Mohammad Reza Salehizadeh

With the ever-increasing penetration of renewable resources, realizing the flexibility of HVAC systems is valuable for maintaining power balance throughout Sustainable Electric Energy Systems (SEESs). The temperature set-point of the buildings has been adjusted to accommodate the thermal needs of the residents, thereby enabling system flexibility. Aggregators have become essential intermediaries for organizing distributed, manageable loads. Within this context, this paper proposes a new aggregation mechanism to realize the flexibility of Heating-Ventilation and Air-Conditioning (HVAC) systems in SEES. For the sake of HVAC control, we employ Model Predictive Control (MPC) technique. Since conventional MPC imposes the online computational burden, we adopt an Explicit MPC (EMPC) strategy to provide fast response. The proposed methodology was implemented in two case studies involving three buildings and 50 buildings to examine scalability. In these case studies, the methodology successfully maximized the flexibility of air conditioning demand, achieving a 76.26% for three buildings and 75.25% for 50 buildings reduction in the maximum power requested by the SEES operator, without compromising the thermal comfort of the occupants in the case of three buildings.

Congestion management in power systems involves controlling and optimizing energy flow to ensure efficient and reliable operation in networked microgrids. The objective is to effectively manage congestion in networked microgrids by balancing energy supply and demand, thereby preventing overloads and ensuring a stable and resilient energy system. One approach to achieve this is through Model Predictive Control (MPC), which can regulate power flow while considering realistic constraints and system dynamics. The results of applying linear MPC for congestion management in networked microgrids are promising when all parameters are deterministic. However, introducing uncertainty into the model poses challenges that linear MPC cannot address. This paper introduces a Chance-Constrained Model Predictive Control (CC-MPC) method to tackle this problem through mathematical reformulation and stochastic optimization. The decision to use CC-MPC over robust MPC or tube MPC was based on the unknown uncertainty of the problem, making stochastic MPC a more suitable option. The results from provided numerical examples demonstrate that the proposed method not only accurately predicts demand response but also effectively manages congestion in both small-scale and large-scale networked microgrids, even while accounting for the effects of uncertainty.

In the oil industry, the leakage of pipelines containing hydrocarbon fluids causes significant environmental and economic damage. Recently, there has been a growing trend in employing data mining techniques for detecting leaks. Among these methods is the Hidden Markov Model, which, despite good results with stationary data, becomes inefficient when a leak causes a drop in the pressure or flow, reducing its accuracy. This paper presents an adaptive Hidden Markov method. Previous methods had low accuracy due to insufficient information for accurate leak detection. They often classified the size and location of leaks broadly. In contrast, the proposed model extracts hidden features to accurately identify the location and size of leaks, even in noisy conditions. Simulating a leak in a section of an oil pipeline in the Iranian Oil Export Corridor demonstrates the proposed method’s superiority over common methods like K-NN, SVM, Naive Bayes, and logistic regression.

A This paper proposes a modified real-time transient modelling (MRTTM) framework to address the critical challenge of leak detection and localization in pipeline transmission systems. Pipelines are essential infrastructure for transporting liquids and gases, but they are susceptible to leaks, with severe environmental and economic impacts. MRTTM tackles this challenge with a three-stage operational process. First, “Data Collection” gathers sensor data from designated observation points. Second, the “Detection” stage identifies leaks. Finally, “Decision-Making” utilizes MRTTM to pinpoint the exact leak magnitude and location. This paper introduces an innovative method designed to significantly enhance pipeline leak detection and localization through the application of artificial intelligence and advanced signal processing techniques. The improved MRTTM framework integrates AI for pattern recognition, state space modelling for leak segment identification, and an extended Kalman filter (EKF) for precise leak location estimation, addressing the limitations of traditional methods. This paper showcases the application of MRTTM through a case study using the K-nearest neighbors (KNN) method on a water transmission pipeline for leak detection. KNN aids in classifying leak patterns and identifying the most likely leak location. Additionally, MRTTM incorporates the EKF, enabling real-time updates during transient events for faster leak identification. Preprocessing sensor data before comparison with the leakage pattern bank (LPB) minimizes false alarms and enhances detection reliability. Overall, the AI-powered MRTTM framework offers a powerful solution for swift and precise leak detection and localization in pipeline systems. The functionality of the framework is examined, and the results effectively approve the effectiveness of this methodology. The experimental results validate the practical utility of the MRTTM framework in real-world applications, demonstrating up to 90% detection accuracy and an F1 score of 0.92.

In recent literature, the value of electric vehicles (EVs) for the resilience enhancement of urban microgrids has been shown. Furthermore, on a larger scale, there has been a growing recognition of the potential of EV cooperation in enhancing the overall resilience of smart cities. To this end, the city can be partitioned into a set of blocks, each encompassing buildings. Within each block, EV traveling time can be ignored. As a step forward, this study presents a Preventive Energy Management (PEM) strategy along with a rescheduling procedure by cooperation of EVs, local distributed energy resources (DERs), and buildings in different city blocks. Based on the available information related to the amount of curtailed loads, two cases are modeled and studied. In the proposed PEM strategy, EV owners’ opt-in preferences such as arrival and departure times, and the city block in which they are willing to give energy services are modeled. As a more realistic consideration, the proposed model does not consider the buildings’ load as a lumped load, instead the PEM strategy is designed to consider each of the buildings separately. The resulting optimization model is flexible enough to enable EVs to switch from one building to another to provide energy in different time slots. By applying disjunctive-constraint-based transformation, the model is recast as a Mixed Integer Linear Programming (MILP) that could be efficiently solved by commercial optimization solvers. The proposed approach is applied to a benchmark and the results are analyzed. According to the results, using EVs in the PEM strategy has been proven to be effective and the importance of the length of the period of service and opt-in preferences for optimal scheduling are highlighted.

A shared pool of grid-scale storage resources called Cloud Energy Storage (CES) can bring substantial benefits to the economical and reliable operation of MGs. However, the investment cost of CES may not be affordable for a single microgrid (MG). As a solution, we propose an approach in which neighboring microgrids in a distribution network collaborate and form a multi-microgrid (multi-MG) to install a shared CES to increase their profit and improve their reliability. Different investment scenarios are evaluated by considering the yearly reward from TSO and DSO. For each of the investment scenarios, TSO and DSO give a yearly reward based on the contribution of CES in peak-shaving and distribution network operation yearly cost reduction. Afterward, a decision table is provided in which, for all investment scenarios, profit, reliability index based on expected energy not supplied (ENS), and TSO-DSO yearly reward are determined. Finally, the microgrids select one of the investment scenarios using a multi-attribute decision-making approach. Simulation results of a case study validate the effectiveness of the proposed collaborative decision-making framework in increasing the economic value of CES investment, reliability enhancement in multi-MG, and peak-shaving.

Maximum power point tracking (MPPT) through an effective control strategy increases the efficiency of solar panels under rapidly changing atmospheric conditions. Due to the nonlinearity of the I–V characteristics of the PV module, the Sliding Mode Controller (SMC) is considered one of the commonly used control approaches for MPPT in the literature. This paper proposed a Backstepping SMC (BSMC) method that ensures system stability using Lyapunov criteria. A fuzzy inference system replaces the saturation function, and a modified SMC is used for MPPT to ensure smooth behavior. The proposed Fuzzy BSMC (FBSMC) parameters are optimized using a Particle Swarm Optimization (PSO) approach. The proposed controller is tested through various case studies on account of MPP’s dependence on temperature and solar radiation. The controller performance is assessed in partial shading conditions as well. The simulation results show that less settling time, a small error, and enhanced power extraction capability are achieved by applying the PSO-based FBSMC approach compared to the conventional BSMC- and ABC-based PI control presented in previous research in different scenarios. Moreover, the proposed approach provides faster adaptation to temperature and solar radiation variation, ensuring faster convergence to the MPP. Finally, the robustness of the proposed controller is validated by providing variation within the system components. The result of the proposed controller clearly indicates the lowest value of RMSE measured between PV voltage and the reference voltage, as well as the RMSE between PV power and maximum power. The results also show that the proposed MPPT controller exhibits the highest dynamic efficiency and mean power.