CIGRE WG C4.605 : “Modelling and aggregation of loads in flexible power networks”

This paper proposes a coordination paradigm for properly coordinating local control actions, taken by many communicating control agents (CAs), in order to maintain multi-area power system voltages within acceptable bounds. The proposed control scheme is inspired by distributed model predictive control (DMPC), and relies on the communication of planned local control actions among neighboring CAs, each possibly operated by an independent transmission system operator (TSO). Each CA, knowing a local model of its own area, as well as reduced-order QSS models of its immediate neighboring areas, and assuming a simpler equivalent PV models for its remote neighbors, performs a greedy local optimization over a finite window in time, communicating its planned control input sequence to its immediate neighbors only. The good performance of the proposed real-time model-based feedback coordinating controller, following major disturbances, is illustrated using time-domain simulation of the well-known realistic Nordic32 test system, assuming worst-case conditions.

/pdf/voltage-coordination-in-multi-area-power-systems-via-2rcp3gg9pa.pdf

Voltage Coordination in Multi-Area Power Systems via Distributed Model Predictive Control

http://faraday1.ucd.ie/archive/papers/eqvshortcir.pdf

Dynamic REI equivalents for short circuit and transient stability analyses

The paper addresses practical capabilities of artificial neural networks (ANNs) in developing measurement-based continuous-time dynamic equivalents for power systems Our method is based on a set of measurements at boundary nodes between a subsystem that is to be modeled in detail ("retained" portion of the system) and the part that is to be replaced by a simplified ("equivalent") model We are particularly interested in combining standard physics-based models with signal-based models derived from measurements We utilize a color-coding scheme to distinguish between physics-based models (clear or white box) at one end, the signal-based models (opaque or black box) at the opposite end, and mixed (gray box) models in the middle The paper also proposes a way for combining classical and ANN-based equivalents in a hybrid model implemented in a standard software environment for transient analysis (in this case, ETMSP) Our conclusions are based on simulations performed on a model of a benchmark multimachine power system derived from the WSCC system

Transient power system analysis with measurement-based gray box and hybrid dynamic equivalents

This paper summarises the model reduction techniques used for the reduction of large-scale linear and nonlinear dynamic models, described by the differential and algebraic equations that are commonly used in control theory. The groups of methods discussed in this paper for reduction of the linear dynamic model are based on singular perturbation analysis, modal analysis, singular value decomposition, moment matching and methods based on a combination of singular value decomposition and moment matching. Among the nonlinear dynamic model reduction methods, proper orthogonal decomposition, the trajectory piecewise linear method, balancing-based methods, reduction by optimising system matrices and projection from a linearised model, are described. Part of the paper is devoted to the techniques commonly used for reduction (equivalencing) of large-scale power systems, which are based on coherency, synchrony, singular perturbation analysis, modal analysis and identification. Two (most interesting) of the described techniques are applied to the reduction of the commonly used New England 10-generator, 39-bus test power system.

/pdf/dynamic-model-reduction-an-overview-of-available-techniques-m1axw535jd.pdf

Dynamic model reduction: An overview of available techniques with application to power systems

The paper proposes an artificial neural network (ANN)-based strategy for identification of reduced-order dynamic equivalents of power systems. This large-signal model is formulated in continuous-time and is therefore compatible with standard models of power system components. In a departure from previous works on the subject, we do not postulate a particular model structure for the equivalent, hence the label nonparametric. The approach uses only measurements at points where internal (retained) and external (reduced) systems are interfaced, and requires no knowledge of parameters and topology of the external subsystem. The procedure consists of two conceptual steps: (1) the first ("bottleneck") ANN is used to extract "states" of the reduced-order equivalent; and (2) the second (recurrent) ANN is embedded in an ordinary differential equations (ODEs) solver, and trained to approximate the "right-hand side," using the states extracted at the first step. We also describe an extension in which a third ANN is used to synthesize missing interface measurements from a historical database of system responses to various disturbances. We illustrate the capabilities of the approach on a multimachine benchmark example derived from the WSCC system.

M. Milosevic

Papers

Identification of nonparametric dynamic power system equivalents with artificial neural networks