Smart EV Charger System Simulation Model

I need a simulation model to emulate the behavior of a smart EV charger system, using MATLAB/Simulink. The model should incorporate the following features:
- Peak and off-peak demand detection
- Recognition of low and high-cost electricity periods
- Use of renewable energy
- Consideration of the impact of EV charging on the grid

Desired outcomes of the simulation model:
- Optimize charging efficiency
- Reduce electricity costs
- Minimize reliance on non-renewable energy sources

The simulation model should consider the following parameters:
- Time of day
- EV charging behavior
- Renewable energy generation
- Grid demand
-Smart charger effect on grid and how it helps

Down below is addition information, i need a matlab simulation please

I. EXECUTIVE SUMMARY The widespread adoption of electric vehicles (EVs) presents both opportunities and challenges for the integration of these vehicles into the existing grid infrastructure. As the number of EVs on the roads continues to grow, the grid faces increased pressure to accommodate their charging needs efficiently while maintaining stability and reliability. The primary objective of this project is to analyze the impact of electric vehicles on the grid and explore how enabling technologies like smart charging can mitigate associated risks. Through the development and simulation of a smart EV charger system using MATLAB/Simulink or Dig silent Power Factory Software, the study aims to assess various aspects, including peak and off-peak demand detection, recognition of low and high-cost electricity periods, and integration of renewable energy. By effectively managing these inputs, the smart EV charger system seeks to reduce electricity costs for consumers and increase the utilization of renewable energy sources, thereby contributing to environmental sustainability.

III. PROJECT OBJECTIVE The primary objective of the project is to assess the impact of electric vehicles (EVs) on the grid and investigate how enabling technologies, particularly smart charging solutions, can mitigate associated risks. The project aims to achieve the following specific objectives: • Analyze Grid Impact: Evaluate the implications of increased EV adoption on grid infrastructure, focusing on challenges such as peak demand management, voltage fluctuations, transformer overloading, and short-circuit risks. • Assess Smart Charging Technologies: Investigate various smart charging technologies and their capabilities in optimizing EV charging patterns to alleviate grid congestion during peak demand periods. • Develop Simulation Model: Develop a comprehensive simulation model using MATLAB/Simulink to emulate the behavior of a smart EV charger system. This model will incorporate parameters such as peak and off-peak demand detection, recognition of low and high-cost electricity periods, and integration of renewable energy sources. • Evaluate Cost Reduction Potential: Assess the potential for cost reduction for consumers through the implementation of smart charging solutions. This includes analyzing the impact on electricity costs and exploring mechanisms to incentivize off-peak charging. • Enhance Renewable Integration: Investigate strategies for integrating renewable energy sources, such as solar power, into the EV charging infrastructure to promote environmental sustainability and reduce reliance on fossil fuels. • Provide Recommendations: Based on the analysis and simulation results, formulate recommendations for stakeholders, including policymakers, utility companies, and EV owners, on the deployment of smart charging technologies and policy frameworks to optimize grid integration of EVs. IV. KNOWLEDGE GAPS AND RESEARCH QUESTIONS Enabling Technologies for Grid Integration of Electric Vehicles is a continuous research topic, but its literature is still scarce in many research areas. Some knowledge gaps are stated below on which this project’s research questions are based. 5 Smart EV Charger Technology and Grid Integration: How can Smart EV chargers identify peak and off-peak demand to reduce strain during busy times? What approaches can handle grid capacity constraints as EV adoption increases? How can Smart EV chargers support the adoption of time-of-use pricing strategies, and which strategies effectively encourage off-peak charging? Integration of Renewable Energy Sources: What are the challenges and opportunities of integrating renewable energy sources into EV charging infrastructure? How can these challenges be addressed effectively? Effects of EV Adoption on Grid Infrastructure: What are the specific effects of increased EV adoption on peak demand management, voltage stability, and grid infrastructure? How do factors like charging patterns, grid capacity, and EV distribution influence these impacts? Smart Charging Technologies: What are the existing smart charging technologies? How do they differ in their ability to mitigate grid congestion caused by EV charging? What technical features and functionalities are required for effective smart charging solutions? Impact Assessment: What challenges does the growing EV adoption pose to grid infrastructure? Specifically regarding peak demand management, voltage stability, and transformer capacity? Grid Congestion Analysis: How does simultaneous charging of multiple EVs during peak periods affect grid congestion? What are the potential consequences in terms of voltage fluctuations, transformer overloading, and short-circuit risks?

Few expected outcomes of this research project are listed below: Development of Smart Charging Solutions: The research aims to develop and implement smart charging algorithms that optimize the charging schedules of electric vehicles based on real-time grid conditions, electricity pricing, and renewable energy availability. These solutions are expected to enhance the integration of electric vehicles into the grid, improve grid stability, and reduce electricity costs for consumers. Insights from Literature Review: The comprehensive literature review conducted at the outset of the project is expected to provide valuable insights into key topics such as electric vehicle grid integration, peak demand management, time-of-use pricing, smart charging solutions, and renewable energy integration. Simulation Analysis: The MATLAB simulations conducted as part of the research will generate a wealth of data regarding charging patterns, grid demand, renewable energy availability, and the performance of smart charging algorithms under various scenarios. The analysis of simulation results is expected to reveal important patterns, trends, and insights that will contribute to a deeper understanding of grid integration challenges and the effectiveness of proposed solutions. Evaluation of Smart Charging Impact: Through rigorous analysis, the impact of smart charging algorithms on grid integration of electric vehicles will be evaluated. Key performance metrics such as reduction in peak demand, increase in utilization of renewable energy, and improvement in grid stability will be assessed.