A method and system for operating neighbouring microgrids is disclosed. The method includes creating a first set of energy management policies associated with a predefined area and a second set of energy management policies associated with a microgrid. The method includes measuring power usage data associated with predefined area and current charge level of batteries within the predefined area. The method further includes determining future power demand of the predefined area, based on predefined analytics performed on the measured power usage data and the current charge level of the batteries. The method includes adapting the data models of predefined area with the data models used in microgrid and utility operations for seamless integration of operations and to optimize overall operations while complying with local, edge and overall policies. The method includes applying the first set of energy management policies within the predefined area based on the determined future power demand.

A charging-discharging device includes a selection unit to acquire estimated load power data indicating estimation of power to be consumed by an electric load, to acquire estimated solar power-generation power data indicating estimation of power to be generated by a solar power generation system, and to select one of a plurality of specific operations regarding usage of power on the basis of the estimated load power data, the estimated solar power-generation power data, operational mode data indicating an operational mode that identifies a power utilization method, price data indicating a price of AC power to be supplied from a commercial system and a price of AC power to be supplied to the commercial system, power conversion efficiency data indicating power conversion efficiency of a power converter at the time of charging or discharging a storage battery, and current time data.

A building system for building data point prediction, the building system comprising one or more memory devices configured to store instructions, that, when executed by one or more processors, cause the one or more processors to receive first building data for a building data point of a building and generate training data, the training data comprising a probability distribution sequence comprising a first probability distribution for the building data point. The instructions cause the one or more processors to train a prediction model based on the training data, receive second building data for the building data point, and predict, for one or more time-steps into the future, one or more second probability distributions with the second building data based on the prediction model, each of the one or more second probability distributions being a probability distribution for the building data point at one of the one or more time-steps.

A charge and discharge control apparatus and method are provided. The charge and discharge control apparatus determines a plurality of adjustment time intervals of a predicted load curve of an electric loop, wherein each adjustment time interval individually corresponds to an adjustment objective. The charge and discharge control apparatus determines a plurality of candidate threshold sets according to the adjustment objectives and a charge and discharge requirement of an energy storage system, wherein each candidate threshold set corresponds to at least one candidate electricity adjustment scheme. The charge and discharge control apparatus determines an objective electricity adjustment scheme from the at least one candidate electricity adjustment scheme so that the energy storage system adjusts the electricity consumption of the electric loop according to the objective electricity adjustment scheme in each adjustment time interval.

A vehicle includes a power system that is couplable to a power network. A controller is configured to communicate with appliances coupled to the power system and programmed to operate the appliances to prevent current demand of the appliances from exceeding a peak operating current of the power system and to balance current demand to prevent exceeding a nominal current rating of the power system for more than a predetermined time.

A central plant optimization system for designing and operating a central plant includes a planning tool, a central plant controller, and an optimization platform. The planning tool is configured to generate a model of the central plant. The central plant controller is configured to receive the model of the central plant from the planning tool and combine the model of the central plant with timeseries data including a timeseries of predicted energy loads to be served by equipment of the central plant. The optimization platform is configured to receive the model of the central plant combined with the timeseries data, construct an optimization problem using the model of the central plant and the timeseries data, solve the optimization problem to determine an optimal allocation of the energy loads across the equipment of the central plant, and provide optimization results to the central plant controller. The central plant controller is configured to use the optimization results to operate the equipment of the central plant to achieve the optimal allocation of the predicted energy loads.

A method for state estimation of a distribution network comprises: (a) obtaining measurements from phasor measurement units (PMUs) placed at buses in the distribution network; (b) constructing a quotient gradient system (QGS) based on a constraint set H that relates the measurements to state variables of the distribution network; (c) integrating the QGS to reach a steady state; (d) identifying one or more of the state variables whose measurement residuals violate a measurement residual constraint in the constraint set H; (e) integrating a reconstructed QGS, which is reconstructed based on the constraint set H by setting the identified one or more state variables to values of corresponding PMU measurements; (f) iterating steps of (d) and (e) until no measurement residuals violate the measurement residual constraint, to thereby obtain the state estimation; and (g) reporting the state estimation to a control system during real-time monitoring of the distribution network.

A grid power configuration may provide a reliable, efficient, inexpensive and environmentally conscious power source to a site, for example, a remote site such as a well services environment. Grid power may be provided for one or more operations at the site by coupling a main breaker to a switchgear unit coupled to one or more loads. The switchgear unit may be coupled to the main breaker via a main power distribution unit and may also be coupled to one or more loads. At least one of a grid power unit and a switchgear unit may be coupled to the main breaker via the main power distribution unit and may also be coupled to one or more additional loads. A control center may be communicatively coupled to the main breaker or any one or more other components to control one or more operations of the grid power configuration.

UAV airways system generally are disclosed. Such UAV airway systems may comprise UAV cargo transportation systems and UAV surveillance and monitoring systems. Such systems preferably overlay and are commensurate with a system of high-voltage power transmission lines of high-voltage transmission system, and electric field actuated (EFA) generators preferably are utilized in UAVs that travel along the transmission lines, in UAV charging stations located along the transmission lines, or in both. Each EFA generator represents a power supply and comprises first and second electrodes separated and electrically insulated from each other for enabling a differential in voltage at the first and second electrodes resulting from a differential in electric field strength experienced by the first and second electrodes arising from the power transmission lines of the high-voltage transmission system.

System, method, and interface for visualized resource allocation and algorithms for the reallocation of resources to achieve a goal. The system analyses an initial state of resource allocation, a cost function for undesirable resources, and a set of potential incremental improvements, each with an associated cost, and determines a step-wise path of applying the incremental improvements to achieve an ultimate resource-allocation goal in an economically feasible way. Simultaneously, a user interface depicts the state of the allocation at the beginning, at the end, and along the path, allowing an intuitive understanding of how the goal will be achieved.