Energy Consumption Of System Research Articles

Power-Efficient Resource Allocation for Active STAR-RIS-Aided SWIPT Communication Systems

Simultaneous wireless information and power transfer (SWIPT) has emerged as a pivotal technology in 6G, offering an efficient means of delivering energy to a large quantity of low-power devices while transmitting data concurrently. To address the challenges of obstructions, high path loss, and significant energy consumption associated with long-distance communication, this work introduces a novel alternating iterative optimization strategy. The proposed approach combines active simultaneous transmission and reflection of reconfigurable intelligent surfaces (STAR-RIS) with SWIPT to maximize spectrum efficiency and reduce overall system energy consumption. This method addresses the considerable energy demands inherent in SWIPT systems by focusing on reducing the power output from the base station (BS) while meeting key constraints: the communication rate for information receivers (IRs) and minimum energy levels for energy receivers (ERs). Given complex interactions between variables, the solution involves an alternating iterative optimization process. In the first stage of this approach, the passive beamforming variables are kept constant, enabling the use of semi-definite relaxation (SDR) and successive convex approximation (SCA) algorithms to optimize active beamforming variables. In the next stage, with active beamforming variables fixed, penalty-based algorithms are applied to fine-tune the passive beamforming variables. This iterative process continues, alternating between active and passive beamforming optimization, until the system converges on a stable solution. The simulation results indicated that the proposed system configuration, which leverages active STAR-RIS, achieves lower energy consumption and demonstrates improved performance compared to configurations utilizing passive RIS, active RIS, and passive STAR-RIS. This evidence suggests that the proposed approach can significantly contribute to advancing energy efficiency in 6G systems.

A system-level CFD simulation model for investigating the energy dissipation mechanism of seawater pump and rotary energy recovery device in SWRO desalination system

Seawater reverse osmosis (SWRO) technology has been universally adopted because of its high efficiency, economic benefits, easy maintenance, and high-pressure seawater pump (HPSP) and energy recovery device (ERD) are essential components, which determine the specific energy consumption of SWRO system. However, it is still unclear about energy dissipation mechanism and mixing characteristics from the perspective of system. For that, this thesis proposes a novel system-level Computational Fluid Dynamics (CFD) model considering all interfaces in HPSP and integrated energy recovery and pressure boost device (IERPBD). To profoundly investigate the system performance under various operating conditions, the pressure and composition distribution, power loss of each interface and mixing rate are numerically calculated and analyzed. Additionally, experiment of the effect of temperature on SWRO system performance is conducted to practically verify the proposed model, which indicates that the presented model could precisely reflect the energy dissipation and mixing characteristics. Besides, it is advised that the rotating speed of HPSP and IERPBD should not be set too high which could increase the mixing rate and energy consumption, and higher temperature might lower the quality of produced freshwater. This study offers a feasible way to reveal the energy dissipation mechanism of SWRO desalination system.

A deep belief network-based energy consumption prediction model for water source heat pump system

To achieve optimal energy efficiency in buildings, accurately forecasting the energy consumption of air conditioning systems is crucial. This study develops an energy consumption prediction model based on a deep belief network, which is constructed according to the principles of a restricted Boltzmann machine. Actual experimental data from a water source heat pump system are collected, and feature variables are selected. The study discusses the impact of model parameters and training set sizes on the performance of energy consumption prediction model. Additionally, the trend in model prediction performance is analyzed through parameter adjustments. The results show that the coefficient of determination (R2) for the optimized model has increased to 0.585. The mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) have been reduced to 6.311, 2.512, and 1.625, respectively. The deep belief network energy consumption prediction model outperforms other common machine learning models for water source heat pump systems.

Theoretical models for predicting ventilation performance of vertical solar chimneys in tunnels

Solar chimney as a reliable renewable energy system has been primarily utilized for building ventilation, but its application in the tunnel is rarely explored. This study develops theoretical models to predict the ventilation performance of vertical solar chimney in urban tunnel. Five temperature distribution types within the chimney cavity are analyzed, including uniform, vertically linear, horizontally semi-parabolic, two piecewise semi-parabolic in the depth direction, and three-dimensional parabolic profiles. The theoretical models consider the effect of chimney configuration, tunnel geometry, glazing materials, and solar radiation intensity on airflow rate through solar chimney. Validation against experimental data and numerical simulation shows that considering three-dimensional temperature distributions results in an average 11 % deviation from validation data, outperforming assumptions of uniform (29.3 % deviation) or lower-dimensional profiles. The volumetric flow rate through solar chimney exponentially decreased with h/w and h/d that the optimum ratio of h/d is 10. The airflow rate linearly increased with 0.14 power of glazing absorptivity. This analysis provides technical guidance for optimizing solar chimney design in tunnels, enhancing natural ventilation, and reducing energy consumption for mechanical ventilation systems.

Study of Low-Temperature Energy Consumption Optimization of Battery Electric Vehicle Air Conditioning Systems Considering Blower Efficiency

Battery electric vehicle (BEV) air conditioning systems often use positive temperature coefficient (PTC) heaters to heat the passenger compartment. The heating process consumes a lot of energy in low-temperature environments, which seriously affects the driving range and user experience. This study aims to reduce the low-temperature energy consumption of the air conditioning system and improve energy efficiency through an innovative optimization method. In this study, the energy consumption composition of the air conditioning system was analyzed, and the goal of minimizing the sum of the total power consumption of the PTC heater and the blower was determined, while the efficiency characteristic of the blower was considered at the same time. The relationship between the average temperature of the passenger compartment measurement points and the PTC power and airflow rate was studied by combining experiments and numerical simulations, and the alternative operating conditions that met the temperature requirement were determined. On this basis, the total power consumption of the air conditioning system was analyzed and optimized. The results show that PTC power, airflow rate, and blower efficiency all have an important influence on the total power consumption of the air conditioning system. The optimized scheme could reduce the theoretical total power from 1315.32 W of the original scheme to 1246.83 W, and the actual total power from 1350.05 W of the original scheme to 1326.56 W, with reductions of 5.21% and 1.74%, respectively. The low-temperature energy consumption optimization method for the BEV air conditioning systems proposed in this study is instructive for the selection of blowers and the design of control strategies for air conditioning systems.

Strategic Evaluation of Sustainable Practices for HVAC Systems in Small and Medium-Sized U.S. Manufacturers

Abstract This paper investigates the often overlooked yet crucial role of Heating, Ventilation, and Air Conditioning (HVAC) systems in advancing sustainable manufacturing practices in the United States. Through all outcomes of the energy assessments conducted by the Industrial Assessment Centers (IACs) in various industrial settings, the current study focuses on the energy consumption of HVAC systems and assesses the impact of their energy-efficient measures on the overall industrial energy usage. In-depth analysis covers both technological and economic facets of resource management practices, utilizing case studies and data from energy assessments on 20,818 small- and medium-sized manufacturing facilities. The results reveal substantial potential for reducing energy consumption, estimated at 71.9 million MMBtu per year, along with annual energy cost savings of approximately $744 million per year and a noteworthy mitigation of 8.7 million metric tons of CO2 emissions per year, all achievable through HVAC system improvements. These findings show the practical significance of sustainable HVAC practices and their potential to improve energy efficiency and mitigate the environmental impact within the manufacturing sector.

Optimal placement of light sensor for improving energy efficiency and visual comfort in smart buildings

The energy consumed by artificial lighting accounts for a significant part of the energy consumption in buildings. Thus, energy consumption can be significantly decreased using optimization techniques, such as the optimum placement of the lighting sensors. In this paper, an optimal placement method of the light sensors is presented to minimize the lighting system's equipment costs and energy consumption and also meet visual comfort under the requirements of the European standard EN12464–1 regarding illuminance-based criteria. In the beginning, based on the illumination measurement grid's mathematical model, the potential location of the sensors is determined. As an average of the dimming levels of the LED lights, the objective function has been introduced. Then, the illuminance and uniformity levels are defined as constraints for the optimization problem. The Battle Royale Optimization (BRO) algorithm is used to solve the optimization problem. Ultimately, based on the results of the BRO algorithm and the calculation of the illuminance deviation, the optimal location of the light sensors is determined. The proposed method is tested in an office room. A fuzzy logic controller is developed to regulate the lighting control system's dimming levels to evaluate the proposed method's performance with other approaches. The comparison results have shown that the proposed method is superior as regards the number of sensors and the optimal sensor position, significant savings in energy consumption of up to 30.8%, and satisfactory visual comfort per the requirements of the European standard EN12464–1.

Machine learning enhanced control co-design optimization of an immersion cooled battery thermal management system

The development of lithium-ion battery technology has ensured that battery thermal management systems are an essential component of the battery pack for next-generation energy storage systems. Using dielectric immersion cooling, researchers have demonstrated the ability to attain high heat transfer rates due to the direct contact between cells and the coolant. However, feedback control has not been widely applied to immersion cooling schemes. Furthermore, current research has not considered battery pack plant design when optimizing feedback control. Uncertainties are inherent in the cooling equipment, resulting in temperature and flow rate fluctuations. Hence, it is crucial to systematically consider these uncertainties during cooling system design to improve the performance and reliability of the battery pack. To fill this gap, we established a reliability-based control co-design optimization framework using machine learning for immersion cooled battery packs. We first developed an experimental setup for 21700 battery immersion cooling, and the experiment data were used to build a high-fidelity multiphysics finite element model. The model can precisely represent the electrical and thermal profile of the battery. We then developed surrogate models based on the finite element simulations in order to reduce computational cost. The reliability-based control co-design optimization was employed to find the best plant and control design for the cooling system, in which an outer optimization loop minimized the cooling system cost while an inner loop ensured battery pack reliability. Finally, an optimal cooling system design was obtained and validated, which showed a 90% saving in cooling system energy consumption.

Indoor PM2.5 and building energy consumption in nine typical residential neighborhoods in Nanjing under different ventilation strategies caused by traffic-related PM2.5 dispersion

Traffic-related PM2.5 seriously endangers human health and affects the energy consumption of building ventilation systems. This study investigates the impact of different ventilation strategies caused by traffic-related PM2.5 dispersion on indoor PM2.5 concentration and building energy consumption of nine typical residential neighborhoods in Nanjing using EnergyPlus software. Results of a third-floor apartment in a six-story slab-type parallel layout residential neighborhood (6F-Sp) show that strategy 1 (window opening, OW) and strategy 7 (natural and mechanical ventilation, NV&MV-TC) can maintain indoor PM2.5 concentration below 25 μg/m3. The NV&MV-TC strategy is chosen to simulate indoor PM2.5 concentrations and cooling/heating energy consumption in nine neighborhoods. The building cooling energy consumption range is 21.2–41.5 kW•h/m2, while the heating energy consumption is 14.9–74.7 kW•h/m2. In the cooling season, the hybrid ventilation strategy shows the lowest cooling energy consumption, followed by window closure with operating indoor air purifiers. Apartments employing window closure with mechanical ventilation have the highest cooling energy consumption. In the heating season, the most energy-efficient strategy is window closure with operating indoor air purifiers, followed by the hybrid ventilation strategy. The indoor PM2.5 concentration negatively correlates with the frontal area index (λf). This study provides valuable information for residential ventilation strategies and guides residents' behaviors in cooling/heating seasons.

Model-based control strategy with linear parameter-varying state-space model for rack-based cooling data centers

Cooling system energy consumption occupies approximately 40% of the total energy consumption in data centers, and efficient control is crucial for improving cooling efficiency and reducing the operational costs of data centers. This paper introduced a novel economic model predictive control (EMPC) strategy based on a linear parameter-varying state-space model. The primary objective was to maintain the thermal environment of data centers while minimizing the energy consumption of the cooling system. Therein, a linear parameter-varying state-space model was developed based on the parameter identification method to precisely predict the temperature field of the rack-based cooling data center in real-time. The energy management and thermal regulation performance of the EMPC and the proportion integration differentiation (PID) controller were comparatively analyzed through simulation experiments. Moreover, the impacts of the server workload level and optimization time domain on the performance of EMPC were comprehensively investigated. The results show that, the proposed EMPC was able to minimize the energy consumption by optimizing the supplied cold air temperature and airflow rate simultaneously. In comparison with the PID controller, EMPC not only achieves a 9.7% energy saving by preventing server over-cooling but also diminishes server temperature fluctuations, promoting the safe and stable operation of the server. The research shows that the EMPC reveals excellent performance along with all server workload levels. In addition, the length of the prediction horizon has a notable impact and three minutes is suggested for the rack-based cooling system.

On the total energy consumption of scalable cache-aided multi-CPU cell-free massive MIMO systems

In conventional cell-free (CF) systems, the scalability and feasibility are hindered by the fact that a single central processing unit (CPU) manages all access points and the stringent synchronization requirement for coherent transmission (CT) across the entire network. Multi-CPU CF systems emerge as a scalable and feasible alternative, yet they still encounter the typical CF system challenge of high downlink transmit energy consumption from extensive fronthaul and backhaul transmissions. To tackle this issue, we propose a scalable downlink partial coherent transmission (PCT) strategy for multi-CPU CF systems to provide high spectral efficiency (SE), reduce transmission time and thereby lower energy consumption, while keeping decoding complexity manageable. Based on the PCT strategy, we introduce scalable cache-aided multi-CPU CF systems and develop the corresponding total energy consumption (TEC) model. A successive convex approximation algorithm is proposed to obtain the suboptimal cache placement to minimize the TEC. Simulation results indicate that the proposed PCT-based scalable cache-aided multi-CPU CF systems can significantly reduce the TEC and consistently maintain the TEC at a relatively low level across all Zipf parameters by leveraging the high SE of CT and the independent transmission of non-coherent transmission.

Research on Variable Speed Variable Displacement Power Unit with High Efficiency and High Dynamic Optimized Matching

For the variable speed variable displacement power unit (VSVDPU), achieving power matching between the permanent magnet synchronous motor (PMSM) and the variable displacement plunger pump (VDPP) is the key to reducing system energy consumption. The control method of adjusting the speed of the PMSM and the displacement of the VDPP is the mainstay of current research and application of the VSVDPU. However, the dynamic properties of the PMSM and VDPP have not been balanced, which affects the control effect of the VSVDPU. This paper proposes a control method of variable speed and variable displacement with low energy consumption and high dynamics. The main idea is based on the efficiency model and dynamic response model of the PMSM and VDPP, and the factors that affect the efficiency and dynamic characteristics of the VSVDPU are analyzed. Guided by the multi-objective optimization algorithm, the optimal combination of speed and displacement under specific working conditions is derived. Simulation and experiment results show that the proposed control method is feasible to improve the efficiency and dynamic characteristics of the VSVDPU.

Novel strategy for efficient energy recovery and pollutant control from sewage sludge and food waste treatment

Considering the high organic matter contents and pollutants in sewage sludge (SS) and food waste (FW), seeking green and effective technology for energy recovery and pollutant control is a big challenge. In this study, we proposed a integrated technology combing SS mass separation by hydrothermal pretreatment, methane production from co-digestion of hydrothermally treated sewage sludge (HSS) centrate and FW, and biochar production from co-pyrolysis of HSS cake and digestate with heavy metal immobilization for synergistic utilization of SS and FW. The results showed that the co-digestion of HSS centrate with FW reduced the NH4+-N concentration and promoted volatile fatty acids conversion, leading to a more robust anaerobic system for better methane generation. Among the co-pyrolysis of HSS cake and digestate, digestate addition improved biochar quality with heavy metals immobilization and toxicity reduction. Following the lab-scale investigation, the pilot-scale verification was successfully performed (except the co-digestion process). The mass and energy balance revealed that the produced methane could supply the whole energy consumption of the integrated system with 26.2 t biochar generation for treating 300 t SS and 120 t FW. This study presents a new strategy and technology validation for synergistic treatment of SS and FW with resource recovery and pollutants control.

The Optimization of Chillers Air-Conditioning in Thailand Supermarkets using a Retail Energy Management System (REMS)

Supermarkets in Thailand continue to grow and there will be more than 1500 branches by 2023 open 18-24 hours a day. In this research, we will present how to increase energy efficiency in retail stores by reducing the energy consumption of compressors of chiller air conditioning systems by using energy management systems in retail stores. Retail energy management systems in retail stores use the Internet of Things to connect to a device to store data and analyze and process from big data, then Artificial Intelligence controls and commands to make the air conditioning system energy efficient. Controlling 10 AHUs using central sensors at the sales area instead of return sensors allows us to save 25% of energy or 12,886 kWh/Year. As for the cold-water pump, it is controlled by an inverter. By using sensors to measure the water flow rate, we save 33% of energy or 19,755 kWh/Year. When controlling the water pump and AHU, the chiller compressor will save energy because when the air handling unit and the water pump have reduced work, the work of the compressor to cool water is reduced as well, resulting in energy savings of 12% or 25,211 kWh/Day. Using such a system reduces the overall energy consumption of the chiller air conditioning system. 17% or 57,852 per year has a payback period of 3 years and can also be extended to other systems within the store or other stores.

Provision for Energy: A Resource Allocation Problem in Federated Learning for Edge Systems

The article explores an energy-efficient method for allocating transmission and computation resources for federated learning (FL) on wireless communication networks. The model being considered involves each user training a local FL model using their limited local computing resources and the data they have collected. These local models are then transmitted to a base station, where they are aggregated and broadcast back to all users. The level of accuracy in learning, as well as computation and communication latency, are determined by the exchange of models between users and the base station. Throughout the FL process, energy consumption for both local computation and transmission must be taken into account. Given the limited energy resources of wireless users, the communication problem is formulated as an optimization problem with the goal of minimizing overall system energy consumption while meeting a latency requirement. To address this problem, we propose an iterative algorithm that takes into account factors such as bandwidth, power, and computational resources. Results from numerical simulations demonstrate that the proposed algorithm can reduce energy consumption compared to traditional FL methods up to 51% reduction.

Energy-saving diagnosis of public buildings based on multi-objective optimization algorithm

ABSTRACT This study focuses on existing ventilation, heating, and air conditioning systems in public buildings and mainly considers three objectives: system energy consumption, indoor thermal comfort, and efficiency. A multi-objective diagnosis system is established, which correlates evaluation indicators with key equipment, forming a clear hierarchical diagnosis system. Meanwhile, particle swarm optimization algorithm is combined with linear weighting method to optimize the operating parameters of key equipment based on the diagnosis results, obtaining the optimal parameters of various operating scenes. For winter conditions, the average system energy consumption is 34.2 W/m2, the average system energy efficiency ratio is 1.5, and the average indicator for indoor thermal environment is 1.06. For summer conditions, the average energy consumption of the systems is 28.9 W/m2, the average energy efficiency ratio is 2.2, and the average indicator for indoor thermal environment is – 0.82. Compared with the measured results, most of the optimized indicators are better, but the system energy efficiency ratio is slightly lower than the measured results for winter conditions. Through the established diagnosis system and optimization method, this research evaluates and optimizes the existing the systems in public buildings. Demonstrating the effectiveness of the established diagnosis system and optimization method.

Controller design and energy analysis for stochastic differential systems with one-sided polynomial growth under finite-time attraction constraint

This article presents a novel controller design approach for stochastic nonlinear systems exhibiting one-sided polynomial growth. The objective is to achieve finite-time attractiveness and estimate control energy consumption. By constructing an appropriate controller and employing inequality techniques, sufficient conditions for achieving finite-time attractiveness in stochastic nonlinear systems are established. Moreover, the article introduces the concept of expected energy consumption for stochastic systems and provides upper bounds on control time and energy consumption. A comprehensive trade-off analysis between control time and energy consumption is conducted to identify the optimal control intensity. The proposed approach is validated through numerical examples, demonstrating its effectiveness in practice.

Methods and algorithms of swarm intelligence for the problems of nonlinear regression analysis and optimization of complex processes, objects, and systems: review and modification of methods and algorithms

The development of high-speed methods and algorithms for global multidimensional optimization and their modifications in various fields of science, technology, and economics is an urgent problem that involves reducing computing costs, accelerating, and effectively searching for solutions to such problems. Since most serious problems involve the search for tens, hundreds, or thousands of optimal parameters of mathematical models, the search space for these parameters grows non-linearly. Currently, there are many modern methods and algorithms of swarm intelligence that solve today's scientific and applied problems, but they require modifications due to the large spaces of searching for optimal model parameters. Modern swarm intelligence has significant potential for application in the energy industry due to its ability to optimize and solve complex problems. It can be used to solve scientific and applied problems of optimizing energy consumption in buildings, industrial complexes, and urban systems, reducing energy losses, and increasing the efficiency of resource use, as well as for the construction of various elements of energy systems in general. Well-known methods and algorithms of swarm intelligence are also actively applied to forecast energy production from renewable sources, such as solar and wind energy. This allows better management of energy sources and planning of their use. The relevance of modifications of methods and algorithms is due to the issues of speeding up their work when solving machine learning problems, in particular, in nonlinear regression models, classification, and clustering problems, where the number of observed data can reach tens and hundreds of thousands or more. The work considers and modifies well-known effective methods and algorithms of swarm intelligence (particle swarm optimization algorithm, bee optimization algorithm, differential evolution method) for finding solutions to multidimensional extremal problems with and without restrictions, as well as problems of nonlinear regression analysis. The obtained modifications of the well-known classic effective methods and algorithms of swarm intelligence, which are present in the work, effectively solve complex scientific and applied tasks of designing complex objects and systems. A comparative analysis of methods and algorithms will be conducted in the next study on this topic. Keywords: optimization, swarm intelligence, mathematical modelling, nonlinear regression, complex objects and systems.

Performance prediction and analysis of a solar assisted medium-deep geothermal heating system

Abstract The solar assisted medium-deep geothermal heating (SAMGH) system is a novel kind of heating system that can combine the benefits of geothermal and solar energy. However, the variations in borehole heat exchanger (BHE) performance and the intermittency of solar energy pose challenges for predicting the overall performance of the coupled system and designing the operational strategies. To conduct simulation on the SAMGH system for performance prediction and analysis, a coaxial medium-deep borehole heat exchanger coupled with the solar energy heating system for an office building in Xi’an was developed. The TRNSYS software was employed to establish the model of the coupled system. A ground source heat pump (GSHP) heating system was used for comparison. The simulation results showed that with the introduction of the solar energy and heat storage modules, the annual operating time of the geothermal system only accounts for 32.06%. The energy consumption of the coupled system can be reduced from 63585 kW to 44586 kW, and the energy consumption proportion of the geothermal system in total value decreased from 69.10% to 40.58%. Therefore, the average coefficient of performance (COP) of the heat pump and the system were improved by 63.71% and 91.77%, respectively. Moreover, because the solar energy is beneficial to the ground heat recovery, the average ground temperature increased from 42.5 °C to 43.88 °C after ten years of operation. The proposed design method and simulation results can serve as a reference for design method and performance analysis of the geothermal and solar coupled system.

Study on the Optimal Configuration of Battery Energy Storage System in Distribution Networks Considering Carbon Capture Units

Background: The implementation of Battery Energy Storage Systems (BESSs) and carbon capture units can effectively reduce the total carbon emissions of distribution networks. However, their widespread adoption has been hindered by the high investment costs associated with the BESSs and power generation costs of carbon capture units. Objective: The objective of this paper is to optimize the location and sizing of BESSs in distribution networks that comprise renewable power plants and coal-fired power units with carbon capture systems. The optimization process aims to minimize the grid’s impact from the configuration while maximizing economic cost savings and the benefits of reducing carbon emissions. Methods: A bi-layer optimization model is proposed to determine the configuration of BESSs. The upper layer of the model optimizes the size and operation strategy of the BESSs to minimize the configuration and power generation costs, using YALMIP and CPLEX optimization tools. Carbon emission reduction benefits are considered through deep peak-shaving and carbon tax. The lower layer of the model aims to optimizes the placement of the BESSs to minimize voltage fluctuation and network loss in the power grid. To achieve this, we improved the efficiency of the Nondominated Sorting Genetic Algorithm II (NSGA-II) to update the BESS’s placement. Results: The IEEE33-bus and IEEE118-bus systems were utilized for simulation and comparison in various scenarios. The findings demonstrate that the proposed configuration method can decrease the cost of investment and power generation. Furthermore, it reduces the degree of node voltage fluctuation and network loss in the distribution network. Conclusion: The study reveals that determining the optimal scale of BESSs can mitigate high energy consumption in carbon capture systems and improve the overall performance of power systems that integrate carbon capture technology and renewable power plants.