Please cite this article in press as: A. Ahmadi, et al., Short term multi-objective hydrothermal scheduling, Electr. Power Syst. Res. (2014), http://dx.doi.org/10.1016/j.epsr.2014.11.015 ARTICLE IN PRESS G Model EPSR-4185; No. of Pages 11 Electric Power Systems Research xxx (2014) xxx–xxx Contents lists available at ScienceDirect Electric Power Systems Research j o ur na l ho mepage: www.elsevier.com/locate/epsr Short term multi-objective hydrothermal scheduling Abdollah Ahmadi a , Mahmoud Sharafi Masouleh b , Mohammadreza Janghorbani c , Navid Yadollahi Ghasemi Manjili d , Adel M. Sharaf e,∗ , Ali Esmaeel Nezhad f a School of Electrical Engineering and Telecommunications, The University of New South Wales, Sydney, Australia b Department of Electrical and Electronic Engineering, Iran University of Science and Technology, Tehran, Iran c Department of Electrical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran d Department of Electrical Engineering, Maziar Institute of Higher Education, Noor, Iran e Sharaf Energy Systems, Inc, Fredericton, NB, Canada f Department of Electrical Engineering, Islamic Azad University, Najafabad Branch, Najafabad, Iran a r t i c l e i n f o Article history: Received 15 July 2014 Received in revised form 14 November 2014 Accepted 16 November 2014 Available online xxx Keywords: Multi-objective optimization Short-term environmental/economic hydrothermal scheduling Normal boundary intersection method Emission minimization Fuzzy decision making a b s t r a c t This paper investigates the short-term environmental/economic hydrothermal scheduling problem. The multi-objective optimization framework is proposed to model the Short-term Hydro Thermal Scheduling (SHTS) problem, while two competing objective functions are supposed to be minimized, simultaneously. The first objective function is to minimize the cost and the second one is to minimize the emissions caused by thermal units. In order to solve the presented multi-objective optimization problem and generate the Pareto optimal front, lexicographic optimization and Normal Boundary Intersection (NBI) method are employed in this paper. The main positive point with this approach is that it avoids the selection of arbitrary parameters and produces a set of evenly distributed points regardless of the objectives’ scales. Afterwards, the most preferred solution among all Pareto solutions is selected utilizing a fuzzy satisfying method. The proposed model is implemented on a sample test system comprising four cascaded hydro generating units as well as three thermal units. Furthermore, the proposed method is implemented on IEEE 118 bus test system. The obtained results show the efficiency of the proposed multi-objective method to solve the SHTS problem compared to other methods recently employed. © 2014 Elsevier B.V. All rights reserved. 1. Introduction One of the most significant issues in power systems is SHTS [1,2] including the determination of the hourly scheduling of avail- able hydro and thermal generating units over the planning horizon (one day/one week) [3]. The most common objective function in a hydrothermal scheduling problem intended to be minimized is the total fuel cost of thermal units [4]. Ref. [4] employs an adaptive chaotic artificial bee colony algorithm to deal with the problem of SHTS while local optimum is skipped using chaotic search added to the artificial bee colony algorithm. Ref. [5] has taken into consideration the nonlinearities, such as valve loading effects pertaining to the thermal units and the prohibited discharge zone of water reservoirs in the SHTS problem wherein a teaching learning based optimization is proposed to solve the problem. The clonal real-coded quantum-inspired evolutionary algorithm with Cauchy mutation is applied to the SHTS problem in Ref. [6], in which ∗ Corresponding author. Tel.: +1 506 453 4561. E-mail address: profdramsharaf@yahoo.ca (A.M. Sharaf). the real-coded rule is used to handle the continuous variables. The progressive hedging method is utilized in Ref. [7] to handle the medium term operation planning problem of hydrothermal power system. The profit maximization of hydro and thermal generating units through taking part in a day-ahead market is investigated in Refs. [8,9] using Mixed Integer Linear Programming (MILP), respec- tively, while MILP method is employed in Refs. [10,11] to model the hydro and thermal generating units in order to maximize the profit of Generation Companies (GENCOs). So far, there are several other approaches proposed to solve the SHTS problem as: dynamic pro- gramming [12,13], Genetic Algorithm (GA) [14–16], Particle Swarm Optimization (PSO) [17,18] and Artificial Neural Networks (ANNs) [19,20]. Refs. [21,22] present the detailed literature review of opti- mization approaches used to solve the SHTS problem. Recently, minimizing the total emission caused by the thermal units is taken into consideration as another objective function in SHTS prob- lem, since concerns on global warming increases drastically. Refs. [23,24] used lexicographic optimization and hybrid augmented weighted epsilon-constraint to minimize the fuel cost and emission for economic/environmental dispatch problem. The non-inferior surface estimation and a fuzzified multi-objective PSO algorithm http://dx.doi.org/10.1016/j.epsr.2014.11.015 0378-7796/© 2014 Elsevier B.V. All rights reserved.