An energy management controller (1) includes a virtual grid generating unit (110) that for each of grids, generates a virtual grid that collectively manages information including information on an energy resource and a wire, in order to carry out efficient power management, and a deal management unit (150) that based on the generated virtual grid, manages a power deal with a consumer-side supplied with power from the energy resource. The energy management controller (1) further includes an RE coloring unit (132) that classifies power by coloring to allow power deals based on RE coloring.

A system for enhanced sequential power system model calibration is provided. The system is programmed to store a model of a device. The model includes a plurality of parameters. The system is also programmed to receive a plurality of events associated with the device, receive a first set of calibration values for the plurality of parameters, generate a plurality of sets of calibration values for the plurality of parameters, for each of the plurality of sets of calibration values, analyze a first event of the plurality of events using a corresponding set of calibration values to generate a plurality of updated sets of calibration values, analyze the plurality of updated sets of calibration values to determine a current updated set of calibration values, and update the model to include the current updated set of calibration values.

In a method and system, voltage and/or current signals on an electrical/power system is monitored. A power event is identified from the monitored voltage and/or current signals. In response to event identification, waveforms of the monitored voltage and/or current signals are captured. Energy-related signals are calculated and extracted from pre-event measurements, event measurements and post-event measurements using the captured waveforms. Additional information associated with the event is identified and calculated by comparing (a) the calculated and used energy-related signals from pre-event measurements, with (b) the calculated and used energy-related signals from post-event measurements.

The present disclosure describes transaction-enabling systems and methods. A system may include a smart contract wrapper configured to access a distributed ledger including a plurality of intellectual property (IP) licensing terms corresponding to a plurality of IP assets, wherein the plurality of IP assets include an aggregate stack of IP, 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 an IP asset to the aggregate stack of IP.

Technology for mitigating impact of a power outage includes a method that determines power regulation data and identifies a power interruption event. The power interruption event is determined to disrupt operation of an electronic device powered by a grid power supply based on the power regulation data. The method mitigates the identified power interruption event by causing an electronic power supply device to transition from receiving power from the grid power supply to receive power from a local power supply where the electronic device is electrically coupled to the electronic power supply device.

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 for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; using outside temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict the time when energy consumption will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; using outside temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict the time when energy consumption will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

An aspect of the present disclosure relates to a method for providing a data structure comprising a refined energy schedule, the method comprising receiving a plurality of energy demand requests, energy storage offers, and/or energy supply offers from a plurality of participants of a power network; determining by a plurality of distributed computational units the refined energy schedule, using an optimization function, under consideration of the plurality of energy demand requests, energy storage offers, and/or energy supply offers, wherein the refined energy schedule is an at least substantially optimal energy schedule; and appending the refined energy schedule to the data structure. A further aspect of the disclosure relates to a data structure, in particular to the data structure provided in the method.

A method and system for predicting regional short-term energy power by taking weather into consideration includes: obtaining meteorological data of all moments in a set time in the future through a network; extracting respectively, from a historical database according to the obtained meteorological data, historical weather station meteorological data, historical network API meteorological data, and historical measured power generation power data within a set time period that meet meteorological conditions corresponding to all the moments; obtaining historical total error data; obtaining real-time error meteorological data; obtaining total error meteorological data; combining the obtained meteorological data of all the moments in the set time in the future with total error meteorological data of all the moments to obtain predicted meteorological data; obtaining predicted power data according to the predicted meteorological data; and optimizing an energy generation plan of a system according to the obtained predicted power data.