Multidimensional scaling analysis of electricity market prices

This paper studies the impact of energy and stock markets upon electricity markets using Multidimensional Scaling (MDS). Historical values from major energy, stock and electricity markets are adopted. To analyze the data several graphs produced by MDS are presented and discussed. This method is useful to have a deeper insight into the behavior and the correlation of the markets. The results may also guide the construction models, helping electricity markets agents hedging against Market Clearing Price (MCP) volatility and, simultaneously, to achieve better financial results.

Short-term electricity prices forecasting in a competetive market by a hybrid Pso-Anfis approach

In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of 1 week. The proposed approach is based on the combination of particle swarm optimization and adaptive-network based fuzzy inference system. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications, to demonstrate its effectiveness regarding forecasting accuracy and computation time. Finally, conclusions are duly drawn.

Demand Response Design and Use Based on Network Locational Marginal Prices

Power systems have been experiencing huge changes mainly due to the substantial increase of distributed generation (DG) and the operation in competitive environments. Virtual Power Players (VPP) can aggregate several players, namely a diversity of energy resources, including distributed generation (DG) based on several technologies, electric storage systems (ESS) and demand response (DR). Energy resources management gains an increasing relevance in this competitive context. This makes the DR use more interesting and flexible, giving place to a wide range of new opportunities. This paper proposes a methodology to support VPPs in the DR programs’ management, considering all the existing energy resources (generation and storage units) and the distribution network. The proposed method is based on locational marginal prices (LMP) values. The evaluation of the impact of using DR specific programs in the LMP values supports the manager decision concerning the DR use. The proposed method has been computationally implemented and its application is illustrated in this paper using a 33-bus network with intensive use of DG.

Analysis of Electricity Market Prices Using Multidimensional Scaling

This paper studies the impact of the energy upon electricity markets using Multidimensional Scaling (MDS). Data from major energy and electricity markets is considered. Several maps produced by MDS are presented and discussed revealing that this method is useful for understanding the correlation between them. Furthermore, the results help electricity markets agents hedging against Market Clearing Price (MCP) volatility.

A green future for European electricity? :Energy sources, policies and further determinants of the household price of electricity

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics

Economic impact of alternative policy measures implemented on the energy activities of the Catalan production system: an input-output analysis

The aim of the paper is to analyse the economic impact of alternative policies implemented on the energy activities of the Catalan production system. Specifically, we analyse the effects of a tax on intermediate energy uses, a reduction in the final production of energy, and a reduction in intermediate energy uses. The methodology involves two versions of the input-output price model: a competitive price formulation and a mark-up price formulation. The input-output price framework will make it possible to evaluate how the alternative measures modify production prices, consumption prices, private welfare, and intermediate energy uses. The empirical application is for the Catalan economy and uses economic data for the year 2001.

The added value from a general equilibrium analyses of increased efficiency in household energy use

The aim of the paper is to identify the added value from using general equilibrium techniques to consider the economy-wide impacts of increased efficiency in household energy use. We take as an illustrative case study the effect of a 5% improvement in household energy efficiency on the UK economy. This impact is measured through simulations that use models that have increasing degrees of endogeneity but are calibrated on a common data set. That is to say, we calculate rebound effects for models that progress from the most basic partial equilibrium approach to a fully specified general equilibrium treatment. The size of the rebound effect on total energy use depends upon: the elasticity of substitution of energy in household consumption; the energy intensity of the different elements of household consumption demand; and the impact of changes in income, economic activity and relative prices. A general equilibrium model is required to capture these final three impacts.

The emergence of biofuels and the co-movement between crude oil and agricultural prices

Biofuels are becoming an alternative to non-renewable energy sources but we know little about the economic mechanisms influencing their prices. This paper studies the interrelationships between the spot prices of oil and those of agricultural commodities used as biofuel feedstocks. Using daily data since 1988, we identify a co-movement after 2005 that does not appear for other food-related commodities and is not due to general economic variables. We also find traces of the co-movement in the prices of a large biofuel stock. The results amount to the first systematic piece of empirical evidence linking spot oil and agricultural markets via the emergence of biofuels.

Adoption Subsidies and Environmental Impacts of Alternative Energy Crops, March 2007

We provide estimates of the costs associated with inducing substantial conversion of land from production of traditional crops to switchgrass. Higher traditional crop prices due to increased demand for corn from the ethanol industry has increased the relative advantage that row crops have over switchgrass. Results indicate that farmers will convert to switchgrass production only with significant conversion subsidies. To examine potential environmental consequences of conversion, we investigate three stylized landscape usage scenarios, one with an entire conversion of a watershed to switchgrass production, a second with the entire watershed planted to continuous corn under a 50% removal rate of the biomass, and a third scenario that places switchgrass on the most erodible land in the watershed and places continuous corn on the least erodible. For each of these illustrative scenarios, the watershed-scale Soil and Water Assessment Tool (SWAT) hydrological model (Arnold et al., 1998; Arnold and Forher, 2005) is used to evaluate the effect of these landscape uses on sediment and nutrient loadings in the Maquoketa Watershed in eastern Iowa.

Energy Information Report, 2011

In its 2007 Session, the Iowa General Assembly passed, and Governor Culver signed into law, extensive and far-reaching state energy policy legislation. This legislation created the Iowa Office of Energy Independence and the Iowa Power Fund. It also required a report to be issued each year detailing: • The historical use and distribution of energy in Iowa. • The growth rate of energy consumption in Iowa, including rates of growth for each energy source. • A projection of Iowa’s energy needs through the year 2025 at a minimum. • The impact of meeting Iowa’s energy needs on the economy of the state, including the impact of energy production and use on greenhouse gas emissions. • An evaluation of renewable energy sources, including the current and future technological potential for such sources. Much of the energy information for this report has been derived from the on-line resources of the Energy Information Administration (EIA) of the United States Department of Energy (USDOE). The EIA provides policy-independent data, forecasts and analyses on energy production, stored supplies, consumption and prices. For complete, economy-wide information, the most recent data available is for the year 2008. For some energy sectors, more current data is available from EIA and other sources and, when available, such information has been included in this report.

Symmetric or asymmetric gasoline prices? A meta-analysis approach

The analysis of price asymmetries in the gasoline market is one of the most studied in the energy economics literature. Nevertheless, the great variability of results makes it very difficult to extract conclusive results on the existence or not of asymmetries. This paper shows through a meta-analysis approach how the industry segment analysed, the quality and quantity of data, the estimator and the model used may explain this heterogeneity of results.

Utilisation of biomass for energy production in the North-West Russian forest industry

Traditionally, fossil fuels have always been the major sources of the modern energy production. However prices on these energy sources have been constantly increasing. The utilization of local biomass resources for energy production can substitute significant part of the required energy demand in different energy sectors. The introduction of the biomass usage can easily be started in the forest industry first as it possesses biomass in a large volume. The forest industry energy sector has the highest potential for the fast bioenergy development in the North-West Russia. Therefore, the question concerning rational and effective forest resources use is important today as well as the utilization of the forestry by-products. This work describes and analyzes the opportunities of utilising biomass, mainly, in the form of the wood by-products, for energy production processes in general, as well as for the northwest Russian forest industry conditions. The study also covers basic forest industry processes and technologies, so, the reader can get familiar with the information about the specific character of the biomass utilization. The work gives a comprehensive view on the northwest forest industry situation from the biomass utilisation point of view. By presenting existing large-scale sawmills and pulp and paper mills the work provides information for the evaluation of the future development of CHP investments in the northwest Russian forest industry.

On Energy Efficient Computing Platforms

In accordance with the Moore's law, the increasing number of on-chip integrated transistors has enabled modern computing platforms with not only higher processing power but also more affordable prices. As a result, these platforms, including portable devices, work stations and data centres, are becoming an inevitable part of the human society. However, with the demand for portability and raising cost of power, energy efficiency has emerged to be a major concern for modern computing platforms. As the complexity of on-chip systems increases, Network-on-Chip (NoC) has been proved as an efficient communication architecture which can further improve system performances and scalability while reducing the design cost. Therefore, in this thesis, we study and propose energy optimization approaches based on NoC architecture, with special focuses on the following aspects. As the architectural trend of future computing platforms, 3D systems have many bene ts including higher integration density, smaller footprint, heterogeneous integration, etc. Moreover, 3D technology can signi cantly improve the network communication and effectively avoid long wirings, and therefore, provide higher system performance and energy efficiency. With the dynamic nature of on-chip communication in large scale NoC based systems, run-time system optimization is of crucial importance in order to achieve higher system reliability and essentially energy efficiency. In this thesis, we propose an agent based system design approach where agents are on-chip components which monitor and control system parameters such as supply voltage, operating frequency, etc. With this approach, we have analysed the implementation alternatives for dynamic voltage and frequency scaling and power gating techniques at different granularity, which reduce both dynamic and leakage energy consumption. Topologies, being one of the key factors for NoCs, are also explored for energy saving purpose. A Honeycomb NoC architecture is proposed in this thesis with turn-model based deadlock-free routing algorithms. Our analysis and simulation based evaluation show that Honeycomb NoCs outperform their Mesh based counterparts in terms of network cost, system performance as well as energy efficiency.

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.

Performance Determinants of Renewable Energy Companies

The purpose of this thesis is to identify the Performance Determinants (PD) of Renewable Energy (RE) companies. It analyzes the background of the RE industry while reflecting simultaneous developments in the fossil based industries. I divided the determinants into two groups: market level and firm level and established hypotheses based on the existing literature. Data from public companies was gathered to construct a Panel Data structure. This is then tested by using a Linear Regression with Fixed Effects model. The model specification was efficient at reflecting the analyzed phenomena. My results showed that both market level and firm level determinants are significant in the RE Industry but the firm level determinants had higher explanatory power (R2). The determinants' relationships were found to follow those from the manufacturing industry more than the utilities' industry. Out of the market level determinants Consumer Price Index (CPI), Interest Rates and Oil prices were significant. Out of the firm level determinants Debt to Assets, Net Investments, Cash flows from operations, Sales and Earnings Before Interests and Taxes (EBIT) were significant. I concluded that this information is valuable for key industry players as they can achieve their objectives faster by elaborating better strategies using these results.