EV Fleet Energy Management Strategy For Smart Microgrids Considering Multiple Objectives: Techno-Economic Perspective DOI

A. Sudhakar,

B. Mahesh Kumar

Arabian Journal for Science and Engineering, Год журнала: 2024, Номер 49(12), С. 16919 - 16939

Опубликована: Июль 14, 2024

Язык: Английский

Market clearing price-based energy management of grid-connected renewable energy hubs including flexible sources according to thermal, hydrogen, and compressed air storage systems DOI
Zhaoyang Qu,

Chuanfu Xu,

Fang Yang

и другие.

Journal of Energy Storage, Год журнала: 2023, Номер 69, С. 107981 - 107981

Опубликована: Июнь 16, 2023

Язык: Английский

Процитировано

169

Capabilities of compressed air energy storage in the economic design of renewable off-grid system to supply electricity and heat costumers and smart charging-based electric vehicles DOI

Farshad Khalafian,

Nahal Iliaee,

Ekaterina Diakina

и другие.

Journal of Energy Storage, Год журнала: 2023, Номер 78, С. 109888 - 109888

Опубликована: Дек. 14, 2023

Язык: Английский

Процитировано

132

Data-Driven hierarchical energy management in multi-integrated energy systems considering integrated demand response programs and energy storage system participation based on MADRL approach DOI Creative Commons
Amin Khodadadi,

Sara Adinehpour,

Reza Sepehrzad

и другие.

Sustainable Cities and Society, Год журнала: 2024, Номер 103, С. 105264 - 105264

Опубликована: Фев. 8, 2024

In this study, an intelligent and data-driven hierarchical energy management approach considering the optimal participation of renewable resources (RER), storage systems (ESSs) integrated demand response (IDR) programs execution based on wholesale retail market signals in multi-integrated system (MIES) structure is presented. The proposed objective function presented four levels, which include minimizing operating costs, environmental pollution risk reducing destructive effects cyberattacks such as false data injection (FDI). implemented central controller local multi-agent deep reinforcement learning method (MADRL). MADRL model formulated Markov decision process equations solved by soft actor-critic Q-learning algorithms two levels offline training online operation. different scenario results show operation cost reduction equivalent to 19.51%, 19.69%, cyber security 24%, 20.24%. has provided important step responding smart cities challenges requirements advantage fast response, high accuracy also computational time burden.

Язык: Английский

Процитировано

33

Artificial intelligence-based methods for renewable power system operation DOI
Yuanzheng Li, Yizhou Ding, Shangyang He

и другие.

Nature Reviews Electrical Engineering, Год журнала: 2024, Номер 1(3), С. 163 - 179

Опубликована: Фев. 9, 2024

Язык: Английский

Процитировано

28

Hierarchical and distributed energy management framework for AC/DC hybrid distribution systems with massive dispatchable resources DOI
Yi Su, Jiashen Teh, Wei Liu

и другие.

Electric Power Systems Research, Год журнала: 2023, Номер 225, С. 109856 - 109856

Опубликована: Сен. 15, 2023

Язык: Английский

Процитировано

42

A novel on intelligent energy control strategy for micro grids with renewables and EVs DOI Creative Commons

Hussaian Basha,

Ramakrishna Reddy K,

C. Dhanamjayulu

и другие.

Energy Strategy Reviews, Год журнала: 2024, Номер 52, С. 101306 - 101306

Опубликована: Фев. 1, 2024

Energy management in Micro Grids (MG) has become increasingly difficult as stochastic Renewable Sources (RES) and Electric Vehicles (EV) have more prevalent. Even challenging is autonomous MG operation with RES since prompt frequency control required. We provide an innovative Management Strategy (EMS) for grid support this academic publication. By integrating EV storage, we seek to decrease reliance on the grid. The EMS consists of three execution phases: Ranking Recommendation (RER), Optimal Power Allocation (OPA) Fleet, Storage (OAES). aim slicing time smaller intervals update energy power scheduling shorter per changes are going system. period 24 h divided into 96 (t) storage requirements (kWh/t) estimated based load together necessary volume. employ approaches that frequently used communication channel allocation optimization accomplish OAES. With two objectives: minimum network loss plus voltage fluctuations, Multi-Objective Optimization Problem (MOOP) solved each 't' OAES Flow (OPF). Pareto-front calculate best amount from fleet 't'. data received fuzzy rule base third stage train intelligent Convolutional Neural Network (CNN), which rank output four decision variables inputs. main goals minimize battery degradation make most it support. aid a MATLAB-based simulation setup heterogeneous entities, primary goal examined put practice On-grid MG.

Язык: Английский

Процитировано

18

Overview of improved dynamic programming algorithm for optimizing energy distribution of hybrid electric vehicles DOI
Xueqin Lü, Songjie He, Yuzhe Xu

и другие.

Electric Power Systems Research, Год журнала: 2024, Номер 232, С. 110372 - 110372

Опубликована: Апрель 8, 2024

Язык: Английский

Процитировано

15

A novel intelligent optimal control methodology for energy balancing of microgrids with renewable energy and storage batteries DOI
Hisham Alghamdi, Taimoor Ahmad Khan, Lyu-Guang Hua

и другие.

Journal of Energy Storage, Год журнала: 2024, Номер 90, С. 111657 - 111657

Опубликована: Май 17, 2024

Язык: Английский

Процитировано

11

Proximal Policy Optimization–Driven Real-Time Home Energy Management System with Storage and Renewables DOI
Ubaid ur Rehman

Process Integration and Optimization for Sustainability, Год журнала: 2025, Номер unknown

Опубликована: Фев. 13, 2025

Язык: Английский

Процитировано

2

Long-term electricity demand forecasting under low-carbon energy transition: Based on the bidirectional feedback between power demand and generation mix DOI
Haowei Jin, Ju’e Guo, T. T. Lei

и другие.

Energy, Год журнала: 2023, Номер 286, С. 129435 - 129435

Опубликована: Ноя. 12, 2023

Язык: Английский

Процитировано

17