A method and controller for controlling electrical activation of elements in a system. A method includes identifying (710) a first element (102) of a system (100) by a control system (600), among a plurality of elements (102, 110, 122) of the system (100), that is to be powered. The method includes determining (712) connected elements (110, 122) of the system (100) by the control system (600). The connected elements (110, 122) are connected to deliver power to the first element (102) directly or indirectly, based on an adjacency matrix (400), and the adjacency matrix (400) identifies connections between each of plurality of elements of the system (100). The method includes identifying (714) at least one of the connected elements (110, 122) to activate by the control system (600), based on the adjacency matrix (400), a health table (500), and the connected elements (110, 122), to deliver power to the first element (102). The method includes activating (716) the at least one of the connected elements (110, 122) by the control system (600), thereby delivering power to the first element (102).

A method for electricity-related security awareness of distributed power supply systems considering spatio-temporal distribution of rainstorms, including: establishing a multi-dimensional parallel parasitic capacitance calculation model of the distributed photovoltaic-energy storage power supply system considering accumulated water depth and micro-terrain environment; performing multi-source spatio-temporal hierarchical correlation analysis between rainstorm spatio-temporal distribution characteristics (including rainfall peak position, cloud movement, rainfall intensity and rainfall duration) and an operating state of the distributed power supply system; constructing a leakage current probability prediction model considering unevenness and randomness of the rainstorm spatio-temporal distribution; and establishing an electricity-related security awareness model based on deep meta-learning.

Example implementations described herein involve a generic dynamic time-series hosting capacity analysis framework, which take into account distributed energy resources (DERs) dynamics and system dynamics. The example implementations described herein can further improve the computational efficiency and accuracy of hosting capacity analysis process. Example implementations can involve systems and methods that receive data input comprising system profiles and topology information of a distribution system having a plurality of DER nodes in an interconnect; execute feeder topology analysis on the topology information to generate output analysis; execute scenario management on the system profiles to generate simulation scenario sets; and load and execute a simulation flow from the simulation scenario sets and the output analysis.

A control device is provided with: a power transfer control means that controls power transfer to and from a DC distribution network; and an exchange means that exchanges, with respect to transfer power to and from the DC distribution network, information indicating an attribute based on a power generation scheme.

Systems, methods, and non-transitory computer-readable storage media for a fast power network simulator. A system configured per this disclosure can use identify a power network, the power network comprising generators, transmission lines, and loads, and receive a model of the power network. The model of the power network can include: models of the generators modeled as differential equations, and models of the transmission lines and the loads modeled as algebraic equations. The system can convert, via a processor, the algebraic equations of the models of the transmission lines and the loads to additional differential equations, then combine, via the processor, the differential equations and the additional differential equations, to yield combined differential equations. The system can then iteratively solve linear equations, via the processor, associated with the combined differential equations, to yield solutions, and output the solutions as part of a power simulation of the power network.

Systems and methods for aggregating and integrating distributed grid element inputs are disclosed. A data platform is provided for a distribution power grid. The data platform provides a crowd-sourced gaming system for identifying grid elements and determining dynamic electric power topology. The data platform also provides an interactive interface for displaying a view of a certain area with identified grid elements. The data platform communicatively connects to the identified grid elements, collects data from the identified grid elements, and manages the distribution power grid.

Systems, methods, and computer-readable media are disclosed. An example method may include receiving a single-line drawing (SLD) of a power substation, the SLD including one or more components, the one or more components including at least one of: a current transformer (CT), a circuit breaker (CB), an isolator, a feeder, and a busbar. The example method may also include analyzing, using an artificial intelligence (AI) system, connection paths associated with the one or more components in the SLD. The example method may also include receiving real-time data relating to a status of the CB and a status of the isolator. The example method may also include providing, based on analyzing the connection paths, and the real-time data relating to the status of the CB and the status of the isolator, an indication of topology information associated with the power substation, the topology information including at least one of: an indication that the CT is a checkzone CT, an indication that the CT is a deadzone CT, an indication of a zone associated with the CT or the CB, or an indication that a zone is unprotectable.

A method, system and computer-readable medium of directed towards improving battery storage is provided. In some embodiments, battery storage formulations are provided and an impact of the constraints on the computational performance of security constrained unit commitment (SCUC) is determined. For example, binary variables may be generally required due to mutual exclusiveness of charging and discharging modes. In some embodiments, valid inequalities may be used to improve state of charge (SOC) constraints. Adding batteries to the Regional Transmission Organizations (RTOs)/Independent System Operators (ISOs) day ahead market clearing cases may reveal an impact of binary variables and the valid inequalities on SCUC solving time. Warm start and lazy constraint techniques may be applied to improve the performance and make the valid inequalities more effective, reducing computation time to acceptable levels for implementation.

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.

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.