Smart Power Grid Research Topics

Smart Grid research topics are one of the developed electrical grid systems and it utilizes digital and other technologies to control and handle the electricity based demands of end users. It is widely employed in many fields or regions. In this we offer some concepts of information related to this research smart grid technology.

  1. Define Smart Grid

At the starting stage of this research we look for the description for smart grid technology. It is an improved electrical grid system which utilizes modern digital communication and managing technologies to effectively handle the electricity consumption, generation, transmission and distribution. The smart grids intend to enhance the energy sustainability, efficiency and reliability during permitting the transition to a more distributed and environmentally friendly energy system. Unlike traditional grids that generally generate in a single way flow of electricity from power plants to consumers, permitting for actual-time monitoring, control, smart grids allow two way interactions among the consumers and utility and optimization of energy usage.

  1. What is Smart Grid?

Next to the description we look for the detailed explanation for this proposed technique. It describes the unique chance to move the energy industry to a novel era of efficiency, reliability and availability which will be dedicated to our environmental and economic health. Over the changeover period, it will be crucial to implement consumer education, testing, improvement of regulations and standards, information sharing and technology improvements to make sure the advantages we predict from the smart grid to become a reality. The advantages that linked with the Smart Grid technology are decreased peak- demand which also assists lower electricity rates, more effective transmission of electricity, improved combination of large-scale renewable energy, enhanced security, Quicker restoration of electricity after power disturbances, Decreased operations and management costs for utilities, and ultimately lower power costs for consumers and Better combination of customer-owner power generation systems, comprising renewable energy systems.

  1. Where Smart Grid used?

After the detailed explanation we interpret where to use this proposed smart grid technology. It is a network on the basis of electricity which utilizes the digital and other improved technologies to handle and control the transport of electricity over all generation sources to meet the differing demands on the basis of end users. Smart grids co-ordinate the requirements and capacities of all grid operators, electricity market stakeholders, generators and end users to work all sections of the system as effectively as possible, environmental effects and decreasing costs while increasing systems resilience, stability, reliability and flexibility. Many technologies will have already reached maturity and so trace the investments offers understandings on levels of arrangements.

  1. Why Smart Grid technology proposed? , previous technology issues

In this research the proposed smart grid serves as a reply to different problems and issues that are linked with traditional electric grids. Some of the major issues that are tackled by smart grid technology will be: Limited Visibility and Control, Resilience and security, Environmental concerns, Aging Infrastructure, Consumer expectations and Participations, Integration of Renewable Energy and Increasing Energy Demand.

  1. Algorithms / protocols

Smart Grid technology is proposed in this research and it overcomes several previous technology issues, now we look through the algorithms or protocols to be utilized for this research. The methods to be utilized are Long-Short-Term-Memory based Recurrent Neural Network with Improved Sparrow Search Algorithm (LSTM-RNN-ISSA), Spatial Temporal Correlation (STC), Blockchain-Based Smart Energy Trading with Adaptive Volt-VAR Optimization (BSET-AVVO), Z-Score Normalization and Distributed Authentication and Authorization (DAA).

  1. Comparative study / Analysis

Here we have given the comparison study or investigating across the existing research and the recommended framework in this section. The primary goal of this study is to achieve an advanced electrical grid system to handle the electrical generations. In comparison to the recommended research we achieve the best finding for this proposed research.

  1. Simulation results / Parameters

For the comparative analysis section we compare various existing methods to give better solutions and then afterwards we compare the parameters to obtain the corresponding results. The parameters or performance metrics that we compared are Throughput with Time (s) and Power Consumption (KW) with Time (h), and the response time (ms) with Number of samples and then Mean Squared Error (MSE) with Number of samples and the average latency (ms) with Number of samples.

  1. Dataset LINKS / Important URL

Here we provide some important links that is useful to overview the Smart Grid technology uses, application and some additional references for any clarification we go through the below offered links:

  1. Smart Grid Applications

Now we see the application for this proposed smart grid technology. It will have an extensive range of applications along various impacts of the electricity supply chain and consumer interaction. Some of the applications are Grid Automation and Control, Demand Response Programs, Energy Storage Management, Predictive Analysis and Forecasting, Consumer Engagement and Energy Management, Grid Security and Resilience, Microgrid Deployment, Electric Vehicle (EV) Charging Infrastructure, Infrastructure of Renewable Energy sources and Advanced Metering Infrastructure (AMI).

  1. Topology for Smart Grid

Let’s see the topology for a smart grid which defines the whole framework and arrangement of elements, like transmission lines, control systems, generation sources and distribution networks. Its topology will differ based on the factors like energy framework, particular goals of the grid execution and geographic location. Some of the common topologies in the smart grid technologies are Mesh Network, Cloud-based Control Systems, Distributed Generation, Virtual Power Plants (VPPs), Hierarchical Structure, Ring Network and Microgrid Configuration.

  1. Environment for Smart Grid

The environment to be employed for this proposed smart grid execution comes across different factors such as economic, social, regulatory and technological considerations. Some of the key aspects are Regulatory Framework, Technological Infrastructure, Data Management and Privacy, Interoperability Standards, Economic Viability, Cybersecurity, Policy and Stakeholder Collaboration and Consumer Engagement and Education.

  1. Simulation tools

Our proposed system Smart Grid technology follows the following software requirements for this research. We implement this research by utilizing the developmental tool namely Python-3.11.4. The operating system that has been employed for this smart grid technology is Windows 10 (64-bit).

  1. Results

Smart Grid technology is proposed in this research and it overcomes numerous existing technology issues by proposing this technology. The proposed method is here compared with different performance metrics or parameters to attain the best results for this research. The tool to be utilized for this research is Python-3.11.4.

Smart Power Grid Research Ideas :

Below we provide are some of the research topics on the basis of Smart Grid technology. These topics will be useful when we have any doubts or clarifications related to this proposed Smart Grid technology:

  1. Research on the Construction of Smart Grid Digital System Based on Big Data Cloud Computing Model Optimization Algorithm
  2. Optimization of Dual-Blockchain Smart Grid Data Sharing Strategy Based on Reverse Auction
  3. Enabling Efficient and Malicious Secure Data Aggregation in Smart Grid With False Data Detection
  4. Improving Smart Grids Security: An Active Learning Approach for Smart Grid-Based Energy Theft Detection
  5. Informed Change-Point Detection Approach for Solar Prosumer Detection and Statistical Verification in Smart Grid
  6. Gradient-Free Accelerated Event-Triggered Scheme for Constrained Network Optimization in Smart Grids
  7. Secured IoT-Based Neighborhood Area Network for Real-Time Energy Data Management in Smart Grids
  8. An Intelligent Controller Assignment Method for QOS and Reliability Improvement in SDN-Smart Grid
  9. A Review on the Evaluation of Feature Selection Using Machine Learning for Cyber-Attack Detection in Smart Grid
  10. Research on Reactive Power and Voltage Cooperative Optimization and Composite Control System in Smart Grid
  11. Design, Modelling, And Simulation of a Smart Grid System with Renewable Energy Penetration
  12. An Improved Framework for Enhancing Security And Efficiency In Reading Smart Grid Technologies
  13. Performance Analysis of OFDM-Based PLC Systems Under Impulsive Noise for Smart Grid Applications
  14. Enhancing Locational FDIA Detection in Smart Grids: A Hyperparameter Optimization Analysis
  15. FLLF: A Fast-Lightweight Location Detection Framework for False Data Injection Attacks in Smart Grids
  16. Design of Reliable IoT Systems With Deep Learning to Support Resilient Demand Side Management in Smart Grids Against Adversarial Attacks
  17. RPMDA: Robust and Privacy-Enhanced Multidimensional Data Aggregation Scheme for Fog-Assisted Smart Grids
  18. Limitation of Reactance Perturbation Strategy Against False Data Injection Attacks on IoT-Based Smart Grid
  19. Communication Network Layer State Estimation Measurement Model for a Cyber-Secure Smart Grid
  20. Federated Learning for Personalized Recommendation in Securing Power Traces in Smart Grid Systems
  21. Adaptive Energy Theft Detection in Smart Grids Using Self-Learning With Dual Neural Network
  22. Secure Consumer-Centric Demand Response Management in Resilient Smart Grid as Industry 5.0 Application With Blockchain-Based Authentication
  23. Locational Detection of False Data Injection Attacks in Smart Grids: A Graph Convolutional Attention Network Approach
  24. TLFed: Federated Learning-based 1D-CNN-LSTM Transmission Line Fault Location and Classification in Smart Grids
  25. Detection of False Data Injection Attacks in Smart Grids via Nonlinear Interval Observer: A Review
  26. Optimizing Smart Grid Integration: A Comprehensive Analysis Review of Load Flow and Advanced Optimization Strategies
  27. A Distributed Coordination Strategy for Heterogeneous Building Flexible Thermal Loads in Responding to Smart Grids
  28. Optimal EV Charging Strategy for Distribution Networks Load Balancing in a Smart Grid Using Dynamic Charging Price
  29. A Novel Approach Based on Machine Learning, Blockchain, and Decision Process for Securing Smart Grid
  30. Fault-Tolerant and Collusion-Resistant Lattice-Based Multidimensional Privacy-Preserving Data Aggregation in Edge-Based Smart Grid
  31. Integrated and Accountable Data Sharing for Smart Grids With Fog and Dual-Blockchain Assistance
  32. Securing Smart Grid Data With Blockchain and Wireless Sensor Networks: A Collaborative Approach
  33. An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems
  34. Two-Timescale Dynamic Resource Management in Smart-Grid Powered Heterogeneous Cellular Networks
  35. Comments on “Enabling Verifiable Privacy-Preserving Multi-Type Data Aggregation in Smart Grids”
  36. A Distributionally Robust Model Predictive Control for Static and Dynamic Uncertainties in Smart Grids
  37. High Performance Platform to Detect Faults in the Smart Grid by Artificial Intelligence Inference
  38. Impulsive Noise Mitigation in NOMA-OFDM Systems Using Time-Domain Interleaving for Smart Grid Applications
  39. Optimization of Internet of Things (IoT) for Smart Grid Energy Management Using Artificial Intelligence (AI) Techniques to Reach SDG7
  40. Locational Detection of False Data Injection Attacks in the Edge Space via Hodge Graph Neural Network for Smart Grids
  41. Review and Comparative Analysis of Deep Learning Techniques for Smart Grid Load Forecasting
  42. Q-Secure-P2-SMA: Quantum-Secure Privacy-Preserving Smart Meter Authentication for Unbreakable Security in Smart Grid
  43. MDA-FLH: Multidimensional Data Aggregation Scheme With Fine-Grained Linear Homomorphism for Smart Grid
  44. Exploring the Potential Application of IEC 61850 to Enable Energy Interconnectivity in Smart Grid Systems
  45. A Proximal ADMM-Based Distributed Optimal Energy Management Approach for Smart Grid With Stochastic Wind Power
  46. ML-Based Energy Consumption and Distribution Framework Analysis for EVs and Charging Stations in Smart Grid Environment
  47. PPMM-DA: Privacy-Preserving Multidimensional and Multisubset Data Aggregation With Differential Privacy for Fog-Based Smart Grids
  48. QFDSA: A Quantum-Secured Federated Learning System for Smart Grid Dynamic Security Assessment
  49. Robust Graph Autoencoder-Based Detection of False Data Injection Attacks Against Data Poisoning in Smart Grids
  50. The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey