Understanding the Impact of Large-Scale Power Grid Architectures on Performance

<p>Grid balancing is a critical system requirement for the power grid in matching the supply to the demand. This balancing has historically been achieved by conventional power generators. However, the increasing level of renewable penetration has brought more variability and uncertainty to the grid (Ela, Diakov et al. 2013, Bessa, Moreira et al. 2014), which has considerable impacts and implications on power system reliability and efficiency, as well as costs. Energy planners have the task of designing infrastructure power systems to provide electricity to the population, wherever and whenever needed. Deciding of the right grid architecture is no easy task, considering consumers’ economic, environmental, and security priorities, while making efficient use of existing resources.</p> <p>In this research, as one contribution, we explore associations between grid architectures and their performance, that is, their ability to meet consumers’ concerns. To do this, we first conduct a correlation analysis study. We propose a generative method that captures path dependency by iteratively creating grids, structurally different. The method would generate alternative grid architectures by subjecting an initial grid to a heuristic choice method for decision making over a fixed time horizon. Second, we also conduct a comparative study to evaluate differences in grid performances. We consider two balancing area operation types, presenting different structures and coordination mechanisms. Both studies are performed with the use of a grid simulation model, Spark! The aim of this model is to offer a meso-scale solution that enables the study of very large power systems over long-time horizons, with a sufficient level of fidelity to perform day-to-day grid activities and support architectural questions about the grids of the future. More importantly, the model reconciles long-term planning with short-term grid operations, enabling long-term projections validation via grid operations and response on a daily basis. This is our second contribution.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Engineering Management & Systems Engineering

Year dc:date.available

2018

Author dc:creator

• Toba, Ange-Lionel

Contributors dc:contributor

• Mamadou Seek
• Ghaith Rabadi
• Adrian Gheorghe
• Jose Padilla

• Discrete event simulation
• Energy planning
• Grid simulation
• Infrastructure systems
• Renewable integration
• Statistical analysis
• Power and Energy
• Systems Engineering

Identifier

9781392018989

OAI identifier oai:identifier

oai:digitalcommons.odu.edu:emse_etds-1141