arXiv subjects

M. Vivienne Liu

Publications and source records attributed to M. Vivienne Liu.

5 recordsLinked to original sources

arXiv2025

Identification of pressure points in modern power systems using transfer entropy

Integration of variable energy resources -- e.g., solar, wind, and hydro -- and end-use electrification increase modern energy systems' weather-dependence. Identifying critical infrastructure constraining the power grid's ability to meet electricity demand under weather-induced shocks and stressors is essential for understanding risks and guiding adaptation. We use transfer entropy to identify predictive pressure points: grid components whose utilization patterns provide early signals of downstream power shortages. We apply this method to simulations of New York State's proposed future grid under various meteorological and technological scenarios, showing that pressure points often arise from complex, system-wide interactions between generation, transmission, and demand. While transfer entropy does not support conclusions about causality, the identified pressure points align with known bottlenecks and offer insight into failure pathways. Furthermore, these pressure points are not easily predicted by high-level scenario features alone, underscoring the need for holistic and adaptive approaches to reliability planning in power systems with intermittent resources.

physics.soc-ph↗

arXiv2025

A Multiobjective Reinforcement Learning Framework for Microgrid Energy Management

The emergence of microgrids (MGs) has provided a promising solution for decarbonizing and decentralizing the power grid, mitigating the challenges posed by climate change. However, MG operations often involve considering multiple objectives that represent the interests of different stakeholders, leading to potentially complex conflicts. To tackle this issue, we propose a novel multi-objective reinforcement learning framework that explores the high-dimensional objective space and uncovers the tradeoffs between conflicting objectives. This framework leverages exogenous information and capitalizes on the data-driven nature of reinforcement learning, enabling the training of a parametric policy without the need for long-term forecasts or knowledge of the underlying uncertainty distribution. The trained policies exhibit diverse, adaptive, and coordinative behaviors with the added benefit of providing interpretable insights on the dynamics of their information use. We employ this framework on the Cornell University MG (CU-MG), which is a combined heat and power MG, to evaluate its effectiveness. The results demonstrate performance improvements in all objectives considered compared to the status quo operations and offer more flexibility in navigating complex operational tradeoffs.

eess.SY↗

arXiv2024

Heterogeneous Vulnerability of Zero-Carbon Power Grids under Climate-Technological Changes

The transition to decarbonized energy systems has become a priority globally to mitigate carbon emissions and, therefore, climate change. However, the vulnerabilities of zero-carbon power grids under climatic and technological changes have not been thoroughly examined. In this study, we focus on modeling the zero-carbon grid using a dataset that captures diverse future climatic-technological scenarios, with New York State as a case study. By accurately representing the topology and operational constraints of the power grid, we identify spatiotemporal heterogeneity in vulnerabilities arising from the interplay of renewable resource availability, high load, and severe transmission line congestion. Our findings reveal a need for 61-105\% more firm, zero-emission capacity to ensure system reliability. Merely increasing wind and solar capacity is ineffective in improving reliability due to transmission congestion and spatiotemporal variations in vulnerabilities. This underscores the importance of considering spatiotemporal dynamics and operational constraints when making decisions regarding additional investments in renewable resources.

physics.soc-ph↗

arXiv2023

Quantifying the multi-scale and multi-resource impacts of large-scale adoption of renewable energy sources

The variability and intermittency of renewable energy sources pose several challenges for power systems operations, including energy curtailment and price volatility. In power systems with considerable renewable sources, co-variability in renewable energy supply and electricity load can intensify these outcomes. In this study, we examine the impacts of renewable co-variability across multiple spatial and temporal scales on the New York State power system, which is undergoing a major transition toward increased renewable generation. We characterize the spatiotemporal co-variability of renewable energy-generating resources and electricity load and investigate the impact of climatic variability on electricity price volatility. We use an accurate, reduced-form representation of the New York power system, which integrates additional wind and solar power resources to meet the state's energy targets through 2030. Our results demonstrate that renewable energy resources can vary up to 17% from the annual average, though combining different resources reduces the overall variation to about 8%. On an hourly basis, renewable volatility is substantially greater and may vary up to 100% above and below average. This results in a 9% variation in annual average electricity prices and up to a 56% variation in the frequency of price spikes. While yearly average price volatility is influenced mainly by hydropower availability, daily and hourly price volatility is influenced by solar and wind availability.

eess.SY↗

arXiv2021

An Open Source Representation for the NYS Electric Grid to Support Power Grid and Market Transition Studies

Under the increasing need to decarbonize energy systems, there is coupled acceleration in connection of distributed and intermittent renewable resources in power grids. To support this transition, researchers and other stakeholders are embarking on detailed studies and analyses of the evolution of this complex system, which require a validated representation of the essential characteristics of the power grid that is accurate for a specific region of interest. For example, the Climate Leadership and Community Protection Act (CLCPA) in New York State (NYS) sets ambitious targets for the transformation of the energy system, opening many interesting research and analysis questions. To provide a platform for these analyses, this paper presents an overview of the current NYS power grid and develops an open-source (https://github.com/AndersonEnergyLab-Cornell/NYgrid) baseline model using publicly available data. The proposed model is validated with real data for power flow and Locational Marginal Prices (LMPs), demonstrating the feasibility, functionality, and consistency of the model. The model is easily adjustable and customizable for various analyses of future configurations and scenarios that require spatiotemporal information about the NYS power grid with data access to all the available historical data and serves as a practical system for general methods and algorithms testing.

eess.SY↗