Optimal energy management for a grid-tied solar PV-battery microgrid: A reinforcement learning approach

There has been a shift towards energy sustainability in recent years, and this shift should continue. The steady growth of energy demand because of population growth, as well as heightened worries about the number of anthropogenic gases released into the atmosphere and deployment of advanced grid technologies, has spurred the penetration of renewable energy resources (RERs) at different locations and scales in the power grid. As a result, the energy system is moving away from the centralized paradigm of large, controllable power plants and toward a decentralized network based on renewables. Microgrids, either grid-connected or islanded, provide a key solution for integrating RERs, load demand flexibility, and energy storage systems within this framework. Nonetheless, renewable energy resources, such as solar and wind energy, can be extremely stochastic as they are weather dependent. These resources coupled with load demand uncertainties lead to random variations on both the generation and load sides, thus challenging optimal energy management. This thesis develops an optimal energy management system (EMS) for a grid-tied solar PV-battery microgrid. The goal of the EMS is to obtain the minimum operational costs (cost of power exchange with the utility and battery wear cost) while still considering network constraints, which ensure grid violations are avoided. A reinforcement learning (RL) approach is proposed to minimize the operational cost of the microgrid under this stochastic setting. RL is a reward-motivated optimization technique derived from how animals learn to optimize their behaviour in new environments. Unlike other conventional model-based optimization approaches, RL doesn't need an explicit model of the optimization system to get optimal solutions. The EMS is modelled as a Markov Decision Process (MDP) to achieve optimality considering the state, action, and reward function. The feasibility of two RL algorithms, namely, conventional Q-learning algorithm and deep Q network algorithm, are developed, and their efficacy in performing optimal energy management for the designed system is evaluated in this thesis. First, the energy management problem is expressed as a sequential decision-making process, after which two algorithms, trading, and non-trading algorithm, are developed. In the trading algorithm case, excess microgrid's energy can be sold back to the utility to increase revenue, while in the latter case constraining rules are embedded in the designed EMS to ensure that no excess energy is sold back to the utility. Then a Q-learning algorithm is developed to minimize the operational cost of the microgrid under unknown future information. Finally, to evaluate the performance of the proposed EMS, a comparison study between a trading case EMS model and a non-trading case is performed using a typical commercial load curve and PV generation profile over a 24- hour horizon. Numerical simulation results indicated that the algorithm learned to select an optimized energy schedule that minimizes energy cost (cost of power purchased from the utility based on the time-varying tariff and battery wear cost) in both summer and winter case studies. However, comparing the non-trading EMS to the trading EMS model operational costs, the latter one decreased cost by 4.033% in the summer season and 2.199% in the winter season. Secondly, a deep Q network (DQN) method that uses recent learning algorithm enhancements, including experience replay and target network, is developed to learn the system uncertainties, including load demand, grid prices and volatile power supply from the renewables solve the optimal energy management problem. Unlike the Q-learning method, which updates the Q-function using a lookup table (which limits its scalability and overall performance in stochastic optimization), the DQN method uses a deep neural network that approximates the Q- function via statistical regression. The performance of the proposed method is evaluated with differently fluctuating load profiles, i.e., slow, medium, and fast. Simulation results substantiated the efficacy of the proposed method as the algorithm was established to learn from experience to raise the battery state of charge and optimally shift loads from a one-time instance, thus supporting the utility grid in reducing aggregate peak load. Furthermore, the performance of the proposed DQN approach was compared to the conventional Q-learning algorithm in terms of achieving a minimum global cost. Simulation results showed that the DQN algorithm outperformed the conventional Q-learning approach, reducing system operational costs by 15%, 24%, and 26% for the slow, medium, and fast fluctuating load profiles in the studied cases

Implementation of second-life batteries as energy storage systems enhancing the interoperability and flexibility of the energy infrastructure in tertiary buildings

The main focus of this project is to evaluate the implementation of second-life batteries for a building stock enabling the energy flexibility schemes like Demand Response (DR). This project will focus particularly on how the building stock and its energy infrastructure (energy storage systems, legacy-assets, communication devices and grid architecture, among others) can participate as innovative energy solutions of the next generation of smart-grids, acting as virtual power plants (VPP) in order to deploy the distributed generation (DG) concept in the actual energy field and paving the way to unlock the demand response (DR) market in the distribution energy network. In addition, the implementation of these technologies will led to plan different business models and the scalability of them in the tertiary building sector. Battery energy storage systems (BESSs) are already being deployed for several stationary applications in a techno-economical feasible way. This project focuses in the study to obtain potential revenues from BESSs built from EVs lithium-ion batteries with varying states of health (SoH). For this analysis, a stationary BESS sizing model is done, using the parameters of a 14 kWh new battery, but also doing a comparison with parameters if the same battery would be 11.2 kWh second-life battery. The comprehensive sizing model consists of several detailed sub-models, considering battery specifications, aging and an operational strategy plan, which allow a technical assessment through a determined time frame. Therefore, battery depreciation and energy losses are considered in this techno-economic analysis. Potential economical feasible applications of new and second-life batteries, such the integration of a Building Integrated Photovoltaics (BIPV), self-consumption schemes, feed-in-tariff schemes and frequency regulation as well as their combined operation are compared. The research includes different electricity price scenarios mostly from the current Spanish energy market. The operation and integration of ICT-IoT technology upgrades is found to have the highest economic viability for this specific case study. A detailed study for this project will enhance the relevant importance of these topics in the energy field and how it will be a disruptive solution for the initial problem statement. A general context is given in order to introduce the main and specific objectives thus to trace an adequate way to follow and achieve them. The development of this master thesis will be coupled with the Demand Response Integration technologies (DRIvE) [10] H2020 EU funded project, currently on-going, considering some of the energy consumption data and initial parameters from the selected case study at COMSA Corporación office building in Barcelona, Spain

Modeling and Communicating Flexibility in Smart Grids Using Artificial Neural Networks as Surrogate Models

Increasing shares of renewable energies and the transition towards electric vehicles pose major challenges to the energy system. In order to tackle these in an economically sensible way, the flexibility of distributed energy resources (DERs), such as battery energy storage systems, combined heat and power plants, and heat pumps, needs to be exploited. Modeling and communicating this flexibility is a fundamental step when trying to achieve control over DERs. The literature proposes and makes use of many different approaches, not only for the exploitation itself, but also in terms of models. In the first step, this thesis presents an extensive literature review and a general framework for classifying exploitation approaches and the communicated models. Often, the employed models only apply to specific types of DERs, or the models are so abstract that they neglect constraints and only roughly outline the true flexibility. Surrogate models, which are learned from data, can pose as generic DER models and may potentially be trained in a fully automated process. In this thesis, the idea of encoding the flexibility of DERs into ANNs is systematically investigated. Based on the presented framework, a set of ANN-based surrogate modeling approaches is derived and outlined, of which some are only applicable for specific use cases. In order to establish a baseline for the approximation quality, one of the most versatile identified approaches is evaluated in order to assess how well a set of reference models is approximated. If this versatile model is able to capture the flexibility well, a more specific model can be expected to do so even better. The results show that simple DERs are very closely approximated, and for more complex DERs and combinations of multiple DERs, a high approximation quality can be achieved by introducing buffers. Additionally, the investigated approach has been tested in scheduling tasks for multiple different DERs, showing that it is indeed possible to use ANN-based surrogates for the flexibility of DERs to derive load schedules. Finally, the computational complexity of utilizing the different approaches for controlling DERs is compared

Study and analysis of the use of flexibility in local electricity markets

In this work an introduction to Local Electricity Markets (LEM) was done and afterwards evolutionary algorithms (EAs) such as Differential Evolution (DE), HybridAdaptive Differential Evolution (HyDE), Hybrid-Adaptive Differential Evolution with Decay Function (HyDE-DF) and Vortex Search (VS) were applied to a market model in order to test its efficiency and scalability. Then, the market model was expanded adding a network model from the BISITE laboratory and again tests using the evolutionary algorithms were performed. In more detail, first a literature review is done about distributed generation, load flexibility, LEM and EAs. Then a cost optimization problem in Local Electricity Markets is analyzed considering fixed-term flexibility contracts between the distribution system operator (DSO) and aggregators. In this market structure, the DSO procures flexibility while aggregators of different types (e.g., conventional demand response or thermo-load aggregators) offer the service. Its then solved the proposed model using evolutionary algorithms based on the well-known differential evolution (DE). First, a parameter-tuning analysis is done to assess the impact of the DE parameters on the quality of solutions to the problem. Later, after finding the best set of parameters for the “tuned” DE strategies, we compare their performance with other self-adaptive parameter algorithms, namely the HyDE, HyDE-DF, and VS. Overall, the algorithms are able to find near-optimal solutions to the problem and can be considered an alternative solver for more complex instances of the model. After this a network model, from BISITE laboratory, is added to the problem and new analyses are performed using evolutionary algorithms along with MATPOWER power flow algorithms. Results show that evolutionary algorithms support from simple to complex problems, that is, it is a scalable algorithm, and with these results it is possible to perform analyses of the proposed market model.Neste trabalho foi feita uma introdução aos Mercados Locais de Eletricidade (MLE) e posteriormente foram aplicados algoritmos evolutivos (AEs) como Differential Evolution (DE), Hybrid-Adaptive Differential Evolution (HyDE), Hybrid-Adaptive Differential Evolution with Decay Function (HyDE-DF) e Vortex Search (VS) a um modelo de mercado a fim de testar a sua eficiência e escalabilidade. O modelo de mercado foi expandido adicionando uma rede do laboratório BISITE e novamente foram realizados testes usando os algoritmos evolutivos. Em mais detalhe, no trabalho primeiro foi feita uma revisão bibliográfica sobre geração distribuída, flexibilidade de carga, MLE e AEs. É analisado um problema de optimização de custos nos MLE, considerando contratos de flexibilidade a prazo fixo entre os agentes. O distribuidor adquire flexibilidade enquanto que os agregadores de diferentes tipos (por exemplo, os agregadores convencionais de resposta à procura ou de carga térmica) oferecem o serviço. Resolve-se depois o modelo proposto utilizando AEs baseados na conhecida DE. É feita uma análise de afinação de parâmetros para avaliar o impacto dos parâmetros DE na qualidade das soluções para o problema. Após encontrarmos o melhor conjunto de parâmetros para as estratégias DE "afinadas", comparamos o seu desempenho com outros algoritmos de parâmetros autoadaptáveis, nomeadamente o HyDE, HyDE-DF, e VS. Globalmente, os algoritmos são capazes de encontrar soluções quase óptimas para o problema e podem ser considerados um solucionador alternativo para instâncias mais complexas do modelo. Então um modelo de rede, do laboratório BISITE, é acrescentado ao problema e novas análises são realizadas utilizando algoritmos evolutivos juntamente com algoritmos de fluxo de potência MATPOWER. Os resultados mostram que os algoritmos evolutivos suportam desde problemas simples a complexos, ou seja, é um algoritmo escalável, e com estes resultados é possível realizar análises do modelo de mercado proposto

Self-organizing Coordination of Multi-Agent Microgrid Networks

abstract: This work introduces self-organizing techniques to reduce the complexity and burden of coordinating distributed energy resources (DERs) and microgrids that are rapidly increasing in scale globally. Technical and financial evaluations completed for power customers and for utilities identify how disruptions are occurring in conventional energy business models. Analyses completed for Chicago, Seattle, and Phoenix demonstrate site-specific and generalizable findings. Results indicate that net metering had a significant effect on the optimal amount of solar photovoltaics (PV) for households to install and how utilities could recover lost revenue through increasing energy rates or monthly fees. System-wide ramp rate requirements also increased as solar PV penetration increased. These issues are resolved using a generalizable, scalable transactive energy framework for microgrids to enable coordination and automation of DERs and microgrids to ensure cost effective use of energy for all stakeholders. This technique is demonstrated on a 3-node and 9-node network of microgrid nodes with various amounts of load, solar, and storage. Results found that enabling trading could achieve cost savings for all individual nodes and for the network up to 5.4%. Trading behaviors are expressed using an exponential valuation curve that quantifies the reputation of trading partners using historical interactions between nodes for compatibility, familiarity, and acceptance of trades. The same 9-node network configuration is used with varying levels of connectivity, resulting in up to 71% cost savings for individual nodes and up to 13% cost savings for the network as a whole. The effect of a trading fee is also explored to understand how electricity utilities may gain revenue from electricity traded directly between customers. If a utility imposed a trading fee to recoup lost revenue then trading is financially infeasible for agents, but could be feasible if only trying to recoup cost of distribution charges. These scientific findings conclude with a brief discussion of physical deployment opportunities.Dissertation/ThesisDoctoral Dissertation Systems Engineering 201

Optimization Under Uncertainty in Building Energy Management

The introduction of decentralized energy resources as well as energy storage systems to the energy system calls for new control and coordination mechanisms and systems. This is also true for buildings. An optimized operation of buildings comprising decentralized generation and energy storage systems can be achieved by a building energy management system. It controls and coordinates the operation of individual devices in a building\u27s energy system to achieve given goals, such as the increase of energy efficiency, the decrease of carbon emissions, the minimization of operating costs or the provision of demand response measures. This thesis picks up on this idea and extends the ongoing research by presenting an approach to the optimized operation of building energy systems that includes the uncertainties in the predictions of the future energy generation and consumption into the control scheme of a building energy management system. To do so, this thesis identified the use of a scenario-based consideration of the uncertainties to be best suited. The presented approach uses a rolling horizon optimization approach with a stochastic two-stage optimization problem, which considers several forecast scenarios in the optimization

Reinforcement learning for EV charging optimization : A holistic perspective for commercial vehicle fleets

Recent years have seen an unprecedented uptake in electric vehicles, driven by the global push to reduce carbon emissions. At the same time, intermittent renewables are being deployed increasingly. These developments are putting flexibility measures such as dynamic load management in the spotlight of the energy transition. Flexibility measures must consider EV charging, as it has the ability to introduce grid constraints: In Germany, the cumulative power of all EV onboard chargers amounts to ca. 120 GW, while the German peak load only amounts to 80 GW. Commercial operations have strong incentives to optimize charging and flatten peak loads in real-time, given that the highest quarter-hour can determine the power-related energy bill, and that a blown fuse due to overloading can halt operations. Increasing research efforts have therefore gone into real-time-capable optimization methods. Reinforcement Learning (RL) has particularly gained attention due to its versatility, performance and real- time capabilities. This thesis implements such an approach and introduces FleetRL as a realistic RL environment for EV charging, with a focus on commercial vehicle fleets. Through its implementation, it was found that RL saved up to 83% compared to static benchmarks, and that grid overloading was entirely avoided in some scenarios by sacrificing small portions of SOC, or by delaying the charging process. Linear optimization with one year of perfect knowledge outperformed RL, but reached its practical limits in one use-case, where a feasible solution could not be found by the solver. Overall, this thesis makes a strong case for RL-based EV charging. It further provides a foundation which can be built upon: a modular, open-source software framework that integrates an MDP model, schedule generation, and non-linear battery degradationElektrifieringen av transportsektorn är en nödvändig men utmanande uppgift. I kombination med ökande solcellsproduktion och förnybara energikällor skapar det ett dilemma för elnätet som kräver omfattande flexibilitetsåtgärder. Dessa åtgärder måste inkludera laddning av elbilar, ett fenomen som har lett till aldrig tidigare skådade belastningstoppar. Ur ett kommersiellt perspektiv är incitamentet att optimera laddningsprocessen och säkerställa drifttid. Forskningen har fokuserat på realtidsoptimeringsmetoder som Deep Reinforcement Learning (DRL). Denna avhandling introducerar FleetRL som en ny RL-miljö för EV-laddning av kommersiella flottor. Genom att tillämpa ramverket visade det sig att RL sparade upp till 83% jämfört med statiska riktmärken, och att överbelastning av nätet helt kunde undvikas i de flesta scenarier. Linjär optimering överträffade RL men nådde sina gränser i snävt begränsade användningsfall. Efter att ha funnit ett positivt business case för varje kommersiellt användningsområde, ger denna avhandling ett starkt argument för RL-baserad laddning och en grund för framtida arbete via praktiska insikter och ett modulärt mjukvaruramverk med öppen källko

Flexibility market for congestion management in smart grids

Mención Internacional en el título de doctorCurrent power systems are facing several sustainability challenges to meet the increasing demand of electricity. In addition, there is a global direction to increase the share of renewable energy sources in the power generation mix and energy efficiency. In the face of all such challenges, smart grids were incepted. Smart grids are modernized power systems that integrate state-of-the art communication and information technology to facilitate the bidirectional flow of information and electricity between the supply and demand sides. The resilience of smart grids can pave the way for having more flexibility at the distribution level of the power systems. Demand response (DR) programs are considered one of the sources of system flexibility and it is one of the main components of smart grids. DR can be defined as the willingness of customers to alter their electricity consumption profile in response to price signals. Transmission system operators have been implementing demand response programs in a straightforward fashion for several years now. For example, by having energy prices that are expensive during on-peak periods and low-priced at off-peak periods. Other type of DR programs introduces price signals when grid reliability is compromised and a reduction in energy consumption is necessary. In this way, customers can plan their activities accordingly in order to save money. Now, a new era of technology, artificial intelligence and the so-called “internet of things”, have provided new ways to explore the full potential of demand response, by allowing to alter loads in a much more dynamic and precise manner, thus optimizing the operation of grid assets. This thesis focuses on one of the main types of DR programs which is demand flexibility. Demand flexibility is the ability of the demand-side customers to adjust their load profiles in response to an external market signal. On the short- and medium-term periods, distribution system operators (DSOs) can take advantage of the flexibility of demand to mitigate network congestions caused by increased peaks or high penetration of renewable energy. On the long-term period, DSOs can include demand flexibility in their network expansion planning process for future demand growth. The optimal usage of demand flexibility can help in postponing needed investments for upgrading the networks’ capacity. Demand flexibility can be acquired through market-based solutions which can deliver cost-efficient flexibility services for several market agents by facilitating competition between different flexibility providers. Market mechanisms are considered by policy makers as the optimal solution for flexibility access. With respect to that, this thesis proposes a comprehensive framework for a distribution-level flexibility market, called “Flex-DLM” that enables and facilitates the trading of demand flexibility between the distribution system operator, as the main buyer, and aggregators, as sellers representing flexible consumers. Two types of demand flexibility services were modelled, which are: 1- Up-regulation flexibility (UREG), which corresponds to load decrease volumes, and 2- Down-regulation flexibility (DREG), which corresponds load increase volumes. In addition, the payback effect, which is a common event to the activation of demand flexibility, is considered for both types of flexibility services. Also, the distribution network constraints were modelled, which represents the power flow constraints of the network, which is key to present a realistic model for the flexibility market. In the Flex-DLM, the DSO is considered as the market operator who is responsible of clearing the market, while making sure the network congestions are mitigated. The Flex-DLM operates on two timeframes which are day-ahead and real-time with an objective to provide the DSO with flexibility products that can help it in the congestion management process. In addition to this, the uncertainty of demand is taken into consideration to prevent the DSO from procuring inaccurate amounts of demand flexibility. A new option is introduced in the day-ahead Flex-DLM, called the right-to-use (RtU) that allows the DSO to reserve the right to activate demand flexibility during the day-ahead period for congestions that have low probability of occurrence on the following operation day. In this way, the DSO can call upon this option in real-time if the congestion takes place. Also, the uncertainty behind the customers’ commitment to the flexibility activation requests and amounts is taken into consideration. In this thesis, the decision-making process of the DSO for optimizing its choice of demand flexibility and minimizing its total cost is modelled. Two methods were carried out for the optimization model proposed in this work. The first method follows a deterministic approach, where the objective is to optimize the DSO’s cost and clear the Flex-DLM during the day-ahead period only, without taking into account the uncertainty of demand and the uncertainty of consumers’ participation. The second method follows probabilistic approach, which considers the demand uncertainty during the day-ahead and real-time periods and models the uncertainty behind the customers’ commitment. Both optimization methods were integrated with an optimal power flow (OPF) solver tool in order to check the technical validity of the activated flexibility services and to make sure that the payback effect does not cause further congestions in the network. The advantage of the proposed framework is that it requires minimum regulatory changes and it does not involve the DSO in any electricity trading. Also, the proposed optimization method can be integrated with any OPF solver tool. Different distribution feeders obtained from a distribution network located in Spain were used to check the validity of the proposed framework and the decision-making process. The case studies are divided into two parts: 1- The first part applies the proposed flexibility framework from a deterministic perspective and 2- The second part applies the Flex-DLM framework considering all uncertainties, which corresponds to the probabilistic optimization approach. Finally, to help the DSO in the long-term planning process of its local network, a cost & benefit analysis is carried out to value the economic impact of implementing demand flexibility programs as an alternate solution to conventional network upgradesLos sistemas de energía actuales se enfrentan a varios desafíos de sostenibilidad para satisfacer la creciente demanda de electricidad. Además, existe una clara tendencia a aumentar la proporción de fuentes renovables de energía en la generación de energía y así como hacia la eficiencia energética. Como parte de la respuesta a estos desafíos, se iniciaron las redes inteligentes. Las redes inteligentes son sistemas de energía modernizados que integran tecnología de comunicación e información de última generación para facilitar el flujo bidireccional de información y electricidad entre la oferta y la demanda. La utilización de las redes inteligentes pretende facilitar el empleo de la flexibilidad en la red de distribución de los sistemas eléctricos. Los programas de gestión de la demanda se consideran una de las fuentes de flexibilidad del sistema y es uno de los puntos sobre los que se apoyan las redes inteligentes. La gestión de la demanda se puede definir como la disposición de los clientes a alterar su perfil de consumo de electricidad en respuesta a las señales de precios. Los operadores de sistemas de transporte han estado implementando programas de respuesta a la demanda de manera directa desde hace varios años. Por ejemplo, la diferencia entre precios altos y bajos en el mercado mayorista introduce un incentivo para el consumo en horas de menor precio. Otro tipo de programas de gestión de la demanda introduce señales de precios cuando la fiabilidad de la red se ve comprometida y es necesaria una reducción en el consumo de energía. De esta manera, los consumidores pueden planificar sus actividades en consecuencia para ahorrar costes. Ahora, una nueva era de la tecnología, la inteligencia artificial y el llamado "internet de las cosas" han proporcionado nuevas formas de explorar el potencial completo de la respuesta de la demanda, al permitir alterar las cargas de una manera mucho más dinámica y precisa, optimizando así la utilización de los activos de red. Esta tesis se centra en uno de los principales tipos de programas de DR que es la flexibilidad de la demanda. La flexibilidad de la demanda es la capacidad de los clientes del lado de la demanda para ajustar sus perfiles de carga en respuesta a una señal del mercado externo. En los períodos a corto y mediano plazo, los operadores de sistemas de distribución pueden aprovechar la flexibilidad de la demanda para mitigar las congestiones en la red causadas por el aumento de los picos de demanda o la alta penetración de energía renovable. En el período a largo plazo, los distribuidores pueden incluir la flexibilidad de la demanda en su proceso de planificación de expansión de la red para el crecimiento futuro de la demanda. El uso óptimo de la flexibilidad de la demanda puede ayudar a posponer las inversiones necesarias para mejorar la capacidad de las redes. La flexibilidad de la demanda se puede conseguir mediante soluciones basadas en el mercado que pueden ofrecer servicios de flexibilidad rentables para varios agentes del mercado al facilitar la competencia entre diferentes proveedores de flexibilidad. Los reguladores suelen considerar que son los mecanismos de mercado los que dan la solución óptima para la gestión de la flexibilidad. En relación con estos temas, esta tesis propone un marco integral para un mercado de flexibilidad a en la red de distribución, denominado “Flex-DLM” que permite y facilita el comercio de flexibilidad de demanda entre el operador del sistema de distribución, como el principal comprador, y los agregadores, como vendedores que representan a los consumidores flexibles. Se han modelado dos tipos de servicios de flexibilidad de demanda, que son: 1- Flexibilidad a subir (UREG), que corresponde a un requerimiento disminución de carga, y 2- Flexibilidad a bajar (DREG), que corresponde a un requerimiento de aumento de carga. Además, el efecto de rebote, o consumo posterior al uso de la flexibilidad, que es un fenómeno común tras la activación de la flexibilidad de la demanda, se tiene en cuenta para ambos tipos de servicios de flexibilidad. Además, se han modelado las restricciones de la red de distribución, que representan las restricciones de flujo de potencia de la red, que es clave para presentar un modelo realista para el mercado de flexibilidad. En el mercado Flex-DLM propuesto, se considera al distribuidor como el operador responsable de despejar el mercado, al tiempo que se encarga de mitigar las congestiones de la red. El Flex-DLM opera en dos marcos de tiempo: el diario y el tiempo real con el objetivo de proporcionar al distribuidor productos flexibles que puedan ayudarlo en el proceso de gestión de la congestión. Además de esto, la incertidumbre de la demanda se tiene en cuenta para evitar que el distribuidor adquiera cantidades incorrectas de flexibilidad de la demanda. Se introduce una nueva opción en el Flex-DLM del día siguiente, denominado derecho de uso que le permite al distribuidor reservar el derecho de activar la flexibilidad de la demanda durante el período del día anterior para congestiones que tienen poca probabilidad de ocurrencia en el siguiente día de operación. De esta manera, el distribuidor puede recurrir a esta opción en tiempo real si se produce la congestión. Además, se tiene en cuenta la incertidumbre sobre del compromiso de cumplimiento de los clientes con los requerimientos y las cantidades de energía activadas durante el proceso de gestión de la flexibilidad. En esta tesis, se modela asimismo el proceso de toma de decisiones del DSO para optimizar su elección de flexibilidad de demanda y minimizar su costo total. Se llevaron a cabo dos métodos para el modelo de optimización propuesto en este trabajo. El primer método sigue un enfoque determinista, donde el objetivo es optimizar el coste de la flexibilidad para el distribuidor y eliminar el Flex-DLM solo durante el mercado diario , sin tener en cuenta la incertidumbre de la demanda y la de la participación de los consumidores. El segundo método sigue un enfoque probabilístico, que considera la incertidumbre de la demanda durante los períodos diarios y en tiempo real y modela la incertidumbre del compromiso de los clientes. Ambos métodos de optimización se integraron con una herramienta de solución de flujo de potencia óptimo (OPF) para verificar la validez técnica de los servicios de flexibilidad activados y asegurar que el efecto de recuperación no cause más congestiones en la red. La ventaja del marco propuesto es que requiere cambios regulatorios mínimos y no involucra al DSO en ningún comercio de electricidad. Además, el método de optimización propuesto se puede integrar con cualquier herramienta de solución OPF. Se han utiliado diferentes líneas de distribución obtenidos de una red de distribución ubicada en España para verificar la validez del marco propuesto y el proceso de toma de decisiones. Los estudios de caso se dividen en dos partes: 1- La primera parte aplica el marco de flexibilidad propuesto desde una perspectiva determinista y 2- La segunda parte aplica el marco Flex-DLM considerando todas las incertidumbres, que corresponden al enfoque de optimización probabilística. Finalmente, para ayudar al distribuidor en el proceso de planificación a largo plazo de su red local, se lleva a cabo un análisis coste - beneficio para valorar el impacto económico de la implementación de programas de flexibilidad de la demanda como una solución alternativa a las actualizaciones de red convencionales.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Hortensia Elena Amaris Duarte.- Secretario: Milan Prodanovic.- Vocal: Barry Patrick Haye

Modeling and Optimizing of Integrated Multi-Modal Energy Systems for Municipal Energy Utilities

The development of sustainable business models is a challenging task since various factors might influence the results of an assessment. Given the complexity at the municipal level, system interdependencies between different alternatives need to be considered. One possibility to support decision makers is to apply energy system optimization models. Existing optimization models, however, ignore the roles different actors play and the resulting impact they have. To address this research issue, this thesis presents an integrated techno-economic optimization framework called IRPopt (Integrated Resource Planning and Optimization). A proven graph-based energy system approach allows the accurate modeling of deployment systems by considering different energy carriers and technical processes. In addition, a graph-based commercial association approach enables the integration of actor-oriented coordination. This is achieved by the explicit modeling of market actors on one layer and technology processes on another layer as well as resource flow interrelations and commercial agreements mechanism among and between the different layers. Using the optimization framework, various optimization problems are solvable on the basis of a generic objective function. For demonstration purposes, this thesis assesses the business models demand response and community storage. The applied examples demonstrate the modeling capabilities of the developed optimization framework. Further, the dispatch results show the usefulness of the described optimization approach