Decision Support for Smart Grid Planning and Operation Considering Reliability

[ES] Esta tesis aporta contribuciones a los temas de los sistemas de energía y la movilidad eléctrica. Por lo tanto, se proponen soluciones innovadoras para la planificación de la red de distribución radial tradicional sin o con pocas unidades de recursos energéticos distribuidos, y para la planificación, operación, reconfiguración, y gestión de recursos energéticos en redes de distribución en media tensión considerando una alta penetración de los recursos energéticos distribuidos en el contexto de las redes inteligentes. Las preocupaciones sobre la disponibilidad de combustibles fósiles y el aumento de los efectos climático causados por su uso generalizado en la generación de electricidad han llevado a varias políticas e incentivos para atenuar estos problemas. Estas medidas contribuyeron a inversiones considerables en fuentes de energía renovables y motivaron muchas iniciativas de redes inteligentes. Aunque el panorama futuro de los sistemas eléctricos modernos parece muy prometedor, la integración a gran escala de fuentes de energía renovables de naturaleza intermitente, como la eólica y la fotovoltaica, plantea nuevos desafíos y limitaciones en la industria eléctrica actual. Hoy en día, el diseño de la red de distribución no está correctamente preparado para alojar una gran cantidad de fuentes de energía renovables distribuidas. Por lo tanto, los operadores del sistema de distribución reconocen la necesidad de cambiar el diseño de la red mediante la planificación y el refuerzo. A medida que aumenta la penetración de las fuentes de energía renovable, un agregador de energía puede proporcionar una generación y demanda altamente flexibles según lo requiere el paradigma de red inteligente. Además, esta entidad puede permitir lograr una alta integración de la oferta de energía renovable y aumentar el valor para los pequeños productores y consumidores que no pueden negociar directamente en el mercado mayorista. Sin embargo, la entidad agregadora de energía necesita herramientas adecuadas de apoyo a la decisión para superar los desafíos complejos y hacer frente a un gran número de recursos energéticos. Por lo tanto, la gestión de recursos energéticos es crucial para que la entidad agregadora de energía reduzca los costos de operación, aumente de los beneficios, reduzca la huella de carbono y mejore la estabilidad del sistema. En la perspectiva mundial actual, muchas personas se están mudando a las ciudades en busca de una mejor calidad de vida, contribuyendo de esta manera a la continua expansión de las áreas urbanas. En consecuencia, el sector de transportes está jugando un papel crítico en las emisiones de dióxido de carbono. Teniendo en cuenta esto, muchas ventajas medioambientales y económicas pueden ser obtenidas del cambio de los motores de combustión interna a los vehículos eléctricos. Sin embargo, este cambio contribuirá a una carga en la red de distribución, dando lugar a la posibilidad de congestión de la red. Por lo tanto, para facilitar la integración de la carga de los vehículos eléctricos en la red de distribución, un modelo de predicción del comportamiento del usuario de un vehículo eléctrico pode ser una herramienta muy importante. Además, el paradigma de la red inteligente está desafiando la estructura de control y operación convencional diseñado para redes de distribución pasivas. De este modo, la reconfiguración de la red de distribución será una estrategia esencial y significativa para el operador del sistema de distribución. En el estado del arte actual se identificó una falta de modelos, estrategias y herramientas de apoyo a la toma de decisiones adecuadas para los dominios de problemas de planificación, operación y gestión de recursos energéticos de redes de distribución en media tensión con una alta penetración de fuentes de energía distribuidas. Por lo tanto, surgen varios desafíos de investigación que llevan a la necesidad de desarrollar modelos nuevos e innovadores que aborden: a) el impacto de las fuentes de energía renovable y la variabilidad de la demanda en la planificación de la expansión a largo plazo, b) el problema de la gestión de los recursos energéticos a gran escala, teniendo en cuenta la demanda, las fuentes de energía renovables, los vehículos eléctricos y la variabilidad de los precios del mercado, c) el análisis de impacto de los precios de carga dinámicos de los vehículos eléctricos en la operación de la red de distribución y en el comportamiento del usuario del vehículo eléctrico. Además, en el contexto de la red de distribución de media tensión radial tradicional, también se verificó la necesidad de modelos innovadores para mejorar la confiabilidad a través de la identificación de nuevas inversiones en los componentes de la red. Por lo tanto, esta tesis propone soluciones innovadoras para hacer frente a todos estos vacíos y problemas. Para ese propósito, las contribuciones de la tesis, resultan en un innovador sistema de apoyo a la decisión llamado Advanced Decision Support Tool for Smart Grid Planning and Operation (SupporGrid). El SupporGrid se compone de un conjunto de modelos diversificados que juntos contribuyen a manejar la complejidad de la planificación tradicional de las redes de distribución radial (PlanTGrid), y para la planificación (PlanSGrid), operación (OperSGrid), y los problemas de gestión de recursos energéticos (ERMGrid) en redes de distribución de media tensión en el paradigma de red inteligente. PlanTGrid incluye un modelo de planificación de expansión para redes de distribución radial tradicionales para identificar la posibilidad de nuevas inversiones al costo mínimo. La planificación de la expansión a largo plazo de las redes de distribución en un contexto de red inteligente con una alta penetración de fuentes de energía renovables distribuidas y que trata las fuentes de incertidumbre se resuelve mediante el uso PlanSGrid. OperSGrid contiene una herramienta de simulación de viajes de los usuarios de los vehículos eléctricos funcionando en conjunto con un modelo de operación y reconfiguración que utiliza descomposición de Benders y precios marginales para comprender el impacto del precio de carga de energía dinámica en ambos lados: la red de distribución y el usuario de vehículo eléctrico. Para hacer frente a la gestión de recursos energéticos a gran escala con problemas de respuesta a la demanda y sistemas de almacenamiento de energía, así como con la variabilidad de la demanda, las fuentes de energía renovable, los vehículos eléctricos y el precio de mercado, ERMGrid incluye un modelo estocástico de dos etapas. Las metodologías desarrolladas para el sistema de soporte de decisiones se han probado y validado en escenarios realistas. Los resultados prometedores logrados en condiciones realistas respaldan la hipótesis de que las metodologías son adecuadas e innovadoras para la planificación de la red de distribución radial tradicional, y para la planificación, operación, reconfiguración y gestión de recursos energéticos a largo plazo de la red de distribución considerando alta penetración de recursos energéticos distribuidos y de vehículos eléctricos en el contexto de red inteligente. Los resultados prometedores logrados en condiciones realistas respaldan la hipótesis de que las metodologías son adecuadas e innovadoras para la planificación de la red de distribución radial tradicional, y para la planificación, operación, reconfiguración y gestión de recursos energéticos a largo plazo de la red de distribución considerando la alta distribución de recursos energéticos y la penetración de vehículos eléctricos. De hecho, este sistema de apoyo a la decisión mejorará el funcionamiento de las redes de distribución de media tensión, permitiendo ahorros para las partes interesadas

Demand Response Driven by Distribution Network Voltage Limit Violation: A Genetic Algorithm Approach for Load Shifting

The residential sector electricity demand has been increasing over the years, leading to an increasing effort of the power network components, namely during the peak demand periods. This demand increasing together with the increasing levels of renewable-based energy generation and the need to ensure the electricity service quality, namely in terms of the voltage profile, is challenging the distribution network operation. Demand response can play an important role in facing these challenges, bringing several benefits, both for the network operation and for the consumer (e.g., increase network components lifetime and consumers bill reduction). The present research work proposes a genetic algorithm-based model to use the consumers’ load flexibility with demand response event participation. The proposed method optimally shifts residential loads to enable the consumers’ participation in demand response while respecting consumers’ preferences and constraints. A realistic low voltage distribution network with 236 buses is used to illustrate the application of the proposed model. The results show considerable energy cost savings for consumers and an improvement in voltage profile.This article is a result of the Project Real-time support infrastructure and energy management for intelligent carbon-neutral smart cities (RETINA) (NORTE-01-0145-FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).info:eu-repo/semantics/publishedVersio

Demand response and dispatchable generation as ancillary services to support the low voltage distribution network operation

The current power systems, namely the low voltage distribution networks, have been suffering considerable changes in recent years. What appeared to be innovation trends nowadays due to technological advances and manufacturing cost reduction has become the new reality in the coming years. Thus, the growing trend of power generation by renewable sources has posed new challenges and new opportunities. Furthermore, the wide installation of “smart meters” and the interest in placing the citizens as core players into the future energy markets and systems operation improves the role of the distribution system operator. In this way, developing new and innovative methodologies to explore the potential mechanisms for providing ancillary services in distribution networks becomes of great importance, namely in low voltage levels. This research paper proposes an innovative methodology to enhance the demand response participation of small consumers and dispatchable distributed renewable energy sources flexibility as ancillary services to mitigate the voltage and congestion issues in low voltage distribution networks. A realistic low voltage distribution network with 236 buses is used to illustrate the application of the proposed model. The results demonstrate a considerable voltage profile and congestion improvements.This work has received funding from the European Union’s Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066). The work has been done also in the scope of projects UIDB/00760/2020, and MAS-Society (PTDC/EEI-EEE/28954/2017), financed by FEDER Funds through COMPETE program and from National Funds through FCT.info:eu-repo/semantics/publishedVersio

Distribution Network Expansion Planning Considering the Flexibility Value for Distribution System Operator

The electric power system has undergone numerous changes over the years. The transformation of the end-users from passive actors to active actors brings implications for the electric power system. The distribution system operator will be able to guide its operations in the function of the active role of the end-users. In many situations, the distribution system operator is carried out to avoid congestion in the distribution networks, and when it happens the distribution system operator is obliged to compensate the affected end-users. This paper presents a model in which distribution system operator can take advantage of the flexibility of the end-users in order to minimize the costs of the investments in distribution network expansion. The investment cost with the presented methodology as show the results has a reduction of 5.77%.This work has received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2019. Ricardo Faia is supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with PhD grant reference SFRH/BD/133086/2017 and Bruno Canizes is supported by FCT Funds through SFRH/BD/110678/2015 PhD scholarshipinfo:eu-repo/semantics/publishedVersio

A Statistical Analysis of Performance in the 2021 CEC-GECCO-PESGM Competition on Evolutionary Computation in the Energy Domain

Evolutionary algorithms (EAs) have emerged as an efficient alternative to deal with real-world applications with high complexity. However, due to the stochastic nature of the results obtained using EAs, the design of benchmarks and competitions where such approaches can be evaluated and compared is attracting attention in the field. In the energy domain, the “2021 CEC-GECCO-PESGM Competition on Evolutionary Computation in the Energy Domain: Smart Grid Applications” provides a platform to test and compare new EAs to solve complex problems in the field. However, the metric used to rank the algorithms is based solely on the mean fitness value (related to the objective function value only), which does not give statistical significance to the performance of the algorithms. Thus, this paper presents a statistical analysis using the Wilcoxon pair-wise comparison to study the performance of algorithms with statistical grounds. Results suggest that, for track 1 of the competition, only the winner approach (first place) is significantly different and superior to the other algorithms; in contrast, the second place is already statistically comparable to some other contestants. For track 2, all the winner approaches (first, second, and third) are statistically different from each other and the rest of the contestants. This type of analysis is important to have a deeper understanding of the stochastic performance of algorithms.This research has received funding from FEDER funds through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under Project POCI01-0145-FEDER-028983; by National Funds through the FCT Portuguese Foundation for Science and Technology, under Projects PTDC/EEI-EEE/28983/2017(CENERGETIC),CEECIND/02814/2017, and UIDB/000760/2020.info:eu-repo/semantics/publishedVersio

Power Quality of Renewable Energy Source Systems: A New Paradigm of Electrical Grids

The power quality delivered by the distribution companies to consumers has always been a relevant issue, especially to industrial consumers, where power quality is directly related to productivity. However, until a few years ago, power quality was mostly synonymous with continuity of service, and the main concern was the minimization of power interruptions. Since the last decade of the twentieth century, power quality has become a strategic issue for all sectors involved in this market, from distribution companies to consumers, with a particular emphasis on industrial consumers as well as equipment manufacturers. The concept of power quality involves a wide range of electromagnetic phenomena that can occur in the power grid. Such changes may occur in different parts of the electrical power system, at customer facilities, or in the distribution network. In recent years, the electric power market has undergone huge transformations, electricity production has become decentralized, and consumers (who can now also be producers) have the opportunity to choose their supplier. The integration of renewable-based microgeneration systems into distribution grids has brought various disturbances to the grid (harmonics, voltage unbalance, voltage fluctuations, frequency deviations, etc.), leading to increasingly degraded power quality. This Special Issue focuses on the analysis of the consequences that renewables-based microgeneration systems have on power networks by finding new solutions for networks management (network optimization models, efficiency, and losses), integrating consumers and micro-producers in order to keep quality parameters at high levels. In this Special Issue, we can see that the interdisciplinarity of these issues is very present among researchers and scholars, who are well aware of the importance and impact that the new paradigm of network management brings in various domains, reflecting on the quality of the contributions submitted. Accordingly, the papers selected for publication cover a wide range of application topics, including electrical mobility, energy storage systems, facility management and control, impact analysis of different types of renewable energy sources, with focus on wind and solar generation, in both low-voltage (LV) and medium-voltage (MV) networks.info:eu-repo/semantics/publishedVersio

Flexibility management model of home appliances to support DSO requests in smart grids

Several initiates have been taken promoting clean energy and the use of local flexibility towards a more sustainable and green economy. From a residential point of view, flexibility can be provided to operators using home-appliances with the ability to modify their consumption profiles. These actions are part of demand response programs and can be utilized to avoid problems, such as balancing/congestion, in distribution networks. In this paper, we propose a model for aggregators flexibility provision in distribution networks. The model takes advantage of load flexibility resources allowing the re-schedule of shifting/real-time home-appliances to provision a request from a distribution system operator (DSO) or a balance responsible party (BRP). Due to the complex nature of the problem, evolutionary computation is evoked and different algorithms are implemented for solving the formulation efficiently. A case study considering 20 residential houses equipped each with seven types of home-appliances is used to test and compare the performance of evolutionary algorithms solving the proposed model. Results show that the aggregator can fulfill a flexibility request from the DSO/BRP by re-scheduling the home-appliances loads for the next 24-h horizon while minimizing the costs associated with the remuneration given to end-users.The present work has been developed under the EUREKA – ITEA2 Project M2MGrids (ITEA-13011), Project SIMOCE (ANI—P2020 17690), and has received funding from FEDER Funds through COMPETE program and from National Funds through FCT under the project UIDB/00760/2020. Joao Soares is supported by FCT under CEECIND/02814/2017 grant.info:eu-repo/semantics/publishedVersio

Investment optimization in distribution network based on fuzzy outage parameters

This paper presents a methodology that aims to increase the probability of delivering power to any load point of the electrical distribution system by identifying new investments in distribution components. The methodology is based on statistical failure and repair data of the distribution power system components and it uses fuzzy-probabilistic modelling for system component outage parameters. Fuzzy membership functions of system component outage parameters are obtained by statistical records. A mixed integer non-linear optimization technique is developed to identify adequate investments in distribution networks components that allow increasing the availability level for any customer in the distribution system at minimum cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a real distribution network

Coordination strategies in distribution network considering multiple aggregators and high penetration of electric vehicles

Given the current state of the society in which we live, in terms of energy pollution, several objectives have been set to try to reduce environmental problems. Some of these goals include an exponential increase in production through renewable energy, and Electric Vehicles (EVs) circulating on roads. Due to this high penetration of distributed energy resources in the electricity grid, several problems may exist: grid congestion, causing severe energy systems damage. Innovative coordination strategies must be developed to mitigate these situations. This paper proposes a methodology to minimize this problem in a smart grid with high penetration of Distributed Generation (DG) and EVs, taking into account multiple aggregators. Initially, the proposed model calculates each aggregator’s profit through some business models and analyzes the network without any congestion strategy. This analysis is then done in the presence of Distribution Locational Marginal Pricing (DLMPs), which the aggregator receives from the Distributed System Operator (DSO). The DSO gets these prices after running the Optimal Power Flow (OPF), where these prices involve the market price, the cost of losses, and the cost of congestion at a given point in the network. Here the aggregators react according to these costs, such as trying to buy flexibility from their customers. In this study, the results prove that dynamic prices are more viable for the power grid by reducing congestion by analyzing each aggregator’s profit.This research has received funding from FEDER funds through the Operational Programme for Competitiveness and Internationalization (COMPETE 2020), under Project POCI-01-0145-FEDER-028983; by National Funds through the FCT Portuguese Foundation for Science and Technology, under Projects PTDC/EEIEEE /28983/2017(CENERGETIC), CEECIND/02814/2017, and UIDB/000760/2020.info:eu-repo/semantics/publishedVersio

Optimal Distribution Grid Operation Using DLMP-Based Pricing for Electric Vehicle Charging Infrastructure in a Smart City

The use of electric vehicles (EVs) is growing in popularity each year, and as a result, considerable demand increase is expected in the distribution network (DN). Additionally, the uncertainty of EV user behavior is high, making it urgent to understand its impact on the network. Thus, this paper proposes an EV user behavior simulator, which operates in conjunction with an innovative smart distribution locational marginal pricing based on operation/reconfiguration, for the purpose of understanding the impact of the dynamic energy pricing on both sides: the grid and the user. The main goal, besides the distribution system operator (DSO) expenditure minimization, is to understand how and to what extent dynamic pricing of energy for EV charging can positively affect the operation of the smart grid and the EV charging cost. A smart city with a 13-bus DN and a high penetration of distributed energy resources is used to demonstrate the application of the proposed models. The results demonstrate that dynamic energy pricing for EV charging is an efficient approach that increases monetary savings considerably for both the DSO and EV users.This work has received funding from FEDER Funds through the COMPETE program, from National Funds through FCT under the project UID/EEA/00760/2019 and from the project PTDC/EEI-EEE/28983/2017-CENERGETIC. Bruno Canizes is supported by FCT Funds through the SFRH/BD/110678/2015 PhD scholarship.info:eu-repo/semantics/publishedVersio