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Network-aware approach for energy storage planning and control in the network with high penetration of renewables

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  • Mahani, Khashayar
  • Farzan, Farbod
  • Jafari, Mohsen A.

Abstract

In this paper, we consider multiple energy storage nodes distributed over a power distribution network, and are purposed for multiple applications. The research problems of interests are to optimally locate these nodes over the distribution network and to create day-ahead plans according to planned applications. The two problems are formulated as stochastic optimization problems, and hourly and time-aggregated approximate solutions are presented. The approximation identifies time periods where load and generation patterns demonstrate low variability, and marks the whole period as a single time zone, thus significantly reducing the number of decision variables and the overall problem size. We show that aggregate and hourly planning solutions are close. The planning problem can handle any number of storage nodes with general topology and load connections, and deterministic or stochastic capacities. In this paper, we focus on network of static energy storages with deterministic capacity. Finally, we build a novel rule based control scheme for the near real time operation of the storage network by mining the statistical relationship between input and optimal charge and discharge patterns.

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  • Mahani, Khashayar & Farzan, Farbod & Jafari, Mohsen A., 2017. "Network-aware approach for energy storage planning and control in the network with high penetration of renewables," Applied Energy, Elsevier, vol. 195(C), pages 974-990.
    • Handle: RePEc:eee:appene:v:195:y:2017:i:c:p:974-990
    DOI: 10.1016/j.apenergy.2017.03.118
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    1. Tabar, Vahid Sohrabi & Abbasi, Vahid, 2019. "Energy management in microgrid with considering high penetration of renewable resources and surplus power generation problem," Energy, Elsevier, vol. 189(C).
    2. Hartmann, Bálint & Divényi, Dániel & Vokony, István, 2018. "Evaluation of business possibilities of energy storage at commercial and industrial consumers – A case study," Applied Energy, Elsevier, vol. 222(C), pages 59-66.
    3. Hannan, M.A. & Faisal, M. & Jern Ker, Pin & Begum, R.A. & Dong, Z.Y. & Zhang, C., 2020. "Review of optimal methods and algorithms for sizing energy storage systems to achieve decarbonization in microgrid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    4. Rana, Md Masud & Romlie, Mohd Fakhizan & Abdullah, Mohd Faris & Uddin, Moslem & Sarkar, Md Rasel, 2021. "A novel peak load shaving algorithm for isolated microgrid using hybrid PV-BESS system," Energy, Elsevier, vol. 234(C).
    5. Jannesar, Mohammad Rasol & Sedighi, Alireza & Savaghebi, Mehdi & Guerrero, Josep M., 2018. "Optimal placement, sizing, and daily charge/discharge of battery energy storage in low voltage distribution network with high photovoltaic penetration," Applied Energy, Elsevier, vol. 226(C), pages 957-966.
    6. Li, Yang & Feng, Bo & Li, Guoqing & Qi, Junjian & Zhao, Dongbo & Mu, Yunfei, 2018. "Optimal distributed generation planning in active distribution networks considering integration of energy storage," Applied Energy, Elsevier, vol. 210(C), pages 1073-1081.
    7. Bai, Linquan & Jiang, Tao & Li, Fangxing & Chen, Houhe & Li, Xue, 2018. "Distributed energy storage planning in soft open point based active distribution networks incorporating network reconfiguration and DG reactive power capability," Applied Energy, Elsevier, vol. 210(C), pages 1082-1091.
    8. Hui Wang & Jun Wang & Zailin Piao & Xiaofang Meng & Chao Sun & Gang Yuan & Sitong Zhu, 2020. "The Optimal Allocation and Operation of an Energy Storage System with High Penetration Grid-Connected Photovoltaic Systems," Sustainability, MDPI, vol. 12(15), pages 1-22, July.
    9. Das, Choton K. & Bass, Octavian & Kothapalli, Ganesh & Mahmoud, Thair S. & Habibi, Daryoush, 2018. "Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm," Applied Energy, Elsevier, vol. 232(C), pages 212-228.
    10. Alipour, Manijeh & Zare, Kazem & Seyedi, Heresh, 2018. "A multi-follower bilevel stochastic programming approach for energy management of combined heat and power micro-grids," Energy, Elsevier, vol. 149(C), pages 135-146.
    11. Das, Choton K. & Bass, Octavian & Mahmoud, Thair S. & Kothapalli, Ganesh & Mousavi, Navid & Habibi, Daryoush & Masoum, Mohammad A.S., 2019. "Optimal allocation of distributed energy storage systems to improve performance and power quality of distribution networks," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    12. Tran, Thomas T.D. & Smith, Amanda D., 2017. "fEvaluation of renewable energy technologies and their potential for technical integration and cost-effective use within the U.S. energy sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1372-1388.
    13. Chen, Scarlett & Kumar, Anikesh & Wong, Wee Chin & Chiu, Min-Sen & Wang, Xiaonan, 2019. "Hydrogen value chain and fuel cells within hybrid renewable energy systems: Advanced operation and control strategies," Applied Energy, Elsevier, vol. 233, pages 321-337.
    14. Mohammadali Kiehbadroudinezhad & Adel Merabet & Homa Hosseinzadeh-Bandbafha, 2022. "Review of Latest Advances and Prospects of Energy Storage Systems: Considering Economic, Reliability, Sizing, and Environmental Impacts Approach," Clean Technol., MDPI, vol. 4(2), pages 1-25, June.

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