The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Economic Allocation for Energy Storage System Considering Wind Power Distribution
Abstract
Energy storage systems play a significant role in both distributed power systems and utility power systems. Among the many benefits of an energy storage system, the improvement of power system cost and voltage profile can be the salient specifications of storage systems. Studies show that improper size and placement of energy storage units leads to undesired power system cost as well as the risk of voltage stability, especially in the case of high renewable energy penetration. To solve the problem, a hybrid multi-objective particle swarm optimization (HMOPSO) approach is proposed in the paper to minimize the power system cost and improve the system voltage profiles by searching sitting and sizing of storage units under consideration of uncertainties in wind power production. Furthermore, the probability cost analysis is first put forward in this paper. The proposed HMOPSO combines multi-objective particle swarm optimization (MOPSO) algorithm with elitist nondominated sorting genetic algorithm (NSGA-II) and probabilistic load flow technique. It also incorporates a five-point estimation method (5PEM) for discretizing wind power distribution. The IEEE 30-bus system is adopted to perform case studies. The simulation results for each case clearly demonstrate the necessity for optimal storage allocation, and the effectiveness of the proposed method.
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
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
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
A new optimizer using particle swarm theory
Eberhart, R., & Kennedy, J. (2002) · MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science · doi:10.1109/mhs.1995.494215 · Cited by 16,067
Multi-Objective Optimization Using Evolutionary Algorithms
Deb, K., & Kalyanmoy, D. (2001) · Andalas University Repository (Andalas University) · Cited by 14,918
Particle swarm optimization: developments, applications and resources
Eberhart, & Shi, Y. (2001) · Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546) · doi:10.1109/cec.2001.934374 · Cited by 4,310
An Improved Particle Swarm Optimization for Nonconvex Economic Dispatch Problems
Park, J.-B., Jeong, Y.-W., Shin, J.-R., & Lee, K. Y. (2010) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2009.2030293 · Cited by 627
Recent advances in particle swarm
Hu, X., Shi, Y., & Eberhart, R. (2004) · Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753) · doi:10.1109/cec.2004.1330842 · Cited by 466
Optimal Allocation and Economic Analysis of Energy Storage System in Microgrids
Chen, C., Duan, S., Cai, T., Liu, B., & Hu, G. (2011) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2011.2116808 · Cited by 369
Kaldellis, J. K., & Zafirakis, D. (2007) · Energy · doi:10.1016/j.energy.2007.07.009 · Cited by 245
A Stochastic Optimization Approach to Rating of Energy Storage Systems in Wind-Diesel Isolated Grids
Abbey, C., & Joos, G. (2009) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2008.2004840 · Cited by 224
Cited by
Sadeghian, H., & Wang, Z. (2020) · Renewable Energy · doi:10.1016/j.renene.2019.09.117 · Cited by 64
Metadata
- Authors
- Wen, S., Lan, H., Fu, Q., Yu, D. C., & Zhang, L.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2015-03
- Volume
- 30
- Issue
- 2
- Pages
- 644-652
- ISSN
- 0885-8950 (ISSN-L)
- 1558-0679
- OpenAlex
- W2053815580
- Cited by
- 287
- References
- 14
- FWCI
- 11.50
- 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: Economic Allocation for Energy Storage System Considering Wind Power Distribution. https://app.cochat.ai/papers/10.1109/tpwrs.2014.2337936
- The paper
- Wen, S., Lan, H., Fu, Q., Yu, D. C., & Zhang, L. (2015). Economic Allocation for Energy Storage System Considering Wind Power Distribution. IEEE Transactions on Power Systems. https://doi.org/10.1109/tpwrs.2014.2337936
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.
