Grid Deployment Research Articles

Multi-node abnormal data identification in distribution networks based on deep learning

With the continuous advancement of the construction of the power grid industry, the lean management of the distribution network is also being fully implemented. Due to unstable communications, system defects, equipment failures, and human factors, data center problems such as missing data and abnormal data collection in the process of power grid deployment and deployment are becoming more and more prominent. To improve the quality and availability of abnormal data in transmission networks, this paper proposes a deep learning-based method for identifying abnormal data of multiple nodes in distribution networks. The dual-channel deep CNN network is used to extract the time series power data features, and the abnormal data is corrected by redistributing the power loss of the transmission line by using the balance calculation relationship. Experiments show that the model proposed in this paper improves the detection rate of abnormal data identification and reduces the false alarm rate compared with traditional machine learning and statistical analysis detection methods. In addition, the abnormal data of the grid is checked and corrected to improve the availability of grid data.

Efficient Strategies for Scalable Electrical Distribution Network Planning Considering Geopositioning

This article presents a heuristic model to find the optimal route or layout of a subway electrical distribution network, obtaining full coverage of users in different scenarios and respecting technical criteria such as maximum distance to avoid voltage drop and capacity. In this way, the location of the transformer substations is achieved through an analysis of candidate sites. The medium voltage network will connect each transformer to a minimum spanning tree (MST), reducing the cost of materials associated with constructing the electrical grid. This work considers the latitude and longitude of each house and electrical count. Georeferenced scenario information is taken from the OpenStreetMap platform to provide an authentic context for distance and location calculations in the deployment of the power grid. The heuristic model offers to decrease time in solving the electrical network layout. As input variables, different powers of the "multi-transformer" transformers are considered to minimize the number of transformers and solve the power supply, reducing the transformers’ oversizing and minimizing the transformers’ idle capacity. The experimentation showed that none exceeded the limit allowed in an urban area of 3.5%.

Smart Grid Challenges in Morocco and an Energy Demand Forecasting with Time Series

Facing development requirements and changes in the global energy context, Morocco has begun a process of diversification of the national energy mix in favor of renewable energy, while ensuring a competitive energy, in terms of costs, availability of products and their security and sustainability. Within this framework, Morocco launched in 2009 a national energy strategy whose major orientations focus on the security of energy supply and the generalization of its access, the preservation of the environment, through the use of renewable energy, energy efficiency, the strengthening of interconnection and regional cooperation. Through this article, the current state of the Moroccan network will be studied, as well as its potential in terms of renewable energy. Some strategies to overcome the challenges facing smart grid deployment in Morocco will also be presented. Then, the long-term energy demand, generation capacity, and renewable energy evolution in Morocco around 2030 will be estimated based on a time series using the artificial neural network method, which can be injected into the grid without causing any transit restrictions on the utility network or on the whole power system. As a result, the wind power available capacity was estimated to be 4087 MW, and the solar power available capacity was estimated to be 4713 MW by 2030. These results will be then compared to those estimated with the mathematical method. As well as, with the accuracy results of similar studies with different time series forecasting techniques. The accuracy value of this study is between 1.2% and 3.5%. So, the performance and viability of the proposed model can be studied.

Smart Grid Systems in Nigeria: Prospects, Issues, Challenges and Way Forward

The ability of the power system to deliver to its consumer electrical energy at an expected level of reliability is correlated with the economic development of a country. The Nigerian power system faces many challenges, varying from overdue infrastructure maintenance, obsolete tools and appliances, insufficient electricity supply, corruption, etc. A gradual shift from manual to smart digital technologies include; smart metering, distributed generation (renewable energy and microgrid), and management using Information and Communication Technology (ICT) tools. In response, research, investments, and upgrade to the power sector are fundamental. This paper discusses and analyses the various smart grid technologies utilised in the Nigerian power system with their effects, impacts, deployment, and integration into the traditional Nigerian power grid. Also discussed are issues and challenges of smart grid deployment and ways of mitigating these challenges.

Benefits of an integrated power and hydrogen offshore grid in a net‐zero North Sea energy system

The North Sea Offshore Grid concept has been envisioned as a promising alternative to: 1) ease the integration of offshore wind and onshore energy systems, and 2) increase the cross-border capacity between the North Sea region countries at low cost. In this paper we explore the techno-economic benefits of the North Sea Offshore Grid using two case studies: a power-based offshore grid, where only investments in power assets are allowed (i.e. offshore wind, HVDC/HVAC interconnectors); and a power-and-hydrogen offshore grid, where investments in offshore hydrogen assets are also permitted (i.e. offshore electrolysers, new hydrogen pipelines and retrofitted natural gas pipelines). In this paper we present a novel methodology, in which extensive offshore spatial data is analysed to define meaningful regions via data clustering. These regions are incorporated to the Integrated Energy System Analysis for the North Sea region (IESA-NS) model. In this optimization model, the scenarios are run without any specific technology ban and under open optimization. The scenario results show that the deployment of an offshore grid provides relevant cost savings, ranging from 1% to 4.1% of relative cost decrease (2.3 bn € to 8.7 bn €) in the power-based, and ranging from 2.8% to 7% of relative cost decrease (6 bn € to 14.9 bn €) in the power-and-hydrogen based. In the most extreme scenario an offshore grid permits to integrate 283 GW of HVDC connected offshore wind and 196 GW of HVDC meshed interconnectors. Even in the most conservative scenario the offshore grid integrates 59 GW of HVDC connected offshore wind capacity and 92 GW of HVDC meshed interconnectors. When allowed, the deployment of offshore electrolysis is considerable, ranging from 61 GW to 96 GW, with capacity factors of around 30%.

Data Mining-Based Cyber-Physical Attack Detection Tool for Attack-Resilient Adaptive Protective Relays

Maintaining proper operation of adaptive protection schemes is one of the main challenges that must be considered for smart grid deployment. The use of reliable cyber detection and protection systems boosts the preparedness potential of the network as required by National Infrastructure Protection Plans (NIPPS). In an effort to enhance grid cyber-physical resilience, this paper proposes a tool to enable attack detection in protective relays to tackle the problem of compromising their online settings by cyber attackers. Implementing the tool first involves an offline phase in which Monte Carlo simulation is used to generate a training dataset. Using rough set classification, a set of If-Then rules is obtained for each relay and loaded to the relays at the initialization stage. The second phase occurs during online operation, with each updated setting checked by the corresponding relay’s built-in tool to determine whether the settings received are genuine or compromised. A test dataset was generated to assess tool performance using the modified IEEE 34-bus test feeder. Several assessment measures have been used for performance evaluation and their results demonstrate the tool’s superior ability to classify settings efficiently using physical properties only.

Fuzzy Algorithms for Diagnosis of Furnace Transformer Insulation Condition

Implementation of the smart transformer concept is critical for the deployment of IIoT-based smart grids. Top manufacturers of power electrics develop and adopt online monitoring systems. Such systems become part of high-voltage grid and unit transformers. However, furnace transformers are a broad category that this change does not affect yet. At the same time, adoption of diagnostic systems for furnace transformers is relevant because they are a heavy-duty application with no redundancy. Creating any such system requires a well-founded mathematical analysis of the facility’s condition, carefully selected diagnostic parameters, and setpoints thereof, which serve as the condition categories. The goal hereof was to create an expert system to detect insulation breach and its expansion as well as to evaluate the risk it poses to the system; the core mechanism is mathematical processing of trends in partial discharge (PD). We ran tests on a 26-MVA transformer installed on a ladle furnace at a steelworks facility. The transformer is equipped with a versatile condition monitoring system that continually measures apparent charge and PD intensity. The objective is to identify the condition of the transformer and label it with one of the generally recognized categories: Normal, Poor, Critical. The contribution of this paper consists of the first ever validation of a single generalized metric that describes the condition of transformer insulation based on the online monitoring of the PD parameters. Fuzzy logic algorithms are used in mathematical processing. The proposal is to generalize the set of diagnostic variables to a single deterministic parameter: insulation state indicator. The paper provides an example of calculating it from the apparent charge and PD power readings. To measure the indicativeness of individual parameters for predicting further development of a defect, the authors developed a method for testing the diagnostic sensitivity of these parameters to changes in the condition. The method was tested using trends in readings sampled whilst the status was degrading from Normal to Critical. The paper also shows a practical example of defect localization. The recommendation is to broadly use the method in expert systems for high-voltage equipment monitoring.

Promises of a truth machine: deception and power in smart grids in India

ABSTRACT Smartness in smart grids and cities is an assemblage of devices that produce data as truth claims and deploy new modes of governance focused on chal (deception, cunning) and bal (power) with a promise to reduce losses and maximise profits. Building on postcolonial geographies, the paper argues that chal (छल) and bal (बल) are central to understanding the deployment of smart grids in India. Managers use the idea of the practice of chal and bal by political citizen-subjects, and the disorder of governance this causes, to justify the deployment of smart infrastructure. However, the paper shows that smartness can be understood as automation, and deployment from a distance, of chal and bal by a capital-state nexus to counter the chaliya (छलिया) (deceptive/cunning/cheat/trickster) citizen-subjects. Planners and implementers expect data and digitalisation to bring order by (re)training and excluding lower-level workers and consumers. These promise to reduce resource losses, making electricity utilities profitable. Rather than being distinctly corporate or clearly in the domain of the state, smart is situated at the fracture of public and private resources, and political and civil spaces. By developing a postcolonial analysis of smart grids and focusing on the ‘inbetweeners’, electricity utility staff and middle-class citizen-subjects, the paper furthers the understanding of smartness and subalternity of elites.

Selection of Smart Grids Projects of Common Interest—Past Experiences and Future Perspectives

This paper discusses the authors’ experience gained with the selection of Projects of Common Interest (PCIs) in the thematic area of smart grids deployment, in the context of the TEN-E Regulation. It presents the framework for assessing candidate electricity smart grids for inclusion in the European Union list of PCIs, in view of the TEN-E Regulation and the existing literature on assessment methodologies for energy infrastructure projects. It also provides an overview of smart grid projects included in the PCI lists, with the aim to shed light on the types of projects and their contribution to accelerating the development of European cross-border energy infrastructure projects to respond to EU energy and climate targets. The paper concludes with discussion of recent regulatory initiatives and their potential implications on the presented methodology.

Reactive grid-assisted co-sputtering of titanium and chromium in a pure nitrogen atmosphere: Uniformity, optics, and structure of the Ti–Cr–N films

This study provides the results of the first attempt to deposit thin films by reactive grid-assisted co-sputtering with the deployment of small grounded grids having two varying apertures for enabling the transfer of particles. The diameter of all the grids utilized was 50 mm, and their apertures' diameters were changed from 5 mm to 15 mm. Furthermore, the ratios of the grid area to the chamber wall area were lower than 0.0161, preventing the formation of ion-rich sheaths and their adverse impact upon discharge and plasma stability. Accordingly, Ti 1 Cr 1-x N films (0.88 < x < 0.97) with low chromium contents were deposited on soda-lime glass substrates by using pure nitrogen as the sputtering gas. The grazing incidence X-ray diffraction patterns of the films, the average thicknesses of which were lower than 50 nm, showed no sign of crystallinity in the films. Ranging from 3.26 to 3.79 eV, the optical bandgap of the films changed by altering the apertures’ size; chromium content, internal stress, and quantum confinement effect appeared to be the main contributing factors. The photoluminescence intensities appertaining to trap state emissions reflected a decreasing trend by increasing the chromium content, which can be ascribed to the capability of the chromium particles included in the surface, structure, and grain boundaries of the films to prevent photoinduced electron-hole pairs from recombination. It was found that not only is the grid-assisted co-sputtering method an effective tool whereby the doping range of conventional co-sputtering methods can be significantly extended but also it can cover a spectrum of surface morphologies. However, in comparison with the conventional co-sputtering method, the utilization of the grids with fairly small apertures can limit the thickness uniformity of the films; this effect may be attenuated by changing the geometry and design of grids.

SUSTAINABLE BUSINESS MODELS, AND EFFECTS OF SMART GRIDS DEVELOPING

The article is devoted to studying sustainable business models and the effects of building "smart" grids. To achieve this goal, we used a scientific review of literature sources from the Scopus® database for 2017-2021. In the course of the research, a sample of 3540 publications on the topic SciVal T.8491 "Business Model Innovation; Sustainable Business; Digital Transformation. " The results of the scientific review highlight and analyze the most active identified institutions and authors who have published work in sources indexed by the Scopus® database on the topic SciVal T.8491. The fundamental directions of research of sustainable business models are identified, which are closely connected with the investigation of digital business models, industry 4.0, cloud technologies, the Internet of Things, directions of adaptation, and dissemination of sustainable business models. In the context of smart grids, sustainable business models are considered from the study of distributed generation, various decentralized energy technologies, from demand management technologies to energy storage technologies in the system. The authors evaluate and prove the effectiveness of the construction of virtual power plants, the possibility of integration into a single system of distributed energy resources and energy storage systems, describe the methodology of operation and control of microgrids given the massive deployment of smart grids. They carry out systematization of business models of smart networks based on the Internet of Things; offer approaches to deploying smart grids based on blockchain technology. This article discusses the environmental and economic effects of smart grids deploying. Thanks to the results of a scientific review of sustainable business models and the effects of building "smart" grids, current energy market players have the opportunity to consider current requirements for the operation of smart grids and, if necessary, revise their business models. Deploying smart infrastructure improves the country's energy independence, achieves targets for the use of alternative (renewable) energy sources, and increases the country's energy security while reducing import dependence on energy resources.

Bird electrocution on power lines: Spatial gaps and identification of driving factors at global scales

Universal energy access is one of the targets of the Sustainable Development Goals (SDGs), and thus the deployment of electricity grids is expected to expand globally in the coming decades. However, the installation of power lines is not biodiversity-friendly. In particular, electrocution on power pylons is a major cause of bird mortality worldwide, including for some severely endangered species. Over the last decades, different studies have improved our understanding of the factors influencing the risk of electrocution in birds, but until now spatial gaps in our knowledge of these impacts and the factors driving global patterns of bird electrocution have not been assessed. In this study, we evaluated data from a total of 114 studies that provided information on bird mortality rates on power lines, and we analyzed the factors driving electrocution rates for all bird species, and then for all raptors and large eagles separately. Our results showed a high spatial distribution bias, as more than 80% of the studies were carried out in developed countries, mostly in Europe and North America. By contrast, no systematic studies have been found for Oceania and very few for South America and Africa. Europe showed the highest electrocution rates for birds, South America for raptor species and Africa for eagles. Socio-economic factors best-explained bird and raptor electrocution rates, while climate-related factors were the most influential for eagles. Contrary to our expectations, factors related to pylon design were the least influential on overall electrocution rates. Variables related to study design showed highly variable levels of influence. This could be due to the lack of standardized protocols. Although bird electrocution has been extensively studied, there are large areas where no studies have been carried out or for which data are inaccessible. This could be because in these areas the power distribution network is still sparse, or that most studies are not public or accessible to the international community. Researchers and managers should promote the publication of studies, as awareness is the first step to solving these problems. The factors identified could be applied globally to the design and planning of power grids and the identification of mortality hotspots. This would help mitigate the creation of new mortality hotspots, especially in developing countries where the installation of new power lines has been growing exponentially in recent years.

Customer engagement in innovative smart grid deployment projects: evidence from Russia.

In recent years, Russian power grid companies have significantly intensified their planning and implementing innovative projects to introduce various elements of smart grid technology. The global experience shows that the best potential outcome from smart grid technologies largely relies on the customers' readiness and adoption. To this end, we assess the Russian consumers' awareness and readiness regarding smart grid technologies by conducting a comprehensive survey. Our empirical study showed that, in general, Russian consumers are unaware of the various aspects of smart grid technologies. On the contrary, a significant share of respondents believes that smart grid technologies can reduce their energy consumption burden through gaining efficiency. The low level of awareness would be detrimental to accrue smart grid innovative projects' potential socio-economic and environmental benefits. We propose several recommendations for policymakers and managers of grid companies for the elimination of the identified risks.

A business-oriented approach for battery energy storage placement in power systems

Battery energy storage systems (BESSs) are gaining increasing importance in the low carbon transformation of power systems. Their deployment in the power grid, however, is currently challenged by the economic viability of BESS projects. To drive the growth of the BESS industry, private, commercial, and institutional investments in large-scale BESS projects are needed. For financiers and investors, choosing an appropriate BESS installation location is a crucial task that requires important considerations. However, so far, studies targeting the BESS placement problem have mainly focused on minimizing operational losses, solving power quality issues, and improving the voltage profile of the system. Implementing such approaches only considers the operational feasibility of BESS at the installation site while ignoring its business feasibility. Therefore, in this paper, we approach the BESS placement problem from a business viewpoint by conducting a stage-level investigation of BESS projects. First, we identify the factors impacting the business feasibility of placement decisions in projects’ construction, connection, operation, and disposal stages and propose cost and time effective measures for making them business friendly. We investigate several factors such as profitability of grid services, integrability of renewable resources, affordability of connection charges, and the usability of BESS capacity. Second, we investigate the business potential of placing BESSs in different grid-levels of the power system and examine BESS connection procedures at those levels. Third, we implement the proposed approach for installing a BESS in the Danish island of Bornholm, for which, we investigate multiple locations of the Bornholm power system.

Line-of-Sight Probability for mmWave-Based UAV Communications in 3D Urban Grid Deployments

The network operators will soon be accommodating a new type of users: unmanned aerial vehicles (UAVs). 5G New Radio (NR) technology operating in the millimeter-wave (mmWave) frequency bands can support the emerging bandwidth-hungry applications facilitated by such aerial devices. To reliably integrate UAVs into the NR-based network infrastructure, new system models that capture the features of UAVs in urban environments are required. As city building blocks constitute one of the primary sources of blockage on the links from the UAV to its serving base station (BS), the corresponding line-of-sight (LoS) probability models are essential for accurate performance evaluation in realistic scenarios. We propose a LoS probability model in UAV communication setups over regular urban grid deployments, which is based on a Manhattan Poisson line process. Our approach captures different building height distributions as well as their dimensions and densities. Under certain characteristic distributions, closed-form expressions for the LoS probability are offered. Our numerical results demonstrate the importance of accounting for the building height distribution type as well as the orientation of the UAV with respect to its BS. By comparing our model with the standard ITU and 3GPP formulations, we establish that the latter provide an overly optimistic approximation for various deployments.

Digital Twins for Intelligent Authorization in the B5G-Enabled Smart Grid

Beyond fifth generation (B5G) communication networks and computation paradigms in the edge are expected to be integrated into power grid infrastructures over the coming years. In this sense, AI technologies will play a fundamental role to efficiently manage dynamic information flows of future applications, which impacts the authorization policies applied in such a complex scenario. This article studies how digital twins can evolve their context awareness capabilities and simulation technologies to anticipate faults or to detect cyber-security issues in real time, and update access control policies accordingly. Our study analyzes the evolution of monitoring platforms and architecture decentralization, including the application of machine learning and blockchain technologies in the smart grid, toward the goal of implementing autonomous and self-learning agents in the medium and long term. We conclude this study with future challenges on applying digital twins to B5G-based smart grid deployments.

Electric Distribution System Planning Methodology Considering Distributed Energy Resources: A Contribution towards Real Smart Grid Deployment

Within the current context of energy transition, the first step to assure cost-efficient and reliable grid operations is the use of adequate planning tools. These tools must evaluate the real needs for reinforcing, replacing and incorporating new network assets considering not only technical and regulatory aspects but also the operational benefits that distributed energy resources (DER) may provide. With this purpose, this paper presents a novel methodology for the analysis and the advanced planning of the active distribution networks in the context of smart grids. The main contributions of the proposed methodology are the following: to incorporate the real capabilities of DER proactively to the planning and operation of distribution networks; to appropriately use the data available in current smart grids; and to promote the efficient use of network capacity in distribution level (efficiency decisions of its users, grid assets in service, access conditions, etc.) through potential operational services, variable in time. The proposed methodology was successfully tested on a real distribution network with 450,000 delivery points on a geographical area of 5000 km2.

Evolutionary Approaches to Fog Node Placement in LV Distribution Networks

Smart grids merge intelligent computing technologies and electrical grid networks for better monitoring, control and management of electrical energy and facilities. The maturity of cloud computing has been the major driving factor for its adoption in smart grid deployments. Despite the elasticity of cloud resources, centrality and long distances to remote data centers cause high latency, high bandwidth consumptions and unstable connectivity, which are undesirable for IoT-based smart grid applications. Fog computing as an extension of cloud computing services to the edges of the network overcomes these challenges and perfectly suit the distributed nature of the low voltage (LV) electrical distribution networks as part of smart grid. The pressing issues with the adoption of fog computing for smart grid applications are finding the best placement plan for fog node locations in LV distribution networks to enhance monitoring and control. The main goal of this work is to present a mathematical model to address the aforementioned issues focusing on minimizing deployment cost and network delay. In addressing this multi-objective problem, a new algorithm, namely Future Search Particle Swarm Non-dominated Sorting Genetic Algorithm (FPNSGA), is proposed based on the combination of the best features of the NSGA-II, SMPSO, and a recently formed algorithm, Future Search. The effectiveness of the algorithm is evaluated based on the benchmarking technique (Weighted Sum approach), the convergence and diversification of the solutions using HV indicators and CPU time. The results from simulations show that the proposed mechanism is very competitive and outperforms other fog planning network schemes.

Study of Modeling of Hybrid Energy System for Optimum Power Extraction from the System to the Grid

With the growing demand of electricity, deployment of micro grid is becoming an attractive option to meet the energy demands. At present, large-scale wind/solar hybrid system is of great potential for development. The large-scale wind/solar hybrid system is of higher reliability compared with wind power generation alone and solar power generation alone However, a grid-connected micro grid suffers from critical stability problems during a power grid failure. For stable operation of the micro grid during a grid failure. In this paper, the transition stability of the micro grid is examined during a power failure

SMART POWER NETWORKS FOR INCREASING THE COMPETITIVENESS OF THE NATIONAL ECONOMY

The article deals with the essence of the national economy competitiveness concept, as well as factors influencing its level, among which an important place is occupied by energy efficiency of the state. Theoretical aspects of the smart grids development are considered, the expediency of their implementation in the context of the national economy competitiveness level increasing is established. A study by reports from the European Commission and the United Nations Economic Commission for Europe has shown that the introduction and deployment of smart grids is one of the priority thematic areas in the implementation of the Trans-European Energy Networks concept. Their use will help to integrate renewable energy sources, optimize the energy market and increase the level of self-regulation of energy use by consumers, as well as to implement the main directions of sustainable development, which today has become an urgent requirement around the world. The ability of smart grids to generate supply and demand information is analyzed, which is important for the integration of an increasing number of renewable energy sources, such as solar and wind energy, while maintaining the stability and efficiency of the energy system. To ensure the effective operation of the network, it is necessary to form a vision and organize the further deployment of technology platforms, as well as to integrate intelligent network applications that will support this vision. The article also examines the functional characteristics of smart grids, the benefits of their use, as well as the main issues related to their use. It is established that the main shortcomings of the smart grids introduction are insufficient confidentiality of customers and the level of information security, as well as network instability and lack of flexibility. The functions of smart grids are grouped according to separate paragraphs, their significance for consumers, investors, environment and competitiveness of the national economy is analyzed. In particular, it is determined that they are more intelligent, decentralized and sustainable than modern networks, and their use will improve the economic efficiency, investment attractiveness and sustainable development.