The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Environmental and economic multi-objective optimization of a household level hybrid renewable energy system by genetic algorithm
Open accessAbstract
The rapid spread of renewables made it essential to design optimal hybrid renewable energy systems (HRES) with the distinctive economic and environmental impacts of each technology in mind. According to a comprehensive literature review, very few studies consider life-cycle environmental impacts in small-scale hybrid renewable energy system optimization. This paper aims to fill this gap by providing a multi-objective design framework for household-scale systems based on the technical modeling of several typical components. Solar photovoltaic, wind turbine, solar heat collector, heat pump, heat storage, battery, and as a novelty, heat insulation thickness are considered. Backup power is either drawn from the grid or produced by a diesel generator in grid-connected and off-grid scenarios, respectively. Single objective optimization using genetic algorithm resulted in the least cost and the least environmental footprint options in a case study of three different locations across Europe. Then, Pareto-optimal solutions between the two extremities were explored with a multi-objective genetic algorithm. Single objective results show substantial differences between environmental and economic optima, while multi-objective optimization proved to be an efficient tool to investigate the trade-offs between the two conflicting goals. Solar photovoltaics is proved to be the most competitive technology to reduce environmental impacts in the case of grid-connected systems. Off-grid systems, however, benefit the most from a balanced mix of different renewable energy sources. Life-cycle impacts in the design of systems involving renewables is proven to be relevant while potential applications of the framework are also revealed. Further research areas, as well as the limitations of the methodology are identified in the conclusion.
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Cited by
Hassan, Q., Algburi, S., Sameen, A. Z., Salman, H. M., & Jaszczur, M. (2023) · Results in Engineering · doi:10.1016/j.rineng.2023.101621 · Cited by 836
Hannan, M. A., Wali, S. B., Ker, P. J., Rahman, M. S. A., Mansor, M., Ramachandaramurthy, V. K., Muttaqi, K. M., Mahlia, T. M. I., & Dong, Z. Y. (2021) · Journal of Energy Storage · doi:10.1016/j.est.2021.103023 · Cited by 676
Extensive comparison of physical models for photovoltaic power forecasting
Mayer, M. J., & Gróf, G. (2021) · Applied Energy · doi:10.1016/j.apenergy.2020.116239 · Cited by 466
Self-citationLiu, Z., Sun, Y., Xing, C., Liu, J., He, Y., Zhou, Y., & Zhang, G. (2022) · Energy and AI · doi:10.1016/j.egyai.2022.100195 · Cited by 288
Lipu, M. S. H., Miah, M. S., Hannan, M. A., Hussain, A., Sarker, M. R., Ayob, A., Saad, M. H. M., & Mahmud, M. S. (2021) · IEEE Access · doi:10.1109/access.2021.3097102 · Cited by 121
Metadata
- Authors
- Mayer, M. J., Szilágyi, A., & Gróf, G.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 269
- Pages
- 115058
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W3022567978
- Cited by
- 401
- References
- 44
- FWCI
- 17.10
- Type
- journal article
- Language
- en
- Open access
- hybrid
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