Deploying sustainable energy technologies to enhance global development and human well being

应对气候紧急情况需要能源系统模型来确保资源的有效利用。这些模式被要求应对的挑战的复杂性已显著增加。模型被要求代表可变的可再生能源和储存，纳入土地、能源和水之间的权衡，并解决不确定性。如果不解决这些挑战、协同作用和权衡取舍，可能会导致资源利用效率低下。这项研究计划解决了可持续发展的主要建模挑战。长期目标通过创建有效的建模工具来支持地方、区域和国际规模的可持续发展，并将这些工具应用于全球的政策制定。短期目标是解决系统模型中的不确定性，开发纳入联合国可持续发展目标(SDGs)的算法，并解决需求侧管理(DSM)和存储带来的挑战。解决能源系统模型不确定性的工作侧重于金融风险，而不是与气候影响有关的风险，也不是未来需求/人口预测。扩大模型所处理的风险将带来更好的政策决策。例如，解决太阳能资源转移的风险可以减少在未来资源不适合的地方安装太阳能电池板的可能性。鉴于应对气候危机所面临的资源限制，将不确定性纳入系统模型将减少资源的低效利用。将可持续发展目标的更多方面纳入模型将解决权衡和协同效应问题。最近的工作将土地、能源和水的关系的各个方面纳入了模型，但这一点需要扩大。将健康和生物多样性纳入其中，将更好地处理权衡取舍问题。例如，目前大多数电气化计划没有考虑燃烧生物质产生的室内空气质量，但在发展中国家，这些影响可能会显著改变电气化的好处。最后，解决需求侧管理和存储问题将是能源系统未来规划的关键。这些新兴技术允许更多可变的可再生能源为减少气候排放做出贡献，但代表它们的建模工具没有得到很好的研究。综合起来，这些模型改进将建立一个强大的研究计划，从而提高决策能力。建立考虑可持续发展权衡的模型对于有效利用资源至关重要。在该项目中工作的总部基地将有机会将他们的建模知识应用于全球各地的可持续发展项目，扩大研究项目的影响。

Addressing the climate emergency requires energy systems models to ensure efficient use of resources. The complexity of the challenges that these models are being asked to address has increased significantly. Models are being asked to represent variable renewable energy and storage, to incorporate trade-offs between land, energy, and water, and to address uncertainties. Not addressing these challenges, synergies and trade-offs can lead to inefficient use of resources. This research program addresses major modelling challenges for sustainable development. The long-term objectives support sustainable development at the local, regional, and international scale through creating effective modelling tools, and apply these tools to policy making around the globe. Short term objectives address uncertainty in systems models, develop algorithms for incorporating the UN Sustainable Development Goals (SDGs), and address challenges posed by demand side management (DSM) and storage. Work addressing uncertainty in energy system models focusses on financial risks and not risks related to climate impacts, nor future demand/population projections. Expanding the risks that models address will result in better policy decisions. For example, addressing the risk of shifts in solar resources can reduce the likelihood of installing solar panels in locations where the future resource is not suitable. Given the resource limitations being faced in addressing the climate crisis, incorporating uncertainty into system models will reduce the inefficient use of resources. Incorporating more aspects of the SDGs into models will address trade-offs and synergies. Recent work incorporates aspects of the nexus of land, energy and water into models, but this needs to be expanded. Expanding the nexus to include health and biodiversity will better address trade-offs. For example, indoor air quality from burning biomass is not currently considered in most electrification plans, but in developing countries these impacts could significantly shift the benefits of electrification. Finally, addressing DSM and storage will be critical in future planning of the energy system. These emerging technologies allow more variable renewable energy sources to contribute to reducing climate emissions, but the modelling tools to represent them are not well studied. Combined, these modelling enhancements will build a strong research program that will enhance decision making. Building models that consider the trade-offs in sustainable development is critical to efficient resource use. HQP working on the program will have the opportunity to apply their modelling knowledge to sustainable development projects around the globe, expanding the impact of the research program.