EFRI-COPN: Neuroscience and Neural Networks for Engineering the Future Intelligent Electric Power Grid

这个项目的基础是Steve Potter和Venyagamoorthy团队之间的一个新的和深入的合作伙伴关系，Steve Potter是在体外寻找神经回路功能的世界先驱（“活神经网络，LNN”），Venyagamoorthy团队领导了自适应的应用，预期优化到电网的组件。这两个团队正在联合起来应对空间复杂性的挑战。以前LNNs的工作主要集中在管理单个控制变量等挑战上，但电网需要数千个相互关联的变量，这些变量必须实时管理。这项在体外进行的新工作将探测由数千个神经元和神经胶质组成的LNNs预测复杂电网模拟器行为的能力，并测试新的生物学习模型解释其能力的能力。如何应对复杂性的新数学概念也将在解决相同的预测挑战中得到测试，并首次尝试将自适应预期控制应用于模拟中的大规模电网控制。对商业电网的测试将主要通过它们与墨西哥、巴西、中国、尼日利亚、新加坡和南非的合作进行。由于缺乏预期优化（以及Venayagamoorthy等人的工作所证明的最佳时移）和储存，目前使用风力发电取代煤炭并减少二氧化碳排放的比例限制在20%左右。如果与足够的存储相结合，这里针对的新算法应该使中国和美国都有可能吸收足够的风能（或太阳能），从而能够在发电过程中实现二氧化碳的零排放。目前看来，美国和中国都有足够的陆上风能资源来实现这一目标。

The basis of this project is a new and deep partnership between Steve Potter, a world pioneer in searching for functional capabilities of neural circuits in vitro ("living neural networks, LNN"), and the Venyagamoorthy team, which has led the application of adaptive, anticipatory optimization to components of the electric power grid.The two groups are combining together to address the challenge of spatial complexity. Previous work on LNNs has focused on challenges like managing a single control variable, but electric power grids entail thousands of interconnected variables which must be managed in real-time. The new work in vitro will probe the ability of LNNs made up of thousands of neurons and glia to predict the behavior of a complicated power grid simulator, and test the ability of new biological learning models to explain their capabilities. New mathematical concepts for how to cope with complexity will also be tested in addressing the same prediction challenge, and in attempting to apply adaptive, anticipatory control for the first time to large scale power grid control in simulation. Testing on commercial electric power grids will mainly occur through their collaborations with Mexico, Brazil, China, Nigeria, Singapore and South Africa.The use of wind power to displace coal and reduce CO2 emissions is currently limited to about 20%, because of the lack of anticipatory optimization (and optimal time-shifting, as demonstrated in the work of Venayagamoorthy et al.) and storage. If combined with adequate storage, the new algorithms aimed at here should make it possible for both China and the US to assimilate enough wind (or solar) power to be able to zero out their emissions of CO2 in power generation. It currently appears that the US and China both have enough onshore wind resources to make this possible.