Abdulraheem Hassan Alobaidi

Microgrids (MGs) have lately received significant attention from researchers as a contemporary solution to better employ the high penetration of renewable energy sources (RESs) to enhance energy sustainability. They can improve the reliability, resilience, and security of distribution systems. However, a distributed energy management framework is required for the optimal operation of distribution systems with multiple microgrids, given the limited communication between the distribution system operator (DSO) and the microgrid operators. Moreover, distribution systems are unbalanced in nature due to the unbalanced connected loads. Thus, modeling the unbalanced power flow in distributed energy management is essential to ensuring the feasibility of operational decisions. This paper proposes a distributed algorithm based on the alternating direction method of multipliers (ADMM) for optimal operation of distribution systems with multi-microgrids, accounting for uncertainty in demand, RESs, and MG operation modes, as well as unbalanced power flow. A modified IEEE 34-bus distribution system with six microgrids is used to validate the effectiveness of the proposed method. The proposed distributed energy management framework can achieve high solution accuracy with limited information shared among operators, as demonstrated in the case study, providing results comparable to those of the centralized energy management approach, with an insignificant 0.24% error in total operating cost. Moreover, numerical results show that compared with the distribution system and microgrids with forecasted loads and PV outputs under normal operation, the proposed stochastic model yields a 0.56% higher total expected operating cost due to uncertainty in load and PV power outputs. When probabilistic MG islanding operation is considered, the total expected operating cost of the distribution system decreases by 1.03% compared with the stochastic solution under normal operation due to the microgrids’ disconnection from the distribution system during islanding in a few scenarios, hence relieving the distribution system of excessive load.

The current landscape of data-centric Internet of Vehicles (IoVs) encompasses a fusion of Human-driven Vehicles, Autonomous Vehicles, Road-Side Units, and edge-based devices engaged in periodic communication. Given the stringent latency requirements inherent in vehicular communications, the emergence of edge-based vehicular Digital Twins (DTs) plays a pivotal role in problem-solving, ensuring rapid response, regulatory compliance, and seamless availability. While these communications serve as the backbone of IoV, they also create an opportune environment for cybercriminals to exploit. Vulnerabilities at the network layer facilitate intrusions, resulting in a surge of data falsification attacks in recent years. Addressing this challenge demands resilient and intelligent threat detection schemes capable of adapting to the dynamic nature of IoV. This study conducts a comprehensive examination of the vulnerabilities in Vehicle-to-DT (V2DT) data communication through the lens of an attacker utilizing False Data Injection Attack (FDIA). It utilizes cutting-edge Blockchain-based decentralized storage and buffering mechanisms for vehicle dynamics data en route to edge-based DTs. Further, deep learning-powered sensor data analysis serves as an additional layer of security. Evaluation of the proposed threat detection and mitigation model demonstrates 100% tamper detection in V2DT communication, coupled with a 96% accurate classification of anomalous driving behaviors, including aggressive driving or FDIAs.

Hydrogen energy promotes sustainable and clean energy and facilitates better utilization of renewable energy sources. The advance in power to hydrogen technology, such as electrolyzers, causes the future shift toward hydrogen storage as a promising technology to store the excess generation in the distribution network. However, the operating decisions of power distribution networks with renewable energy and power to hydrogen technology are affected by the stochastic nature of generation and load, which calls for sophisticated, more advanced optimization techniques to determine the optimal operating mechanism under these uncertain factors. Moreover, the centralized approach to solving such a problem requires an extensive communication infrastructure and processing of large system data. This paper proposes an advanced optimization methodology to optimize the operation of distribution networks with hydrogen storage and fuel cells, aiming to reduce the communication burden and address generation and load uncertainties. The problem is formulated as a distributionally robust optimization in which the ambiguity set is constructed based on Wasserstein distance to address the uncertainties in the electricity/hydrogen demands, solar photovoltaic (PV) generation, and hydrogen reserve. The proposed formulation coordinates the optimal schedule of the generation assets in the integrated power distribution-hydrogen systems using Benders decomposition under the worst probability distribution of uncertain variables. The modified IEEE 34-bus distribution network with two hydrogen storage systems is used to evaluate the performance of the proposed algorithm. The numerical results show that the total expected operation cost in the proposed DRO algorithm is 1.98% higher than those obtained using a deterministic approach due to considering the generation and demand uncertainties. Moreover, the out-of-sample performance shows that the proposed DRO algorithm obtained 0.48% lower expected operation cost compared to the stochastic programming counterpart when different scenarios are tested.

The rapid expansion of extended electric vehicle (xEV) adoption necessitates optimizing energy storage systems (ESS) management for enhanced performance, longevity, and reliability. However, traditional ESS management approaches struggle with real-time state estimation, energy optimization, and predictive maintenance, leading to inefficiencies in battery utilization and sustainability. This paper addresses these challenges by proposing an advanced ESS framework that integrates digital twin (DT) technology with the twin-delayed deep deterministic policy gradient (TD3) algorithm, a state-of-the-art reinforcement learning method derived from the deep deterministic policy gradient (DDPG). This integration enables precise real-time estimation of critical ESS states, including state of charge (SoC), state of health (SoH), state of energy (SoE), and remaining useful life (RUL), thereby enhancing predictive maintenance and operational efficiency. The proposed framework facilitates proactive battery health monitoring, generates early warnings for potential failures, and enables intelligent battery swapping via DT-driven ESS control. By dynamically adapting ESS control strategies, the TD3 algorithm optimizes energy distribution, reduces energy consumption, and improves overall vehicle performance. With a prediction accuracy of 99.8% for SoC, SoH, and SoE, the proposed approach effectively addresses key challenges in xEV ESS management like cell balancing, dynamic charging rate adjustment, battery swapping decisions, and energy optimization, contributing to a more sustainable and efficient EV ecosystem.

A fuzzy cascaded PI−PD (FCPIPD) controller is proposed in this paper to optimize load frequency control (LFC) in the linked electrical network. The FCPIPD controller is composed of fuzzy logic, proportional integral, and proportional derivative with filtered derivative mode controllers. Utilizing renewable energy sources (RESs), a dual-area hybrid AC/DC electrical network is used, and the FCPIPD controller gains are designed via secretary bird optimization algorithm (SBOA) with aid of a novel objective function. Unlike the conventional objective functions, the proposed objective function is able to specify the desired LFCs response. Under different load disturbance situations, a comparison study is conducted to compare the performance of the SBOA-based FCPIPD controller with the one-to-one (OOBO)-based FCPIPD controller and the earlier LFC controllers published in the literature. The simulation’s outcomes demonstrate that the SBOA-FCPIPD controller outperforms the existing LFC controllers. For instance, in the case of variable load change and variable RESs profile, the SBOA-FCPIPD controller has the best integral time absolute error (ITAE) value. The SBOA-FCPIPD controller’s ITAE value is 0.5101, while sine cosine adopted an improved equilibrium optimization algorithm-based adaptive type 2 fuzzy PID controller and obtained 4.3142. Furthermore, the work is expanded to include electric vehicle (EV), high voltage direct current (HVDC), generation rate constraint (GRC), governor dead band (GDB), and communication time delay (CTD). The result showed that the SBOA-FCPIPD controller performs well when these components are equipped to the system with/without reset its gains. Also, the work is expanded to include a four-area microgrid system (MGS), and the SBOA-FCPIPD controller excelled the SBOA-CPIPD and SBOAPID controllers. Finally, the SBOA-FCPIPD controller showed its superiority against various controllers for the two-area conventionally linked electrical network.

A major priority for practicing engineers in an electric power system is preserving the stability of frequency and voltage levels. Any change in these two factors will impact the efficiency and lifespan of the machines connected to the power supply. Therefore, this paper provides a control approach utilizing the Interval Type-2 Fuzzy Sets- Proportional Integral Derivative (IT2FSs-PID) controller and Advanced Thyristor Controlled Series Capacitor (ATCSC) with a combined Load Frequency Control-Automatic Voltage Regulator (LFC-AVR). Several inspections were implemented to demonstrate the controller’s strength, including various disturbances in the power system. The LFC-AVR was studied using two different dynamic models, referred to as open and closed loops on the Generation Rate Constraint (GRC) forms. A comparison was made using different techniques from the literature using the same model. Before using the approach, the frequency deviation of area-1 had a very large settling time value, which was caused by system instability. However, after implementing the approach, this value decreased to 4.9236 s. Finally, an additional ATCSC was added to the proposed model to observe its effect on the power system. The simulation was implemented using MATLAB/SIMULINK tools.

Distribution service restoration plays a vital role in mitigating the adverse impacts of power outages stemming from extreme weather conditions. With incentives toward reducing the carbon footprint of power generation on the environment and the reliance on fossil fuel resources, the future distribution networks are equipped with distributed renewable energy sources. This paper reviews the state-of-the-art frameworks proposed for service restoration in the distribution networks with renewable generation technologies. First, the objectives of the distribution service restoration and the challenges to accomplishing this task are discussed. Later, the existing approaches to restoring the distribution networks and the control architectures are presented. The contribution of microgrid technology and the models used to incorporate renewable energy resources in distribution service restoration are discussed. In this context, the application of artificial intelligence to distribution service restoration is highlighted, and potential research opportunities in distribution service restoration are presented.