Mobility under COVID-19: Understanding transport mode choice through agent-based modelling

问题或挑战在区域能源系统中，经济风险、监管不确定性和技术锁定都影响着决策者的选择。因此，模型参数因其固有的不确定性而受到严格审查。最近，已经提出了考虑不确定性的优化方法。然而，模型参数不确定性的量化、传播和管理仍然面临挑战。此外，目前的模型根本不适合预测未来能源需求的演变。在经济动荡、组织重组和环境脆弱性的背景下，我们可能会看到能源消费的历史趋势发生变化。目前，我们还没有有效地吸收这些变化的机制。该项目将研究数字技术和新数据源在量化区域能源系统优化中的不确定性方面的作用。它将探索结合统计和数值建模的混合模型，以改善管理和减少模型结果的不确定性。将特别强调为有效决策而传达不确定因素。博士项目描述博士项目将在以下方面扩展最近的区域能源优化模型：(a)表示额外的组件和技术(例如。(b)开发一种系统的方法来量化模型输入中的不确定性，特别是那些与系统弹性有关的不确定性。这将通过利用数字技术(c)通过统计和数值模拟的新组合传播、管理和交流不确定性来实现。MRes组件-对最先进的随机优化进行彻底的文献回顾-上面列出的组件(A)，以及相关数字技术的识别。博士-预期结果，对知识和实践的贡献-随机能源系统优化的工具-整个系统生命周期的数据同化以评估系统运行的变化-通过适当的计算平台有效地显示和交流模型输出。

Problem or ChallengeIn district energy systems, economic risk, regulatory uncertainty, and technology lock-in all weigh on decision makers' choices. Accordingly, model parameters are coming under scrutiny for their inherent uncertainty. More recently, optimisation methods which account for uncertainty have been proposed. However, the quantification, propagation, and management of uncertainty in model parameters continues to pose challenges. Furthermore, current models are fundamentally ill-suited to project future evolutions of energy demand. In the context of economic volatilities, organizational restructuring, and environmental vulnerabilities, we are likely to see shifts in historic trends of energy consumption. At the moment we have no mechanism to efficiently assimilate these shifts.MRes/PhD project objectivesThis project will investigate the role of digital technologies and new data sources to quantify uncertainties in district energy system optimization. It will explore hybrid-models that combine statistical and numerical modelling for improved management and reduction of uncertainties in model outcomes. Specific emphasis will be on communication of uncertainties for effective decision-making.PhD project descriptionThe PhD will extend a recent model of district energy optimization in the following aspects: (a) representation of additional components and technologies (for eg. EV charging within the district energy network), (b) develop a systematic methodology to quantify uncertainties in model inputs, especially those pertaining to resilience of the system. This will be carried out by exploiting digital technologies (c) propagation, management, and communication of uncertainties through novel combinations of statistical and numerical modelling.MRes component- A thorough literature review of the state-of-the-art in stochastic optimization- The component (a) listed above, along with identification of relevant digital technologies. PhD - Expected Outcomes, Contributions to Knowledge & Practice- A tool for stochastic energy system optimization- Data assimilation across the system lifetime to assess changes in system operation- Efficient display and communication of model outputs through appropriate computational platforms.