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A data driven multi-state model for distribution system flexible planning utilizing hierarchical parallel computing

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  • Ye, Chengjin
  • Ding, Yi
  • Song, Yonghua
  • Lin, Zhenzhi
  • Wang, Lei

Abstract

With the development of smart grid and electricity market, the uncertainty for power flow is greatly aggravated, and thus leads to a great challenge on the traditional expansion methods for distribution systems to satisfy the future demands. In this paper, a data-driven multi-state distribution system expansion planning (DSEP) model is explored. Innovatively, amplitude values and profiles of uncertain factors in distribution systems are considered separately. Based on the massive historical measurement data, kernel density estimation and adaptive clustering are utilized to aggregate the typical amplitudes and profiles of time-varying variables respectively without prior knowledge. Consolidating all the uncertain factors, a multi-state model is established which extends DSEP into the deterministic initial planning and the probabilistic re-planning. The minimization of the overall planning cost is considered as the objective, which takes the initial planning costs and the expected costs of the initial plans being adapted to other future states into account. In this way, the flexibility of DSEP can be greatly enhanced and extra investments caused by frequent adjustments of plans are reduced. To avoid the rapid growth of CPU time due to multi-state model utilization, an integrated differential evolution and cross entropy algorithm implemented on a three-hierarchy parallel platform is proposed. The feasibilities of the proposed data-driven multi-state DSEP model and the parallel integrated solution method are demonstrated by numerical studies on a realistic 61-bus distribution system.

Suggested Citation

  • Ye, Chengjin & Ding, Yi & Song, Yonghua & Lin, Zhenzhi & Wang, Lei, 2018. "A data driven multi-state model for distribution system flexible planning utilizing hierarchical parallel computing," Applied Energy, Elsevier, vol. 232(C), pages 9-25.
    • Handle: RePEc:eee:appene:v:232:y:2018:i:c:p:9-25
    DOI: 10.1016/j.apenergy.2018.09.202
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    1. Mokryani, Geev & Hu, Yim Fun & Pillai, Prashant & Rajamani, Haile-Selassie, 2017. "Active distribution networks planning with high penetration of wind power," Renewable Energy, Elsevier, vol. 104(C), pages 40-49.
    2. Zheng, Menglian & Wang, Xinhao & Meinrenken, Christoph J. & Ding, Yi, 2018. "Economic and environmental benefits of coordinating dispatch among distributed electricity storage," Applied Energy, Elsevier, vol. 210(C), pages 842-855.
    3. Diaf, S. & Notton, G. & Belhamel, M. & Haddadi, M. & Louche, A., 2008. "Design and techno-economical optimization for hybrid PV/wind system under various meteorological conditions," Applied Energy, Elsevier, vol. 85(10), pages 968-987, October.
    4. Zou, Dexuan & Li, Steven & Wang, Gai-Ge & Li, Zongyan & Ouyang, Haibin, 2016. "An improved differential evolution algorithm for the economic load dispatch problems with or without valve-point effects," Applied Energy, Elsevier, vol. 181(C), pages 375-390.
    5. Reuven Rubinstein, 1999. "The Cross-Entropy Method for Combinatorial and Continuous Optimization," Methodology and Computing in Applied Probability, Springer, vol. 1(2), pages 127-190, September.
    6. Mokryani, Geev & Hu, Yim Fun & Papadopoulos, Panagiotis & Niknam, Taher & Aghaei, Jamshid, 2017. "Deterministic approach for active distribution networks planning with high penetration of wind and solar power," Renewable Energy, Elsevier, vol. 113(C), pages 942-951.
    7. Spiliotis, Konstantinos & Ramos Gutierrez, Ariana Isabel & Belmans, Ronnie, 2016. "Demand flexibility versus physical network expansions in distribution grids," Applied Energy, Elsevier, vol. 182(C), pages 613-624.
    8. Yang, Hongming & Xiong, Tonglin & Qiu, Jing & Qiu, Duo & Dong, Zhao Yang, 2016. "Optimal operation of DES/CCHP based regional multi-energy prosumer with demand response," Applied Energy, Elsevier, vol. 167(C), pages 353-365.
    9. Ali Mohammadi & Mohammad Javad Dehghani & Elham Ghazizadeh, 2018. "Game Theoretic Spectrum Allocation in Femtocell Networks for Smart Electric Distribution Grids," Energies, MDPI, vol. 11(7), pages 1-18, June.
    10. Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P. & Bouzerdoum, A., 2017. "Short-term electricity demand forecasting using autoregressive based time varying model incorporating representative data adjustment," Applied Energy, Elsevier, vol. 205(C), pages 790-801.
    11. Dirk P. Kroese & Sergey Porotsky & Reuven Y. Rubinstein, 2006. "The Cross-Entropy Method for Continuous Multi-Extremal Optimization," Methodology and Computing in Applied Probability, Springer, vol. 8(3), pages 383-407, September.
    12. Li, Zhigang & Qiu, Feng & Wang, Jianhui, 2016. "Data-driven real-time power dispatch for maximizing variable renewable generation," Applied Energy, Elsevier, vol. 170(C), pages 304-313.
    13. Luo, Xuan & Hong, Tianzhen & Chen, Yixing & Piette, Mary Ann, 2017. "Electric load shape benchmarking for small- and medium-sized commercial buildings," Applied Energy, Elsevier, vol. 204(C), pages 715-725.
    14. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    15. Li, Gong & Shi, Jing, 2010. "Application of Bayesian model averaging in modeling long-term wind speed distributions," Renewable Energy, Elsevier, vol. 35(6), pages 1192-1202.
    16. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    17. Xie, Shiwei & Hu, Zhijian & Zhou, Daming & Li, Yan & Kong, Shunfei & Lin, Weiwei & Zheng, Yunfei, 2018. "Multi-objective active distribution networks expansion planning by scenario-based stochastic programming considering uncertain and random weight of network," Applied Energy, Elsevier, vol. 219(C), pages 207-225.
    18. Yang, Yanhong & Pei, Wei & Huo, Qunhai & Sun, Jianjun & Xu, Feng, 2018. "Coordinated planning method of multiple micro-grids and distribution network with flexible interconnection," Applied Energy, Elsevier, vol. 228(C), pages 2361-2374.
    19. Munkhammar, Joakim & Rydén, Jesper & Widén, Joakim, 2014. "Characterizing probability density distributions for household electricity load profiles from high-resolution electricity use data," Applied Energy, Elsevier, vol. 135(C), pages 382-390.
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    Cited by:

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    3. Li, Hao & Zhong, Shengyuan & Wang, Yongzhen & Zhao, Jun & Li, Minxia & Wang, Fu & Zhu, Jiebei, 2020. "New understanding on information’s role in the matching of supply and demand of distributed energy system," Energy, Elsevier, vol. 206(C).
    4. Wang, Sheng & Shao, Changzheng & Ding, Yi & Yan, Jinyue, 2019. "Operational reliability of multi-energy customers considering service-based self-scheduling," Applied Energy, Elsevier, vol. 254(C).

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