Hybrid Dynamic-Evolutionary Programming for Multi-Objective Long-Term Malaysia Generation Mix

  • Abstract
  • Keywords
  • References
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  • Abstract

    A Hybrid Dynamic-Evolutionary Programming (HDPEP) is proposed to find an optimal solution formulti-objective power generation mix model. The present contribution is intended to develop a method to facilitate simultaneous modelling of multi-objective optimization considering the cost of power generation, carbon emission and power system reliability. The study introduces the implementation of Evolutionary Programming (EP) via weighted sum method (WSM) approach within the HDPEP framework to optimize the weighted coefficient in providing accurate decision for generation mix planning. The EP-WSM reduces ‘discrimination’ when choosing the weight values of each objective function. The proposed HDPEP were compared with non-optimal weighted approach. Results show that the HDPEP model provides a better performance in providing the lowest Multi-Objectives Index (MOI)in solving multi-objective power generation mix problem.


  • Keywords

    Multi-objective optimization, generation mix, evolutionary programming, dynamic programming, weighted sum method.

  • References

      [1] Abbasi, A. R., & Seifi, A. R. (2014). Energy expansion planning by considering electrical and thermal expansion simultaneously. Energy Conversion and Management, 83, 9–18.

      [2] Abbasi, A. R., & Seifi, A. R. (2014). Simultaneous integrated stochastic electrical and thermal energy expansion planning. IET Generation, Transmission and Distribution, 8(6).

      [3] Abon, S. A., Dahlan, N. Y., & Mat Yassin, Z. (2015). Evolutionary programming based generation mix model for Malaysia considering cost, environmental and reliability factors. Journal of Electrical Systems, 2015(Specialissue3), 26–34.

      [4] Afful-Dadzie, A., Afful-Dadzie, E., Iddrisu, A., & Banuro, J. K. (2017). Power generation capacity planning under budget constraint in developing countries.

      [5] Aghaei, J., Akbari, M. A., Roosta, A., & Baharvandi, A. (2013a). Electric Power Systems Research Multiobjective generation expansion planning considering power system adequacy. Electric Power Systems Research, 102, 8–19.

      [6] Aghaei, J., Akbari, M. A., Roosta, A., & Baharvandi, A. (2013b). Multiobjective generation expansion planning considering power system adequacy. Electric Power Systems Research, 102, 8–19.

      [7] Aliyu, A. S., Ramli, A. T., & Saleh, M. A. (2013). Nigeria electricity crisis: Power generation capacity expansion and environmental ramifications. Energy, 61, 354–367.

      [8] Cacchiani, V., & D ’ambrosio, C. (2016). A branch-and-bound based heuristic algorithm for convex multi-objective MINLPs. European Journal of Operational Research, 260, 920–933.

      [9] Chen, F., Huang, G., & Fan, Y. (2015). A linearization and parameterization approach to tri-objective linear programming problems for power generation expansion planning. Energy, 87, 240–250.

      [10] Dehghan, S., Amjady, N., & Kazemi, A. (2014). Two-stage robust generation expansion planning: A mixed integer linear programming model. IEEE Transactions on Power Systems, 29(2), 584–597.

      [11] Georgiou, P. N. (2016). A bottom-up optimization model for the long-term energy planning of the Greek power supply sector integrating mainland and insular electric systems. Computers and Operations Research, 66, 292–312.

      [12] Ghaderi, A., Parsa Moghaddam, M., & Sheikh-El-Eslami, M. K. (2014). Energy efficiency resource modeling in generation expansion planning. Energy, 68, 529–537.

      [13] Gitizadeh, M., Kaji, M., & Aghaei, J. (2013). Risk based multiobjective generation expansion planning considering renewable energy sources. Energy, 50(1), 74–82.

      [14] Habib, M. A., & Chungpaibulpatana, S. (2014). Electricity generation expansion planning with environmental impact abatement: Case study of Bangladesh. In Energy Procedia (Vol. 52, pp. 410–420).

      [15] Hemmati, R., Hooshmand, R., & Khodabakhshian, A. (2013). Reliability constrained generation expansion planning with consideration of wind farms uncertainties in deregulated electricity market. Energy Conversion and Management, 76, 517–526.

      [16] Jadidoleslam, M., Bijami, E., Amiri, N., Ebrahimi, a., & Askari, J. (2012). Application of Shuffled Frog Leaping Algorithm to Long Term Generation Expansion Planning. International Journal of Computer and Electrical Engineering, 4(2), 115–120.

      [17] Jadidoleslam, M., & Ebrahimi, A. (2015). Reliability constrained generation expansion planning by a modified shuffled frog leaping algorithm. International Journal of Electrical Power and Energy Systems, 64, 743–751.

      [18] Javadi, M. S., Mashhadi, H. R., Saniei, M., & Gutiérrez-Alcaraz, G. (2013). Multi-objective expansion planning approach: distant wind farms and limited energy resources integration. IET Renewable Power Generation, 7(6), 652–668.

      [19] Khodaei, A., & Shahidehpour, M. (2013). Microgrid-based co-optimization of generation and transmission planning in power systems. IEEE Transactions on Power Systems, 28(2), 1582–1590.

      [20] Koltsaklis, N. E., Dagoumas, A. S., Kopanos, G. M., Pistikopoulos, E. N., & Georgiadis, M. C. (2014). A spatial multi-period long-term energy planning model: A case study of the Greek power system. Applied Energy, 115, 456–482.

      [21] Leibowicz, B. D., & Larsen, P. H. (2013). Carbon Emissions Caps and the Impact of a Radical Change in Nuclear Electricity Costs, 3(1), 60–74.

      [22] Majewski, D. E., Wirtz, M., Lampe, M., & Bardow, A. (2017). Robust multi-objective optimization for sustainable design of distributed energy supply systems. Computers and Chemical Engineering, 102.

      [23] Majidi, M., Nojavan, S., Esfetanaj, N. N., Najafi-Ghalelou, A., & Zare, K. (2017). A multi-objective model for optimal operation of a battery/PV/fuel cell/grid hybrid energy system using weighted sum technique and fuzzy satisfying approach considering responsible load management.

      [24] Majidi, M., Nojavan, S., & Zare, K. (2017). A cost-emission framework for hub energy system under demand response program. https://doi.org/10.1016/j.energy.2017.06.003

      [25] Marler, R. T., & Arora, J. S. (2004). Survey of multi-objective optimization methods for engineering. Struct Multidisc Optim, 26, 369–395.

      [26] Mavalizadeh, H., & Ahmadi, A. (2014). Hybrid expansion planning considering security and emission by augmented epsilon-constraint method. International Journal of Electrical Power and Energy Systems, 61, 90–100.

      [27] Mohd Shokri, S. M., & Dahlan, N. Y. (2014). An Application of the Multi-Objective Approach for the Evaluation of Long-Term Electrical Generation Optimum Mix: a Case Study. International Review of Electrical Engineering (IREE), 9(5), 991.

      [28] Mohd Shokri, S. M., Dahlan, N. Y., & Ahmad, N. H. (2015). Optimum Generation Mix Possibilities for Malaysia Power Sector in 2030. Applied Mechanics and Materials, 785, 521–525.

      [29] Mohd Shokri, S. M., Dahlan, N. Y., & Mohamad, H. (2017). Multi-Objective Sensitivity Analyses for Power Generation Mix: Malaysia Case Study. International Journal on Advanced Science, Engineering and Information Technology, 7(4).

      [30] Mutalib, N. A. H. A., Dahlan, N. Y., Abon, S. A., Rajemi, M. F., M.N.M, N., & Baharum, F. (2014). Optimum generation mix for Malaysia’s additional capacity using evolutionary programming. In 2014 IEEE International Conference on Power and Energy (PECon) (pp. 65–70). IEEE.

      [31] Nazemi, A., Ghaderi, S. F., Moghadam, S. K., & Farsaei, A. (2016). Trade-off Curves and Elasticity Analysis in Multi Fuel Options System and Combined Problem, 6(3), 646–654.

      [32] Pereira, A. J. C., & Saraiva, J. T. (2013). A long term generation expansion planning model using system dynamics – Case study using data from the Portuguese/Spanish generation system. Electric Power Systems Research, 97, 41–50.

      [33] Pineda, S., Morales, J. M., Ding, Y., & Stergaard, J. (2014). Impact of equipment failures and wind correlation on generation expansion planning. Electric Power Systems Research, 116, 451–458.

      [34] Priya, G. S. K., & Bandyopadhyay, S. (2017). Multi-objective pinch analysis for power system planning. Applied Energy, 202, 335–347.

      [35] Promjiraprawat, K., & Limmeechokchai, B. (2013). Multi-Objective and Multi-Criteria Optimization for Power Generation Expansion Planning with CO2 Mitigation in Thailand. Songklanakarin Journal of Sicence and Technology, 35(3), 349–359.

      [36] Sadeghi, H., Rashidinejad, M., & Abdollahi, A. (2016). A comprehensive sequential review study through the generation expansion planning.

      [37] Talib, K. M., Musirin, I., & Kalil, M. R. (2007). Power Flow Solvability Identification and Calculation Algorithm Using Evolutionary Programming Technique P ower, (December), 0–4.

      [38] Venkatachary, S. K., Prasad, J., & Samikannu, R. (2017). Cost Optimization of Micro grids Using Homer : A Case Study in, 7(5), 323–339.

      [39] Yoza, A., Yona, A., Senjyu, T., & Funabashi, T. (2014). Optimal capacity and expansion planning methodology of PV and battery in smart house. Renewable Energy, 69, 25–33.

      [40] Zhang, Q., Mclellan, B. C., Tezuka, T., & Ishihara, K. N. (2013). An integrated model for long-term power generation planning toward future smart electricity systems. Applied Energy, 112, 1424–1437.

      [41] Zhu, Q., Luo, X., Zhang, B., & Chen, Y. (2017). Mathematical modelling and optimization of a large-scale combined cooling, heat, and power system that incorporates unit changeover and time-of-use electricity price.




Article ID: 27955
DOI: 10.14419/ijet.v7i4.19.27955

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