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可再生能源、转换、储能与需求协同的综合能源系统双层优化设计
Bi-Level Optimal Design of Integrated Energy System With Synergy of Renewables, Conversion, Storage, and Demand
| 作者 | Lizhi Zhang · Hui Zhang · Fan Li · Bo Sun |
| 期刊 | IEEE Transactions on Industry Applications |
| 出版日期 | 2025年1月 |
| 技术分类 | 储能系统技术 |
| 技术标签 | 储能系统 |
| 相关度评分 | ★★★★★ 5.0 / 5.0 |
| 关键词 | 综合能源系统 双级优化设计方法 沼气-太阳能-风能 容量与运行优化 案例验证 |
语言:
中文摘要
将沼气、太阳能和风能结合的综合能源系统(IESs)在有效利用可再生能源方面显示出巨大潜力,这对实现碳中和至关重要。其能源和经济性能的提升依赖于优化设计方法,该方法需要考虑容量与运行的联合优化,以及沼气生产、能源转换、存储和需求之间的协同作用。因此,本研究提出了一种沼气 - 太阳能 - 风能综合能源系统的双层优化设计方法。首先,建立了㶲枢纽模型,以准确描述能源转换过程中能量数量和质量的变化。然后,将综合能源系统的容量与运行联合优化问题构建为一个双层迭代模型,并采用基于多属性加权的全时间序列聚类方法来获取典型源 - 荷场景。第一层旨在最大化成本和㶲节约,并确定可再生能源、能源转换和存储组件的额定容量;第二层通过纳入沼气生产的热力学模型和电力需求响应计划,协同优化能源转换、存储和需求组件的运行方案。并且建立了这两层之间的迭代优化机制。此外,开发了一种结合遗传算法和序列二次规划法的混合算法来求解该双层模型。最后,通过案例研究验证了所提方法的可行性和有效性。
English Abstract
Integrated energy systems (IESs) that combine biogas, solar, and wind energy sources demonstrate considerable potential for effective utilization of renewable energy, which is instrumental for achieving carbon neutrality. The enhancement in their energetic and economic performances relies on optimal design methods that need to consider the combined optimization of capacity and operation and synergy between biogas production, energy conversion, storage, and demand. Therefore, this study proposes a bi-level optimal design method for a biogas–solar–wind IES. First, an exergy hub model is established to accurately describe the variations in the energy quantity and quality resulting from energy conversion processes. Then, the combined capacity and operation optimization problem of the IES is formulated as a bi-level iterative model, and a full-time-series clustering method based on multi-attribute weighting is employed to obtain typical source–load scenarios. The first level is designed to maximize the cost and exergy savings and determine the rated capacities of renewables, energy conversion and storage components; the second level synergistically optimizes the operation schemes of energy conversion, storage, and demand components by incorporating a thermodynamic model of biogas production along with an electrical demand response program. And the iterative optimization mechanisms between these two levels are established. Moreover, a hybrid algorithm combining a genetic algorithm and sequential quadratic programming method is developed to solve the bi-level model. Finally, the feasibility and effectiveness of the proposed method are verified through case studies.
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SunView 深度解读
从阳光电源的业务视角来看,这篇论文提出的双层优化设计方法对我们在综合能源系统领域的战略布局具有重要参考价值。该研究通过建立火用枢纽模型,不仅关注能量数量的平衡,更深入到能量品质的分析层面,这与我们在多能互补系统中追求的高效转换目标高度契合。
论文的核心价值在于其系统性的优化框架:上层优化确定光伏、风电、储能等设备的额定容量,下层协同优化各组件的运行策略。这种方法论可直接应用于我们的"源网荷储一体化"解决方案设计中。特别是其全时序聚类方法能够有效处理新能源出力的波动性,为我们的储能系统EMS(能量管理系统)算法优化提供了新思路。将需求响应与生物质产气热力学模型相结合的协同优化策略,也为我们拓展氢能、生物质能等新业务板块提供了技术路径。
从技术成熟度看,双层优化模型的理论框架已较为完善,但工程化应用仍面临挑战。遗传算法与序贯二次规划的混合求解方法虽然有效,但在大规模实际项目中的计算效率和收敛性需要验证。对阳光电源而言,关键机遇在于将此方法嵌入我们的iSolarCloud智慧能源管理平台,开发面向工业园区、微电网等场景的智能规划工具。技术挑战主要集中在多能源耦合模型的精度提升、实时优化算法的轻量化,以及与我们现有逆变器、储能PCS等硬件产品的深度融合。建议组织跨部门技术攻关,优先在示范项目中验证其经济性和可靠性。