Abstract

LALLJITH, Sumant; SWANSON, Andrew G.  and  GOUDARZI, Arman. An intelligent alternating current-optimal power flow for reduction of pollutant gases with incorporation of variable generation resources. J. energy South. Afr. [online]. 2020, vol.31, n.1, pp.40-61. ISSN 2413-3051.  https://doi.org/10.17159/2413-3051/2020/v31i1a7008.

Frequent escalations in fuel costs, environmental concerns, and the depletion of non-renewable fuel reserves have driven the power industry to significant utilisation of renewable energy resources. These resources cannot satisfy the entire system load demand because of the intermittent nature of variable generation resources (VGRs) such as wind and solar. Therefore, there is a need to optimally schedule the generating units (thermal and VGRs) to reduce the amount of fuel used and the level of emissions produced. In this study, an AC-power flow in conjunction with combined economic and environmental dispatch approach through the implementation of a modified constricted coefficient particle swarm optimisation was used to minimise the fuel cost and the level of emission gases produced. The approach was applied to the Institute of Electric and Electronic Engineers 30 bus test system through three different load conditions: base-load, increase-load and critical-load. The results showed the practicality of the proposed approach for the simultaneous reduction of the total generation cost and emission levels on a large electrical power grid while maintaining all the physical and operational constraints of the system. HIGHLIGHTS: • Considering several physical and environmental constraints of generating units. • Proposing a metaheuristic method based on swarm intelligence for solving AC-OPF problem. • Incorporation of variable generation resources in electricity spot markets. • Maximisation of social welfare and minimisation of total generation cost, while reducing the volume of pollutant gases.

Keywords : combined economic and emission dispatch; modified constricted coefficient particle swarm optimisation; metaheuristic optimal power flow; variable generation resources.

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