Abstract
Power generation planning (PGP) is a vigorous stage in power system planning subsequently forecasting the load demand. The nature of the PGP problem is highly constrained, dynamic, discrete, nonlinear, stochastic, and mixed-integer. The amalgamation of the renewable energy sources in strategic PGP generates the complexity in terms of feeble reliability because of variability. The meta-heuristic algorithms are referred to as efficient solution techniques for the optimization of challenging and minimum-cost PGP. In this paper, a novel PGP optimizer has been proposed to plan the power generation plants reflecting the minimum cost and attaining an explicit reliability evaluation for specific planning prospect. The formulated and proposed optimizer is called teaching–learning-based optimization employing correction matrix method-with-indicators (TLBO-CMMI). In TLBO-CMMI, a new radix-5-based mapping procedure has been developed for the representation of population search agents. An innovative constraint handling procedure known as correction matrix method-with-indicators and incremental cost-based strategy to compute variable operation and maintenance cost, have been embedded from our previous research work. The optimizer has been employed to perform the PGP for different test scenarios in the literature including reliability and emission constraints. The optimizer gives promising results considering minimum cost and computational time when compared with results by current methods in the literature. The suitability of the optimizer has also been assessed by applying to an actual scenario of the Pakistan power system to create a viable power generation plan.
Similar content being viewed by others
References
http://www.ntdc.com.pk/energy (2019). Accessed 12 Jan 2020
Ardakani F, Ardehali M (2017) Optimization of mixed-integer non-linear electricity generation expansion planning problem based on newly improved gravitational search algorithm. AUT J Electr Eng 49(2):161–172
Ashraf MM, Malik TN (2017) Design of a three-phase multistage axial flux permanent magnet generator for wind turbine applications. Turk J Electr Eng Comput Sci 25(1):520–538
Ashraf MM, Malik TN (2019) Least cost generation expansion planning in the presence of renewable energy sources using correction matrix method with indicators-based discrete water cycle algorithm. J Renew Sustain Energy 11(5):056301
Ashraf MM, Waqas AB, Malik TN (2017) Grid connected wind energy conversion system for peak load sharing using fuzzy logic controller. Int J Renew Energy Res (IJRER) 7(4):1767–1778
Association IET (2006) Wien automatic system planning (WASP) package: a computer code for power generating system expansion planning version WASP-IV with user interface user’s manual. IAEA, Vienna, pp 13–150
Bhuvanesh A, Jaya Christa ST, Kannan S, Karuppasamy Pandiyan M (2019) Multistage multiobjective electricity generation expansion planning for Tamil Nadu considering least cost and minimal GHG emission. Int Trans Electr Energy Syst 29(2):e2708
Biswas PP, Suganthan PN, Qu BY, Amaratunga GA (2018) Multiobjective economic-environmental power dispatch with stochastic wind-solar-small hydro power. Energy 150:1039–1057
da Luz T, Moura P (2019) Power generation expansion planning with complementarity between renewable sources and regions for 100% renewable energy systems. Int Trans Electr Energy Syst 29(7):e2817
David A, Zhao RD (1989) Integrating expert systems with dynamic programming in generation expansion planning. IEEE Trans Power Syst 4(3):1095–1101
Heard BP, Brook BW, Wigley TM, Bradshaw CJ (2017) Burden of proof: a comprehensive review of the feasibility of 100% renewable-electricity systems. Renew Sustain Energy Rev 76:1122–1133
Hua B, Baldick R, Wang J (2018) Representing operational flexibility in generation expansion planning through convex relaxation of unit commitment. IEEE Trans Power Syst 33(2):2272–2281
Jadidoleslam M, Bijami E, Amiri N, Ebrahimi A, Askari J (2012) Application of shuffled frog leaping algorithm to long term generation expansion planning. Int J Comput Electr Eng 4(2):115
Jadidoleslam M, Ebrahimi A (2015) Reliability constrained generation expansion planning by a modified shuffled frog leaping algorithm. Int J Electr Power Energy Syst 64:743–751
Jenkins RT, Joy D (1974) Wein automatic system planning package (WASP): an electric utility optimal generation expansion planning computer code. Report, Oak Ridge National Lab, Tenn (USA)
Kamran M (2018) Current status and future success of renewable energy in pakistan. Renew Sustain Energy Rev 82:609–617
Kannan S, Slochanal SMR, Padhy NP (2005) Application and comparison of metaheuristic techniques to generation expansion planning problem. IEEE Trans Power Syst 20(1):466–475
Kannan S, Slochanal SMR, Subbaraj P, Padhy NP (2004) Application of particle swarm optimization technique and its variants to generation expansion planning problem. Electr Power Syst Res 70(3):203–210
Koltsaklis NE, Dagoumas AS (2018) State-of-the-art generation expansion planning: a review. Appl Energy 230:563–589
Koltsaklis NE, Georgiadis MC (2015) A multi-period, multi-regional generation expansion planning model incorporating unit commitment constraints. Appl Energy 158:310–331
Milligan MR (2001) A sliding window technique for calculating system LOLP contributions of wind power plants. National Renewable Energy Laboratory, Golden
Mo B, Hegge J, Wangensteen I (1991) Stochastic generation expansion planning by means of stochastic dynamic programming. IEEE Trans Power Syst 6(2):662–668
Nawaz U, Malik TN, Ashraf MM (2019) Least-cost generation expansion planning using whale optimization algorithm incorporating emission reduction and renewable energy sources. Int Trans Electr Energy Syst 30(3):e12238
Oree V, Hassen SZS, Fleming PJ (2017) Generation expansion planning optimisation with renewable energy integration: a review. Renew Sustain Energy Rev 69:790–803
Park JB, Park YM, Won JR, Lee KY (2000) An improved genetic algorithm for generation expansion planning. IEEE Trans Power Syst 15(3):916–922
Park Y, Park J, Won J (1998) A hybrid genetic algorithm/dynamic programming approach to optimal long-term generation expansion planning. Int J Electr Power Energy Syst 20(4):295–303
Park YM, Won JR, Park JB, Kim DG (1999) Generation expansion planning based on an advanced evolutionary programming. IEEE Trans Power Syst 14(1):299–305
Pereira S, Ferreira P, Vaz A (2017) Generation expansion planning with high share of renewables of variable output. Appl Energy 190:1275–1288
Rafique MM, Rehman S (2017) National energy scenario of Pakistan-current status, future alternatives, and institutional infrastructure: an overview. Renew Sustain Energy Rev 69:156–167
Rajesh K, Bhuvanesh A, Kannan S, Thangaraj C (2016) Least cost generation expansion planning with solar power plant using differential evolution algorithm. Renew Energy 85:677–686
Rajesh K, Kannan S, Thangaraj C (2016) Least cost generation expansion planning with wind power plant incorporating emission using differential evolution algorithm. Int J Electr Power Energy Syst 80:275–286
Rajesh K, Karthikeyan K, Kannan S, Thangaraj C (2016) Generation expansion planning based on solar plants with storage. Renew Sustain Energy Rev 57:953–964
Rao RV, Patel V (2013) An improved teaching–learning-based optimization algorithm for solving unconstrained optimization problems. Sci Iran 20(3):710–720
Rao RV, Savsani VJ, Vakharia D (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315
Rashidaee SA, Amraee T (2018) Generation expansion planning considering the uncertainty of yearly peak loads. In: 2018 IEEE international conference on environment and electrical engineering and 2018 IEEE industrial and commercial power systems Europe (EEEIC/I&CPS Europe), pp 1–4. IEEE
Rashidaee SA, Amraee T, Fotuhi-Firuzabad M (2018) A linear model for dynamic generation expansion planning considering loss of load probability. IEEE Trans Power Syst 33:6924–6934
Rice JA (2003) Mathematical statistics and data analysis. China Machine Press, Beijing
Sadeghi H, Rashidinejad M, Abdollahi A (2017) A comprehensive sequential review study through the generation expansion planning. Renew Sustain Energy Rev 67:1369–1394
Seifi H, Sepasian MS (2011) Electric power system planning: issues. Algorithms and solutions. Springer, Berlin
Shakeel SR, Takala J, Shakeel W (2016) Renewable energy sources in power generation in Pakistan. Renew Sustain Energy Rev 64:421–434
Xifan W, McDonald J (1994) Modern power system planning. Mcgraw-HillBook Company, London
Yu K, Wang X, Wang Z (2016) An improved teaching–learning-based optimization algorithm for numerical and engineering optimization problems. J Intell Manuf 27(4):831–843
Zhu J, Chow My (1997) A review of emerging techniques on generation expansion planning. IEEE Trans Power Syst 12(4):1722–1728
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ashraf, M.M., Malik, T.N. A hybrid teaching–learning-based optimizer with novel radix-5 mapping procedure for minimum cost power generation planning considering renewable energy sources and reducing emission. Electr Eng 102, 2567–2582 (2020). https://doi.org/10.1007/s00202-020-01044-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00202-020-01044-0