Abstract: Disclosed herein are methods, systems, and devices for utilizing distributed reinforcement learning and consensus control to most effectively generate and utilize energy. In some embodiments, individual turbines within a wind farm may communicate to reach a consensus as to the desired yaw angle based on the wind conditions.

Abstract: An example device includes a processor configured to receive a plurality of voltage values representing respective voltage magnitudes at voltage nodes in a first portion of a power system and determine, for each voltage node, a respective value of first and second voltage-constraint coefficients. The processor is also configured to receive a power value corresponding to a connection point of the first portion of the power system with a second portion of the power system and determine for the connection point, a respective value of first and second power-constraint coefficients. The processor is also configured to cause at least one energy resource connected to the first portion of the power system to modify an output power of the at least one energy resource based on the value of the first and second voltage-constraint coefficients for each voltage node and the value of the first and second power-constraint coefficients.

Abstract: Disclosed herein are methods, systems, and devices for utilizing distributed reinforcement learning and consensus control to most effectively generate and utilize energy. In some embodiments, individual turbines within a wind farm may communicate to reach a consensus as to the desired yaw angle based on the wind conditions.

Abstract: The present disclosure provides techniques for estimating network states using asynchronous measurements by leveraging network inertia. For example, a device configured in accordance with the techniques of the present disclosure may receive electrical parameter values corresponding to at least one first location within a power network and determine, based on the electrical parameter values and a previous estimated state of the power network, an estimated value of unknown electrical parameters that correspond to a second location within the power network. The device may further cause at least one device within the power network to modify operation based on the estimated value of the unknown electrical parameters. The leveraging of network inertia may obviate the need for probabilistic models or pseudo-measurements.

Abstract: Disclosed herein are methods, systems, and devices for utilizing distributed reinforcement learning and consensus control to most effectively generate and utilize energy. In some embodiments, individual turbines within a wind farm may communicate to reach a consensus as to the desired yaw angle based on the wind conditions.

Abstract: An example device includes a processor configured to receive a plurality of voltage values representing respective voltage magnitudes at voltage nodes in a first portion of a power system and determine, for each voltage node, a respective value of first and second voltage-constraint coefficients. The processor is also configured to receive a power value corresponding to a connection point of the first portion of the power system with a second portion of the power system and determine for the connection point, a respective value of first and second power-constraint coefficients. The processor is also configured to cause at least one energy resource connected to the first portion of the power system to modify an output power of the at least one energy resource based on the value of the first and second voltage-constraint coefficients for each voltage node and the value of the first and second power-constraint coefficients.

Abstract: The present disclosure provides techniques for estimating network states using asynchronous measurements by leveraging network inertia. For example, a device configured in accordance with the techniques of the present disclosure may receive electrical parameter values corresponding to at least one first location within a power network and determine, based on the electrical parameter values and a previous estimated state of the power network, an estimated value of unknown electrical parameters that correspond to a second location within the power network. The device may further cause at least one device within the power network to modify operation based on the estimated value of the unknown electrical parameters. The leveraging of network inertia may obviate the need for probabilistic models or pseudo-measurements.

Abstract: An example device includes a processor configured to receive a plurality of voltage values representing respective voltage magnitudes at voltage nodes in a first portion of a power system and determine, for each voltage node, a respective value of first and second voltage-constraint coefficients. The processor is also configured to receive a power value corresponding to a connection point of the first portion of the power system with a second portion of the power system and determine for the connection point, a respective value of first and second power-constraint coefficients. The processor is also configured to cause at least one energy resource connected to the first portion of the power system to modify an output power of the at least one energy resource based on the value of the first and second voltage-constraint coefficients for each voltage node and the value of the first and second power-constraint coefficients.

Abstract: The present disclosure provides techniques for network-cognizant droop control in power systems, such as a power distribution system. An example device includes a processor configured to determine, based on (i) a model representing a structure of a power system that includes a plurality of energy resources and (ii) an indication of predicted uncontrollable power injections in the power system, for each controllable energy resource in the plurality of energy resources, a respective value of a first droop coefficient and a respective value of a second droop coefficient. The processor may be further configured to cause at least one controllable energy resource in the plurality of energy resources to modify an output power of the at least one energy resource based on the respective value of the first droop coefficient and the respective value of the second droop coefficient.

Abstract: An example device includes a processor configured to receive a plurality of voltage values representing respective voltage magnitudes at voltage nodes in a first portion of a power system and determine, for each voltage node, a respective value of first and second voltage-constraint coefficients. The processor is also configured to receive a power value corresponding to a connection point of the first portion of the power system with a second portion of the power system and determine for the connection point, a respective value of first and second power-constraint coefficients. The processor is also configured to cause at least one energy resource connected to the first portion of the power system to modify an output power of the at least one energy resource based on the value of the first and second voltage-constraint coefficients for each voltage node and the value of the first and second power-constraint coefficients.

Abstract: An example device includes a processor configured to receive a plurality of voltage values corresponding to voltage nodes in a first portion of a power system and determine, for each voltage node, a respective value of first and second voltage-constraint coefficients. The processor is also configured to receive a power value corresponding to a connection point of the first portion of the power system with a second portion of the power system and determine for the connection point, a respective value of first and second power-constraint coefficients. The processor is also configured to cause at least one energy resource connected to the first portion of the power system to modify an output power of the at least one energy resource based on the value of the first and second voltage-constraint coefficients for each voltage node and the value of the first and second power-constraint coefficients.

Abstract: An example device includes a processor configured to receive a plurality of voltage measurements corresponding to nodes in a distribution network, and determine, for each respective node: a value of a first coefficient, based on a previous value of the first coefficient, a minimum voltage value for the node, and a voltage measurement that corresponds to the node, and a value of a second coefficient based on a previous value of the second coefficient, a maximum voltage value for the node, and the voltage measurement. The processor of the example device is also configured to cause an inverter-interfaced energy resource connected to the distribution network to modify its output power based on the value of the first coefficient for each node and the value of the second coefficient for each node.

Abstract: Decentralized methods for computing optimal real and reactive power setpoints for residential photovoltaic (PV) inverters are described. Optimal power flow techniques are described that select which inverters will provide ancillary services and compute their optimal real and reactive power setpoints according to specified performance criteria and economic objectives.

Abstract: The present disclosure provides techniques for network-cognizant droop control in power systems, such as a power distribution system. An example device includes a processor configured to determine, based on (i) a model representing a structure of a power system that includes a plurality of energy resources and (ii) an indication of predicted uncontrollable power injections in the power system, for each controllable energy resource in the plurality of energy resources, a respective value of a first droop coefficient and a respective value of a second droop coefficient. The processor may be further configured to cause at least one controllable energy resource in the plurality of energy resources to modify an output power of the at least one energy resource based on the respective value of the first droop coefficient and the respective value of the second droop coefficient.

Abstract: An example device includes a processor configured to receive a plurality of voltage values corresponding to voltage nodes in a first portion of a power system and determine, for each voltage node, a respective value of first and second voltage-constraint coefficients. The processor is also configured to receive a power value corresponding to a connection point of the first portion of the power system with a second portion of the power system and determine for the connection point, a respective value of first and second power-constraint coefficients. The processor is also configured to cause at least one energy resource connected to the first portion of the power system to modify an output power of the at least one energy resource based on the value of the first and second voltage-constraint coefficients for each voltage node and the value of the first and second power-constraint coefficients.

Abstract: An example system includes a simulation system that emulates a first portion of an electrical system and controls, based on the first portion, electrical inputs to a device under test. The system also includes an observation device, operatively coupled to the simulation system, that receives a delayed version of a remote emulation value, which represents a second portion of the electrical system. The remote emulation value is generated by another, physically separate simulation system. The observation device is further configured to determine, based on the delayed version of the remote emulation value and a model of the second portion, a respective real-time estimation of the remote emulation value, and output, to the simulation system, the respective real-time estimation. The simulation system is further configured to emulate the first portion based on the respective real-time estimation of the remote emulation value.

Abstract: An example device includes a processor configured to receive a plurality of voltage measurements corresponding to nodes in a distribution network, and determine, for each respective node: a value of a first coefficient, based on a previous value of the first coefficient, a minimum voltage value for the node, and a voltage measurement that corresponds to the node, and a value of a second coefficient based on a previous value of the second coefficient, a maximum voltage value for the node, and the voltage measurement. The processor of the example device is also configured to cause an inverter-interfaced energy resource connected to the distribution network to modify its output power based on the value of the first coefficient for each node and the value of the second coefficient for each node.

Abstract: Decentralized methods for computing optimal real and reactive power setpoints for residential photovoltaic (PV) inverters are described. Optimal power flow techniques are described that select which inverters will provide ancillary services and compute their optimal real and reactive power setpoints according to specified performance criteria and economic objectives.

Abstract: This disclosure describes techniques for constructing power spectral density (PSD) maps representative of the distribution of radio frequency (RF) power as a function of both frequency and space (geographic location). For example, the disclosure describes techniques for construction PSD maps using robust basis pursuit forms of signal expansion.

Abstract: This disclosure describes techniques for constructing power spectral density (PSD) maps representative of the distribution of radio frequency (RF) power as a function of both frequency and space (geographic location). For example, the disclosure describes techniques for construction PSD maps using robust basis pursuit forms of signal expansion.