Hardware and software for power system and market studies using AI and bigdata techniques

大型互联电力系统对加拿大人的经济和社会福祉具有战略意义，因为社会比以往任何时候都更加依赖电力，因为我们的大部分能源需求，特别是热能和交通运输，正以非常快的速度实现电气化，这是由气候变化以及与之相关的更多现有和更便宜的可再生能源(RE)的整合所推动的，如风能和太阳能发电。这些巨大而复杂的网格被认为是人类发明的最复杂的系统，传统上是通过软件模拟来学习和研究，而不是基于硬件的研究。在此背景下，我们在滑铁卢的研究小组专注于电力系统研究，主要基于计算机建模和模拟大小电网的数学模型，用于稳定性、控制、运行、经济、规划和电力市场研究。在过去的二十年里，我们的研究小组开发了一些最相关的数学模型，用于电网技术文献中的计算机仿真和研究，并得到了多个奖项和认可的证明，这些模型被世界各地的研究人员和公用事业公司广泛参考和使用。 我们的研究是在4台服务器和各种软件包的帮助下完成的，这是我们团队完全依赖的。所有这些硬件和软件工具都是用NSERC、OCE、研究合同和滑铁卢基金开发、购买、维护和更新的，这些工具对我们很好，使我们也能够与滑铁卢大学和其他加拿大大学的其他同事以及来自海外机构的研究人员建立合作关系。然而，这些服务器和电力系统建模和仿真软件包正在变得过时，因此迫切需要升级。此外，目前市场上可用的软件版本具有许多我们现有版本所缺乏的高级功能。事实上，我们最老的服务器由于陈旧和过度使用，在执行所需处理任务方面变得相当缓慢，这正在影响我们研究成果的质量和数量。 鉴于上述主要限制，我们正在申请研究工具和仪器(RTI)资金，用最新的更先进的版本取代上述服务器，特别是因为我们开始研究人工智能(AI)、机器学习(ML)和电力系统的大数据应用。由即将到来的拨款和合同支持的这一不断发展的新研究方向将需要一种基于多个图形处理单元(GPU)的不同类型的服务器，这对于执行与人工智能相关的软件是必不可少的。图形处理器是FAST ML的最佳选择，因为数据科学模型训练涉及通过并行计算大大增强的计算。

Large interconnected electric power systems are strategic for the economic and social well-being of Canadians, since society depends more than ever on electricity, due to the fact that most of our energy demand, in particular thermal and transportation, are being electrified at a very rapid rate, driven by climate change and the associated integration of ever more present and cheaper Renewable Energy Sources (RES) such as wind and solar generation. These large and complex grids, which are considered the most complex systems invented by humanity, have traditionally been studied and researched through software simulations, rather than hardware-based studies. In this context, our research group at Waterloo has focused on power systems research that is primarily based on computer modeling and simulation of mathematical models of large and small power grids with RES for stability, control, operation, economics, planning, and electricity market studies. For over the past two decades, our research group has developed some of the most relevant mathematical models for computer simulations and studies in the power grid technical literature, as attested by multiple awards and recognitions, which has been widely referred to and used by researchers and utilities around the world. Our research has been accomplished with the help of 4 servers and various software packages, on which our group is fully dependent. All this hardware and software tools, which have been developed, purchased, maintained, and updated with NSERC, OCE, research contracts, and Waterloo funds, have served us well, allowing us to also establish collaborations with other colleagues at Waterloo and other Canadian universities, as well as with researchers from overseas institutions. However, these servers and power system modeling and simulation software packages are becoming outdated and hence need urgent upgrade. Furthermore, the software versions currently available in the market have many advanced features that our existing versions are lacking. In fact, our oldest server has become rather slow in performing the required processing tasks because of its age and overuse, which is affecting the quality and quantity of our research output. In view of the above major constraints, we are applying for the Research Tools and Instrument (RTI) funds to replace the aforementioned server with an up-to-date more advanced version, especially since we are starting to pursue research in artificial intelligence (AI), machine learning (ML), and big-data applications to power systems. This evolving new research direction being supported by upcoming grants and contracts would require a different type of server based on multiple Graphic Processing Units (GPUs), which are indispensable for executing AI-related software. GPUs are the best options for fast ML as data science model training involves calculations that are greatly enhanced by parallel computations.