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New understanding on information’s role in the matching of supply and demand of distributed energy system

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  • Li, Hao
  • Zhong, Shengyuan
  • Wang, Yongzhen
  • Zhao, Jun
  • Li, Minxia
  • Wang, Fu
  • Zhu, Jiebei

Abstract

According to the entropy theory, the increase in the thermodynamic entropy of energy is reduced under the control of the information entropy. However, the quantitative analysis of this process remains difficult. In this study, energy properties are conferred upon information to enable an uncertain event to be measured. First, the entropy theory was adopted to describe the uncertainty of the energy in a distributed energy system. Further, a method was devised to optimize the configurations of the distributed energy system based on minimize total entropy generation. Finally, using the example of load forecasting, the impact of introducing information in the form of negative entropy on the ability of the system to improve the alignment between supply and demand is quantitatively elucidated. The negative entropy caused by information utilization increased from 356.14 to 638.68 kWh/K, with the load forecasting errors decreased from 30% to 10%. The information entropy was also applied to describe the uncertainty of the On-site energy fraction, by increasing the capacity of energy storage, the uncertainty decreased from 1.81 to 1.80 nat, while the load forecasting could decreased it to 1.71 nat, 1.54 nat, 1.29 nat with the load forecasting errors at 30%, 20% and 10%, respectively.

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  • 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).
  • Handle: RePEc:eee:energy:v:206:y:2020:i:c:s0360544220311439
    DOI: 10.1016/j.energy.2020.118036
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    1. Ferracuti, Francesco & Fonti, Alessandro & Ciabattoni, Lucio & Pizzuti, Stefano & Arteconi, Alessia & Helsen, Lieve & Comodi, Gabriele, 2017. "Data-driven models for short-term thermal behaviour prediction in real buildings," Applied Energy, Elsevier, vol. 204(C), pages 1375-1387.
    2. Gupta, M.K. & Kaushik, S.C. & Ranjan, K.R. & Panwar, N.L. & Reddy, V. Siva & Tyagi, S.K., 2015. "Thermodynamic performance evaluation of solar and other thermal power generation systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 567-582.
    3. Pang, Zhihong & O'Neill, Zheng, 2018. "Uncertainty quantification and sensitivity analysis of the domestic hot water usage in hotels," Applied Energy, Elsevier, vol. 232(C), pages 424-442.
    4. Cao, Sunliang & Hasan, Ala & Sirén, Kai, 2014. "Matching analysis for on-site hybrid renewable energy systems of office buildings with extended indices," Applied Energy, Elsevier, vol. 113(C), pages 230-247.
    5. Zhang, Sheng & Huang, Pei & Sun, Yongjun, 2016. "A multi-criterion renewable energy system design optimization for net zero energy buildings under uncertainties," Energy, Elsevier, vol. 94(C), pages 654-665.
    6. Luo, Yu & Shi, Yixiang & Zheng, Yi & Gang, Zhongxue & Cai, Ningsheng, 2017. "Mutual information for evaluating renewable power penetration impacts in a distributed generation system," Energy, Elsevier, vol. 141(C), pages 290-303.
    7. Fu, Xueqian & Li, Gengyin & Zhang, Xiurong & Qiao, Zheng, 2018. "Failure probability estimation of the gas supply using a data-driven model in an integrated energy system," Applied Energy, Elsevier, vol. 232(C), pages 704-714.
    8. Smarra, Francesco & Jain, Achin & de Rubeis, Tullio & Ambrosini, Dario & D’Innocenzo, Alessandro & Mangharam, Rahul, 2018. "Data-driven model predictive control using random forests for building energy optimization and climate control," Applied Energy, Elsevier, vol. 226(C), pages 1252-1272.
    9. Fu, Xueqian & Sun, Hongbin & Guo, Qinglai & Pan, Zhaoguang & Xiong, Wen & Wang, Li, 2017. "Uncertainty analysis of an integrated energy system based on information theory," Energy, Elsevier, vol. 122(C), pages 649-662.
    10. Karunathilake, Hirushie & Hewage, Kasun & Mérida, Walter & Sadiq, Rehan, 2019. "Renewable energy selection for net-zero energy communities: Life cycle based decision making under uncertainty," Renewable Energy, Elsevier, vol. 130(C), pages 558-573.
    11. 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.
    12. Silva, R. & Pérez, M. & Berenguel, M. & Valenzuela, L. & Zarza, E., 2014. "Uncertainty and global sensitivity analysis in the design of parabolic-trough direct steam generation plants for process heat applications," Applied Energy, Elsevier, vol. 121(C), pages 233-244.
    13. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "Uncertainty and global sensitivity analysis for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 214(C), pages 219-238.
    14. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    15. Fu, Xueqian & Zhang, Xiurong, 2018. "Failure probability estimation of gas supply using the central moment method in an integrated energy system," Applied Energy, Elsevier, vol. 219(C), pages 1-10.
    16. Fan, Cheng & Xiao, Fu & Yan, Chengchu & Liu, Chengliang & Li, Zhengdao & Wang, Jiayuan, 2019. "A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning," Applied Energy, Elsevier, vol. 235(C), pages 1551-1560.
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