Multicriteria optimization in the problem of computer-aided design of hybrid solar energy systems
DOI:
https://doi.org/10.15587/1729-4061.2021.234202Keywords:
hybrid solar energy system, multicriteria optimization, automated designAbstract
This paper reports an approach to optimizing the structure of a hybrid solar energy system (HSES), used in the task of automated design, under two modes: independent and connected to the network. The proposed HSES includes a solar energy system (SES), an energy storage system (ESS) powered by rechargeable batteries (RBs), a set of diesel generators (DGs), and a network-connecting system. This paper has identified models of the HSES elements' power and proposed a control algorithm based on rules that assess the state of the system during operation.
The energy models in conjunction with the control algorithm make it possible to model the system's operation stage over a predefined time interval. The proposed approach is based on solving a multicriteria optimization problem (MCO). MCO takes into consideration the minimization of system costs and the total cost of the system, minimizing fuel use, maximizing reliability, and minimizing the use of non-renewable energy sources. A solution to the MCO problem is based on using a Pareto-optimal solution search algorithm, underlying which is the NSGA-II genetic algorithm employing the proposed set of crossbreeding, mutation, and breeding operators. The devised procedure makes it possible to determine the structure of HSES, which includes a set of the number of solar panels, RBs, and DGs. The result is three variants of HSES for a household for two people (Kyiv, Ukraine), under an autonomous mode and in the regime connected to the electricity grid. Given the possibility of selling electricity at a green tariff during the year, the reported solution makes it possible to reduce the estimated cost of the system by up to 45 %. The use of simulation has helped conduct a detailed analysis of the system's performance throughout the year
References
- Farret, F. A., Godoy Simoes, M. (2017). Integration of renewable sources of energy. John Wiley & Sons, 688.
- Aziz, A., Tajuddin, M., Adzman, M., Ramli, M., Mekhilef, S. (2019). Energy Management and Optimization of a PV/Diesel/Battery Hybrid Energy System Using a Combined Dispatch Strategy. Sustainability, 11 (3), 683. doi: https://doi.org/10.3390/su11030683
- Zheng, X.-K., Li, K., Wang, R., Zhang, T. (2017). Operation Management of a Hybrid Renewable Energy Systems Base on Multi-Objective Optimal under Uncertainties. 2017 IEEE International Conference on Energy Internet (ICEI). doi: https://doi.org/10.1109/icei.2017.18
- Ma, G., Xu, G., Chen, Y., Ju, R. (2016). Multi‐objective optimal configuration method for a standalone wind–solar–battery hybrid power system. IET Renewable Power Generation, 11 (1), 194–202. doi: https://doi.org/10.1049/iet-rpg.2016.0646
- Song, Y., Liu, Y., Wang, R., Ming, M. (2019). Multi-Objective Configuration Optimization for Isolated Microgrid With Shiftable Loads and Mobile Energy Storage. IEEE Access, 7, 95248–95263. doi: https://doi.org/10.1109/access.2019.2928619
- Upadhyay, S., Sharma, M. P. (2014). A review on configurations, control and sizing methodologies of hybrid energy systems. Renewable and Sustainable Energy Reviews, 38, 47–63. doi: https://doi.org/10.1016/j.rser.2014.05.057
- Abedini, M., Moradi, M. H., Hosseinian, S. M. (2016). Optimal management of microgrids including renewable energy scources using GPSO-GM algorithm. Renewable Energy, 90, 430–439. doi: https://doi.org/10.1016/j.renene.2016.01.014
- Delgado, C., Dominguez-Navarro, J. A. (2014). Optimal design of a hybrid renewable energy system. 2014 Ninth International Conference on Ecological Vehicles and Renewable Energies (EVER). doi: https://doi.org/10.1109/ever.2014.6844008
- Bernal-Agustín, J. L., Dufo-López, R. (2006). Economical and environmental analysis of grid connected photovoltaic systems in Spain. Renewable Energy, 31 (8), 1107–1128. doi: https://doi.org/10.1016/j.renene.2005.06.004
- Caballero, F., Sauma, E., Yanine, F. (2013). Business optimal design of a grid-connected hybrid PV (photovoltaic)-wind energy system without energy storage for an Easter Island's block. Energy, 61, 248–261. doi: https://doi.org/10.1016/j.energy.2013.08.030
- Cho, J.-H., Chun, M.-G., Hong, W.-P. (2016). Structure Optimization of Stand-Alone Renewable Power Systems Based on Multi Object Function. Energies, 9 (8), 649. doi: https://doi.org/10.3390/en9080649
- Eriksson, E. L. V., Gray, E. M. (2017). Optimization and integration of hybrid renewable energy hydrogen fuel cell energy systems – A critical review. Applied Energy, 202, 348–364. doi: https://doi.org/10.1016/j.apenergy.2017.03.132
- Singh, R., Bansal, R. C. (2019). Optimization of an Autonomous Hybrid Renewable Energy System Using Reformed Electric System Cascade Analysis. IEEE Transactions on Industrial Informatics, 15 (1), 399–409. doi: https://doi.org/10.1109/tii.2018.2867626
- Hosseinalizadeh, R., Shakouri G, H., Amalnick, M. S., Taghipour, P. (2016). Economic sizing of a hybrid (PV–WT–FC) renewable energy system (HRES) for stand-alone usages by an optimization-simulation model: Case study of Iran. Renewable and Sustainable Energy Reviews, 54, 139–150. doi: https://doi.org/10.1016/j.rser.2015.09.046
- Olatomiwa, L., Mekhilef, S., Huda, A. S. N., Sanusi, K. (2015). Techno‐economic analysis of hybrid PV –diesel–battery and PV –wind–diesel–battery power systems for mobile BTS: the way forward for rural development. Energy Science & Engineering, 3 (4), 271–285. doi: https://doi.org/10.1002/ese3.71
- Aguiar, R., Collares-Pereira, M. (1992). TAG: A time-dependent, autoregressive, Gaussian model for generating synthetic hourly radiation. Solar Energy, 49 (3), 167–174. doi: https://doi.org/10.1016/0038-092x(92)90068-l
- Singh, R., Bansal, R. C., Singh, A. R. (2018). Optimization of an isolated photo-voltaic generating unit with battery energy storage system using electric system cascade analysis. Electric Power Systems Research, 164, 188–200. doi: https://doi.org/10.1016/j.epsr.2018.08.005
- Bokopane, L., Kusakana, K., Vermaak, H. J. (2015). Optimal energy management of an isolated electric Tuk-Tuk charging station powered by hybrid renewable systems. 2015 International Conference on the Domestic Use of Energy (DUE). doi: https://doi.org/10.1109/due.2015.7102981
- Bakhtiari, H., Naghizadeh, R. A. (2018). Multi‐criteria optimal sizing of hybrid renewable energy systems including wind, photovoltaic, battery, and hydrogen storage with ɛ‐constraint method. IET Renewable Power Generation, 12 (8), 883–892. doi: https://doi.org/10.1049/iet-rpg.2017.0706
- Coppitters, D., De Paepe, W., Contino, F. (2020). Robust design optimization and stochastic performance analysis of a grid-connected photovoltaic system with battery storage and hydrogen storage. Energy, 213, 118798. doi: https://doi.org/10.1016/j.energy.2020.118798
- Díaz, G., Gómez-Aleixandre, J., Coto, J., Conejero, O. (2018). Maximum income resulting from energy arbitrage by battery systems subject to cycle aging and price uncertainty from a dynamic programming perspective. Energy, 156, 647–660. doi: https://doi.org/10.1016/j.energy.2018.05.122
- Ramesh, M., Saini, R. P. (2020). Effect of different batteries and diesel generator on the performance of a stand-alone hybrid renewable energy system. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 1–23. doi: https://doi.org/10.1080/15567036.2020.1763520
- Yusoff, Y., Ngadiman, M. S., Zain, A. M. (2011). Overview of NSGA-II for Optimizing Machining Process Parameters. Procedia Engineering, 15, 3978–3983. doi: https://doi.org/10.1016/j.proeng.2011.08.745
- Ming, M., Wang, R., Zha, Y., Zhang, T. (2017). Multi-objective optimization of hybrid renewable energy system using an enhanced multi-objective evolutionary algorithm. Energies, 10 (5), 674. doi: https://doi.org/10.3390/en10050674
- Nujoom, R., Wang, Q., Mohammed, A. (2018). Optimisation of a sustainable manufacturing system design using the multi-objective approach. The International Journal of Advanced Manufacturing Technology, 96 (5-8), 2539–2558. doi: https://doi.org/10.1007/s00170-018-1649-y
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Виктор Михайлович Синеглазов, Денис Петрович Карабецкий, Елена Ильинична Чумаченко
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.