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Multiobjective environmental/techno-economic approach for strategic bidding in energy markets


  • Vahidinasab, V.
  • Jadid, S.


This paper describes a method for developing optimal bidding strategy based on a bilevel optimization, considering suppliers' emission of pollutants. The proposed methodology employs supply function equilibrium (SFE) model to represent the strategic behavior of each supplier. Locational marginal pricing mechanism is also assumed for settling the market and calculating the supplier profit. It is modeled as a bilevel optimization problem in which the upper-level subproblem maximizes individual supplier payoff and the lower-level subproblem solves the Independent System Operator's market clearing problem. In this paper, the multiobjective optimal power flow is used to solve market clearing mechanism with supplier emission of pollutants, as extra objectives, subject to the transmission limits and supplier physical constraints. To illustrate the proposed approach under different conditions an IEEE 30-bus test system together with a number of case studies are used.

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  • Vahidinasab, V. & Jadid, S., 2009. "Multiobjective environmental/techno-economic approach for strategic bidding in energy markets," Applied Energy, Elsevier, vol. 86(4), pages 496-504, April.
  • Handle: RePEc:eee:appene:v:86:y:2009:i:4:p:496-504

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    References listed on IDEAS

    1. Klemperer, Paul D & Meyer, Margaret A, 1989. "Supply Function Equilibria in Oligopoly under Uncertainty," Econometrica, Econometric Society, vol. 57(6), pages 1243-1277, November.
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    Cited by:

    1. Chang, Hsueh-Hsien & Yang, Hong-Tzer, 2009. "Applying a non-intrusive energy-management system to economic dispatch for a cogeneration system and power utility," Applied Energy, Elsevier, vol. 86(11), pages 2335-2343, November.
    2. Zou, Dexuan & Li, Steven & Wang, Gai-Ge & Li, Zongyan & Ouyang, Haibin, 2016. "An improved differential evolution algorithm for the economic load dispatch problems with or without valve-point effects," Applied Energy, Elsevier, vol. 181(C), pages 375-390.
    3. Shayegan-Rad, Ali & Badri, Ali & Zangeneh, Ali, 2017. "Day-ahead scheduling of virtual power plant in joint energy and regulation reserve markets under uncertainties," Energy, Elsevier, vol. 121(C), pages 114-125.
    4. Zhang, Ni & Yan, Yu & Su, Wencong, 2015. "A game-theoretic economic operation of residential distribution system with high participation of distributed electricity prosumers," Applied Energy, Elsevier, vol. 154(C), pages 471-479.
    5. Ghadikolaei, Hadi Moghimi & Tajik, Elham & Aghaei, Jamshid & Charwand, Mansour, 2012. "Integrated day-ahead and hour-ahead operation model of discos in retail electricity markets considering DGs and CO2 emission penalty cost," Applied Energy, Elsevier, vol. 95(C), pages 174-185.
    6. Kuo, Cheng-Chien, 2010. "Wind energy dispatch considering environmental and economic factors," Renewable Energy, Elsevier, vol. 35(10), pages 2217-2227.
    7. Li, Gong & Shi, Jing & Qu, Xiuli, 2011. "Modeling methods for GenCo bidding strategy optimization in the liberalized electricity spot market–A state-of-the-art review," Energy, Elsevier, vol. 36(8), pages 4686-4700.
    8. Harris, A.R. & Rogers, Michelle Marinich & Miller, Carol J. & McElmurry, Shawn P. & Wang, Caisheng, 2015. "Residential emissions reductions through variable timing of electricity consumption," Applied Energy, Elsevier, vol. 158(C), pages 484-489.
    9. Esmaili, Masoud & Shayanfar, Heidar Ali & Amjady, Nima, 2010. "Congestion management enhancing transient stability of power systems," Applied Energy, Elsevier, vol. 87(3), pages 971-981, March.
    10. Tolis, Athanasios I. & Rentizelas, Athanasios A., 2011. "An impact assessment of electricity and emission allowances pricing in optimised expansion planning of power sector portfolios," Applied Energy, Elsevier, vol. 88(11), pages 3791-3806.
    11. Roche, Robin & Idoumghar, Lhassane & Suryanarayanan, Siddharth & Daggag, Mounir & Solacolu, Christian-Anghel & Miraoui, Abdellatif, 2013. "A flexible and efficient multi-agent gas turbine power plant energy management system with economic and environmental constraints," Applied Energy, Elsevier, vol. 101(C), pages 644-654.
    12. Vithayasrichareon, Peerapat & MacGill, Iain F., 2014. "Incorporating short-term operational plant constraints into assessments of future electricity generation portfolios," Applied Energy, Elsevier, vol. 128(C), pages 144-155.
    13. Niknam, Taher, 2010. "A new fuzzy adaptive hybrid particle swarm optimization algorithm for non-linear, non-smooth and non-convex economic dispatch problem," Applied Energy, Elsevier, vol. 87(1), pages 327-339, January.
    14. Wei, Wei & Liu, Feng & Wang, Jianhui & Chen, Laijun & Mei, Shengwei & Yuan, Tiejiang, 2016. "Robust environmental-economic dispatch incorporating wind power generation and carbon capture plants," Applied Energy, Elsevier, vol. 183(C), pages 674-684.
    15. Behrangrad, Mahdi & Sugihara, Hideharu & Funaki, Tsuyoshi, 2011. "Effect of optimal spinning reserve requirement on system pollution emission considering reserve supplying demand response in the electricity market," Applied Energy, Elsevier, vol. 88(7), pages 2548-2558, July.
    16. Hosseini, Seyyed Ahmad & Amjady, Nima & Shafie-khah, Miadreza & Catalão, João P.S., 2016. "A new multi-objective solution approach to solve transmission congestion management problem of energy markets," Applied Energy, Elsevier, vol. 165(C), pages 462-471.
    17. Esmaili, Masoud & Amjady, Nima & Shayanfar, Heidar Ali, 2011. "Multi-objective congestion management by modified augmented [epsilon]-constraint method," Applied Energy, Elsevier, vol. 88(3), pages 755-766, March.
    18. Chen, Yizhong & He, Li & Li, Jing & Cheng, Xi & Lu, Hongwei, 2016. "An inexact bi-level simulation–optimization model for conjunctive regional renewable energy planning and air pollution control for electric power generation systems," Applied Energy, Elsevier, vol. 183(C), pages 969-983.
    19. Li, Y.F. & Li, Y.P. & Huang, G.H. & Chen, X., 2010. "Energy and environmental systems planning under uncertainty--An inexact fuzzy-stochastic programming approach," Applied Energy, Elsevier, vol. 87(10), pages 3189-3211, October.


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