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Summary Microgrids have lately proved to be the most efficient type of power system delivering uninterrupted power to a local area incurring negligible amount of transmission loss. Inclusion of the available renewable energy sources among the various distributed generation (DG) sources in a microgrid have decreased the emission of harmful toxic gases to a great extent. Dynamic economic dispatch indicates the economic dispatch of a microgrid allocating the timely optimal output of the distributed generation sources. However, the setup of a microgrid involves a huge amount of capital out of which installation cost, operation and maintenance cost, and depreciation cost instigated through the passing of time are a few. This paper uses three soft computing techniques viz. particle swarm optimization, differential evolution, and differential evolution with local global neighborhoods to perform a novel dynamic cost analysis of a microgrid and minimize its overall cost which includes fuel, emission, operation and maintenance cost, installation, and depreciation costs. Four different case studies are pulled off on a microgrid, and a comparative analysis is done to identify the best case. Numerical results and figures also point out the best optimization technique which was able to give the most efficient and minimal cost for the microgrid.

Power system instability primarily results from the deviation of the frequency from its predefined rated value. This deviation causes voltage collapse, which further leads to sudden blackouts of the power system network. It is often triggered by a lack of reactive capacity. The solution to the reactive capacity problem can be obtained in two stages. In the first stage, the vulnerable buses, also known as ‘weak buses’, where voltage failure might occur are identified, and the Var compensating devices are mounted at those locations. The proposed approach utilizes three simple vulnerable bus detection methods: the fast voltage stability index, line stability index, and voltage collapse proximity index (VCPI). In the second stage, various optimization algorithms are implemented to determine the optimal setting of Var sources, such as particle swarm optimization, differential evolution, the whale optimization algorithm, the grasshopper optimization algorithm, the salp swarm algorithm, grey wolf optimization, and oppositional grey wolf optimization (OGWO). The results indicate that the best approach to poor bus recognition is the VCPI, and the OGWO technique provides a much less expensive system than other optimization strategies used for problems of optimal reactive power planning.

Economic dispatch of power is no more a sole concern for utilities. Instead, the utilities focus on reducing toxic gases emitted to the atmosphere due to the maximum utilisation of conventional fossil-fuelled generators to meet the surging demand for electricity. This can be carried out by involving renewable energy sources (RES) to generate clean power compensating the depletion in the availability of fossil fuels. This article performs combined economic emission disfpatch (CEED) on four dynamic systems with and without the involvement of RES. Two methods for solving CEED, namely the price-penalty factor (ppf) method and the fractional programming (FP) method, are used to perform CEED for all the four test systems, and a comparative analysis between them is made based on the least emission of harmful and toxic gases into the atmosphere. A novel hybrid (CSA-JAYA) algorithm is used as the optimisation tool for the study. Numerical results manifest that the FP method of solving CEED is economic and emits less toxic gases to the atmosphere than the ppf method. The proposed hybrid CSA-JAYA outperformed a long list of algorithms from recent literature in consistently providing better and superior quality solutions.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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