Negotiating the selling price of hydropower energy using multi-agent systems in BOT
Abstract
During the feasibility study of BOT (Build-Operate-Transfer) hydropower investments, the selling price of energy is the most critical parameter that impacts the net present value (NPV) estimated by the investors. Investors usually consider the price of energy guaranteed by the government during their feasibility studies which is the worst case scenario. However, it is apparent that negotiations that take place between investor and broker determine the price of energy which is affected by various sources of uncertainty associated with the energy demand and country conditions. The objective of this study was to make a realistic estimate of the investor’s selling price by modeling the negotiation process between investor and broker using a multi-agent system (MAS). Thus, the factors affecting the negotiation process were identified, a negotiation protocol between the parties was set up, negotiation scenarios were determined, and modelled by using a MAS. The model was tested on a hydropower investment in Turkey and generated more realistic results compared to the current practice. Investors and brokers may benefit from this study because it considers the potential changes in the market as well as the negotiating postures of parties under different scenarios.
Keyword : renewable energy, hydropower investment, multi-agent system, negotiation
How to Cite
Akcay, E. C., Dikmen, I., Birgonul, M. T., & Arditi, D. (2019). Negotiating the selling price of hydropower energy using multi-agent systems in BOT. Journal of Civil Engineering and Management, 25(5), 441-450. https://doi.org/10.3846/jcem.2019.9874
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Shoham, Y. (1993). Agent-oriented programming. Artificial Intelligence, 60(1), 51-92. https://doi.org/10.1016/0004-3702(93)90034-9
Song, J., Song, D., & Zhang, D. (2015). Modeling the concession period and subsidy for BOT waste-to-energy incineration projects. Journal of Construction Engineering Management, 141(10). https://doi.org/10.1061/(ASCE)CO.1943-7862.0001005
Taghaddos, H., Hermann, U., AbouRizk, S., & Mohamed, Y. (2014). Simulation-based multiagent approach for scheduling modular construction. Journal of Computing in Civil Engineering, 28(2). https://doi.org/10.1061/(ASCE)CP.1943-5487.0000262
Tah, J. H. (2005). Towards an agent-based construction supply network. Automation in Construction, 14(3), 353-359. https://doi.org/10.1016/j.autcon.2004.08.003
Taylor, J. E., Levitt, R., & Villarroel, J. A. (2009). Simulating learning dynamics in project networks. Journal of Construction Engineering and Management, 135(10), 1009-1015. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000065
Turkish Contractors Association. (2017). Retrieved from https://www.tmb.org.tr/
Xue, X., Li, X., Shen, Q., & Wang, Y. (2005). An agent-based framework for supply chain coordination in construction. Automation in Construction, 14(3), 413-430. https://doi.org/10.1016/j.autcon.2004.08.010
Xue, X. L., Shen, Q. P., O’Brien, W., & Ren, Z. M. (2009). Improving agent-based negotiation efficiency in construction supply chains: A relative entropy method. Automation in Construction, 18(7), 975-982. https://doi.org/10.1016/j.autcon.2009.05.002
Zayed, T., & Chang, L. (2002). Prototype model for build-operate-transfer risk assessment. Journal of Management in Engineering, 18(1), 7-16. https://doi.org/10.1061/(ASCE)0742-597X(2002)18:1(7)
Zhu, L., Zhao, X., & Chua, D. K. H. (2016). Agent-based debt terms’ bargaining model to improve negotiation inefficiency in PPP projects. Journal of Computing in Civil Engineering, 30(6). https://doi.org/10.1061/(ASCE)CP.1943-5487.0000571
Cleary, P. J. (2001). The negotiation handbook. M. E. Sharpe, Inc.
De Oliveira, E., Fonseca, J., & Steiger-Garcao, A. (1997). MACIV: A DAI based resource management system. Applied Artificial Intelligence, 11(6), 525-550. https://doi.org/10.1080/088395197118055
Du, J., & El-Gafy, M. (2012). Virtual organizational imitation for construction enterprises: Agent-based simulation framework for exploring human and organizational implications in construction management. Journal of Computing in Civil Engineering, 26(3), 282-297. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000122
El-Adaway, I., & Kandil, A. (2010). Multi-agent system for construction dispute resolution (MAS-COR). Journal of Construction Engineering and Management, 136(3), 303-315. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000144
EMMC. (2018). Energy market management company. Retrieved from https://www.epias.com.tr/en
Farshchian, M. M., Heravi, G., & AbouRizk, S. (2017). Optimizing the owner’s scenarios for budget allocation in a portfolio of projects using agent-based simulation. Journal of Construction Engineering and Management, 143(7). https://doi.org/10.1061/(ASCE)CO.1943-7862.0001315
González-Briones, A., Chamoso, P., De La Prieta, F., Demazeau, Y., & Corchado, J. M. (2018). Agreement technologies for energy optimization at home. Sensors, 18(5), 1633. https://doi.org/10.3390/s18051633
González-Briones, A., Prieto, J., De La Prieta, F., Herrera-Viedma, E., & Corchado, J. M. (2018). Energy optimization using a case-based reasoning strategy. Sensors, 18(3), 865. https://doi.org/10.3390/s18030865
Karakas, K. (2010). Development of a multi agent system for negotiation of cost overrun in international construction projects (MSc thesis). Middle East Technical University.
Karakas, K., Dikmen I., & Birgonul, M. T. (2013). Multiagent system to simulate risk-allocation and cost-sharing processes in construction projects. Journal of Computing in Civil Engineering, 27(3), 307-319. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000218
Kim, K., & Paulson, B. (2003). Agent-based compensatory negotiation methodology to facilitate distributed coordination of project schedule changes. Journal of Computing in Civil Engineering, 17(1), 10-18. https://doi.org/10.1061/(ASCE)0887-3801(2003)17:1(10)
Kraus, S., Wilkenfeld, J., & Zlotkin, G. (1995). Multiagent negotiation under time constraints. Artificial Intelligence, 75(2), 297-345. https://doi.org/10.1016/0004-3702(94)00021-R
Molinero, C., & Núñez, M. (2011). Planning of work schedules through the use of a hierarchical multi-agent system. Automation in Construction, 20(8), 1227-1241. https://doi.org/10.1016/j.autcon.2011.05.006
Mostafavi, A., Abraham, D., DeLaurentis, D., Sinfield, J., Kandil, A., & Queiroz, C. (2015). Agent-based simulation model for assessment of financing scenarios in highway transportation infrastructure systems. Journal of Computing in Civil Engineering, 30(2). https://doi.org/10.1061/(ASCE)CP.1943-5487.0000482
Ng, S. T., & Li, W. (2006). Parallel bargaining protocol for automated sourcing of construction suppliers. Automation in Construction, 15(3), 365-373. https://doi.org/10.1016/j.autcon.2005.07.004
Ozdoganm, I. D., & Birgonul, M. T. (2000). A decision support framework for project sponsors in the planning stage of build-operate-transfer (BOT) projects. Construction Management and Economics, 18(3), 343-353. https://doi.org/10.1080/014461900370708
Ren, Z., & Anumba, C. (2004). Multi-agent systems in construction – state of the art and prospects. Automation in Construction, 13(3), 421–434. https://doi.org/10.1016/j.autcon.2003.12.002
Shen, L. Y., & Wu, Y. Z. (2005). Risk concession model for build operate transfer contract projects. Journal of Construction Engineering Management, 131(2), 211-220. https://doi.org/10.1061/(ASCE)0733-9364(2005)131:2(211)
Shoham, Y. (1993). Agent-oriented programming. Artificial Intelligence, 60(1), 51-92. https://doi.org/10.1016/0004-3702(93)90034-9
Song, J., Song, D., & Zhang, D. (2015). Modeling the concession period and subsidy for BOT waste-to-energy incineration projects. Journal of Construction Engineering Management, 141(10). https://doi.org/10.1061/(ASCE)CO.1943-7862.0001005
Taghaddos, H., Hermann, U., AbouRizk, S., & Mohamed, Y. (2014). Simulation-based multiagent approach for scheduling modular construction. Journal of Computing in Civil Engineering, 28(2). https://doi.org/10.1061/(ASCE)CP.1943-5487.0000262
Tah, J. H. (2005). Towards an agent-based construction supply network. Automation in Construction, 14(3), 353-359. https://doi.org/10.1016/j.autcon.2004.08.003
Taylor, J. E., Levitt, R., & Villarroel, J. A. (2009). Simulating learning dynamics in project networks. Journal of Construction Engineering and Management, 135(10), 1009-1015. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000065
Turkish Contractors Association. (2017). Retrieved from https://www.tmb.org.tr/
Xue, X., Li, X., Shen, Q., & Wang, Y. (2005). An agent-based framework for supply chain coordination in construction. Automation in Construction, 14(3), 413-430. https://doi.org/10.1016/j.autcon.2004.08.010
Xue, X. L., Shen, Q. P., O’Brien, W., & Ren, Z. M. (2009). Improving agent-based negotiation efficiency in construction supply chains: A relative entropy method. Automation in Construction, 18(7), 975-982. https://doi.org/10.1016/j.autcon.2009.05.002
Zayed, T., & Chang, L. (2002). Prototype model for build-operate-transfer risk assessment. Journal of Management in Engineering, 18(1), 7-16. https://doi.org/10.1061/(ASCE)0742-597X(2002)18:1(7)
Zhu, L., Zhao, X., & Chua, D. K. H. (2016). Agent-based debt terms’ bargaining model to improve negotiation inefficiency in PPP projects. Journal of Computing in Civil Engineering, 30(6). https://doi.org/10.1061/(ASCE)CP.1943-5487.0000571