Quick Calculation of Magnetic Flux Density in Electrical Facilities

[EN] The World Health Organization (WHO) warns that the presence of magnetic fields due to the circulation of industrial frequency electrical currents may have repercussions on the health of living beings. Hence, it is crucially important that we are able to quantify these fields under the normal operating conditions of the facilities, both in their premises and in their surroundings, in order to take the appropriate corrective measures and assure the safety conditions imposed, in force, by regulations. For this purpose, CRMag® software has been developed. Using the simplified Maxwell equations for low frequencies, CRMag® calculates and represents the magnetic flux density (MFD) that electrical currents produce in the environment. Users can easily model electrical facilities through a friendly and simple data entry. MFDs calculated by CRMag® have been validated in real facilities and laboratory tests. With this software, exposure levels can be studied in any hypothetical scenario, even in inaccessible zones. This allows designers to guarantee that legal limits (occupational, general population, or precautionary levels related to epidemiological studies) are fulfilled. A real case study has been described to show how the reconfiguration of conductors in a distribution transformer substation (DTS) allows significant reductions in MFD in some points outside the facility.This work has been possible thanks to the support of the Universitat Politecnica de Valencia.Roldán-Blay, C.; Roldán-Porta, C. (2020). Quick Calculation of Magnetic Flux Density in Electrical Facilities. Applied Sciences. 10(3):1-20. https://doi.org/10.3390/app10030891S120103Feychting, M., & Alhbom, M. (1993). Magnetic Fields and Cancer in Children Residing Near Swedish High-voltage Power Lines. American Journal of Epidemiology, 138(7), 467-481. doi:10.1093/oxfordjournals.aje.a116881WERTHEIMER, N., & LEEPER, E. (1979). ELECTRICAL WIRING CONFIGURATIONS AND CHILDHOOD CANCER. American Journal of Epidemiology, 109(3), 273-284. doi:10.1093/oxfordjournals.aje.a112681Green, L. M., Miller, A. B., Villeneuve, P. J., Agnew, D. A., Greenberg, M. L., Li, J., & Donnelly, K. E. (1999). A case-control study of childhood leukemia in Southern Ontario, Canada, and exposure to magnetic fields in residences. International Journal of Cancer, 82(2), 161-170. doi:10.1002/(sici)1097-0215(19990719)82:23.0.co;2-xGreen, L. M., Miller, A. B., Agnew, D. A., Greenberg, M. L., Li, J., Villeneuve, P. J., & Tibshirani, R. (1999). Cancer Causes and Control, 10(3), 233-243. doi:10.1023/a:1008919408855McBride, M. L., Gallagher, R. P., Theriault, G., Armstrong, B. G., Tamaro, S., Spinelli, J. J., … Choi, W. (1999). Power-Frequency Electric and Magnetic Fields and Risk of Childhood Leukemia in Canada. American Journal of Epidemiology, 149(9), 831-842. doi:10.1093/oxfordjournals.aje.a009899Tynes, T., & Haldorsen, T. (1997). Electromagnetic Fields and Cancer in Children Residing Near Norwegian High-Voltage Power Lines. American Journal of Epidemiology, 145(3), 219-226. doi:10.1093/oxfordjournals.aje.a009094Ahlbom, A., Day, N., Feychting, M., Roman, E., Skinner, J., Dockerty, J., … Verkasalo, P. K. (2000). A pooled analysis of magnetic fields and childhood leukaemia. British Journal of Cancer, 83(5), 692-698. doi:10.1054/bjoc.2000.1376GUIDELINES FOR LIMITING EXPOSURE TO TIME-VARYING ELECTRIC AND MAGNETIC FIELDS (1 Hz TO 100 kHz). (2010). Health Physics, 99(6), 818-836. doi:10.1097/hp.0b013e3181f06c86Exposure to Extremely Low Frequency Fieldshttps://www.who.int/peh-emf/publications/facts/fs322/en/Boletín Oficial del Estadohttps://www.boe.es/buscar/pdf/2001/BOE-A-2001-18256-consolidado.pdfIEEE Standard for Safety Levels With Respect to Human Exposure to Electromagnetic Fields, 0-3 kHz. (s. f.). doi:10.1109/ieeestd.2002.94143International Agency for Research on Cancer Classifies Radiofrequency Electromagnetic Fields as Possibly Carcinogenic to Humans, World Health Organization, Lyon, 2011https://www.iarc.fr/wp-content/uploads/2018/07/pr208_E.pdfKavet, R., Dovan, T., & Reilly, J. P. (2012). The relationship between anatomically correct electric and magnetic field dosimetry and publishedelectric and magnetic field exposure limits. Radiation Protection Dosimetry, 152(4), 279-295. doi:10.1093/rpd/ncs064Boletín Oficial del Estadohttps://www.boe.es/boe/dias/2014/06/09/pdfs/BOE-A-2014-6084.pdfKheifets, L., Afifi, A., Monroe, J., & Swanson, J. (2010). Exploring exposure–response for magnetic fields and childhood leukemia. Journal of Exposure Science & Environmental Epidemiology, 21(6), 625-633. doi:10.1038/jes.2010.38Zhao, L., Liu, X., Wang, C., Yan, K., Lin, X., Li, S., … Liu, X. (2014). Magnetic fields exposure and childhood leukemia risk: A meta-analysis based on 11,699 cases and 13,194 controls. Leukemia Research, 38(3), 269-274. doi:10.1016/j.leukres.2013.12.008Calvente, I., Fernandez, M. F., Villalba, J., Olea, N., & Nuñez, M. I. (2010). Exposure to electromagnetic fields (non-ionizing radiation) and its relationship with childhood leukemia: A systematic review. Science of The Total Environment, 408(16), 3062-3069. doi:10.1016/j.scitotenv.2010.03.039Bunch, K. J., Keegan, T. J., Swanson, J., Vincent, T. J., & Murphy, M. F. G. (2014). Residential distance at birth from overhead high-voltage powerlines: childhood cancer risk in Britain 1962–2008. British Journal of Cancer, 110(5), 1402-1408. doi:10.1038/bjc.2014.15Sermage-Faure, C., Demoury, C., Rudant, J., Goujon-Bellec, S., Guyot-Goubin, A., Deschamps, F., … Clavel, J. (2013). Childhood leukaemia close to high-voltage power lines – the Geocap study, 2002–2007. British Journal of Cancer, 108(9), 1899-1906. doi:10.1038/bjc.2013.128Bunch, K. J., Swanson, J., Vincent, T. J., & Murphy, M. F. G. (2015). Magnetic fields and childhood cancer: an epidemiological investigation of the effects of high-voltage underground cables. Journal of Radiological Protection, 35(3), 695-705. doi:10.1088/0952-4746/35/3/695Frei, P., Poulsen, A. H., Mezei, G., Pedersen, C., Cronberg Salem, L., Johansen, C., … Schuz, J. (2013). Residential Distance to High-voltage Power Lines and Risk of Neurodegenerative Diseases: a Danish Population-based Case-Control Study. American Journal of Epidemiology, 177(9), 970-978. doi:10.1093/aje/kws334Crespi, C. M., Swanson, J., Vergara, X. P., & Kheifets, L. (2019). Childhood leukemia risk in the California Power Line Study: Magnetic fields versus distance from power lines. Environmental Research, 171, 530-535. doi:10.1016/j.envres.2019.01.022Bavastro, D., Canova, A., Freschi, F., Giaccone, L., & Manca, M. (2014). Magnetic field mitigation at power frequency: Design principles and case studies. 2014 IEEE Industry Application Society Annual Meeting. doi:10.1109/ias.2014.6978470Paraskevopoulos, A. A. P., Bourkas, P. D., & Karagiannopoulos, C. G. (2009). Magnetic induction measurements in high voltage centers of 150/20kV. Measurement, 42(8), 1188-1194. doi:10.1016/j.measurement.2009.03.007Nicolaou, C. P., Papadakis, A. P., Razis, P. A., Kyriacou, G. A., & Sahalos, J. N. (2012). Experimental measurement, analysis and prediction of electric and magnetic fields in open type air substations. Electric Power Systems Research, 90, 42-54. doi:10.1016/j.epsr.2012.03.014Nicolaou, C. P., Papadakis, A. P., Razis, P. A., Kyriacou, G. A., & Sahalos, J. N. (2011). Simplistic numerical methodology for magnetic field prediction in open air type substations. Electric Power Systems Research, 81(12), 2120-2126. doi:10.1016/j.epsr.2011.08.003Navarro-Camba, E., Segura-García, J., & Gomez-Perretta, C. (2018). Exposure to 50 Hz Magnetic Fields in Homes and Areas Surrounding Urban Transformer Stations in Silla (Spain): Environmental Impact Assessment. Sustainability, 10(8), 2641. doi:10.3390/su1008264

Improving the benefits of demand response participation in facilities with distributed energy resources

[EN] Demand response has proven to be a distributed energy resource of great potential over the last decades for electrical systems operation. However, small or medium size facilities generally have a very limited ability to participate in demand response programs. When a facility includes several generation resources, energy storage systems, or even demand flexibility, the decision-making becomes considerably harder because of the amount of variables to be considered. This paper presents a method to facilitate end users' decision-making in demand response participation. The method consists of an algorithm that uses demand and generation forecasts and costs of the available resources. Depending on the energy to be reduced in a program, the algorithm obtains the optimal schedule and facilitates decision making, helping end users to decide when and how to participate. With this method, end users' capability to participate in these programs is clearly increased. In addition, the method is contrasted by simulations based on real programs developed at the Campus de Vera of the Universitat Politècnica de València. The simulations carried out show that the developed method allows end users to take advantage of the potential of their facilities to provide demand response services and obtain the maximum possible benefit.[EN] La resposta a la demanda ha demostrat ser un recurs d'energia distribuïda de gran potencial en les últimes dècades per a l'operació de sistemes elèctrics. No obstant això, les instal·lacions xicotetes o mitjanes generalment tenen una capacitat molt limitada per a participar en programes de resposta a la demanda. Quan una instal·lació inclou diversos recursos de generació, sistemes d'emmagatzematge d'energia o fins i tot flexibilitat de la demanda, la presa de decisions es torna considerablement més difícil a causa de la quantitat de variables que han de considerar-se. Aquest article presenta un mètode per a facilitar la presa de decisions dels usuaris finals en la participació en la resposta a la demanda. El mètode consisteix en un algorisme que utilitza els pronòstics de demanda i generació i els costos dels recursos disponibles. Depenent de l'energia que haja de reduir-se en un programa, l'algorisme obté el programa òptim i facilita la presa de decisions, ajudant els usuaris finals a decidir quan i com participar. Amb aquest mètode, la capacitat dels usuaris finals per a participar en aquests programes s'incrementa clarament. A més, el mètode es contrasta mitjançant simulacions basades en programes reals desenvolupats al Campus de Vera de la Universitat Politècnica de València. Les simulacions realitzades mostren que el mètode desenvolupat permet als usuaris finals aprofitar el potencial de les seues instal·lacions per a proporcionar serveis de resposta a la demanda i obtindre el màxim benefici possible.[ES] La respuesta a la demanda ha demostrado ser un recurso de energía distribuida de gran potencial en las últimas décadas para la operación de sistemas eléctricos. Sin embargo, las instalaciones pequeñas o medianas generalmente tienen una capacidad muy limitada para participar en programas de respuesta a la demanda. Cuando una instalación incluye varios recursos de generación, sistemas de almacenamiento de energía o incluso flexibilidad de la demanda, la toma de decisiones se vuelve considerablemente más difícil debido a la cantidad de variables que deben considerarse. Este artículo presenta un método para facilitar la toma de decisiones de los usuarios finales en la participación en la respuesta a la demanda. El método consiste en un algoritmo que utiliza los pronósticos de demanda y generación y los costes de los recursos disponibles. Dependiendo de la energía que deba reducirse en un programa, el algoritmo obtiene el programa óptimo y facilita la toma de decisiones, ayudando a los usuarios finales a decidir cuándo y cómo participar. Con este método, la capacidad de los usuarios finales para participar en estos programas se incrementa claramente. Además, el método se contrasta mediante simulaciones basadas en programas reales desarrollados en el Campus de Vera de la Universitat Politècnica de València. Las simulaciones realizadas muestran que el método desarrollado permite a los usuarios finales aprovechar el potencial de sus instalaciones para proporcionar servicios de respuesta a la demanda y obtener el máximo beneficio posible.This work has been possible thanks to the "Programa de Formacion del Profesorado Universitario (FPU). Convocatoria 2013. Estancias Breves". This study was carried out thanks to a grant within this program for a short stay at Brunel University London (Uxbridge, London). The authors want to acknowledge the Ministerio de Educacion, Cultura y Deporte for this program and the Brunel University staff, especially Prof. Maria Kolokotroni, for hosting Carlos Roldan Blay and helping him during his research stay. In addition, this research work has been made possible with the support of the GV/2015/068-Ayudas para la realizacion de proyectos de I + D para grupos de investigacion emergentes.Roldán-Blay, C.; Escrivá-Escrivá, G.; Roldán-Porta, C. (2019). Improving the benefits of demand response participation in facilities with distributed energy resources. Energy. 169:710-718. https://doi.org/10.1016/j.energy.2018.12.102S71071816

An optimisation algorithm for distributed energy resources management in micro-scale energy hubs

[EN] In this paper, a new algorithm for optimal management of distributed energy resources in facilities with distributed generation, energy storage systems and specific loads - energy hubs - is shown. This method consists of an iterative algorithm that manages optimal energy flows to obtain the minimum energy cost based on availability of each resource, prices and expected demand. A simulation tool has been developed to run the algorithm under different scenarios. Eight different scenarios of an energy hub have been simulated to illustrate the operation of this method. These scenarios consist of a demand curve under different conditions related to the existence or absence of renewable energy sources and energy storage systems and different electricity tariffs for grid supply. Partial results in the iterative process of the developed algorithm are shown and the results of these simulations are analysed. Results show a good level of optimisation of energy resources by means of optimal use of renewable energy sources and optimal management of energy storage systems. Moreover, the impact of this optimised management on carbon dioxide emissions is analysed. (C) 2017 Elsevier Ltd. All rights reserved.This research work has been made possible with the support of the Programa de Apoyo a la Investigación y Desarrollo (PAID-06-12) de la Universitat Politècnica de València (Spain) and the GV/2015/068-Ayudas para la realización de proyectos de I+D para grupos de investigación emergentes.Roldán-Blay, C.; Escrivá-Escrivá, G.; Roldán-Porta, C.; Álvarez, C. (2017). An optimisation algorithm for distributed energy resources management in micro-scale energy hubs. Energy. 132:126-135. https://doi.org/10.1016/j.energy.2017.05.038S12613513

Optimal Generation Scheduling with Dynamic Profiles for the Sustainable Development of Electricity Grids

[EN] The integration of renewable generation in electricity networks is one of the most widespread strategies to improve sustainability and to deal with the energy supply problem. Typically, the reinforcement of the generation fleet of an existing network requires the assessment and minimization of the installation and operating costs of all the energy resources in the network. Such analyses are usually conducted using peak demand and generation data. This paper proposes a method to optimize the location and size of different types of generation resources in a network, taking into account the typical evolution of demand and generation. The importance of considering this evolution is analyzed and the methodology is applied to two standard networks, namely the Institute of Electrical and Electronics Engineers (IEEE) 30-bus and the IEEE 118-bus. The proposed algorithm is based on the use of particle swarm optimization (PSO). In addition, the use of an initialization process based on the cross entropy (CE) method to accelerate convergence in problems of high computational cost is explored. The results of the case studies highlight the importance of considering dynamic demand and generation profiles to reach an effective integration of renewable resources (RRs) towards a sustainable development of electric systems.The stay of the corresponding author that made this research possible was funded by a grant "Jose Castillejo" number CAS18/00291 of the Spanish Ministerio de Educacion, Cultura y Deporte.Roldán-Blay, C.; Miranda, V.; Carvalho, L.; Roldán-Porta, C. (2019). Optimal Generation Scheduling with Dynamic Profiles for the Sustainable Development of Electricity Grids. Sustainability. 11(24):1-26. https://doi.org/10.3390/su11247111S1261124Höök, M., & Tang, X. (2013). Depletion of fossil fuels and anthropogenic climate change—A review. Energy Policy, 52, 797-809. doi:10.1016/j.enpol.2012.10.046Van de Ven, D. J., & Fouquet, R. (2017). Historical energy price shocks and their changing effects on the economy. Energy Economics, 62, 204-216. doi:10.1016/j.eneco.2016.12.009Osório, G., Shafie-khah, M., Lujano-Rojas, J., & Catalão, J. (2018). Scheduling Model for Renewable Energy Sources Integration in an Insular Power System. Energies, 11(1), 144. doi:10.3390/en11010144Lasseter, R. H. (2011). Smart Distribution: Coupled Microgrids. Proceedings of the IEEE, 99(6), 1074-1082. doi:10.1109/jproc.2011.2114630Moriarty, P., & Honnery, D. (2016). Can renewable energy power the future? Energy Policy, 93, 3-7. doi:10.1016/j.enpol.2016.02.051Ghosh, S., Ghoshal, S. P., & Ghosh, S. (2010). Optimal sizing and placement of distributed generation in a network system. International Journal of Electrical Power & Energy Systems, 32(8), 849-856. doi:10.1016/j.ijepes.2010.01.029Gomez-Gonzalez, M., López, A., & Jurado, F. (2012). Optimization of distributed generation systems using a new discrete PSO and OPF. Electric Power Systems Research, 84(1), 174-180. doi:10.1016/j.epsr.2011.11.016Li, Y., Li, Y., Li, G., Zhao, D., & Chen, C. (2018). Two-stage multi-objective OPF for AC/DC grids with VSC-HVDC: Incorporating decisions analysis into optimization process. Energy, 147, 286-296. doi:10.1016/j.energy.2018.01.036Yassine, A. A., Mostafa, O., & Browning, T. R. (2017). Scheduling multiple, resource-constrained, iterative, product development projects with genetic algorithms. Computers & Industrial Engineering, 107, 39-56. doi:10.1016/j.cie.2017.03.001Dias, B. H., Oliveira, L. W., Gomes, F. V., Silva, I. C., & Oliveira, E. J. (2012). Hybrid heuristic optimization approach for optimal Distributed Generation placement and sizing. 2012 IEEE Power and Energy Society General Meeting. doi:10.1109/pesgm.2012.6345653Prakash, D. B., & Lakshminarayana, C. (2016). Multiple DG Placements in Distribution System for Power Loss Reduction Using PSO Algorithm. Procedia Technology, 25, 785-792. doi:10.1016/j.protcy.2016.08.173Hung, D. Q., Mithulananthan, N., & Bansal, R. C. (2013). Analytical strategies for renewable distributed generation integration considering energy loss minimization. Applied Energy, 105, 75-85. doi:10.1016/j.apenergy.2012.12.023Syahputra, R., Robandi, I., & Ashari, M. (2015). Reconfiguration of Distribution Network with Distributed Energy Resources Integration Using PSO Algorithm. TELKOMNIKA (Telecommunication Computing Electronics and Control), 13(3), 759. doi:10.12928/telkomnika.v13i3.1790Ueckerdt, F., Brecha, R., & Luderer, G. (2015). Analyzing major challenges of wind and solar variability in power systems. Renewable Energy, 81, 1-10. doi:10.1016/j.renene.2015.03.002Kansal, S., Kumar, V., & Tyagi, B. (2013). Optimal placement of different type of DG sources in distribution networks. International Journal of Electrical Power & Energy Systems, 53, 752-760. doi:10.1016/j.ijepes.2013.05.040De Magalhaes Carvalho, L., Leite da Silva, A. M., & Miranda, V. (2018). Security-Constrained Optimal Power Flow via Cross-Entropy Method. IEEE Transactions on Power Systems, 33(6), 6621-6629. doi:10.1109/tpwrs.2018.2847766Zimmerman, R. D., Murillo-Sanchez, C. E., & Thomas, R. J. (2011). MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education. IEEE Transactions on Power Systems, 26(1), 12-19. doi:10.1109/tpwrs.2010.2051168Matpower 7.0 User’s Manual; PSERC, USAhttps://matpower.org/docs/manual.pdfWang, H., Murillo-Sanchez, C. E., Zimmerman, R. D., & Thomas, R. J. (2007). On Computational Issues of Market-Based Optimal Power Flow. IEEE Transactions on Power Systems, 22(3), 1185-1193. doi:10.1109/tpwrs.2007.901301Abdi, H., Beigvand, S. D., & Scala, M. L. (2017). A review of optimal power flow studies applied to smart grids and microgrids. Renewable and Sustainable Energy Reviews, 71, 742-766. doi:10.1016/j.rser.2016.12.102Red Eléctrica de Españahttp://www.ree.esOperador del Mercado Ibérico-Polo Español S.Ahttp://www.omie.esAlsac, O., & Stott, B. (1974). Optimal Load Flow with Steady-State Security. IEEE Transactions on Power Apparatus and Systems, PAS-93(3), 745-751. doi:10.1109/tpas.1974.293972The IEEE 30-Bus Test Systemhttps://labs.ece.uw.edu/pstca/pf30/pg_tca30bus.htmOpen Energy Information—Transparent Cost Databasehttps://openei.org/apps/TCDB/Real Decreto 1955/2000, de 1 de Diciembre, Por el Que se Regulan las Actividades de Transporte, Distribución, Comercialización, Suministro y Procedimientos de Autorización de Instalaciones de Energía Eléctrica, (in Spanish)https://www.boe.es/boe/dias/2000/12/27/pdfs/A45988-46040.pdfThe IEEE 118-Bus Test Systemhttps://labs.ece.uw.edu/pstca/pf118/pg_tca118bus.ht

Herramientas básicas de visualización 3D en Matlab

En este artículo se describen las principales herramientas de visualización de figuras tridimensionales en programas como Matlab y otros del mismo entorno. Se muestra el uso de cada herramienta y las distintas opciones que ofrece. Además, en ocasiones se comparan las versiones modernas con las más antiguas, indicando las diferencias y el modo de cambiar a la versión clásica.Roldán Blay, C.; Roldán Porta, C. (2017). Herramientas básicas de visualización 3D en Matlab. http://hdl.handle.net/10251/83007DE

Smart Cooperative Energy Supply Strategy to Increase Reliability in Residential Stand-Alone Photovoltaic Systems

[EN] In reliability studies of isolated energy supply systems for residential buildings, supply failures due to insufficient generation are generally analysed. Recent studies conclude that this kind of analysis makes it possible to optimally design the sizes of the elements of the generation system. However, in isolated communities or rural areas, it is common to find groups of dwellings in which micro-renewable sources, such as photovoltaic (PV) systems, can be installed. In this situation, the generation and storage of several houses can be considered as an interconnected system forming a cooperative microgrid (CoMG). This work analyses the benefits that sharing two autonomous installations can bring to each one, from the point of view of reliability. The method consists of the application of a random sequential Monte Carlo (SMC) simulation to the CoMG to evaluate the impact of a simple cooperative strategy on the reliability of the set. The study considers random failures in the generation systems. The results show that the reliability of the system increases when cooperation is allowed. Additionally, at the design stage, this allows more cost-effective solutions than single sizing with a similar level of reliability.This work was supported by Conselleria de Educacion, Investigacion, Cultura y Deporte (Grant No. AICO/2019/001).Roldán-Blay, C.; Roldán-Porta, C.; Quiles Cucarella, E.; Escrivá-Escrivá, G. (2021). Smart Cooperative Energy Supply Strategy to Increase Reliability in Residential Stand-Alone Photovoltaic Systems. Applied Sciences. 11(24):1-19. https://doi.org/10.3390/app112411723S119112

Optimal sizing and design of renewable power plants in rural microgrids using multi-objective particle swarm optimization and branch and bound methods

[EN] As energy prices rise, optimizing renewable power plant sizing is vital, especially in areas with unreliable electricity supply due to distant transmission lines. This study addresses this issue by optimizing a renewable power plant portfolio for a Spanish municipality facing such challenges. The presented approach involves a systematic method. Firstly, energy demand is thoroughly analyzed. Next, available renewable resources are explored and optimal plant placements are determined. A multi-objective particle swarm optimization algorithm is then used to size each plant, minimizing annualized costs and grid energy imports. The most suitable feasible optimum is selected from theoretical configurations using branch and bound techniques, prioritizing practicality. In the specific case analyzed, the results show a 20-year Internal Rate of Return of 8.33 %. This is achieved with the following capacities for each plant: 750 kW of photovoltaic solar energy, 160 kW of turbine-based generation, 180 kW of hydroelectric pumping, 160 kW for the biomass plant, and 200 kW from the wind turbine. This study offers an innovative solution to energy challenges, providing practical insights for cost-efficient, sustainable projects.This result is part of the Project TED2021-130464B-I00 (INASO-LAR), funded by MCIN/AEI/10.13039/501100011033 and by European Union "NextGenerationEU"/PRTR.Roldán-Blay, C.; Escrivá-Escrivá, G.; Roldán-Porta, C.; Dasí-Crespo, D. (2023). Optimal sizing and design of renewable power plants in rural microgrids using multi-objective particle swarm optimization and branch and bound methods. Energy. 284. https://doi.org/10.1016/j.energy.2023.12931828

Optimising a Biogas and Photovoltaic Hybrid System for Sustainable Power Supply in Rural Areas

[EN] This paper proposes a method for evaluating the optimal configuration of a hybrid system (biomass power plant and photovoltaic plant), which is connected to the electrical grid, to achieve minimum energy costs. The study is applied to a small ruralmunicipality in the Valencian Community, Spain, as an energy community. The approach takes into account the daily energy demand variation and price curves for energy that are either imported or exported to the grid. The optimal configuration is determined by the highest internal rate of return (IRR) over a 12-year period while providing a 20% discount in electricity prices for the energy community. The approach is extrapolated to an annual period using the statistical data of sunny and cloudy days, considering 23.8% of the year as cloudy. The methodology provides a general procedure for hybridising both plants and the grid to meet the energy needs of a small rural population. In the analysed case, an optimal combination of 140 kW of rated power from the biogas generator was found, which is lower than the maximum demand of 366 kW and 80 kW installed power in the photovoltaic plant, resulting in an IRR of 6.13% over 12 years. Sensitivity studies for data variations are also provided.This project has received funding from the European Union's Horizon 2020 research and innovation programme under the grant agreement No. 101000470 (Natural and Synthetic Microbial Communities for Sustainable Production of Optimised Biogas-Micro4Biogas).Roldán-Porta, C.; Roldán-Blay, C.; Dasí-Crespo, D.; Escrivá-Escrivá, G. (2023). Optimising a Biogas and Photovoltaic Hybrid System for Sustainable Power Supply in Rural Areas. Applied Sciences. 13(4):1-20. https://doi.org/10.3390/app1304215512013

Particle Swarm Optimization Method for Stand-Alone Photovoltaic System Reliability and Cost Evaluation Based on Monte Carlo Simulation

[EN] In rural regions with limited access to the power grid, self-reliance for electricity generation is paramount. This study focuses on enhancing the design of stand-alone photovoltaic installations (SAPV) to replace conventional fuel generators thanks to the decreasing costs of PV modules and batteries. This study presents a particle swarm optimization (PSO) method for the reliable and cost-effective sizing of SAPV systems. The proposed method considers the variability of PV generation and domestic demand and optimizes the system design to minimize the total cost of ownership while ensuring a high level of reliability. The results show that for the PSO method with 500 iterations, the error is around 2%, and the simulation time is approximately 2.25 s. Moreover, the PSO method allows a much lower number of iterations to be used in the Monte Carlo simulation, with a total of 100 iterations used to obtain the averaged results. The optimization results, encompassing installed power, battery capacity, reliability, and annual costs, reveal the effectiveness of our approach. Notably, our discretized PSO algorithm converges, yielding specific parameters like 9900 W of installed power and a battery configuration of five 3550 Wh units for the case study under consideration. In summary, our work presents an efficient SAPV system design methodology supported by concrete numerical outcomes, considering supply reliability and installation and operational costs.Vicerrectorado de Investigacion de la Universitat Politecnica de Valencia (PAID-11-22)Quiles Cucarella, E.; Marquina-Tajuelo, A.; Roldán-Blay, C.; Roldán-Porta, C. (2023). Particle Swarm Optimization Method for Stand-Alone Photovoltaic System Reliability and Cost Evaluation Based on Monte Carlo Simulation. Applied Sciences. 13(21). https://doi.org/10.3390/app132111623132

Accurate Sizing of Residential Stand-Alone Photovoltaic Systems Considering System Reliability

[EN] In rural areas or in isolated communities in developing countries it is increasingly common to install micro-renewable sources, such as photovoltaic (PV) systems, by residential consumers without access to the utility distribution network. The reliability of the supply provided by these stand-alone generators is a key issue when designing the PV system. The proper system sizing for a minimum level of reliability avoids unacceptable continuity of supply (undersized system) and unnecessary costs (oversized system). This paper presents a method for the accurate sizing of stand-alone photovoltaic (SAPV) residential generation systems for a pre-established reliability level. The proposed method is based on the application of a sequential random Monte Carlo simulation to the system model. Uncertainties of solar radiation, energy demand, and component failures are simultaneously considered. The results of the case study facilitate the sizing of the main energy elements (solar panels and battery) depending on the required level of reliability, taking into account the uncertainties that affect this type of facility. The analysis carried out demonstrates that deterministic designs of SAPV systems based on average demand and radiation values or the average number of consecutive cloudy days can lead to inadequate levels of continuity of supply.This work has been supported by research funds of the Universitat Politecnica de Valencia.Quiles Cucarella, E.; Roldán-Blay, C.; Escrivá-Escrivá, G.; Roldán-Porta, C. (2020). Accurate Sizing of Residential Stand-Alone Photovoltaic Systems Considering System Reliability. Sustainability. 12(3):1-18. https://doi.org/10.3390/su12031274S118123Twaha, S., & Ramli, M. A. M. (2018). A review of optimization approaches for hybrid distributed energy generation systems: Off-grid and grid-connected systems. 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