Blockchain And AI Technologies For Smart Energy – Hongjian Sun (1)

The x-axis is the time step of the energy scheduling. The y-axis is the power with the unit of MW. realise the load curtailment and load shifting. The agents coordination allows the practical market operations to be applied into the scheduling process. 5.7.5 Research Summary This example research proposes a multi-agent system design for low-carbon smart grids in both the generation scheduling and demand-side management for the day- ahead and real-time operations. The results of case studies demonstrate the effective- ness of designed control and management model and the possibility to employ smart grid technologies into the multi-agent system.

Through multi-objective optimisation scheduling, the minimum payment bills of consumers, minimum carbon emissions, and maximum profits for generators have been realised through fairly dispatching. With the demand-side management including the load shifting and load curtailment, the scheduling brings the real-time operation to the objective demand curve. 5.8 The deployment of smart meters facilitates increasing number of consumers to pro- duce or store electricity at home, forming the role of prosumers [51].

However, the ■Blockchain and Artificial Intelligence Technologies for Smart Energy Systems economic potential of individual prosumer’s energy scheduling is limited, whereas multiple types of energy sources, loads, and energy behaviours require high- dimensional optimisation and accurate modelling. This research example proposes a prosumer-centric low carbon pricing patterns recognition through using the convo- lutional neural networks to scale to high-dimensional systems and generalise unseen inputs through extracting features from images. The deep network architecture with multiple layers of representations is compatible with various system conditions to abstract inherent dynamic price elasticities of generation, consumption, and carbon emissions from prosumers.

The uncertainties caused by distributed renewable energy sources and flexible demand are considered through using the proposed scenarios gen- eration algorithm. Case studies demonstrate that the proposed approach is capable of capturing the underlying hidden features of prosumer-centric pricing patterns and providing an accurate evaluation of scheduling potentials. 5.8.1 Introduction Increasing energy demand has driven global energy-related carbon emissions rising to a historic high. The power sector accounts for nearly two-thirds of emissions growth [52]. Facing this environmental challenge, low carbon policy targets on phasing out fossil fuel based power generation through facilitating distributed renewable energy sources and charging carbon taxes from high-emission sources.

for Smart Energy Systems Present energy systems are undergoing a radical transformation, driven by the urgent need to address the climate change crisis. At the same time, we are witnessing the sharp growth of en- ergy data and a revolution of advanced technologies, with artificial intelligence (AI) and Block- chain emerging as two of the most transformative technologies of our time. The convergence of these two technologies has the potential to create a paradigm shift in the energy sector, enabling the development of smart energy systems that are more resilient, efficient, and sustainable.

This book situates itself at the forefront of this paradigm shift, providing a timely and compre- hensive guide to AI and Blockchain technologies in the energy system. Moving from an intro- duction to the basic concepts of smart energy systems, this book proceeds to examine the key challenges facing the energy system, and how AI and Blockchain can be used to address these challenges. Research examples are presented to showcase the role and impact of these new tech- nologies, while the latest developed testbeds are summarised and explained to help researchers accelerate their development of these technologies.

This book is an indispensable guide to the current changes in the energy system, being of par- ticular use to industry professionals, from researchers to management, looking to stay ahead of technological developments. Hongjian Sun, Professor at Durham University, UK. He is the Head of Durham Smart Grid Lab- oratory and leads Smart Grid research at Durham, with over 180 papers in refereed journals and international conferences.

His research has been funded by EU H2020, EU ERDF, EPSRC, BEIS, Ofgem, Innovate UK, and industry. He is also the Editor-in-Chief for the IET Smart Grid journal. Weiqi Hua, Assistant Professor at the University of Birmingham, UK. He took postdoctoral positions at the University of Oxford, UK, and Cardiff University, UK, after receiving his Ph.D. from the University of Durham, UK. He is an Editorial Board Member of Applied Energy, and Editorial Board Member of Oxford Open Energy.

Minglei You, Assistant Professor at the University of Nottingham, UK.

This is a short excerpt from the opening of “” by Unknown, quoted for review and introduction purposes. All rights belong to the copyright holders.

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