Summary

High-capacity hybrid thermoacoustic electric generators (HTAEGs) are ideal in different small- and micro-scale energy systems, especially in space nuclear power systems. In this work, an HTAEG with a post-positioned gas spring is proposed. To demonstrate the superiority of the gas-spring-post-positioned design on high-capacity HTAEGs, an HTAEG prototype is modeled, built, and tested from the perspective of thermoacoustics accordingly. Experimental results demonstrate an output electric power of 15.0 kW. Furthermore, it could achieve the highest efficiency of 39.2% with an output electric power of 11.1 kW. Given that a 15-kW power output is an ultra-high level on a single-piston HTAEG of this type to date, and the achieved efficiency on the prototype is also encouraging, this work marks an important milestone in the development of high-capacity HTAEGs. It also demonstrates that the gas-spring-post-positioned design has significant advantages and enormous potential for application.

Introduction

The thermoacoustic electric generator (TAEG) emerges as a promising dynamic conversion technology due to its exceptional reliability, extremely long lifespan, versatility in heat source utilization, and high intrinsic efficiency.1 These attributes render it particularly well suited for space and submarine thermoelectric applications.2,3 Within the TAEG family, a hybrid one (traditionally called the free-piston Stirling engine,4 currently recognized as a generalized thermoacoustic engine5) that incorporates a solid mass to tune the internal acoustic field transcends the conventional gas-tuning systems in the aspects of power density and thermal-to-electrical efficiency.6

Most recently, dramatic progress, e.g., 16-year non-stop operation and over 39% thermal-to-electrical efficiency,7 has stoked up the enthusiasm for the hybrid thermoacoustic electric generator (HTAEG) in space application. However, the current achievements are all confined in a rather small power level (i.e., around 100 W), not sufficient to cover various application scenarios. Scaling up to tens of kilowatt power while maintaining high reliability simultaneously is formidably challenging, and so far, there is no public literature reported on it. The main obstacle is the spring force mechanism that is imperative to tune the acoustic field. For low- and medium-power HTAEGs (ranging from tens of watts to kilowatts), planar springs are commonly employed as the spring force mechanism for their excellent compactness and radial rigidity.8 These planar springs can be positioned either at the bounce space or compression space, resulting in two distinct designs for HTAEG. Meanwhile, both designs have been extensively utilized in numerous HTAEG prototypes, with their reliability well demonstrated through ground tests.7

Results and discussion

System configuration and numerical study
Figure 1 presents the schematics of an MGS-HTAEG and a PGS-HTAEG. The PGS-HTAEG is primarily a helium-filled pressure vessel that houses a hybrid thermoacoustic engine (HTAE) and a linear alternator (LA). The HTAE comprises an ambient heat exchanger (AHX), a regenerator, a hot heat exchanger (HHX), and a displacer. Meanwhile, the LA consists of an electromagnetic conversion circuit consisting of outer and inner stators, moving magnets, and coils, along with a power piston supported by gas bearings. Within the HTAEG, a combination of the AHX, the regenerator, and the HHX constitutes its thermoacoustic core, designed to transform external thermal energy into acoustic energy. By heating the HHX while rejecting heat from the AHX, helium within the regenerator undergoes oscillatory flow due to the thermoacoustic effect. Consequently, thermal energy is converted into acoustic energy. The displacer plays a pivotal role in modulating the acoustic field and facilitating the transfer of acoustic power. When the acoustic pressure fluctuation generated by the thermoacoustic core pushes the power piston to move reciprocating in the cylinder, the moving magnets mounted on the power piston move in a stationary coil accordingly, and the acoustic power is converted to electric power through electromagnetic induction.

Abstract

In recent years, with the increasing demand for intelligent society, intelligent photonics has developed rapidly. Machine learning (ML), as a subset of artificial intelligence (AI), has played an important role in the intelligent evolution of optical fiber sensors. Its impact extends beyond enhancing sensor performance by introducing innovative problem-solving approaches. Specifically, ML algorithms have become instrumental in signal demodulation and elevating the efficacy of discrete and distributed sensors, and have also greatly promoted the development of optical fiber speckle pattern processing. This paper presents the latest advancements in ML-based optical fiber sensors, outlines the problems faced by conventional demodulation methods and the common ML algorithms applied in optical fiber sensors, and emphasizes key applications. Additionally, this paper delves into the challenges and future development of this emerging research direction.

Introduction

Due to advancements in powerful computing tools, hardware, widespread use of cloud data, and the evolution of the Internet of Things, the accessibility of large datasets has significantly improved. This has promoted the emergence of many efficient machine learning (ML) algorithms. These algorithms, aided by continuous advancements in data analysis technology, have demonstrated effectiveness in training models and solving specific problems. If ML algorithms are complex and deep enough in terms of layers, then ML can become so-called deep learning (DL) [1]. Regardless of the name, ML seems to have a wide range of applications in modern society, because the achievements of artificial intelligence (AI) often exceed those achieved by manual control design.

In 2021, Nature Photonics further advanced its exploration of the hot topic of optics/photonics through a focused issue called “Machine learning of Light”. As an important type of microphysics, optics seems to have similarities in complexity with some of the “black box” processes in ML. In the Q&A session, David Pille and Aydogan Ozcan discussed the two primary drivers behind the current application of ML in photonics [2]. One direction is to use AI methods on hardware to design optical structures and devices with specific performance. Another direction is to use optical systems as fully optical/hybrid statistical inference models to achieve AI calculations.

Optics and photonics applied to ML computing offer unique advantages, allowing for ultrafast calculations at extreme frame rates and low energy consumption [3], [4], [5], [6]. For instance, Feldmann et al. [7] proposed a fully optical neural synaptic system. They use wavelength division multiplexing technology to implement a scalable circuit architecture for photonic neural networks (NNs) and use optical pulse signals to regulate the phase change materials that encapsulate neurons. This phase change material can regulate transmittance and reflectivity through a controllable laser. This system can perform both supervised and unsupervised learning. The work successfully demonstrates direct pattern recognition in the full optical system. This photonic NN has the potential to leverage the inherent high-speed and high bandwidth of optical systems, thereby enabling the direct processing of full optical communication and visual data. However, logic gates, computation, and NNs are all linear (passive) systems that cannot address nonlinear (active) issues. The lack of effective nonlinear optical processes as activation functions for many nodes poses challenges for all-optical implementation [8].

Conclusion and future perspectives

This paper reviews the application of the ML algorithm in optical fiber sensors. In recent years, the advent of ML has greatly impacted optical development. Compared with static demodulation algorithms and complex spatial optical paths, the ML algorithm offers better signal processing mechanisms for optics. ML algorithms enable automated processing, analysis, and recognition of spectrums, thereby enhancing the efficiency and accuracy of spectrum analysis. Using ML algorithms to classify and identify the spectrum of different substances, enabling qualitative and quantitative analysis of substances. By learning from known corresponding spectrums and building models, ML algorithms can predict and analyze unknown spectrums. ML algorithms can extract and recognize features from the spectrum, analyze the content and proportion of various components, and reveal the intrinsic nature of substances. With the continuous development of spectroscopy and ML algorithms, the application prospects of ML in spectroscopy will become increasingly widespread. This advancement will contribute to more efficient and accurate analytical methods in chemical analysis, environmental monitoring, food safety, and other fields, promoting progress in scientific research and expanding application fields.

Abstract

A novel time–current-rate-based inverse characteristic curve for relays in a DC microgrid is proposed in this paper. Line current rise rate is used as actuating quantity, ensuring quick line fault clearing (relay operating time is in order of a few . The advantage of using line current rise rate as actuating quantity is that for a line short-circuit fault, it does not vary significantly for grid-connected and islanded modes of operation and varying network topologies of DC microgrid. Consequently, using the proposed characteristic, a single set of optimal relay settings is obtained using an optimization solver in MATLAB. The obtained settings ensure reliable and selective coordination between primary and backup relays for various pole-to-ground and pole-to-pole line faults under different operating conditions with multiple sources in two different 4-bus 400V low voltage DC microgrids. The maximum relay operating time for a high resistance fault with the proposed curve is for the first considered low voltage DC microgrid. Simulations in the Real Time Digital Simulator and comparisons with previous schemes (one comparison on the second considered DC microgrid), conventional standard inverse, and extremely inverse curves for DC microgrid protection indicate the effectiveness of the proposed scheme.

Introduction

The ease of integration of solar Photo-Voltaic (PV) and Battery Energy Storage Systems (BESSs) based renewable energy resources in a DC microgrid, compared to an AC microgrid, has led to the increased expansion and research interest of DC microgrids over AC microgrids [1], [2]. Also, DC microgrid can be a great way to power remote towns, shipboards, spaceships, and grids with sensitive loads, where the quality of power is crucial [3], [4]. However, the rate of rise of current is very high in a DC microgrid during a line fault due to the rapid discharge of the DC link capacitor and the associated low line impedance [5]. Hence, in general, the DC microgrid protection schemes follow a three-time frame based protection methodology in which the protection of the line, feeder, and the source should be done within a few milliseconds, and seconds, respectively [6]. Several DC Circuit Breakers (CBs) have been developed to support the quick isolation of lines during a fault in a DC microgrid, among which the solid-state CBs have a response time in the range of a few

Conclusion

This paper proposes a novel inverse time relay characteristic for the protection of DC microgrids against line faults using line current rise rate as an actuating quantity. It can coordinate relays in a DC microgrid with non-unit fault detection schemes or act as a backup scheme for relays with communication-based primary method. The relay coordination using the proposed characteristic is tested on a V LVDC microgrid. A single set of optimal relay settings ( and ) and a common set of values of and in (8) are obtained, considering all possible modes of operation, network configurations, and worst-case PG, PP LRFs and PG HRFs. Consequently, the proposed approach does not require an online update of and with a change in operating condition of DC microgrid. The efficacy of the proposed curve is validated with the obtained optimal unique relay settings for various PG, PP LRFs, PG HRFs for GC, islanded modes of operation, ring, radial network configurations, and intermittency of generation from renewable energy sources. Zero relay miscoordinations with a maximum primary relay operating time with the proposed curve are and for LRF and HRF faults, respectively, proving the effectiveness of the proposed technique in LVDC microgrid.

Further, the issues of using SI and EI curves to coordinate relays in a LVDC microgrid using line current and line current rise rate as actuating quantity and issues with using line current as actuating quantity in proposed scheme are discussed. It is observed that the conventional EI curve with line current rise rate may be used for relay coordination in DC microgrid. However, the high relay operating time (maximum primary relay operating time being ) may lead to prolonged line fault isolation. Additionally, the proposed scheme is fast and reliable, compared to EI curves, since the sum of primary relay operating times, considering all fault scenarios, is with the proposed curve, compared to the EI curve, which gives . The comparison with [16], [17] reveals the efficacy of the proposed scheme with the relay operating times in the proposed scheme being in the range of a few for line faults in LVDC microgrid.

Summary

Electric double-layer capacitors (EDLCs) have the potential to replace aluminum electrolytic capacitors for alternating current line-filtering, aligning with the trend of device miniaturization. However, it is challenging for EDLCs to simultaneously achieve rapid ion migration, electrical response, and high areal and volumetric capacitances. Here, we demonstrate that three-dimensional, structurally integrated multi-layer carbon tube (3D-MLCT) frameworks are used as electrodes for high-performance filtering EDLCs. By simply increasing the number of tube layers, a high specific areal capacitance of 3.08 mF cm−2 at 120 Hz is achieved with a phase angle of −80.1°, exhibiting excellent capacitance and fast frequency response. The outstanding performance of the 3D-MLCT is attributed to the high density, high orientation, and high integrality of carbon tube arrays that facilitate and accelerate ion distribution onto the electrode surface. The findings of this work provide a solution for developing next-generation miniaturized filter capacitors with high capacitance and fast frequency response.

Introduction

Converting alternating current (AC) into direct current (DC) is essential for powering electronics.1,2,3 In the process, filter capacitors play a crucial role in smoothing the ripples in the rectified DC signal.4,5,6,7 Aluminum electrolytic capacitors (AECs) dominate this field but occupy the most significant volume in circuits.8,9,10 Electric double-layer capacitors (EDLCs), storing energy physically through reversible ion adsorption at the electrode-electrolyte interface, have been anticipated for fast frequency response applications.11,12,13,14,15 EDLCs are considered promising alternative devices for AC line-filtering to meet the demands of miniaturized electronic devices owing to their several-orders-of-magnitude-higher specific capacitance than commercial AECs.16,17,18,19,20,21,22,23 However, conventional EDLCs behave more like resistors than capacitors and possess poor frequency response performance at an indicator frequency of 120 Hz because of their tortuous and complicated pore structure as well as the high resistances of the electrodes.24,25,26,27

The first filtering EDLC was reported by Miller et al.,1 with discrete vertically oriented graphene (VOG) nanosheets grown on nickel substrates as electrodes, demonstrating an outstanding AC line-filtering performance. Subsequently, various carbon-nanomaterial-based electrodes with high orientation, thin thickness, and macro-porous structures have been explored for filtering EDLCs.28,29,30,31,32,33,34,35 However, the specific areal (CA) and volumetric capacitances (CV) are still at a low level—the bottleneck of miniaturized filter devices.36,37,38,39

Results and discussion

The fabricating process of 3D-MLCT is schematically illustrated in Figure 1. A 3D-AAO template with interconnected vertical and lateral channels was synthesized by anodizing impure aluminum foil in phosphoric acid electrolyte at 0°C under a voltage of 195 V (Figure S1).40,41 The template was placed into a horizontal tube furnace and heated to 1,000°C at 10°C/min under an Ar atmosphere. The first CT layer was deposited on the pore walls of the 3D-AAO template with a flow of acetylene gas under vacuum conditions for 50 min. Subsequently, an AlOx layer was deposited via an ALD process at 250°C on the inner wall of the first CT layer using trimethyl aluminum (TMA) and ozone (O3) as the precursors.42 The second CT layer was deposited on the AlOx surface via a second-time CVD process. Similarly, repeating the ALD and subsequent CVD processes can prepare a third CT layer. Finally, the 3D-MLCT was obtained after removing the AlOx layer(s) and 3D-AAO template in hydrofluoric acid solution, and the spacing of adjacent CT layers can be precisely controlled by the thickness of the AlOx layers. Furthermore, the size of the 3D-CT films can be adjusted by varying the area of the 3D-AAO as needed, and we prepared a 3D-CT film with an area of approximately 24 cm2 (see Figure S2).

Abstract

Application of micro-grids (MGs) as a solution for future energy systems are significantly increasing in recent years. On the other hand, more stringent requirements are added to grid codes (GCs) of low-voltage distribution networks. Accordingly, this paper proposes a low-voltage ride-through (LVRT) control scheme for four-wire multi-source MGs. The novel strategy is composed of two layers for controlling each phase of the grid-connected sources independently. The primary layer contains a reverse-droop function for each phase and fractional-order sliding-mode-control (FOSMC). The secondary layer determines the total requested reactive power for each phase during symmetrical and asymmetrical voltage drops. Then, the mentioned power is shared among the phases of each inverter based on the reactive power-sharing strategy and GC requirements. Based on the proposed grid-following controller, the voltage of faulty phases is compensated by reactive power injection. In addition, power quality indexes are kept in acceptable ranges during abnormalities, and the FOSMC performs better than the conventional methods. The effectiveness of the proposed scheme is verified through offline simulations in MATLAB/Simulink as well as validation by real-time results.

Introduction

1.1. Background
The utilization of small-scale distributed energy resources (DERs) in low and medium-voltage networks is increasing, and as a result, their sudden and collective disconnection from the power system might cause voltage collapse on the grid during severe voltage perturbations. Therefore, the severe impact of the DERs cannot be ignored since their contribution to the power supply is considerable. Thus, the incremental usage of DER systems has become leverage for setting new requirements for power systems. These regulations are called grid codes (GCs), which vary from country to country and cover some grid-connected power sources such as wind turbines and photovoltaic (PV) systems [1]. The mentioned regulations force the sources to stay connected to the main grid during temporary voltage sags and support it by injecting reactive current. An essential requirement of the GC, which has gained much attraction in recent studies, is the low-voltage ride-through (LVRT) capability of the distributed generators (DGs). LVRT curves of the German and Danish GCs are highlighted in Fig. 1a [2]. Besides, in Fig. 1b amount of the reactive power injection per voltage drop is depicted for different scenarios [3]. Most studies cover symmetrical voltage disturbances that only include a minor percentage of the faults such as [4,5], etc. However, asymmetrical faults such as single-phase short-circuits are more probable with an 85 % occurrence rate [6]. In most cases, the conventional three-phase control methods for compensating voltage drops are not applicable for asymmetrical faults since they might affect the operation of safe phases by deteriorating voltage imbalance. Also, reactive power-sharing and reference power tracking in multi-source MGs are important operation factors, which has not been addressed sufficiently in previous studies.

Conclusion

MGs integration into power systems has boosted the system’s reliability and caused operational challenges for the network operators. Hence, some countries have considered a set of requirements to improve the functionality of their power systems. For this purpose, in this paper, an LVRT scheme is proposed for four-wire MGs, which can control each phase of the DGs independently through a grid-following method. The local controller of the DGs, known as the primary layer, utilizes reverse-droop control for voltage and frequency regulation and a nonlinear FOSMC. On the other hand, the secondary layer fulfills the modern GC requirements by calculating the required reactive power for the faulty phases. Next, this amount is shared between the corresponding phases of the DGs based on their free capacity. The effectiveness of the presented controller is proved by analyzing the results obtained from implementing 30 % single-phase, two-phase, and three-phase voltage sags. Accordingly, each DG phase injects reactive power during voltage sags according to the determined amount. Also, comparing the injected reactive power with the amounts of German GC demonstrates that the reactive power injection is done with acceptable accuracy. The results point out that during the external sags, the voltage of PCC, inverter terminals, and load terminals are restored to their nominal values, 30 % compensation, as if there is no perturbation. Consequently, the presented controller can realize the GC requirements and ride through during severe voltage conditions. Moreover, this control method keeps the power quality indices like THD and voltage imbalance factors in acceptable ranges during the sags. Also, the superiority of designed FOSMC over conventional current controllers such as PI and SMC are demonstrated. This outperformance is highlighted with several aspects such as MPAE index, THD values, voltage compensation, and reactive power tracking during the imposed voltage sags. It is shown that the best value for the alpha is 0.76, by which the response of the controller is fast and accurate. Finally, the simulations are validated through real-time results. Our future work is focused on the LVRT capability of the MGs including both grid-forming and grid-following inverters. In addition, the effect of grid strength and R/X ratio of the power lines can be discussed in the future investigations, which has not been addressed sufficiently in this literature.

Abstract

In recent years, floating photovoltaic (FPV) systems have emerged as a promising technology for generating renewable energy using the surface of water bodies such as reservoirs, lakes, and oceans. FPV systems offer several advantages over traditional land-based solar arrays, including increased land-use efficiency, reduced water evaporation, and improved cooling and maintenance. However, like all solar power systems, FPVs are subject to variability and intermittency due to changes in weather, seasons, and time of day. The environmental impact is discussed along with the deployment consideration and the feasibility for a better understanding of the system. Challenges associated with this are addressed by progressed research suggesting the integration of FPVs with various energy storage and hybrid systems. The most promising areas researched in this paper look at hybrid FPV hydropower plants (HPP), in parts of the world experiencing droughts HPP is not working to its optimum capacity. A review of available literature has been conducted on the topic of offshore and onshore floating solar electricity generation using floating solar photovoltaics to identify the challenges and opportunities presented. This work looks at a variety of other hybrid FPV energy sources with varying technology readiness levels. This paper concludes with the possibility of integrating different renewable technologies with existing FPVs and highlights the boons of doing so with some examples. Ultimately, current as well as future perspectives have been provided which consolidate the current research being done and give recommendations for future research work.

Introduction

One of the biggest challenges that is faced by the world is global warming due to which both humans and the planet are suffering as a whole, this is directly linked to the burning of the fossil fuels that we rely on to facilitate our daily life. The Intergovernmental Panel on Climate Change (IPCC) set out the impacts of global warming 1.5 °C above preindustrial levels [1]. We are not currently on course to limit 21st-century global warming to under 1.5 °C or even 2 °C [2]. Another key issue is the lack of clean electrification for a vast population which is hampering the development of the planet in tackling the climate change issue as many rely on the burning of fossil fuel for various daily requirements from generating electricity to cooking. It has been estimated that about 675 million people are still forced to live in the dark most of them belong to sub-Saharan Africa according to 2021 data. Though there has been an increase in the rate of access to electricity from 87% in 2015 to 91% in 2019, this has provided electricity to nearly 800 million people [3]. This is where solar PV can play a substantial role, solar PV has the benefit of being a renewable energy source, producing electricity from solar irradiance without any greenhouse emission [4].

However, there are challenges that must be addressed in order to fully realize the potential of solar energy and traditional photovoltaics [5]. These challenges include land usage, intermittency, storage, and integration into existing energy grids. One promising and upcoming alternative to traditional land-based photovoltaics is Floating Photovoltaics (FPV) or flotavoltaics [6]. The majority of renewable energy sources, such as biomass, solar, and others, take considerable footprint areas to generate electricity on a larger scale, which restricts the use of land for agriculture [[7], [8], [9]]. This sparked the discussion over whether land should be used for food production or energy production [10,11], encouraging research into offshore renewable technologies [12], and led to the creation of the floating photovoltaic (PV) array concept for the production of commercial electricity [13]. FPV technology is a concept in which solar panels are placed on platforms that float on water bodies such as natural lakes, man-made reservoirs, and the seas and oceans [14]. Fig. 1 shows a typical standalone floating photovoltaic system with all the components including an inverter, pontoons, solar panels, and cables connected to the grid.

Conclusion

This review article has examined the current state of research on the integration of floating photovoltaics with different storage and hybrid systems, including batteries, pumped hydro storage, compressed air energy storage, hydrogen storage and mixed energy storage options as well as the hybrid systems of FPV wind, FPV aquaculture, and FPV hydrogen production. The findings suggest that such systems have the potential to significantly increase the efficiency and reliability of renewable energy generation, as well as provide additional flexibility in managing electricity supply and demand.

FPV has many benefits over ground-mounted such as reduced land costs, increased efficiency, reduced soiling, reduced shading, reduced evaporation, and lower visual impact. Hybrid FPV has all of these benefits while being able to reduce its lifetime costs when paired with another technology a reduction in O&M costs, grid infrastructure and cable pooling. Overall, hybrid FPV has all the benefits of FPV plus reduced costs, while enhancing another technology. While FPV is set to rise this will initially be in land first with offshore following, the priority should be covering HPP first due to its complementary nature and benefits for both technologies. Countries, which have HPP reservoirs, should be looking into all the benefits of combining FPV with it. As FPV becomes more common it will be seen moving offshore, as the offshore wind increases it creates the opportunity for FPV to become a hybrid system.

Abstract

The research on emotional intelligence in social robots is growing. This paper provides a thematic analysis of the studies on robots and emotional intelligence, synthesising and evaluating current knowledge and research topics. In addition, based on the thematic analysis of the studies, it also provides a conceptual framework explaining the emotional intelligence of robots that includes both actors (human and robot) in a human-robot interaction setting. The findings are based on the analysis of 252 studies published until the end of 2022 and indexed in the Scopus database. The results unveiled two main themes (robot design-technical developments and characteristics and human-robot interaction), including sub-themes and topics that emerged in the literature. Finally, the themes and sub-themes were evaluated through a critical discussion to develop a conceptual framework for robots and emotional intelligence.

Introduction

1.1. Rationale
A robot is a “programmed actuated mechanism with a degree of autonomy to perform locomotion, manipulation or positioning” [1]. While robots are widely used in manufacturing [2,3] for producing goods, they find their way into agriculture [4] and the delivery of services [[5], [6], [7]] as well. Social robots help children with autism [8], assist the elderly [9], and redefine the meaning we put in sexual relationships [10].

One of the main directions of robotics research is on human-robot interaction (HRI) [11]. The topic has been researched in the context of industrial robots [12], service robots [13], and social robots [14]. A key component of HRI in the context of social robots are the emotions of humans and the emotional intelligence of both parties in the relationship [11,15] because emotions trigger specific affective, cognitive, and behavioural responses [16]. For example, when humans experience positive emotions in their interactions with robots, they are more likely to use robots and less likely to object to them [17]. Additionally, humans’ perceptions of the emotional skills of robots are positively related to the perceived appropriateness of robot use in service contexts, e.g. museums in galleries, as found by Webster and Ivanov [18].

To elicit respective emotional reactions in humans, robots need to have some degree of emotional intelligence. Emotional intelligence (EI) is defined as “the capacity to reason about emotions, and of emotions to enhance thinking. It includes the abilities to accurately perceive emotions, to access and generate emotions so as to assist thought, to understand emotions and emotional knowledge, and to reflectively regulate emotions so as to promote emotional and intellectual growth” ([19]:197). EI has four aspects measured through the Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT) ([19]:200, [20], [21]:281–282).

Conclusion

This paper provided a thematic analysis of the studies on robots and emotional intelligence. The results uncovered two main: (i) robot design-technical developments and characteristics, and (ii) human-robot interaction. The robot design-technical developments and characteristics include sensory aspects (facial expression and voice-related), cognitive aspects and behavioural aspects, visual aspects, and other aspects. The “human-robot interaction” category comprises two main sub-themes, namely settings (health, education, and other settings) and attitudes (perceptions toward robots).

Abstract

The fast transition to the electrification of the energy system, combined with an exponential growth of the market share of electric vehicles, is leading to a tight interrelation between electric energy production and transportation, two prominent sectors in fossil fuels consumption and greenhouse gas emissions. Accelerating this process, the management of electric fluxes, aiming at optimizing production and demand coupling, plays a crucial role in reaching the net-zero emission target. The proposed software platform is designed to optimally manage the energy fluxes for a solar powered parking lot, serving a fleet of electric vehicles; the real-time knowledge of energy production and demand, in conjunction with forecasted power generation, allows the maximization of renewable energy self-consumption, thus reducing the exchange with the external grid. The software platform can work either in design mode, allowing the dimensioning of the various parking lot components, or in real-time mode managing instantaneously the energy balance. As a case study, it is tested on the 2019 parking lot mobility data of a research center, assuming a complete transformation of the then existing fleet of employees’ cars to electric vehicles. A comparison of the resulting energy flows with those projected by an established commercial tool is performed, as well as a preliminary economic evaluation. Both consistency of the simulation results and favorable economics validate the presented smart charging algorithm and Internet of Things platform for the real-time energy management of a solar parking lot.

Introduction

The steep growth of anthropogenic greenhouse gas (GHG) emissions related to the use of fossil resources requires fast and challenging solutions; transportation and electricity production sectors are responsible for around two thirds of global CO2 emissions. According to the numbers published by the U.S. Energy Information Administration (EIA) in its monthly energy review of August 2022 [1], the total consumption of primary energy in the U.S.A. in the year 2021 was 28.5 Million terawatt-hours (TWh) or 2.4546 Billion tonnes of oil equivalent (toe), 7.9 Million TWh (0.6782 Billion toe) of which are used by the transportation sector and 10.8 Million TWh (0.9267 Billion toe) by the electric power sector. 94% of the total energy used for transportation came from petroleum and natural gas and 6% from biofuels and electricity [2]. In 2021, according to EIA estimates [3], cars, light trucks, and motorcycles accounted for the largest shares of total U.S. transportation sector energy consumption, with light-duty vehicles (cars, small trucks, vans, sport utility vehicles, and motorcycles) accounting for 54.2%, commercial and freight trucks 4.5%, jets, planes, and other aircraft 8.7%, boat, ships, and other watercraft 4.6%, trains and buses 2.6%, the military sector 2.0%, pipelines 2.8%, lubricants 0.5%. Globally, carbon dioxide emissions associated with transportation accounted in 2021 for 7620 Mt; 600 Mt less than pre pandemic level by 2019 [4].

Since the onset of the industrial revolution, ever increasing amounts of GHG have been released into the atmosphere. While the energy mix and corresponding supply chains shifted from coal to oil and more recently towards natural gas, the prosperity of humankind still relies on fossil fuels and energy conversion systems based on fuels combustion. Even today, transportation and electricity production are heavily reliant on CO2 emitting technologies. But there is a silver lining, as technological improvements coupled with a strong cost reduction enabled the fast growth of the total installed energy capacity of renewable energy sources for electricity production (RES-E). Together with powerful battery energy storage systems (BESSs), they will impose drastic changes in these two historically conservative sectors. Today, the fast growth of electric vehicles (EVs) related technologies, as well as their forecasted market penetration, reaching the record of 6.6 million vehicles sold in 2021 [5], generates new scenarios impacting the electric energy production and transportation market. Based on existing climate-focused policy pledges and announcements, the International Energy Agency predicts for 2030 EVs to represent more than 30% of vehicles sold globally across all modes (excluding two- and three-wheelers) and the global electricity demand for EVs to reach 1100 TWh; then about 4% of the total final electricity demand [5].

Conclusions

This paper presented the SPEM IoT platform, capable of simulating, designing and real time managing the smart charging of EV fleets in a RES-E driven parking lot.

A deterministic rule-based algorithm was introduced for the fine-grained management of the energy fluxes between EVs and the different electric components of a solar parking lot. Since the algorithm does not utilize any assumption on the future mobility and energy demand it can handle unforeseen changes avoiding critical situations.

As it was shown, intra-day deferral serves to shift EV charging demand towards the time of maximum production, guided by MPC, constantly analyzing the momentary situation of 1) intermittent solar production, 2) stored energy and energy storage availability, 3) charging station occupancy and 4) solar production forecast.

An extensive case study was conducted, where the design mode of SPEM was applied to the historic situation of an Italian Research Center in 2019, assuming the substitution of all employee’s cars by EVs. As a benchmark for the validation of SPEM, HOMER® Grid, a commercial software for the modeling, design and simulation of electrical infrastructure, was used.

Abstract

We study a radio resource allocation problem in mobile communication systems. As the distinct characteristic of this problem, a common data transmission rate is used on all channels allocated to a user. Because the channels differ in their quality, for each user the achievable rate varies by channel. Thus allocating more channels does not necessarily increase the total rate, as the common rate is constrained to be the lowest one supported by the allocated channels. Radio resource allocation subject to the common-rate constraint is of practical relevance, though little attention has been paid to modeling and solving the problem. We take a mathematical optimization perspective with focus on modeling. We first provide a complexity analysis. Next, several integer linear programming (ILP) formulations for the problem, including compact as well as non-compact models, are derived. The bulk of our analysis consists in a rigorous comparative study of their linear programming (LP) relaxations, to reveal the relationship between the formulations in terms of bounding. Computational experiments are presented to illustrate the numerical performance in bounding and LP-assisted problem solving. Our theoretical analysis and numerical results together serve the aim of setting a ground for the next step of developing model-based and tailored optimization methods.

Introduction

Mobile communication systems have been evolving rapidly in the past decades. From an optimization standpoint, a key research topic for mobile networks is radio resource management for utilizing the radio resource efficiently, particularly because the amount of data traffic is constantly growing in the current 4th generation (4G) systems, and the trend will remain or even accelerate in the 5th generation (5G) networks. Along with this growth, 5G networks target a plethora of new services (e.g., vehicular communications), making resource management more challenging than before, as pointed out in Calabrese et al. (2018).

4G/5G networks deploy orthogonal frequency division multiple access (OFDMA). In essence, data transmission takes place in two dimensions, namely time and frequency that are divided into time slots and channels, respectively.1 Resource management for channel allocation in a time slot, as well as that across multiple time slots, is commonly called scheduling. In any time slot, a user may be scheduled to receive transmission from its base station (BS) on multiple channels, and a channel may be scheduled for at most one user of this BS. The latter is what the term “orthogonal” refers to in OFDMA. Among the BSs, the channels are reused, i.e., there is generally no dedicated (sub)set of channels for a BS. As a result, a channel is exposed to interference, if the channel is also scheduled in some nearby BSs in the same time slot. Note that even if interference is not present, the channels are still frequency-selective (Tse and Viswanath, 2005), meaning that they vary in quality and hence the achievable data rate from a user’s perspective.

Conclusions and future work

We have studied mathematical modeling of a resource optimization problem with common-rate channel allocation in mobile communication systems. We have demonstrated that the problem admits an array of integer programming formulations. The study shows that modeling does matter, analytically as well as numerically, in terms of performance in bounding and LP-based problem solving. Moreover, there is a clear correlation between the accuracy of LP and the quality of integer solution derived thereby.

As we have observed, straightforward schemes of constructing integer solutions are not practical in solution time, whereas for our resource allocation problem, the ultimate target is real-time optimization. To approach this goal with model-based optimization, we make several observations. First, to deliver a solution rapidly, the amount of computation has to be very small, and thus the LP relaxation may have to be solved approximately, followed by some primal heuristic (both admitting massive parallel computation). Second, non-compact models are also of interest, as restricting the number of columns or rows leads to approximation of the problem (and the LP relaxation). Finally, with presence of rate lower bound, obtaining solution feasibility is of clear significance, and one may need to trade solution quality against feasibility. Developing and implementing algorithmic notions along these lines form interesting topics for forthcoming research.

Abstract

Solar energy can play an important role in meeting global energy needs in a sustainable and environmentally friendly manner. However, despite solar energy’s accelerated growth in recent years, its level of diffusion is highly uneven when looked at on a global scale. The solar photovoltaic (PV) companies involved in the sales of PV systems are central to fostering diffusion. A company’s ability to devise and deliver value offerings that match customers’ needs is vital in encouraging the adoption of solar PV technology. The extent to which a company can address market needs and deliver value often depends on the business model it has adopted. The extant research has explored business models based on ownership structures, financing options, the effect of regulatory regimes and policies, industry practices, alliances, and business models for distributed generation and large-scale utility companies. However, the research to date, has mostly neglected the business models of solar companies involved in the sales and installation of solar PV. This qualitative study based on twenty semi-structured interviews contributes to the existing knowledge by exploring how sales and installation companies can enhance solar photovoltaic adoption by transforming customer interactions and engagement practices, which is a key element of a company’s business model. Companies’ ability to communicate value offerings and address consumer concerns is important in enhancing diffusion. The study highlights that transforming customer interaction and engagement practices can help companies broaden customer reach, improve the dissemination of information, reduce transaction costs and efficiently utilise market insights and trends.

Introduction

Renewable energy technologies (RETs) can play an important role in meeting global energy needs in a sustainable and environmentally friendly manner. Countries across the globe are looking to adopt measures and ways to support their development and increase their share of the energy mix [3]. Solar energy, in particular, has the potential to be used in large-scale commercial facilities, as well as serving needs at the household level [4]. Its improved efficiency, decreasing price and supportive policy regime have made solar one of the leading forms of renewable energy in the world [5], [6]. However, it is widely believed that this growth is far lower than its potential in most regions, and efforts should be made by all stakeholders to foster its development [7], [8]. The widespread adoption and use of RETs is a complex and multifarious process influenced by a number of personal, socioeconomic, technical, market-related and regulatory factors [9], [10], [11], [12]. The process is particularly challenging as the technology is disruptive in nature, and its successful diffusion requires changes in the existing structure and approaches as well as a change in mindset [13], [14].

Solar Photovoltaic (PV) companies, directly involved in interaction with consumers, dissemination and sales, become an important actor in this regard [15], [16], [17]. Companies’ ability to devise and deliver value offerings that match customer needs can play a vital role in encouraging adoption. Teece [18] suggests that the extent to which a company can address market needs and deliver value depends on the business model it has adopted. Business models direct a company’s course of action, help channel resources, address challenges and provide opportunities for growth [19], [20], [21]. RET companies often face challenges in developing market-centric business models that can enable them to thrive independently [22], [23]. A number of studies have explored business models for companies operating in the solar industry. However, an overwhelming majority of the existing research has focused on business models based on ownership structures [24], [25], [26], [27], financing options [1], [2], the effect of regulatory regimes and policies [28], [29], industry practices [30], alliances [31], business models for distributed generation [32], [33], [34] and large-scale utility companies [22]. To date, the research has overlooked customer interaction and engagement practices in the business models of conventional solar PV companies involved in the sales and installation of solar systems [35], [36], [37], [38].

Results

The result highlights that transforming customer interaction and engagement practices can help companies broaden customer reach, improve the dissemination of information, reduce transaction costs and efficiently utilise market knowledge and insights (Fig. 3).

4.1. Broadening customer reach
4.1.1. Effective means to reach customer
Companies need to adopt effective means to reach customers. Conventionally, in the early phase of market development, many companies have relied on direct approaches to reach customers through door-to-door sales, telephone marketing or similar measures. The approach worked for some but has largely been a difficult endeavour for companies seeking growth and high sales volumes due to the effort and cost involved in materialising a single sale. As one group sales manager suggested: “It was quite common for companies to directly reach out to customers. This seemed to have worked as companies were able to generate sales. … However, companies soon realised that the process is too labour-intensive and is costing too much, mainly in terms of labour hours.” On the other hand, the expense of conventional mass media marketing makes it unaffordable for companies operating in the PV sector. The manager further suggested, “…. for the vast majority of companies, the use of media such as electronic and print media has been out of reach. Companies selling solar systems are hardly of a size to afford such a thing [medium].”

Abstract

Solar energy forecasting has seen tremendous growth by using weather and photovoltaic (PV) parameters. This study presents new approach that predicts solar energy production by using the scheduled, unscheduled maintenance activities and weather data. The dataset is obtained from the 1MW solar power plant of PDEU (our university), which has 12 structured columns and 1 unstructured column with manual text entries about different scheduled and unscheduled maintenance activities, and weather conditions on the daily basis. The unstructured column is used to create new features by using Hash-Map, flag words and stop words. The solar power generation forecasting is formulated as a vector auto regression (VAR) optimization problem and total power generation forecasting is presented with the results of four different cases. The results have shown that the root mean square percentage error (RMSPE) in total power generation forecasting is less than 10% for different lag (p) values. The vector auto regression can forecast the unscheduled maintenance activities like Grid failure, Inverter Failure, scheduled maintenance activity like module cleaning, weather activity like cloudy along with total power generation forecasting for effective and efficient management of solar power plants. The power generation decay is different for all the PV sets which show the variations in the impacts of weather, aging and maintenance on the solar power plant. This research work has proven that the peaks of total power generation forecasting and prediction can be tracked in a better way by using daily unscheduled, scheduled maintenance activities and weather conditions.

Introduction

Solar energy forecasting is an emerging area over the last decade by using historical time series data collected through a weather station (such as weather variables wind speed and direction, solar irradiance, and temperature). The scheduled and unscheduled maintenance activities are carried out frequently for efficient and effective management of solar power plant. Another important parameter that contributes slowly but effectively is the age of the Solar PV module. The degradation of PV modules is further impacted by the conditions such as High temperature, solar cell shedding, discoloration, and delamination, etc. India has been endowed with non-depleting renewable energy resources like solar, hydro, geothermal, and wind and it is currently ranked no. 3rd globally in the renewable energy market. According to MNRE sources, about 5,000 trillion kWh of energy is falling on India’s land mass annually, with the majority receiving between 4 and 7 kWh per square meter per day. This is going to be one of the fastest-developing industries to boost rural electrification as well as the economy. Currently, there are more than 35 solar power plants in India. These plants are managed and operated by various government organizations. The operational expenditure of MW to GW solar photovoltaic power plants imposes a huge cost. Artificial Intelligence based solutions such as forecasting and recommendation-based systems can provide fruitful insights to reduce such expenditure.

Conclusions and Future Work

The maintenance activities like “grid failure”, “inverter failure”, “module cleaning” and the weather parameter “cloudy” have huge impact on the solar power generation forecasting and prediction. The optimum value of lag p=12 results in lowest RMSPE, RMSE and MAE for different cases. The error is reduced due to inclusion of duration for maintenance activities in this model for case 2. The VAR model used in this work is capable of forecasting all the four features as well as power generation target variable simultaneously. The predicted power generation is able to follow the trends of original power generation when the thresholding is applied to the predicted values of maintenance features (activities). The results have shown that the root mean square percentage error (RMSPE) in total power generation forecasting is less than 10% for different lag (p) values for all cases. The vector auto regression can forecast the unscheduled maintenance activities like Grid failure, Inverter Failure, scheduled maintenance activity like module cleaning, weather activity like cloudy along with total power generation forecasting which result in effective and efficient management of solar power plant. The power generation decay is different for all the PV sets which show the variation in the impacts of weather, aging and maintenance on the solar power plant. The effects of aging, dust and other environmental factors can be considered in the model as a future scope to improve the solar power generation forecasting and prediction.

Abstract

In recent times, there has been a drastic increase in the number of road accidents and their severity. These accidents mostly happen due to human error. Some common human errors like distracted driving, drowsy driving, driving at high speeds and delayed reaction times of the drivers result in fatal accidents that cause loss of life and damage to property. These factors can be eliminated by means of self-driving cars. A driverless car (or self-driving car or autonomous car) is a vehicle that can travel from one point to another by self-navigating without any human intervention by using sensors, cameras, radar, GPS, and AI (Artificial Intelligence). We will be implementing a Driverless Car using Deep Learning, OpenCV, DonkeyCar, TensorFlow and C++/Python.

Introduction

There has been a large spike in the number of automobile accidents these days. According to the U.S. Department of Transportation (USDOT), it has been estimated that about 20,160 people have died in road accidents in the first six months of 2021 in the U.S. alone. In India, data from the National Crime Records Bureau (NCRB) shows that in 2020, an average of 37 people died per 100 car crashes which results in a total of about 1.3 lakh deaths. Also, we need to note that these numbers include the period of lockdown due to the Covid-19 pandemic, during which there was minimal traffic movement.The major causes of these catastrophic accidents include distracted driving, drowsy and drunk driving. These factors lead to increased human error, which in turn increases the risk of having an accident. We can overcome the dangerous consequences of human error using autonomous vehicles or driverless cars. Most leading automobile manufacturers across the globe have started research and development of semi-autonomous and fully autonomous cars.

Conclusion & Future Scope

Driver errors while driving are the most prominent cause of traffic accidents. Driverless cars have the following benefits related to road transport: reduce stress due to driving, reduce driver cost, provide mobility of non-drivers, increased safety, and decrease in traffic congestion. Even a person with disabilities can make use of autonomous vehicles the same as any other individual and can even customize the vehicles for more convenience. A self driving car not only increases safety, but it also improves efficiency and productivity of the people who spend most of their time stuck in traffic and trying to reach their work places. Donkey is a self-driving car platform which uses a high-level Python library to attain autonomous driving. We have trained the Donkeycar to drive on its own based on driving style and path. This uses a supervised learning technique often referred to as behavioral cloning. Next, we carry out steering and throttle calibration. The reason for calibrating the car is to make it drive consistently. Later, the car is driven using a web controller/ physical joystick controller. As the driving car becomes reliable, we used Keras to train a neural network to drive like a human.

Abstract

The use of smart inverter capabilities of distributed energy resources (DERs) enhances the grid reliability but in the meanwhile exhibits more vulnerabilities to cyber-attacks. This paper proposes a deep reinforcement learning (DRL)-based defense approach. The defense problem is reformulated as a Markov decision making process to control DERs and minimizing load shedding to address the voltage violations caused by cyber-attacks. The original soft actor-critic (SAC) method for continuous actions has been extended to handle discrete and continuous actions for controlling DERs’ setpoints and loadshedding scenarios. Numerical comparison results with other control approaches, such as Volt-VAR and Volt-Watt on the modified IEEE 33-node, show that the proposed method can achieve better voltage regulation and have less power losses in the presence of cyber-attacks.

Introduction

S Mart control of distributed energy resources (DER) in distribution systems is bringing a fundamental shift in how these networks are maintained within the security limits. Historically, control methods were designed based on conventional approaches, where cyber threats have not been paid attention [1]. The idea of introducing internet protocols in the electrical network to use more advanced protection and control components has created the need to defend the cyberattacks. Furthermore, a study on [2], [3] has showed that only 62% of cyber-attacks can be recognized after they cause massive damage to the system, which makes it a critical issue for system designers. Nowadays, the digital transformation of the electrical distribution systems has forced lots of restrictions and regulations that must be applied for achieving a secure and resilient system [4]. In the context of cyberphysical security [5], [6], smart attackers can initiate false data injection attacks (FDI) [7], where a slight change in any of the controllable devices (i.e., smart inverters, smart ring main units and digital relays), can result in disturbing the networking security without being detected by existing defense approaches. In this paper, we propose a learning-based approach for the mitigation of cyber-attacks on connected loads and DERs. Deep reinforcement learning (DRL) was opted for its superior capability of learning the power system constraints and achieving optimal control strategy

Numerical Results

TS A modified IEEE 33-node system with three-phase loads and 4 DERs that are utility-owned (i.e., each DER consists of 1 ES unit and 1 PV with installed capacities of 500 kW each unit), is used for testing. The system is modeled using OpenDSS and is configured to be in the grid connected mode. At the first solution evaluated using OpenDSS, no voltage violation has been observed during the normal operation. In addition, 4 tie switches have been added to the network an sectionalizers are considered for operation. The learning environment is designed according to OpenAI Gym [14], which is a common interfacing library to define DRL environment for the agent. The SAC algorithm is implemented using PyTorch. Specifically, in the SAC, both actor and critic networks are designed as feed-forward neural networks with three hidden layers of 50, 100 and 50 neurons and a ReLU activation function for each layer. Other SAC hyper-parameters are as follows: Adam optimizer is used with a learning rate of 0.0001 and discount factor γ is set to 0.9. The target network is updated by tau = 0.001 and random process is applied for better exploration with α = 0.1, β = 0.1 ρ = 10 and µ = 0.1; the replay buffer size is 100000 with batch size 256. The offline DRL training spends around 3 hours and 30 minutes on a laptop computer with 3.6GHz Intel i7 processor and 32.0 GB RAM. The proposed DRL defense algorithm is compared with other control algorithms, such as the Volt-VAR and Volt-Watt using the default control curves in OpenDSS [15]). Also, the MPC algorithm is implemented following [16] based on the problem formulation in Section II using the same control and state variables for the proposed DRL algorithm. Attacks are initiated for few timesteps by changing the load and DER setpoints (% power change of loads and/or DERs) in OpenDSS using python interface.