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 microcontroller powered by a power management integrated circuit (PMIC) includes a plurality of cores. A first core of the microcontroller can be configured to implement a system power transient management component. One or more other or second cores of the microcontroller can be configured to implement one or more applications. The system power transient management component implemented by the first core can be configured to dynamically identify an expected load transient event to occur in the microcontroller, determine power control data to optimize a response to the identified expected load transient event, the power control data comprising a power control mode and associated parameters, and provide the power control data to the power management integrated circuit (PMIC).

Apparatus and associated methods relate to automatically load matching, in time, energy physically generated and transmitted to a consumption location across at least one tracking and processing infrastructure. In an illustrative example, a load pool (LP) may be created based on energy consumed at a physical location at one or more selected time periods. A generation pool (GP) may, for example, be created based on energy generated and physically available for consumption at the physical location during the time periods. Associations may be created, for example, between measurements in the GP of energy generated and transmitted and measurements in the LP of energy consumed. The associations may be created as a function of predetermined privileges associated with the consumption location and generation locations and/or physical transmission links corresponding to the GP during the time periods. Various embodiments may advantageously determine environmental impact based on location and time-based load matching.

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.

A computer implemented method for controlling a load aggregator for a grid includes receiving a predicted power demand over a horizon of time steps associated with one of at least two buildings, aggregating the predicted power demand at each time step to obtain an aggregate power demand, applying a learnable convolutional filter on the aggregate power demand to obtain a target load, computing a difference between the predicted power demand of the one building with the target load to obtain a power shift associated with the one building over the horizon of time steps, apportioning the power shift according to a learnable weighted vector to obtain an apportioned power shift, optimizing the learnable weighted vector and the learnable convolutional filter via an evolutionary strategy based update to obtain an optimized apportioned power shift, and transmitting the optimized apportioned power shift to a building level controller associated with the one building.

A server that manages energy of a power grid by using a plurality of energy storage resources includes a loss obtaining unit and a selector. The loss obtaining unit obtains for each of the plurality of energy storage resources, energy loss including retention loss and input and output loss, the energy loss being caused in storing energy in each energy storage resource. When surplus electric power occurs in the power grid, the selector selects at least one energy storage resource for storing surplus electric power from among the plurality of energy storage resources based on the energy loss caused in storing surplus electric power.

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.

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.