Search Results (3,287)

Power systems are undergoing a transformative transition as consumers seek greater participation in managing electricity systems. This shift has given rise to the concept of “prosumers,” individuals who both consume and produce electricity, primarily through renewable energy sources. While renewables offer undeniable environmental benefits, they also introduce significant energy management challenges. One major concern is the variability in energy consumption patterns within households, which can lead to inefficiencies. Also, improper energy management can result in economic losses due to unbalanced energy control or inefficient systems. Home Energy Management Systems (HEMSs) have emerged as a promising solution to address these challenges. A well-designed HEMS enables users to achieve greater efficiency in managing their energy consumption, optimizing asset usage while ensuring cost savings and system reliability. This paper presents a comprehensive systematic review of optimization techniques applied to HEMS development between 2019 and 2024, focusing on key technical and computational factors influencing their advancement. The review categorizes optimization techniques into two main groups: conventional methods, emerging techniques, and machine learning methods. By analyzing recent developments, this study provides an integrated perspective on the evolving role of HEMSs in modern power systems, highlighting trends that enhance the efficiency and effectiveness of energy management in smart grids. Unifying taxonomy of HEMSs (2019–2024) and integrating mathematical, heuristic/metaheuristic, and ML/DRL approaches across horizons, controllability, and uncertainty, we assess algorithmic complexity versus tractability, benchmark comparative evidence (cost, PAR, runtime), and highlight deployment gaps (privacy, cybersecurity, AMI/HAN, and explainability), offering a novel synthesis for AI-enabled HEMS. Full article

Active control of injection quantity during start-up idle optimizes automotive diesel engine starting performance, aligning with low-carbon goals. Conventional methods rely on a calibrated demand torque map adjusted by speed, temperature, and pressure variations, requiring extensive labor for calibration and limiting energy-saving and emission improvements. To address this problem, this paper proposes a transient injection quantity active control method for the start-up process based on the variation characteristics of target speed. Firstly, the target speed variation characteristics of the start-up process are optimized by setting different accelerations. Secondly, a transient injection quantity control strategy for the start-up process is proposed based on the target speed variation characteristics. Finally, the control strategy proposed in this paper was compared with the conventional starting injection quantity control method to verify its effectiveness. The results show that the start-up idle control strategy proposed in this paper reduces the cumulative fuel consumption of the start-up process by 25.9% compared to the conventional control method while maintaining an essentially unchanged start-up time. The emissions of hydrocarbon (HC), carbon monoxide (CO), and nitrogen oxides (NOx) exhibit peak reductions of 12.4%, 32.5%, and 62.9%, respectively, along with average concentration drops of 27.2%, 35.1%, and 41.0%. Speed overshoot decreases by 25%, and fluctuation time shortens by 23.6%. The results indicate that the proposed control method not only avoids complicated calibration work and saves labor and material resources but also effectively improves the starting performance, which is of great significance for the diversified development of automotive power sources. Full article

Electricity price spikes are the most important characteristic of the electricity price time series. Operationally, they result from various stresses in the power system or the strategic bidding behavior of market participants. These high prices are important as they represent economic opportunities in the form of profits and savings. Theoretically, price spikes are defined as prices that exceed a threshold over a typically short duration. This definition serves as the basis for several established modeling approaches in the literature. In general, the threshold component determines the design of a price spike model, often overlooking the duration aspect. Therefore, this paper presents a simple yet informative model to quantify the duration of electricity price spikes using historical price data from different market jurisdictions. We approach the problem through the lens of survival analysis, a widely used technique for evaluating time-to-event data. Specifically, we use the Kaplan–Meier (KM) estimator, which enables a nonparametric evaluation of the survival (duration) of price spikes over time. We refer to this as the price spike duration model. Full article

Electrical grids are currently facing new demands due to increased power consumption, growing interconnections, and limitations regarding transmission capacity. These factors introduce considerable challenges for the dispatch and operation of large-scale power systems, often resulting in congestion, energy losses, and high operating costs. To address these issues, this study presents a transmission line switching strategy, which is formulated as an optimal power flow problem with binary variables and solved via mixed-integer nonlinear programming. The proposed methodology was tested using MATLAB’s MATPOWER toolbox version 8.1, focusing on power systems with five and 3374 nodes. The results demonstrate that operating costs can be reduced by redistributing power generation while observing the system’s reliability constraints. In particular, disconnecting line 6 in the 5-bus system yielded a 13.61% cost reduction, and removing line 1116 in the 3374-bus system yielded cost savings of 0.0729%. These findings underscore the potential of transmission line switching in enhancing the operational efficiency and sustainability of large-scale power systems. Full article

The optimal control of cooling water systems is of great significance for energy saving inchiller plants. Previously optimal control methods optimize the flow rate, temperature or temperature difference setpoints but cannot control pumps and cooling tower fans directly. This study proposes a direct optimal control method for pumps and fans based on derivativecontrol strategyby decoupling water flow rate optimization and airflow rate optimization, which can make the total power of chillers, pumps and fans approach a minimum. Simulations for different conditions were performed for the validation and performance analysis of the optimal control strategy. The optimization algorithms and implementation methods of direct optimal control were developed and validated by experiment. The simulation results indicate that total power approaches a minimum when the derivative of total power with respect to water/air flow rate approaches zero. The power-saving rate of the studied chiller plant is 13.2% at a plant part-load ratio of 20% compared to the constant-speed pump/fan mode. The experimental results show that the direct control method, taking power frequency as a controlled variable, can make variable frequency drives regulate their output frequencies to be equal to the optimized power frequencies of pumps and fansin a timely manner. Full article

This study investigates the application and transformation potential of smart city concepts along Zhongyang Road in Hsinchu City, Taiwan. By introducing evaluation mechanisms such as the Smart City Maturity Index (SCMI) and the Composite Key Performance Indicator (CKPI), the research systematically analyzes the effectiveness of implementations across areas including transportation, energy, governance, and citizen engagement. Furthermore, Formula (1) is applied to assess the improvement in average delay time after the integration of smart technologies, while Formula (2) quantifies the annual energy savings achieved by replacing conventional streetlights with solar-powered ones, demonstrating tangible energy-saving and carbon-reduction benefits. The findings indicate that cross-sector collaboration and technological integration can significantly enhance urban operational efficiency and sustainability, providing valuable insights for the development of other new towns. Full article

Personal comfort systems (PCSs), which provide targeted heating or cooling to specific body parts, have emerged as a promising solution to enhance occupant comfort while reducing energy use in buildings. Among the many factors influencing PCS performance, heat transfer mechanisms (HTMs) play a pivotal role. However, a critical gap remains in the literature regarding the identification of optimal HTMs for achieving both thermal comfort and energy efficiency in PCSs. To address this gap, our study investigates the impact of conduction, convection, and radiation in PCSs on thermal comfort enhancement and energy performance under both heating and cooling modes. A meta-analysis was conducted, extracting data from 64 previous studies to evaluate the effects of HTMs of PCSs on thermal sensation vote (TSV), overall comfort (OC) and corrective energy power (CEP). Results indicate that PCSs typically improve users’ thermal sensation and comfort by about one scale unit in both heating and cooling modes. Radiative HTM is the most effective individual method, while combined conductive and convective HTMs perform best overall. Most PCSs operate efficiently, consuming less than 200 W/°C, with conduction in heating and convection in cooling being recommended for optimal comfort and energy efficiency. These findings suggest that selecting optimal HTMs for PCSs is crucial for achieving maximum comfort performance and energy savings. Data on combined HTMs of PCSs remain limited, underscoring the need for further research in this area. Future research should prioritize optimizing HTMs, especially radiative and combined methods, to maximize comfort and energy savings in PCS design. Full article

Electric vehicle (EV) adoption is a critical step toward achieving sustainable transportation and reducing carbon emissions, especially in regions like Saudi Arabia that are undergoing rapid urban development and energy diversification. However, the widespread adoption of EVs is hindered by a variety of interrelated economic, infrastructural, and policy-related factors. This study aims to systematically identify and structure these influencing factors using Interpretive Structural Modeling (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis. Based on a thorough literature review and expert consultation, 17 key factors affecting EV adoption in Saudi Arabia were identified. The ISM results reveal that purchase price, long-term savings, resale value, urban planning, and accessibility are among the most influential drivers of adoption. The MICMAC analysis complements these insights by categorizing the variables based on their driving and dependence power. The developed hierarchical model provides insights into the complex interdependencies among these factors and offers a strategic framework to support policymakers and stakeholders in accelerating EV uptake. The study contributes to a deeper understanding of the dynamics influencing EV adoption in emerging markets. Full article

With the growing global deployment of Fiber-to-the-Home (FTTH) networks driven by the demand for ensuring high-capacity broadband services, mobile network operators (MNOs) face challenges of excessive energy consumption (EC) of wired optical access networks (OANs). This paper presents a comprehensive review of methods aimed at improving the energy efficiency (EE) of wired access passive optical networks (PONs) and active optical networks (AONs). The most important energy management and power-saving methods for Optical Line Terminals (OLTs) and Optical Network Units (ONUs), as key OAN components, are overviewed in the paper. Special attention in the paper is further given to analyzing the impact of a constant increase in the number of subscribers and average data rate per subscriber on global instantaneous power and annual energy consumption trends of FTTH Gigabit PONs (GPONs) and FTTH point-to-point (P-t-P) networks. The analysis combines the real ONU/OLT device-level power profiles and the number of installed OLT and ONU devices with data traffic and subscriber growth projections for the period 2025–2035. A comparative EE analysis is performed for different MNO FTTH OAN architectures and technologies, point-of-presence (PoP) subscriber capacities, and GPON-to-P-t-P subscriber distribution ratios. The findings indicate that different FTTH PON and AON architectures, FTTH technologies, and PON-to-AON subscriber distributions can yield significantly different EE gains in the future. This review paper can serve as a decision-making guide for MNOs in balancing performance and sustainability goals, and as a reference for researchers, engineers, and policymakers engaged in designing next-generation wired optical access networks with minimized environmental impact. Full article

Using the Kaya identity and LMDI method, this study analyzes the influence of population, GDP per capita, energy intensity, and carbon intensity on Xinjiang’s carbon emissions, and compares the effects of industrial structure, energy intensity, and carbon intensity on the industrial sectors during the Eighth to Twelfth Five-Year Plan (FYP) periods. Key findings are as follows: (1) Xinjiang’s carbon emissions center on resource- and energy-intensive sectors, emissions from sectors such as extraction of petroleum and natural gas, fuel processing, chemicals, ceramics and cement, iron and steel, and non-ferrous and power generation accounted for 62% of carbon emissions in 2015; (2) after the Sixth FYP, GDP per capita effect turned into the core driver of carbon emission growth, while the population effect played an auxiliary role. Meanwhile, the energy intensity effect exerted a marked inhibitory impact on the increase in carbon emissions, yet the restraining effect of carbon intensity was comparatively limited; (3) during the Eighth to Twelfth FYPs, carbon emission growth was mainly attributed to industrial structure effects of the mining and washing of coal, extraction of petroleum and natural gas, fuel processing, chemicals, ceramics and cement, iron and steel, non-ferrous and power generation. Energy intensity and carbon intensity effects in various industries inhibited emission growth. Based on new trends in Xinjiang’s socioeconomic development, policy recommendations proposed including promoting the low-carbon transformation of industrial structure, profound restructuring of energy consumption, and improving energy efficiency by advancing energy-saving technology. Full article

Laboratory and industrial tests were conducted to study the impact of grinding media material on key indicators such as grinding product particle size, sodium cyanide consumption, gold recovery rate, unit power consumption, and ball consumption. Laboratory test results indicate that the reasonable mixing of ceramic and steel balls can achieve an increase of more than 2.8% in the fineness of the grinding product (−0.038 mm), an increase of 0.3% in the gold recovery rate, and a decrease of 1.3 kg/t in the consumption of sodium cyanide. Industrial trial studies indicate that, compared to the traditional steel ball scheme, using a ceramic ball to steel ball mass ratio of 3:1 under conditions of processing 50,000 tons of gold concentrate annually can save a total of 1.31 million yuan in annual ball consumption, electricity consumption, and cyanide consumption costs. Additionally, the improved recovery rate generates an additional economic benefit of 3.63 million yuan, resulting in an annual comprehensive economic benefit increase of 4.94 million yuan. In summary, in gold cyanide leaching grinding, the mixture ratio between ceramic balls and steel balls demonstrates significant potential for energy conservation, cost reduction, and efficiency enhancement, providing a theoretical basis and technical support for subsequent process optimization and green gold extraction. Full article

With the rapid growth in the penetration of Electric Vehicles (EVs), their large-scale uncoordinated charging behavior presents significant challenges to the hosting capacity of traditional distribution networks (DNs). The novelty of this paper lies in its methodology, which integrates a Markov Chain Monte Carlo (MCMC) method for realistic load profiling with a bi-level optimization framework for Time-of-Use (TOU) pricing, whose effectiveness is then rigorously evaluated through an Optimal Power Flow (OPF)-based assessment of the grid’s hosting capacity. First, to compensate for the limitations of historical data, the MCMC method is employed to simulate the uncoordinated charging process of a large-scale EV fleet. Second, the bi-level optimization model is constructed to formulate a globally optimal TOU tariff that maximizes charging cost savings for EV users. At the same time, its lower-level simulates the optimal economic response of the EV user population. Finally, the change in the minimum daily hosting capacity is calculated based on the OPF. Case study simulations for IEEE 33-bus and IEEE 69-bus systems demonstrate that the proposed model effectively shifts charging loads to off-peak hours, achieving stable user cost savings of 20.95%. More importantly, the findings reveal substantial security benefits from this economic strategy, validated across diverse network topologies. In the 33-bus system, the minimum daily capacity enhancement ranged from 174.63% for the most vulnerable node to 2.44% for the strongest node. In the 69-bus system, vulnerable nodes still achieved a significant 78.62% improvement. This finding highlights the limitations of purely economic assessments and underscores the necessity of the proposed integrated framework for achieving precise, location-dependent security planning. Full article

The development of a multifunctional invention requires several refinements for optimizing each function. This study presents a Theory of Inventive Problem Solving (TRIZ)-based conceptual framework for enhancing an innovative multifunctional assistive technology device that integrates the functionalities of a rollator walker, wheelchair, Pilates chair, and stepladder. The limitations of the multifunctional rollator walker were identified from the user feedback of a foundational work and were then addressed by identifying the engineering and physical contradictions and problem modeling using Su-field analysis. Through TRIZ Inventive Principles, the proposed design eliminates common trade-offs between portability, stability, and usability. The conceptual enhancement incorporates features such as deployable steps, the utilization of high strength–to–weight ratio material, foldability, a passive mechanical brake-locking system, retractable armrests, the incorporation of spring-assist hinges, and the use of large tires with vibration-dampening hubs. This study contributes a novel, user-focused, and space-saving mobility solution that aligns with the evolving demands of assistive technology, laying the groundwork for future iterations involving smart control, power assist, and modular enhancements. Full article

With growing urban populations and rapid technological advancement, major cities worldwide are facing pressing challenges from surging energy demands. Interestingly, substantial unused space within residential buildings offers potential for installing renewable energy systems coupled with energy storage. This study innovatively proposes a grid-connected photovoltaic (PV) system integrated with pumped hydro storage (PHS) and battery storage for residential applications. A novel optimization algorithm is employed to achieve techno-economic optimization of the hybrid system. The results indicate a remarkably short payback period of about 5 years, significantly outperforming previous studies. Additionally, a threshold is introduced to activate pumping and water storage during off-peak nighttime electricity hours, strategically directing surplus power to either the pump or battery according to system operation principles. This nighttime water storage strategy not only promises considerable cost savings for residents, but also helps to mitigate grid stress under time-of-use pricing schemes. Overall, this study demonstrates that, through optimized system sizing, costs can be substantially reduced. Importantly, with the nighttime storage strategy, the payback period can be shortened even further, underscoring the novelty and practical relevance of this research. Full article

This paper presents a unified modeling framework for quantifying power and energy consumption in motor-driven systems operating under variable frequency control and soft starter conditions. By formulating normalized expressions for voltage, current, and power factor as functions of motor speed, the model enables accurate estimation of instantaneous and cumulative energy use using only measurable electrical quantities. The effect of soft starter operation during startup is incorporated through ramp-based profiles, while variable frequency control is modeled through dynamic speed modulation. Analytical results show that variable speed control can achieve energy savings of up to 36.1% for sinusoidal speed profiles and up to 42.9% when combined with soft starter operation, with the soft starter alone contributing a consistent 8.6% reduction independent of the power factor. To support energy optimization under uncertain demand scenarios, a two-stage stochastic optimization framework is developed for motor sizing and control assignment, and four physics-guided machine learning models—MLP, LSTM, GRU, and XGBoost—are benchmarked to forecast normalized energy ratios from key electrical parameters, enabling rapid and interpretable predictions. The proposed framework provides a scalable, interpretable, and practical tool for monitoring, diagnostics, and smart energy management of industrial motor-driven systems. Full article