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团队之间新的深度合作，前者是体外神经回路功能研究（“活神经网络，LNN”）的世界先驱，后者领导了自适应、预期优化在电网组件中的应用。这两个团队正在联合起来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。这项新的体外研究将探索由数千个神经元和神经胶质细胞组成的LNN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。新的数学概念，如何科普复杂性也将在解决同样的预测挑战，并在尝试应用自适应，预期控制的第一次大规模电网控制模拟测试。商业电网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。由于缺乏预期优化（以及最佳时移，如Venayagamoorthy等人的工作所示），使用风力发电替代煤炭并减少二氧化碳排放量目前仅限于约20%。和存储。如果与足够的存储相结合，针对这里的新算法应该使中国和美国能够吸收足够的风能（或太阳能），从而能够将发电过程中的二氧化碳排放量降至零。 目前看来，美国和中国都有足够的陆上风力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.