Contingency based congestion management and cost minimization using bee colony optimization technique

Rahim, Maisarah Abd; Musirin, Ismail; Abidin, Izham Zainal; Othman, Muhammad Murtadha

doi:10.1109/pecon.2010.5697705

Cited by 10 publications

(2 citation statements)

References 16 publications

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“…The constructive BCO constructs a population of solutions from scratch in a number of constructive stages consisting of a number of constructive moves in a single iteration; while improvement BCO improves a given population of complete solutions in a number of improvement stages consisting of a number of improvement moves in a single iteration. Since its inception, BCOc has been successfully applied to solve many complex optimization problems in science and engineering (Teodorović and Dell'Orco, 2005;Teodorovic et al, 2006;Wong et al, 2008a;Wong et al, 2008b;Wong et al, 2009;Wong et al, 2010;Chaiyatham et al, 2009;Low et al, 2009;Zeng et al, 2010;Rahim et al, 2010;Arabnejad et al, 2011;Huang and Lin, 2011;Chaiyatham and Ngamroo, 2012;Davidović et al, 2012;Todorovic and Petrovic, 2013;Teodorović et al, 2013;Amador-Angulo and Castillo, 2014;Alzaqebah and Abdullah, 2015). Davidović et al (2011) observed that BCOc did not yield suitable solutions for benchmarked problems and investigated BCOi to obtain better solutions.…”

Section: Improvement Based Bcomentioning

confidence: 99%

Materialized View Selection using Improvement based Bee Colony Optimization

Kumar

Arun

2015

International Journal of Software Science and Computational Intelligence

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In the present information age, data and information are vital not just for the survival of any corporate entity, but also to provide it with an edge over its competitors. Data warehouses have become the foundational databases of almost every corporation. However, extracting new information from these data warehouses takes hours, and even days, which is practically unacceptable. Materialized views have been popularly used to facilitate fast information extraction. However, the selection of appropriate views, which significantly accelerate information synthesis is an NP-Complete problem. The aim of this paper is to select near optimal sets of views for materialization using the improvement bee colony optimization algorithm. The experimental results indicate that the improvement bee colony optimization algorithm performs better than the constructive bee colony optimization algorithm and the fundamental view selection algorithm HRUA. The views thus selected would significantly minimize the response time of analytical queries, when materialized, resulting in efficient strategic decision making.

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Section: Improvement Based Bcomentioning

confidence: 99%

Materialized View Selection using Improvement based Bee Colony Optimization

Kumar

Arun

2015

International Journal of Software Science and Computational Intelligence

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“…When a contingency occurs in a branch, the remaining branches in the network can experience greater loading and be at a higher risk of network congestion [4][5][6][7]. While traditional security analysis uses the N À 1 criterion this does not account for variation in output of wind leading to post contingency congestion and curtailment of wind.…”

Section: Introductionmentioning

confidence: 99%

Assessment of post-contingency congestion risk of wind power with asset dynamic ratings

Banerjee

Jayaweera

Islam

2015

International Journal of Electrical Power & Energy Systems

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a b s t r a c tLarge scale integration of wind power can be deterred by congestion following an outage that results in constrained network capacity. Post outage congestion can be mitigated by the application of event control strategies; however they may not always benefit large wind farms. This paper investigates this problem in detail and proposes an advanced mathematical framework to model network congestion as functions of stochastic limits of network assets to capture post contingency risk of network congestion resulting through the constrained network capacity that limits high penetration of wind. The benefit of this approach is that it can limit the generation to be curtailed or re-dispatched by dynamically enhancing the network latent capacity in the event of outages or as per the need. The uniqueness of the proposed mathematical model is that it converts conventional thermal constraints to dynamic constraints by using a discretized stochastic penalty function with quadratic approximation of constraint relaxation penalty. The case study results with large and small network models suggest that the following an outage, wind utilization under dynamic line rating can be increased considerably if the wind power producers maintain around a 15% margin of operation. IntroductionNetwork congestion is an undesirable result of insufficient capacity being available on a network to transport electricity from generation to loads. It leads to highly variable locational marginal prices (LMP) at nodes usually with high prices at load points which are affected by congestion compared to those which are not. A number of publications have used LMP as an indicator of network congestion [1][2][3]. In systems with large amount of wind power, network congestion hinders effective integration and utilization of wind as extra wind generated has to be curtailed thereby leading to uncertainty in revenue for wind power producers. The dynamic nature of wind results in large variations in power output over a short period of time, which makes effective utilization of wind an even bigger challenge in congested networks.Network congestion has a greater impact in networks under contingency. When a contingency occurs in a branch, the remaining branches in the network can experience greater loading and be at a higher risk of network congestion [4][5][6][7]. While traditional security analysis uses the N À 1 criterion this does not account for variation in output of wind leading to post contingency congestion and curtailment of wind. Therefore, even when a network seems to have no congestion and utilizes wind effectively, there is a high risk that any contingency will drastically change the situation.A number of sources agree that the true thermal capacity of a transmission line is considerably higher than the rated values [8-12] since ratings are calculated under the worst case weather assumption although such operating conditions occurs rarely in practice. It is possible to exploit this property by using dynamic line ratings (DLR) which model ...

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