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This study explores the integration and optimization of battery energy storage systems (BESSs) and hydrogen energy storage systems (HESSs) within an energy management system (EMS), using Kangwon National University's Samcheok campus as a case study. This research focuses on designing BESSs and HESSs with specific technical specifications, such as energy capacities and power ratings, and their integration into the EMS. By employing MATLAB-based simulations, this study analyzes energy dynamics, grid interactions, and load management strategies under various operational scenarios. Real-time data from the campus are utilized to examine energy consumption, renewable energy generation, grid power fluctuations, and pricing dynamics, providing key insights for system optimization. This study finds that a BESS manages energy fluctuations between 0.5 kWh and 3.7 kWh over a 24 h period, with battery power remaining close to 4 W for extended periods. Grid power fluctuates between −5 kW and 75 kW, while grid prices range from 75 to 120 USD/kWh, peaking at 111 USD/kWh. Hydrogen energy storage varies from 1 kWh to 8 kWh, with hydrogen power ranging from −40 kW to 40 kW. Load management keeps power stable at around 35 kW, and PV power integration peaks at 48 kW by the 10th h. The findings highlight that BESSs and HESSs effectively manage energy distribution and storage, improving system efficiency, reducing energy costs by approximately 15%, and enhancing grid stability by 20%. This study underscores the potential of BESSs and HESSs in stabilizing grid operations and integrating renewable energy. Future directions include advancements in storage technologies, enhanced EMS capabilities through artificial intelligence and machine learning, and the development of smart grid infrastructures. Policy recommendations stress the importance of regulatory support and stakeholder collaboration to drive innovation and scale deployment, ensuring a sustainable energy future. [ABSTRACT FROM AUTHOR]

This paper proposes a per-machine linear dispatching rule learning approach to improve the scheduling of re-entrant flow shop such as semiconductor fab. Finding an optimal schedule of a complex manufacturing system is intractable; hence, a dispatching rule as a heuristic approach is widely used in actual practice. Also, to develop a good dispatching rule, an automated methodology for developing heuristics, also known as a hyper-heuristic, has been studied extensively. However, most of the literature has focused on finding a single-sophisticated dispatching rule, in which every machine uses the same rule. Such an approach often shows suboptimal performance when the optimal dispatching rule is different on each machine. To solve this problem, we introduce a simple and effective per-machine dispatching rule learning approach, in which each machine has one linear dispatching rule that is optimised by the Gradient-based Evolutionary Strategy (GES). This method is sample-efficient and can be applied to non-differentiable objective functions such as average Cycle Time. The proposed approach was mainly compared to two popular methods based on Genetic Programming (GP) and the Genetic Algorithm (GA) on a four-station and eight-machine re-entrant flow shop. Numerical results show that the proposed approach outperforms widely used methods. [ABSTRACT FROM AUTHOR]