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Open Access

Simultaneous Optimization of Renewable Energy and Energy Storage Capacity with the Hierarchical Control

Zhaodi Shi( ), Weisheng Wang, Yuehui Huang, Pai Li, Ling Dong
State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems, China Electric Power Research Institute, Beijing 100192, China
State Grid Qinghai Electric Power Company, Xining 810000, China
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Abstract

To fully consider the complementary role of different energy sources and reduce the curtailment of renewable energy (RE) in high RE penetration systems, a hierarchical optimization algorithm is proposed to simultaneously optimize the capacity of RE generation and energy storage systems (ESS). Time sequence simulation (TSS) technology is adopted to fully consider the regional RE resource characteristics and make the model more reliable. An optimization model for evaluating ESS capacity is established at a lower level. To overcome the high dimensional complexity of time sequence data, this paper re-formulates this sub-model as a consensus problem, which can be solved by a distributed approach to minimize the system’s total investment costs. At the upper level, the model for assessing the proportion of wind and solar capacity is developed by maximizing the RE generation. The golden section Fibonacci tree optimization (GSFTO) algorithm is utilized to improve the efficiency and solution accuracy. The results show that the algorithm and model are feasible and applicable for the identified purposes, which can provide a useful guidance for the development of power generation and the energy storage capacity in high RE penetration systems.

Keywords

Consensus problemenergy storageplanningrenewabletime sequence simulation (TSS)

References

[1]
F. Birol, “World energy outlook,” International Energy Agency, Paris, France, pp. 23–29, 2014.
[2]
W. W. Kim, J. S. Shin, and J. O. Kim, “Operation strategy of multi-energy storage system for ancillary services,”IEEE Transactions on Power Systems, vol. 32, no. 6, pp. 4409–4417, Nov. 2017.
Crossref Google Scholar
[3]
Y. S. Sun, Z. X. Zhao, M. Yang, D. Q. Jia, W. Pei, and B. Xu, “Overview of energy storage in renewable energy power fluctuation mitigation,”CSEE Journal of Power and Energy Systems, vol. 6, no. 1, pp. 160–173, Mar. 2020.
Google Scholar
[4]
Z. X. Lu, H. B. Li, and Y. Qiao, “Flexibility evaluation and supply/demand balance principle of power system with high-penetration renewable electricity,”Proceedings of the CSEE, vol. 37, no. 1, pp. 9–19, Jan. 2017.
Google Scholar
[5]
A. V. Savkin, M. Khalid, and V. G. Agelidis, “A constrained monotonic charging/discharging strategy for optimal capacity of battery energy storage supporting wind farms,”IEEE Transactions on Sustainable Energy, vol. 7, no. 3, pp. 1224–1231, Jul. 2016.
Crossref Google Scholar
[6]
S. Dutta and R. Sharma, “Optimal storage sizing for integrating wind and load forecast uncertainties,”in 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), 2012, pp. 1–7.
Crossref Google Scholar
[7]
M. E. Khodayar, M. Shahidehpour, and L. Wu, “Enhancing the dispatch ability of variable wind generation by coordination with pumped-storage hydro units in stochastic power systems,”IEEE Transactions on Power Systems, vol. 28, no. 3, pp. 2808–2818, Aug. 2013.
Crossref Google Scholar
[8]
L. Shi, Y. Luo, G. Y. Tu, and N. Shi, “Energy storage sizing method considering dispatch ability of wind farm,”Transactions of China Electrotechnical Society, vol. 28, no. 5, pp. 120–127, 134, May. 2013.
Google Scholar
[9]
O. Mégel, J. L. Mathieu, and G. Andersson, “Scheduling distributed energy storage units to provide multiple services under forecast error,”International Journal of Electrical Power & Energy Systems, vol. 72, pp. 48–57, Nov. 2015.
Crossref Google Scholar
[10]
S. Lakshminarayana, Y. J. Xu, H. V. Poor, and T. Q. S. Quek, “Cooperation of storage operation in a power network with renewable generation,”IEEE Transactions on Smart Grid, vol. 7, no. 4, pp. 2108–2122, Jul. 2016.
Crossref Google Scholar
[11]
P. Yang and A. Nehorai, “Joint optimization of hybrid energy storage and generation capacity with renewable energy,”IEEE Transactions on Smart Grid, vol. 5, no. 4, pp. 1566–1574, Jul. 2014.
Crossref Google Scholar
[12]
H. Alharbi and K. Bhattacharya, “Stochastic optimal planning of battery energy storage systems for isolated microgrids,”IEEE Transactions on Sustainable Energy, vol. 9, no. 1, pp. 211–227.Jan. 2018.
Crossref Google Scholar
[13]
S. S. Thale, R. G. Wandhare, and V. Agarwal, “A novel reconfigurable microgrid architecture with renewable energy sources and storage,”IEEE Transactions on Industry Applications, vol. 51, no. 2, pp. 1805–1816, Mar. 2015.
Crossref Google Scholar
[14]
A. Kargarian, G. Hug, and J. Mohammadi, “A multi-time scale co-optimization method for sizing of energy storage and fast-ramping generation,”IEEE Transactions on Sustainable Energy, vol. 7, no. 4, pp. 1351–1361, Oct. 2016.
Crossref Google Scholar
[15]
J. J. Dong, F. Gao, X. H. Guan, Q. Z. Zhai, and J, Wu, “Storage-reserve sizing with qualified reliability for connected high renewable penetration micro-grid,”IEEE Transactions on Sustainable Energy, vol. 7, no. 2, pp. 732–743, Apr. 2016.
Crossref Google Scholar
[16]
D. Bhowmik and A. K. Sinha, “Cost-based allocation model for hybrid power system considering solar, wind and thermal generations separately,”IET Generation, Transmission & Distribution, vol. 11, no. 18, pp. 4576–4587, Dec. 2017.
Crossref Google Scholar
[17]
E. Nasrolahpour, J. Kazempour, H. Zareipour, and W. D. Rosehart, “Impacts of ramping inflexibility of conventional generators on strategic operation of energy storage facilities,”IEEE Transactions on Smart Grid, vol. 9, no. 2, pp. 1334–1344, Mar. 2018.
Crossref Google Scholar
[18]
C. J. López-Salgado, O. Añó, and D. M. Ojeda-Esteybar, “Stochastic unit commitment and optimal allocation of reserves: a hybrid decomposition approach,”IEEE Transactions on Power Systems, vol. 33, no. 5, pp. 5542–5552, Sep. 2018.
Crossref Google Scholar
[19]
M. Alsayed, M. Cacciato, G. Scarcella, and G. Scelba, “Multicriteria optimal sizing of photovoltaic-wind turbine grid connected systems,”IEEE Transactions on Energy Conversion, vol. 28, no. 2, pp. 370–379, Jun. 2013.
Crossref Google Scholar
[20]
S. F. Santos, D. Z. Fitiwi, M. Shafie-Khah, A. W. Bizuayehu, C. M. P. Cabrita, and J. P. S. Catalão, “New multistage and stochastic mathematical model for maximizing RES hosting capacity-Part I: problem formulation,”IEEE Transactions on Sustainable Energy, vol. 8, no. 1, pp. 304–319, Jan. 2017.
Crossref Google Scholar
[21]
A. Arabali, M. Ghofrani, M. Etezadi-Amoli, and M. S. Fadali, “Stochastic performance assessment and sizing for a hybrid power system of solar/wind/energy storage,”IEEE Transactions on Sustainable Energy, vol. 5, no. 2, pp. 363–371, Apr. 2014.
Crossref Google Scholar
[22]
E. F. M. Abreu, P. Canhoto, V. Prior, and R. Melicio, “Solar resource assessment through long-term statistical analysis and typical data generation with different time resolutions using GHI measurements,”Renewable Energy, vol. 127, pp. 398–411, Nov. 2018.
Crossref Google Scholar
[23]
T. Qiu, B. L. Xu, Y. S. Wang, Y. Dvorkin, and D. S. Kirschen, “Stochastic multistage coplanning of transmission expansion and energy storage,”IEEE Transactions on Power Systems, vol. 32, no. 1, pp. 643–651, Jan. 2017.
Crossref Google Scholar
[24]
M. Trifkovic, M. Sheikhzadeh, K. Nigim, and P. Daoutidis, “Modeling and control of a renewable hybrid energy system with hydrogen storage,”IEEE Transactions on Control Systems Technology, vol. 22, no. 1, pp. 169–179, Jan. 2014.
Crossref Google Scholar
[25]
Y. Cao, Y. H. Huang, Y. Yuan, M. Wang, P. Li, and S. Q. Guo, “Stratified optimization algorithm for optimal proportion of wind and solar capacity based on time sequence simulation,”Proceedings of the CSEE, vol. 35, no. 5, pp. 1072–1078, Mar. 2015.
Google Scholar
[26]
S. Baros and M. D. Ilić. “A consensus approach to real-time distributed control of energy storage systems in wind farms,”IEEE Transactions on Smart Grid, vol. 10, no. 1, pp. 613–625, Jan. 2019.
Crossref Google Scholar
[27]
Q. Y. Sun, R. K. Han, H. G. Zhang, J. G. Zhou, and J. M. Guerrero, “A multiagent-based consensus algorithm for distributed coordinated control of distributed generators in the energy Internet,”IEEE Transactions on Smart Grid, vol. 6, no. 6, pp. 3006–3019, Nov. 2015.
Crossref Google Scholar
[28]
Y. Dong, D. J. Lv, X. Wang, Y. M. Wang, P. Li, and X. L. Shi, “A global randomness-based probability convergence analysis of Fibonacci tree optimization algorithm,”Control and Decision, vol. 33, no. 3, pp. 439–446, Mar. 2018.
Google Scholar
[29]
F. Alismail, P. Xiong, and C. Singh, “Optimal wind farm allocation in multi-area power systems using distributional robust optimization approach,”IEEE Transactions on Power Systems, vol. 33, no. 1, pp. 536–544, Jan. 2018.
Crossref Google Scholar
[30]
X. X. Zhu and Z. H. Han, “Research on LS-SVM wind speed prediction method based on PSO,”Proceedings of the CSEE, vol. 36, no. 23, pp. 6337–6342, Dec. 2016.
Google Scholar
[31]
X. D. Ke, N. Lu, and C. L. Jin, “Control and size energy storage systems for managing energy imbalance of variable generation resources,”IEEE Transactions on Sustainable Energy, vol. 6, no. 1, pp. 70–78, Jan. 2015.
Crossref Google Scholar
[32]
P. D. Brown, J. A. P. Lopes, and M. A. Matos, “Optimization of pumped storage capacity in an isolated power system with large renewable penetration,”IEEE Transactions on Power Systems, vol. 23, no. 2, pp. 523–531, May. 2008.
Crossref Google Scholar
[33]
Y. M. Wang, L. Wu, and S. X. Wang. “A fully-decentralized consensus-based ADMM approach for DC-OPF with demand response,”IEEE Transactions on Smart Grid, vol. 8, no. 6, pp. 2637–2647, Nov. 2017.
Crossref Google Scholar
[34]
S. H. Zhang, X. L. Shi, P. Li, Y. Dong, and S. C. Li, “Golden section Fibonacci tree optimization algorithm for multimodal function optimization,”Acta Electronica Sinica, vol. 45, no. 4, pp. 791–798, Apr. 2017.
Google Scholar
[35]
J. Z. Shi and D. M. You, “Golden section adaptive speed control of ultrasonic motors,”Transactions of China Electrotechnical Society, vol. 28, no. 6, pp. 59–65, Jun. 2013.
Google Scholar
[36]
M. Ding, M. J. Chu, R. Bi, and W. H. Shi, “Wind power accommodation capability evaluation based on sequential Monte Carlo probabilistic production simulation and its application,”Electric Power Automation Equipment, vol. 36, no. 9, pp. 67–73, Sep. 2016.
Google Scholar
CSEE Journal of Power and Energy Systems
Volume 8 Issue 1,
January 2022
Pages 95-104
DOI: 10.17775/CSEEJPES.2019.01470
Cite this article:
Shi Z, Wang W, Huang Y, et al. Simultaneous Optimization of Renewable Energy and Energy Storage Capacity with the Hierarchical Control. CSEE Journal of Power and Energy Systems, 2022, 8(1): 95-104. https://doi.org/10.17775/CSEEJPES.2019.01470

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Received: 04 July 2019
Revised: 26 September 2019
Accepted: 07 January 2020
Published: 13 February 2020
© 2019 CSEE
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