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.

A method for generating a net load forecast for a utility grid, the grid including intermittent distributed energy resources and loads, comprising: defining two or more load forecast zones, each zone being associated with a load profile type and a climate zone type; assigning each of the loads to one of the zones based on the load profile and climate zone types associated with the load; assigning each of the energy resources to at least one of the zones based on the climate zone type associated with the energy resource; for each zone, generating an electrical energy consumption forecast for loads, an electric power generation forecast for energy resources, and a net load forecast from the electrical energy consumption and electric power generation forecasts; combining the net load forecast for each zone to generate the net load forecast for the grid; and, presenting the net load forecast on a display.

A method is proposed for real-time economic dispatch in power system operation, especially in systems that include renewable power sources that may cause heavy deviations from a generation schedule due to their uncertain outputs. According to the method an optimal generation schedule is obtained based on forecasted load data. This schedule has to be adjusted if the actual load and renewable energy source outputs unexpectedly deviate from the forecasted value. An algorithm employed in the method is capable of dictating generation adjustments which minimize total generation costs. The algorithm is a modification of the base point and participation factor method. It differs from existing methods in that a precise model of transmission losses is adopted in the algorithm to achieve higher accuracy in optimization. The proposed method also has significant advantage in execution speed so that it is suitable for real-time operation.

Systems and methods for probability forecasts and an energy transmission and/or energy distribution network are provided. Operational management may be carried out using a network control system with systematic consideration of forecast uncertainties. The probability of a distribution network being operable in a stable manner (e.g., with N-1 certainty) in a planning period is included. The system includes a forecaster for forecasts for a planning period, a forecast analyzer connected to the forecasts from the at least one forecaster, and elements for further information for outputting estimated forecast uncertainties. The system also includes a stability probability analyzer connected to the forecasts from the at least one forecaster, the estimated forecast uncertainties from the forecast analyzer, and elements for further information for outputting at least one item of information relating to an N-1 stability of the distribution network in the planning period.

The present disclosure is directed to systems and methods for controlling an electrical system using simulation-based setpoints. Some embodiments include control methods that enable recalculation of the optimal setpoints during demand windows. Some embodiments include a multi-mode controller to control an electrical system in a charge mode and a demand mode. Some embodiments include techniques for load and generation learning and prediction. Some embodiments include consideration of external data, such as weather.

A system includes a database storing demand response data, the demand response data including demand response agreement parameters, demand response load and energy demand characteristics of one or more demand response customers, the demand response load characteristics including power consumption capacity of each of one or more demand response loads, an aggregator to aggregate the demand response loads based on the demand response data and forecast data into a demand response portfolio, a monitor to monitor power demand of one or more demand response customers and one or more power grids, and a dispatcher to notify the one or more demand response customers of the demand response portfolio and to notify a utility of a response from the one or more demand response customers whether to control the demand response load to return the power consumption capacity of the demand response load back to the one or more power grids.

Transaction-enabling systems including a controller are disclosed. The controller can access a distributed ledger including an instruction set, tokenize the instruction set, interpret an instruction set access request, and, in response to the instruction set access request, provide a provable access to the instruction set.

A method for predicting electrical energy production of a photovoltaic system included in at least one aircraft is described. The method includes determining a first predicted amount of solar irradiance for each of a plurality of geographical points, determining a second predicted amount of solar irradiance received by the at least one aircraft along a flight path of the at least one aircraft, wherein the flight path includes a subset of the plurality of geographical points, and wherein the second predicted amount is based at least in part on the first predicted amount, and determining a predicted amount of electrical energy produced by the photovoltaic system along the flight path, based at least in part on the second predicted amount.

The present disclosure provides systems and methods for managing electrical energy generated by a renewable microgrid. One or more processors of a system may store priorities for one or more consumer loads; forecast an amount of available electrical energy for a time period; allocate a first electrical energy amount to a first consumer load to a first energy limit for the time period; determine more electrical energy is forecast to be available from the RES or the ESS for the time period; identify a second consumer load of the one or more consumer loads; allocate a second electrical energy amount to the second consumer load for the time period; and direct electrical energy from the RES or the ESS to the first and second consumer loads according to the allocations.

An energy saving support system according to an embodiment is configured to provide a consumer, who has an electric load to which electric energy is supplied from an electric power supply system within a house, with energy consumption-related information through a photo frame or the like. The information providing apparatus is configured to acquire an acceptability level, which stepwise indicates a degree of interest of the consumer in the energy consumption-related information, and to determine the energy consumption-related information to be newly provided to the consumer based on the acceptability level.