Design of Ancillary Services for Battery Energy Storage Systems to Mitigate Voltage Unbalance in Power Distribution Networks

power system, voltage unbalance issues are expected to exacerbate. Single{phase connectedphotovoltaic (PV) panels may cause unequal three{phase power ows, resultingin unbalanced grid currents and voltages. In addition, the random charging behaviour ofPlug{in Hybrid Electric Vehicles (PHEVs) equipped with single{phase on{board chargersis expected to further contribute to voltage unbalance rise as the number of thesedevices grows. If voltage unbalance increases to unacceptable levels, it may have adverseeects on power system operation and on the equipment connected to it. Traditionally,the phase swapping technique has been deployed by distribution system operators forvoltage unbalance mitigation, while other mitigating techniques include the deploymentof power electronics-based devices. The majority of the devices reported in the literatureare based on three-phase congurations, including series and parallel active power lters,unied power quality conditioners (UPQCs), static synchronous compensators (STATCOMs)and, more recently, three-phase distributed generation (DG) inverters.This research proposes the use of single-phase battery energy storage systems (BESSs)for the provision of phase balancing services, which has been considered only in a few literatureworks, with most of these research papers focusing on three-phase BESSs. In thisthesis, a novel control strategy is proposed for single-phase BESS units to compensatevoltage unbalance by injecting both active and reactive power simultaneously. The proposedapproach is based on the coordinated operation of three independent single-phaseBESS inverters using local voltage and current measurements.Initially, a comprehensive literature review is performed with the following aims: arobust classication of the ancillary services currently oered by BESSs, harmonisation ofthe notation found in the literature for ancillary services, and identication of potentialfuture applications of BESSs to power grids with large number of Low Carbon Technologies(LCTs). Then, the eectiveness of the proposed voltage unbalance compensationmethod is validated in the simulation environment, where two realistic models of distributionsystems are developed. Next, the impact of increasing PV and EV penetrationlevels on voltage unbalance for a typical UK distribution system is assessed based on adeterministic approach. The control strategy is validated experimentally by carrying outHardware-In-The-Loop (HIL) tests. Finally, an equivalent model of the distribution systemand BESS inverter is derived, which allows to carry out a preliminary probabilisticstudy to cater for the uncertainties related to the location and size of the PVs and EVs,and to evaluate the voltage unbalance levels without and with the BESSs controlled toprovide voltage unbalance compensation.It is concluded that the proposed BESS control system may eectively reduce thevoltage unbalance levels under various loading and generating conditions

Location Awareness in Multi-Agent Control of Distributed Energy Resources

The integration of Distributed Energy Resource (DER) technologies such as heat pumps, electric vehicles and small-scale generation into the electricity grid at the household level is limited by technical constraints. This work argues that location is an important aspect for the control and integration of DER and that network topology can inferred without the use of a centralised network model. It addresses DER integration challenges by presenting a novel approach that uses a decentralised multi-agent system where equipment controllers learn and use their location within the low-voltage section of the power system. Models of electrical networks exhibiting technical constraints were developed. Through theoretical analysis and real network data collection, various sources of location data were identified and new geographical and electrical techniques were developed for deriving network topology using Global Positioning System (GPS) and 24-hour voltage logs. The multi-agent system paradigm and societal structures were examined as an approach to a multi-stakeholder domain and congregations were used as an aid to decentralisation in a non-hierarchical, non-market-based approach. Through formal description of the agent attitude INTEND2, the novel technique of Intention Transfer was applied to an agent congregation to provide an opt-in, collaborative system. Test facilities for multi-agent systems were developed and culminated in a new embedded controller test platform that integrated a real-time dynamic electrical network simulator to provide a full-feedback system integrated with control hardware. Finally, a multi-agent control system was developed and implemented that used location data in providing demand-side response to a voltage excursion, with the goals of improving power quality, reducing generator disconnections, and deferring network reinforcement. The resulting communicating and self-organising energy agent community, as demonstrated on a unique hardware-in-the-loop platform, provides an application model and test facility to inspire agent-based, location-aware smart grid applications across the power systems domain

Power Losses Estimation in Low Voltage Smart Grids

Mención Internacional en el título de doctorOne of the European Union Targets was to replace at least 80% of all traditional energy meters with electronic smart meters by 2020. However, by the end of 2020, the European region (EU 27 including the UK) had installed no more than 150 million smart electricity meters, representing a penetration rate of 50% for smart meters. By 2026, It is expected that there will be more than 227 million smart meters in households due to the updated planning and target numbers, which will affect many European markets, including western and northern Europe. This scenario would contribute to the general purpose of building a more sustainable distribution system for the future. This thesis contributes to the field of power losses estimation and optimization in low-voltage (LV) smart grids in large-scale distribution areas. To contextualize the importance of the research, it has been necessary to explain the unbalanced nature of low voltage distribution networks where there is a huge deployment of smart meter rollout, and there is also uncertainty related to renewable energy generation. Main results of the thesis have been applied in two smart grid research projects: the national project OSIRIS (Optimizaci´on de la Supervisi´on Inteligente de la Red de Distribuci´on) and the European project IDE4L (Ideal Grid For All ). Smart metering infrastructure allows distributor system operators (DSOs) to have detailed information about the customers energy consumption or generation. Smart meters measure the active and reactive energy consumption/generation of customers using different discrete time resolutions which range from 15-60 min. A large-scale smart meter rollout allows service providers to gain information about the energy consumed and produced by each customer in near-real time. This knowledge can be used to compute the aggregated network power losses at any given time. In this case, network power losses are calculated by means of customers’ smart meters measurements, in terms of both active and reactive energy consumption, and by the energy measured by the smart meter supervisor located at the secondary substation (SS). The problem of network losses estimation becomes more challenging as a results of the existence of not-technical losses due to electricity fraud or smart meter measurements anomalous (null or extremely high) or even because there are customers’ smart meters that can be out of service. One of the differential keys of LV smart grids is the presence of single-phase loads and unbalanced operation, which makes it necessary to adopt a complete three-phase model of the LV distribution network to calculate the real value of the power losses. This scenario makes the process of power loss estimation a computationally intensive problem. The challenge is even greater when estimating the power losses of large-scale distribution networks, composed of thousands of SSs. In recent years, environmental concerns have led to the increasing integration of a considerable number of distributed energy resources (DERs) into LV smart grids. This fact prompts DSOs and regulators to provide the maximum energy efficiency in their networks (i.e., the smallest power loss values) and maximum sustainable energy consumption. Detailed understanding of the network’s behavior in terms of power losses and the use of electricity is necessary to achieve this energy efficiency. However, the above scenario presents some drawbacks. The integration of DERs units, such as photovoltaic (PV) panels, into distribution networks can produce an increment of network power losses if the DERs units are not optimally located, coordinated, or controlled. Additionally, the network can experience technical contingencies such as cable’s overloads and nodal over-voltages or can lead to an inefficient system operation due to high energy losses or cables that exceed thermal limits. Moreover, there is a great uncertainty associated with the distributed power generation from PVs because its energy generation depend on weather conditions, including ambient temperature and solar irradiance, which are highly intermittent and fluctuating. Uncertainty is also present in some loads with stochastic behavior, such as plug-in electric vehicles (PEV), which adds an uncertainty layer and makes their optimal integration more complex. Therefore, DSOs require advanced methods to estimate power losses in unbalanced large-scale LV smart grids under uncertain situations. Such estimations would facilitate the deployment of policies and practices that lead to a safe and efficient integration of DERs in the form of flexibility mechanisms. In this context, flexibility mechanisms are essential to achieve optimal operation conditions under extreme uncertainty. Flexibility mechanisms can be deployed to tackle the imbalance between generation and demand that results from the uncertainty that is latent in LV smart grids. These flexibility mechanisms are based on modifying the normal power consumption (for the demand side) or power generation (for the generation side), according to a flexibility scheduling at the request of the network operator. In summary, DSOs face the challenge of managing network losses over large geographical areas where there are hundreds of secondary substations and thousands of feeders, with multiple customers and an ever-increasing presence of renewable DERs. Power losses estimation is thus paramount to improve network energy efficiency in the context of the European Union energy policies. This situation is complicated by the unbalanced operation of those networks and the presence of uncertainty. To address these challenges, this thesis focuses on the following objectives: 1. Power losses estimation in unbalanced LV smart grids under uncertainty. 2. Power losses estimation in unbalanced LV smart grids in large areas with a presence of DERs. 3. Flexibility scheduling for power losses minimization in unbalanced smart grids under uncertainty. The mentioned objectives are achieved by taking advantage of smart metering infrastructures, machine and deep learning models and mathematical programming techniques which allows DSOs to reduce their total power losses within the distribution network. This approach entails using flexibility mechanisms to operate the distribution network optimally and enhance the load management and DG expansion planning. According to the objectives identified earlier, the main contributions of this thesis are the following: 1. Power losses estimation in unbalanced LV smart grids under uncertainty conditions. An optimization-based procedure to estimate load consumption of non-telemetered customers. A Markov chain-based process to estimate intra-hour load demand for data having a low resolution and for non-telemetered customers or customers which smart meters provide incorrect measurements. 2. Power losses estimation in unbalanced LV smart grids in large-scale areas with a presence of DERs. A data mining approach to reduce a high-dimensionality dataset in smart grids to yield a reduced set of relevant features. A clustering process to obtain representative feeders within a large-scale distribution area of smart grids. A deep learning-based power losses estimator for large-scale LV smart grids. The method is formulated as a deep neural network that uses as input features the power load demand and power generation of a set of representative feeders. The model gives, as output, the power losses of the whole area. 3. Flexibility scheduling for power losses minimization in unbalanced smart grids under uncertainty. A robust optimization model for the flexibility scheduling optimization model for unbalanced smart grids with distributed resources, such as PV panels and PEV devices.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidenta: Natalia Alguacil Conde.- Secretario: Pablo Ledesma Larrea.- Vocal: Samuele Grill

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

Power Quality

Electrical power is becoming one of the most dominant factors in our society. Power generation, transmission, distribution and usage are undergoing signifi cant changes that will aff ect the electrical quality and performance needs of our 21st century industry. One major aspect of electrical power is its quality and stability – or so called Power Quality. The view on Power Quality did change over the past few years. It seems that Power Quality is becoming a more important term in the academic world dealing with electrical power, and it is becoming more visible in all areas of commerce and industry, because of the ever increasing industry automation using sensitive electrical equipment on one hand and due to the dramatic change of our global electrical infrastructure on the other. For the past century, grid stability was maintained with a limited amount of major generators that have a large amount of rotational inertia. And the rate of change of phase angle is slow. Unfortunately, this does not work anymore with renewable energy sources adding their share to the grid like wind turbines or PV modules. Although the basic idea to use renewable energies is great and will be our path into the next century, it comes with a curse for the power grid as power fl ow stability will suff er. It is not only the source side that is about to change. We have also seen signifi cant changes on the load side as well. Industry is using machines and electrical products such as AC drives or PLCs that are sensitive to the slightest change of power quality, and we at home use more and more electrical products with switching power supplies or starting to plug in our electric cars to charge batt eries. In addition, many of us have begun installing our own distributed generation systems on our rooft ops using the latest solar panels. So we did look for a way to address this severe impact on our distribution network. To match supply and demand, we are about to create a new, intelligent and self-healing electric power infrastructure. The Smart Grid. The basic idea is to maintain the necessary balance between generators and loads on a grid. In other words, to make sure we have a good grid balance at all times. But the key question that you should ask yourself is: Does it also improve Power Quality? Probably not! Further on, the way how Power Quality is measured is going to be changed. Traditionally, each country had its own Power Quality standards and defi ned its own power quality instrument requirements. But more and more international harmonization efforts can be seen. Such as IEC 61000-4-30, which is an excellent standard that ensures that all compliant power quality instruments, regardless of manufacturer, will produce of measurement instruments so that they can also be used in volume applications and even directly embedded into sensitive loads. But work still has to be done. We still use Power Quality standards that have been writt en decades ago and don’t match today’s technology any more, such as fl icker standards that use parameters that have been defi ned by the behavior of 60-watt incandescent light bulbs, which are becoming extinct. Almost all experts are in agreement - although we will see an improvement in metering and control of the power fl ow, Power Quality will suff er. This book will give an overview of how power quality might impact our lives today and tomorrow, introduce new ways to monitor power quality and inform us about interesting possibilities to mitigate power quality problems. Regardless of any enhancements of the power grid, “Power Quality is just compatibility” like my good old friend and teacher Alex McEachern used to say. Power Quality will always remain an economic compromise between supply and load. The power available on the grid must be suffi ciently clean for the loads to operate correctly, and the loads must be suffi ciently strong to tolerate normal disturbances on the grid

Electromagnetic fast-transients in LV networks with ubiquitous small-scale embedded generation

Small-scale embedded generation projects rated below 16A per phase are being integrated into low-voltage distribution networks in ever increasing numbers. Seen from the network operator's perspective as little more than negative load, the commissioning of such generators is subject to compliance with the Fit and Forget connection requirements of ENA Engineering Recommendation G83/1. This thesis has sought to quantify the electromagnetic switching transient implications of integrating very large volumes of embedded generation into the UK's low-voltage supply networks. Laboratory testing of a converter-interfaced PV source has been undertaken to characterise typical switching transient waveshapes, and equivalent representative source models have been constructed in EMTP-ATP. A detailed frequency-dependent travelling wave equivalent of the DNO-approved Generic UK LV Distribution network model has been developed and, by means of extensive statistical simulation studies, used to quantify the cumulative impact of geographically localised generators switching in response to common network conditions. It is found that the magnitude of generator-induced voltage and current transients is dependent on the number of concurrently switched generators, and on their relative locations within the network. A theoretical maximum overvoltage of 1.72pu is predicted at customer nodes remote from the LV transformer terminals, for a scenario in which all households have installed embedded generation. Latent diversity in switch pole closing and inrush inception times is found to reduce predicted peak transient voltages to around 25-40% of their theoretical maxima.EThOS - Electronic Theses Online ServiceGBUnited Kingdo