Energy Forecasting and Control Methods for Energy Storage Systems in Distribution Networks

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.

Introduces electrical load forecasting and its use Describes stochastic and predictive control modelling Provides a basis for further research and spurs

Auteur
Professor William Holderbaum has been working at the University of Glasgow, the University of Reading, Manchester Metropolitan University and currently held the professor status in Control Engineering at Aston University in the UK. He has played major leadership roles in research, whilst maintaining a very strong international reputation and an extensive list of publications and PhDs supervisions. Over the years He has applied his control expertise to several applications and in particular rehabilitation engineering and energy transmission, storage for electrical systems, and power systems. Professor Holderbaum was involved in the Thames Valley Vision (TVV) project, a £30M low carbon network fund project. The aim of the projects is to assist distribution network operators in the UK to prepare for the future low carbon economy by testing various technological and operational solutions. One of the main tasks of the TVV is the smart control of storage devices for a range of applications including, peak demand reduction, voltage control and phase balancing. Furthermore, Professor William Holderbaum was the leader of an EU funded project "Delivering Sustainable Energy Solutions to Ports" which has the aim of reducing greenhouse gases emissions in ports which involved analyse the energy flows of ports to eventually develop innovative control techniques to reduce fuel consumption and enable energy recovery using energy storage devices. 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.

Contenu
Introduction.- Basic tools.- Short term load forecasting.- Control strategies in low voltage network for energy saving.- Optimal control with load forecasting.- Case study: Energy saving based on optimal control and load forecasts.- Conclusion.