Intelligent-Energy-Control-Strategy-for-Grids-with-EVs-and-RES.pptx

intelligent smart grid control

1. Intelligent Energy Control Strategy for Microgrids with EVs and RES eH b y V N S R Murthy Research Scholar

2. Objectives of the work: As the world moves towards a more sustainable energy future, the integration of electric vehicles (EVs) and renewable energy sources (RES) into the power grid presents both challenges and opportunities. An intelligent energy control strategy is crucial to optimizing the grid's efficiency, reliability, and responsiveness in this evolving landscape. This work will explore the key components and benefits of such a strategy, highlighting its potential to transform the way we generate, distribute, and consume electricity . Energy management in Micro Grids (MG) has become increasingly difficult as stochastic Renewable Energy Sources (RES) and Electric Vehicles (EV) have become more prevalent. Even more challenging is autonomous MG operation with RES since prompt frequency control is required. By integrating RES and EV storage, we seek to decrease reliance on the grid. The EMS consists of three execution phases: Ranking for EV Recommendation (RER), Optimal Power Allocation (OPA) for Fleet, and EV Storage Allocation (OAES). The aim of slicing the time in to smaller in intervals is to update the energy and power scheduling in shorter intervals as per the changes are going on in the system. eH The Pareto-front is used to calculate the best amount of power from each fleet in each 't'. The data received from the fuzzy rule base is used in the third stage to train an intelligent Convolutional Neural Network (CNN), which has rank of EV as an output and four decision variables as inputs. The main goals in this stage are to minimize battery degradation and to make the most of it for MG support. With the aid of a MATLAB-based simulation setup and heterogeneous entities, the primary goal of EMS is examined and put into practice in an On-grid MG.

3. Renewable Energy Integration Harnessing Renewable Sources The integration of RES, such as solar, wind, and hydropower, into the grid is essential for reducing reliance on fossil fuels and achieving sustainability. However, the intermittent and variable nature of these sources poses a challenge for grid stability and reliability. Balancing Supply and Demand An intelligent energy control strategy must effectively manage the fluctuations in renewable energy generation to ensure a consistent and reliable supply of electricity. This may involve the use of energy storage systems, demand-side management, and advanced forecasting techniques. Optimizing Grid Integration Seamless integration of RES into the grid requires advanced control algorithms, communication protocols, and grid infrastructure upgrades. The goal is to maximize the utilization of renewable sources while maintaining grid stability and power quality.

4. Electric Vehicle Integration 1 Charging Infrastructure The widespread adoption of EVs necessitates the development of a comprehensive charging infrastructure, including both public and private charging stations. An intelligent energy control strategy must optimize the placement and management of these charging points to ensure efficient and user-friendly charging experiences. 2 Vehicle-to-Grid (V2G) Integration By enabling bidirectional energy flow between EVs and the grid, the intelligent energy control strategy can leverage EV batteries as distributed energy storage resources. This V2G integration can help balance supply and demand, provide ancillary services, and improve grid resilience. 3 Intelligent Charging Algorithms The control strategy must employ advanced algorithms to intelligently manage EV charging, taking into account factors such as user preferences, grid conditions, and renewable energy availability. This optimization can help minimize the impact of EVs on the grid and maximize the benefits of their integration.

5. Energy Storage Integration 1 Balancing Supply and Demand Energy storage systems, such as batteries, can play a crucial role in balancing the fluctuations between renewable energy generation and electricity demand. The intelligent energy control strategy can optimize the operation of these storage systems to ensure a stable and reliable power supply. 2 Frequency Regulation and Grid Ancillary Services Energy storage can provide fast-acting frequency regulation and other ancillary services to the grid, helping to maintain grid stability and power quality. The control strategy can leverage these capabilities to enhance the grid's resilience and responsiveness. 3 Peak Shaving and Load Shifting By strategically charging and discharging energy storage systems, the control strategy can help reduce peak electricity demand and shift loads to periods of lower demand. This can lead to significant cost savings and improved grid efficiency. 4 Resilience and Emergency Response Energy storage can provide backup power and emergency response capabilities during grid disruptions or power outages. The control strategy can optimize the use of these resources to ensure continuity of service and enhance the overall resilience of the power grid.

6. Benefits of Intelligent Energy Control Improved Grid Efficiency The intelligent energy control strategy can optimize the utilization of grid resources, reduce energy losses, and minimize the need for additional generation capacity, leading to significant improvements in overall grid efficiency. Increased Reliability and Resilience By effectively managing the integration of RES and EVs, and leveraging energy storage systems, the control strategy can enhance the grid's ability to withstand disruptions and maintain a stable and reliable power supply. Cost Savings and Reduced Emissions The optimization of energy generation, distribution, and consumption can result in cost savings for both utilities and consumers, while also contributing to the reduction of greenhouse gas emissions and the achievement of sustainability goals.

7. Strategies for Intelligent Energy Control 1 Dynamic Pricing 2 Demand Response 3 Vehicle-to-Grid (V2G) Integration Enabling bidirectional energy flow between EVs and the grid can allow EV batteries to serve as distributed energy storage, providing additional flexibility and resilience to the system. Coordinating with consumers to temporarily adjust their energy consumption in response to grid conditions can help mitigate peak demand and enhance grid stability. Implementing variable electricity rates that reflect real-time grid conditions can incentivize consumers to shift their energy usage and EV charging to off-peak hours, reducing strain on the grid.

8. Flowchart representation of proposed EV control strategy for MG with EMS

9. Conclusion: An innovative EMS for MG with grid support is suggested in the work. The goal was to use RES and EV storage to lessen reliance on the grid. The proposed EMS had three execution phases: OAES, OPF, and RER. The estimated storage need (kWh/t) is based on the predicted demand and RES and is based on dividing the 24-h period into 96 time intervals (t). They established the required amount of storage and the charging and discharging time zones (G2V and V2G, respectively). The main goal is "load flattening," which aims to lessen the MG’s reliance on the grid. Three approaches were first used to obtain OAES, but the OAIWF algorithm performed the best. Based on OAES, OPF will be obtained for each "t" by resolving a MOOP with the dual goals of reducing voltage variation and network power loss. The OPF for the fleet was derived for each "t" from the Pareto-front, where we took into account a solution that improves performance while still achieving both MOOP goals. The CNN will be trained using the information obtained from the fuzzy rule base in the third stage, which comprised four decision variables as inputs and the rank of EV as an output. During this phase, the key goals were to avoid battery deterioration and optimize its use for MG support. A MATLAB-based smart grid system with heterogeneous entities will be used to implement the suggested EMS, and various case studies were examined. Different combinations of CNN inputs have been used to study the effects of decision variables in RER. It has been researched how the Pareto-front choice affects voltage and power loss.

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