Ancillary Services in Hybrid AC/DC Low Voltage Distribution Networks

In the last decade, distribution systems are experiencing a drastic transformation with the advent of new technologies. In fact, distribution networks are no longer passive systems, considering the current integration rates of new agents such as distributed generation, electrical vehicles and energy storage, which are greatly influencing the way these systems are operated. In addition, the intrinsic DC nature of these components, interfaced to the AC system through power electronics converters, is unlocking the possibility for new distribution topologies based on AC/DC networks. This paper analyzes the evolution of AC distribution systems, the advantages of AC/DC hybrid arrangements and the active role that the new distributed agents may play in the upcoming decarbonized paradigm by providing different ancillary services.Ministerio de Economía y Competitividad ENE2017-84813-RUnión Europea (Programa Horizonte 2020) 76409

Wide-Area Time-Synchronized Closed-Loop Control of Power Systems And Decentralized Active Distribution Networks

The rapidly expanding power system grid infrastructure and the need to reduce the occurrence of major blackouts and prevention or hardening of systems against cyber-attacks, have led to increased interest in the improved resilience of the electrical grid. Distributed and decentralized control have been widely applied to computer science research. However, for power system applications, the real-time application of decentralized and distributed control algorithms introduce several challenges. In this dissertation, new algorithms and methods for decentralized control, protection and energy management of Wide Area Monitoring, Protection and Control (WAMPAC) and the Active Distribution Network (ADN) are developed to improve the resiliency of the power system. To evaluate the findings of this dissertation, a laboratory-scale integrated Wide WAMPAC and ADN control platform was designed and implemented. The developed platform consists of phasor measurement units (PMU), intelligent electronic devices (IED) and programmable logic controllers (PLC). On top of the designed hardware control platform, a multi-agent cyber-physical interoperability viii framework was developed for real-time verification of the developed decentralized and distributed algorithms using local wireless and Internet-based cloud communication. A novel real-time multiagent system interoperability testbed was developed to enable utility independent private microgrids standardized interoperability framework and define behavioral models for expandability and plug-and-play operation. The state-of-theart power system multiagent framework is improved by providing specific attributes and a deliberative behavior modeling capability. The proposed multi-agent framework is validated in a laboratory based testbed involving developed intelligent electronic device prototypes and actual microgrid setups. Experimental results are demonstrated for both decentralized and distributed control approaches. A new adaptive real-time protection and remedial action scheme (RAS) method using agent-based distributed communication was developed for autonomous hybrid AC/DC microgrids to increase resiliency and continuous operability after fault conditions. Unlike the conventional consecutive time delay-based overcurrent protection schemes, the developed technique defines a selectivity mechanism considering the RAS of the microgrid after fault instant based on feeder characteristics and the location of the IEDs. The experimental results showed a significant improvement in terms of resiliency of microgrids through protection using agent-based distributed communication

Impact Assessment of Hypothesized Cyberattacks on Interconnected Bulk Power Systems

The first-ever Ukraine cyberattack on power grid has proven its devastation by hacking into their critical cyber assets. With administrative privileges accessing substation networks/local control centers, one intelligent way of coordinated cyberattacks is to execute a series of disruptive switching executions on multiple substations using compromised supervisory control and data acquisition (SCADA) systems. These actions can cause significant impacts to an interconnected power grid. Unlike the previous power blackouts, such high-impact initiating events can aggravate operating conditions, initiating instability that may lead to system-wide cascading failure. A systemic evaluation of "nightmare" scenarios is highly desirable for asset owners to manage and prioritize the maintenance and investment in protecting their cyberinfrastructure. This survey paper is a conceptual expansion of real-time monitoring, anomaly detection, impact analyses, and mitigation (RAIM) framework that emphasizes on the resulting impacts, both on steady-state and dynamic aspects of power system stability. Hypothetically, we associate the combinatorial analyses of steady state on substations/components outages and dynamics of the sequential switching orders as part of the permutation. The expanded framework includes (1) critical/noncritical combination verification, (2) cascade confirmation, and (3) combination re-evaluation. This paper ends with a discussion of the open issues for metrics and future design pertaining the impact quantification of cyber-related contingencies

Risk-Based Machine Learning Approaches for Probabilistic Transient Stability

Power systems are getting more complex than ever and are consequently operating close to their limit of stability. Moreover, with the increasing demand of renewable wind generation, and the requirement to maintain a secure power system, the importance of transient stability cannot be overestimated. Considering its significance in power system security, it is important to propose a different approach for enhancing the transient stability, considering uncertainties. Current deterministic industry practices of transient stability assessment ignore the probabilistic nature of variables (fault type, fault location, fault clearing time, etc.). These approaches typically provide a conservative criterion and can result in expensive expansion plans or conservative operating limits. With the increasing system uncertainties and widespread electricity market deregulation, there is a strong inevitability to incorporate probabilistic transient stability (PTS) analysis. Moreover, the time-domain simulation approach, for transient stability evaluation, involving differential-algebraic equations, can be very computationally intensive, especially for a large-scale system, and for online dynamic security assessment (DSA). The impact of wind penetration on transient stability is critical to investigate, as it does not possess the inherent inertia of synchronous generators. Thus, this research proposes risk-based, machine learning (ML) approaches, for PTS enhancement by replacing circuit breakers, including the impact of wind generation. Artificial Neural Network (ANN) was used for predicting the benefit-cost ratio (BCR) to reduce the computation effort. Moreover, both ANN and support vector machine (SVM) were used and consequently, were compared, for PTS classification, for online DSA. The training of the ANN and SVM was accomplished using suitable system features as inputs, and PTS status indicator as the output. DIgSILENT PowerFactory and MATLAB was utilized for transient stability simulations (for obtaining training data for ML algorithms), and applying ML algorithms, respectively. Results obtained for the IEEE 14-bus test system demonstrated that the proposed ML methods offer a fast approach for PTS prediction with a fairly high accuracy, and thereby, signifying a strong possibility for ML application in probabilistic DSA. Advisor: Sohrab Asgarpoo

Modeling and Real-Time Scheduling of DC Platform Supply Vessel for Fuel Efficient Operation

DC marine architecture integrated with variable speed diesel generators (DGs) has garnered the attention of the researchers primarily because of its ability to deliver fuel efficient operation. This paper aims in modeling and to autonomously perform real-time load scheduling of dc platform supply vessel (PSV) with an objective to minimize specific fuel oil consumption (SFOC) for better fuel efficiency. Focus has been on the modeling of various components and control routines, which are envisaged to be an integral part of dc PSVs. Integration with photovoltaic-based energy storage system (ESS) has been considered as an option to cater for the short time load transients. In this context, this paper proposes a real-time transient simulation scheme, which comprises of optimized generation scheduling of generators and ESS using dc optimal power flow algorithm. This framework considers real dynamics of dc PSV during various marine operations with possible contingency scenarios, such as outage of generation systems, abrupt load changes, and unavailability of ESS. The proposed modeling and control routines with real-time transient simulation scheme have been validated utilizing the real-time marine simulation platform. The results indicate that the coordinated treatment of renewable based ESS with DGs operating with optimized speed yields better fuel savings. This has been observed in improved SFOC operating trajectory for critical marine missions. Furthermore, SFOC minimization at multiple suboptimal points with its treatment in the real-time marine system is also highlighted

Power quality enhancement in electricity networks using grid-connected solar and wind based DGs

The integration of DG into utility networks has significantly increased over the past years primarily as a result of growing energy demand, coupled with the environmental impacts posed by conventional fossil fuel-based power generation. The prominent DG technologies which are capable of meeting bulk energy demands and are clean energy sources are wind and solar energy sources. The resources for solar and wind based DG are available in abundance in most geographical locations in South Africa and the rest of Africa. Through the Renewable Energy Independent Power Producer Procurement Programme (REIPPPP) introduced by the South African government in 2011, 3 920 MW of renewable energy has been procured to date. Out of this, solar and wind energy constitute 2 200 MW and 960 MW, respectively. Grid integration of solar and wind-based intermittent DGs may however pose negative impacts on the quality of power supplied by the utility network. Some of the detrimental impacts of DG include voltage fluctuations, flicker, etc. which are in general categorised as power quality (PQ) problems. The proper planning of DG integration is required to mitigate the negative impacts they pose on system's PQ to ensure that the performance of the utility network is enhanced in terms of the overall PQ improvement of the system. This dissertation reviews general PQ problems in utility networks with DG integration and whether poor planning of DG integration affects PQ negatively. The work emphasizes on the impacts of grid integration of wind and solar PV sources on power quality. It investigates the manner in which wind and solar energy systems differ in their impacts and capacity to improve PQ of the network in terms of a number of factors such as point of integration and capacity of DG, type of DG, network loading, etc. The role of grid-integrated DG in PQ improvement in electricity network is also investigated by exploring different PQ improvement techniques. The networks considered for the grid integration of DG for PQ improvement in this work are the IEEE 9-bus sub-transmission network at the nominal voltage of 230kV and the IEEE 33-bus distribution network at the nominal voltage of 12 kV. The aspects essential for facilitating proper planning of DG integration for PQ improvement and total loss reduction are investigated and the comparative analysis is made between grid integration of wind and solar PV based DGs. The simulations of different case studies in this work are done using DIgSILENT PowerFactory version 14.1 as well as coding in MATLAB. The cases studies conducted are aimed at facilitating the proper planning of grid integration of wind and solar PV-based DGs by comparing their PQ improvement capabilities under different scenarios. First the investigation of how their location and capacity affect the network voltage profiles and active power losses is conducted. Their ability to improve the system's PQ is also studied by observing PQ improvement strategies such as voltage control, installation of energy storage and the optimal placement of DGs under different scenarios. In order to account for the weakness of most South African utility grids, PQ improvement in weak networks with DG integration is also studied by investigating how DG integration in networks with different grid strengths affect the system's PQ. The results provide an understanding of the role of grid integration of wind and solar based DGs on PQ which is useful in the planning of grid integration of RE, particularly in South African electricity networks. The results revealed that the location and capacity of integrated DGs indeed affect the quality of power as well as active power losses in the grid. It is also established that a significant improvement in network's PQ and line loss reduction can be achieved in networks with wind and solar integration. The results however indicated that wind and solar PV based DGs differ in their impacts and capacity to improve the quality of power in the network. Furthermore, the results revealed that wind and solar plants integration into weak utility grids may pose adverse impacts on the system's PQ. It was however established that including reactive power control devices such as STATCOM and SVC at the PCC can successfully improve the system's PQ and enable grid code compliance in electricity networks with DG integration

Transient stability assessment of hybrid distributed generation using computational intelligence approaches

Includes bibliographical references.Due to increasing integration of new technologies into the grid such as hybrid electric vehicles, distributed generations, power electronic interface circuits, advanced controllers etc., the present power system network is now more complex than in the past. Consequently, the recent rate of blackouts recorded in some parts of the world indicates that the power system is stressed. The real time/online monitoring and prediction of stability limit is needed to prevent future blackouts. In the last decade, Distributed Generators (DGs) among other technologies have received increasing attention. This is because DGs have the capability to meet peak demand, reduce losses, due to proximity to consumers and produce clean energy and thus reduce the production of CO₂. More benefits can be obtained when two or more DGs are combined together to form what is known as Hybrid Distributed Generation (HDG). The challenge with hybrid distributed generation (HDG) powered by intermittent renewable energy sources such as solar PV, wind turbine and small hydro power is that the system is more vulnerable to instabilities compared to single renewable energy source DG. This is because of the intermittent nature of the renewable energy sources and the complex interaction between the DGs and the distribution network. Due to the complexity and the stress level of the present power system network, real time/online monitoring and prediction of stability limits is becoming an essential and important part of present day control centres. Up to now, research on the impact of HDG on the transient stability is very limited. Generally, to perform transient stability assessment, an analytical approach is often used. The analytical approach requires a large volume of data, detailed mathematical equations and the understanding of the dynamics of the system. Due to the unavailability of accurate mathematical equations for most dynamic systems, and given the large volume of data required, the analytical method is inadequate and time consuming. Moreover, it requires long simulation time to assess the stability limits of the system. Therefore, the analytical approach is inadequate to handle real time operation of power system. In order to carry out real time transient stability assessment under an increasing nonlinear and time varying dynamics, fast scalable and dynamic algorithms are required. Transient Stability Assessment Of Hybrid Distributed Generation Using Computational Intelligence Approaches These algorithms must be able to perform advanced monitoring, decision making, forecasting, control and optimization. Computational Intelligence (CI) based algorithm such as neural networks coupled with Wide Area Monitoring System (WAMS) such as Phasor Measurement Unit (PMUs) have been shown to successfully model non-linear dynamics and predict stability limits in real time. To cope with the shortcoming of the analytical approach, a computational intelligence method based on Artificial Neural Networks (ANNs) was developed in this thesis to assess transient stability in real time. Appropriate data related to the hybrid generation (i.e., Solar PV, wind generator, small hydropower) were generated using the analytical approach for the training and testing of the ANN models. In addition, PMUs integrated in Real Time Digital Simulator (RTDS) were used to gather data for the real time training of the ANNs and the prediction of the Critical Clearing Time (CCT)

Voltage stability of power systems with renewable-energy inverter-based generators: A review

© 2021 by the authors. The main purpose of developing microgrids (MGs) is to facilitate the integration of renewable energy sources (RESs) into the power grid. RESs are normally connected to the grid via power electronic inverters. As various types of RESs are increasingly being connected to the electrical power grid, power systems of the near future will have more inverter-based generators (IBGs) instead of synchronous machines. Since IBGs have significant differences in their characteristics compared to synchronous generators (SGs), particularly concerning their inertia and capability to provide reactive power, their impacts on the system dynamics are different compared to SGs. In particular, system stability analysis will require new approaches. As such, research is currently being conducted on the stability of power systems with the inclusion of IBGs. This review article is intended to be a preface to the Special Issue on Voltage Stability of Microgrids in Power Systems. It presents a comprehensive review of the literature on voltage stability of power systems with a relatively high percentage of IBGs in the generation mix of the system. As the research is developing rapidly in this field, it is understood that by the time that this article is published, and further in the future, there will be many more new developments in this area. Certainly, other articles in this special issue will highlight some other important aspects of the voltage stability of microgrids