Integration of Massive Plug-in Hybrid Electric Vehicles into Power Distribution Systems: Modeling, Optimization, and Impact Analysis

With the development of vehicle-to-grid (V2G) technology, it is highly promising to use plug-in hybrid electric vehicles (PHEVs) as a new form of distributed energy resources. However, the uncertainties in the power market and the conflicts among different stakeholders make the integration of PHEVs a highly challenging task. Moreover, the integration of PHEVs may lead to negative effects on the power grid performance if the PHEV fleets are not properly managed. This dissertation studies various aspects of the integration of PHEVs into power distribution systems, including the PHEV load demand modeling, smart charging algorithms, frequency regulation, reliability-differentiated service, charging navigation, and adequacy assessment of power distribution systems. This dissertation presents a comprehensive methodology for modeling the load demand of PHEVs. Based on this stochastic model of PHEV, a two-layer evolution strategy particle swarm optimization (ESPSO) algorithm is proposed to integrate PHEVs into a residential distribution grid. This dissertation also develops an innovative load frequency control system, and proposes a hierarchical game framework for PHEVs to optimize their charging process and participate in frequency regulation simultaneously. The potential of using PHEVs to enable reliability-differentiated service in residential distribution grids has been investigated in this dissertation. Further, an integrated electric vehicle (EV) charging navigation framework has been proposed in this dissertation which takes into consideration the impacts from both the power system and transportation system. Finally, this dissertation proposes a comprehensive framework for adequacy evaluation of power distribution networks with PHEVs penetration. This dissertation provides innovative, viable business models for enabling the integration of massive PHEVs into the power grid. It helps evolve the current power grid into a more reliable and efficient system

Zoning and occupancy-moderation for residential space-conditioning under demand-driven electricity pricing

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 138-144).Occupancy-moderated zonal space-conditioning (OZS) refers to the partitioning of a residence into different zones and independently operating the space-conditioning equipment of each zone based on its occupancy. OZS remains largely unexplored in spite of its potential to reduce the cost of space-conditioning. Despite the excitement surrounding cloud-connected devices like mobile phones and tablet computers, the benefit of using them to aid energy management agents (EMAs) in reducing space-conditioning cost under demand-driven pricing of electricity is not well understood. We develop a novel framework and the algorithms to enable an EMA to implement OZS for multiple inhabitants under a demand-driven pricing scheme for electricity. We further investigate the effects that influencing factors can have on the effectiveness of OZS under different scenarios using Monte Carlo simulations. The simulation results demonstrate that OZS is realizable on a simple home computer and can achieve significant space-conditioning cost reductions in practice. In our studies, both the financial operating cost of space-conditioning and the cost associated with discomfort are included in a single aggregate cost function. We then expand the simulations to study the cost reduction that is achievable when using cloud-connected devices to provide remote schedule updates to an EMA. This part of the study reveals that reduction in space-conditioning cost is appreciable if a working resident remotely updates an EMA at mid-day of his return time in the evening. In addition, we establish a directly proportional relationship between the level of space-conditioning cost reduction achievable and the variance of return time. Based on the research findings, we further offer recommendations and ideas for future research on the use of OZS and remote schedule updates to different stakeholders like policy-makers and homeowners.by Woei Ling Leow.Ph.D

Management of Distributed Energy Storage Systems for Provisioning of Power Network Services

Because of environmentally friendly reasons and advanced technological development, a significant number of renewable energy sources (RESs) have been integrated into existing power networks. The increase in penetration and the uneven allocation of the RESs and load demands can lead to power quality issues and system instability in the power networks. Moreover, high penetration of the RESs can also cause low inertia due to a lack of rotational machines, leading to frequency instability. Consequently, the resilience, stability, and power quality of the power networks become exacerbated. This thesis proposes and develops new strategies for energy storage (ES) systems distributed in power networks for compensating for unbalanced active powers and supply-demand mismatches and improving power quality while taking the constraints of the ES into consideration. The thesis is mainly divided into two parts. In the first part, unbalanced active powers and supply-demand mismatch, caused by uneven allocation and distribution of rooftop PV units and load demands, are compensated by employing the distributed ES systems using novel frameworks based on distributed control systems and deep reinforcement learning approaches. There have been limited studies using distributed battery ES systems to mitigate the unbalanced active powers in three-phase four-wire and grounded power networks. Distributed control strategies are proposed to compensate for the unbalanced conditions. To group households in the same phase into the same cluster, algorithms based on feature states and labelled phase data are applied. Within each cluster, distributed dynamic active power balancing strategies are developed to control phase active powers to be close to the reference average phase power. Thus, phase active powers become balanced. To alleviate the supply-demand mismatch caused by high PV generation, a distributed active power control system is developed. The strategy consists of supply-demand mismatch and battery SoC balancing. Control parameters are designed by considering Hurwitz matrices and Lyapunov theory. The distributed ES systems can minimise the total mismatch of power generation and consumption so that reverse power flowing back to the main is decreased. Thus, voltage rise and voltage fluctuation are reduced. Furthermore, as a model-free approach, new frameworks based on Markov decision processes and Markov games are developed to compensate for unbalanced active powers. The frameworks require only proper design of states, action and reward functions, training, and testing with real data of PV generations and load demands. Dynamic models and control parameter designs are no longer required. The developed frameworks are then solved using the DDPG and MADDPG algorithms. In the second part, the distributed ES systems are employed to improve frequency, inertia, voltage, and active power allocation in both islanded AC and DC microgrids by novel decentralized control strategies. In an islanded DC datacentre microgrid, a novel decentralized control of heterogeneous ES systems is proposed. High- and low frequency components of datacentre loads are shared by ultracapacitors and batteries using virtual capacitive and virtual resistance droop controllers, respectively. A decentralized SoC balancing control is proposed to balance battery SoCs to a common value. The stability model ensures the ES devices operate within predefined limits. In an isolated AC microgrid, decentralized frequency control of distributed battery ES systems is proposed. The strategy includes adaptive frequency droop control based on current battery SoCs, virtual inertia control to improve frequency nadir and frequency restoration control to restore system frequency to its nominal value without being dependent on communication infrastructure. A small-signal model of the proposed strategy is developed for calculating control parameters. The proposed strategies in this thesis are verified using MATLAB/Simulink with Reinforcement Learning and Deep Learning Toolboxes and RTDS Technologies' real-time digital simulator with accurate power networks, switching levels of power electronic converters, and a nonlinear battery model

Impact analysis of domestic building energy demand and electric vehicles charging on low voltage distribution networks

There are lots of worldwide attentions paid to the greenhouse gas (GHG) emissions, which can result in serious climate change issues. Hence, finding ways to save energy and GHG emissions become important. Moreover, the energy demand from the residential sector accounts for around 30% of the total energy demand, which shows that it can be a potential way to contribute to reducing GHG emissions. Furthermore, the electric vehicle (EV) is going to play an important role in reducing GHG emissions, however, with the growth of EVs in the community, the low voltage (LV) distribution network (DN) will be affected directly. Therefore, investigating reducing the energy demand from domestic dwellings and minimising the impacts of EVs charging on dwellings and DNs become significantly important. Firstly, the energy demand of a domestic dwelling is modelled in the EnergyPlus. Potential energy savings from building material, photovoltaic/thermal (PV/T) panels, LED lights and occupants’ behaviours are analysed and improving the energy efficiency is investigated. Then, coupling by EnergyPlus and Matlab through Building Control Virtual Test Bed (BCVTB) interface, the Dwelling-EV Integration Model (DEIM) is established as the foundation for impact analysis of EVs charging on the energy demand in the dwellings and DNs. An individual domestic dwelling is modelled. Then load-shifting method and the battery storage energy system (BSES) are used to reduce the peak power demand in the dwelling, which are proved to be feasible and be able to smooth the daily power demand profile. III Further, in order to solve the issues caused by EVs charging, such as voltage drop, power loss etc. on DN, the impacts of EVs charging on the LV DN are analysed based on a typical network, and the concept of dwelling’s micro-grid, consisting of the PV and a battery storage system, is proposed. The dwelling’s micro-grid is used to minimise the impacts of EVs charging, and it is proved to be useful for reducing the voltage drop, the voltage disqualification rate and the power loss. Finally, an ordered charging strategy (OCS) of EVs using the expected power is proposed to minimise unbalanced load and increasing unqualified voltage caused by EVs charging. Additionally, the OCS using the expected power is combined with the BSES to further reduce the impacts. This method not only reduces the capacity of BSES, makes the voltage of DN qualify, but also smoothes the daily power demand. It solves the voltage drop caused by random EVs charging and overcomes the disadvantage of the large deployment of EVs on the DN

Book of abstracts of the 2nd International Conference of TEMA: mobilizing projects

Based on its Human Capital and Capacities, the Centre for Mechanical Technology and Automation (TEMA) embraces a mission aiming to contribute to a sustainable industry, with specially focus on the surrounding SMEs, and to the wellbeing of society. Sustainable manufacturing aims to contribute to the development of a sustainable industry by developments and innovations on manufacturing engineering and technologies, to increase productivity, improve products quality and reduce waste in production processes. Technologies for the Wellbeing wishes to contribute to the wellbeing of society by the development of supportive engineering systems focusing on people and their needs and intending to improve their quality of life. TEMA intends to maximize its national and international impact in terms of scientific productivity and its transfer to society by tackling the relevant challenges of our time. TEMA is aware of the major challenges of our days, not only confined to scientific issues but also the societal ones, (a strategic pillar of the Horizon 2020 program), at the same time placing an effort to have its research disseminated, in high impact journals to the international scientific community. (...)publishe

DYNAMIC DISCRETE CHOICE MODELS FOR CAR OWNERSHIP MODELING

With the continuous and rapid changes in modern societies, such as the introduction of advanced technologies, aggressive marketing strategies and innovative policies, it is more and more recognized by researchers in various disciplines from social science to economics that choice situations take place in a dynamic environment and that strong interdependencies exist among decisions made at different points in time. The increasing concerns about climate change, the development of high-tech vehicles, and the extensive applications of demand models in economics and transportation areas motivate this research on vehicle ownership based on disaggregate discrete choices. Over the next five to ten years, dramatic changes in the automotive marketplace are expected to occur and new opportunities might arise. Therefore, a methodology to model dynamic vehicle ownership choices is formulated and implemented in this dissertation for short and medium-term planning. In the proposed dynamic model framework, the car ownership problem is described as a regenerative optimal stopping problem; when a purchase is made, the current vehicle state (vehicle age, mileage driven, etc.) is regenerated. The model allows the estimation of the probability of buying a new vehicle or postponing this decision; if the decision to buy is made, the model further investigates the vehicle type choices. Dynamic models explicitly account for consumers' expectations of future vehicle quality or market evolution, arising endogenously from their purchase decisions. Both static and dynamic formulations are applied first to simulated data in order to test the ability to recover the true underlying parameters of the synthetic population. Results obtained attest that the dynamic model outperforms the static MNL in terms of goodness of fit, parameters bias and predictive power. In particular, it is found that MNL captures the general trends in choice probabilities, but fails to recover peaks in demand and behavioral changes due to rapidly evolving external conditions. The extension to a real case study required a data collection effort. A preliminary pilot survey was designed and executed in the State of Maryland in fall 2010; the survey was self-administrated and web-based. Choices were made under the hypothesis that an interval time period of six months passed from a decision to the successive decision and choices over a hypothetical time period of six years were recorded. Finally, the application of dynamic discrete choice models to vehicle ownership decisions in the context of the introduction of new technology is proposed. Results from the real case study confirm our initial expectations, as the model fit is significantly superior to the fit of the static model

Wind Power

This book is the result of inspirations and contributions from many researchers of different fields. A wide verity of research results are merged together to make this book useful for students and researchers who will take contribution for further development of the existing technology. I hope you will enjoy the book, so that my effort to bringing it together for you will be successful. In my capacity, as the Editor of this book, I would like to thanks and appreciate the chapter authors, who ensured the quality of the material as well as submitting their best works. Most of the results presented in to the book have already been published on international journals and appreciated in many international conferences

Efficient operation of recharging infrastructure for the accommodation of electric vehicles: a demand driven approach

Large deployment and adoption of electric vehicles in the forthcoming years can have significant environmental impact, like mitigation of climate change and reduction of traffic-induced air pollutants. At the same time, it can strain power network operations, demanding effective load management strategies to deal with induced charging demand. One of the biggest challenges is the complexity that electric vehicle (EV) recharging adds to the power system and the inability of the existing grid to cope with the extra burden. Charging coordination should provide individual EV drivers with their requested energy amount and at the same time, it should optimise the allocation of charging events in order to avoid disruptions at the electricity distribution level. This problem could be solved with the introduction of an intermediate agent, known as the aggregator or the charging service provider (CSP). Considering out-of-home charging infrastructure, an additional role for the CSP would be to maximise revenue for parking operators. This thesis contributes to the wider literature of electro-mobility and its effects on power networks with the introduction of a choice-based revenue management method. This approach explicitly treats charging demand since it allows the integration of a decentralised control method with a discrete choice model that captures the preferences of EV drivers. The sensitivities to the joint charging/parking attributes that characterise the demand side have been estimated with EV-PLACE, an online administered stated preference survey. The choice-modelling framework assesses simultaneously out-of-home charging behaviour with scheduling and parking decisions. Also, survey participants are presented with objective probabilities for fluctuations in future prices so that their response to dynamic pricing is investigated. Empirical estimates provide insights into the value that individuals place to the various attributes of the services that are offered by the CSP. The optimisation of operations for recharging infrastructure is evaluated with SOCSim, a micro-simulation framework that is based on activity patterns of London residents. Sensitivity analyses are performed to examine the structural properties of the model and its benefits compared to an uncontrolled scenario are highlighted. The application proposed in this research is practice-ready and recommendations are given to CSPs for its full-scale implementation.Open Acces