Hybrid wind-diesel power plant valuation and optimization using real option theory and dynamic programming

El sistema de energía eólica-diesel híbrido tiene un gran potencial en la prestación de suministro de energía a comunidades remotas. En comparación con los sistemas tradicionales de diesel, las plantas de energía híbridas ofrecen grandes ventajas tales como el suministro de capacidad de energía extra para "microgrids", reducción de los contaminantes y emisiones de gases de efecto invernadero, y la cobertura del riesgo de aumento inesperado del precio del combustible. El principal objetivo de la presente tesis es proporcionar nuevos conocimientos para la evaluación y optimización de los sistemas de energía híbrido eólico-diesel considerando las incertidumbres. Dado que la energía eólica es una variable estocástica, ésta no puede ser controlada ni predecirse con exactitud. La naturaleza incierta del viento como fuente de energía produce serios problemas tanto para la operación como para la evaluación del valor del sistema de energía eólica-diesel híbrido. Por un lado, la regulación de la potencia inyectada desde las turbinas de viento es una difícil tarea cuando opera el sistema híbrido. Por otro lado, el bene.cio económico de un sistema eólico-diesel híbrido se logra directamente a través de la energía entregada a la red de alimentación de la energía eólica. Consecuentemente, la incertidumbre de los recursos eólicos incrementa la dificultad de estimar los beneficios globales en la etapa de planificación. La principal preocupación del modelo tradicional determinista es no tener en cuenta la incertidumbre futura a la hora de tomar la decisión de operación. Con lo cual, no se prevé las acciones operativas flexibles en respuesta a los escenarios futuros. El análisis del rendimiento y simulación por ordenador en el Proyecto Eólico San Cristóbal demuestra que la incertidumbre sobre la energía eólica, las estrategias de control, almacenamiento de energía, y la curva de potencia de aerogeneradores tienen un impacto significativo sobre el rendimiento del sistema. En la presente tesis, se analiza la relación entre la teoría de valoración de opciones y el proceso de toma de decisiones. La opción real se desarrolla con un modelo y se presenta a través de ejemplos prácticos para evaluar el valor de los sistemas de energía eólica-diesel híbridos. Los resultados muestran que las opciones operacionales pueden aportar un valor adicional para el sistema de energía híbrida, cuando esta flexibilidad operativa se utiliza correctamente. Este marco se puede aplicar en la optimización de la operación a corto plazo teniendo en cuenta la naturaleza dependiente de la trayectoria de la política óptima de despacho, dadas las plausibles futuras realizaciones de la producción de energía eólica. En comparación con los métodos de valoración y optimización existentes, el resultado del caso de estudio numérico muestra que la política de operación resultante del modelo de optimización propuesto presenta una notable actuación en la reducción del con- sumo total de combustible del sistema eólico-diesel. Con el .n de tomar decisiones óptimas, los operadores de plantas de energía y los gestores de éstas no deben centrarse sólo en el resultado directo de cada acción operativa, tampoco deberían tomar decisiones deterministas. La forma correcta es gestionar dinámicamente el sistema de energía teniendo en cuenta el valor futuro condicionado en cada opción frente a la incertidumbre. ABSTRACT Hybrid wind-diesel power systems have a great potential in providing energy supply to remote communities. Compared with the traditional diesel systems, hybrid power plants are providing many advantages such as providing extra energy capacity to the micro-grid, reducing pollution and greenhouse-gas emissions, and hedging the risk of unexpected fuel price increases. This dissertation aims at providing novel insights for assessing and optimizing hybrid wind-diesel power systems considering the related uncertainties. Since wind power can neither be controlled nor accurately predicted, the energy harvested from a wind turbine may be considered a stochastic variable. This uncertain nature of wind energy source results in serious problems for both the operation and value assessment of the hybrid wind-diesel power system. On the one hand, regulating the uncertain power injected from wind turbines is a difficult task when operating the hybrid system. On the other hand, the economic profit of a hybrid wind-diesel system is achieved directly through the energy delivered to the power grid from the wind energy. Therefore, the uncertainty of wind resources has increased the difficulty in estimating the total benefits in the planning stage. The main concern of the traditional deterministic model is that it does not consider the future uncertainty when making the dispatch decision. Thus, it does not provide flexible operational actions in response to the uncertain future scenarios. Performance analysis and computer simulation on the San Cristobal Wind Project demonstrate that the wind power uncertainty, control strategies, energy storage, and the wind turbine power curve have a significant impact on the performance of the system. In this dissertation, the relationship between option pricing theory and decision making process is discussed. A real option model is developed and presented through practical examples for assessing the value of hybrid wind-diesel power systems. Results show that operational options can provide additional value to the hybrid power system when this operational flexibility is correctly utilized. This framework can be applied in optimizing short term dispatch decisions considering the path-dependent nature of the optimal dispatch policy, given the plausible future realizations of the wind power production. Comparing with the existing valuation and optimization methods, result from numerical example shows that the dispatch policy resulting from the proposed optimization model exhibits a remarkable performance in minimizing the total fuel consumption of the wind-diesel system. In order to make optimal decisions, power plant operators and managers should not just focus on the direct outcome of each operational action; neither should they make deterministic decisions. The correct way is to dynamically manage the power system by taking into consideration the conditional future value in each option in response to the uncertainty.

Bases teóricas dos processos de medicalização: um olhar sobre as forças motrizes

Introdução: O tema medicalização emerge como objeto de estudo no campo da sociologia da saúde, a partir da década de 70, nas vozes de Irving Zola, Ivan Illich, Peter Conrad e Michel Foucalt; as quais indicaram a crescente influência da medicina em campos que até então não lhe pertenciam. E, no decorrer dos anos, o termo vem sendo apropriado por vários campos: na saúde, na educação, na psicologia, entre outros. Esta configuração levou alguns estudiosos da primeira década do século XXI, a se preocupar com o uso impreciso e vago do conceito de medicalização na produção científica. Neste sentido, este estudo busca olhar para os processos de medicalização, tomando-o em sua pluralidade a fim de discernir as principais forças motrizes e coteja-las com as mudanças na contemporaneidade. Objetivo: Recuperar as forças motrizes contidas nas principais contribuições dos autores primários sobre os processos de medicalização. Método: Realizou-se um exercício hermenêutico composto pelos seguintes passos: leitura profunda do texto, fichamento dos aspectos centrais que caracterizam as diversas concepções sobre medicalização. Interpretação do conteúdo por meio da abstração dos núcleos de sentido e dos referenciais teóricos que lhe dão suporte. Resultados: A partir deste movimento reflexivo e crítico conseguiu-se desvelar quatro conceitos nucleares que representam as principais forças motrizes: a indústria, as instituições, o Estado e a sociedade. Zola oferece indícios que o Estado e a indústria teriam levado a sociedade à dependência da medicina. Para Illich, a medicina, por si só, detém o poder comparada as outras instituições. Para Michel Foucault, a medicina deixou de ser uma ciência pura e transformou-se numa instituição subordinada a um sistema econômico e de poder, enfim a uma lógica subjacente aos princípios e regras de governo. Em contrapartida, para Conrad a medicalização não constitui um empreendimento exclusivo da medicina, prevalecendo os interesses de outras instituições e organizações sociais. O sentido com que cada um desses conceitos é usado difere entre os autores e a distinção desses aspectos é chave para compreender a contribuição efetiva de cada um. Da mesma forma, ocorre quando os autores discutem as consequências e os efeitos causados pelos processos de medicalização. Alguns autores direcionam seus efeitos para os indivíduos, num processo de exacerbação da individualização; enquanto que outros focam os efeitos da medicalização nas políticas de saúde e na questão econômica associada ao oneroso custo financeiro para a sociedade e o país. Considerações finais: A recuperação e a compreensão dos significados subjacentes às principais forças motrizes presentes nas contribuições de cada autor apresentadas nesta investigação constituem-se em passo importante para subsidiar a reflexão sobre processos concretos de medicalização no início do século XXI, um período marcado por aceleradas transformações, no qual, entre outros aspectos, a medicina e várias instituições têm sido crescentemente, capturadas para satisfazer, de um lado o consumismo, e de outro, a avidez pelo lucro do mercado capitalista; ao mesmo tempo em que forças desagregadoras atravessam os sujeitos impactando sua autonomia e identidade política, social e econômica.

Algebraic derivative estimation applied to nonlinear control of magnetic levitation.

The subject of this thesis is the real-time implementation of algebraic derivative estimators as observers in nonlinear control of magnetic levitation systems. These estimators are based on operational calculus and implemented as FIR filters, resulting on a feasible real-time implementation. The algebraic method provide a fast, non-asymptotic state estimation. For the magnetic levitation systems, the algebraic estimators may replace the standard asymptotic observers assuring very good performance and robustness. To validate the estimators as observers in closed-loop control, several nonlinear controllers are proposed and implemented in a experimental magnetic levitation prototype. The results show an excellent performance of the proposed control laws together with the algebraic estimators.

Fault location in power distribution networks with distributed generation.

This research presents the development and implementation of fault location algorithms in power distribution networks with distributed generation units installed along their feeders. The proposed algorithms are capable of locating the fault based on voltage and current signals recorded by intelligent electronic devices installed at the end of the feeder sections, information to compute the loads connected to these feeders and their electric characteristics, and the operating status of the network. In addition, this work presents the study of analytical models of distributed generation and load technologies that could contribute to the performance of the proposed fault location algorithms. The validation of the algorithms was based on computer simulations using network models implemented in ATP, whereas the algorithms were implemented in MATLAB.

Particle Swarm Optimization Based Reactive Power Dispatch for Power Networks with Distributed Generation

Reactive power is critical to the operation of the power networks on both safety aspects and economic aspects. Unreasonable distribution of the reactive power would severely affect the power quality of the power networks and increases the transmission loss. Currently, the most economical and practical approach to minimizing the real power loss remains using reactive power dispatch method. Reactive power dispatch problem is nonlinear and has both equality constraints and inequality constraints. In this thesis, PSO algorithm and MATPOWER 5.1 toolbox are applied to solve the reactive power dispatch problem. PSO is a global optimization technique that is equipped with excellent searching capability. The biggest advantage of PSO is that the efficiency of PSO is less sensitive to the complexity of the objective function. MATPOWER 5.1 is an open source MATLAB toolbox focusing on solving the power flow problems. The benefit of MATPOWER is that its code can be easily used and modified. The proposed method in this thesis minimizes the real power loss in a practical power system and determines the optimal placement of a new installed DG. IEEE 14 bus system is used to evaluate the performance. Test results show the effectiveness of the proposed method.

Dynamical Approach for Real-Time Monitoring of Agricultural Crops

In this paper, a novel approach for exploiting multitemporal remote sensing data focused on real-time monitoring of agricultural crops is presented. The methodology is defined in a dynamical system context using state-space techniques, which enables the possibility of merging past temporal information with an update for each new acquisition. The dynamic system context allows us to exploit classical tools in this domain to perform the estimation of relevant variables. A general methodology is proposed, and a particular instance is defined in this study based on polarimetric radar data to track the phenological stages of a set of crops. A model generation from empirical data through principal component analysis is presented, and an extended Kalman filter is adapted to perform phenological stage estimation. Results employing quad-pol Radarsat-2 data over three different cereals are analyzed. The potential of this methodology to retrieve vegetation variables in real time is shown.

Contribution to Real-Time Estimation of Crop Phenological States in a Dynamical Framework Based on NDVI Time Series: Data Fusion With SAR and Temperature

In this study, a methodology based in a dynamical framework is proposed to incorporate additional sources of information to normalized difference vegetation index (NDVI) time series of agricultural observations for a phenological state estimation application. The proposed implementation is based on the particle filter (PF) scheme that is able to integrate multiple sources of data. Moreover, the dynamics-led design is able to conduct real-time (online) estimations, i.e., without requiring to wait until the end of the campaign. The evaluation of the algorithm is performed by estimating the phenological states over a set of rice fields in Seville (SW, Spain). A Landsat-5/7 NDVI series of images is complemented with two distinct sources of information: SAR images from the TerraSAR-X satellite and air temperature information from a ground-based station. An improvement in the overall estimation accuracy is obtained, especially when the time series of NDVI data is incomplete. Evaluations on the sensitivity to different development intervals and on the mitigation of discontinuities of the time series are also addressed in this work, demonstrating the benefits of this data fusion approach based on the dynamic systems.

Energy Transition in Europe’s Power House. Alleingang, avant-garde or blackout? IES Policy Brief Issue 2012/03/October 2012

Summary. The transformation of Germany’s energy sector will further exacerbate current network fluctuations and intensify the need for modifications in Europe’s power system. Cross-border power transfers will have to increase in order to overcome national limitations for absorbing large volumes of intermittent renewables like wind and solar power. In order to establish such an infrastructure on a European scale, the energy transition needs to be guided by an economic approach designed to prevent further fractures in the Internal Electricity Market. Moreover, constructive negotiations with neighbouring countries on market designs and price signals will be important preconditions for a successful energy transition in Europe.

Consensus, contradiction, and conciliation of interests: the geo-economics of the Energy Union. EPC Policy Brief, 8 July 2015

European Union energy policy calls for nothing less than a profound transformation of the EU's energy system: by 2050 decarbonised electricity generation with 80-95% fewer greenhouse gas emissions, increased use of renewables, more energy efficiency, a functioning energy market and increased security of supply are to be achieved. Different EU policies (e.g., EU climate and energy package for 2020) are intended to create the political and regulatory framework for this transformation. The sectorial dynamics resulting from these EU policies already affect the systems of electricity generation, transportation and storage in Europe, and the more effective the implementation of new measures the more the structure of Europe's power system will change in the years to come. Recent initiatives such as the 2030 climate/energy package and the Energy Union are supposed to keep this dynamic up. Setting new EU targets, however, is not necessarily the same as meeting them. The impact of EU energy policy is likely to have considerable geo-economic implications for individual member states: with increasing market integration come new competitors; coal and gas power plants face new renewable challengers domestically and abroad; and diversification towards new suppliers will result in new trade routes, entry points and infrastructure. Where these implications are at odds with powerful national interests, any member state may point to Article 194, 2 of the Lisbon Treaty and argue that the EU's energy policy agenda interferes with its given right to determine the conditions for exploiting its energy resources, the choice between different energy sources and the general structure of its energy supply. The implementation of new policy initiatives therefore involves intense negotiations to conciliate contradicting interests, something that traditionally has been far from easy to achieve. In areas where this process runs into difficulties, the transfer of sovereignty to the European level is usually to be found amongst the suggested solutions. Pooling sovereignty on a new level, however, does not automatically result in a consensus, i.e., conciliate contradicting interests. Rather than focussing on the right level of decision making, European policy makers need to face the (inconvenient truth of) geo-economical frictions within the Union that make it difficult to come to an arrangement. The reminder of this text explains these latter, more structural and sector-related challenges for European energy policy in more detail, and develops some concrete steps towards a political and regulatory framework necessary to overcome them.

State energy policy hearings

Hearings held in Sacramento, Feb. 15-Mar. 9, 1973

Dynamics of loads on power systems using field measurements /

"May 1984."

Operations and Planning in Sustainable Power Systems

Thesis (Ph.D.)--University of Washington, 2016-06

Eigenvalue sensitivity analysis for probabilistic small signal stability assessment

This paper presents a method to analyze the first order eigenvalue sensitivity with respect to the operating parameters of a power system. The method is based on explicitly expressing the system state matrix into sub-matrices. The eigenvalue sensitivity is calculated based on the explicitly formed system state matrix. The 4th order generator model and 4th order exciter system model are used to form the system state matrix. A case study using New England 10-machine 39-bus system is provided to demonstrate the effectiveness of the proposed method. This method can be applied into large scale power system eigenvalue sensitivity with respect to operating parameters.

Approximate Bayesian techniques for inference in stochastic dynamical systems

This thesis is concerned with approximate inference in dynamical systems, from a variational Bayesian perspective. When modelling real world dynamical systems, stochastic differential equations appear as a natural choice, mainly because of their ability to model the noise of the system by adding a variant of some stochastic process to the deterministic dynamics. Hence, inference in such processes has drawn much attention. Here two new extended frameworks are derived and presented that are based on basis function expansions and local polynomial approximations of a recently proposed variational Bayesian algorithm. It is shown that the new extensions converge to the original variational algorithm and can be used for state estimation (smoothing). However, the main focus is on estimating the (hyper-) parameters of these systems (i.e. drift parameters and diffusion coefficients). The new methods are numerically validated on a range of different systems which vary in dimensionality and non-linearity. These are the Ornstein-Uhlenbeck process, for which the exact likelihood can be computed analytically, the univariate and highly non-linear, stochastic double well and the multivariate chaotic stochastic Lorenz '63 (3-dimensional model). The algorithms are also applied to the 40 dimensional stochastic Lorenz '96 system. In this investigation these new approaches are compared with a variety of other well known methods such as the ensemble Kalman filter / smoother, a hybrid Monte Carlo sampler, the dual unscented Kalman filter (for jointly estimating the systems states and model parameters) and full weak-constraint 4D-Var. Empirical analysis of their asymptotic behaviour as a function of observation density or length of time window increases is provided.