Overview
- Introduces electrical load forecasting and its use
- Describes stochastic and predictive control modelling
- Provides a basis for further research and spurs
Part of the book series: Lecture Notes in Energy (LNEN, volume 85)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (6 chapters)
Keywords
About this book
This book describes the stochastic and predictive control modelling of electrical systems that can meet the challenge of forecasting energy requirements under volatile conditions.
The global electrical grid is expected to face significant energy and environmental challenges such as greenhouse emissions and rising energy consumption due to the electrification of heating and transport. Today, the distribution network includes energy sources with volatile demand behaviour, and intermittent renewable generation. This has made it increasingly important to understand low voltage demand behaviour and requirements for optimal energy management systems to increase energy savings, reduce peak loads, and reduce gas emissions.
Electrical load forecasting is a key tool for understanding and anticipating the highly stochastic behaviour of electricity demand, and for developing optimal energy management systems. Load forecasts, especially of the probabilistic variety, can support moreinformed planning and management decisions, which will be essential for future low carbon distribution networks. For storage devices, forecasts can optimise the appropriate state of control for the battery. There are limited books on load forecasts for low voltage distribution networks and even fewer demonstrations of how such forecasts can be integrated into the control of storage.
This book presents material in load forecasting, control algorithms, and energy saving and provides practical guidance for practitioners using two real life examples: residential networks and cranes at a port terminal.
Authors and Affiliations
About the authors
Dr. Feras Alasali is an assistant Professor in the department of electrical engineering at the Hashemite University, Jordan with more than 5 years experience in optimal and predictive control models for energy storage systems and LV network applications. He received his BSc and MSc degrees in electrical power Engineering at the Al-Yarmouk University . After graduation he worked in Electrical Distribution Company (EDCO), Jordan, as a metering and protection engineer and then as project manager with more than 10 years experience in HV/MV substation projects, KSA . He received his PhD from the University of Reading in 2019 in electrical power Engineering and his research interests are focused around control models for distribution generation and LV network, load forecasting and power protection systems.
Mr. Ayush Sinha is working as Research Associate with research interest as to apply and optimize machine learning algorithms on demand response optimization and cyber security of critical infrastructure while pursuing PhD at Indian Institute of Information technology Allahabad, India. He has 4 years of research experience in the C3i HUB IIT Kanpur sponsored project(Risk Averse Resilience Framework for Critical Infrastructure Security), and in the Indo-Norway Project(CPSEC) in the field of “Machine Learning Approach for Cyber Security”- (under joint collaboration of IIT Kanpur, IIIT Allahabad and Norwegian University of Science& Technology, Gjowik-Norway). He received his graduation in Mathematics(BHU, India) and postgraduation in Computer Application(MNNIT Allahabad, India) and in Software Systems(BITS Pilani, India). After postgraduation, he worked 9 years for multinationals like Tata Consultancy Services(India) and Ciena India Pvt. Ltd. (Indian and Canada) as a senior Java developer in the field of Telecommunications, Layer zero control plane for optical fiber and Banking/Finance.
Bibliographic Information
Book Title: Energy Forecasting and Control Methods for Energy Storage Systems in Distribution Networks
Book Subtitle: Predictive Modelling and Control Techniques
Authors: William Holderbaum, Feras Alasali, Ayush Sinha
Series Title: Lecture Notes in Energy
DOI: https://doi.org/10.1007/978-3-030-82848-6
Publisher: Springer Cham
eBook Packages: Energy, Energy (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-030-82847-9Published: 08 January 2023
Softcover ISBN: 978-3-030-82850-9Published: 09 January 2024
eBook ISBN: 978-3-030-82848-6Published: 07 January 2023
Series ISSN: 2195-1284
Series E-ISSN: 2195-1292
Edition Number: 1
Number of Pages: XVI, 204
Number of Illustrations: 7 b/w illustrations, 95 illustrations in colour
Topics: Energy Storage, Energy Systems, Control and Systems Theory, Energy Policy, Economics and Management