Internet-of-Things technology and applications for clean energy systems

As a critical technology for clean and sustainable energy transition, Internet of Things (IoT) is becoming increasingly popular for its use in extending connectivity into multiple energy resources. Based on the heterogeneous networking integration of devices, IoT has the potential of achieve seamless management of various facilities, thus enabling real-time optimisation of supply chains and dynamic response to energy system dispatch. In addition, IoT can help improve the visibility and controllability of distributed energy resources and leverage flexible loads via extensive connections among numerous devices. This special issue has received wide attention from the research community. The five papers selected for publication in this issue are briefly introduced as follows.

In ‘Architecture and function analysis of integrated energy service stations considering cyber–physical integration’, Liu et al. proposed integrated energy service stations (IESSs), which comprise substations, integrated multi-energy conversion stations, data centres, communication base stations and other functional units. Two feasible schemes were then designed to realise the construction of IESSs, including entity IESSs which require refined planning and construction, and virtual IESSs which involve transformation based on existing substation resources. The motivation and practical implementation for constructing IESSs are discussed. Finally, future research interests regarding IESSs are summarised.

In ‘A survey on policies, modelling and security of cyber–physical systems in smart grids’, Wang et al. provided an overview of the policy drivers for and barriers to the implementation of cyber–physical systems (CPSs). With the widespread deployment of behind-the-metre distributed energy resources (DERs), there is an increasing demand to model hardware, software and their interactions in a smart grid environment. This paper reviewed the modelling and applications of an intelligent CPS for a decentralised energy system. The integration of DERs and the supportive infrastructure can cause a modern power system to become more vulnerable to external threats such as terrorist attacks and therefore less reliable as a secure system. The latest progress in CPS implementation was summarised considering critical infrastructure identification and protection as well as risk assessment and methods for mitigating cyber threats and attacks.

In ‘Strategic PMU placement to alleviate power system vulnerability against cyber attacks’, Khare et al. presented a strategic phasor measurement unit (PMU) placement scheme to reduce cyber vulnerability of power systems to cyber attacks. A multi-stage PMU placement strategy was developed to alleviate power system vulnerability to possible false data injection attacks, where forward dynamic programming was used to distribute the capital cost of PMUs over a certain period. "The authors also proposed an index to quantify the vulnerability of the nodes of a grid to false data injection attacks. This index could be useful in selecting and prioritising an optimal set of candidate buses for PMU placement in a specific deployment stage.

In ‘Consensus-based decentralized energy trading for distributed energy resources’, Wang et al. proposed a fully decentralised transactive energy management method using a consensus-based algorithm. A virtual pool was designed for prosumers to trade energy and exchange information with the support of IoT technologies. The consensus-based algorithm enables prosumers to obtain an optimal energy schedule independently but in a coordinated manner without revealing personal data. Practical data were used to perform simulations and validate the proposed algorithm, which demonstrated both the efficiency and effectiveness of the consensus-based decentralised transactive energy management strategy.

In ‘Hybrid clustering-based bad data detection of PMU measurements’, Zhu et al. introduced the objective of bad PMU data detection and presented an illustrative bad data instance. Three clustering methods, including linear regression, density-based spatial clustering of applications with noise (DBSCAN), and Gaussian mixture models (GMM), were combined for bad PMU data detection. A statistical analysis and bound modification of data clustering were performed to further improve detection accuracy. The proposed hybrid clustering-based bad data detection method is unsupervised and can be applied to online bad PMU data detection over a short computational period.