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储能系统技术 ★ 5.0

基于生物相变材料的热能储存系统需求响应最优控制

Optimal control of a bio-based phase change material thermal energy storage for demand response

作者 Aneesh Chandra Nunn · Olav Galtelan · Laurent Georges · Yi Zong
期刊 Energy Conversion and Management
出版日期 2025年1月
卷/期 第 326 卷
技术分类 储能系统技术
相关度评分 ★★★★★ 5.0 / 5.0
关键词 Developed control oriented numerical model for a latent thermal energy storage unit.
语言:

中文摘要

摘要 本研究提出了一种创新性基于生物蜡相变材料的枕板式热能储存装置在为一栋四层高的研究建筑提供空间供暖时的最优控制策略的设计与开发。ZEB实验室中的水力供暖系统包括电动热泵、热能储存装置以及水力散热器。为模拟该热系统的相变动态特性,建立了面向控制的数值动态模型,并进行了验证。为了可靠且准确地预测建筑物的逐时供暖负荷,开发了一个14节点的电阻–电容(Resistance–Capacitance)热网络模型,用作决策支持工具。基于已验证的系统模型,进一步开发了一种基于模型的最优预测控制策略,用于热能储存装置的实时运行控制。该控制策略旨在最优利用热能储存装置的储能能力,以产生应对随时间变化的电价信号的需求灵活性。与基于规则的控制方法相比,在一个月的运行期间,所开发的最优控制策略表现出高度的灵活性——其灵活性因子值接近1,表明系统处于最大灵活性运行状态。此外,结果表明该系统平均每天可提供100 kW h至200 kW h的储能容量,显示出优化运行后的热能储存装置具备提供电网辅助服务的能力。除了实现需求灵活性外,最优充电调度还平均降低了约40%至50%的能耗与能源成本。因此,所开发的最优控制策略展现出显著的能力,能够生成并最大化需求灵活性,智能转移负荷、提供电网服务,并降低能源消耗与成本。

English Abstract

Abstract In this study, the design and development of an optimal control strategy for the operation of an innovative bio-wax phase change material based pillow plate thermal energy storage unit delivering space heating to a four-storey-high research building is presented. The hydronic heating system in the ZEB-laboratory comprises an electric driven heat pump, the thermal storage unit and hydronic radiators. Numerical control-oriented dynamic models to simulate the phase-change dynamics of the thermal system are developed and validated. To predict the hourly heating load of the building reliably and accurately, a 14-node Resistance–Capacitance thermal network model is developed to be employed as a decision support tool. An optimal model-based predictive control strategy based on the validated system models is developed for application in real-time operation of the thermal storage unit. The control strategy is designed to optimally utilize the energy storage capability of the thermal energy storage unit to generate demand flexibility in response to time-varying electricity price signals. In comparison to a rule based control , the developed optimal control demonstrates a high degree of flexibility – as quantified by values of flexibility factor close to 1 being obtained – indicating a system operating with maximum flexibility, during one month of operation. Further, results demonstrate the availability of storage capacity of 100 kW h–200 kW h per day on average, indicating the capability of the optimized operation of the thermal energy storage unit to provide grid ancillary services. In addition to being demand flexible, the optimal charging schedule reduces the energy consumption and cost by about 40% – 50% on average. Thus, the developed optimal control strategy demonstrates a significant capability to generate and maximize demand flexibility to shift loads intelligently, provide grid services, and reduce energy cost and consumption.
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SunView 深度解读

该研究的模型预测控制(MPC)策略与阳光电源ST系列储能变流器及PowerTitan系统高度契合。文中基于动态模型的需求响应优化可直接应用于我司储能系统的能量管理策略,通过响应分时电价实现40%-50%的成本削减。其柔性因子接近1的控制效果验证了储能系统提供电网辅助服务的能力,可增强我司ESS解决方案在工商业建筑场景的竞争力。建议将该MPC算法集成至iSolarCloud平台,结合虚拟同步发电机(VSG)技术,提升储能系统在需求侧响应和削峰填谷场景的智能调度能力。