Impact analysis of renewable energy Distributed Generation in deregulated electricity markets: A Context of Transmission Congestion Problem

Abstract

In the present markets, transmission congestion is contemplated to play a crucial role in the initiation of cascading outages, which compels the system to collapse. It is to be alleviated as soon as possible to maximise profits; or else the cost effective supply will be restricted from reaching the end consumers. Furthermore, global warming, the most serious issue being overlooked by conventional congestion management approaches, causes severe environmental consequences like increase in temperature and declining water supplies. The incorporation of renewable energy (RE) or associated technologies is indeed an excellent countermeasure to global warming. Hence, in this work, optimal capacities of solar based Distributed Generation (DG) are inserted to acknowledge the transmission network's N-1 contingency scenario. A multi-objective framework utilising the Grey Wolf Optimizer (GWO) is presented to obtain the exact size of solar DG by minimizing the costs, voltage deviations and power losses of the network simultaneously. Also, a new heuristic ranking based on the MW flow and net power injections is proposed to determine the most critical buses for the optimal placement of the solar DG. To assess the viability of the proposed approach, it is evaluated using a variety of case studies, including constant and voltage dependent load models in both planning stage and real time operation stage. The capacities of unity and 0.9 lagging power factor (pf) DGs have been obtained for standard IEEE system to check the effectiveness of the approach. The final optimal solutions obtained by GWO are compared with the existing algorithms available in the literature to show the superiority and the potentiality in solving the multi-objective framework. The simulation results emphasise the importance of the proposed approach in terms of costs, voltages and losses as it delivers better performance when compared to existing models in the literature.