Band 25, Heft 6

California’s electric grid has established and allowed for a large swathe of land with varying topography, geology, and biota to become inhabited and maintained at a level of modernization that most Americans have become accustomed to in the 21st century. However, while the residents, commerce, and overall populations of both have increased and progressed, the energy infrastructure that supports them has not. Instead, in some instances, the transmission infrastructure has ignited fires in the state’s wildlands – some with devastating effects – the point sources of power production have aged and not necessarily kept up with a shifting climate or demanded load, and new sources of power generation and energy storage have not penetrated the energy marketplace fast enough or to great effect. This paper delineates just a portion of some of California’s trials in the coming years as it transitions to a decarbonized electric grid and light-duty fleet. Most calculations were carried out using data prior to December 31, 2019 and generically treats succeeding data (2020–2021) as unrepresentative of former years or unlikely to be accurate of the years succeeding the Corona Virus Outbreak of 2020.

In this paper, an adaptive centralized energy management algorithm is developed for an islanded bipolar DC microgrid (BDCMG). The proposed microgrid consists of a solar photovoltaic (SPV) system, a wind energy conversion system (WECS), and a battery energy storage system (BESS). The SPV and WECS are operated in maximum power point tracking mode to extract maximum power. The adaptive control algorithm focus on power sharing at a bipolar DC bus based on variations in wind speed, solar irradiance, battery state of charge (SoC), and load. Furthermore, this algorithm maintains voltage balance and voltage regulation on a bipolar DC bus without the use of a communication link and provides effective energy management through coordinated control of renewable energy sources and BESS. The efficacy of the proposed bipolar DC microgrid is simulated and validated on a lab scale prototype to verify its feasibility, and it is observed that the results are found to be satisfactory.

Aiming at the low efficiency of distributed new energy information collection in distribution network and the poor encryption of information transmission process, a distributed new energy information collection model based on Beidou communication is proposed. The new energy information acquisition terminal, data acquisition and transmission equipment, Beidou satellite communication module and dispatching master terminal are designed respectively to form the hardware module of the distribution network distributed new energy information acquisition model. Use Beidou communication to collect new energy information, realize the encrypted transmission of new energy information based on Beidou short message, and complete the construction of distribution network distributed new energy information collection model based on Beidou communication. The experimental results show that the bandwidth allocation effect of the Beidou benchmark station of the model is good, the Beidou communication distance is long, the data encryption can be completed in a small space by using the model, the transmission efficiency of new energy data is high, and the data integrity can be effectively guaranteed.

This study is dedicated to develop a single-phase modified Y-source inverter ( m -YSI) topology having a high boost factor and low switch voltage stress by adding a switch to the low voltage region. Thanks to this structure, continuous input current and wide control range are achieved regardless of winding factor. Since an external switch is used in the recommended modified Y-source inverter, the modulation index and duty ratio are controlled independently. Detailed analyses of the modified Y-source inverter were performed using Matlab/Simulink and its performance was compared with conventional Y-source inverters. Compared to selected conventional Y-source inverters, the proposed m -YSI has almost three times less voltage stress. A lower-cost topology has been obtained in which state-of-the-art control methods can be applied, thanks to a wide control range and circuit components with a low power ratio. This proposed model can be used as a double-stage micro-inverter in photovoltaic systems.

This paper proposes the implementation of a power electronics interface for driving agricultural loads. The interface would utilize the untapped power generated by pre-installed solar-photovoltaic (SPV) based pumping systems during non-pumping periods. This architecture is used to increase the percentage utilization of the SPV pumping system, which is currently at around 50 %. The implemented system includes a dual stage converter, consisting of a boost converter and an H-bridge inverter, to facilitate the interface between low-voltage (LV) SPV systems and single-phase AC agricultural loads, while maintaining the desired voltage level. The key advantage of this system is its transformer less operation to boost SPV output from 90 V DC to 220 V AC, resulting in reduced size and cost. This paper presents an analysis of the operating modes, system modeling, validation in simulation, and experimental implementation of 1 kW system. Additionally, an improved step-size perturb and observe (P&O) technique is employed in the implemented interface to extract optimum power from a SPV. This interface is suitable for incorporating an off-grid SPV pump to power other loads during non-pumping and sunny hours. Furthermore, a comparison of the proposed interface with existing structures demonstrates operational superiority. Furthermore, the system can be efficiently used to drive various single-phase agrarian loads for farmers’ use in unelectrified remote areas.

Insulated gate bipolar transistor (IGBT) is one of the most used devices for high-power-density and high-voltage applications such as wind turbines, electric vehicles, and smart grids. However, the field of IGBT research is still in its infancy owing to the failures that can happen to the module due to its temperature rise. Hence, the junction temperature T j measurement of the active region is essential for analyzing and predicting the degradation state of the IGBT device. In this paper, an overall study, including uses of the governing thermal equation to estimate the junction temperature T j , experimental analysis of the IGBT component during operation, and a numerical finite element (FE) study, was conducted. The thermal management FE model is conducted to predict temperature variation along time and heat spreading inside the IGBT layers. To investigate the material’s properties and structure change during operation impacting the junction temperature, scanning electron microscopy (SEM) and energy X-ray dispersive spectroscopy (EDS) were performed on the IGBT power module. Results were used to interpret the difference between the experimental and the predicted temperature values. For effectiveness validation purposes, results obtained from the proposed FE model are compared with the results of the experimental thermo-sensitive electrical parameters (TSEP) method. Good agreement was found between the experiment and the proposed FE model.

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.

The classification of power quality (PQ) disturbances is a critical task for both utilities and industry. PQ issues cause power system equipment to fail. PQ disruptions also cause significant disruption in the paper and semiconductor industries, with significant financial implications as well as technological difficulties. Deep learning based approaches are used for automatic PQ disturbance classification, which requires huge amounts of data. A PQ disturbance dataset consisting of 12 PQ disturbances is developed using wavelet transform and MATLAB software. In this paper, an autoencoder is used to reduce the dimensionality of power quality disturbances data from higher dimensionality space, which consists of 72 input features, to lower dimensionality space, which consists of 21 input features. Based on data extracted from the autoencoder, a radial basis function neural network is used to identify the type of PQ disturbances. Based on the simulation results, it is observed that radial basis function neural network is able to distinguish the type of PQ disturbance with 92 % accuracy.

The grounding scheme is one of the main elements for protection system to mitigate the effect of unwanted lightning strikes or operational failures due to faults in generation, transmission and distribution systems. Desert sand soil has a very low electric conductivity, causing weakness in grounding system. To mitigate problems, the soil is supported with a high conductivity agent to adjust the soil conductivity to acceptable levels. A high-cost and non-renewable commercial product can be added to soils to increase their conductivity. This study brings innovation to conventional soil-enhancement materials. A newly developed mixture is proposed, which is composed of waste-activated carbon received from water purification industries and wood ash from agricultural wastes. First, mixture samples with different compositions of available waste materials were prepared. Then, experimental tests were performed and optimized with a combined genetic algorithm (GA) and fuzzy ranking method to estimate the optimal percentage volume value of each material involved in the developed mixture. To validate the effectiveness of the developed mixture, the results were compared with a commercial product available in the market. Also, the obtained results using GA are compared with those obtained by particle swarm optimization (PSO) to appreciate the best GA solutions. The effectiveness of using the developed mixture and the commercial product in reducing the resistance-to-ground of a rod driven in high and low resistivity soils is evaluated. Finally, a sample of the developed mixture was checked to be non-corrosive material for copper grounding rods.

Moving towards clean and sustainable transportation system, electrification of railway systems along with the use of electric vehicles (EVs) are of great interest. For economic operation of such systems, the peer-to-peer (P2P) energy trading policy became more applicable. Therefore, this paper proposes a double-layer individual-based optimization algorithm for P2P based optimal energy management (EM) of a smart distribution network incorporating flexible smart railway substation taking into consideration multiple energy sources of the traction system and the wayside distribution network with the integration of both EVs and energy hubs (EHs). For efficient operation of the railway substation, captured regenerative braking energy (RBE) are considered. In addition, photovoltaic (PV) units are used on station’s and platforms’ roof-tops for clean energy generation along with a multistory parking garage of electric vehicles (EVs). The wayside distribution network of district distribution area with flexible loads also contains several energy sources including distributed generation and multiple EHs. Optimal energy management is carried out including optimal demand side management of flexible loads and EHs. The optimization study takes into consideration several operational uncertainties arising from several components of the system. The simulation results show the feasibility of applying the proposed EM algorithm with an improvement of the energy economics of the system. The results show a reduction of 6.5 % in energy cost of the distribution network loads, 48 % in net energy cost of EHs, 70.8 % in net energy cost of EVs, and 59.1 % in the cost of energy bought by the traction substation.

Presently the term “Power Quality” is gaining more and more attention due to different kinds of non-linearities induced by various converters used in the industries. Power quality is all about assessing and maintaining optimal quality of power during various stages of the power system ranging from generation to distribution. This research intends to provide a better understanding to various researchers about the applications of the Distribution Static Compensator (D-STATCOM) in mitigating various power quality concerns typically focused on harmonics elimination and voltage profile improvement. D-STATCOM is designed and installed in the distribution network to maintain reactive power demand, harmonic mitigation, and power factor correction. A Synchronous Reference Frame Theory (SRFT) based control technique is adopted to extract reference current. The behavior of the system with and without D-STATCOM is analyzed in detail for various loads. As a result, this study presents various ideas, adjustments, and compensating methods for several loading conditions. Finally, it is found that the current THD has been considerably improved under various loading conditions.

This paper describes a three-phase VSC based 4-switch Grid-Tied Solar PV-DSTATCOM using Modified Synchronous Reference Frame (MSRF) Theory to achieve the Unity Power Factor (UPF) operation, Zero Voltage Regulation (ZVR) operation, perform harmonics compensation and compensation against load unbalancing. The DC-Link voltage is set stable and voltage balancing is done across the split-capacitors under all load-charge conditions. Coordinated design of DSTATCOM consists of integrated Grid-Tied Solar Photovoltaic (PV) array and a DC-to-DC boost converter circuit for improvement of Photovoltaic energy/power. Maximum Power Point Tracking (MPPT) is used to provide the fixed power which is trapped and transferred from the array to the boost converter. UPF operation and ZVR are not available together. So, models are designed separately to accomplish one of the objectives (UPF and ZVR) for linear, non-linear and unbalanced loads. Simulation models are first designed in MATLAB software and then in Real-Time using RT-LAB having MASTER and CONSOLE blocks for Computation and Communication. In building of the model, Real-Time Digital Simulator (OPAL-RT 4510) is used in which hardware synchronised models are embedded, and hardware real-time results are displayed on the synchronised four-channel oscilloscope RIGOL-MSO5204.