Resurrection of carbon dioxide as refrigerant in solar thermal absorption cooling systems

The existing air conditioning and cold storage systems use conventional compressor based systems, compelling more electricity and greenhouse gasses (GHG) emissions. The incumbent cooling system uses synthetic refrigerants (CFCs, HCFC, and HFCs) that outperform natural refrigerants but are banned or under time bared permission due to their harmful effects. The global community of (196 parties till 2017) has ratified Paris Accord to limit GHG emissions and use low Global Warming Potential (GWP) refrigerants, and after the ban on existing synthetic refrigerants, quested for suitable natural working fluids and retrofitting in the existing system. Among ASHRAE envisaged natural refrigerants, CO2 has resurrected as an emerging refrigerant after the availability of high pressure technologies. The proposed design of solar assisted absorption chiller employing CO2 as a heat transfer fluid for a commercial dwelling is simulated for a dwelling in the hot and humid, moderate and sun adverse region (Lahore, 31.5204° N, 74.3587° E) to assess the thermal properties of the proposed design. A thermal storage tank with immersed heat exchangers augmented to meet the intermittency of solar energy. A solar evacuated glass tube collector (EGTC) with U-shaped copper tubes is used to collect solar heat energy. Integration of renewable energy (RE) systems is inevitable due to the persistent energy crisis and climate change situations. Solar energy is a promising source of energy abundantly available in hot areas. CO2 is a natural refrigerant that outperforms ASHRAE envisaged natural refrigerants due to the low critical point. A solar thermal cooling system employing a 35.2 kW absorption chiller driven via heat energy harnessed with EGTC using R-744 supported by an auxiliary furnace is simulated in a TRNSYS® Simulation environment. The simulated system covers the cooling requirements of a large three-room dwelling in Lahore, Pakistan. The proposed design comprises an R-744-based solar heating system combined with a hot water-fired absorption chiller. The results were dynamically simulated for the hot climate of Lahore, Pakistan, with average yearly maintained temperatures of 23 °C, 26 °C, and 21 °C for the three rooms and 0.21 solar fraction for the whole year

Circulating Current Reduction in MMC-HVDC System Using Average Model

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 4th harmonics in the upper and lower arm currents of an MMC. Less capacitor energy variations are obtained 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

An Inventive Method for Eco-Efficient Operation of Home Energy Management Systems

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

An Efficient Demand Side Management System with a New Optimized Home Energy Management Controller in Smart Grid

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

QoS-Oriented Optimal Relay Selection in Cognitive Radio Networks

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

A Weighted-Sum PSO Algorithm for HEMS: A New Approach for the Design and Diversified Performance Analysis

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

Thigh-length compression stockings and DVT after stroke

Controversy exists as to whether neoadjuvant chemotherapy improves survival in patients with invasive bladder cancer, despite randomised controlled trials of more than 3000 patients. We undertook a systematic review and meta-analysis to assess the effect of such treatment on survival in patients with this disease