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Optimal design of distributed energy resource systems under large-scale uncertainties in energy demands based on decision-making theory

ABSTRACT
This study focuses on the optimal design of distributed energy resource (DER) systems with consideration of large-scale uncertainty of energy demands based on decision-making theory. Five integrated modeling and optimization frameworks are developed through the combined use of mixed integer linear programming (MILP) and uncertainty decision-making criteria (including optimistic criterion, pessimistic criterion, Hurwicz criterion, Laplace criterion, and minimax regret criterion). Superstructure-based MILP models are used for the optimal design and optimal operation of the system where the objective function is to minimize the annual cost. The uncertainty of energy demands is represented by assuming a set of possible scenarios. The proposed methods are applied to the planning of a DER system for a hotel in Guangzhou, China and their validity and effectiveness are verified. Results show that each method has its specific feature. Optimistic method is risky and recommends a relative small-scale system, while pessimistic method is conservative presenting a relative large-scale system. Hurwicz method is with great subjectivity, making different decisions at different values of optimism coefficient. Both Laplace method and minimax regret method identify a moderate-scale system as the best alternative. Sensitivity analyses on the energy demand scenarios are conducted and results show that the five methods have high sensitivity to the choice of scenarios.
KEYWORDS
PAPER SUBMITTED: 2017-07-18
PAPER REVISED: 2018-07-17
PAPER ACCEPTED: 2018-07-18
PUBLISHED ONLINE: 2018-09-22
DOI REFERENCE: https://doi.org/10.2298/TSCI170718199Y
REFERENCES
  1. Ackermann T., et al., Distributed generation: a definition, Electric Power Systems Research, 57 (2001),3,pp. 195-204
  2. Pepermans G., et al., Distributed generation: definition, benefits and issues, Energy Policy, 33 (2005),6,pp. 787-798
  3. Alanne K., Saari A., Distributed energy generation and sustainable development, Renewable & Sustainable Energy Reviews, 10 (2006),6,pp. 539-558
  4. Akorede M.F., et al., Distributed energy resources and benefits to the environment, Renewable & Sustainable Energy Reviews, 14 (2010),2,pp. 724-734
  5. Li C.Z., et al., Sensitivity analysis of energy demands on performance of CCHP system, Energy Conversion and Management, 49 (2008),12,pp. 3491-3497
  6. Yang Y., et al., Application of monte carlo method in uncertainty evaluation for cogeneration systems, Proceedings of the Chinese Society of Electrical Engineering, 33 (2013),2,pp. 16-23
  7. Zhou Z., et al., A two-stage stochastic programming model for the optimal design of distributed energy systems, Applied Energy, 103 (2013),135-144
  8. Carpaneto E., et al., Cogeneration planning under uncertainty. Part II: Decision theory-based assessment of planning alternatives, Applied Energy, 88 (2011),4,pp. 1075-1083
  9. Yokoyama R., Ito K., Robust Optimal Design of a Gas Turbine Cogeneration Plant Based on Minimax Regret Criterion,Proceedings, pp. ALL-10
  10. Carpaneto E., et al., Cogeneration planning under uncertainty Part I: Multiple time frame approach, Applied Energy, 88 (2011),4,pp. 1059-1067
  11. Yang Y., et al., Optimal design of distributed energy resource systems based on two-stage stochastic programming, Applied Thermal Engineering, 110 (2017),1358-1370
  12. French S., Decision theory : an introduction to the mathematics of rationality 1993
  13. Anders G.J., Probability concepts in electric power systems, John Wiley and Sons Inc, New York,USA, 1989
  14. Miranda V., Proenca L.M., Why risk analysis outperforms probabilistic choice as the effective decision support paradigm for power system planning, Ieee Transactions on Power Systems, 13 (1998),2,pp. 643-648
  15. Miranda V., Proenca L.M., Probabilistic choice vs risk analysis - Conflicts and synthesis in power system planning, Ieee Transactions on Power Systems, 13 (1998),3,pp. 1038-1043
  16. Bisschop J., et al., Paragon Decision Technology, AIMMS 3.12. Copyright © 1989-2015 by AIMMS B.V., Haarlem., 2014
  17. Yang Y., et al., Optimal design of distributed energy resource systems coupled with energy distribution networks, Energy, 85 (2015),433-448
  18. Yang Y., et al., An MILP (mixed integer linear programming) model for optimal design of district-scale distributed energy resource systems, Energy, 90 (2015),1901-1915
  19. Yokoyama R., Ito K., Optimal design of gas turbine cogeneration plants in consideration of discreteness of equipment capabilities, Journal of Engineering for Gas Turbines and Power-Transactions of the Asme, 128 (2006),2,pp. 336-343
  20. Mehleri E.D., et al., Optimal design and operation of distributed energy systems: Application to Greek residential sector, Renewable Energy, 51 (2013),331-342
  21. Li H.W., et al., Thermal-economic optimization of a distributed multi-generation energy system - A case study of Beijing, Applied Thermal Engineering, 26 (2006),7,pp. 709-719
  22. Ren H.B., et al., Economic optimization and sensitivity analysis of photovoltaic system in residential buildings, Renewable Energy, 34 (2009),3,pp. 883-889
  23. Hawkes A.D., Leach M.A., Modelling high level system design and unit commitment for a microgrid, Applied Energy, 86 (2009),7-8,pp. 1253-1265
  24. Farmer R., Simple cycle OEM design ratings, Gas Turbine World 2012 GTW Handbook, 29 (2012),70-80