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An integrated, systematic data-driven supply-demand side management method for smart integrated energy systems

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  • Su, Huai
  • Chi, Lixun
  • Zio, Enrico
  • Li, Zhenlin
  • Fan, Lin
  • Yang, Zhe
  • Liu, Zhe
  • Zhang, Jinjun

Abstract

Different energy systems become highly connected to provide better flexibility. However, this change poses new challenges for system management considering the diversity of demands, complexities of the energy networks, uncertainties, etc. This work develops a smart Supply-Demand Side Management method to overcome these challenges. The main objectives of this Supply-Demand Side Management framework are improving system efficiency and smoothing energy load, through flexible supply planning and dynamic pricing. Firstly, the customer response analysis method is proposed by combining the Deep Learning model and the economic model. Then, the energy network simulation model is used to coordinate the Supply-Demand Side Management strategies and the overall energy system capacity. A method is proposed to introduce the compressibility of natural gas in the management framework to offset the uncertain disturbances. Finally, a multi-objective decision method is developed to find the optimal strategy. The results of the application on a typical integrated energy system show that the proposed method can reduce the energy load fluctuation by 4%–8% under different planning horizons, and improve the system efficiency by reducing energy loss and increasing the profitability. The results also present a possibility of the development toward resilient Integrated Energy Systems by managing the buffer capacity of natural gas pipeline networks.

Suggested Citation

  • Su, Huai & Chi, Lixun & Zio, Enrico & Li, Zhenlin & Fan, Lin & Yang, Zhe & Liu, Zhe & Zhang, Jinjun, 2021. "An integrated, systematic data-driven supply-demand side management method for smart integrated energy systems," Energy, Elsevier, vol. 235(C).
    • Handle: RePEc:eee:energy:v:235:y:2021:i:c:s0360544221016649
    DOI: 10.1016/j.energy.2021.121416
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    as
    1. Behrangrad, Mahdi, 2015. "A review of demand side management business models in the electricity market," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 270-283.
    2. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    3. Hajibandeh, Neda & Shafie-khah, Miadreza & Osório, Gerardo J. & Aghaei, Jamshid & Catalão, João P.S., 2018. "A heuristic multi-objective multi-criteria demand response planning in a system with high penetration of wind power generators," Applied Energy, Elsevier, vol. 212(C), pages 721-732.
    4. Su, Huai & Zhang, Jinjun & Zio, Enrico & Yang, Nan & Li, Xueyi & Zhang, Zongjie, 2018. "An integrated systemic method for supply reliability assessment of natural gas pipeline networks," Applied Energy, Elsevier, vol. 209(C), pages 489-501.
    5. Rajanna, S. & Saini, R.P., 2016. "Employing demand side management for selection of suitable scenario-wise isolated integrated renewal energy models in an Indian remote rural area," Renewable Energy, Elsevier, vol. 99(C), pages 1161-1180.
    6. Norman, Thomas W.L., 2015. "Learning, hypothesis testing, and rational-expectations equilibrium," Games and Economic Behavior, Elsevier, vol. 90(C), pages 93-105.
    7. Ju, Liwei & Zhao, Rui & Tan, Qinliang & Lu, Yan & Tan, Qingkun & Wang, Wei, 2019. "A multi-objective robust scheduling model and solution algorithm for a novel virtual power plant connected with power-to-gas and gas storage tank considering uncertainty and demand response," Applied Energy, Elsevier, vol. 250(C), pages 1336-1355.
    8. Lu, Xiaoxing & Li, Kangping & Xu, Hanchen & Wang, Fei & Zhou, Zhenyu & Zhang, Yagang, 2020. "Fundamentals and business model for resource aggregator of demand response in electricity markets," Energy, Elsevier, vol. 204(C).
    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. Chi, Lixun & Su, Huai & Zio, Enrico & Zhang, Jinjun & Li, Xueyi & Zhang, Li & Fan, Lin & Zhou, Jing & Bai, Hua, 2020. "Integrated Deterministic and Probabilistic Safety Analysis of Integrated Energy Systems with bi-directional conversion," Energy, Elsevier, vol. 212(C).
    11. Laib, Oussama & Khadir, Mohamed Tarek & Mihaylova, Lyudmila, 2019. "Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks," Energy, Elsevier, vol. 177(C), pages 530-542.
    12. Srinivasan, Dipti & Rajgarhia, Sanjana & Radhakrishnan, Bharat Menon & Sharma, Anurag & Khincha, H.P., 2017. "Game-Theory based dynamic pricing strategies for demand side management in smart grids," Energy, Elsevier, vol. 126(C), pages 132-143.
    13. Kabir, M.N. & Mishra, Y. & Bansal, R.C., 2016. "Probabilistic load flow for distribution systems with uncertain PV generation," Applied Energy, Elsevier, vol. 163(C), pages 343-351.
    14. Zeng, Qing & Fang, Jiakun & Li, Jinghua & Chen, Zhe, 2016. "Steady-state analysis of the integrated natural gas and electric power system with bi-directional energy conversion," Applied Energy, Elsevier, vol. 184(C), pages 1483-1492.
    15. Su, Huai & Zio, Enrico & Zhang, Jinjun & Li, Xueyi, 2018. "A systematic framework of vulnerability analysis of a natural gas pipeline network," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 79-91.
    16. Luo, X.J. & Fong, K.F., 2019. "Development of integrated demand and supply side management strategy of multi-energy system for residential building application," Applied Energy, Elsevier, vol. 242(C), pages 570-587.
    17. Yoon, Yourim & Kim, Yong-Hyuk, 2016. "Effective scheduling of residential energy storage systems under dynamic pricing," Renewable Energy, Elsevier, vol. 87(P2), pages 936-945.
    18. Hu, Yuan & Bie, Zhaohong & Ding, Tao & Lin, Yanling, 2016. "An NSGA-II based multi-objective optimization for combined gas and electricity network expansion planning," Applied Energy, Elsevier, vol. 167(C), pages 280-293.
    19. Zio, E. & Bazzo, R., 2011. "Level Diagrams analysis of Pareto Front for multiobjective system redundancy allocation," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 569-580.
    20. Roy, Anthony & Auger, François & Dupriez-Robin, Florian & Bourguet, Salvy & Tran, Quoc Tuan, 2020. "A multi-level Demand-Side Management algorithm for offgrid multi-source systems," Energy, Elsevier, vol. 191(C).
    21. Anand, Hithu & Ramasubbu, Rengaraj, 2018. "A real time pricing strategy for remote micro-grid with economic emission dispatch and stochastic renewable energy sources," Renewable Energy, Elsevier, vol. 127(C), pages 779-789.
    22. Wang, Yongli & Ma, Yuze & Song, Fuhao & Ma, Yang & Qi, Chengyuan & Huang, Feifei & Xing, Juntai & Zhang, Fuwei, 2020. "Economic and efficient multi-objective operation optimization of integrated energy system considering electro-thermal demand response," Energy, Elsevier, vol. 205(C).
    23. Das, Saborni & Basu, Mousumi, 2020. "Day-ahead optimal bidding strategy of microgrid with demand response program considering uncertainties and outages of renewable energy resources," Energy, Elsevier, vol. 190(C).
    24. Su, Huai & Zio, Enrico & Zhang, Jinjun & Xu, Mingjing & Li, Xueyi & Zhang, Zongjie, 2019. "A hybrid hourly natural gas demand forecasting method based on the integration of wavelet transform and enhanced Deep-RNN model," Energy, Elsevier, vol. 178(C), pages 585-597.
    25. Zio, E. & Bazzo, R., 2011. "A clustering procedure for reducing the number of representative solutions in the Pareto Front of multiobjective optimization problems," European Journal of Operational Research, Elsevier, vol. 210(3), pages 624-634, May.
    26. Sharma, Vishal & Yang, Dazhi & Walsh, Wilfred & Reindl, Thomas, 2016. "Short term solar irradiance forecasting using a mixed wavelet neural network," Renewable Energy, Elsevier, vol. 90(C), pages 481-492.
    27. Sheikhi, Aras & Bahrami, Shahab & Ranjbar, Ali Mohammad, 2015. "An autonomous demand response program for electricity and natural gas networks in smart energy hubs," Energy, Elsevier, vol. 89(C), pages 490-499.
    28. Sharadga, Hussein & Hajimirza, Shima & Balog, Robert S., 2020. "Time series forecasting of solar power generation for large-scale photovoltaic plants," Renewable Energy, Elsevier, vol. 150(C), pages 797-807.
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    3. He, Shuaijia & Gao, Hongjun & Liu, Junyong & Zhang, Xi & Chen, Zhe, 2022. "Distribution system planning considering peak shaving of energy station," Applied Energy, Elsevier, vol. 312(C).

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