Spatial and Temporal Analysis of Renewable Power Generation Portfolios

Open Access
Shahriari, Mehdi
Graduate Program:
Energy and Mineral Engineering
Doctor of Philosophy
Document Type:
Date of Defense:
April 01, 2018
Committee Members:
  • Seth Adam Blumsack, Dissertation Advisor
  • Seth Adam Blumsack, Committee Chair
  • Guido Cervone, Committee Member
  • Jeffrey Brownson, Committee Member
  • Susan W Stewart, Outside Member
  • Renewable Energy
  • Electricity Market
  • Portfolio Optimization
The increase in renewable energy nameplate capacity has resulted in significant rise in net electricity generation from renewable sources. This increased renewable power penetration results in higher renewable integration cost. Since renewable generation technologies are weather dependent and their output cannot be predicted without uncertainty, the system operator needs to hold a larger portfolio of back-up power to maintain system reliability. One of the most common methods to address this drawback, is to spread wind turbines over space which tends to reduce output variability; this happens as distance between the turbines increases, the correlation between them decreases. In this study we are interested in studying the variability of interconnected wind farms over different spatial and temporal scales. The focus is mainly on analyzing the benefits of aggregation of wind generators over multiple spatial and temporal scales. We will also study the benefits of hybrid wind and solar systems and quantify the benefits of such systems. First, we incorporate a variance minimizing algorithm that sequentially connects wind farms. Our aim is to find the relationship between geographical scale and variance minimizing number of wind farms over different temporal scales. Our results show that the number of wind farms required to achieve minimum output volatility grows with spatial scale of aggregation. Further, we find that optimizing wind farm deployment to achieve output variance minimization over one-time scale does not imply minimizing output variance over other time scales. Further, we use the mean variance portfolio (MVP) theory to create optimal portfolios of wind and solar generators over various spatial and temporal scales. Our results indicate that using MVP will increase wind and solar capacity value. We observed that adding solar to wind portfolios will increase the capacity value of the portfolio and decrease portfolio risk. Creating portfolios in larger geographic areas and in regions with better renewable resources will decrease portfolio risk. Finally, we study the capacity value of wind and solar resources in electricity capacity markets. We consider different policy structures and analyze their benefits and shortcomings. A new measure is introduced that considers the spatial variability of wind and solar generators and has the potential to increase system reliability and generators’ revenue from capacity markets. Our results indicate that the capacity value of renewable generation technologies varies over space and time. We then use a brute force optimization method to explore revenue opportunities for wind and solar generators in electricity capacity market.