# Modified Invasive Weed Optimization with Dual Mutation Technique for Dynamic Economic Dispatch

**Topics:**Optimization, Electric power, Electricity generation

**Pages:**17 (4987 words)

**Published:**August 23, 2013

R. Sharma, Member, IEEE , Niranjan Nayak, Member, IEEE ,Krishnanand K. R. and P. K. Rout, Member, IEEE

Abstract-- Dynamic economic dispatch (DED) is one of the main functions of power system operation and control. It determines the optimal operation of units with predicted load demands over a certain period of time with an objective to minimize total production cost while the system is operating within its ramp rate limits. This paper presents DED based on Invasive Weed Optimization (IWO) technique for the determination of the global or near global optimum dispatch solution. In the present case, load balance constraints, operating limits, valve-point loading, ramp constraints, and network losses using loss coefficients are incorporated. Numerical results for a sample test system (10- unit) have been presented to demonstrate the performance and applicability of the proposed method. Index Terms-- dynamic economic dispatch, invasive weed optimization algorithm, non-smooth cost function, valvepoint effect. I. INTRODUCTION NE of the most important aspects of power system operation is its obligation to supply power to the customers economically [1]. Power system economic load dispatch is the process of allocating generation among the available generating units subject to load and other operational constraints such that the cost of operation is minimum [2], [3]. And now a day’s quality requirements of power utilities are so severe, that the operators have to sort out possible means of minimizing the production cost so as to offer the most competitive price to its customers. This has led to the adoption of system models and other operational constraints more analogous to real life situations. Traditional optimization techniques can never accurately model the system according to mathematical solutions [4],[5]. To solve the DED problem, it is assumed that a thermal unit commitment has been *Corresponding Author Renu Sharma is with Dept of ICE, Siksha ‘O’ Anusandhan University1, Bhubaneswar, Orissa, 751030 INDIA(e-mail: renusharma_india@yahoo.com) Niranjan Nayak is with Electrical Engg Dept, Siksha ‘O’ Anusandhan University1,Bhubaneswar,Orissa,751030INDIA(e-mail: niranjannayak.el.kec@gmail.com) Krishnanand K. R is with MDRC, Siksha ‘O’ Anusandhan University1, Bhubaneswar, Orissa, 751030 INDIA(e-mail: krishkr09@gmail.com), P. K. Rout is with Dept of EEE, Siksha ‘O’ Anusandhan University1, Bhubaneswar, Orissa, 751030 INDIA(e-mail: pkrout_india@yahoo.com),

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978-1-4673-0136-7/11/$26.00 ©2011 IEEE

previously determined [6].DED considers the constraints imposed on the systems by the generator ramp rate limits because mathematically DED is considered as second–order dynamic optimization problem [6]. To extend the life of equipments, the gradients for temperature and pressure inside the boiler and turbine should be kept within the limit. This mechanical constraint is transformed into a limit on the rate of increase or decrease of electrical power output .This limit is called ramp rate limit which distinguishes DED from static economic dispatch problem [7]. The DED can be solved by dividing the total load dispatch period into a number of small intervals, during that period load demand is assumed to be constant, and the system is considered to be time invariant for that period. Traditional approach of a DED with N units and T time intervals would require the solution of an optimization problem of size N×T— a considerably more complex task. Recently, hybrid EPsequential quadratic programming (SQP) [6], deterministically guided PSO [8], and hybrid PSO-SQP [9] methods were proposed to solve the DED problem with non-smooth fuel cost functions. Simulated Annealing (SA) [10] has also been employed for the solution of the DED problem. The DED problem becomes heavily constrained as these utilize the traditional approach of a DED, in which...

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