Energy Forecasting and Control Methods for Energy Storage Systems in Distribution Networks: Predictive Modelling and Control Techniques

This book discusses the modeling of electrical systems for energy management, focusing on forecasting energy requirements under volatile conditions. It highlights the importance of understanding low voltage demand behavior and developing optimal energy management systems to increase energy savings, reduce peak loads, and reduce gas emissions. The book provides practical guidance for practitioners using real-life examples and covers topics such as load forecasting, control algorithms, and energy saving.

Format: Hardback
Length: 204 pages
Publication date: 08 January 2023
Publisher: Springer Nature Switzerland AG

The demand for energy is expected to increase significantly in the coming years, posing significant challenges for the global electrical grid. With the electrification of heating and transport, the grid will be required to meet the growing energy needs while also reducing greenhouse emissions and mitigating the impact of climate change. One of the key challenges facing the grid is the unpredictable nature of energy demand. Energy sources such as wind and solar power are intermittent, making it difficult to predict their availability and output in real-time. This variability in demand has led to the development of energy management systems that can optimize the use of energy resources and reduce waste.

One of the critical components of these systems is electrical load forecasting. Load forecasting is the process of predicting the future energy demand of a specific area or network. By understanding the patterns and trends in energy consumption, utilities can optimize their energy production and distribution to meet the demand without overloading the grid.

There are several types of load forecasting, including statistical forecasting, machine learning, and artificial intelligence. Statistical forecasting involves using historical data to predict future demand patterns. Machine learning algorithms use neural networks to analyze large amounts of data and make predictions based on patterns. Artificial intelligence algorithms use a combination of machine learning and statistical modeling to make predictions.

Load forecasting is essential for developing optimal energy management systems. By predicting the future energy demand, utilities can optimize their energy production and distribution to reduce waste and increase efficiency. This can lead to significant cost savings for consumers and businesses and help to reduce the environmental impact of energy production.

However, there are limited books on load forecasting for low voltage distribution networks, and even fewer demonstrations of how such forecasts can be integrated into the control of storage. This book aims to address this gap by presenting material on load forecasting, control algorithms, and energy saving, with practical guidance for practitioners using two real-life examples: residential networks and cranes at a port terminal.

In conclusion, the demand for energy is expected to increase significantly in the coming years, posing significant challenges for the global electrical grid. Load forecasting is a critical component of energy management systems that can help utilities optimize their energy production and distribution to meet the demand while reducing waste and increasing efficiency. This book presents material on load forecasting, control algorithms, and energy saving, with practical guidance for practitioners using two real-life examples: residential networks and cranes at a port terminal. By understanding the patterns and trends in energy consumption, utilities can develop optimal energy management systems that will help to meet the challenges of the future.

Weight: 500g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030828479
Edition number: 1st ed. 2023