The Impact of Emissions Trading on the Price of Electricity in Nord Pool : Market Power and Price Determination in the Nordic Electricity Market

The objective of this thesis is to find out how dominant firms in a liberalised electricity market will react when they face an increase in the level of costs due to emissions trading, and how this will effect the price of electricity. The Nordic electricity market is chosen as the setting in which to examine the question, since recent studies on the subject suggest that interaction between electricity markets and emissions trading is very much dependent on conditions specific to each market area. There is reason to believe that imperfect competition prevails in the Nordic market, thus the issue is approached through the theory of oligopolistic competition. The generation capacity available at the market, marginal cost of electricity production and seasonal levels of demand form the data based on which the dominant firms are modelled using the Cournot model of competition. The calculations are made for two levels of demand, high and low, and with several values of demand elasticity. The producers are first modelled under no carbon costs and then by adding the cost of carbon dioxide at 20€/t to those technologies subject to carbon regulation. In all cases the situation under perfect competition is determined as a comparison point for the results of the Cournot game. The results imply that the potential for market power does exist on the Nordic market, but the possibility for exercising market power depends on the demand level. In season of high demand the dominant firms may raise the price significantly above competitive levels, and the situation is aggravated when the cost of carbon dioixide is accounted for. Under low demand leves there is no difference between perfect and imperfect competition. The results are highly dependent on the price elasticity of demand.

The effect of electricity tariffs over the operating costs of the Spanish wastewater treatments plants

Given the complex structure of electricity tariffs and their steady growth in Spanish, we've studied its effect over the operating costs of the wastewater treatment plants (WWTP), concluding that in the last three years the revisions of electricity rates have meant increases in electricity costs of 64.5% in the rate 3.1.A and 79.1% in the rate 6.1. This has caused the cost of electricity, which was the most important, has increased from a 44% of total operating costs in the year 2009, to more than a 56% in the year 2012.

A techno-economic assessment of the use of fast pyrolysis bio-oil from UK energy crops in the production of electricity and combined heat and power

This thesis investigates the cost of electricity generation using bio-oil produced by the fast pyrolysis of UK energy crops. The study covers cost from the farm to the generator’s terminals. The use of short rotation coppice willow and miscanthus as feedstocks was investigated. All costs and performance data have been taken from published papers, reports or web sites. Generation technologies are compared at scales where they have proved economic burning other fuels, rather than at a given size. A pyrolysis yield model was developed for a bubbling fluidised bed fast pyrolysis reactor from published data to predict bio-oil yields and pyrolysis plant energy demands. Generation using diesel engines, gas turbines in open and combined cycle (CCGT) operation and steam cycle plants was considered. The use of bio-oil storage to allow the pyrolysis and generation plants to operate independently of each other was investigated. The option of using diesel generators and open cycle gas turbines for combined heat and power was examined. The possible cost reductions that could be expected through learning if the technology is widely implemented were considered. It was found that none of the systems analysed would be viable without subsidy, but with the current Renewable Obligation Scheme CCGT plants in the 200 to 350 MWe range, super-critical coal fired boilers co-fired with bio-oil, and groups of diesel engine based CHP schemes supplied by a central pyrolysis plant would be viable. It was found that the cost would reduce with implementation and the planting of more energy crops but some subsidy would still be needed to make the plants viable.

Defining expected cost for the air conditioning to avoid a price spike of electricity market under DSR model

This work presents a demand side response model (DSR) which assists small electricity consumers, through an aggregator, exposed to the market price to proactively mitigate price and peak impact on the electrical system. The proposed model allows consumers to manage air-conditioning when as a function of possible price spikes. The main contribution of this research is to demonstrate how consumers can minimise the total expected cost by optimising air-conditioning to account for occurrences of a price spike in the electricity market. This model investigates how pre-cooling method can be used to minimise energy costs when there is a substantial risk of an electricity price spike. The model was tested with Queensland electricity market data from the Australian Energy Market Operator and Brisbane temperature data from the Bureau of Statistics during hot days on weekdays in the period 2011 to 2012.

Cost-effectiveness of electricity energy efficiency programs

We analyze the cost-effectiveness of electric utility ratepayer-funded programs to promote demand-side management (DSM) and energy efficiency (EE) investments. We specify a model that relates electricity demand to previous EE DSM spending, energy prices, income, weather, and other demand factors. In contrast to previous studies, we allow EE DSM spending to have a potential longterm demand effect and explicitly address possible endogeneity in spending. We find that current period EE DSM expenditures reduce electricity demand and that this effect persists for a number of years. Our findings suggest that ratepayer funded DSM expenditures between 1992 and 2006 produced a central estimate of 0.9 percent savings in electricity consumption over that time period and a 1.8 percent savings over all years. These energy savings came at an expected average cost to utilities of roughly 5 cents per kWh saved when future savings are discounted at a 5 percent rate. Copyright © 2012 by the IAEE. All rights reserved.

Demand-side response model to avoid spike of electricity price

The aim of this work is to develop a demand-side-response model, which assists electricity consumers exposed to the market price to independently and proactively manage air-conditioning peak electricity demand. The main contribution of this research is to show how consumers can optimize the energy cost caused by the air conditioning load considering to several cases e.g. normal price, spike price, and the probability of a price spike case. This model also investigated how air-conditioning applies a pre-cooling method when there is a substantial risk of a price spike. The results indicate the potential of the scheme to achieve financial benefits for consumers and target the best economic performance for electrical generation distribution and transmission. The model was tested with Queensland electricity market data from the Australian Energy Market Operator and Brisbane temperature data from the Bureau of Statistics regarding hot days from 2011 to 2012.

Systems modelling of the socio-technical aspects of residential electricity use and network peak demand

Provision of network infrastructure to meet rising network peak demand is increasing the cost of electricity. Addressing this demand is a major imperative for Australian electricity agencies. The network peak demand model reported in this paper provides a quantified decision support tool and a means of understanding the key influences and impacts on network peak demand. An investigation of the system factors impacting residential consumers’ peak demand for electricity was undertaken in Queensland, Australia. Technical factors, such as the customers’ location, housing construction and appliances, were combined with social factors, such as household demographics, culture, trust and knowledge, and Change Management Options (CMOs) such as tariffs, price,managed supply, etc., in a conceptual ‘map’ of the system. A Bayesian network was used to quantify the model and provide insights into the major influential factors and their interactions. The model was also used to examine the reduction in network peak demand with different market-based and government interventions in various customer locations of interest and investigate the relative importance of instituting programs that build trust and knowledge through well designed customer-industry engagement activities. The Bayesian network was implemented via a spreadsheet with a tick box interface. The model combined available data from industry-specific and public sources with relevant expert opinion. The results revealed that the most effective intervention strategies involve combining particular CMOs with associated education and engagement activities. The model demonstrated the importance of designing interventions that take into account the interactions of the various elements of the socio-technical system. The options that provided the greatest impact on peak demand were Off-Peak Tariffs and Managed Supply and increases in the price of electricity. The impact in peak demand reduction differed for each of the locations and highlighted that household numbers, demographics as well as the different climates were significant factors. It presented possible network peak demand reductions which would delay any upgrade of networks, resulting in savings for Queensland utilities and ultimately for households. The use of this systems approach using Bayesian networks to assist the management of peak demand in different modelled locations in Queensland provided insights about the most important elements in the system and the intervention strategies that could be tailored to the targeted customer segments.

Cost of wind energy: comparing distant wind resources to local resources in the midwestern United States.

The best wind sites in the United States are often located far from electricity demand centers and lack transmission access. Local sites that have lower quality wind resources but do not require as much power transmission capacity are an alternative to distant wind resources. In this paper, we explore the trade-offs between developing new wind generation at local sites and installing wind farms at remote sites. We first examine the general relationship between the high capital costs required for local wind development and the relatively lower capital costs required to install a wind farm capable of generating the same electrical output at a remote site,with the results representing the maximum amount an investor should be willing to pay for transmission access. We suggest that this analysis can be used as a first step in comparing potential wind resources to meet a state renewable portfolio standard (RPS). To illustrate, we compare the cost of local wind (∼50 km from the load) to the cost of distant wind requiring new transmission (∼550-750 km from the load) to meet the Illinois RPS. We find that local, lower capacity factor wind sites are the lowest cost option for meeting the Illinois RPS if new long distance transmission is required to access distant, higher capacity factor wind resources. If higher capacity wind sites can be connected to the existing grid at minimal cost, in many cases they will have lower costs.

ANALYSIS AND SIMULATION OF ENERGY USE AND COST AT A MUNICIPAL WASTEWATER TREATMENT PLANT

The cost of electricity, a major operating cost of municipal wastewater treatment plants, is related to influent flow rate, power price, and power load. With knowledge of inflow and price patterns, plant operators can manage processes to reduce electricity costs. Records of influent flow, power price, and load are evaluated for Blue Plains Advanced Wastewater Treatment Plant. Diurnal and seasonal trends are analyzed. Power usage is broken down among treatment processes. A simulation model of influent pumping, a large power user, is developed. It predicts pump discharge and power usage based on wet-well level. Individual pump characteristics are tested in the plant. The model accurately simulates plant inflow and power use for two pumping stations [R2 = 0.68, 0.93 (inflow), R2 =0.94, 0.91(power)]. Wet-well stage-storage relationship is estimated from data. Time-varying wet-well level is added to the model. A synthetic example demonstrates application in managing pumps to reduce electricity cost.

A strategic review of electricity systems models

Electricity systems models are software tools used to manage electricity demand and the electricity systems, to trade electricity and for generation expansion planning purposes. Various portfolios and scenarios are modelled in order to compare the effects of decision making in policy and on business development plans in electricity systems so as to best advise governments and industry on the least cost economic and environmental approach to electricity supply, while maintaining a secure supply of sufficient quality electricity. The modelling techniques developed to study vertically integrated state monopolies are now applied in liberalised markets where the issues and constraints are more complex. This paper reviews the changing role of electricity systems modelling in a strategic manner, focussing on the modelling response to key developments, the move away from monopoly towards liberalised market regimes and the increasing complexity brought about by policy targets for renewable energy and emissions. The paper provides an overview of electricity systems modelling techniques, discusses a number of key proprietary electricity systems models used in the USA and Europe and provides an information resource to the electricity analyst not currently readily available in the literature on the choice of model to investigate different aspects of the electricity system.

Economic optimization of wind farms in function of the cost of energy produced

Esta tese apresenta um estudo sobre otimização económica de parques eólicos, com o objetivo de obter um algoritmo para otimização económica de parques eólicos através do custo da energia produzida. No estudo utilizou-se uma abordagem multidisciplinar. Inicialmente, apresentam-se as principais tecnologias e diferentes arquiteturas utilizadas nos parques eólicos. Bem como esquemas de funcionamento e gestão dos parques. São identificadas variáveis necessárias e apresenta-se um modelo dimensionamento para cálculo dos custos da energia produzida, tendo-se dado ênfase às instalações onshore e ligados a rede elétrica de distribuição. É feita uma análise rigorosa das características das topologias dos aerogeradores disponíveis no mercado, e simula-se o funcionamento de um parque eólico para testar a validade dos modelos desenvolvidos. Também é implementado um algoritmo para a obtenção de uma resposta otimizada para o ciclo de vida económico do parque eólico em estudo. A abordagem proposta envolve algoritmos para otimização do custo de produção com multiplas funções objetivas com base na descrição matemática da produção de eletricidade. Foram desenvolvidos modelos de otimização linear, que estabelece a ligação entre o custo económico e a produção de eletricidade, tendo em conta ainda as emissões de CO2 em instrumentos de política energética para energia eólica. São propostas expressões para o cálculo do custo de energia com variáveis não convencionais, nomeadamente, para a produção variável do parque eólico, fator de funcionamento e coeficiente de eficiência geral do sistema. Para as duas últimas, também é analisado o impacto da distribuição do vento predominante no sistema de conversão de energia eólica. Verifica-se que os resultados obtidos pelos algoritmos propostos são similares às obtidas por demais métodos numéricos já publicados na comunidade científica, e que o algoritmo de otimização económica sofre influência significativa dos valores obtidos dos coeficientes em questão. Finalmente, é demonstrado que o algoritmo proposto (LCOEwso) é útil para o dimensionamento e cálculo dos custos de capital e O&M dos parques eólicos com informação incompleta ou em fase de projeto. Nesse sentido, o contributo desta tese vem ser desenvolver uma ferramenta de apoio à tomada de decisão de um gestor, investidor ou ainda agente público em fomentar a implantação de um parque eólico.

Estimating the increasing cost of commercial buildings in Australia due to greenhouse emissions trading

The ratification of the Kyoto Protocol by most industrial nations will result in an international greenhouse emissions trading market by or before 2008. Calculating the quantity of embodied energy in commercial buildings has therefore taken on added significance because it is in the creation of energy that most greenhouse gas that causes global warming is released. For energy efficient commercial buildings in Australia, the embodied energy can typically represent between 10 and 20 years of operational energy. When greenhouse emissions trading is introduced in Australia the cost of energy will rise significantly, particularly electricity which relies primarily on burning fossil fuels for generation. This will affect not only the operating energy costs of buildings (light, power & heating/cooling) but also the cost of building materials and construction. Early estimates of the potential cost of future greenhouse emission permits in Australia vary between $IO/tonne to $180Itonne. This cost would be imposed primarily on the producers of energy and passed on by them to consumers via higher energy costs. For a typical commercial building this could lead to an increase in the total procurement cost of buildings of up to 20% due to the energy embodied during the construction or refurbishment of the building. To assist in evaluating these potential cost increases McKean & Park, Sinclair Knight Merz and Deakin University have developed a web-based Carbon Cost Calculator for commercial buildings.