Combined Power Research Articles

Emerging Technologies and Applications of AI Chips: Integrating Deep Learning Algorithms with Advanced Hardware Architectures

Due to the ongoing promotion of the latest technological revolution and industrial upgrading, as well as the widespread use of artificial intelligence and deep learning, the intelligent Internet of Things terminal has reached a more advanced stage of development. The combination of training data, model algorithms, and processing power is the basis for advancing artificial intelligence. The semiconductor business plays a crucial role in shaping the future of AI algorithms and the computing power industry. This study explores the progression of artificial intelligence, and essential technologies, and examines the categorization and structure of AI chips. The process of deep learning was then analyzed, demonstrating the application of AI in real life. Next, this study showcases distinct AI chip-related models, specifically designed for the automated implementation of Convolutional Neural Networks (CNNs) on Field-Programmable Gate Arrays (FPGAs). The study then proceeds to examine the unique procedure and the significance of the resulting data. The future of AI was examined in the domains of space exploration, healthcare, and autonomous driving, along with future forecasts.

Genetic Diversity and Forensic Utility of X-STR Loci in Punjabi and Kashmiri Populations: Insights into Population Structure and Ancestry

Background: X-chromosomal short tandem repeats (X-STRs) are crucial in forensic applications, particularly in complex kinship cases, and play an important role in population genetics. However, there is limited data on X-STR variation in Pakistani populations, especially among ethnic groups like Kashmiri and Punjabi. Methodology: This study investigates the forensic and genetic properties of 12 X-STRs from the Investigator Argus X-12 Kit (QIAGEN, Hilden, Germany) in 125 families (75 Kashmiri, 50 Punjabi) from Azad Jammu and Kashmir and Punjab, Pakistan. Results: In both populations, a total of 222 alleles were identified across the 12 X-STR loci (Punjabi 171 alleles, Kashmiri 161 alleles), with allele frequencies ranging from 0.0056 to 0.3033. DXS10148 was the most polymorphic locus with 28 alleles, while DXS7132 was the least polymorphic with 9 alleles. Most loci were in linkage equilibrium, except for the DXS10135/DXS10148 pair in males, with no loci exhibiting significant linkage disequilibrium in females. The combined power of discrimination was 0.999 999 9977 for Kashmiri males, 0.999 999 999 999 9746 for Kashmiri females, and 0.999 999 999 999 9781 for Punjabi females. In Kashmiri males, 34, 31, 28, and 32 haplotypes were observed across the four linkage groups (LG1, LG2, LG3, and LG4), though these groups did not form stable haplotypes, as indicated by Linkage Equilibrium within and significant Linkage Disequilibrium between groups. Conclusions: Genetic structure analysis using Principal Component Analysis and STRUCTURE revealed distinct clustering patterns for the Kashmiri and Punjabi populations, indicating unique genetic backgrounds and ancestry influences, particularly distinguishing them from East Asian populations. This study provides a comprehensive analysis of X-STR variation in Punjabi and Kashmiri populations, offering valuable insights for forensic and population genetic studies.

Acidity and Salinization of Soil Following the Application of Ashes from Biomass Combustion Under Different Crop Plant Species Cultivation

Ashes from biomass combustion (BAs) are a valuable source of plant nutrients, making them suitable for fertilizing crops. BAs also contain components that directly affect soil environmental conditions, leading to improved growth and development of plants. Their deacidifying properties allow BAs to serve as a substitute for calcium fertilizers. However, they contain substantial amounts of components that can increase soil salinity, which can have negative effects. The aim of this study was to assess the impact of BAs on changes in pH and salinity of haplicluvisol soil under the cultivation of various plant species. The study also analyzed the effects of BAs on the content of total forms of calcium (Ca), magnesium (Mg), potassium (K), and sodium (Na) in the soil. The BAs used in the experiment were sourced from a combined heat and power plant that combusts forest and agricultural biomass. These BAs are distributed as a product for fertilizing agricultural land. However, their application is not subjected to further monitoring. The results indicated that the application of different doses of BAs significantly affected the pH of the analyzed soil. Compared to control objects, a significant increase in pH was observed, with these changes dependent on the species of the cultivated plant. Additionally, even the smallest doses of BAs caused an increase in the electrolytic conductivity (EC) of soil solutions, which serves as a measure of soil salinity. Despite the increase in the average EC value, the application of BAs did not alter the salinity class. The use of BAs also significantly influenced other analyzed parameters. An increase in the average content of total forms of Ca, Mg, K, and Na in the soil was observed, along with a higher degree of soil saturation with alkaline cations, compared to the control and the soil condition before the experiment. The changes in the analyzed soil parameters were significantly influenced not only by the different doses of BAs but also by the species of the cultivated plant. The greatest fluctuations in the obtained values were observed in soil under winter rape cultivation, while the smallest fluctuations were noted in soil under spring barley and potato cultivation.

Design of a high power combiner in HF band

Power combiners are commonly used in power amplifiers to achieve high power, especially in long-range and medium-range wireless communication device transmission systems. This paper proposes designing and implementing a high-power two-way power combiner operating in the HF (High Frequency) band, aiming to achieve an output power of over 1 kW. The power combiner is designed, implemented, and tested in the laboratory with the following achieved parameters: insertion loss between output and input better than 3.4 dB and isolation between inputs better than 26 dB. The experimental results showed that the proposed power combiner had better performance than existing combiners on the market. Mainly, power combining efficiency is over 88%, the standing wave ratio at ports is less than 1.25, and the operating temperature of the power combiner is less than 50 °C, meeting the set requirements.

Fuel Cell-Based Distributed Robust Optimal Scheduling for Combined Heat and Power Supply

At present, the safe operation of integrated energy systems is significantly affected by the considerable uncertainty inherent to wind and photovoltaic power generation. Based on this, this paper proposes an optimal scheduling model for integrated electricity, heat, and hydrogen-based energy systems on distributed robust optimization (DRO). Firstly, a combined heat and power microgrid system considering hydrogen energy systems was constructed based on the thermoelectric cogeneration characteristics of fuel cells and electrolyzers. Then, a data-driven two-stage distribution robust optimization scheduling model is built by combining typical historical data of wind power output, photovoltaic power output, and load. The results show that the distributed robust method reduces the running cost by 6% compared to the deterministic method. The proposed method and model are capable of meeting the demand for thermoelectric loads within the microgrid in a more cost-effective manner, thereby achieving stable and independent operation of the system.

Data-driven stochastic dynamic economic dispatch for combined heat and power systems using particle swarm optimization

In modern power systems, the uncertain behavior of renewable energy sources (RESs) may result in deviations from the optimal operating dispatches for the various power sources. The electric power generation from Combined Heat and Power (CHP) units is characterized by high efficiency with less pollution. To use CHP units more efficiently, the dynamic economic dispatch problem is applied to obtain the optimal power and heat sources’ outputs to satisfy heat and power demands while meeting the different operational constraints. In this paper, data-driven stochastic optimization is used to model these uncertainties utilizing the Generative Adversarial Networks in scenario generation that are accurate and do not require fitting models or modeling probability distributions. Then, the Fast Forward Selection technique is used to decrease the number of scenarios to improve system tractability. The results obtained indicate that the variability in electrical load, photovoltaic (PV), and wind turbine (WT) output significantly impacts the overall operational costs of the power system. Therefore, it is essential to incorporate the uncertainties associated with electrical load, PV, and WT when aiming for accurate results in economic dispatch. the study found that the overall operation cost of the dynamic economic dispatch of the electrical system for 24 hours after integrating RES achieved a daily savings of 2.5 % in the first scenario. Furthermore, the findings demonstrate that RESs positively contribute to reducing the total operational costs of the power system. The economic dispatch of CHP systems is influenced by the integration of RESs and the uncertainties associated with electrical parameters.

Part-load performance analysis of a modular biomass boiler with a combined heat and power industrial Rankine cycle and supplementary sCO2 Brayton cycle

Abstract The addition of a supplementary high efficiency cycle integrated with an existing steam power cycle may increase energy efficiency and net generation. In this paper, part load performance and operation of a modular biomass boiler with an existing industrial Rankine steam heat and power cycle and supplementary supercritical CO2 (sCO2) Brayton cycle is analysed. The aim is to leverage the high efficiency of the sCO2 cycle by retrofitting sCO2 heaters in the existing biomass boiler, increasing net power output and thermal efficiency. With nominal load performance previously investigated, understanding part-load performance and operation is vital to determine cycle feasibility. A quasi-steady-state one dimensional thermofluid network model was used to simulate the integrated cycle performance for loads ranging from 100% to 60%. The model solves the mass, energy, momentum, and species balance equations, capturing detailed component characteristics. Two control methodologies are explored for the sCO2 Brayton cycle, namely inventory control and inventory control combined with throttling valve control. Inventory control is selected as the better performing control strategy for load following, maintaining high thermal efficiency across partial loads. At 60% load, the sCO2 compressor operates near the pseudo-critical point, leading to a sharp decrease in sCO2 cycle capacity, which requires careful management of inventory control. Two sCO2 heater configurations are investigated, namely a single convective-dominant heater, and a dual heater configuration with a radiative and a convective heater. The single heater configuration is preferred to minimise adverse impacts on the Rankine cycle superheaters.

Botnets Unveiled: A Comprehensive Survey on Evolving Threats and Defense Strategies

ABSTRACTBotnets have emerged as a significant internet security threat, comprising networks of compromised computers under the control of command and control (C&C) servers. These malevolent entities enable a range of malicious activities, from denial of service (DoS) attacks to spam distribution and phishing. Each bot operates as a malicious binary code on vulnerable hosts, granting remote control to attackers who can harness the combined processing power of these compromised hosts for synchronized, highly destructive attacks while maintaining anonymity. This survey explores botnets and their evolution, covering aspects such as their life cycles, C&C models, botnet communication protocols, detection methods, the unique environments botnets operate in, and strategies to evade detection tools. It analyzes research challenges and future directions related to botnets, with a particular focus on evasion and detection techniques, including methods like encryption and the use of covert channels for detection and the reinforcement of botnets. By reviewing existing research, the survey provides a comprehensive overview of botnets, from their origins to their evolving tactics, and evaluates how botnets evade detection and how to counteract their activities. Its primary goal is to inform the research community about the changing landscape of botnets and the challenges in combating these threats, offering guidance on addressing security concerns effectively through the highlighting of evasion and detection methods. The survey concludes by presenting future research directions, including using encryption and covert channels for detection and strategies to strengthen botnets. This aims to guide researchers in developing more robust security measures to combat botnets effectively.

Hourly electricity CO[formula omitted] intensity profiles based on the real operation of large-scale natural gas combined cycle cogeneration plants

The decarbonization of energy systems is a major challenge that requires complex cross-sectoral strategies that need to be supported by energy modeling. As many technologies rely on electricity, an accurate estimation of its CO2 intensity is of utmost importance for the reliability of modeling results. When electricity is generated from fossil sources, its CO2 intensity depends on several parameters. This research paper presents an hourly calculation of the CO2 intensity of power generation from natural gas combined cycles, based on real data from several years of operation of three plants. As these plants are also operated in combined heat and power mode, two alternative allocation methods are compared. The results confirm the variability of CO2 intensity based on the different operation strategies of the plants and the share of heat and electricity generated. The hourly analysis shows average values in the range of 230–250 gCO2/kWh in winter, rising to around 330–370 gCO2/kWh in summer. The real CO2 intensity profiles presented in this paper can be integrated into energy planning models to improve their ability to estimate the potential benefits of different decarbonization solutions by including the effect of the operational profiles over the day and the year.

U-Net Semantic Segmentation-Based Calorific Value Estimation of Straw Multifuels for Combined Heat and Power Generation Processes

This paper proposes a system for real-time estimation of the calorific value of mixed straw fuels based on an improved U-Net semantic segmentation model. This system aims to address the uncertainty in heat and power generation per unit time in combined heat and power generation (CHPG) systems caused by fluctuations in the calorific value of straw fuels. The system integrates an industrial camera, moisture detector, and quality sensors to capture images of the multi-fuel straw. It applies the improved U-Net segmentation network for semantic segmentation of the images, accurately calculating the proportion of each type of straw. The improved U-Net network introduces a self-attention mechanism in the skip connections of the final layer of the encoder, replacing traditional convolutions by depthwise separable convolutions, as well as replacing the traditional convolutional bottleneck layers with Transformer encoder. These changes ensure that the model achieves high segmentation accuracy and strong generalization capability while maintaining good real-time performance. The semantic segmentation results of the straw images are used to calculate the proportions of different types of straw and, combined with moisture content and quality data, the calorific value of the mixed fuel is estimated in real time based on the elemental composition of each straw type. Validation using images captured from an actual thermal power plant shows that, under the same conditions, the proposed model has only a 0.2% decrease in accuracy compared to the traditional U-Net segmentation network, while the number of parameters is significantly reduced by 74%, and inference speed is improved 23%.

Comparison of Aiming Point Strategy Algorithms and Their Assessment for Volumetric Solar Receivers

The present work summarizes the work done within the scope of CATION, CHLOE, and HECTOR projects related to the aiming point strategies optimization algorithms applied to volumetric solar receivers. Several optimization methods have been applied in the literature for optimizing the aiming strategy of solar power tower plants. However, the use of these algorithms for the requirements of volumetric solar receivers and solar fuel applications has not been accomplished. Herein, the problem is formulated as a constrained optimization problem whose objective function is a combination of the total power on the receiver surface and the flux homogeneity. A comparison of the results is presented in this work. It will be seen that TABU search and SA algorithms are the most efficient in terms of computation time and, in addition, the first one shows very good results in power and spillage. The rest of the mentioned algorithms are much slower and, in general, the results are slightly worse.

Modeling of calculations on the performance optimization of a double-flash geothermal renewable energy-based combined heat/power plant coupled by transcritical carbon dioxide rankine cycle

This study investigates the integration of a double-flash geothermal system with a transcritical CO2 Rankine cycle, presenting a groundbreaking approach for enhancing efficient and sustainable energy generation. By harnessing renewable geothermal resources, this combined heat and power system aims to minimize greenhouse gas emissions, contributing to a more sustainable future. The research emphasizes multi-objective optimization to maximize net power output, net heating capacity, and overall system efficiency. The expanded stream pressures from the expansion valves, denoted as P2 and P6, are systematically varied to evaluate their impact on system performance. Significant findings reveal that as P6 increases from 260 kPa to 640 kPa, net power output rises dramatically from 2688 kW to 3163 kW. The optimal conditions for peak performance are identified at P2 of 820 kPa and P6 of 640 kPa, resulting in impressive outcomes: a net power output of 3392 kW, a net heating capacity of 14,586 kW, and an overall efficiency of 52.65 %. By delineating these relationships and establishing benchmarks for performance, this research offers valuable insights for the scientific community, policymakers, and industry stakeholders aiming to advance geothermal energy technologies.

Bi-Level Operation Optimization and Performance Analysis for a Distributed Energy System with Energy Network

The increasing energy crisis and environmental problems promote the development of distributed energy systems (DESs) that utilize combined heat and power technology, renewable energy technology, and waste heat recovery technology to meet various load requirements. Although there is existing work and research on a variety of DESs, most previous studies tend to focus on energy production with little consideration of a distributed energy network and its energy loss, which results in large errors in energy-efficiency calculations and performance analyses. In this paper, a new DES model is proposed with full consideration of an energy network and the full use of solar energy, terrestrial heat, and exhaust gases. At the same time, an effective bi-level optimization method is also proposed for the daily operation of the DES in order to improve the system performance and benefits in the energy, the economy, and the environment. Specifically, the co-generation energy station and water heating network in the DES are optimized separately with two different optimization models. The first-level optimization model is used to seek the optimal values of water mass flow rate and water temperature of the water heating network, and the second-level optimization model is built to determine the optimal energy purchasing strategy and the optimal energy outputs of the co-generation energy station. In order to verify the advantages and effectiveness of the proposed system and method, a contrastive simulation study is undertaken to provide a comparison of the global optimization method. Simulation results show that energy loss, energy cost, and energy consumption of the DES using the bi-level optimization operation strategy only account for 22.51%, 33.42%, and 51.31% of the quantities of the global optimization method, respectively. The hourly curves of the optimal operating variables also demonstrate that the proposed bi-level optimization method can improve the operating stability of the DES better than the global optimization method.

Closing the Loop between Plastic Waste Management and Energy Cogeneration: An Innovative Design for a Flexible Pyrolysis Small-Scale Unit

This study proposes a simplified unit that can be employed in an industrial facility for the utilization of its own abundant plastic waste, primarily from discarded packaging, to achieve full or partial energy autonomy. By converting this waste into synthetic pyrolysis oil equivalent to 91,500 L, the industry can power a combined heat and power generation unit. The proposed unit was designed with a focus on maintaining high temperatures efficiently while minimizing oxygen exposure to protect the integrity of hydrocarbons until they transform into new compounds. Pyrolysis stands as a foundational procedure, paving the way for subsequent thermochemical transformations such as combustion and gasification. This study delves into the factors affecting pyrolysis and presents analytically the mathematical formulations and relevant calculations in order to effectively design and apply a real-life system. On this basis, fuels from plastic waste can be produced, suitable for utilization in typical equipment meant to produce heat, estimated for six months’ operation and 800 MWh of electricity. This study enhances the transition towards a more circular and resource-efficient economy with technologies that unlock the latent energy contained within the discarded matter. Additionally, it demonstrates the feasibility of a moderate investment in a co-generation system for industries utilizing 568 tonnes of plastic waste per year. The design and accurate calculations of this study highlight the theoretical potential of this technology, promoting environmental sustainability and resource conservation.

Experimental and theoretical assessment of the effects of electrical load variation on the operability of a small-scale Organic Rankine Cycle (ORC)-based unit equipped with a hermetic scroll expander

Scroll expanders are widely adopted in small-scale Organic-Rankine-Cycles-(ORCs)-units integrated with renewable energy sources for low power residential Combined Heat and Power Production (CHP). These expanders are often connected to variable direct loads without the possibility to impose its speed externally. Despite the effect of electric load variation on expander speed is known, a minor attention is focused on its impact on the plant operability and regulation. To fill this gap, a wide experimental analysis is carried out on a solar micro-cogenerative ORC-based power unit equipped with a 1 kW hermetic scroll expander connected with a variable electric load. The experimental behaviour of the ORC-unit was assessed for different values of load resistance (from 20 Ω up to 80 Ω). Results show that properly acting on the load resistance the ORC-power unit can be improved up to 50 % in terms of efficiency and power production. Moreover, if the expander is properly designed its self-regulatory capability allows to achieve similar performance of an equivalent speed-controlled expander tested on the same plant. Finally, a new theoretical model reproducing the impact of main operating quantities on expander behaviour was developed and experimentally validated paving the way to a novel model-based control approach.

Artificial Intelligence Application for Assessment/Optimization of a Double-Flash Geothermal Scheme Tailored Combined Heat/Power Plant

Utilizing the capabilities of artificial intelligence can lead to the development of energy systems that are more efficient, sustainable, and resilient. The integration of machine learning techniques within these systems provides substantial benefits and is essential for enhancing overall performance. As the global community confronts challenges like climate change and rising energy demands, machine learning will play an increasingly vital role in defining the future of energy systems. This research examines how effective regression-based machine learning techniques are for analyzing and optimizing the performance of a geothermal combined heat and power system. It focuses on creating both linear and quadratic models to assess electricity generation, heat production, and the efficiency of the entire system. The evaluation of these models is performed through residual analysis and R-squared statistics. Results indicate that quadratic models surpass linear ones, with linear model achieving an R-squared value of 88.56% for power generation, while the quadratic model reaches an impressive R-squared level of 99.88%. Furthermore, the study demonstrates that quadratic machine learning models hold significant promise for optimizing system performance, shown by desirability metrics exceeding 0.99. This research highlights the importance of regression-based machine learning methods in analyzing and improving geothermal combined heat and power systems.

Home energy management system for residential fuel cell-based combined heat and power system

The goal of this paper is to propose an optimization scheme for enhancing power dispatch and load scheduling for residential fuel cell-based combined heat and power systems (FC-CHPS) using mixed integer linear programming (MILP), considering the lifetime degradation of both the fuel cell system (FCS) and battery energy storage systems (BESS). The scheme is applied in the home energy management system that oversees the electric and thermal power of residential FC-CHPS. First, the nonlinearity in the relationship between the natural gas consumption and output electric power, and between the residual thermal power and output electric power of the fuel cell system (FCS) in the FC-CHPS is approximated using piecewise linearization. Then, the piecewise linearization is integrated with MILP to derive optimal power dispatch and load scheduling solutions, while accounting for the nonlinearity of the FCS. Next, cost functions representing the lifetime degradation of FCS and BESS are formulated. These cost functions result in nonlinear optimization constraints. Therefore, innovative methods are proposed to linearize these constraints, allowing the continued use of MILP as the optimization method and factoring in the lifetime degradation of FCS and BESS. Compared to the mixed-integer non-linear programming (MINLP) method that does not linearize the non-linearity in the FCS or the lifetime degradation of the FCS and BESS, the proposed MILP scheme achieves the identical objective function value. Furthermore, the computation time for the proposed MILP scheme is 99.94 % lower than that required by the MINLP method. The proposed scheme provides accurate results in short computation times.

A theoretical thermodynamic investigation on solar-operated combined electric power, heating, and ejector cooling cycle driven by an ORC turbine waste heat, tri-generation cycle system

The current study investigated a solar thermal power plant to simultaneously produce multiple energy outputs, including electric power, process heating, and cooling. This integrated approach aligns with the concept of a tri-generation system, where three different forms of energy are produced from a single source. The study involves a parametric analysis, where the impact of each influencing parameter including, turbine inlet temperature, and turbine inlet pressure is systematically varied with DNI ranging from 600 W/m2 to 1000 W/m2 to understand its effects on both first-law and second-law efficiencies. Further, the exergy analysis shows the irreversibility's occur within the system. Notably, the central receiver and heliostat are identified as major sources of exergy loss. The comparison to a combined power and heating (CPH) cycle without an ORC-ejector indicates that the additional technologies significantly contribute to improving the system's performance, approximately 3.97 % and 2.3 % for both efficiencies, respectively.

Quantifying the informativity of emission lines to infer physical conditions in giant molecular clouds. I. Application to model predictions

Observations of ionic, atomic, or molecular lines are performed to improve our understanding of the interstellar medium (ISM). However, the potential of a line to constrain the physical conditions of the ISM is difficult to assess quantitatively, because of the complexity of the ISM physics. The situation is even more complex when trying to assess which combinations of lines are the most useful. Therefore, observation campaigns usually try to observe as many lines as possible for as much time as possible. We have searched for a quantitative statistical criterion to evaluate the full constraining power of a (combination of) tracer(s) with respect to physical conditions. Our goal with such a criterion is twofold. First, we want to improve our understanding of the statistical relationships between ISM tracers and physical conditions. Secondly, by exploiting this criterion, we aim to propose a method that helps observers to make their observation proposals; for example, by choosing to observe the lines with the highest constraining power given limited resources and time. We propose an approach based on information theory, in particular the concepts of conditional differential entropy and mutual information. The best (combination of) tracer(s) is obtained by comparing the mutual information between a physical parameter and different sets of lines. The presented analysis is independent of the choice of the estimation algorithm ( neural network or $ minimization). We applied this method to simulations of radio molecular lines emitted by a photodissociation region similar to the Horsehead Nebula. In this simulated data, we considered the noise properties of a state-of-the-art single dish telescope such as the IRAM 30m telescope. We searched for the best lines to constrain the visual extinction or the ultraviolet illumination field . We ran this search for different gas regimes, namely translucent gas, filamentary gas, and dense cores. The most informative lines change with the physical regime ( cloud extinction). However, the determination of the optimal (combination of) line(s) to constrain a physical parameter such as the visual extinction depends not only on the radiative transfer of the lines and chemistry of the associated species, but also on the achieved mean signal-to-noise ratio. The short integration time of the CO isotopologue $J=1-0$ lines already yields much information on the total column density for a large range of ( ) space. The best set of lines to constrain the visual extinction does not necessarily combine the most informative individual lines. Precise constraints on the radiation field are more difficult to achieve with molecular lines. They require spectral lines emitted at the cloud surface ( and lines). This approach allows one to better explore the knowledge provided by ISM codes, and to guide future observation campaigns.

Sky localization of space-based detectors with time-delay interferometry

The accurate sky localization of gravitational wave (GW) sources is an important scientific goal for space-based GW detectors. The main differences between future space-based GW detectors, such as Laser Interferometer Space Antenna (LISA), Taiji, and TianQin, include the time-changing orientation of the detector plane, the arm length, the orbital period of the spacecraft and the noise curve. Because of the effects of gravity on three spacecraft, it is impossible to maintain the equality of the arm length, so the time-delay interferometry (TDI) method is needed to cancel out the laser frequency noise for space-based GW detectors. Extending previous work based on equal-arm Michelson interferometer, we explore the impacts of different first-generation TDI combinations and detector's constellations on the sky localization for monochromatic sources. We find that the sky localization power is almost unaffected by the inclusion of the TDI Michelson (X, Y, Z) combination in the analysis. We also find that the variation in the sky localization power for different TDI combinations is entirely driven by the variation in the sensitivities of these combinations. For the six particular TDI combinations studied, the Michelson (X, Y, Z) combination is the best for source localization.