Integration of microgrids into electricity distribution networks

Existing electricity distribution system is under pressure because implementation of distributed generation changes the grid configuration and also because some customers demand for better distribution reliability. In a short term, traditional network planning does not offer techno-economical solutions for the challenges and therefore the idea of microgrids is introduced. Islanding capability of microgrids is expected to enable better reliability by reducing effects of faults. The aim of the thesis is to discuss challenges in integration of microgrids into distribution networks. Study discusses development of microgrid related smart grid features and gives estimation of the guideline of microgrid implementation. Thesis also scans microgrid pilots around the world and introduces the most relevant projects. Analysis reveals that the main focus of researched studies is on low voltage microgrids. This thesis extends the idea to medium voltage distribution system and introduces challenges related to medium voltage microgrid implementation. Differences of centralized and distributed microgrid models are analyzed and the centralized model is discovered to be easiest to implement into existing distribution system. Preplan of medium voltage microgrid pilot is also carried out in this thesis.

Price spike forecasting in a competitive day-ahead energy market

Electricity price forecasting has become an important area of research in the aftermath of the worldwide deregulation of the power industry that launched competitive electricity markets now embracing all market participants including generation and retail companies, transmission network providers, and market managers. Based on the needs of the market, a variety of approaches forecasting day-ahead electricity prices have been proposed over the last decades. However, most of the existing approaches are reasonably effective for normal range prices but disregard price spike events, which are caused by a number of complex factors and occur during periods of market stress. In the early research, price spikes were truncated before application of the forecasting model to reduce the influence of such observations on the estimation of the model parameters; otherwise, a very large forecast error would be generated on price spike occasions. Electricity price spikes, however, are significant for energy market participants to stay competitive in a market. Accurate price spike forecasting is important for generation companies to strategically bid into the market and to optimally manage their assets; for retailer companies, since they cannot pass the spikes onto final customers, and finally, for market managers to provide better management and planning for the energy market. This doctoral thesis aims at deriving a methodology able to accurately predict not only the day-ahead electricity prices within the normal range but also the price spikes. The Finnish day-ahead energy market of Nord Pool Spot is selected as the case market, and its structure is studied in detail. It is almost universally agreed in the forecasting literature that no single method is best in every situation. Since the real-world problems are often complex in nature, no single model is able to capture different patterns equally well. Therefore, a hybrid methodology that enhances the modeling capabilities appears to be a possibly productive strategy for practical use when electricity prices are predicted. The price forecasting methodology is proposed through a hybrid model applied to the price forecasting in the Finnish day-ahead energy market. The iterative search procedure employed within the methodology is developed to tune the model parameters and select the optimal input set of the explanatory variables. The numerical studies show that the proposed methodology has more accurate behavior than all other examined methods most recently applied to case studies of energy markets in different countries. The obtained results can be considered as providing extensive and useful information for participants of the day-ahead energy market, who have limited and uncertain information for price prediction to set up an optimal short-term operation portfolio. Although the focus of this work is primarily on the Finnish price area of Nord Pool Spot, given the result of this work, it is very likely that the same methodology will give good results when forecasting the prices on energy markets of other countries.

The Impact of Acquisitions on Share Pricing and Long Term Performance – Empirical Evidence from the Global Automotive Industry

Automotive industry has faced intense consolidation pressure, which has lead to increasing number of M&As. However, empirical evidence has given controversial results suggesting that most of M&As are value destructive for acquiring companies and for acquiring companies’ shareholders. The objective of this master’s thesis is to examine how acquiring companies’ shareholders react to acquisition announcement and is the reaction in line with the long-term performance. This study uses empirical evidence from automotive industry, which has been characterized as an industry that holds large amount of vertical and horizontal synergies. Transaction data consists of 65 acquisitions made by publicly listed companies between 2008-2010. The short-term impact is tested by applying event study methodology while the long term operative performance is examined with accounting study methodology. The event study results indicate that during the three days after acquisition (t= 0-2), the acquiring firms’ stocks generate an abnormal return of 1.22% on average across all acquisitions. When long term performance is studied it is evident that acquiring companies perform better than the industry median pre- and post-transaction but there is no statistically significant evidence that the performance has increased. The only performance ratio indicating statistically significant decrease is Return on Equity (ROE). On long-term horizontal acquisitions seem to outperform conglomerate ones but otherwise deal characteristics do not have any statistically significant impact.