A power conditioning system (PCS) includes: a grid blackout determiner, a voltage controller, and a processor electrically connected to the grid blackout determiner and the voltage controller. The processor is configured to identify a state of a grid as a blackout state or an unstable state based on at least one of an amplitude or a frequency of a voltage of the grid that is detected by the grid blackout determiner, control the voltage controller to adjust, based on the identified state of the grid being the blackout state or the unstable state, load voltage input to the voltage controller to be equal to a command voltage, and adjust, based on the identified state of the grid being the blackout state or the unstable state, a first frequency of the detected voltage of the grid to a second frequency that is different from the first frequency.

Aspects of the present invention relate to a method for controlling a renewable power plant connected to a power network to reduce deviation of a measured frequency of the power network from a target frequency. The method comprises determining a forecasted power gradient over a forecast interval defined between a first time point and a second time point, and, at a third time point during the forecast interval, controlling the power plant to output active power according to a minimum active power level if the measured frequency at the third time point is below the target frequency, controlling the power plant to output active power according to a maximum active power level if the measured frequency at the third time point is above the target frequency. The maximum and minimum active power levels are based on the forecasted power gradient.

Demand response methodologies for primary frequency response (PFR) for under or over frequency events. Aspects of the present disclosure include methods for controlling a fleet of distributed energy resources equipped for PFR and quantifying in real time an amount of primary frequency control capacity available in the fleet. In some examples, the DERs may be configured to consume and discharge electrical energy in discrete energy packets and be equipped with a frequency response local control law that causes each DER to independently and instantaneously interrupt an energy packet in response to local frequency measurements indicating a grid disturbance event has occurred.

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.

An example system includes an aggregator configured to receive a service collaboration request and iteratively determine, based on minimum and maximum power values for DERs under its management, an optimized operation schedule. The aggregator may also be configured to iteratively determine, based on the optimized operation schedule, an estimated flexibility range for devices under its management and output an indication thereof. The system may also include a power management unit (PMU) configured to iteratively receive the indication and determine, based on a network model that includes the estimated flexibility range, a reconfiguration plan and an overall optimized operation schedule for the network. The PMU may also be configured to iteratively cause reconfiguration of the network based on the plan. The PMU and aggregator may also be configured to iteratively, at a fast timescale, cause energy resources under their management to modify operation based on the overall optimized operation schedule.

A method predicts a grid state of an electrical power distribution grid, in which a central computer arrangement is used to receive measured values from measuring devices. A state estimation device is used to predict a future grid state, wherein the prediction of the future grid state is taken as a basis for ascertaining measures to guarantee stability of the power distribution grid. The prediction is made for multiple times within a predefined time window. A first prediction device is used to ascertain a prediction for a first portion of the multiple times on the basis of a voltage var control method, and in that a second prediction device is used to ascertain a prediction for a second portion of the multiple times on the basis of a neural network method.

A computer-implemented method for identifying a topology of a power distribution grid having a number of transformers includes acquiring measurement signals of one or more electrical quantities pertaining to nodes of the power distribution grid. A graph representation is generated using the measurement signals and grid topological information, wherein the measurement signals pertaining to respective nodes are used to derive node features and the grid topological information is used to encode edges representing certain and uncertain connections between the nodes. The graph representation is processed using a graph neural network to classify the nodes and output a mapping of each of the nodes to one of the transformers, whereby a status of the uncertain connections is determined.

Method and apparatus configured to receive a plurality of power flow data from at least a grid monitoring device connected to a grid network including a plurality of nodes, generate a power flow allocation for at least a node in the network as a function of the at least a power consumption datum and the at least a generation datum, determine a carbon flow as a function of the power flow allocation and a first set of stored relational rules, generate an objective function of a carbon flow and a second set of stored relational rules, minimize the objective function of a carbon flow as a function of the carbon optimization model and an optimization algorithm, generate a grid modification as a function of the minimization; and modify a grid parameter of the grid network as a function of the grid modification.

A method of enhanced backward/forward sweep based power flow analysis is described. The method can include performing a backward sweep to determine first branch currents of a radial distribution network based on nodal voltages determined at a last iteration. The radial distribution network can include nodes and branches that are sequentially numbered and belong to different layers. A forward sweep is determined to determine first nodal voltages of the radial distribution network based on the first branch currents. Second branch currents of the radial distribution network are determined based on the first nodal voltages. The second branch current of the respective node is a sum of a nodal injection current of the respective node that is updated based on the first nodal voltage of the respective node, and if available, the first branch currents of branches emanating from the respective node.

A method tests the configuration of an aggregated DERs system using distributed asset managers in a decentralized hardware-in-the-loop (“HIL”) scheme. The managers contain the model of the asset they are meant to control. The method programs an asset manager with a model of a DERs asset. A plurality of asset managers are connected to a central controller. The plurality of asset managers are also connected to a simplified hardware-in-the-loop platform. The simplified HIL platform is configured to solve a network model, a load model, a non-controllable asset model, and a grid model. The method tests the DERs system control structure by using: (a) the simplified HIL platform to solve the network model, the load model, the non-controllable asset model, and the grid model, and (b) the asset manager to solve the model of the DERs asset, without any simulation between the central controller and the distributed asset managers.