Static Economic Dispatch Incorporating UPFC Using Artificial Bee Colony Algorithm
Static economic dispatch is a real-time problem in power system network. Here, the real power output of each generating unit is calculated with respect to forecasted load demand over a time horizon while satisfying the system constraints. This paper expla
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Abstract Static economic dispatch is a real-time problem in power system network. Here, the real power output of each generating unit is calculated with respect to forecasted load demand over a time horizon while satisfying the system constraints. This paper explains the impact of unified power flow controller (UPFC) in static economic dispatch (SED) using artificial bee colony (ABC) algorithm. UPFC is a converter (shunt and series)-based FACTS device, which can control all the parameters in a transmission line individually or simultaneously. ABC algorithm that imitates the foraging behavior of honey bees is used as an optimization tool. The impact of UPFC in reducing the generation cost, loss, and improving voltage profile, power flow are demonstrated. The studies are carried out in an IEEE 118 bus test system and a practical South Indian 86 bus utility.
Keywords Economic dispatch Artificial bee colony troller Voltage source converter
Unified power flow con-
1 Introduction Economic dispatch of generating units is one of the significant functions of contemporary energy management system. The static economic dispatch problem (SED) can be formulated as a constrained optimization problem which reduces the
S. Sreejith (&) Velamuri Suresh P. Ponnambalam School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India e-mail: [email protected] Velamuri Suresh e-mail: [email protected] P. Ponnambalam e-mail: [email protected] © Springer Science+Business Media Singapore 2016 M. Pant et al. (eds.), Proceedings of Fifth International Conference on Soft Computing for Problem Solving, Advances in Intelligent Systems and Computing 436, DOI 10.1007/978-981-10-0448-3_63
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total generation cost within committed units satisfying system equality and inequality constraints. In conventional methods the cost curves of power generators are usually assumed to be quadratic and monotonically increasing functions. A variety of nonlinearities are present in the cost curves of modern generating units due to valve point loading. This results in inaccurate assumptions and the results in approximate solutions. On other hand, the evolutionary methods such as differential evolution (DE)¸ particle swarm optimization (PSO), genetic algorithms (GA), and bacterial foraging (BF) are free from these convexity assumptions and perform better since they have excellent parallel search capability. Therefore, the above methods are particularly popular for solving nonlinear and nonconvex optimization problems. In dynamic programming [1] the shape of cost curves is unrestricted, but it consumes more time and suffers from dimensional issues. Optimization methods like dynamic programming [1], genetic algorithm [2–4], evolutionary programming [3], and particle swarm optimization [5–7] solve nonconvex optimization problems in a faster rate and efficient manner. ABC algorithm is used efficiently [8] for solving constrained optimization problems [9], so ABC algorithm is used as an optim
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