Authors: Gourav Sakharkar, Akash Jagnade, Siddheshwar Sakarge, Sankalp Challawar, Dr. Vaishnavi Dhok, Prof. Pallavi Barekar
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Power industry applications for smart grid are quite promising. In recent years, network life extension has attracted a lot of study attention. By examining the business needs of the Smart grid, this study enhances the wireless sensor network LEACH algorithm. To ensure the rationality of the cluster head selection, the remaining energy and node density are first taken into consideration. The cluster radius of various sizes is then constructed while clustering using a non-uniform clustering process. To achieve the goal of balancing the energy load, data is transmitted to the base station using the multi-hop mode, and the distance between the cluster head nodes and the remaining energy are utilised as the selection criteria for the next-hop relay node. In order to provide the computational intelligence required to create the Smart Grid, this paper discusses the future of the electric energy system, covering every aspect from power generation to substations, distribution, and customer feedback loops.
The current electricity grid struggles with inefficient monitoring, automation, fault diagnosis, and communications, which raises the risk of a regional system failure. In order to increase the effectiveness, dependability, and safety of power distribution, the smart grid is a new generation of electric power networks that modernises electric power grid networks utilising cutting-edge sensors and distributed computing technology. Three basic subsystems make up the electric power grid: power generation, power transmission and distribution, and client facilities.
To improve control by combining automation and self-healing capabilities, smart grid needs online monitoring, diagnostics, and protection. In recent years, wired networks have gained recognition as a technology with great potential for achieving seamless, energy-efficient, dependable smart grid monitoring and control.
Through the three primary smart grid components, wired networks are used. The "Smart Grid Simulation with LEACH Clustering" project aims to create a dynamic and energy-efficient smart grid simulation using MATLAB. Smart grids are modern power systems that integrate advanced communication and control technologies to optimise energy distribution and consumption. In this simulation, we design a smart grid with multiple nodes connected to a base station, where power generation is time-varying, and energy consumption is managed using the LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm.
The simulation app offers an intuitive user interface, allowing users to adjust simulation parameters such as the duration of the simulation, power generation capacity, and the number of consumers in the network. The time-varying power generation is modelled based on a sinusoidal function, while the LEACH algorithm dynamically forms clusters and selects cluster heads to optimise energy efficiency.
Throughout the simulation, users can observe real-time graphs displaying power consumption by nodes and power balance at the base station. The integration of LEACH clustering enhances energy distribution, enabling nodes to efficiently communicate with the base station, ultimately prolonging the network's lifetime and improving overall energy management.
The project provides valuable insights into the behaviour of smart grid systems and the benefits of employing advanced clustering algorithms for energy optimization.
It serves as a powerful tool for researchers, students, and energy management professionals to study and understand the dynamics of smart grid networks and explore energy-efficient strategies to build sustainable power systems for the future. Although wired networks provide numerous benefits for the smart grid technologies, they also have many drawbacks because of their special qualities, resource limitations, and the challenging and intricate electric-power environment
II. LITERATURE SURVEY
The LEACH protocol was suggested by W. Heinzelman and Balakrishnan for WSN . A LEACH is an algorithm that organises the network nodes into small clusters and chooses a CH from each cluster. At first, the node senses its destination and then transmits the pertinent message to the CH. Then, the CH transmits the gathered information to the BS. LEACH protocol’s main objective is to enhance energy efficiency by employing a random integer to implement a rotation-based CH selection technique. The LEACH procedures are designed to work in many rounds. There are two phases in each round. A unique LEACH protocol in a heterogeneous network that matched the simulated outcomes to those of the LEACH homogeneous system was introduced when they simulated the protocol across a-meter region. Sharma discovered that 10 nodes contain more energy than the other 90, extending the lifespan of the system and improving the performance of the wireless sensor network. Dynamic K value protocol (DK-LEACH) proposes optimum clustering. Within the unequal energy distribution, this strategy minimises energy consumption. The same cluster and different cluster distances are both relevant elements in this case.
User Interface (UI) Setup: The Simulation is built using MATLAB's App Designer, and it contains various UI components like sliders, labels, numeric edit fields, and an axis to plot the simulation results.
Deletion of UI Figure: When the interface is deleted or closed, the delete method ensures that the UI Figure is properly closed.
The methodology focuses on setting up the UI, defining simulation parameters, implementing the smart grid simulation algorithm, handling clustering with LEACH, and updating the UI based on user input. The combination of these components results in a Smart Grid Simulation app that provides insights into power consumption and balance for different simulation scenarios.
a. Open MATLAB and Start GUIDE: Open MATLAB, and in the Command Window, type guide to launch the GUIDE tool.
b. Create a New GUI: In GUIDE, click on "New" to create a new GUI. Choose "Blank GUI" as the template.
c. Design the GUI: Design the GUI by dragging and dropping components (e.g., sliders, buttons, textboxes) from the Component Palette onto the GUI canvas. Create components for the simulation parameters you want to adjust, such as simulation time, power generation, number of consumers, etc.
d. Set Component Properties: After adding components, you can customise their properties in the "Property Inspector" to set default values, limits, and other relevant options.
e. Add Callbacks: For each component that needs to change the simulation parameters, add "Callback" functions that will be triggered when the user interacts with the components. In these callbacks, you can update the simulation parameters based on the user's inputs.
f. Generate GUI Code: Once you've designed the GUI and added the necessary callbacks, click on "Generate M-File" to create the code that defines the GUI and its behaviour.
g. Integrate with Smart Grid Simulation Code: Integrate the GUI code with the existing smart grid simulation code. In the callback functions, update the simulation parameters based on user inputs, and then call the smart grid simulation code to run the simulation with the adjusted parameters.
h. Run the GUI: Save the GUI and run it in MATLAB to interactively adjust the simulation parameters and observe the updated results on the plot.
Creating a complete GUI might involve additional customization and complexity, depending on your specific requirements. You can find more information about GUIDE and GUI development in MATLAB's official documentation and tutorials. Additionally, MATLAB also provides other GUI development options, such as App Designer, which you may consider depending on your preference and the complexity of the GUI you want to create.
IV. SIMULATION AND ANALYSIS
The Smart Grid Simulation project aimed to model and simulate a smart grid system with multiple power generation sources and consumers, along with the implementation of the LEACH protocol for clustering. The simulation was designed to study the power consumption and balance dynamics over time.
Throughout the simulation, we observed the following key findings:
Time-Varying Power Generation: The use of a sinusoidal function to model time-varying power generation provided interesting insights into how power generation fluctuates over the course of the day. The inclusion of solar power generation, based on solar panel efficiency, also contributed to the overall power generation capacity.
VI. POWER GENERATION SOURCES
In our smart grid simulation model, we have utilised the following energy sources:
These diverse energy sources contribute to a balanced and efficient smart grid, ensuring a more sustainable and reliable power supply.
The \"Smart Grid Simulation with LEACH Clustering\" project has provided valuable hands-on experience in understanding smart grid dynamics, optimising energy management, and using clustering algorithms for network efficiency. This simulation serves as a useful tool for researchers, energy professionals, and policymakers to study and enhance the performance of smart grid systems, ultimately contributing to the advancement of sustainable energy solutions for the future. In conclusion, the Smart Grid Simulation app provided valuable insights into the behaviour of a smart grid system under various scenarios. The ability to visualise power consumption, power balance, and the effects of the LEACH protocol on clustering contributes to better understanding and decision-making for smart grid management and optimization. It is important to note that this simulation is a simplified model of a real-world smart grid system. For a more accurate representation, additional factors such as power transmission losses, real-world consumer behaviour, and renewable energy variability could be incorporated. The app can be further enhanced and expanded to include more features for a comprehensive smart grid analysis. Overall, the Smart Grid Simulation project serves as a valuable tool for researchers, engineers, and policymakers to study and optimise smart grid systems for a sustainable and efficient energy future.
 Yu Xinxin, Yan Wenpeng. Review of Smart Grid. Journal of China Electrical Engineering. 2009, 3,(4):1--8  Yide L, An L. Wireless Sensor Network Applications in Smart Grid: Recent Trends and Challenges. International Journal of Distributed Sensor Networks.2012, 6(38):383--388.  Heinzelman W, Chandrakasan A, Balakrishnan H. Energy-Efficient Protocol For Wireless Micro Sensor Networks. Hawaii International Conference On System Sciences. 2011 5(9):3005--3014.  Lu Xianling, Wang Yingying, Wang Hongbin, Xu Baoguo. Non-uniform clustering algorithm for energy balance in wireless sensor networks. computer science.2013, 40(5):393--396  Xiangqing Ling, Junwei Han, Jin Lu, Xia wei. Research of Wireless Sensor Network Multi-path Routing Algorithm in Distribution Communications. ICEICE 2012:3826--3829  Li Chengfa, Ye Mao, Chen Guihai. An Energy-efficient Unequal Clustering Mechanism For Wireless Sensor Networks.//Proc.of The 2nd IEEE International Conference On Mobile Ad hoc and Sensor System. 2005, 8(36):521--525.  A.M. Youssef, M.F. Younis, M. Youssef, A.K. Agrawala, Distributed formation of overlapping multi-hop clusters in wireless sensor networks. IEEE Global Communications Conference. 2020, 1(28):58--62.  Su Bing, ZHANG Wei. Routing protocol for wireless sensor networks based on non-uniform clustering. Computer Measurement & Control. 2017, 5(2):325--327.  Lai Chao, Jiang Wenxian. Energy Balanced Routing Algorithm for Wireless Sensor Networks Based on Cluster Head Expectation. Small Computer System. 2016, 12(6):1391--1396  Sun Yan-qing, Peng Jian, Liu Tang, et al. Uneven clustering routing protocol based on dynamic partition for wireless sensor network. Journal on Communications, 2018, 35(1):198--206.  Sun Yan-qing, Peng Jian, Liu Tang, et al. Uneven clustering routing protocol based on dynamic partition for
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