Tre essay om elektrisitetspriser

The thesis explores how the Nord Pool power market prices forward-looking information. Specifically, the thesis investigates the efficiency, or the biasedness, of the derivatives side of the power market, how temperature is priced by the market, and how to account for uncertainty in future information. The thesis consists of an introductory chapter which comprises the context and background for the three studies, including a summary. Using data on current and future-looking information from futures prices, the thesis brings new evidence on how the efficiency of the futures market at Nord Pool has developed. Furthermore, the thesis investigates relationship between the relationship between temperature and power prices and the price forecasting information implied in weather forecasts.The thesis provides new insight into the effect of future looking information on the electricity spot prices. The findings indicate that futures on the Nord Pool spot price has been unbiased estimates of the future spot price since 2008. Furthermore, the effect of temperature on price were investigated and were found to be seasonally dependent, different across Norwegian price zones, and different across price quantiles. Also, the information from temperature forecasts is beneficial to use in price forecasts for up to 9 days ahead, but the uncertainty in temperature forecasts has to be accounted for

Micro (Wind) Generation: \u27Urban Resource Potential & Impact on Distribution Network Power Quality\u27

Of the forms of renewable energy available, wind energy is at the forefront of the European (and Irish) green initiative with wind farms supplying a significant proportion of electrical energy demand. This type of distributed generation (DG) represents a ‘paradigm shift’ towards increased decentralisation of energy supply. However, because of the distance of most DG from urban areas where demand is greatest, there is a loss of efficiency. The solution, placing wind energy systems in urban areas, faces significant challenges. The complexities associated with the urban terrain include planning, surface heterogeneity that reduces the available wind resource and technology obstacles to extracting and distributing wind energy. Yet, if a renewable solution to increasing energy demand is to be achieved, energy conversion systems where populations are concentrated, that is cities, must be considered. This study is based on two independent strands of research into: low voltage (LV) power flow and modelling the urban wind resource. The urban wind resource is considered by employing a physically-based empirical model to link wind observations at a conventional meteorological site to those acquired at urban sites. The approach is based on urban climate research that has examined the effects of varying surface roughness on the wind-field above buildings. The development of the model is based on observational data acquired at two locations across Dublin representing an urban and sub-urban site. At each, detailed wind information is recorded at a height about 1.5 times the average height of surrounding buildings. These observations are linked to data gathered at a conventional meteorological station located at Dublin Airport, which is outside the city. These observations are linked through boundary-layer meteorological theory that accounts for surface roughness. The resulting model has sufficient accuracy to assess the wind resource at these sites and allow us to assess the potential for micro–turbine energy generation. One of the obstacles to assessing this potential wind resource is our lack of understanding of how turbulence within urban environments affects turbine productivity. This research uses two statistical approaches to examine the effect of turbulence intensity on wind turbine performance. The first approach is an adaptation of a model originally derived to quantify the degradation of power performance of a wind turbine using the Gaussian probability distribution to simulate turbulence. The second approach involves a novel application of the Weibull Distribution, a widely accepted means to probabilistically describe wind speed and its variation. On the technological side, incorporating wind power into an urban distribution network requires power flow analysis to investigate the power quality issues, which are principally associated with imbalance of voltage on distribution lines and voltage rise. Distribution networks that incorporate LV consumers must accommodate a highly unbalanced load structure and the need for grounding network between the consumer and grid operator (TN-C-S earthing). In this regard, an asymmetrical 3-phase (plus neutral) power flow must be solved to represent the range of issues for the consumer and the network as the number of wind-energy systems are integrated onto the distribution network. The focus in this research is integrating micro/small generation, which can be installed in parallel with LV consumer connections. After initial investigations of a representative Irish distribution network, a section of an actual distribution network is modelled and a number of power flow algorithms are considered. Subsequently, an algorithm based on the admittance matrix of a network is identified as the optimal approach. The modelling thereby refers to a 4-wire representation of a suburban distribution network within Dublin city, Ireland, which incorporates consumer connections at single-phase (230V-N). Investigations relating to a range of network issues are considered. More specifically, network issues considered include voltage unbalance/rise and the network neutral earth voltage (NEV) for increasing levels of micro/small wind generation technologies with respect to a modelled urban wind resource. The associated power flow analysis is further considered in terms of the turbulence modelling to ascertain how turbulence impinges on the network voltage/voltage-unbalance constraints

Empirical Studies on Financial Stability and Natural Capital

This dissertation develops and applies empirical methods to find policy-relevant answers regarding financial stability and network effects, predicting food insecurity risks, and understanding the financial relevance of natural capital. Chapter 2 proposes a dynamic network effect (DNE) model to study network effects, which refer to entities affecting their neighbors due to the proximity to each other. The smooth marginalized particle filter (SMPF) is shown to be a well-suited estimator in Monte Carlo simulations. Chapter 3 applies the DNE model to explain contagion among the largest Eurozone banks. Supervisory asset holding data allow the construction of a bank business model similarity network. The associated time-varying network effects help resolve the credit spread puzzle, especially during turbulent times. Chapter 4 proposes a stochastic framework to forecast food insecurity risks using LASSO variable selection, a panel vector-autoregression and Bayesian priors to incorporate expert opinions. The model is stochastic and can inform vulnerability and risk assessments. Chapter 5 asks how 1% growth in natural capital affects a country’s government bond yields. Comparisons across countries lead to problematic insights, due to the ingrained income bias. Instead, within-country comparisons over the recent past, estimated using interactive fixed-effects, are unaffected by the bias and show that renewable natural capital tend to lower borrowing costs

Transient numerical simulation of heat transfer processes during drilling of geothermal wells

The transient thermal history of a well drilling system has been identified as oneof the main problems that the geothermal well drilling industry needs to solve. Inparticular, the estimation of temperatures, in and around a geothermal well duringdrilling (circulation) and shut-in (thermal recovery) conditions, is required.To overcome this problem, a computer simulator (WELLTHER) has beendeveloped which uses a direct solution method to solve the finite difference equationsdescribing the transient heat transfer processes in a wellbore during drilling and shut-inoperations in the presence of the lost circulation to the formation. The new computersimulator uses a numerical model to account for the transient convective heat transfer inthe formation surrounding a well, due to lost circulation. This feature of the presentsimulator is important, since previous wellbore simulators consider the heat transferprocess in the formation (rock) as a merely conductive problem. The WELLTHERsimulator is capable of accounting for these losses at any point in the well and it hasbeen applied to the study of several Mexican geothermal wells. The results show that theeffect of lost circulation on the shut-in temperature profiles can be reproducedsatisfactorily. Likewise, a parametric analysis, carried out using the simulator,indicates that a number of assumptions made in previous numerical models are invalidand that certain factors ignored in previous models have a significant effect on thedynamic wellbore temperature distribution.Finally, a coupling of the new simulator with another computer code(STATIC TEMP) can be used as a tool to infer more reliably the static formationtemperatures in geothermal systems

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Analysis of market incentives on power system planning and operations in liberalised electricity markets

The design of liberalised electricity markets (e.g., the energy, capacity and ancillary service markets) is a topic of much debate, regarding their ability to trigger adequate investment in generation capacities and to incentivize flexible power system operation. Long-term generation investment (LTGI) models have been widely used as a decision-support tool for generation investments and design of energy policy. Of particular interest is quantification of uncertainty in model outputs (e.g., generation projections or system reliability) given a particular market design while accounting for uncertainties in input data as well as the discrepancies between the model and the reality. Unfortunately, the standard Monte Carlo based techniques for uncertainty quantification require a very large number of model runs which may be impractical to achieve for a complex LTGI model. In order to enable efficient and fully systematic analysis, it is therefore necessary to create an emulator of the full model, which may be evaluated quickly for any input and which quantifies uncertainty in the output of the full model at inputs where it has not been run. The case study shows results from the Great Britain power system exemplar which is representative of LTGI models used in real policy processes. In particular, it demonstrates the application of Bayesian emulation to a complex LTGI model that requires a formal calibration, uncertainty analysis, and sensitivity analysis. In power systems with large amounts of variable generation, it is important to provide sufficient incentives for operating reserves as a main source of generation flexibility. In the traditional unit commitment (UC) model, the demand for operating reserves is fixed and inelastic, which does not reflect the marginal value of operating reserves in avoiding the events of load shedding and wind curtailment. Besides, the system-wide reserve constraint assumes that the operating reserve can be delivered to any location freely, which is not true in real-world power system operations. To recognize the value and deliverability of operating reserves, dynamic zonal operating reserve demand curves are introduced to an enhanced deterministic UC model for co-optimizing the day-ahead schedules for energy and operating reserves. In the case study on the RTS-73 test system, comparisons are made between the choices of reserve policies (e.g., single, seasonal or dynamic zones) and of different reserve zonal partitioning methods. Results suggest that the enhanced deterministic UC model produces on average lower operational cost, higher system reliability and higher energy and reserve revenues than the traditional one. Finally, we discuss future directions of methodological research arising from current energy system challenges and the computer models developed for better understanding of the impacts of market incentives on power system planning and operations