Optimal Planning of Renewable Energy Management with Energy Storage for an Industrial Microgrid by a New Hybrid Model Based on Deep Machine Learning in Golgohar Industrial and Mining Complex

Keynia, Farshid; Memarzadeh, Gholamreza; Khaksarpour, Reza; Abbasinia, Ali

doi:10.22052/eem.2026.257360.1137

Optimal Planning of Renewable Energy Management with Energy Storage for an Industrial Microgrid by a New Hybrid Model Based on Deep Machine Learning in Golgohar Industrial and Mining Complex

Document Type : Original Article

Authors

¹ Department of Energy Management and Optimization, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran;

² Valiasr University, Rafsanjan, Iran.

³ Golgohar Industrial Complex.

10.22052/eem.2026.257360.1137

Abstract

Renewable energy sources have attracted much attention as a priority option to reduce costs and reduce greenhouse gas emissions from fossil fuels. However, their use causes uncertainty and reduces reliability in industries. Moreover, rapid fluctuations in renewable energy generation can strain the grid infrastructure and require investment in advanced grid management systems and energy storage solutions to maintain system stability and reliability. This paper proposes a comprehensive framework for optimizing the performance and profitability of a microgrid. To this end, first, using data mining techniques to accurately predict net load and electricity price using appropriate inputs for a hybrid forecasting model with a cascade neural network architecture and deep learning. Based on the results obtained, the proposed algorithm achieves a considerable average prediction accuracy for net load prediction and for electricity price prediction, which demonstrates its effectiveness in modeling the inherent complexities of these variables. Furthermore, this paper examines the impact of energy storage on microgrid profitability. The results show that the incorporation of an optimally sized energy storage can significantly increase the microgrid revenue and highlight the economic benefits of energy storage in mitigating the challenges of renewable energy integration.

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