In the present paper is shown a methodology for characterization of the electric load carried out using as baseline the information obtained from the measurement of the electrical parameters at the medium voltage level by means of the phasorial measurement units (PMUs) and low voltage level by means of the smart meters (SM) in a smart campus. A network topology is also developed for the connection of both PMUs and SMs to the control center using the Ethernet network that is available in the place, the control center is the site where all the optimization algorithms are merged for the energy management system (EMS) on smart campus. Load characterization is done by means of the ZIP model, in which the impedance, current, active and reactive power constants are calibrated. Then, simply with the voltage information obtained from the measurements, the load characterization curve over time is shown. For the location of the PMUs, the clustering technique of k-medoids is used, where the number of clusters represents the number of PMUs in the SEP which is calculated by a MILP optimization considering the 100% observability of the SEP, in terms of the location of SM is performed in each of the substations of the SEP on the low voltage side. Finally, a comparison of load characterization at the medium and low voltage level is performed, obtaining very similar values.
|Título de la publicación alojada||Proceedings - 2017 International Conference on Information Systems and Computer Science, INCISCOS 2017|
|Editorial||Institute of Electrical and Electronics Engineers Inc.|
|Número de páginas||6|
|ISBN (versión digital)||9781538626443|
|Estado||Publicada - 29 mar. 2018|
|Evento||2nd International Conference on Information Systems and Computer Science, INCISCOS 2017 - Quito, Ecuador|
Duración: 23 nov. 2017 → 25 nov. 2017
Serie de la publicación
|Nombre||Proceedings - 2017 International Conference on Information Systems and Computer Science, INCISCOS 2017|
|Conferencia||2nd International Conference on Information Systems and Computer Science, INCISCOS 2017|
|Período||23/11/17 → 25/11/17|
Nota bibliográficaPublisher Copyright:
© 2017 IEEE.