ABSTRACT
Abstract
An AI-based platform for enabling intelligent orchestration and management of power and energy is disclosed. The platform includes a system configured to perform automated and coordinated governance of a set of energy entities that are operationally coupled within an energy grid and a set of distributed edge energy resources. At least one of the distributed edge energy resources is operationally independent of the energy grid.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation-in-part of PCT Application No. PCT/US22/50932 filed Nov. 23, 2022, which claims the benefit of U.S. Provisional Application Nos. 63/375,225 filed Sep. 10, 2022, 63/302,016 filed Jan. 21, 2022, 63/299,727 filed Jan. 14, 2022, 63/291,311 filed Dec. 17, 2021, and 63/282,510 filed Nov. 23, 2021.
This application is a continuation of PCT Application No. PCT/US22/50924 filed Nov. 23, 2022, which claims the benefit of U.S. Provisional Application Nos. 63/375,225 filed Sep. 10, 2022, 63/302,016 filed Jan. 21, 2022, 63/299,727 filed Jan. 14, 2022, 63/291,311 filed Dec. 17, 2021, and 63/282,510 filed Nov. 23, 2021.
The entire disclosures of the above applications are incorporated by reference.
BACKGROUND
Energy remains a critical factor in the world economy and is undergoing an evolution and transformation, involving changes in energy generation, storage, planning, demand management, consumption and delivery systems and processes. These changes are enabled by the development and convergence of numerous diverse technologies, including more distributed, modular, mobile and/or portable energy generation and storage technologies that will make the energy market much more decentralized and localized, as well as a range of technologies that will facilitate management of energy in a more decentralized system, including edge and Internet of Things networking technologies, advanced computation and artificial intelligence technologies, transaction enablement technologies (such as blockchains, distributed ledgers and smart contracts) and others. The convergence of these more decentralized energy technologies with these networking, computation and intelligence technologies is referred to herein as the âenergy edge.â
The energy market is expected to evolve and transform over the next few decades from a highly centralized model that relies on fossil fuels and a managed electrical grid to a much more distributed and decentralized model that involves many more localized generation, storage, and consumption systems. During that transition, a hybrid system will likely persist for many years in which the conventional grid becomes more intelligent, and in which distributed systems will play a growing role. A need exists for a platform that facilitates management and improvement of legacy infrastructure in coordination with distributed systems.
SUMMARY
An AI-based energy edge platform is provided herein with a wide range of features, components and capabilities for management and improvement of legacy infrastructure and coordination with distributed systems to support important use cases for a range of enterprises. The platform may incorporate emerging technologies to enable ecosystem and individual energy edge node efficiencies, agility, engagement, and profitability. Embodiments may be guided by, and in some cases integrated with, methodologies and systems that are used to forecast, plan for, and manage the demand and utilization of energy in greater distributed environments. Embodiments may use AI, and AI enablers such as IoT, which may be deployed in vastly denser data environments (reflecting the proliferation of smart energy systems and of sensors in the IoT), as well as technologies that filter, process, and move data more effectively across communication networks. Embodiments of the platform may leverage energy market connection, communication, and transaction enablement platforms. Embodiments may employ intelligent provisioning, data aggregation, and analytics. Among many use cases the platform may enable improvements in the optimization of energy generation, storage, delivery and/or enterprise consumption in operations (e.g., buildings, data centers, and factories, among many others), the integration and use of new power generation and energy storage technologies and assets (distributed energy resources, or âDERsâ), the optimization of energy utilization across existing networks and the digitalization of existing infrastructure and supporting systems.
BRIEF DESCRIPTION OF THE DRAWINGS
The present disclosure will become more fully understood from the detailed description and the accompanying drawings.
FIG. 1 is a schematic diagram that presents an introduction of platform and main elements, according to some embodiments.
FIGS. 2 A and 2 B are schematic diagrams that present an introduction of main subsystems of a major ecosystem, according to some embodiments.
FIG. 3 is a schematic diagram that presents more detail on distributed energy generation systems, according to some embodiments.
FIG. 4 is a schematic diagram that presents more detail on data resources, according to some embodiments.
FIG. 5 is a schematic diagram that presents more detail on configured energy edge stakeholders, according to some embodiments.
FIG. 6 is a schematic diagram that presents more detail on intelligence enablement systems, according to some embodiments.
FIG. 7 is a schematic diagram that presents more detail on AI-based energy orchestration, according to some embodiments.
FIG. 8 is a schematic diagram that presents more detail on configurable data and intelligence, according to some embodiments.
FIG. 9 is a schematic diagram that presents a dual-process learning function of a dual-process artificial neural network, according to some embodiments.
FIG. 10 through FIG. 37 are schematic diagrams of embodiments of neural net systems that may connect to, be integrated in, and be accessible by the platform for enabling intelligent transactions including ones involving expert systems, self-organization, machine learning, artificial intelligence and including neural net systems trained for pattern recognition, for classification of one or more parameters, characteristics, or phenomena, for support of autonomous control, and other purposes in accordance with embodiments of the present disclosure.
FIG. 38 is a schematic view of an exemplary embodiment of a quantum computing service according to some embodiments of the present disclosure.
FIG. 39 illustrates quantum computing service request handling according to some embodiments of the present disclosure.
FIG. 40 is a diagrammatic view of a thalamus service and how it coordinates within the modules in accordance with the present disclosure.
FIG. 41 is another diagrammatic view of a thalamus service and how it coordinates within the modules in accordance with the present disclosure.
DETAILED DESCRIPTION
FIG. 1 : Introduction of Platform and Main Elements
In embodiments, provided herein is an AI-based energy edge platform 102 , referred to herein for convenience in some cases as simply the platform 102 , including a set of systems, subsystems, applications, processes, methods, modules, services, layers, devices, components, machines, products, sub-systems, interfaces, connections, and other elements working in coordination to enable intelligent, and in some cases autonomous or semi-autonomous, orchestration and management of power and energy in a variety of ecosystems and environments that include distributed entities (referred to herein in some cases as âdistributed energy resourcesâ or âDERsâ) and other energy resources and systems that generate, store, consume, and/or transport energy and that include IoT, edge and other devices and systems that process data in connection with the DERs and other energy resources and that can be used to inform, analyze, control, optimize, forecast, and otherwise assist in the orchestration of the distributed energy resources and other energy resources.
In embodiments, the platform 102 enables a set of configured stakeholder energy edge solutions 108 , with a wide range of functions, applications, capabilities, and uses that may be accomplished, without limitation, by using or orchestrating a set of advanced energy resources and systems 104 , including DERs and others. The configured stakeholder energy edge solution 108 may integrate, for example, domain-specific stakeholder data, such as proprietary data sets that are generated in connection with enterprise operations, analysis and/or strategy, real-time data from stakeholder assets (such as collected by IoT and edge devices located in proximity to the assets and operations of the stakeholder), stakeholder-specific energy resources and systems 104 (such as available energy generation, storage, or distribution systems that may be positioned at stakeholder locations to augment or substitute for an electrical grid), and the like into a solution that meets the stakeholder's energy needs and capabilities, including baseline, period, and peak energy needs to conduct operations such as large-scale data processing, transportation, production of goods and materials, resource extraction and processing, heating and cooling, and many others.
In embodiments, the AI-based energy edge platform 102 (and/or elements thereof) and/or the set of configured stakeholder energy edge solutions 108 may take data from, provide data to and/or exchange data with a set of data resources for energy edge orchestration 110 .
The AI-based energy edge platform 102 may include, integrate with, exchange data with and/or otherwise link to a set of intelligence enablement systems 112 , a set of AI-based energy orchestration, optimization, and automation systems 114 and a set of configurable data and intelligence modules and services 118 .
The set of intelligence enablement systems 112 may include a set of intelligent data layers 130 , a set of distributed ledger and smart contract systems 132 , a set of adaptive energy digital twin systems 134 , and/or a set of energy simulation systems 136 .
The set of AI-based energy orchestration, optimization, and automation systems 114 may include a set of energy generation orchestration systems 138 , a set of energy consumption orchestration systems 140 , a set of energy marketplace orchestration systems 146 , a set of energy delivery orchestration systems 147 , and a set of energy storage orchestration systems 142 .
The set of configurable data and intelligence modules and services 118 may include a set of energy transaction enablement systems 144 , a set of stakeholder energy digital twins 148 and a set of data integrated microservices 150 that may enable or contribute to enablement of the set of configured stakeholder energy edge solutions 108 .
The AI-based energy edge platform 102 may include, integrate with, link to, exchange data with, be governed by, take inputs from, and/or provide outputs to one or more artificial intelligence (AI) systems, which may include models, rule-based systems, expert systems, neural networks, deep learning systems, supervised learning systems, robotic process automation systems, natural language processing systems, intelligent agent systems, self-optimizing and self-organizing systems, and others as described throughout this disclosure and in the documents incorporated by reference herein. Except where context specifically indicates otherwise, references to AI, or to one or more examples of AI, should b
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation-in-part of PCT Application No. PCT/US22/50932 filed Nov. 23, 2022, which claims the benefit of U.S. Provisional Application Nos. 63/375,225 filed Sep. 10, 2022, 63/302,016 filed Jan. 21, 2022, 63/299,727 filed Jan. 14, 2022, 63/291,311 filed Dec. 17, 2021, and 63/282,510 filed Nov. 23, 2021.
This application is a continuation of PCT Application No. PCT/US22/50924 filed Nov. 23, 2022, which claims the benefit of U.S. Provisional Application Nos. 63/375,225 filed Sep. 10, 2022, 63/302,016 filed Jan. 21, 2022, 63/299,727 filed Jan. 14, 2022, 63/291,311 filed Dec. 17, 2021, and 63/282,510 filed Nov. 23, 2021.
The entire disclosures of the above applications are incorporated by reference.
BACKGROUND
Energy remains a critical factor in the world economy and is undergoing an evolution and transformation, involving changes in energy generation, storage, planning, demand management, consumption and delivery systems and processes. These changes are enabled by the development and convergence of numerous diverse technologies, including more distributed, modular, mobile and/or portable energy generation and storage technologies that will make the energy market much more decentralized and localized, as well as a range of technologies that will facilitate management of energy in a more decentralized system, including edge and Internet of Things networking technologies, advanced computation and artificial intelligence technologies, transaction enablement technologies (such as blockchains, distributed ledgers and smart contracts) and others. The convergence of these more decentralized energy technologies with these networking, computation and intelligence technologies is referred to herein as the âenergy edge.â
The energy market is expected to evolve and transform over the next few decades from a highly centralized model that relies on fossil fuels and a managed electrical grid to a much more distributed and decentralized model that involves many more localized generation, storage, and consumption systems. During that transition, a hybrid system will likely persist for many years in which the conventional grid becomes more intelligent, and in which distributed systems will play a growing role. A need exists for a platform that facilitates management and improvement of legacy infrastructure in coordination with distributed systems.
SUMMARY
An AI-based energy edge platform is provided herein with a wide range of features, components and capabilities for management and improvement of legacy infrastructure and coordination with distributed systems to support important use cases for a range of enterprises. The platform may incorporate emerging technologies to enable ecosystem and individual energy edge node efficiencies, agility, engagement, and profitability. Embodiments may be guided by, and in some cases integrated with, methodologies and systems that are used to forecast, plan for, and manage the demand and utilization of energy in greater distributed environments. Embodiments may use AI, and AI enablers such as IoT, which may be deployed in vastly denser data environments (reflecting the proliferation of smart energy systems and of sensors in the IoT), as well as technologies that filter, process, and move data more effectively across communication networks. Embodiments of the platform may leverage energy market connection, communication, and transaction enablement platforms. Embodiments may employ intelligent provisioning, data aggregation, and analytics. Among many use cases the platform may enable improvements in the optimization of energy generation, storage, delivery and/or enterprise consumption in operations (e.g., buildings, data centers, and factories, among many others), the integration and use of new power generation and energy storage technologies and assets (distributed energy resources, or âDERsâ), the optimization of energy utilization across existing networks and the digitalization of existing infrastructure and supporting systems.
BRIEF DESCRIPTION OF THE DRAWINGS
The present disclosure will become more fully understood from the detailed description and the accompanying drawings.
FIG. 1 is a schematic diagram that presents an introduction of platform and main elements, according to some embodiments.
FIGS. 2 A and 2 B are schematic diagrams that present an introduction of main subsystems of a major ecosystem, according to some embodiments.
FIG. 3 is a schematic diagram that presents more detail on distributed energy generation systems, according to some embodiments.
FIG. 4 is a schematic diagram that presents more detail on data resources, according to some embodiments.
FIG. 5 is a schematic diagram that presents more detail on configured energy edge stakeholders, according to some embodiments.
FIG. 6 is a schematic diagram that presents more detail on intelligence enablement systems, according to some embodiments.
FIG. 7 is a schematic diagram that presents more detail on AI-based energy orchestration, according to some embodiments.
FIG. 8 is a schematic diagram that presents more detail on configurable data and intelligence, according to some embodiments.
FIG. 9 is a schematic diagram that presents a dual-process learning function of a dual-process artificial neural network, according to some embodiments.
FIG. 10 through FIG. 37 are schematic diagrams of embodiments of neural net systems that may connect to, be integrated in, and be accessible by the platform for enabling intelligent transactions including ones involving expert systems, self-organization, machine learning, artificial intelligence and including neural net systems trained for pattern recognition, for classification of one or more parameters, characteristics, or phenomena, for support of autonomous control, and other purposes in accordance with embodiments of the present disclosure.
FIG. 38 is a schematic view of an exemplary embodiment of a quantum computing service according to some embodiments of the present disclosure.
FIG. 39 illustrates quantum computing service request handling according to some embodiments of the present disclosure.
FIG. 40 is a diagrammatic view of a thalamus service and how it coordinates within the modules in accordance with the present disclosure.
FIG. 41 is another diagrammatic view of a thalamus service and how it coordinates within the modules in accordance with the present disclosure.
DETAILED DESCRIPTION
FIG. 1 : Introduction of Platform and Main Elements
In embodiments, provided herein is an AI-based energy edge platform 102 , referred to herein for convenience in some cases as simply the platform 102 , including a set of systems, subsystems, applications, processes, methods, modules, services, layers, devices, components, machines, products, sub-systems, interfaces, connections, and other elements working in coordination to enable intelligent, and in some cases autonomous or semi-autonomous, orchestration and management of power and energy in a variety of ecosystems and environments that include distributed entities (referred to herein in some cases as âdistributed energy resourcesâ or âDERsâ) and other energy resources and systems that generate, store, consume, and/or transport energy and that include IoT, edge and other devices and systems that process data in connection with the DERs and other energy resources and that can be used to inform, analyze, control, optimize, forecast, and otherwise assist in the orchestration of the distributed energy resources and other energy resources.
In embodiments, the platform 102 enables a set of configured stakeholder energy edge solutions 108 , with a wide range of functions, applications, capabilities, and uses that may be accomplished, without limitation, by using or orchestrating a set of advanced energy resources and systems 104 , including DERs and others. The configured stakeholder energy edge solution 108 may integrate, for example, domain-specific stakeholder data, such as proprietary data sets that are generated in connection with enterprise operations, analysis and/or strategy, real-time data from stakeholder assets (such as collected by IoT and edge devices located in proximity to the assets and operations of the stakeholder), stakeholder-specific energy resources and systems 104 (such as available energy generation, storage, or distribution systems that may be positioned at stakeholder locations to augment or substitute for an electrical grid), and the like into a solution that meets the stakeholder's energy needs and capabilities, including baseline, period, and peak energy needs to conduct operations such as large-scale data processing, transportation, production of goods and materials, resource extraction and processing, heating and cooling, and many others.
In embodiments, the AI-based energy edge platform 102 (and/or elements thereof) and/or the set of configured stakeholder energy edge solutions 108 may take data from, provide data to and/or exchange data with a set of data resources for energy edge orchestration 110 .
The AI-based energy edge platform 102 may include, integrate with, exchange data with and/or otherwise link to a set of intelligence enablement systems 112 , a set of AI-based energy orchestration, optimization, and automation systems 114 and a set of configurable data and intelligence modules and services 118 .
The set of intelligence enablement systems 112 may include a set of intelligent data layers 130 , a set of distributed ledger and smart contract systems 132 , a set of adaptive energy digital twin systems 134 , and/or a set of energy simulation systems 136 .
The set of AI-based energy orchestration, optimization, and automation systems 114 may include a set of energy generation orchestration systems 138 , a set of energy consumption orchestration systems 140 , a set of energy marketplace orchestration systems 146 , a set of energy delivery orchestration systems 147 , and a set of energy storage orchestration systems 142 .
The set of configurable data and intelligence modules and services 118 may include a set of energy transaction enablement systems 144 , a set of stakeholder energy digital twins 148 and a set of data integrated microservices 150 that may enable or contribute to enablement of the set of configured stakeholder energy edge solutions 108 .
The AI-based energy edge platform 102 may include, integrate with, link to, exchange data with, be governed by, take inputs from, and/or provide outputs to one or more artificial intelligence (AI) systems, which may include models, rule-based systems, expert systems, neural networks, deep learning systems, supervised learning systems, robotic process automation systems, natural language processing systems, intelligent agent systems, self-optimizing and self-organizing systems, and others as described throughout this disclosure and in the documents incorporated by reference herein. Except where context specifically indicates otherwise, references to AI, or to one or more examples of AI, should be understood to encompass these various alternative methods and systems; for example, without limitation, an AI system described for enabling any of a wide variety of functions, capabilities and solutions described herein (such as optimization, autonomous operation, prediction, control, orchestration, or the like) should be understood to be capable of implementation by operation on a model or rule set; by training on a training data set of human tag, labels, or the like; by training on a training data set of human interactions (e.g., human interactions with software interfaces or hardware systems); by training on a training data set of outcomes; by training on an AI-generated training data set (e.g., where a full training data set is generated by AI from a seed training data set); by supervised learning; by semi-supervised learning; by deep learning; or the like. For any given function or capability that is described herein, neural networks of various types may be used, including any of the types described herein or in the documents incorporated by reference, and, in embodiments, a hybrid set of neural networks may be selected such that within the set a neural network type that is more favorable for performing each element of a multi-function or multi-capability system or method is implemented. As one example among many, a deep learning, or black box, system may use a gated recurrent neural network for a function like language translation for an intelligent agent, where the underlying mechanisms of AI operation need not be understood as long as outcomes are favorably perceived by users, while a more transparent model or system and a simpler neural network may be used for a system for automated governance, where a greater understanding of how inputs are translated to outputs may be needed to comply with regulations or policies.
AI-Based Energy Orchestration, Optimization and Automation Systems
In embodiments, the platform may employ demand forecasting, including automated forecasting by artificial intelligence or by taking a data stream of forecast information from a third party. Among other things, forecasting demand helps inform site selection and intelligently planned network expansion. In embodiments, machine learning algorithms may generate multiple forecastsâsuch as about weather, prices, solar generation, energy demand, and other factorsâand analyze how energy assets can best capture or generate value at different times and/or locations.
In embodiments, AI-based energy orchestration, optimization, and automation systems 114 may enable energy pattern optimization, such as by analyzing building or other operational energy usage and seeking to reshape patterns for optimization (e.g., by modeling demand response to various stimuli).
The AI-based energy orchestration, optimization, and automation systems 114 may be enabled by the set of intelligence enablement systems 112 that provide functions and capabilities that support a range of applications and use cases.
Subsystems and Modules of Intelligence Enablement Systems
Intelligent Data Layers
The intelligence enablement systems 112 may include a set of intelligent data layers 130 , such as a set of services (including microservices), APIs, interfaces, modules, applications, programs, and the like which may consume any of the data entities and types described throughout this disclosure and undertake a wide range of processing functions, such as extraction, cleansing, normalization, calculation, transformation, loading, batch processing, streaming, filtering, routing, parsing, converting, pattern recognition, content recognition, object recognition, and others. Through a set of interfaces, a user of the platform 102 may configure the intelligent data layers 130 or outputs thereof to meet internal platform needs and/or to enable further configuration, such as for the stakeholder energy edge solutions 108 . The intelligent data layers 130 , intelligence enablement systems 112 more generally, and/or the configurable data and intelligence modules and services 118 may access data from various sources throughout the platform 102 and, in embodiments, may operate from the set of shared data resources 130 , which may be contained in a centralized database and/or in a set of distributed databases, or which may consist of a set of distributed or decentralized data sources, such as IoT or edge devices that produce energy-relevant event logs or streams. The intelligent data layers 130 may be configured for a wide range of energy-relevant tasks, such as prediction/forecasting of energy consumption, generation, storage or distribution parameters (e.g., at the level of individual devices, subsystems, systems, machines, or fleets); optimization of energy generation, storage, distribution or consumption (also at various levels of optimization); automated discovery, configuration and/or execution of energy transactions (including microtransactions and/or larger transactions in spot and futures markets as well as in peer-to-peer groups or single counterparty transactions); monitoring and tracking of parameters and attributes of energy consumption, generation, distribution and/or storage (e.g., baseline levels, volatility, periodic patterns, episodic events, peak levels, and the like); monitoring and tracking of energy-related parameters and attributes (e.g., pollution, carbon production, renewable energy credits, production of waste heat, and others); automated generation of energy-related alerts, recommendations and other content (e.g., messaging to prompt or promote favorable user behavior); and many others.
Distributed Ledger and Smart Contract Systems
Energy edge intelligence enablement systems 112 may include a smart contract system 132 for handling a set of smart contracts, each of which may optionally operate on a set of blockchain-based distributed ledgers. Each of the smart contracts may operate on data stored in the set of distributed ledgers or blockchains, such as to record energy-related transactional events, such as energy purchases and sales (in spot, forward and peer-to-peer markets, as well as direct counterparty transactions), relevant service charges and the like; transaction relevant energy events, such as consumption, generation, distribution and/or storage events, and other transaction-relevant events often associated with energy, such as carbon production or abatement events, renewable energy credit events, pollution production or abatement events, and the like. The set of smart contracts handled by the smart contract system 132 may consume as a set of inputs any of the data types and entities described throughout this disclosure, undertake a set of calculations (optionally configured in a flow that takes inputs from disparate systems in a multi-step transaction), and provide a set of outputs that enable completion of a transaction, reporting (optionally recorded on a set of distributed ledgers), and the like. Energy transactional enablement systems 144 may be enabled or augmented by artificial intelligence, including to autonomously discover, configure, and execute transactions according to a strategy and/or to provide automation or semi-automation of transactions based on training and/or supervision by a set of transaction experts. In embodiments, the smart contract systems 132 may be used by the energy transactional enablement systems 144 (described elsewhere in this disclosure) to configure transactional solutions.
Adaptive Energy Digital Twin Systems
Any entity, analytic results, output of artificial intelligence, state, operating condition, or other feature noted throughout this disclosure may, in embodiments, be presented in a digital twin, such as the adaptive energy digital twin 134 , which is widely applicable, and/or the stakeholder energy digital twin 148 , which is configured for the needs of a particular stakeholder or stakeholder solution. The adaptive energy digital twin 134 may, for example, provide a visual or analytic indicator of energy consumption by a set of machines, a group of factories, a fleet of vehicles, or the like; a subset of the same (e.g., to compare energy parameters by each of a set of similar machines to identify out-of-range behavior); and many other aspects. A digital twin may be adaptive, such as to filter, highlight, or otherwise adjust data presented based on real-time conditions, such as changes in energy costs, changes in operating behavior, or the like.
Energy Simulation Systems
In embodiments, a set of energy simulation systems 136 is provided, such as to develop and evaluate detailed simulations of energy generation, demand response and charge management, including a simulation environment that simulates the outcomes of use of various algorithms that may govern generation across various generations assets, consumption by devices and systems that demand energy, and storage of energy. Data can be used to simulate the interaction of non-controllable loads and optimized charging processes, among other use cases. The simulation environment may provide output to, integrate with, or share data with the set of advanced energy digital twin systems 134 .
In embodiments, as more enterprises embrace hybrid infrastructure, uptime is becoming more complex, requiring backup and failover strategies that span cloud, colocation, on-premises facilities, and edge infrastructure. This may include AI-based algorithms for automatically managing energy for devices and systems in such devices. For example, artificial intelligence may enable autonomous data center cooling and industrial control. In embodiments, DERs 128 may be integrated into or with, for example, AI-driven computing infrastructure, smart PDUs, UPS systems, energy-enabled air flow management systems, and HVAC systems, among others.
Introduction of Main Subsystems and Modules of AI-Based Energy Orchestration, Optimization, and Automation Systems
The set of AI-based energy orchestration, optimization, and automation systems 114 may include the set of energy generation orchestration systems 138 , the set of energy consumption orchestration systems 140 , the set of energy storage orchestration systems 142 , the set of energy marketplace orchestration systems 146 and the set of energy delivery orchestration systems 147 , among others. For example, the energy delivery orchestration systems 147 may enable orchestration of the delivery of energy to a point of consumption, such as by fixed transmission lines, wireless energy transmission, delivery of fuel, delivery of stored energy (e.g., chemical or nuclear batteries), or the like, and may involve autonomously optimizing the mix of energy types among the foregoing available resources based on various factors, such as location (e.g., based on distance from the grid), purpose or type of consumption (e.g., whether there is a need for very high peak energy delivery, such as for power-intensive production processes), and the like.
Configurable Data and Intelligence Modules and Services
In embodiments, the platform 102 may include a set of configurable data and intelligence modules and services 118 . These may include energy transaction enablement systems 144 , stakeholder energy digital twins 148 , energy-related data integrated microservices 150 , and others. Each module or service (optionally configured in a microservices architecture) may exchange data with the various data resources 110 in order to provide a relevant output, such as to support a set of internal functions or capabilities of the platform 102 and/or to support a set of functions or capabilities of one or more of the configured stakeholder energy edge solutions 108 . As one example among many, a service may be configured to take event data from an IoT device that has cameras or sensors that monitor a generator and integrate it with weather data from a public data resource 162 to provide a weather-correlated timeline of energy generation data for the generator, which in turn may be consumed by a stakeholder energy edge solution 108 , such as to assist with forecasting day-ahead energy generation by the generator based on a day-ahead weather forecast. A wide range of such configured data and intelligence modules and services 118 may be enabled by the platform 102 , representing, for example, various outputs that consist of the fusion or combination of the wide range of energy edge data sources handled by the platform, higher-level analytic outputs resulting from expert analysis of data, forecasts and predictions based on patterns of data, automation and control outputs, and many others.
Energy Transaction Enablement Systems
Configurable data and intelligence modules and services 118 may include energy transaction enablement systems 144 . Transaction enablement systems 144 may include a set of smart contracts, which may operate on data stored in a set of distributed ledgers or blockchains, such as to record energy-related transactional events, such as energy purchases and sales (in spot, forward and peer-to-peer markets, as well as direct counterparty transactions) and relevant service charges; transaction relevant energy events, such as consumption, generation, distribution and/or storage events, and other transaction-relevant events often associated with energy, such as carbon production or abatement events, renewable energy credit events, pollution production or abatement events, and the like. The set of smart contracts may consume as a set of inputs any of the data types and entities described throughout this disclosure, undertake a set of calculations (optionally configured in a flow that takes inputs from disparate systems in a multi-step transaction), and provide a set of outputs that enable completion of a transaction, reporting (optionally recorded on a set of distributed ledgers), and the like. Energy transactional enablement systems 144 may be enabled or augmented by artificial intelligence, including to autonomously discover, configure, and execute transactions according to a strategy and/or to provide automation or semi-automation of transactions based on training and/or supervision by a set of transaction experts. Autonomy and/or automation (supervised or semi-supervised) may be enabled by robotic process automation, such as by training a set of intelligent agents on transactional discovery, configuration, or execution interactions of a set of transactional experts with transaction-enabling systems (such as software systems used to configure and execute energy trading activities).
As energy is increasingly produced and consumed in local, decentralized markets, the energy market is likely to follow patterns of other peer-to-peer or shared economy markets, such as ride sharing, apartment sharing and used goods markets. Technology enables the bypassing of top-down or centralized energy supply and enables operators to create platforms that can manage and monetize spare capacity, such as through the leasing and trading of assets and outputs.
As more distributed or peer-to-peer transactive energy markets develop, the platform 102 may include systems or link to, integrate with, or enable other platforms that facilitate P2P trading, wholesale contracts, renewable energy certificate (REC) tracking, and broader distributed energy provisioning, payment management and other transaction elements. In embodiments, the foregoing may use blockchain, distributed ledger and/or smart contract systems 132 .
In embodiments, with increased transparency, choice, and flexibility, consumers will be able to participate actively in energy markets, by generating, storing, and selling, as well as consuming electricity.
In embodiments, transactional elements may be configured by energy transaction enablement systems 144 to optimize energy generation, storage, or consumption, such as utility time of use charges. Shifting energy demand away from high-priced time periods with IoT-based platforms that can identify periods where energy costs are the least expensive.
Stakeholder Energy Digital Twins
The configurable data and intelligence modules and services 118 may include one or more stakeholder energy digital twins 148 , which may, in embodiments, include set of digital twins that are configured to represent a set of stakeholder entities that are relevant to energy, including stakeholder-owned and stakeholder-operated energy generation resources, energy distribution resources, and/or energy distribution resources (including representing them by type, such as indicating renewable energy systems, carbon-producing systems, and others); stakeholder information technology and networking infrastructure entities (e.g., edge and IoT devices and systems, networking systems, data centers, cloud data systems, on premises information technology systems, and the like); energy-intensive stakeholder production facilities, such as machines and systems used in manufacturing; stakeholder transportation systems; market conditions (e.g., relating to current and forward market pricing for energy, for the stakeholder's supply chain, for the stakeholders product and services, and the like), and others. The digital twins 148 may provide real-time information, such as provided sensor data from IoT and edge devices, event logs, and other information streams, about status, operating conditions, and the like, particularly relating to energy consumption, generation, storage, and or distribution.
The stakeholder energy digital twin 148 may provide a visual, real-time view of the impact of energy on all aspects of an enterprise. A digital twin may be role-based, such as providing visual and analytic indicators that are suitable for the role of the user, such as financial reporting information for a CFO; operating parameter information for a power plant manager; and energy market information for an energy trader.
Data Integrated Microservices
The configurable data and intelligence modules and services 118 may include configurable data integrated microservices 150 , such as organized in a service-oriented architecture, such that various microservices can be grouped in series, in parallel, or in more complex flows to create higher-level, more complex services that each provide a defined set of outputs by processing a defined set of outputs, such as to enable a particular stakeholder solution 108 or to facilitate AI-based orchestration, optimization and/or automation systems 114 . The configurable data and intelligence modules and services 118 may, without limitation, be configured from various functions and capabilities of the intelligent data layers 130 , which in turn operate on various data resources for energy edge orchestration 110 and/or internal event logs, outputs, data streams and the like of the platform 102 .
FIGS. 2 A- 2 B : Introduction of Main Subsystems of Major Ecosystem Components Data Resources for Energy Edge Orchestration
Referring to FIG. 2 A , the data resources for energy edge orchestration 110 may include a set of Edge and IoT Networking Systems 160 , a set of Public data resources 162 , and/or a set of Enterprise data resources 168 , which in embodiments may use or be enabled by an Adaptive Energy Data Pipeline 164 that automatically handles data processing, filtering, compression, storage, routing, transport, error correction, security, extraction, transformation, loading, normalization, cleansing and/or other data handling capabilities involved in the transport of data over a network or communication system. This may include adapting one or more of these aspects of data handling based on data content (e.g., by packet inspection or other mechanisms for understanding the same), based on network conditions (e.g., congestion, delays/latency, packet loss, error rates, cost of transport, quality of service (QoS), or the like), based on context of usage (e.g., based on user, system, use case, application, or the like, including based on prioritization of the same), based on market factors (e.g., price or cost factors), based on user configuration, or other factors, as well as based on various combinations of the same. For example, among many others, a least-cost route may be automatically selected for data that relates to management of a low-priority use of energy, such as heating a swimming pool, while a fastest or highest-QoS route may be selected for data that supports a prioritized use or energy, such as support of critical healthcare infrastructure.
Referring to FIG. 2 B , the platform 102 and orchestration may include, integrate, link to, integrate with, use, create, or otherwise handle, a wide range of data resources for the advanced energy resources and systems 104 , the configured stakeholder energy edge solutions 108 , and/or the energy edge orchestration 110 . In embodiments, elements of the advanced energy resources and systems 104 , the configured stakeholder energy edge solutions 108 , and/or the energy edge orchestration 110 may be the same as, similar to, or different from corresponding elements shown in FIG. 1 . The data resources 110 may include separate databases, distributed databases, and/or federated data resources, among many others.
Edge and IoT Networking Systems
A wide range of energy-related data may be collected and processed (including by artificial intelligence services and other capabilities), and control instructions may be handled, by a set of edge and IoT networking systems 160 , such as ones integrated into devices, components or systems, ones located in IoT devices and systems, ones located in edge devices and systems, or the like, such as where the foregoing are located in or around energy-related entities, such as ones used by consumers or enterprises, such as ones involved in energy generation, storage, delivery or use. These include any of the wide range of software, data and networking systems described herein.
Public Data Resources
In embodiments, the platform 102 may track various public data resources 162 , such as weather data. Weather conditions can impact energy use, particularly as they relate to HVAC systems. Collecting, compiling, and analyzing weather data in connection with other building information allows building managers to be proactive about HVAC energy consumption. A wide range of public data resources 162 may include satellite data, demographic and psychographic data, population data, census data, market data, website data, ecommerce data, and many other types.
Enterprise Data Resources
Enterprise data resources 168 may include a wide range of enterprise resources, such as enterprise resource planning data, sales and marketing data, financial planning data, accounting data, tax data, customer relationship management data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, operating data, and many others.
Subsystems and Modules of Advanced Energy Resources and Systems
In embodiments, the advanced energy resources and systems 104 may include distributed energy resources 128 , or âDERsâ 128 . More decentralized energy resources will mean that more individuals, networked groups, and energy communities will be capable of generating and sharing their own energy and coordinating systems to achieve ultimate efficacy. The DER 128 may be a small- or medium-scale unit of power generation and/or storage that operates locally and may be connected to a larger power grid at the distribution level. That is, the DER systems 128 may be either connected to the local electric power grid or isolated from the grid in stand-alone applications.
Transformed Energy Infrastructure
The advanced energy resources and <figure-callout id="104" label="systems" filenames="US12298
CLAIMS
Claims ( 23 )
The invention claimed is:
1. An artificial-intelligence-based (AI-based) platform for enabling intelligent orchestration and management of power and energy, the AI-based platform comprising:
a system configured to perform automated and coordinated governance of a set of energy entities that are operationally coupled within an energy grid and a set of distributed edge energy resources,
wherein at least one of the set of distributed edge energy resources is operationally independent of the energy grid,
wherein the system is configured to facilitate governance of a mining environment,
wherein the system includes mine-level Internet of Things (IoT) sensing of the mining environment,
wherein the mine-level IoT sensing includes ground-penetrating sensing of unmined portions of the mining environment, wearable devices for detecting physiological status of miners, and secure recording and resolution of transactions and transaction-related events, and
wherein the system includes smart contracts for automatically allocating proceeds derived from the mining environment.
2. The AI-based platform of claim 1 , wherein:
the system is further configured to adapt a transport of data over at least one of a network or a communication system, and
the adapting is based on at least one of:
a congestion condition,
a delay condition,
a latency condition,
a packet loss condition,
an error rate condition,
a cost of transport condition,
a quality-of-service (QoS) condition,
a usage condition,
a market factor condition, or
a user configuration condition.
3. The AI-based platform of claim 1 , further comprising an adaptive energy digital twin that represents at least one of:
an energy stakeholder entity,
an energy distribution resource,
a stakeholder information technology,
a networking infrastructure entity,
an energy-dependent stakeholder production facility,
a stakeholder transportation system,
a market condition, or
an energy usage priority condition.
4. The AI-based platform of claim 1 , further comprising an adaptive energy digital twin that is configured to perform at least one of:
providing a visual indicator of energy consumption by one or more energy consumers,
providing an analytic indicator of the energy consumption,
filtering energy data,
highlighting the energy data, or
adjusting the energy data.
5. The AI-based platform of claim 1 , further comprising an adaptive energy digital twin that is configured to generate an indicator of energy consumption by at least one of:
one or more machines,
one or more factories, or
one or more vehicles in a vehicle fleet,
wherein the indicator is at least one of visual or analytic.
6. The AI-based platform of claim 1 , wherein the system is further configured to perform at least one of:
extracting energy-related data,
detecting errors in the energy-related data,
correcting errors in the energy-related data,
transforming the energy-related data,
converting the energy-related data,
normalizing the energy-related data,
cleansing the energy-related data,
parsing the energy-related data,
detecting patterns in the energy-related data,
detecting content in the energy-related data,
detecting objects in the energy-related data,
compressing the energy-related data,
streaming the energy-related data,
filtering the energy-related data,
loading the energy-related data,
storing the energy-related data,
routing the energy-related data,
transporting the energy-related data, or
maintaining security of the energy-related data.
7. The AI-based platform of claim 1 , further comprising at least one of an AI-based model or an AI-based algorithm, wherein:
the at least one of the AI-based model or the AI-based algorithm is trained based on a training data set, and
the training data set is based on at least one of:
one or more human tags,
one or more human labels,
one or more human interactions with a hardware system,
one or more human interactions with a software system,
one or more human interactions with a hardware and software system,
one or more outcomes,
one or more AI-generated training data samples,
a supervised learning training process,
a semi-supervised learning training process, or
a deep learning training process.
8. The AI-based platform of claim 1 , wherein:
the system is further configured to orchestrate delivery of energy to one or more points of consumption, and
the delivery of the energy includes at least one of:
one or more fixed transmission lines,
one or more instances of wireless energy transmission,
one or more deliveries of fuel, or
one or more deliveries of stored energy.
9. The AI-based platform of claim 1 , wherein:
the system is further configured to record, in at least one of a distributed ledger or a blockchain, one or more energy-related events, and
the one or more energy-related events include at least one of:
an energy purchase event,
an energy sale event,
a service charge associated with the energy purchase event,
a service charge associated with the energy sale event,
an energy consumption event,
an energy generation event,
an energy distribution event,
an energy storage event,
a carbon emission production event,
a carbon emission abatement event,
a renewable energy credit event,
a pollution production event, or
a pollution abatement event.
10. The AI-based platform of claim 1 , wherein:
at least one of the set of distributed edge energy resources is deployed in an off-grid environment, and
the off-grid environment includes at least one of:
an off-grid energy generation system,
an off-grid energy storage system, or
an off-grid energy mobilization system.
11. The AI-based platform of claim 1 , wherein the system includes at least one of:
mass spectrometry,
computer-vision-based sensing of mined materials,
asset tagging of smart containers, or
an automated system for recording, reporting, and assessing compliance with contractual, regulatory, and legal policy requirements.
12. The AI-based platform of claim 1 , wherein:
the system includes a set of carbon-aware edge energy solutions, and
the set of carbon-aware edge energy solutions includes exploring, configuring, and implementing a set of policies regarding carbon generation.
13. The AI-based platform of claim 12 , wherein the set of carbon-aware edge energy solutions requires energy production by a mining environment to be monitored to track carbon emissions generated by the mining environment.
14. The AI-based platform of claim 12 , wherein the set of carbon-aware edge energy solutions requires energy production by a mining environment to offset carbon generation by the mining environment.
15. The AI-based platform of claim 1 , further comprising:
a user interface,
wherein the system includes a set of automated energy policy deployment solutions that are configurable via user interaction with the user interface.
16. The AI-based platform of claim 1 , wherein:
the system includes an intelligent agent trained to generate policies related to governance of the mining environment, and
the intelligent agent being is trained on a training set of historical data, feedback from outcomes, and human policy-setting interactions.
17. The AI-based platform of claim 1 , wherein the system facilitates governance of the mining environment by implementing policies including at least one of:
setting maximum energy usage for an entity for a time period,
setting maximum energy cost for an entity for a time period,
setting maximum carbon production for an entity for a time period,
setting maximum pollution emissions for an entity for a time period,
setting carbon offset requirements,
setting renewable energy credit requirements,
setting energy mix requirements,
setting profit margin minimums based on energy and other marginal costs for a production entity, or
setting minimum storage baselines for energy storage entities.
18. The AI-based platform of claim 1 , wherein the system includes a set of energy governance smart contract solutions configured to allow a user of the AI-based platform to at least one of design, generate, or deploy a smart contract that automatically provides a degree of governance of a set of energy transaction.
19. The AI-based platform of claim 1 , wherein the system includes a set of automated energy financial control solutions configured to allow a user of the AI-based platform to at least one of design, generate, configure, or deploy a policy related to control of financial factors related to at least one of energy generation, storage, delivery, or utilization.
20. An artificial-intelligence-based (AI-based) method for enabling intelligent orchestration and management of power and energy, the method comprising:
performing automated and coordinated governance of a set of energy entities that are operationally coupled within an energy grid and a set of distributed edge energy resources, wherein at least one of the set of distributed edge energy resources is operationally independent of the energy grid;
facilitating governance of a mining environment;
performing mine-level Internet of Things (IoT) sensing of the mining environment, wherein the mine-level IoT sensing includes ground-penetrating sensing of unmined portions of the mining environment, wearable devices for detecting physiological status of miners, and secure recording and resolution of transactions and transaction-related events; and
implementing smart contracts for automatically allocating proceeds derived from the mining environment.
21. The AI-based method of claim 20 , further comprising:
adapting a transport of data over at least one of a network or a communication system,
wherein the adapting is based on at least one of:
a congestion condition,
a delay condition,
a latency condition,
a packet loss condition,
an error rate condition,
a cost of transport condition,
a quality-of-service (QoS) condition,
a usage condition,
a market factor condition, or
a user configuration condition.
22. The AI-based method of claim 20 , further comprising at least one of:
performing mass spectrometry,
sensing mined materials using computer vision,
asset tagging smart containers, or
automatedly recording, reporting, and assessing compliance with at least one of contractual, regulatory, or legal policy requirements.
23. The AI-based method of claim 20 , further comprising:
training an intelligent agent to generate policies related to governance of the mining environment,
wherein the training is based on a training set of at least one of historical data, feedback from outcomes, or human policy-setting interactions.
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2021-11-23
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AI-based energy edge platform, systems, and methods having a digital twin of decentralized infrastructure
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Process-aware AI-based energy edge platform, systems, and methods
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AI-based energy edge platform, systems, and methods having a robotic process automation system
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Pending
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2021-11-23
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2021-11-23
2023-12-08
AI-Based Energy Edge Platform, Systems, and Methods That Recommend Operating Parameters Based on Energy Demands Within a Defined Domain
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Intelligent Orchestration Systems for Energy and Power Management of Heterogeneous Energy-Related Systems and Devices
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Pending
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Pending
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2021-11-23
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Pending
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