Combining the wavelet transform and forecasting models to predict gas forward prices

This paper presents a forecasting technique for forward energy prices, one day ahead. This technique combines a wavelet transform and forecasting models such as multi- layer perceptron, linear regression or GARCH. These techniques are applied to real data from the UK gas markets to evaluate their performance. The results show that the forecasting accuracy is improved significantly by using the wavelet transform. The methodology can be also applied to forecasting market clearing prices and electricity/gas loads.

Investement appraisal in a deregulated electricity market

This thesis analyses the impact of deregulation on the theory and practice of investment decision making in the electricity sector and appraises the likely effects on its long term future inefficiency. Part I describes the market and its shortcomings in promoting an optimal generation margin and plant mix and in reducing prices through competition. A full size operational model is developed to simulate hour by hour operation of the market and analyse its features. A relationship is established between the SMP and plant mix and between the LOLP and plant margin and it is shown bow a theoretical optimum can be derived when the combined LOLP payments and the capital costs of additional generation reach a minimum. A comparison of prices against an idealised bulk supply tariff is used to show how energy prices have risen some 12% in excess of what might have occurred under the CEGB regime. This part concludes with proposals to improve the marlprices so as to maximise his income. The optimal pricing strategy for the transmitter is also derived and consumer response is simulated .The concept of transmission uplift is developed and the operational model is extended to include transmission constraints and then used to establish monthly incremental transmission constraint cost functions. It is shown how these can be used to appraise investment options and optimally plan outages. Part 4 concludes by discussing the regulatory framework and its limitations in improving efficiency or encouraging the optimum levels of investment. The principal findings of the thesis are reviewed and potential market improvement are described. This part concludes with a discussion of alternative market structures and likely future developments.

Energy forward price prediction with a hybrid adaptive model

This paper presents a forecasting technique for forward electricity/gas prices, one day ahead. This technique combines a Kalman filter (KF) and a generalised autoregressive conditional heteroschedasticity (GARCH) model (often used in financial forecasting). The GARCH model is used to compute next value of a time series. The KF updates parameters of the GARCH model when the new observation is available. This technique is applied to real data from the UK energy markets to evaluate its performance. The results show that the forecasting accuracy is improved significantly by using this hybrid model. The methodology can be also applied to forecasting market clearing prices and electricity/gas loads.

Techniques to improve forecasting models: applications to energy demand and price

This thesis is a study of three techniques to improve performance of some standard fore-casting models, application to the energy demand and prices. We focus on forecasting demand and price one-day ahead. First, the wavelet transform was used as a pre-processing procedure with two approaches: multicomponent-forecasts and direct-forecasts. We have empirically compared these approaches and found that the former consistently outperformed the latter. Second, adaptive models were introduced to continuously update model parameters in the testing period by combining ?lters with standard forecasting methods. Among these adaptive models, the adaptive LR-GARCH model was proposed for the fi?rst time in the thesis. Third, with regard to noise distributions of the dependent variables in the forecasting models, we used either Gaussian or Student-t distributions. This thesis proposed a novel algorithm to infer parameters of Student-t noise models. The method is an extension of earlier work for models that are linear in parameters to the non-linear multilayer perceptron. Therefore, the proposed method broadens the range of models that can use a Student-t noise distribution. Because these techniques cannot stand alone, they must be combined with prediction models to improve their performance. We combined these techniques with some standard forecasting models: multilayer perceptron, radial basis functions, linear regression, and linear regression with GARCH. These techniques and forecasting models were applied to two datasets from the UK energy markets: daily electricity demand (which is stationary) and gas forward prices (non-stationary). The results showed that these techniques provided good improvement to prediction performance.

The effects of the changing energy situation on demand for industrial heat exchangers

The Project arose during a period in which the World was still coming to terms with the effects and implications of the so called 'energy crisis' of 1973/74. Serck Heat Transfer is a manufacturer of heat exchangers which transfer heat between fluids of various sorts. As such the company felt that past and possible future changes in the energy situation could have an impact upon the demand for its products. The thesis represents the first attempt to examine the impact of changes in the energy situation (a major economic variable) on the long term demand for heat exchangers. The scope of the work was limited to the United Kingdom, this being the largest single market for Serek's products. The thesis analyses industrial heat exchanger markets and identifies those trends which are related to both the changing energy situation and the usage of heat exchangers. These trends have been interpreted In terms of projected values of heat exchanger demand. The projections cover the period 197S to the year 2000. Also examined in the thesis is the future energy situation both internationally and nationally and it is found that in the long term there will be increasing pressure on consumers to conserve energy through rising real prices. The possibility of a connection between energy consumption and heat exchanger demand is investigated and no significant correlation found. This appears to be because there are a number of determinants of demand besides energy related factors and also there is a wide diversity of individual markets for heat exchangers. Conclusions are that in all markets, bar one, the changing energy situation should lead to a higher level of heat exchanger demand than would otherwise be the case had the energy situation not changed. It is also pointed out that it is misleading to look at changes in one influence on the demand for a product and ignore others.

Increase of energy recovery from sewage sludge

The use of the pyrolysis process to obtain valuable products from biomass is amongst the technologies being investigated as a source for renewable energy. The pyrolysis process yields products such as biochar, bio-oil and non condensable gases. The main objective of this project is to increase energy recovery from sewage sludge by utilising the intermediate pyrolysis process. The intermediate pyrolysis has a residence time ranging from 5 to 10 minutes. The main product yields from sewage sludge pyrolysis are 50 wt% biochar, 40 wt% bio-oil and 10 wt% non condensable gases. The project was carried out on a pilot plant scale reactor with a load capacity of 20 kg/h. This enabled a high yield of biochar and bio-oil. The characterisation of the products indicated that the organic phase of the bio-oil had good fuel properties such as having high energy content of 39 MJ/kg, low acid number of 21.5, high flash point of 150 and viscosity of 35 cSt. An increase in pyrolysis experiments enabled large quantities of pyrolysis oil production. Co-pyrolysis of sewage sludge was carried out on laboratory scale with mixed wood, rapeseed and straw. It found that there was an increase in bio-oil quantity with rapeseed while co-pyrolysis with wood helped to mask the smell of the sludge pyrolysis oil. Engine test were successfully carried out in an old Lister engine with pyrolysis oil fractions of 30% and 50% blended with biodiesel. This indicates that these pyrolysis oil fractions can be used in similar engine types without any problems however long term effects in ordinary engines are unknown. An economic evaluation was carried out about the implementation of the intermediate pyrolysis process for electricity production in a CHP using the pyrolysis oil. The prices of electricity per kWh were found to be very high.

An intelligent broker agent for energy trading:an MDP approach

This paper details the development and evaluation of AstonTAC, an energy broker that successfully participated in the 2012 Power Trading Agent Competition (Power TAC). AstonTAC buys electrical energy from the wholesale market and sells it in the retail market. The main focus of the paper is on the broker’s bidding strategy in the wholesale market. In particular, it employs Markov Decision Processes (MDP) to purchase energy at low prices in a day-ahead power wholesale market, and keeps energy supply and demand balanced. Moreover, we explain how the agent uses Non-Homogeneous Hidden Markov Model (NHHMM) to forecast energy demand and price. An evaluation and analysis of the 2012 Power TAC finals show that AstonTAC is the only agent that can buy energy at low price in the wholesale market and keep energy imbalance low.

The main criteria of biomass selection for energy generation in Brazil

The principal aim of this paper is to examine the criteria assisting in the selection of biomass for energy generation in Brazil. To reach the aim, this paper adopts case study and survey research methods to collect information from four biomass energy case companies and solicits opinions from experts. The data gathered are analysed in line with a wide range of related data, including selection criteria for biomass and its importance, energy policies in Brazil, availability of biomass feedstock in Brazil and its characteristics, as well as status quo of biomass-based energy in Brazil. The findings of the paper demonstrate that there are ten main criteria in biomass selection for energy generation in Brazil. They comprise geographical conditions, availability of biomass feedstock, demand satisfaction, feedstock costs and oil prices, energy content of biomass feedstock, business and economic growth, CO2 emissions of biomass end-products, effects on soil, water and biodiversity, job creation and local community support, as well as conversion technologies. Furthermore, the research also found that these main criteria cannot be grouped on the basis of sustainability criteria, nor ranked by their importance as there is correlation between each criterion such as a cause and effect relationship, as well as some overlapping areas. Consequently, this means that when selecting biomass more comprehensive consideration is advisable.