An energy control system (ECS) for controlling an energy storage system (ESS's) that includes energy storage devices(s) or an energy storage combination (ESDC's) including≥1 of the energy storage devices and ≥1 of the energy storage combinations. A power conversion system is coupled to an output of the ESDC, and a transformer is coupled to an output of the power conversion system. The ECS includes an ECS server and an ESS adapter configured for providing an interface between ECS server and the ESS. The ECS server is configured for reading status data from the ESS and submitting schedules including selected charging and discharging times to the ESS, monitoring or displaying a variance between an expected performance of the ESS based on the schedules and an actual ESS performance, and responsive to the variance being determined to be above a predetermined threshold, sending an update of the schedules to the ESS.

Systems and methods are provided for managing an energy storage system. In one example, a method may include directing energy stored in the energy storage system to a cryptocurrency mining system and another consumer responsive to conditions. Other consumers may include a third party consumer of energy and conditions may include current and predicted cryptocurrency values and current and predicted availability of cyclically available renewable energy.

An AI-based platform for enabling intelligent orchestration and management of power and energy is disclosed. The AI-based platform includes a set of modular, distributed energy systems that are configurable based on local demand requirements. In some embodiments, the local demand requirements are forecast by demand forecasting algorithm operating on a set of edge networking devices that are linked to a set of systems that consume energy. In some embodiments, at least one of the energy systems is configured by the AI-based platform to be located in proximity to a location and time of demand and/or to be located in proximity to a location and time of demand. In some embodiments, at least one of the energy systems is configured by the AI-based platform to generate energy at a point of local demand and/or to deliver a modular generation system to a location of demand.

An REPP may include a renewable energy source (RES), a first meter associated with a first load, a second meter associated with a second load, a first ESS electrically coupled to the RES and the first meter, a second ESS electrically coupled to the RES and the first meter through a switch, and a controller configured to set a first charge/discharge for the first ESS and a second charge/discharge for the second ESS such that the REPP delivers power to the first load longer than the RES produces power, in response to a trigger condition, actuate the switch such that the second ESS is electrically coupled to the second meter, and set a fourth charge/discharge for the second ESS such that the second ESS maintains a portion of its charge in reserve for the second load.

A method may include obtaining irradiance data at a first time and a second time from sensors, determining whether one or more solar modules of a plurality of networked power plants will be covered by a shadow or shade at a third time based on the irradiance data, and generating, based on the determination, a power output prediction for each power plant of the networked power plants at the third time. The method may further include receiving power delivery profiles for first and second loads, adjusting a power output of one or more power plants of the networked power plants based at least in part on the power output prediction and the power delivery profiles for the first and second loads, and allocating a combined power output of the power plants to the first and second loads based on first and second load reliability thresholds.

A method may include obtaining irradiance data at a first time and a second time from sensors, determining whether one or more solar modules of a plurality of networked power plants will be covered by a shadow or shade at a third time based on the irradiance data, and generating, based on the determination, a power output prediction for each power plant of the networked power plants at the third time. The method may further include receiving power delivery profiles for first and second loads, adjusting a power output of one or more power plants of the networked power plants based at least in part on the power output prediction and the power delivery profiles for the first and second loads, and allocating a combined power output of the power plants to the first and second loads based on first and second load reliability thresholds.

Systems and methods for aggregating and integrating distributed grid element inputs are disclosed. A data platform is provided for a distribution power grid. The data platform provides a crowd-sourced gaming system for identifying grid elements and determining dynamic electric power topology. The data platform also provides an interactive interface for displaying a view of a certain area with identified grid elements. The data platform communicatively connects to the identified grid elements, collects data from the identified grid elements, and manages the distribution power grid.

A system and method for seasonal energy consumption determination using verified energy loads with the aid of a digital computer are provided. A digital computer is operated, including: obtaining energy loads for a building measured over a seasonal time period; obtaining outdoor temperatures for the building as measured over the seasonal time period; verifying stability of the energy loads, including: evaluating the energy loads over time; and identifying at least one of one or more discontinuities and one or more irregularities in the energy loads based on the evaluation. Operating the computer further includes: determining a baseload energy consumption using at least some of those of the energy loads; calculating seasonal fuel consumption rates and balance point temperatures; and disaggregating seasonal fuel consumption based on the baseload energy consumption, seasonal fuel consumption rates, balance point temperatures, and at least some of the outdoor temperatures into component loads of consumption.

An example transaction-enabled system may include a smart contract wrapper to access a distributed ledger comprising intellectual property (IP) licensing terms corresponding to IP assets, wherein the IP licensing terms include an apportionment of royalties among owning entities in the distributed ledger. The smart contract wrapper may interpret an IP description value and an IP addition request, and, in response to the IP addition request and the IP description value, to add the apportionment of royalties corresponding to the IP description value. At least one of the plurality of IP assets comprises an instruction set and an operation on the distributed ledger provides provable access to the instruction set. A royalty apportionment wrapper apportions royalties from at least one royalty generating element to owning entities in response to the IP licensing terms.

Aspects of the disclosure include a power device having a first input configured to be coupled to a main power source, a second input configured to be coupled to a back-up power source, an output configured to be coupled to a load, the load being configured to perform a procedure, and at least one controller configured to determine a required energy for the load to perform the procedure, estimate an amount of available energy predicted to be available to the power device during the procedure, the available energy being derived from at least the back-up power source, determine whether the amount of available energy predicted to be available is equal to or greater than the required energy, and prevent power from being provided at the output responsive to determining that the amount of available energy predicted to be available is less than the required energy.