A coordinated voltage regulation algorithm of a power distribution grid with multiple photovoltaic distributed generators based on active power curtailment and on-line tap changer

The aim of this research is to manage the voltage of an active distribution grid with a low X/R ratio and multiple Photovoltaic Distributed Generators (PVDGs) operating under varying conditions. This is achieved by providing a methodology for coordinating three voltage-based controllers implementing an Adaptive Neuro-Fuzzy Inference System (ANFIS). The first controller is for the On-Line Tap Changer (OLTC), which computes its adequate voltage reference. Whereas the second determines the required Active Power Curtailment (APC) setpoint for PVDG units with the aim of regulating the voltage magnitude and preventing continuous tap operation (the hunting problem) of OLTC. Finally, the last component is an auxiliary controller designed for reactive power adjustment. Its function is to manage voltage at the Common Coupling Point (CCP) within the network. This regulation not only aids in preventing undue stress on the OLTC but also contributes to a modest reduction in active power generated by PVDGs. The algorithm coordinating between these three controllers is simulated in MATLAB/SIMULINK and tested on a modified IEEE 33-bus power distribution grid (PDG). The results revealed the efficacy of the adopted algorithm in regulating voltage magnitudes in all buses compared to the traditional control method.Peer ReviewedPostprint (published version

Micro (Wind) Generation: \u27Urban Resource Potential & Impact on Distribution Network Power Quality\u27

Of the forms of renewable energy available, wind energy is at the forefront of the European (and Irish) green initiative with wind farms supplying a significant proportion of electrical energy demand. This type of distributed generation (DG) represents a ‘paradigm shift’ towards increased decentralisation of energy supply. However, because of the distance of most DG from urban areas where demand is greatest, there is a loss of efficiency. The solution, placing wind energy systems in urban areas, faces significant challenges. The complexities associated with the urban terrain include planning, surface heterogeneity that reduces the available wind resource and technology obstacles to extracting and distributing wind energy. Yet, if a renewable solution to increasing energy demand is to be achieved, energy conversion systems where populations are concentrated, that is cities, must be considered. This study is based on two independent strands of research into: low voltage (LV) power flow and modelling the urban wind resource. The urban wind resource is considered by employing a physically-based empirical model to link wind observations at a conventional meteorological site to those acquired at urban sites. The approach is based on urban climate research that has examined the effects of varying surface roughness on the wind-field above buildings. The development of the model is based on observational data acquired at two locations across Dublin representing an urban and sub-urban site. At each, detailed wind information is recorded at a height about 1.5 times the average height of surrounding buildings. These observations are linked to data gathered at a conventional meteorological station located at Dublin Airport, which is outside the city. These observations are linked through boundary-layer meteorological theory that accounts for surface roughness. The resulting model has sufficient accuracy to assess the wind resource at these sites and allow us to assess the potential for micro–turbine energy generation. One of the obstacles to assessing this potential wind resource is our lack of understanding of how turbulence within urban environments affects turbine productivity. This research uses two statistical approaches to examine the effect of turbulence intensity on wind turbine performance. The first approach is an adaptation of a model originally derived to quantify the degradation of power performance of a wind turbine using the Gaussian probability distribution to simulate turbulence. The second approach involves a novel application of the Weibull Distribution, a widely accepted means to probabilistically describe wind speed and its variation. On the technological side, incorporating wind power into an urban distribution network requires power flow analysis to investigate the power quality issues, which are principally associated with imbalance of voltage on distribution lines and voltage rise. Distribution networks that incorporate LV consumers must accommodate a highly unbalanced load structure and the need for grounding network between the consumer and grid operator (TN-C-S earthing). In this regard, an asymmetrical 3-phase (plus neutral) power flow must be solved to represent the range of issues for the consumer and the network as the number of wind-energy systems are integrated onto the distribution network. The focus in this research is integrating micro/small generation, which can be installed in parallel with LV consumer connections. After initial investigations of a representative Irish distribution network, a section of an actual distribution network is modelled and a number of power flow algorithms are considered. Subsequently, an algorithm based on the admittance matrix of a network is identified as the optimal approach. The modelling thereby refers to a 4-wire representation of a suburban distribution network within Dublin city, Ireland, which incorporates consumer connections at single-phase (230V-N). Investigations relating to a range of network issues are considered. More specifically, network issues considered include voltage unbalance/rise and the network neutral earth voltage (NEV) for increasing levels of micro/small wind generation technologies with respect to a modelled urban wind resource. The associated power flow analysis is further considered in terms of the turbulence modelling to ascertain how turbulence impinges on the network voltage/voltage-unbalance constraints

Discrete Event Simulations

Considered by many authors as a technique for modelling stochastic, dynamic and discretely evolving systems, this technique has gained widespread acceptance among the practitioners who want to represent and improve complex systems. Since DES is a technique applied in incredibly different areas, this book reflects many different points of view about DES, thus, all authors describe how it is understood and applied within their context of work, providing an extensive understanding of what DES is. It can be said that the name of the book itself reflects the plurality that these points of view represent. The book embraces a number of topics covering theory, methods and applications to a wide range of sectors and problem areas that have been categorised into five groups. As well as the previously explained variety of points of view concerning DES, there is one additional thing to remark about this book: its richness when talking about actual data or actual data based analysis. When most academic areas are lacking application cases, roughly the half part of the chapters included in this book deal with actual problems or at least are based on actual data. Thus, the editor firmly believes that this book will be interesting for both beginners and practitioners in the area of DES

Development of a synthetic solar irradiance generator that produces time series with high temporal and spatial resolutions using readily available mean hourly observations

Photovoltaics (PV) have seen rapid global penetration into the low voltage (LV) electricity distribution grid year-on-year. The result of high PV penetration levels is grid impacts of voltage fluctuations, harmonic distortions and reverse flow among others. Research that attempts to quantify the maximum allowable PV penetration into the LV grid before experiencing detrimental impacts is an important. The most commonly reported barrier to enabling grid impact analysis is the lacking availability of high-resolution and geographically flexible solar irradiance data. As an alternative, synthetically generated solar irradiance data can be used. There is a distinct lack of synthetic solar irradiance generators that can derive high resolution and statistically accurate solar irradiance data using only readily available inputs. This thesis presents the development of two synthetic generators: the Solar Irradiance Generator (SIG), and the Spatially Decorrelating Solar Irradiance Generator (SDSIG). The SIG proves the concept that synthetic minutely irradiance time series can be generated using readily available mean hourly observations of total cloud amount, atmospheric pressure, wind speed and cloud base height. The SDSIG presents the first ever methodology to synthetically generate unique and spatially decorrelating minutely irradiance time series for any number of uniquely orientated and tilted houses inside a spatial domain using the same inputs as the SIG. The SDSIG employs (1) Markov chains, to derive stochastic weather variable time series, (2) synthetic representations of clouds in the sky, using a novel method called cloud fields, (3) globally flexible irradiance estimation models, and (4) distributions of clear-sky irradiance by total cloud amount, to create the irradiance time series. The SDSIG outputs are temporally validated using metrics of ramp rates, variability indices and irradiance magnitude frequencies against real world observations at two UK sites and two USA sites, representing three distinct climates. Daily 2-sample Kolmogorov-Smirnov tests of each metric passed a minimum of 95.34% of the time with a 99% confidence limit. The lowest CDF correlation coefficient between modelled and observed data for all metrics and sites was R=0.908; the mean was R=0.987. The SDSIG outputs are spatially validated at Oahu, HI USA, showing R=0.955, RMSE=0.01 and MAPE=0.865% when comparing modelled and observed spatial correlation versus site separation. The SDSIG outputs are applied to a grid impacts power flow model of an LV grid with increasing PV penetration to test the over voltage metric of daily on-load tap changer (OLTC) operations. Using correlating irradiance time series at each house in the LV grid overestimates OLTC operations in every instance of PV penetration when compared to using spatially decorrelating irradiance time series from the SDSIG

Decentralised Energy

As the contribution of renewable energy increases to meet climate change objectives, so does the extent to which electricity is generated by individuals and communities, moving a centralised system to a more decentralised one, with associated system management and integration challenges. However, new digital and storage technologies are enabling local system management, and reducing the need for, and cost of, centralised planning. Creating the right regulatory environment is crucial, but it is especially challenging given the pace of technology change. This book describes the deploying renewable energy in selected countries and describes how each stage is affected by the regulatory and industrial environments, as gives examples of how new business models that enable greater penetration of renewables and more localised grid management and consumer engagement are being deployed

Reliability assessment of distribution networks incorporating regulator requirements, generic network equivalents and smart grid functionalities

Over the past decades, the concepts and methods for reliability assessment have evolved from analysing the ability of individual components to operate without faults and as intended during their lifetime, into the comprehensive approaches for evaluating various engineering strategies for system planning, operation and maintenance studies. The conventional reliability assessment procedures now receive different perspectives in different engineering applications and this thesis aims to improve existing approaches by incorporating in the analysis: a) a more detailed and accurate models of LV and MV networks and their reliability equivalents, which are important for the analysis of transmission and sub-transmission networks, b) the variations in characteristics and parameters of LV and MV networks in different areas, specified as “generic” UK/Scottish highly-urban, urban, sub-urban and rural network models, c) the relevant requirements for network reliability performance imposed by Regulators on network operators, d) the actual aggregate load profiles of supplied customers and their correlation with typical daily variations of fault probabilities and repair times of considered network components, and e) some of the expected “smart grid” functionalities, e.g., increased use of network automation and reconfiguration schemes, as well as the higher penetration levels of distributed generation/storage resources. The conventional reliability assessment procedures typically do not include, or only partially include the abovementioned important factors and aspects in the analysis. In order to demonstrate their importance, the analysis presented in the thesis implements both analytical and probabilistic reliability assessment methods in a number of scenarios and study cases with improved and more detailed “generic” LV and MV network models and their reliability equivalents. Their impact on network reliability performance is analysed and quantified in terms of the frequency and duration of long and short supply interruptions (SAIFI and SAIDI), as well as energy not supplied (ENS). This thesis addresses another important aspect of conventional approaches, which often, if not always, provide separate indicators for the assessment of system-based reliability performance and for the assessment of customer-based reliability performance. The presented analysis attempts to more closely relate system reliability performance indicators, which generally correspond to a fictitious “average customer”, to the actual “best-served” and “worst-served” customers in the considered networks. Here, it is shown that a more complex metric than individual reliability indicators should be used for the analysis, as there are different best-served and worst-served customers in terms of the frequency and duration of supply interruptions, as well as amounts of not supplied energy. Finally, the analysis in the thesis considers some aspects of the anticipated transformation of existing networks into the future smart grids, which effectively require to re-evaluate the ways in which network reliability is approached at both planning and operational stages. Smart grids will feature significantly higher penetration levels of variable renewable-based distributed generation technologies (with or without energy storage), as well as the increased operational flexibility, automation and remote control facilities. In this context, the thesis evaluates some of the considered smart grid capabilities and functionalities, showing that improved system reliability performance might result in a deterioration of power quality performance. This is illustrated through the analysis of applied automation, reconfiguration and automatic reclosing/remote switching schemes, which are shown to reduce frequency and duration of long supply interruptions, but will ultimately result in more frequent and/or longer voltage sags and short interruptions. Similarly, distributed generation/storage resources might have strong positive impact on system reliability performance through the reduced power flows in local networks and provision of alternative supply points, even allowing for a fully independent off-grid operation in microgrids, but this may also result in the reduced power quality levels within the microgrids, or elsewhere in the network, e.g. due to a higher number of switching transfers and transients

Decentralised Energy

As the contribution of renewable energy increases to meet climate change objectives, so does the extent to which electricity is generated by individuals and communities, moving a centralised system to a more decentralised one, with associated system management and integration challenges. However, new digital and storage technologies are enabling local system management, and reducing the need for, and cost of, centralised planning. Creating the right regulatory environment is crucial, but it is especially challenging given the pace of technology change. This book describes the deploying renewable energy in selected countries and describes how each stage is affected by the regulatory and industrial environments, as gives examples of how new business models that enable greater penetration of renewables and more localised grid management and consumer engagement are being deployed

State-of-the-Art Assessment of Smart Charging and Vehicle 2 Grid services

Electro-mobility – especially when coupled smartly with a decarbonised grid and also renewable distributed local energy generation, has an imperative role to play in reducing CO2 emissions and mitigating the effects of climate change. In parallel, the regulatory framework continues to set new and challenging targets for greenhouse gas emissions and urban air pollution. • EVs can help to achieve environmental targets because they are beneficial in terms of reduced GHG emissions although the magnitude of emission reduction really depends on the carbon intensity of the national energy mix, zero air pollution, reduced noise, higher energy efficiency and capable of integration with the electric grid, as discussed in Chapter 1. • Scenarios to limit global warming have been developed based on the Paris Agreement on Climate Change, and these set the EV deployment targets or ambitions mentioned in Chapter 2. • Currently there is a considerable surge in electric cars purchasing with countries such as China, the USA, Norway, The Netherlands, France, the UK and Sweden leading the way with an EV market share over 1%. • To enable the achievement of these targets, charging infrastructures need to be deployed in parallel: there are four modes according to IEC 61851, as presented in Chapter 2.1.4. • The targets for SEEV4City project are as follow: o Increase energy autonomy in SEEV4-City sites by 25%, as compared to the baseline case. o Reduce greenhouse gas emissions by 150 Tonnes annually and change to zero emission kilometres in the SEEV4-City Operational Pilots. o Avoid grid related investments (100 million Euros in 10 years) by introducing large scale adoption of smart charging and storage services and make existing electrical grids compatible with an increase in electro mobility and local renewable energy production. • The afore-mentioned objectives are achieved by applying Smart Charging (SC) and Vehicle to Grid (V2G) technologies within Operational Pilots at different levels: o Household. o Street. o Neighbourhood. o City. • SEEV4City aims to develop the concept of 'Vehicle4Energy Services' into a number of sustainable business models to integrate electric vehicles and renewable energy within a Sustainable Urban Mobility and Energy Plan (SUMEP), as introduced in Chapter 1. With this aim in mind, this project fills the gaps left by previous or currently running projects, as reviewed in Chapter 6. • The business models will be developed according to the boundaries of the six Operational Pilots, which involve a disparate number of stakeholders which will be considered within them. • Within every scale, the relevant project objectives need to be satisfied and a study is made on the Public, Social and Private Economics of Smart Charging and V2G. • In order to accomplish this work, a variety of aspects need to be investigated: o Chapter 3 provides details about revenue streams and costs for business models and Economics of Smart Charging and V2G. o Chapter 4 focuses on the definition of Energy Autonomy, the variables and the economy behind it; o Chapter 5 talks about the impacts of EV charging on the grid, how to mitigate them and offers solutions to defer grid investments; o Chapter 7 introduces a number of relevant business models and considers the Economics of Smart Charging and V2G; o Chapter 8 discusses policy frameworks, and gives insight into CO2 emissions and air pollution; o Chapter 9 defines the Data Collection approach that will be interfaced with the models; o Chapter 10 discusses the Energy model and the simulation platforms that may be used for project implementation