On advancing business intelligence in the electricity retail market

In recent decades, business intelligence (BI) has gained momentum in real-world practice. At the same time, business intelligence has evolved as an important research subject of Information Systems (IS) within the decision support domain. Today’s growing competitive pressure in business has led to increased needs for real-time analytics, i.e., so called real-time BI or operational BI. This is especially true with respect to the electricity production, transmission, distribution, and retail business since the law of physics determines that electricity as a commodity is nearly impossible to be stored economically, and therefore demand-supply needs to be constantly in balance. The current power sector is subject to complex changes, innovation opportunities, and technical and regulatory constraints. These range from low carbon transition, renewable energy sources (RES) development, market design to new technologies (e.g., smart metering, smart grids, electric vehicles, etc.), and new independent power producers (e.g., commercial buildings or households with rooftop solar panel installments, a.k.a. Distributed Generation). Among them, the ongoing deployment of Advanced Metering Infrastructure (AMI) has profound impacts on the electricity retail market. From the view point of BI research, the AMI is enabling real-time or near real-time analytics in the electricity retail business. Following Design Science Research (DSR) paradigm in the IS field, this research presents four aspects of BI for efficient pricing in a competitive electricity retail market: (i) visual data-mining based descriptive analytics, namely electricity consumption profiling, for pricing decision-making support; (ii) real-time BI enterprise architecture for enhancing management’s capacity on real-time decision-making; (iii) prescriptive analytics through agent-based modeling for price-responsive demand simulation; (iv) visual data-mining application for electricity distribution benchmarking. Even though this study is from the perspective of the European electricity industry, particularly focused on Finland and Estonia, the BI approaches investigated can: (i) provide managerial implications to support the utility’s pricing decision-making; (ii) add empirical knowledge to the landscape of BI research; (iii) be transferred to a wide body of practice in the power sector and BI research community.

Forecasting financial weather - can we foresee market sentiment? Quantitative analysis of nodal prices in the New Zealand Electricity Spot Market

Time series of hourly electricity spot prices have peculiar properties. Electricity is by its nature difficult to store and has to be available on demand. There are many reasons for wanting to understand correlations in price movements, e.g. risk management purposes. The entire analysis carried out in this thesis has been applied to the New Zealand nodal electricity prices: offer prices (from 29 May 2002 to 31 March 2009) and final prices (from 1 January 1999 to 31 March 2009). In this paper, such natural factors as location of the node and generation type in the node that effects the correlation between nodal prices have been reviewed. It was noticed that the geographical factor affects the correlation between nodes more than others. Therefore, the visualisation of correlated nodes was done. However, for the offer prices the clear separation of correlated and not correlated nodes was not obtained. Finally, it was concluded that location factor most strongly affects correlation of electricity nodal prices; problems in visualisation probably associated with power losses when the power is transmitted over long distance.

Maintenance and construction outsourcing in a public-owned electricity network company

Euroopan sähkösektori on ollut viimeisen vuosikymmenen suurten mullistusten kourissa. Sähkömarkkinoiden avautumisen jälkeen monopoliliiketoimintaa harjoittavien sähköyhtiöiden on ollut pakko parantaa tuottavuuttaan. Ratkaisuksi tähän on etsitty apua huolto- ja rakennustoimintojen ulkoistamisella. Ulkoistaminen on kuitenkin uusi menetelmä tällä sektorilla. Tämän tutkielman tavoitteena on selvittää syyt, jotka tanskalaisella sähköverkkoyhtiöllä oli huolto- ja rakennustoimintojen ulkoistamiseen, sekä löytää siitä saatavat hyödyt ja siihen sisältyvät riskit. Tutkimus suoritetaan käyttäen apuna kirjallisuutta, saatavilla olevia due diligence-, sekä muita raportteja ja analyysejä, sekä tapausta koskettavien tahojen haastatteluja.Lisäksi sähköverkkoalan asiantuntijoiden kanssa käytyjä konsultointia käytetäänselvitykseen. Tutkimus osoittaa, että perimmäiset ajurit huolto- ja rakennustoimintojen ulkoistamiseen tulivat lainmuutosten ja vapautuneiden sähkömarkkinoiden asettamista paineista. Kunnallisessa organisaatiossa parantaa tehokkuutta ulkoistamalla jotain toimintoja yksityisomisteiselle palvelun tuottajalle. Muut ulkoistamisesta odotetut hyödyt olivat alentuneet kustannukset, virtaviivaisempi organisaation ja sähköverkkoyhtiön tehottomista osista eroon pääseminen ennen sen myymistä.

Change of Electricity Distribution Industry: Drivers and Opening Business Opportunities

As a result of the recent regulatory amendments and other development trends in the electricity distribution business, the sector is currently witnessing radical restructuring that will eventually impact the business logics of the sector. This report represents upcoming changes in the electricity distribution industry and concentrates on the factors that are expected to be the most fundamental ones. Electricity network companies nowadays struggle with legislative and regulatory requirements that focus on both the operational efficiency and the reliability of electricity distribution networks. The forces that have an impact on the distribution network companies can be put into three main categories that define the transformation at a general level. Those are: (1) a requirement for a more functional marketplace for energy, (2) environmental aspects (combating climate change etc.), and (3) a strongly emphasized requirement for the security of energy supply. The first point arises from the legislators’ attempt to increase competition in electricity retail markets, the second one concerns both environmental protection and human safety issues, and the third one indicates societies’ reduced willingness to accept interruptions in electricity supply. In the future, regulation of electricity distribution business may lower the threshold for building more weather-resistant networks, which in turn means increased underground cabling. This development pattern is reinforced by tightening safety and environmental regulations that ultimately make the overhead lines expensive to build and maintain. The changes will require new approaches particularly in network planning, construction, and maintenance. The concept for planning, constructing, and maintaining cable networks is necessary because the interdependencies between network operations are strong, in other words, the nature of the operation requires a linkage to other operations.

Regime Switching Models for Electricity Time Series that Capture Fat Tailed Distributions

In the power market, electricity prices play an important role at the economic level. The behavior of a price trend usually known as a structural break may change over time in terms of its mean value, its volatility, or it may change for a period of time before reverting back to its original behavior or switching to another style of behavior, and the latter is typically termed a regime shift or regime switch. Our task in this thesis is to develop an electricity price time series model that captures fat tailed distributions which can explain this behavior and analyze it for better understanding. For NordPool data used, the obtained Markov Regime-Switching model operates on two regimes: regular and non-regular. Three criteria have been considered price difference criterion, capacity/flow difference criterion and spikes in Finland criterion. The suitability of GARCH modeling to simulate multi-regime modeling is also studied.

A comparison of electricity spot prices simulation using ARMA-GARCH and mean-reverting models

The aim of this work is to compare two families of mathematical models for their respective capability to capture the statistical properties of real electricity spot market time series. The first model family is ARMA-GARCH models and the second model family is mean-reverting Ornstein-Uhlenbeck models. These two models have been applied to two price series of Nordic Nord Pool spot market for electricity namely to the System prices and to the DenmarkW prices. The parameters of both models were calibrated from the real time series. After carrying out simulation with optimal models from both families we conclude that neither ARMA-GARCH models, nor conventional mean-reverting Ornstein-Uhlenbeck models, even when calibrated optimally with real electricity spot market price or return series, capture the statistical characteristics of the real series. But in the case of less spiky behavior (System prices), the mean-reverting Ornstein-Uhlenbeck model could be seen to partially succeeded in this task.

Stress testing and risk evaluation of new capacity payment algorithm proposed by Market Council

In the Russian Wholesale Market, electricity and capacity are traded separately. Capacity is a special good, the sale of which obliges suppliers to keep their generating equipment ready to produce the quantity of electricity indicated by the System Operator. The purpose of the formation of capacity trading was the maintenance of reliable and uninterrupted delivery of electricity in the wholesale market. The price of capacity reflects constant investments in construction, modernization and maintenance of power plants. So, the capacity sale creates favorable conditions to attract investments in the energy sector because it guarantees the investor that his investments will be returned.

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.

Testing for granger-causality in the Finnish roundwood market

Summary

Logistic services as competitive means : segmenting the retail market for softwood lumber

Abstract

Land use response to agricultural policy and market movements on Finnish dairy-farms

Selostus: Tuki- ja hintamuutosten vaikutus maitotilojen pellon käyttöön

Whole sales market for fresh potato in Finland

Selostus: Suomen ruokaperunamarkkinoiden toimivuus