홈 › Papers

Solar Power driven EV Charging Optimization with Deep Reinforcement Learning

2022-11-17 · Stavros Sykiotis, Christoforos Menos-Aikateriniadis, Anastasios Doulamis, Nikolaos Doulamis, Pavlos S. Georgilakis

Power sector decarbonization plays a vital role in the upcoming energy transition towards a more sustainable future. Decentralized energy resources, such as Electric Vehicles (EV) and solar photovoltaic systems (PV), are continuously integrated in residential power systems, increasing the risk of bottlenecks in power distribution networks. This paper aims to address the challenge of domestic EV charging while prioritizing clean, solar energy consumption. Real Time-of-Use tariffs are treated as a price-based Demand Response (DR) mechanism that can incentivize end-users to optimally shift EV charging load in hours of high solar PV generation with the use of Deep Reinforcement Learning (DRL). Historical measurements from the Pecan Street dataset are analyzed to shape a flexibility potential reward to describe end-user charging preferences. Experimental results show that the proposed DQN EV optimal charging policy is able to reduce electricity bills by an average 11.5\% by achieving an average utilization of solar power 88.4

📄 PDF Abstract BibTeX arXiv:2211.09479

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Methods 이 논문이 사용한 방법론

Q-Learning Q-Learning is an off-policy temporal difference control algorithm: $$Q\left(S\_{t}, A\_{t}\right) \leftarrow Q\left(S\_{t}, A\_{t}\right) + \alpha\left[R_{t+1} +…

Convolution A convolution is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the…

Electric Electric is an energy-based cloze model for representation learning over text. Like BERT, it is a conditional generative model of tokens given their contexts. However,…

Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…

DQN A DQN, or Deep Q-Network, approximates a state-value function in a Q-Learning framework with a neural network. In the Atari…

Optimal Charging Profile Design for Solar-Powered Sustainable UAV Communication Networks

2023-02-13 · Longxin Wang, Saugat Tripathi, Ran Zhang, Nan Cheng 외

This work studies optimal solar charging for solar-powered self-sustainable UAV communication networks, considering the day-scale time-variability of solar radiation and user service demand. The objective is to optimally…

Deep Reinforcement Learning

Optimal activity and battery scheduling algorithm using load and solar generation forecast

2021-12-06 · Rui Yuan, Nam Trong Dinh, Yogesh Pipada, S. Ali Pourmouasvi

In this report, we provide a technical sequence on tackling the solar PV and demand forecast as well as optimal scheduling problem proposed by the IEEE-CIS 3rd technical challenge on predict + optimize for activity and b…

Missing Values Scheduling Time Series Time Series Analysis+1

Analyzing Power Quality Implications of High Level Charging Rates of Electric Vehicle Within Distribution Networks

2021-06-28 · Arash Farokhi Soofi, Reza Bayani, Saeed D. Manshadi

This paper investigates the impact of the charging level of high penetration level of Electric Vehicles (EVs) on the power quality of the electricity distribution network. The EV owners tend to charge their EVs as fast a…

Agent-Based Decentralized Energy Management of EV Charging Station with Solar Photovoltaics via Multi-Agent Reinforcement Learning

2025-05-24 · Jiarong Fan, Chenghao Huang, Hao Wang

In the pursuit of energy net zero within smart cities, transportation electrification plays a pivotal role. The adoption of Electric Vehicles (EVs) keeps increasing, making energy management of EV charging stations criti…

energy management Management Multi-agent Reinforcement Learning

Optimizing a domestic battery and solar photovoltaic system with deep reinforcement learning

2021-09-10 · Alexander J. M. Kell, A. Stephen McGough, Matthew Forshaw

A lowering in the cost of batteries and solar PV systems has led to a high uptake of solar battery home systems. In this work, we use the deep deterministic policy gradient algorithm to optimise the charging and discharg…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning (RL)