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An MILP Method for Design of Distributed Energy Resource System Considering Stochastic Energy Supply and Demand

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  • Zhigang Duan

    (Beijing Key Laboratory of Urban oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing 102249, China
    Sino-Pipeline International Company Limited, Dongzhimen North Street No. 9, Dongcheng District, Beijing 100010, China)

  • Yamin Yan

    (Beijing Key Laboratory of Urban oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing 102249, China)

  • Xiaohan Yan

    (Beijing Key Laboratory of Urban oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing 102249, China)

  • Qi Liao

    (Beijing Key Laboratory of Urban oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing 102249, China)

  • Wan Zhang

    (Beijing Key Laboratory of Urban oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing 102249, China)

  • Yongtu Liang

    (Beijing Key Laboratory of Urban oil and Gas Distribution Technology, China University of Petroleum-Beijing, Fuxue Road No. 18, Changping District, Beijing 102249, China)

  • Tianqi Xia

    (Center for Spatial Information Science, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa-shi 277-8568, Chiba, Japan)

Abstract

A distributed energy resource (DER) system, which can be defined as a medium or small energy conversion and utilization system with various functions for meeting multiple targets, is directly oriented towards users and achieves on-site production and energy supply according to users’ demands. Optimization research on system construction has recently become an important issue. In this paper, simple stochastic mathematical equations were used to interpret the optimal design problem of a DER system, and based on this, a novel method for solving the optimization problem, which has multi-dimensional stochastic uncertainties (involving the price of input-energy and energy supply and demand), was put forward. A mixed-integer linear programming (MILP) model was established for the optimal design of the DER system by combining the ideas of mean value and variance, aiming to minimize the total costs, including facility costs, energy purchase costs, and loss caused by energy supply shortage, and considering the energy balance and facility performance constraints. In the end, a DER system design for an office building district in Xuzhou, China, was taken as an example to verify the model. The influences of uncertainty on the selection of system facilities and the economic evaluation were analyzed. The result indicated that uncertainty of energy demand played a significant role in optimal design, whereas energy price played a negligible role. With respect to economy, if uncertainties are not considered in system design, it will result in a short supply, and therefore the total cost will increase considerably. The calculation convergence was compared with previous work. The implementation results showed the practicality and efficiency of the proposed method.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2017:i:1:p:22-:d:124081
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    References listed on IDEAS

    as
    1. Henning, Dag, 1997. "MODEST—An energy-system optimisation model applicable to local utilities and countries," Energy, Elsevier, vol. 22(12), pages 1135-1150.
    2. Falke, Tobias & Krengel, Stefan & Meinerzhagen, Ann-Kathrin & Schnettler, Armin, 2016. "Multi-objective optimization and simulation model for the design of distributed energy systems," Applied Energy, Elsevier, vol. 184(C), pages 1508-1516.
    3. Marmidis, Grigorios & Lazarou, Stavros & Pyrgioti, Eleftheria, 2008. "Optimal placement of wind turbines in a wind park using Monte Carlo simulation," Renewable Energy, Elsevier, vol. 33(7), pages 1455-1460.
    4. Basak, Prasenjit & Chowdhury, S. & Halder nee Dey, S. & Chowdhury, S.P., 2012. "A literature review on integration of distributed energy resources in the perspective of control, protection and stability of microgrid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 5545-5556.
    5. Alarcon-Rodriguez, Arturo & Ault, Graham & Galloway, Stuart, 2010. "Multi-objective planning of distributed energy resources: A review of the state-of-the-art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(5), pages 1353-1366, June.
    6. Yang, Yun & Zhang, Shijie & Xiao, Yunhan, 2015. "An MILP (mixed integer linear programming) model for optimal design of district-scale distributed energy resource systems," Energy, Elsevier, vol. 90(P2), pages 1901-1915.
    7. Omu, Akomeno & Choudhary, Ruchi & Boies, Adam, 2013. "Distributed energy resource system optimisation using mixed integer linear programming," Energy Policy, Elsevier, vol. 61(C), pages 249-266.
    8. Kavvadias, K.C. & Maroulis, Z.B., 2010. "Multi-objective optimization of a trigeneration plant," Energy Policy, Elsevier, vol. 38(2), pages 945-954, February.
    9. Morvaj, Boran & Evins, Ralph & Carmeliet, Jan, 2016. "Optimization framework for distributed energy systems with integrated electrical grid constraints," Applied Energy, Elsevier, vol. 171(C), pages 296-313.
    10. Akorede, Mudathir Funsho & Hizam, Hashim & Pouresmaeil, Edris, 2010. "Distributed energy resources and benefits to the environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(2), pages 724-734, February.
    11. Di Somma, M. & Yan, B. & Bianco, N. & Graditi, G. & Luh, P.B. & Mongibello, L. & Naso, V., 2017. "Multi-objective design optimization of distributed energy systems through cost and exergy assessments," Applied Energy, Elsevier, vol. 204(C), pages 1299-1316.
    12. Ghatikar, Girish & Mashayekh, Salman & Stadler, Michael & Yin, Rongxin & Liu, Zhenhua, 2016. "Distributed energy systems integration and demand optimization for autonomous operations and electric grid transactions," Applied Energy, Elsevier, vol. 167(C), pages 432-448.
    13. Rongxiang Yuan & Timing Li & Xiangtian Deng & Jun Ye, 2016. "Optimal Day-Ahead Scheduling of a Smart Distribution Grid Considering Reactive Power Capability of Distributed Generation," Energies, MDPI, vol. 9(5), pages 1-17, April.
    14. Calvillo, C.F. & Sánchez-Miralles, A. & Villar, J. & Martín, F., 2016. "Optimal planning and operation of aggregated distributed energy resources with market participation," Applied Energy, Elsevier, vol. 182(C), pages 340-357.
    15. Liu, Liuchen & Zhu, Tong & Pan, Yu & Wang, Hai, 2017. "Multiple energy complementation based on distributed energy systems – Case study of Chongming county, China," Applied Energy, Elsevier, vol. 192(C), pages 329-336.
    16. Mavrotas, George & Florios, Kostas & Vlachou, Dimitra, 2010. "Energy planning of a hospital using Mathematical Programming and Monte Carlo simulation for dealing with uncertainty in the economic parameters," MPRA Paper 105754, University Library of Munich, Germany.
    17. Kopanos, Georgios M. & Georgiadis, Michael C. & Pistikopoulos, Efstratios N., 2013. "Energy production planning of a network of micro combined heat and power generators," Applied Energy, Elsevier, vol. 102(C), pages 1522-1534.
    18. Lozano, Miguel A. & Ramos, Jose C. & Serra, Luis M., 2010. "Cost optimization of the design of CHCP (combined heat, cooling and power) systems under legal constraints," Energy, Elsevier, vol. 35(2), pages 794-805.
    19. Ruan, Yingjun & Liu, Qingrong & Zhou, Weiguo & Firestone, Ryan & Gao, Weijun & Watanabe, Toshiyuki, 2009. "Optimal option of distributed generation technologies for various commercial buildings," Applied Energy, Elsevier, vol. 86(9), pages 1641-1653, September.
    20. Rong, Aiying & Lahdelma, Risto, 2005. "An efficient linear programming model and optimization algorithm for trigeneration," Applied Energy, Elsevier, vol. 82(1), pages 40-63, September.
    21. Ren, Hongbo & Zhou, Weisheng & Nakagami, Ken'ichi & Gao, Weijun & Wu, Qiong, 2010. "Multi-objective optimization for the operation of distributed energy systems considering economic and environmental aspects," Applied Energy, Elsevier, vol. 87(12), pages 3642-3651, December.
    22. Mehleri, E.D. & Sarimveis, H. & Markatos, N.C. & Papageorgiou, L.G., 2013. "Optimal design and operation of distributed energy systems: Application to Greek residential sector," Renewable Energy, Elsevier, vol. 51(C), pages 331-342.
    23. Pengwei Su & Xue Tian & Yan Wang & Shuai Deng & Jun Zhao & Qingsong An & Yongzhen Wang, 2017. "Recent Trends in Load Forecasting Technology for the Operation Optimization of Distributed Energy System," Energies, MDPI, vol. 10(9), pages 1-13, August.
    24. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
    25. Mehleri, Eugenia D. & Sarimveis, Haralambos & Markatos, Nikolaos C. & Papageorgiou, Lazaros G., 2012. "A mathematical programming approach for optimal design of distributed energy systems at the neighbourhood level," Energy, Elsevier, vol. 44(1), pages 96-104.
    26. Milan, Christian & Stadler, Michael & Cardoso, Gonçalo & Mashayekh, Salman, 2015. "Modeling of non-linear CHP efficiency curves in distributed energy systems," Applied Energy, Elsevier, vol. 148(C), pages 334-347.
    27. Yang, Yun & Zhang, Shijie & Xiao, Yunhan, 2015. "Optimal design of distributed energy resource systems coupled with energy distribution networks," Energy, Elsevier, vol. 85(C), pages 433-448.
    28. Silvente, Javier & Aguirre, Adrián M. & Zamarripa, Miguel A. & Méndez, Carlos A. & Graells, Moisès & Espuña, Antonio, 2015. "Improved time representation model for the simultaneous energy supply and demand management in microgrids," Energy, Elsevier, vol. 87(C), pages 615-627.
    29. Zhou, Zhe & Zhang, Jianyun & Liu, Pei & Li, Zheng & Georgiadis, Michael C. & Pistikopoulos, Efstratios N., 2013. "A two-stage stochastic programming model for the optimal design of distributed energy systems," Applied Energy, Elsevier, vol. 103(C), pages 135-144.
    30. Wee, Jung-Ho, 2011. "Molten carbonate fuel cell and gas turbine hybrid systems as distributed energy resources," Applied Energy, Elsevier, vol. 88(12), pages 4252-4263.
    31. Buoro, D. & Casisi, M. & De Nardi, A. & Pinamonti, P. & Reini, M., 2013. "Multicriteria optimization of a distributed energy supply system for an industrial area," Energy, Elsevier, vol. 58(C), pages 128-137.
    32. Kaplanis, S. & Kaplani, E., 2007. "A model to predict expected mean and stochastic hourly global solar radiation I(h;nj) values," Renewable Energy, Elsevier, vol. 32(8), pages 1414-1425.
    33. Zhang, Haoran & Liang, Yongtu & Liao, Qi & Yan, Xiaohan & Shen, Yun & Zhao, Yabin, 2017. "A three-stage stochastic programming method for LNG supply system infrastructure development and inventory routing in demanding countries," Energy, Elsevier, vol. 133(C), pages 424-442.
    34. Steen, David & Stadler, Michael & Cardoso, Gonçalo & Groissböck, Markus & DeForest, Nicholas & Marnay, Chris, 2015. "Modeling of thermal storage systems in MILP distributed energy resource models," Applied Energy, Elsevier, vol. 137(C), pages 782-792.
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