Optimal sizing and operation of a hybrid energy systems via response surface methodology (RSM).

Hybrid energy systems (HESs) are the most important sources of energy demand-supply, have developed significantly around the world. Microgrids, renewable energy sources, remote telecommunications stations, greenhouses, etc., are being considered as HESs applications. Optimal sizing of these systems is considered as one of the important issues related to energy management. In this paper, the Response Surface Methodology (RSM) is proposed for the optimal sizing of a Photovoltaic (PV) system in a HESs. The suggested procedure solves the optimization problem by considering the factors affecting PV output power about the environmental conditions of the HESs. Providing a mathematical model for each of the input parameters and the ability to assessment the sensitivity of each of the input variables are the most important advantages of the proposed technique. In this paper, the RSM provides the most optimal sizing related to the PV system by considering climatic and geographical factors in the study site, and technical and economic issues related to the HESs. The optimal model obtained is evaluated by the Analysis of Variance (ANOVA) evaluation method, which is one of the important techniques of statistical evaluation. It should be noted that the RSM technique can be utilized to optimize all components of any HES.

A peer-and self-group competitive behavior-based socio-inspired approach for household electricity conservation.

This paper proposes a knowledge-based decision-making system for energy bill assessment and competitive energy consumption analysis for energy savings. As humans have a tendency toward comparison between peers and self-groups, the same concept of competitive behavior is utilized to design knowledge-based decision-making systems. A total of 225 house monthly energy consumption datasets are collected for Maharashtra state, along with a questionnaire-based survey that includes socio-demographic information, household appliances, family size, and some other parameters. After data collection, the pre-processing technique is applied for data normalization, and correlation technique-based key features are extracted. These features are used to classify different house categories based on consumption. A knowledge-based system is designed based on historical datasets for future energy consumption prediction and comparison with actual usage. These comparative studies provide a path for knowledgebase system design to generate monthly energy utilization reports for significant behavior changes for energy savings. Further, Linear Programming and Genetic Algorithms are used to optimize energy consumption for different household categories based on socio-demographic constraints. This will also benefit the consumers with an electricity bill evaluation range (i.e., normal, high, or very high) and find the energy conservation potential (kWh) as well as a cost-saving solution to solve real-world complex electricity conservation problem.

Towards net zero: A technological review on the potential of space-based solar power and wireless power transmission.

The global need for energy is increasing at a high rate and is expected to double or increase by 50%, according to some studies, in 30 years. As a result, it is essential to look into alternative methods of producing power. Solar photovoltaic (PV) power plants utilize the sun's clean energy, but they're not always dependable since they depend on weather patterns and requires vast amount of land. Space-based solar power (SBSP) has emerged as the potential solution to this issue. SBSP can provide 24/7 baseload carbon-free electricity with power density over 10 times greater than terrestrial alternatives while requiring far less land. Solar power is collected and converted in space to be sent back to Earth via Microwave or laser wirelessly and used as electricity. However, harnessing its full potential necessitates tackling substantial technological obstacles in wireless power transmission across extensive distances in order to efficiently send power to receivers on the ground. When it comes to achieving a net-zero goal, the SBSP is becoming more viable option. This paper presents a review of wireless power transmission systems and an overview of SBSP as a comprehensive system. To introduce the state-of-the-art information, the properties of the system and modern SBSP models along with application and spillover effects with regard to different sectors was examined. The challenges and risks are discussed to address the key barriers for successful project implementation. The technological obstacles stem from the fact that although most of the technology is already available none are actually efficient enough for deployment so with, private enterprises entering space race and more efficient system, the cost of the entire system that prevented this notion from happening is also decreasing. With incremental advances in key areas and sustained investment, SBSP integrated with other renewable could contribute significantly to cross-sector decarbonization.

Maximum power extraction from solar PV systems using intelligent based soft computing strategies: A critical review and comprehensive performance analysis.

This paper shows a comprehensive review on various maximum power point tracking (MPPT) techniques of the solar photovoltaic (PV) cell. It is well understood that power from a solar PV array is sometimes not sufficient, so it is required to extract the maximum power to meet the load demand. In this regard, different techniques were used for comparative analysis like perturb and observe (P & O), fuzzy logic control (FLC), incremental conductance (IC), ripple correction control (RCC), artificial neural network (ANN), particle swarm optimization (PSO), lyapunov control scheme (LCS), and fisher discrimination dictionary learning (FDDL). The performance of MPPT is also examined under the conditions like effect of shading, irradiance, etc. After reviewing the literature, it has been observed that maximum power at different sets of irradiations is extracted with ANN in comparison to other techniques. Subsequently, the least deviations about maximum power point are attained with IC while comparing with other techniques and FDDL has been found the best technique for attaining the minimum total harmonic distortion (THD). In addition to this, it is also detected that the least switching losses are attained with PSO in comparison to others. To this end, it has been concluded that each method has its significance for the extraction of maximum power from the source and dominance over other methods for smart energy systems. The researchers may find this critical review to be a valuable resource in choosing an appropriate soft computing method for the given parameters.

A novel on design and implementation of hybrid MPPT controllers for solar PV systems under various partial shading conditions.

At present, fossil fuel-based power generation systems are reducing drastically because of their less availability in nature. In addition, it produces hazardous gasses and high environmental pollution. So, in this work, the solar natural source is selected for generating the electricity. Due to the nonlinear behavior of PV, achieving maximum voltage from the Photovoltaic (PV) system is a more tough job. In this work, various hybrid optimization controllers are studied for tracing the working power point of the PV under different Partial Shading Conditions. The studied hybrid optimization MPPT methods are equated in terms of oscillations across MPP, output power extraction, settling time of the MPP, dependency on the PV modeling, operating duty value of the converter, error finding accuracy of MPPT, algorithm complexity, tracking speed, periodic tuning required, and the number of sensing parameters utilized. Based on the simulative comparison results, it has been observed that the modified Grey Wolf Optimization based ANFIS hybrid MPPT method provides good results when equated with the other power point tracking techniques. Here, the conventional converter helps increase the PV source voltage from one level to another level. The proposed system is investigated by using the MATLAB/Simulink tool.

A review on self-healing featured soft robotics.

Soft robots are becoming more popular because they can solve issues stiff robots cannot. Soft component and system design have seen several innovations recently. Next-generation robot-human interactions will depend on soft robotics. Soft material technologies integrate safety at the material level, speeding its integration with biological systems. Soft robotic systems must be as resilient as biological systems in unexpected, uncontrolled situations. Self-healing materials, especially polymeric and elastomeric ones, are widely studied. Since most currently under-development soft robotic systems are composed of polymeric or elastomeric materials, this finding may provide immediate assistance to the community developing soft robots. Self-healing and damage-resilient systems are making their way into actuators, structures, and sensors, even if soft robotics remains in its infancy. In the future, self-repairing soft robotic systems composed of polymers might save both money and the environment. Over the last decade, academics and businesses have grown interested in soft robotics. Despite several literature evaluations of the soft robotics subject, there seems to be a lack of systematic research on its intellectual structure and development despite the rising number of articles. This article gives an in-depth overview of the existing knowledge base on damage resistance and self-healing materials' fundamental structure and classifications. Current uses, problems with future implementation, and solutions to those problems are all included in this overview. Also discussed are potential applications and future directions for self-repairing soft robots.

Corrigendum to "Energy saving potential analysis applying factory scale energy audit - A case study of food production" [Heliyon 9 (3), (March 2023) Article e14216].

[This corrects the article DOI: 10.1016/j.heliyon.2023.e14216.].

Energy saving potential analysis applying factory scale energy audit - A case study of food production.

An energy audit (EA) is a crucial step in boosting factory energy efficiency and obtaining certification for cleaner manufacturing. The results of a preliminary energy audit carried out at a sizable industrial facility in Jordan that creates some of the most well-known foods in the Middle East are presented in this study. The monthly demand of the factory for diesel ranged from 75,251.545 to 166,666.67 L. The factory energy model which is used to examine the impact of various energy-saving practices on the factory's primary energy consumption, was developed with the help of the energy audit. It has been established that optimizing the factory's energy use and the boiler systems' performance with regards to diesel consumption can withstand an expected monthly financial savings of 14205.85 Jordanian Dinar (JD). This has allowed a reduction in energy use of up to 18%. The CO2 harmful emissions were also decreased. Additionally, it is estimated that switching from the proposed motors to energy-efficient motors will cost less overall over time, saving around 3472.314 JD/month or 0.33576/year on average. Moreover, it was discovered that a total of 772.82021 Ton CO2/year emissions may be avoided each year.

Trends and prospects of geothermal energy as an alternative source of power: A comprehensive review.

The world has capitalized on numerous renewable energy resources by developing its energy infrastructure mainly around solar, biomass, and hydro energy. However, geothermal energy has not yet been developed at a significant scale, despite reports from 62 wells showing evidence of geothermal gradients ranging from 20.8 °C/km to 48.7 °C/km in various areas of the world. Recent studies suggest that Bangladesh also has a huge potential for geothermal energy. This review extensively reports on exploiting the range of geothermal temperature in various direct and indirect energy application sectors including but not limited to the agriculture and industrial sector of Bangladesh. Additionally, the authors have analyzed and proposed adaptable measures to harness the abundance of geothermal energy. Furthermore, a comparative and possible solution has been discussed extensively for implementing a geothermal powerplant by analyzing techno-economic costs, policies, and systems of other countries in the world. Further, this review also shows the prospect of geothermal energy for Bangladesh as a case study.

A slotted plus-shaped antenna with a DGS for 5G Sub-6 GHz/WiMAX applications.

A slotted plus-shaped patch antenna (PSPA) with defected ground structure (DGS) is modelled and proposed for 5G Sub-6 GHz and WiMAX applications by using computer simulation technology (CST) MWS suite. The PSPA incorporates a rectangular slotted plus-shaped metal patch and a DGS. The PSPA is designed on a Rogers RT5880 (lossy) substrate with a compact dimension of 20 × 35 × 0.79 mm 3 . Its reflection coefficient is -52.06 dB resonating at 3.12 GHz and operates over a wider bandwidth of 2.56 GHz (2.67-5.23 GHz) to accommodate suitable Sub-6 GHz bands. The PSPA has a good gain (2.44 dB), directivity (2.53 dBi), and VSWR (1.005) at 3.12 GHz with omnidirectional radiation characteristics. The maximum efficiency of the proposed PSPA is about 98% for almost loss free power radiation. The apex of estimated gain and directivity are 4.65 dB and 4.95 dBi. The impact of different physical parameters on the antenna performance has also been studied and analysed in this paper. Initially, the proposed PSPA has been investigated by using time domain (TD) solver of CST then again it is buttressed by applying frequency domain (FD) solver of CST. Furthermore, the design has also been verified by high-frequency structure simulator (HFSS) as well as FEKO (a computational electromagnetics software). All the simulators show a very good agreement in results.

Modeling the SARS-CoV-2 parallel transmission dynamics: Asymptomatic and symptomatic pathways.

Asymptomatic transmission of the coronavirus disease and the infected individual prediction has become very important in the COVID-19 outbreak study. The asymptomatic and symptomatic transmission studies are still ongoing to assess their impacts on disease monitoring and burden. However, there has been limited research on how asymptomatic and symptomatic transmissions together can affect the coronavirus disease outbreak. A mathematical model is therefore needed to be developed in order to assess the effect of these transmissions on the coronavirus disease dynamics. This paper develops a mathematical model concerning asymptomatic and symptomatic disease transmission processes in the COVID-19 outbreak. The model sensitivity has been analysed in terms of the variance of each parameter, and the local stability at two equilibrium points have been discussed in terms of the basic reproduction number (R0). It is found that the disease-free equilibrium gets stable for R0 < 1 whereas the endemic equilibrium becomes stable for R0 > 1 and unstable otherwise. The proportion of the effect of asymptomatic and symptomatic transmission rates on R0 is calculated to be approximately between 1 and 3. The results demonstrate that asymptomatic transmission has a significant impact compared to symptomatic transmission in the disease outbreak. Outcomes of this study will contribute to setting an effective control strategy for the COVID-19 outbreak.

Denial-of-Service Attack on IEC 61850-Based Substation Automation System: A Crucial Cyber Threat towards Smart Substation Pathways.

The generation of the mix-based expansion of modern power grids has urged the utilization of digital infrastructures. The introduction of Substation Automation Systems (SAS), advanced networks and communication technologies have drastically increased the complexity of the power system, which could prone the entire power network to hackers. The exploitation of the cyber security vulnerabilities by an attacker may result in devastating consequences and can leave millions of people in severe power outage. To resolve this issue, this paper presents a network model developed in OPNET that has been subjected to various Denial of Service (DoS) attacks to demonstrate cyber security aspect of an international electrotechnical commission (IEC) 61850 based digital substations. The attack scenarios have exhibited significant increases in the system delay and the prevention of messages, i.e., Generic Object-Oriented Substation Events (GOOSE) and Sampled Measured Values (SMV), from being transmitted within an acceptable time frame. In addition to that, it may cause malfunction of the devices such as unresponsiveness of Intelligent Electronic Devices (IEDs), which could eventually lead to catastrophic scenarios, especially under different fault conditions. The simulation results of this work focus on the DoS attack made on SAS. A detailed set of rigorous case studies have been conducted to demonstrate the effects of these attacks.

RTDS implementation of an improved sliding mode based inverter controller for PV system.

This paper proposes a novel approach for testing dynamics and control aspects of a large scale photovoltaic (PV) system in real time along with resolving design hindrances of controller parameters using Real Time Digital Simulator (RTDS). In general, the harmonic profile of a fast controller has wide distribution due to the large bandwidth of the controller. The major contribution of this paper is that the proposed control strategy gives an improved voltage harmonic profile and distribute it more around the switching frequency along with fast transient response; filter design, thus, becomes easier. The implementation of a control strategy with high bandwidth in small time steps of Real Time Digital Simulator (RTDS) is not straight forward. This paper shows a good methodology for the practitioners to implement such control scheme in RTDS. As a part of the industrial process, the controller parameters are optimized using particle swarm optimization (PSO) technique to improve the low voltage ride through (LVRT) performance under network disturbance. The response surface methodology (RSM) is well adapted to build analytical models for recovery time (Rt), maximum percentage overshoot (MPOS), settling time (Ts), and steady state error (Ess) of the voltage profile immediate after inverter under disturbance. A systematic approach of controller parameter optimization is detailed. The transient performance of the PSO based optimization method applied to the proposed sliding mode controlled PV inverter is compared with the results from genetic algorithm (GA) based optimization technique. The reported real time implementation challenges and controller optimization procedure are applicable to other control applications in the field of renewable and distributed generation systems.