Grid System Research Articles

Low-damage etching of poly-Si and SiO2 via a low-energy electron beam in inductively coupled CF4 plasma

Abstract Electron-assisted etching of poly-Si and SiO2 is performed via a grid system in inductively coupled CF4 plasma. The feasibility of electron-assisted etching is discussed with a focus on the low-surface damage of the etching. The etch rate increases with electron beam energy, which indicates that the electrons assist the surface etching process. To verify this, etching of poly-Si and SiO2 is performed in several plasma conditions, which leads to differences in etch rate that depend on the presence or absence of radicals and electron beams. Poly-Si and SiO2 are not etched without radicals of CF4 plasma, but they are etched when such radicals are present. When the electron beam and radicals exist simultaneously, the etch rate increases more dramatically than in the case of a CF4 plasma without an electron beam, demonstrating that the electron beam assists the etching process. Optical emission spectroscopy is employed to verify the F radical does not affect the etch rate increase. The surface roughness is measured after electron-assisted etching and compared with the surface roughness after ion-assisted etching.

Distributed battery energy storage systems for deferring distribution network reinforcements under sustained load growth scenarios

Energy storage systems can be leveraged in electricity distribution network planning as mitigation alternatives to traditional grid reinforcements if they are strategically installed and operated to reduce congestion and voltage limit violations. This paper examines the technical and economic viability of distributed battery energy storage systems owned by the system operator as an alternative to distribution network reinforcements. The case study analyzes the installation of battery energy storage systems in a real 500-bus Spanish medium voltage grid under sustained load growth scenarios. The results show that, in general, dedicated battery energy storage systems are only a cost-efficient alternative in distribution system planning under very specific conditions, such as when low load growth rates are expected. Nevertheless, they are only required for peak shaving a few days per year. For the analyzed case study, the recoverable portion of their total cost through deferral of distribution system upgrades is higher than the fraction of cycles required for peak shaving under all sustained load growth scenarios. Therefore, it is also explored if mobile battery energy storage systems, capital grants, and revenue stacking can enable battery energy storage systems to become an efficient distribution system planning alternative.

Medium-Duty Delivery Truck Integrated Bidirectional Wireless Power Transfer System With Grid and Stationary Energy Storage System Connectivity

Medium-Duty Delivery Truck Integrated Bidirectional Wireless Power Transfer System With Grid and Stationary Energy Storage System Connectivity

Communication Technology for Renewable Energy Access Grid System Based on Multi-source Heterogeneous Data Fusion

Abstract Incorporating renewable energy solutions into the power grid is essential for facilitating a shift in energy sources. Ensuring the effectiveness and dependability of communication technologies within smart distribution grids is vital, as these grids serve as the link between end users and the central station, thereby contributing to the grid’s stable functioning and the effective dissemination of energy. This study introduces a framework for communication that tackles the challenges associated with the integration of multi-source, heterogeneous data in smart distribution grids. The framework leverages multi-source heterogeneous data fusion and capitalizes on edge computing to efficiently process and consolidate data from diverse sensors and devices. An optimal weight allocation algorithm is proposed to mitigate data redundancy and to improve the energy efficiency of communication. The paper demonstrates, through theoretical examination and empirical tests, the proposed system’s enhanced communication capabilities, especially in terms of data transmission effectiveness and energy usage optimization. The discussion highlights the advantages of smart distribution networks over conventional communication methods and offers insights for further advancements in communication technologies within this sector.

Secure smart grid implementation with automatic data integrity attack location prediction and exalted energy theft detection

Smart grid systems use digital communication technology to efficiently monitor and manage electricity distribution. However, there is a need for improve the robustness of smart grid systems. Hence a novel “Secure Smart Grid Implementation with Automatic Data Integrity Attack Location Prediction and Exalted Energy Theft Detection” has been proposed. Current detection methods are not good at identifying data integrity attacks. So a novel Automatic Deterministic Attack Location Prediction Model detects data integrity attacks using a Stacked Watermarking BiGRU-based Deep Deterministic Q network, extracting system matrix and state time series variation. Moreover, Energy theft detection algorithms, which are intended to detect dishonest clients, provide erroneous findings because of linear interpolation processes and randomised near misses, which ignore important data points. Thus a novel Exalted Energy Theft Detector with Secure Encoding is introduced where Amortized Multicode GAN method secures smart grids by detecting energy theft and preventing firmware code modifications, utilising fractional multi-level quantum encoding to identify injection locations. As a result, the proposed model has an accuracy of 97.1%, recall of 96.25%, error rate is 1.36%, F1-score is 96.20%, sensitivity is 96.10%, specificity is 96.10%, MAPE is 0.48%, MSE is 0.0001 and RMSE is 0.017.

Research on distributed optimal scheduling method based on consensus

Abstract With access to large-scale distributed power sources, the physical structure of the power grid system tends to be distributed. The application of traditional centralized optimal scheduling methods will lead to a surge in communication overhead and computational complexity, which will affect the performance and efficiency of the solution. In this paper, the minimum operating cost of the system is taken as the goal, and the distributed direct current optimal power flow (DC-OPF) optimization calculation of the photovoltaic-storage power generation system is considered to approximately solve the economic dispatch problem. The DC optimal power flow model with energy storage charging penalty is constructed. Then, the distributed optimization method based on the consensus alternating direction multiplier method (ADMM) is adopted. By decoupling the objective function and constraints of the whole system problem, the fully distributed DC-OPF calculation is realized by introducing the global consensus variable to deal with the boundary buses. Finally, the improved IEEE 30-bus example is used to test the method, and the simulation results verify the convergence and accuracy of the method.

A Comparative Study of the Effect of Weather Conditions on Solar Irradiance Forecasting Using Various Methods in Yogyakarta, Indonesia

Abstract The government’s plan to build utility-scale solar power plants for the next decade will impact the high penetration of solar energy into the grid. This problem is associated with the intermittent nature of solar electricity, which can disrupt the grid system’s stability and reliability if it is not tackled. Furthermore, the complicated weather conditions in Indonesia make this variable challenging to predict. Therefore, one of the solutions offered is the solar irradiance forecasting technique that helps the operator during planning. This paper will look at the differences in these weather characteristics in predicting solar irradiance using three forecasting methods: LSTM, RNN, and ARIMA. The data is taken from direct measurements installed in the center of the island of Java, namely Yogyakarta. The result of this work shows that among LSTM, RNN, and ARIMA forecasting models, the highest forecast accuracy has been performed by LSTM with RMSE, MAPE, MAE, and R2 are 33.01 W/m2, 7.72%, 24.51 W/m2 and 0.96 respectively. Subsequently, partially cloudy, cloudy, or rainy weather has a worse predicting performance than weather with a sunny or clear sky. However, they are still regarded as reasonable forecasts.

Analysis of distributed smart grid system on the national grids

Analysis of distributed smart grid system on the national grids

Nitrogen migration and gasification characteristics of pulverized coal using the novel gasification-combustion technology

This study proposes a feasible gasification-combustion technology for coal-fired boilers operating at low loads for the purpose of providing strong support for stable output of the power grid system that accommodates more renewable energy power. In this technology, a multi-nozzle impinging entrained-flow gasifier is used for the first time to preheat pulverized coal (PC), and the gasified fuel enters a down-fired combustor (DFC) for complete combustion with air-staged method. A 75 kW novel self-sustained gasification-combustion test rig was constructed. The stability and feasibility of the test rig were verified, and the gasification characteristics and nitrogen migration mechanism of PC under loads of 45 kW, 55 kW, 65 kW and 75 kW were investigated. The results demonstrated that the gasifier could continuously and steadily provide high-temperature gasified gas-char mixture to the combustor. The gas-char mixture, consisting of coal gas which was rich in high-concentration flammable gases (CO, H2, and CH4) and char with enlarged specific surface area, was easy to ignite, which could enhance the flame stability of boilers operating at low loads. Additionally, more than 61.6 % of coal-N was released in the gasifier. The liberated coal-N primarily transformed to N2 and NH3 instead of NOx, while the amount of NH3 produced was influenced by the gasification temperature. The gasified gas-char mixture entering the combustor achieved stable combustion with uniform temperature distribution. Before the injection of tertiary air, the released char-N and the NH3 in coal gas transformed primarily to N2, and there was no production of NO due to the strong reducing environment. The lowest NOx emission was 122.31 mg/Nm3 (@6% O2) at 65 kW, and the combustion efficiencies under all loads exceeded 99.62%, indicating that the gasification-combustion technology could enable efficient combustion and reduce NOx emission. Hopefully, the proposed technology could provide an innovative solution for the stable combustion of PC boilers at low loads and provide a feasible way to achieve clean combustion.

A detection model for false data injection attacks in smart grids based on graph spatial features using temporal convolutional neural networks

Due to the intelligence and opening of future generation cyber–physical power systems, smart grids are vulnerable to the emergency of cyber-attacks aiming at the cyber–physical systems. Among these attacks, false data injection (FDI) attacks are particularly concerning in smart grids due to their stealthiness. Attackers can tamper with data transmitted through communication networks, influencing the analysis results of the control center, leading it to make wrong decisions and thereby undermining the stability of smart grids. The inability of traditional bad data detection models to detect false data injection attacks poses huge security risks to smart grids. For this reason, a new attack detection model using spatial–temporal features is proposed. The proposed detection model includes the following two parts: spatial features extraction and temporal convolutional network. First, graph convolutional operations are employed to disentangle the interactions among buses and extract spatial features of measurement; Then, the temporal convolutional network model is used to extract the temporal features. Through these processes, the proposed detection model can effectively identify the injected false data injection attacks in smart grids. The detection performance and robustness of the proposed FDI attack detection model are tested through simulations on IEEE 14-bus and 118-bus grid systems.

Long term demand forecasting in Jakarta distribution grid system using fuzzy logic and artificial neural network method

Abstract In order to attain equilibrium between energy supply and demand, reliance on conventional methods for precise long-term electricity demand forecasting is no longer viable. The utilization of artificial intelligence, such as fuzzy logic and artificial neural network (ANN) models, emerges as a prospective solution in the current dynamic scenario. This research explores long-term electricity demand forecasting within the Jakarta distribution grid system, employing various fuzzy logic and ANN approaches including Sugeno, Mamdani, Bayesian Regularization, and the Levenberg algorithm. The analysis incorporates time series data spanning 2016 to 2019, encompassing electricity load demand, economic factors, and demographic variables, processed using MATLAB. The outcomes of the four forecasting methods reveal an average error range of 2 to 3%. The findings indicate that employing fuzzy logic and ANN methods for long-term electricity demand forecasting can yield a forecast error of less than 3%. The study recommends future research enhancements through the inclusion of additional time series data and a more refined system.

Optimal solution for PV integrated with smart grid connected fast charging EV station: A case study for Jaipur

Current research study investigates the optimal solution for integrating photovoltaic (PV) solar panels with a smart grid-connected fast-charging electric vehicle (EV) station in Jaipur, India, using HOMER software for simulation. The study aims to develop a cost-effective and sustainable charging infrastructure specifically designed for e-rickshaws and personal vehicles, targeting reduced electricity costs. The findings demonstrate that a hybrid Solar and Grid-integrated EV charging station significantly lowers operational costs while promoting renewable energy adoption. The optimized Solar + grid system, compared to a traditional grid-only model, achieves substantial savings, with an average annual energy bill reduction of ₹26,17,806. Despite an initial capital expenditure of ₹50,22,895, the system's financial viability is affirmed by a rapid payback period of 1.9 years (simple) or 2.1 years (discounted) and an impressive internal rate of return (IRR) of 51.3%. Over a 25-year project lifespan, the system is projected to generate total savings of ₹6,54,45,150. The analysis considers various financial metrics, including monthly and annual energy costs, demand charges, and fixed charges, providing a comprehensive overview of the economic benefits. The results underline the importance of incorporating solar power into EV charging stations to reduce overall energy expenses and enhance long-term sustainability. This study offers valuable insights for policymakers, energy managers, and stakeholders, highlighting the economic and environmental advantages of solar-integrated charging solutions in urban settings.

Systematic Survey Analysis of the Application of Artificial Intelligence Base Network on Grid Computing Techniques

A smart grid is a contemporary electrical system that supports two-way communication and utilizes the concept of demand response. In order to increase the smart grid's dependability and enhance the consistency, efficiency, and efficiency of the electrical supply, stability prediction is required. The true test for smart grid system designers and specialists will therefore be the increase of renewable energy. With the goal of integrating the electric utility infrastructure into the advanced communication era of today, both in terms of function and architecture, this program has achieved great strides toward modernizing and expanding it. In this study, researchers used the Systematic literature review method which identifies, evaluates and interprets all relevant research results related to certain research questions, certain topics, or phenomena of concern. The study review on how a smart grid applied different deep learning techniques and how renewable energy can be integrated into a system where grid control is essential for energy management. The article discusses the idea of a smart grid and how reliable it is when renewable energy sources are present. Globally, a change in electric energy is needed to reduce greenhouse gas emissions, prevent global warming, reduce pollution, and boost energy security.

Designing Energy-efficient DC Robotic Machines with Advanced Cyber Security for a Smart Grid System

In this paper, an energy-efficient direct current (DC) robotic machine (E2DCRM) is developed to revolutionize industrial automation, eliminating the rectification stage using brushless alternating direct current (BADC). E2DCRM is integrated with an advanced intelligent grid structure for enhancing the smooth and soft connection process. The emphasis on threats occurs because malicious attacks are overcome using deep learning algorithms. In this approach, a hybrid deep learning-based system is used to eliminate malicious attacks. The communication protocols might operate directly over the power lines, thus eliminating the need for rectification and enabling seamless integration with the smart grid. This research offers a comprehensive, integrated solution combining energy-efficient DC robotics with state-of-the-art cyber security measures. The proposed methodologies address current industrial limitations and set the stage for a resilient and sustainable future in innovative and safe grid technology.

A Systematic Survey on the Application of Artificial Intelligence (ai) Baseline Networks on Grid Computing Techniques - Challenges, Novelty and Prospects

A smart grid is a contemporary electrical system that supports two-way communication and utilizes the concept of demand response. To increase the smart grid's dependability and enhance the consistency, efficiency, and efficiency of the electrical supply, stability prediction is required. The true test for smart grid system designers and specialists will therefore be the increase of renewable energy. To integrate the electric utility infrastructure into the advanced communication era of today, both in terms of function and architecture, this program has made great strides toward modernizing and expanding it. The study reviews how a smart grid applied different deep learning techniques and how renewable energy can be integrated into a system where grid control is essential for energy management. The article discusses the idea of a smart grid and how reliable it is when renewable energy sources are present. Globally, a change in electric energy is needed to reduce greenhouse gas emissions, prevent global warming, reduce pollution, and boost energy security.

Synergetic UPQC Application for Power Quality Enhancement in Microgrid Distribution System: SCSO Approach

Nowadays, Hybrid renewable energy systems (HRES) are increasingly being integrated into grid-connected load systems to lower losses and boost dependability. When the HRES system is connected to the grid system to satisfy needed load demand, power quality issues arise because of imbalanced load circumstances, non-linear load, and critical load. So this manuscript proposed a synergetic Unified Power Quality Conditioner (UPQC) application for power quality enhancement in microgrid distribution systems. The Sand Cat Swarm Optimization (SCSO) technique is based on the proposed sand cat swarm optimization method. The proposed strategy's primary goal is to regulate power flow, minimize harmonic distortion in both voltage and current waveforms, and maximize UPQC. By then, the proposed method's performance has been implemented into practice on the MATLAB platform and contrasted with several currently used techniques. The proposed technique displays the low Total Harmonic Distortion (THD) and operation time in all existing approaches like Moth Flame Optimization (MFO), Genetic Algorithm (GA), Salp Swarm Algorithm (SSA), Ant Lion Optimizer (ALO), Improved Bat Search Algorithm (IBSA), Lion Algorithm (LA), Artificial Bee Colony (ABC), Atom Search Optimization (ASO) and Crow Search Algorithm (CSA). The proposed method attains THD as before UPQC is 31%, after UPQC is 3%, load current is 1.42%, and load voltage is 2%. It also achieves a low operation time of 506ms and a low settling time of 0.15s compared to the existing methods. The significant improvement in power quality metrics demonstrates the effectiveness of the proposed SCSO technique.

A Power Grid Topological Error Identification Method Based on Knowledge Graphs and Graph Convolutional Networks

Precise and comprehensive model development is essential for predicting power network balance and maintaining power system analysis and optimization. The development of big data technologies and measurement systems has introduced new challenges in power grid modeling, simulation, and fault prediction. In-depth analysis of grid data has become vital for maintaining steady and safe operations. Traditional knowledge graphs can structure data in graph form, but identifying topological errors remains a challenge. Meanwhile, Graph Convolutional Networks (GCNs) can be trained on graph data to detect connections between entities, facilitating the identification of potential topological errors. Therefore, this paper proposes a method for power grid topological error identification that combines knowledge graphs with GCNs. The proposed method first constructs a knowledge graph to organize grid data and introduces a new GCN model for deep training, significantly improving the accuracy and robustness of topological error identification compared to traditional GCNs. This method is tested on the IEEE 30-bus system, the IEEE 118-bus system, and a provincial power grid system. The results demonstrate the method’s effectiveness in identifying topological errors, even in scenarios involving branch disconnections and data loss.

High-uniformity liquid-cooling network designing approach for energy storage systems by graph-coupled genetic algorithm

Electrochemical battery energy storage stations have been widely used in power grid systems and other fields. Controlling the temperature of numerous batteries in the energy storage station to be uniform and appropriate is crucial for their safe and efficient operation. Thus, effective thermal management is required. In this work, an approach for rapid and efficient design of the liquid cooling system for the stations was proposed. A hydraulic solution model for the liquid-cooling network was established based on graph theory principles, and the genetic algorithm was employed for automatic system optimization to achieve high cost-effectiveness and ideal temperature distribution. Experiments were conducted to validate the accuracy of the hydraulic model and evaluate the efficacy of the optimization strategy. The flow rate calculated from the hydraulic model agreed well with the experiments, with a discrepancy smaller than 3.5 %, and the uniformity of flow rates was improved to as high as 70 % after the optimization. Furthermore, the applicability of this approach was also confirmed across various sizes of energy storage systems.

Anti-islanding image detection and optimization of distributed power supply using neural network architecture search

Anti-islanding detection (AILD) for distributed power sources plays an important role on the stable operation of power grid systems and the safety of electrical systems. In order to improve the detection accuracy, we propose neural network architecture search (NAS) based approach for anti-islanding image detection and optimization of distributed power sources. It combines the fast region-based convolutional neural network (Faster-R-CNN) with the differentiable architecture search (Darts), utilizing electrical signal image data acquired from cameras or photovoltaic (PV) inverters, thereby enhancing the accuracy and efficiency of detection. We conduct a comparative analysis of two methods for obtaining distributed power supply (DPS) anti-islanding image data: camera-based acquisition and PV inverter-based acquisition. Our observation reveals that the image data acquired through cameras is more conducive for the learning process of DCNN. The proposed algorithm was compared with other convolutional neural network (CNN) models, validating its performance superiority. This provides a novel perspective and methodology for the advancement of AILD for distributed power sources, and enhances the security and stability of DPS.

Access to energy and women's human capital in sub-Saharan Africa

This study examines the link between access to energy and women's human capital by focusing on women's life expectancy and school enrollment. To evaluate this relationship, we take a supranational perspective by using data from a panel of 34 sub-Saharan African countries over a 21-year period from 2000 to 2020. To do so, the ordinary least squares estimation technique applied to a fixed effects specification was adopted. It is found that for the whole sample, access to electricity has a significant positive impact on both life expectancy and school enrollment rates of women. Nevertheless, access to clean fuels and technologies for cooking significantly only impacts women's life expectancy but has no significant effect on women's school enrollment rate. Ultimately, we conclude that access to energy improves women's human capital, with a higher impact on life expectancy than on education. From a policy standpoint, action on both women's life expectancy and school enrollment should prioritize access to electricity. In order to increase women's human capital, governments must give the energy sector priority investment by setting up a grid system or promote the use of renewable energies such as micro-hydro and solar systems.