US20190244310A1 - Method and instrumentation for sustainable energy load flow management system performing as resilient adaptive microgrid system - Google Patents
Method and instrumentation for sustainable energy load flow management system performing as resilient adaptive microgrid system Download PDFInfo
- Publication number
- US20190244310A1 US20190244310A1 US16/237,074 US201816237074A US2019244310A1 US 20190244310 A1 US20190244310 A1 US 20190244310A1 US 201816237074 A US201816237074 A US 201816237074A US 2019244310 A1 US2019244310 A1 US 2019244310A1
- Authority
- US
- United States
- Prior art keywords
- loads
- energy
- critical
- data
- storage
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
- 238000000034 method Methods 0.000 title claims abstract description 28
- 230000003044 adaptive effect Effects 0.000 title abstract description 3
- 238000003860 storage Methods 0.000 claims abstract description 30
- 238000004146 energy storage Methods 0.000 claims abstract description 9
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 9
- 239000002551 biofuel Substances 0.000 claims abstract description 5
- 229910052739 hydrogen Inorganic materials 0.000 claims abstract description 5
- 239000001257 hydrogen Substances 0.000 claims abstract description 5
- 125000004435 hydrogen atom Chemical class [H]* 0.000 claims abstract description 5
- 230000005855 radiation Effects 0.000 claims abstract description 4
- 238000007726 management method Methods 0.000 claims description 11
- 238000004088 simulation Methods 0.000 claims description 10
- 239000000446 fuel Substances 0.000 claims description 7
- 238000001556 precipitation Methods 0.000 claims description 7
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 6
- 230000006870 function Effects 0.000 claims description 5
- 238000004891 communication Methods 0.000 claims description 4
- 239000007788 liquid Substances 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 claims description 2
- 238000012544 monitoring process Methods 0.000 claims description 2
- 238000005381 potential energy Methods 0.000 claims description 2
- 238000005057 refrigeration Methods 0.000 claims description 2
- 230000005465 channeling Effects 0.000 claims 1
- 238000013500 data storage Methods 0.000 claims 1
- 230000000694 effects Effects 0.000 abstract description 12
- 238000006243 chemical reaction Methods 0.000 abstract 1
- 230000008569 process Effects 0.000 description 7
- 238000013480 data collection Methods 0.000 description 3
- 238000009826 distribution Methods 0.000 description 3
- 238000010248 power generation Methods 0.000 description 3
- 230000001932 seasonal effect Effects 0.000 description 3
- 230000032683 aging Effects 0.000 description 2
- 230000003466 anti-cipated effect Effects 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 239000000428 dust Substances 0.000 description 2
- 238000010438 heat treatment Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 239000002028 Biomass Substances 0.000 description 1
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- CBENFWSGALASAD-UHFFFAOYSA-N Ozone Chemical compound [O-][O+]=O CBENFWSGALASAD-UHFFFAOYSA-N 0.000 description 1
- 238000004378 air conditioning Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 239000003225 biodiesel Substances 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 230000001186 cumulative effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000029087 digestion Effects 0.000 description 1
- 238000010410 dusting Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 238000005086 pumping Methods 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000013024 troubleshooting Methods 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
- 238000009423 ventilation Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
-
- G06F17/5009—
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- 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
-
- 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
- H02J13/00004—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 the power network being locally controlled
-
- H02J13/0006—
-
- 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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- 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
-
- 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
-
- 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
-
- 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
-
- 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
- H02J2310/12—The local stationary network supplying a household or a building
- H02J2310/14—The load or loads being home appliances
-
- 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
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
-
- 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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
-
- 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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/30—Arrangements for balancing of the load in a network by storage of energy using dynamo-electric machines coupled to flywheels
-
- 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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
-
- 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
-
- 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
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
-
- 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
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
- Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
-
- Y02B70/3266—
-
- 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
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02B90/20—Smart grids as enabling technology in buildings sector
-
- Y02B90/222—
-
- 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
-
- Y02E10/563—
-
- Y02E10/566—
-
- 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
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
-
- Y02E10/763—
-
- 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
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
-
- Y02E40/72—
-
- 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/12—Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation
- Y04S10/123—Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation the energy generation units being or involving renewable energy sources
-
- 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/12—Energy storage units, uninterruptible power supply [UPS] systems or standby or emergency generators, e.g. in the last power distribution stages
-
- 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
-
- 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/242—Home appliances
Definitions
- Intelligent control of energy generation, storage, and usage is indispensable for use in a standalone supply system powered by local and renewable energy resources intended to provide uninterrupted power for mission-critical infrastructures.
- the probabilistic nature of wind and solar energy sources requires weather forecast information to manage and prioritize the available storage level, critical loads, non-critical loads that need to be fed on a certain time schedule, and deferrable loads. This is especially important since these local power generation systems will need to be able to provide power during seasonal and daily adjustments of anticipated renewable resource availability. For example, not only may there be seasonal adjustments to the amount of sunlight available to a specific location, there may also be daily fluctuations in the clarity of the atmosphere due to fog, smog and other variables that can impact the availability of solar energy on a particular day.
- the Sustainable Energy Load Flow Management System (SelfMaster.TM) is outlined in FIG. 1 .
- SelfMaster.TM is a microcomputer-based controller.
- User input defines a schedule of activities and priority of energy usage.
- SelfMaster.TM receives forecast data from a weather service and input data from sensors monitoring generated power, storage levels, and electric demand in real time.
- the computer software continuously updates a database of actual component input-output characteristics.
- the near-future performance of the system is simulated using estimated states of the generated and stored energy, scheduled activities, and forecast data within a reasonable time interval.
- the storage level is assessed and optimal load distribution is determined for critical, deferrable, and non-critical loads.
- Computer interface circuits turn the loads on or off and provide output for variable frequency drives (VFD) that control heating, ventilation, and air conditioning (HVAC) devices.
- VFD variable frequency drives
- HVAC heating, ventilation, and air conditioning
- the present invention relates in general to the field of renewable energy, particularly adaptive control of electric load, energy storage, and activities in a stand-alone sustainable power system based on data collection, data communications, computer software, and electronic interface circuits.
- the invention in certain embodiments uses simulation techniques to forecast the energy generation and storage levels.
- FIG. 1 Outline of a sustainable standalone micro grid supplied by renewable energy sources and controlled by SelfMaster.TM.
- FIG. 2 Interaction of SelfMaster.TM. with a micro grid, power supply units, weather service provider, user, and other SelfMaster.TM. units.
- FIG. 3 Flowchart of energy management process performed by SelfMaster.TM.
- FIG. 4 Flowchart of the “Observation Routine” process
- FIG. 5 Data logging system
- FIG. 6 Flowchart of the resource estimation process
- FIG. 7 Flowchart of the simulation, scheduling, and control processes
- SelfMaster.TM. The purpose of SelfMaster.TM. is to control load flow and storage in an isolated micro-grid isolated or potentially isolated from a larger grid as depicted in FIG. 1 .
- the micro-grid is supplied by a group of wind turbines, solar PV arrays, and an optional auxiliary backup generator.
- the backup generator may be a combination of generators driven by a multi-fuel combustion engine, compressed air, flywheel, or fuel cell stack. Solar thermal collectors provide heating source.
- a hybrid storage system is a combination of primary and secondary storages. Primary storage is always a battery bank for short-term energy backup. Secondary storage may be hydrogen, biofuel, or compressed air tank, flywheel, or hot water tanks.
- the electric load is grouped as “Critical Load,” “Non-critical Load,” and “Deferrable Load.”
- Critical load consists of internal supply for sensors, computing devices, and controllers; communication, control, signalization (IT), and emergency lighting.
- Non-critical load is the equipment, appliances, and devices that are needed for a comfortable life or regular functions. Audiovisual devices, daytime artificial lighting, and part of the variable frequency drives (VFD) for HVAC circulation are examples to non-critical load.
- Deferrable loads can be supplied at a convenient time frame based on the energy state of the micro-grid. Due to the thermal capacity and longer time constant of the HVAC systems, part of the VFD load can be considered as deferrable load. Refrigeration, pumping for water storage, and device rechargers are also deferrable loads.
- SelfMaster is the central control unit that manages the load and storage based on current and estimated future states. Interaction of SelfMaster.TM. with an isolated micro-grid is shown in FIG. 2 . In this figure, arrows show data flow and lines represent power connections. Data collection points are shown with a dot on the power lines. It is assumed that data collection sensors do not affect the voltage and current values on the power lines.
- the major components of SelfMaster.TM. are Observer, Resource Estimator, Simulator, Scheduler, and Controller routines. Each of these components is considered as a separate virtual device created in the computer software as separate functions. Data flow between these functions is shown in FIG. 2 . An operator may interact with SelfMaster.TM. via a user interface to enter input data and monitor the system performance.
- User inputs consist of a list of activities to be scheduled, needed resources (such as space allocation, temperature, lighting, and equipment), and priority level of each activity. Data is logged both at the micro-grid and/or at a remote location. In addition, several SelfMaster units may communicate with each other to control a cluster of micro grids.
- the flowchart in FIG. 3 shows the overall operation of SelfMaster.TM.. Collected data is processed to determine the status of the micro-grid. If the battery bank is full, then the excess energy will be stored in non-electrical form such as (but not limited to) hydrogen, methane, other gaseous or liquid fuels, biofuel production, thermal (hot water), kinetic (flywheel), or potential energy (compressed air, pumped water).
- non-electrical form such as (but not limited to) hydrogen, methane, other gaseous or liquid fuels, biofuel production, thermal (hot water), kinetic (flywheel), or potential energy (compressed air, pumped water).
- Weather forecast data for the following given number of days is automatically downloaded from a weather station (such as National Weather Service-NWS) database every hour.
- the hourly generation, storage, and consumption values are estimated for a given time interval through real-time simulation based on the forecast information and user defined load profiles.
- the anticipated storage level is checked at every simulation and deferred load is scheduled to optimize the energy balance. If the storage level is expected to fall below a user defined critical level, then SelfMaster will start available auxiliary generation to charge the battery bank until the first upcoming simulation indicates an adequate level of electric storage.
- the forecast data relevant to the operation of SelfMaster.TM. are temperature (.theta.), surface wind speed (V), and percent sky cover (C).
- the computer program sends a SOAP request to the NDFD XML server through the Internet.
- the SOAP response received from the server is converted to a data table and stored in a file.
- Data acquisition hardware and software collect the DC voltage and current outputs and cell temperatures of the series connected PV modules. If the PV array is generating power, the DC output of the charge controllers and AC output values of the inverters are recorded simultaneously to compute the actual efficiencies and update the PV database.
- a separate data acquisition system collects the output voltage, current, and frequency of the wind generators. If any of the wind turbines is generating power, then the DC output of the charge controllers and AC output values of the inverters are recorded simultaneously to compute the actual efficiencies and update the wind turbine (WT) database.
- WT wind turbine
- Energy stored in the primary storage is determined by recording the actual charge and discharge amp-hours.
- the energy reserve available in the secondary storage is evaluated based on non-electrical quantities, such as temperature, pressure, volume of fuel, etc., depending on the type of energy stored.
- the amount of stored fuel is converted to electrical energy equivalent using the specific value of the stored substance such as hydrogen, methane, biomass, biodiesel, or anaerobic digestion products.
- the “Observation Routine” receives inputs from sensors, a weather forecast service, and user interface. Sensors and data acquisition hardware collect electrical and non-electrical quantities such as voltages, currents, temperatures, liquid level, and pressures, etc.
- a local weather station on site provides current temperature, wind speed, sky cover, and precipitation data at the actual location. Forecast data is periodically downloaded from a weather station to record hourly temperature, wind speed, sky cover, and probability of snow precipitation for a given number of days. Collected data is stored in a local memory device and also sent to a remote storage device. In addition, the observer routine computes the actual efficiency of the generation units and updates the databases.
- FIG. 4 shows a flowchart explaining the observation process.
- the computer program downloads periodically weather forecast data for the following 105 hours from National Oceanic and Atmospheric Administration's (NOAA) National Weather Service NWS) database.
- NOAA updates the US National Digital Forecast Database (NDFD) every hour with forecasts data produced on a 3-hourly basis for up to three days ahead and on a 6-hourly basis up to six days ahead.
- NDFD provides gridded data for a location specified by either the postal (zip) code or GPS coordinates.
- Perez et al. (2010) present a validation of the NDFD short-term forecast.
- Kim and Augenbroe (2012) discuss the adequacy of NDFD forecasts for building automation and control processes. According to the evaluations presented in these publications, NDFD provides an acceptable level of accuracy up to a forecast horizon of six hours.
- FIG. 5 A schematic outline of the local data logging system is shown in FIG. 5 .
- the data logger and sub-meters are off-the-shelf monitoring devices available on the market.
- the devices communicate with the data logger via RS 485 connection using the MODBUS protocol.
- the serial data communication is described in the document “MODBUS Organization, (2012).”
- the data logger supplies data to a local computer and provides remote access through the Internet.
- the “Resource Estimation Routine,” named hereafter “estimator”, uses the database created by the “observer”. It computes the estimated power generation for the period of time covered by the weather forecast using four sources of data listed below.
- the first step of the estimation process is to correlate the actual PV generation to the sky cover and snow precipitation history recorded over the last N p number of days.
- a reasonable default value of 30-day is selected for N p to record the seasonal variation of the solar path, average temperature, shading, snow, and dust cover, or any loss of energy due to equipment faults.
- Extraterrestrial global radiation given in W/m 2 does not depend on the sky cover conditions.
- a number of methods to estimate the “clear sky irradiation” at a location on the earth surface were compared by Reno et al. (2012). Simple clear sky models only based on geometric calculations can be used in estimation of the global irradiation since the sky cover data already include atmospheric parameters considered in more complex models. Average errors of various models as a percentage of measured irradiance for 30 sites in the US are compared in FIG. 22 of Reno et al. (2012).
- z is the zenith angle calculated for the location and time
- I 0 is the extraterrestrial normal incident irradiation
- a m represents the air mass
- T L is the atmospheric (Linke) turbidity reformulated by P. Ineichen and R. Perez, (2002)
- the “Atmospheric Efficiency” E.sub.a is defined here as the ratio of the actual DC power generated by the PV array and the DC power this array would produce for the GHI calculated for the given location and time.
- the atmospheric efficiency is zero at night.
- the E a value obtained at a given instant during daytime is a function of many factors such as cloudiness, clearness, water vapor content, and ozone layer thickness.
- the estimator first obtains a linear correlation of the computed atmospheric efficiency values and the recorded sky cover values at the observation times. As well as the atmospheric conditions, shading, dusting, and minor defects of the modules are included in this correlation.
- the “Resource Estimation Routine” is outlined in FIG. 6 .
- the purpose of this routine is to estimate available energy sources, electric storage, and energy capacity of stored fuel at the current time and for the following N.sub.f number of hours.
- the sky cover (cloudiness) index provided in the NDFD is not directly related to the solar irradiation received at the earth surface.
- microclimate, shading, dust cover, and aging affect the output power of PV modules.
- FIG. 7 shows a flowchart for the simulation and scheduling, and control routines.
- the “Simulation Routine”, named hereafter “simulator”, receives actual power generation data and performance characteristics from the database generated by the observer.
- a Monte-Carlo simulation is performed to estimate the generated power P g and cumulative stored energy W s (t) over the forecast horizon of N f hours.
- An operator enters planned activities by specifying the priority level, planned start and end times, light, heat, and equipment needed for each activity.
- the scheduling routine estimates the energy needed for the requested activities (W a ) and tries to place them at the requested time slots on the schedule. The difference between available and needed energy at all instants is computed.
- the scheduling routine may shift deferrable loads to obtain an optimal load distribution.
- the scheduler suggests better time frames available for the requested activities or recommends the user to reschedule or revise the request.
- the simulator and scheduler routines interact to find an optimal activity schedule that can be supplied by the available resources. If the iterations converge to the optimal load distribution over the forecast horizon, then the final schedule is forwarded to the controller, which sends signals to the switching hardware to turn on or off groups of critical, non-critical, and deferrable loads as well as activate the secondary storage or auxiliary generation units if needed.
Abstract
Disclosed is a method and instrumentation for predictive and adaptive controllers devised to ensure uninterrupted operation of standalone electrical supply systems powered by sustainable energy sources. The device, herein referred to as SelfMaster, is an expert system that manages the energy conversion, storage, and consumption in an isolated electric grid based on data collected during past and current operation of the system and predicted future states of the primary energy sources, storage level, and demand. The sustainable primary energy sources managed by SelfMaster may include, but are not limited to, wind force, solar radiation, and biofuels. The energy storage system is a combination of batteries, hydrogen, biofuel, and hot water tanks. Electric demand consists of critical, non-critical, and deferrable loads identified according to the activities supported by the supply system.
Description
- This application is a continuation of and claims priority from U.S. patent application Ser. No. 13/889867, filed May 8, 2013, which is a non-provisional of U.S. Provisional Patent Application No. 61/643,987, filed May 8, 2012, each of which are incorporated herein by reference in their entirety.
- This application includes material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent disclosure, as it appears in the Patent and Trademark Office files or records, but otherwise reserves all copyright rights whatsoever.
- This application includes material, which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent disclosure, as it appears in the Patent and Trademark Office files or records, but otherwise reserves all copyright rights whatsoever.
- Intelligent control of energy generation, storage, and usage is indispensable for use in a standalone supply system powered by local and renewable energy resources intended to provide uninterrupted power for mission-critical infrastructures. The probabilistic nature of wind and solar energy sources requires weather forecast information to manage and prioritize the available storage level, critical loads, non-critical loads that need to be fed on a certain time schedule, and deferrable loads. This is especially important since these local power generation systems will need to be able to provide power during seasonal and daily adjustments of anticipated renewable resource availability. For example, not only may there be seasonal adjustments to the amount of sunlight available to a specific location, there may also be daily fluctuations in the clarity of the atmosphere due to fog, smog and other variables that can impact the availability of solar energy on a particular day.
- The Sustainable Energy Load Flow Management System (SelfMaster.™) is outlined in
FIG. 1 . SelfMaster.™ is a microcomputer-based controller. User input defines a schedule of activities and priority of energy usage. SelfMaster.™ receives forecast data from a weather service and input data from sensors monitoring generated power, storage levels, and electric demand in real time. The computer software continuously updates a database of actual component input-output characteristics. The near-future performance of the system is simulated using estimated states of the generated and stored energy, scheduled activities, and forecast data within a reasonable time interval. The storage level is assessed and optimal load distribution is determined for critical, deferrable, and non-critical loads. Computer interface circuits turn the loads on or off and provide output for variable frequency drives (VFD) that control heating, ventilation, and air conditioning (HVAC) devices. - The present invention relates in general to the field of renewable energy, particularly adaptive control of electric load, energy storage, and activities in a stand-alone sustainable power system based on data collection, data communications, computer software, and electronic interface circuits.
- It is an object of the invention to provide a controller to manage the load and energy storage in a standalone electric supply system powered by renewable energy sources or a local micro-grid.
- It is a further object of the invention to provide a continuously updated database to make actual component characteristics available for accurate estimation of future energy balance.
- It is a further object of the invention to provide mass-producible product that can meet the needs of a majority of users of renewable energy systems.
- It is a further object of the invention to provide an improved apparatus and software that meets needs for uninterruptible sustainable power supply for mission critical loads.
- It is a further object of the invention to define the “Internal Critical Load” that is required over and above the external critical loads, to ensure the continuous reliable operation of the energy management system.
- The invention in certain embodiments uses simulation techniques to forecast the energy generation and storage levels.
-
FIG. 1 : Outline of a sustainable standalone micro grid supplied by renewable energy sources and controlled by SelfMaster.™. -
FIG. 2 : Interaction of SelfMaster.™. with a micro grid, power supply units, weather service provider, user, and other SelfMaster.™. units. -
FIG. 3 : Flowchart of energy management process performed by SelfMaster.™. -
FIG. 4 : Flowchart of the “Observation Routine” process -
FIG. 5 : Data logging system -
FIG. 6 : Flowchart of the resource estimation process -
FIG. 7 : Flowchart of the simulation, scheduling, and control processes - The purpose of SelfMaster.TM. is to control load flow and storage in an isolated micro-grid isolated or potentially isolated from a larger grid as depicted in
FIG. 1 . The micro-grid is supplied by a group of wind turbines, solar PV arrays, and an optional auxiliary backup generator. The backup generator may be a combination of generators driven by a multi-fuel combustion engine, compressed air, flywheel, or fuel cell stack. Solar thermal collectors provide heating source. A hybrid storage system is a combination of primary and secondary storages. Primary storage is always a battery bank for short-term energy backup. Secondary storage may be hydrogen, biofuel, or compressed air tank, flywheel, or hot water tanks. The electric load is grouped as “Critical Load,” “Non-critical Load,” and “Deferrable Load.” Critical load consists of internal supply for sensors, computing devices, and controllers; communication, control, signalization (IT), and emergency lighting. Non-critical load is the equipment, appliances, and devices that are needed for a comfortable life or regular functions. Audiovisual devices, daytime artificial lighting, and part of the variable frequency drives (VFD) for HVAC circulation are examples to non-critical load. Deferrable loads can be supplied at a convenient time frame based on the energy state of the micro-grid. Due to the thermal capacity and longer time constant of the HVAC systems, part of the VFD load can be considered as deferrable load. Refrigeration, pumping for water storage, and device rechargers are also deferrable loads. - SelfMaster is the central control unit that manages the load and storage based on current and estimated future states. Interaction of SelfMaster.TM. with an isolated micro-grid is shown in
FIG. 2 . In this figure, arrows show data flow and lines represent power connections. Data collection points are shown with a dot on the power lines. It is assumed that data collection sensors do not affect the voltage and current values on the power lines. The major components of SelfMaster.™. are Observer, Resource Estimator, Simulator, Scheduler, and Controller routines. Each of these components is considered as a separate virtual device created in the computer software as separate functions. Data flow between these functions is shown inFIG. 2 . An operator may interact with SelfMaster.™. via a user interface to enter input data and monitor the system performance. User inputs consist of a list of activities to be scheduled, needed resources (such as space allocation, temperature, lighting, and equipment), and priority level of each activity. Data is logged both at the micro-grid and/or at a remote location. In addition, several SelfMaster units may communicate with each other to control a cluster of micro grids. - Outline of the Operation
- The flowchart in
FIG. 3 shows the overall operation of SelfMaster.™.. Collected data is processed to determine the status of the micro-grid. If the battery bank is full, then the excess energy will be stored in non-electrical form such as (but not limited to) hydrogen, methane, other gaseous or liquid fuels, biofuel production, thermal (hot water), kinetic (flywheel), or potential energy (compressed air, pumped water). - Weather forecast data for the following given number of days is automatically downloaded from a weather station (such as National Weather Service-NWS) database every hour. The hourly generation, storage, and consumption values are estimated for a given time interval through real-time simulation based on the forecast information and user defined load profiles. The anticipated storage level is checked at every simulation and deferred load is scheduled to optimize the energy balance. If the storage level is expected to fall below a user defined critical level, then SelfMaster will start available auxiliary generation to charge the battery bank until the first upcoming simulation indicates an adequate level of electric storage.
- The forecast data relevant to the operation of SelfMaster.™. are temperature (.theta.), surface wind speed (V), and percent sky cover (C). The computer program sends a SOAP request to the NDFD XML server through the Internet. The SOAP response received from the server is converted to a data table and stored in a file.
- Data acquisition hardware and software collect the DC voltage and current outputs and cell temperatures of the series connected PV modules. If the PV array is generating power, the DC output of the charge controllers and AC output values of the inverters are recorded simultaneously to compute the actual efficiencies and update the PV database.
- Similarly, a separate data acquisition system collects the output voltage, current, and frequency of the wind generators. If any of the wind turbines is generating power, then the DC output of the charge controllers and AC output values of the inverters are recorded simultaneously to compute the actual efficiencies and update the wind turbine (WT) database.
- Energy stored in the primary storage (battery bank) is determined by recording the actual charge and discharge amp-hours.
- The energy reserve available in the secondary storage is evaluated based on non-electrical quantities, such as temperature, pressure, volume of fuel, etc., depending on the type of energy stored. The amount of stored fuel is converted to electrical energy equivalent using the specific value of the stored substance such as hydrogen, methane, biomass, biodiesel, or anaerobic digestion products.
- Observation Routine
- The “Observation Routine” named hereafter “observer” receives inputs from sensors, a weather forecast service, and user interface. Sensors and data acquisition hardware collect electrical and non-electrical quantities such as voltages, currents, temperatures, liquid level, and pressures, etc. A local weather station on site provides current temperature, wind speed, sky cover, and precipitation data at the actual location. Forecast data is periodically downloaded from a weather station to record hourly temperature, wind speed, sky cover, and probability of snow precipitation for a given number of days. Collected data is stored in a local memory device and also sent to a remote storage device. In addition, the observer routine computes the actual efficiency of the generation units and updates the databases.
- Manufacturers usually give typical catalog specifications of wind turbines, solar PV modules, and converters based on factory tests and guaranteed rated values. However, the actual efficiency of these components depends on environmental conditions, aging, and possible faults or damages during service. The observer updates periodically the actual state of each power supply component for more accurate estimation. It also generates warning or alarm signals when critical generation issues occur. Data collected by sensors is logged in (both at the device and possibly in a remote location as part of a power management and network management system for the micro-grid) for reporting, troubleshooting, and future reference and, when appropriate, communicated to other elements of the micro-grid.
-
FIG. 4 shows a flowchart explaining the observation process. The computer program downloads periodically weather forecast data for the following 105 hours from National Oceanic and Atmospheric Administration's (NOAA) National Weather Service NWS) database. NOAA updates the US National Digital Forecast Database (NDFD) every hour with forecasts data produced on a 3-hourly basis for up to three days ahead and on a 6-hourly basis up to six days ahead. NDFD provides gridded data for a location specified by either the postal (zip) code or GPS coordinates. Perez et al. (2010) present a validation of the NDFD short-term forecast. Kim and Augenbroe (2012) discuss the adequacy of NDFD forecasts for building automation and control processes. According to the evaluations presented in these publications, NDFD provides an acceptable level of accuracy up to a forecast horizon of six hours. - While other forecast methods presented in Heinemann et al. (2006) and Perez et al. (2007) can provide more accurate forecasts beyond 6-hour horizon, SelfMaster.™. uses the NDFD for the following reasons:
-
- a. Temperature, surface wind speed, sky cover index, and chance of snow precipitation are available in the database
- b. Service continuity and quality control is ensured by the US National Oceanic and Atmospheric Agency (NOAA)
- c. Service is updated every hour for a grid of 5-km spatial resolution for the contiguous US
- d. NDFD is an open source web service accessible to public through Simple Object Access Protocol (SOAP) in the Extensible Markup Language (XML) format.
- A schematic outline of the local data logging system is shown in
FIG. 5 . The data logger and sub-meters are off-the-shelf monitoring devices available on the market. The devices communicate with the data logger via RS485 connection using the MODBUS protocol. The serial data communication is described in the document “MODBUS Organization, (2012).” The data logger supplies data to a local computer and provides remote access through the Internet. - Resource Estimation Routine
- The “Resource Estimation Routine,” named hereafter “estimator”, uses the database created by the “observer”. It computes the estimated power generation for the period of time covered by the weather forecast using four sources of data listed below.
-
- a. Forecasted wind speed, sky cover, and snow precipitation data downloaded from NDFD
- b. Actual generation data collected by the local data acquisition system
- c. Extraterrestrial global solar radiation data obtained from NREL SolPos software
- d. Characteristics of solar PV modules and wind turbines provided by the manufacturers
- The first step of the estimation process is to correlate the actual PV generation to the sky cover and snow precipitation history recorded over the last Np number of days. A reasonable default value of 30-day is selected for Np to record the seasonal variation of the solar path, average temperature, shading, snow, and dust cover, or any loss of energy due to equipment faults. Extraterrestrial global radiation given in W/m2 does not depend on the sky cover conditions. A number of methods to estimate the “clear sky irradiation” at a location on the earth surface were compared by Reno et al. (2012). Simple clear sky models only based on geometric calculations can be used in estimation of the global irradiation since the sky cover data already include atmospheric parameters considered in more complex models. Average errors of various models as a percentage of measured irradiance for 30 sites in the US are compared in
FIG. 22 of Reno et al. (2012). - Ineichen and Perez model proves to be reasonably adequate with 5% Root Mean Square Error (RMSE). Expression (1) below describes the Global Horizontal Irradiation (GHI) for the Ineichen and Perez model.
-
GHI=c g1 *I )*cos(z)*e −c g 2 M a [fh1 +fh2 L (T−1)] e 1.80.01*M a (1) - In this expression z is the zenith angle calculated for the location and time, I0 is the extraterrestrial normal incident irradiation, Am represents the air mass, and TL is the atmospheric (Linke) turbidity reformulated by P. Ineichen and R. Perez, (2002)
-
c g1=5.09*e −5h+0.868 -
c g2=3.92*e −5h+0.0387 (2) - In (2) h is the elevation.
- The “Atmospheric Efficiency” E.sub.a is defined here as the ratio of the actual DC power generated by the PV array and the DC power this array would produce for the GHI calculated for the given location and time. The atmospheric efficiency is zero at night. The Ea value obtained at a given instant during daytime is a function of many factors such as cloudiness, clearness, water vapor content, and ozone layer thickness. The estimator first obtains a linear correlation of the computed atmospheric efficiency values and the recorded sky cover values at the observation times. As well as the atmospheric conditions, shading, dusting, and minor defects of the modules are included in this correlation.
- Resource Estimation Methodology
- The “Resource Estimation Routine” is outlined in
FIG. 6 . The purpose of this routine is to estimate available energy sources, electric storage, and energy capacity of stored fuel at the current time and for the following N.sub.f number of hours. - The sky cover (cloudiness) index provided in the NDFD is not directly related to the solar irradiation received at the earth surface. In addition, microclimate, shading, dust cover, and aging affect the output power of PV modules.
- Simulation and Scheduling Routines
-
FIG. 7 shows a flowchart for the simulation and scheduling, and control routines. The “Simulation Routine”, named hereafter “simulator”, receives actual power generation data and performance characteristics from the database generated by the observer. A Monte-Carlo simulation is performed to estimate the generated power Pg and cumulative stored energy Ws(t) over the forecast horizon of Nf hours. - An operator (user) enters planned activities by specifying the priority level, planned start and end times, light, heat, and equipment needed for each activity. The scheduling routine estimates the energy needed for the requested activities (Wa) and tries to place them at the requested time slots on the schedule. The difference between available and needed energy at all instants is computed.
-
- If the simulations do not guarantee sufficient energy at all instants for the requested activities
-
- the scheduling routine may shift deferrable loads to obtain an optimal load distribution.
- If the needed energy is still not available, the scheduler suggests better time frames available for the requested activities or recommends the user to reschedule or revise the request.
- The simulator and scheduler routines interact to find an optimal activity schedule that can be supplied by the available resources. If the iterations converge to the optimal load distribution over the forecast horizon, then the final schedule is forwarded to the controller, which sends signals to the switching hardware to turn on or off groups of critical, non-critical, and deferrable loads as well as activate the secondary storage or auxiliary generation units if needed.
Claims (15)
1. A system and method of managing microgrids using Observer, Resource Estimator, Simulator, Scheduler, and Controller routines each considered as a separate virtual device created in the computer software as separate functions, comprising the steps of:
using a processor of a computing device to check collected processed data to determine the status of the micro-grid;
using the processor to determine if an electric battery bank is full, and if so then to command channeling excess energy to be stored in non-electrical form;
using the processor to determine if a secondary energy storage system is full, and if so then to command diverting power into dummy loads;
if the battery bank is not full, then using the processor to determine the likely available energy given data collected and forecasted in an observer routine, the step using a resource estimator routine to determine an amount of likely available energy required for an adequate level of electric storage, and then to:
i) run a load scheduler which limits use of deferrable loads by sending signals from a controlling computer to turn off deferrable loads according to priorities until a simulation indicates an adequate level of electric storage;
ii) then, if the level of electric storage is not indicated to be adequate, command on the use of the secondary energy storage system; then,
iii) provide alarms and alerts to the system management system showing the use of the secondary energy storage system; then,
iv) check the rate of the energy storage system and continue to use the auxiliary energy system until the rate changes in order to meet the adequate level of electric storage;
v) compare an amount of energy reserve in the secondary storage system and the observer routine to determine projected power coming into the system, and using the resource estimator routine to determine the amount of time the auxiliary storage system will provide the needed amount of energy and provide alerts and alarms to the system management system;
using the processor to determine if the auxiliary energy storage system continues to discharge, and if so then, determine priorities of critical loads and begin to reduce the critical loads, maintaining the power management and alert system as the most critical load; then
continue to send alarms and alerts to the management system;
using the processor to determine if the auxiliary management system continues to discharge at an unacceptable rate, and if so then begin the final safe and orderly shut down of the system while maintaining a minimum of power and system management of the system;
wherein each of the above steps utilizes at least one particular machine, said at least one particular machine comprising a computer and related industrial controls necessary to adjust power sources, storage systems and power using applications.
2. The method of claim 1 further comprising a step of collecting data to form a knowledge base for identification of actual component characteristics.
3. The method of claim 1 further comprising a step of the use of an expert system that learns the behavior of the components evaluating archived data.
4. The method of claim 1 further comprising steps of estimating the future states of stochastic wind and solar resources, including temperature and precipitation, upon weather forecast data.
5. The method of claim 4 , further comprising a step of using multiple sensors positioned to receive wind speed, solar radiation, sky cover, precipitation, and shading factors.
6. The method of claim 1 further comprising steps of assessing the amount of energy needed for critical loads in an energy system and considering the energy storage system itself as the most critical load to be maintained in order to protect the other critical loads.
7. The method of claim 1 further comprising a step of coordinating with a sponsored services routine whereby loads are provided to applications and users from accounts of those willing to pay for those loads.
8. The method of claim 1 further comprising a step of provisioning the microgrid with electromagnetic pulse protection.
9. The method of claim 1 , wherein the non-electric form comprises at least one selected from the group consisting of: hydrogen, methane, other gaseous or liquid fuels, biofuel production, thermal, kinetic, or potential energy.
10. The method of claim 1 , wherein the non-electric form comprises hot water.
11. The method of claim 1 , wherein the non-electric form comprises a flywheel.
12. The method of claim 1 , wherein the non-electric form comprises compressed air or pumped water.
13. The method of claim 1 , wherein the deferrable loads are comprised of one or more loads required for critical infrastructure or applications supported by the micro-grid.
14. The method of claim 13 , wherein the deferrable loads comprise at least one load selected from the group: communications loads, HVAC loads, water systems loads; surveillance loads, monitoring and alarm systems loads; lighting system loads, access control loads, refrigeration loads, computer system loads, data storage system loads, and medical equipment loads.
14. The method of claim 22, wherein the loads are also provisioned with electromagnetic pulse protection so that the combination of the micro-grids and the loads can continue to operate in island-mode without power from the outside grid despite external electromagnetic interference.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US16/237,074 US20190244310A1 (en) | 2012-05-08 | 2018-12-31 | Method and instrumentation for sustainable energy load flow management system performing as resilient adaptive microgrid system |
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201261643987P | 2012-05-08 | 2012-05-08 | |
US13/889,867 US10169832B2 (en) | 2013-05-08 | 2013-05-08 | Method and instrumentation for sustainable energy load flow management system performing as resilient adaptive microgrid system |
US16/237,074 US20190244310A1 (en) | 2012-05-08 | 2018-12-31 | Method and instrumentation for sustainable energy load flow management system performing as resilient adaptive microgrid system |
Related Parent Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US13/889,867 Continuation US10169832B2 (en) | 2012-05-08 | 2013-05-08 | Method and instrumentation for sustainable energy load flow management system performing as resilient adaptive microgrid system |
Publications (1)
Publication Number | Publication Date |
---|---|
US20190244310A1 true US20190244310A1 (en) | 2019-08-08 |
Family
ID=51865424
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US13/889,867 Expired - Fee Related US10169832B2 (en) | 2012-05-08 | 2013-05-08 | Method and instrumentation for sustainable energy load flow management system performing as resilient adaptive microgrid system |
US16/237,074 Abandoned US20190244310A1 (en) | 2012-05-08 | 2018-12-31 | Method and instrumentation for sustainable energy load flow management system performing as resilient adaptive microgrid system |
Family Applications Before (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US13/889,867 Expired - Fee Related US10169832B2 (en) | 2012-05-08 | 2013-05-08 | Method and instrumentation for sustainable energy load flow management system performing as resilient adaptive microgrid system |
Country Status (1)
Country | Link |
---|---|
US (2) | US10169832B2 (en) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110854891A (en) * | 2019-11-08 | 2020-02-28 | 中国农业大学 | Power distribution network pre-disaster resource allocation method and system |
CN111130133A (en) * | 2019-12-27 | 2020-05-08 | 广东电网有限责任公司电力调度控制中心 | Joint scheduling method and related device for distributed energy storage battery |
CN111200288A (en) * | 2020-01-07 | 2020-05-26 | 武汉烽火富华电气有限责任公司 | Park microgrid system demand response method based on neural network |
WO2023089640A1 (en) * | 2021-11-17 | 2023-05-25 | Hitachi Energy Switzerland Ag | Method and system for operating an energy management system |
Families Citing this family (35)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10289080B2 (en) | 2012-10-11 | 2019-05-14 | Flexgen Power Systems, Inc. | Multi-generator applications using variable speed and solid state generators for efficiency and frequency stabilization |
US9312699B2 (en) | 2012-10-11 | 2016-04-12 | Flexgen Power Systems, Inc. | Island grid power supply apparatus and methods using energy storage for transient stabilization |
US9553517B2 (en) | 2013-03-01 | 2017-01-24 | Fllexgen Power Systems, Inc. | Hybrid energy storage system and methods |
JP6163121B2 (en) * | 2014-02-26 | 2017-07-12 | サンケン電気株式会社 | Independent operation system |
US10574055B2 (en) | 2014-12-30 | 2020-02-25 | Flexgen Power Systems, Inc. | Transient power stabilization device with active and reactive power control |
WO2017007713A1 (en) * | 2015-07-06 | 2017-01-12 | Locus Energy, Inc. | Solar irradiance modeling augmented with atmospheric water vapor data |
US10975846B2 (en) | 2015-07-29 | 2021-04-13 | General Electric Company | Method and system to optimize availability, transmission, and accuracy of wind power forecasts and schedules |
CN105244917B (en) * | 2015-11-04 | 2018-07-13 | 江西宝象科技有限公司 | With the micro-grid system for adjusting the energy and balancing the load function |
CN105279582B (en) * | 2015-11-20 | 2019-01-04 | 水电十四局大理聚能投资有限公司 | Super short-period wind power prediction technique based on dynamic correlation feature |
US10839302B2 (en) | 2015-11-24 | 2020-11-17 | The Research Foundation For The State University Of New York | Approximate value iteration with complex returns by bounding |
EP3381102B1 (en) * | 2015-11-25 | 2021-07-07 | HPS Home Power Solutions GmbH | Domestic energy generation installation and operating method for operating a domestic energy generation installation |
CN105627578A (en) * | 2016-01-20 | 2016-06-01 | 周丽娜 | Safe and efficient energy-saving boiler |
US10371729B2 (en) | 2016-01-21 | 2019-08-06 | International Business Machines Corporation | Real-time estimation of solar irradiation |
DE102016005296A1 (en) * | 2016-04-29 | 2017-11-02 | Bundesrepublik Deutschland, vertreten durch das Bundesministerium der Verteidigung, vertreten durch das Bundesamt für Ausrüstung, Informationstechnik und Nutzung der Bundeswehr | Energy supply system for storage technology |
CN105893325A (en) * | 2016-06-03 | 2016-08-24 | 江西理工大学 | Method for judging stability of metal mine artificial pillar |
US20190214823A1 (en) * | 2016-06-07 | 2019-07-11 | Nec Corporation | Energy management system, guide server and energy management method |
US10454277B2 (en) * | 2016-06-08 | 2019-10-22 | Faith Technologies, Inc. | Method and apparatus for controlling power flow in a hybrid power system |
US11461513B2 (en) * | 2016-08-18 | 2022-10-04 | Cato | Data center power scenario simulation |
KR102610913B1 (en) * | 2016-12-12 | 2023-12-06 | 한국전자통신연구원 | Hybrid self generator using vibration source and wind source and wireless sensor using the same |
EP3343717A1 (en) | 2016-12-27 | 2018-07-04 | Vito NV | Hierarchical implicit controller for shielded system in a grid |
CN106786767A (en) * | 2017-01-17 | 2017-05-31 | 无锡协鑫分布式能源开发有限公司 | The integrated micro grid control system of demand sidelight storage |
DE102017203249A1 (en) * | 2017-02-28 | 2018-08-30 | Viessmann Werke Gmbh & Co Kg | Energy management procedure for an energy system and energy system |
CN108808653A (en) * | 2017-05-02 | 2018-11-13 | 南京理工大学 | A kind of wind-light storage micro-capacitance sensor stored energy capacitance Optimal Configuration Method considering controllable burden |
CN110323786A (en) * | 2018-03-28 | 2019-10-11 | 华北电力大学 | Dispatching method and device based on micro-capacitance sensor |
CN109063902A (en) * | 2018-07-17 | 2018-12-21 | 广东工业大学 | A kind of short-term load forecasting method, device, equipment and storage medium |
CN109595125A (en) * | 2018-12-13 | 2019-04-09 | 海南大学 | Space is mobile can energy storage equipment by kinetic energy and active |
CN112290583B (en) * | 2019-07-12 | 2023-07-04 | 阳光电源股份有限公司 | DC coupling off-grid hydrogen production system and control cabinet power supply device and control method thereof |
CN110571794B (en) * | 2019-08-26 | 2023-07-25 | 国家电网公司东北分部 | Transient model equivalent calculation method suitable for doubly-fed wind power plant |
US11444473B2 (en) | 2019-10-15 | 2022-09-13 | Inventus Holdings, Llc | Dynamic battery charging for maximum wind/solar peak clipping recapture |
CN110635510B (en) * | 2019-10-23 | 2021-01-26 | 河北工业大学 | Cooperative control method for non-grid-connected wind power water electrolysis hydrogen production system |
GB2592218B (en) * | 2020-02-19 | 2022-06-22 | Conductify Ltd | A method for managing an energy system |
CN111525621B (en) * | 2020-05-20 | 2021-06-18 | 国网安徽省电力有限公司经济技术研究院 | Distributed coordination control method and system for building group direct current power distribution system |
JP2022109049A (en) * | 2021-01-14 | 2022-07-27 | トヨタ自動車株式会社 | Information processing apparatus, information processing system and program |
CN114301072A (en) * | 2021-11-22 | 2022-04-08 | 深圳供电局有限公司 | Micro-grid scheduling method |
EP4311060A1 (en) * | 2022-07-19 | 2024-01-24 | Siemens Schweiz AG | Power management |
Citations (42)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6285749B1 (en) * | 1997-11-17 | 2001-09-04 | Charles L. Manto | System and method for providing universal telecommunications service and third party payer services |
US20020025028A1 (en) * | 1998-11-17 | 2002-02-28 | Manto Charles L. | System and method for providing sponsored of universal telecommunications service and third party payer services |
US20030119528A1 (en) * | 2001-12-26 | 2003-06-26 | Boathouse Communication Partners, Llc | System and method for an automated intermediary to broker remote transaction between parties based on actively managed private profile information |
US20050084081A1 (en) * | 2001-08-31 | 2005-04-21 | Manto Charles L. | System and method for providing interoperable and on-demand telecommunications service |
US20060192435A1 (en) * | 2005-02-26 | 2006-08-31 | Parmley Daniel W | Renewable energy power systems |
US20070105445A1 (en) * | 2005-10-27 | 2007-05-10 | Instant Access Networks, Llc | System and method for providing certifiable electromagnetic pulse and rfi protection through mass-produced shielded containers and rooms |
US7274975B2 (en) * | 2005-06-06 | 2007-09-25 | Gridpoint, Inc. | Optimized energy management system |
US7333880B2 (en) * | 2002-12-09 | 2008-02-19 | Enernoc, Inc. | Aggregation of distributed energy resources |
US20080179887A1 (en) * | 2007-01-26 | 2008-07-31 | Hironari Kawazoe | Hybrid power generation of wind-power generator and battery energy storage system |
US20090076661A1 (en) * | 2007-07-25 | 2009-03-19 | Ken Pearson | Apparatus, system, and method to manage the generation and use of hybrid electric power |
US20090295162A1 (en) * | 2007-09-27 | 2009-12-03 | Hitachi Engineering & Services Co., Ltd. | Wind power generation system of a type provided with power storage system |
US20090319090A1 (en) * | 2008-06-19 | 2009-12-24 | Honeywell International Inc. | Energy optimization system |
US20090326726A1 (en) * | 2008-06-25 | 2009-12-31 | Versify Solutions, Llc | Aggregator, monitor, and manager of distributed demand response |
US20090326724A1 (en) * | 2007-03-01 | 2009-12-31 | Wisconsin Alumni Research Foundation | Control of combined storage and generation in distributed energy resources |
US20100198421A1 (en) * | 2009-01-30 | 2010-08-05 | Board Of Regents, The University Of Texas System | Methods and Apparatus for Design and Control of Multi-port Power Electronic Interface for Renewable Energy Sources |
US20100217550A1 (en) * | 2009-02-26 | 2010-08-26 | Jason Crabtree | System and method for electric grid utilization and optimization |
US20100308765A1 (en) * | 2009-04-01 | 2010-12-09 | Eaglepicher Technologies, Llc | Hybrid energy storage system, renewable energy system including the storage system, and method of using same |
US20110093127A1 (en) * | 2009-10-16 | 2011-04-21 | David L. Kaplan | Distributed energy resources manager |
US20110106328A1 (en) * | 2009-11-05 | 2011-05-05 | General Electric Company | Energy optimization system |
US8024073B2 (en) * | 2009-08-21 | 2011-09-20 | Allure Energy, Inc. | Energy management system |
US20110227343A1 (en) * | 2011-02-10 | 2011-09-22 | Mitsubishi Heavy Industries, Ltd. | Wind power plant and wind-power-plant control method |
US20110276194A1 (en) * | 2010-05-10 | 2011-11-10 | Emalfarb Hal A | System and method for energy management |
US20120083930A1 (en) * | 2010-09-30 | 2012-04-05 | Robert Bosch Gmbh | Adaptive load management: a system for incorporating customer electrical demand information for demand and supply side energy management |
US20120143385A1 (en) * | 2010-12-06 | 2012-06-07 | Goldsmith Steven Y | Computing architecture for autonomous microgrids |
US20120150679A1 (en) * | 2012-02-16 | 2012-06-14 | Lazaris Spyros J | Energy management system for power transmission to an intelligent electricity grid from a multi-resource renewable energy installation |
US20120173035A1 (en) * | 2009-09-10 | 2012-07-05 | Rikiya Abe | Multi-terminal power conversion device, multi-terminal power transfer device, and power network system |
US8222765B2 (en) * | 2009-02-13 | 2012-07-17 | First Solar, Inc. | Photovoltaic power plant output |
US8280799B2 (en) * | 2003-08-20 | 2012-10-02 | New Virtus Engineering, Inc. | Method and systems for predicting solar energy production |
US20120283888A1 (en) * | 2011-05-05 | 2012-11-08 | State Grid Corporation Of China (Sgcc) | Seamless Transition Method and Apparatus for Micro-grid Connect/Disconnect from Grid |
US20120283890A1 (en) * | 2011-05-05 | 2012-11-08 | State Grid Corporation Of China (Sgcc) | Control Apparatus for Micro-grid Connect/Disconnect from Grid |
US20130015703A1 (en) * | 2011-07-16 | 2013-01-17 | Rouse Gregory C | Microgrid |
US20130076140A1 (en) * | 2011-09-28 | 2013-03-28 | Thomas Francis Darden | Systems and methods for microgrid power generation and management |
US20130079943A1 (en) * | 2011-09-28 | 2013-03-28 | Ii Thomas Francis Darden | Systems and methods for microgrid power generation management with selective disconnect |
US20130099565A1 (en) * | 2011-04-15 | 2013-04-25 | Deka Products Limited Partnership | Modular power conversion system |
US20130169064A1 (en) * | 2010-09-10 | 2013-07-04 | Samsung Sdi Co., Ltd. | Energy storage system and controlling method of the same |
US20130342020A1 (en) * | 2012-06-25 | 2013-12-26 | Honeywell International Inc. | Fuel efficiency optimization for microgrid systems employing multiple generators |
US8704390B2 (en) * | 2010-12-07 | 2014-04-22 | Vestas Wind Systems A/S | Dynamic adjustment of power plant output based on electrical grid characteristics |
US8706650B2 (en) * | 2009-01-14 | 2014-04-22 | Integral Analytics, Inc. | Optimization of microgrid energy use and distribution |
US20140129040A1 (en) * | 2012-11-06 | 2014-05-08 | Ali Emadi | Adaptive energy management system |
US20140249680A1 (en) * | 2011-09-30 | 2014-09-04 | Johnson Controls Technology Company | Systems and methods for controlling energy use in a building management system using energy budgets |
US20140350743A1 (en) * | 2012-08-27 | 2014-11-27 | Nec Laboratories America, Inc. | Tiered power management system for microgrids |
US9188109B2 (en) * | 2012-02-16 | 2015-11-17 | Spyros James Lazaris | Virtualization, optimization and adaptation of dynamic demand response in a renewable energy-based electricity grid infrastructure |
-
2013
- 2013-05-08 US US13/889,867 patent/US10169832B2/en not_active Expired - Fee Related
-
2018
- 2018-12-31 US US16/237,074 patent/US20190244310A1/en not_active Abandoned
Patent Citations (44)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6285749B1 (en) * | 1997-11-17 | 2001-09-04 | Charles L. Manto | System and method for providing universal telecommunications service and third party payer services |
US20020025028A1 (en) * | 1998-11-17 | 2002-02-28 | Manto Charles L. | System and method for providing sponsored of universal telecommunications service and third party payer services |
US20050084081A1 (en) * | 2001-08-31 | 2005-04-21 | Manto Charles L. | System and method for providing interoperable and on-demand telecommunications service |
US20030119528A1 (en) * | 2001-12-26 | 2003-06-26 | Boathouse Communication Partners, Llc | System and method for an automated intermediary to broker remote transaction between parties based on actively managed private profile information |
US7333880B2 (en) * | 2002-12-09 | 2008-02-19 | Enernoc, Inc. | Aggregation of distributed energy resources |
US8280799B2 (en) * | 2003-08-20 | 2012-10-02 | New Virtus Engineering, Inc. | Method and systems for predicting solar energy production |
US20060192435A1 (en) * | 2005-02-26 | 2006-08-31 | Parmley Daniel W | Renewable energy power systems |
US7274975B2 (en) * | 2005-06-06 | 2007-09-25 | Gridpoint, Inc. | Optimized energy management system |
US20070105445A1 (en) * | 2005-10-27 | 2007-05-10 | Instant Access Networks, Llc | System and method for providing certifiable electromagnetic pulse and rfi protection through mass-produced shielded containers and rooms |
US20080179887A1 (en) * | 2007-01-26 | 2008-07-31 | Hironari Kawazoe | Hybrid power generation of wind-power generator and battery energy storage system |
US20090326724A1 (en) * | 2007-03-01 | 2009-12-31 | Wisconsin Alumni Research Foundation | Control of combined storage and generation in distributed energy resources |
US20090076661A1 (en) * | 2007-07-25 | 2009-03-19 | Ken Pearson | Apparatus, system, and method to manage the generation and use of hybrid electric power |
US20090295162A1 (en) * | 2007-09-27 | 2009-12-03 | Hitachi Engineering & Services Co., Ltd. | Wind power generation system of a type provided with power storage system |
US8334606B2 (en) * | 2007-09-27 | 2012-12-18 | Hitachi Engineering & Services Co., Ltd. | Wind power generation system of a type provided with power storage system |
US20090319090A1 (en) * | 2008-06-19 | 2009-12-24 | Honeywell International Inc. | Energy optimization system |
US20090326726A1 (en) * | 2008-06-25 | 2009-12-31 | Versify Solutions, Llc | Aggregator, monitor, and manager of distributed demand response |
US8706650B2 (en) * | 2009-01-14 | 2014-04-22 | Integral Analytics, Inc. | Optimization of microgrid energy use and distribution |
US20100198421A1 (en) * | 2009-01-30 | 2010-08-05 | Board Of Regents, The University Of Texas System | Methods and Apparatus for Design and Control of Multi-port Power Electronic Interface for Renewable Energy Sources |
US8222765B2 (en) * | 2009-02-13 | 2012-07-17 | First Solar, Inc. | Photovoltaic power plant output |
US20100217550A1 (en) * | 2009-02-26 | 2010-08-26 | Jason Crabtree | System and method for electric grid utilization and optimization |
US20100308765A1 (en) * | 2009-04-01 | 2010-12-09 | Eaglepicher Technologies, Llc | Hybrid energy storage system, renewable energy system including the storage system, and method of using same |
US8024073B2 (en) * | 2009-08-21 | 2011-09-20 | Allure Energy, Inc. | Energy management system |
US8174381B2 (en) * | 2009-08-21 | 2012-05-08 | Allure Energy, Inc. | Mobile energy management system |
US20120173035A1 (en) * | 2009-09-10 | 2012-07-05 | Rikiya Abe | Multi-terminal power conversion device, multi-terminal power transfer device, and power network system |
US20110093127A1 (en) * | 2009-10-16 | 2011-04-21 | David L. Kaplan | Distributed energy resources manager |
US20110106328A1 (en) * | 2009-11-05 | 2011-05-05 | General Electric Company | Energy optimization system |
US20110276194A1 (en) * | 2010-05-10 | 2011-11-10 | Emalfarb Hal A | System and method for energy management |
US20130169064A1 (en) * | 2010-09-10 | 2013-07-04 | Samsung Sdi Co., Ltd. | Energy storage system and controlling method of the same |
US20120083930A1 (en) * | 2010-09-30 | 2012-04-05 | Robert Bosch Gmbh | Adaptive load management: a system for incorporating customer electrical demand information for demand and supply side energy management |
US20120143385A1 (en) * | 2010-12-06 | 2012-06-07 | Goldsmith Steven Y | Computing architecture for autonomous microgrids |
US8704390B2 (en) * | 2010-12-07 | 2014-04-22 | Vestas Wind Systems A/S | Dynamic adjustment of power plant output based on electrical grid characteristics |
US20110227343A1 (en) * | 2011-02-10 | 2011-09-22 | Mitsubishi Heavy Industries, Ltd. | Wind power plant and wind-power-plant control method |
US20130099565A1 (en) * | 2011-04-15 | 2013-04-25 | Deka Products Limited Partnership | Modular power conversion system |
US20120283888A1 (en) * | 2011-05-05 | 2012-11-08 | State Grid Corporation Of China (Sgcc) | Seamless Transition Method and Apparatus for Micro-grid Connect/Disconnect from Grid |
US20120283890A1 (en) * | 2011-05-05 | 2012-11-08 | State Grid Corporation Of China (Sgcc) | Control Apparatus for Micro-grid Connect/Disconnect from Grid |
US20130015703A1 (en) * | 2011-07-16 | 2013-01-17 | Rouse Gregory C | Microgrid |
US20130076140A1 (en) * | 2011-09-28 | 2013-03-28 | Thomas Francis Darden | Systems and methods for microgrid power generation and management |
US20130079943A1 (en) * | 2011-09-28 | 2013-03-28 | Ii Thomas Francis Darden | Systems and methods for microgrid power generation management with selective disconnect |
US20140249680A1 (en) * | 2011-09-30 | 2014-09-04 | Johnson Controls Technology Company | Systems and methods for controlling energy use in a building management system using energy budgets |
US20120150679A1 (en) * | 2012-02-16 | 2012-06-14 | Lazaris Spyros J | Energy management system for power transmission to an intelligent electricity grid from a multi-resource renewable energy installation |
US9188109B2 (en) * | 2012-02-16 | 2015-11-17 | Spyros James Lazaris | Virtualization, optimization and adaptation of dynamic demand response in a renewable energy-based electricity grid infrastructure |
US20130342020A1 (en) * | 2012-06-25 | 2013-12-26 | Honeywell International Inc. | Fuel efficiency optimization for microgrid systems employing multiple generators |
US20140350743A1 (en) * | 2012-08-27 | 2014-11-27 | Nec Laboratories America, Inc. | Tiered power management system for microgrids |
US20140129040A1 (en) * | 2012-11-06 | 2014-05-08 | Ali Emadi | Adaptive energy management system |
Non-Patent Citations (3)
Title |
---|
Hajizadeh et al. ("Intelligent power management strategy of hybrid distributed generation system", Electrical Power and Energy Systems 29 (2007) 783–795) (Year: 2007) * |
Morais et al. ("Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming ", Renewable Energy 35 (2010) 151–156) (Year: 2010) * |
Palma-Behnke et al. ("Energy Management System for a Renewable based Microgrid with a Demand Side Management Mechanism", IEEE, 2011, pp 1-8) (Year: 2011) * |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110854891A (en) * | 2019-11-08 | 2020-02-28 | 中国农业大学 | Power distribution network pre-disaster resource allocation method and system |
CN111130133A (en) * | 2019-12-27 | 2020-05-08 | 广东电网有限责任公司电力调度控制中心 | Joint scheduling method and related device for distributed energy storage battery |
CN111200288A (en) * | 2020-01-07 | 2020-05-26 | 武汉烽火富华电气有限责任公司 | Park microgrid system demand response method based on neural network |
WO2023089640A1 (en) * | 2021-11-17 | 2023-05-25 | Hitachi Energy Switzerland Ag | Method and system for operating an energy management system |
Also Published As
Publication number | Publication date |
---|---|
US20140337002A1 (en) | 2014-11-13 |
US10169832B2 (en) | 2019-01-01 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US20190244310A1 (en) | Method and instrumentation for sustainable energy load flow management system performing as resilient adaptive microgrid system | |
US10261536B2 (en) | Systems and methods for optimizing microgrid power generation and management with predictive modeling | |
US9979198B2 (en) | Systems and methods for microgrid power generation and management | |
Cau et al. | Energy management strategy based on short-term generation scheduling for a renewable microgrid using a hydrogen storage system | |
Washom et al. | Ivory tower of power: Microgrid implementation at the University of California, San Diego | |
Kudo et al. | Forecasting electric power generation in a photovoltaic power system for an energy network | |
US9639904B2 (en) | Systems and methods for minimizing energy costs for a power consumption system that has access to off-grid resources | |
US20130046415A1 (en) | Programmable power management controller | |
US20140252855A1 (en) | Microgrid control system | |
Shufian et al. | Modeling and analysis of cost-effective energy management for integrated microgrids | |
JP2010233352A (en) | Power supply system, and device for control of distributed power plant | |
KR101927759B1 (en) | optimum control system for photovoltaic energy generation system | |
Liu et al. | Evolution towards dispatchable PV using forecasting, storage, and curtailment: A review | |
Miller et al. | Integrating high levels of wind in island systems: Lessons form Hawaii | |
Ramahatana et al. | Economic optimization of micro-grid operations by dynamic programming with real energy forecast | |
WO2013049547A2 (en) | Systems and methods for optimizing microgrid power generation management with selective disconnect and predictive modeling | |
Funabashi et al. | Field tests of a microgrid control system | |
Brka | Optimisation of stand-alone hydrogen-based renewable energy systems using intelligent techniques | |
Van Haaren | Utility scale photovoltaic plant variability studies and energy storage optimization for ramp rate control | |
Naoi et al. | Demand and supply simulations considering detailed forecast, scheduling and control functions for Japanese Power System with a massive integration of renewable energy sources | |
Manabe et al. | Cooperative control of energy storage systems and biogas generator for multiple renewable energy power plants | |
Onaolapo | Reliability study under the smart grid paradigm using computational intelligent techniques and renewable energy sources. | |
Moniruzzaman et al. | An Overview of Frequency Control as a Criterion of Power System Reliability and International Survey of Determining Operating Reserve | |
Delfino et al. | An Energy Management Platform for Smart Microgrids | |
Sandherr et al. | Modeling analysis for solar/wind-powered microgrid on Tangier Island |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
STPP | Information on status: patent application and granting procedure in general |
Free format text: NON FINAL ACTION MAILED |
|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION |