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Combining agent-based residential demand modeling with design optimization for integrated energy systems planning and operation

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  • Zhang, Zhihui
  • Jing, Rui
  • Lin, Jian
  • Wang, Xiaonan
  • van Dam, Koen H.
  • Wang, Meng
  • Meng, Chao
  • Xie, Shan
  • Zhao, Yingru

Abstract

When optimizing the design of integrated energy systems, their energy demands are usually assumed as given input. However, the demand of energy systems may not always be available information or it can be uncertain, particularly for projects at the planning stage. Moreover, the overall energy demand is dependent on the behavior of many individuals, which might change as a result of technical, economic or policy interventions. Therefore, this study proposes a holistic approach to combine the demand modeling with design and dispatch optimization for integrated energy systems. The approach can be decomposed into two stages. Firstly, at demand simulation stage, Agent Based Modeling is adopted to generate uncertain demand scenarios for a case study community and the energy-consuming activities for various types of households living in different kinds of apartments are simulated based on probability models and demographic information. Dozens of demand scenarios are obtained via iterative simulation, and k-means clustering approach is further applied to generate representative stochastic scenarios. Secondly, at system optimization stage, the uncertain demand scenarios are used as input of an established stochastic Mixed Integer Linear Programming model, by which the system design and dispatch strategy can be optimized simultaneously. The case study shows that the obtained optimal solutions can save 36% of annual total cost compared to the business-as-usual baseline scenario.

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  • Zhang, Zhihui & Jing, Rui & Lin, Jian & Wang, Xiaonan & van Dam, Koen H. & Wang, Meng & Meng, Chao & Xie, Shan & Zhao, Yingru, 2020. "Combining agent-based residential demand modeling with design optimization for integrated energy systems planning and operation," Applied Energy, Elsevier, vol. 263(C).
    • Handle: RePEc:eee:appene:v:263:y:2020:i:c:s0306261920301355
    DOI: 10.1016/j.apenergy.2020.114623
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    as
    1. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    2. Tian, Wei & Heo, Yeonsook & de Wilde, Pieter & Li, Zhanyong & Yan, Da & Park, Cheol Soo & Feng, Xiaohang & Augenbroe, Godfried, 2018. "A review of uncertainty analysis in building energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 285-301.
    3. Koltsaklis, Nikolaos E. & Nazos, Konstantinos, 2017. "A stochastic MILP energy planning model incorporating power market dynamics," Applied Energy, Elsevier, vol. 205(C), pages 1364-1383.
    4. Lin, Haiyang & Wang, Qinxing & Wang, Yu & Liu, Yiling & Sun, Qie & Wennersten, Ronald, 2017. "The energy-saving potential of an office under different pricing mechanisms – Application of an agent-based model," Applied Energy, Elsevier, vol. 202(C), pages 248-258.
    5. Sharifzadeh, Mahdi & Lubiano-Walochik, Helena & Shah, Nilay, 2017. "Integrated renewable electricity generation considering uncertainties: The UK roadmap to 50% power generation from wind and solar energies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 385-398.
    6. Yin, Rongxin & Kara, Emre C. & Li, Yaping & DeForest, Nicholas & Wang, Ke & Yong, Taiyou & Stadler, Michael, 2016. "Quantifying flexibility of commercial and residential loads for demand response using setpoint changes," Applied Energy, Elsevier, vol. 177(C), pages 149-164.
    7. Zhigang Duan & Yamin Yan & Xiaohan Yan & Qi Liao & Wan Zhang & Yongtu Liang & Tianqi Xia, 2017. "An MILP Method for Design of Distributed Energy Resource System Considering Stochastic Energy Supply and Demand," Energies, MDPI, vol. 11(1), pages 1-23, December.
    8. Chakrabarti, Auyon & Proeglhoef, Rafael & Turu, Gonzalo Bustos & Lambert, Romain & Mariaud, Arthur & Acha, Salvador & Markides, Christos N. & Shah, Nilay, 2019. "Optimisation and analysis of system integration between electric vehicles and UK decentralised energy schemes," Energy, Elsevier, vol. 176(C), pages 805-815.
    9. Jing, Rui & Wang, Meng & Liang, Hao & Wang, Xiaonan & Li, Ning & Shah, Nilay & Zhao, Yingru, 2018. "Multi-objective optimization of a neighborhood-level urban energy network: Considering Game-theory inspired multi-benefit allocation constraints," Applied Energy, Elsevier, vol. 231(C), pages 534-548.
    10. J. R. Riley & U. Greggers & A. D. Smith & D. R. Reynolds & R. Menzel, 2005. "The flight paths of honeybees recruited by the waggle dance," Nature, Nature, vol. 435(7039), pages 205-207, May.
    11. Zheng, Xuyue & Wu, Guoce & Qiu, Yuwei & Zhan, Xiangyan & Shah, Nilay & Li, Ning & Zhao, Yingru, 2018. "A MINLP multi-objective optimization model for operational planning of a case study CCHP system in urban China," Applied Energy, Elsevier, vol. 210(C), pages 1126-1140.
    12. Jing, Rui & Wang, Meng & Zhang, Zhihui & Wang, Xiaonan & Li, Ning & Shah, Nilay & Zhao, Yingru, 2019. "Distributed or centralized? Designing district-level urban energy systems by a hierarchical approach considering demand uncertainties," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    13. Hu, Maomao & Xiao, Fu & Wang, Lingshi, 2017. "Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model," Applied Energy, Elsevier, vol. 207(C), pages 324-335.
    14. Li, Miao & Mu, Hailin & Li, Nan & Ma, Baoyu, 2016. "Optimal design and operation strategy for integrated evaluation of CCHP (combined cooling heating and power) system," Energy, Elsevier, vol. 99(C), pages 202-220.
    15. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach," Applied Energy, Elsevier, vol. 222(C), pages 932-950.
    16. Jing, Rui & Wang, Meng & Brandon, Nigel & Zhao, Yingru, 2017. "Multi-criteria evaluation of solid oxide fuel cell based combined cooling heating and power (SOFC-CCHP) applications for public buildings in China," Energy, Elsevier, vol. 141(C), pages 273-289.
    17. Yu, Zhun (Jerry) & Haghighat, Fariborz & Fung, Benjamin C.M. & Morofsky, Edward & Yoshino, Hiroshi, 2011. "A methodology for identifying and improving occupant behavior in residential buildings," Energy, Elsevier, vol. 36(11), pages 6596-6608.
    18. Wang, Xiaonan & Palazoglu, Ahmet & El-Farra, Nael H., 2015. "Operational optimization and demand response of hybrid renewable energy systems," Applied Energy, Elsevier, vol. 143(C), pages 324-335.
    19. Noor, Sana & Yang, Wentao & Guo, Miao & van Dam, Koen H. & Wang, Xiaonan, 2018. "Energy Demand Side Management within micro-grid networks enhanced by blockchain," Applied Energy, Elsevier, vol. 228(C), pages 1385-1398.
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