly decline.
  • DC nano/micro -grids can potentially be more efficient because many loads require DC power (e.g., LED lighting systems, electric vehicle charging, variable speed drives, monitors, computers, laptops, mobile phones, servers, batteries, etc.), and these loads need extra power conversion stages to operate in an AC system. Additionally, most renewable energy systems are either inherently DC in nature (batteries, solar panels) or incorporate a DC stage (inverter connected wind turbines).
  • loads require DC power (e.g., LED lighting systems, electric vehicle charging, variable speed drives, monitors, computers, laptops, mobile phones, servers, batteries, etc.)
  • DC stage inverter connected wind turbines
  • FIG. 1 shows a general block diagram of an AC nano/micro-grid.
  • the AC nano/micro-grid includes a common AC bus that distributes power within the system.
  • the grid includes DC/AC converters that couples DC power sources (e.g. wind turbines, solar energy cells, energy storage units, etc.) to the AC bus.
  • the AC bus also couples to AC loads and, by way of a breaker, to the main AC power grid.
  • all the components of the nano/micro -grid are connected to this AC bus.
  • FIG. 2 shows a general block diagram of a DC nano/micro-grid.
  • a common DC bus distributes power within the system.
  • the DC bus couples to DC power sources (e.g. a battery, wind turbines, and solar power cells) by way of DC/DC converters.
  • DC/DC converters e.g. a battery, wind turbines, and solar power cells
  • the DC bus couples to the main power grid by way of a DC/AC converter.
  • DC loads are also coupled to this common DC bus.
  • Various components of the DC nano/micro -grid are connected to this DC bus through appropriate power converters.
  • the present invention provides systems and methods relating to an AI-based power distribution system.
  • the power distribution system uses a controller that receives relevant power data from dynamic observer modules that monitor the various components of a nano/micro power grid.
  • the dynamic observer modules monitor the power parameters of various energy sources as well as power loads and the main power grid. Based on data from the modules, the controller uses AI-based methods and techniques to control and manage power flow within the nano/micro grid to work towards optimal power flow and resource utilization.
  • the present invention provides a control subsystem for managing a distribution of power in an energy network, the subsystem comprising:
  • a dynamic grid observer module receiving voltage and current data from a power grid
  • At least one dynamic power source observer module receiving voltage and current data from at least one power source
  • a dynamic load observer module receiving voltage and current data from at least one power load
  • FIGURE 1 is a block diagram of an AC bus-based nano/micro-grid according to the prior art
  • FIGURE 2 is a block diagram of a DC bus-based nano/micro-grid according to the prior art
  • FIGURE 3 illustrates a block diagram for an AI-based power distribution system using an AC bus
  • FIGURE 4 is a block diagram for an AI-based power distribution system using a DC bus
  • FIGURE 5 is a block diagram illustrating the components in an AI-base power distribution component
  • FIGURE 6 is a block diagram of the modules in a control subsystem according to one aspect ofthe invention.
  • FIGURE 7 is a schematic diagram illustrating the source of data for some ofthe dynamic observer modules
  • FIGURE 8 is a diagram of inputs and output s ofthe dynamic observer blocks noted for Figure 7;
  • FIGURE 9 is a diagram of an AI-based power flow controller that receives the output of the dynamic observer blocks illustrated in Figure 8.
  • FIGURE 10 illustrates the concept of design layers of a Smart Junction Box (SJB)/AI-based power distribution system according to one aspect of the present invention
  • FIGURE 11 is flowchart of applied supervised machine learning on the system according to one aspect of the present invention.
  • FIGURE 12 block diagram of an output provider for one aspect of the present invention.
  • FIG. 3 shown is an artificial intelligence (Al)-based AC power
  • the grid architecture is based on a smart power distribution system component 20 that optimally distributes power within the system 10.
  • the component 20 couples to the main AC power grid 30 and to AC loads 40.
  • power sources 50A, 50B, 50C, 50D are also coupled to the component 20.
  • These power sources are illustrated as solar energy cells 50A, wind turbines 50B, and energy storage modules (e.g. batteries) 50C.
  • a generator 50D is also illustrated.
  • these energy sources are coupled to the component 20 by way of DC/ AC converters 60 A, 60B, 60C.
  • the AI-based power distribution component 20 effectively optimizes the power flow within the grid in the islanded mode of operation. It controls both the power flow within the grid as well as the power flow between the smaller grid and the main power grid in the grid-connected mode. In addition, in the islanded mode, the AI- based power distribution component adjusts the settings for the power generation side and load balances such that the maximum efficiency is achieved. This component 20 also increases the reliability of the nano/micro grid by intelligently distributing power within the grid. In addition to its power distribution and management capabilities, the AI-based power distribution component also distributes various grid related power data as may be necessary to various other modules and components. Because of this capability, the component 20 can be integrated with and be advantageous for future smart-grid applications.
  • the nano/micro -grid system 100 in Figure 4 includes a suitable AI -based power distribution component 110 similar to the component 20 in Figure 3.
  • this component 110 is for use with a DC-bus based grid system 100.
  • the component 110 is coupled to the main power grid 30 by way of a DC/AC converter 120.
  • the component 110 is coupled to power sources 130A, 130 B, 130 C by way of suitable DC/DC converters 140A, 140B, 140C.
  • the power sources are solar energy cells 130A, wind turbines 140B, and energy storage module 140C.
  • An AC/DC converter 150 may be used with the wind turbine 140B.
  • DC load 160 is also coupled to the component 110.
  • FIG. 5 is a block diagram illustrating the internal structure of the AI-based power distribution component that forms part of the grids in Figures 3 and 4.
  • the component 110 includes a communications subsystem 170, a control subsystem 180, and drive circuits 190.
  • the bus 200 couples the various power sources (shown as solar power generation, wind power generation, and energy storage in Figure 5) to the control circuitry as well as to the loads 210 and to the main power grid 30.
  • the coupling to the bus 200 is done by way of suitable switches 210A-210F, each of which is controlled by the control subsystem 180.
  • Sensors 220A-220F measure and sense relevant parameters of power coming from or going to the various power sources/loads coupled to the bus 200. These parameters are received by the control subsystem 180 and are used to determine the power flow and the power distribution within the nano/micro-grid to ensure optimal efficiency and utilization of resources.
  • FIG. 6 shows an exemplary arrangement for the internal modules ofthe control subsystem illustrated in Figure 5.
  • the control subsystem 180 includes an AI-based power flow controller 230. Coupled to this controller 230 and sending relevant power data (including voltage and current measurements) to the controller 230 are a dynamic grid observer module 240, a dynamic power generation observer module 250, a dynamic storage observer module 260, and a dynamic load observer module 270. Each of these modules receives real-time or near real-time voltage and current readings from their relevant sources.
  • the grid observer module 240 receives data regarding the main power grid
  • the power generation observer module 250 receives data regarding the various power sources that generate power (e.g.
  • the dynamic storage observer module 260 receives data regarding the energy storage components coupled to the common bus (e.g. the batteries), and the dynamic load observer module receives data regarding the various loads coupled to the nano/micro -grid system.
  • These dynamic observer modules dynamically estimate the relevant data regarding load, generation capability, storage status, and grid status.
  • These observer modules provide the required information to the controller 230 to allow the controller to calculate, estimate, and control power flow within the nano/micro -grid system such that optimal power flow and/or optimal resource utilization/management is achieved.
  • FFT Fast Fourier Transform
  • Figure 7 shows the conceptual design of dynamic observers based on nano/micro-grid learning on Riemannian Manifolds.
  • Figure 8 shows a block diagram of the dynamic observer blocks noted above .
  • the main block receives the current and voltage from the respective components and generates a vector identifying the required parameters.
  • the dynamic observers for the components have similar structures with only the output vector (parameters) being different for each observer.
  • the Riemannian metrics for different components will be different and will be determined based on the underlying structure of the dynamics for each component.
  • FIG. 9 shows a block diagram of the AI-Based Power Flow Controller block.
  • This block receives the signals from dynamic observer blocks noted in Figure 8 (i.e., the dynamic grid observer, the dynamic generation observer, the dynamic storage observer, and the dynamic load observer).
  • Each dynamic observer block produces a vector of signals, which will be the inputs to an AI-based method or algorithm used by the controller block in Figure 9.
  • the output of the AI-Based Power Flow Controller block are the switching signals for the AI-Based Power Distribution System.
  • a number of vectors output by the block in Figure 8 operate as inputs to the main controller block in Figure 9.
  • Grd 0 is an input vector to be used by the AI-based method or algorithm and this input vector includes the grid parameters.
  • Gen 0 is also an input vector to the AI -based method or algorithm and this input vector includes the parameters for the renewable energy sources. Another of these input vectors to the AI-based method or algorithm is ESS 0 . This input vector to the AI-based method includes the ESS parameters. Ld 0 is another input vector to the AI-base method or algorithm and this input vector includes the load parameters.
  • Ss, Sw, SESS, SEV, SG, and SL are the output vectors of the AI-based method or algorithm and these output vectors show the switch
  • the system of the present invention is designed as a Smart
  • junction Box or as an AI-based AC power distribution system.
  • This system is conceptually designed as a three layer design as shown in Figure 10.
  • the base- layer or bottom-most layer
  • the middle layer consists of communication solution components including protocols, security, and data package arrangements.
  • the third layer or top-most layer is an Artificial Intelligence (AI) component that acts as a ruling or controlling layer that determines and controls the behavior of the system. All the relay states are decided by this top-most layer.
  • AI Artificial Intelligence
  • the Artificial Intelligence (AI) component in the system is achieved by machine learning.
  • machine learning two major categories for machine learning are supervised and unsupervised machine learning.
  • Supervised machine learning is used for the system of the present invention due to the usefulness of the teaching or training dataset in supervised machine learning.
  • a data flow diagram illustrates the logic and data flow for the system. As shown in Figure 11, a teaching or training dataset is sent to a machine learning subsystem to be trained.
  • the training data set labelled as "experience” in Figure 11, is used to train the machine learning subsystem.
  • the output of the machine learning subsystem is sent to a cost function block and these two blocks form the model representation stage of the process.
  • the training datasets or the "experience" datasets are gathered from experience or from previous instances of the system.
  • the system must learn or be trained to decide which relay combination (n.b. : relay combination means which relays should be on or off) is applicable for a given current state. Therefore, the system output is the state of all relays.
  • the system can be fed data that includes the instant amount of power from a wind turbine, a solar cell, a power grid, and other
  • the hypothesis for a decision function is generated in the next step of the process.
  • the output of the system is a binary output for the relays (i.e. each relay is to be set as either on or off)
  • the problem to be solved by the system is a classification problem, i.e., which set or sets of input data is/are to be mapped to which set or sets of output data.
  • the system can use logistic regression for the model representation step (see Figure 11). Using a sigmoid function as a hypothesis and finding a suitably related cost function leads to completing the model representation step.
  • the learned hypothesis is obtained by optimizing the cost function.
  • This learned hypothesis provides the coefficients for the decision function, and, by having these coefficients, the system now has what is required to know how to act in a new situation. It should be noted that decision function calculations are done offline by mathematical tools to ensure fast and accurate system results. After such calculations, the coefficients are extracted and provided to the decision functions on the system. To render the system more accurate, a recursive method such as the Gradient Boosting Algorithm (GBA) can be used during the learning process/leaming/training phase of the process. The GBA improves the accuracy of the hypothesis generated by optimizing errors based on inputs to thereby reduce the uncertainty.
  • GBA Gradient Boosting Algorithm
  • a logic verification unit can be used.
  • the logic verification unit can function to supervise the system's stability.
  • user preferences may be considered when determining the system output. Therefore, as illustrated in Figure 12, the output of the trained system (listed as "taught machine” in Figure 12) along with user preferences are both fed into logic check/verification unit.
  • This logic verification/check unit can test/determine if the new settings will/can affect system stability. If the new settings are not suitable (e.g. the new settings will cause system instability), the logic verification unit can ignore the changes and can reports a fault/error.
  • This logic verification unit can also consider user preferences for other possible situations.
  • the logic verification unit may enter safe mode after the number of faults detected has reached a threshold number. This hybrid solution involving the logic verification unit and the threshold number of faults can contribute to a working system in all states.
  • system AI is a hybrid solution which uses a classification solution based on supervised machine learning along with a logic verification unit prior to a final output stage.
  • system inputs include the instant power for sinks and sources where such data implicitly has time and relay status information. User preferences are also considered in the final stage where it will be applicable.
  • system AI is a hybrid solution which uses a classification solution based on supervised machine learning along with a logic verification unit prior to a final output stage.
  • system inputs include the instant power for sinks and sources where such data implicitly has time and relay status information.
  • User preferences are also considered in the final stage where it will be applicable.
  • a dataset was produced by gathering real-world available power analysis of sources and sinks. For example, to find available wind power, the wind profile obtained from an airport was used and, for this dataset, the wind speed was converted to a power rate based on a generic 1KW wind turbine. The house load was estimated based on the usage of two residential units. All other assumptions were made on the same bases. After input/output power calculations and preparations, the relays were assigned by considering following rules: * There is a restriction on the total power on the BUS.
  • Energy Storage System could be sink/source of power based on its reported state of charge .
  • WIND is not main source of power but it could provide some power.
  • the system has the capability to communicate with a PC and with Android-based devices.
  • the communications interface on third party devices are informed about the internal state of the system. Those interfaces are designed to show voltage, current, and relay status. This information will be available through wireless serial connection.
  • the Zigbee protocol is used in the system’s communication section which provides a reliable wireless communication in a wide range of frequency options and with a wide range of free wireless channels in each region of the world.
  • data can be retrieved from a serial Comm port.
  • the Comm port setting is, preferably, set on 115200-8-h-I (baud-bits-parity-stop).
  • the same data packet can be received on Android based devices which are equipped with an OTG communication port. Handshaking is not used in one implementation that uses serial communication.
  • the data packet structure may be as follows:
  • Receiving packet in application side includes 33 bytes of data. Additionally, the packet starts with an "@" symbol and ends with a symbol. Also, packets would include as a delimiter between information fields.
  • the system may report 10 channels of Analog to Digital Converter (ADC) results. These channels are connected to different sensors and the data being sensed correspond with desired data input points. In one implementation, 6 current sensors + 3 voltage sensors + 1 reserved will be reported to the system.
  • ADC Analog to Digital Converter
  • each bit of a relay status byte representing the status of the relay in a packet will represent a closed relay contact by a one (1) in the data while an open relay contact will be represented by a zero (0) in the data.

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Theoretical Computer Science (AREA)
  • Software Systems (AREA)
  • Medical Informatics (AREA)
  • Evolutionary Computation (AREA)
  • Data Mining & Analysis (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Artificial Intelligence (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

L'invention concerne un système de distribution d'énergie basé sur l'intelligence artificielle utilisant un contrôleur qui reçoit des données d'alimentation pertinentes à partir de modules d'observation dynamiques qui surveillent les divers composants d'un nano/micro réseau électrique. Les modules d'observation dynamiques surveillent les paramètres d'alimentation de diverses sources d'énergie ainsi que les charges de puissance et le réseau électrique principal. Sur la base des données provenant des modules, le contrôleur utilise des techniques basées sur l'intelligence artificielle pour commander et gérer la circulation de l'électricité dans le nano/micro réseau afin d'œuvrer pour obtenir une circulation d'électricité optimale et une utilisation de ressources optimale.
PCT/CA2019/051224 2018-08-30 2019-08-30 Système de distribution d'énergie de nano-micro réseau électrique Ceased WO2020041903A1 (fr)

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US201862725193P 2018-08-30 2018-08-30
US62/725,193 2018-08-30

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113689024A (zh) * 2021-06-03 2021-11-23 上海交通大学 一种基于Boosting感知的深度学习多微网弹性组合决策方法
CN114330909A (zh) * 2021-12-31 2022-04-12 西安交通大学 一种共享储能与多微网分布式协调优化运行方法
CN116596162A (zh) * 2023-07-05 2023-08-15 浙江正泰新能源开发有限公司 一种光伏发电量测算方法、装置、设备及存储介质

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Publication number Priority date Publication date Assignee Title
US20110106321A1 (en) * 2009-11-03 2011-05-05 Spirae, Inc. Dynamic distributed power grid control system
US9590421B2 (en) * 2013-04-23 2017-03-07 Varentec, Inc. Dynamic power flow controllers

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Publication number Priority date Publication date Assignee Title
US20110106321A1 (en) * 2009-11-03 2011-05-05 Spirae, Inc. Dynamic distributed power grid control system
US9590421B2 (en) * 2013-04-23 2017-03-07 Varentec, Inc. Dynamic power flow controllers

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113689024A (zh) * 2021-06-03 2021-11-23 上海交通大学 一种基于Boosting感知的深度学习多微网弹性组合决策方法
CN114330909A (zh) * 2021-12-31 2022-04-12 西安交通大学 一种共享储能与多微网分布式协调优化运行方法
CN116596162A (zh) * 2023-07-05 2023-08-15 浙江正泰新能源开发有限公司 一种光伏发电量测算方法、装置、设备及存储介质
CN116596162B (zh) * 2023-07-05 2023-10-20 浙江正泰新能源开发有限公司 一种光伏发电量测算方法、装置、设备及存储介质

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