US-12620039-B2 - Governance engines for energy- and power-related facilities and systems

Abstract

Disclosed herein are AI-based platforms for enabling intelligent orchestration and management of power and energy. In various embodiments, a set of edge devices is configured to communicate with at least one energy generation facility, energy storage facility, and/or energy consumption system and automatically execute a set of preconfigured policies that govern energy generation, energy storage, or energy consumption of the respective energy generation facilities, energy storage facilities, or energy consumption systems. In some embodiments, the automatically executed policies are a set of contextual policies that adjust based on the current status of a set of energy generation entities in an energy grid.

Inventors

• Charles H. Cella
• Andrew Cardno

Assignees

• STRONG FORCE EE PORTFOLIO 2022, LLC

Dates

Publication Date

20260505

Application Date

20230615

Claims (20)

1. 1 . An artificial intelligence-based (AI-based) platform for enabling intelligent orchestration and management of power and energy, the AI-based platform comprising: a set of edge devices configured to, communicate with an energy system associated with an energy use, store a set of preconfigured policies, wherein, the set of preconfigured policies encodes a set of operational priorities, the energy use is associated with an energy use priority, and the energy use includes at least one of, energy generation of an energy generation facility, energy storage of an energy storage facility, or energy consumption of an energy consumption system, adapt at least one communication parameter of a communication system for transport of a dataset, wherein, the dataset is associated with the energy use priority, and transport the dataset, via the communication system, using the at least one communication parameter, wherein transporting the dataset includes: determining whether a set of transmission criteria has been met, in response to a determination that the set of transmission criteria has been met, transport the dataset, and in response to a determination that the set of transmission criteria has not been met: comparing the energy use priority to the set of operational priorities, and in response to a determination that the energy use priority is aligned with the set of operational priorities, transport the dataset, and, otherwise, delay the transport of the dataset.
2. 2 . The AI-based platform of claim 1 , wherein the set of preconfigured policies is a set of contextual policies that adjusts based on a current status of a set of energy generation entities in an energy grid.
3. 3 . The AI-based platform of claim 1 , wherein the set of preconfigured policies is a set of contextual policies that adjusts based on a current status of a set of energy generation entities in an energy generation environment that includes an energy grid and a set of distributed energy resources that operate independently of the energy grid.
4. 4 . The AI-based platform of claim 1 , wherein the set of preconfigured policies is a set of contextual policies that adjusts based on a current status of a set of energy storage entities in an energy grid.
5. 5 . The AI-based platform of claim 1 , wherein, the set of preconfigured policies is a set of contextual policies that adjusts based on a current status of a set of energy storage entities in an energy storage environment that includes an energy grid and a set of distributed energy resources that operate independently of the energy grid, and the set of preconfigured policies is a set of contextual policies that adjusts based on the current status of a set of energy delivery entities in an energy grid.
6. 6 . The AI-based platform of claim 1 , wherein the set of preconfigured policies is a set of contextual policies that adjusts based on a current status of a set of energy transmission entities in an energy transmission environment that includes an energy grid and a set of distributed energy resources that operate independently of the energy grid.
7. 7 . The AI-based platform of claim 1 , wherein the set of preconfigured policies is a set of contextual policies that adjusts based on a current status of a set of energy consumption entities that consume energy from an energy grid.
8. 8 . The AI-based platform of claim 1 , wherein the set of preconfigured policies is a set of contextual policies that adjusts based on a current status of a set of energy consumption entities that consume energy from an energy grid and from a set of distributed energy resources that operate independently of the energy grid.
9. 9 . The AI-based platform of claim 1 , wherein, at least one edge device of the set of edge devices is further configured to adjust the set of preconfigured policies based on at least one contextual factor, and the at least one contextual factor includes at least one of, historical data of energy transactions, at least one operational factor, at least one market factor, at least one anticipated market behavior, or at least one anticipated customer behavior.
10. 10 . The AI-based platform of claim 1 , wherein the adapting the at least one communication parameter is based on at least one of, the priority associated with the energy use, at least one market factor, 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, or a user configuration condition.
11. 11 . 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.
12. 12 . The AI-based platform of claim 1 , further comprising an adaptive energy digital twin that is configured to perform at least one of, providing at least one of a visual or an analytic indicator of energy consumption by at least one energy consumer, filtering energy data, highlighting energy data, or adjusting energy data.
13. 13 . The AI-based platform of claim 1 , further comprising an adaptive energy digital twin that is configured to generate at least one of a visual or an analytic indicator of energy consumption by at least one of, at least one machine, at least one factory, or at least one vehicle in a vehicle fleet.
14. 14 . The AI-based platform of claim 1 , wherein at least one edge device of the set of edge devices is further configured to perform at least one of, extracting energy-related data, detecting errors in energy-related data, correcting errors in energy-related data, transforming energy related-data, converting energy related-data, normalizing energy-related data, cleansing energy-related data, parsing energy-related data, detecting patterns in energy-related data, detecting content in energy-related data, detecting objects in energy-related data, compressing energy-related data, streaming energy-related data, filtering energy-related data, loading energy-related data, storing energy-related data, routing energy-related data, transporting energy-related data, or maintaining security of energy-related data.
15. 15 . The AI-based platform of claim 1 , wherein: at least one policy of the set of preconfigured policies is based on at least one public data resource, and the at least one public data resource includes at least one of, a weather data resource, a satellite data resource, a census resource, a population resource, a demographic resource, a psychographic data resource, a market data resource, or an ecommerce data resource.
16. 16 . The AI-based platform of claim 1 , wherein: at least one policy of the set of preconfigured policies is based on at least one enterprise data resource, and the at least one enterprise data resource includes at least one of, resource planning data, sales data, marketing data, financial planning data, demand planning data, supply chain data, procurement data, pricing data, customer data, product data, or operating data.
17. 17 . The AI-based platform of claim 1 , wherein, at least one edge device of the set of edge devices includes at least one of at least one AI-based model or algorithm, the at least one AI-based model or algorithm is trained based on a training data set, and the training data set is based on at least one of, at least one human tag, at least one human label, at least one human interaction with at least one of a hardware system or a software system, at least one outcome, at least one AI-generated training data sample, a supervised learning training process, a semi-supervised learning training process, or a deep learning training process.
18. 18 . The AI-based platform of claim 1 , wherein, at least one edge device of the set of edge devices is configured to orchestrate delivery of energy to at least one point of consumption, and the delivery of the energy includes at least one of, at least one fixed transmission line, at least one instance of wireless energy transmission, at least one delivery of fuel, or at least one delivery of stored energy.
19. 19 . The AI-based platform of claim 1 , wherein, at least one edge device of the set of edge devices is configured to record, in a distributed ledger, at least one energy-related event, and the at least one energy-related event includes at least one of, an energy purchase event, an energy sale event, a service charge associated with an energy purchase event, a service charge associated with an 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.
20. 20 . The AI-based platform of claim 1 , wherein, at least one edge device of the set of edge devices 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.

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 “energy edge.” 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 “DERs”), 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. 2A and 2B 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