【學術亮點】Energy Management Scheduling of a Smart Factory with Carbon Capture and Storage, Carbon Emission Quota Cap-and-Trade, and Green Energy trading
Ecological Agriculture: Assessment of Forest Carbon Sink and Ecological Economy under Climate Change【 Department of Forestry / Liu, Wan-Yu / Distinguished Professor】
生態農業：氣候變遷下森林碳匯與生態經濟評估【森林學系/柳婉郁特聘教授】
| 論文篇名 | 英文：Energy Management Scheduling of a Smart Factory with Carbon Capture and Storage, Carbon Emission Quota Cap-and-Trade, and Green Energy trading 中文：智慧工廠的能源管理調度：結合碳捕捉、碳儲存與碳排放配額總量管制與交易，以及綠色能源交易 |
| 期刊名稱 | Energy |
| 發表年份, 卷數,起迄頁數 | 2025, 333, no. 137231 |
| 作者 | Lin, Chun-Cheng; Zhang, Shi-Yu; Chou, Yu-Lun; Liu, Wan-Yu(柳婉郁)* |
| DOI | 10.1016/j.energy.2025.137231 |
| 中文摘要 | 為實現淨零排放，許多政府對工廠設定碳排放配額(Carbon Quotas, CQs)上限。儘管工廠可透過製程與材料改良或認證的碳抵換機制以降低排放，但要完全符合配額上限仍具挑戰性。參與政府主導的「總量管制與交易制度」(Cap-and-Trade, CAT)可使工廠透過碳權交易以達成各自的排放上限。以往能源管理調度相關研究大多忽略在 CAT 制度下提升碳配額交易的潛力，特別是在與碳捕捉與封存(Carbon Capture and Storage, CCS)系統整合時。本研究提出一套整合電池儲能系統、CCS、碳配額交易及綠色能源交易的工廠能源管理調度系統。本研究建立一個混合整數規劃(Mixed-Integer Programming, MIP)模型，以實現具成本效益的能源管理調度，包含電力使用、能源儲存、碳捕捉與封存、綠色能源交易及碳配額交易等決策。模擬結果顯示，LTCTP機制能有效激勵低排放，實現碳交易獲利，產生額外收益，並使碳交易績效較定價提升31.18%。本研究同時提出一種結合簡化和諧搜尋(SHS)和自適應變動鄰域搜尋法(SAVNS)的新演算法，以提升全域與局部搜尋效率。結果顯示該演算法具有較高的求解品質和穩定性，並能透過整合CCS和LTCTP模型，顯著降低總成本。 |
| 英文摘要 | Many governments have capped carbon emission quotas (CQs) on factories to reach net-zero emissions. Despite efforts through improvements in manufacturing processes and materials or certified carbon offsetting, factories often find it challenging to meet these CQ caps. Participation in government-led cap-and-trade (CAT) systems allows factories to trade surplus CQs to meet their respective caps. Previous studies on energy management scheduling has largely ignored the potential for optimizing CQ trading within CAT systems, especially when integrated with carbon capture and storage (CCS) systems. This study proposes an energy management scheduling system for factories that integrates battery energy storage systems, CCS, CQ trading, and green energy trading. We develop a mixed-integer programming model for cost-efficient energy management scheduling, encompassing decisions on electricity usage, energy storage, carbon capture and storage, green energy trading, and CQ trading. Simulation results demonstrate that the LTCTP mechanism effectively incentivizes lower emissions and enables profitable carbon trading, generating surplus revenue and improving carbon trading performance by 31.18 % compared to fixed pricing. This study also features a new algorithm combining simplified harmony search (SHS) with self-adaptive variable neighborhood search (SAVNS) to enhance both global and local search efficiencies. Experimental results demonstrate high solution quality and stability of the algorithm, significantly reducing total costs by integrating CCS and the LTCTP model. |
| 發表成果與本中心研究主題相關性 | 此研究與永續農業中心研究主題之淨零碳排與碳匯、’碳信用額度密切相關。 |
