Competitive Bidding in Electricity Markets with Carbon Emission by Using Particle Swarm Optimization

被引:0
|
作者
Dwivedi, K. [1 ]
Kumar, Y. [1 ]
Agnihotri, G. [1 ]
机构
[1] Maulana Azad Natl Inst Technol Bhopal, Dept Elect Engn, Bhopal, MP, India
关键词
Bidding; Electricity market; PSO; Carbon Emission; UNIT-COMMITMENT;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this oligopolistic electricity market, the maximization of profit for generators is mainly dealt with the used bidding strategies. For selling of electricity with maximum profit power companies required suitable bidding models that includes power operating constraints and price uncertainty within the market. In this paper, we present particle swarm optimization (PSO) algorithms to determine bid prices and quantities under the rules of a competitive power market with using emission as a constraint. The Objective of this paper is the potential impacts of emissions trading on power industries and electricity markets. Increasing environmental issues and regulations have forced Generation companies (GENCOs) to review the policies being used for long term planning. Constraints on CO2 emission are restricted the GENCOs to adopt the green technologies.
引用
收藏
页码:1078 / 1082
页数:5
相关论文
共 50 条
  • [31] Adapting particle swarm optimization to stock markets
    Nenortaite, J
    Simutis, R
    5TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, PROCEEDINGS, 2005, : 520 - 525
  • [32] Dynamic Particle Swarm Optimization for Financial Markets
    Atiah, Frederick Ditliac
    Helbig, Marde
    2018 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI), 2018, : 2337 - 2344
  • [33] Optimal Strategic Bidding and Financial Risk Assessment in Restructured Electricity Market using Particle Swarm Optimisation
    Sahoo, Ahhilipsa
    Mahapatra, Arnrit Anand
    2016 INTERNATIONAL CONFERENCE ON RECENT ADVANCES AND INNOVATIONS IN ENGINEERING (ICRAIE), 2016,
  • [34] Bacteria Foraging Optimization Algorithm based Strategic Bidding in Electricity Markets
    Jain, A. K.
    Srivastava, S. C.
    Singh, S. N.
    Srivastava, L.
    2014 EIGHTEENTH NATIONAL POWER SYSTEMS CONFERENCE (NPSC), 2014,
  • [35] Bidding strategy of IPP in Competitive Electricity market using FACLPSO
    Mallick, Ranjan Kumar
    Agrawal, Ramachandra
    Hota, Prakash Kumar
    2016 INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, AND OPTIMIZATION TECHNIQUES (ICEEOT), 2016, : 1447 - 1452
  • [36] Intelligent strategic bidding in competitive electricity markets using multi-agent simulation and deep reinforcement learning
    Wu, Jiahui
    Wang, Jidong
    Kong, Xiangyu
    APPLIED SOFT COMPUTING, 2024, 152
  • [37] Development of bidding strategies in electricity markets using possibility theory
    Li, Y
    Wen, FS
    Wu, FF
    Ni, YX
    Qiu, JJ
    POWERCON 2002: INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY, VOLS 1-4, PROCEEDINGS, 2002, : 182 - 187
  • [38] Optimal bidding strategies in electricity markets using reinforcement learning
    Wu, QH
    Guo, J
    Turner, DR
    Wu, ZX
    Zhou, XX
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2004, 32 (02) : 175 - 192
  • [39] Strategic bidding in network constrained electricity markets using FAPSO
    Bajpai, Prabodh
    Singh, Sri Niwas
    INTERNATIONAL JOURNAL OF ENERGY SECTOR MANAGEMENT, 2008, 2 (02) : 274 - 296
  • [40] Portfolio Optimization for Electricity Market Participation with Particle Swarm
    Faia, Ricardo
    Pinto, Tiago
    Vale, Zita
    Solteiro Pires, E. J.
    2015 26TH INTERNATIONAL WORKSHOP ON DATABASE AND EXPERT SYSTEMS APPLICATIONS (DEXA), 2015, : 62 - 67