The REAM lab has a myriad of projects that analyze renewable energy integration, ranging from long-term planning to real-time operations. The following is a non-exhaustive list of research threads:
- Long-term planning in power systems
- Real-time operations of grids with high penetration of renewable energy
- Development and application of machine learning and AI methods for power systems controller design using safety guarantees from control theory
- Electricity market redesign for the efficient integration of renewable energy
- Distributed energy resources deployment (e.g. to mitigate wildfire risk), operation and tariff schemes designs
Control of Power Dynamics with Variable and Low Inertia
As more non-synchronous RES participate in power systems, the system’s inertia decreases and becomes time dependent, challenging the ability of existing control schemes to maintain frequency stability. System operators, research laboratories, and academic institutes have expressed the importance to adapt to this new power system paradigm. However, power dynamics have been modeled as time-invariant, by not modeling the variability in the system’s inertia. To address this, we propose a new modeling framework for power system dynamics to simulate a time-varying evolution of rotational inertia coefficients in a network. Power dynamics are modeled as a hybrid system with discrete modes representing different rotational inertia regimes of the network.
Using this new hybrid model for power dynamics, we present a framework to design a fixed learned controller based on datasets of optimal time-varying LQR controllers. We test the performance of the controller in a twelve-bus system. By adding virtual inertia we can guarantee stability of high-renewable (low-inertia) modes. The novelty of our work is to propose a design framework for a stable controller with fixed gains for time-varying power dynamics. This is relevant because it would be simpler to implement a proportional controller with fixed gains compared to a time-varying control. To expand this work, we introduce a framework to learn sparse time-invariant frequency controllers in a power system network with a time-varying evolution of rotational inertia. We design a controller that uses as features the system’s states. In other words, we design a control proportional to the angles and frequencies. Virtual inertia is included in the controllers to ensure stability. One of the findings is that it is possible to restrict communication between the nodes by reducing the number of features in the controller (from 22 to 10 in our case study) without disrupting performance and stability. Furthermore, once communication between nodes has reached a threshold, increasing it beyond this threshold does not improve performance or stability. There is a correlation between optimal feature selection in sparse controllers and the topology of the network.
Zero Emissions in the Western North American Grid
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Stochastic Long-term Power System Planning in Western North America under Climate Change
Typical electricity-grid capacity expansion models make investment decisions with fixed inputs (e.g., fixed electricity demands and hydro-power availability). The resultant electricity supply system may not be robust to future climate change-driven uncertainties in energy demand and supply. We present the first climate change stochastic long-term (2050) capacity expansion and operation electricity grid model for the Western North America electricity region, with high temporal and spatial resolution. The Stochastic SWITCH WECC model generates a least cost portfolio of power plants that is robust to varying future climate conditions using a multi-stage optimization approach with varying electricity-demand and hydropower-availability inputs under three climate change scenarios. Results show that an optimal robust electricity supply portfolio in the WECC for 2050 has about 4% higher overall installed capacity than the average mix of the three scenarios modeled separately, and about 5.6% higher installed gas capacity, due to the greater need for operational flexibility under the wider range of possible conditions.
Modeling of Long-Duration Storage for Decarbonization of California and the Western US Energy System
The challenge is to meet all of California’s and Western US’ clean-energy goals with low cost solutions. Low-cost solar and wind electricity are a partial solution, but the public would also like low-cost electricity when solar and wind electricity are not available (at night and/or on calm days). While many technologies show promise to provide the needed storage and/or demand management, none is established in a way that gives confidence or that elucidates how a zero-carbon electricity grid will function.
Modeling of the Western US electricity grid requires thousands of assumptions and the assumptions can vary widely. The state of California can implement time of use rates or demand management programs that can help to shift load to times when renewable electricity is available. These actions have the potential to greatly accelerate the adoption of new storage technologies for different durations if they are well planned. Our challenge is to develop and implement mathematical methods to operate and optimize the capacity expansion of the grid, and with this framework, study the roles and cost targets of long-duration storage technologies.
Wave Energy Technology Assessment for Optimal Grid Integration and Blue Economy Advancement
The marine energy potential in the United States is twice the current total national electricity consumption. Until recently, it has been cost prohibitive to take advantage of this tremendous resource. Dramatic cost reductions are now taking place with a diverse suite of technology options, and significant further reductions are possible through technological learning and by co-locating wave energy with other blue economy markets, specifically offshore wind, seawater desalination, and aquaculture. By quantifying the benefits of co-location before the build-out of these assets, the technical, regulatory, and economic opportunities can be maximized. We propose the first in-depth national study of the techno-economic performance of wave and combined wind-wave farms for utility-scale grid connection and powering blue economy industries, such as seawater desalination. This analysis has the potential to guide wave technology designs, stimulate the marine energy markets, and disrupt the national energy landscape.
Learning to Control in Power Systems: Design and Analysis Guidelines for Concrete Safety Problems
Rapid progress in machine learning and artificial intelligence (AI) has brought renewed attention to its applicability in power systems for modern forms of control that help integrate higher levels of renewable generation and address increasing levels of uncertainty and variability. In this work we discuss these new applications and shine light on the most relevant new safety risks and considerations that emerge when relying on learning for control purposes in electric grid operations. We build on recent taxonomical work in AI safety and focus on four concrete safety problems. We draw on two case studies, one in frequency regulation and one in distribution system control, to exemplify these problems and show mitigating measures. We then provide general guidelines and literature to help people working on integrating learning capabilities for control purposes to make safety risks a central tenet of design.
Wildfire Risk Mitigation and Power Systems Resilience
Electricity market design to allow the participation of distributed energy resources (DER) and microgrids in the market (TSO-DSO coordination). The objective of this work is to design an effective scheme to aid the penetration of DER independently and within microgrids, while at the same time ensuring reliability in the events of wildfire.
Electricity Market and Ancillary Services Redesign
As more renewable energy participates in power systems, the markets and requirements that govern its operation require to be modernized to be able to operate efficiently and reliably in this new setting. This projects aims to design new ancillary services that use state of the art mathematical methods (machine learning, reinforcement learning, control theory, optimization) to guarantee safe and reliable system’s operation.