Modelling aggregate demand-side flexibility in distribution networks with electrified heat and transport

该项目旨在模拟配电网络（DN）中大量住宅和中小型商业终端用户聚集的需求侧灵活性。通过该项目开发的算法将通过评估灵活负载的时变容量来促进DN的更灵活运行，以响应配电系统运营商（DSO）目前采购的灵活服务，即：维持，安全，动态和恢复。总体灵活性将被描述为可用容量的数量及其持续时间，这是由最终用户的日常行为驱动并受其舒适度约束的，具有不同操作模式、启动时间、最大延迟时间等的单个负载聚合的结果。这种灵活性分析，对应于已经在实践中使用的更大的灵活资源（例如，分布式发电机或存储），将为中小型最终用户提供多种灵活的服务。这将增加DN的整体灵活性。此外，利用住宅和商业用户的灵活性潜力将对环境产生重大影响，因为这些用户对电力使用和全球温室气体排放都有很大贡献。该项目的研究结果可以进一步与智能电表数据相补充，以制定针对住宅和商业用户的电价和激励措施，通过减少效率和需求响应（DR）计划结果的不确定性，支持更加协调的采购灵活性。这项研究的主要受益者将是dso、聚合者和dn1一级的其他DR责任方。灵活性模型的问题不仅对报告需求端的DR潜力（通常是聚合者的角色）很重要，而且对DR方案的结果、费率设计和灵活性评估也更有信心，这些都与dso高度相关。该项目为dsos带来的主要好处之一是在投资激励措施和基础设施时支持决策制定，从而实现对整个网络灵活负载的控制。

This project aims to model demand-side flexibility coming from aggregation of a largenumber of residential and small and medium-size commercial end-users in the distribution network (DN). Thealgorithms developed through this project will facilitate more flexible operation of the DN by assessing the timevarying capacity available from flexible loads, in response to flexible services currently procured by thedistribution system operator (DSO), namely: Sustain, Secure, Dynamic and Restore. The aggregate flexibility willbe described as the amount of available capacity and its duration, as a result of aggregating individual loads withdifferent operating modes, start times, maximum deferral times, etc., driven by the end-users' daily behaviourand constrained by their comfort. Such flexibility profiling, corresponding to that of larger flexible resourcesalready employed in practice (e.g., distributed generators or storage), will make provision of multiple flexibleservices accessible to small and medium-size end-users. This will result in increased flexibility of the DN as awhole. Furthermore, harnessing flexibility potential of residential and commercial users would have significantenvironmental implications, as these contribute to a large share to both, electrical usage and global greenhousegas emissions. The findings of the project could be further complemented with smart meter data to developtariffs and incentives for residential and commercial users, supporting more coordinated procurement offlexibility by reducing uncertainty of efficiency and outcome of the demand response (DR) programmes.The main beneficiaries of the research would be DSOs, aggregators and other DR responsible parties at the DNlevel. The question of flexibility modelling is not only important for reporting DR potential at the demand side(commonly, an aggregator's role), but also for more confident estimation of the outcome of DR programmes, tariff design and flexibility assessment, which are highly relevant to DSOs. One of the main benefits for DSOsbrought by this project would be in supporting decision making when investing into incentives and infrastructureallowing network-wide control of flexible loads.