This study presents the efficiency of the wind-driven optimisation (WDO) approach in solving non-convex economic dispatch problems with point-valve effect. The best economic dispatch for a power system is one wherein the system can generate energy at a low cost. The calculation of the generating cost is subject to a number of constraints, such as the power demand for the entire system and the generation limit for each generator unit in the system. In addition, the system should also produce low power loss. The WDO optimisation technique is developed based on the concept of natural wind movement, which serves as a stabiliser to equalise the inequality of air pressure in the atmosphere. One major advantage of WDO over other techniques is its search accuracy. The proposed algorithm has been implemented in two systems, namely, the 10-generator and 40-generator systems. Both systems were tested in a Matlab environment. To highlight the capabilities of WDO, the results using this proposed technique are compared with the results obtained using flower pollination algorithm, moth flame optimisation, particle swarm optimisation and evolutionary programming techniques to determine the efficiency of the proposed approach in solving economic dispatch. The simulation results show the capability of WDO in determining the optimal power generation value with minimum generation cost and low rate of power loss.

<p>This paper proposes the optimal generator allocation to solve Economic Dispatch (ED) problem in power system using Moth Flame Optimizer (MFO). With this approach, the optimum power for each unit generating in the system will be searched based on the power constraints per unit and the amount of power demand. The objective function of this study is to minimize the total cost of generation. The amount of power loss is measured to determine the effectiveness of the proposed technique. The performance of the MFO technique is also compared to the evolutionary programming (EP) and Particle Swarm Optimization (PSO) methods. Five- and thirty-bus power system networks are selected as test systems and simulated using MATLAB. Based on simulation results, MFO provides better results in regulating the optimum power generation with minimum generation cost and power loss, compared to EP and PSO.</p>

This study presents the efficiency of the Flower Pollination Algorithm (FPA) in solving economic dispatch. The best economic dispatch for a power system is that the system can generate energy at low generation costs. The calculation of the generating cost is subject to a number of constraints, such as the power demand for the entire system and the generation limit for each generator unit in the system. In addition, the system should also produce low power loss to reduce the impact of greenhouse gas emission. FPA optimization technique is developed based on the transfer of pollen from one flower to another on the same tree or another tree using natural pollinators such as honey bees, birds, water, or wind. Among the advantages of FPA over other techniques are simplicity in computational formulas and fast search simulation time. The proposed algorithm has been implemented in two systems namely IEEE 9 bus 3 generator system and IEEE 30 bus 6 generator. Both of these systems were tested in a Matlab environment. To highlight the capabilities of FPA, the results using this proposed technique are compared with the Moth Flame Algorithm (MFA) technique to determine the efficiency of the proposed approach in solving economic dispatch. MFA is an optimization technique that has been widely used in finding optimal results, especially in engineering research. The simulation results show that FPA performs better than MFA in determining the optimal power generation value with minimum generation cost and low rate of power loss.