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Parra Laita, Íñigo de la

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https://academica-e.unavarra.es/handle/2454/49742

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Parra Laita

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Íñigo de la

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Ingeniería Eléctrica, Electrónica y de Comunicación

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ISC. Institute of Smart Cities

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0000-0002-4391-8140

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88770

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810130

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2019 - 20202
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Subject: Energy management ×

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Now showing 1 - 2 of 2
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    PublicationOpen Access
    Identification of critical parameters for the design of energy management algorithms for Li-ion batteries operating in PV power plants
    (IEEE, 2020) Berrueta Irigoyen, Alberto; Soto Cabria, Adrián; Marcos Álvarez, Javier; Parra Laita, Íñigo de la; Sanchis Gúrpide, Pablo; Ursúa Rubio, Alfredo; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Institute of Smart Cities - ISC; Ingeniería Eléctrica, Electrónica y de Comunicación; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa, ReBMS PJUPNA1904; Gobierno de Navarra / Nafarroako Gobernua, 0011-1411-2018-000029 GERA
    Lithium-ion batteries are gaining importance for a variety of applications due to their price decrease and characteristics improvement. For a proper use of such storage systems, an energy management algorithm (EMA) is required. A number of EMAs, with various characteristics, have been published recently, given the diverse nature of battery problems. The EMA of deterministic battery problems is usually based on an optimization algorithm. The selection of such an algorithm depends on a few problem characteristics, which need to be identified and closely analyzed. The aim of this article is to identify the critical optimization problem parameters that determine the most suitable EMA for a Li-ion battery. With this purpose, the starting point is a detailed model of a Li-ion battery. Three EMAs based on the algorithms used to face deterministic problems, namely dynamic, linear, and quadratic programming, are designed to optimize the energy dispatch of such a battery. Using real irradiation and power price data, the results of these EMAs are compared for various case studies. Given that none of the EMAs achieves the best results for all analyzed cases, the problem parameters that determine the most suitable algorithm are identified to be four, i.e., desired computation intensity, characteristics of the battery aging model, battery energy and power capabilities, and the number of optimization variables, which are determined by the number of energy storage systems, the length of the optimization problem, and the desired time step.
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    PublicationOpen Access
    Critical comparison of energy management algorithms for lithium-ion batteries in renewable power plants
    (IEEE, 2019) Berrueta Irigoyen, Alberto; Soto Cabria, Adrián; García Solano, Miguel; Parra Laita, Íñigo de la; Sanchis Gúrpide, Pablo; Ursúa Rubio, Alfredo; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Institute of Smart Cities - ISC; Ingeniería Eléctrica, Electrónica y de Comunicación; Universidad Pública de Navarra / Nafarroako Unibertsitate Publikoa
    Lithium-ion batteries are gaining importance for a variety of applications due to their price decrease and characteristics improvement. A good energy management strategy is required in order to increase the profitability of an energy system using a Li-ion battery for storage. The vast number of management algorithms that has been proposed to optimize the achieved profit, with diverse computational power requirements and using models with different complexity, raise doubts about the suitability of an algorithm and the required computation power for a particular application. The performance of three energy management algorithms based on linear, quadratic, and dynamic programming are compared in this work. A realistic scenario of a medium-sized PV plant with a constraint of peak shaving is used for this comparison. The results achieved by the three algorithms are compared and the grounds of the differences are analyzed. Among the three compared algorithms, the quadratic one seems to be the most suitable for renewableenergy applications, given the undue simplification of the battery aging required by the linear algorithm and the discretization and computational power required by a dynamic algorithm.