Search

Modular-multilevel converters (MMCs) are considered a useful technology in high-voltage direct current (hvdc) transmission systems. Since it offers many advantages like modular configuration with scalable output voltage. Voltage ripples on sub-module (SM) capacitors in MMC and circulating currents are two major challenges, which can affect the converter stability and performance. Conventionally, direct modulation method is used to insert or bypass SMs in MMC producing circulating current and SMs capacitor voltage oscillations. The closed loop and open loop control methods are used to overcome drawbacks in direct modulation method. However, these methods rely on the accurate measurements and estimation of SM capacitor voltages. A flat bottom current method is proposed in this paper to minimize the amplitude of ripple voltages on SM capacitors in MMC-based HVDC systems. Limited capacitor energy changes leading to reduce capacitor voltage ripples are observed by the proposed methodology using direct-modulation method without using circulating current suppression controller. The proposed method is tested on a two-terminal MMC-based HVDC system implemented by vector control approach in PSCAD/EMTDC software. Analytical results are verified by simulation results in reducing capacitor voltage ripples based on the direct modulation approach.

Integration of Device-to-Device (D2D) communication into the cellular network can greatly enhance the spectrum utilization as well as creating additional communication opportunities. D2D can be combined with cognitive radio to further enhance spectrum utilization and cellular network performance. Unlike the traditional D2D communication approach which employs only cellular spectrum, cognitive D2D can enable sensing and utilizing non-cellular spectrum opportunistically as well, thus allowing to offload cellular base station traffic to non-cellular spectrum such as WiFi, Bluetooth, or TV white-spaces. Cognitive D2D users (cDUs) must vacate channel for primary users and hand-off to another secondary channel which renders it quite challenging to meet Quality of Service (QoS) requirements of multi-class cDUs in the presence of higher primary network load. In this work, an enhanced hybrid spectrum access scheme has been developed based on non-switching spectrum hand-off for multi-class DUs, utilizing both interweave and hybrid interweave underlay spectrum access strategies. Further, lower priority cDUs with non-real-time traffic remains in the system and wait for channels to become available, rather than being dropped from the system due to lack of secondary channels. A Continuous-Time Markov Chain (CTMC) has been developed to analyze the performance of the proposed scheme. For comparison, several cases, ranging from the simple cellular network to complex cellular-cognitive-D2D with hybrid-spectrum-access, have been analyzed. The main focus of the analysis is to compare the efficacy of enhanced hybrid spectrum access scheme with individual interweave and hybrid interweave underlay spectrum access strategies in terms of QoS provisioning for multi-class cDUs. The results depict improvement in throughput, spectrum utilization, and extended data delivery time for the proposed scheme and validate the suitability of the proposed scheme to meet QoS requirements for both delay-sensitive and delay-tolerant users of the multi-class cognitive D2D communication system.

Hydrogen drives the big wheel of nature. Hydrogen nuclear fusion in the sun produces light and heat. Solar flux reaching the earth's surface in an hour is far more than global annual energy demand. Photosynthesis traps 100-TW solar energy annually into biomass on land at 0.1% efficiency that is about six times more than global yearly energy demand. All photosynthetic organisms (photoautotroph) annually convert 100-billion tons of carbon in the atmosphere into biomass. The rampant rise in energy demand requires to replicate natural photosynthesis process artificially to convert solar energy and Carbon dioxide (CO2) in liquid and burnable gaseous fuels. Chemists, physicists and biologists are collaborating to develop suitable catalysts for artificial photosynthesis. There is a consensus the sun can fuel transport sector by hydrogen and power grid by photo-electricity. It is well in time to develop a full spectrum of solar technologies instead of keeping ourselves plugged to hydrocarbon honey. Photocathodes and catalysts can mediate water splitting using nature-inspired artificial photosynthesis. Economic hydrogen production can accomplish the grand energy transition from fossil fuels to sustainable and renewable energy sources. This paper reviews the recent advances in artificial photosynthesis technologies and presents our work on the microbial fuel cell for hydrogen production and points out technical barriers and operational challenges.

Water, energy and food nexus of Indus Water Treaty (IWT) is presented in the light of water governance. The water governance doctrine refers to social, economic, administrative and political systems influencing the transboundary water use and management. Water governance means who gets what water, when and how much, and who has the right to water related benefits. Indus Water Treaty is cited to be one of the few successful settlements of boundary water basin conflicts that has stood the test of times since last six decades. Riparian states have opportunities of harvesting water, power and agriculture by compliance to accords in letter and spirit. IWT restrains both (India) and lower (Pakistan) riparian from pulling out of accord from fear of deadly consequences. IWT is founded on World Bank’s professionally prepared comprehensive terms and conditions keeping in mind future regional developments. This treaty has given control of three eastern rivers (Ravi, Beas and Sutlej) to India and three western rivers (Indus, Jhelum and Chenab) to Pakistan. IWT gives both countries genuine share of eastern/western waters for domestic use, agriculture and electricity generation using Run-of-River Plants, subject to observing minimum level of water flow into lower riparian at Head Marala Barrage. Water, energy and food nexus of this treaty bonds upper and lower riparian to comply with water governance principles. Upper riparian diverted 34 million acre feet (MAF) water out of eastern rivers before entering into Pakistan and launched run-of-river power plants spree on western rivers in last two decades. Restricting water flow to lower riparian in the name of pond filling needs attention. Unrestricted use of water in run-of-river power plants is pointed out to be a limiting factor in Indus Water Treaty. Continuum of cooperation has room for collaboration under Indus Water Treaty. India and Pakistan can sort out disputes by dialogue, in light of rights and needs, rather Harmon Doctrine. Keywords: Water, Power, Energy, Agriculture, Dams, Climate change, Run-of-River Plants

Fog devices are beginning to play a key role in relaying data and services within the Internet-of-Things (IoT) ecosystem. These relays may be static or mobile, with the latter offering a new degree of freedom for performance improvement via careful relay mobility design. Besides that, power conservation has been a prevalent issue in IoT networks with devices being power-constrained, requiring optimal power-control mechanisms. In this paper, we consider a multi-tier fog-based IoT architecture where a mobile/static fog node acts as an amplify and forward relay that transmits received information from a sensor node to a higher hierarchically-placed static fog device, which offers some localized services. The outage probability of the presented scenario was efficiently minimized by jointly optimizing the mobility pattern and the transmit power of the fog relay. A closed-form analytical expression for the outage probability was derived. Furthermore, due to the intractability and non-convexity of the formulated problem, we applied an iterative algorithm based on the steepest descent method to arrive at a desirable objective. Simulations reveal that the outage probability was improved by 62.7% in the optimized-location fixed-power (OLFP) scheme, 79.3% in the optimized-power fixed-location (OPFL) scheme, and 94.2% in the optimized-location optimized-power (OLOP) scheme, as against the fixed-location and fixed-power (FLFP) scheme (i.e., without optimization). Lastly, we present an optimal relay selection strategy that chooses an appropriate relay node from randomly distributed relaying candidates., Comment: Accepted and Published in IEEE Internet of Things Journal

A cognitive radio network can be employed in any wireless communication systems, including military communications, public safety, emergency networks, aeronautical communications, and wireless-based Internet of Things, to enhance spectral efficiency. The performance of a cognitive radio network (CRN) can be enhanced through the use of cooperative relays with buffers; however, this incurs additional delays which can be reduced by using virtual duplex relaying that requires selection of a suitable relay pair. In a virtual duplex mode, we mimic full-duplex links by using simultaneous two half-duplex links, one transmitting and the other one receiving, in such a way that the overall effect of duplex mode is achieved. The relays are generally selected based on signal-to-interference-plus-noise ratio (SINR). However, other factors such as power consumption and buffer capacity can also have a significant impact on relay selection. In this work, a multiobjective relay selection scheme is proposed that simultaneously takes into account throughput, delay performance, battery power, and buffer status (i.e., both occupied and available) at the relay nodes while maintaining the required SINR. The proposed scheme involves the formulation of four objective functions to, respectively, maximize throughput and buffer space availability while minimizing the delay and battery power consumption. The weighted sum approach is then used to combine these objective functions to form the multiobjective optimization problem and an optimal solution is obtained. The assignments of weights to objectives have been done using the rank sum (RS) method, and several quality-of-service (QoS) profiles have been considered by varying the assignment of weights. The results gathered through simulations demonstrate that the proposed scheme efficiently determines the optimal solution for each application scenario and selects the best relay for the respective QoS profile. The results are further verified by using the genetic algorithm (GA) and particle swarm optimization (PSO) techniques. Both techniques gave identical solutions, thus validating our claim.

In this paper, we propose an innovative self-organizing medium access control mechanism for a distributed cognitive radio network (CRN) in which utilization is maximized by minimizing the collisions and missed opportunities. This is achieved by organizing the users of the CRN in a queue through a timer and user ID and providing channel access in an orderly fashion. To efficiently organize the users in a distributed, ad hoc network with less overhead, we reduce the sensing period through parallel sensing wherein the users are divided into different groups and each group is assigned a different portion of the primary spectrum band. This consequently augments the number of discovered spectrum holes which then are maximally utilized through the self-organizing access scheme. The combination of two schemes augments the effective utilization of primary holes to above 95%, even in impasse situations due to heavy primary network loading, thereby achieving higher network throughput than that achieved when each of the two approaches are used in isolation. By efficiently combining parallel sensing with the self-organizing MAC (PSO-MAC), a synergy has been achieved that affords the gains which are more than the sum of the gains achieved through each one of these techniques individually. In an experimental scenario with 50% primary load, the network throughput achieved with combined parallel sensing and self-organizing MAC is 50% higher compared to that of parallel sensing and 37% better than that of self-organizing MAC. These results clearly demonstrate the efficacy of the combined approach in achieving optimum performance in a CRN.

The focus of research efforts in cognitive radio networks (CRNs) has primarily remained confined to maximizing the utilization of the discovered resources. However, it is also important to enhance the user satisfaction in CRNs by finding a suitable match between the secondary users and the idle channels available from the primary network while taking into consideration not only the quality of service (QoS) requirements of the secondary users but the quality of the channels as well. In this work, the Gale Shapley matching theory was applied to find the best match, so that the most suitable channels from the available pool were allocated that satisfy the QoS requirements of the secondary users. Before applying matching theory, two objective functions were defined from the secondary user&rsquo, s perspective as well as from the channel&rsquo, s perspective. The objective function of secondary users is the weighted sum of the data rate of the secondary users and the probability of reappearance of the primary user on the channel. Whereas, the objective function of the channel is the maximum utilization of the channel. The weight factors included in the objective functions allow for diverse service classes of secondary users (SUs) or varying channel quality characteristics. The objective functions were used in developing the preference lists for the secondary users and the idle channels. The preference lists were then used by the Gale Shapely matching algorithm to determine the most suitably matched SU-channel pairs. The performance of the proposed scheme was evaluated using Monte&ndash, Carlo simulations. The results show significant improvement in the overall satisfaction of the secondary users with the proposed scheme in comparison to other contemporary techniques. Further, the impact of changing the weight factors in the objective functions on the secondary user&rsquo, s satisfaction and channel utilization was also analyzed.

■■■ Finding holes from the underutilized portion of spectrum at various times and locations is the most important function in cognitive radio networks (CRNs). This requires efficient sensing policy at the MAC layer that can discover more idle channels in less time. Whereas, the sensing policy depends on the channel sensing order that decides how a secondary user senses the primary user band in minimum period of time. Spectrum sensing policies for searching idle channels from the underutilized primary band can significantly affect the performance of secondary user in terms of throughput, reliability, and energy efficiency. In this paper, we have analyzed MAC protocol structure for ad hoc radio networks which used random channel sensing. This results in poor performance, either due to the channels being skipped or the time for sensing the band being significantly longer. We propose a parallel sensing scheme with sequential channel selection order as part of MAC protocol, which can discover all the free channels in the primary user band in less time. For the proposed scheme, we have performed analysis over the number of channels sensed and the number of idle channels discovered. Furthermore, energy efficiency and throughput of the system have also been evaluated. The results show considerable improvement for the proposed scheme when compared with the contemporary scheme.

A cognitive radio network has a high promise to improve spectrum utilization, through exploitation of spectrum holes from the primary network. However, in a distributed environment, employing contention based random access, collisions and unattempted slots result in waste of the discovered holes. This waste is even costlier for a CRN as resources are consumed in finding the holes. In this paper, we have proposed a self-organizing distributed CR-MAC protocol so that the contention is not required for medium access, thereby, minimizing the waste of opportunity due to collisions or for being left idle in random access, consequently, improving the spectral efficiency. This is achieved by organizing and queuing the active secondary users through a timer value and directing them in an orderly fashion to use the discovered holes. The proposed scheme successfully assigns a channel to more than 99% of the secondary users in less than 5% of the idle slots from the primary network, compared to that required in contention based random access schemes.

Hypnosis-level regulation is the major goal of anesthesia infusion during surgical activities. Anesthesia involves maintaining the suitable hypnosis level and pain-free feeling by injecting several drugs in combination creating a synergistic effect. This research work focuses on the analysis of two nonlinear control techniques, i.e., sliding mode control and backstepping, to regulate depth of hypnosis of the patients during surgery. The primary motive in introducing the nonlinear approaches in anesthesia infusion systems is the limitation of linear control strategies regarding the inadequate handling of large inter-patient variability. Nonlinear approaches, exhibiting excellent properties of accuracy, provide an a variability, uncertain systems, sliding mode control, backstepping, nonlinear control.

The spectrum handoff is highly critical as well as challenging in a cognitive radio ad hoc network (CRAHN) due to lack of coordination among secondary users (SUs), which leads to collisions among the SUs and consequently affects the performance of the network in terms of spectrum utilization and throughput. The target channel selection mechanism as part of handoff process can play an enormously significant role in minimizing the collisions among the SUs and improving the performance of a cognitive radio network (CRN). In this paper, an enhanced target channel selection scheme based on imperfect channel state prediction is proposed for the spectrum handoff among the SUs in a CRAHN. The proposed scheme includes an improved frame structure that increases coordination among the SUs in the ad hoc environment and helps in organizing the SUs according to the shortest job first principle during channel access. Unlike the existing prediction-based spectrum handoff techniques, the proposed scheme takes into account the accuracy of channel state prediction, the SUs affected due to false prediction are compensated by allowing them to contend for channel access within the same transmission cycle and thus enabling them to achieve higher throughput. The proposed scheme has been compared with the contemporary spectrum handoff schemes and the results have demonstrated substantial improvement in throughput and extended data delivery time by virtue of the reduced number of collisions.

This paper presents demand side load management using a smart controller. Normal time and load slabs based revolving load shedding techniques affect equally all small and large consumers. Small homes have simple light and fan loads in summer compared to fully air conditioned posh bungalows. Utility blanket load shedding techniques affect equally low and high load customers equally. In summer, April to September, domestic air conditioner loads are 46 to 47% of domestic loads. Utilities certainly want to reduce load on 11kV feeders during peak hours without effecting small consumers by blanket load shedding. There is no technology in market that can selectively disconnect large loads and permit small loads. Introduction of smart devices into consumer premises can facilitate them shutdown themselves by detecting system health conditions. Voltage and frequency of power system informs the system health condition as temperature and pulse rate tell the health condition of a patient. Priority based load management is a good alternative to incorporate renewable energy but not best for live load management. Utility wants to control consumer loads through local smart circuit breakers or AMI under bottom up approach. Intelligent circuit breakers controlling luxurious, auxiliary and essential loads can be prioritized to implement the smart grid option yet in an unsustainable forced manner. A smart load controller may automatically detect system overloading and use standard HEC-12/ZigBee wireless technologies to switch off luxury loads in multiple story buildings. If the same idea is implemented by all consumers, then demand side load management can become a living reality. Demand side live line load management can also be accomplished by advance metering infrastructures (AMI) if manufactured using smart load management functions inside it that are not available in common AMI meters in market. Presence of live line load controller in all homes adapt the load according to available utility supply capacities that might need utility level arrangement

Modular multilevel converters (MMCs) are quickly emerging as a suitable technology for a voltage-source converter-based high-voltage direct-current (VSC-HVDC) transmission systems due to its numerous advantages as reported in literature. However, for a large DC-network, MMCs require large numbers of sub-modules (SMs) and switches, which makes its modeling very challenging and computationally complex using electromagnetic transient (EMT) programs. Average Value Model (AVM) provides a relatively better solution to model MMCs by combining cells as an arm equivalent circuit. Circulating current is an important issue related to the performance and stability of MMCs. Due to circulating currents, power loss in a converter increases as root mean square (RMS) values of the arm current increases. The traditional method for inserting SMs in each arm is based on direct modulation, which does not compensate for the arm voltage oscillations, and generates circulating current in each leg of a three-phase MMC. This paper presents a new method for reducing the circulating current by adding 2nd and 4thharmonics in the upper and lower arm currents of an MMC. Less capacitor energy variations reobtained by the proposed method compared to traditional direct modulation methods. The proposed method is tested on a common symmetrical monopole (point-to-point) MMC-HVDC system using vector current control strategy in PSCAD/EMTDC software. Analytical and simulation results show the effectiveness of the new method in minimizing the circulating current and arm voltage oscillation reductions as compared to the direct modulation approach.

This research focuses on a decomposed-weighted-sum particle swarm optimization (DWS-PSO) approach that is proposed for optimal operations of price-driven demand response (PDDR) and PDDR-synergized with the renewable and energy storage dispatch (PDDR-RED) based home energy management systems (HEMSs). The algorithm for PDDR-RED-based HEMS is developed by combining a DWS-PSO-based PDDR scheme for load shifting with the dispatch strategy for the photovoltaic (PV), storage battery (SB), and power grid systems. Shiftable home appliances (SHAs) are modeled for mixed scheduling (MS). The MS includes advanced as well as delayed scheduling (AS/DS) of SHAs to maximize the reduction in the net cost of energy ( C E ). A set of weighting vectors is deployed while implementing algorithms and a multi-objective-optimization (MOO) problem is decomposed into single-objective sub-problems that are optimized simultaneously in a single run. Furthermore, an innovative method to carry out the diversified performance analysis (DPA) of the proposed algorithms is also proposed. The method comprises the construction of a diversified set of test problems (TPs), defining of performance metrics, and computation of the metrics. The TPs are constructed for a set of standardized dynamic pricing signal and for scheduling models for MS and DS. The simulation results show the gradient of the tradeoff line for the reduction in C E and related discomfort for DPA.

Analysis of losses in power distribution system and techniques to mitigate these are two active areas of research especially in energy scarce countries like Pakistan to increase the availability of power without installing new generation. Since total energy losses account for both technical losses (TL) as well as non-technical losses (NTLs). Utility companies in developing countries are incurring of major financial losses due to non-technical losses. NTLs lead to a series of additional losses, such as damage to the network (infrastructure and the reduction of network reliability) etc. The purpose of this paper is to perform an introductory investigation of non-technical losses in power distribution systems. Additionally, analysis of NTLs using consumer energy consumption data with the help of Linear Regression Analysis has been carried out. This data focuses on the Low Voltage (LV) distribution network, which includes: residential, commercial, agricultural and industrial consumers by using the monthly kWh interval data acquired over a period (one month) of time using smart meters. In this research different prevention techniques are also discussed to prevent illegal use of electricity in the distribution of electrical power system.

The unfettered rise in electricity demand due to overpopulation, industrialization, and lack of adequate generation augment stress on utilities’ ability to serve their customers. The situation usually worsens when the demand exceeds production. The peak demands are catered by operating expensive spinning reserves or resort to blanket load shedding by utilities during the peak hours. The first option increases the cost of electricity which is undesirable whereas indiscriminate load shedding is also unacceptable. Utility companies practice direct control methods such as utility integrated control over consumer loads during peak hours to minimize the gap between supply and demand. We present a frequency and voltage relaying based demand side management (DSM) scheme to reduce the load on 11 kV distribution feeders during peak hours without resorting to blanket load shedding. In this scheme, an energy management system (EMS) employing Underfrequency and Undervoltage (UFUV) relays is implemented at consumers’ premises instead of 220 kV distribution substations stations. This system monitors the power system’s vital signals, voltage and frequency, to manage load. The EMS controller automatically detects the frequency and voltage changes in the system and responds to respective loading patterns. The dynamic voltage (v), current (i) and frequency (f) signals are acquired from zero crossing detectors and power sources are analyzed and stored. The transducer with output voltage range in millivolt (mv), signal conditioning techniques are applied to match data acquisition range (0–10V). The predefined range are computed with dynamic values to send tripping signal to control device. In order to counter sag, swell or minor deviation a 2–3 cycles time delay is added in calculations. The integration of the proposed DSM system has shown up to 97% power supply reliability. Efficient energy management reduces Green House Gas (GHG) emissions, which is a significant step towards cleaner production.

A demand response (DR) based home energy management systems (HEMS) synergies with renewable energy sources (RESs) and energy storage systems (ESSs). In this work, a three-step simulation based posteriori method is proposed to develop a scheme for eco-efficient operation of HEMS. The proposed method provides the trade-off between the net cost of energy ( C E n e t ) and the time-based discomfort ( T B D ) due to shifting of home appliances (HAs). At step-1, primary trade-offs for C E n e t , T B D and minimal emissions T E M i s s are generated through a heuristic method. This method takes into account photovoltaic availability, the state of charge, the related rates for the storage system, mixed shifting of HAs, inclining block rates, the sharing-based parallel operation of power sources, and selling of the renewable energy to the utility. The search has been driven through multi-objective genetic algorithm and Pareto based optimization. A filtration mechanism (based on the trends exhibited by T E M i s s in consideration of C E n e t and T B D ) is devised to harness the trade-offs with minimal emissions. At step-2, a constraint filter based on the average value of T E M i s s is used to filter out the trade-offs with extremely high values of T E M i s s . At step-3, another constraint filter (made up of an average surface fit for T E M i s s ) is applied to screen out the trade-offs with marginally high values of T E M i s s . The surface fit is developed using polynomial models for regression based on the least sum of squared errors. The selected solutions are classified for critical trade-off analysis to enable the consumer choice for the best options. Furthermore, simulations validate our proposed method in terms of aforementioned objectives.

Over the years, vertical handover necessity estimation has attracted the interest of numerous researchers. Despite the attractive benefits of integrating different wireless platforms, mobile users are confronted with the issue of detrimental handover. This paper uses extensive geometric and probabilistic techniques to develop a realistic and novel model for the coverage area of a wireless local area network (WLAN) cell with an aim to minimize unnecessary handover and handover failure of a mobile node (MN) traversing the WLAN cell from a third-generation network. The dwell time is estimated along with the threshold values to ensure an optimal handover decision by the MN, while the probability of unnecessary handover and probability of handover failure are kept within tolerable bounds. Monte Carlo simulations were carried out to assess the behavior of the proposed and existing models. Simulation results showed that our proposed model is more robust and capable of keeping unnecessary handover probability and handover failure probability closest to a predefined probability benchmark. For model validation, the Nash--Sutcliffe coefficient was used to compute the efficiency of the proposed model, which achieved a value of 98.82{\%} indicating the accuracy of the model.\vs{2mm

Type 1 diabetes mellitus is one of the most common childhood diseases which, if not carefully mitigated, can pose menacing risks like hyperglycemia and ketoacidosis, ultimately leading to heart disease, kidney failure and other life-threatening complications. In order to reduce these risks, two existing control schemes are implemented with the purpose of simulation-based comparative analysis between linear and nonlinear control strategies. In this regards, H1 is selected as a nonlinear strategy whereas lead-lag compensation is the choice from the class of classical linear strategies. The designed control scheme consists of three main components: a controller, an Insulin Feedback Loop (IFL) and a Safety Mechanism (SM). IFL is designed such that it modifies the loop gain for the reduction of postprandial hypoglycemia risks, and the SM predicts the future glucose levels and acts accordingly by modifying the controller output to reduce the risk of hypoglycemia and hyperglycemia. The comparison of the transient and dynamic performances in terms of rise time, settling time and overshoot of both controllers is analysed. It is observed that the H1 gives a far sturdy response as compared to the lead-lag controller.

The traditional power grid is inadequate to overcome modern day challenges. As the modern era demands the traditional power grid to be more reliable, resilient, and cost-effective, the concept of smart grid evolves and various methods have been developed to overcome these demands which make the smart grid superior over the traditional power grid. One of the essential components of the smart grid, home energy management system (HEMS) enhances the energy efficiency of electricity infrastructure in a residential area. In this aspect, we propose an efficient home energy management controller (EHEMC) based on genetic harmony search algorithm (GHSA) to reduce electricity expense, peak to average ratio (PAR), and maximize user comfort. We consider EHEMC for a single home and multiple homes with real-time electricity pricing (RTEP) and critical peak pricing (CPP) tariffs. In particular, for multiple homes, we classify modes of operation for the appliances according to their energy consumption with varying operation time slots. The constrained optimization problem is solved using heuristic algorithms: wind-driven optimization (WDO), harmony search algorithm (HSA), genetic algorithm (GA), and proposed algorithm GHSA. The proposed algorithm GHSA shows higher search efficiency and dynamic capability to attain optimal solutions as compared to existing algorithms. Simulation results also show that the proposed algorithm GHSA outperforms the existing algorithms in terms of reduction in electricity cost, PAR, and maximize user comfort.

This paper presents design and development of solar power monitoring and control through GSM network in rural application. This system includes a GSM mobile and GSM hardware installed at solar system with 12VDC power for solar power monitoring along with temperatures (ambient and battery). This system is designed to conceptualizing how much solar power transferred to batteries and temperature conditions for that instant of time. Hardware is developed for the continuous update to the targeted station using GSM. The developed hardware gets the signal from the installed location calculate the real time power and temperature parameters. This information transferred to targeted mobile station through GSM interface using texting service (SMS). At the receiving end, power monitoring system is used to maintain the power to batteries profile locally. An easy, cost proficient and consistent working model of whole system has been developed which may be incorporated for data acquisition. Also the same system can use for uninterrupted power supply (UPS) systems.

Smart grid plays a significant role in decreasing of electricity consumption cost through Demand Side Management (DSM). Smart homes, a part of smart grid contributes a lot in minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to scheduling of home appliances. This scheduling problem is considered as an optimization problem. Meta-heuristic algorithms have attracted increasing attention in last few years for solving optimization problems. Hence, in this study we propose an efficient scheme in Home Energy Management System (HEMS) using Genetic Algorithm (GA) and Cuckoo search algorithm to solve optimization problem. The proposed scheme is implemented on a single smart home and a smart building; comprising of thirty smart homes. Real Time Pricing (RTP) signals are used in term of electricity cost estimation for both single smart home and a smart building. Experimental results demonstrate the extremely effectiveness of our proposed scheme for single and multiple smart homes in terms of electricity cost and Peak to Average Ratio (PAR) minimization. Moreover, our proposed scheme obtains the desired tradeoff between electricity cost and user waiting time.

Energy crisis has become a formidable challenge for Pakistan; since, it is threatening Pakistan's economic and social goals as well as international environmental obligations. In order to respond to this challenge and its associated problems, the government of Pakistan (GoP) conceived a Renewable Energy Policy in 2006 to increase investments in renewable energies (RE) sector —with particular focus on wind and solar energies (WSE). But, power system of Pakistan (PSP) is weak and, thus, vulnerable to impacts of integration of WSE; that is, its security, stability and reliability is highly likely to be disturbed. In this paper, we analyze possible impacts on PSP after integration of WSE. Analysis reveals problems, like, thermal overloading, voltage violations and generators’ oscillations following integration of WSE. Afterwards, we propose solutions to mitigate and reduce these undesirable impacts.

Goal. The aim of this paper is to conduct a comprehensive comparative analysis between five different controllers for a drug infusion system in total intravenous anesthesia (TIVA) administration. Methods. The proposed method models a dilution chamber with first order exponential decay characteristics to represent the pharmacokinetic decay of a drug. The dilution chamber is integrated with five different control techniques with a simulation-based comparative analysis performed between them. The design process is conducted using MATLAB SISOTOOL. Results. The findings show that each controller has its own merits and demerits. The results generated using MATLAB signify and confirm the effectiveness of PI and cascaded lead controllers, with cascaded lead controller as the best control technique to automate and control the propofol delivery. Conclusion. In this paper, different control techniques for measurement-based feedback-controlled propofol delivery is confirmed with promising results. Significance. The comparative analysis showed that this drug infusion platform, merged with the proper control technique, will perform eminently in the field of total intravenous anesthesia.

Cognitive radio networks have emerged to exploit optimally the scarcely-available radio spectrum resources to enable evolving 5G wireless communication systems. These networks tend to cater to the ever-increasing demands of higher data rates, lower latencies and ubiquitous coverage. By using the buffer-aided cooperative relaying, a cognitive radio network can enhance both the spectral efficiency and the range of the network, although, this could incur additional end-to-end delays. To mitigate this possible limitation of the buffer-aided relaying in the underlay cognitive network, a virtual duplex multi-hop scheme, referred as buffer-aided multi-hop relaying, is proposed, which improves throughput and reduces end-to-end delays while keeping the outage probability to a minimum as well. This scheme simultaneously takes into account the inter-relay interference and the interference to the primary network. The proposed scheme is modeled as a Markov chain, and Monte Carlo simulations under various scenarios are conducted to evaluate several key performance metrics such as throughput, outage probability, and average packet delay. The results show that the proposed scheme outperforms many non-buffer-aided relaying schemes in terms of outage performance. When compared with other buffer-aided relaying schemes such as max-max, max-link, and buffer-aided relay selection with reduced packet delay, the proposed scheme demonstrated better interference mitigation without compromising the delay performance as well.

This paper proposes an economic hybrid energy system for electrification of an off-grid remote site, Rakhi Gaj in Pakistan. Electrical load demand of water pumping station and primary household's appliances is addressed using these systems. Levelized Cost of Energy (LCOE) using a hybrid micro grid (PV-diesel-wind) is computed to evaluate the importance of renewable resources at the site. The metrological data of the site is acquired from nearest weather station while irradiance is computed analytically using Angstrom as well as De Wit's method. This analytical data is compared with the solar satellite data of NASA (National Aero Space Agency) to improve validity of solar resource inputs. It is evaluated in the research that electricity cost using diesel generator ($ 0.316/kWh) is significantly minimized ($ 0.230/kWh) by integrating renewable technologies. Sensitivity analysis is also performed by varying diesel prices and wind speeds to explore different options for domestic users. National Renewable Energy Laboratory's (NREL) software, Hybrid Optimization Model for Electric Renewable (HOMER) is used for modelling and simulation.