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Enhancing Smart Grid Resilience through Hybrid Forecasting of Renewable Energy Generation and Dynamic Load Balancing

Authors: Pankaj Pachauri

Published: 2025-05-02 11:02:31.242

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Abstract

The integration of renewable energy sources (RES) into the smart grid presents significant challenges and opportunities. Intermittency and variability in RES generation, coupled with fluctuating demand, can strain grid stability and reliability. This paper proposes a hybrid forecasting model that combines machine learning techniques with statistical methods to predict renewable energy generation and dynamic load balancing strategies to enhance smart grid resilience. The forecasting model integrates Long Short-Term Memory (LSTM) networks with Autoregressive Integrated Moving Average (ARIMA) models to improve prediction accuracy. The dynamic load balancing strategy employs a multi-objective optimization algorithm, considering both cost minimization and grid stability. Simulation results demonstrate the effectiveness of the proposed approach in mitigating the impact of RES intermittency, reducing overall energy costs, and improving grid reliability under various operational scenarios. The paper concludes with a discussion of the limitations and potential future research directions in this critical area of smart grid management.

Keywords

Smart Grid, Renewable Energy Forecasting, Load Balancing, Hybrid Forecasting, Machine Learning, Optimization Algorithms, Resilience, Energy Management, Dynamic Pricing, Demand Response

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How to Cite

Pankaj Pachauri, (2025-05-02 11:02:31.242). Enhancing Smart Grid Resilience through Hybrid Forecasting of Renewable Energy Generation and Dynamic Load Balancing. JANOLI International Journal of Applied Engineering and Management, Volume UIh3MC5UrwhGKptS6jkQ, Issue 3.

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