Open Access
Issue |
RAIRO-Oper. Res.
Volume 55, Number 3, May-June 2021
|
|
---|---|---|
Page(s) | 1767 - 1785 | |
DOI | https://doi.org/10.1051/ro/2021065 | |
Published online | 23 June 2021 |
- A. Akbari-Dibavar, B. Mohammadi-Ivatloo and K. Zare, Electricity market pricing: uniform pricing vs. pay-as-bid pricing. Electricity Markets (2020) 19–35. [Google Scholar]
- L.M. Ausubel, P. Cramton, M. Pycia, M. Rostek and M. Weretka, Demand reduction and inefficiency in multi-unit auctions. Rev. Econ. Stud. 81 (2014) 1366–1400. [Google Scholar]
- B. Biaisa and A.M. Faugeron-Crouzetb, I.P.O. Auctions, English, Dutch, …. French, and Internet. J. Financ. Intermed. 11 (2002) 9–36. [Google Scholar]
- C. Böhringer and A. Lange, On the design of optimal grandfathering schemes for emission allowances. Eur. Econ. Rev. 49 (2005) 2041–2055. [Google Scholar]
- E. Bonabeau, Agent-based modeling: Methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. U.S.A. 99 (2002) 7280–7287. [Google Scholar]
- K. Cao, X. Xu, Q. Wu and Q. Zhang, Optimal production and carbon emission reduction level under cap-and-trade and low carbon subsidy policies. J. Clean. Prod. 167 (2017) 505–513. [Google Scholar]
- E. Cardona Santos, H. Storm and S. Rasch, The cost-effectiveness of conservation auctions in the presence of asset specificity: an agent-based model. Land Use Policy 102 (2021) 104907. [Google Scholar]
- Q. Chai, Z. Xiao, K. Hung Lai and G. Zhou, Can carbon cap and trade mechanism be beneficial for remanufacturing? Int. J. Prod. Econ. 203 (2018) 311–321. [Google Scholar]
- F. Chr Matthes, K. Neuhoff, Auctioning in the European Union Emissions Trading Scheme. ko-Institut & University of Cambridge (2007). [Google Scholar]
- S. Clò, Grandfathering, auctioning and carbon leakage: assessing the inconsistencies of the new ETS Directive. Energy Policy 38 (2010) 2420–2430. [Google Scholar]
- R.-G. Cong and Y.-M. Wei, Auction design for the allocation of carbon emission allowances: Uniform or discriminatory price? Int. J. Energy Environ. 1 (2010) 533–546. [Google Scholar]
- R.G. Cong and Y.M. Wei, Experimental comparison of impact of auction format on carbon allowance market. Renew. Sustain. Energy Rev. 16 (2012) 4148–4156. [Google Scholar]
- P. Cramton and S. Kerr, Tradable carbon permits auctions: How and why to auction not grandfather. Energy Policy 30 (2002) 333–345. [Google Scholar]
- N.C. Dormady, Carbon auctions, energy markets & market power: an experimental analysis. Energy Econ. 44 (2014) 468–482. [Google Scholar]
- S. Du, W. Tang and M. Song, Low-carbon production with low-carbon premium in cap-and-trade regulation. J. Clean. Prod. 134 (2016) 652–662. [Google Scholar]
- T. Hattori and S. Takahashi, Discriminatory Versus Uniform Auction: Evidence From JGB Market. Available SSRN (2020). [Google Scholar]
- J. Hu and M.P. Wellman, Multiagent reinforcement learning theoretical frame work and an algorithm. ICML 98 (1998) 242–250. [Google Scholar]
- J. Hu and M.P. Wellman, Nash Q-learning for general-sum stochastic games. J. Mach. Learn. Res. 4 (2004) 1039–1069. [Google Scholar]
- M.X. Jiang, D.X. Yang, Z.Y. Chen and P.Y. Nie, Market power in auction and efficiency in emission permits allocation. J. Environ. Manage. 183 (2016) 576–584. [PubMed] [Google Scholar]
- D. Matthäus, Designing effective auctions for renewable energy support. Energy Policy 142 (2020) 11462. [Google Scholar]
- S. Min Yu, Y. Fan, L. Zhu and W. Eichhammer, Modeling the emission trading scheme from an agent-based perspective: system dynamics emerging from firms’ coordination among abatement options. Eur. J. Oper. Res. 286 (2020) 1113–1128. [Google Scholar]
- M.J. Poursalimi Jaghargh and H.R. Mashhadi, An analytical approach to estimate structural and behavioral impact of renewable energy power plants on LMP. Renew. Energy 163 (2021) 1012–1022. [Google Scholar]
- M. Rahimiyan and H. Rajabi Mashhadi, Supplier’s optimal bidding strategy in electricity pay-as-bid auction: comparison of the Q-learning and a model-based approach. Electr. Power Syst. Res. 78 (2008) 165–175. [Google Scholar]
- M. Rahimiyan and H. Rajabi Mashhadi, An adaptive Q-Learning algorithm developed foragent-based computational modeling of electricity market. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40 (2010) 547–556. [Google Scholar]
- S.M. Sadr, H. Rajabi Mashhadi and M.E. Hajiabadi, Evaluation of price-sensitive loads’ impacts on LMP and market power using LMP decomposition. Iran. J. Electr. Electron. Eng. 12 (2016) 154–167. [Google Scholar]
- T.W. Sandholm and R.H. Crites, On multiagent Q-learning in a semi-competitive domain. In: International Joint Conference on Artificial Intelligence (1995) 191–205. [Google Scholar]
- W. Sugiyarto, An Analysis of the Performance of the Indonesian Treasuries Market. Queensland University of Technology (2020). [Google Scholar]
- L. Tang, J. Wu, L. Yu and Q. Bao, Carbon allowance auction design of China’s emissions trading scheme: a multi-agent-based approach. Energy Policy 102 (2017) 30–40. [Google Scholar]
- G. Tesauro and J.O. Kephart, Pricing in agent economies using multi-agent Q-learning. Auton. Agent. Multi. Agent. Syst. 5 (2002) 289–304. [Google Scholar]
- J.J.D. Wang and J.F. Zender, Auctioning divisible goods. Econ. Theory 19 (2002) 673–705. [Google Scholar]
- U. Wilensky and W. Rand, An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo. Mit Press (2015). [Google Scholar]
- G. Xiong, T. Hashiyama and S. Okuma, An electricity supplier bidding strategy through Q-Learning. IEEE Power Eng. Soc. Summer Meeting 3 (2002) 1516–1521. [Google Scholar]
- G. Xiong, S. Okuma and H. Fujita, Multi-agent based experiments on uniform price and pay-as-bid electricity auction markets. In: 2004 IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies. Proceedings (2004) 72–76. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.