Contingency Analysis of a Grid of Connected EVs for Primary Frequency Control of an Industrial Microgrid Using Efficient Control Scheme

• URL: http://arxiv.org/abs/2402.01608v1
• Date: Fri, 2 Feb 2024 18:14:16 GMT
• Title: Contingency Analysis of a Grid of Connected EVs for Primary Frequency Control of an Industrial Microgrid Using Efficient Control Scheme
• Authors: J.N. Sabhahit, S.S. Solanke, V.K. Jadoun, H. Malik, F.P. Garc\'ia M\'arquez, J.M. Pinar-P\'erez
• Abstract summary: Electric vehicles (EVs) can operate as both a load and a source. Industrial Microgrids are made up of different energy sources such as wind farms and PV farms, storage systems, and loads. A proposed control scheme for frequency management is presented.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: After over a century of internal combustion engines ruling the transport sector, electric vehicles appear to be on the verge of gaining traction due to a slew of advantages, including lower operating costs and lower CO2 emissions. By using the Vehicle-to-Grid (or Grid-to-Vehicle if Electric vehicles (EVs) are utilized as load) approach, EVs can operate as both a load and a source. Primary frequency regulation and congestion management are two essential characteristics of this technology that are added to an industrial microgrid. Industrial Microgrids are made up of different energy sources such as wind farms and PV farms, storage systems, and loads. EVs have gained a lot of interest as a technique for frequency management because of their ability to regulate quickly. Grid reliability depends on this quick reaction. Different contingency, state of charge of the electric vehicles, and a varying number of EVs in an EV fleet are considered in this work, and a proposed control scheme for frequency management is presented. This control scheme enables bidirectional power flow, allowing for primary frequency regulation during the various scenarios that an industrial microgrid may encounter over the course of a 24-h period. The presented controller will provide dependable frequency regulation support to the industrial microgrid during contingencies, as will be demonstrated by simulation results, achieving a more reliable system. However, simulation results will show that by increasing a number of the EVs in a fleet for the Vehicle-to-Grid approach, an industrial microgrid\'s frequency can be enhanced even further.

Related papers

• Smart Ride and Delivery Services with Electric Vehicles: Leveraging Bidirectional Charging for Profit Optimisation [8.899491864225464]
  We introduce the Electric Vehicle Orienteering Problem with V2G (EVOP-V2G)<n>This involves navigating dynamic electricity prices, charging station selection, and route constraints.<n>We propose two near-optimal metaheuristic algorithms: one evolutionary (EA) and the other based on large neighborhood search (LNS)
  arXiv  Detail & Related papers  (2025-06-25T13:15:52Z)
• Dynamic Rolling Horizon Optimization for Network-Constrained V2X Value Stacking of Electric Vehicles Under Uncertainties [4.43248614452072]
  Electric vehicle (EV) coordination can provide significant benefits through vehicle-to-everything (V2X) This work aims to develop a V2X value-stacking framework, including vehicle-to-building (V2B), vehicle-to-grid (V2G), and energy trading.
  arXiv  Detail & Related papers  (2025-02-13T13:06:56Z)
• Electric Vehicles coordination for grid balancing using multi-objective Harris Hawks Optimization [0.0]
  The rise of renewables coincides with the shift towards Electrical Vehicles (EVs) posing technical and operational challenges for the energy balance of the local grid. Coordinating power flow from multiple EVs into the grid requires sophisticated algorithms and load-balancing strategies. This paper proposes an EVs fleet coordination model for the day ahead aiming to ensure a reliable energy supply and maintain a stable local grid.
  arXiv  Detail & Related papers  (2023-11-24T15:50:37Z)
• Charge Manipulation Attacks Against Smart Electric Vehicle Charging Stations and Deep Learning-based Detection Mechanisms [49.37592437398933]
  "Smart" electric vehicle charging stations (EVCSs) will be a key step toward achieving green transportation. We investigate charge manipulation attacks (CMAs) against EV charging, in which an attacker manipulates the information exchanged during smart charging operations. We propose an unsupervised deep learning-based mechanism to detect CMAs by monitoring the parameters involved in EV charging.
  arXiv  Detail & Related papers  (2023-10-18T18:38:59Z)
• Federated Reinforcement Learning for Electric Vehicles Charging Control on Distribution Networks [42.04263644600909]
  Multi-agent deep reinforcement learning (MADRL) has proven its effectiveness in EV charging control. Existing MADRL-based approaches fail to consider the natural power flow of EV charging/discharging in the distribution network. This paper proposes a novel approach that combines multi-EV charging/discharging with a radial distribution network (RDN) operating under optimal power flow.
  arXiv  Detail & Related papers  (2023-08-17T05:34:46Z)
• Identifying drivers and mitigators for congestion and redispatch in the German electric power system with explainable AI [0.0]
  We provide a data-driven analysis of congestion in the German transmission grid. We develop an explainable machine learning model to predict the volume of redispatch and countertrade. We show that, as expected, wind power generation is the main driver, but hydropower and cross-border electricity trading also play an essential role.
  arXiv  Detail & Related papers  (2023-07-24T09:19:38Z)
• Federated Reinforcement Learning for Real-Time Electric Vehicle Charging and Discharging Control [42.17503767317918]
  This paper develops an optimal EV charging/discharging control strategy for different EV users under dynamic environments. A horizontal federated reinforcement learning (HFRL)-based method is proposed to fit various users' behaviors and dynamic environments. Simulation results illustrate that the proposed real-time EV charging/discharging control strategy can perform well among various factors.
  arXiv  Detail & Related papers  (2022-10-04T08:22:46Z)
• Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
  This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing. We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
  arXiv  Detail & Related papers  (2022-06-28T17:08:31Z)
• A Reinforcement Learning Approach for Electric Vehicle Routing Problem with Vehicle-to-Grid Supply [2.6066825041242367]
  We present QuikRouteFinder that uses reinforcement learning (RL) for EV routing to overcome these challenges. Results from RL are compared against exact formulations based on mixed-integer linear program (MILP) and genetic algorithm (GA) metaheuristics.
  arXiv  Detail & Related papers  (2022-04-12T06:13:06Z)
• An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning [50.85048976506701]
  This paper proposes a novel solution to range anxiety based on a federated-learning model. It is capable of estimating battery consumption and providing energy-efficient route planning for vehicle networks.
  arXiv  Detail & Related papers  (2021-11-13T15:03:44Z)
• Risk Adversarial Learning System for Connected and Autonomous Vehicle Charging [43.42105971560163]
  We study the design of a rational decision support system (RDSS) for a connected and autonomous vehicle charging infrastructure (CAV-CI) In the considered CAV-CI, the distribution system operator (DSO) deploys electric vehicle supply equipment (EVSE) to provide an EV charging facility for human-driven connected vehicles (CVs) and autonomous vehicles (AVs) The charging request by the human-driven EV becomes irrational when it demands more energy and charging period than its actual need. We propose a novel risk adversarial multi-agent learning system (ALS) for CAV-CI to solve
  arXiv  Detail & Related papers  (2021-08-02T02:38:15Z)
• Efficient algorithms for electric vehicles' min-max routing problem [4.640835690336652]
  An increase in greenhouse gases emission from the transportation sector has led companies and the government to elevate and support the production of electric vehicles (EV) With recent developments in urbanization and e-commerce, transportation companies are replacing their conventional fleet with EVs to strengthen the efforts for sustainable and environment-friendly operations. deploying a fleet of EVs asks for efficient routing and recharging strategies to alleviate their limited range and mitigate the battery degradation rate.
  arXiv  Detail & Related papers  (2020-08-07T18:45:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.