Electricity can be provided to small-scale communities like commercial areas and villages through microgrid, one of the small-scale, advanced, and independent electricity systems out of the grid. Microgrid is an appropriate choice for specific purposes reducing emission and generation cost and increasing efficiency, reliability, and the utilization of renewable energy sources. The main objective of this paper is to elucidate the combined economic emission dispatch CEED problem in the microgrid to attain optimal generation cost. A combined cost optimization approach is examined to minimize operational cost and emission levels while satisfying the load demand of the microgrid. With this background, the authors proposed a novel improved mayfly algorithm incorporating Levy flight to resolve the combined economic emission dispatch problem encountered in microgrids. The islanded mode microgrid test system considered in this study comprises thermal power, solar-powered, and wind power generating units. The simulation results were considered for 24 hours with varying power demands. The minimization of total cost and emission is attained for four different scenarios. Optimization results obtained for all scenarios using IMA give a comparatively better reduction in system cost than MA and other optimization algorithms considered revealing the efficacy of IMA taken for comparison with the same data. The proposed IMA algorithm can solve the CEED problem in a grid-connected microgrid.

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Summary The purpose of the combined economic emission dispatch of generation in an electric power is to offer the finest schedule for the generating units which must run with both lesser fuel cost and emission levels concurrently thereby fulfilling the system equality and inequality constraints. This paper presents an interior search algorithm (ISA) for solving multi‐objective combined economic emission dispatch (CEED) problems. Simulation results obtained substantiate the efficiency of ISA algorithm in solving the CEED problems. The effectiveness of the proposed ISA is investigated on five various test systems which comprise three‐unit system, IEEE 30‐bus system, 10‐unit system, 20‐unit system, and Taiwan 40‐unit generating system. Simulation results and their comparison with other meta‐heuristic optimization techniques reveal that the proposed ISA is proficient in solving CEED problem of fossil fuel generators.

Summary The main goal behind the combined economic emission dispatch (CEED) is to reduce the costs incurred upon fuel and emission for the generating units available without any intention to violate the generator and security constraints. Hence, the CEED must be handled after considering two challenging goals such as the costs involved with emission and fuel. In this paper, chaotic self‐adaptive interior search algorithm (CSAISA) was proposed to solve the CEED problems, considering the nonlinear behavior of generators in terms of valve point effects, prohibited operating zones, and security constraints. The proposed algorithm was tested for its effectiveness using 11‐generating units (without security), IEEE‐30 bus system, and IEEE‐118 bus system with security constraints. The results of the proposed CSAISA were compared with interior search algorithm (ISA), harmony search algorithm (HSA), differential evolution (DE), particle swarm optimization (PSO), and genetic algorithm (GA). To conclude, the proposed CSAISA outperformed all other algorithms in terms of convergence speed, implementation time, and solution quality, which was tested using performance metrics.

This paper presents cuckoo search algorithm (CSA) for solving non-convex economic load dispatch (ELD) problems of fossil fuel fired generators considering transmission losses and valve point loading effect. CSA is a new meta-heuristic optimisationCopyright © 2017 Institute of Advanced Engineering and Science. All rights reserved. IntroductionELD (Economic load dispatch) is one of the most important optimization problems in power system operation and control. ELD allocates the load demand among the committed generators at minimum operating cost while satisfying the physical and operational constraints. The ELD problem is a highly constrained nonlinear non-convex optimization problem [7] and branch and bound [8] and mixed integer programming [9] have been applied. All of these conventional optimization techniques can solve economic load dispatch problem under the assumption that the incremental fuel cost curves of the generating units are monotonically increasing piecewiselinear functions. On the other hand, the ELD problem has the characteristics of high nonconvexity and nonlinearity. Also large steam turbines contain a number of steam admission valves which contribute non-convexity in the cost function of the generating units. Classical calculus based optimization techniques fail to address these types of issues satisfactorily and lead to sub optimal solutions making huge revenue loss over time. The classical optimization techniques are not good enough to solve this ELD problem which has inherently nonlinear and discontinuous objective function. Conventional optimization techniques depend on the existence of the first and the second derivatives of the fitness function and on the estimation of these derivatives in large search space. Hence the practical ELD problem can be formulated as nonconvex objective function subject to non-linear constraints, which is difficult to be solved by the conventional optimization techniques.Recently, many attempts have been examined to overcome the limitations of the conventional optimization techniques such as meta-heuristic optimization techniques, for example simulated annealing (SA) [10] [18]. The application of the meta-heuristic optimization techniques to global optimization problems turn out to be attractive since they have improved global search abilities over conventional optimization techniques. The meta-heuristic optimization techniques appear to be evolving and promising and have become the most extensively used tools for