A number of emerging trends will substantially alter the operation and control of the electric grid over the next several decades. These trends include: ensuring resiliency under severe weather events, increasing integration of renewable electricity generation, supporting changing electricity demand patterns, and the improving cost effectiveness of distributed energy resources. With today’s grid, operators calculate and schedule the amount of power that needs to be generated based off anticipated demand. However, as electricity markets and infrastructure evolve, these calculations become more complex and the probability for error increases. The current optimization tools are not scalable to emerging energy requirements. Improved optimization tools are essential to manage a next-generation electricity system that includes more renewable power, demand response, storage systems, and technologies that integrate natural gas infrastructure with the electric grid.
Project Innovation + Advantages:
The team led by Pacific Northwest National Laboratory (PNNL) will develop a High-Performance Power-Grid Optimization (HIPPO) technology to reduce grid resource scheduling times to within a fraction of current speeds, which can lead to more flexible and reliable real-time operation. The team will leverage advances in optimization algorithms and deploy high-performance computing technologies to significantly improve the performance of grid scheduling. HIPPO will provide inter-algorithm parallelization and allow algorithms to share information during their solution process, with the objective of reducing computing time by efficiently using computational power. New algorithms will leverage knowledge of the underlying system, operational experience, and past solutions to improve performance and avoid previously encountered mistakes.
If successful, innovations from this project could enhance the ability to manage the electric grid and enable grid-level integration of current and future smart grid technologies.
Improved grid optimization could permit tighter integration among grid analytical tools and provide greater grid resilience through the ability to detect, isolate, and recover from failures.
Faster grid calculations could reduce energy losses on the grid, helping to avoid energy-related greenhouse gas emissions.
The technology will yield grid operational benefits and significant financial savings.