Modelos para el análisis dinámico y la predicción a corto plazo de los precios de la electricidad en mercados liberalizados

En esta tesis se va a describir y aplicar de forma novedosa la técnica del alisado exponencial multivariante a la predicción a corto plazo, a un día vista, de los precios horarios de la electricidad, un problema que se está estudiando intensivamente en la literatura estadística y económica reciente. Se van a demostrar ciertas propiedades interesantes del alisado exponencial multivariante que permiten reducir el número de parámetros para caracterizar la serie temporal y que al mismo tiempo permiten realizar un análisis dinámico factorial de la serie de precios horarios de la electricidad. En particular, este proceso multivariante de elevada dimensión se estimará descomponiéndolo en un número reducido de procesos univariantes independientes de alisado exponencial caracterizado cada uno por un solo parámetro de suavizado que variará entre cero (proceso de ruido blanco) y uno (paseo aleatorio). Para ello, se utilizará la formulación en el espacio de los estados para la estimación del modelo, ya que ello permite conectar esa secuencia de modelos univariantes más eficientes con el modelo multivariante. De manera novedosa, las relaciones entre los dos modelos se obtienen a partir de un simple tratamiento algebraico sin requerir la aplicación del filtro de Kalman. De este modo, se podrán analizar y poner al descubierto las razones últimas de la dinámica de precios de la electricidad. Por otra parte, la vertiente práctica de esta metodología se pondrá de manifiesto con su aplicación práctica a ciertos mercados eléctricos spot, tales como Omel, Powernext y Nord Pool. En los citados mercados se caracterizará la evolución de los precios horarios y se establecerán sus predicciones comparándolas con las de otras técnicas de predicción. ABSTRACT This thesis describes and applies the multivariate exponential smoothing technique to the day-ahead forecast of the hourly prices of electricity in a whole new way. This problem is being studied intensively in recent statistics and economics literature. It will start by demonstrating some interesting properties of the multivariate exponential smoothing that reduce drastically the number of parameters to characterize the time series and that at the same time allow a dynamic factor analysis of the hourly prices of electricity series. In particular this very complex multivariate process of dimension 24 will be estimated by decomposing a very reduced number of univariate independent of exponentially smoothing processes each characterized by a single smoothing parameter that varies between zero (white noise process) and one (random walk). To this end, the formulation is used in the state space model for the estimation, since this connects the sequence of efficient univariate models to the multivariate model. Through a novel way, relations between the two models are obtained from a simple algebraic treatment without applying the Kalman filter. Thus, we will analyze and expose the ultimate reasons for the dynamics of the electricity price. Moreover, the practical aspect of this methodology will be shown by applying this new technique to certain electricity spot markets such as Omel, Powernext and Nord Pool. In those markets the behavior of prices will be characterized, their predictions will be formulated and the results will be compared with those of other forecasting techniques.

Determining electric vehicle charging point locations considering drivers' daily activities

In this paper the daily temporal and spatial behavior of electric vehicles (EVs) is modelled using an activity-based (ActBM) microsimulation model for Flanders region (Belgium). Assuming that all EVs are completely charged at the beginning of the day, this mobility model is used to determine the percentage of Flemish vehicles that cannot cover their programmed daily trips and need to be recharged during the day. Assuming a variable electricity price, an optimization algorithm determines when and where EVs can be recharged at minimum cost for their owners. This optimization takes into account the individual mobility constraint for each vehicle, as they can only be charged when the car is stopped and the owner is performing an activity. From this information, the aggregated electric demand for Flanders is obtained, identifying the most overloaded areas at the critical hours. Finally it is also analyzed what activities EV owners are underway during their recharging period. From this analysis, different actions for public charging point deployment in different areas and for different activities are proposed.

A techno-economic comparison of power production by biomass fast pyrolysis with gasification and combustion

This paper presents an assessment of the technical and economic performance of thermal processes to generate electricity from a wood chip feedstock by combustion, gasification and fast pyrolysis. The scope of the work begins with the delivery of a wood chip feedstock at a conversion plant and ends with the supply of electricity to the grid, incorporating wood chip preparation, thermal conversion, and electricity generation in dual fuel diesel engines. Net generating capacities of 1–20 MWe are evaluated. The techno-economic assessment is achieved through the development of a suite of models that are combined to give cost and performance data for the integrated system. The models include feed pretreatment, combustion, atmospheric and pressure gasification, fast pyrolysis with pyrolysis liquid storage and transport (an optional step in de-coupled systems) and diesel engine or turbine power generation. The models calculate system efficiencies, capital costs and production costs. An identical methodology is applied in the development of all the models so that all of the results are directly comparable. The electricity production costs have been calculated for 10th plant systems, indicating the costs that are achievable in the medium term after the high initial costs associated with novel technologies have reduced. The costs converge at the larger scale with the mean electricity price paid in the EU by a large consumer, and there is therefore potential for fast pyrolysis and diesel engine systems to sell electricity directly to large consumers or for on-site generation. However, competition will be fierce at all capacities since electricity production costs vary only slightly between the four biomass to electricity systems that are evaluated. Systems de-coupling is one way that the fast pyrolysis and diesel engine system can distinguish itself from the other conversion technologies. Evaluations in this work show that situations requiring several remote generators are much better served by a large fast pyrolysis plant that supplies fuel to de-coupled diesel engines than by constructing an entire close-coupled system at each generating site. Another advantage of de-coupling is that the fast pyrolysis conversion step and the diesel engine generation step can operate independently, with intermediate storage of the fast pyrolysis liquid fuel, increasing overall reliability. Peak load or seasonal power requirements would also benefit from de-coupling since a small fast pyrolysis plant could operate continuously to produce fuel that is stored for use in the engine on demand. Current electricity production costs for a fast pyrolysis and diesel engine system are 0.091/kWh at 1 MWe when learning effects are included. These systems are handicapped by the typical characteristics of a novel technology: high capital cost, high labour, and low reliability. As such the more established combustion and steam cycle produces lower cost electricity under current conditions. The fast pyrolysis and diesel engine system is a low capital cost option but it also suffers from relatively low system efficiency particularly at high capacities. This low efficiency is the result of a low conversion efficiency of feed energy into the pyrolysis liquid, because of the energy in the char by-product. A sensitivity analysis has highlighted the high impact on electricity production costs of the fast pyrolysis liquids yield. The liquids yield should be set realistically during design, and it should be maintained in practice by careful attention to plant operation and feed quality. Another problem is the high power consumption during feedstock grinding. Efficiencies may be enhanced in ablative fast pyrolysis which can tolerate a chipped feedstock. This has yet to be demonstrated at commercial scale. In summary, the fast pyrolysis and diesel engine system has great potential to generate electricity at a profit in the long term, and at a lower cost than any other biomass to electricity system at small scale. This future viability can only be achieved through the construction of early plant that could, in the short term, be more expensive than the combustion alternative. Profitability in the short term can best be achieved by exploiting niches in the market place and specific features of fast pyrolysis. These include: •countries or regions with fiscal incentives for renewable energy such as premium electricity prices or capital grants; •locations with high electricity prices so that electricity can be sold direct to large consumers or generated on-site by companies who wish to reduce their consumption from the grid; •waste disposal opportunities where feedstocks can attract a gate fee rather than incur a cost; •the ability to store fast pyrolysis liquids as a buffer against shutdowns or as a fuel for peak-load generating plant; •de-coupling opportunities where a large, single pyrolysis plant supplies fuel to several small and remote generators; •small-scale combined heat and power opportunities; •sales of the excess char, although a market has yet to be established for this by-product; and •potential co-production of speciality chemicals and fuel for power generation in fast pyrolysis systems.

An exergy based engineering and economic analysis of sustainable building

To achieve the goal of sustainable development, the building energy system was evaluated from both the first and second law of thermodynamics point of view. The relationship between exergy destruction and sustainable development were discussed at first, followed by the description of the resource abundance model, the life cycle analysis model and the economic investment effectiveness model. By combining the forgoing models, a new sustainable index was proposed. Several green building case studies in U.S. and China were presented. The influences of building function, geographic location, climate pattern, the regional energy structure, and the technology improvement potential of renewable energy in the future were discussed. The building’s envelope, HVAC system, on-site renewable energy system life cycle analysis from energy, exergy, environmental and economic perspective were compared. It was found that climate pattern had a dramatic influence on the life cycle investment effectiveness of the building envelope. The building HVAC system energy performance was much better than its exergy performance. To further increase the exergy efficiency, renewable energy rather than fossil fuel should be used as the primary energy. A building life cycle cost and exergy consumption regression model was set up. The optimal building insulation level could be affected by either cost minimization or exergy consumption minimization approach. The exergy approach would cause better insulation than cost approach. The influence of energy price on the system selection strategy was discussed. Two photovoltaics (PV) systems—stand alone and grid tied system were compared by the life cycle assessment method. The superiority of the latter one was quite obvious. The analysis also showed that during its life span PV technology was less attractive economically because the electricity price in U.S. and China did not fully reflect the environmental burden associated with it. However if future energy price surges and PV system cost reductions were considered, the technology could be very promising for sustainable buildings in the future.

An Exergy Based Engineering and Economic Analysis of Sustainable Building

To achieve the goal of sustainable development, the building energy system was evaluated from both the first and second law of thermodynamics point of view. The relationship between exergy destruction and sustainable development were discussed at first, followed by the description of the resource abundance model, the life cycle analysis model and the economic investment effectiveness model. By combining the forgoing models, a new sustainable index was proposed. Several green building case studies in U.S. and China were presented. The influences of building function, geographic location, climate pattern, the regional energy structure, and the technology improvement potential of renewable energy in the future were discussed. The building’s envelope, HVAC system, on-site renewable energy system life cycle analysis from energy, exergy, environmental and economic perspective were compared. It was found that climate pattern had a dramatic influence on the life cycle investment effectiveness of the building envelope. The building HVAC system energy performance was much better than its exergy performance. To further increase the exergy efficiency, renewable energy rather than fossil fuel should be used as the primary energy. A building life cycle cost and exergy consumption regression model was set up. The optimal building insulation level could be affected by either cost minimization or exergy consumption minimization approach. The exergy approach would cause better insulation than cost approach. The influence of energy price on the system selection strategy was discussed. Two photovoltaics (PV) systems – stand alone and grid tied system were compared by the life cycle assessment method. The superiority of the latter one was quite obvious. The analysis also showed that during its life span PV technology was less attractive economically because the electricity price in U.S. and China did not fully reflect the environmental burden associated with it. However if future energy price surges and PV system cost reductions were considered, the technology could be very promising for sustainable buildings in the future.

Economic Development of Urban Areas

In this dissertation I study the development of urban areas. At the aggregate level I investigate how they may be affected by climate change policies and by being designated the seat of governmental power. At the household level I study with coauthors how microfinance could improve the health of urban residents. In Chapter 1, I investigate how local employment may be affected by electricity price increases, which is a likely consequence of climate change policies. I outline how previous studies that find large, negative effects may be biased. To overcome these biases I develop a novel estimation strategy that blends border-pair regressions with the synthetic control methodology. I show the conditions for consistent estimation. Using this estimator, I find no effect of contemporaneous price changes on employment. Consistent with the longer time-frame for manufacturing decisions, I do find evidence for negative effects from perceived permanent price shocks. These estimates are much smaller than previous research has found. National capital cities are often substantially larger than other cities in their countries. In Chapter 2, I investigate whether there is a causal effect from being a capital by studying the 1960 relocation of the Brazilian capital from Rio de Janeiro to Brasília. Using a synthetic controls strategy I find that losing the capital had no significant effects on Rio de Janeiro in terms of population, employment, or gross domestic product (GDP). I find that Brasília experienced large and significant increases in population, employment, and GDP. I find evidence of large spillovers from the public to the private sector. Chapter 3 investigates how microfinance could increase the uptake of costly health goods. We study the effect of time payments (micro-loans or micro-savings) on willingness-to-pay (WTP) for a water filter among households in the slums of Dhaka, Bangladesh. We find that time payments significantly increase WTP: compared to a lump-sum up-front purchase, median WTP increases 83% with a six-month loan and 115% with a 12-month loan. We find that households are quite patient with respect to consumption of health inputs. We find evidence for the presence of credit and savings constraints.

Cascade hydropower capacity increase profitability analysis with optimisation modelling

The electricity market and climate are both undergoing a change. The changes impact hydropower and provoke an interest for hydropower capacity increases. In this thesis a new methodology was developed utilising short-term hydropower optimisation and planning software for better capacity increase profitability analysis accuracy. In the methodology income increases are calculated in month long periods while varying average discharge and electricity price volatility. The monthly incomes are used for constructing year scenarios, and from different types of year scenarios a long-term profitability analysis can be made. Average price development is included utilising a multiplier. The method was applied on Oulujoki hydropower plants. It was found that the capacity additions that were analysed for Oulujoki were not profitable. However, the methodology was found versatile and useful. The result showed that short periods of peaking prices play major role in the profitability of capacity increases. Adding more discharge capacity to hydropower plants that initially bypassed water more often showed the best improvements both in income and power generation profile flexibility.

Gestão nos Serviços com Sistemas de Monitorização e Implementação do Smart Pricing

A presente dissertação foi elaborada no âmbito do Mestrado em Engenharia Electrotécnica (MEE) no Instituto Superior de Engenharia do Porto (ISEP), em regime empresarial, na empresa PH Energia Lda. Tem-se verificado que, ao longo dos últimos anos, os mercados estão cada vez mais competitivos, tornando-se quase imperativo que as empresas apostem numa boa otimização dos processos produtivos. Produzir cada vez mais, mais rapidamente e com menos recursos disponíveis, ou seja, de forma eficiente, são os desafios de todas as empresas que pretendem permanecer no mercado. Neste contexto surge o tema de tese, “Gestão nos Serviços com Sistemas de Monitorização e Implementação do Smart Pricing”, cujo objetivo tem como base principal a otimização das plataformas da PH Energia numa cultura de melhoria contínua e orientação para o cliente e promover aplicação da tarifa indexada e Smart Pricing em empresas de maneira a que exista uma maior poupança. Ao longo desta dissertação, foram desenvolvidos cálculos associados à monitorização e gestão nos serviços, bem como demonstrada a viabilidade dos mesmos na aplicação de tarifasindexadas e Smart Pricing no setor empresarial e, para finalizar, a compensação que é possível obter ao deslocar o diagrama de cargas, mantendo sempre o mesmo consumo. Na elaboração deste trabalho fez-se o cruzamento de duas plataformas informáticas designadas GesEnergy e Kisense, com ajuda da empresa VPS que tem como parceria a empresa Energia Simples. Em relação ao plano indexado, foram realizados dois estudos de dois balcões do Banco Popular de Portugal de forma a explicitar quando e como deve ser aplicada a tarifa indexada, gestão da procura, bem como deve ser deslocação do consumo, de forma a abranger as horas mais vantajosas em que o preço de energia elétrica é mais baixo.

Coordinated Scheduling of Wind-Thermal Gencos in Day-Ahead Markets

This paper presents a stochastic mixed-integer linear programming approach for solving the self-scheduling problem of a price-taker thermal and wind power producer taking part in a pool-based electricity market. Uncertainty on electricity price and wind power is considered through a set of scenarios. Thermal units are modeled by variable costs, start-up costs and technical operating constraints, such as: ramp up/down limits and minimum up/down time limits. An efficient mixed-integer linear program is presented to develop the offering strategies of the coordinated production of thermal and wind energy generation, aiming to maximize the expected profit. A case study with data from the Iberian Electricity Market is presented and results are discussed to show the effectiveness of the proposed approach.

Bidding Decision of Wind-Thermal GenCo in Day-Ahead Market

This paper deals with the self-scheduling problem of a price-taker having wind and thermal power production and assisted by a cyber-physical system for supporting management decisions in a day-ahead electric energy market. The self-scheduling is regarded as a stochastic mixed-integer linear programming problem. Uncertainties on electricity price and wind power are considered through a set of scenarios. Thermal units are modelled by start-up and variable costs, furthermore constraints are considered, such as: ramp up/down and minimum up/down time limits. The stochastic mixed-integer linear programming problem allows a decision support for strategies advantaging from an effective wind and thermal mixed bidding. A case study is presented using data from the Iberian electricity market.

Bidding strategy of wind-thermal energy producers

This paper presents a stochastic mixed-integer linear programming approach for solving the self-scheduling problem of a price-taker thermal and wind power producer taking part in a pool-based electricity market. Uncertainty on electricity price and wind power is considered through a set of scenarios. Thermal units are modelled by variable costs, start-up costs and technical operating constraints, such as: forbidden operating zones, ramp up/down limits and minimum up/down time limits. An efficient mixed-integer linear program is presented to develop the offering strategies of the coordinated production of thermal and wind energy generation, having as a goal the maximization of profit. A case study with data from the Iberian Electricity Market is presented and results are discussed to show the effectiveness of the proposed approach.

The impact of the clean development mechanism (CDM) on the cost price of wind power electricity : a China study

As a renewable energy source, wind power is playing an increasingly important role in China’s electricity supply. Meanwhile, China is also the world’s largest market for Clean Development Mechanism (CDM) wind power projects. Based on the data of 27 wind power projects of Inner Mongolia registered with the Executive Board of the United Nations (EB) in 2010, this paper constructs a financial model of Net Present Value (NPV) to analyze the cost of wind power electricity. A sensitivity analysis is then conducted to examine the impact of different variables with and without Certified Emission Reduction (CER) income brought about by the CDM. It is concluded that the CDM, along with static investment and annual wind electricity production, is one of the most significant factors in promoting the development of wind power in China. Additionally, wind power is envisaged as a practical proposition for competing with thermal power if the appropriate actions identified in the paper are made.

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.

Response of residential electricity demand to price: The effect of measurement error

In this paper we present an empirical analysis of the residential demand for electricity using annual aggregate data at the state level for 48 US states from 1995 to 2007. Earlier literature has examined residential energy consumption at the state level using annual or monthly data, focusing on the variation in price elasticities of demand across states or regions, but has failed to recognize or address two major issues. The first is that, when fitting dynamic panel models, the lagged consumption term in the right-hand side of the demand equation is endogenous. This has resulted in potentially inconsistent estimates of the long-run price elasticity of demand. The second is that energy price is likely mismeasured.

A congestion management and transmission price simulator for competitive electricity markets

In a liberalized electricity market, the Transmission System Operator (TSO) plays a crucial role in power system operation. Among many other tasks, TSO detects congestion situations and allocates the payments of electricity transmission. This paper presents a software tool for congestion management and transmission price determination in electricity markets. The congestion management is based on a reformulated Optimal Power Flow (OPF), whose main goal is to obtain a feasible solution for the re-dispatch minimizing the changes in the dispatch proposed by the market operator. The transmission price computation considers the physical impact caused by the market agents in the transmission network. The final tariff includes existing system costs and also costs due to the initial congestion situation and losses costs. The paper includes a case study for the IEEE 30 bus power system.