State estimation in electrical distribution systems

The changes occurring in electrical systems, due to technical and economic reasons, and the requirements of a reliable and efficient delivery of the power supply to the customers demand a smarter management of the current grids at all the levels. In particular, the distribution level, until now managed without a detailed monitoring of its operating conditions, requires significant reinforcements, in terms of both measurement infrastructure and control functionalities, to deal with the changes in a t. In this context, Distribution System State Estimation tools play a key role, sin e they allow the estimation of the operating conditions of the distribution grid, representing the essential link between the measurements gathered from the field and the control functions envisaged in future Distribution Management Systems. In this thesis, the focus has been on the development of appropriate procedures to accurately perform DSSE. Several goals have been pursued. First of all, a DSSE algorithm specifically on conceived for the distribution systems and tailored to the features of these networks has been designed. The proposed estimator allows the proper processing of all the types of measurement, in luding both conventional measurements and new generation synchrophasors provided by Phasor Measurement Units. Particular attention has been paid to the measurement model to be used within the DSSE algorithm, since its implementation an strongly a e t the a ura y a hiev- able in the estimation results. Moreover, a simple method to handle the equality constraints, well suited to the proposed estimator, has been presented to improve its computational efficency. The proposed estimator has been riti ally analyzed and compared to other approaches available in the literature, in order to highlight strengths and weaknesses of the on eived solution. In the second part of the thesis, the problem of DSSE has been analyzed from a wider perspective, aiming at highlighting the impact of different measurement as- pe ts on the estimation results. The impact of measurement type and placement on the estimation a ura y of the different electrical quantities has been deeply investigated, supporting the empiric cal results through a detailed mathematical analysis. Such study an provide important guidelines for the choice of the measurement infrastructure to be deployed in future distribution systems in order to a achieve spe i a ura y targets for the estimation of the different electrical quantities. The possibility to enhan e the estimation a ura y by properly considering the measurement correlations has also been investigated. Developed analysis shows that different sources of correlation an exist in the measurements used as input to the DSSE algorithm. Performed simulations prove that the in lusion of these orrelations in the DSSE model an lead to significant benefits on the estimation a ura y. Finally, a possible de entralized multi-area ar hite ture, designed to handle large distribution networks, has been proposed. Su h a solution has been on eived duly taking into a ount the opposite requirements of a ura y, computational e ien y and low communication costs desirable in realistic scenarios. Even in this case, particular attention has been fo used on the proper modeling of the measurements in order to a hieve estimation results as a urate as possible. To this purpose, a mathematical analysis has been developed to assess the correlations arising in the proposed multi-area approach. Test results on rm the validity of the developed analysis and, above all, prove the importance of a proper consideration of all the measurement aspects for enhacing the a ura y of the estimations. In conclusion, in this thesis, the problem of state estimation in future distribution system has been deeply analyzed, focusing in particular on the issues related to the measurement modeling and processing. The analysis and the results presented in this thesis shows how the achievement of a really smart management and control of future distribution grids (as expected in the Smart Grid scenario) is strictly dependent on the smart deployment, processing and management of the measurements in the network.