柔性区域供热网络的全局优化
Global Optimization of Flexible District Heating Networks
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中文总结 AI 辅助
本文针对区域供热网络的多时间步长运行全局优化问题，提出改进的空间分支定界算法及时间分解方法，可高效求解含循环的网络实例，在小型网络上时间分解方法能快速得到最优解。
中文摘要 AI 辅助
区域供热网络是实现低碳供热的核心工具。在该领域，它们面临着应对日益多样化、部分时变的可再生能源、储热装置以及网状拓扑结构的挑战。本文基于一种稳定且符合实际的非线性网络模型，研究此类区域供热网络在多个时间步长上的运行全局优化问题。为了提升单时间步长空间分支定界算法的求解性能，引入了以下新的方法要素：排除循环流动、利用供回水网络的对称性、简化温度混合约束、新型原始启发式算法及分支规则。将所提方法在一组含循环和多个供应商的生成基准网络实例及真实世界基准网络实例上进行评估。在生成基准网络实例上，使用这些方法后，已求解实例数量增加一倍以上，运行时间缩短一半以上；在真实世界基准网络实例上，所得算法能在合理运行时间内生成具有质量保证的解。对于通过储热装置耦合的多个时间步长，研究了一种时间分解方法，在实践中合理的假设下，该方法可得到最优解。在一个小型示例网络上，该分解方法能在不到一秒内计算出最优解，而求解完整的时间耦合问题无法在一小时内完成。
英文摘要
District heating networks are a central tool to achieve low-carbon heat supplies. In this realm, they face the challenge of dealing with increasingly heterogeneous, partially time-varying renewable sources, thermal storage, and meshed topologies. This paper examines global optimization of the operation of such district heating networks over multiple time steps, based on a stationary, yet realistical nonlinear network model. To accelerate the solution performance of a spatial branch-and-bound algorithm for one time step, the following new methodological ingredients are introduced: exclusion of cyclic flow, symmetry exploitation between supply and return networks, reduction of temperature mixing constraints, novel primal heuristics and branching rules. The proposed methods are evaluated on a set of generated and real-world benchmark network instances with cycles and several suppliers. On the generated benchmark instances, using these methods more than doubles the number of solved instances and more than halves the runtime. For the real-world benchmark instances, the resulting algorithm produces solutions with guaranteed quality in reasonable run time. For multiple time steps that are coupled by a storage, a time decomposition approach is investigated. Under assumptions that are reasonable in practice, this approach is shown to yield an optimal solution. On a small example network, this decomposition is able to compute optimal solutions in less than a second, while solving the complete time-coupled problem is not possible within one hour.
