Multi-objective congestion management by modified augmented e-constraint method Masoud Esmaili a,⇑ , Nima Amjady b , Heidar Ali Shayanfar a a Centre of Excellence for Power System Automation and Operation, Iran University of Science and Technology, Tehran, Iran b Department of Electrical Engineering, Semnan University, Semnan, Iran article info Article history: Received 6 April 2010 Received in revised form 29 August 2010 Accepted 19 September 2010 Available online 13 October 2010 Keywords: Multi-objective congestion management Augmented e-constraint technique Efficient solution Solution preference Weighting method abstract Congestion management is a vital part of power system operations in recent deregulated electricity mar- kets. However, after relieving congestion, power systems may be operated with a reduced voltage or tran- sient stability margin because of hitting security limits or increasing the contribution of risky participants. Therefore, power system stability margins should be considered within the congestion man- agement framework. The multi-objective congestion management provides not only more security but also more flexibility than single-objective methods. In this paper, a multi-objective congestion manage- ment framework is presented while simultaneously optimizing the competing objective functions of con- gestion management cost, voltage security, and dynamic security. The proposed multi-objective framework, called modified augmented e-constraint method, is based on the augmented e-constraint technique hybridized by the weighting method. The proposed framework generates candidate solutions for the multi-objective problem including only efficient Pareto surface enhancing the competitiveness and economic effectiveness of the power market. Besides, the relative importance of the objective functions is explicitly modeled in the proposed framework. Results of testing the proposed multi- objective congestion management method on the New-England test system are presented and compared with those of the previous single objective and multi-objective techniques in detail. These comparisons confirm the efficiency of the developed method. Crown Copyright Ó 2010 Published by Elsevier Ltd. All rights reserved. 1. Introduction Nowadays, transmission networks in competitive electricity markets are heavily loaded up to near their stability limits to achieve a more economical operating point. In a power market, all participants try to maximize their profit by employing their own bidding strategies [1]. However, the electric power that can be transmitted between two locations on a transmission network is limited by several transfer limits such as thermal limits, voltage limits, and stability limits. When such a limit is reached, the sys- tem is said to be congested [2]. Ensuring that the power system operates within its limits is vital to maintain power system intact. Congestion management, which is to control the transmission sys- tem so that transfer limits are observed, is perhaps the fundamen- tal transmission management problem [3]. The methods generally adopted to manage congestion include rescheduling generator out- puts, supplying reactive power support, or physically curtail trans- actions or involuntary load shedding. System operators generally prefer the first option to supply demand as much as possible by existing mechanisms; if not possible, they use load shedding as the last resort to manage congestion and retain system security. Generators participate in the congestion management market by bidding for up and down their production. Also, demands can bid as demand side bidding (DSB) [4] for up and down their loads. While choosing generators or demands for re-dispatching, the least cost option is picked up to minimize total rescheduling cost or to maximize total social benefit. Recent major blackouts in North America and Europe [5] have rekindled the security requirements of power systems, an impor- tant matter that has been regretfully neglected in favor of more financial concerns in recent years. This renewed interest calls also for research on congestion management with more secure solu- tions. Conventional congestion management may not provide suf- ficient security level to find a more economical solution [6]. Since congestion management is a mathematical optimization problem satisfying a set of constraints, some variables may hit their limits. For instance, a few branches can be fully loaded; or, some voltages can be set at their lower limit; or, the generation of critical gener- ators can be increased. Although there is no violation, the system may be vulnerable against disturbances. In other words, the stabil- ity margin of the network may be low after relieving congestion. 0306-2619/$ - see front matter Crown Copyright Ó 2010 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.apenergy.2010.09.014 ⇑ Corresponding author. Tel.: +98 21 88891886; fax: +98 21 88731293. E-mail address: msdesmaili@gmail.com (M. Esmaili). Applied Energy 88 (2011) 755–766 Contents lists available at ScienceDirect Applied Energy journal homepage: www.elsevier.com/locate/apenergy