Analysis of a Mid-size Industry LoadManagement Aggregator on the Swedish Nordic Control Market

Abstract: The vast amount of research on the global climate are converging to more homogeneous results and it becomes commonly accepted that the scientists' warnings has to be taken into account. As a result, politicians set high goals for the penetration of \green electricity", trying to limit the global warming by phasing out fossil fueled power plants. The European Union has set the “20–20–20”-targets that aims to reduce greenhouse gas emissions, increase the share of renewable energy and also increase the eciency of the energy use. This has led to increases of wind power, solar power and other innovative forms of Distributed Generation (DG) on the electricity grids. In order to successfully integrate this power, the need for Balance power increases due to the unreliability of forecasts of nature's forces. In the Nordic countries, there is abundant quantities of hydro-power to balance the system, but there is a discussion on how much DG it can cover which has not lead yet to any denite conclusions. The rapid development of ICT has opened up a broad range of possibilities for Smart Grid solutions. This thesis was conducted to evaluate the technical and economic potential for an Aggregator to act on the Nordic Control market. The Aggregator is an actor that pools consumers in order to gather a signicant amount of power and then use their exibility for dierent purposes. This thesis will deal with exibility provided by Swedish midsize industries. Through a literature study, an understanding of the Nordic electricity system and the market was acquired. Interesting electricity-heavy processes were found within a few different branches by performing interviews and a couple of study visits. But because of the limited time that was assigned to the project, it was chosen to focus on freeze storages, that seemed like the most promising load. The study of the market and the chosen load led to the construction of a few different mathematical models. First of all the Aggregator business case was applied on deterministic data from the year 2012. The Aggregator prot was maximized for the whole year, to give an approximation of the magnitude of the prot. Then, a model that generated realistic, stochastic scenarios of the relevant parameters in the system was constructed to enable further analysis of the possibilities in dierent future scenarios. The results showed that there is a prot to be made, without too extensive investments. Though, due to the problematics of forecasting the Control Market, the Aggregator needs to have a carefully worked through strategy for its bidding on the Control Market. . .