國立高雄大學圖資館 |

Language: English

Back

Artificial intelligence enabled computational methods for smart grid forecast and dispatch

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligence enabled computational methods for smart grid forecast and dispatchby Yuanzheng Li ... [et al.].
other author:	Li, Yuanzheng.
Published:	Singapore :Springer Nature Singapore :2023.
Description:	1 online resource (xviii, 260 p.) :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Smart power grids.
Online resource:	https://doi.org/10.1007/978-981-99-0799-1
ISBN:	9789819907991$q(electronic bk.)

Artificial intelligence enabled computational methods for smart grid forecast and dispatch
Artificial intelligence enabled computational methods for smart grid forecast and dispatch[electronic resource] /by Yuanzheng Li ... [et al.]. - Singapore :Springer Nature Singapore :2023. - 1 online resource (xviii, 260 p.) :ill., digital ;24 cm. - Engineering applications of computational methods,v. 142662-3374 ;. - Engineering applications of computational methods ;v.2..

Chapter 1: Introduction for Smart Grid Forecast and Dispatch -- Chapter 2: Review for Smart Grid Forecast -- Chapter 3: Review for Smart Grid Dispatch -- Chapter 4: Deep Learning Based Densely Connected Network for Load Forecast -- Chapter 5: Reinforcement Learning Assisted Deep Learning for Probabilistic Charging Power Forecast of Electric Vehicles -- Chapter 6: Dense Skip Attention based Deep Learning for Day-Ahead Electricity Price Forecast with a Drop-Connected Structure -- Chapter 7: Dirichlet Process Mixture Model Based on Relevant Data for Uncertainty Characterization of Net Load -- Chapter 8: Extreme Learning Machine for Economic Dispatch with High Penetration of Wind Power -- Chapter 9: Data-driven Bayesian Assisted Optimization Algorithm for Dispatch of Highly Renewable Energy Power Systems -- Chapter 10: Multi-objective Optimization Approach for Coordinated Scheduling of Electric Vehicles-Wind Integrated Power Systems -- Chapter 11: Deep Reinforcement Learning Assisted Optimization Algorithm for Many-Objective Distribution Network Reconfiguration -- Chapter 12: Federated Multi-Agent Deep Reinforcement Learning Approach via Physic-Informed Reward for Multi-Microgrid Energy Management -- Chapter 13: Supply Function Game Based Energy Management Between Electric Vehicle Charging Stations and Electricity Distribution System.

With the increasing penetration of renewable energy and distributed energy resources, smart grid is facing great challenges, which could be divided into two categories. On the one hand, the endogenous uncertainties of renewable energy and electricity load lead to great difficulties in smart grid forecast. On the other hand, massive electric devices as well as their complex constraint relationships bring about significant difficulties in smart grid dispatch. Owe to the rapid development of artificial intelligence in recent years, several artificial intelligence enabled computational methods have been successfully applied in the smart grid and achieved good performances. Therefore, this book is concerned with the research on the key issues of artificial intelligence enabled computational methods for smart grid forecast and dispatch, which consist of three main parts. (1) Introduction for smart grid forecast and dispatch, in inclusion of reviewing previous contribution of various research methods as well as their drawbacks to analyze characteristics of smart grid forecast and dispatch. (2) Artificial intelligence enabled computational methods for smart grid forecast problems, which are devoted to present the recent approaches of deep learning and machine learning as well as their successful applications in smart grid forecast. (3) Artificial intelligence enabled computational methods for smart grid dispatch problems, consisting of edge-cutting intelligent decision-making approaches, which help determine the optimal solution of smart grid dispatch. The book is useful for university researchers, engineers, and graduate students in electrical engineering and computer science who wish to learn the core principles, methods, algorithms, and applications of artificial intelligence enabled computational methods.

ISBN: 9789819907991$q(electronic bk.)

Standard No.: 10.1007/978-981-99-0799-1doiSubjects--Topical Terms:

559143
Smart power grids.

LC Class. No.: TK3105

Dewey Class. No.: 621.31

Artificial intelligence enabled computational methods for smart grid forecast and dispatch
LDR:04296nmm a2200337 a 4500 001 637783
003 DE-He213
005 20230505093646.0
006 m d
007 cr nn 008maaau
008 230921s2023 si s 0 eng d
020 $a 9789819907991$q(electronic bk.)
020 $a 9789819907984$q(paper)
024 7 $a 10.1007/978-981-99-0799-1 $2 doi
035 $a 978-981-99-0799-1
040 $a GP $c GP
041 0 $a eng
050 4 $a TK3105
072 7 $a THRD $2 bicssc
072 7 $a TEC046000 $2 bisacsh
072 7 $a THY $2 thema
082 0 4 $a 621.31 $2 23
090 $a TK3105 $b .A791 2023
245 0 0 $a Artificial intelligence enabled computational methods for smart grid forecast and dispatch $h [electronic resource] / $c by Yuanzheng Li ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2023.
300 $a 1 online resource (xviii, 260 p.) : $b ill., digital ; $c 24 cm.
490 1 $a Engineering applications of computational methods, $x 2662-3374 ; $v v. 14
505 0 $a Chapter 1: Introduction for Smart Grid Forecast and Dispatch -- Chapter 2: Review for Smart Grid Forecast -- Chapter 3: Review for Smart Grid Dispatch -- Chapter 4: Deep Learning Based Densely Connected Network for Load Forecast -- Chapter 5: Reinforcement Learning Assisted Deep Learning for Probabilistic Charging Power Forecast of Electric Vehicles -- Chapter 6: Dense Skip Attention based Deep Learning for Day-Ahead Electricity Price Forecast with a Drop-Connected Structure -- Chapter 7: Dirichlet Process Mixture Model Based on Relevant Data for Uncertainty Characterization of Net Load -- Chapter 8: Extreme Learning Machine for Economic Dispatch with High Penetration of Wind Power -- Chapter 9: Data-driven Bayesian Assisted Optimization Algorithm for Dispatch of Highly Renewable Energy Power Systems -- Chapter 10: Multi-objective Optimization Approach for Coordinated Scheduling of Electric Vehicles-Wind Integrated Power Systems -- Chapter 11: Deep Reinforcement Learning Assisted Optimization Algorithm for Many-Objective Distribution Network Reconfiguration -- Chapter 12: Federated Multi-Agent Deep Reinforcement Learning Approach via Physic-Informed Reward for Multi-Microgrid Energy Management -- Chapter 13: Supply Function Game Based Energy Management Between Electric Vehicle Charging Stations and Electricity Distribution System.
520 $a With the increasing penetration of renewable energy and distributed energy resources, smart grid is facing great challenges, which could be divided into two categories. On the one hand, the endogenous uncertainties of renewable energy and electricity load lead to great difficulties in smart grid forecast. On the other hand, massive electric devices as well as their complex constraint relationships bring about significant difficulties in smart grid dispatch. Owe to the rapid development of artificial intelligence in recent years, several artificial intelligence enabled computational methods have been successfully applied in the smart grid and achieved good performances. Therefore, this book is concerned with the research on the key issues of artificial intelligence enabled computational methods for smart grid forecast and dispatch, which consist of three main parts. (1) Introduction for smart grid forecast and dispatch, in inclusion of reviewing previous contribution of various research methods as well as their drawbacks to analyze characteristics of smart grid forecast and dispatch. (2) Artificial intelligence enabled computational methods for smart grid forecast problems, which are devoted to present the recent approaches of deep learning and machine learning as well as their successful applications in smart grid forecast. (3) Artificial intelligence enabled computational methods for smart grid dispatch problems, consisting of edge-cutting intelligent decision-making approaches, which help determine the optimal solution of smart grid dispatch. The book is useful for university researchers, engineers, and graduate students in electrical engineering and computer science who wish to learn the core principles, methods, algorithms, and applications of artificial intelligence enabled computational methods.
650 0 $a Smart power grids. $3 559143
650 0 $a Artificial intelligence. $3 194058
650 1 4 $a Energy Grids and Networks. $3 914991
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Computer Modelling. $3 913151
700 1 $a Li, Yuanzheng. $3 944316
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Engineering applications of computational methods ; $v v.2. $3 869168
856 4 0 $u https://doi.org/10.1007/978-981-99-0799-1
950 $a Energy (SpringerNature-40367)