Control and Optimization of Energy Storage in AC and DC Power Grids

Energy storage attracts attention nowadays due to the critical role it will play in the power generation and transportation sectors. Electric vehicles, as moving energy storage, are going to play a key role in the terrestrial transportation sector and help reduce greenhouse emissions. Bulk hybrid energy storage will play another critical role for feeding the new types of pulsed loads on ship power systems. However, to ensure the successful adoption of energy storage, there is a need to control and optimize the charging/discharging process, taking into consideration the customer preferences and the technical aspects. In this dissertation, novel control and optimization algorithms are developed and presented to address the various challenges that arise with the adoption of energy storage in the electricity and transportation sectors. Different decentralized control algorithms are proposed to manage the charging of a mass number of electric vehicles connected to different points of charging in the power distribution system. The different algorithms successfully satisfy the preferences of the customers without negatively impacting the technical constraints of the power grid. The developed algorithms were experimentally verified at the Energy Systems Research Laboratory at FIU. In addition to the charge control of electric vehicles, the optimal allocation and sizing of commercial parking lots are considered. A bi-layer Pareto multi-objective optimization problem is formulated to optimally allocate and size a commercial parking lot. The optimization formulation tries to maximize the profits of the parking lot investor, as well as minimize the losses and voltage deviations for the distribution system operator. Sensitivity analysis to show the effect of the different objectives on the selection of the optimal size and location is also performed. Furthermore, in this dissertation, energy management strategies of the onboard hybrid energy storage for a medium voltage direct current (MVDC) ship power system are developed. The objectives of the management strategies were to maintain the voltage of the MVDC bus, ensure proper power sharing, and ensure proper use of resources, where supercapacitors are used during the transient periods and batteries are used during the steady state periods. The management strategies were successfully validated through hardware in the loop simulation

Innovations in Electric Vehicle Technology: A Review of Emerging Trends and Their Potential Impacts on Transportation and Society

The adoption of electric vehicles (EVs) has gained significant momentum in recent years, driven by the need to reduce greenhouse gas emissions, improve air quality, and achieve sustainable transportation. This study presents a comprehensive review of emerging trends in EV technology and their potential impacts on transportation and society. The study explores various areas of innovation in the field of EVs, including battery technology, wireless charging, vehicle-to-grid (V2G) communication, lightweight materials, autonomous driving, vehicle-to-everything (V2X) communication, circular economy approaches, advanced charging infrastructure, energy storage, and social and behavioral innovations. This study reveals that battery technology advancements are driving the adoption of EVs. Lithium-ion batteries have improved energy density, charging speed, and lifespan. Alternative battery technologies, like solid-state and lithium-sulfur batteries, show promise for even higher energy density, faster charging, and increased safety. Wireless charging technology is emerging, with high-power and high-efficiency systems potentially addressing concerns about charging infrastructure and range anxiety. V2G communication allows EVs to serve as mobile energy storage units, contributing to grid stability, load balancing, and renewable energy integration. Lightweight materials, like advanced composites and lightweight metals, can significantly reduce the weight of EVs, improving energy efficiency and overall performance. Autonomous driving technologies have the potential to improve safety, reduce congestion, and optimize energy use. V2X communication enables a wide range of applications, like intelligent traffic management and enhanced safety features. Circular economy approaches, including designing EVs with recyclability and reusability in mind, using recycled materials in manufacturing, and developing end-of-life recycling and repurposing strategies, can minimize the environmental impact of EVs and contribute to their sustainability

A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

The energy management system is executed in microgrids for optimal integration of distributed energy resources (DERs) into the power distribution grids. To this end, various strategies have been more focused on cost reduction, whereas effectively both economic and technical indices/factors have to be considered simultaneously. Therefore, in this paper, a two-layer optimization model is proposed to minimize the operation costs, voltage fluctuations, and power losses of smart microgrids. In the outer-layer, the size and capacity of DERs including renewable energy sources (RES), electric vehicles (EV) charging stations and energy storage systems (ESS), are obtained simultaneously. The inner-layer corresponds to the scheduled operation of EVs and ESSs using an integrated coordination model (ICM). The ICM is a fuzzy interface that has been adopted to address the multi-objectivity of the cost function developed based on hourly demand response, state of charges of EVs and ESS, and electricity price. Demand response is implemented in the ICM to investigate the effect of time-of-use electricity prices on optimal energy management. To solve the optimization problem and load-flow equations, hybrid genetic algorithm (GA)-particle swarm optimization (PSO) and backward-forward sweep algorithms are deployed, respectively. One-day simulation results confirm that the proposed model can reduce the power loss, voltage fluctuations and electricity supply cost by 51%, 40.77%, and 55.21%, respectively, which can considerably improve power system stability and energy efficiency.</jats:p

Electric vehicle as a service (EVaaS):applications, challenges and enablers

Under the vehicle-to-grid (V2G) concept, electric vehicles (EVs) can be deployed as loads to absorb excess production or as distributed energy resources to supply part of their stored energy back to the grid. This paper overviews the technologies, technical components and system requirements needed for EV deployment. Electric vehicle as a service (EVaaS) exploits V2G technology to develop a system where suitable EVs within the distribution network are chosen individually or in aggregate to exchange energy with the grid, individual customers or both. The EVaaS framework is introduced, and interactions among EVaaS subsystems such as EV batteries, charging stations, loads and advanced metering infrastructure are studied. The communication infrastructure and processing facilities that enable data and information exchange between EVs and the grid are reviewed. Different strategies for EV charging/discharging and their impact on the distribution grid are reviewed. Several market designs that incentivize energy trading in V2G environments are discussed. The benefits of V2G are studied from the perspectives of ancillary services, supporting of renewables and the environment. The challenges to V2G are studied with respect to battery degradation, energy conversion losses and effects on distribution system

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

A Framework for Multifunctional Green Infrastructure Investment in Camden, NJ

This study demonstrates a decision-support framework for planning Green Infrastructure (GI) systems that maximize urban ecosystem services in Camden, NJ. Seven key ecosystem services are evaluated (urban agriculture expansion, combined sewer overflow reduction, heat island reduction, flooding reduction, capacity building/green jobs expansion, fitness expansion, and stress reduction), to produce a normalized value for each service for each drainage sub-basin within the city. Gaps in ecosystem services are then mapped and utilized to geographically prioritize different kinds of multifunctional GI. Conceptual designs are developed for four site typologies: parks, schools, vacant lots, and brownfield sites. For one demonstration site, additional analysis is presented on urban engagement, life cycle cost reduction, and new sources of funding. What results is an integrated, long-term vision where multifunctional GI systems can be readily customized to meet multiple needs within urban communities. This study provides a portable and replicable framework for leveraging the regulatory requirement to manage stormwater to meet broader urban revitalization goals, all through a decentralized network of green infrastructure assets

The New AC/DC Hybrid Microgrid Paradigm: Analysis and Operational Control

AC/DC hybrid microgrids (HMGs) represent a promising architecture that allows the hosting of innovative dc energy resources, such as renewables, and modern dc loads, such as electric vehicles, thereby reducing the number of conversion stages and offering other technical and cost benefits. Such advantages have prompted power distribution planners to begin investigating the possibility of hybridizing existing ac grids and designing new ac/dc hybrid clusters, referred to as microgrids, as a step toward an envisioned smart grid that incorporates multiple ac/dc microgrids characterized by "plug-and-play" features. Despite their potential, when either islanded or interfaced with the main grid, HMGs create challenges with respect to system operation and control, such as difficulties related to precise power sharing, voltage stability during a contingency, the control and management of power transfer through the interlinking converters (ICs), and the coordination of local distributed energy resources (DERs) with the hosting main grid. An understanding of HMGs and their operational philosophy during islanding will assuredly pave the way toward the realization of a future smart grid that includes a plug-and-play feature and will alleviate any operational challenges. However, the planning and operation of such islanded and hybrid systems are reliant on a powerful and efficient power flow analysis tool. To this end, this thesis introduces a novel unified, generic, flexible power flow algorithm for islanded/isolated HMGs. The developed algorithm is generic in the sense that it includes consideration of the unique characteristics of islanded HMGs: a variety of possible topologies, droop controllability of the DERs and bidirectionality of the power flow in the ICs. The new power flow formulation is flexible and permits the easy incorporation of any changes in the DER operating modes and the IC control schemes. The developed algorithm was validated against a detailed time-domain model and applied for the analysis of a variety of operational and control aspects in islanded HMGs, including the problem of imprecise power sharing and droop control of the ICs. The proposed load flow program can form the basis of and provide direction for further studies of islanded HMGs. This thesis also presents a deeper look at the problem of inaccurate active and reactive power sharing in islanded droop-based HMGs and proposes a unified and universal power sharing scheme that can simultaneously ensure precise power sharing in both ac and dc subgrids. Test results demonstrate the capability of the developed scheme with respect to achieving exact power sharing not only among DERs in proportion to their ratings but also among ICs that interface adjacent ac and dc microgrids. The developed unified power sharing scheme would assist system planners with the effective design of droop characteristics for DERs and ICs, which would result in enhancements such as the avoidance of converter overloading and the achievement of precise load sharing. Another operational aspect that was thoroughly investigated for this thesis is the possibility of voltage instability/collapse in islanded HMGs during contingencies. This research unveiled the possibility of voltage instability in HMGs that include constant power loads and a mix of synchronous-based and converter-based generating units. As indicated by the voltage stability analysis presented here, despite the fact that healthy microgrids have far-reaching loadability boundaries, the voltage at some ac/dc load buses can unexpectedly collapse during abnormal conditions. The analysis also revealed that fine tuning the droop characteristics of DERs and ICs can enlarge the voltage stability margin and safeguard the entire microgrid against collapse during contingencies, all without the sacrifice of a single load. A final component of this thesis is the proposal of a two-stage stochastic centralized dispatch scheme for ac/dc hybrid distribution systems. The developed dispatch scheme coordinates the operation of a variety of DERs, such as distributed generators and energy storage systems. It also ensures the coordinated charging of electric vehicles and models the degradation of their batteries that occurs due to the vehicle-to-grid action. The energy coordination problem has been formulated as a two-stage day-ahead resource scheduling problem: the intermittent supply; the variable demand, which includes electric vehicles; and the fluctuating real-time energy price are all modelled as random variables. The first stage produces day-ahead dispatch decisions for the dispatchable DG units. For a set of possible scenarios over the next 24 h, the second stage determines appropriate corrective decisions with respect to the import/export schedule, storage charging/discharging cycles, and electric vehicle charging/discharging patterns. The simulation results demonstrate the effectiveness of the developed scheme for optimally coordinating the various components of future ac/dc hybrid smart grids. Despite its substantial merits and value as a host for ac and dc technologies, a smart grid with HMGs creates previously unexperienced operational challenges for system planners and operators. The work completed for this thesis could help pave the way for the realization of ac/dc hybrid smart grids in years to come

PV Charging and Storage for Electric Vehicles

Electric vehicles are only ‘green’ as long as the source of electricity is ‘green’ as well. At the same time, renewable power production suffers from diurnal and seasonal variations, creating the need for energy storage technology. Moreover, overloading and voltage problems are expected in the distributed network due to the high penetration of distributed generation and increased power demand from the charging of electric vehicles. The energy and mobility transition hence calls for novel technological innovations in the field of sustainable electric mobility powered from renewable energy. This Special Issue focuses on recent advances in technology for PV charging and storage for electric vehicles