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Abdullahi, B, Ibrahim, Y M, Ibrahim, A and Bala, K (2019) Development of e-tendering evaluation system for Nigerian public sector. Journal of Engineering, Design and Technology , 18(01), 122–49.

Acheamfour, V K, Kissi, E, Adjei-Kumi, T and Adinyira, E (2019) Review of empirical arguments on contractor pre-qualification criteria. Journal of Engineering, Design and Technology , 18(01), 70–83.

Adu, E T and Opawole, A (2019) Assessment of performance of teamwork in construction projects delivery in South-Southern Nigeria. Journal of Engineering, Design and Technology , 18(01), 230–50.

Dadzie, J, Runeson, G and Ding, G (2019) Assessing determinants of sustainable upgrade of existing buildings. Journal of Engineering, Design and Technology , 18(01), 270–92.

Hellas, M S, Chaib, R and Verzea, I (2019) Artificial intelligence treating the problem of uncertainty in quantitative risk analysis (QRA). Journal of Engineering, Design and Technology , 18(01), 40–54.

Hulio, Z H and Jiang, W (2019) An assessment of effects of non-stationary operational conditions on wind turbine under different wind scenario. Journal of Engineering, Design and Technology , 18(01), 102–21.

Ibn Majdoub Hassani, Z, El Barkany, A, Jabri, A, El Abbassi, I and Darcherif, A M (2019) Hybrid approach for solving the integrated planning and scheduling production problem. Journal of Engineering, Design and Technology , 18(01), 172–89.

Luo, Z, Chen, Y, Cen, K, Pan, H, Zhong, M and He, J (2019) Research on comprehensive environmental impact assessment of shale gas development. Journal of Engineering, Design and Technology , 18(01), 1–20.

Mengistu, D G and Mahesh, G (2019) Dimensions for improvement of construction management practice in Ethiopian construction industry. Journal of Engineering, Design and Technology , 18(01), 21–39.

Neshat, N, Hadian, H and Rahimi Alangi, S (2019) Technological learning modelling towards sustainable energy planning. Journal of Engineering, Design and Technology , 18(01), 84–101.

  • Type: Journal Article
  • Keywords: Technological learning; Generation expansion planning; Multi-agent based modelling;
  • ISBN/ISSN: 1726-0531
  • URL: https://doi.org/10.1108/JEDT-03-2019-0085
  • Abstract:
    Obviously, the development of a robust optimization framework is the main step in energy and climate policy. In other words, the challenge of energy policy assessment requires the application of approaches which recognize the complexity of energy systems in relation to technological, social, economic and environmental aspects. This paper aims to develop a two-sided multi-agent based modelling framework which endogenizes the technological learning mechanism to determine the optimal generation plan. In this framework, the supplier agents try to maximize their income while complying with operational, technical and market penetration rates constraints. A case study is used to illustrate the application of the proposed planning approach. The results showed that considering the endogenous technology cost reduction moves optimal investment timings to earlier planning years and influences the competitiveness of technologies. The proposed integrated approach provides not only an economical generation expansion plan but also a cleaner one compared to the traditional approach. Design/methodology/approach To the best of the authors’ knowledge, so far there has not been any agent-based generation expansion planning (GEP) incorporating technology learning mechanism into the modelling framework. The main contribution of this paper is to introduce a multi-agent based modelling for long-term GEP and undertakes to show how incorporating technological learning issues in supply agents behaviour modelling influence on renewable technology share in the optimal mix of technologies. A case study of the electric power system of Iran is used to illustrate the usefulness of the proposed planning approach and also to demonstrate its efficiency. Findings As seen, the share of the renewable technology agents (geothermal, hydropower, wind, solar, biomass and photovoltaic) in expanding generation increases from 10.2% in the traditional model to 13.5% in the proposed model over the planning horizon. Also, to incorporate technological learning in the supply agent behaviour leads to earlier involving of renewable technologies in the optimal plan. This increased share of the renewable technology agents is reasonable due to their decreasing investment cost and capability of cooperation in network reserve supply which leads to a high utilization factor. Originality/value To the best of the authors’ knowledge, so far there hasn’t been any agent-based GEP paying attention to this integrated approach. The main contribution of this paper is to introduce a multi-agent based modelling for long-term GEP and undertakes to show how incorporating technological learning issues in supply agents behaviour modelling influence on renewable technology share in the optimal mix of technologies. A case study of the electric power system of Iran is used to illustrate the usefulness of the proposed planning approach and also to demonstrate its efficiency.

Othman, A A E and Khalil, M H (2019) Divergent heritage sustainability: a threefold approach through lean talent management. Journal of Engineering, Design and Technology , 18(01), 150–71.

Patel, T D, Haupt, T C and Bhatt, T (2019) Fuzzy probabilistic approach for risk assessment of BOT toll roads in Indian context. Journal of Engineering, Design and Technology , 18(01), 251–69.

Soltani, M, Aouag, H and Mouss, M D (2019) An integrated framework using VSM, AHP and TOPSIS for simplifying the sustainability improvement process in a complex manufacturing process. Journal of Engineering, Design and Technology , 18(01), 211–29.

Syed Abu Bakar, S P, Jaafar, M and Muhibudin, M (2019) Intensifying business success of Malaysian housing development firms through entrepreneurial learning. Journal of Engineering, Design and Technology , 18(01), 190–210.

Yap, J B H and Toh, H M (2019) Investigating the principal factors impacting knowledge management implementation in construction organisations. Journal of Engineering, Design and Technology , 18(01), 55–69.