Capacity expansion under uncertainty in an oligopoly using indirect reinforcement-learning

被引:15
|
作者
Oliveira, Fernando S. [1 ]
Costa, Manuel L. G. [2 ]
机构
[1] Natl Univ Singapore, NUS Business Sch, Singapore, Singapore
[2] Univ Porto, Fac Econ, Ctr Econ & Finance, Porto, Portugal
关键词
OR in energy; Capacity expansion; Computational learning; Electricity markets; Oligopoly; INVESTMENT; MARKETS; OPTIONS; IMPACT; FLEXIBILITY; GENERATION; STRATEGIES; CONTRACTS; ECONOMICS; BEHAVIOR;
D O I
10.1016/j.ejor.2017.11.013
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
We model capacity expansion in oligopolistic markets, with endogenous prices, under uncertainty, considering multiple production technologies. As this environment is complex, capacity expansion is the outcome of a learning process by individual firms. We propose indirect reinforcement-learning to model the interaction between price determination and capacity decisions, in the context of an oligopoly game. We apply our model to the analysis of the Iberian electricity market, considering multiple technologies, focusing on how subsidies, the prices of CO2 emissions and gas affect the capacity expansion policies. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:1039 / 1050
页数:12
相关论文
共 50 条
  • [31] A Deep Reinforcement Learning Approach to Sensor Placement under Uncertainty
    Jabini, Amin
    Johnson, Erik A.
    IFAC PAPERSONLINE, 2022, 55 (27): : 178 - 183
  • [32] Inland port and waterway capacity expansion model under demand uncertainty
    Guo, Liquan
    Ng, Adolf K. Y.
    Long, Jiancheng
    Li, Xixi
    COMPUTERS & INDUSTRIAL ENGINEERING, 2024, 190
  • [33] ROBUST OPTIMIZATION FOR POWER-SYSTEMS CAPACITY EXPANSION UNDER UNCERTAINTY
    MALCOLM, SA
    ZENIOS, SA
    JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, 1994, 45 (09) : 1040 - 1049
  • [34] Grasp Approach Under Positional Uncertainty Using Compliant Tactile Sensing Modules and Reinforcement Learning
    Galaiya, Viral Rasik
    de Oliveira, Thiago Eustaquio Alves
    Jiang, Xianta
    da Fonseca, Vinicius Prado
    2024 IEEE CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING, CCECE 2024, 2024, : 424 - 428
  • [35] Autonomous Driving Systems for Decision-Making Under Uncertainty Using Deep Reinforcement Learning
    Haklidir, Mehmet
    Temeltas, Hakan
    2022 30TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU, 2022,
  • [36] Generation Capacity Expansion Planning under Demand Uncertainty Using Stochastic Mixed-Integer Programming
    Gandulfo, William
    Gil, Esteban
    Aravena, Ignacio
    2014 IEEE PES GENERAL MEETING - CONFERENCE & EXPOSITION, 2014,
  • [37] Reinforcement Learning Framework for Modeling Spatial Sequential Decisions under Uncertainty
    Truc Viet Le
    Liu, Siyuan
    Lau, Hoong Chuin
    AAMAS'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS & MULTIAGENT SYSTEMS, 2016, : 1449 - 1450
  • [38] Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs
    Chen, Fanfei
    Martin, John D.
    Huang, Yewei
    Wang, Jinkun
    Englot, Brendan
    2020 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2020, : 6140 - 6147
  • [39] Modelling and Predictive Monitoring of Business Processes under Uncertainty with Reinforcement Learning
    Bousdekis, Alexandros
    Kerasiotis, Athanasios
    Kotsias, Silvester
    Theodoropoulou, Georgia
    Miaoulis, Georgios
    Ghazanfarpour, Djamchid
    SENSORS, 2023, 23 (15)
  • [40] Reinforcement Learning for Robust Header Compression (ROHC) Under Model Uncertainty
    Jing, Shusen
    Zhang, Songyang
    Ding, Zhi
    IEEE Transactions on Machine Learning in Communications and Networking, 2024, 2 : 1033 - 1044