Determination of Pumped Storage Capacity Combining the Entropy Weighting Method and Principal Component Analysis
Abstract
The aim of the study is to evaluate methods for determining appropriate pumped storage capacities. This is especially relevant, since pumped storage units are, today, viewed as some of the best means of storing large amounts intermittently-produced power in order to meet peak demands on power supply grids. The determination of appropriate pumped storage capacity is a problem of integrated decision-making. The entropy weighting method and principal component analysis are combined to determine the optimum pumped storage capacity, taking into account several representative indices, whilst using the syntropy method to standardize the data indicators. The entropy weighting method is used to determine the weighting of the indicators, while principal component analysis offers reduction of the dimensions. The optimal solution is then determined by ranking the evaluation values for each design. This method can avoid subjectivity in the weighting assignment and simplifies the calculations to an evaluation problem which contains multiple evaluation indices. Using the principle of energy-saving scheduling, the peak-shaving method is applied to the dispatching over a typical daily load in order to verify the rationality of the calculated pumped storage capacity. The example analysis, here, shows that it is reasonable to determine the optimum pumped storage capacity by using this combination of the entropy weighting method and principal component analysis.References
[1] A. Poullikkas, Optimization analysis for pumped energy storage systems in small isolated power systems, Journal of Power Technologies 93(2) (2013) 78-89.
[2] S. Papaefthimiou, E. Karamanou, S. Papathanassiou, M. Papadopoulos, Operating policies for wind-pumped storage hybrid power stations in island grids, IET Renewable Power Generation 3 (3) (2009) 293–307.
[3] P. D. Brown, J. A. Pecas Lopes, M. A. Matos, Optimization of pumped storage capacity in an isolated power system with large renewable penetration, IEEE Trans on Power Systems 23 (2) (2008) 523–531.
[4] S. V. Papaefthymiou, E. G. Karamanou, S. A. Papathanassiou, M. P. Papadopoulos, A wind-hydro-pumped storage station leading to high RES penetration in the autonomous island system of Ikaria, IEEE Transactions on Sustainable Energy 1 (3) (2010) 163–172.
[5] S. M. Chen, S. J. Niou, Fuzzy multiple attributes group decision-making based on fuzzy preference relations, Expert Systems with Applications 38 (4) (2011) 3865–3872.
[6] A. Soroudi, M. Ehsan, R. Caire, N. Hadjsaid, Possibilistic evaluation of distributed generations impacts on distribution networks, IEEE Transactions on Power Systems 26 (4) (2011) 2293–2301.
[7] M. T. Isaai, A. Kanani, M. Tootoonchi, H. R. Afzali, Intelligent timetable evaluation using fuzzy AHP, Expert Systems with Applications 38 (4) (2011) 3718–3723.
[8] H. K. Chan, X. J. Wang, G. R. T. White, N. Yip, An extended Fuzzy-AHP approach for the evaluation of green product designs, IEEE Transactions on Engineering Management 60 (2) (2013) 327–339.
[9] Y. H. Chiang, Using a combined AHP and PLS path modelling on blog site evaluation in Taiwan , Computers in Human Behavior 29 (4) (2013) 1325–1333.
[10] W. H. Chen, Quantitative decision-making model for distribution system restoration, IEEE Transactions on Power Systems 25 (1) (2010) 313–321.
[11] Q. Wang, D. M. Kilgour, K. W. Hipel, Fuzzy real options for risky project evaluation using least squares Monte-Carlo simulation, IEEE Systems Journal 5(3) (2011) 385–395.
[12] S. K. Tyagi, M. Akram, Human reliability evaluation for offshore platform musters using intuitionistic Fuzzy sets, IEEE Transactions on Fuzzy Systems 21 (6) (2013) 1115–1122.
[13] D. Liu, M. Liu, Application of Sample Entropy on Measuring Precipitation Series Complexity in Jiansanjiang Branch Bureau of China, Nature Environment and Pollution Technology 12 (2) (2013) 249–254.
[14] B. Hsiao, C. C. Chern, C. R. Chiu, Performance evaluation with the entropy based weighted russell measure in data envelopment analysis, Expert Systems with Applications 38 (8) (2011) 9965–9972.
[15] S. Bouhouche, L. L. Yazid, H. Tarek, B. Jurgen, Inferential sensor-based adaptive principal components analysis for mechanical properties prediction and evaluation, Measurement 46(9) (2013) 3683–3690.
[16] A. Banimostafa, S. Papadokonstantakis, K. Hungerbühler, Evaluation of EHS hazard and sustainability metrics during early process design stages using principal component analysis , Process Safety and Environmental Protection 90 (1) (2012) 8–26.
[17] M. F. Qi, Z. G. Fu, Y. Jing, Y. Ma, A comprehensive evaluation method of power plant units based on information entropy and principal component analysis, Proceedings of CSEE 33 (2) (2013) 58–64.
[18] X. Q. Wen, Y. H. Wang, C. Chen, S. G. Peng, Evaluation on land intensive utilization based on the combination of principal component analysis and entropy value method, Journal of Anhui Agri. Sci. 36 (28) (2008) 12372–12373.
[19] D. P. Guo, Research on benefits and economy for pumped storage station, M.Eng dissertation, Dalian University of Technology, China (Sep 2001)25-54.
[20] F. Cao, L. Z. Zhang, Determination of pumped-storage plant capacity with peak-regulation proportion, Electric Power Automation Equipment 27 (6) (2007) 47–50.
[21] J. C. Cui, D. H. Liu, W. L. Liang, F. Xie, H. Y. Chen, Analysis on economic and environmental benefit of pumped-storage station, Electric Power 40 (1) (2007) 5–10.
[22] Z. A. Zhang, X. G. Cai, Determination of pumped storage capacity based on entropy, Electric Machines and Control 18 (3) (2014) 34–39.
[23] H. Z. Nie, S. Nie, Y. Qiao, P. Lv, Comprehensive decision-making of alternative transmission network planning based on principal component analysis, Power System Technology 34 (6) (2010) 134–137.
[2] S. Papaefthimiou, E. Karamanou, S. Papathanassiou, M. Papadopoulos, Operating policies for wind-pumped storage hybrid power stations in island grids, IET Renewable Power Generation 3 (3) (2009) 293–307.
[3] P. D. Brown, J. A. Pecas Lopes, M. A. Matos, Optimization of pumped storage capacity in an isolated power system with large renewable penetration, IEEE Trans on Power Systems 23 (2) (2008) 523–531.
[4] S. V. Papaefthymiou, E. G. Karamanou, S. A. Papathanassiou, M. P. Papadopoulos, A wind-hydro-pumped storage station leading to high RES penetration in the autonomous island system of Ikaria, IEEE Transactions on Sustainable Energy 1 (3) (2010) 163–172.
[5] S. M. Chen, S. J. Niou, Fuzzy multiple attributes group decision-making based on fuzzy preference relations, Expert Systems with Applications 38 (4) (2011) 3865–3872.
[6] A. Soroudi, M. Ehsan, R. Caire, N. Hadjsaid, Possibilistic evaluation of distributed generations impacts on distribution networks, IEEE Transactions on Power Systems 26 (4) (2011) 2293–2301.
[7] M. T. Isaai, A. Kanani, M. Tootoonchi, H. R. Afzali, Intelligent timetable evaluation using fuzzy AHP, Expert Systems with Applications 38 (4) (2011) 3718–3723.
[8] H. K. Chan, X. J. Wang, G. R. T. White, N. Yip, An extended Fuzzy-AHP approach for the evaluation of green product designs, IEEE Transactions on Engineering Management 60 (2) (2013) 327–339.
[9] Y. H. Chiang, Using a combined AHP and PLS path modelling on blog site evaluation in Taiwan , Computers in Human Behavior 29 (4) (2013) 1325–1333.
[10] W. H. Chen, Quantitative decision-making model for distribution system restoration, IEEE Transactions on Power Systems 25 (1) (2010) 313–321.
[11] Q. Wang, D. M. Kilgour, K. W. Hipel, Fuzzy real options for risky project evaluation using least squares Monte-Carlo simulation, IEEE Systems Journal 5(3) (2011) 385–395.
[12] S. K. Tyagi, M. Akram, Human reliability evaluation for offshore platform musters using intuitionistic Fuzzy sets, IEEE Transactions on Fuzzy Systems 21 (6) (2013) 1115–1122.
[13] D. Liu, M. Liu, Application of Sample Entropy on Measuring Precipitation Series Complexity in Jiansanjiang Branch Bureau of China, Nature Environment and Pollution Technology 12 (2) (2013) 249–254.
[14] B. Hsiao, C. C. Chern, C. R. Chiu, Performance evaluation with the entropy based weighted russell measure in data envelopment analysis, Expert Systems with Applications 38 (8) (2011) 9965–9972.
[15] S. Bouhouche, L. L. Yazid, H. Tarek, B. Jurgen, Inferential sensor-based adaptive principal components analysis for mechanical properties prediction and evaluation, Measurement 46(9) (2013) 3683–3690.
[16] A. Banimostafa, S. Papadokonstantakis, K. Hungerbühler, Evaluation of EHS hazard and sustainability metrics during early process design stages using principal component analysis , Process Safety and Environmental Protection 90 (1) (2012) 8–26.
[17] M. F. Qi, Z. G. Fu, Y. Jing, Y. Ma, A comprehensive evaluation method of power plant units based on information entropy and principal component analysis, Proceedings of CSEE 33 (2) (2013) 58–64.
[18] X. Q. Wen, Y. H. Wang, C. Chen, S. G. Peng, Evaluation on land intensive utilization based on the combination of principal component analysis and entropy value method, Journal of Anhui Agri. Sci. 36 (28) (2008) 12372–12373.
[19] D. P. Guo, Research on benefits and economy for pumped storage station, M.Eng dissertation, Dalian University of Technology, China (Sep 2001)25-54.
[20] F. Cao, L. Z. Zhang, Determination of pumped-storage plant capacity with peak-regulation proportion, Electric Power Automation Equipment 27 (6) (2007) 47–50.
[21] J. C. Cui, D. H. Liu, W. L. Liang, F. Xie, H. Y. Chen, Analysis on economic and environmental benefit of pumped-storage station, Electric Power 40 (1) (2007) 5–10.
[22] Z. A. Zhang, X. G. Cai, Determination of pumped storage capacity based on entropy, Electric Machines and Control 18 (3) (2014) 34–39.
[23] H. Z. Nie, S. Nie, Y. Qiao, P. Lv, Comprehensive decision-making of alternative transmission network planning based on principal component analysis, Power System Technology 34 (6) (2010) 134–137.
Published
2014-08-27
How to Cite
ZHANG, Zhan’an; CAI, Xingguo; PARKUN, Destung.
Determination of Pumped Storage Capacity Combining the Entropy Weighting Method and Principal Component Analysis.
Journal of Power Technologies, [S.l.], v. 94, n. 3, p. 165--175, aug. 2014.
ISSN 2083-4195.
Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/608>. Date accessed: 11 dec. 2024.
Issue
Section
Energy Engineering and Technology
Keywords
Pumped storage, Capacity determination, Integrated decision-making, Entropy weighting method, Principal component analysis
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).