12 research outputs found

    INTEGRATING ELECTRIC VEHICLES INTO SMART GRID USING IEC 61850 AND ISO/IEC 15118 STANDARDS

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    The development of Electric Vehicle (EV) technology is evolving quickly due to the worlds growing concerns in environmental protection and energy conservation. The world is struggling to minimize CO2 emissions and fossil fuel dependency in transportation sector. Standardized communication interface is a key factor for the successful integration of electric vehicle into smart grid, interoperability of charging infrastructure and mass-market acceptance of E-Mobility. The deployment of the electric vehicle in large scale would be one of the feasible solutions because of its economical and environmentally friendly features. However, large deployment of the electric vehicle arises challenges at the grid level such as peak load impacts and charging control. Therefore, it is very crucial to investigate how to integrate electric vehicle into smart grid so that to avoid these effects. This thesis describes how the vehicle-to-grid communication interface (V2G CI) currently being developed in ISO/IEC 15118 can be connect to IEC 61850-7-420 standard for Distributed Energy Resources (DER). The client-server application is implemented to simulate EV charging process according to the ISO/IEC 15118 standard. Also the new logical nodes for monitoring and controlling EV and Electric Vehicle Supply Equipment (EVSE) are implemented for the simulation of the concept.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

    Wireless Authentication Solution and TTCN-3 based Test Framework for ISO-15118 Wireless V2G Communication

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    Vehicle to grid (V2G) communication for electric vehicles and their charging points is already well established by the ISO 15118 standard. The standard allows vehicles to communicate with the charging station using the power cable, i.e. a wired link, but it is improved to enable wireless (WLAN) links as well. This paper aims to provide an implementation accomplishes a wireless authentication solution (WAS). With that the electric vehicles can establish V2G connection when approaching the charging pool, then identify and authenticate the driver and/or the vehicle. Furthermore, the paper presents a TTCN-3 based validation and verification (V&V) framework in order to test the conformance of the prototype implementation against the standard

    Demand Side Management of Electric Vehicles in Smart Grids: A survey on strategies, challenges, modeling, and optimization

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    The shift of transportation technology from internal combustion engine (ICE) based vehicles to electricvehicles (EVs) in recent times due to their lower emissions, fuel costs, and greater efficiency hasbrought EV technology to the forefront of the electric power distribution systems due to theirability to interact with the grid through vehicle-to-grid (V2G) infrastructure. The greater adoptionof EVs presents an ideal use-case scenario of EVs acting as power dispatch, storage, and ancillaryservice-providing units. This EV aspect can be utilized more in the current smart grid (SG) scenarioby incorporating demand-side management (DSM) through EV integration. The integration of EVswith DSM techniques is hurdled with various issues and challenges addressed throughout thisliterature review. The various research conducted on EV-DSM programs has been surveyed. This reviewarticle focuses on the issues, solutions, and challenges, with suggestions on modeling the charginginfrastructure to suit DSM applications, and optimization aspects of EV-DSM are addressed separatelyto enhance the EV-DSM operation. Gaps in current research and possible research directions have beendiscussed extensively to present a comprehensive insight into the current status of DSM programsemployed with EV integration. This extensive review of EV-DSM will facilitate all the researchersto initiate research for superior and efficient energy management and EV scheduling strategies andmitigate the issues faced by system uncertainty modeling, variations, and constraints

    Electric vehicle integration in a real-time market

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    Coordinated Optimal Voltage Control in Distribution Networks with Data-Driven Methods

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    Voltage control is facing significant challenges with the increasing integration of photovoltaic (PV) systems and electric vehicles (EVs) in active distribution networks. This is leading to major transformations of control schemes that require more sophisticated coordination between different voltage regulation devices in different timescales. Except for conventional Volt/Var control (VVC) devices such on-load tap change (OLTC) and capacitor banks (CBs), inverter-based PVs are encouraged to participate in voltage regulation considering their flexible reactive power regulation capability. With the vehicle to grid (V2G) technology and inverter-based interface at charging stations, the charging power of an EV can be also controlled to support voltages. These emerging technologies facilitate the development of two-stage coordinated optimal voltage control schemes. However, these new control schemes pursue a fast response speed with local control strategies in shorter snapshots, which fails to track the optimal solutions for the distribution system operation. The voltage control methods mainly aim to mitigate voltage violations and reduce network power loss, but they seldom focus on satisfying the various requirements of PV and EV customers. This may discourage customer-owned resources from participating in ancillary services such as voltage regulation. Moreover, model-based voltage control methods highly rely on the accurate knowledge of power system models and parameters, which is sometimes difficult to obtain in real-life distribution networks. The goal of this thesis is to propose a data-driven two-stage voltage control framework to fill the research gaps mentioned above, showing what frameworks, models and solution methods can be used in the optimal voltage control of modern active distribution systems to tackle the security and economic challenges posed by high integration of PVs and EVs

    Efficiency and Sustainability of the Distributed Renewable Hybrid Power Systems Based on the Energy Internet, Blockchain Technology and Smart Contracts-Volume II

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    The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems, and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications, such as hybrid and microgrid power systems based on the Energy Internet, Blockchain technology, and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above

    Modelling and analysis of smart localised energy system for a sustainable future power network

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    Combating the increasing effect of climate change and averting future energy crisis resulting partly due to our continued dependence on conventional energy sources requires exploring aggressively more sustainable means of generating and utilising energy. Currently, most developed countries are transitioning slowly from a fossil fuel dominated energy system to a sustainable and renewable energy based system. However, for the results of these transitions to be impactful and reduce the global temperature rise to the expected 1.5oC, the approach must be wholistic and encompassing. Although there are a lot of ongoing research in the areas of renewable energy integration into the grid, however, there seems to be a dearth of such studies in some specific aspect of the power system application. Consequently, this thesis models and performs several analyses on a smart localised energy system with the aim of decarbonising some aspects of the future power network. The study investigated the dynamics of residential power demand in Nigeria and modelled the residential energy consumption profile. An excel-based algorithm was developed and applied to the developed model. The results of the residential energy consumption was based on the appliance energy end use methodology. This was used to develop a load profile indicative of a typical urban residential energy demand in Nigeria and employed to predict the effects of residential loads on the power system. Following the frequent use of diesel generators by municipal councils to power street lighting, several case studies demonstrating how to optimise street lighting energy consumption and improve energy efficiency were carried out using simple economic analysis indices such as Life Cycle Cost (LCC), Annualized Life Cycle Cost (ALCC), Net Present Cost (NPC), Cost of Energy (COE), and Return on Investment (ROI). The solar photovoltaic (SPV) system had the lowest LCC and ALCC, thus making it the most economically viable option. The response of the power system to Distributed Energy Resources (DERs) integration was also investigated. Data from a real low voltage (LV) distribution network in Nigeria was obtained and used in modelling the network using PSCAD/EMTDC software package. Different impact studies considering addition of distributed generation sources and increase in the load were performed. Volt-VAr optimisation (VVO) was performed to enable the inverter-based PV systems participate actively in voltage regulation by the provision of flexible reactive power support. A net total of 1.359 MVAr and 1.301 MVAr respectively are utilised from the inverter to regulate voltage within the acceptable limits, hence reducing the substation reactive power by 19.8% and 18.9% respectively during the controlled case study. Also, the total active power loss did reduce from 0.437 MW to 0.172 MW while the deviation of consumer voltages from the nominal system voltage was reduced by 33.4% during the controlled case studies. Overall, the VVO did enhance power quality and reliability by improving the feeder voltage profile and reducing the active power losses in the network. Lastly, to decarbonise some operation of the power system and improve the system resilience, DERs integrated black start restoration (BSR) strategy was implemented. The formulated BSR problem was implemented as a dynamic optimisation problem and the simulation was performed on the Nigerian 330 kV 48-bus system. The mixed-integer linear programming (MILP) technique was adopted and modelled to suit the nature of the BSR method developed. The black start power restoration sequence and the development of a viable restoration strategy were actualised. The simulation of the MILP model was achieved in MATLAB® using the IBM CPLEXTM solver. For the Nigerian 330 kV 48-bus system analysed, it was observed that most loads were optimally restored before the 30th time step for a black start operation. Both the experimental and numerical methodology were adopted in the validation of energy storage system (ESS) adopted for the proposed BSR simulated study. The optimal battery power availability for participating in restoration was reached in less than 50 minutes, with ESS optimally contributing to power restoration achieving 4.3% & 18.1% for Kaduna and Jos respectively

    Power System State Estimation and Renewable Energy Optimization in Smart Grids

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    The future smart grid will benefit from real-time monitoring, automated outage management, increased renewable energy penetration, and enhanced consumer involvement. Among the many research areas related to smart grids, this dissertation will focus on two important topics: power system state estimation using phasor measurement units (PMUs), and optimization for renewable energy integration. In the first topic, we consider power system state estimation using PMUs, when phase angle mismatch exists in the measurements. In particular, we build a measurement model that takes into account the measurement phase angle mismatch. We then propose algorithms to increase state estimation accuracy by taking into account the phase angle mismatch. Based on the proposed measurement model, we derive the posterior Cramér-Rao bound on the estimation error, and propose a method for PMU placement in the grid. Using numerical examples, we show that by considering the phase angle mismatch in the measurements, the estimation accuracy can be significantly improved compared with the traditional weighted least-squares estimator or Kalman filtering. We also show that using the proposed PMU placement strategy can increase the estimation accuracy by placing a limited number of PMUs in proper locations. In the second topic, we consider optimization for renewable energy integration in smart grids. We first consider a scenario where individual energy users own on-site renewable generators, and can both purchase and sell electricity to the main grid. Under this setup, we develop a method for parallel load scheduling of different energy users, with the goal of reducing the overall cost to energy users as well as to energy providers. The goal is achieved by finding the optimal load schedule of each individual energy user in a parallel distributed manner, to flatten the overall load of all the energy users. We then consider the case of a micro-grid, or an isolated grid, with a large penetration of renewable energy. In this case, we jointly optimize the energy storage and renewable generator capacity, in order to ensure an uninterrupted power supply with minimum costs. To handle the large dimensionality of the problem due to large historical datasets used, we reformulate the original optimization problem as a consensus problem, and use the alternating direction method of multipliers to solve for the optimal solution in a distributed manner
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