Economic Load Dispatch with PEV Using Evolutionary Techniques

Tejaswita Khobaragade; K. T. Chaturvedi

Abstract

In this paper, the Economic Load Dispatch (ELD) problem is solved by using four different evolutionary techniques Social Spider Algorithm (SSA), Particle Swarm Optimization (PSO), and Teaching Learning Based Optimization (TLBO) with PEV on 20-unit thermal generation station. The most recent Self-Learning Teaching Learning Based Optimization (SL-TLBO) is introduced, including a weighting factor w for adjusting the learning range. A comprehensive study demonstrates that the unique algorithm has the potential to give significant economic and environmental benefits to power system operators while also providing competitive charging strategies for policymakers and PEV aggregators. In this paper, a 15-unit thermal system is addressed, and PEVs are studied along with several evolutionary strategies that meet the restrictions. Various techniques, such as the use of Plug-in Electric Vehicles (PEVs) and Renewable Energy Resources (RERs), have already been recommended to limit the exponential increase in these GHG emissions. The performance of each technique has been evaluated and different results obtained. The performance analysis has been studied with load demand among all the 20-unit thermal generation stations at minimum generation cost and attain the system constraints.

Abstract

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