A planning tool for reliability assessment of overhead distribution lines in hybrid AC/DC grids

Integration of DC grids into AC networks will realize hybrid AC/DC grids, a new energetic paradigm which will become widespread in the future due to the increasing availability of DC-based generators, loads and storage systems. Furthermore, the huge connection of intermittent renewable sources to distribution grids could cause security and congestion issues affecting line behaviour and reliability performance. This paper aims to propose a planning tool for congestion forecasting and reliability assessment of overhead distribution lines. The tool inputs consist of a single line diagram of a real or synthetic grid and a set of 24-h forecasting time series concerning climatic conditions and grid resource operative profiles. The developed approach aims to avoid congestions criticalities, taking advantage of optimal active power dispatching among “congestion-nearby resources”. A case study is analysed to validate the implemented control strategy considering a modified IEEE 14-Bus System with introduction of renewables. The tool also implements reliability prediction formulas to calculate an overhead line reliability function in congested and congestions-avoided conditions. A quantitative evaluation underlines the reliability performance achievable after the congestion strategy action

Real-Time Cyber-Physical Power System Testbed for Optimal Power Flow Study using Co-Simulation Framework

Today’s power system is transforming into an increasingly complex entity, consisting of numerous components, such as transmission lines, controllable loads, and especially different types of distributed renewable energy sources (DERs). With the growing integration of DERs into the grid, multiple operational challenges arise, including overvoltage, undervoltage, or increased energy losses. Resolving these issues demands the implementation of both advanced and effective control strategies. As the dynamic power system evolves by incorporating new technologies, these control strategies need to consider other different technical aspects, such as communication protocols or real-time considerations. Additionally, the rise of smart metering devices has transformed conventional power systems into cyber-physical power systems (CPPS), which can integrate the advanced control strategies into the cyber layer. Given the operating challenges and the integration of diverse technologies, it is proposed that a CPPS testbed platform constitutes an ideal solution for developing and validating technologies in future smart grids. For this purpose, this paper introduces a co-simulation framework for implementing a CPPS testbed, utilising the real-time simulator, Typhoon HIL, within a laboratory environment. Additionally, it presents a proposed optimal power flow (OPF) control strategy that emphasises two key objectives, minimisation of operating costs and power network loss. The investigation is illustrated by a modified version of IEEE 39-bus test system with the high integration of DERs. The findings indicate that adopting a CPPS testbed can be advantageous for implementing real-time research on monitoring and control in a wide area network.publishedVersio

Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm

The deployment of utility-scale energy storage systems (ESSs) can be a significant avenue for improving the performance of distribution networks. An optimally placed ESS can reduce power losses and line loading, mitigate peak network demand, improve voltage profile, and in some cases contribute to the network fault level diagnosis. This paper proposes a strategy for optimal placement of distributed ESSs in distribution networks to minimize voltage deviation, line loading, and power losses. The optimal placement of distributed ESSs is investigated in a medium voltage IEEE-33 bus distribution system, which is influenced by a high penetration of renewable (solar and wind) distributed generation, for two scenarios: (1) with a uniform ESS size and (2) with non-uniform ESS sizes. System models for the proposed implementations are developed, analyzed, and tested using DIgSILENT PowerFactory. The artificial bee colony optimization approach is employed to optimize the objective function parameters through a Python script automating simulation events in PowerFactory. The optimization results, obtained from the artificial bee colony approach, are also compared with the use of a particle swarm optimization algorithm. The simulation results suggest that the proposed ESS placement approach can successfully achieve the objectives of voltage profile improvement, line loading minimization, and power loss reduction, and thereby significantly improve distribution network performance

Systematic mapping of power system models: Expert survey

The power system is one of the main subsystems of larger energy systems. It is a complex system in itself, consisting of an ever-changing infrastructure used by a large number of actors of very different sizes. The boundaries of the power system are characterised by ever-evolving interfaces with equally complex subsystems such as gas transport and distribution, heating and cooling, and, increasingly, transport. The situation is further complicated by the fact that electricity is only a carrier, able to fulfil demand for such things as lighting, heat or mobility. One specific and fundamental feature of the electricity system is that demand and generation must match at any time, while satisfying technical and economic constraints. In most of the world’s power systems, only relatively small quantities of electricity can be stored, and only for limited periods of time. A detailed analysis of supply and demand is thus needed for short time intervals. Mathematical models facilitate power system planning, operation, transmission and distribution, demonstrating problems that need to be solved over different timescales and horizons. The use of modelling to understand these processes is not only vital for the system’s direct actors, i.e. the companies involved in the generation, trade, transmission, distribution and use of electricity, but also for policy-makers and regulators. Power system models can provide evidence to support policy-making at European Union, Member State and Regional level. As a consequence of the growth in computing power, mathematical models for power systems have become more accessible. The number of models available worldwide, and the degree of detail they provide, is growing fast. A proper mapping of power system models is therefore essential in order to: - provide an overview of power system models and their applications available in, or used by, European organisations; - analyse their modelling features; - identify modelling gaps. Few reviews have been conducted to date of the power system modelling landscape. The mission of the Knowledge for the Energy Union Unit of the Joint Research Centre (JRC) is to support policies related to the Energy Union by anticipating, mapping, collating, analysing, quality checking and communicating all relevant data/knowledge, including knowledge gaps, in a systematic and digestible way. This report therefore constitutes: - From the energy modelling perspective, a useful mapping exercise that could help promote knowledge-sharing and thus increase efficiency and transparency in the modelling community. It could trigger new, unexplored avenues of research. It also represents an ideal starting point for systematic review activities in the context of the power system. - From the knowledge management perspective, a useful blueprint to be adopted for similar mapping exercises in other thematic areas. Finally, this report is aligned with the objectives of the European Commission's Competence Centre on Modelling, (1) launched on 26 October 2017 and hosted by the JRC, which aims to promote a responsible, coherent and transparent use of modelling to support the evidence base for European Union policies. In order to meet the objectives of this report, an online survey was used to collect detailed and relevant information about power system models. The participants’ answers were processed to categorise and describe the modelling tools identified. The survey, conducted by the Knowledge for the Energy Union Unit of the JRC, comprised a set of questions for each model to ascertain its basic information, its users, software characteristics, modelling properties, mathematical description, policy-making applications, selected references, and more. The survey campaign was organised in two rounds between April and July 2017. 228 surveys were sent to power system experts and organisations, and 82 questionnaires were completed. The answers were processed to map the knowledge objectively. (2) The main results of the survey can be summarised as follows: - Software-related features: about two thirds of the models require third-party software such as commercial optimisation solvers or off-the-shelf software. Only 14% of the models are open source, while 11% are free to download. - Modelling-related features: models are mostly defined as optimisation problems (78%) rather than simulation (33%) or equilibrium problems (13%). 71% of the models solve a deterministic problem while 41% solve probabilistic or stochastic problems. - Modelled power system problems: the economic dispatch problem is the most commonly modelled problem with a share of approximately 70%, followed by generation expansion planning, unit commitment, and transmission expansion planning, with around 40‒43% each. Most of the models (57%) have non-public input data while 31% of models use open input data. - Modelled technologies: hydro, wind, thermal, storage and nuclear technologies are widely taken into account, featuring in around 83‒94% of models. However, HVDC, wave tidal, PSTs, and FACTS (3) are not often found unless the analysis is specifically performed for those technologies. - Applicability in the context of European energy policy: more than half of the mapped models (56%) were used to answer a specific policy question. Of the five Energy Union strategic dimensions, integration of the European Union internal energy market was addressed the most often (27%), followed by climate action (23%), research, innovation and competitiveness (21%), and energy efficiency (15%). This report includes JRC recommendations based on the results of the survey, on future research avenues for power system modelling and its applicability within the Energy Union strategic dimensions. More attention should be paid, for example, to model uncertainty features, and collaboration among researchers and practitioners should be promoted to intensify research into specific power system problems such as AC (4) optimal power flow. The report includes factsheets for each model analysed, summarising relevant characteristics based on the participants’ answers. While this report represents a scientific result per se, one of the expected (and welcomed) outcomes of this mapping exercise is to raise awareness of power system modelling activities among European policy makers.JRC.C.7-Knowledge for the Energy Unio

Smart management strategies of utility-scale energy storage systems in power networks

Power systems are presently experiencing a period of rapid change driven by various interrelated issues, e.g., integration of renewables, demand management, power congestion, power quality requirements, and frequency regulation. Although the deployment of Energy Storage Systems (ESSs) has been shown to provide effective solutions to many of these issues, misplacement or non-optimal sizing of these systems can adversely affect network performance. This present research has revealed some novel working strategies for optimal allocation and sizing of utility-scale ESSs to address some important issues of power networks at both distribution and transmission levels. The optimization strategies employed for ESS placement and sizing successfully improved the following aspects of power systems: performance and power quality of the distribution networks investigated, the frequency response of the transmission networks studied, and facilitation of the integration of renewable generation (wind and solar). This present research provides effective solutions to some real power industry problems including minimizationof voltage deviation, power losses, peak demand, flickering, and frequency deviation as well as rate of change of frequency (ROCOF). Detailed simulation results suggest that ESS allocation using both uniform and non-uniform ESS sizing approaches is useful for improving distribution network performance as well as power quality. Regarding performance parameters, voltage profile improvement, real and reactive power losses, and line loading are considered, while voltage deviation and flickers are taken into account as power quality parameters. Further, the study shows that the PQ injection-based ESS placement strategy performs better than the P injection-based approach (in relation to performance improvement), providing more reactive power compensations. The simulation results also demonstrate that obtaining the power size of a battery ESS (MVA) is a sensible approach for frequency support. Hence, an appropriate sizing of grid-scale ESSs including tuning of parameters Kp and Tip (active part of the PQ controller) assist in improving the frequency response by providing necessary active power. Overall, the proposed ESS allocation and sizing approaches can underpin a transition plan from the current power grid to a future one

Regulation of Disturbance Magnitude for Locational Frequency Stability Using Machine Learning

Power systems must maintain the frequency within acceptable limits when subjected to a disturbance. To ensure this, the most significant credible disturbance in the system is normally used as a benchmark to allocate the Primary Frequency Response (PFR) resources. However, the overall reduction of system inertia due to increased integration of Converter Interfaced Generation (CIG) implies that systems with high penetration of CIG require more frequency control services, which are either costly or unavailable. In extreme cases of cost and scarcity, regulating the most significant disturbance magnitude can offer an efficient solution to this problem. This paper proposes a Machine Learning (ML) based technique to regulate the disturbance magnitude of the power system to comply with the frequency stability requirements i.e., Rate of Change of Frequency (RoCoF) and frequency nadir. Unlike traditional approaches which limit the disturbance magnitude by using the Centre Of Inertia (COI) because the locational frequency responses of the network are analytically hard to derive, the proposed method is able to capture such complexities using data-driven techniques. The method does not rely on the computationally intensive RMS-Time Domain Simulations (TDS), once trained offline. Consequently, by considering the locational frequency dynamics of the system, operators can identify operating conditions (OC) that fulfil frequency requirements at every monitored bus in the network, without the allocation of additional frequency control services such as inertia. The effectiveness of the proposed method is demonstrated on the modified IEEE 39 Bus network

Low Voltage Distribution Networks Modeling and Unbalanced (Optimal) Power Flow: A Comprehensive Review

The rapid increase of distributed energy resources (DERs) installation at residential and commercial levels can pose significant technical issues on the voltage levels and capacity of the network assets in distribution networks. Most of these issues occur in low-voltage distribution networks (LVDNs) or near customer premises. A lack of understanding of the networks and advanced planning approaches by distribution network providers (DNSPs) has led to rough estimations for maximum DERs penetration levels that LVDNs can accommodate. These issues might under- or over-estimate the actual hosting capacity of the LVDNs. Limited available data on LVDNs' capacity to host DERs makes planning, installing, and connecting new DERs problematic and complex. In addition, the lack of transparency in LVDN data and information leads to model simplifications, such as ignoring the phase imbalance. This can lead to grossly inaccurate results. The main aim of this paper is to enable the understanding of the true extent of local voltage excursions to allow more targeted investment, improve the network's reliability, enhance solar performance distribution, and increase photovoltaic (PV) penetration levels in LVDNs. Therefore, this paper reviews the state-of-the-art best practices in modeling unbalanced LVDNs as accurately as possible to avoid under- or over-estimation of the network's hosting capacity. In addition, several PV system modeling variations are reviewed, showing their limitations and merits as a trade-off between accuracy, computational burden, and data availability. Moreover, the unbalanced power flow representations, solving algorithms, and available tools are explained extensively by providing a comparative study between these tools and the ones most commonly used in Australia. This paper also presents an overview of unbalanced optimal power flow representations with their related objectives, solving algorithms, and tools

Voltage Stability Margin Estimation Using Machine Learning Tools

Real-time voltage stability assessment, via conventional methods, is a difficult task due to the required large amount of information, high execution times and computational cost. Based on these limitations, this technical work proposes a method for the estimation of the voltage stability margin through the application of artificial intelligence algorithms. For this purpose, several operation scenarios are first generated via Monte Carlo simulations, considering the load variability and the n-1 security criterion. Afterwards, the voltage stability margin of PV curves is determined for each scenario to obtain a database. This information allows structuring a data matrix for training an artificial neural network and a support vector machine, in its regression version, to predict the voltage stability margin, capable of being used in real time. The performance of the prediction tools is evaluated through the mean square error and the coefficient of determination. The proposed methodology is applied to the IEEE 14 bus test system, showing so promising results

Online identification of cascading events in power systems with renewable generation using machine learning

This PhD project deals with the Modelling of Cascading Events in Power Systems and their Online Identification with Machine Learning, considering the integration of Renewable Energy Sources. Cascading events involve highly complex dynamic phenomena and in some cases can pose significant challenges to the stability and reliability of power grids, leading even to blackouts. The intermittent nature of renewable generation introduces additional complexities, as the system dynamic behavior following a contingency becomes more unpredictable. Consequently, there is an increasing need for cascading event identification methods that can effectively handle these emerging challenges and ensure secure network operation. Machine Learning methods can extract complex relationships from power system data, by capturing the underlying dynamics, offering a promising tool for the accurate and timely identification of the online system state. In addition, due to the extensive installation of Phasor Measurement Units in modern power systems, it is possible to acquire measurement data related to electrical system variables in close-to-real time. The thesis first delves into the understanding of cascading events appearance, as defined by the discrete action of protection devices, using detailed dynamic simulations and considering uncertainties associated with network operating conditions, contingencies and renewable generation. To address the online nature of the problem, supervised machine learning methods that utilize measurement data are developed. Different contemporary machine learning approaches are investigated, to identify the most suitable techniques for the detection of the appearance of cascading events, formulated as a binary classification problem, and the prediction of the reason of the upcoming cascading event, formulated as a multi-class classification problem. Furthermore, this thesis explores the challenges associated with the application of machine learning models on power system data, such as the online inference time, class imbalance, practical considerations related to measurement data and investigates techniques for model explainability to enhance the trustworthiness of the developed models. The contributions of this thesis lie in the development of machine learning-based techniques for online identification of cascading events in power systems, enabling more proactive and efficient situational awareness. These insights have the potential to significantly enhance the resilience and stability of power grids, minimizing the risk of large-scale blackouts and improving the overall reliability of the system. Georgios Nakas is sponsored through Engineering & Physical Sciences Research Council (EPSRC) Research Excellence Award (REA) and is supervised by Dr. Panagiotis Papadopoulos and Professor Graeme Burt.This PhD project deals with the Modelling of Cascading Events in Power Systems and their Online Identification with Machine Learning, considering the integration of Renewable Energy Sources. Cascading events involve highly complex dynamic phenomena and in some cases can pose significant challenges to the stability and reliability of power grids, leading even to blackouts. The intermittent nature of renewable generation introduces additional complexities, as the system dynamic behavior following a contingency becomes more unpredictable. Consequently, there is an increasing need for cascading event identification methods that can effectively handle these emerging challenges and ensure secure network operation. Machine Learning methods can extract complex relationships from power system data, by capturing the underlying dynamics, offering a promising tool for the accurate and timely identification of the online system state. In addition, due to the extensive installation of Phasor Measurement Units in modern power systems, it is possible to acquire measurement data related to electrical system variables in close-to-real time. The thesis first delves into the understanding of cascading events appearance, as defined by the discrete action of protection devices, using detailed dynamic simulations and considering uncertainties associated with network operating conditions, contingencies and renewable generation. To address the online nature of the problem, supervised machine learning methods that utilize measurement data are developed. Different contemporary machine learning approaches are investigated, to identify the most suitable techniques for the detection of the appearance of cascading events, formulated as a binary classification problem, and the prediction of the reason of the upcoming cascading event, formulated as a multi-class classification problem. Furthermore, this thesis explores the challenges associated with the application of machine learning models on power system data, such as the online inference time, class imbalance, practical considerations related to measurement data and investigates techniques for model explainability to enhance the trustworthiness of the developed models. The contributions of this thesis lie in the development of machine learning-based techniques for online identification of cascading events in power systems, enabling more proactive and efficient situational awareness. These insights have the potential to significantly enhance the resilience and stability of power grids, minimizing the risk of large-scale blackouts and improving the overall reliability of the system. Georgios Nakas is sponsored through Engineering & Physical Sciences Research Council (EPSRC) Research Excellence Award (REA) and is supervised by Dr. Panagiotis Papadopoulos and Professor Graeme Burt