A comprehensive review of renewables and electric vehicles hosting capacity in active distribution networks

© Copyright 2023 The Author(s). The excessive integration of renewable distributed generation (RDG) and electric vehicles (EVs) could be considered the two most problematic elements representing the greatest threat to the distribution network (DN) technical operation. In order to avoid going beyond technical limitations, the term hosting capacity (HC) was proposed to define the highest permitted amount of distributed generation (DG) or EVs that can be integrated safely into the DN. The connection of RDGs was first brought to the attention of researchers and DN operators since it accounts for the most notable portion of these technical issues. Hence, the phrase ‘DG-HC’ was initially proposed and evolved significantly over the last few years. Currently, EV integration in most DNs worldwide is still low, but given the worldwide support for clean transportation options, expectations are raised for a significant increase. As a result, it is anticipated that over the next years, the effect of EV integration on the DN will be highly noticeable, requiring greater attention from researchers and DN operators to define the accepted limits of EV penetration levels, ‘EV-HC,’ which is expected to pass along the same line of DG-HC. This article provides an in-depth review of both DG-HC and EV-HC. It first analyses how the DG-HC research has grown over the years and then studies the published EV-HC papers, illustrating to what extent there is a similarity between them and, finally, employs these analyses to expect future development in the EV-HC research area. This article includes the different uses of the term HC, the most common performance indices of DG-HC, the various methods for assessing DG-HC, the different techniques for DG-HC enhancement, the effects of integrating EVs on the DG-HC, and finally, calculating and enhancing methods for EV-HC

Probabilistic hosting capacity and risk analysis for distribution networks

Hereby I present a PhD thesis by publications. Altogether, the thesis includes: a) two journal papers, b) three IEEE conference papers. The journals include IEEE Transactions on Industrial Informatics while the second has been submitted. The conference list includes World Renewable Energy Congress (WREC), Asian conference on energy, power and transportation electrification (ACEPT) and IEEE Conference on Probabilistic Methods Applied in Power Systems (PMAPS). The PMAPS conference is the only event that exclusively discusses probability and statistic methods applied to power system analysis. The thesis presents several novel methods. The first novelty is the development of a new probabilistic model for estimating the solar radiation incident to residential roofs which is compatible with the Australian meteorological conditions. The second is the development of new probabilistic approach called “probabilistic hosting capacity” to estimate the hosting capacity of distribution networks. The third one is the utilization of sparse grid numerical approximation techniques in handling the uncertainty computations. The last contribution is the new assessment method for quantifying the risk of connecting a large number of correlated distributed generators (DGs) into the distribution networks. In glance, these contributions are highlighted in the following paragraphs. The development of the probabilistic method to estimate the solar irradiation is aimed to represent the uncertainty of produced power from residential solar panels. By utilizing the relation between clearness index and diffuse fraction, a probability density function (PDF) of produced power is derived from the total radiance quantity incident of a tilted area to the horizontal plane. Given the characteristics of the day time and the place, the uncertainty associated with power production by solar panels can be probabilistically estimated from the total solar irradiation of a tilted area. Two mathematical models are proposed: the first utilizes the HDKR (Hay, Davies, Klucher, Reindl) mathematical representation for total irradiance, while the second one involves the use of Hay-Davies mathematical representation. Without losing the scope of the work, only the first model is compared with real data obtain from a site in Adelaide. The second model is used for conducting the power flow calculations due to the low computational time is required to deliver results. The interest in the development of probabilistic hosting capacity comes as DGs in the distribution networks rely mainly on the renewable energy. Probabilistic hosting capacity is aimed to deliver a probabilistic estimate of the maximum amount of DGs that can be connected into the existing distribution network without jeopardizing the utility’s system operation and/or customers’ connected appliances. The approach is built up after defining the main uncertainties, resulted from the stochastic behaviours of the small-scale of wind turbines and solar panels as well as domestic loads. The impacts of these uncertainties on the operation of a distribution network are assessed by establishing a set of operational performance indices and the use of the probability of occurrence notion. Three types of hazardous impacts are defined (tolerable, critical and serious). The approach is time-dependent and includes network bi-directionality feature which complies with the fundamentals of automation approaches for active distribution networks. The third contribution is the use of sparse grid numerical techniques (SGTs) as an efficient tool to handle the uncertainty computation which is multi-dimensional problem. It replaces the use of classical numerical techniques based on tensor product grids which suffers from the curse of dimensionality. Additionally, the SGT in comparison with Monte Carlo Technique (MCT) is able to achieve improved efficiency in computation with acceptable accuracy. The last contribution is the development of a new risk analysis approach to quantify the effect of increasing levels of DG penetration on distribution networks. The proposed novel analysis utilises the following techniques and concepts: the Nataf transformation to represent spatial correlation of the DGs connected in the same distribution network; the consideration of likelihood (relative frequency of event occurrence) as well as severity (accumulative depth of event occurrence) of the performance indices in assessing the operation of distribution networks with the increase of DG connections. The Nataf transformation was used to ensure the rank correlation modelling among the non-Gaussian uncertainty representations in which the inter-dependences are modelled. The risk components, likelihood and severity, are visualized along with the increase of correlated DG connections. The purpose of this analysis is to provide an estimate of degree of risk in assessing the operational performance of a distribution network as whole, instead of the traditional methods that assess the network by parts, such as assessing individually a line or bus. The effectiveness of developed methods in this thesis is demonstrated by performing tests on two actual distribution networks: small and large. The small network consists of 11 buses with one substation transformer; while the existing large distribution network, situated in South Australia, consists of 59 (11/0.4 kV) feeder-transformers serving commercial, residential and industrial loads. The large network is segmented into different zones according to their likelihood of having DGs. The results are visualized, analysed and discussed for each proposed methods or approaches. All system modelling and algorithms are performed using MATLAB software and implemented on the distribution networks modelled in the industry accepted software OpenDSS, introduced by Electrical Power Research Institute (EPRI).Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 201

Robust Computational Frameworks for Power Grid Reliability, Vulnerability and Resilience Analysis

The power grid is one of the largest man-made critical infrastructures. It has been designed to distribute electric power from generating units to residential, commercial and industrial end-users. Due to the continuous increasing of electrical penetration, the availability and reliability of network is of paramount importance. In addition, the continuous increasing of renewable generators posed a further challenges to the stability of the network due to their dependencies on environmental changes, which are drifting weather scenarios towards extremes. Hence, resilience is becoming a major concern for the future power grid. In order to respond promptly to those important changes, the resilience of the such critical infrastructure has to be augmented. This can only be achieved with the availability of robust computational models that allow to design a better network, robustly validated and updated the results. Ideally, a computational framework for the assessment of power grid resilience should capture all the relevant physical interactions between components, subsystems and the system as a whole. Furthermore, uncertain and heterogeneous environmental factors have to be accounted for and their effect on safety-related metrics explicitly modelled and quantified. This is necessary to reveal power grid risks, hazards and identity situation for which an immediate safety and resilience enhancement is necessary. In this thesis, the existing power grid safety-related concepts (i.e. reliability, risk, vulnerability and resilience) and ancillary uncertainty quantification methods are analysed. The major weakness in existing quantification frameworks has been identified as the way a lack of data required by the frameworks and the treatment of such imprecise information. To overcome this limitation, a novel and robust methods for the uncertainty quantification in power grid safety-critical evaluations has been developed. The main contributions of this dissertation are a set of novel tools for the assessment of power grid reliability, vulnerability and resilience and accounting for a rigorous treatment of lack of data uncertainty. These methods have a limited need for artificial model assumptions, which might alter the quality of the available information and, with it, the validity of safety-critical decisions. One of the key elements for a resilient grid is the system ability to learn from past events, improving the grid structure, operations and policies. For this reason, a Reinforcement Learning framework for optimal decision-making under uncertainty has been investigated. This allows to equip the systems with learning capabilities, which is a fundamental component of the resilience concept, and it optimizes operation and maintenance decisions. The developed frameworks can be used to investigate the effect of threatening scenarios (such as extreme weather conditions, multiple contingencies and cascading events) on the grid safety performance. The validity of the approaches has been tested on scaled-down power grids and prognostic health management as well on realistic models of existing systems (e.g. the IEEE reliability test system). These tools provide a valuable contribution to the research community and industrial practitioners as they can help to discern whether the available information suffices to answer a reliability, vulnerability or resilience related question. If the information is limited and additional data has to be gathered, the method informs the decision-maker with the most relevant and sensitive factors, i.e. a basic indication on where to start collecting data so that an expected reduction in uncertainty is maximised

UKERC Energy Strategy Under Uncertainties An Integrated Systematic Analysis of Uncertainties in UK Energy Transition Pathways

energy systems. It is the hub of UK energy research and the gateway between the UK and the international energy research communities. Our interdisciplinary, whole systems research informs UK policy development and research strategy. www.ukerc.ac.uk The Meeting Place- hosting events for the whole of the UK energy research community-www.ukerc.ac.uk/support/TheMeetingPlace National Energy Research Network- a weekly newsletter containing news, jobs, event, opportunities and developments across the energy field- www.ukerc.ac.uk/support/NERN Research Atlas- the definitive information resource for current and past UK energy research and development activity

Sensitivity based planning and operation of modern distribution systems

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringMajor Professor Not ListedThe power system is undergoing numerous changes due to the rapid increase in energy demand, rising concerns of climate change, and increased engagement of consumers in the energy market. Consumers are now motivated to invest in distributed energy resources (DERs), e.g., rooftop photovoltaic systems, due to their environmental advantages. The number of electric vehicles (EVs) is also increasing due to their reliability and low carbon footprint. Despite their numerous benefits, the rapid onset of DERs and EVs introduces new technical challenges to distribution systems including (1) complex system operation due to reverse power flows, (2) voltage instability issues; and (3) increased power losses due to poor DER and EV planning as well as their temporal uncertainty. Existing methods to improve the planning and operation of distribution systems in the presence of these technologies use available data from measurement devices in the grid together with traditional load flow analysis. However, some of the major limitations of existing impact-analysis techniques include (1) inability to capture uncertainty, (2) high computational burden; and (3) lack of foresight. This dissertation addresses these research gaps by proposing computationally efficient, yet accurate, sensitivity frameworks that help simplify planning and operation of modern distribution systems. First, a novel probabilistic sensitivity framework is developed to quantify the impact of grid-edge technologies, e.g., DERs and EVs, on line losses for balanced and unbalanced distribution systems. Results show that the developed approaches offer high approximation accuracy and four-orders faster execution time when compared to classical approaches. Secondly, this dissertation develops a novel preemptive voltage monitoring approach based on low-complexity probabilistic voltage sensitivity analysis that predicts the probability distribution of node voltage magnitudes, which is then used to identify nodes that may violate the nominal operational limits with high probability. The proposed approach offers over 95\% accuracy in predicting voltage violations. To address the complexity-accuracy trade-off with existing planning methods, this dissertation develops a novel spatio-temporal sensitivity approach to analyze both spatial and temporal uncertainties associated with DER injections. The spatio-temporal framework is used to quantify voltage violations for various PV penetration levels and subsequently determine the hosting capacity of the system without the need to examine a large number of scenarios. This framework is further extended for EV charging station allocation to ensure minimum active power losses and voltage deviations. Thirdly, this dissertation develops a new system voltage influencer (SVI) paradigm that identifies strategic locations in the system that have the highest influence on node voltages. The SVI nodes are ranked and used within a stochastic control setup to eliminate voltage violations. The development of SVI paradigm is essential given the increased number of behind-the-meter and utility-controlled DERs, where it is becoming difficult to select optimal control points and counter the impact of the introduced uncertainties. The developed approaches in this dissertation help system operators quickly reveal impending voltage and loss issues resulting from power changes at the grid edge

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