Optimal Energy Management of Automotive Battery Systems Including Thermal Dynamics and Aging

2014 ◽  
pp. 219-236 ◽  
Author(s):  
Antonio Sciarretta ◽  
Domenico di Domenico ◽  
Philippe Pognant-Gros ◽  
Gianluca Zito
Keyword(s):  
Energy Management ◽  
Thermal Dynamics ◽  
Optimal Energy ◽  
Battery Systems ◽  
Optimal Energy Management

scholarly journals Optimal energy management and sizing of renewable energy and battery systems in residential sectors via a stochastic MILP model

2020 ◽  
Vol 187 ◽  
pp. 106483 ◽  
Author(s):  
Meisam Farrokhifar ◽  
Farid Hamzeh Aghdam ◽  
Arman Alahyari ◽  
Ali Monavari ◽  
Amin Safari
Keyword(s):  
Renewable Energy ◽  
Energy Management ◽  
Optimal Energy ◽  
Battery Systems ◽  
Optimal Energy Management ◽  
Milp Model

scholarly journals Corrigendum to “Optimal energy management of a PV-WTG-BSS-DG microgrid system” [Energy 217 (2021) 119358]

Energy ◽  
2021 ◽  
pp. 120468
Author(s):  
T. Adefarati ◽  
R.C. Bansal ◽  
M. Bettayeb ◽  
R. Naidoo
Keyword(s):  
Energy Management ◽  
Optimal Energy ◽  
Optimal Energy Management ◽  
System Energy

scholarly journals Optimal Energy Management of a Grid-Tied Solar PV-Battery Microgrid: A Reinforcement Learning Approach

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2700
Author(s):  
Grace Muriithi ◽  
Sunetra Chowdhury
Keyword(s):  
Renewable Energy ◽  
Reinforcement Learning ◽  
Energy Management ◽  
Renewable Energy Sources ◽  
Critical Role ◽  
Winter Season ◽  
Learning Approach ◽  
Solar Pv ◽  
Optimal Energy ◽  
Optimal Energy Management

In the near future, microgrids will become more prevalent as they play a critical role in integrating distributed renewable energy resources into the main grid. Nevertheless, renewable energy sources, such as solar and wind energy can be extremely volatile as they are weather dependent. These resources coupled with demand can lead to random variations on both the generation and load sides, thus complicating optimal energy management. In this article, a reinforcement learning approach has been proposed to deal with this non-stationary scenario, in which the energy management system (EMS) is modelled as a Markov decision process (MDP). A novel modification of the control problem has been presented that improves the use of energy stored in the battery such that the dynamic demand is not subjected to future high grid tariffs. A comprehensive reward function has also been developed which decreases infeasible action explorations thus improving the performance of the data-driven technique. A Q-learning algorithm is then proposed to minimize the operational cost of the microgrid under unknown future information. To assess the performance of the proposed EMS, a comparison study between a trading EMS model and a non-trading case is performed using a typical commercial load curve and PV profile over a 24-h horizon. Numerical simulation results indicate that the agent learns to select an optimized energy schedule that minimizes energy cost (cost of power purchased from the utility and battery wear cost) in all the studied cases. However, comparing the non-trading EMS to the trading EMS model operational costs, the latter one was found to decrease costs by 4.033% in summer season and 2.199% in winter season.

scholarly journals Optimal Energy Management in Combined Heat and Power System via A Decentralized Consensus-Based ADMM

2020 ◽  
Vol 53 (2) ◽  
pp. 4026-4031
Author(s):  
Xu Zhou ◽  
Suli Zou ◽  
Peng Wang ◽  
Zhongjing Ma
Keyword(s):  
Power System ◽  
Energy Management ◽  
Combined Heat And Power ◽  
Optimal Energy ◽  
Optimal Energy Management
Sign in / Sign up

Export Citation Format

Share Document