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基于有限理性的集中供热耦合绿电调峰系统运行优化

Operation optimization of central heating system coupled with green power peak-shaving based on bounded rationality

  • 摘要: 针对集中供热调节时滞性强、室内温度热不均匀性大等问题,集中供热调峰系统通过集中供热基础保障、调峰系统波动补充的组合供热形式实现调节优化,成为目前北方供热的主要系统形式之一。同时,耦合分布式绿电的集中供热调峰系统可促进建筑与电网的交互、提升绿电消纳,并满足居民个性化热舒适需求。为此,提出一种面向个性化供热需求、耦合分布式绿电的集中供热调峰系统,并将居民参与集中供热需求响应的有限理性纳入建模。采用“费用-舒适”模型刻画异质性用户供热需求响应特性,构建企业、用户双层定价与调度优化框架,实现了绿电消纳与供热收益的协同优化。以西安市某小区为算例进行仿真,结果表明,绿电日均消纳功率达到10~18 MW,供热企业运行成本降低36.5%,用户温度达标率由2.9%提升至90.3%。该方法可缓解居民理想化需求响应假设带来的调度偏差,提高系统经济性与末端舒适保障水平,为绿电参与供热调峰及差异化定价机制设计提供参考。

     

    Abstract: To address the pronounced problems of long regulation time lag and severe indoor temperature unevenness in central heating, the central-heating peak-shaving systems achieves regulation optimization through a combined heating mode of basic central-heating guarantee and fluctuating supplement by the peak shaving system, and has become one of the prevailing heating configurations in northern China. Meanwhile, the central-heating peak-shaving system coupled with distributed green power can facilitate building–grid interaction, enhance renewable power accommodation, and satisfy residents' personalized thermal comfort demands. Therefore, this paper proposes a central-heating peak-shaving system coupled with distributed green power and oriented toward personalized heat demands, and incorporates the bounded rationality of residents participating in central-heating demand response into the modeling. A cost–comfort model is employed to characterize the demand-response behaviors of heterogeneous users, and a bi-level pricing and scheduling optimization framework for heating enterprises and users is established to achieve coordinated optimization of green power accommodation and heating benefits. Taking a residential community in Xi'an for case study, the results show that the daily average accommodated green power reaches 10~18 MW, the operating cost of the heating enterprises is reduced by 36.5%, and the user temperature compliance rate is increased from 2.9% to 90.3%. The proposed approach can alleviate the scheduling deviation caused by the idealized assumption of residents' demand response, improve system economy and terminal comfort guarantee level, and provide a reference for the participation of green power in heating peak shaving and the design of differentiated pricing mechanisms.

     

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