The Challenge 

Electricity grids in the United States are rapidly evolving due to technical advancements, cost declines, and policy objectives that have resulted in increasing investment in variable renewable energy (VRE), e.g., wind, solar, and energy storage resources; and increasing interdependence with other energy systems such as transport electrification and green hydrogen production. The U.S. government has established ambitious decarbonization goals, led by the Biden Administration’s target to produce 100% of electricity from carbon-free resources by 2035. These factors introduce new challenges and increase the complexity of power system planning and operations. Specifically, wind and solar generation is weather and location dependent - only producing electricity when and where the wind blows or sun shines. As result, generation profiles from wind and solar generators and uncertain. As the relative share of wind and solar generation increases, these factors will increasingly require new models, tools, market rules and operational practices to ensure that our power systems are able to deliver reliable, clean and affordable electricity to consumers.

Argonne’s Approach 

The Electricity Market Team at Argonne leads a range of research activities that are designed to improve decision-making for the planning and operation of modern power systems. We are developing a range of complex analytical models that can simulate and optimize future grids while considering the impacts of system operation with highly resolved temporal and geographic representation. These tools consider the physical characteristics of generation and transmission assets and the complex interactions between system infrastructure, required grid services, operational economics, and national, state and local policy. Argonne researchershave developed an integrated national-scale power system simulation framework, the Argonne Low-carbon Electricity Analysis Framework (A-LEAF) that includes a number of capabilities of crucial importance to analyzing and optimizing the ongoing power system transition.

Generation and transmission expansion planning

Generation and transmission expansion planning models determine the optimal time, location, and size of new generation and transmission assets that are needed to guarantee reliable provision of electricity to consumers, while ensuring that system costs are also minimized. Such models have traditionally been executed with relatively coarse temporal and geographic resolution, considering a subset of days to be representative of an entire year for example. This approach has generally been appropriate when generation investments are centered around a small number of large coal, natural gas or nuclear-fuel power plants. However, as electricity is increasingly provided by wind and solar generators that are more geographically dispersed, it is important to represent system geography with greater detail. As wind, solar and storage resources vary their generation (and charging) profiles on much finer time scales, it is also important to capture sub-hourly decision making in expansion planning models. Argonne researchers are developing cutting generation and transmission expansion planning capabilities that will capture these complex factors that drive investment and retirement decisions in power systems of the future including: 

• Modeling of investment and retirement decision-making of profit-seeking entities using game-theoretic and agent-based frameworks.
• Increasing temporal resolution to capture sub-hourly volatility of solar and storage operations in expansion problems.
• Increasing the length of representative periods to capture the dynamics of long-duration energy storage in expansion problems.
• Improving best practices in co-optimization of generation and transmission expansion planning.

Short-term production cost simulation

Short-term production cost simulation models determine the least-cost system operational strategies for individual power plants, typically for every hour of a year and often with greater geographic resolution than expansion planning models. These models explicitly consider security-constrained unit commitment and economic dispatch formulations and include more detailed representations of emerging technologies such as energy storage. Argonne researchers are currently developing multiple innovations to improve best practices in production cost modeling including:

• Capturing the impacts of system uncertainty through scenario-based stochastic optimization methods.
• Enhancing representation of technologies with high levels of operational complexity such as hydropower and energy storage.
• Implementing advanced mathematical algorithms to reduce computational times and enhance model scalability.
• Leveraging the high-performance computing capabilities at Argonne to conduct high-fidelity analysis of large, real-world systems.