Adaptive Grasshopper Optimization Algorithm for Multi-Objective Dynamic Optimal Power Flow in Renewable Energy Integrated Microgrid
Authors: P. Sobha Rani, M. S. Giridhar, K. Radha Rani, Varaprasad Janamala
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Global warming has prompted several governments to adopt more sustainable policies in all areas. Incorporating renewable energy sources (RES) and adopting electric vehicles (EVs) are examples of such practises. Today's electrical distribution networks (EDNs) are becoming more reliable microgrids (MG) that can operate grid-connected or self-healing. As a result, the fluctuating nature of RES and EVs has raised numerous technical and economic concerns. This research proposes a novel multi-objective dynamic optimum power flow (OPF) addressing total load dispatch cost minimization and network security margin maximisation for various load profiles. A composite load model is proposed considering residential, industrial, commercial, EVs, agricultural loads. The proposed optimization issue is tackled using an adaptive grasshopper optimization algorithm (AGOA), a meta-heuristic grasshopper optimization technique with adaptive control parameter (AGOA). A modified IEEE 33-bus benchmark test system with PV units and reactive power compensation devices is used for simulation over 24-hour horizon. The suggested AGOA's computing efficiency is compared for two scenarios. By combining good exploration and exploitation features with adaptive regulating variables, the AGOA outperformed in terms of global optima. Also, the techno-economics of MG operation and control are improved significantly. In scenario 1, the network is configured in a radial topology, with average operational costs, distribution losses, voltage variation, and transmission loadability of 1117.72 $/h, 82.4803 kW, 0.0058 p.u., and 0.7910 p.u., respectively, over a 24-hour period. In scenario 2, the network is run as a meshed network, with network performance of 1113.36 $/h, 43.15 kW, 0.0019 p.u., and 0.8524 p.u., respectively. This suggests that switching from radial to meshed configuration can result in lower losses, a better voltage profile, and increased loadability, as well as the applicability of the suggested methodology for managing uncertainty in modern EDNs.