This paper proposes a two-stage stochastic energy management (EM) in an isolated microgrid (MG) to decide for the day-ahead optimal dispatch. The dispatch aims to effectively manage the MG power sources, including intermittent renewable energy sources (RESs), battery energy storage systems (BESSs), and diesel generators such that the expected operation costs, reactive power costs, spinning reserve, and load shedding are minimized. The Generative Adversarial Networks (GANs) is utilized in this paper as a data driven scenario generation method to model the uncertainties in the output power of the RESs to be used in the stochastic programming formulation. Then, the fast forward scenario reduction algorithm is used to reduce the number of scenarios with the help of SCENRED/GAMS software. Usually, fuel consumption costs of diesel generators are considered to be dependent on active power generation only. However, neglecting the related reactive power costs might result in increased operation costs and deviations in the dispatches from the optimal solutions. Hence, this paper co-optimizes the costs related to both active and reactive powers of diesel generators. In addition, this study considers the reactive power capability of inverter-interfaced distributed energy resources (DERs). Moreover, the detailed models for the different resources are presented, especially for diesel generators where the actual capability curves are used instead of the widely used box constraints. The problem is formulated as a nonlinear programming problem in the General Algebraic Modeling System (GAMS) software and is solved by the CONOPT solver.

The high penetration of renewable energy sources (RESs) in modern power systems poses two conflicting issues. First one is the reduction in the operation costs resulted from RESs utilization instead of the expensive fossil fuel thermal generating units. However, the RESs are characterized by its uncertain and intermittent behavior that their output power is not dispatchable and not known exactly due to forecast errors. Therefore, reserves are scheduled in the day-ahead market to meet the uncertain supply from RESs which add some costs to the system. The decision maker should be aware of those two conflicting objectives in order to operate the system in the optimal way with minimum operation costs. The problem of the market clearing is formulated as Mixed Integer Linear Programming (MILP) problem using GAMS software. It is considered a two stage stochastic programming with the objective of minimizing the expected total operation energy and reserve costs while satisfying the various operational constraints. The results show the effectiveness of the RESs integration in different cases with the consideration of load shedding, RESs curtailment and transmission congestion. As shown from the results, when the network is congested, the operation costs are increased due to the load shedding and RESs curtailment events occur. Moreover, scheduled reserves are increased to face the uncertainty of RESs. As the RESs power penetration increases with no network congestion, a decreasing trend in the operation cost can be seen. However, this trend is less marked as the uncertainty of RESs generation increases. For the network congested case, the reduction rate of the operation cost is decreased for increasing RESs power penetration levels.

Due to the limitations and high cost of fossil fuel energy sources, combined heat and power units (CHPs) are gaining more attention recently as they are more efficient and less pollutant than conventional sources. In order to use CHP units more efficiently, the economic dispatch problem (EDP) is applied to obtain the optimal power and heat sources’ outputs to satisfy heat and power demands while meeting the different operational constraints. The problem is nonlinear and non-convex which requires heuristic technique to be used to solve this complex problem. Particle Swarm Optimization (PSO) is utilized due to its effectiveness in solving complex problems with less number of iterations. The EDP main objective is to obtain optimaloutput power and heat of each unit while the total generation cost is minimized. The obtained results justify the superiority of the proposed method in solving such complicated problems.Due to the limitations and high cost of fossil fuel energy sources, combined heat and power units (CHPs) are gaining more attention recently as they are more efficient and less pollutant than conventional sources. In order to use CHP units more efficiently, the economic dispatch problem (EDP) is applied to obtain the optimal power and heat sources’ outputs to satisfy heat and power demands while meeting the different operational constraints.

Nowadays, direct torque control (DTC) of AC drives is one of the most interesting fast dynamic response motor control techniques. Permanent magnet synchronous motors (PMSM) are now replacing induction motors (IM) in a wide range of applications because of their salient merits such as high efficiency and compactness. Combining DTC and PMSM in one drive may result in excellent performance not only for torque dynamics but also for efficiency. DTC relies on the estimation of the stator flux and torque to be compared with the set points of the torque and flux. In the literature, there are various flux estimation methods and each one has its own merits and demerits. Comparisons of these various methods are discussed in this paper showing their effect on the drive performance through MATLAB/Simulink simulations.

Direct Torque Control (DTC) is a prominent control strategy for induction motor drives, valued for its fast dynamic response and minimal dependence on motor parameters. This paper presents a comparative analysis of two DTC schemes: conventional hysteresis-based DTC and Space Vector Modulation (SVM)-based DTC, focusing on their performance in high-dynamic-response drive systems. The conventional hysteresis DTC, despite its simplicity and rapid torque response, suffers from high torque and flux ripples and variable switching frequency, which can degrade drive efficiency and precision. In contrast, SVM-DTC mitigates these drawbacks by maintaining a constant switching frequency, reducing torque ripple, and enhancing overall system efficiency, albeit at the cost of increased computational complexity. The study evaluates these schemes in terms of torque and flux control accuracy, switching losses, and dynamic response, providing a comprehensive assessment of their suitability for modern high-performance motor drive applications. MATLAB/Simulink is used to make modeling and simulation of the two schemes. From the obtained simulation results, both conventional and SVM-DTC have extremely fast torque dynamics. A key difference is the apparent smoothness of the torque, flux, and current waveforms of SVM-DTC compared to the conventional DTC. SVM-DTC provides a 30% reduction in torque ripples and a 20% increased improvement in overall system efficiency compared to conventional DTC. Additionally, SVM-DTC achieves a 2% faster dynamic response. However, SVM-DTC requires a more complex algorithm due to the SVM calculation itself, compared to the simpler hysteresis comparators and lookup table of conventional DTC.