Karimi, Ali (2024) Development of Tools for Smart Real-Time Optimal Operation of Multi-Energy Systems. [Tesi di dottorato]
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| Item Type: | Tesi di dottorato |
|---|---|
| Resource language: | English |
| Title: | Development of Tools for Smart Real-Time Optimal Operation of Multi-Energy Systems |
| Creators: | Creators Email Karimi, Ali ali.karimi@unina.it |
| Date: | 10 December 2024 |
| Number of Pages: | 154 |
| Institution: | Università degli Studi di Napoli Federico II |
| Department: | Ingegneria Industriale |
| Dottorato: | Ingegneria industriale |
| Ciclo di dottorato: | 37 |
| Coordinatore del Corso di dottorato: | nome email Grassi, Michele michele.grassi@unina.it |
| Tutor: | nome email Gimelli, Alfredo UNSPECIFIED Assadi, Mohsen UNSPECIFIED |
| Date: | 10 December 2024 |
| Number of Pages: | 154 |
| Keywords: | Multi Energy Systems, Optimization, Artificial intelligence, Deep reinforcement learning, mixed integer linear programing |
| Settori scientifico-disciplinari del MIUR: | Area 09 - Ingegneria industriale e dell'informazione > ING-IND/09 - Sistemi per l'energia e l'ambiente |
| Additional information: | Cycle 37 is correct. |
| Date Deposited: | 18 Nov 2025 14:47 |
| Last Modified: | 02 Sep 2026 08:06 |
| URI: | https://www.fedoa.unina.it/id/eprint/16295 |
Collection description
The global energy landscape is undergoing rapid transformation, driven by the urgent need for sustainable solutions to address climate change, energy security, and economic growth. Multi-Energy Systems (MES) present a promising solution, allowing for the integration and optimization of diverse energy resources within a single framework. This thesis explores the operational challenges and optimization potential of MES through a comparative analysis of four control strategies: Rule-Based Control, Hourly Optimized Control, 24-Hour Forecasted Optimized Control, and Deep Reinforcement Learning (DRL). Each control algorithm was evaluated on key performance metrics, including operational costs, energy consumption, carbon emissions, and system reliability. The results reveal distinct advantages and limitations of each control strategy. The Rule-Based approach, while simple, relies heavily on grid dependency, leading to higher emissions and operational costs. Hourly and 24-Hour Optimized strategies provide enhanced efficiency by leveraging predictive analytics, though the 24-Hour approach exhibited a higher grid reliance during low-cost periods, impacting emissions. The DRL algorithm emerged as the most efficient, achieving the lowest emissions, reduced grid dependency, and improved operational cost savings, leveraging adaptive learning capabilities to optimize real-time energy management dynamically. This work underscores the importance of tailored MES control strategies and highlights the potential of DRL to adaptively manage complex energy systems, thereby supporting the development of resilient, sustainable energy infrastructures aligned with global climate targets. The findings offer a valuable framework for future MES applications, optimizing energy efficiency and sustainability in diverse sectors.
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