Energy-related greenhouse gas emissions are estimated to increase by 1.5 billion tons by the end of 2021 from 2.3 billion tons in 2020. This is the second-largest increase in history and the largest annual rise since 2010, stated by the International Energy Agency (IEA). The electric grid is the primary source of over 25% of these emissions. In the face of this warning, we must find and build a sustainable solution for our climate while considering the impacts of economic recovery from the Covid crisis.
Data-Empowered Grid Transformation
There is a significant push, driven primarily due to climate change concerns, to reduce the use of carbon-based fuels to slow the warming trend by increasing the capacity and generation of renewable energy sources. This is simultaneous with the efforts in improving grid resilience against many climate change-induced extreme events. These events include hurricanes, floods, and wildfires, to name a few, that are currently surging in terms of both intensity and the frequency of occurrence. All in all, the power grid’s transformation needs to be expedited to adapt to climate change. This achievement of the 20th century needs to be reimagined and re-engineered to meet the needs of the 21st century.
As research has shown, data will be at the core of this transformation. Significant investments in smart grid technology over the past two decades have resulted in advanced grid monitoring and measurement, resulting in the continual collection of myriads of data from various parts of the grid. Computing tools have accordingly resurfaced as enabling means that can convert data to information and ultimately to actions. Traditional computing relies on large operating margins to ensure a secure and resilient grid operation. Today, these extensive operating margins cause an estimated US$5-US$15B in additional energy delivery costs, which will further increase as renewable energy grows over the next several decades.
Many power engineering research and development roadmaps, as adopted by DOE, NSF, Utilities, and ISOs, call for new analytics and computational methods to develop sustained innovation for the planning and operation of the fast-evolving power grid. This is considering that the grid operation will be ever more complex, including an interplay between physics, economics, and uncertainties of many forms.
Rethinking Computing
Grid analytics signifies a set of Software-as-a-Service (SaaS) tools to support accurate modeling appropriate for various time scales of decision making at different layers of such complex interconnected grid. Given this growing complexity, the traditional computing methods may become ineffective to address the new set of challenges or offer innovative solutions. A viable and emerging solution is Quantum Computing technology. Quantum Computing is developed based on quantum mechanical phenomena that describe the nature and conduct of energy and matter at the level of fundamental subatomic particles. A quantum computer operates by controlling the behavior of these particles to achieve desired computation. Quantum computers mark a step forward in computing capacity, which is potentially superior to a modern supercomputer by offering extensive efficiency growth. Based on the laws of quantum physics, a quantum computer can achieve enormous processing power over multi-state capacity and can execute multiple functions simultaneously by using possible permutations.
Taking an Initiative
Through funding from the Advanced Industries Grant Program at the Colorado Office of Economic Development and International Trade (OEDIT), Dr. Amin Khodaei, professor at the University of Denver, and Dr. Rozhin Eskandarpour, the CEO of Resilient Entanglement (RE), have investigated possible solutions utilizing quantum models and quantum mechanical phenomena to address the critical challenges of the power sector. The team focuses on the core of today’s energy technology movement by developing a quantum solution to one of the most fundamental power system problems — DC power flow. The DC power flow problem is a numerical analysis based on the physics of the grid, which is the keystone of electric utilities’ decision-making in grid operation, control, and planning. Initial studies conducted on a practical quantum computer showed a potentially significant speedup compared to a classical computer.
This will in turn transform current grid practices, for example by enabling a faster grid recovery and shorter power outages for residents in disaster areas in the aftermath of an extreme weather event, helping to save lives, restore comfort, and secure communication within the community. The team’s objective was to provide a new analytical approach that, by leveraging exascale computing, can prove the concept and achieve a theoretically proven speedup, thus resulting in potentially significant savings through transformed decision making.
Power flow is the most widely used power system analysis technique, either as a stand-alone application or embedded in other applications; therefore, its fast and accurate solution is of utmost significance for grid operators. We based our studies on the Harrow-Hassidim-Lloyd (HHL) quantum algorithm, which has a proven theoretical speedup over classical algorithms in solving a system of linear equations. Practical studies on a quantum computer were conducted using the WSCC 9-bus system. The results showed that a quantum computer could provide higher processing power and solve this fundamental grid problem up to 30 times faster than a classical computer for large-scale power grids. It was further shown that this conclusion holds true even for cases that quantum hardware adds delays to computations.
From the Rocky Mountains to the Coasts
The team is currently building partnerships with other researchers in the state of Colorado to develop and create a quantum computing-based platform to address exascale stochastic and dynamic grid behavior. The team draws on a valued collaboration between the University of Denver, Resilient Entanglement, the National Renewable Energy Laboratory, and the Colorado Office of Economic Development and International Trade.
Developing the foundational capabilities for understanding and analyzing the next-generation electric grid is a multidisciplinary endeavor and requires experts in electric power engineering, physics, mathematics, statistics, operations research, computer science, and economics. Therefore, to meet today’s most prominent energy challenges, we need researchers and entrepreneurs with a long-term vision and a collaborative mindset as well as profound experiences in investing across the energy computation, quantum computing, and a strong commitment to setting the standard for energy excellence to build the next-generation electric grid.
Acknowledgment
This work was supported by the Advanced Industries Grant Program of the Colorado Office of Economic Development and International Trade, and the University of Denver.
