WO2020023998A1 - Améliorations apportées à la détermination et à la modification d'un état de fonctionnement - Google Patents

Améliorations apportées à la détermination et à la modification d'un état de fonctionnement Download PDF

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Publication number
WO2020023998A1
WO2020023998A1 PCT/AU2019/050621 AU2019050621W WO2020023998A1 WO 2020023998 A1 WO2020023998 A1 WO 2020023998A1 AU 2019050621 W AU2019050621 W AU 2019050621W WO 2020023998 A1 WO2020023998 A1 WO 2020023998A1
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WO
WIPO (PCT)
Prior art keywords
data
optionally
asset
operational
status
Prior art date
Application number
PCT/AU2019/050621
Other languages
English (en)
Inventor
Stephen ASHFIELD
Garry Harding
Taylor WOODS
Craig PHASEY
Scott Rowan
Kutri SIHVOLA
Timothy CERVENJAK
Shane HEMMENS
Timothy Anderson
Douglas HOLLETT
Stephen HANNAM
Liam DENSLEY
Will HENDERSON
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Nova Professional Services Pty Ltd
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Publication date
Priority claimed from AU2018902740A external-priority patent/AU2018902740A0/en
Application filed by Nova Professional Services Pty Ltd filed Critical Nova Professional Services Pty Ltd
Publication of WO2020023998A1 publication Critical patent/WO2020023998A1/fr

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    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling
    • 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
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • 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
    • G06Q50/02—Agriculture; Fishing; Mining
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Definitions

  • a computer implemented method comprising the steps: receiving data associated with an identified problem; using an Option Generator to analyse the data and generate at least one option to solve the problem; modelling the problem and / or at least one option using a Modelling Engine to generate a modelling output; and optionally testing and / or simulating the modelling output and / or the at least one option, optionally using a Digital Emulator.
  • the received data can be of any suitable type and can be from diverse types and sources.
  • the invention also provides a computer implemented method comprising the steps:
  • the invention also provides a computer implemented system comprising: an Option Generator to analyse received data associated with an identified problem and generate at least one option to solve the problem; a Modelling Engine to model the problem and / or at least one option to generate a modelling output; and a Digital Emulator for testing the modelling output and / or the at least one option.
  • the invention also provides a computer implemented system comprising: an Option Generator to analyse received data associated with an identified problem and generate at least one option to solve the problem; a Modelling Engine to model the problem and / or at least one option to generate a modelling output; a Scenario Generator to generate at least one scenario associated with the modelling output and the at least one option; optionally a Digital Emulator for testing and / or simulating the modelling output and / or the at least one option.
  • the Option Generator derives one or more operational objectives from an inputted problem statement; processes the operational objectives to do one or more of: determine at least one value criterion; defines the problem; derive at least one performance requirement; identifies and optionally manages one or more opportunities based on the performance requirements and the problem definition; prioritise the one or more opportunities according to the value criteria into a series of initiatives.
  • the Option Generator separates the identified problem into a plurality of constituent problems; for each constituent problem, identifies one or more potential solutions, optionally by matching one or more constituent problem characteristics in a problem / solution data base; optionally iterates one or more potential solutions to arrive at least one option to solve the problem.
  • the Option Generator maps out the problem space and the solution space in a way that simulates their value; optionally generates outputs that can be directly imported into the Digital Emulator; can optionally auto-generate Integrated Playbooks for the Option Generator; can optionally compare the performance of different asset configurations and identify areas for expanding the operational envelope.
  • integrated playbooks comprise a suggested list to provide the operator with an optimised set of intervention actions to respond to an event.
  • An Option Generator may generate a comparison of one or more historical data and / or outcomes, with modelled data and / or outcomes to generate a curve of expected outcomes.
  • the Modelling Engine may use information about the asset network and organisation as inputs wherein the asset inputs optionally comprise information such as physical properties, interfaces and other attributes and the organisation inputs optionally comprise information such as organisational structure, requirements, capabilities, Key Performance Indicators (KPIs) and goals.
  • asset inputs optionally comprise information such as physical properties, interfaces and other attributes
  • organisation inputs optionally comprise information such as organisational structure, requirements, capabilities, Key Performance Indicators (KPIs) and goals.
  • KPIs Key Performance Indicators
  • Simulation inputs may comprise specified input conditions and for example asset network information provided by a GIS Network Generator.
  • a GIS Network Generator may be in the form of software derived from interface requirements to provide necessary information to each platform in the appropriate format.
  • These inputs may be used by an engineering modeller (comprising one or more of Physics and / or Chemistry) according to the invention to solve for the required properties at each network point and generate a simulated dataset and wherein if the results generated by the simulated dataset exceed the specified boundary conditions at any point, the input conditions are adjusted, and the simulation is re calculated.
  • One or more inputs can be analysed and arranged into a set of requirements for problem and solution space modelling.
  • a Modelling Engine may conduct preliminary testing of operational concepts.
  • One or more process steps may be iterated optionally automatically and optionally periodically to update their output based on the latest available information and / or decision weightings.
  • Some preferred embodiments of the invention comprise a Scenario Generator.
  • the Scenario Generator may use input data and physical condition data, coupled with modelled Digital Emulator outputs, which are optionally analysed using the Modelling Engine module to generate a series of possible scenario outcomes.
  • the Scenario Generator may use input and physical conditions data, coupled with simulation outputs, which are then analysed through the Modelling Engine to generate a series of expected and possible scenario outcomes. Typically generated outcomes can be stored for future analysis and display.
  • the process of scenario generation is continually updated with new incoming data.
  • Some embodiments of the invention comprise a step of calculation of a series of engineering-based and simulated scenarios which in turn can be assessed based upon a set of both static and dynamic value requirements. In some embodiments there is a step of generating a forecast.
  • One or more value scenarios may be combined iteratively against one or more Option Generator parameters in order to optimize value and outcomes in accordance with operator requirements.
  • Some preferred embodiments comprise a Digital Emulator which can simulate system performance faster than real time.
  • Some embodiments of a method according to the invention comprise one or more of: a source of GIS data which is optionally used to create a virtual model of a physical system of interest within a Digital Emulator; a mapping module to support mapping and visualisations and optionally to (i) supply operating data associated with a model and optionally (ii) assemble network input for engineering-based analysis into the correct format to support modelling; a solver to clean and / or simplify data, and optionally remove one or more unnecessary nodes and parse the output to one or more location based on objective; an interface module to pass formatted GIS and operational data to a engineering simulation module; a engineering simulation module to enable simulation of the process; and a survival time module to calculate survival time.
  • Survival Time preferably comprises estimation of the time until assets are expected to stop producing when the network capacity reaches saturation. Survival Time can be based on simulated data from the engineering model and determines the amount of time before limiting thresholds are met in the chosen asset group.
  • the Modelling Engine may also create an enterprise architecture model which may be customised optionally using one or more of a physical aspect, a functional aspect, an operating aspect, a KPI.
  • a system according to the invention may comprise an enterprise architecture model. It may also comprise a engineering modeller to solve for the required properties and / or operation of a network segment.
  • an engineering modeller may undergo one or more of: receiving engineering data; based on one or more input conditions, processing the data to solve for one or more properties in a network segment; optionally repeating one or more steps for one or more subsequent simulated cycles;
  • a traffic light status green, amber, red
  • a survival time for one or more assets may also be calculated optionally based upon one or more of operational, physical and economic constraints on the asset.
  • the status and / or properties of one or more assets in a network may be displayed in near real-time optionally using production data and simulation outputs.
  • prediction of the future state of one or more assets in a network may be made and optionally based on the latest measured network data.
  • An aspect of the invention is that it may comprise a plurality of Digital Emulators which may optionally be interlaced or interconnected.
  • the invention also relates to a system for determining an operational state comprising : a data store; a Central Processing Unit (CPU); a memory; a source of Internet Of Things (IOT) sensor data; a source of contextual, non-IOT data; a technical rule engine; and an integrated rule engine; wherein the technical rule engine is operable to process IOT sensor data and at least some contextual, non-IOT data to determine status data and the integrated rule engine is operable to process the status data and optionally additional contextual, non-IOT data to arrive at an operational state.
  • CPU Central Processing Unit
  • IOT Internet Of Things
  • the invention also relates to a method for determining an operational state comprising: receiving into a data store IOT sensor data; receiving into a data store contextual non IOT data; using a technical rule engine to process IOT sensor data and at least some contextual, non-IOT data to determine status data; using an integrated rule engine is operable to process the status data and optionally additional contextual, non-IOT data to arrive at an operational state.
  • the IOT sensor comprises one or more of: a flow sensor, a pressure sensor, a temperature sensor, a vibration sensor, a revolutions sensor, a cycle time sensor, a gas sensor, a Global Positioning System (GPS) sensor, a proximity sensor, a chemical sensor, a water quality sensor, a smoke sensor, a light sensor, an infrared sensor, a mechanical sensor, a level sensor, an image sensor, a motion detection sensor, an accelerometer sensor, a gyroscopic sensor, a humidity sensor, a moisture sensor, a fluid level sensor, a vessel fullness sensor, a spectral sensor, a colour sensor, an ultrasonic sensor, an energy sensor, and an optical sensor.
  • GPS Global Positioning System
  • the IOT sensor data comprises one or more of: equipment condition, local weather, asset status, asset station throughput, asset flowrate, asset station expected downtime, an asset type, an asset valve position, an asset pressure, an asset and asset network connectivity, a compression station status, a compression station throughput, a compression station flowrate, a compression station failure code, a compression station expected downtime, an asset type, an asset valve position, an asset pressure, an compression asset flow rate, an asset and asset network connectivity, an asset and compression station connectivity, an asset distance from compression station, an asset and connected network with elevation profile, a flow rate, a pressure, a temperature, a vibration, a number of revolutions, a cycle time, an RPM, a gas, a GPS location, an object proximity, a sensed chemical, a water quality, a sensed smoke, a sensed light, a sensed infrared signal, a mechanical movement, a level, an image, a detected motion, an acceleration, an orientation, a humidity, a moisture, a
  • Some embodiments of the invention comprise an IOT effector to receive and give effect to instructions at an IOT site.
  • the IOT effector may for example comprise one or more of: a valve, a switch, a gate, a light, a speaker, a camera, an alarm, a warning light, a heat source, an energy source, an automated intervention, a mechanical device and a mechanical isolator.
  • the contextual non IOT data comprises one or more of: weather, power data or energy data (such as price, availability, generation type) economic, share price, government policy, business data, actual vs predicted price, market data, volumes of sales, market participants, demand, supply, cost, safety, event probability, risk, financial, spatial, temporal, environmental, a tolerance envelope, a modelling output, a simulation output, a network survival time, a network line pack, a network safe operating limit, contracts data, ownership data, land owner sentiment, land access rights, access to well terms and conditions, joint venture requirements, contractual requirements, work crew and maintenance data, maintenance status, maintenance history, operating and maintenance operating instructions, proximity to maintenance crew via GPS tracking, road access, an operating procedure, a policy, a shutdown procedure, a restart procedure, a failure procedure.
  • power data or energy data such as price, availability, generation type
  • a technical rule engine according to the invention may be at least part of a Modelling Engine and the integrated rule engine may be at least part of a
  • Status data may for example comprise one or more of: an asset status, an asset network status, a system status, an operational status, an output status, an input status, an efficiency status, a risk status, a safety status.
  • the status as described herein can be of any suitable type, for example it may be one or more of: real-world, replica, digitally derived, Digital Emulator derived, past, present or future.
  • Some embodiments of the invention process an operational status and optionally other data and do one or more of: supply information; provide a recommendation; provide an investment recommendation, make a forecast, forecast a failure, predict an outcome;
  • Some embodiments of the invention comprise a Modelling Engine which optionally uses one or more of an environment model, a physical condition model, a risk model, an uncertainty quantification, an engineering model, an economic model, a financial model, an operational model, a business model, and a business requirement model.
  • the invention also encompasses a system or method for use in relation to one or more of an oil and / or gas well, an oil and / or gas field.
  • technical rules may comprise one or more of: oil and gas flow, bottom hole pressure, well and tubulars sizes, casing size, wellhead pressure, bubble point, gas and liquid chemistry, paraffin content, wax content, asphaltene content, decline rate, viscosity, gravity, Gas to Oil Ratio (GOR)
  • operational rules comprise one or more of: maximum open flow, minimum flow, oil / gas ratio, oil / water ratio, condensate yield, flowing tubing pressure, anulus pressure
  • status types may comprise one or more of: producing, shut-in, choked back, down for work over, abandoned, plugged, temporarily abandoned
  • operational states may comprise one or more of: profitable, uneconomic, maintaining market requirement, maintaining domestic requirement, minimum economic flow rate, minimum per well production.
  • the invention also encompasses a system or method for use in relation to one or more of oil and / or gas platforms and / or production facilities.
  • technical rules may comprise one or more of: oil and gas flow, surface pressure, flowing pressure, water content, GOR, temperature, vibration, corrosion, platform export rate of oil and gas, water rate, temperature, parasitic load
  • daily volume status types may comprise one or more of: producing, shut-in, partial shut-in, standby, wait on weather/conditions, regulatory hold
  • operational states may comprise one or more of: economic, non-economic, abandoned, temporarily abandoned.
  • the invention also encompasses a system or method for use in relation to one or more of pipelines and / or gas gathering systems.
  • technical rules may comprise one or more of: pipe size, rate, GOR, pressure, cathodic protection, line length, depth, location, temperature, vibration, expansion, line pack, pressure changes, minimum rate, maximum rate, hydrate formation, obstructions, slugging, corrosion, emissions, minimum line volume, status types may comprise one or more of: operating, shut-in, start-up operations, pigging; and operational states may comprise one or more of: minimum throughput, maximum throughput, economic, non-economic, design capacity,
  • the invention also encompasses a system or method for use in relation to one or more of LNG facilities.
  • technical rules may comprise one or more of: gas input, gas type and chemistry, gas BTU, liquids content, temperature, volume, flow rate, train throughput, efficiency, offtake, storage volume, operating temperature, throughput, gas loss, emissions, boil off status types may comprise one or more of: operating, offloading, start-up, shutdown; and operational states may comprise one or more of: economic operations, tanker loading, shutdown, cold start, uneconomic
  • the invention also encompasses a system or method for use in relation to one or more chemical and / or refining facilities.
  • technical rules may comprise one or more of: input feedstock chemistry, pressure, temperature, tank sizes, vibration, emissions, reactants, catalysts, process conditions, process diagrams, safety requirements, output ratios, output products, emissions restrictions, volumes, daily output, schedules, status types may comprise one or more of: operating, maintenance, shutdown, start-up, cold start, restricted operations; and operational states may comprise one or more of: profitable, uneconomic, breakeven, abandon, closeout,
  • the invention also encompasses a system or method for use in relation to one or more compressor stations.
  • technical rules may comprise one or more of: power requirement, inlet and outlet pressure, gas density, RPM, vibrations, temperature, efficiency, volume, minimum pressure, maximum pressure, gas pressure, power use, maintenance, safety, status types may comprise one or more of: operating, shutdown, on demand, spinning; and operational states may comprise one or more of: economic, non-economic, shutdown, mothballed.
  • the invention also encompasses a system or method use in relation to one or more oil and / or gas processing facilities.
  • technical rules may comprise one or more of: tank volumes and sizes, gas volume and rate, oil volume and rate, gas chemistry, GOR, water rate, water ratio, emissions, pump rate, sulphur content, chemicals use, export volumes, water disposal, emissions control, minimum throughput, product specifications, safety status types may comprise one or more of: operating, standby, non- operating, shutdown; and operational states may comprise one or more of: economic, non-economic, abandoned, dismantled
  • the invention also encompasses a system or method for use in relation to one or more mining operations.
  • technical rules may comprise one or more of: ore chemistry, grade, type, mineralogy, size, depth, temperature, distance, personnel count, trucks, hourly and daily volume, emissions, downtime, safety, daily rate, ore grade, ore size, loading rate, treatment rate, operational expenditure, status types may comprise one or more of: operating, minimum operations, standby, non operating operational states may comprise one or more of: economic, non-economic, abandoned, dismantled
  • the invention also encompasses a system or method for use in relation to one or more automated transport systems.
  • technical rules may comprise one or more of: number of vehicles, vehicle type, speed, location, wear indicators, tire pressure, fuel use, emissions, daily volume/throughput, safety, operational time, time between repairs, near miss stats, status types may comprise one or more of: operating, standby, shutdown, charging, available, no tavailable, maintenance and repairs; and operational states may comprise one or more of: economic, non-economic.
  • the invention also encompasses a system or method for use at least partially in relation to one or more distributed energy grids and / or energy microgrids for example by use of an Energy Management function and / or module as described herein.
  • technical rules may comprise one or more of: meteorological conditions, weather, weather forecast, current demand, customer demand, demand projection, generation rate, demand forecasts, smart meter data, synchro phaser data, inverter data, Frequency, historical generation profiles, wind energy profiles, solar irradiance profiles, generator production, generator curtailment, prosumer use and production, restricted allotments, pricing mechanisms.
  • status types may comprise one or more of: operating, ramping, generator curtailment, baseload generation, demand increase, demand decrease, load shedding; and operational states may comprise one or more of: supplying the customer, maintaining load/demand curve, profitable, uneconomic.
  • power is managed using an Energy Management module or function as described herein.
  • the invention enables energy use to be modified and the sale of excess energy as required.
  • the invention combines the benefits of enterprise digitalisation with the ability to participate in the energy market.
  • the invention may for example allow an operation to optimise its output while leveraging electricity consumption and/or generation with participation on the energy market.
  • the invention also encompasses a system or method for use in relation to one or more cyber protection and / or resilience systems.
  • the invention also encompasses a system and or method for use in relation to one or more water distribution and / or wastewater collection systems.
  • technical rules comprise one or more of: tank and pipe volumes and sizes, water volume and flow rate, water chemistry, water ratio, emissions, pump rate, chemicals use, water release volumes, wastewater input volumes, water and wastewater disposal, emissions control, minimum throughput, product specifications, safety status types comprise one or more of: operating, standby, non-operating, shutdown operational states comprise one or more of: economic, non-economic, abandoned, dismantled Throughout this specification (including any claims which follow), unless the context requires otherwise, the word‘comprise’, and variations such as‘comprises’ and ‘comprising’, will be understood to imply the inclusion of a stated integer or step or group of integers or steps but not the exclusion of any other integer or step or group of integers or steps.
  • Figure 1 a is a block diagram showing an overview of an example system according to the invention.
  • Figure 1 b is a block diagram showing an overview of an example system according to the invention comprising a virtual power plant.
  • Figure 1 c is a process flow diagram showing an example physical system according to the invention.
  • Figure 1 d is a block diagram showing a high level structure of an example Digital Enterprise Suite
  • Figure 1 e is a block diagram showing a high level structure of an example Digital Enterprise Suite comprising a virtual power plant.
  • Figure 2a is a process flow diagram showing operation of an example Option Generator according to the invention
  • Figure 2b is a block diagram showing operational workflow of an example Digital Enterprise Suite
  • Figure 2c is a process flow diagram showing example functionality of a Modelling Engine according to the invention.
  • Figure 2d is a process flow diagram showing example functionality of a Modelling Engine comprising a virtual power plant according to the invention.
  • Figure 3a is a process flow diagram showing operational workflow of an example Scenario Generator according to the invention.
  • Figure 3b is a block diagram showing an example functional hierarchy according to the invention
  • Figure 3c is a process flow diagram showing an example process flow for simulation according to the invention.
  • Figure 4a is a process flow diagram showing functionality of an example graphical user interface.
  • Figure 4b is a process flow diagram showing functionality of an example graphical user interface.
  • Figure 4c is a block diagram showing an example functional flow chart according to the invention.
  • Figure 4d is a block diagram showing an example functional flow chart comprising a virtual power plant according to the invention
  • Figure 5 is a block diagram showing example simulation replay functions according to the invention
  • FIG. 6 is a block diagram showing example mode selection according to the invention.
  • Figure 7 is a block diagram showing example historic replay according to the invention.
  • Figure 8 is a block diagram showing an example what-if version of historic replay according to the invention.
  • Figure 9a is a block diagram showing example live monitoring according to the invention.
  • Figure 9b is a block diagram showing example live monitoring including energy data according to the invention
  • Figure 10 is a block diagram showing example prediction / look ahead according to the invention
  • Figure 1 1 a is a block diagram showing an example representative Digital Emulator
  • Figure 1 1 b is a block diagram showing an example representative Digital Emulator
  • Figure 12a is a block diagram showing an example technology stack according to the invention
  • Figure 12b is a block diagram showing an example technology stack comprising a virtual power plant according to the invention
  • FIGS 13 to 15 depict further example embodiments of the implementation of Energy Management.
  • Figure 16 is a time-scaled graph showing demonstrating utilisation of residual energy as saleable energy into the marketplace
  • Figure 17 is a time-scaled graph showing demonstrating utilisation of energy as saleable energy into the marketplace
  • Figures 18 to 24 are screen shots showing functionality for an example implementation of a method and system according to the invention set out in Example 2.
  • Figure 25 is an example dashboard for certain embodiments of a method and system of the invention.
  • Figures 26 to 43 are flowcharts, system diagrams and wireframes for an example reduced version of an example system and method according to the invention.
  • the system and method of the invention is depicted in one example form in Figure 1 a, herein as a Digital Enterprise Suite and may be comprised of the following main components: Physical System (to be optimised), Simulations, Modelling Engine, Option Generator, Scenario Generator, Graphical User Interface (GUI) and Data Store.
  • the Physical System and Simulations provide operational data and simulated data respectively to the system of the invention.
  • the Modelling Engine provides asset structure information and the Option Generator provides information about operational objectives to the Digital Enterprise Suite.
  • the Scenario Generator uses these inputs, in conjunction with the stored data, to compute operational values and assist to generate a set of recommended options. These options are displayed via the Graphical User Interface and also fed back to the Option Generator for the refinement of the operational objectives.
  • the system has ongoing operational data which is input into the model, coupled with a digital simulation which inputs a mirrored simulation dataset as well. These data are then assessed with a Modelling Engine, which provides engineering-based integrated engineering solutions across the breadth of the problem.
  • the Option Generator imparts operational objectives which have been generated bottom-up through an iterative process.
  • the Option Generator and the Modelling Engine can be used to generate a statistically broad and diverse set of possible outcomes for example within the Scenario Generator.
  • the goal of these scenarios is to accurately model, engineer and address as many as possible of the outcomes which could impact the operational integrity, safety and durability of the physical system.
  • the resultant outputs and original data are then stored for further use, reference and baseline calculations.
  • the output also goes to the GUI within which operational value and decision scenarios can be considered. Since the physical system is complex, and typically includes commercial, safety, operational and production characteristics, these unique considerations need to be weighed and valued in order to make the proper decision.
  • This operational valuation can be static (i.e. based upon a constant or unchanging set of criteria) or it can be dynamic according to various market, production or operational priorities.
  • the system and method of the invention provides an important means by which such complex physical systems can be managed in a high-fidelity fashion.
  • the system and method of the invention is depicted in one example form in Figure 1 b, herein as a Digital Enterprise Suite and may be comprised of the following main components: Physical System (to be optimised), Simulations, Modelling Engine, Option Generator, Scenario Generator, Graphical User Interface (GUI), Data Store and Virtual Power Plant.
  • the Physical System and Simulations provide operational data and simulated data respectively to the system of the invention.
  • the Modelling Engine provides asset structure information and the Option Generator provides information about operational objectives to the Digital Enterprise Suite.
  • the Virtual Power Plant provides energy market data to the Scenario Generator.
  • the Scenario Generator uses these inputs, in conjunction with the stored data, to compute operational values and assist to generate a set of recommended options. These options are displayed via the Graphical User Interface and also fed back to the Option Generator for the refinement of the operational objectives.
  • the system has ongoing operational data which is input into the model, coupled with a digital simulation which inputs a mirrored simulation dataset as well. These data are then assessed with a Modelling Engine, which provides engineering-based integrated engineering solutions across the breadth of the problem.
  • the Option Generator imparts operational objectives which have been generated bottom-up through an iterative process.
  • the Option Generator and the Modelling Engine can be used to generate a statistically broad and diverse set of possible outcomes for example within the Scenario Generator.
  • the goal of these scenarios is to accurately model, engineer and address as many as possible of the outcomes which could impact the operational integrity, safety and durability of the physical system.
  • the resultant outputs and original data are then stored for further use, reference and baseline calculations.
  • the output also goes to the GUI within which operational value and decision scenarios can be considered. Since the physical system is complex, and typically includes commercial, safety, operational and production characteristics, these unique considerations need to be weighed and valued in order to make the proper decision.
  • This operational valuation can be static (i.e. based upon a constant or unchanging set of criteria) or it can be dynamic according to various market, production or operational priorities.
  • the system and method of the invention provides an important means by which such complex physical systems can be managed in a high-fidelity fashion.
  • the Physical System provides operational data to the Digital Enterprise Suite, based on the configuration and properties of the asset network. For each asset in the asset network, three datasets are generated and stored. The GIS data is read, corrected and then parsed by the GIS Network Generator. The operational data is read from sensors attached to each asset. The asset properties are read from information provided about the asset network. This process is repeated for each asset in the network and the complete dataset is then made available to the Digital Enterprise Suite.
  • a digital enterprise representation of complex and diverse datasets allows greater control of operating equipment and facilities, and enhancement of critical factors such as flow rate, equipment and facility failure, power use, and component integrity.
  • the example used for present purposes is of an operating wellfield with associated producing wells, flowlines, gathering lines, compression, field batteries and facilities, power supply, road and access infrastructure, and control systems.
  • the challenge is to optimize gas production and both capital expenditure and operating expenditure, through integrated management of rates, pressures, power and cost. This is done in part by minimizing well failures and shut-ins, avoidance of well workovers and re-drills, careful management of pressure and flow regimes in tubular systems, and precise management of power requirements and costs for operations.
  • the Digital Enterprise Suite targets a physical system which is to be optimized; it can be as variable as a pipeline system, a plant, a power grid or any industrial setting such as a mine.
  • the method and system of the invention hereto referred as Digital Enterprise Suite comprises an Option Generator, Digital Emulator and Modelling Engine, as shown in figure 1 d.
  • the method and system of the invention hereto referred as Digital Enterprise Suite comprises an Option Generator, Digital Emulator, Modelling Engine and Virtual Power Plant, as shown in Figure 1 e.
  • Option Generator ( Figure 2a) - this problem-solving algorithm for complex problems breaks issues down into constituent problems and formulates the set of opportunities for value creation.
  • the Option Generator consists of four phases and includes the ongoing refinement of potential initiatives that eventually become options for the Decision-Making process.
  • the Option Generator can provide the solution hypotheses to be tested for value and producibility, and as a basis for innovation.
  • the Option Generator enables the determination of the operational objectives, initiatives, value criteria and performance requirements, based on the client requirements that are unique to each problem.
  • the operational objectives are derived from the client problem statement. These objectives then inform the assessment of value, which lead to the determination of the value criteria.
  • the objectives are also used to define the problem and derive the performance requirements. These performance requirements, along with the problem definition, enable the identification and management of opportunities.
  • the opportunities are then prioritised according to the value criteria into a series of initiatives.
  • FIG. 2b A representative operational work flow of a system and method according to the invention is shown in Figure 2b.
  • Modelling Engine ( Figure 2c) - this toolset and related technologies map out the problem space and the solution space in a way that simulates their value.
  • the Modelling Engine generates outputs that can be directly imported into the Digital Emulator and can auto-generate Integrated Playbooks for the Option Generator.
  • the Modelling Engine can compare the performance of different asset configurations and identify areas for expanding the operational envelope.
  • the Modelling Engine uses information about the asset network and organisation as inputs.
  • the asset inputs may include information such as physical properties, interfaces and other attributes.
  • the organisation inputs may include information such as organisational structure, requirements, skills and competencies, KPIs and goals. These inputs are then analysed into a set of requirements for problem and solution space modelling. Preliminary testing of operational concepts is also conducted by the Modelling Engine. The results of the modelling, as well as the Modelling Engine structure and attributes, are stored for later use.
  • the environment and / or platform of the invention may have one or more of the following features:
  • Intangible and tangible value and benefits - value provided by options is not always tangible, there will almost always be intangible aspects such as company reputation, liability exposure, the engagement of staff, or strategic alignment. Intangible value can be measured in a similar way to tangible value and must be considered as part of decision making throughout the process. • Continuous Decision-Making - to accelerate change and reduce the cumulative risk and pressures of a big-bang approach, a continuous approach to decision making is employed. This aims to ensure initial value is realised quickly and allows available funds to be leveraged as they become ready. The weightings of decision criteria may change with time, in response to changes in environment and policy.
  • Multi-Criteria Decision-Making Analysis has been proven in multi-stakeholder, financially constrained environments.
  • MCDA is integrated as follows; o both tangible and intangible value and benefits are considered.
  • o Value and benefits are considered throughout the Option Generator as opportunities are refined, right up until funding decisions are made in the decision phase.
  • o Criteria are generated under the Option Generator and fed into the Decision Options tool. Weightings of decision criteria are centrally generated.
  • User stories that are pushed upwards from the client need to be framed as opportunities and considered in context under the MCDA process.
  • o Decision options are generated in a distributed fashion, and then managed centrally.
  • Decision option meta-data is generated in distributed Option Generator workshops using an electronic proforma, and then recorded and visualised.
  • the workshops form a vital aspect of engaging client staff in the whole decision process.
  • Line-of-sight (traceability) between decision options and the value they provide is essential and is maintained along with the meta-data used.
  • o Decision-making for options to enter the deploy stage can be held as needed, but still take a whole of system approach to ensure best choices are made and made based upon objective evidence from the Modelling Engine and Digital Emulator testing.
  • Modelling Engine ( Figure 2d) - this toolset and related technologies map out the problem space and the solution space in a way that simulates their value.
  • the Modelling Engine generates outputs that can be directly imported into the Digital Emulator and can auto-generate Integrated Playbooks for the Option Generator.
  • the Modelling Engine can compare the performance of different asset configurations and identify areas for expanding the operational envelope.
  • the Modelling Engine uses information about the asset network and organisation as inputs.
  • the asset inputs may include information such as physical properties, interfaces and other attributes.
  • the organisation inputs may include information such as organisational structure, requirements, skills and competencies, KPIs and goals.
  • Virtual Power Plant inputs may for example include market data, for example from an organisation such as the Australian Energy Market Operator (AEMO), Electricity Grid Status data, Environmental Data.
  • AEMO Australian Energy Market Operator
  • Electricity Grid Status data Environmental Data.
  • Operational data is accessible from multiple points and presented at several levels;
  • Past (Historic Replay) - operations can be replayed using adjusted operational parameters, to virtually test ways to enhance end-to-end performance through“what- if scenarios.
  • the What-if function provides a‘sandpit’ environment to test and evaluate the impact of different operating scenarios on the asset network.
  • Future (Look Ahead) - operations data is generated by simulations to show expected performance of the operation. Future data is also used to validate and optimise performance of cases yet to be developed. When operational failures occur, an alert may be flagged, and operators are automatically presented with options for work-arounds to select from by the suite controller. In some embodiments, in all modes, operators can drill down into meta-data to investigate anomalies. Assets, people and operating parameters are all tracked providing their whereabouts, status and tasking information.
  • the Scenario Generator uses input and physical conditions data, coupled with simulation outputs, which are then analysed through the Modelling Engine to generate a series of expected and possible scenario outcomes.
  • the generated outcomes are stored for future analysis and display.
  • the overall process is governed by the operational objectives developed by the Option Generator and is repeated for each variable of interest e.g.
  • the Scenario Generator utilises raw input data and physical conditions data (such as VPP market data or physical asset data), coupled with modelled Digital Emulator outputs, which are then analysed through the Modelling Engine module to generate a series of possible scenario outcomes. This overall process is in turn governed by the Option Generator module. This results in a comparison of past and historical data and outcomes, with modelled outcomes which are assessed both per expected deterministic results, and probabilistic outcomes based upon the historical data. The Scenario Generator combines this output, to generate a curve of expected outcomes.
  • This process of scenario generation is continually updated with new incoming data. It is also replicated using the same overall dataset, for additional required variables which are connected to the system. As an example, a set of physical model scenarios would be different from scenarios which are based upon power availability and demand. Similarly, a scenario based upon modelled power cost, while by necessity coupled to the equipment which is being powered, is nonetheless separate from the scenario for flowing material such as liquid or gas.
  • the combination of the Option Generator, Scenario Generator and Modelling Engine module allows calculation of a series of engineering-based and simulated scenarios which in turn can be assessed based upon a set of both static and dynamic value requirements. Examples of a static requirement would be production always being higher than a base level point; power costs never exceeding a threshold; water levels always below a prescribed point. Dynamic values include rates which are dependent upon price, time of day, or other more complex interactions of outside factors and input data.
  • a further component of the value calculation is the ability to look ahead over varying time periods, to predict system behaviour and accordingly, to anticipate and not simply react to incoming data points. This is an important aspect of a model according to the invention. Because the model includes prior and historical data, and also includes forecasting (such as weather, price trends and system behaviour based upon prior experience), it conveys the ability to react and respond in real time rather than with a significant computational lag.
  • the value scenarios are then combined in an iterative fashion against the Option Generator parameters, in order to optimize value and outcomes in accordance with operator requirements.
  • complex and unique physical systems can be combined in a fashion which cannot be done by an individual operator, and at a speed not attainable without the system and method of the invention.
  • the resulting instructions are available both in the visualization graphics to primary system operators, and also are immediately deployable to control devices through, for example, a Supervisory Control and Data Acquisition (SCADA) system.
  • SCADA Supervisory Control and Data Acquisition
  • the system and method of the invention predicts emergent phenomena produced by complex organisations. This property is supported by two important aspects:
  • Anchoring in real time Use of a digital emulation of the target system backed up by engineering-based models that can simulate system performance faster than real time.
  • a Digital Emulator according to the invention can continuously display the Digital Emulator predicted system performance alongside the sensed actual system performance. Under normal operating conditions the predicted performance will converge with the sensed performance. When the system exhibits complex behaviours beyond the ability of the Digital Emulator to predict, divergence between the predicted and sensed performance will indicate anomalous conditions.
  • the system and method of the invention consists of a set of federated subsidiary Digital Emulators. Each subsidiary Digital Emulator is created or imported at sufficient accuracy to support operations at the level of abstraction of the subsidiary (i.e.
  • the Digital Emulator federates the subsidiary Digital Emulators based on the overall enterprise model, architected for example using the Modelling Engine. As the set of federated Digital Emulators increases in size and the fidelity of the subsidiary Digital Emulators increases, the system and method of the invention will produce emergent phenomena in the same manner as the real enterprise. Through this mechanism, as the system and method of the invention grows to address additional functions within an enterprise, the whole system evolves to more accurately predict the complex behaviour of the enterprise.
  • a Digital Enterprise Suite can allow for downstream ore-blending by making smart selections of ore when it is first mined further upstream.
  • a Digital Enterprise Suite according to the invention overlays actual and virtual data visually for the operator to provide better insight into problem areas, allowing better optimisation.
  • FIG. 3b The top-level functional hierarchy of a preferred embodiment of the system and method of the invention is shown in Figure 3b.
  • Digital Emulator a multi-agent simulation of operation of an asset, used for monitoring, testing, and control (including for example, autonomous control) for best overall business outcomes.
  • a Digital Emulator is used for fine testing of decision options to provide confidence in value delivery, even before any decision has been made.
  • Geospatial Information System (GIS) data is used to create a virtual model of the physical system within the Digital Emulator.
  • the GIS data may for example cover all network and sensor components.
  • Data collected directly from the field instruments represents the operational“experience” of the asset. It is used to enable the scenario replay and simulation functions.
  • the data files may for example contain time series data based on the geographic area, data types and parameters, and data range chosen. Selected data includes both static and time series data.
  • the Simulator inputs are the specified input conditions and the asset network information provided by the GIS Network Generator. These inputs are used by an Engineering Modeller according to the invention to solve for the properties at each network point and generated a simulated dataset. If the results generated by the simulated dataset exceed the specified boundary conditions at any point, the input conditions are adjusted, and the simulation is re calculated. The simulation results are then stored for later use and provided to the Digital Enterprise Suite for analysis.
  • mapping module By use of a mapping module, the formatted GIS data is used to support and / or integrate mapping and visualisations. It also supplies the operating data associated with the model. The mapping module is also used to assemble the network input for engineering-based analysis, into the correct format to support the modelling function.
  • the data is parsed into a solver comprised of several customised routines and data scripts.
  • the tool removes unnecessary nodes, only maintaining them at critical points. The remaining nodes are all uniquely identifiable due to a string of information attached; such as pressure, rate and valve position.
  • the outputs are parsed to different places based upon objective; the database for rendering of the physical system, and to generate the physical connections of the network model.
  • An interface module provides the interface to pass formatted GIS and operations data to a engineering simulation module to enable simulation of the physical layout and process the outputs from the simulation. For each simulation, this engineering simulation module simulates forward in time in discrete time cycles for a fixed number of iterations, once the initial starting conditions have been read in. This solves for parameters such as pressures and flows, or other required parameters, at each node throughout the network.
  • the results are a prediction from the initial start point through to the end of the simulation period.
  • the results of each individual simulation run has a unique Run Identification (ID), and can be stored to allow for future playback and data manipulation.
  • ID Run Identification
  • Survival Time is calculated using a state-based table which identifies a Class and Completion type for each component.
  • the state-based table is used as the point of reference, to be compared to the absolute conditions at each node, at all points in time during the simulation period.
  • the Graphical User Interface ( Figure 4a) initially reads in the Modelling Engine model information and network mapping overlay to confirm that the displayed asset network is correct. Then, for each selected view, it generates a display view based on the imported operational data, simulation results. Multiple views can be selected by the user. The user can also select the display mode to be used - historical reply, live monitoring or look ahead.
  • GUI Graphical User Interface
  • a dashboard also allows for visualization either by individual component, region, or asset type. Assets can be displayed either in ascending or descending order, with traffic lights (according to critical values), safety margins, time to failure.
  • a pop-up window consisting of engineering and process data related to that asset.
  • CAD Computer Aided Drafting
  • the pop-up window also includes line graphs, simulated data and time profiles.
  • a slider bar provides functionality to replay an entire scenario as well as step through individual simulation steps.
  • the replay controls also provide the ability to automatically play forward or backward through the entire simulation.
  • the Graphical User Interface ( Figure 4b) initially reads in the Modelling Engine model information and network mapping overlay to confirm that the displayed asset network is correct. Then, for each selected view, it generates a display view based on the imported operational data, simulation results and Virtual Power Plant associated data. Multiple views can be selected by the user. The user can also select the display mode to be used - historical reply, live monitoring or look ahead.
  • GUI Graphical User Interface
  • a dashboard also allows for visualization either by individual component, region, or asset type. Assets can be displayed either in ascending or descending order, with traffic lights (according to critical values), safety margins, time to failure. Visualisation may also be provided to show generation or load reduction opportunities.
  • a pop-up window consisting of engineering and process data related to that asset.
  • CAD Computer Aided Drafting
  • the pop-up window also includes line graphs, simulated data and time profiles.
  • a slider bar provides functionality to replay an entire scenario as well as step through individual simulation steps.
  • the replay controls also provide the ability to automatically play forward or backward through the entire simulation.
  • the combination of existing data, the Modelling Engine, forecast operating conditions and the Option Generator allows a predictive look-ahead at critical factors and data points. In practice this means being able to predict when certain values might exceed acceptable limits leading to failure; being able to predict pressures, vibration or temperatures (as just a few examples) means greater system assurance. This is achieved through a combination of progressive learning through the modelled system, coupled with historical operating data.
  • An Enterprise Architecture Model is created using the Modelling Engine.
  • the Modelling Engine model is sufficiently broad in scope to cover the whole asset.
  • the Modelling Engine model forms the basis of the enterprise model and is adapted and customised to incorporate specific details about the primary asset.
  • the model is built using a number of representative‘views’ and hierarchy levels, as defined in Systems Modelling Language (SysML), in order to capture all components of the operation and how they are physically and functionally connected.
  • SysML Systems Modelling Language
  • a full structural SysML model of the physical asset shows interfaces between asset / enterprise components and assigned properties and attributes.
  • a representative set of requirements, Key Performance Indicators (KPIs) and goals will be included in the Modelling Engine model in order to show how these organisational drivers flow down to physical asset components and business functions and how changes to the asset impact these drivers.
  • KPIs Key Performance Indicators
  • FIG. 4c is a block diagram showing an example functional flow chart according to the invention.
  • the raw GIS data is obtained through a data export, for example in comma separated values (.CSV) format.
  • the CSV file is imported into a mapping module with a mapping tool and re-projected using a common co-ordinate system and then merged into one single data set covering the whole asset network.
  • the merged data set is used to create a new file such as a CSV file which is then imported into the GIS Network Generator for further processing.
  • Operational data such as individual asset class, pressure, flowrate and temperature are obtained through data export for example in CSV format.
  • Each tag can for example be parsed in individual CSV files to the GIS Network Generator.
  • the files are time sampled at a rate selected by the Operator.
  • the exported operational data is imported into the GIS Network Generator along with the Exported GIS Data.
  • the GIS Network Generator processes all the files and for example may generate three files.
  • the first file which details the asset network which is fed into the IOT environment to create the Points of Interest (POIs).
  • POIs Points of Interest
  • the second file is created based on the asset network, which is further simplified for use in the Engineering Modeller.
  • This file forms the structural basis of the Engineering Modeller simulation.
  • the asset network files include static information such as asset class, asset operating class and locations.
  • the third file is an interface file that highlights the real flow properties and asset settings for each of the nodes in the selected network, it also provides a place for the Engineering Modeller to parse simulated pressures and flows for each of the nodes. Furthermore, the file details how many cycles the simulation will do and the time increment between each cycle.
  • the Engineering Modeller imports the interface and network files manually before discretising the network and solving for the flow properties throughout the network segment. The calculations are repeated for each simulation cycle based on a single set of input conditions.
  • the Engineering Modeller is tuned for example, manually and through a feedback loop, where it checks against real data to ensure the prediction is accurate (within a reasonable margin to account for single phase simulation versus multi-phase reality).
  • a set of threshold criteria are chosen to reflect currently understood operating limits for assets based on their class and surface completion type. These limits can be used as the rules that drive the state-based formatting of the assets presented on the dashboard.
  • Each of the thresholds can be tailored to the class and surface completion type for the asset.
  • Current operator knowledge and control system setpoints are used to populate the limits table. Pressures below‘state will indicate an under-pressure situation, between‘state and‘state2’ is an under-pressure warning status, between‘state2’ and‘state3’ is the nominal operating condition,‘state3’ and‘state4’ is an over-pressure warning status and finally, greater than‘state4’ is an over-pressure scenario.
  • the IOT dashboard represents the culmination of real data and simulated data over a chosen scenario and can for example display a simplified status, such as a traffic light status (green, amber, red) for each asset based on its physical configuration and therefore inferred setpoints, with respect to the current operating information.
  • the states represent the ranges for each of the operating conditions and therefore the colour status for each of the asset groups in the dashboard.
  • Figure 4d is a block diagram showing an example functional flow chart according to the invention.
  • the raw GIS data is obtained through a data export, for example in comma separated values (.CSV) format.
  • the CSV file is imported into a mapping module with a mapping tool and re-projected using a common co-ordinate system and then merged into one single data set covering the whole asset network.
  • the merged data set is used to create a new file such as a CSV file which is then imported into the GIS Network Generator for further processing.
  • Operational data such as individual asset class, pressure, flowrate and temperature are obtained through data export for example in CSV format.
  • Each tag can for example be parsed in individual CSV files to the GIS Network Generator.
  • the files are time sampled at a rate selected by the Operator.
  • Market data such as AEMO power price, demand and supply are obtained along with environmental data such as temperature, season, time of day, cloud cover, humidity for example through .CSV.
  • the files are time stamped at a rate appropriate for the application. This market data is obtained through a Virtual Power Plant interface and then imported into the Scenario Generator.
  • the exported operational data is imported into the GIS Network Generator along with the Exported GIS Data.
  • the GIS Network Generator processes all the files and for example may generate three files.
  • the first file which details the asset network which is fed into the IOT environment to create the Points of Interest (POIs).
  • POIs Points of Interest
  • the second file is created based on the asset network, which is further simplified for use in the Engineering Modeller.
  • This file forms the structural basis of the Engineering Modeller simulation.
  • the asset network files include static information such as asset class, asset operating class and locations.
  • the third file is an interface file that highlights the real flow properties and asset settings for each of the nodes in the selected network, it also provides a place for the Engineering Modeller to parse simulated pressures and flows for each of the nodes. Furthermore, the file details how many cycles the simulation will do and the time increment between each cycle.
  • the Engineering Modeller imports the interface and network files manually before discretising the network and solving for the flow properties throughout the network segment. The calculations are repeated for each simulation cycle based on a single set of input conditions.
  • the Engineering Modeller is tuned for example, manually and through a feedback loop, where it checks against real data to ensure the prediction is accurate (within a reasonable margin to account for single phase simulation versus multi-phase reality).
  • the simulation data from the Engineering Modeller is parsed back through the same interface file, with all the remaining information now calculated and included.
  • This file can be imported into an IOT database location where it is called for the dashboard.
  • a set of threshold criteria are chosen to reflect currently understood operating limits for assets based on their class and surface completion type. These limits can be used as the rules that drive the state-based formatting of the assets presented on the dashboard. Each of the thresholds can be tailored to the class and surface completion type for the asset. Current operator knowledge and control system setpoints are used to populate the limits table.
  • the IOT dashboard represents the culmination of real data and simulated data over a chosen scenario and can for example display a simplified status, such as a traffic light status (green, amber, red) for each asset based on its physical configuration and therefore inferred setpoints, with respect to the current operating information.
  • the states represent the ranges for each of the operating conditions and therefore the colour status for each of the asset groups in the dashboard.
  • a data packet is sent back to the Virtual Power Plant describing the quantity of available dispatchable load or generation to be issued back into the power market for sale.
  • This data includes quantity of power, the duration of availability and the time to respond along with an agreement to provide and commit to these via contract.
  • the dashboard may take the information provided by all aforementioned systems and present a GUI based upon the prescribed formatting.
  • a prioritised list of assets can be displayed in the dashboard which are formatted based on the pressures at the point in the simulation, down to each increment of cycle time.
  • the status, set as traffic light colours, will demonstrate the condition of each Asset Group within the chosen catchment.
  • the colour black may be used to denote Assets that are not operational.
  • the dashboard may include a map function where each of the assets and associated connections are rendered as an overlay from the GIS data and the GIS Network Generator.
  • Each asset on the map may have an associated colour to signify its status. Green denotes assets within normal operating limits, orange denotes assets on the fringes of normal operating limits, red denotes assets that are at risk of failure and black denotes assets that are not operating.
  • a mapping tool may be used within the dashboard to represent the location of assets, asset connections and other meaningful items such as intersections, drains and valves. Further network information and simulated data can be overlayed on the network using this tool.
  • a graph can be configured on an asset-by-asset basis to demonstrate the difference between real data in the chosen scenario, and the simulated data for the identical period of time, based on the data returned from the Engineering Model.
  • the IOT environment takes the Network file from the GIS Network Generator and the GIS data file before interpreting the data to create the network visualisation.
  • This function allows the user to configure the runtime environment. It allows the user to scroll through the simulated scenario data, visualise detailed asset information and see the status of each asset over the simulation period. Example available modes are shown in Figure 6.
  • a historic replay function allows the user to visualise historical operational events in the asset network using stored production data. This will enable standardisation and continuous improvement of field operations leading to optimised procedures. Historic replay can also support after-action review of incidents by processing a chosen scenario in historical data to discover possible causes, understand failure modes and verify correct actions were taken.
  • the workflow for historic replay mode is shown in Figure 7.
  • the user selects the desired time period and the historic operational dataset for that time period is then displayed and visualised.
  • the historic replay mode also provides a sandpit environment to test and evaluate the impact of different operating parameters on the asset network. This function gives network/production operators the ability to rapidly forecast the impact of a range of possible actions and select the optimum response.
  • Live Monitoring displays the status and properties of the asset network in near real-time using production data and simulation outputs. This can be achieved for example by taking known instrumented points and predicting values throughout the entire asset network based on the network structure model. These predicted measurements can then be used in conjunction with 3D mapping to produce a network model that shows areas of the system operating state including those areas in danger, for example due to high pressures, mass flow rates or velocities.
  • FIG. 9a An example workflow for the live monitoring function is shown in Figure 9a.
  • a near-live simulated dataset is generated and automatically updated based on the operational dataset.
  • FIG. 9b An example workflow for the live monitoring function is shown in Figure 9b.
  • a near-live simulated dataset is generated and automatically updated based on the operational dataset.
  • the Live Monitoring function will generate a suggested asset load shaping or additional generation list to provide the operator with an optimised set of intervention actions to respond to a power price event; otherwise known as Dynamic Playbooks.
  • the Look Ahead function provides a constant prediction of the future state of the asset network based on the latest measured network data. The prediction is continually updated and refreshed as new data is collected. The Look Ahead function will give the operator an understanding of what will happen to the asset network if no action is taken over the look ahead period.
  • FIG. 10 An example workflow for the look ahead function is shown in Figure 10.
  • a user may select the time increment and time period for the simulation as well as any stop condition for the simulation.
  • a forward-looking simulated dataset is then generated based on these conditions for the operational dataset.
  • the system and method of the invention may contain one to many Digital Emulators.
  • Each Digital Emulator is a digital replica of one or more physical assets, processes and systems used to specify, verify, predict, plan and automate optimal end-to-end operations performance in near real time.
  • a representative Digital Emulator is shown in Figure 1 1 a and 1 1 b.
  • FIG. 12a An example technology stack according to one embodiment of the invention is shown in Figure 12a.
  • the invention provides energy management which may for example be at least partially facilitated by an Energy Management module.
  • energy management functionality provides a platform to drive down power costs and importantly, control energy demand and in some preferred embodiments create an avenue to monetise excess power generation capacity.
  • the additional insights and control afforded by the system and method of the invention enables energy use to be shaped to greatest effect - whether it be maximum product, minimum time, maximum financial return, most efficient energy use and so on.
  • the invention :
  • the energy management function enables smart operation of both critical and non-safety critical systems to help the invention user to overcome the burden of grid-based power prices whilst enhancing predictive maintenance, safe dynamic load shaping, smart asset tracking and event planning.
  • the invention comprises a Historic Replay function which allows a user to visualise historical operational events in the energy network using stored production data. This will enable standardisation and continuous improvement of field operations leading to optimised power procedures.
  • Historic replay can also support after-action review of incidents/ events by processing a chosen scenario in historical data to discover possible causes, understand failure modes and verify correct actions were taken.
  • the invention comprises a Dynamic Playbooks function which can generate a suggested asset load shaping list to provide an operator with an optimised set of intervention actions to respond to a power price event.
  • a suggested power generation profile is prescribed to capitalise on a potential energy market condition.
  • Dynamic Playbooks provide a mechanism to capture existing knowledge and business rules around asset status and operational outcomes with respect to power consumption. It also allows operator adjustment of asset load shaping rules.
  • the invention comprises Live Monitoring to alert an operator to unexpected or unsafe conditions which may be building in the network such as (a) transient power usage that may exceed set operating limits or (b) contractual caps or (c) other unexpected conditions.
  • This function supports network operations and decision making to ensure the ongoing integrity of the power network.
  • the Live Monitoring function provides the operator with a near-real time picture of the power network.
  • the invention comprises a Look Ahead function which provides a constant prediction of the future state of the energy network based on the latest measured network and energy market data. The prediction is continually updated and refreshed as new data is collected.
  • the Look Ahead function gives the operator an understanding of what will happen to the power network if no action is taken over the Look Ahead period.
  • a key metric provided by Look Ahead is the energy cost against production, which is an estimation of the cost of energy against the benefit of production.
  • the invention comprises a What-if function which provides a‘sandpit’ environment to test and evaluate the impact of different operating scenarios on the asset network.
  • the What-if function gives network/production operators the ability to rapidly forecast the impact of a range of possible actions and select the optimum response with respect to power consumption or monetisation.
  • the Energy Management aspect of the invention provides access to the Energy Market and combined situational awareness of the operational asset and the energy market place in one platform. This will enable significant opportunities to reduce power cost and monetise excess energy whilst providing decision support for the operator to manage the asset.
  • the overall method and system of the invention has many benefits, for example linking various cost centres to outcomes, insights and data provided by invention drives multiple efficiencies. These may for example include contractual performance KPI’s, reporting agility, improved response and logistical planning activities.
  • Figure 13 depicts an example embodiment of the invention with one example
  • This embodiment enables the visualisation of assets and generation capability, along with a cost benefit calculator augmenting near real time information such as gas and power prices; balancing value of operational output and power cost as per market price.
  • a dashboard according to this embodiment provides insight into the current power usage, percentage of maximum power, efficiency and gas throughput for each of the identified assets.
  • there is an integrated power price prediction tool based on intelligent prediction algorithms absorbing Energy Market data (for example Australian Energy Market Operator (AEMO) data as depicted in Figure 13), along with Meteorology data (Bureau of Meteorology (BOM) data) to give predictive insights into the price of power over the upcoming period.
  • AEMO Australian Energy Market Operator
  • BOM Boau of Meteorology
  • Management Module provides a near-real time situational awareness of the status of the asset in terms of energy consumption and generation, efficiency and cost. Combined with leading indicators from the power market, it flags opportunities for power cost reduction in the form of operational decision support instructions and suggested plays.
  • Figure 14 depicts an example embodiment of the invention with another example implementation of Energy Management comprising creation of saleable power.
  • the system and method of the invention can provide a capability to sell power back into the energy grid.
  • a user entity has non-critical and critical assets suitable for delivering regulated power during times of significant power demand for example: photovoltaics, distributed Gas-Turbine Alternators (GTA’s) and diesel generators.
  • GTA Gas-Turbine Alternators
  • Each of these assets may be used as saleable energy platforms, especially when power prices are high.
  • the Energy may be used as saleable energy platforms, especially when power prices are high.
  • Management module will predict appropriate windows of time within which to sell power onto a power market which may for example include duration and quantity.
  • production may be altered (for example reduced) in response to current or predicted energy costs.
  • Figure 15 depicts an example embodiment of the invention with another example implementation of Energy Management comprising shaping of power assets.
  • other asset infrastructure can be leveraged for example by load shifting to enable sale of additional power into the marketplace.
  • one or more models are used, for example based on causal and other relationships between assets.
  • One or more of such models may be used to monitor one or more variables for example Line Pack and Survival Time (measure of time to single asset failure) in order to determine how long assets can be turned down (such as for example compressors).
  • the method and system of the invention comprises use of a playbook to implement a preferred load shaping methodology so as to ensure the cost / benefit of adjusting operation of an asset (for example by turning it down) is worthwhile in exchange for the sale of power.
  • Some embodiments of a method according to the invention comprise the step of developing such a model from data for example, live, pre-existing or test data.
  • the digital representation will provide information on Survival Time and Line Pack to help the gas business determine whether they are able to ramp down compressions facilities to reduce exposure to the high power prices, and suggest a duration for turn down based on prices and asset capabilities.
  • the cost of standby generation is typically of the order of $140/MW/FI.
  • the spot price varies on a five minute basis. Most of the time the spot price is below the cost of production but in daily peaks the price will often gust to $200 - $300. (See Figure 16). In price spikes, when demand gets out of synch with supply, the price can rapidly increase to $14,400 per MW/FI.
  • the system and method of the invention allows organic generation to be sold into the market whenever the spot price exceeds the production cost. As an added benefit, running the generators under load helps with their reliability. This is a second rapid income with minimal change to operations.
  • a PTA is a hedge against fluctuations in the spot price. This hedge represents an opportunity through the system and method of the invention. If spot price is overlain on daily load for a company, it is likely that regular or schedulable down times do not align with the power peaks . However, by selectively re-scheduling down times to align with peak prices the amount of power available for re-sale through the system and method of the invention at high price points is maximised. Consequently, the system and method of the invention can dramatically increase income at a simple cost of selectively rescheduling existing activities and implementing efficiency savings without impacting production.
  • the next step is to progressively adjust operations to minimise the power use during price peaks and spikes and recover production during low price periods (Figure 17).
  • the actual electricity load of a company will be below the maximum permissible load.
  • the spot price is usually below what the company is paying for electricity (otherwise the retailer is going out of business). During spikes the price rises dramatically above what is being paid.
  • the modelling and monitoring functions of the invention are used to monitor and predict operations in real time.
  • these allow the company to prepare for periods of high price by ramping up production, so that production can be curtailed during price spikes. Beyond simply avoiding high prices, the ability to shape load will enable demand management. So, rather than paying a capped price for power during a spike, the company will be paid to not use that power. This has the potential to develop significant income.
  • load shaping is a method of storing electricity in processes rather than in chemical batteries or pumped hydro.
  • This‘process storage’ is available through appropriate digitalisation of control systems, and it does not require acres of lithium ion storage shelters.
  • the system may include at least one processor that is configured to execute software instructions from a memory accessed by the processor.
  • the software instructions may be configured to cause the processor to carry out various acts and functions described herein.
  • the described software instructions may be included in one or more components of an energy management module that is configured to control and manage electrical power associated with one or more commercial and / or industrial locations.
  • the described data processing system may include at least one display device and at least one input device.
  • the display device may include an LCD display screen, monitor, and/or a projector.
  • the input devices may include a mouse, pointer, touch screen, touch pad, drawing tablet, track ball, buttons, keypad, keyboard, camera, motion sensing device that captures motion gestures, and/or any other type of input device capable of providing the inputs described herein.
  • the processor, memory, software instructions, display device, and input device may be included as part of a data processing system corresponding to a PC, workstation, server, or any other type of computing system, or any combination thereof.
  • the described software instructions may enable (via a user interface) different types of Distributed Energy Resources (DERs) to be aggregated together to form an aggregated energy resource controlled by a management system and other additional software.
  • DERs Distributed Energy Resources
  • a DER as defined herein as corresponding to a decentralized electrical grid-connected device that may generate electricity, consume electricity, or both, and which is controllable.
  • DERs may include load assets that consume electrical power such as pumps, valves, heaters, turbines, lifting devices, reaction vessels, air conditioners, baseboard heaters, building lighting systems, mining equipment and plant, gas and oil equipment and plant, other controllable loads, or any combination thereof.
  • DERs may also include power output assets such as solar power panels, wind turbines, generators, other power generation assets that output power, or any combination thereof.
  • DERs may also include storage assets such as batteries, pumped hydro that may both consume power (when charging) or output power (when discharging).
  • the operational characteristics associated with the operation of a particular type of DER may be represented by one more forecast algorithms usable to forecast how the particular resource will output or consume power in the future.
  • a forecast algorithm for a reaction vessel may be capable of determining for one or more time periods and time intervals therein (e.g., each minute, hour, and/or day) in the future, how much electrical energy will be required based on forecasts of production requirements and operational requirements (which may for example change with weather or other factors) in the future.
  • a forecast algorithm for a production asset such as a solar panel installation may forecast based on forecasts associated with sunrises, sunsets, cloud cover, precipitation, solar irradiation and/or any other factor that effects the amount of sunlight a solar panel is expected to receive during such time periods.
  • the system may specify one or more power related operational parameters, for example target production requirements, or maximum and/or minimum amounts of power for which the DER should be configured to consume and/or output during a specified time period.
  • power related operational parameters for example target production requirements, or maximum and/or minimum amounts of power for which the DER should be configured to consume and/or output during a specified time period.
  • the processor is configured to determine a forecasted energy deficit or excess for one or more of the assets or DERs. Such a forecast may indicate an amount of additional power that is either (a) excess to requirements and available for sale into an energy market, or (b) needed to be purchased to cover the loads when the load assets are forecasted to consume more power than the power output assets that are included.
  • the system and method of the invention may be configured to generate data representative of either an offer or a bid for power to an energy trading market system.
  • Such an offer or bid may correspond to an offer to sell or purchase the forecasted amount of power.
  • such a bid may represent the aggregated DER as an individual load class (e.g., as a single source from the perspective of the market system).
  • the software instructions may be configured to access one or more market systems via an online interface, or other type of communication system.
  • the software instructions may cause the processor to submit data corresponding to the offer or bid and may also cause the processor to receive confirmation that the offer or bid has been accepted.
  • the system may be configured to repeat the forecast process for example at pre-set time intervals and for example provide instructions to adjust either the offer or bid (if allowed by the market) or operation of one or more of the assets so as to adjust energy use and / or production to match the offer or bid requirements.
  • such dispatches for the subset of DERs may correspond to either the respective first control limit power forecast for the respective DER or the respective second control limit power forecast for the respective DER, based on whether the requested reserve capacity is either greater than or less than the updated reserve capacity forecast.
  • the power focussed digital representation utilises simulation, emulation and analytics to drive situational awareness of energy consuming and producing assets.
  • generation such as gas turbines, solar panels, wind turbines, generators
  • curtailment by (a) selling residual, non-critical asset, energy back into the energy marketplace, (b) load shaping operations to avoid the high power prices with forecast technology and (c) sell the reduced load back into the marketplace.
  • the computer-executable instructions may include a routine, a sub-routine, programs, applications, modules, libraries, and/or the like. Still further, results of acts of the methodologies may be stored in a computer-readable medium, displayed on a display device, and/or the like.
  • a Digital Enterprise Suite comprised of the following main components: Physical System (to be optimised), Simulations, Modelling Engine, Option Generator, Scenario Generator, Graphical User Interface (GUI) and Data Store.
  • Physical System to be optimised
  • Simulations Modelling Engine
  • Option Generator Option Generator
  • Scenario Generator Graphical User Interface
  • Data Store Data Store.
  • the Physical System and Simulations provide operational data and simulated data respectively to the system of the invention.
  • the Modelling Engine provides asset structure information and the Option Generator provides information about operational objectives to the Digital Enterprise Suite.
  • the Scenario Generator uses these inputs, in conjunction with the stored data, to compute operational values and assist to generate a set of recommended options. These options are displayed via the Graphical User Interface and also fed back to the Option Generator for the refinement of the operational objectives
  • the Digital Enterprise Suite targets a physical system which is to be optimized; it can be as variable as a pipeline system, a plant, a power grid or any industrial setting such as a mine.
  • the system has ongoing operational data which is input into the model, coupled with a digital simulation which inputs a mirrored simulation dataset as well.
  • These data are then assessed with the Modelling Engine, which provides physics-based integrated engineering solutions across the breadth of the problem.
  • the Option Generator imparts operational objectives which have been generated bottom-up through an iterative process.
  • the Option Generator and the Modelling Engine can be used to generate a statistically broad and diverse set of possible outcomes within the Scenario Generator.
  • the goal of these scenarios is to accurately model, engineer and address as many as possible of the outcomes which could impact the operational integrity, safety and durability of the physical system.
  • the resultant outputs and original data are then stored for further use, reference and baseline calculations.
  • This example implementation of a method and system of the invention focuses on the operation of an underground block cave mine.
  • On the left had side of each example page is a live information tool panel that provides:
  • operational aspects managed and displayed according to this example comprise:
  • a user can use the three dimensional mapping tool to navigate to the asset(s) the user wishes to see more information about and click on it (them), at this point a window on the top right hand corner opens and displays information relevant to that specific asset.
  • Figure 18 shows an example user interface and functionality for Conveyor Belt digital management with virtual power plant / energy management functionality. This interface shows the conveyor belt and associated data. Note the power consumption for this part of the operation is shown.
  • Figure 19 shows an example user interface and functionality for Mine Rock Crusher digital management with virtual power plant / energy management functionality. This interface shows the rock crusher and associated data. Note the power consumption for this part of the operation is shown.
  • Figure 20 shows an example user interface and functionality for Mine LHD Truck digital management with virtual power plant / energy management functionality. This interface shows the rock crusher and associated data.
  • Figures 21 and 22 show an example user interfaces and functionality for Mine Ventilation System digital management with virtual power plant / energy management functionality. Note the power consumption for this part of the operation is shown. Figure 22 further comprises mapping data.
  • Figure 23 shows an example user interface for Mine Operational Control digital management with virtual power plant / energy management functionality. This provides the important power control aspects of the system by showing overall mine operational throughput and the power price sell rates plus the control modes. Manual/Auto Control for the mine and manual/auto virtual power plant controls.
  • Figure 24 shows an example user interface for Mine Power Consumption digital management with virtual power plant / energy management functionality. This example interface shows a status of the mines power consumption.
  • Figure 25 is an example dashboard for certain embodiments of a method and system of the invention.
  • the left panel displays a decision engine and simple cost / benefit data in dollar terms for cumulative power cost, cumulative power revenue, cumulative gas revenue and net benefit.
  • the upper right panel displays tracking data for demand versus price.
  • the lower right panel displays asset overview data.
  • This example sets out a reduced functionality version of the system and method of the invention, herein referred to as the Virtual Power Exchange (VPx).
  • VPx Virtual Power Exchange
  • VPx is the combination of Digital Twin (DT) and Virtual Power Plant (VPP) technology that enables energy load shaping and excess energy harvesting of an asset.
  • DT Digital Twin
  • VPP Virtual Power Plant
  • the VPx solution enables energy informed operations through digital data connectivity and power-based insight sharing. It promotes confident decision making at all levels based on centralised information and analysis. With increased awareness and clarity regarding operational power usage, VPx allows clients to monetise existing assets through the sale of power on the National Electricity Market (NEM) and taking advantage of demand response opportunities.
  • NEM National Electricity Market
  • the Virtual Power Plant aggregates distributed energy resources (DER) to behave like a power plant and can respond dynamically to market signals.
  • the Virtual Power Plant is connected to the Decentralised Energy Exchange (DeX, a digital energy marketplace) which connects energy suppliers with buyers of energy services such as retailers and distribution network operators.
  • a Virtual Power Plant may also participate directly in the National Energy Market. Operators of the Virtual Power Plant see the current status of the energy market they are participating in, are notified of power supply opportunities from the Digital Twin (DT) and the energy market and can send out calls for energy.
  • the Digital Twin collects asset data, operational data, information on external influences and VPP data to monitor the asset, track revenue and plan for future
  • the VPx provides a pathway for clients to save on and monetise their power through self-generation of power, reducing non-critical load power usage, load shaping critical loads and managing component efficiencies.
  • Figure 27 shows an alternative example configuration of a VPx platform and its interaction with a VPP platform, user interfaces etc.
  • Figure 28 provides a more detailed breakdown of the main components and interfaces that make up the VPx solution of Figure 26. Functionality of the VPx is split between Digital Twin (DT) core components and third-party energy management software components - Peak Response (PR) and Virtual Power Plant. The VPx solution also requires interfaces to client-side asset data and external data sources that may influence the operation of the asset.
  • DT Digital Twin
  • PR Peak Response
  • Virtual Power Plant The VPx solution also requires interfaces to client-side asset data and external data sources that may influence the operation of the asset.
  • the VPx-R is a working but functionally reduced version of the VPx system designed to demonstrate important principles.
  • VPx-R consists of 3 individual products and the interfaces between them. These individual products are: a) Virtual Power Plant (VPP) Software, b) Peak Response (PR) Software, and c) The Digital Twin (DT).
  • Figure 29 shows a high level schematic of the components of VPx-R.
  • VPx-R consists of an interactive user interface that can schedule maintenance and operations, view and monitor the asset and its power consumption with the ability to forecast asset availability, and track revenue made from the asset.
  • An external interface with asset data is in this example, limited to a set of asset data in the VPx-R.
  • the Digital Twin within VPx-R internally interfaces with Peak Response software for the transfer of asset and energy availability data, market data and notifications of market opportunities.
  • the internal Digital Twin functionality includes: a) Decision support for setting minimum sell price of power based on market data and asset requirements, operational limits, recommended commands and other functionalities used in the user interface. b) A data storage platform for asset information, processed data, decision support and data displayed on the user interface. c) A platform for analysing and processing data. d) A simulation of asset control via notifications of asset status.
  • the client physical asset is defined by its criticality and is further categorised by the asset load type.
  • the physical asset can be one of the following:
  • Asset Information Information that is received from the asset, or about the asset
  • Metadata is stored in a form specific to the client.
  • the information could include the assets availability, health, maintenance status, history and operating state.
  • External Information Information that influences the VPx solution including weather data from the Bureau of Meteorology and energy market data from Australian Energy Market Operator (AEMO).
  • AEMO Australian Energy Market Operator
  • NEM National Electricity Market
  • Retailers. Retailers and distributers are participants in the energy markets (NEM and deX).
  • deX The Decentralised Energy Exchange (deX) is a digital marketplace where buyers and sellers of distributed energy services can meet and transact.
  • the deX may for example be provided and run by a third party.
  • the Peak Response component monitors asset data (from the Digital Twin) and provides information to the Virtual Power Plant.
  • the Peak Response component is also a platform that passes commands and information from the Virtual Power Plant to the Digital Twin.
  • VPP Virtual Power Plant Platform and Operator.
  • the Virtual Power Plant (VPP) platform aggregates distributed energy resources (DER) to behave like a power plant and can respond dynamically to market signals.
  • the Virtual Power Plant platform or module may in some cases be provided by a third party operator, such as GreenSync.
  • the Virtual Power Plant is connected to the Decentralised Energy Exchange, which connects energy suppliers with buyers of energy services such as retailers and distribution network operators.
  • a Virtual Power Plant may also participate directly in the National Energy Market. Operators of the Virtual Power Plant (for example, a Joint Venture or existing VPP operator) see the current status of the energy market they are participating in, are notified of power supply opportunities from the Digital Twin and the energy market and can send out calls for energy.
  • Asset information including the assets availability, health, history, maintenance status and operational state.
  • External information that could influence operations including weather and energy market data.
  • Data Analytics A data analytics platform within the Digital Twin that analyses and processes data.
  • Decision Logic and Functionality Processes within the Digital Twin that provide decision support for setting the minimum sell price of power based on market data and asset requirements, operational limits, recommended commands and other functionalities used in the user interface.
  • Asset Modelling and Simulation Processes within the Digital Twin that creates a digital model of the asset, simulates the assets mathematical / physical properties or provide a statistical model for the asset.
  • the Asset Operator GUI will provide access to the supervision, understanding, control and configuration of the generating asset(s) that form part of the client’s operation.
  • the Asset Operator will use this GUI to understand the current and historical state of the generating asset, set thresholds for available power supply (capacity, schedule and price), receive alerts/notifications of upcoming supply transactions and supervise the asset during dispatch of contracted power.
  • Asset Control A function within the Digital Twin that enables the agreed power capacity to be delivered to the Virtual Power Plant by controlling the asset. Switching on / off or turning up / down the asset can occur by:
  • Asset Focused Alerts A Digital Twin function for asset focused alerts will provide maintenance actions and close-out activities, alerts on breaching asset operational limits, and safety and emergency alerts.
  • Interface 1 An interface may exist for the client to directly access or manage asset information. For example, if the client manually processes asset information to create a new asset data set, or extracts data for use in other enterprise applications.
  • the Asset Operator GUI provides the interactive interface between the client and the VPx solution.
  • Asset information (historic and real-time) stored on the client site is transferred to the DT platform for storage and processing.
  • Interface 4 Physical asset data is transferred to a storage facility on the client site.
  • Interface 5 There is an interface between the Digital Twin and the physical asset to provide asset control and allow for direct collection of real-time asset information using Internet of Things (loT), smart devices or other means.
  • LoT Internet of Things
  • Interface 6 External information required for the functionality of the VPx that is not provided by other interfaces is transferred to the Digital Twin. Examples of such transferable data include weather data from the Bureau of Meteorology and energy market data from AEMO.
  • Interface 7 There is an interface that provides external information, such as energy market data, to the Peak Response Module.
  • Interface 8 The Digital Twin interfaces with Peak Response Software for the transfer of asset and energy availability data, market data and notifications of market opportunities.
  • Interface 9. An internal interface exists so information from the Peak Response Software can be aggregated to create a singular Virtual Power Plant. The interface allows energy opportunities and supply decisions to be separated from a singular Virtual Power Plant platform to each client specific Digital Twin and asset through the Peak Response Software.
  • Interface 10 There is an interface that allows the Virtual Power Plant to sell and purchase energy directly from the National Energy Market.
  • the Virtual Power Plant can meet and transact with other buyers and sellers of distributed energy services in the deX.
  • Interface A Data stored is used to create physical models and simulations of the asset.
  • Interface B Data stored is applied to the decision logic and functionality model.
  • Interface C Asset modelling and simulations are displayed on the user interface.
  • Interface D Stored data is displayed on the user interface.
  • Interface E Recommended commands and close-out activities are displayed on the user interface.
  • Interface F Operational data, workflows and contractual complexity information is stored.
  • Interface G The asset is controlled and monitored by the user interface. Asset control is performed by system and user input from the user interface. The user interface provides notifications of asset control and displays the asset status.
  • Interface FI Operational data, workflows and contractual complexity information is displayed on the user interface where applicable.
  • Interface I Asset focused alerts / warnings are displayed on the user interface. Users address the alerts / warnings via the interface.
  • Interface J Stored raw data is transferred to an analytics platform. Processed data is then stored through the interface.
  • the use case for the VPx-R is defined as follows:
  • the use case for the VPx-R is a subset of the use case(s) for the full VPx solution.
  • This use case consists of a client with an asset which has excess generation capacity that the client would like to monetise.
  • the use case can be summarised in the use case diagram at Figure 30. The two actors considered are the client (VPx user) and the VPP operator.
  • Figure 31 sets out the functional hierarchy and a breakdown of the subordinate functions that must be performed by the VPx-R system in order to address the identified Use Case. These functions are jointly met by the system components, including software, hardware, data sets, people and processes.
  • the green boxes in Figure 5 represent functions performed by the GreenSync platform and VPP Operator, and are therefore out of scope for the VPx prototype.
  • FIG 32 is a functional flow diagram of an example Minimum Viable Product (MVP) for the VPx-R. It shows how the system components interact to deliver the outcomes described in the system use case.
  • MVP Minimum Viable Product
  • the MVP is the minimum mandatory system functionality that must be in place in order to satisfy the use case.
  • the MVP is built in a scalable format such that further functionality can be added to the prototype system without modifying or changing the baseline system.
  • the GUI developed for the Asset Operator provides access to the Digital Twin components of the VPx for the supervision, understanding, control and configuration of the generating asset(s) that form part of the client’s operation.
  • the Asset Operator uses this GUI to understand the current and historical state of the generating asset, set thresholds for available power supply (capacity, schedule and price), receive alerts/notifications of upcoming supply transactions and also supervise the asset during dispatch of contracted power.
  • the VPP Operator can interface with the VPP software component of the VPx to view the current energy market conditions and opportunities, set bids for energy supply and also see additional parameters that may affect the dispatch or wholesale price of electricity (eg. weather, asset availability, network conditions, etc).
  • the User can select the asset of interest using the Select Asset dropdown. Once selected, the asset information is shown in the main window on the page.
  • the user can choose to view further asset details by using the button on the top left.
  • Figure 34 shows that after selecting the Asset Details a data table opens in the foreground, enabling the user to view key metrics such as frequency, current, standby status and daily revenue. The user can choose to hide the data table by again selecting the asset details button.
  • the user may select the "Create New Power Requirement" function for example, because the generator is required for a period of time.
  • the User can add a description of the requirement, a start time and end time as well as the required power. Additional notes can also be added for future reference.
  • the User can select maintenance event which automatically sets the required power to maximum for the duration of the event - prohibiting any energy market contracts.
  • Figure 37 shows that by hovering over the operational requirement plot, a user can see the details of each operational event.
  • the User wishes to change the minimum price that their energy can be sold for on the market.
  • the User hovers over the current Short Run Marginal Cost and the recommended minimum appears, based on asset data.
  • Figure 41 when a maintenance event as scheduled by the user is due to commence the user is alerted. Again, the user is not required to make a decision.
  • Figure 42 shows that the history tab allows a user to view previous data for the asset. This includes a data table of metrics such as revenue, % utilisation and maintenance time over different periods.
  • the Operational Power Usage and Available Power Charts are again present but with history data.
  • the user may slide back and forward in time using the slider and may adjust the zoom using the drop- down menu.
  • the Future Events tab shows a similar layout to the History tab with the sliding scale Power Usage and Available Power charts.
  • the top of the view shows an itemised list of each upcoming maintenance event as entered by the user and each upcoming energy contract. The user cannot make edits in this view.
  • Table 1 below contains example user inputs to the system made through the GUI. In this case it is limited to the“scheduled power requirements”. This is how the user inputs the required power requirements that they need (ie. This is how they‘reserve’ capacity from the asset) and the remainder gets offered to the energy market. This data stream is part of Interface 2 as set out in Figures 28 and 29 and described above.
  • Table 2 contains the telemetry data stream from an example virtual asset, in this case a 15MW diesel-powered generator. This is the asset data stream which is Interface 3 as set out in Figures 28 and 29 and described above.
  • the data table contains an example data stream covering 3hrs of input telemetry.

Abstract

Selon l'invention, un procédé mis en œuvre par ordinateur comprend les étapes consistant : à recevoir des données associées à un problème identifié ; à utiliser un générateur d'options pour analyser les données et générer au moins une option pour résoudre le problème ; à modéliser le problème et/ou au moins une option au moyen d'un moteur de modélisation pour générer une sortie de modélisation ; et éventuellement à tester et/ou simuler la sortie de modélisation et/ou la ou les options à l'aide, facultativement, d'un émulateur numérique.
PCT/AU2019/050621 2018-07-29 2019-06-17 Améliorations apportées à la détermination et à la modification d'un état de fonctionnement WO2020023998A1 (fr)

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AU2018902740A AU2018902740A0 (en) 2018-07-29 Operational state determination
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AU2019900161A AU2019900161A0 (en) 2019-01-20 Operational state determination and modification
AU2019900386A AU2019900386A0 (en) 2019-02-07 Improvements to operational state determination and modification
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US11663679B2 (en) 2019-10-11 2023-05-30 International Business Machines Corporation Generating mode change alerts with automatic detection from sensor data
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CN114362169A (zh) * 2022-01-13 2022-04-15 国网江苏省电力有限公司镇江供电分公司 一种计及光储型虚拟电厂边际成本的分层协调调控方法
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