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Having continuous decrease in inertia and being sensitive to load/generation variation are considered crucial challenging problems for modern power grids. The main cause of these problems is the increased penetration capacities of renewables. An unbalanced load with generation power largely affects grids' frequency and voltage profiles. Load frequency control (LFC) mechanisms are extensively presented to solve these problems. In the literature, LFC methods are still lacking in dealing with system uncertainty, parameter variation, structure changes, and/or disturbance rejection. Therefore, this paper proposes an improved LFC methodology using the hybrid one plus proportional integral double-integral derivative (1+PII2D) cascaded with fractional order proportional-integral-derivative (FOPID), namely, the proposed 1+PII2D/FOPID controller. The contribution of superconducting magnetic energy storage devices (SMES) is considered in the proposed design, also considering hybrid high-voltage DC and AC transmission lines (hybrid HVDC/HVAC). An optimized design of proposed 1+PII2D/FOPID controller is proposed using a new application of the recently presented powerful artificial rabbits optimizers (ARO) algorithm. Various performance comparisons, system changes, parameter uncertainties, and load/generation profiles and changes are considered in the proposed case study. The results proved superior regulation of frequency using proposed 1+PII2D/FOPID control and the ARO optimum parameters. [ABSTRACT FROM AUTHOR]

Recently, wide installations of photovoltaic (PV) systems have been achieved in the electrical power systems. However, fluctuated output power of the PV generation and/or fluctuated load demands represent critical factors for the operation of PV systems. Thence, energy storage systems (ESSs) are highly needed for improving the supply reliability of PV generation systems. Among existing ESSs, the proton exchange membrane fuel cell (PEMFC) systems represent long-lifetime, efficient, and cost-effective solutions for PV systems. However, nonlinear behaviour exists in the output of PEMFC systems, which depends on the operating cell temperature, and the membrane water contents. The output of PEMFC has a unique operating maximum power point (MPP) for each operating combination of membrane water content and temperature, which requires MPP tracking (MPPT) control loop. Therefore, this paper presents a fuzzy logic control (FLC) MPPT method for enhancing the operation of PEMFC systems. The traditional MPPT methods in the literature employ three sensors, including voltage, water content, and temperature. The proposed controller employs only the PEMFC output current and voltage electrical signals. Compared to the classical fixed step size perturb and observe (P and O) and hill climbing MPPT methods, the proposed method represents variable step size MPPT method. In addition, compared to the widely employed incremental conductance (INC) and incremental resistance (INR) MPPT methods, the proposed method benefits the wide operating and adaptive step size MPPT operation due to using the FLC approach. The proposed method is simple and can be implemented using low cost microcontrollers. The design procedures, operating principle, and performance verification of the proposed method are presented in this paper. [ABSTRACT FROM AUTHOR]

Hydrocarbons, carbon monoxide and other pollutants from the transportation sector harm human health in many ways. Fuel cell (FC) has been evolving rapidly over the past two decades due to its efficient mechanism to transform the chemical energy in hydrogen-rich compounds into electrical energy. The main drawback of the standalone FC is its slow dynamic response and its inability to supply rapid variations in the load demand. Therefore, adding energy storage systems is necessary. However, to manage and distribute the power-sharing among the hybrid proton exchange membrane (PEM) fuel cell (FC), battery storage (BS), and supercapacitor (SC), an energy management strategy (EMS) is essential. In this research work, an optimal EMS based on a spotted hyena optimizer (SHO) for hybrid PEM fuel cell/BS/SC is proposed. The main goal of an EMS is to improve the performance of hybrid FC/BS/SC and to reduce the amount of hydrogen consumption. To prove the superiority of the SHO method, the obtained results are compared with the chimp optimizer (CO), the artificial ecosystem-based optimizer (AEO), the seagull optimization algorithm (SOA), the sooty tern optimization algorithm (STOA), and the coyote optimization algorithm (COA). Two main metrics are used as a benchmark for the comparison: the minimum consumed hydrogen and the efficiency of the system. The main findings confirm that the minimum amount of hydrogen consumption and maximum efficiency are achieved by the proposed SHO based EMS. [ABSTRACT FROM AUTHOR]

Enhancing power cycling capability of power semiconductor devices is highly demanded in order to increase the long-term reliability of multilevel inverters. Ageing of power switches and their cooling systems leads to their accelerated damage due to excess power losses and junction temperatures. Therefore, thermal stresses relief (TSR) is the most effective solution for lifetime extension of power semiconductor devices. This paper presents a new TSR carrier-based pulse width modulation (TSRPWM) strategy for extending the lifetime of semiconductor switches in single-phase multilevel inverters. The proposed strategy benefits the inherent redundancy among switching states in multilevel inverters to optimally relieve the thermally stressed device. The proposed algorithm maintains the inverter operation without increased stresses on healthy switches and without reduction of the output power ratings. In addition, the proposed algorithm preserves voltage balance of the dc-link capacitors. The proposed strategy is validated on a single-phase five-level T-type inverter system with considering different locations of thermal stresses detection. Experimental prototype of the selected case study is built to verify the results. Moreover, comparisons with the most featured strategies in literature are given in detail. [ABSTRACT FROM PUBLISHER]

Simulation tools allow changes in electrical network, timetabling, and train speed profiles, which is fundamental for the planning and development of electric railways. When simulating electric railways, it may be challenging to adequately represent their nonlinear characteristics resulting from the rectifier substations, overvoltage protection circuits, and the continuous changes of the load according to the train position. The power exchange between trains and the change of the rail resistance between them require high temporal resolution and thus results in high computational complexity. Given these goals and challenges, the main objective of this work is to develop a sufficiently simple, fast to simulate, yet accurate, simulation method based on real-world assumptions. Further, the simulation method should be extendable to energy storage systems (ESSs) and control to allow designing control methods for energy management to maximise the energy efficiency of electric railways. This article describes the development of a framework for modelling the power flow within an electric rail network referenced to a case study example. In this article, a test scenario is presented comprising of eight trains running on a double rail track in order to verify the model against expected observations. The simulation model includes two DC traction substations separated by 2 km and three passenger stations separated by 1 km. The model operation requirements and parameters are based on the Yizhuang Line in Beijing Metro. The simulation methodology developed for this work differs from other methods reported in pertinent literature as it facilitates modifications to the model by avoiding the common approach of forming iterative methods for solving the power flow. The proposed test scenario allows the model to be verified for accuracy. The verification is accomplished via different methods and the obtained results confirm that the proposed modelling approach is accurate as the error between the supplied and consumed energy is only 0.11%. [ABSTRACT FROM AUTHOR]