HYBRID SOLAR-WIND INTEGRATION USING AN ADAPTIVE NETWORK RECONFIGURATION METHOD AND CONTROLLING FOR UNCERTAINTY-AWARE SMART GRID BY OPTIMIZATION ALGORITHM
By
Ghaith M. Fadhil
,
Ghaith M. Fadhil
Faculty of Electrical and Computer Engineering, University of Tabriz, 29 Bahman boulevard
, Tabriz
, Iran
Civil Engineering Department, College of Engineering, Al-Qasim Green University
, Babylon
, Iraq
In this work, a new optimization approach for the exploitation and smooth integration of hybrid renewable sources (HRs), including PV solar/wind turbines in addition to a dynamic reconfiguration process of electricity distribution microgrids, is proposed. A crucial novelty of this work is the definition of a multi-scenario optimization framework that allows to compare devices at various levels (of complexity) across different system conditions, and which has not been thoroughly investigated yet in the literature. Further, the study presents one of the most detailed and operational-realistic representations of an IEEE 84-bus Taiwan Power Company (TPC) distribution system model (in a unique dataset containing exact switch status, impedance properties, and power injection location). This network model serves as a scalable benchmark for grid optimization studies and utility-scale PV deployment. Moreover, the proposed method adopts a variant of particle swarm optimization algorithm to minimize the operational cost along with the variance-based penalty function in consideration of uncertainty associated with renewable power generation. This combination of cost effectiveness and uncertainty management in the context of a single objective function increases the stability and flexibility in grid functions. Then, the approach is verified for three operational modes: a base scenario without any renewable integration, a PSO-tuned scenario with PV and WT but ignoring network reconfiguration, and an integrated (renewables together with reconfiguration). The final formation achieved after optimization can minimize the power losses from 4.924 MW to around 0.002 MW and reduce the operational cost to $1.954/MWh, as reported in results. Such results validate the effectiveness of our proposed strategy for facilitating cost-efficient and robust operation of smart grid.
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