相关论文: Solar Power driven EV Charging Optimization with D…
With recent developments in vehicle and battery technologies, electric vehicles (EVs) are rapidly getting established as a sustainable alternative to traditional fossil-fuel vehicles. This has made the large-scale electrification of…
For electrifying the transportation sector, deploying a strategically planned and efficient charging infrastructure is essential. This paper presents a two-phase approach for electric vehicle (EV) charger deployment that integrates spatial…
This paper presents a decentralized Multi-Agent Reinforcement Learning (MARL) approach to an incentive-based Demand Response (DR) program, which aims to maintain the capacity limits of the electricity grid and prevent grid congestion by…
Electric vehicle (EV) charging patterns are highly uncertain in both location, time, and duration particularly in association with the predicted high demand for electric mobility in the future. An EV can be charged at home, at charging…
Increasing adoption of solar photovoltaic (PV) presents new challenges to modern power grid due to its variable and intermittent nature. Fluctuating outputs from PV generation can cause the grid violating voltage operation limits. PV smart…
This paper addresses the critical integration of electric vehicles (EVs) into the electricity grid, which is essential for achieving carbon neutrality by 2050. The rapid increase in EV adoption poses significant challenges to the existing…
The rapid increase in Electric Vehicle (EV) adoption provides a promising solution for reducing carbon emissions and fossil fuel dependency in transportation systems. However, the increasing numbers of EVs pose significant challenges to the…
As the share of renewable energy sources in the present electric energy mix rises, their intermittence proves to be the biggest challenge to carbon free electricity generation. To address this challenge, we propose an electricity pricing…
Deep Reinforcement Learning (DRL) solutions are becoming pervasive at the edge of the network as they enable autonomous decision-making in a dynamic environment. However, to be able to adapt to the ever-changing environment, the DRL…
Electric vehicles (EVs) are increasingly integrated into power grids, offering economic and environmental benefits but introducing challenges due to uncoordinated charging. This study addresses the profit maximization problem for multiple…
The growing adoption of hybrid electric vehicles (HEVs) presents a transformative opportunity for revolutionizing transportation energy systems. The shift towards electrifying transportation aims to curb environmental concerns related to…
Models of multi-agent systems can be found all around us. One of the objectives of multi-agent systems is to define local control laws in order to achieve a desired global state of the system. This paper utilises the concept of Distributed…
The installation of electric vehicle (EV) charging stations in buildings is inevitable, as states push for increased EV adoption to support decarbonization efforts. This transition could force the need for grid infrastructure upgrades and…
Renewable energy resources (RERs) have been increasingly integrated into large-scale distributed power systems. Considering uncertainties and voltage fluctuation issues introduced by RERs, in this paper, we propose a deep reinforcement…
Electric vehicles (EVs) can be considered as flexible mobile battery storages in microgrids. For multiple microgrids in an area, coordinated scheduling on charging and discharging are required to avoid power exchange spikes between the…
The electric vehicle routing problem with time windows (EVRPTW) is a complex optimization problem in sustainable logistics, where routing decisions must minimize total travel distance, fleet size, and battery usage while satisfying strict…
We propose a novel way to use Electric Vehicles (EVs) as dynamic mobile energy storage with the goal to support grid balancing during peak load times. EVs seeking parking in a busy/expensive inner city area, can get free parking with a…
This paper introduces a deep reinforcement learning (RL) framework for optimizing the operations of power plants pairing renewable energy with storage. The objective is to maximize revenue from energy markets while minimizing storage…
Hybrid fast-charging stations with battery storage and local renewable generation can facilitate low-carbon electric vehicle (EV) charging, while reducing the stress on the distribution grid. This paper proposes energy management strategies…
The power consumption of households has been constantly growing over the years. To cope with this growth, intelligent management of the consumption profile of the households is necessary, such that the households can save the electricity…