Calm or storm? : Wind power actors perceptions of Finnish wind power and its future

Wind power has grown fast internationally. It can reduce the environmental impact of energy production and increase energy security. Finland has turbine industry but wind electricity production has been slow, and nationally set capacity targets have not been met. I explored social factors that have affected the slow development of wind power in Finland by studying the perceptions of Finnish national level wind power actors. By that I refer to people who affect the development of wind power sector, such as officials, politicians, and representatives of wind industries and various organisations. The material consisted of interviews, a questionnaire, and written sources. The perceptions of wind power, its future, and methods to promote it were divided. They were studied through discourse analysis, content analysis, and scenario construction. Definition struggles affect views of the significance and potential of wind power in Finland, and also affect investments in wind power and wind power policy choices. Views of the future were demonstrated through scenarios. The views included scenarios of fast growth, but in the most pessimistic views, wind power was not thought to be competitive without support measures even in 2025, and the wind power capacity was correspondingly low. In such a scenario, policy tool choices were expected to remain similar to ones in use at the time of the interviews. So far, the development in Finland has followed closely this pessimistic scenario. Despite the scepticism about wind electricity production, wind turbine industry was seen as a credible industry. For many wind power actors as well as for the Finnish wind power policy, the turbine industry is a significant motive to promote wind power. Domestic electricity production and the export turbine industry are linked in discourse through so-called home market argumentation. Finnish policy tools have included subsidies, research and development funding, and information policies. The criteria used to evaluate policy measures were both process-oriented and value-based. Feed-in tariffs and green certificates that are common elsewhere have not been taken to use in Finland. Some interviewees considered such tools unsuitable for free electricity markets and for the Finnish policy style, dictatorial, and being against western values. Other interviewees supported their use because of their effectiveness. The current Finnish policy tools are not sufficiently effective to increase wind power production significantly. Marginalisation of wind power in discourses, pessimistic views of the future, and the view that the small consumer demand for wind electricity represents the political views of citizens towards promoting wind power, make it more difficult to take stronger policy measures to use. Wind power has not yet significantly contributed to the ecological modernisation of the energy sector in Finland, but the situation may change as the need to reduce emissions from energy production continues.

Investment incentives and valuation of power sector infrastructure assets with increasing interconnection, intermittency and decarbonisation

This dissertation applies a variety of quantitative methods to electricity and carbon market data, utility company accounts data, capital and operating costs to analyse some of the challenges associated with investment in energy assets. In particular, three distinct research topics are analysed within this general theme: the efficiency of interconnector trading, the optimal sizing of intermittent wind facilities and the impact of carbon pricing on the cost of capital for investors are researched in successive sections.

Addressing the technical and market challenges to high wind power integration in Ireland

Over the last decade there has been a rapid global increase in wind power stimulated by energy and climate policies. However, as wind power is inherently variable and stochastic over a range of time scales, additional system balancing is required to ensure system reliability and stability. This paper reviews the technical, policy and market challenges to achieving ambitious wind power penetration targets in Ireland’s All-Island Grid and examines a number of measures proposed to address these challenges. Current government policy in Ireland is to address these challenges with additional grid reinforcement, interconnection and open-cycle gas plant. More recently smart grid combined with demand side management and electric vehicles have also been presented as options to mitigate the variability of wind power. In addition, the transmission system operators have developed wind farm specific grid codes requiring improved turbine controls and wind power forecasting techniques.

Essays on energy derivatives pricing and financial risk management

This thesis consists of an introductory chapter (essay I) and five more empirical essays on electricity markets and CO2 spot price behaviour, derivatives pricing analysis and hedging. Essay I presents the structure of the thesis and electricity markets functioning and characteristics, as well as the type of products traded, to be analyzed on the following essays. In the second essay we conduct an empirical study on co-movements in electricity markets resorting to wavelet analysis, discussing long-term dynamics and markets integration. Essay three is about hedging performance and multiscale relationships in the German electricity spot and futures markets, also using wavelet analysis. We concentrate the investigation on the relationship between coherence evolution and hedge ratio analysis, on a time-frequency-scale approach, between spot and futures which conditions the effectiveness of the hedging strategy. Essays four, five and six are interrelated between them and with the other two previous essays given the nature of the commodity analyzed, CO2 emission allowances, traded in electricity markets. Relationships between electricity prices, primary energy fuel prices and carbon dioxide permits are analyzed on essay four. The efficiency of the European market for allowances is examined taking into account markets heterogeneity. Essay five analyzes stylized statistical properties of the recent traded asset CO2 emission allowances, for spot and futures returns, examining also the relation linking convenience yield and risk premium, for the German European Energy Exchange (EEX) between October 2005 and October 2009. The study was conducted through empirical estimations of CO2 allowances risk premium, convenience yield, and their relation. Future prices from an ex-post perspective are examined to show evidence for significant negative risk premium, or else a positive forward premium. Finally, essay six analyzes emission allowances futures hedging effectiveness, providing evidence for utility gains increases with investor’s preference over risk. Deregulation of electricity markets has led to higher uncertainty in electricity prices and by presenting these essays we try to shed new lights about structuring, pricing and hedging in this type of markets.

Data mining contributions to characterize MV consumers and to improve the suppliers-consumers settlements

This paper deals with the establishment of a characterization methodology of electric power profiles of medium voltage (MV) consumers. The characterization is supported on the data base knowledge discovery process (KDD). Data Mining techniques are used with the purpose of obtaining typical load profiles of MV customers and specific knowledge of their customers’ consumption habits. In order to form the different customers’ classes and to find a set of representative consumption patterns, a hierarchical clustering algorithm and a clustering ensemble combination approach (WEACS) are used. Taking into account the typical consumption profile of the class to which the customers belong, new tariff options were defined and new energy coefficients prices were proposed. Finally, and with the results obtained, the consequences that these will have in the interaction between customer and electric power suppliers are analyzed.

Long-term price range forecast applied to risk management using regression models

Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level α. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.

Intelligent electric vehicle heuristic for energy resource management using simulated annealing

The smart grid concept appears as a suitable solution to guarantee the power system operation in the new electricity paradigm with electricity markets and integration of large amounts of Distributed Energy Resources (DERs). Virtual Power Player (VPP) will have a significant importance in the management of a smart grid. In the context of this new paradigm, Electric Vehicles (EVs) rise as a good available resource to be used as a DER by a VPP. This paper presents the application of the Simulated Annealing (SA) technique to solve the Energy Resource Management (ERM) of a VPP. It is also presented a new heuristic approach to intelligently handle the charge and discharge of the EVs. This heuristic process is incorporated in the SA technique, in order to improve the results of the ERM. The case study shows the results of the ERM for a 33-bus distribution network with three different EVs penetration levels, i. e., with 1000, 2000 and 3000 EVs. The results of the proposed adaptation of the SA technique are compared with a previous SA version and a deterministic technique.

Virtual power producers market strategies

This paper presents MASCEM - a multi-agent based electricity market simulator. MASCEM uses game theory, machine learning techniques, scenario analysis and optimisation techniques to model market agents and to provide them with decision-support. This paper mainly focus on the MASCEM ability to provide the means to model and simulate Virtual Power Producers (VPP). VPPs are represented as a coalition of agents, with specific characteristics and goals. The paper detail some of the most important aspects considered in VPP formation and in the aggregation of new producers and includes a case study.

Transmission costs allocation based on optimal re-dispatch

Congestion management of transmission power systems has achieve high relevance in competitive environments, which require an adequate approach both in technical and economic terms. This paper proposes a new methodology for congestion management and transmission tariff determination in deregulated electricity markets. The congestion management methodology is based on a reformulated optimal power flow, whose main goal is to obtain a feasible solution for the re-dispatch minimizing the changes in the transactions resulting from market operation. The proposed transmission tariffs consider the physical impact caused by each market agents in the transmission network. The final tariff considers existing system costs and also costs due to the initial congestion situation and losses. This paper includes a case study for the 118 bus IEEE test case.

A data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.

Multilevel negotiation in smart grids for VPP management of distributed resources

A multilevel negotiation mechanism for operating smart grids and negotiating in electricity markets considers the advantages of virtual power player management.

MV producers and consumers agents characterization with DSM techniques

This paper consist in the establishment of a Virtual Producer/Consumer Agent (VPCA) in order to optimize the integrated management of distributed energy resources and to improve and control Demand Side Management DSM) and its aggregated loads. The paper presents the VPCA architecture and the proposed function-based organization to be used in order to coordinate the several generation technologies, the different load types and storage systems. This VPCA organization uses a frame work based on data mining techniques to characterize the costumers. The paper includes results of several experimental tests cases, using real data and taking into account electricity generation resources as well as consumption data.

Data mining applications in power systems - case-studies and future trends

Presently power system operation produces huge volumes of data that is still treated in a very limited way. Knowledge discovery and machine learning can make use of these data resulting in relevant knowledge with very positive impact. In the context of competitive electricity markets these data is of even higher value making clear the trend to make data mining techniques application in power systems more relevant. This paper presents two cases based on real data, showing the importance of the use of data mining for supporting demand response and for supporting player strategic behavior.

Cyber-ambient intelligent training of operators in power systems control centres

Cyber-Physical Systems and Ambient Intelligence are two of the most important and emerging paradigms of our days. The introduction of renewable sources gave origin to a completely different dimension of the distribution generation problem. On the other hand, Electricity Markets introduced a different dimension in the complexity, the economic dimension. Our goal is to study how to proceed with the Intelligent Training of Operators in Power Systems Control Centres, considering the new reality of Renewable Sources, Distributed Generation, and Electricity Markets, under the emerging paradigms of Cyber-Physical Systems and Ambient Intelligence. We propose Intelligent Tutoring Systems as the approach to deal with the intelligent training of operators in these new circumstances.

Rentabilidade e risco na comercialização agregada de energia elétrica

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica na Área de Especialização de Energia