Advancing Energy Transition: A Circular Economy Framework for Co-Optimizing Energy, Water, and Emissions in Low-Carbon Systems
The urgent global need for sustainable energy, water, and climate solutions requires bold, integrated strategies. This study introduces a transformative framework that pioneers the co-optimization of energy, water, and emissions, advancing the energy transition while addressing key global challenges...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11068994/ |
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| Summary: | The urgent global need for sustainable energy, water, and climate solutions requires bold, integrated strategies. This study introduces a transformative framework that pioneers the co-optimization of energy, water, and emissions, advancing the energy transition while addressing key global challenges. By harnessing renewable energy sources (RES), carbon capture (CC) technologies, and reverse osmosis (RO) desalination systems within a circular economy context, this approach disrupts traditional thinking, laying the groundwork for a pathway to a resilient, low-carbon economy. Current models fail to address the dynamic interplay between RES, CC, and desalination, especially given the complexities of temperature-sensitive desalination processes and operational uncertainties. This paper introduces an innovative solution providing a systemic, co-optimized strategy that balances not only intermittent energy generation and water demand but also integrates risk-based demand response (DR) tailored for desalination plants, a first-of-its-kind approach that minimizes emissions and operational costs in real-time. We employ a novel risk-multi-objective stochastic (RMOBS) optimization method, coupled with a two-stage stochastic programming model, to address uncertainties through hidden Markov processes and conditional value-at-risk (CVaR) techniques. This integrated methodology allows us to achieve unprecedented results: a 9% reduction in peak load, 4% cost savings, and a 6.6% decrease in CO2 emissions in benchmark IEEE test systems. When scaled to larger grids, the framework achieves a 5% cost reduction, a 19.7% reduction in emissions, and a 5.7% reduction in peak load, demonstrating both scalability and robustness across diverse system configurations. |
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| ISSN: | 2169-3536 |