Maximum Achievable Energy Efficiency Research Articles

Pathway toward Scalable Energy-Efficient Li-Mediated Ammonia Synthesis.

Lithium-mediated ammonia synthesis (LiMAS) is an emerging electrochemical method for NH3 production, featuring a meticulous three-step process involving Li+ electrodeposition, Li nitridation, and Li3N protolysis. The essence lies in the electrodeposition of Li+, a critical phase demanding current oscillations to fortify the solid-electrolyte interface (SEI) and ensure voltage stability. This distinctive operational cadence orchestrates Li nitridation and Li3N protolysis, profoundly influencing the NH3 selectivity. Increasing N2 pressure enhances the NH3 faradaic efficiency (FE) up to 20 bar, beyond which proton availability controls selectivity between Li nitridation and Li3N protolysis. The proton donor, typically alcohols, is a key factor, with 1-butanol observed to yield the highest NH3 FE. Counterion in the Li salt is also observed to be significant, with larger anions (e.g., exemplified by BF4-) improving SEI stability, directly impacting LiMAS efficacy. Notably, we report a peak NH3 FE of ∼70% and an NH3 current density of ∼-100 mA/cm2 via a delicate balance of process conditions, encompassing N2 pressure, proton donor, Li salt, and their respective concentrations. In contrast to the recent literature, we find that the theoretical maximum energy efficiency of LiMAS hinges significantly on the proton source, with LiMAS utilizing H2O calculated to have a maximum achievable energy efficiency of 27.8%. Despite inherent challenges, a technoeconomic analysis suggests high-pressure LiMAS to be more feasible than both ambient LiMAS and a modified green Haber-Bosch process. Our analysis finds that, at a 100 mA/cm2 NH3 current density and a 6 V cell voltage, LiMAS delivers green NH3 at an all-inclusive cost of $456 per ton, significantly lower than conventional cost barriers. Our economic analysis underscores high-pressure LiMAS as a potentially transformative technology that may revolutionize large-scale NH3 production, paving the way for a sustainable future.

Energy, exergy and economic analysis of a novel solar driven CCHP system powered by organic Rankine cycle and photovoltaic thermal collector

• A novel solar-driven CCHP system is proposed. • The maximum achievable energy efficiency is 70.78%. • The maximum achievable exergy efficiency is 10.70%. • The highest energy and exergy efficiency cannot be achieved together. • The payback period of the proposed system is 6.4 years. Combing solar energy systems and polygeneration units is an optimal way of producing useful outputs by utilizing a renewable energy source. Most of the polygeneration systems studied in the literature utilize only one solar collector whereas the studies on multiple solar source polygeneration systems are limited. On the other hand, multiple source polygeneration systems are of great interest in zero energy buildings. Hence, this study proposes a novel polygeneration system powered by two solar sources. Photovoltaic thermal and parabolic trough collectors are used in a series configuration with water as working fluid for this purpose. The proposed system is equipped with an ejector - compression refrigeration cycle (VCRC) with two different temperature levels (above zero and sub-zero) and an organic Rankine cycle (ORC) to provide cooling, heating, and power. The water out coming from the photovoltaic thermal (PVT) unit is used to cool the VCRC condenser, and hence it is preheated before entering the parabolic trough collector (PTC) resulting in reduced sizing and the cost of the PTC unit. In this study, the impact of parameters such as water flow rate in PVT-PTC circuit, the effect of working fluid in the ORC cycle, and solar irradiation are investigated using the first and second laws of thermodynamic and economic analysis. The results show that in the same conditions, R123, R600, R245fa, R600a refrigerants have higher energy efficiency, respectively, so that the highest energy efficiency of 70.78% and exergy efficiency of 10.70% were calculated for R123 refrigerant. Furthermore, it is shown that the highest energy and exergy efficiency cannot be achieved together. Also, the results showed a payback period of 6.4 years for the proposed system.

Tradeoff Between Ergodic Energy Efficiency and Spectral Efficiency in D2D Communications Under Rician Fading Channel

This paper aims at investigating the tradeoff between ergodic energy efficiency (EE) and spectral efficiency (SE) for device-to-device (D2D) communications underlaying cellular networks. Assuming average-based resource allocation, we propose a multiobjective optimization problem (MOOP) approach to deal with the EE and SE maximization problem. Specifically, the formulated MOOP results in the maximization of the total ergodic sum rate of the D2D users and the minimization of the total average transmit power of the D2D transmitters. Two practical scenarios are considered, i.e., a limited interference scenario, which includes the sparsely deployed scenario, and a densely deployed scenario, where for each scenario the maximum achievable EE and SE are derived. Specifically, considering the limited interference scenario, an upper bound is imposed on the received interference across different D2D communications, and closed-form expressions for the optimal power allocation, ergodic sum rate, and maximum EE are obtained. In the general, densely deployed scenario, however, the cross D2D network interference needs to be taken into account. We provide an optimal solution for the power control based on sequential fractional programming to tradeoff between complexity and performance gain. Simulation results unveil an interesting tradeoff to strike a balance between EE and SE.

Single-Solution-Based Vortex Search Strategy for Optimal Design of Offshore and Onshore Natural Gas Liquefaction Processes

Propane-Precooled Mixed Refrigerant (C3MR) and Single Mixed Refrigerant (SMR) processes are considered as optimal choices for onshore and offshore natural gas liquefaction, respectively. However, from thermodynamics point of view, these processes are still far away from their maximum achievable energy efficiency due to nonoptimal execution of the design variables. Therefore, Liquefied Natural Gas (LNG) production is considered as one of the energy-intensive cryogenic industries. In this context, this study examines a single-solution-based Vortex Search (VS) approach to find the optimal design variables corresponding to minimal energy consumption for LNG processes, i.e., C3MR and SMR. The LNG processes are simulated using Aspen Hysys and then linked with VS algorithm, which is coded in MATLAB. The results indicated that the SMR process is a potential process for offshore sites that can liquefy natural gas with 16.1% less energy consumption compared with the published base case. Whereas, for onshore LNG production, the energy consumption for the C3MR process is reduced up to 27.8% when compared with the previously published base case. The optimal designs of the SMR and C3MR processes are also found via distinctive well-established optimization approaches (i.e., genetic algorithm and particle swarm optimization) and their performance is compared with that of the VS methodology. The authors believe this work will greatly help the process engineers overcome the challenges relating to the energy efficiency of LNG industry, as well as other mixed refrigerant-based cryogenic processes.

Energy Efficiency Gain of Cellular Base Stations with Large-Scale Antenna Systems for Green Information and Communication Technology

Due to the ever-increasing data demand of end users, the number of information and communication technology (ICT)-related devices and equipment continues to increase. This induces large amounts of heat emissions, which can cause serious environmental pollution. In recent times, signal transmission systems such as cellular base stations (BSs) have been constructed everywhere and these emit a large carbon footprint. Large-scale antenna systems (LSASs) that use a large amount of transmission antennas to serve a limited number of users can increase energy efficiency (EE) of BSs based on the beamforming effect, and thus can be a promising candidate to reduce the carbon footprint of the ICT field. In this paper, we discuss the necessary schemes to realize LSASs and show the expected EE gain of the LSAS with enough practicality. There are many obstacles to realize the high EE LSAS, and even though several studies have shown separate schemes to increase the EE and/or throughput (TP) of LSASs, few have shown combinations of schemes, and presented how much EE gain can be achieved by the schemes in the overall system. Based on the analysis in this paper, we believe more detailed work for the realization of high energy efficient BSs with LSASs is possible because this paper shows the necessary schemes and the maximum achievable energy efficiency gain as a reference. Extensive analysis and simulation results show that with proper implementation of the power amplifier/RF module and a robust channel estimation scheme, LSASs with 600 transmitter (TX) antennas can achieve 99.4 times more EE gain compared to the current systems, thereby resulting in significant reduction of carbon footprints.

Energy Efficiency of Multiuser Multiantenna Random Cellular Networks With Minimum Distance Constraints

Compared with conventional regular hexagonal cellular models, random cellular network models resemble real cellular networks much more closely. However, most studies of random cellular networks are based on the Poisson point process and do not take into account the fact that adjacent base stations (BSs) should be separated with a minimum distance to avoid strong interference among each other. In this paper, based on the hard core point process (HCPP), we propose a multi-user multi-antenna random cellular network model with the aforementioned minimum distance constraint for adjacent BSs. Taking into account the effects of small scale fading and shadowing, interference and capacity models are derived for the multi-user multi-antenna HCPP random cellular networks. Furthermore, a spectrum efficiency model as well as an energy efficiency model is presented, based on which, the maximum achievable energy efficiency of the considered multi-user multiantenna HCPP random cellular networks is investigated. Simulation results demonstrate that the energy efficiency of conventional Poison point process (PPP) cellular networks is underestimated when the minimum distance between adjacent BSs is ignored.

Optimization of Base-Station Density for High Energy-Efficient Cellular Networks With Sleeping Strategies

The base station (BS) density configuration is a crucial factor in improving energy efficiency (EE) in cellular networks, particularly when the sleeping strategy is used to reduce energy consumption. In this paper, the optimization of BS density for enhancing EE through traffic-aware sleeping strategies in both one- and two-tier cellular networks is researched, where the BS location is modeled as a Poisson point process. In the one-tier scenario, the EE optimization objective function is proved to be quasi-concave, and the optimal BS density solution is derived, where the maximum achievable EE is demonstrated to be independent of user density. In the two-tier scenario, both the activation probability of BSs and the coverage probability are analyzed, where the EE optimization objective function is proved to be not necessarily quasi-concave. To handle this nonconvex issue, an equivalent optimization problem that jointly optimizes the ratio and weighted sum of BS densities is proposed and solved by a dynamic gradient iterative algorithm. Meanwhile, a data fitting method is utilized to quantitatively analyze EE performances, in which the relationship between the optimal BS density and the user density is approximately linear when the user density is sufficiently high. Simulation results verify the relevant derivations and reflect that the sleeping strategy can not only save energy but improve the quality of radio links as well.

Energy-Efficient Resource Allocation for Wireless Powered Communication Networks

This paper considers a wireless powered communication network (WPCN), where multiple users harvest energy from a dedicated power station and then communicate with an information receiving station. Our goal is to investigate the maximum achievable energy efficiency (EE) of the network via joint time allocation and power control while taking into account the initial battery energy of each user. We first study the EE maximization problem in the WPCN without any system throughput requirement. We show that the EE maximization problem for the WPCN can be cast into EE maximization problems for two simplified networks via exploiting its special structure. For each problem, we derive the optimal solution and provide the corresponding physical interpretation, despite the nonconvexity of the problems. Subsequently, we study the EE maximization problem under a minimum system throughput constraint. Exploiting fractional programming theory, we transform the resulting nonconvex problem into a standard convex optimization problem. This allows us to characterize the optimal solution structure of joint time allocation and power control and to derive an efficient iterative algorithm for obtaining the optimal solution. Simulation results verify our theoretical findings and demonstrate the effectiveness of the proposed joint time and power optimization.

Iterative Energy-Efficient Stable Matching Approach for Context-Aware Resource Allocation in D2D Communications

Energy efficiency (EE) is critical to fully achieve the huge potentials of device-to-device (D2D) communications with limited battery capacity. In this paper, we consider the two-stage EE optimization problem, which consists of a joint spectrum and power allocation problem in the first stage, and a context-aware D2D peer selection problem in the second stage. We provide a general tractable framework for solving the combinatorial problem, which is NP-hard due to the binary and continuous optimization variables. In each stage, user equipments (UEs) from two finite and disjoint sets are matched in a two-sided stable way based on the mutual preferences. First, the preferences of UEs are defined as the maximum achievable EE. An iterative power allocation algorithm is proposed to optimize EE under a specific match, which is developed by exploiting nonlinear fractional programming and Lagrange dual decomposition. Second, we propose an iterative matching algorithm, which first produces a stable match based on the fixed preferences, and then dynamically updates the preferences according to the latest matching results in each iteration. Finally, the properties of the proposed algorithm, including stability, optimality, complexity, and scalability, are analyzed in detail. Numerical results validate the efficiency and superiority of the proposed algorithm under various simulation scenarios.

Multi-Objective Energy-Efficient Resource Allocation for Multi-RAT Heterogeneous Networks

Heterogeneous network (HetNet) integrated with multiple radio access technologies (RATs) is a promising technique for satisfying the exponentially increasing traffic demand of future cellular systems. In this paper, we investigate energy-efficient resource allocation in a multi-RAT HetNet, aimed at maximizing the energy efficiency (EE) for each individual user while guaranteeing the quality-of-service (QoS) requirement. Since the EE cannot be simultaneously maximized for every user, a multiple-objective optimization problem (MOOP) is formulated. To find its Pareto optimal solution, we first introduce the concept of Utopia EE, defined as the maximum achievable EE, for each user. Then, using the weighted Tchebycheff method, a single-objective optimization problem (SOOP) is formulated, which can achieve Pareto optimal solution of the original MOOP. The SOOP is a generalized fractional programming problem that aims to minimize the maximum of several quasiconvex fractional functions. We further transform the problem into an equivalent but better tractable one, and develop an iterative algorithm to effectively solve it. Numerical results demonstrate that the proposed algorithm yields fast convergence, high system EE, and flexible EE tradeoff.

Energy-Efficient Power Allocation Over Nakagami- <inline-formula> <tex-math notation="TeX">$m$</tex-math></inline-formula> Fading Channels Under Delay-Outage Constraints

This paper presents an energy-efficient power allocation strategy for Nakagami-m flat-fading channels with a delay-outage probability constraint. The operating input transmit power value is limited to P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> . The energy efficiency (EE), expressed in units of b/J/Hz, is represented as the ratio of the effective capacity to the sum of transmission power (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> ) and circuit power (P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> ). Since the EE-maximization objective function is quasi-concave, a unique global maximum exists. By using fractional programming, we develop an EE-optimal power allocation strategy that consists of two steps: 1) obtaining the power level P̅ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">un</sub> , at which the maximum EE can be achieved, and 2) distributing the power optimally based on the minimum of P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max</sub> and P̅ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">un</sub> . We prove that while P̅ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">un</sub> monotonically increases with P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> , the maximum achievable EE is a monotonically decreasing function of P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> . The analysis further allows us to derive the EE of three important cases: non-fading channels, extremely stringent delay-limited systems, and systems with no delay constraints. Simulation results confirm analytical derivations and further show the effects of the circuit power, fading duration, and fading severeness on the achievable EE and effective capacity of a delay-limited fading channel.

Energy-Efficient Power Adaptation over a Frequency-Selective Fading Channel with Delay and Power Constraints

This paper presents an energy-efficient power allocation for a multicarrier link over a frequency-selective fading channel with a delay-outage probability constraint. The power adaptation maximizes the system energy efficiency (EE), formulated as the ratio of the achieved effective capacity (EC) to the total expenditure power, including both transmission power and rate-independent circuit power. We prove that this objective function is quasi-concave in the transmission power, and derive the global optimum solution using fractional programming. Based on the obtained solution, we develop a power adaptation algorithm consisting of two steps: (i) establishing the optimum average power level corresponding to the maximum achievable EE with no transmit power constraint, and then (ii) for a given power constraint, jointly distributing the power over time and frequency based on the constraint and the optimum power level found in the first step. Analytical results show that the proposed EE-based power allocation has a structure similar to the allocation that maximizes the EC, but with a different cut-off threshold. Our proposed joint EE-optimal power allocation provides significant EE gains over both the joint spectral-efficient and independent-subcarrier EE-based power allocation schemes, where the rate-energy tradeoff becomes more pronounced with higher frequency selectivity.

Achieving Maximum Energy-Efficiency in Multi-Relay OFDMA Cellular Networks: A Fractional Programming Approach

In this paper, the joint power and subcarrier allocation problem is solved in the context of maximizing the energy-efficiency (EE) of a multi-user, multi-relay orthogonal frequency division multiple access (OFDMA) cellular network, where the objective function is formulated as the ratio of the spectral-efficiency (SE) over the total power dissipation. It is proven that the fractional programming problem considered is quasi-concave so that Dinkelbach's method may be employed for finding the optimal solution at a low complexity. This method solves the above-mentioned master problem by solving a series of parameterized concave secondary problems. These secondary problems are solved using a dual decomposition approach, where each secondary problem is further decomposed into a number of similar subproblems. The impact of various system parameters on the attainable EE and SE of the system employing both EE maximization (EEM) and SE maximization (SEM) algorithms is characterized. In particular, it is observed that increasing the number of relays for a range of cell sizes, although marginally increases the attainable SE, reduces the EE significantly. It is noted that the highest SE and EE are achieved, when the relays are placed closer to the BS to take advantage of the resultant line-of-sight link. Furthermore, increasing both the number of available subcarriers and the number of active user equipment (UE) increases both the EE and the total SE of the system as a benefit of the increased frequency and multi-user diversity, respectively. Finally, it is demonstrated that as expected, increasing the available power tends to improve the SE, when using the SEM algorithm. By contrast, given a sufficiently high available power, the EEM algorithm attains the maximum achievable EE and a suboptimal SE.