The rapid growth of electric vehicle (EV) penetration has led to more flexible and reliable vehicle-to-grid-enabled cyber-physical systems (V2G-CPSs). However, the increasing system complexity also makes them more vulnerable to cyber-physical threats. Coordinated cyber attacks (CCAs) have emerged as a major concern, requiring effective detection and mitigation strategies within V2G-CPSs. Digital twin (DT) technologies have shown promise in mitigating system complexity and providing diverse functionalities for complex tasks such as system monitoring, analysis, and optimal control. This paper presents a resilient and secure framework for CCA detection and mitigation in V2G-CPSs, leveraging a smart DT-enabled approach. The framework introduces a smarter DT orchestrator that utilizes long short-term memory (LSTM) based actor-critic deep reinforcement learning (LSTM-DRL) in the DT virtual replica. The LSTM algorithm estimates the system states, which are then used by the DRL network to detect CCAs and take appropriate actions to minimize their impact. To validate the effectiveness and practicality of the proposed smart DT framework, case studies are conducted on an IEEE 30 bus system-based V2G-CPS, considering different CCA types such as malicious V2G node or control command attacks. The results demonstrate that the framework is capable of accurately estimating system states, detecting various CCAs, and mitigating the impact of attacks within 5 seconds.

Smart grids leverage the data collected from smart meters to make important operational decisions. However, they are vulnerable to False Data Injection (FDI) attacks in which an attacker manipulates meter data to disrupt the grid operations. Existing works on FDI are based on a simple threat model in which a single grid operator has access to all the data, and only some meters can be compromised.

The sustaining evolution of sensing and advancement in communications technologies has revolutionized prognostics and health management for various electrical equipment toward data-driven ways. This revolution delivers a promising solution for the health monitoring problem of the heat pump (HP) system, a vital device widely deployed in modern buildings for heating use, to timely evaluate its operation status to avoid unexpected downtime. Many HPs were practically manufactured and installed many years ago, resulting in fewer sensors available due to technology limitations and cost control at that time. It raises a dilemma to safeguard HPs at an affordable cost. In this article, we propose a hybrid scheme by integrating industrial Internet-of-Things (IIoT) and intelligent health monitoring algorithms to handle this challenge. To start with, an IIoT network is constructed to sense and store measurements. Specifically, temperature sensors are properly chosen and deployed at the inlet and outlet of the water tank to measure water temperature. Second, with temperature information, we propose an unsupervised learning algorithm named mixture slow feature analysis (MSFA) to timely evaluate the health status of the integrated HP. Characterized by frequent operation switches of different HPs due to the variable demand for hot water, various heating patterns with different heating speeds are observed. Slowness, a kind of dynamics to measure the varying speed of steady distribution, is properly considered in MSFA for both heating pattern division and health evaluation. Finally, the efficacy of the proposed method is verified through a real integrated HP with five connected HPs installed ten years ago. The experimental results show that MSFA is capable of accurately identifying health status of the system, especially failure at a preliminary stage compared to its competing algorithms.

Global warming, and the resultant climate change, has become an urgent global issue. One potential partial solution to this problem and the focus of this paper is to design energy management mechanisms for buildings that encourage users’ acceptance of the technology for increased environmental sustainability. Particularly, this paper focuses on the design of an energy management technique for the air-conditioning (AC) systems in residential buildings that considers users’ views on how they use such technology. Note that while a large number of energy management mechanisms are available in the literature for ACs, most of these studies, however, do not consider how well the users may accept their use in the building. To address this issue, this paper first synthesizes somemotivational psychologyliterature to understand users’ attitudes toward adopting such management techniques for the ACs within the building. Then, the obtained insights from various motivational models are incorporated into the design of an energy management scheme that encourages consumers to accept the technology that reduces electricity consumption, the cost of electricity, peak power from the grid; and the generation of CO2in the residential building. Finally, some experimental results are provided to illustrate how the designed energy management mechanism validates the motivational psychology models in terms of providing various benefits to the users, and thus shows the potential of being accepted by them.

Solar water heating (SWH) systems are well known and effective structures that transfer solar energy into thermal energy with hot water as the storage. The efficiency of an SWH system is mainly based on well-designed solar collectors and proper operation mechanisms. Although the most existing literature has focused on the efficiency enhancement of solar collectors, limited studies are devoted to improve the operating mechanism. As such, this paper studies the control mechanisms of the SWH system with a purpose so as to help the building manager to achieve targeted energy management goals. In particular, three control approaches are presented for improving the operational efficiency of the SWH system by controlling auxiliary heaters, such as heat pumps, electric heaters, and circulation pumps. The proposed approaches are developed based on different requirements of information such as the hot water demand, weather, and electricity price. Moreover, three various energy management objectives are studied with considering different scenarios in terms of real weather pattern and hot water demand of a commercial building. The results validate that the proposed approaches can improve the operation of the SWH system according to various operation objectives.