Implementation of flower pollination algorithm for solving economic load dispatch and combined economic emission dispatch problems in power systems A.Y. Abdelaziz a , E.S. Ali b, * , S.M. Abd Elazim b a Electric Power and Machine Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt b Electric Power and Machine Department, Faculty of Engineering, Zagazig University, Zagazig, Egypt article info Article history: Received 25 May 2015 Received in revised form 22 January 2016 Accepted 7 February 2016 Available online xxx Keywords: Flower pollination algorithm Economic load dispatch Combined economic emission dispatch Emission constraints Valve point loading effect Swarm intelligence abstract ELD (Economic Load Dispatch ) is the process of allocating the required load between the available generation units such that the cost of operation is minimized. The ELD problem is formulated as a nonlinear constrained optimization problem with both equality and inequality constraints. The dual- objective CEED (Combined Economic Emission Dispatch ) problem is considering the environmental impacts that accumulated from emission of gaseous pollutants of fossil-fueled power plants. In this paper, an implementation of FPA (Flower Pollination Algorithm ) to solve ELD and CEED problems in power systems is discussed. Results obtained by the proposed FPA are compared with other optimization algorithms for various power systems. The results introduced in this paper show that the proposed FPA outlasts other techniques even for large scale power system considering valve point effect in terms of total cost and computational time. © 2016 Elsevier Ltd. All rights reserved. 1. Introduction ED (Economic Dispatch ) problem has become a crucial task in the operation and planning of power system [1]. It is very complex to solve because of a nonlinear objective function and a large number of constraints. ED in power system deals with the deter- mination of optimum generation schedule of available generators so that the total cost of generation is minimized within the system constraints [2,3]. Well known long-established techniques such as gradient method [4], lambda iteration method [5,6], linear pro- gramming [7], quadratic programming [8], lagrangian multiplier method [9], classical technique based on co-ordination equations [10] are applied to solve ELD (Economic Load Dispatch) problems. However, these conventional methods require the incremental cost curves to be monotonically increasing or piece-wise linear. Practi- cally the input to output characteristics of the generating units is highly nonlinear and non-smooth. During the last decades many researches and techniques had dealt with ELD problems. FLC (Fuzzy Logic Control ) has attracted the attention in control applications. In contrast with the con- ventional techniques, FLC formulates the control action in terms of linguistic rules drawn from the behavior of a human operator rather than in terms of an algorithm synthesized from a model of the system [11e 14]. However, it requests more fine tuning and simulation before operational. Another technique likes ANN (Artificial Neural Network ) has its own advantages and disad- vantages. The characteristics of the system is enhanced by ANN, but the main problem of this technique is the long training time, the selecting number of layers and the number of neurons in each layer [15e18]. An alternative approach is to employ EA (Evolutionary Algo- rithm ) techniques. Due to its ability to treat nonlinear objective functions, EA is believed to be very effective to deal with ELD problem. Among the EA techniques, GA ( Genetic Algorithm ) is introduced in Refs. [19,20], but it requires a very long run time depending on the size of the system under study. Also, it gives rise to repeat revisiting of the same suboptimal solutions. SA ( Simu- lated Annealing ) is illustrated in Refs. [21,22], but this technique might fail by getting trapped in one of the local optimal. EP ( Evolutionary Programming ) is discussed in Ref. [23], but it has a * Corresponding author. E-mail addresses: almoatazabdelaziz@hotmail.com (A.Y. Abdelaziz), ehabsalimalisalama@yahoo.com (E.S. Ali), sahareldeep@yahoo.com (S.M. Abd Elazim). Contents lists available at ScienceDirect Energy journal homepage: www.elsevier.com/locate/energy http://dx.doi.org/10.1016/j.energy.2016.02.041 0360-5442/© 2016 Elsevier Ltd. All rights reserved. Energy 101 (2016) 506e518