Majlesi Journal of Energy Management Vol. 5, No. 1, March 2016 19 Combined Heat and Power Economic Dispatch Problem Solution using Particle Swarm Optimization with Unique Inertia Factor Naser Ghorbani 1 , Payam Farhadi 2 1- Eastern Azarbayjan Electric Power Distribution Company, Tabriz, Iran Email: Naser.Ghorbani@yahoo.com(Corresponding author) 2-Young Researchers Club, Parsabad Moghan Branch, Islamic Azad University, Parsabad Moghan, Iran Email: pfarhadi@iaupmogan.ac.ir Received September 2015 Revised October 2015 Accepted January 2016 ABSTRACT: Combined heat and power economic dispatch (CHPED) is one of the important issues in power systems. CHPED is a challenging optimization problem of non-linear and non-convex type. Thus, evolutionary and heuristic algorithms are employed as effective tools in solving this problem. This paper proposes a new approach to solve CHPED problem using particle swarm optimization with unique inertia factor (PSO-UIF) algorithm in which there is no similar inertia coefficient for all population. Contrary to the particle swarm optimization (PSO), in the proposed method, there is an inertia weight, in each iteration, for each member of the population. Hence, in the proposed method, some populations have unique inertia coefficient and consequently a unique velocity in looking for the global optimum points. In order to examine the proposed algorithm's capabilities two test systems are optimized considering valve-point effect, system power loss and system constraints. The numerical results were compared to those of the other existing techniques. The result of comparisons shows the effectiveness and superiority of the proposed method. KEYWORDS: combined heat and power, non-convex, particle swarm optimization, valve-point. 1. INTRODUCTION The aim of solving CHPED problem is to determine optimal heat and power of generating units with the minimized cost of total system and to meet constraints of the problem. The heat and power demand should be also met. The presence of heat-power feasibility constraints of cogeneration units may result in more complicated ED problem in comparison to conventional economic dispatch problems [1]. In recent two decades, much research has been reported in literature for solving CHPED problem using mathematical methods and optimization algorithms [2- 3]. Dual dependency of heat and power production in CHP units makes the CHPED problem a complicated optimization problem, which needs powerful optimization techniques to solve it. The CHPED problem will be more complex if the effects of the valve-point in cost function and system losses are taken into account. Considering valve-point effects make the CHPED problem non-convex, which cannot be solved directly through the mathematical approaches. Heuristic algorithms can optimize various problems by generating random numbers without considering complexity and constraints of the problem [4]. Thus, various intelligent techniques, including Evolutionary programming (EP) [5], harmony search algorithm (HSA) [6], differential evolution (DE) [7], bee colony optimization (BCO) [8] and PSO with time varying acceleration coefficients (PSO-TVAC) [1] have been proposed to successfully solve CHPED problem with convex and non-convex fuel cost function. Some problems such as CHPED have many maximum and minimum local points. Algorithms that looking for this search space will have a descending slope, gets trapped in these local points. This leads to convergence to non-similar responses in each implementation of the program. Only strategies, which are able to analyze an extensive search space, can pass through these local optimum points [9]. In order to obtain appropriate results for these cases, the searching strategy needs to have efficient and robust extracting components. PSO is a population based, self-adaptive, modern heuristic search method developed by Kennedy and Eberhart in 1995 inspired by the movement of a flock of birds searching for food. In PSO, the search for optimal solution is conducted using population particles, and each particle modifies its position