Abstract
This research article presents an innovative approach to enhance sustainable power generation and grid support by integrating real-time modeling and optimization with Molten Salt Energy Storage (MSES) and a Supercritical Steam Cycle (s-SC). As renewable energy usage grows, intermittent resource availability challenges grid stability and reliable power supply. To address this, we develop a system that merges real-time modeling and optimization for precise control of MSES and s-SC components. This integration ensures uninterrupted energy generation, storage, and distribution, optimizing renewable energy use during high-demand periods. Mathematical models and simulations assess the system's dynamic behavior, performance, and economic viability. Rigorous techno-economic analysis highlights cost-effectiveness and environmental benefits. Findings reveal exceptional energy efficiency and grid support, making it a promising solution for sustainable power generation and grid stability amid renewable energy growth. Real-time modeling and optimization emerge as crucial components in modern energy systems. The Combined Heat and Power (CHP) system achieves 56% energy efficiency with off-design impacts considered and 63.61% without. Also, the overall system exergy efficiency decreased from 73.36% at design to approximately 63.55% under off-design scenarios. Regarding the economic aspect, the levelized cost of storage (LCOS) for the CHP system is estimated at 114.4 €/MWh with off-design conditions and 106.8 €/MWh without.
| Original language | English |
|---|---|
| Journal | Process Safety and Environmental Protection |
| Volume | 182 |
| Pages (from-to) | 866-879 |
| ISSN | 0957-5820 |
| DOIs | |
| Publication status | Published - 2024 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Combined Heat and Power
- Molten Salt Energy Storage
- Real-Time Modeling
- Renewable Energy Integration
- Supercritical Steam Cycle
- Sustainable Power Generation
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