Optimal self-unit commitment of combined cycle power plants. Bridging the gap between the state of the art and current regulation of electricity and natural gas markets.

Abstract

Los CCGTs desempeñarán un papel esencial en la transición hacia la descarbonización del sector eléctrico y su correcto modelado es de vital importancia en mercados marginalistas, ya que, suelen ser decisivos en el precio de casación. Uno de los problemas fundamentales a los que se enfrenta una compañía generadora (GenCo) es el “self unit commitment” (self-UC), que consiste en decidir el estado de acoplamiento de los generadores propios en cada periodo del horizonte considerado. Los modelos de optimización de self-UC son precisamente las herramientas que utilizan las GenCos para planificar su operativa diaria. Los CCGTs tienen la complejidad añadida de recibir el gas a través de una red de gasoductos. Un operador (TSO) gestiona la red gas, y en su operación incurre en costes que traslada a los usuarios mediante tarifas de acceso de terceros a la red (ATR). Una GenCo propietaria de CCGTs que participa en el mercado eléctrico debe considerar tanto las tarifas ATR como las opciones disponibles para aprovisionarse de gas (contratos bilaterales con proveedores y compras en mercados spot). Otro aspecto importante es el régimen impositivo de los generadores. Los impuestos asociados a la generación eléctrica pueden gravar distintos conceptos: generación de energía, ingresos de mercado, consumo de combustible, o emisiones de gases de efecto invernadero. Además, estos impuestos pueden ser distintos según la tecnología, e incluso depender de la zona geográfica. Estas posibles diferencias entre generadores hacen que sea importante modelar los impuestos con detalle para evaluar con precisión el coste real asociado a la explotación. Por último, como en cualquier mercado, se espera que los agentes traten de maximizar sus beneficios. En competencia perfecta, esta situación no requiere consideraciones adicionales. Sin embargo, con un número reducido de agentes, existen normas específicas para limitar comportamientos estratégicos. Por tanto, las GenCos deben tener en cuenta dicha regulación en su planificación. Ante la relevancia de los CCGTs en el sistema eléctrico, surge la necesidad de resolver las carencias de los modelos existentes. Por tanto, esta tesis comienza explicando en detalle los problemas presentados, y posteriormente presenta varios modelos que los abordan, aportando ejemplos que demuestran su utilidad. El Capítulo 3 propone el modelado de las tarifas de ATR y las compras de gas a nivel de portfolio. El Capítulo 4 ofrece un modelo que representa los ingresos individuales de cada generador para poder considerar correctamente sus impuestos y la propiedad compartida de los activos de generación. Por último, el Capítulo 5 presenta dos enfoques diferentes para limitar los comportamientos estratégicos. Las mejoras en la representación del gas se basan en la regulación europea (cuyo objetivo es similar al de otros países), y aquellas relacionadas con el modelado de los ingresos son extensibles a otras tecnologías de generación. De hecho, la correcta representación de los impuestos gana importancia cuando existen diversas tecnologías sujetas a tasas distintas. Por último, respecto a los potenciales usuarios de los modelos propuestos, está claro que para las GenCos tienen una aplicación directa en la planificación de su propia operación. Además, también son herramientas útiles para reguladores u operadores del sistema, proporcionándoles medios con los que simular y estudiar los comportamientos esperados de los agentes. Finalmente, tres anexos proporcionan herramientas necesarias para optimizar el self-UC con CCGTs. El primero está dedicado al modelado del self-UC en sí, y a considerar la incertidumbre con optimización estocástica. El segundo presenta los cambios necesarios en caso de tener CCGTs con múltiples configuraciones de turbinas de gas y vapor. Finalmente, el tercero presenta un ejemplo de ejecución en un entorno de computación en la nube, un estándar que está ganando relevancia en la industria.

Gas Fired Units (GFUs), especially Combined Cycle Gas Turbines (CCGTs), will play an essential role in the transition to the decarbonization of the power industry. One of the fundamental problems faced by a Generation Company (GenCo) with GFUs is known as the Unit Commitment (UC) problem, and as it affects its own generation, this problem is also known in the literature as self Unit Commitment (self-UC). The UC is one of the most studied problems in the power systems literature, consisting in deciding the commitment status of the generation units. The UC is critical because the units take a minimum time to start up and shut down so that the commitment state in a given hour is linked to the surrounding periods, whereas with a unit already connected, varying the load level from one hour to another is more flexible. In particular, the correct modeling of GFUs is vital in deregulated market environments organized as marginal markets since, in most situations, this type of units is decisive for the resulting market cleared price. The GFUs have an added complexity not applicable to other thermal units such as coal or nuclear, that is receiving their fuel supply through a gas network. This gas network is in some ways analogous to the electricity network, but it has its own characteristics that need to be considered for the GFUs operation. The gas network has a Transmission System Operator (TSO) in charge of its operation. This operation entails associated costs that the TSO passes on to the gas network users through the so-called Third Party Access (TPA) tariffs. From the point of view of a GenCo that owns GFUs and participates in the electricity market, two fundamental issues must be considered concerning the fuel supply for its units: the TPA tariffs that must be paid to extract gas from the network at the power plants where the GFUs are located, and the available options to acquire the gas. Regarding gas procurement, there are mainly two options, bilateral contracts with suppliers and purchases in gas spot markets. Irrespective of whether the company has bilateral agreements with suppliers, if it operates in an area with a sufficiently liquid gas spot market, the price of that market will be its supply cost. On the one hand, if the GenCo buys on the gas spot market directly, that price is the cost for the GFUs. On the other hand, if it has bilateral contracts, the market price becomes the opportunity cost of using the gas already acquired for electricity generation as an alternative to selling that gas to the spot market. Another aspect to consider that is usually simplified is the tax scheme to which the generation units are subject. Taxes are part of the generation units operating costs. A careful review of current taxation systems shows different tax types. For instance, taxes charge concepts such as the energy generation [e/MWh], the market revenue [market price percentage] or greenhouse gas emissions [e/ton]. Additionally, these taxes can differ depending on the technology used to generate electricity (coal, gas, hydro, nuclear, etc.), and even they can depend on geographical areas belonging to the same market. These possible differences make it essential to model them in detail to take them into account correctly. Finally, as in any other market, agents are expected to try to maximize their expected profits. In an environment with perfect competition, this situation requires no additional consideration. However, when the number of GenCos is reduced, there are specific regulations aiming to limit possible strategic behaviors of dominant players. In the case of the power sector, those rules have widely been implemented; therefore, agents participating in the electricity market need to account for such regulation in their own planning. Considering the vital role that GFUs will continue to play in the future, the need to solve the lack of state-of-the-art models to address issues that significantly affect the real operation of these units in the market becomes essential. Therefore, this thesis begins by explaining in detail the problems briefly presented in this summary, starting with a detailed review of how the gas system works, focusing on TPA tariffs in the European Union. In addition, it also reviews the different taxes applied to the electricity generation activity and how these problems are tackled in the optimization modeling literature. These issues were not found in the literature on the short-term self-UC optimization models, which are the tools GenCos use in their day-to-day operation planning. Additionally, the relevance of the TPA consideration, that accounts for more than 10% of the total operation cost, has been confirmed by our own experience in collaborations with the industry. Therefore, several modeling improvements have been developed and are proposed in this document. The different chapters present several models with their respective case studies to demonstrate their usefulness in the subjects they address. They are not specific models for each one of the issues but instead represent the different improvements that would have to be implemented to cover each of the concepts discussed, and they could, in fact, be integrated. Chapter 3 proposes modeling TPA tariffs and gas purchases at the portfolio level. Chapter 4 offers a model that represents the individual revenues of each unit to correctly consider its taxes and the possibility that different agents share the ownership of generation assets. Lastly, Chapter 5 presents two different approaches to limit the strategic behaviors that could result from applying the models found in the literature. The thesis’s objective is general, and therefore, the developments presented are not only valid for a GenCo operating CCGTs in a specific country. The improvements in the representation of gas are based on European regulation (whose objective is similar to that of other countries), and those related to income modeling are extensible to other generation technologies. In fact, the correct tax representation gains importance when the portfolio has several technologies subject to different levies. Finally, regarding the potential users of the proposed models, it is clear that GenCos find a direct application in planning their own operation. In addition, they are also helpful tools for regulators or System Operator (SO), providing them with the means to simulate and study the expected behavior of the agents participating in the market. Finally, three appendices are presented with the objective of providing all the necessary tools to optimize the self-UC with GFUs. The first appendix is dedicated to the UC modeling and how to take uncertainty into account by using stochastic programming. The second appendix presents the formulation changes that should be implemented in case of having CCGTs with multiple configurations of gas and steam turbines. Such consideration is thought to be valuable since this type of unit is very common in the industry. Finally, the third appendix presents an example of how to run a self-UC optimization model in a cloud computing environment, a standard that is gaining traction in the industry.