This paper proposes the exchange market algorithm (EMA) to solve the combined economic and emission dispatch (CEED) problems in thermal power plants. The EMA is a new, robust and efficient algorithm to exploit the global optimum point in optimization problems. Existence of two seeking operators in EMA provides a high ability in exploiting global optimum point. In order to show the capabilities of EMA in solving CEED problem, several experimentations are conducted on systems with 6, 10, and 40 generation units applying valve-point effects and network power losses in a multi objective function consists of system fuel cost and emission level. The obtained results are compared with other advanced techniques such as Strength Pareto evolutionary algorithm, non-dominating sorting genetic algorithm II, multi objective evolutionary algorithm, fuzzy clustering-based particle swarm optimization, multi objective differential evolution, gravitational search algorithm, modified bacterial foraging algorithm, etc. The results well demonstrate the practical advantage of the exchange market algorithm over the other approaches. Manuscript profile

This paper proposes per unit coding for combined economic emission load dispatch problem. In the proposed coding, it is possible to apply the percent effects of elements in any number and with high accuracy in objective function. In the proposed per unit coding, each function is transformed into per unit form based on its own maximum value and has a value from 0 to 1. In this paper, particle swarm optimization is used for solving economic emission load dispatch problem. In order to show the advantages of the proposed method, 25 independent case studies are conducted on systems holding three and six power units with different influence percentages of each function are investigated. The obtained results are compared with those of other methods such as Biogeography Based Optimization, Tabu Search, NSGA-II and etc. The obtained results properly show the superiority of the proposed method to combine economic emission dispatch problem over the penalty factor technique and other conventional combined approaches. Manuscript profile

In this paper particle swarm optimization with smart inertia factor (PSO-SIF) algorithm is proposed to solve combined heat and power economic dispatch (CHPED) problem. The CHPED problem is one of the most important problems in power systems and is a challenging non-convex and non-linear optimization problem. The aim of solving CHPED problem is to determine optimal heat and power of generating units with the minimized cost of total system and satisfied constraints of problem. In proposed algorithm inertia coefficients are controlled with respect to cost function in each population. So, each population has unique inertia coefficient and as a result unique velocity in convergent direction for the best group solution. In order to examine the proposed algorithm's capabilities and find optimum solution for CHPED problem, two test systems considering valve-point effect, system power loss and system constraints are optimized. The obtained results demonstrate the superiority of the proposed method in solving non-convex CHPED problem over other new and efficient algorithms. Manuscript profile