A method includes collecting energy resource data for the specific geographic location over a predetermined time period, calculating power curves and matrices for at least two energy technologies based on the collected energy resource data, estimating the potential of generated electric power over time of the at least two energy technologies based on the calculated power curves and matrices, the time period, and the characteristic parameters of each of the at least two energy technologies, simulating different base load and power variations based on the estimation of the potential generated electric power and different distribution of the electric power generation of the at least two energy technologies, identifying an optimal distribution of the at least two energy technologies by analyzing the base load and power variations for each simulation, and choosing the distribution of the electric power generation with the highest base load and lowest power variation.

A power distribution arrangement for distributing AC power to loads requiring AC power is disclosed. The power distribution arrangement comprises a power distribution substation comprising transformers, switches, buses, and feeders, a DC transmission line, and at least one control unit. The control unit may control operation of the switches to selectively connect or disconnect one or more feeders to or from at least one transformer via one or more buses and to selectively connect or disconnect the DC transmission line to or from one or more feeders via at least one bus, whereby AC power is distributed to the loads via the feeders. The control unit may control operation of the switches based on: loading in and a power transfer rating of respective feeders and transformers, and any power transfer via the DC transmission line from the other power distribution substation to the at least one bus.

Systems and methods for determining a location of an event in an electric power delivery system using a machine learning engine are provided. The machine learning engine may be trained based on a topology of the electric power delivery system, where the topology may be a layout of line sections and corresponding sensors of the electric power delivery system. Based on the topology, one or more training matrices that indicate possible event locations may be generated. In turn, the machine learning engine may be trained using the training matrices and logistic regression models to determine locations of events that occur during operation of the electric power delivery system.

Disclosed is an electrical power system having a transmission system with a first voltage, a distribution network with a second voltage, and a coupling unit having a coupling ratio, and a distributed generation unit DGU to generate the electric power and be in a communication with the distribution network. The system may also have a control unit to control the coupling unit and the DGU in an emergency operating mode to bring and/or to keep the value of the first voltage between a first threshold and a third threshold, and/or the value of the second voltage between a second threshold and a fourth threshold, the first threshold corresponding to the value of the first voltage at the time of entry into the emergency operating mode and the second threshold corresponding to the value of the second voltage at the time of entry into the emergency operating mode.

A demand adjustment control system includes an obtainer, a determiner, and a controller. The obtainer obtains a target adjustment amount according to a temporary demand adjustment request. The determiner determines one or more target devices each of which is to be a target of demand adjustment control from a device group in a facility, based on the target adjustment amount obtained by the obtainer. The controller executes the demand adjustment control on the one or more target devices to cause an adjustment amount achieved by the one or more target devices to fall within a range of the target adjustment amount during a period in which demand adjustment is being requested. The device group includes one or more first devices of which demand adjustment control mode is not changed during the period and one or more second devices of which demand adjustment control mode can be changed during the period.

A micro utility system. The micro utility system may include a portable container configured to house an energy storage system (ESS) and solar panel storage structures; a portable solar panel structure having two or more solar panels coupled to each other at one end, wherein the two or more solar panels are coupled to at least two wheels at a distal end of the portable solar panel structure; and circuitry configured to receive electrical power from the portable solar panel structure, wherein the circuitry includes a processor configured by machine-readable instructions to direct electrical energy from the portable solar panel structure or the ESS to a load.

Disclosed is a centralized system and method for metering, tracking, and monetizing distributed digital energy assets. The system includes the creation and utilization of a secure digital energy-asset that acts as a key, authorizing specialized circuits to generate a specified amount of electrical energy. Digital information passed through the system includes data necessary to ascribe attribution (i.e., branding, trail of ownership, etc.) and permit monetization (i.e., kWhs, time, dollars, geography, etc.). Functionality and/or value that is ascribed to the digital energy asset can be enhanced by appending additional information and/or utility. The system also includes a means to normalize and evaluate a potentially infinite number of measurements commonly used in business models across multiple industries to a simplified listing of measurements required to track and monetize energy alone within the system.

A server includes a processing device. The server manages a plurality of vehicles. The processing device sets priority levels for the vehicles. The processing device sets the priority level of a vehicle in which its switching device is in a closed state to be higher than the priority level of a vehicle in which its switching device is in an open state. The processing device selects, based on the priority levels, a participating vehicle to be used for demand-increasing demand response from among the vehicles.

The application relates to an electric converter for converting AC or DC input into an electric AC or DC output. A swap circuit with controllable electric switches serves to selectively swap connection of a plurality of DC power banks (DCBs) between an input terminal and an output terminal, thus selectively connecting the DCBs to an electric source or an electric load. The DCBs are formed as series of interconnected submodules (SMs) each having electric energy storage elements (ESEs) and a switching circuit for selectively by-passing or connecting the ESEs. By properly controlling the swap circuit and the switching of the SMs, the converter can be used for DC-AC, DC-DC, AC-DC, or AC-AC conversion, allowing multilevel output.

A method includes generating a time-varying charge/discharge control signal for an electrical storage device, wherein generating the time-varying charge/discharge control signal comprises identifying a prioritization order of a stack of simultaneously operating control modes, the stack of simultaneously operating control modes including a staging mode and at least two additional control modes, each control mode of the stack comprising a plurality of control signal candidate values; identifying an intersection of one or more control signal candidate values from the plurality of control signal candidate values of each control mode of the stack according to the prioritization order; and determining, based on the prioritization order, at least one time-varying charge/discharge control signal for the electrical energy storage device from the intersection of control signal candidate values.