Abstract
The development of smart energy systems using the principles of Industry 4.0 to energize smart cities is of significant interest. On the other hand, digital twin systems are gaining popularity as they are expected to provide greater insights into the design, development, and maintenance processes of complex systems. This chapter first presents various salient operational requirements in energizing the smart cities through renewable energies, virtual power plants, and demand side management technologies. The tenets of digital twins and Industry 4.0 are the key drivers in the developmental process of cyber physical energy systems. The chapter illustrates the process of replicating the twins—physical and virtual systems—to function in synchronization for effective management. The digital transformation process of developing cyber physical systems from the conceptual living labs to the fully functional digital twins is presented.
TopIntroduction
Smart cities are coming up in good numbers across the world and are expected to be equipped by smart technologies. They bring several concepts (Industry 4.0, Circular Economy etc.) and technologies (IIoT, Big data, Renewable energies) together and operate with digital platforms where information can be shared in a smart fashion for specific purposes (K.S. Sastry, 2020). One of the most essential aspects of smart cities is their energy consumption, which is higher than that of their conventional contemporaries. Smart cities are supposed to meet a significant portion of their energy demands on their own through renewable energies and efficient energy conservation processes. This aspect brings in Demand Side Management (DSM), Renewable energies (REs), and circular economy (CE) (K. S. Sastry Musti, 2020). However, DSM and CE overlap in principles, objectivity and philosophies. Fundamentally smart cities are driven by fourth industrial revolution. Industry 4.0 advocates the use of Industrial Internet of Things (IIoT) that can combine measurement of data, analyzing the data through analytics and communication technologies and platforms. Dembski et. al(2020) presented various aspects of smart cities and provided foundation for the need of using digital twinning principles for their development (Ali et. al, 2021). Thus, it can be seen that using the concept of digital twins for smart cities is of significant interest. Energy consumption patterns of smart cities can be significantly higher and different when compared to legacy cities. Designing power distribution system networks for smart cities by itself is a challenging task. Laamarti et al (2020) has explained the ISO standards for developing smart cities based on digital twin approach. Yu et. al (2016) presented the need to establish cyber-physical systems for smart grids. A reliable and highly functional real-time measurement, monitoring and control system needs to be in place so that a typical smart city is able to efficiently a) conserve energy, b) serve consumers as per the quality standards c) manage energy resources etc. Hence, managing the energy related functions and services in a typical smart city are different from the regular non-smart cities (Onile et. al, 2021).
A well-defined smart grid can better serve a smart city to meet its energy demands. However, the challenge is that designing a new smart grid for an upcoming smart city; and/or transforming an existing non-automated power distribution network into smart grid. For instance, information on load consumption patterns is vital for determining type of DSM initiative. If the peak load is too high, then Time of Use tariff can be set to a high value so that consumers are forced to reduce the consumption to an extent. On the other hand, if the base load consumption is too low, power tariffs can be lowered to encourage consumers to increase the energy consumption. Similarly, DSM initiatives such as load shifting and valley filling etc., are heavily dependent energy consumption patterns which in turn can be obtained from precise measurements of data over the day in 30-minute intervals. Similarly, adoption of CE by smart cities can result in efficient energy conservation and thus make them more self-reliant; however, this requires significant monitoring of resources and even waste. Once a well-tested design model is available for typical a smart city that resolves above mentioned problems, then such model needs to be replicated elsewhere, in other words, the design needs to be replicated in the industry 4.0 era, following the principles of digital twinning Castelli et., al (2019).
Key Terms in this Chapter
Circular Economy: A system that operates on the 4R principles – Reuse, Reduce, Repair, and Recycling and thus results in significant direct savings in energy and finances. Circular economy is also known for creating new range of employment prospects in semi-formal services, especially in recycling and repair sectors.
Digital Transformation: A systematic process for converting legacy, un-automated systems into fully automated digital systems. Digital transformation process generally involves significant improvements to existing system especially provides the industry 4.0 capabilities such as sensors to identify the changes, communication for information exchange and real-time control, and the combinations of these capabilities.
Smart City: An urban environment that is setup to provide modern and futuristic features and services to the residents. Smart cities are typically capable of meeting the energy demands on their own and thus do not depend much on the external sources. Information visualization, automated facilities and services, environmentally friendly living spaces are key elements in a smart city.
Living Lab: A conceptual framework for developing innovative products and ecosystems. Typically, the development is expected to be carried out through open and transparent processes and using real-life environments. The development cycle depends on the continuous testing and feedback in order to produce a long lasting and sustainable product(s).
Duck Curve Phenomenon: A graph that represents the difference between the load and the electricity generation typically over a given day. Duck curve also illustrates the instance where excess solar energy generation can lead to either loss of load or capacity.
Cyber Physical System: A composite system that consists of both hardware and software. It is used to automate most of the monitoring and control large industrial and organizational processes.
Digital Twin: A virtual model of a physical system that can function in tandem with the physical system (or the twin) in real-time. This facilitates several simulations for different case scenarios to study the behavior and thus paving the way for improvements in design and operation.