Search for: All records

Climate change impacts the electric power system by affecting both the load and generation. It is paramount to understand this impact in the context of renewable energy as their market share has increased and will continue to grow. This study investigates the impact of climate change on the supply of renewable energy through applying novel metrics of intermittency, power production and storage required by the renewable energy plants as a function of historical climate data variability. Here we focus on and compare two disparate locations, Palma de Mallorca in the Balearic Islands and Cordova, Alaska. The main results of this analysis of wind, solar radiation and precipitation over the 1950–2020 period show that climate change impacts both the total supply available and its variability. Importantly, this impact is found to vary significantly with location. This analysis demonstrates the feasibility of a process to evaluate the local optimal mix of renewables, the changing needs for energy storage as well as the ability to evaluate the impact on grid reliability regarding both penetration of the increasing renewable resources and changes in the variability of the resource. This framework can be used to quantify the impact on both transmission grids and microgrids and can guide possible mitigation paths. 

Cordova is a town of approximately 2,000 people located on the southern coast of Alaska. A power grid for a town this size, with a large seasonal fishing economy, is considered a moderate to large sized microgrid in terms of power produced. Understanding the vulnerabilities and risks of failures in such a grid is important for planning and operations. Investigating these characteristics in the context of complex system dynamics is a novel approach. The analysis of Cordova’s microgrid is a case study relevant to a large class of microgrid communities. We analyze the outage data based on size, cause characteristics, and load demand on the system and find long time correlations and power laws in the failure size distributions. Finally we apply a risk metric to give a single numerical value to the risk of an outage occurring during certain time periods and under certain conditions. 

The OPA model calculates the long-term risk of cascading blackouts by simulating cascading outages and the slow process of network upgrade in response to blackouts. We validate OPA on a detailed 19402 bus network model of the Western Electricity Coordinating Council (WECC) interconnection with publicly available data. To do this, we examine scalings on a series of WECC interconnection models with increasing detail. The most detailed, 19402 bus network has more tree structures at the edges of the main mesh structure, and we extend the OPA model to account for this. The higher-risk cascading outages are the large cascades that extend across interconnections, so validating cascading models on large networks is crucial to understanding how the real grid behaves. Finally, exploring networks with mixed mesh and tree like structure has implications for the risk analysis for both the transmission grid and other network infrastructures. 

We use North American Electric Reliability Corporation historical data to give improved estimates of distributions of blackout size, time correlations, and waiting times for the Eastern and Western interconnections of the North American grid. We then explain and estimate the implications of the power law region (heavy tails) in the empirical distribution of blackout size in the historical data for the Western interconnection. Annual mean blackout size has high variability and the risk of large blackouts exceeds the risk of medium size blackouts. Ways to communicate blackout risk are discussed.