Competition in the supply option market

This paper develops a multi-attribute competition model for procurement of short life cycle products. In such an environment, the buyer installs dedicated production capacity at the suppliers before the demand is realized. Final production orders are decided after demand materializes. Of course, the buyer is reluctant to bear all the capacity and inventory risk, and thus signs flexible contracts with several suppliers. We model the suppliers' offers as option contracts, where each supplier charges a reservation price per unit of capacity, and an execution price per unit of delivered supply. These two parameters illustrate the trade-off between total price and flexibility of the contract, and are both important to the buyer. We model the interaction between the suppliers and the buyer as a game in which the suppliers are the leaders and the buyer is the follower. Specifically, suppliers compete to provide supply capacity to the buyer and the buyer optimizes its expected profit by selecting one or more suppliers. We characterize the suppliers' equilibria in pure strategies for a class of customer demand distributions. In particular, we show that this type of interaction gives rise to cluster competition. That is, in equilibrium, suppliers tend to be clustered in small groups of two or three suppliers each, such that within the same group all suppliers use similar technologies and offer the same type of contract. Finally, we show that in equilibrium, the supply chain inefficiencies, i.e., the loss of profit due to competition, are in general at most 25% of the profit of a centralized supply chain, for a wide class of demand distributions.supplier portfolio; supplier competition;

Uncertainty management in multiobjective hydro-thermal self-scheduling under emission considerations

In this paper, a stochastic multiobjective framework is proposed for a day-ahead short-term Hydro Thermal Self-Scheduling (HTSS) problem for joint energy and reserve markets. An efficient linear formulations are introduced in this paper to deal with the nonlinearity of original problem due to the dynamic ramp rate limits, prohibited operating zones, operating services of thermal plants, multi-head power discharge characteristics of hydro generating units and spillage of reservoirs. Besides, system uncertainties including the generating units\u27 contingencies and price uncertainty are explicitly considered in the stochastic market clearing scheme. For the stochastic modeling of probable multiobjective optimization scenarios, a lattice Monte Carlo simulation has been adopted to have a better coverage of the system uncertainty spectrum. Consequently, the resulting multiobjective optimization scenarios should concurrently optimize competing objective functions including GENeration COmpany\u27s (GENCO\u27s) profit maximization and thermal units\u27 emission minimization. Accordingly, the ε-constraint method is used to solve the multiobjective optimization problem and generate the Pareto set. Then, a fuzzy satisfying method is employed to choose the most preferred solution among all Pareto optimal solutions. The performance of the presented method is verified in different case studies. The results obtained from ε-constraint method is compared with those reported by weighted sum method, evolutionary programming-based interactive Fuzzy satisfying method, differential evolution, quantum-behaved particle swarm optimization and hybrid multi-objective cultural algorithm, verifying the superiority of the proposed approach

Competition in the Supply Option Market

This paper develops a multiattribute competition model for procurement of short life-cycle products. In such an environment, the buyer installs dedicated production capacity at the suppliers before demand is realized. Final production orders are decided after demand materializes. Of course, the buyer is reluctant to bear all the capacity and inventory risk, and thus signs flexible contracts with several suppliers. We model the suppliers' offers as option contracts, where each supplier charges a reservation price per unit of capacity and an execution price per unit of delivered supply. These two parameters illustrate the trade-off between total price and flexibility of a contract, which are both important to the buyer. We model the interaction between suppliers and the buyer as a game in which the suppliers are the leaders and the buyer is the follower. Specifically, suppliers compete to provide supply capacity to the buyer, and the buyer optimizes its expected profit by selecting one or more suppliers. We characterize the suppliers' equilibria in pure strategies for a class of customer demand distributions. In particular, we show that this type of interaction gives rise to cluster competition. That is, in equilibrium suppliers tend to be clustered in small groups of two or three suppliers each, such that within the same group all suppliers use similar technologies and offer the same type of contract. Finally, we show that in equilibrium, supply chain inefficiencies—i.e., the loss of profit due to competition—are at most 25% of the profit of a centralized supply chain.United States. Office of Naval Research (contract N00014-95-1-0232)United States. Office of Naval Research (contract N00014-01-1-0146)National Science Foundation (U.S.) (contract DMI-0085683)National Science Foundation (U.S.) (DMI-0245352)National Science Foundation (U.S.) (CMMI-0758069)Massachusetts Institute of Technology. Center for Digital BusinessUniversity of Navarra. IESE Business School (CIIL International Center for Logistics Research

Plantas virtuales de energía para la integración de fuentes renovables de generación distribuida en sistemas de demanda de agua y energía.

Las instalaciones de bombeo de agua para riego son grandes consumidores de energía eléctrica además de ser un complejo sistema de gestión de agua. España es el país de la Unión Europea con mayor extensión para el regadío con casi 4 millones de hectáreas, destacando Aragón con una superficie de 415.998 hectáreas. No obstante, la agricultura se está enfrentando a serios problemas. Por un lado, como consecuencia del cambio climático, los periodos de sequía son cada vez más frecuentes y la disponibilidad de agua para el regadío agrícola se está reduciendo. Además, España se sitúa como uno de los países de la Unión Europea con el mayor coste de electricidad, debido entre otras razones por los costes regulados que afectan especialmente a los consumos estacionales, como es el caso de los sistemas de bombeo de agua para riego que concentra su consumo principalmente entre los meses de mayo a septiembre. La obligación de contratación de la potencia eléctrica para todo el año sin la posibilidad de modificarla durante el mismo perjudica seriamente a este sector. Para reducir el coste de suministro eléctrico, las comunidades de regantes están invirtiendo en instalaciones de generación renovable.La rápida penetración de estas fuentes de generación en el marco de un mercado eléctrico cada vez más competitivo requiere de nuevas tecnologías y sistemas de operación para hacer frente a los nuevos retos técnicos y económicos derivados de la integración óptima de los recursos disponibles. Las plantas virtuales de energía aparecen, así como ejes claves para hacer posible esta integración. Dada la tendencia actual de generar energía de forma distribuida es primordial el control conjunto de las unidades de producción para conseguir el mayor rendimiento del sistema.Esta tesis doctoral realizada por compendio de publicaciones pretende ofrecer soluciones a los problemas planteados anteriormente. El objetivo principal de esta investigación es el estudio, desarrollo y aplicación de nuevos modelos de operación óptima integrada de la generación y el consumo de energía eléctrica junto con las infraestructuras de agua de los sistemas generales de regadío, integrando los recursos de producción eléctrica, la demanda horaria de electricidad y la gestión del agua mediante el modelado matemático de una planta virtual de energía que participa en el mercado eléctrico mayorista para maximizar el beneficio de operación.En primer lugar, se realiza una clasificación y evaluación de los trabajos publicados en los últimos años sobre el modelado de plantas virtuales de energía con participación en distintos tipos de mercados eléctricos. La clasificación se basa en los criterios más relevantes para el modelado, tales como el objetivo del problema, el tipo de problema matemático y método de resolución, los tipos de mercados eléctricos y la aplicabilidad del modelo a casos reales de estudio. Además, se identifican los retos todavía pendientes en este campo de estudio, entre los que destaca la aplicación simultánea de varias estrategias de compra-venta de energía de la planta virtual de energía en los distintos mercados energéticos, además de la utilización de técnicas de inteligencia artificial con el fin de proporcionar al modelo de planta virtual de energía un método de aprendizaje capaz de garantizar un margen de anticipación en sus decisiones.Posteriormente, para analizar el alcance real de la gestión de energía de acuerdo al diseño y operación de una planta virtual de energía, se desarrolla y aplica un nuevo modelo matemático de despacho horario técnico-económico a un gran sistema energético real de bombeo de agua para riego agrícola con recursos de producción renovable que evacuan directamente su producción a la red de distribución (centrales hidroeléctricas y un parque eólico), plantas de autoconsumo fotovoltaico asociadas a cada estación de bombeo y demanda eléctrica para maximizar el beneficio de operación conjunto de la planta virtual de energía. El comportamiento del modelo se ilustra para un año entero.Como ampliación del modelo anterior y afrontar el complejo reto de gestión eficiente del binomio agua-energía en las instalaciones de bombeo de agua, se desarrolla y aplica un nuevo modelo de despacho horario con la integración de los recursos energéticos e hídricos para la optimización de los costes de energía y de los cargos por demanda máxima en un gran sistema de regadío para un año entero, convirtiéndose en un modelo de tipo no lineal mixto-entero. A partir de los resultados obtenidos, el modelo con integración de la gestión del binomio agua-energía consigue aumentar el autoconsumo de energía renovable y ahorrar costes de suministro eléctrico al reducir la potencia contratada anualmente en los periodos horarios con mayor coste energético.Por otra parte, la aplicación de fuentes de energía renovable debe considerar el acoplamiento de la producción de electricidad con la demanda de energía eléctrica de las estaciones de bombeo y contemplar las limitaciones técnicas de las instalaciones hidráulicas de bombeo, almacenamiento y distribución del agua. Por tanto, por último, se propone el desarrollo de un modelo matemático de despacho a corto plazo con gestión técnica y económica para obtener la programación horaria óptima de los equipos de bombeo, minimizando los costes de operación de una estación real de bombeo de agua con autoconsumo fotovoltaico, sujeto a las restricciones eléctricas e hidráulicas de los sistemas de bombeo, y garantizando la demanda de riego. A partir de los resultados obtenidos, se puede comprobar que gracias a la combinación de instalaciones fotovoltaicas de autoconsumo y variadores de velocidad se consigue maximizar el porcentaje de energía autoconsumida y así, reducir los costes energéticos de la estación de bombeo además de mejorar la gestión del agua.En definitiva, esta tesis doctoral pone de manifiesto la importancia de desarrollar estrategias de gestión óptima de fuentes de generación eléctrica renovable e infraestructuras de agua para minimizar los costes energéticos y mejorar la eficiencia energética.<br /

Reserve services provision by demand side resources in systems with high renewables penetration using stochastic optimization

It is widely recognized that renewable energy sources are likely to represent a significant portion of the production mix in many power systems around the world, a trend expected to be increasingly followed in the coming years due to environmental and economic reasons. Among the different endogenous renewable sources that may be used in order to achieve reductions in the carbon footprint related to the electricity sector and increase the economic efficiency of the generation mix, wind power generation has been one of the most popular options. However, despite the potential benefits that arise from the integration of these resources in the power system, their large-scale integration leads to additional problems due to the fact that their production is highly volatile. As a result, apart from the typical sources of uncertainty that the System Operators have to face, such as system contingencies and intra-hour load deviations, through the deployment of sufficient levels of reserve generation, additional reserves must be kept in order to maintain the balance between the generation and the consumption. Furthermore, a series of other problems arise, such as efficiency loss because of ramping of conventional units, environmental costs because of increased emissions due to suboptimal unit commitment and dispatch and more costly system operation and maintenance. Recently, it has been recognized that apart from the generation side, several types of loads may be deployed in order to provide system services and especially, different types of reserves, through demand response. The contribution of demand side reserves to accommodate higher levels of wind power generation penetration is likely to be of substantial importance in the future and therefore, the integration of these resources in the system operations needs to be thoroughly studied. This thesis deals with the aspects of demand response as regards the integration of wind power generation in the power system. First, a mapping of the current status of demand response internationally is attempted, followed also by a discussion concerning the opportunities, the benefits and the barriers to the widespread adoption of demand side resources. Then, several joint energy and reserve market structures are developed which explicitly incorporate demand side resources that may contribute to energy and reserve services. Two-stage stochastic programming is employed in order to capture the uncertainty of wind power generation. Moreover, several aspects of demand response are considered such as the capability of providing contingency and load following reserves, the appropriate modeling of industrial consumer processes load and the load recovery effect. Finally, this thesis investigates the effect of demand side resources on the risk that is associated with the decisions of the System Operator through appropriate risk management techniques, proposing also a novel methodology of handling risk as an alternative to the commonly used technique.It is widely recognized that renewable energy sources are likely to represent a significant portion of the production mix in many power systems around the world, a trend expected to be increasingly followed in the coming years due to environmental and economic reasons. Among the different endogenous renewable sources that may be used in order to achieve reductions in the carbon footprint related to the electricity sector and increase the economic efficiency of the generation mix, wind power generation has been one of the most popular options. However, despite the potential benefits that arise from the integration of these resources in the power system, their large-scale integration leads to additional problems due to the fact that their production is highly volatile. As a result, apart from the typical sources of uncertainty that the System Operators have to face, such as system contingencies and intra-hour load deviations, through the deployment of sufficient levels of reserve generation, additional reserves must be kept in order to maintain the balance between the generation and the consumption. Furthermore, a series of other problems arise, such as efficiency loss because of ramping of conventional units, environmental costs because of increased emissions due to suboptimal unit commitment and dispatch and more costly system operation and maintenance. Recently, it has been recognized that apart from the generation side, several types of loads may be deployed in order to provide system services and especially, different types of reserves, through demand response. The contribution of demand side reserves to accommodate higher levels of wind power generation penetration is likely to be of substantial importance in the future and therefore, the integration of these resources in the system operations needs to be thoroughly studied. This thesis deals with the aspects of demand response as regards the integration of wind power generation in the power system. First, a mapping of the current status of demand response internationally is attempted, followed also by a discussion concerning the opportunities, the benefits and the barriers to the widespread adoption of demand side resources. Then, several joint energy and reserve market structures are developed which explicitly incorporate demand side resources that may contribute to energy and reserve services. Two-stage stochastic programming is employed in order to capture the uncertainty of wind power generation. Moreover, several aspects of demand response are considered such as the capability of providing contingency and load following reserves, the appropriate modeling of industrial consumer processes load and the load recovery effect. Finally, this thesis investigates the effect of demand side resources on the risk that is associated with the decisions of the System Operator through appropriate risk management techniques, proposing also a novel methodology of handling risk as an alternative to the commonly used technique

Multistage scenario trees generation for renewable energy systems optimization

The presence of renewables in energy systems optimization have generated a high level of uncertainty in the data, which has led to a need for applying stochastic optimization to modelling problems with this characteristic. The method followed in this thesis is multistage Stochastic Programming (MSP). Central to MSP is the idea of representing uncertainty (which, in this case, is modelled with a stochastic process) using scenario trees. In this thesis, we developed a methodology that starts with available historical data; generates a set of scenarios for each random variable of the MSP model; define individual scenarios that are used to build the initial stochastic process (as a fan or an initial scenario tree); and builds the final scenario trees that are the approximation of the stochastic process. The methodology proposes consists of two phases. In the first phase, we developed a procedure similar to Muñoz et al. (2013), with the difference being that the VAR models are used to predict the next day for each random parameter of the MSP models. In the second phase, we build scenario trees from the Forward Tree Construction Algorithm(FTCA), developed by Heitsch and Römisch (2009a); and an adapted version of DynamicTree Generation with a Flexible Bushiness Algorithm (DTGFBA), developed by Pflugand Pichler (2014, 2015). This methodology was used to generate scenario trees for two MSP models. A first model, Multistage Stochastic Wind Battery Virtual Power Plantmodel (MSWBVPP model) and to a second model, which is the Multistage StochasticOptimal Operation of Distribution Networks model (MSOODN model). We developed extensive computational experiments for the MSWBVPP model and generated scenario trees with real data, which were based on MIBEL prices and wind power generation of the real wind farm called Espina, located in Spain. For the MSOODN model, we obtained scenario trees by also using real data from the power load provided by FEEC-UNICAMP and photovoltaic generation of a distribution grid located in Brazil. The results show that the scenario tree generation methodology proposed in this thesis can obtain suitable scenario trees for each MSP model. In addition, results were obtained for the model using the scenario trees as input data. In the case of the MSWBVPP model, we solved three different case studies corresponding to three different hypotheses on the virtual power plant’s participation in electricity markets. In the case of the MSOODN model, two test cases were solved, with the results indicating that the EDN satisfied the limits imposed for each test case. Furthermore, the BESS case gave good results when taking into account the uncertainty in the model. Finally, the MSWBVPP model was used to study the relative performance of the FTCA and DTGFBA scenario trees, specifically by analyzing the value of the stochastic solution for the 366 daily optimal bidding problems. To this end, a variation of the classical VSS (the so-called “Forecasted Value of the Stochastic Solution”, FVSS) was defined and used together with the classical VSS.a presencia de energías renovables en la optimización de sistemas energéticos hagenerado un alto nivel de incertidumbre en los datos, lo que ha llevado a la necesidad de aplicar técnicas de optimización estocástica para modelar problemas con estas características. El método empleado en esta tesis es programación estocástica multietapa (MSP, por sus siglas en inglés). La idea central de MSP es representar la incertidumbre (que en este caso es modelada mediante un proceso estocástico), mediante un árbol de escenarios. En esta tesis, desarrollamos una metodología que parte de una data histórica, la cual está disponible; generamos un conjunto de escenarios por cada variable aleatoria del modelo MSP; definimos escenarios individuales, que luego serán usados para construir el proceso estocástico inicial (como un fan o un árbol de escenario inicial); y, por último, construimos el árbol de escenario final, el cual es la aproximación del proceso estocástico. La metodología propuesta consta de dos fases. En la primera fase, desarrollamos un procedimiento similar a Muñoz et al. (2013), con la diferencia de que para las predicciones del próximo día para cada variable aleatoria del modelo MSP usamos modelos VAR. En la segunda fase construimos árboles de escenarios mediante el "Forward Tree Construction Algorithm (FTCA)", desarrollado por Heitsch and Römisch (2009a); y una versión adaptada del "Dynamic Tree Generation with a Flexible Bushiness Algorithm (DTGFBA)", desarrolado por Pflug and Pichler (2014, 2015). Esta metodología fue usada para generar árboles de escenarios para dos modelos MSP. El primer modelo fue el "Multistage Stochastic Wind Battery Virtual Power Plant model (modelo MSWBVPP)", y el segundo modelo es el "Multistage Stochastic Optimal Operation of Distribution Networks model (MSOODN model)". Para el modelo MSWBVPP desarrollamos extensivos experimentos computacionales y generamos árboles de escenarios a partir de datos realesde precios MIBEL y generación eólica de una granja eólica llamada Espina, ubicada en España. Para el modelo MSOODN obtuvimos árboles de escenarios basados en datos reales de carga, provistos por FEEC-UNICAMP y de generación fotovoltaica de una red de distribución localizada en Brasil. Los resultados muestran que la metodología de generación de árboles de escenarios propuesta en esta tesis, permite obtener árboles de escenarios adecuados para cada modelo MSP. Adicionalmente, obtuvimos resultados para los modelos MSP usando como datos de entrada los árboles de escenarios. En el caso del modelo MSWBVPP, resolvimos tres casos de estudio correspondiente a tres hipótesis basadas en la participación de una VPP en los mercados de energía. En el caso del modelo MSOODN, dos casos de prueba fueron resueltos, mostrando que la EDN satisface los límites impuestos para cada caso de prueba, y además, que el caso con BESS da mejores resultados cuando se toma en cuenta el valor la incertidumbre en el modelo. Finalmente, el modelo MSWBVPP fue usado para estudiar el desempeño relativo de los árboles de escenarios FTCA y DTGFBA, específicamente, analizando el valor de la solución estocástica para los 366 problemas de oferta óptima. Para tal fin, una variación del clásico VSS (denominado "Forecasted Value of the Stochastic Solution", FVSS) fue definido y usado junto al clásico VSS