Experimental modeling of a web-winding machine: LPV approaches

This chapter presents the identification of a web-winding system as a linear parameter varying (LPV) system with the reel radius as the time-varying parameter. This system is nonlinear, time-varying and input–output unstable. Two identification methods are considered: in the first one, an LPV model is estimated in a single step using a novel approach based on sparse identification and set membership optimality evaluation. In the second one, several local linear time-invariant (LTI) models are identified using classical identification algorithms, and the overall LPV model is constructed as a weighted sum of the local models. The two methods are applied to experimental data measured on a real web-winding machine

A Set-Membership approach to short-term electric load forecasting

[EN] This work presents a model for the short-term forecast of electric load, based on Set-Membership techniques. The model is formed by a periodic component and an adaptive non-linear autoregressive component. The identifications set of the non-linear model is increased at each estimation step. The model is evaluated in a case study with more than 13.000 samples of hourly sampled energy demand, registered during three years at a rural town in Colombia. The performance of the estimator is evaluated and confronted to a linear autoregressive model and a standard Set-Membership model with fixed identification set. Results show that the proposed estimator is able to predict demand with an RMS error below 2.5% for validation data, using just a 5% of the available dataset for the model identification.[ES] En este artículo se propone un modelo para la predicción de demanda de energía eléctrica a corto plazo empleando técnicas de estimación Set Membership. El modelo está compuesto por una componente periódica y una componente no-lineal auto-regresiva generada por un modelo no-paramétrico adaptable que incorpora datos recientes al conjunto de identificación en cada iteración del algoritmo. El modelo es evaluado en un caso de estudio con mas de 13,000 muestras de demanda horaria a lo largo de tres años, registradas en un municipio rural de Colombia. El desempeño del estimador se compara con un modelo lineal auto-regresivo y un modelo Set Membership con conjunto de identificación fijo. Los resultados muestran que el estimador propuesto logra predecir la demanda de energía con un error RMS inferior al 2.5 % en datos de validación, empleando solo un 5 % de los datos disponibles para la construcción del modelo.Este trabajo fue financiado por el Fondo de Ciencia, Tecnología e Innovación del Sistema General de Regalías (SGR),Gobernación de Cundinamarca (Colombia), convenio especial de cooperación No. SCTeI 016 de 2015. El trabajo de Jimena Díaz fue financiado por una beca del Departamento Cundinamarca-Fundación CEIBA a través del Proyecto Fortalecimiento del Departamento de Cundinamarca en sus Capacidades de Investigación en Ciencia, Tecnología e Innovación.Diaz, J.; Vuelvas, J.; Ruiz, F.; Patiño, D. (2019). Modelo de predicción de demanda de energía eléctrica mediante técnicas Set-Membership. Revista Iberoamericana de Automática e Informática. 16(4):467-479. https://doi.org/10.4995/riai.2019.9819SWORD467479164Acosta, A., González, A., Zamarreno, J., Álvarez, V., 2011. Modelo para la predicción energética de una instalación hotelera. Revista Iberoamericana de Automática e Informática Industrial RIAI 8 (4), 309 - 322. https://doi.org/10.1016/j.riai.2011.09.001Alam, A., Upadhyay, S., Murthy, C. H., Reddy, M. J. B., Jana, K. C., Mohanta, D. K., 2012. Reliability evaluation of solar photovoltaic microgrid. In: Environment and Electrical Engineering (EEEIC), 2012 11th International Conference on. pp. 490-495. https://doi.org/10.1109/EEEIC.2012.6221427Alfares, H. K., Nazeeruddin, M., 2002. Electric load forecasting: Literature survey and classification of methods. International Journal of Systems Science 33 (1), 23-34. https://doi.org/10.1080/00207720110067421Bordons, C., Torres, F. G., Valverde, L., 2015. Gestión Óptima de la energía en microrredes con generación renovable. Revista Iberoamericana de Automática e Informática Industrial RIAI 12 (2), 117 - 132. https://doi.org/10.1016/j.riai.2015.03.001Castano, J., Ruiz, F., 2013. Set membership identification of an excimer lamp for fast simulation. Control Engineering Practice 21 (1), 96 - 104. https://doi.org/10.1016/j.conengprac.2012.09.013Dang, H. Q., 2014. Time series outlier detection in spacecraft data. Ph.D. thesis, Knowledge Engineering Group, TU Darmstadt.Dwijayanti, S., Hagan, M., 2013. Short Term Load Forecasting Using a Neural Network Based Time Series Approach. 2013 1st International Conference on Artificial Intelligence, Modelling and Simulation (1), 17-22. https://doi.org/10.1109/AIMS.2013.11Hanmandlu, M., Chauhan, B. K., 2011. Load forecasting using hybrid models. IEEE Transactions on Power Systems 26 (1), 20-29. https://doi.org/10.1109/TPWRS.2010.2048585He, Y., Xu, Q.,Wan, J., Yang, S., 2016. Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function. Energy 114, 498-512. https://doi.org/10.1016/j.energy.2016.08.023Hu, Z., Bao, Y., Xiong, T., Chiong, R., 2015. Hybrid filter-wrapper feature selection for short-term load forecasting. Engineering Applications of Artificial Intelligence 40, 17-27. https://doi.org/10.1016/j.engappai.2014.12.014Kapgate, D., Mohod, S., 2014. Hybrid wavenet model for short term electrical load forecasting. In: 2014 Conference on IT in Business, Industry and Government (CSIBIG). No. 1. pp. 1-8. https://doi.org/10.1109/CSIBIG.2014.7057008Lopes, J. P., Hatziargyriou, N., Mutale, J., Djapic, P., Jenkins, N., 2007. Integrating distributed generation into electric power systems: A review of drivers, challenges and opportunities. Electric Power Systems Research 77 (9), 1189 - 1203. https://doi.org/10.1016/j.epsr.2006.08.016Mikati, M., Santos, M., Armenta, C., 2012. Modelado y simulación de un sistema conjunto de energía solar y eólica para analizar su dependencia de la red eléctrica. Revista Iberoamericana de Automática e Informática Industrial RIAI 9 (3), 267 - 281. https://doi.org/10.1016/j.riai.2012.05.010Milanese, M., Novara, C., 2004. Set Membership identification of nonlinear systems. Automatica 40 (6), 957-975. https://doi.org/10.1016/j.automatica.2004.02.002Milanese, M., Novara, C., Nov 2005. Set membership prediction of nonlinear time series. IEEE Transactions on Automatic Control 50 (11), 1655-1669. https://doi.org/10.1109/TAC.2005.858693Nose-Filho, K., Lotufo, A. D. P., Minussi, C. R., 2011b. Short-Term Multinodal Load Forecasting Using a Modified General Regression Neural Network. IEEE Transactions on Power Delivery 26 (4), 2862-2869. https://doi.org/10.1109/TPWRD.2011.2166566Oliveira, M. O., Marzec, D. P., Bordin, G., Bretas, a. S., Bernardon, D., 2011. Climate change e_ect on very short-term electric load forecasting. 2011 IEEE Trondheim PowerTech 190, 1-7. https://doi.org/10.1109/PTC.2011.6019249Parkpoom, S., Harrison, G., Bialek, J., 2004. Climate change impacts on electricity demand. 39th International Universities Power Engineering Conference, 2004. UPEC 2004. 3 (Table I), 1342-1346Sadaei, H. J., Enayatifar, R., Abdullah, A. H., Gani, A., 2014. Short-term load forecasting using a hybrid model with a refined exponentially weighted fuzzy time series and an improved harmony search. International Journal of Electrical Power & Energy Systems 62 (from 2005), 118-129. https://doi.org/10.1016/j.ijepes.2014.04.026Singh, N. K., Singh, A. K., Tripathy, M., 2015. A comparative study of BPNN, RBFNN and ELMAN neural network for short-term electric load forecasting: A case study of Delhi region. 9th International Conference on Industrial and Information Systems, ICIIS 2014. https://doi.org/10.1109/ICIINFS.2014.7036502Ueckerdt, F., Brecha, R., Luderer, G., 2015. Analyzing major challenges of wind and solar variability in power systems. Renewable Energy 81, 1 - 10. https://doi.org/10.1016/j.renene.2015.03.002Yalcinoz, T., Eminoglu, U., 2005. Short term and medium term power distribution load forecasting by neural networks. Energy Conversion and Management 46 (9-10), 1393-1405. https://doi.org/10.1016/j.enconman.2004.07.00

Coordination of specialised energy aggregators for balancing service provision

In the present context of evolution of the power and energy systems, more flexibility is required on the generation and demand side, to cope with the increasing uncertainty mostly introduced by variable renewable energy resources. This paper presents a conceptual framework that encompasses different types of aggregators, including local network aggregators, demand-side general aggregators, specialised energy aggregators (SEAs), and energy community aggregators. In this framework, this paper focuses on the coordination of SEAs to provide balancing services to the system operator. Each SEA manages a specific type of load, so that these loads can be managed by exploiting their control capabilities in a detailed way considering response time, dynamics and available flexibility. Moreover, the presence of the SEAs increases the privacy protection of the users, as only the information on a specific type of user's load is sent to the SEA. The SEA Coordinator interacts with the Balancing Service Provider aimed at procuring frequency containment, frequency restoration and replacement reserve services. This paper contains the SEA Coordinator formulation, information exchange and control operation strategies. Case study applications are presented by using SEAs for three specific types of loads (thermoelectric refrigerator, water booster pressure systems and electric vehicle charging stations). The results show how the control algorithm of the SEA Coordinator is effective in providing balancing services at different timings with the different types of loads. Various scenarios are considered, comparing an ideal situation without command propagation delays with realistic situations that take into account the command propagation delays

La sectorización de las inversiones extranjeras en Colombia: Efectos en la economía nacional

Tesis (Economista).--Universidad de Cartagena. Facultad de Ciencias Económicas. Programa de Economía, 1986.El presente estudio tiene por objeto determinar si las inversiones extranjeras pueden ser una solución para la dificil situacion economica del pais