Abstract: A system determines a location and a size of a new power generation or power regulating unit within a current system architecture of a power system including a plurality of power generation units. The system comprises a controller including a processor and a memory, computer-readable logic code stored in the memory which, when executed by the processor, causes the controller to execute a hybrid algorithm as a combination of a data-driven algorithm and a model-based algorithm to determine an optimal location and size of the new power generation or power regulating unit. The data-driven algorithm encodes a location and a size information. The controller to enable the model-based algorithm to optimize performance of a selected location and size of the new power generation or power regulating unit, which is based on a linearized system or a nonlinear system to provide guidance for the data-driven algorithm to incorporate physical rules and verify a new system architecture.

Abstract: A method for configuring a controller of a dynamical system includes obtaining a control data manifold formed by a plurality of stored control points, each representative of a state signal specifying a state of the dynamical system and an assigned control signal. Each state signal is mapped to a multi-dimensional state space. The assigned control signal is generated by a first control algorithm as a function of the state signal. The method includes detecting patches on the control data manifold by identifying control points on the control data manifold that belong to a common local approximation function, and training a classifier to classify control points into different patches. The method further includes training a respective regression model for each detected patch for approximating a relationship between state signals and the control signals in that patch, to create an explicit rule-based control algorithm.

Abstract: A computer-implemented method for online calibration of power system model against a power system includes iteratively approximating the power system model, at sequential optimization steps, around a moving design point defined by parameter values of a set of calibration parameters of the power system model. At each optimization step, an approximated system model is used to transform a dynamic input signal into a model output signal, which is compared with measurement signals obtained from measurement devices installed in the power system that define an actual power system output signal generated in response to the dynamic input signal. Parameter values of the calibration parameters adjusted in a direction to minimize an error between the model output signal and the actual power system output signal. The power system model is calibrated against the power system based on resulting optimal values of the calibration parameters.

Abstract: A method for adding assets to a distribution network includes using a placement generation engine to generate discrete placements of assets to be added to the distribution network subject to asset-installation constraint(s). Each placement is defined by a mapping of an asset, from multiple assets of different sizes, to a placement location defined by a node or branch of the distribution network. Each placement is used to update an operational circuit model of the distribution network for tuning control parameters of one or more controllers of the distribution network for robust operation over a range of load and/or generation scenarios. A cost function is evaluated for each placement based on a simulated operation. Parameters of the placement generation engine are iteratively adjusted based on the evaluated cost functions to arrive at an optimal placement and sizing of assets to be added to the distribution network.

Abstract: According to some possible implementations, a method may include determining one or more inputs to a model of a system and one or more outputs from the model. The method may include identifying a continuous portion of the model to be discretized. The method may include discretizing the continuous portion of the model, using at least one of a continuous linear representation for the model or a frequency response associated with the continuous linear representation, to generate a discrete linear representation for the continuous portion of the model. The method may include outputting information associated with the discrete linear representation to permit the continuous portion of the model to be implemented on one or more processors.

Abstract: According to some possible implementations, a method may include determining one or more inputs to a model of a system and one or more outputs from the model. The method may include identifying a continuous portion of the model to be discretized. The method may include discretizing the continuous portion of the model, using at least one of a continuous linear representation for the model or a frequency response associated with the continuous linear representation, to generate a discrete linear representation for the continuous portion of the model.