Integrated Thermal and Energy Management of Connected Hybrid Electric Vehicles Using Deep Reinforcement Learning

The climate-adaptive energy management system holds promising potential for harnessing the concealed energy-saving capabilities of connected plug-in hybrid electric vehicles. This research focuses on exploring the synergistic effects of artificial intelligence control and traffic preview to enhance the performance of the energy management system (EMS). A high-fidelity model of a multi-mode connected PHEV is calibrated using experimental data as a foundation. Subsequently, a model-free multistate deep reinforcement learning (DRL) algorithm is proposed to develop the integrated thermal and energy management (ITEM) system, incorporating features of engine smart warm-up and engine-assisted heating for cold climate conditions. The optimality and adaptability of the proposed system is evaluated through both offline tests and online hardware-in-the-loop tests, encompassing a homologation driving cycle and a real-world driving cycle in China with real-time traffic data. The results demonstrate that ITEM achieves a close to dynamic programming fuel economy performance with a margin of 93.7%, while reducing fuel consumption ranging from 2.2% to 9.6% as ambient temperature decreases from 15°C to -15°C in comparison to state-of-the-art DRL-based EMS solutions

A Trip Planning-Assisted Energy Management System for Connected PHEVs: Evaluation and Enhancement

The built-in Energy Management System (EMS) of Plug-in Hybrid Electric Vehicles (PHEVs) plays an important role in the fuel efficiency of these vehicles. Recently, it has been revealed that prior knowledge of the upcoming trip can assist EMS to enhance the distribution of power between the energy sources, i.e. the engine and the motor-generators used in PHEVs, resulting in lower fuel consumptions. This dissertation intends to further investigate on a Trip Planning-assisted EMS (TP-assisted EMS), by studying its feasibility for online implementation, and evaluating its performance and robustness with respect to the trip data uncertainties in various practical scenarios, to ultimately answer this question: Does the TP-assisted EMS function as a reliable system for PHEVs which can outperform conventional methods? This research starts with improving upon an existing Trip Planning module with an emphasis on its online integration with the EMS module. In particular, the power-balance model of PHEVs is introduced, which is computationally inexpensive and yet adequately accurate to be used for the optimizations involved in the Trip Planning module. To speed up the optimizations, the use of Particle Swarm Optimization (PSO) algorithm is suggested. These modifications result in the reduction of computational time, making TP-assisted EMS module suitable for online implementations. Once the TP-assisted EMS module has been integrated with a high-fidelity model of the baseline PHEV, namely, 2013 Toyota Prius PHEV, its performance and sensitivity/robustness have been extensively studied through Monte Carlo simulations, where numerous samples of standard as well as real-world drive cycles have been tested. However, in order to use these data for Model-in-the-Loop (MIL) and Hardware-in-the-Loop (HIL) tests, a Micro-trip Generator block has been developed. This block automatically segments the drive cycles, similar to the way that trip information is obtained in practice, making the simulation samples compatible with the Trip Planning module. Statistical analyses of the simulation results show that the TP-assisted EMS is a superior controller compared to the conventional EMS strategies. Moreover, these simulations present one of the first sensitivity analyses that have been performed in the context of TP-assisted EMS for PHEVs, showing that this system is robust despite the existence of random disturbances and meanwhile has low sensitivity against variations of the design parameters

Predictive Energy Management in Connected Vehicles: Utilizing Route Information Preview for Energy Saving

This dissertation formulates algorithms that use preview information of road terrain and traffic flow for reducing energy use and emissions of modern vehicles with conventional or hybrid powertrains. Energy crisis, long term energy deficit, and more restrictive environmental protection policies require developing more efficient and cleaner vehicle powertrain systems. An alternative to making advanced technology engines or electrifying the vehicle powertrain is utilizing ambient terrain and traffic information in the energy management of vehicles, a topic which has not been emphasized in the past. Today\u27s advances in vehicular telematics and advances in GIS (Geographic Information System), GPS (Global Positioning Systems), ITS (Intelligent Transportation Systems), V2V (Vehicle to Vehicle) communication, and VII (Vehicle Infrastructure Integration ) create more opportunities for predicting a vehicle\u27s trip information with details such as the future road grade, the distance to the destination, speed constraints imposed by the traffic flow, which all can be utilized for better vehicle energy management. Optimal or near optimal decision-making based on this available information requires optimal control methods, whose fundamental theories were well studied in the past but are not directly applicable due to the complexity of real problems and uncertainty in the available preview information. This dissertation proposes the use of optimal control theories and tools including Pontryagin minimum principle, Dynamic Programming (DP) which is a numerical realization of Bellman\u27s principle of optimality, and Model Predictive Control (MPC) in the optimization-based control of hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and conventional vehicles based on preview of future route information. The dissertation includes three parts introduced as follows: First, the energy saving benefit in HEV energy management by previewing future terrain information and applying optimal control methods is explored. The potential gain in fuel economy is evaluated, if road grade information is integrated in energy management of hybrid vehicles. Real-world road geometry information is taken into account in power management decisions by using both Dynamic Programming (DP) and a standard Equivalent Consumption Minimization Strategy (ECMS), derived using Pontryagin minimum principle. Secondly, the contribution of different levels of preview to energy management of plug-in hybrid vehicles (PHEVs) is studied. The gains to fuel economy of plug-in hybrid vehicles with availability of velocity and terrain preview and knowledge of distance to the next charging station are investigated. Access to future driving information is classified into full, partial, or no future information and energy management strategies for real-time implementation with partial future preview are proposed. ECMS as well as Dynamic Programming (DP) is systematically utilized to handle the resulting optimal control problems with different levels of preview. We also study the benefit of future traffic flow information preview in improving the fuel economy of conventional vehicles by predictive control methods. According to the time-scale of the preview information and its importance to the driver, the energy optimization problem is decomposed into different levels. In the microscopic level, a model predictive controller as well as a car following model is employed for predictive adaptive cruise control by stochastically forecasting the driving behavior of the lead car. In the macroscopic level, we propose to incorporate the estimated macroscopic future traffic flow information and optimize the cost-to-go by utilizing a two-dimension Dynamic Programming (2D-DP). The algorithm yields the optimal trip velocity as the reference velocity for the driver or a low level controller to follow. Through the study, we show that energy use and emissions can be reduced considerably by using preview route information. The methodologies discussed in this dissertation provide an alternative mean for the automotive industry to develop more efficient and environmentally friendly vehicles by relying mostly on software and information and with minimal hardware investments

Analyzing the Improvements of Energy Management Systems for Hybrid Electric Vehicles Using a Systematic Literature Review: How Far Are These Controls from Rule-Based Controls Used in Commercial Vehicles?

Featured Application This work is useful for researchers interested in the study of energy management systems for hybrid electric vehicles. In addition, it is interesting for institutions related to the market of this type of vehicle. The hybridization of vehicles is a viable step toward overcoming the challenge of the reduction of emissions related to road transport all over the world. To take advantage of the emission reduction potential of hybrid electric vehicles (HEVs), the appropriate design of their energy management systems (EMSs) to control the power flow between the engine and the battery is essential. This work presents a systematic literature review (SLR) of the more recent works that developed EMSs for HEVs. The review is carried out subject to the following idea: although the development of novel EMSs that seek the optimum performance of HEVs is booming, in the real world, HEVs continue to rely on well-known rule-based (RB) strategies. The contribution of this work is to present a quantitative comparison of the works selected. Since several studies do not provide results of their models against commercial RB strategies, it is proposed, as another contribution, to complete their results using simulations. From these results, it is concluded that the improvement of the analyzed EMSs ranges roughly between 5% and 10% with regard to commercial RB EMSs; in comparison to the optimum, the analyzed EMSs are nearer to the optimum than commercial RB EMSs

Optimisation-based Approaches for Evaluating the Aggregation of EVs and PVs in Unbalanced Low-Voltage Networks

214 p.In the near future, it is expected that the distribution system operators face different technical challenges derived from the massification of electric mobility and renewable energy sources in the low voltage networks. The purpose of this thesis is to define different smart coordination strategies among different agents involved in the low voltage networks such as the distribution system operator, the aggregators and the end-users when significant penetration levels of these resources are adopted. New models for representing the uncertainty of the photovoltaic output power and the connection of the electric vehicles are introduced. A new energy boundary model for representing the flexibility of electric vehicles is also proposed. In combination with the above models, four optimisation models were proposed as coordination strategies into three different approaches: individual, population, and hybrid. The first model was defined at the aggregator level, whereas the other models were proposed at the distribution system operator level. Complementary experimental cases about the proposed optimisation model in the individual-based approach and the quadratic formulation in the hybrid approach for the PV power curtailment were carried out to test its response in real-time. Simulations results demonstrated that the proposed coordination strategies could effectively manage critical insertion levels of electric vehicles and photovoltaic units in unbalanced low voltage networks

Data-driven analyses of future electric personal mobility

Personal mobility is moving towards the era of electrification. Adopting electric vehicles (EV) is widely regarded as an effective solution to energy crisis and air pollution. Many automakers have announced their roadmap to electrification in the next 1-2 decades. At the same time, limited electric range and insufficient charging infrastructure are still obstacles to EV large-scale adoption. However, with the emerging technologies of ride-hailing, connected vehicles, and autonomous vehicles, these obstacles are being solved effectively, and the EV market penetration is expected to increase significantly. Among the many kinds of electric mobility, electric taxis and personal battery electric vehicles (BEV) especially are gaining increasing popularity and acceptance among customers. This dissertation studies the future challenges of electric taxis and personal BEVs. First, this dissertation examines the BEV feasibility from the spatial-temporal travel patterns of taxis. The BEV feasibility of a taxi is quantified as the percentage of occupied trips that can be completed by BEVs during a year. It is found that taxis with certain characteristics are more suitable for switching to BEVs, such as fewer daily shifts, shorter daily driving distance, and higher likelihood to dwell at the borough of Manhattan. Second, we model and simulate the operations of electric autonomous vehicle (EAV) taxis. EAV taxis are dispatched by the optimization-based model and the neural network-based model. The neural network dispatch model is able to learn the optimal dispatch strategies and runs much faster. The EAV taxis dispatched by the neural network-based model can improve operational efficiency in term of less empty travel distance and smaller fleet size. Third, this dissertation proposes a cumulative prospect theory (CPT) based modeling framework to describe charging behavior of BEV drivers. A BEV mass-market scenario is constructed using 2017 National Household Travel Survey data. By applying the CPT-based charging behavior model, we examine the battery state-of-charge when drivers decide to charge their vehicles, charging timing and locations, and charging power demand profiles under the mass-market scenario. In addition, sensitivity analyses with respect to drivers’ risk attitude and public charger network coverage are conducted

Transportation Behavioral Data and Climate Change

In 2017, transportation became the largest single source of greenhouse gas emissions from the United States. Globally, the 2014 Intergovernmental Panel on Climate Change report found that, without far more aggressive policies, “transportation emissions could increase at a faster rate than emissions from other energy end use sectors” reaching 12 Gt CO2-eq/year by 2050 (Sims et al., 2014). The overwhelming challenge of combatting these emissions is made far more difficult by the fact that so little is known about transportation behavior. To use a cliché – if we can’t measure it, we can’t manage it. And transportation must be managed if we are to avoid the most catastrophic consequences of climate change. In this dissertation, I propose that better data collection is necessary to achieve reduction of transportation-related emissions. Happily, advances in technology make this more feasible today than at any time in the past. The costs of massive computing resources have gone down, the world is swarming with mobile devices like smartphones and connected cars collecting massive (if messy) amounts of data, and new techniques in data science and machine learning have emerged to help get clean answers out of all that data in a privacy-appropriate manner. In some cases, these new techniques will displace older ones. In other cases, the old ways have inherent advantages. In other cases yet, fusing new and old techniques will yield the most productive results.In Chapter One, I lay out a framework to organize the types of transportation behavioral data that must be collected regularly to adequately measure and manage transportation’s impact on climate. This builds on classic climate impact frameworks, adapting them to the particular measurement challenges presented by transportation. In Chapter Two, I provide a history of US transportation data collection since World War II as well as a review of traditional, modern, and emerging transportation data collection technologies. I then map each technology onto each behavioral data collection need identified in Chapter One, matching each behavior to the best respective data collection technique.Chapters Three and Four provides an example of analysis done using the traditional data collection techniques, notably Household and Commercial Travel Surveys, to explore changes in PMT related to shopping and retail freight since 1969, as well as freight for fuel transportation. They demonstrate and take advantage of the key benefits of traditional techniques: that they go back in history, that they collect clearly stated trip purposes, vehicle occupancies, demographics (including gender, an important demographic but particularly difficult to deduce from the new data collection sources), trip distances, chaining behavior, commodities logged, and more. As it turns out, these benefits are critical: the historical trends of the past 40 years allow behavioral insight that would not have been possible with a shorter term study, and gender dynamics are key to understanding the behaviors at hand. However, the analysis in Chapters Three and Four also highlights some of the key limitations of survey-based analysis. The fact that data was only collected every five to ten years severely limits the analysis, such as limiting the exploration that can be done on the impacts of the Great Recession. In addition, fallibilities in human memory are especially pronounced in short trips, trip chains, and non-work related trips, all of particular importance to this study. Chapters Five lays out theoretically, and then Chapter Six demonstrates via case study in India, how personal GPS diary devices can be used to log detailed data about individual trips. It demonstrates the key benefit of this data – highly individualized characteristics. Taking the example of vehicle electrification, this chapter demonstrates two ways such granular data is important: in one example, such data to give feedback to an individual to influence their car buying behavior. In the second, the granularity found with this new data collection techniques reveals the importance of highly localized policy making and emissions modeling based on driving patterns in different cities.Chapter Seven uses the emerging technology of mass amounts of locational data, collected passively via smart phones, to explore how urban density at home and work interacts with total, work-related, and non-work-related miles driven. This demonstrates the great strength of this type of data – massive sample size combined with high spatial granularity and longitudinal data collection. These strengths enable the analysis at statistically meaningful scale of patterns across many geographies, individuals, and times of year. Thus, this data can shed light on questions about the relationship of density and miles travelled which previously have not been answered conclusively due to data constraints

New strategies for the massive introduction of electric vehicles in the operation and planning of Smart Power Systems

En el contexto actual, donde el calentamiento climático es cada vez más importante, existe la necesidad de limitar el consumo de combustibles fósiles. De esta manera, el transporte es uno de los sectores en los que más se están generando cambios en cuanto a la sostenibilidad. El vehículo eléctrico aparece como una solución para este cambio paulatino ya que no contamina localmente y su balance energético es muy eficiente. Así, se han propuesto diferentes programas para el crecimiento del vehículo eléctrico en el parque automotor. Sin embargo, el cambio de vehículos de gasolina por vehículos eléctricos genera desafíos en varios aspectos, como el impacto que ocasiona en la red eléctrica una implantación masiva: caídas de tensión, pérdidas de potencia, problemas con la calidad de la electricidad, inversiones importantes, etc. Se han planteado algunas soluciones en la parte operativa, pero muchas de ellas no han tomado en cuenta la flexibilidad de los usuarios, lo cual es muy importante para la adopción de vehículos eléctricos. De igual manera, en muchas ocasiones, en la literatura se asumen valores para ciertas variables (estado de carga, recorrido, tipo de batería, etc) que pueden cambiar según el comportamiento de cada usuario, lo que modificaría las previsiones realizadas. Finalmente, pocos trabajos han estudiado el impacto de lo vehículos eléctricos en redes eléctricas cuya gestión energética es más complicada debido a su aislamiento de una macrored y con alta penetración de energías renovables, como lo son las microredes. En este marco, esta tesis propone un enfoque novedoso en cuanto a la participación de los usuarios de vehículos eléctricos en la operación y planificación de diferentes sistemas eléctricos de potencia. Esta trata de algunos aspectos principales: disminución de costos de carga, participación en servicios de regulación, aprovechamiento de energía renovable, así como la planificación de generación de una microred incorporando vehículos eléctricos. En una primera parte, se presenta un análisis del vehículo eléctrico y su interacción en sistemas de potencia. De igual manera, se presentan los trabajos de investigación relacionados sobre la temática. En base al análisis de dichos trabajos, esta tesis propone una nueva metodología para optimizar la carga de los vehículos eléctricos. Se propone la participación de un nuevo agente del mercado eléctrico, el Agregador de vehículos eléctricos. Tendrá que gestionar la carga de dichos vehículos en una importante zona, coordinar con el operador de la red para evitar fallos y minimizar los costos de carga. De igual manera, se considera la diferente flexibilidad de los usuarios ya qu podrán escoger una tarifa que se adapte a su disponibilidad en espera y pagar el precio por aquello. La metodología ha sido aplicada a un caso de estudio a la red de Quito, Ecuador. Se propone también la participación en servicios de regulación, necesitando esta vez de usuarios que sean más flexibles al dejar su vehículo conectado a la red. Se considera las tarifas de la parte anterior para realizar dicho estudio. De igual manera, se aplicó al caso de estudio de la red de Quito, Ecuador. Con el crecimiento de las energías renovables, como solar y eólica, la gestión de la electricidad se vuelve más compleja. Con vistas a utilizar el exceso de energía renovable, se propone una tarifa de electricidad que permita al agregador de cargar los diferentes vehículos, tomando en cuenta precios bajos en periodos en donde la energía renovable esté en exceso. Finalmente, se plantea a planificación de generación de una microred que incluya la introducción masiva de vehículos eléctricos. Se aplicó al caso de las islas de Santa Cruz y Baltra, Galápagos, Ecuador, estudiando el impacto en los costos y en el medio ambiente de nueva generación y considerando la variación del precio del diésel debido a su incertidumbre.In the current context, where global warming is growing progressively, it is fundamental to limit fossil fuels consumption. Hence, transportation is one of the sectors in which several changes are occurring considering the sustainability. The Electric Vehicle appears as a new solution for this gradual change; it does not pollute locally and its energy's balance is very efficient. So, different programs have been proposed for the growth of electric vehicles in the automotive market. Nevertheless, the change from internal combustion vehicles to electric vehicles generates challenges in several aspects, such as the impact in the electric grid of a massive introduction of electric vehicles: voltage drops, power losses, quality of electricity issues, important investments, among others. Several solutions in operation have been formulated, but most of them do not consider the flexibility of users, which is a significant criterion for the electric vehicle acquisition. Moreover, in several works of the literature, many variables are assumed (stateof- charge, routes, type of battery, etc), which can vary significantly depending on the user, so also the results. Finally, few works have studied the impact of electric vehicles in very complex power systems, as the ones that are isolated from a macrogrid and because of significant penetration of renewable energy sources, such as microgrids. In this context, this thesis proposes a novel approach to the participation of the electric vehicle users in operation and planning of different electric power systems. This thesis is intended to cover various topics: charging costs decrease, regulation services participation, use of an excess of renewable energy, and the power generation planning of a microgrid considering the introduction of electric vehicles. In a first part, an analysis of the electric vehicle and its interaction with power systems is presented. Additionally, the principal works on the topic are summarized. Based on the analysis of these works, this thesis proposes a new methodology for optimizing the charge of electric vehicles. The participation of a new agent of the electricity market, the electric vehicle aggregator, is proposed. It has the ability to manage the charge of the electric vehicles in a zone with significant size, to coordinate with the grid operator in order to avoid troubles and to minimize charging costs. Furthermore, the different flexibility of electric vehicle users is considered because they will choose an EV customer choice product (CCP) that is adapted to their waiting needs and to the cost they can pay. The methodology has been applied to a case study in the grid of Quito, Ecuador. The participation in regulation services has been also considered to discuss this participation in Ancillary services. The CCPs from the part before are considered for performing such study but assuming more involvement from the electric vehicle users. The case study of Quito, Ecuador, was also studied. With the growth of renewable energies, such as solar and wind, the electricity management becomes more complicated. In order to use the excess of renewable energy, an EV charging mechanism for the aggregator is proposed, based on low prices when the renewable energy is in excess. Finally, a power generation planning for a microgrid is proposed, considering the massive introduction of electric vehicles. The case of the Santa Cruz and Baltra islands, Galapagos, Ecuador are studied to determine its costs and environmental impacts, based on diesel costs sensitivity studies to account for its uncertainty.En el context actual, on l'escalfament climàtic és cada vegada més important, hi ha la necessitat de limitar el consum de combustibles fòssils. El transport és un dels sectors en els quals més s'estan generant canvis pel que fa a la sostenibilitat. El vehicle elèctric apareix com una solució per a aquest canvi gradual ja que no contamina localment i el seu balanç energètic és molt eficient. Així, s'han proposat diferents programes per al creixement del vehicle elèctric al parc automotor. No obstant això, el canvi de vehicles de gasolina per vehicles elèctrics generen desafiaments en diversos aspectes, com son l'impacte que ocasiona a la xarxa elèctrica una implantació massiva: caigudes de tensió, pèrdues de potència, problemes amb la qualitat de l'electricitat, inversions importants, disminució de la vida útil dels transformadors, etc. S'han plantejat algunes solucions a la part operativa, però moltes d'elles no han tingut en compte la flexibilitat dels usuaris, la qual cosa és molt important per a l'adopció de vehicles elèctrics. De la mateixa manera, en moltes ocasions, en la literatura s'assumeixen valors per certes variables (estat de càrrega, recorregut, tipus de bateria, etc.) que poden canviar segons el comportament de cada usuari, el que modificaria les previsions realitzades. Finalment indicar que pocs treballs han estudiat l'impacte del que vehicles elèctrics en xarxes elèctriques on la gestió energètica és més complicada a causa del seu aïllament d'una macroxarxa i amb alta penetració d'energies renovables, com ho són les microxarxes. En aquest marc, aquesta tesi proposa un enfocament nou pel que fa a la participació dels usuaris de vehicles elèctrics en l'operació i planificació de diferents sistemes elèctrics de potència. Aquesta tracta alguns aspectes principals: disminució de costos de càrrega, participació en serveis de regulació, aprofitament d'energia renovable, així com la planificació de generació d'una microxarxa incorporant vehicles elèctrics. En una primera part, es presenta una anàlisi del vehicle elèctric i la seva interacció en sistemes de potència. De la mateixa manera, es presenten els treballs de recerca relacionats sobre la temàtica. En base a l'anàlisi d'aquests treballs, aquesta tesi proposa una nova metodologia per optimitzar la càrrega dels vehicles elèctrics. Es proposa la participació d'un nou agent del mercat elèctric, el Agregador de vehicles elèctrics. Haurà de gestionar la càrrega d'aquests vehicles en una important zona, coordinar amb l'operador de la xarxa per evitar fallades i minimitzar els costos de càrrega. De la mateixa manera es considera la diferent flexibilitat dels usuaris ja que podran escollir una tarifa que s'adapti a la seva disponibilitat en espera i pagar el preu per allò. La metodologia ha estat aplicat a un cas d'estudi a la xarxa de Quito, Equador. Es proposa també la participació en serveis de regulació, necessitant aquest cop d'usuaris que siguin més flexibles en deixar el seu vehicle connectat a la xarxa. Es consideren les tarifes de la part anterior per a realitzar dit estudi. De la mateixa manera, es va aplicar al cas d'estudi de la xarxa de Quito, Equador. Amb el creixement de les energies renovables, com solar i eòlica, la gestió de l'electricitat es torna més complexa. Amb vista a utilitzar l'excés d'energia renovable, es proposa un tarifa d'electricitat que permeti a l'agregador de carregar els diferents vehicles, especialment en períodes on l'energia renovable estigui en excés. Finalment, es planteja la planificació de generació d'una microxarxa que inclogui la introducció massiva de vehicles elèctrics. En concret, es va aplicar al cas de la illes de Santa Cruz i Baltra, Galápagos, Equador, estudiant l'impacte de la nova generació en els costos i en el medi ambient i considerant la variació del preu del dièsel, causa de la seva incertesa.Clairand Gómez, JM. (2018). New strategies for the massive introduction of electric vehicles in the operation and planning of Smart Power Systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/110971TESI

Power Management and Protection in MT-HVDC Systems with the Integration of High-Voltage Charging Stations

Due to the significant increase of the long-distance electricity demand, effective use of Distributed Generations (DGs) in power system, and the challenges in the expansion of new transmission lines to improve the reliability of power system reliability, utilizing Multi-Terminal HVDC (MT-HVDC) technology is an applicable, reliable, and cost-effective solution in hybrid AC/DC grids. MT-HVDC systems have flexibility in terms of independent active and reactive power flow (reversible control) and voltage control. Interconnecting two AC grids with different frequencies and transmitting electricity for the long-distance with low power-losses, which leads to less operation and maintenance costs, can be done through the MT-HVDC systems. The integration of large-scale remote DGs, e.g., wind farms, solar power plants, etc., and high-voltage charging stations for Electric Vehicles (EVs) into the power grid have different issues, such as economic, technical, and environmental challenges of transmission and network expansion/operation of both AC and DC grids. In details, damping oscillation, voltage support at different buses, operation of grid-connected inverters to the off-shore and on-shore AC systems, integrating of existing converter stations in MT-HVDC systems without major changes in control system, evaluation of communication infrastructure and also reactive power and filtering units’ requirements in MT-HVDC systems are the technical challenges in this technology. Therefore, a reliable MT-HVDC system can be a possible mean of resolving all the above-mentioned issues. MT-HVDC systems need a control system that can bring stability to the power system during a certain period of the operation/planning time while providing effective and robust electricity. This thesis presents an improved droop-based control strategy for the active and reactive power-sharing on the large-scale MT-HVDC systems integrating different types of AC grids considering the operation of the hybrid AC/DC grids under normal/contingency conditions. The main objective of the proposed strategy is to select the best parameters of the local terminal controllers at the site of each converter station (as the primary controller) and a central master controller (supervisory controller) to control the Power Flow (PF) and balance the instantaneous power in MT-HVDC systems. In this work, (1) various control strategies of MT-HVDC systems are investigated to propose (2) an improved droop-based power-sharing strategy of MT-HVDC systems while the loads (e.g., high-voltage charging stations) in power systems have significant changes, to improve the frequency response and accuracy of the PF control, (3) a new topology of a fast proactive Hybrid DC Circuit Breaker (HDCCB) to isolate the DC faults in MT-HVDC grids in case of fault current interruption. The results from this research work would include supporting energy adequacy, increasing renewable energy penetration, and minimizing losses when maintaining system integrity and reliability. The proposed strategies are evaluated on different systems, and various case scenarios are applied to demonstrate their feasibility and robustness. The validation processes are performed using MATLAB software for programming, and PSCAD/EMTDC and MATLAB/Simulink for simulation