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

Quality of Service and Associated Communication Infrastructure for Electric Vehicles †

Transportation electrification is pivotal for achieving energy security and emission reduction goals. Electric vehicles (EVs) are at the forefront of this transition, driving the development of new EV technologies and infrastructure. As this trend gains momentum, it becomes essential to enhance the quality of service (QoS) of EVs to encourage their widespread adoption. This paper has been structured with two primary aims to effectively address the above timely technological needs. Firstly, it comprehensively reviews the various QoS factors that influence EVs’ performance and the user experience. Delving into these factors provides valuable insights into how the QoS can be improved, thereby fostering the increased use of EVs on our roads. In addition to the QoS, this paper also explores recent advancements in communication technologies vital for facilitating in-formation exchanges between EVs and charging stations. Efficient communication systems are crucial for optimizing EV operations and enhancing user experiences. This paper presents expert-level technical details in an easily understandable manner, making it a valuable resource for researchers dedicated to improving the QoS of EV communication systems, who are tirelessly working towards a cleaner, more efficient future in transportation. It consolidates the current knowledge in the field and presents the latest discoveries and developments, offering practical insights for enhancing the QoS in electric transportation. A QoS parameter reference map, a detailed classification of QoS parameters, and a classification of EV communication technology references are some of the key contributions of this review paper. In doing so, this paper contributes to the broader objectives of promoting transportation electrification, enhancing energy security, and reducing emissions

Using Smart Grids to Enhance Use of Energy-Efficiency and Renewable-Energy Technologies

The picture of electrification across the Asia Pacific Economic Cooperation (APEC) economies is complex. APEC members are in various states of smart grid development, ranging from no activity, conducting demonstrations, and engaging in joint projects with other economies. Each member economy has unique attributes that influence the benefits of smart grid capabilities and affect the priorities given to deployment strategies. To help provide insights into this complex topic, this work surveys APEC economies and characterizes the status of smart grid activities. It also identifies APEC economies that are actively pursuing smart grid capabilities to address environmental and economic sustainability goals. Finally, the report explores the potential application of smart grid capabilities to resolve renewable-integration and energy-efficiency concerns so future directions or roadmaps in this area can be developed by interested economies

Econometric framework for electricity infrastructure modernization in Saudi Arabia, An

2017 Summer.Includes bibliographical references.The electricity infrastructure in Saudi Arabia is facing several challenges represented by demand growth, high peak demand, high level of government subsidies, and system losses. This dissertation aims at addressing these challenges and proposing a multi-dimensional framework to modernize the electricity infrastructure in Saudi Arabia. The framework proposes four different scenarios—identified by two dimensions—for the future electric grid. The first and second dimensions are characterized by electricity market deregulation and Smart Grid technologies (SGTs) penetration, respectively. The framework analysis estimates global welfare (GW) and economic feasibility of the two dimensions. The first dimension quantifies the impact of deregulating the electricity market in Saudi Arabia. A non-linear programming (NLP) algorithm optimizes consumers surplus, producers surplus, and GW. The model indicates that deregulating the electricity market in Saudi Arabia will improve market efficiency. The second dimension proposes that allowing the penetration of SGTs in the Saudi electricity infrastructure is expected to mitigate the technical challenges faced by the grid. The dissertation examines the priorities of technologies for penetration by considering some key performance indicators (KPIs) identified by the Saudi National Transformation Program, and Saudi Vision 2030. A multi-criteria decision making (MCDM) algorithm—using the fuzzy Analytic Hierarchy Process (AHP)—evaluates the prioritization of SGTs to the Saudi grid. The algorithm demonstrates the use of triangular fuzzy numbers to model uncertainty in planning decisions. The results show that advanced metering infrastructure (AMI) technologies are the top priority for modernizing the Saudi electricity infrastructure; this is followed by advanced assets management (AAM) technologies, advanced transmission operations (ATO) technologies, and advanced distribution operations (ADO) technologies. SGTs prioritization is followed by a detailed cost benefit analysis (CBA) conducted for each technology. The framework analysis aims at computing the economic feasibility of SGTs and estimating their outcomes and impacts in monetary values. The framework maps Smart Grid assets to their functions and benefits to estimate the feasibility of each Smart Grid technology and infrastructure. Discounted cash flow (DCF) and net present value (NPV) models, benefit/cost ratio, and minimum total cost are included in the analysis. The results show that AAM technologies are the most profitable technologies of Smart Grid to the Saudi electricity infrastructure, followed by ADO technologies, ATO technologies, and AMI technologies. Considering the weights resulting from the fuzzy AHP and the economic analysis models for each infrastructure, the overall ranking places AAM technologies as the top priority of SGTs to the Saudi electricity infrastructure, followed by AMI technologies, ADO technologies, and ATO technologies. This dissertation has contributed to the existing body of knowledge in the following areas: • Proposing an econometric framework for electricity infrastructure modernization. The framework takes into account technical, economic, environmental, societal, and policy factors. • Building an NLP algorithm to optimize a counterfactual deregulation of a regulated electricity market. The algorithm comprises short run price elasticity of electricity demand (ε), level of technical efficiency improvement, and discount rate (r). • Proposing an MCDM model using AHP and fuzzy set theory to prioritize SGTs to electricity infrastructures. • Adapting a Smart Grid asset-function-benefit linkage model that maps SGTs to their respected benefits. • Conducting a detailed CBA to estimate the economic feasibility of SGTs to the Saudi electricity infrastructure, This work opens avenues for more analysis on electricity infrastructure modernization. Measuring risk impact and likelihood is one area for future research. In fact, risk assessment is an important factor in determining the economic feasibility of the modernization. Probabilistic economic analysis can be applied to assess the risk associated with the implantation of the previously mentioned dimensions. The parameters used for the economic analysis, such as economic life of a project, and the discount rate, are usually deterministic. However, a probabilistic method can be applied to capture the uncertainty of the parameters. Another area for future research is the integration of both dimensions into one model in which GW resulted from market deregulation and SGTs insertion are summed

Plug-in electric vehicle deployment and integration with the electric grid

Key battery, semiconductor, and software technologies have sufficiently progressed over the past few decades to enable viable plug-in electric vehicle (PEV) alternatives to conventional vehicles. Alternatives to petroleum-based fuels for transportation are sought to address concerns over energy security, foreign oil derived U.S. trade deficits, oil related geopolitical entanglements, and emissions. The various types of PEVs have substantially different characteristics. The types and key attributes of PEVs, charging standards, and charging locations are described. The likely scenario for PEV-Grid interactions over the next decade is synthesized from the analysis of the technologies available to and circumstances of vehicle manufacturers, utilities, and supplier firms. PEV adoption considerations are evolving. Many lessons have been learned from the first generation of PEVs that were introduced starting in late 2010. Technology, market, and policy drivers of emerging trends in the diffusion of PEVs are explored more in-depth. PEVs as electric loads are unique in that they are large, flexible, and intelligent. These attributes can not only provide utilities a new source of revenue, but also improve grid stability and economics. Actions, technologies, and policies that utilities can deploy to increase adoption are discussed. Actions are explored to make the overall PEV ownership experience superior to a conventional vehicle. This dissertation also describes research of the capability for PEVs in Vehicle to Home (V2H) scenarios, where the vehicle acts as a residential battery storage system and/or a backup generator in a residential microgrid configuration during a grid outage. Residential energy data collected from a smart grid testbed is used with a custom PEV model to assess the performance (in terms of duration and power output) of a BEV or PHEV used for backup power. Our earlier results quantify the extent to which photovoltaic (PV) generation and the characteristics of a PEV (battery size, gasoline availability) affect the backup duration during an electric grid outage. Strategies to further increase backup duration and non-continuous self-sustaining off-grid alternatives were found in our early V2H research. Varied amounts of load curtailment and PHEV engine-generator control improvements are modeled in subsequent research.Electrical and Computer Engineerin

K-Means and Alternative Clustering Methods in Modern Power Systems

As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies

Impact of vehicle to grid in the power system dynamic behaviour

This work was supported in part by FCT-Fundação para a Ciência e a Tecnologia de Portugal, under the grant SFRH/BD/47973/2008 and within the framework of the Project "Green Island" with the Reference MIT-PT/SES-GI/0008/2008, by the European Commission within the framework of the European Project MERGE - Mobile Energy Resources in Grids of Electricity, contract nr. 241399 (FP7) and by INESC Porto - Instituto de Engenharia de Sistemas e Computadores do PortoTese de doutoramento. Sistemas Sustentáveis de Energia. Universidade do Porto. Faculdade de Engenharia. 201

Online Coordinated Charging of Plug-In Electric Vehicles in Smart Grid to Minimize Cost of Generating Energy and Improve Voltage Profile

This Ph.D. research highlights the negative impacts of random vehicle charging on power grid and proposes four practical PEV coordinated charging strategies that reduce network and generation costs by integrating renewable energy resources and real-time pricing while considering utility constraints and consumer concerns

Business Models for SEEV4-City Operational Pilots: From a generic SEEV4-City business model towards improved specific OP business models

This report, led by Northumbria University, provides a final analysis by project partners regarding Business Models for SEEV4-City Operational pilots. It is part of a collection of reports published by the project covering a variation of specific and cross-cutting analysis and evaluation perspectives and spans 6 operational pilots