Short - Term Bidding Strategies for a Generation Company in the Iberian Electricity Market

La posada en marxa del Mercat Ibèric de l'Electricitat va introduir al sector elèctric espanyol un seguit de nous mecanismes de participació que han forçat els agents a renovar les seves polítiques de gestió. D'aquesta nova situació sorgeix l'oportunitat d'estudiar noves estratègies d'oferta a curt termini per a companyies de generació price-taker que participin diàriament al Mercat Ibèric de l'Electricitat. Aquestes estratègies se centraran al mercat diari, ja que és aquí on es negocia un 80% de l'electricitat que es consumeix diàriament a Espanya i on s'integren gran part de la resta de mecanismes de participació. La liberalització dels mercats elèctrics obre a noves tècniques d'optimització els problemes clàssics de gestió de l'energia. En particular, atesa la incertesa que l'existència del mercat ocasiona als preus, les tècniques de programació estocàstiques es converteixen en la forma més natural per abordar aquests problemes. Als mercats elèctrics el preu es fixa horàriament com a resultat d'un procés de casació , és a dir que quan l'agent ha d'efectuar la seva oferta desconeix el preu al qual li vindrà remunerada l'energia. Aquesta incertesa fa imprescindible l'ús de tècniques estadístiques per obtenir informació del mercat i introduir-la als models d'optimització. En aquest aspecte, una de les contribucions d'aquesta tesi és l'estudi dels preus del mercat de l'electricitat a Espanya i el seu modelat mitjançant models factorials. D'altra banda, s'hi es descriuen els nous mecanismes presents al Mercat Ibèric de l'Electricitat que afecten directament la producció física de les unitats. En particular, s'inclou el modelat detallat dels contractes de futurs físics i bilaterals i de la seva inclusió a l'oferta del mercat diari per part de les companyies de generació. Als models presentats, es tenen en compte explícitament les regles del mercat, així com les clàssiques restriccions d'operació de les unitats, tant tèrmiques com de cicle combinat. A més, es deriva i es demostra l'expressió de la funció d'oferta. Per tant, els models construïts són una eina per decidir l'assignació de les unitats, la generació dels contractes de futurs físics i bilaterals a través seu i l'oferta òptima d'una companyia de generació. Un cop s'han cobert aquests objectius, es presenta una millora dels models mitjançant la inclusió de la seqüència de mercats de molt curt termini per tal de modelar la influència que tenen en l'oferta al mercat diari. Aquests mercats es casen just abans i durant el dia en què l'energia ha de ser consumida, i això permetrà veure com la possibilitat d'augmentar els beneficis participant-hi afecta directament les estratègies d'oferta òptima del mercat diari. Els models presentats en aquest treball han estat provats amb dades reals provinents del Mercat Ibèric de l'Electricitat i d'una companyia de generació que hi opera. Els resultats obtinguts són adequats i es discuteixen al llarg del documentLa puesta en marcha del Mercado Ibérico de la Electricidad introdujo en el sector eléctrico español una serie de nuevos mecanismos de participación que han forzado a los agentes a renovar sus políticas de gestión. De esta nueva situación surge la oportunidad de estudiar nuevas estrategias de oferta para las compañías de generación. Esta tesis se enmarca en las estrategias de oferta a corto plazo para compañías de generación price-taker que participen diariamente en el Mercado Ibérico de la Electricidad. Estas estrategias se centraran en el mercado diario ya que es donde se negocia un 80% de la electricidad consumida diariamente en España y es donde se integran gran parte del resto de los mecanismos de participación. La liberalización de los mercados eléctricos permite aplicar nuevas técnicas de optimización a los problemas clásicos de gestión de la energía. En concreto, dada la incertidumbre en el precio existente en el mercado, las técnicas de programación estocástica se convierten en la forma más natural para abordar estos problemas. En los mercados eléctricos el precio se fija horariamente como resultado de un proceso de casación, es decir, cuando el agente debe efectuar sus ofertas desconoce el precio al que la energía le será pagada. Esta incertidumbre hace imprescindible el uso de técnicas estadísticas para obtener información del mercado e introducirla en los modelos de optimización. En este aspecto, una de las contribuciones de esta tesis es el estudio del precio de la electricidad en España y su modelado mediante modelos factoriales. Se describen los nuevos mecanismos presentes en el Mercado Ibérico de la Electricidad que afectan directamente a la producción física de las unidades. En particular, se incluye una modelización detallada de los contratos de futuros físicos y bilaterales y su inclusión en la oferta enviada al mercado diario por las compañías de generación. En los modelos presentados se tiene en cuenta explícitamente las reglas del mercado así como las clásicas restricciones de operación de las unidades, tanto térmicas como de ciclo combinado. La expresión de la función de oferta óptima se deriva y se demuestra. Por lo tanto, los modelos construidos son una herramienta para decidir la asignación de unidades, la generación de los contratos de futuros físicos y bilaterales a través de ellas y la oferta óptima de una compañía de generación. Una vez alcanzados estos objetivos, se presenta una mejora del modelo con la inclusión de la secuencia de mercados de muy corto plazo. El objetivo es modelar la influencia que esta tiene en la oferta al mercado diario. Estos mercados se casan justo antes y durante el día en el que la energía va a ser consumida y se verá cómo la posibilidad de aumentar los beneficios participando en ellos afecta a las estrategias de oferta óptima del mercado diario. Los modelos presentados en este trabajo se han probado con datos reales procedentes del Mercado Ibérico de la Electricidad y de una compañía de generación que opera en él. Los resultados obtenidos son adecuados y se discuten a lo largo del documento.The start-up of the Iberian Electricity Market introduced a set of new mechanisms in the Spanish electricity sector that forced the agents participating in the market to change their management policies. This situation created a great opportunity for studying the bidding strategies of the generation companies in this new framework. This thesis focuses on the short-term bidding strategies of a price-taker generation company that bids daily in the Iberian Electricity Market. We will center our bidding strategies on the day-ahead market because 80% of the electricity that is consumed daily in Spain is negotiated there and also because it is the market where the new mechanisms are integrated. The liberalization of the electricity markets opens the classical problems of energy management to new optimization approaches. Specifically, because of the uncertainty that the market produces in the prices, the stochastic programming techniques have become the most natural way to deal with these problems. Notice that, in deregulated electricity markets the price is hourly fixed through a market clearing procedure, so when the agent must bid its energy it is unaware of the price at which it will be paid. This uncertainty makes it essential to use some statistic techniques in order to obtain the information coming from the markets and to introduce it in the optimization models in a suitable way. In this aspect, one of the main contributions of this thesis has been the study the Spanish electricity price time series and its modeling by means of factor models. In this thesis, the new mechanism introduced by the Iberian Market that affects the physical operation of the units is described. In particular, it considers in great detail the inclusion of the physical futures contracts and the bilateral contracts into the day-ahead market bid of the generation companies. The rules of the market operator have been explicitly taken into account within the mathematical models, along with all the classical operational constraints that affect the thermal and combined cycle units. The expression of the optimal bidding functions are derived and proved. Therefore, the models built in this thesis provide the generation company with the economic dispatch of the committed futures and bilateral contracts, the unit commitment of the units and the optimal bidding strategies for the generation company. Once these main objectives were fulfilled, we improved the previous models with an approach to the modeling of the influence that the sequence of very short markets have on optimal day-ahead bidding. These markets are cleared just before and during the day in which the electricity will be consumed and the opportunity to obtain benefits from them changes the optimal day-ahead bidding strategies of the generation company, as it will be shown in this thesis. The entire models presented in this work have been tested using real data from a generation company and Spanish electricity prices. Suitable results have been obtained and discussed

Total variation bounds on the expectation of periodic functions with applications to recourse approximations

We derive a lower and upper bound for the expectation of periodic functions, depending on the total variation of the probability density function of the underlying random variable. Using worst-case analysis we derive tighter bounds for functions that are periodically monotone. These bounds can be used to evaluate the performance of approximations for both continuous and integer recourse models. In this paper, we introduce a new convex approximation for totally unimodular recourse models, and we show that this convex approximation has the best worst-case error bound possible, improving previous bounds with a factor 2. Moreover, we use similar analysis to derive error bounds for two types of discrete approximations of continuous recourse models with continuous random variables. Furthermore, we derive a tractable Lipschitz constant for pure integer recourse models

Manipulating Code Annotations

This thesis concerns language theory and metaprogramming, more specifically, a kind of metaprogramming performed during program execution. This thesis proposes a technique that help simplify and partially automate many tasks involved on these two aspects of software design and development. A powerful and smart metaprogramming mechanism, which works at runtime on virtual machine level, and which is applicable to any language supported by the virtual machine itself is provided. The mechanism is based on simple source code annotations. Applications of this technique varies from code specialization to code reuse and deployment. Different examples of application are provided

Adaptive and flexible approaches for water resources planning under uncertainty

Planning for water supply infrastructure includes identifying interventions that cost-effectively secure an acceptably reliable water supply. In investigating a range of feasible interventions, water planners are challenged by two main factors. First, uncertainty is inherent in the predictions of future demands and supplies due for example to hydrological variability and climate change. This makes fixed invest-ment plans brittle as they are likely to fail if future conditions turn out to be different than assumed. Therefore, adaptability to changing future conditions is increasingly viewed as a valuable strategy of water planning. However, there is a lack of approaches that explicitly seek to enhance the adaptivity of water resource system developments. Second, water resource system development typically af¬fects multiple societal groups with at times competing interests. The diversity of objectives in water resource systems mean that considering trade-offs between competing objectives implied by the highest performing interventions is useful. Nonetheless, few multi-objective applications have aimed at adaptive scheduling of interventions in long-term water resource planning. This thesis introduces two novel decision-making approaches that address these two challenges in turn. Both approaches apply principles of real option analysis via two different formulations (1) a multistage stochastic mathematical programme and (2) a multi-objective evolutionary algorithm coupled to a river basin simula¬tion. In both cases, a generalised scenario tree construction algorithm is used to efficiently approximate the probabilistic uncertainty. The tree consists of possible investment paths with multiple decision stages to allow for frequent and regu¬lar modifications to the investment strategies. Novel decision-relevant metrics of adaptivity and flexibility are introduced, evolving their definition in the context of water resources planning. The approaches are applied to London’s urban water resources planning problem. Results from this thesis demonstrate that there is value in adopting adaptive and flexible plans suggesting that flexibility in activating, delaying and replacing en-gineering projects should be considered in water supply intervention scheduling. To evaluate the implementation of Real Option Analysis (ROA), the use of two metrics is proposed: the Value of the Stochastic Solution (VSS) and the Expected Value of Perfect Information (EVPI) that quantify the value of adopting adaptive and flexible plans respectively. The investment decisions results are a mixture of ‘long-term’ and ‘contingency schemes’ that are optimally chosen considering different futures. The VSS shows that by considering uncertainty, adaptive invest-ment decisions avoid £100 million NPV cost, 15% of the total NPV. The EVPI demonstrates that optimal delay and early decisions have £50 million NPV, 6% of total NPV. Additionally, a comparison study of alternative optimisation approaches to water supply capacity expansion problem demonstrate that there is benefit in waiting to allow for improvements around supply uncertainty in the case of London’s urban water resources planning problem. The results from the case study suggest that the proposed adaptive planning approach achieves substantial improvement in performance compared to alternative optimisation approaches with fixed plans saving more than £377 million, reducing NPV cost by 35%. Using a multi-objective multi-stage real-options formulation of the water planning problem, the trade-offs between a long-term water management plan’s resilience and its financial costs under supply and demand uncertainty are explored. The set of trade-off solutions consist of different investment plans that are adaptive to demand growth, approximated by a scenario tree, while robust to the effects of climate change supply uncertainty, represented by an ensemble of supply (hydro-logical) scenarios. Results show that, by being adaptive to demand uncertainty, the total NPV of the most resilient plans is lowered by 58.7%. The value in de¬laying investments by waiting for more accurate supply and demand estimates is 28.9% of total NPV. It should be noted that the results from the case study are indicative and should not be considered prescriptively as they are based in a simplified representation of London’s water supply system and should be further tested with the more detailed simulation model employed by the water utility which includes the latest proposed option designs, includes requirements to supply neighbouring water utilities, and considers more objectives

Successive discretization procedures for stochastic programming with recourse

Includes bibliographical references (leaves [30]-[31]).by Randall Hiller

Processing techniques development, volume 3. Part 2: Data preprocessing and information extraction techniques

There are no author-identified significant results in this report

Optimal Highway Safety Improvement Investments by Dynamic Programming [Dec. 1974]

The process of determining which projects to implement under a given budget, and which to defer until later, is central to the planning and management of highway systems. With a limited budget for construction, maintenance, and safety improvements, investments which will produce the optimal benefits must be chosen. This is often impossible to accomplish without the aid of a computer because of the complexity of the problem. Dynamic programming has been tested and verified as an efficient method for selecting priority projects to derive maximum benefits. The applicability of dynamic programming to the safety improvement program is demonstrated in this study. There are several approaches to priority programming as it is related to the capital allocation problem. Benefit-cost, present worth, and rate-of-return calculations have traditionally been used as an integral part of the transportation planning process. Construction and maintenance programs continually face the task of having to assign priorities when insufficient funds are available to complete all projects. Safety improvement programs, which were initially funded through the Highway Safety Act of 1966 and expanded through the Federal-Aid Highway Act of 1973, have become so large that they are unmanageable without a clear and concise means of priority allocation. A dynamic programming procedure was developed in this study which selects the optimal combination of safety improvement projects for a given budget. The type of dynamic programming being considered here is multistage. Multistage is defined as cost optimization of several projects, each with one or more alternatives. All safety improvement costs are dealt with in terms of present worth with consideration given to construction or installation cost, yearly maintenance cost, present interest rate, and the expected life of the improvement. The option of staging installation of safety improvements over a number of years was excluded from this analysis. All possible combinations of improvements were input as alternatives for each of the 61 projects involved in this study. The input consisted of the designated budget for the safety improvement program, the improvement cost, and the benefits derived from each improvement. The accuracy and reliability of dynamic programming is dependent upon the accuracy of benefits and costs used as input

Scalable invoice-based B2B payments with microservices

Paying by invoice has several advantages for businesses over conventional payment methods such as Debit/Credit cards. An invoice not only allows a buyer to make the purchase on credit but also contains the tax information for each purchased item. Businesses need to save this information in their financial records and report it to the authorities. The core challenge in an invoice-based payment method is the ability to make an accurate credit decision for a given purchase. Such a credit decision requires information about the buying company such as their credit rating. The company information is gathered in real time from different third-party sources. In this context, Enterpay Oy provides an invoiced-based B2B payment solution and is growing its payment service to European countries. In order to support this expansion, Enterpay needs to develop new capabilities such as the ability to detect fraudulent purchases. These new features require the application architecture to be flexible in terms of technology. For example, different components of the service should be built with the most suited programming language, libraries, and frameworks. The goal of this thesis is to enable efficient scaling and high availability for Enterpay's payment service. Thus, we have migrated from a monolithic a microservice-based architecture. This transition allows us to choose the best suited technology for the business case of the given microservice. We extracted various modules from the original monolithic application, which have different scalability criteria. We built these modules as Docker containers, which run as independent microservices. We used Kubernetes as the container orchestration framework and deployed the microservice in Amazon Web Services (AWS). Finally, we conducted experiments to measure the performance of the service with the new architecture. We found that this architecture not only scales faster but also recovers from instance failures quicker than the previous solution. Additionally, we noticed that the average response time of service request is similar in both architectures. Finally, we observed that new microservices can be built using different technology stack and deployed conveniently in the same Kubernetes cluster

Online partial evaluation of sheet-defined functions

We present a spreadsheet implementation, extended with sheet-defined functions, that allows users to define functions using only standard spreadsheet concepts such as cells, formulas and references, requiring no new syntax. This implements an idea proposed by Peyton-Jones and others. As the main contribution of this paper, we then show how to add an online partial evaluator for such sheet-defined functions. The result is a higher-order functional language that is dynamically typed, in keeping with spreadsheet traditions, and an interactive platform for function definition and function specialization. We describe an implementation of these ideas, present some performance data from microbenchmarks, and outline desirable improvements and extensions.Comment: In Proceedings Festschrift for Dave Schmidt, arXiv:1309.455