Study of value factors as a metric for Swedish electricity

Abstract: The Swedish electricity market was historically based on predictable and controllable power plants. The introduction variable renewable energy (VRE) sources in the market have led to less predictability and larger short-term variations in the generation profiles. These effects are compounded as the market share of VRE sources are increasing, and in Sweden’s case as the nuclear power plants are being phased out. Different metrics of value are calculated to shed light on the economic potential of new power sources. A not yet commonly used metric is the value factor of a technology, which represents the net increase or net decrease in revenue due to if the generation coincides with a high or low spot price of electricity. The thesis seeks to calculate the value factors and analyse their place within the northern European electricity market. In order to calculate the value factors and analysing them, the acquisition of datasets for varying market variables was required. The three sources of these datasets was for this thesis the ENTSO-E Transparency Platform, Nord Pool and Svenska kraftnät. These sources combined could supply datasets for market variables dating back to 2015 for Sweden and each country with an international connection with Sweden. This limits the scope of this analysis to 2015 - 2019 for Sweden and six other countries. The value factors were calculated for each Swedish electric price region divided into five categorical technologies, wind-, solar-, hydro-, nuclear- and heat power. The results from this only gave concrete results for two technologies. Wind power are shown to generally have a value factor below one and hydro power in the two northern regions have a value factor above one. This indicates that the market is saturated for wind power while in demand for hydro power from northern Sweden. Every other pair of technology and region vary as to not indicate whether the market is in demand of it or not. Analysing the association of variables was accomplished using a correlation study. Variables that consistently have a critical correlation factor, either linear or monotonic, are identified as associated variables. Out of these pairs of associated variables, the ones with a shared trend in either correlation or normalized regression with the trend of the appropriate value factor are identified as associated with the same value factor. This resulted in several associated variables for each value factor. Neither of these associations can by this methodology be identified as having a causal relation, it only displays correlations which could be incidental.