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• The reciprocity relationship between critical tuples of measurements and measuring units is addressed; • Efficient enumerative algorithms are proposed to identify criticalities of measurements from critical devices and vice versa; • The reduced capability to observe the system state is pointed out, which compromises the debugging of spurious measurements and precipitates unobservability. State estimation (SE) plays an essential role in energy management systems. It greatly depends on the availability of redundant, diverse, and strategically placed measurements. Insufficient supply of such measurements compromises SE, weakening its capability to detect/identify spurious data. Individual or group of measurements whose unavailability renders unobservability are termed critical for the SE. Similarly, one can characterize a single device or a group of them as critical, essential to the network observability, which means that the unavailability of the measurements these devices serve causes unobservability. This paper establishes the reciprocal relationship between critical tuples of measurements and measurement devices. Efficient enumerative algorithms are proposed to identify criticalities of measurements from critical devices and vice versa. Illustrative examples performed on the IEEE-30 bus system show how the proposed approach obtains adequate solutions for grid operation. [ABSTRACT FROM AUTHOR]

The art of estimating future values of a random process, based upon previously observed or estimated values, is usually known as a priori estimation, prediction, or forecasting. Power system state estimation process can be enhanced if state/measurement forecasts are incorporated into it. Important research efforts have been made in this direction bringing a fresh perspective to the state estimation problem. This paper (Part I) presents a comprehensive survey of forecasting-aided state estimators. It gathers up-covering a period of three decades-research results on the main benefits achieved by state estimators with forecasting capability regarding: data redundancy, innovation analysis, observability, filtering, bad data, and network configuration and parameter error processing. Aspects of modeling, forecasting techniques, and computational effort are also addressed. The second of this two-paper series presents the results of the implementation of a forecasting-aided state estimator in the energy management system of the LIGHT Services of Electricity, a company which provides electric energy to Rio de Janeiro, Brazil. [ABSTRACT FROM AUTHOR]

This paper is the second part of a two-part paper addressing power system forecasting-aided state estimation. Part I presented a comprehensive survey on the subject, including future prospects. Part II is devoted to presentation and discussion of numerical results obtained with the implementation of a forecasting-aided state estimator in the energy management system of LIGHT Services of Electricity, which is company that supplies Rio de Janeiro, Brazil. This estimator is submitted to different situations, such as: normal operating conditions, presence of spurious measurements (including those which are critical), and occurrence of unpredictable sudden changes in the state of the LIGHT system. [ABSTRACT FROM AUTHOR]