Accommodating energy price volatility in life cycle cost analysis of asphalt pavements

Energy price is related to more than half of the total life cycle cost of asphalt pavements. Furthermore, the fluctuation related to price of energy has been much higher than the general inflation and interest rate. This makes the energy price inflation an important variable that should be addressed when performing life cycle cost (LCC) studies re- garding asphalt pavements. The present value of future costs is highly sensitive to the selected discount rate. Therefore, the choice of the discount rate is the most critical element in LCC analysis during the life time of a project. The objective of the paper is to present a discount rate for asphalt pavement projects as a function of interest rate, general inflation and energy price inflation. The discount rate is defined based on the portion of the energy related costs during the life time of the pavement. Consequently, it can reflect the financial risks related to the energy price in asphalt pavement projects. It is suggested that a discount rate sensitivity analysis for asphalt pavements in Sweden should range between –20 and 30%.

Primary production, financial constraints and economic instability:Zambia since independence.

A systematic analysis is presented of the economic consequences of the abnormally high concentration of Zambia's exports on a commodity whose price is exceptionally unstable. Zambian macro-economic variables in the post-independence years are extensively documented, showing acute instability and decline, particularly after the energy price revolution and the collapse of copper prices. The relevance of stabilization policies designed to correct short-term disequilibrium is questioned. It is, therefore, a pathological case study of externally induced economic instability, complementing other studies in this area which use cross-country analysis of a few selected variables. After a survey of theory and issues pertaining to development, finance and stabilization, the emergence of domestic and foreign financial constraints on the Zambian economy is described. The world copper industry is surveyed and an examination of commodity and world trade prices concludes that copper showed the highest degree of price instability. Specific aspects of Zambia's economy identified for detailed analysis include: its unprofitable mining industry, external payments disequilibrium, a constrained government budget, potentially inflationary monetary growth, and external indebtedness. International comparisons are used extensively, but major copper exporters are subjected to closer scrutiny. An appraisal of policy options concludes the study.

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.

Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their MSEs are 0.02314 and 0.15384 respectively.

A heat metering system for energy management on an industrial site

Faced with a future of rising energy costs there is a need for industry to manage energy more carefully in order to meet its economic objectives. A problem besetting the growth of energy conservation in the UK is that a large proportion of energy consumption is used in a low intensive manner in organisations where they would be responsibility for energy efficiency is spread over a large number of personnel who each see only small energy costs. In relation to this problem in the non-energy intensive industrial sector, an application of an energy management technique known as monitoring and targeting (M & T) has been installed at the Whetstone site of the General Electric Company Limited in an attempt to prove it as a means for motivating line management and personnel to save energy. The objective energy saving for which the M & T was devised is very specific. During early energy conservation work at the site there had been a change from continuous to intermittent heating but the maintenance of the strategy was receiving a poor level of commitment from line management and performance was some 5% - 10% less than expected. The M & T is concerned therefore with heat for space heating for which a heat metering system was required. Metering of the site high pressure hot water system posed technical difficulties and expenditure was also limited. This led to a ‘tin-house' design being installed for a price less than the commercial equivalent. The timespan of work to achieve an operational heat metering system was 3 years which meant that energy saving results from the scheme were not observed during the study. If successful the replication potential is the larger non energy intensive sites from which some 30 PT savings could be expected in the UK.

Energy conservation in the steel industry

The last few years have witnessed an unprecedented increase in the price of energy available to industry in the United Kingdom and worldwide. The steel industry, as a major consumer of energy delivered in U.K. (8% of national total and nearly 25% of industrial total) and whose energy costs currently form some 28% of the total manufacturing cost, is very much aware of the need to conserve energy. Because of the complexities of steelmaking processes it is imperative that a full understanding of each process and its interlinking role in an integrated steelworks is understood. An analysis of energy distribution shows that as much as 70% of heat input is dissipated to the environment in a variety of forms. Of these, waste gases offer the best potential for energy conservation. The study identifies areas for and discusses novel methods of energy conservation in each process. Application of these schemes in BSC works is developed and their economic incentives highlighted. A major part of this thesis describes design, development and testing of a novel ceramic rotary regenerator for heat recovery from high temperature waste gases, where no such system is available. The regenerator is a compact, efficient heat exchanger. Application of such a system to a reheating furnace provides a fuel saving of up to 40%. A mathematical model developed is verified on the pilot plant. The results obtained confirm the success of the concept and material selection and outlines the work needed to develop an industrial unit. Last, but not least, the key position of an energy manager in an energy conservation programme is identified and a new Energy Management Model for the BSC is developed.

Putting service relations to the test:how can negative consumer reactions of price increases be reduced?

Price increases seem to be an adequate way to improve the earnings of companies. This fact becomes especially crucial because of increased price competition in many markets. Price increases might lead to negative customer reactions, such as a lower perceived utility or a lower loyalty intention. Therefore, the question for managers remains how prices can be increased without losing customers. Results of our experimental study suggest that customers of energy suppliers rate the perceived utility of the offer relatively better when the price increase is combined with an additional modification of the product or accompanied by a new service. It becomes clear that intensifying service relations can offset the negative effects of price increases.

Novel biomass conversion routes:ammonia from biomass, and marine macroalgae for energy

This thesis presents a techno-economic investigation of the generation of electricity from marine macroalgae (seaweed) in the UK (Part 1), and the production of anhydrous ammonia from synthesis gas (syngas) generated from biomass gasification (Part 2). In Part 1, the study covers the costs from macroalgae production to the generation of electricity via a CHP system. Seven scenarios, which varied the scale and production technique, were investigated to determine the most suitable scale of operation for the UK. Anaerobic digestion was established as the most suitable technology for macroalgae conversion to CHP, based on a number of criteria. All performance and cost data have been taken from published literature. None of the scenarios assessed would be economically viable under present conditions, although the use of large-scale electricity generation has more potential than small-scale localised production. Part 2 covers the costs from the delivery of the wood chip feedstock to the production of ammonia. Four cases, which varied the gasification process used and the scale of production, were investigated to determine the most suitable scale of operation for the UK. Two gasification processes were considered, these were O2-enriched air entrained flow gasification and Fast Internal Circulating Fluidised Bed. All performance and cost data have been taken from published literature, unless otherwise stated. Large-scale (1,200 tpd) ammonia production using O2-enriched air entrained flow gasification was determined as the most suitable system, producing the lowest ammonia-selling price, which was competitive to fossil fuels. Large-scale (1,200 tpd) combined natural gas/biomass syngas ammonia production also generated ammonia at a price competitive to fossil fuels.

Making energy service companies (ESCOS) future fit

Energy service companies (ESCOs) are faced with a range of challenges and opportunities associated with the rapidly changing and flexible requirements of energy customers (end users) and rapid improvements in technologies associated with energy and ICT. These opportunities for innovation include better prediction of energy demand, transparency of data to the end user, flexible and time dependent energy pricing and a range of novel finance models. The liberalisation of energy markets across the world has leads to a very small price differential between suppliers on the unit cost of energy. Energy companies are therefore looking to add additional layers of value using service models borrowed from the manufacturing industry. This opens a range of new product and service offerings to energy markets and consumers and has implications for the overall efficiency, utility and price of energy provision.

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.