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

该项目的基础是史蒂夫·波特(Steve Potter)和Venyagamoorthy团队之间的新的深入合作伙伴关系。Steve Potter是在体外寻找神经网络功能能力的世界先驱，Venyagamoorthy团队领导了对电网组件的自适应预期优化应用。两个团队正在联合起来，以应对空间复杂性的挑战。以前关于LNN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。这项新的体外工作将探索由数千个神经元和胶质细胞组成的神经网络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。如何处理复杂性的新数学概念也将在解决同样的预测挑战，并首次尝试在模拟中将自适应预期控制应用于大规模电网控制中进行测试。商业电网的测试将主要通过与墨西哥、巴西、中国、尼日利亚、新加坡和南非的合作进行。由于缺乏预见性优化(以及最佳时移，Venayagamoorthy等人的工作证明了这一点)，目前使用风力发电取代煤炭和减少二氧化碳排放的比例限制在20%左右。和储藏室。如果与足够的存储相结合，针对这里的新算法应该会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.