A computer-implemented method and system is provided. The system adaptively switches prediction strategies to optimize time-variant energy demand and consumption of built environments associated with renewable energy sources. The system analyzes a first, second, third, fourth and a fifth set of statistical data. The system derives of a set of prediction strategies for controlled and directional execution of analysis and evaluation of a set of predictions for optimum usage and operation of the plurality of energy consuming devices. The system monitors a set of factors corresponding to the set of prediction strategies and switches a prediction strategy from the set of derived prediction strategies. The system predicts a set of predictions for identification of a potential future time-variant energy demand and consumption and predicts a set of predictions. The system manipulates an operational state of the plurality of energy consuming devices and the plurality of energy storage and supply means.

A charge calculation apparatus, system and method allow for a controller to administer an electric power charging operation to one or more client devices, such as an electric vehicle. The vehicle provides its ID to the controller, which in turn calculates a tax according to the vehicle making the request, and other factors such as taxing jurisdiction, amount of electricity used, timing, etc. By controlling the charging operation in this way, the taxing authorities are able to collect tax revenue for use in maintaining roads from the users of those roads by monitoring which vehicles are using electricity to operate the vehicles on the roads. By keeping track of the vehicle's movement within different tax jurisdictions, the tax may be apportioned amongst the different taxing authorities.

The present disclosure provides a computer-implemented method for ranking one or more control schemes for controlling peak loading conditions and abrupt changes in energy pricing of one or more built environments associated with renewable energy sources. The computer-implemented method includes analysis of a first set of statistical data, a second set of statistical data, a third set of statistical data, a fourth set of statistical data and a fifth set of statistical data. Further, the computer-implemented method includes identification and execution of the one or more control schemes. In addition, the computer-implemented method includes scoring the one or more control schemes by evaluating a probabilistic score. Further, the computer-implemented method includes ranking the one or more control schemes to determine relevant control schemes for controlling real time peak loading conditions and abrupt changes in energy pricing associated with the one or more built environments.

A device may detect occurrence of an event. The device may receive energy storage information and solar charging information. The energy storage information may be associated with an energy storage component. The solar charging information may be associated with a solar array. The energy storage information and the solar charging information may be received based on the occurrence of the event. The device may set, based on at least one of the energy storage information or the solar charging information, a time value and/or a radio operation state. The device may determine, based on the time value, to determine location information. The device may determine location information. The device may transmit the location information based on the radio operation state.

A method for managing a microgrid, the microgrid having an intermittent energy source and energy storage, comprising: using a microgrid asset management system, dispatching the energy storage to maintain optimal power flow to and from a power grid by: producing a generation forecast and a generation forecast error probability distribution for an intermittent energy source coupled to the microgrid from a historic generation forecast and one or more measurements; generating random intermittent energy source generation inputs from at least one of historic generation data, the historic generation forecast, and the generation forecast error probability distribution; using the random intermittent energy source generation inputs, calculating a microgrid performance value using a microgrid performance model and a microgrid financial value using a microgrid financial model; and, selecting an energy storage dispatch scenario to optimize operation of the microgrid using at least one of the microgrid performance value and the microgrid financial value.

Systems and methods for operating a series of generators configured to provide power to a motor or motors. Generators generally operate at different efficiency levels depending on the operating capacity. A computation component can analyze the current efficiency of the generators and determine if there is an alternative power distribution among the existing generators that would result in a more efficient operation of the system.

Systems and methods manage electrical loads in a grid by applying Robust principal component analysis (R-PCA) to decompose annual load profiles into low-rank components and sparse components; extracting one or more predetermined features; constructing a similarity graph; selecting submodular cluster centers through the constructed similarity graph; determining a cluster assignment based on selected centers; and applying the clustering assignment for load analysis.

The technical field includes machine, manufacture, process, and product produced thereby, as well as necessary intermediates, which pertain to power sources, units thereof, computer systems used to facilitate operation of one or more power sources.

An optimization and management system using a global linearization approach and mixed integer linear programming to perform the dispatch and to schedule a river system for ten days forward while subject to real world, hourly physical, biological, environmental, and recreational constraints. The optimizer system uses a combination of slack variables and stitching methods to approximate a highly nonlinear optimization problem, thereby generating realtime generation schedules to promote to the management authority.

A method performs state estimation of a distribution network based on real time measurement values. The method includes calculating load scaling factors at least for non-redundantly measurable parts of the distribution network, determining whether each of the load scaling factors related to a non-redundantly measurable part of the distribution network is within a given range (RLSF), and discarding the measurement values related to the corresponding scaling factor for all load scaling factors outside the given range (RLSF). The network state of the distribution network is then estimated based on the remaining measurement values.