Modular Optimization and Simulation of Energy Systems

能源系统的模块化优化与仿真

• 批准号: RGPIN-2017-04455
• 负责人: Evins, Ralph
• 金额: $ 1.89万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=650357
• 关键词: Modular; Optimization; Simulation; Energy; Systems

The unique context of energy systems requires efficient, adaptable design tools able to quickly explore new areas and emerging problems. Two exciting new developments in machine learning, hyper-heuristics (optimizing the optimizer) and fitting of meta-models using statistical emulators, can be combined in a modular fashion to provide such tools.***Preventing disastrous levels of climate change, ensuring energy security and achieving a sustainable future all require novel energy systems. These will be less centralised and ‘top-down' since local balancing of demand and supply is critical temporally and spatially. Analysis of these systems must span from buildings (which are now active players in energy markets) to district, city and national infrastructure. ***This research program aims to investigate the holistic, integrated modelling, design and optimization of diverse energy systems. It will leverage the unique benefits of a modular modelling environment (mod2e), encompassing simulation and analysis, optimization, hyper-heuristics and statistical meta-models amongst others. ***Existing simulation tools address single design options rather than extensive design-space exploration. There has been considerable success in applying computational optimization methods to find good solutions across a broad design-space, but further progress requires a different approach in which optimization and modelling are coupled more closely.***The underlying methodology is the modularisation of simulation, optimization and meta-modelling elements to form a modular modelling environment (mod2e). New and existing techniques will be combined more effectively, then applied to diverse energy systems problems with academic and commercial partners.***The modularisation of optimization will allow tuning of optimizers for particular sub-problems using hyper-heuristics, which extends the field of meta-heuristics to ‘optimizing the optimizer'. Statistical meta-models allow time-consuming simulations to be replaced by fast approximations which give adequate accuracy during the early stages of optimization.***Models can be easily reused and reconfigured to address wide-ranging design problems to meet new research challenges. This flexibility will enable an adaptive process rather than the solution of a static problem. Extensions will cover semi-autonomous module configuration and expansion to additional domains such as city planning and electric vehicle deployment.***The impact of the research program will be to deliver more useful, more powerful, more holistic simulations and optimizations by embracing modularity. Faster, more detailed exploration of complex design-spaces will allow broader questions to be explored in greater depth. This will enable significant improvements in how energy systems for buildings, districts and cities can be designed and operated to meet future challenges.

能源系统的独特环境要求高效、适应性强的设计工具能够快速探索新领域和新出现的问题。机器学习领域的两个令人兴奋的新发展，hyper-automistics（优化优化器）和使用统计仿真器拟合元模型，可以以模块化的方式组合起来，以提供这样的工具。防止灾难性的气候变化、确保能源安全和实现可持续的未来都需要新的能源系统。这些将不那么集中和“自上而下”，因为当地的需求和供应平衡在时间和空间上都至关重要。对这些系统的分析必须从建筑物（现在是能源市场的活跃参与者）到地区、城市和国家基础设施。 * 该研究计划旨在研究不同能源系统的整体，综合建模，设计和优化。它将利用模块化建模环境（mod 2 e）的独特优势，包括模拟和分析，优化，超物理学和统计元模型等。* 现有的模拟工具解决单一的设计选项，而不是广泛的设计空间探索。在应用计算优化方法在广泛的设计空间中找到良好的解决方案方面取得了相当大的成功，但进一步的进展需要一种不同的方法，其中优化和建模更紧密地结合在一起。基本方法是模拟，优化和元建模元素的模块化，形成一个模块化的建模环境（mod 2 e）。新的和现有的技术将更有效地结合起来，然后与学术和商业伙伴一起应用于各种能源系统问题。优化的模块化将允许使用超优化来调整特定子问题的优化器，这将元优化领域扩展到“优化优化器”。统计元模型允许耗时的模拟被快速近似所取代，从而在优化的早期阶段提供足够的准确性。模型可以很容易地重复使用和重新配置，以解决广泛的设计问题，以满足新的研究挑战。这种灵活性将使一个适应性的过程，而不是静态的问题的解决方案。扩展将涵盖半自动模块配置和扩展到其他领域，如城市规划和电动汽车部署。该研究计划的影响将是通过采用模块化来提供更有用，更强大，更全面的模拟和优化。对复杂设计空间的更快、更详细的探索将允许更深入地探索更广泛的问题。这将大大改善建筑物、地区和城市的能源系统的设计和运营，以应对未来的挑战。