Integration of an energy management tool and digital twin for coordination and control of multi-vector smart energy systems

Abstract

As Internet of Things (IoT) technologies enable greater communication between energy assets in smart cities, the operational coordination of various energy networks in a city or district becomes more viable. Suitable tools are needed that can harness advanced control and machine learning techniques to achieve environmental, economic and resilience objectives. In this paper, an energy management tool is presented that can offer optimal control, scheduling, forecasting and coordination services to energy assets across a district, enabling optimal decisions under user-defined objectives. The tool presented here can coordinate different subsystems in a district to avoid the violation of high-level system constraints and is designed in a generic fashion to enable transferable use across different energy sectors. The work demonstrates the potential for a single open-source optimisation framework to be applied across multiple energy vectors, providing local government the opportunity to manage different assets in a coordinated fashion. This is shown through case studies that integrate low-carbon communal heating for social housing with electric vehicle charge-point management to achieve high-level system constraints and local government objectives in the borough of Greenwich, London. The paper illustrates the theoretical methodology, the software architecture and the digital twin-based testing environment underpinning the proposed approach.

Smart city based on digital twins

“…Digital twins first appeared and played a role in industries of product and manufacturing design, and later emerged in industries such as aerospace (Liu et al 2020), automation (Talkhestani et al 2018), shipbuilding (Mondoro and Grisso 2019), healthcare (Jimenez et al 2020) and energy (Enders and Hoßbach 2019;O'Dwyer et al 2020). In recent years, with the rapid development of technologies and industries such as the Internet of Things(IoT) (Kaur et al 2020), big data (Tao et al 2018), cloud computing (Khatib et al 2019) and artificial intelligence(AI) (Kharchenko et al 2020), the construction basis of smart city has gradually evolved from the original static 3D modeling level towards the digital twin level that combines dynamic digital technology and static 3D model (Yan et al 2019), which forms a new concept that digital twin city assists smart city construction.…”

Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis

“…The virtual replicas of the electricity grid, transportation routes and greenhouse electricity generation facilitate predictive maintenance, fault diagnosis, lifecycle management, cost-effective generation, loss reduction, and usage analysis. The DT model considers various parameters, such as temperature, flow rate, virtual energy flow models and fuel characteristics using different data-capturing techniques [64][65][66]. Moreover, the DT model can estimate the remaining useful life of equipment, and schedule a maintenance activity (i.e., predictive maintenance) to minimize the downtime and cost of production [67].…”

Digital Twin (DT) in Smart Energy Systems - Systematic Literature Review of DT as a growing solution for Energy Internet of the Things (EIoT)

“…EIoT of State Grid Corporation of China, and EU-funded Sharing Cities project (SHARING CITIES) applied in England (Greenwich) are examples of bringing EIoT in action [67,68]. Despite, the researchers and practitioners try to bring EIoT in action but still there are big challenges in practice and some researchers question the feasibility of IoT in such a complex system.…”

“…In the final list multiple articles highlight applications of DT in smart grids [68,[123][124][125][126][127][128][129][130]. In fact, smart grid is an energy infrastructure.…”

“…DT can succor modern monitoring systems to deal with cutting edge technologies. DT can provide efficient solutions to deal with the complexity of systems [68,123,[131][132][133][134][135][136][137]137]…”