PresentationThe concept of Industry 4.0 is well known in manufacturing. This "fourth industrial revolution" entails adopting emerging innovations such as the Internet of Things (IoT), Data Science, Machine Learning (ML) and Cloud Computing. Actionable data helps improve decision making, reduce vulnerabilities and risk factors. This will certainly benefit in optimizing production, improving flexibility and efficiency within a “smart factory” environment.
Driven by new regulations, new market structures and new energy resources, the smart grid (or Smart Grid) has been the catalyst for profound changes in the way electricity is generated, distributed, managed and consumed. The Smart Grid evolved the traditional power grid by using a two-way flow of electricity and information to create a fully automated power grid. However, those pioneering technologies must grow to adapt to the demands of today's digital society. The current digital revolution - called Energy 4.0 - seeks to incorporate all these disruptive innovations into Smart Grids when managing distributed energy assets. Essentially, digitization means that the energy infrastructure will be increasingly integrated with the communications infrastructure in what has come to be known as the Internet of Energy (IoE).
In today's digital landscape it will be possible to access data and knowledge that until now were simply inconceivable. This title aims to address the landscape towards which Smart Grids are evolving, due to the arrival of ubiquitous technologies such as IoT. It will be the advanced exploitation of the massive amounts of data generated from the IoT sensors that will become the main engine to transform the Smart Grid concept, focused on infrastructure, towards the paradigm of the “Digital Energy” network, focused on service (“Energy as a Service”, EaaS). Furthermore, collective intelligence will improve decision-making processes and empower citizens.
The IoT sensorics, the prediction of energy consumption patterns with the use of Data Science, Data Mining, Machine Learning or Blockchain should be the answer to the questions of energy saving and optimization of the use of resources energetic. Also technologies such as wireless energy transmission, microgrids and digital control of power electronics or cyber-physical systems. By committing to this digitization through the IoE, it will contribute to the improvement of the planning, control and operation of electrical systems, as well as their flexibility, productivity and stability, and the conversion of sustainable energy. The digital transformation of energy is also expected to generate new and emerging business models, open innovation and the stimulation of entrepreneurship in electric energy.
The digital transformation of the electricity sector can improve the efficiency of power generation and the transmission and distribution of electricity, while providing consumers with more capabilities and options around their energy use. Digital tools also improve power supply quality and reliability. And ultimately it reduces the cost of generating, transmitting and delivering electricity. With digitization, opportunities will also appear for Energy 4.0 companies to establish new business models or energy production and supply strategies. The result will be improved economic, environmental and social performance in these critical systems of our society.
Titles as specialized as the one proposed here are not yet common, only a close reference such as the Postgraduate course in Digital Energy at the UPC, also this course: Smart Grid: Sensing, Data Analytics and Control at Stanford University, although perhaps the most outstanding is the Advanced Digital Energy Systems MSc from Cranfield University.
The objective of this degree, therefore, is to prepare Digital Energy professionals, with their challenges both at the generation and demand levels, paying special attention to aspects of energy quality, reliability and resilience. Specifically, the course presents the following objectives:Introduce the fundamentals of the Internet of Energy, including technical and regulatory knowledge of the Smart Grids framework.
Apply advanced tools in the optimal use of distributed energy resources
Apply techniques from the Internet of Energy, Machine Learning or Data Science in energy management.