The impact of smart grid technology on dielectrics and electrical insulation

Delivery of the Smart Grid is a topic of considerable interest within the power industry in general, and the IEEE specifically. This paper presents the smart grid landscape as seen by the IEEE Dielectrics and Electrical Insulation Society (DEIS) Technical Committee on Smart Grids. We define the various facets of smart grid technology, and present an examination of the impacts on dielectrics within power assets. Based on the trajectory of current research in the field, we identify the implications for asset owners and operators at both the device level and the systems level. The paper concludes by identifying areas of dielectrics and insulation research required to fully realize the smart grid concept. The work of the DEIS is fundamental to achieving the goals of a more active, self-managing grid

Utilisation of transformer condition monitoring data

Electricity grids are getting older and demand of electricity is rising. The critical com-ponents in electricity transmission systems should be monitored for assessing the need for maintenance. The electricity grid works more reliable when the condition infor-mation of important components are available continuously and thus larger catastrophic failures are preventable. Transformers are one of the critical components in electricity transmission. It is im-portant that they operate continuously. Transformers are reliable and long life compo-nents but the older the transformer is, the more sensitive it is about to fail. Condition monitoring provides improved data on the condition of transformer. With on-line condi-tion monitoring it is possible to detect developing failures and then a corrective action can be made in time. This study focuses on the utilization of transformer condition monitoring system in tra-ditional grid and in upcoming smart grid. The aim is to find out, where the condition monitoring data is needed in electricity transmission and distribution system manage-ment and how it is possible to carry the information to right place. This thesis introduces first the basics of a power system, the construction of a trans-former, transformer condition monitoring methods and condition monitoring data pro-cess. After that the management of a power system within traditional and smart grid is analyzed. The asset management process of both type power systems is explored through case study of transformer failure situations. In traditional power system the transformer maintenance bases mostly on time scheduled inspections. In smart grid the management is all time aware on the condition information of transformers which al-lows using of better fault prevention strategies.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Practical applications of multi-agent systems in electric power systems

The transformation of energy networks from passive to active systems requires the embedding of intelligence within the network. One suitable approach to integrating distributed intelligent systems is multi-agent systems technology, where components of functionality run as autonomous agents capable of interaction through messaging. This provides loose coupling between components that can benefit the complex systems envisioned for the smart grid. This paper reviews the key milestones of demonstrated agent systems in the power industry and considers which aspects of agent design must still be addressed for widespread application of agent technology to occur

Data Mining in Smart Grids

Effective smart grid operation requires rapid decisions in a data-rich, but information-limited, environment. In this context, grid sensor data-streaming cannot provide the system operators with the necessary information to act on in the time frames necessary to minimize the impact of the disturbances. Even if there are fast models that can convert the data into information, the smart grid operator must deal with the challenge of not having a full understanding of the context of the information, and, therefore, the information content cannot be used with any high degree of confidence. To address this issue, data mining has been recognized as the most promising enabling technology for improving decision-making processes, providing the right information at the right moment to the right decision-maker. This Special Issue is focused on emerging methodologies for data mining in smart grids. In this area, it addresses many relevant topics, ranging from methods for uncertainty management, to advanced dispatching. This Special Issue not only focuses on methodological breakthroughs and roadmaps in implementing the methodology, but also presents the much-needed sharing of the best practices. Topics include, but are not limited to, the following: Fuzziness in smart grids computing Emerging techniques for renewable energy forecasting Robust and proactive solution of optimal smart grids operation Fuzzy-based smart grids monitoring and control frameworks Granular computing for uncertainty management in smart grids Self-organizing and decentralized paradigms for information processin

Electric Power Conversion and Micro-Grids

This edited volume is a collection of reviewed and relevant research chapters offering a comprehensive overview of recent achievements in the field of micro-grids and electric power conversion. The book comprises single chapters authored by various researchers and is edited by a group of experts in such research areas. All chapters are complete in themselves but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on electric power conversion, micro-grids, and their up-to-the-minute technological advances and opens new possible research paths for further novel developments

Aging aware adaptive control of Li-ion battery energy storage system for flexibility services provision

Battery energy storage systems (BESSs) play a major role as flexible energy resource (FER) in active network management (ANM) schemes by bridging gaps between non-concurrent renewable energy sources (RES)-based power generation and demand in the medium-voltage (MV) and low-voltage (LV) electricity distribution networks. However, Lithium-ion battery energy storage systems (Li-ion BESS) are prone to aging resulting in decreasing performance, particularly its reduced peak power output and capacity. BESS controllers when employed for providing technical ancillary i.e. flexibility services to distribution (e.g. through ANM) or transmission networks must be aware of changing battery characteristics due to aging. Particularly of importance is BESSs' peak power changes aiding in protection of the Li-ion BESS by restricting its operation limits of it for safety reasons and improving its lifetime in the long run. In this paper, firstly an architecture for ANM scheme is designed considering Li-ion BESSs as one of the FERs in an existing smart grid pilot (Sundom Smart Grid, SSG) in Vaasa, Finland. Further, Li-ion BESS controllers are designed to be adaptive in nature to include its aging characteristics, i.e. tracking the changing peak power as the aging parameter, when utilised for ANM operation in the power grid. Peak power capability of the Li-ion nickel‑manganese‑cobalt (NMC) chemistry-based battery cell has been calculated with the experimental data gathered from accelerated aging tests performed in the laboratory. Impact of such aging aware and adaptive Li-ion BESS controllers on the flexibility services provision for power system operators needs will be analysed by means of real-time simulation studies in an existing SSG pilot./© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Observer techniques for estimating the state-of-charge and state-of-health of VRLABs for hybrid electric vehicles

The paper describes the application of observer-based state-estimation techniques for the real-time prediction of state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Specifically, an approach based on the well-known Kalman filter, is employed, to estimate SoC, and the subsequent use of the EKF to accommodate model non-linearities to predict battery SoH. The underlying dynamic behaviour of each cell is based on a generic Randles' equivalent circuit comprising of two-capacitors (bulk and surface) and three resistors, (terminal, transfer and self-discharging). The presented techniques are shown to correct for offset, drift and long-term state divergence-an unfortunate feature of employing stand-alone models and more traditional coulomb-counting techniques. Measurements using real-time road data are used to compare the performance of conventional integration-based methods for estimating SoC, with those predicted from the presented state estimation schemes. Results show that the proposed methodologies are superior with SoC being estimated to be within 1% of measured. Moreover, by accounting for the nonlinearities present within the dynamic cell model, the application of an EKF is shown to provide verifiable indications of SoH of the cell pack

Power System State Estimation and Renewable Energy Optimization in Smart Grids

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

Power Electronics and Energy Management for Battery Storage Systems

The deployment of distributed renewable generation and e-mobility systems is creating a demand for improved dynamic performance, flexibility, and resilience in electrical grids. Various energy storages, such as stationary and electric vehicle batteries, together with power electronic interfaces, will play a key role in addressing these requests thanks to their enhanced functionality, fast response times, and configuration flexibility. For the large-scale implementation of this technology, the associated enabling developments are becoming of paramount importance. These include energy management algorithms; optimal sizing and coordinated control strategies of different storage technologies, including e-mobility storage; power electronic converters for interfacing renewables and battery systems, which allow for advanced interactions with the grid; and increase in round-trip efficiencies by means of advanced materials, components, and algorithms. This Special Issue contains the developments that have been published b researchers in the areas of power electronics, energy management and battery storage. A range of potential solutions to the existing barriers is presented, aiming to make the most out of these emerging technologies