Technical Papers
Jun 19, 2014

Influence of Bidding Mechanism and Spot Market Characteristics on Market Power of a Large Genco Using Hybrid DE/BBO

Publication: Journal of Energy Engineering
Volume 141, Issue 3

Abstract

Generation company (Genco) bidding in an electricity market (EM) aims to maximize its profit under uncertain market characteristics and a regulated bidding mechanism. This paper addresses the strategic bidding for a large price maker Genco and empirically investigates the effect of a step-wise multiple segment bidding mechanism and EM characteristics, such as demand and rivals’ behavior, on its market power (MP) potential and efficiency. The methodology of using novel hybrid differential evolution with biogeography-based optimization (DE/BBO), employing the sinusoidal migration model, is proposed for strategic bidding. DE exploration with BBO exploitation enhances global optimization. Uncertain rival behavior is modeled as normal distribution and simulated by the Monte Carlo technique. The proposed approach is validated for large Genco bidding in spot EM, under changing market characteristics and bidding segments. The implicit MP potential and efficiency of Genco for corresponding strategies is assessed using the criteria of expected profit, risk of profit variance, and failure rate of Genco. This assessment discovers an underlying correlation between the market characteristics and bidding segments, which would aid Genco in optimizing its bidding strategy and market performance.

Get full access to this article

View all available purchase options and get full access to this article.

References

Attaviriyanupap, K. P., Tanaka, H. E., and Hasegawa, J. (2005). “New bidding strategy formulation for day-ahead energy and reserve markets based on evolutionary programming.” Int. J. Electr. Power Energy Syst., 27(3), 157–167.
Azadani, N. E., Hosseinian, S. H., and Moradzadeh, B. (2010). “Generation and reserve dispatch in a competitive market using constrained particle swarm optimization.” Int. J. Electr. Power Energy Syst., 32(1), 79–86.
Azadeh, A., Ghaderi, S. F., Pourvalikhan Nokhandan, B., and Sheikhalishahi, M. (2012). “A new GA-approach for optimizing bidding strategy from viewpoint of profit maximization of a Genco.” Expert Syst. Appl., 39(1), 1565–1574.
Bajpai, P., and Singh, S. N. (2007). “Fuzzy adaptive particle swarm optimization for bidding strategy in uniform price spot market.” IEEE Trans. Power Syst., 22(4), 2152–2160.
Bhattacharya, A., and Chattopadhyay, P. K. (2010a). “Biogeography based optimization for different economic load dispatch problems.” IEEE Trans. Power Syst., 25(2), 1064–1077.
Bhattacharya, A., and Chattopadhyay, P. K. (2010b). “Hybrid differential evolution with biogeography based optimization for solution of economic load dispatch.” IEEE Trans. Power Syst., 25(4), 1955–1964.
Careri, F., Genesi, C., Marannino, P., Montagna, M., Rossi, S., and Siviero, I. (2010). “Strategic bidding in a day ahead market by coevolutionary genetic algorithms.” Power and Energy Society General Meeting, IEEE, Minneapolis, MN, 1–8.
David, A. K., and Wen, F. (2000). “Strategic bidding in competitive electricity market: A literature survey.” Power and Energy Society General Meeting, IEEE, Seattle, WA, 2168–2173.
Gong, W., Cai, Z., and Ling, C. X. (2010). “DE/BBO: A hybrid differential evolution with biogeography based optimization for global numerical optimization.” Soft Comput., 15(4), 645–665.
Jain, P., Agarwal, A., Gupta, N., Paliwal, U., Bhakar, R., and Singh, S. N. (2012a). “Genco’s profit maximization based on biogeography-based optimization.” National Power Syst. Conf.-2012, IIT BHU, Varanasi, India.
Jain, P., Agarwal, A., Gupta, N., Sharma, R., Paliwal, U., and Bhakar, R. (2012b). “Profit maximization of a generation company based on biogeography based optimization.” Power and Energy Society General Meeting, IEEE, San Diego, CA, 1–6.
Li, C., Svoboda, A. J., Guan, X., and Singh, H. (1999). “Revenue adequate bidding strategies in competitive electricity markets.” IEEE Trans. Power Syst., 14(2), 492–497.
Ma, H. (2010). “An analysis of the equilibrium of migration models for biogeography-based optimization.” Inf. Sci., 180(18), 3444–3464.
Ma, L., Wen, F. S., and David, A. K. (2002). “A preliminary study on strategic bidding in electricity market with step-wise bidding protocol.” Power and Energy Society Transmission and Distribution Conf. and Exhib.: Asia Pacific, Vol. 3, IEEE, 1960–1965.
Pindoriya, N. M., and Singh, S. N. (2009). “Day ahead self scheduling of thermal generator in competitive electricity market using hybrid PSO.” 15th Int. Conf. on Intelligent System Applications to Power Systems, IEEE, 1–6.
Richter, C. W., Jr., and Sheble, G. B. (1998). “Genetic algorithm evolution of utility bidding strategies for the competitive market place.” IEEE Trans. Power Syst., 13(1), 256–261.
Richter, C. W., Jr., Sheble, G. B., and Ashlock, D. (1999). “Comprehensive bidding strategies with genetic programming/finite state automata.” IEEE Trans. Power Syst., 14(4), 1207–1212.
Shahidehpour, M., Yamin, H., and Li, Z. (2002). Market operations in electric power systems: Forecasting, scheduling and risk management, Wiley, New York.
Simon, D. (2008). “Biogeography based optimization.” IEEE Trans. Evol. Comput., 12(6), 702–713.
Singh, S. N., and Erlich, I. (2007). “Particle swarm based optimal estimation of block incremental cost curve.” 14th Int. Conf. on Intelligent Systems Applications to Power Systems, IEEE, 1–7.
Soleymani, S. (2011). “Bidding strategy of generation companies using PSO combined with simulated annealing method in the pay as bid markets.” Int. J. Electr. Power Energy Syst., 33(7), 1272–1278.
Song, H., Liu, C. C., Lawarree, J., and Dahlgren, R. W. (2000). “Optimal electricity supply bidding by Markov decision process.” IEEE Trans. Power Syst., 15(2), 618–624.
Stoft, S. (2002). Power system economics: Designing markets for electricity, Wiley, New York.
Storn, R., and Price, K. (2008). “Home differential evolution, 2008.” 〈http://www.ICSI.Berkely.edu/∼storn/code.html〉 (Jul. 2013).
Wang, L., Yu, C. W., and Wen, F. (2008). “The impacts of different bidding segment numbers on bidding strategies of generation companies.” Electr. Power Syst. Res., 78(3), 458–463.
Wen, F. S., and David, A. K. (2001a). “Optimal bidding strategies and modelling of imperfect information among competitive generators.” IEEE Trans. Power Syst., 16(1), 15–21.
Wen, F. S., and David, A. K. (2001b). “Strategic bidding for electricity supply in a day ahead energy market.” Electr. Power Syst. Res., 59(3), 197–206.
Yucekaya, A. D., Valenzuelaa, J., and Dozier, G. (2009). “Strategic bidding in electricity markets using particle swarm optimization.” Electr. Power Syst. Res., 79(2), 335–345.

Information & Authors

Information

Published In

Go to Journal of Energy Engineering
Journal of Energy Engineering
Volume 141Issue 3September 2015

History

Received: Sep 14, 2013
Accepted: Apr 18, 2014
Published online: Jun 19, 2014
Discussion open until: Nov 19, 2014
Published in print: Sep 1, 2015

Permissions

Request permissions for this article.

Authors

Affiliations

Prerna Jain [email protected]
Assistant Professor, Dept. of Electrical Engineering, Malaviya National Institute of Technology Jaipur, JLN Marg, Jaipur, Rajasthan 302015, India (corresponding author). E-mail: [email protected]; [email protected]
Rohit Bhakar [email protected]
Prize Fellow, Dept. of Electronic and Electrical Engineering, Univ. of Bath, Bath BA2 7AY, U.K. E-mail: [email protected]
S. N. Singh [email protected]
Professor, Dept. of Electrical Engineering, IIT Kanpur, Kanpur, Uttar Pradesh 208016, India. E-mail: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share