SYSTEM AND METHOD FOR MACHINE FORWARD ENERGY PURCHASE BASED ON MODEL SIMULATION ON A DIGITAL TWIN
Systems and methods for machine forward energy purchase based on model simulation on a digital twin are disclosed. An example system may include an energy and compute facility including at least one of an energy source or an energy utilization requirement, and a controller. The controller may include a facility model circuit to operate a digital twin for the facility; a facility description circuit to interpret a set of parameters from the digital twin for the facility; and a facility configuration circuit to operate an adaptive learning system, wherein the adaptive learning system adjusts a facility configuration based on the set of parameters from the digital twin based, at least in part, on the energy source or the energy utilization requirement, and an energy credit forward market.
1 . A transaction-enabling system, comprising:
an energy and compute facility comprising:
at least one of an energy source or an energy utilization requirement; and
a controller, comprising:
a facility model circuit structured to operate a digital twin for the facility;
a facility description circuit structured to interpret a set of parameters from the digital twin for the facility; and
a facility configuration circuit structured to operate an adaptive learning system, wherein the adaptive learning system is configured to adjust a facility configuration based on the set of parameters from the digital twin based at least in part on the energy source or the energy utilization requirement and an energy credit forward market.
2 . The system of claim 1 , wherein the adaptive learning system comprises at least one of a machine learning system and an artificial intelligence (AI) system.
3 . The system of claim 1 , wherein adjusting the facility configuration further comprises performing a purchase or sale transaction on the energy credit forward market.
4 . The system of claim 1 , wherein the facility configuration circuit is structured to adaptively improve one of an output value of the facility or a cost of operation of the facility using executed transactions on the energy credit forward market.
5 . The system of claim 1 , wherein the energy and compute facility further comprises a networking task.
6 . The system of claim 5 , wherein adjusting the facility configuration further comprises performing a transaction-enabling purchase or sale transaction on at least one of a network bandwidth spot market, or a network bandwidth forward market.
7 . The system of claim 1 , further comprising:
wherein the facility description circuit is further structured to interpret detected conditions, wherein the detected conditions comprise at least one condition selected from the conditions consisting of: an input resource for the facility; a facility resource; an output parameter for the facility; or an external condition related to an output of the facility; and
wherein the facility model circuit is further structured to update the digital twin for the facility in response to the detected conditions.
8 . The system of claim 1 , further comprising an associated regenerative energy facility, and an energy requirement for at least one of a compute task, a networking task, or an energy consumption task.
9 . The system of claim 8 , wherein the controller further comprises:
an energy requirement circuit structured to determine an amount of energy for the associated regenerative energy facility to service the at least one of the compute task, the networking task, or the energy consumption task in response to the energy requirement for the at least one of the compute task, the networking task, or the energy consumption task.
10 . The system of claim 9 , further comprising an energy distribution circuit structured to adaptively improve an energy delivery of energy produced by the associated regenerative energy facility between the at least one of the compute task, the networking task, or the energy consumption task.
11 . A method, comprising:
operating a model comprising a digital twin for a facility;
interpreting a set of parameters from the digital twin for the facility; and
operating an adaptive learning system, thereby adjusting a facility configuration based on the set of parameters from the digital twin for the facility based at least in part on an energy source or an energy utilization requirement of the facility and an energy credit forward market.
12 . The method of claim 11 , wherein adjusting the facility configuration further comprises performing a purchase or sale transaction on an energy forward market.
13 . The method of claim 11 , wherein adjusting the facility configuration further comprises performing a purchase or sale transaction on at least one of a spectrum spot market or a spectrum forward market.
14 . The method of claim 11 , wherein adjusting the facility configuration further comprises performing a purchase or sale transaction on at least one of a compute resource spot market or a compute resource forward market.
15 . The method of claim 11 , wherein adjusting the facility configuration further comprises performing a purchase or sale transaction on at least one of a network bandwidth spot market, or a network bandwidth forward market.
16 . The method of claim 11 , further comprising:
interpreting detected conditions relative to the facility, wherein the detected conditions comprise at least one condition selected from the conditions consisting of: an input resource for the facility; a facility resource; an output parameter for the facility; and an external condition related to an output of the facility; and
operating the adaptive learning system, thereby updating the digital twin for the facility in response to the detected conditions.
17 . The method of claim 11 , further comprising operating an associated regenerative energy facility having an energy requirement for at least one of a compute task, a networking task, or an energy consumption task.
18 . The method of claim 17 , further comprising determining an amount of energy for the associated regenerative energy facility to service the at least one of the compute task, the networking task, or the energy consumption task in response to the energy requirement for the at least one of the compute task, the networking task, or the energy consumption task.
19 . The method of claim 18 , further comprising adaptively improving an energy delivery of energy produced by the associated regenerative energy facility between the at least one of the compute task, the networking task, or the energy consumption task.
20 . The method of claim 11 , wherein the adaptive learning system comprises at least one of a machine learning system and an artificial intelligence (AI) system.