Frontiers in Energy

, Volume 12, Issue 4, pp 518–528 | Cite as

Multi-objective optimization of a hybrid distributed energy system using NSGA-II algorithm

  • Hongbo Ren
  • Yinlong Lu
  • Qiong WuEmail author
  • Xiu Yang
  • Aolin Zhou
Research Article


In this paper, a multi-objective optimization model is established for the investment plan and operation management of a hybrid distributed energy system. Considering both economic and environmental benefits, the overall annual cost and emissions of CO2 equivalents are selected as the objective functions to be minimized. In addition, relevant constraints are included to guarantee that the optimized system is reliable to satisfy the energy demands. To solve the optimization model, the non-dominated sorting generic algorithm II (NSGA-II) is employed to derive a set of non-dominated Pareto solutions. The diversity of Pareto solutions is conserved by a crowding distance operator, and the best compromised Pareto solution is determined based on the fuzzy set theory. As an illustrative example, a hotel building is selected for study to verify the effectiveness of the optimization model and the solving algorithm. The results obtained from the numerical study indicate that the NSGA-II results in more diversified Pareto solutions and the fuzzy set theory picks out a better combination of device capacities with reasonable operating strategies.


multi-objective optimization hybrid distributed energy system non-dominated sorting generic algorithm II fuzzy set theory Pareto optimal solution 


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This work was supported in part by the National Natural Science Foundation of China (Grant No. 71804106), Shanghai Sailing Program (No. 17YF1406800), Shanghai Chenguang Program (No. 17CG57) and The Key Fund of Shanghai Science Technology Committee (No. 16020500900).


  1. 1.
    International Energy Agency. Energy, Climate Change & Environment: 2016 Insights. Technical Report, 2016Google Scholar
  2. 2.
    World Economic Forum and Accenture. Digital Transformation of Industries Electricity Industry. Technical Report, 2016Google Scholar
  3. 3.
    Liu M, Shi Y, Fang F. Combined cooling, heating and power systems: a survey. Renewable & Sustainable Energy Reviews, 2014, 35: 1–22CrossRefGoogle Scholar
  4. 4.
    Qiu Y, Jiang J, Chen D. Development and present status of multienergy distributed power generation system. In: IEEE Power Electronics and Motion Control Conference, Florence, Italy, 2016Google Scholar
  5. 5.
    Ju L, Tan Z, Li H, Tan Q K, Yu X B, Song X H. Multi-objective operation optimization and evaluation model for CCHP and renewable energy based hybrid energy system driven by distributed energy resources in China. Energy, 2016, 111: 322–340CrossRefGoogle Scholar
  6. 6.
    Evins R. Multi-level optimization of building design, energy system sizing and operation. Energy, 2015, 90: 1775–1789CrossRefGoogle Scholar
  7. 7.
    Mehleri E D, Sarimveis H, Markatos N C, Papageorgiou L G. Optimal design and operation of distributed energy systems: application to Greek residential sector. Renewable Energy, 2013, 51(2): 331–342CrossRefGoogle Scholar
  8. 8.
    Cardoso G, Stadler M, Bozchalui M C, Sharma R, Marnay C, Barbosa-Póvoa A, Ferrão P. Optimal investment and scheduling of distributed energy resources with uncertainty in electric vehicle driving schedules. Energy, 2014, 64(1): 17–30CrossRefGoogle Scholar
  9. 9.
    Voll P, Klaffke C, Hennen M, Bardow A. Automated superstructurebased synthesis and optimization of distributed energy supply systems. Energy, 2013, 50(1): 374–388CrossRefGoogle Scholar
  10. 10.
    Bakken B H, Skjelbred H I, Wolfgang O. Transport: Investment planning in energy supply systems with multiple energy carriers. Energy, 2007, 32(9): 1676–1689CrossRefGoogle Scholar
  11. 11.
    Falke T, Krengel S, Meinerzhagen A K, Schnettler A. Multiobjective optimization and simulation model for the design of distributed energy systems. Applied Energy, 2016, 184:1508–1516CrossRefGoogle Scholar
  12. 12.
    Duan Z, Yan Y, Yan X, Liao Q, Zhang W, Liang Y T, Xia T. An MILP method for design of distributed energy resource system considering stochastic energy supply and demand. Energies, 2017, 11(1): 22CrossRefGoogle Scholar
  13. 13.
    Di Somma M, Yan B, Bianco N, Luh P B, Graditi G, Mongibello L, Naso V. Multi-objective operation optimization of a Distributed Energy System for a large-scale utility customer. Applied Thermal Engineering, 2016, 101: 752–761CrossRefGoogle Scholar
  14. 14.
    Hu M, Cho H. A probability constrained multi-objective optimization model for CCHP system operation decision support. Applied Energy, 2014, 116(116): 230–242CrossRefGoogle Scholar
  15. 15.
    Zeng R, Li H, Liu L, Zhang X, Zhang G. A novel method based on multi-population genetic algorithm for CCHP–GSHP coupling system optimization. Energy Conversion and Management, 2015, 105: 1138–1148CrossRefGoogle Scholar
  16. 16.
    Gan L K, Shek J K H, Mueller M A. Optimised operation of an offgrid hybrid wind-diesel-battery system using genetic algorithm. Energy Conversion and Management, 2016, 126: 446–462CrossRefGoogle Scholar
  17. 17.
    Ghaem Sigarchian S, Orosz M S, Hemond H F, Malmquist A. Optimum design of a hybrid PV–CSP–LPG microgrid with Particle Swarm Optimization technique. Applied Thermal Engineering, 2016, 109: 1031–1036CrossRefGoogle Scholar
  18. 18.
    Fazlollahi S, Mandel P, Becker G, Maréchal F. Methods for multiobjective investment and operating optimization of complex energy systems. Energy, 2012, 45(1): 12–22CrossRefGoogle Scholar
  19. 19.
    Wang J, Zhai Z, Jing Y, Zhang C. Particle swarm optimization for redundant building cooling heating and power system. Applied Energy, 2010, 87(12): 3668–3679CrossRefGoogle Scholar
  20. 20.
    Soheyli S, Shafiei Mayam M H, Mehrjoo M. Modeling a novel CCHP system including solar and wind renewable energy resources and sizing by a CC-MOPSO algorithm. Applied Energy, 2016, 184: 375–395CrossRefGoogle Scholar
  21. 21.
    Zitzler E, Thiele L. Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 1999, 3(4): 257–271CrossRefGoogle Scholar
  22. 22.
    Zitzler E, Thiele L. An Evolutionary Algorithm for Multiobjective Optimization: The Strength Pareto Approach. TIK-Report, 1998Google Scholar
  23. 23.
    Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182–197CrossRefGoogle Scholar
  24. 24.
    Wang H, He C, Liu Y. Pareto optimization of power system reconstruction using NSGA-II algorithm. In: Asia-Pacific Power and Energy Engineering Conference, Chengdu, China, 2010Google Scholar
  25. 25.
    Farina M, Amato P. A fuzzy definition of optimality for manycriteria optimization problems. IEEE Transactions on Systems, Man, and Cybernetics. Part A, Systems and Humans, 2004, 34(3): 315–326CrossRefGoogle Scholar
  26. 26.
    Sheng W, Liu Y, Meng X, Zhang T. An Improved Strength Pareto Evolutionary Algorithm 2 with application to the optimization of distributed generations. Computers & Mathematics with Applications (Oxford, England), 2012, 64(5): 944–955CrossRefGoogle Scholar
  27. 27.
    Weber C, Keirstead J, Samsatli N, Shah N, Fisk D. Trade-offs between layout of cities and design of district energy systems. In: Proceedings of the 23rd international conference on efficiency, cost, optimization, simulation and environmental impact of energy systems, Lausanne, Switzerland, 2010Google Scholar
  28. 28.
    Shanghai Municipal Development & Reform Commission. Notice on implementing linkage adjustment of coal and electricity prices by Shanghai Price Bureau. 2016–01–05,
  29. 29.
    National Development and Reform Commission. Notice on giving full play to price leverage to promote the healthy development of photovoltaic industry by the State Development and Reform Commission. 2013–08–26,
  30. 30.
    Shanghai Municipal Development & Reform Commission. Notice on issuing the special supporting fund for renewable energy and new energy development in Shanghai. 2016–11–16,
  31. 31.
    Huo X L, Zhou W G, Ying-Jun R. The integrated optimization of sizing and operation strategy for BCHP (Buildings Cooling, Heating and Power) systems. Natural Gas Industry, 2009, 29(8): 119–122Google Scholar
  32. 32.
    Ren H, Wu Q, Gao W, Zhou W. Optimal operation of a grid-connected hybrid PV/fuel cell/battery energy system for residential applications. Energy, 2016, 113: 702–712CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Hongbo Ren
    • 1
  • Yinlong Lu
    • 1
  • Qiong Wu
    • 1
    Email author
  • Xiu Yang
    • 2
  • Aolin Zhou
    • 1
  1. 1.College of Energy and Mechanical EngineeringShanghai University of Electric PowerShanghaiChina
  2. 2.College of Electrical EngineeringShanghai University of Electric PowerShanghaiChina

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