Strategizing EV Charging and Renewable Integration in Texas
- URL: http://arxiv.org/abs/2310.17056v1
- Date: Wed, 25 Oct 2023 23:34:25 GMT
- Title: Strategizing EV Charging and Renewable Integration in Texas
- Authors: Mohammad Mohammadi and Jesse Thornburg
- Abstract summary: This study explores the convergence of electric vehicles (EVs), renewable energy, and smart grid technologies in the context of Texas.
Research focuses on grid stability concerns, uncoordinated charging patterns, and the complicated relationship between EVs and renewable energy sources.
- Score: 2.429429123532487
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Exploring the convergence of electric vehicles (EVs), renewable energy, and
smart grid technologies in the context of Texas, this study addresses
challenges hindering the widespread adoption of EVs. Acknowledging their
environmental benefits, the research focuses on grid stability concerns,
uncoordinated charging patterns, and the complicated relationship between EVs
and renewable energy sources. Dynamic time warping (DTW) clustering and k-means
clustering methodologies categorize days based on total load and net load,
offering nuanced insights into daily electricity consumption and renewable
energy generation patterns. By establishing optimal charging and
vehicle-to-grid (V2G) windows tailored to specific load characteristics, the
study provides a sophisticated methodology for strategic decision-making in
energy consumption and renewable integration. The findings contribute to the
ongoing discourse on achieving a sustainable and resilient energy future
through the seamless integration of EVs into smart grids.
Related papers
- A Survey of AI-Powered Mini-Grid Solutions for a Sustainable Future in Rural Communities [0.18783379094746652]
This paper reviews various forecasting models, including statistical methods, machine learning algorithms, and hybrid approaches.
It explores public datasets and tools such as Prophet, NeuralProphet, and N-BEATS for model implementation and validation.
The survey concludes with recommendations for future research, addressing challenges in model adaptation and optimisation for real-world applications.
arXiv Detail & Related papers (2024-07-17T20:23:38Z) - 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) - A Review on AI Algorithms for Energy Management in E-Mobility Services [4.084938013041068]
E-mobility, or electric mobility, has emerged as a pivotal solution to address pressing environmental and sustainability concerns.
This paper seeks to explore the potential of artificial intelligence (AI) in addressing various challenges related to effective energy management in e-mobility systems.
arXiv Detail & Related papers (2023-09-26T16:34:35Z) - Deep Reinforcement Learning-Based Battery Conditioning Hierarchical V2G
Coordination for Multi-Stakeholder Benefits [3.4529246211079645]
This study proposes a multi-stakeholder hierarchical V2G coordination based on deep reinforcement learning (DRL) and the Proof of Stake algorithm.
The multi-stakeholders include the power grid, EV aggregators (EVAs), and users, and the proposed strategy can achieve multi-stakeholder benefits.
arXiv Detail & Related papers (2023-08-01T01:19:56Z) - EVKG: An Interlinked and Interoperable Electric Vehicle Knowledge Graph
for Smart Transportation System [5.600639345303369]
We present an EV-centric knowledge graph (EVKG) as a comprehensive, cross-domain, and open geospatial knowledge management system.
The EVKG encapsulates essential EV-related knowledge, including EV adoption, electric vehicle supply equipment, and electricity transmission network.
Using six competency questions, we demonstrate how the EVKG can be used to answer various types of EV-related questions.
arXiv Detail & Related papers (2023-04-10T23:01:02Z) - Distributed Energy Management and Demand Response in Smart Grids: A
Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems.
In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
arXiv Detail & Related papers (2022-11-29T01:18:58Z) - 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) - An Energy and Carbon Footprint Analysis of Distributed and Federated
Learning [42.37180749113699]
Classical and centralized Artificial Intelligence (AI) methods require moving data from producers (sensors, machines) to energy hungry data centers.
Emerging alternatives to mitigate such high energy costs propose to efficiently distribute, or federate, the learning tasks across devices.
This paper proposes a novel framework for the analysis of energy and carbon footprints in distributed and federated learning.
arXiv Detail & Related papers (2022-06-21T13:28:49Z) - 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) - Investigating Underlying Drivers of Variability in Residential Energy
Usage Patterns with Daily Load Shape Clustering of Smart Meter Data [53.51471969978107]
Large-scale deployment of smart meters has motivated increasing studies to explore disaggregated daily load patterns.
This paper aims to shed light on the mechanisms by which electricity consumption patterns exhibit variability.
arXiv Detail & Related papers (2021-02-16T16:56:27Z) - Risk-Aware Energy Scheduling for Edge Computing with Microgrid: A
Multi-Agent Deep Reinforcement Learning Approach [82.6692222294594]
We study a risk-aware energy scheduling problem for a microgrid-powered MEC network.
We derive the solution by applying a multi-agent deep reinforcement learning (MADRL)-based advantage actor-critic (A3C) algorithm with shared neural networks.
arXiv Detail & Related papers (2020-02-21T02:14:38Z)
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.