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

The transition to decarbonized energy systems has become a priority at regional, national and global levels as a critical strategy to mitigate carbon emissions and therefore climate change. However, the vulnerabilities of the proposed zero-carbon power grid 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 a broad variety of future climatic-technological scenarios, with New York State (NYS) as a case study. By accurately capturing 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 30-65\% more firm, zero-emission capacity to ensure system reliability. Merely increasing wind and solar capacity is ineffective in improving reliability due to the spatial and temporal variations in vulnerabilities. This underscores the importance of considering spatiotemporal dynamics and operational constraints when making decisions regarding additional investments in renewable resources.

[1]  S. Steinschneider,et al.  Quantifying the multi-scale and multi-resource impacts of large-scale adoption of renewable energy sources , 2023, ArXiv.

[2]  M. Dahlhausen,et al.  ComStock Reference Documentation: Version 1 , 2023 .

[3]  S. Steinschneider,et al.  Evaluating the intensity, duration, and frequency of flexible energy resources needed in a zero-emission, hydropower reliant power system , 2023, Oxford Open Energy.

[4]  N. Frick,et al.  End-Use Load Profiles for the U.S. Building Stock: Practical Guidance on Accessing and Using the Data , 2022 .

[5]  S. Steinschneider,et al.  Establishing Opportunities and Limitations of Forecast Use in the Operational Management of Highly Constrained Multiobjective Water Systems , 2022, Journal of Water Resources Planning and Management.

[6]  C. Bataille,et al.  Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Chapter 11 , 2022 .

[7]  D. Faranda,et al.  A climate-change attribution retrospective of some impactful weather extremes of 2021 , 2022, Weather and Climate Dynamics.

[8]  Jeffrey A. Sward,et al.  An Open Source Representation for the NYS Electric Grid to Support Power Grid and Market Transition Studies , 2021, IEEE Transactions on Power Systems.

[9]  G. Luderer,et al.  Impact of declining renewable energy costs on electrification in low-emission scenarios , 2021, Nature Energy.

[10]  I Keppo,et al.  Exploring the possibility space: taking stock of the diverse capabilities and gaps in integrated assessment models , 2021, Environmental Research Letters.

[11]  Iva Ridjan Skov,et al.  EnergyPLAN – Advanced analysis of smart energy systems , 2021 .

[12]  C. Goodess,et al.  Quantifying the sensitivity of european power systems to energy scenarios and climate change projections , 2021, Renewable Energy.

[13]  Yilu Liu,et al.  A Review of Clean Electricity Policies—From Countries to Utilities , 2020, Sustainability.

[14]  R. Vautard,et al.  Impacts of climate change on energy systems in global and regional scenarios , 2020, Nature Energy.

[15]  T. Hong,et al.  Quantifying the impacts of climate change and extreme climate events on energy systems , 2020, Nature Energy.

[16]  António Moniz,et al.  A review of multi-criteria decision making approaches for evaluating energy storage systems for grid applications , 2019, Renewable and Sustainable Energy Reviews.

[17]  S. Steinschneider,et al.  Summer Covariability of Surface Climate for Renewable Energy across the Contiguous United States: Role of the North Atlantic Subtropical High , 2018, Journal of Applied Meteorology and Climatology.

[18]  Paula Ferreira,et al.  Planning for a renewable future in the Brazilian power system , 2018, Energy.

[19]  Jesse D. Jenkins,et al.  The Role of Firm Low-Carbon Electricity Resources in Deep Decarbonization of Power Generation , 2018, Joule.

[20]  Shuba V. Raghavan,et al.  Translating climate change and heating system electrification impacts on building energy use to future greenhouse gas emissions and electric grid capacity requirements in California , 2018, Applied Energy.

[21]  R. Pietzcker,et al.  Application of a high-detail energy system model to derive power sector characteristics at high wind and solar shares , 2017 .

[22]  Igor Linkov,et al.  Impacts of rising air temperatures on electric transmission ampacity and peak electricity load in the United States , 2016 .

[23]  D. Maraun Bias Correcting Climate Change Simulations - a Critical Review , 2016, Current Climate Change Reports.

[24]  Kenny Gruchalla,et al.  Eastern Renewable Generation Integration Study , 2016 .

[25]  Veronika Eyring,et al.  Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization , 2015 .

[26]  C. Frantzidis,et al.  Response to Reviewers Reviewer #1 , 2010 .

[27]  Paula Varandas Ferreira,et al.  Renewable energy scenarios in the Portuguese electricity system , 2014 .

[28]  Neven Duić,et al.  A 100% renewable energy system in the year 2050: The case of Macedonia , 2012 .

[29]  Yvonne Scholz,et al.  Renewable energy based electricity supply at low costs - Development of the REMix model and application for Europe , 2012 .

[30]  R. Schaeffer,et al.  Energy sector vulnerability to climate change: A review , 2012 .

[31]  P. Forster,et al.  Climate change impacts on future photovoltaic and concentrated solar power energy output , 2011 .

[32]  Vincent Marchau,et al.  Addressing deep uncertainty using adaptive policies: introduction to section 2 , 2010 .

[33]  R. Iman Latin Hypercube Sampling , 2006 .

[34]  Cintia Bertacchi Uvo,et al.  Impacts of the North Atlantic Oscillation on Scandinavian Hydropower Production and Energy Markets , 2005 .

[35]  K. S. Swarup,et al.  Effect of temperature on short term load forecasting using an integrated ANN , 2004 .

[36]  Richard J. Beckman,et al.  A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output From a Computer Code , 2000, Technometrics.

[37]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[38]  Corinna Cortes,et al.  Boosting Decision Trees , 1995, NIPS.

[39]  K. Doering,et al.  A Spatiotemporal Analysis of New York State Grid Transition under the CLCPA Energy Strategy , 2023, HICSS.