Households’ willingness to engage in demand response in the Finnish retail electricity market: an empirical study

If electricity users adjusted their consumption patterns according to time-variable electricity prices or other signals about the state of the power system, generation and network assets could be used more efficiently, and matching intermittent renewable power generation with electricity demand would be facilitated. This kind of adjustment of electricity consumption, or demand response, may be based on consumers’ decisions to shift or reduce electricity use in response to time-variable electricity prices or on the remote control of consumers’ electric appliances. However, while demand response is suggested as a solution to many issues in power systems, actual experiences from demand response programs with residential customers are mainly limited to short pilots with a small number of voluntary participants, and information about what kinds of changes consumers are willing and able to make and what motivates these changes is scarce. This doctoral dissertation contributes to the knowledge about what kinds of factors impact on residential consumers’ willingness and ability to take part in demand response. Saving opportunities calculated with actual price data from the Finnish retail electricity market are compared with the occurred supplier switching to generate a first estimate about how large savings could trigger action also in the case of demand response. Residential consumers’ motives to participate in demand response are also studied by a web-based survey with 2103 responses. Further, experiences of households with electricity consumption monitoring systems are discussed to increase knowledge about consumers’ interest in getting more information on their electricity use and adjusting their behavior based on it. Impacts of information on willingness to participate in demand response programs are also approached by a survey for experts of their willingness to engage in demand response activities. Residential customers seem ready to allow remote control of electric appliances that does not require changes in their everyday routines. Based on residents’ own activity, the electricity consuming activities that are considered shiftable are very limited. In both cases, the savings in electricity costs required to allow remote control or to engage in demand response activities are relatively high. Nonmonetary incentives appeal to fewer households.

Supplier Energy Audits in Thermal Power Plants

This master’s thesis handles an operating model for an electric equipment supplier conducted sale oriented energy audit for pumping, fan and other motor applications at power plants. The study goes through the largest factors affecting internal electricity use at a power plant, finds an energy audit –like approach for the basis of information gathering and presents the information needed for conducting the analysis. The model is tested in practice at a kraft recovery boiler of a chemical pulping mill. Targets chosen represent some of the largest electric motor applications in the boiler itself and in its fuel handling. The energy saving potential of the chosen targets is calculated by simulating the energy consumption of the alternatives for controlling the targets, and thereafter combining the information with the volume flow duration curve. Results of the research are somewhat divaricated, as all the information needed is not available in the automation system. Some of the targets could be simulated and their energy saving potential calculated quite easily. At some of the targets chosen the monitoring was not sufficient enough for this and additional measurements would have been needed to base the calculations on. In traditional energy audits, energy efficiency of pump and fan applications is not necessarily examined. This means that there are good possibilities for developing the now presented targeted energy audit procedure basis further.

Lämminvesivaraajan ohjaus automaatiolla aurinkosähköä tuottavassa kotitaloudessa

Hajautettu sähköntuotanto aurinkopaneeleilla on Suomessa kasvussa. Kotitalouksiin asennettujen aurinkopaneelijärjestelmien määrä kasvaa jatkuvasti, mutta suuri osa tuotetusta sähköenergiasta syötetään sähköverkkoon. Tämä johtuu aurinkosähkön tuotannon painottumisesta kesäpäiviin, jolloin kotitalouksien kulutus on pienimmillään. Suurin hyöty itse tuotetusta energiasta saadaan kuitenkin käyttämällä se tuotantokohteessa, jolla minimoidaan energiansiirto sähköverkon ja kotitalouden rajapinnassa. Siirtämällä kotitalouden suurimpia kuormia aurinkosähkön mukaan ohjatuksi, voidaan saavuttaa merkittäviä parannuksia tuotetun sähkön omakäyttöasteessa. Helpoimmillaan tämä onnistuu kellokytkimellä, joka ajoittaa kulutuksen parhaimman tuotannon ajalle. Tämä ei kuitenkaan poista ongelmaa tilanteissa, jossa aurinkosähkön tuotanto on häiriintynyt esimerkiksi pilvisyyden takia aamupäivällä ja huipputuotanto saavutetaan vasta iltapäivän puolella. Saatavilla on jo useita järjestelmiä, jotka ohjaavat kodin laitteita tuotannon mukaisesti. Suuri osa näistä järjestelmistä on kuitenkin suunniteltu toimimaan vain tuotetun energiamäärän mukaisesti, ottamatta huomioon kotitaloudessa olevaa muuta, automaation piiriin kuulumatonta kulutusta. Tässä kandidaatin työssä vertaillaan sähköenergian eri mittaustapoja ja niiden vaikutusta siirretyn energian laskennalliseen määrään. Lisäksi työssä tutkitaan lämminvesivaraajan kuormanohjausta käyttäen termostaatti-, kellokytkin- ja logiikkaohjausta. Työssä esitelty logiikkaohjaus hyödyntää siirretyn energian mittausta sähköverkon ja kotitalouden rajapinnassa, ottaen automaattisesti huomioon myös talouden muun kulutuksen. Työssä esitellään myös esimerkkilaitteisto, jolla suunniteltu logiikka voidaan toteuttaa.

Modelling of changes in electricity end-use and their impacts on electricity distribution

The electricity distribution sector will face significant changes in the future. Increasing reliability demands will call for major network investments. At the same time, electricity end-use is undergoing profound changes. The changes include future energy technologies and other advances in the field. New technologies such as microgeneration and electric vehicles will have different kinds of impacts on electricity distribution network loads. In addition, smart metering provides more accurate electricity consumption data and opportunities to develop sophisticated load modelling and forecasting approaches. Thus, there are both demands and opportunities to develop a new type of long-term forecasting methodology for electricity distribution. The work concentrates on the technical and economic perspectives of electricity distribution. The doctoral dissertation proposes a methodology to forecast electricity consumption in the distribution networks. The forecasting process consists of a spatial analysis, clustering, end-use modelling, scenarios and simulation methods, and the load forecasts are based on the application of automatic meter reading (AMR) data. The developed long-term forecasting process produces power-based load forecasts. By applying these results, it is possible to forecast the impacts of changes on electrical energy in the network, and further, on the distribution system operator’s revenue. These results are applicable to distribution network and business planning. This doctoral dissertation includes a case study, which tests the forecasting process in practice. For the case study, the most prominent future energy technologies are chosen, and their impacts on the electrical energy and power on the network are analysed. The most relevant topics related to changes in the operating environment, namely energy efficiency, microgeneration, electric vehicles, energy storages and demand response, are discussed in more detail. The study shows that changes in electricity end-use may have radical impacts both on electrical energy and power in the distribution networks and on the distribution revenue. These changes will probably pose challenges for distribution system operators. The study suggests solutions for the distribution system operators on how they can prepare for the changing conditions. It is concluded that a new type of load forecasting methodology is needed, because the previous methods are no longer able to produce adequate forecasts.

Analysis of Patterns in Electricity Spot Market Time Series

Due to its non-storability, electricity must be produced at the same time that it is consumed, as a result prices are determined on an hourly basis and thus analysis becomes more challenging. Moreover, the seasonal fluctuations in demand and supply lead to a seasonal behavior of electricity spot prices. The purpose of this thesis is to seek and remove all causal effects from electricity spot prices and remain with pure prices for modeling purposes. To achieve this we use Qlucore Omics Explorer (QOE) for the visualization and the exploration of the data set and Time Series Decomposition method to estimate and extract the deterministic components from the series. To obtain the target series we use regression based on the background variables (water reservoir and temperature). The result obtained is three price series (for Sweden, Norway and System prices) with no apparent pattern.

Analysis of electricity distribution network operation business models and capitalization of control room functions with DMS

Electricity distribution network operation (NO) models are challenged as they are expected to continue to undergo changes during the coming decades in the fairly developed and regulated Nordic electricity market. Network asset managers are to adapt to competitive technoeconomical business models regarding the operation of increasingly intelligent distribution networks. Factors driving the changes for new business models within network operation include: increased investments in distributed automation (DA), regulative frameworks for annual profit limits and quality through outage cost, increasing end-customer demands, climatic changes and increasing use of data system tools, such as Distribution Management System (DMS). The doctoral thesis addresses the questions a) whether there exist conditions and qualifications for competitive markets within electricity distribution network operation and b) if so, identification of limitations and required business mechanisms. This doctoral thesis aims to provide an analytical business framework, primarily for electric utilities, for evaluation and development purposes of dedicated network operation models to meet future market dynamics within network operation. In the thesis, the generic build-up of a business model has been addressed through the use of the strategicbusiness hierarchy levels of mission, vision and strategy for definition of the strategic direction of the business followed by the planning, management and process execution levels of enterprisestrategy execution. Research questions within electricity distribution network operation are addressed at the specified hierarchy levels. The results of the research represent interdisciplinary findings in the areas of electrical engineering and production economics. The main scientific contributions include further development of the extended transaction cost economics (TCE) for government decisions within electricity networks and validation of the usability of the methodology for the electricity distribution industry. Moreover, DMS benefit evaluations in the thesis based on the outage cost calculations propose theoretical maximum benefits of DMS applications equalling roughly 25% of the annual outage costs and 10% of the respective operative costs in the case electric utility. Hence, the annual measurable theoretical benefits from the use of DMS applications are considerable. The theoretical results in the thesis are generally validated by surveys and questionnaires.

Greenhouse gas emissions from peat and biomass-derived fuels, electricity and heat — Estimation of various production chains by using LCA methodology

More discussion is required on how and which types of biomass should be used to achieve a significant reduction in the carbon load released into the atmosphere in the short term. The energy sector is one of the largest greenhouse gas (GHG) emitters and thus its role in climate change mitigation is important. Replacing fossil fuels with biomass has been a simple way to reduce carbon emissions because the carbon bonded to biomass is considered as carbon neutral. With this in mind, this thesis has the following objectives: (1) to study the significance of the different GHG emission sources related to energy production from peat and biomass, (2) to explore opportunities to develop more climate friendly biomass energy options and (3) to discuss the importance of biogenic emissions of biomass systems. The discussion on biogenic carbon and other GHG emissions comprises four case studies of which two consider peat utilization, one forest biomass and one cultivated biomasses. Various different biomass types (peat, pine logs and forest residues, palm oil, rapeseed oil and jatropha oil) are used as examples to demonstrate the importance of biogenic carbon to life cycle GHG emissions. The biogenic carbon emissions of biomass are defined as the difference in the carbon stock between the utilization and the non-utilization scenarios of biomass. Forestry-drained peatlands were studied by using the high emission values of the peatland types in question to discuss the emission reduction potential of the peatlands. The results are presented in terms of global warming potential (GWP) values. Based on the results, the climate impact of the peat production can be reduced by selecting high-emission-level peatlands for peat production. The comparison of the two different types of forest biomass in integrated ethanol production in pulp mill shows that the type of forest biomass impacts the biogenic carbon emissions of biofuel production. The assessment of cultivated biomasses demonstrates that several selections made in the production chain significantly affect the GHG emissions of biofuels. The emissions caused by biofuel can exceed the emissions from fossil-based fuels in the short term if biomass is in part consumed in the process itself and does not end up in the final product. Including biogenic carbon and other land use carbon emissions into the carbon footprint calculations of biofuel reveals the importance of the time frame and of the efficiency of biomass carbon content utilization. As regards the climate impact of biomass energy use, the net impact on carbon stocks (in organic matter of soils and biomass), compared to the impact of the replaced energy source, is the key issue. Promoting renewable biomass regardless of biogenic GHG emissions can increase GHG emissions in the short term and also possibly in the long term.

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.

Promoting Growth in the Use of Bioenergy in Europe by Converting Existing Coal-Fired Power Plants to Biomass

Repowering existing power plants by replacing coal with biomass might offer an interesting option to ease the transition from fossil fuels to renewable energy sources and promote a fur-ther expansion of bioenergy in Europe, on account of the potential to decrease greenhouse gas emissions, as well as other pollutants (SOx, NOx, etcetera). In addition, a great part of the appeal of repowering projects comes from the opportunity to reuse the vast existing invest-ment and infrastructure associated with coal-based power generation. Even so, only a limited number of experiences with repowering are found. Therefore, efforts are required to produce technical and scientific evidence to determine whether said technology might be considered feasible for its adoption within European conditions. A detailed evaluation of the technical and economic aspects of this technology constitutes a powerful tool for decision makers to define the energy future for Europe. To better illustrate this concept, a case study is analyzed. A Slovakian pulverized coal plant was used as the basis for determining the effects on perfor-mance, operation, maintenance and cost when fuel is shifted to biomass. It was found that biomass fuel properties play a crucial role in plant repowering. Furthermore, results demon-strate that this technology offers renewable energy with low pollutant emissions at the cost of reduced capacity, relatively high levelized cost of electricity and sometimes, a maintenance-intensive operation. Lastly, regardless of the fact that existing equipment can be reutilized for the most part, extensive additions/modifications may be required to ensure a safe operation and an acceptable performance.

Assessment of techno-economic use cases for small-scale island grids

The main objective of the study was to define the methodology for assessing the limits for application island grids instead of interconnecting with existing grid infrastructure. The model for simulation of grid extension distance and levelised cost of electricity has been developed and validated by the case study in Finland. Thereafter, sensitivities of the application limits were examined with the respect to operational environment, load conditions, supply security and geographical location. Finally, recommendations for the small-scale rural electrification projects in the market economy environment have been proposed.

Distributed energy resources in an electricity retailer’s short-term profit optimization

Liberalization of electricity markets has resulted in a competed Nordic electricity market, in which electricity retailers play a key role as electricity suppliers, market intermediaries, and service providers. Although these roles may remain unchanged in the near future, the retailers’ operation may change fundamentally as a result of the emerging smart grid environment. Especially the increasing amount of distributed energy resources (DER), and improving opportunities for their control, are reshaping the operating environment of the retailers. This requires that the retailers’ operation models are developed to match the operating environment, in which the active use of DER plays a major role. Electricity retailers have a clientele, and they operate actively in the electricity markets, which makes them a natural market party to offer new services for end-users aiming at an efficient and market-based use of DER. From the retailer’s point of view, the active use of DER can provide means to adapt the operation to meet the challenges posed by the smart grid environment, and to pursue the ultimate objective of the retailer, which is to maximize the profit of operation. This doctoral dissertation introduces a methodology for the comprehensive use of DER in an electricity retailer’s short-term profit optimization that covers operation in a variety of marketplaces including day-ahead, intra-day, and reserve markets. The analysis results provide data of the key profit-making opportunities and the risks associated with different types of DER use. Therefore, the methodology may serve as an efficient tool for an experienced operator in the planning of the optimal market-based DER use. The key contributions of this doctoral dissertation lie in the analysis and development of the model that allows the retailer to benefit from profit-making opportunities brought by the use of DER in different marketplaces, but also to manage the major risks involved in the active use of DER. In addition, the dissertation introduces an analysis of the economic potential of DER control actions in different marketplaces including the day-ahead Elspot market, balancing power market, and the hourly market of Frequency Containment Reserve for Disturbances (FCR-D).