The integrity of Internet of Things (IoT) devices has caused a fast spread in an era of data-driven decision-making across businesses. This tutorial survey provides a comprehensive review of current IoT data handling advances, focusing on data quality management (DQM). The article starts with the key aspects of IoT data management. In this regard, we shed light on the data source, volume and velocity, variety, lifecycle, security and privacy, scalability and distribution processing, anomaly detection, and energy efficiency. Then, We present a comprehensive taxonomy of IoT DQM based on the application type, such as smart cities, healthcare, agriculture, environmental monitoring, retail and supply chain, and smart grids (SGs). As IoT data processing, analysis, and security play a significant role in DQM; this tutorial survey carefully addresses how modern technologies maintain this role. More particularly, this work investigates the use of edge computing for real-time data processing and the incorporation of synthetic data to supplement restricted resources incorporating the issues of managing the massive datasets created by IoT implementations. In addition, the paper addresses the use of machine learning (ML) algorithms for in-depth analysis of IoT data streams, DQ evaluation protocols, and detection tactics during data transfer. Moreover, the article investigates complete security measures for protecting sensitive data, such as access control regulations and several security techniques, including authentication, encryption, and secure communication protocols that enable IoT data management. Besides, blockchain technology’s significant roles in this regard have been comprehensively addressed. Along with summarizing and reviewing the latest efforts in DQM in IoT-based systems, we shed light on their strong and weak points and discuss upcoming trends and potential difficulties in IoT data management. Last but not least, we continue by emphasizing the cumulative impact of these advances and shedding light on the open issues and challenges. Finally, this in-depth tutorial survey aims to be a significant resource for academics, practitioners, and stakeholders interested in the changing environment of IoT data management, with a particular emphasis on DQ.

Dynamic wireless charging (DWC) facilitates energy transfer from the electric grid to moving electric vehicles (EVs) via charging pads (CPs) positioned along roadways. To maximize satisfied charging requests, given the limited supply capacity, dynamic charging coordination is required to determine suitable CPs for mobile EVs. Charging coordination necessitates EV owners to share their information (i.e., the identities and locations) with charging service providers (CSPs) to allocate the best CP for charging. However, charging coordination raises privacy concerns due to the exchange of private information. Moreover, a fast authentication mechanism is then required between EVs and CPs to initiate the charging process. In addition to the privacy limitation, existing DWC strategies lack the presence of multiple CSPs, which is a crucial aspect given the significant growth of the EV market. Consequently, centralization arises, with a single CSP overseeing the entire network. This paper proposes a semi-decentralized privacy-preserving networking strategy utilizing a specially designed consortium blockchain to support dynamic charging coordination, authentication, and billing while ensuring user anonymity and data unlinkability. Our proposed strategy leverages a novel semi-decentralized K-times group signature scheme and distributed random number generators to achieve privacy and decentralization. Simulation results showed that the proposed method reduces the EV authentication time to 0.1 ms while limiting storage requirements to just 4 MB per block at each EV. Additionally, the proposed strategy showed improved security and privacy features when compared with IBM’s privacy-preserving blockchain (Identity Mixer).

Abstract The concept of interconnected multi-microgrids (MMGs) is presented as a promising solution for the improvement in the operation, control, and economic performance of the distribution networks. The energy management of the MMGs is a strenuous and challenging task, especially with the integration of renewable energy resources (RERs) and variation in the loading due to the intermittency of these resources and the stochastic nature of the load demand. In this regard, the energy management of the MMGs is optimized with optimal inclusion of a hybrid system consisting of a photovoltaic (PV) and a wind turbine (WT)-based distributed generation (DGs) under uncertainties of the generated powers and the load variation. A modified Capuchin Search Algorithm (MCapSA) is presented and applied for the energy management of the MMGs. The MCapSA is based on enhancing the searching abilities of the standard Capuchin Search Algorithm (CapSA) using three improvement strategies including the quasi-oppositional-based learning (QOBL), the random movement-based Levy flight distribution, and the exploitation mechanism of the prairie dogs in the prairie dog optimization (PDO). The optimized function is a multi-objective function that comprises of the cost and the voltage deviation reduction along with stability enhancement. The effectiveness of the proposed technique is verified on standard benchmark functions and the obtained results. Then, the proposed method is used for energy management of IEEE 33-bus and 69-bus MMGs at uncertainties conation. The results depict that the energy management with inclusion of WTs and PVs using the proposed technique can reduce the cost and summation of the VD by 46.41% and 62.54%, and the VSI is enhanced by 15.1406% for the first MMG. Likewise, for the second MMG, the cost and summation of the VD are reduced by 44.19% and 39.70%, and the VSI is enhanced by 4.49%.

This article proposes a new mathematical formulation for optimal operation scheduling for a remote photovoltaic (PV) farm and distributed electric vehicle charging stations (D-EVCSs), which are owned by a private entity. The proposed model is formulated to maximize the profit of the D-EVCSs private investor through optimal electric vehicle (EV) charging coordination and pricing mechanism. The proposed pricing mechanism aims to achieve an expected revenue by the private investor, while guaranteeing a low charging price for EVs to ensure the EV owners’ satisfaction. For the EV supply application, D-EVCS integrates a battery storage system and rooftop PV. However, the rooftop PV installation is constrained to the limited footprint of the EVCS. For this reason, the investment in a remote PV farm and its impact on the expected revenue and the EVs charging price is introduced in this article. The power generated by the remote PV farm can be transacted to the D-EVCSs using a power purchase agreement through the utility grid infrastructure. In such a transaction, the electricity service and distribution fees are paid to the utility grid for overseeing the PV farm power transaction. The proposed model also considers the opportunity for D-EVCSs to participate in the provision of operating reserve and demand response ancillary services. Different case studies are evaluated for the purpose of validating the effectiveness of the proposed model.

A multistage approach to passive islanding detection is proposed that utilizes a decision tree (DT) like classification algorithm. The novelty of the proposed method is centered on the way in which features are passed to subsequent stages of the DT. Feature sets extracted using different sized time windows are passed to successive stages of the tree. This provides two important advantages: 1) cases that can be easily determined as either islanding or nonislanding events are flagged as soon as possible without waiting for the full feature set to become available; 2) because the algorithm allows for the use of different sized time windows, features are analyzed in time-scales that fit their natural patterns of temporal evolution. In this article, the proposed classifier is trained and tested using a database of feature vectors, obtained using PSCAD, which were designed to reflect a variety of commonly encountered events on an IEEE 34-bus distribution system. One of the key requirements for the proposed algorithm was that easy cases should be flagged as soon as possible; this property was confirmed by the observation that most events ($\approx$79%) were detected within 10–20 ms, while at the same time retaining a very high detection rate overall cases ($>\!99$%).

This paper proposes a new approach for optimal operation of an Electric Vehicle (EV) battery-swapping station (BSS) based on Rolling-Horizon optimization (RHO). The BSS has several swapping bays such that each can accommodate an EV for swapping single or multiple battery units. The proposed BSS model considers serving different types of EVs using a heterogeneous battery stock. The charging of the depleted batteries (DBs) is performed using continuously controlled variable chargers which makes it more flexible for providing grid services. While previous studies focused on day-ahead modeling of BSSs, our study considers BSS dynamic scheduling. The goal is to maximize the daily profit using an RHO mechanism to provide optimal swapping and charging/discharging processes. The problem is defined as mixed-integer nonlinear programming (MINLP), then it’s linearized into a mixed-integer linear problem (MILP) to reduce the computational complexity. To predict the EV’s swapping demand, a long short-term memory (LSTM) recurrent neural network is utilized as a time series forecasting engine. The proposed model is validated through a set of case studies comparing the LSTM-based RHO mechanism versus unscheduled operation and day-ahead scheduling. Simulation results demonstrate that the proposed dynamic scheduling mechanism increases the profit between 10% and 25.7% compared to the day-ahead scheduling. Furthermore, the number of EVs served using the proposed approach increases between 11% and 14% compared to the day-ahead model.

As the penetration rate of flexible loads and distributed energy resources in the distribution networks increases, congestion management techniques that utilize demand-side management (DSM) have been developed. These are indirect methods that rely on information exchange between the distribution network operator, aggregators, and consumers’ meters to encourage customers to change their demand to relieve congestion. Cyber attacks against aggregators can compromise the operation of DSM-based congestion management methods, and hence, affect the security and reliability of electrical networks. In this article, the vulnerability of indirect congestion management methods to load-altering attacks is studied. An optimization algorithm is developed to determine the aggregators a cyber attacker would compromise, via minimum alteration of their load profiles, to cause congestion problems. The impact of such attacks on congestion and consumers’ electricity bill is then studied. A mitigation scheme is formulated to determine the most critical aggregators in the network. The security of these aggregators is then reinforced to mitigate such cyber attacks.

Advanced metering infrastructure (AMI) networks are vulnerable against electricity theft cyber attacks. Different from the existing research that exploits shallow machine learning architectures for electricity theft detection, this paper proposes a deep neural network (DNN)-based customer-specific detector that can efficiently thwart such cyber attacks. The proposed DNN-based detector implements a sequential grid search analysis in its learning stage to appropriately fine tune its hyper-parameters, hence, improving the detection performance. Extensive test studies are carried out based on publicly available real energy consumption data of 5000 customers and the detector's performance is investigated against a mixture of different types of electricity theft cyber attacks. Simulation results demonstrate a significant performance improvement compared with state-of-the-art shallow detectors.

Although the future impact of plug-in electric vehicles (PEVs) on distribution grids is disputed, all parties agree that mass operation of PEVs will greatly affect load profiles and grid assets. The large-scale penetration of domestic energy storage, such as with photovoltaics (PVs), into the edges of low-voltage grids is increasing the amount of customer-generated electricity. Distribution grids, which are inherently unbalanced, tend to become even more so with the uneven spread of PVs and PEVs. In combination, PEVs and local generation could provide voltage support for distribution networks, and support increased penetration. This paper develops an interactive energy management system for incorporating PEVs in demand response (DR). Using this system, owners can immediately choose whether they want to discharge their PEV battery back into the grid. The system not only provides owners with a flexible scheme for contributing to DR but also ensures that, through real-time collaboration of PEVs and PVs, the three-phase grid operates within acceptable voltage unbalance. An extensive performance evaluation using MATLAB/GAMS simulation of the 123-bus test system verifies the effectiveness of the proposed approach.