Electric Power Grid Research Articles

Tri-level mixed-binary linear programming: Solution approaches and application in defending critical infrastructure

Decentralized decision-making is becoming more ubiquitous in different organizations that often follow a hierarchical structure. To model these problems, multi-level programming has been suggested as a suitable methodology for modeling the interaction between the different levels of decisions. However, multi-level programming, even for the case of bi-levels, is known to be strongly NP-hard. To address this computational challenge, we develop three different heuristic-based approaches for solving a specific class of tri-level programming problems, in which the leader has direct control over the follower’s decisions to a certain extent, with a common objective function shared at all levels. As expected, each heuristic type offers a trade-off between solution quality and computational time. To illustrate our solution approach, we present an application for defending critical infrastructure to improve its resilience against intentional attacks. In this context we use a defender-attacker-defender model and apply it to electrical power grids. We also propose a modified implementation of a widely adopted enumeration algorithm in this area, with a warm-starting solution technique that significantly enhanced the computational performance of the enumeration algorithm. We test our solution approaches on three electrical transmission networks and present the results of our numerical computations as well as some insights.

The Role of the Electric Vehicle in Smart Homes: Assessment and Future Perspectives

The wide spreading of electric mobility is imposing the application of new technologies of hardware and software for supporting new operation paradigms, not only in the perspective of improving smart grids, but also each time more in the perspective of boosting the preponderance of smart homes. Concretely, nowadays it is clear that the emergent technologiesfor smart homes, targeting power quality and a more efficient power management, are also altering the conventional electrical power grids toward smart grids. In this panorama, by relating the strategic pillars of electric mobility and smart homes, this paper has as main objective the characterization of the role of the electric vehicle (EV) in smart homes, presenting a broad assessment based on the state of the art, as well as creating a link with upcoming advanced operations. From the smart home point of view, since it should be equipped to deal with the requirements of the EV, the assessment of both on-board and off-board EV battery charging systems are considered along the paper. Furthermore, the presence of technologies such as energy storage systems, renewable energy sources and dc electrical appliances in smart homes towards sustainability is also reflected in this paper, but framed from the point of view of an off-board EV battery charging system, since it is more convenient for enabling the interface of these mentioned technologies. Looking the paper as a whole, a relevant contribution is the discussion of future operations for the EV in smart homes, also envisioning the opportunity for ac, dc, or hybrid power grids. Illustrative and strategic results are shown, covering all these features with specific details, not only based on numerical simulations, but also based on experimental results, which were obtained with a proof-of-concept laboratory prototype.

The periodic transient kinetics method for investigation of kinetic process dynamics under realistic conditions: Methanation as an example

Due to rising interest for the integration of chemical energy storage into the electrical power grid, the unsteady-state operation of chemical reactors is gaining more and more attention with emphasis on heterogeneously catalyzed reactions. The transient response of those reactions is influenced by effects on different length scales, ranging from the active surface via the individual porous catalyst particle up to the full-scale reactor. The challenge, however, is to characterize unsteady-state effects under realistic operation conditions and to assign them to distinct transport processes. Therefore, the periodic transient kinetics (PTK) method is introduced, which allows for the separation of kinetic process dynamics at different length scales experimentally under realistic operation conditions. The methodology also provides the capability for statistical analysis of the experimental results and therefore improved reliability of the derived conclusions. Therefore, the PTK method provides the experimental basis for model-based derivation of reaction kinetics valid under dynamic conditions. The applicability of the methodology is demonstrated for the methanation reaction chosen as an example process for heterogeneously catalyzed reactions relevant for chemical energy storage purposes.

Analysis and Adequacy Methodology for Voltage Violations in Distribution Power Grid

This paper proposes a computational process development capable of filling the electric power sector shortage regarding voltage non-conformities identification in electric distribution power grid accounting for loads dynamic behavior at medium and low voltages. Actual distribution power grid data are used, with georeferencing to signal voltage transgressions locations, generate a report with voltage transgression indices and financial reimbursement values provided by legislation. The methodology compares regulatory requirements and makes available in software some possible actions in an attempt to adjust voltage levels, avoiding inconvenience and penalties for energy utilities providers. The method involves a data extractor construction for electricity provider company’s databases, computer simulations and comparison of obtained results with values established in electricity quality control standards. Thus, finding non-conformity locations and determining network adjustments to correct tension indexes in permanent regulation. The proposal features a reduction in electricity utilities operating costs, increasing efficiency in operation and energy quality available to consumers.

Management of Smart Grids: A Review

Electrical power grid has witnessed continuous changes over the past century but phenomenal developments are visible in the last two decades or so. The demand of 24x7x365 power supply, and development in socio-economic status of individuals and nations the demand of electrical power hence grid complexity is rising. Moreover shifting to renewables has opened a whole new can of worms. The conventional approach of grid management is unable to cater the needs of such a complex demand and supply needs. The present work is an approach addressing various modalities, modes of operation and management of smart grids. This includes demand side management in household and non-household scenarios, ICT and cyber security concerns.

Critical Risk Indicators (CRIs) for the electric power grid: a survey and discussion of interconnected effects.

The electric power grid is a critical societal resource connecting multiple infrastructural domains such as agriculture, transportation, and manufacturing. The electrical grid as an infrastructure is shaped by human activity and public policy in terms of demand and supply requirements. Further, the grid is subject to changes and stresses due to diverse factors including solar weather, climate, hydrology, and ecology. The emerging interconnected and complex network dependencies make such interactions increasingly dynamic, posing novel risks, and presenting new challenges to manage the coupled human–natural system. This paper provides a survey of models and methods that seek to explore the significant interconnected impact of the electric power grid and interdependent domains. We also provide relevant critical risk indicators (CRIs) across diverse domains that may be used to assess risks to electric grid reliability, including climate, ecology, hydrology, finance, space weather, and agriculture. We discuss the convergence of indicators from individual domains to explore possible systemic risk, i.e., holistic risk arising from cross-domain interconnections. Further, we propose a compositional approach to risk assessment that incorporates diverse domain expertise and information, data science, and computer science to identify domain-specific CRIs and their union in systemic risk indicators. Our study provides an important first step towards data-driven analysis and predictive modeling of risks in interconnected human–natural systems.

A Bi-Level Model for Detecting and Correcting Parameter Cyber-Attacks in Power System State Estimation

Power system state estimation is an important component of the status and healthiness of the underlying electric power grid real-time monitoring. However, such a component is prone to cyber-physical attacks. The majority of research in cyber-physical power systems security focuses on detecting measurements False-Data Injection attacks. While this is important, measurement model parameters are also a most important part of the state estimation process. Measurement model parameters though, also known as static-data, are not monitored in real-life applications. Measurement model solutions ultimately provide estimated states. A state-of-the-art model presents a two-step process towards simultaneous false-data injection security: detection and correction. Detection steps are χ2 statistical hypothesis test based, while correction steps consider the augmented state vector approach. In addition, the correction step uses an iterative solution of a relaxed non-linear model with no guarantee of optimal solution. This paper presents a linear programming method to detect and correct cyber-attacks in the measurement model parameters. The presented bi-level model integrates the detection and correction steps. Temporal and spatio characteristics of the power grid are used to provide an online detection and correction tool for attacks pertaining the parameters of the measurement model. The presented model is implemented on the IEEE 118 bus system. Comparative test results with the state-of-the-art model highlight improved accuracy. An easy-to-implement model, built on the classical weighted least squares solution, without hard-to-derive parameters, highlights potential aspects towards real-life applications.

Echo state network applied for classification of medium voltage insulators

Insulators are components of electrical power grid that have the function of mechanically supporting cables and isolating electrical potential. The proper functioning of the insulators is essential for the continuity in the supply of electrical energy. When an insulator has its properties damaged, disruptive discharges may shut down the system and impairs the network’s reliability. For this reason, classifying adverse conditions is a critical task to keep the system running. In this paper, the echo state network is used for the classification of the insulators based on the ultrasound signal. Hypertuning is applied to automate the evaluation of the parameters and optimize the network to have a general application. The insulators are evaluated in the laboratory under controlled conditions from the application of 7.95 kV (phase-to-ground), under the same conditions that are found in the field. The assessment is made on perforated and contaminated insulators, which were removed from service due to defects. The echo state network achieves 87.36 % accuracy for the multiclassification and 99.99 % for the specific classification of drilling. For comparative analysis, the multilayer perceptron and support-vector machines are evaluated based on the fast fourier transform. The results show that echo state network is promising to classify the evaluated conditions, being more accurate than the multilayer perceptron and support-vector machines based on fast fourier transform.

Threat Assessment Model in Electrical Power Grid Environment

At the heart of every critical national infrastructure, there is a SCADA system. The wide range of supported applications has made this life-saving management system the key element of every industrial control infrastructure in various sectors. The complexity and sophistication of SCADA systems have been increased dramatically over the last few years. Various cyberattacks have targeted these systems by exploiting zero-day vulnerabilities or targeting a nearby Internet-enabled appliance or computer system. To this end, many efforts have been proposed previously to model the threats on SCADA systems to eliminate potential danger and diminish the catastrophic consequences in the physical domain. The main contribution of this work is to present a multi-facets threat assessment model to aid security teams to systematically perform various cybersecurity analysis practices such as penetration testing, vulnerability assessment and risk analysis.

Energy-efficient distributed heterogeneous clustered spectrum-aware cognitive radio sensor network for guaranteed quality of service in smart grid

The development of a modern electric power grid has triggered the need for large-scale monitoring and communication in smart grids for efficient grid automation. This has led to the development of smart grids, which utilize cognitive radio sensor networks, which are combinations of cognitive radios and wireless sensor networks. Cognitive radio sensor networks can overcome spectrum limitations and interference challenges. The implementation of dense cognitive radio sensor networks, based on the specific topology of smart grids, is one of the critical issues for guaranteed quality of service through a communication network. In this article, various topologies of ZigBee cognitive radio sensor networks are investigated. Suitable topologies with energy-efficient spectrum-aware algorithms of ZigBee cognitive radio sensor networks in smart grids are proposed. The performance of the proposed ZigBee cognitive radio sensor network model with its control algorithms is analyzed and compared with existing ZigBee sensor network topologies within the smart grid environment. The quality of service metrics used for evaluating the performance are the end-to-end delay, bit error rate, and energy consumption. The simulation results confirm that the proposed topology model is preferable for sensor network deployment in smart grids based on reduced bit error rate, end-to-end delay (latency), and energy consumption. Smart grid applications require prompt, reliable, and efficient communication with low latency. Hence, the proposed topology model supports heterogeneous cognitive radio sensor networks and guarantees network connectivity with spectrum-awareness. Hence, it is suitable for efficient grid automation in cognitive radio sensor network–based smart grids. The traditional model lacks these capability features.

Adaptive FOPI controller based on the fuzzy supervisory for wind power conversion system equipped by a doubly fed induction generator

Wind power systems have non-linear dynamics and contain many uncertainties such as the parametric uncertainty and the unknown external disturbances. For these reasons, it is a crucial task to design the robust control systems to assure a robust response of wind systems during uncertainties without deteriorating the supplied power quality or stressing the static power converters. This paper designs an adaptive fractional-order proportional-integral (FOPI) control system for a wind power conversion system (WPCS) equipped by a doubly fed induction generator (DFIG) in the electric power grid (EPG) connected mode. The designed adaptive control system combines the robust and intelligent nature of the fuzzy supervisory system (FSS) and simple structure of FOPI controller, where the fuzzy rules are utilized to adjust the FOPI controller parameters based on the error and its rate, resulting in an adaptive fuzzy FOPI (AFFOPI) control system, which can keep the control action touch of the standard FOPI controller and, at the same time, provides the robustness during uncertainties without effect on the power quality supplied to the EPG. Pursuant to the vector control technology of the DFIG, the designed adaptive fuzzy FOPI controllers are applied in both the external loop of power control and in the internal loop of rotor current control, simultaneously. The feasibility of the presented adaptive FOPI control is validated through the numerical results obtained, under different running conditions, using the Matlab/Simulink software.

A Data-Driven Long Time-Series Electrical Line Trip Fault Prediction Method Using an Improved Stacked-Informer Network.

The monitoring of electrical equipment and power grid systems is very essential and important for power transmission and distribution. It has great significances for predicting faults based on monitoring a long sequence in advance, so as to ensure the safe operation of the power system. Many studies such as recurrent neural network (RNN) and long short-term memory (LSTM) network have shown an outstanding ability in increasing the prediction accuracy. However, there still exist some limitations preventing those methods from predicting long time-series sequences in real-world applications. To address these issues, a data-driven method using an improved stacked-Informer network is proposed, and it is used for electrical line trip faults sequence prediction in this paper. This method constructs a stacked-Informer network to extract underlying features of long sequence time-series data well, and combines the gradient centralized (GC) technology with the optimizer to replace the previously used Adam optimizer in the original Informer network. It has a superior generalization ability and faster training efficiency. Data sequences used for the experimental validation are collected from the wind and solar hybrid substation located in Zhangjiakou city, China. The experimental results and concrete analysis prove that the presented method can improve fault sequence prediction accuracy and achieve fast training in real scenarios.

A Hierarchical Resilience Enhancement Framework for Interdependent Critical Infrastructures

Resilience is becoming a key concept for risk assessment and safety management of interdependent critical infrastructures (ICIs). This work proposes a resilience enhancement framework for ICIs. With reference to the accidental event, ex-ante and ex-post solutions for enhancing system resilience are analysed and included into a hierarchical model of resilience enhancement strategies (RES). To provide specific resilience enhancement solutions for ICIs, we integrate the hierarchical model with a model predictive control-based dynamic model of ICI system operation. The relationships between the solutions implemented and their impacts on the system parameters are discussed. A multi-objective optimization (MOO) problem is defined, with the objectives of simultaneously minimizing RES cost and maximizing ICIs resilience. The fast non-dominated sorting genetic algorithm NSGA-II is used to solve the MOO problem. For exemplification, a case study is considered, involving interdependent natural gas network and electric power grid. The results show that the resilience enhancement framework is effective in finding optimal RESs for given ICIs.

Forecasting the hydroelectric power generation of GCMs using machine learning techniques and deep learning (Almus Dam, Turkey)

Renewable energy is one of the most important factors for developed and sustainable societies. However, its utilization in electrical power grid systems can be very challenging regarding rates predictably. Renewable energy depends mainly on environmental conditions such as rainfall-runoff ratios and temperature. Because of that, the expected power production heavily fluctuates, which makes the prediction and calculation of feed-in into the power grid very challenging. The accurate forecasting of energy production is a very crucial issue for power management process. This paper presents the results of deploying Machine Learning Techniques in short-term forecasting of the amount of energy produced of General Circulation Models (GCMs) Data by Almus Dam and Hydroelectric Power Plant in Tokat, Turkey. The study demonstrates the use of modeling techniques in hydropower forecasting process using the predicted monthly hydroelectric power generation data of GCMs from 2018 to 2080. Decision Tree, Deep Learning, Generalized Linear, Gradient Boosted Trees and Random Forest models are utilized to forecast the hydropower production. The results show that the correlation value of the gradient boosted trees model equals 0.717, which means that the gradient boosted trees model is the most successful model for the present data. The gradient boosted trees model used in the prediction process for each GCM in each scenario is 4.5 and 8.5. The results show that there are small differences between the models, which means that the predictions are going in similar directions for all these models.

Technical Design And Operational Control Of A Decentralized Microgrid In Rural Area

Microgrids incorporating Renewable Energy (RE) sources are being used nowadays to overcome the lack of electric power supply or grid instabilities in rural areas. Microgrids are decentralized and performant solutions to distribute electric power and to supply the consumers of a community with energy. They can be used to provide stable electrical energy to hospitals, companies and residential areas and therefore, they can contribute significantly to rural development. Based on renewable sources, they are climate neutral as well. Very often, in regular operation, a Microgrid is connected with an utility national or another distributed grid. In case of an utility grid fault occurrence, the Microgrid can still provide power since it incorporates renewable sources. However, since renewable sources like photovoltaics or wind power are volatile in supply, grid instabilities, voltage and frequency fluctuations and harmonic distortions in the Microgrid can occur. This paper focuses on developing a Microgrid (M.G.) model using MATLAB Simulink and analyzing its issues at different operational modes assuming a photovoltaic generator and a coupling to an utility grid as power sources. In order to analyze and predict the behavior of the Microgrid, deep learning methods based on Auto Regressive Moving Average (ARIMA) and Artificial Neural Networks (ANN) will be applied. It is shown that these methods allow to optimize the operation modes of the Microgrid. For instance, a balance between power supply and demand at different times could be reached and lead to economic efficiency and feasibility.

Sun, Wind and Waves: EV Fossil Fuel Use and Emissions on an Isolated, Oil-Dependent Hawaiian Island

Electric power grids in remote communities around the world tend to be highly oil-dependent, unlike large, interconnected grids. Consequently, self-contained power grids such as the Hawaiian Islands’ have become testbeds for aggressive renewable energy integration (PV, wind, and ocean energy) and transportation electrification. However, there remains a lack of critical analysis for remote communities to determine the benefits of transitioning from internal combustion engine (ICE) vehicles to plug-in electric vehicles (EVs). This case study examines the impacts of this transition to EVs and renewable power generation on fossil fuel use and CO2 emissions on the oil-dependent Island of Oahu, Hawaii. Average passenger EVs were found to consume seven times less fossil fuel (the equivalent of 66 gallons of gasoline (GGe), than their gasoline-powered counterparts (455 gallons) in 2020. Average EVs also cut emissions in half, (2 MTCO2 versus 4 MTCO2). Several renewable power and EV transition scenarios were modeled to assess impacts out to 2050. Fossil fuel use and emissions plummet with more clean power and increasing EV numbers. By 2045, in the most ambitious scenario, all gasoline- and diesel-powered vehicles (passenger and freight) will consume a total of 8.8 billion GGe, and EVs 0.090 billion GGe (1%). ICE CO2 emissions will total 80 MMT, and EVs 4.4 MMT (5.5%). By 2050, the anticipated transition to electric passenger and freight vehicles combined with renewable power will lead to 99% less fossil fuel consumed, and 93% less CO2 emitted.

Energy Transition on Islands with the Presence of Electric Vehicles: A Case Study for Porto Santo

Facilitating high-RES (Renewable Energy Resources) penetration via integrated resource management is considered a promising strategy on different islands worldwide. For this work, the Portuguese island of Porto Santo is established as a test bench using actual data from the island. Given its geographical condition and energy needs, integrating the management of different resources (namely, the electric power grid with the water supply system, intensive in-land transportation electrification, and the energy storage applications) is analyzed by this work to achieve a power grid relying entirely on RES. The energy storage utilization and the purposeful manipulations in demand patterns have been perceived as instruments to reduce RES availability and consumption mismatch. Electric Vehicles (EV) could be perceived as a reliable alternative to centralized storage systems, acting either as a load or power resource (generator), providing the required flexibility for power systems to uptake the increased RES and maintaining the balance of supply and demand. This means that EVs could contribute to greening both the power system and the transport sectors. Hence, the impact of the EVs’ penetration level on the island was assessed through a gradual increase in the EVs’ total number (from 0 to a fleet containing 2500 vehicles). Furthermore, a collaboration between the water supply (seawater desalination) and the energy sector is proposed. The obtained results revealed that the optimized management of resources could significantly help the overall energy system (power grid) to rely only on RES (solar and wind energies). The curtailments decreased relatively (maximizing the RES share), while the polluter conventional power plant remained off over the simulation periods.

Energy Community Flexibility Solutions to Improve Users’ Wellbeing

Energy communities, mostly microgrid based, are a key stakeholder of modern electrical power grids. Operating a microgrid based energy community is a challenging topic due to the involved uncertainties, complexities and often conflicting objectives. The aim of this paper is to present a novel methodology demonstrating that energy community flexibility can contribute to each community member’s wellbeing when a grid fault occurs. A three-house energy community will be modelled considering as consumption sources non-controllable and controllable devices in each house. As power supply sources, PV systems installed in a community’s houses are considered, as well as the power obtained from main grid. Each house’s flexibility inside the community will be studied to improve the management of loads during a fault occurrence. Moreover, three different scenarios will be considered with different available power in the community. With these simulations, it was possible to understand that houses’ energy flexibility can be used under a fault situation, either to maintain the users’ wellbeing or to change the energy flow. Furthermore, energy flexibility can be used to create better energy price markets, to improve the resilience of the grid, or even to consider electrical vehicles’ connection to a community’s grid.

Interface Converters for Residential Battery Energy Storage Systems: Practices, Difficulties and Prospects

Recent trends in building energy systems such as local renewable energy generation have created a distinct demand for energy storage systems to reduce the influence and dependency on the electric power grid. Under the current market conditions, a range of commercially available residential energy storage systems with batteries has been produced. This paper addresses the area of energy storage systems from multiple directions to provide a broader view on the state-of-the-art developments and trends in the field. Present standards and associated limitations of storage implementation are briefly described, followed by the analysis of parameters and features of commercial battery systems for residential applications. Further, the power electronic converters are reviewed in detail, with the focus on existing and perspective non-isolated solutions. The analysis covers well-known standard topologies, including buck-boost and bridge, as well as emerging solutions based on the unfolding inverter and fractional/partial power converters. Finally, trends and future prospects of the residential battery storage technologies are evaluated.

Unified Power Converter Based on a Dual-Stator Permanent Magnet Synchronous Machine for Motor Drive and Battery Charging of Electric Vehicles

An electric vehicle (EV) usually has two main power converters, namely one for the motor drive system and another for the battery-charging system. Considering the similarities between both converters, a new unified power converter for motor drive and battery charging of EVs is propounded in this paper. By using a single unified power converter, the cost, volume, and weight of the power electronics are reduced, thus also making possible a reduction in the final price of the EV. Moreover, the proposed unified power converter has the capability of bidirectional power flow. During operation in traction mode, the unified power converter controls motor driving and regenerative braking. Additionally, during operation in battery-charging mode, with the EV plugged into the electrical power grid, the unified power converter controls the power flow for slow or fast battery charging (grid-to-vehicle (G2V) mode), or for discharging of the batteries (vehicle-to-grid (V2G) mode). Specifically, this paper presents computer simulations and experimental validations for operation in both motor-driving and slow battery-charging mode (in G2V and V2G modes). It is demonstrated that the field-oriented control used in the traction system presents good performance for different values of mechanical load and that the battery-charging system operates with high levels of power quality, both in G2V and in V2G mode.