| 田壁源,刘倩汝,戚红艳,徐海奇,刘 琪,常喜强,张新燕.阶梯分时碳价下考虑能流-碳流耦合的综合能源园区优化调度[J].电力需求侧管理,2024,26(6):37-43 |
| 阶梯分时碳价下考虑能流-碳流耦合的综合能源园区优化调度 |
| Optimal dispatch of PIES considering energy carbon flow coupling under carbon trading mechanism |
| 投稿时间：2024-07-23 修订日期：2024-09-02 |
| DOI：10. 3969 / j. issn. 1009-1831. 2024. 06. 006 |
| 中文关键词: 综合能源系统 碳排放强度 碳流密度 阶梯分时碳价 需求响应 |
| 英文关键词: integrated energy system carbon flow density carbon flow density ladder time sharing carbon price demand response |
| 基金项目:国家自然科学基金（51667018） |
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| 摘要点击次数: 34 |
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| 中文摘要: |
| 建立了一种基于多能潮流-碳流耦合及碳-电市场协同的综合能源园区（PIES）两阶段低碳优化调度模型。研究了基于统一能源母线结构的综合能源型园区（park-level integrated energy system,PIES）多能流虚拟碳流传输机理,引入能源母线“碳流密度”的概念和储能调度周期内碳排放守恒原则,设计并构建了PIES的多能供应、耦合及消费等环节碳排放核算系统；利用区域电网的碳流密度对园区碳排放进行精确核算,并以PIES经济调度为一阶段优化模型；构建以阶梯型分时碳价为信号的需求响应低碳优化调度为二阶段优化模型,利用用户减碳意愿及自身负荷侧调节能力降低总碳排放量；在一个典型PIES中进行了算例分析。结果表明,所提优化模型能根据区域电网的碳流密度趋势做出及时响应,并利用PIES多能互补及用户侧需求响应的灵活性最小化碳排放,实现深度减排的目标。 |
| 英文摘要: |
| A two-stage low-carbon optimization scheduling model for PIES based on multi energy flow coupling and carbon electricity market synergy is established. First, the park-level integrated energy system（PIES）multi energy flow virtual carbon flow transmission mechanism based on the unified energy bus structure is studied, the concept of energy bus“carbon flow density”and the principle of carbon emission conservation within the energy storage and scheduling cycle are introduced, and the PIES multi energy supply, consumption carbon emission accounting system is designed. Second, the carbon flow density of the regional power grid is used to calculate the carbon emissions, and PIES economic dispatching is one-stage. A two-stage optimization model of low-carbon DR optimal scheduling with the signal of stepped time-sharing carbon price is constructed to reduce the total carbon emissions by using users’willingness, and load side regulation capacity. Finally, a typical PIES is used to analyze numerical example. The results show that the proposed optimization model can make timely response according to the carbon flow density trend of the regional power grid, and user side DR to minimize its own carbon emissions and achieve the goal of deep emission reduction. |
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