Mathematical programming-based models for the distribution networks' decarbonization

(English) Climate change is pushing to decarbonize worldwide economies and forcing fossil fuel-based power systems to evolve into power systems based mainly on renewable energies sources (RES). Thus, increasing the energy generated from renewables in the energy supply mix involves transversal challenges at operational, market, political and social levels due to the stochasticity associated with these technologies and their capacity to generate energy at a small scale close to the consumption point. In this regard, the power generation uncertainty can be handled through battery storage systems (BSS) that have become competitive over the last few years due to a significant price reduction and are a potential alternative to mitigate the technical network problems associated with the intermittency of the renewables, providing flexibility to store/supply energy when is required. On the other hand, the capacity of low-cost generation from small-scale power systems (distributed or decentralized generation (DG)) represents an opportunity for both customers and the power system operators. i.e., customers can generate their energy, reduce their network dependency, and participate actively in eventual local energy markets (LEM), while the power system operator can reduce the system losses and increase the power system quality against unexpected external failures. Nevertheless, incorporating these structures and operational frameworks into distribution networks (DN) requires developing sophisticated tools to support decision-making related to the optimal integration of the distributed energy resources (DER) and assessing the performance of new DNs with high DERs penetration under different operational scenarios. This thesis addresses the distribution networks' decarbonization challenge by developing novel algorithms and applying different optimization techniques through three subtopics. The first axis addresses the optimal sizing and allocation of DG and BSS into a DN from deterministic and stochastic approaches, considering the technical network limitation, the electric vehicle (EV) presence, the users capacity to modify their load consumption, and the DG capability to generate reactive power for voltage stability. Besides, a novel algorithm is developed to solve the deterministic and stochastic models for multiple scenarios providing an accurate DERs capacity that should be installed to decrease the external network dependency. The second subtopic assesses the DN capacity to face unlikely scenarios like primary grid failure or natural disasters preventing the energy supply through a deterministic model that modifies the unbalance DN topology into multiple virtual microgrids (VM) balanced, considering the power supplied by DG and the flexibility provided by the storage devices (SD) and demand response (DR). The third axis addresses the emerging transactive energy (TE) schemes in DNs with high DERs penetration at a residential level through two stochastic approaches to model a Peer-to-peer (P2P) energy trading. To this end, the capability of a P2P energy trading scheme to operate on different markets as day-ahead, intraday, flexibility, and ancillary services (AS) market is assessed, while an algorithm is developed to manage the users' information under a decentralized design.(Català) El cambio climático está obligando a descarbonizar las economías de todo el mundo forzando a los sistemas de energía basados en combustibles fósiles a evolucionar hacia sistemas de energía basados principalmente en fuentes de energía renovables (FER). Así, incrementar la energía generada a partir de renovables en el mix energético está implicando retos transversales a nivel operativo, de mercado, político y social debido a la estocasticidad asociada a estas tecnologías y su capacidad de generar electricidad a pequeña escala cerca al punto de consumo. En este sentido, la incertidumbre en la generación de energía eléctrica puede ser manejada a través de sistemas de almacenamiento en baterías (BSS) que se han vuelto competitivos en los últimos años debido a una importante reducción de precios y son una potencial alternativa para mitigar los problemas técnicos de red asociados a la intermitencia de las renovables, proporcionando flexibilidad para almacenar/suministrar energía cuando sea necesario. Por otro lado, la capacidad de generación a bajo costo a partir de sistemas eléctricos de pequeña escala (generación distribuida o descentralizada (GD)) representa una oportunidad tanto para los clientes como para los operadores del sistema eléctrico. Es decir, los clientes pueden generar su energía, reducir su dependencia de la red y participar activamente en eventuales mercados locales de energía (MLE), mientras que el operador del sistema eléctrico puede reducir las pérdidas del sistema y aumentar la calidad del sistema eléctrico frente a fallas externas inesperadas. Sin embargo, incorporar estas estructuras y marcos operativos en las redes de distribución (RD) requiere desarrollar herramientas sofisticadas para apoyar la toma de decisiones relacionadas con la integración óptima de los recursos energéticos distribuidos (RED) y evaluar el desempeño de las nuevas RD con alta penetración de RED bajo diferentes escenarios de operación. Esta tesis aborda el desafío de la descarbonización de las redes de distribución mediante el desarrollo de algoritmos novedosos y la aplicación de diferentes técnicas de optimización a través de tres dimensiones. El primer eje aborda el dimensionamiento y localización óptimos de GD y BSS en una RD desde enfoques determinísticos y estocásticos, considerando la limitación técnica de la red, la presencia de vehículos eléctricos (VE), la capacidad de los usuarios para modificar su consumo de carga y la capacidad de GD para generar potencia reactiva para la estabilidad del voltaje. Además, se desarrolla un algoritmo novedoso para resolver los modelos determinísticos y estocásticos para múltiples escenarios proporcionando una capacidad precisa de RED que debe instalarse para disminuir la dependencia de la red externa. El segundo subtema evalúa la capacidad de la RD para enfrentar escenarios improbables como fallas en la red primaria o desastres naturales que impidan el suministro de energía, a través de un modelo determinista que modifica la topología de la RD desequilibrada en múltiples microrredes virtuales (MV) balanceadas, considerando la potencia suministrada por GD y la flexibilidad proporcionada por los dispositivos de almacenamiento y respuesta a la demanda (DR). El tercer eje aborda los esquemas emergentes de energía transactiva en RDs con alta penetración de RED a nivel residencial a través de dos enfoques estocásticos para modelar un comercio de energía Peer-to-peer (P2P). Para ello, se evalúa la capacidad de un esquema de comercialización de energía P2P para operar en diferentes mercados como el mercado diario, intradiario, de flexibilidad y de servicios complementarios, a la vez que se desarrolla un algoritmo para gestionar la información de los usuarios bajo un esquema descentralizado.Postprint (published version

Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods

In view of scarcity of traditional energy resources and environmental issues, renewable energy resources (RERs) are introduced to fulfill the electricity requirement of growing world. Moreover, the effective utilization of RERs to fulfill the varying electricity demands of customers can be achieved via demand response (DR). Furthermore, control techniques, decision variables and offered motivations are the ways to introduce DR into distribution network (DN). This categorization needs to be optimized to balance the supply and demand in DN. Therefore, intelligent algorithms are employed to achieve optimized DR. However, these algorithms are computationally restrained to handle the parametric load of uncertainty involved with RERs and power system. Henceforth, this paper focuses on the limitations of intelligent algorithms for DR. Furthermore, a comparative study of different intelligent algorithms for DR is discussed. Based on conclusions, quantum algorithms are recommended to optimize the computational burden for DR in future smart grid

Decision-making under uncertainty in short-term electricity markets

In the course of the energy transition, the share of electricity generation from renewable energy sources in Germany has increased significantly in recent years and will continue to rise. Particularly fluctuating renewables like wind and solar bring more uncertainty and volatility to the electricity system. As markets determine the unit commitment in systems with self-dispatch, many changes have been made to the design of electricity markets to meet the new challenges. Thereby, a trend towards real-time can be observed. Short-term electricity markets are becoming more important and are seen as suitable for efficient resource allocation. Therefore, it is inevitable for market participants to develop strategies for trading electricity and flexibility in these segments. The research conducted in this thesis aims to enable better decisions in short-term electricity markets. To achieve this, a multitude of quantitative methods is developed and applied: (a) forecasting methods based on econometrics and machine learning, (b) methods for stochastic modeling of time series, (c) scenario generation and reduction methods, as well as (d) stochastic programming methods. Most significantly, two- and three-stage stochastic optimization problems are formulated to derive optimal trading decisions and unit commitment in the context of short-term electricity markets. The problem formulations adequately account for the sequential structure, the characteristics and the technical requirements of the different market segments, as well as the available information regarding uncertain generation volumes and prices. The thesis contains three case studies focusing on the German electricity markets. Results confirm that, based on appropriate representations of the uncertainty of market prices and renewable generation, the optimization approaches allow to derive sound trading strategies across multiple revenue streams, with which market participants can effectively balance the inevitable trade-off between expected profit and associated risk. By considering coherent risk metrics and flexibly adaptable risk attitudes, the trading strategies allow to substantially reduce risk with only moderate expected profit losses. These results are significant, as improving trading decisions that determine the allocation of resources in the electricity system plays a key role in coping with the uncertainty from renewables and hence contributes to the ultimate success of the energy transition

Market-oriented micro virtual power prosumers operations in distribution system operator framework

As the European Union is on track to meet its 2020 energy targets on raising the share of renewable energy and increasing the efficiency in the energy consumption, considerable attention has been given to the integration of distributed energy resources (DERs) into the restructured distribution system. This thesis proposes market-oriented operations of micro virtual power prosumers (J.lVPPs) in the distribution system operator framework, in which the J.lVPPs evolve from home-oriented energy management systems to price-taking prosumers and to price-making prosumers. Considering the diversity of the DERs installed in the residential sector, a configurable J.l VPP is proposed first to deliver multiple energy services using a fuzzy logic-based generic algorithm. By responding to the retail price dynamics and applying load control, the J.lVPP achieves considerable electricity bill savings, active utilisation of energy storage system and fast return on investment. As the J.lVPPs enter the distribution system market, they are modelled as price-takers in a two-settlement market first and a chance-constrained formulation is proposed to derive the bidding strategies. The obtained strategy demonstrates its ability to bring the J.l VPP maximum profit based on different composition of DERs and to maintain adequate supply capacity to meet the demand considering the volatile renewable generation and load forecast. Given the non-cooperative nature of the actual market, the J.l VPPs are transformed into price-makers and their market behaviours are studied in the context of electricity market equilibrium models. The resulted equilibrium problems with equilibrium constraints (EPEC) are presented and solved using a novel application of coevolutionary approach. Compared with the roles of home-oriented energy management systems and price-taking prosumers, the J.lVPPs as price­ making prosumers have an improved utilisation rate of the installed DER capacity and a guaranteed profit from participating in the distribution system market

Market-oriented micro virtual power prosumers operations in distribution system operator framework

As the European Union is on track to meet its 2020 energy targets on raising the share of renewable energy and increasing the efficiency in the energy consumption, considerable attention has been given to the integration of distributed energy resources (DERs) into the restructured distribution system. This thesis proposes market-oriented operations of micro virtual power prosumers (J.lVPPs) in the distribution system operator framework, in which the J.lVPPs evolve from home-oriented energy management systems to price-taking prosumers and to price-making prosumers. Considering the diversity of the DERs installed in the residential sector, a configurable J.l VPP is proposed first to deliver multiple energy services using a fuzzy logic-based generic algorithm. By responding to the retail price dynamics and applying load control, the J.lVPP achieves considerable electricity bill savings, active utilisation of energy storage system and fast return on investment. As the J.lVPPs enter the distribution system market, they are modelled as price-takers in a two-settlement market first and a chance-constrained formulation is proposed to derive the bidding strategies. The obtained strategy demonstrates its ability to bring the J.l VPP maximum profit based on different composition of DERs and to maintain adequate supply capacity to meet the demand considering the volatile renewable generation and load forecast. Given the non-cooperative nature of the actual market, the J.l VPPs are transformed into price-makers and their market behaviours are studied in the context of electricity market equilibrium models. The resulted equilibrium problems with equilibrium constraints (EPEC) are presented and solved using a novel application of coevolutionary approach. Compared with the roles of home-oriented energy management systems and price-taking prosumers, the J.lVPPs as price­ making prosumers have an improved utilisation rate of the installed DER capacity and a guaranteed profit from participating in the distribution system market

Optimal Home Energy Management System for Committed Power Exchange Considering Renewable Generations

This thesis addresses the complexity of SH operation and local renewable resources optimum sizing. The effect of different criteria and components of SH on the size of renewable resources and cost of electricity is investigated. Operation of SH with the optimum size of renewable resources is evaluated to study SH annual cost. The effectiveness of SH with committed exchange power functionality is studied for minimizing cost while responding to DR programs