最大化热电联产机组电力收益的区域供热网络最优储热容量配置
Optimal Heat Storage Sizing for District Heating Networks to Maximize Electricity Revenue from Combined Heat and Power Units
AI总结：
本文针对区域供热网络储热容量优化难题，提出基于物理原理的数学优化方法，在比利时第三代CHP-DHN案例中使20年总成本降低16.5%，凸显了该方法的优势。
AI中文摘要：
在区域供热网络（DHN）中集成储热装置，可支持应对热需求波动、能源价格变化以及可再生能源间歇性等动态特性。常见应用场景为配备热电联产（CHP）机组的区域供热网络，储热装置可将热量抽取时段转移至电价有利的时期。尽管储热装置在DHN中的优势已得到充分证实，但由于DHN在热源、生产消费模式、网络热损失及燃料成本等方面存在多样性，确定最优储热容量仍具挑战性。本文提出一种可扩展的自动化方法，利用数学优化对DHN中的短期储热装置进行优化。该基于非线性物理原理的方法同时对DHN、热生产者及储热装置进行建模，通过最优储热容量配置最小化总经济成本，明确考虑随时间变化的热生产成本、热需求及热损失。该方法在比利时一个拥有30个用户的第三代CHP-DHN上进行了验证，其规模超过了以往基于物理原理的储热容量研究。对于该系统，最优储热装置的集成将20年总成本从441万欧元降至368万欧元，减少了73万欧元（降幅16.5%）。该成本减少源于通过将热量抽取转移至低电价时期实现了107万欧元的热生产成本降低，而储热装置的投资为32.3万欧元。与常用的简化储热容量方法对比显示，该方法无法确定最优储热容量，证明了所提出的基于整体物理原理的优化方法在确保可行设计、准确成本评估及最优储热容量配置方面的优势。
英文摘要：
Integrating heat storages in district heating networks (DHNs) supports managing dynamic characteristics such as heat-demand variations and changing energy prices, and the intermittency of renewable sources. A common application are DHNs with combined heat and power (CHP) units, where a storage allows shifting heat extraction to periods with favorable electricity prices. Although the benefits of heat storage in DHNs are well established, determining the optimal storage size remains challenging due to the diversity of DHNs in terms of heat sources, production and consumption patterns, network heat losses, and fuel costs. This paper presents a scalable, automated methodology for optimizing short-term heat storage in DHNs using mathematical optimization. The nonlinear, physics-based approach models the DHN, heat producers, and storages simultaneously to minimize total economic cost through optimal storage sizing, explicitly considering time-varying heat-production costs, heat demands, and heat losses. The methodology is demonstrated on a 3rd-generation CHP-DHN in Belgium with 30 consumers, exceeding the scale of previous physics-based storage-sizing studies. For this system, optimal storage integration reduces the 20-year cost by 730 k EUR (16.5%), from 4.41 M EUR to 3.68 M EUR. The reduction results from a 1.07 M EUR decrease in heat-production cost achieved by shifting heat extraction to periods of low electricity prices, while the storage investment amounts to 323 k EUR. A comparison to a commonly used simplified storage sizing method, which fails to identify the optimal storage size, demonstrates the advantage of the proposed holistic, physics-based optimization approach in ensuring feasible designs, accurate cost assessments, and optimal storage sizing.
