The Economic Feasibility of a Wind Power Producer Operating in the Icelandic Regulating Market

Author: Gaynor Kirsten McIlraith
Year: 2018
Supervisors: Ewa Lazarczyk Carlson and Stefán Kári Sveinbjörnsson

Abstract

Over the past decade, rapid growth of renewable energy technologies has resulted in substantial installed wind power capacities around the world. Integrating wind production into electricity markets has been a wide-spread challenge due to the volatile nature of wind speeds and correspondingly intermittent power output. Variable output can cause large imbalances in the system that must be corrected for by other generators. As the integration of wind power into existing electricity markets becomes more prevalent, wind farms face a growing responsibility to reduce these imbalances. Some countries have begun to hold wind farms balancing responsible, however the extent of responsibility varies between markets. This thesis evaluates the economic feasibility of a theoretical wind farm operating within the Icelandic regulating market. Under the current market structure in Iceland, all market participants are treated uniformly, and a dual-pricing strategy is administered to correct for the imbalances in the system. This project simulated potential power imbalances that would be caused by the theoretical wind farm using limited forecast data and modelled the associated financial outcomes that arose as a result of failing to uphold scheduled commitments. The economic viability of the wind farm was analyzed for three primary scenarios. These scenarios involve the wind farm operating in the following capacities: as a standalone entity without supplementary balancing, as an entity with access to external balancing capacity provided by a hydropower plant, and as an internally balanced entity using self-proportioning strategies. It was determined that the current market structure in Iceland is not suited to support a standalone wind farm, and internal balancing strategies offered only minor improvements. External balancing with hydropower proved to drastically reduce imbalancing errors and provide a financially positive result for the wind farm, thus demonstrating the best operational perspective. Ultimately, it was clear that developing an accurate prediction model for a wind farm is critical to perform successfully.