The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Data-driven configuration optimization of an off-grid wind/PV/hydrogen system based on modified NSGA-II and CRITIC-TOPSIS
Abstract
Abstract This paper proposes a data-driven two-stage multi-criteria decision-making (MCDM) framework to investigate the optimal configuration of a stand-alone wind/PV/hydrogen system. In the first stage, a modified non-dominated sorting genetic algorithm (NSGA)-II based on reinforcement learning is utilized to determine a set of Pareto solutions. The objectives considered are to minimize the levelized cost of energy (LCOE), the loss of power supply possibility (LPSP) and the power abandonment rate (PAR), simultaneously. In the second stage, the Criteria Importance Though Intercrieria Correlation (CRITIC) method is utilized to determine the weight of the three objectives, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach is employed to select the unique best solution from Pareto solutions. To verify the effectiveness, the framework is applied to the wind/PV/hydrogen system located in Aksay Kazak Autonomous County, Gansu Province, China to meet an off-grid industrial park’s load demand of 1603 kWh/day and peak load of 117.17 kW. The result states that the optimal system, which consists of 83.2 kW PV panels, 160 kW wind turbines, 20 kW fuel cells, 54 kW electrolyzers and 450 m3 hydrogen storage tanks, owns the LCOE of 0.226 $/kWh, the LPSP of 4.01% and the PAR of 2.15%.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Determining objective weights in multiple criteria problems: The critic method
Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995) · Computers & Operations Research · doi:10.1016/0305-0548(94)00059-h · Cited by 3,413
Hydrogen energy, economy and storage: Review and recommendation
Abe, J. O., Popoola, A. P. I., Ajenifuja, E., & Popoola, O. M. (2019) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2019.04.068 · Cited by 3,315
Application of multi-criteria decision making to sustainable energy planning—A review
Pohekar, S. D., & Ramachandran, M. (2004) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2003.12.007 · Cited by 1,938
Economics of converting renewable power to hydrogen
Glenk, G., & Reichelstein, S. (2019) · Nature Energy · doi:10.1038/s41560-019-0326-1 · Cited by 1,360
Review of software tools for hybrid renewable energy systems
Sinha, S., & Chandel, S. S. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.01.035 · Cited by 991
Ramli, M. A. M., Bouchekara, H. R. E. H., & Alghamdi, A. S. (2018) · Renewable Energy · doi:10.1016/j.renene.2018.01.058 · Cited by 548
Multiobjective Immune Algorithm with Nondominated Neighbor-Based Selection
Gong, M., Jiao, L., Du, H., & Bo, L. (2008) · Evolutionary Computation · doi:10.1162/evco.2008.16.2.225 · Cited by 486
Guo, S., & Zhao, H. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.08.082 · Cited by 464
Dufo‐López, R., Bernal-Agustín, J. L., Yusta, J. M., Dominguez-Navarro, J. A., Ramírez-Rosado, I. J., Lujano, J., & Aso, I. (2011) · Applied Energy · doi:10.1016/j.apenergy.2011.04.019 · Cited by 460
Cited by
Le, T. S., Nguyen, T. N., Bui, D.-K., & Ngo, T. D. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.120817 · Cited by 270
Al-Othman, A., Tawalbeh, M., Martis, R., Dhou, S., Orhan, M., Qasim, M., & Ghani Olabi, A. (2022) · Energy Conversion and Management · doi:10.1016/j.enconman.2021.115154 · Cited by 258
Robust multi-objective optimization of methanol steam reforming for boosting hydrogen production
Bayat, M., & Garmroodi Asil, A. (2021) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2021.06.164 · Cited by 25
Yang, M., Yan, G., Zhang, Y., Zhang, T., & Ai, C. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e13805 · Cited by 19
“Subsequently, the analytic hierarchy process (AHP) [23], TOPSIS entropy weight [24], and other comprehensive decision-making methods were applied to set the weight between multiple objectives and select the optimal design scheme.”
Method
Metadata
- Authors
- Xu, C., Ke, Y., Li, Y., Chu, H., & Wu, Y.
- Venue
- Energy Conversion and Management
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 215
- Pages
- 112892
- ISSN
- 0196-8904 (ISSN-L)
- OpenAlex
- W3024366305
- Cited by
- 386
- References
- 39
- FWCI
- 15.72
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Data-driven configuration optimization of an off-grid wind/PV/hydrogen system based on modified NSGA-II and CRITIC-TOPSIS. https://app.cochat.ai/papers/10.1016/j.enconman.2020.112892
- The paper
- Xu, C., Ke, Y., Li, Y., Chu, H., & Wu, Y. (2020). Data-driven configuration optimization of an off-grid wind/PV/hydrogen system based on modified NSGA-II and CRITIC-TOPSIS. Energy Conversion and Management. https://doi.org/10.1016/j.enconman.2020.112892
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
