Reduce Energy Costs Research Articles

A Framework for Distributed Orchestration of Cyber-Physical Systems: An Energy Trading Case Study

The increasing number of active energy consumers, also known as energy prosumers, is dramatically changing the electricity system. New products and services that adopt the concept of dynamic pricing are available to the market, where demand and price forecasting are applied to determine schedule loads and prices. Throughout this manuscript, a novel framework for energy trading among prosumers is introduced. Rather than solving the problem in a centralized manner, the proposed orchestrator relies on a distributed game theory to determine optimal bids. Experimental results validate the efficiency of proposed solution, since it achieves average energy cost reduction of 2×, as compared to the associated cost from the main grid. Additionally, the hardware implementation of the introduced framework onto a low-cost embedded device achieves near real-time operation with comparable performance to state-of-the-art computational intensive solvers.

Digitalization of Adjustment and Maintenance Processes for Optimizing Energy Consumption in the Oil and Gas Sector

This article examines the impact of digitalization on adjustment and maintenance processes in the oil and gas sector aimed at optimizing energy consumption. Modern technologies such as big data analysis, the Internet of Things (IoT), and Machine Learning (ML) algorithms are discussed, highlighting how they assist companies in reducing energy consumption and enhancing operational efficiency. Examples of the implementation of these solutions in major companies are provided, demonstrating significant reductions in energy costs and equipment downtime. The advantages of contemporary approaches over traditional adjustment and maintenance methods are emphasized.

Synergistic Effects of Energy Storage Systems and Demand-Side Management in Optimizing Zero-Carbon Smart Grid Systems

The urgent need to mitigate climate change and reduce reliance on fossil fuels has driven the global shift towards renewable energy sources (RESs). However, the intermittent nature of RESs poses significant challenges to the widespread adoption of Zero-Carbon Smart Grids (ZCSGs). This study proposes a synergistic framework to address this hurdle. It utilizes energy storage systems (ESSs) by comparing Vanadium redox flow batteries (VRFBs) and Lithium ion batteries (LIBs) to identify the most suitable option for ZCSGs, with precise models enabling robust performance evaluation. Moreover, an accurate demand-side management (DSM) strategy considering power elasticity to manage discrepancies between electricity load, RES generation, and ESS availability is introduced for estimating fair, dynamic tariffs. An advanced load and weather-forecasting strategy is introduced for improving grid planning and management. An advanced optimization algorithm enhances grid stability and efficiency. Simulations demonstrate significant reductions in carbon footprint, peak power demand, and reliance on fossil fuels. The study finds that VRFBs outperform LIBs in cost and security, and dynamic tariffs based on accurate DSM significantly reduce energy costs. This work explores the challenges and opportunities of this integrated approach, offering policy recommendations and future research directions for truly optimized ZCSG implementation.

A multi-objective energy-efficient scheduling algorithm for solving hybrid flow workshops with parallel heterospeed machines

Abstract. In hybrid flow assembly workshops, minimizing completion time and reducing energy consumption are two key indicators of efficient workshop management, which are often inversely related. This study investigates how parallel heterospeed machines with adjustable times can reduce energy costs and assembly times at the production system level. To address these issues, we propose the MO-HFSP-Batch algorithm based on the enhanced NSGA-II algorithm, aimed at improving the algorithm's optimization capability. Given the trade-offs between optimization objectives and the high computational complexity of the proposed multi-objective mixed integer programming, a three-tier chromosome encoding structure was introduced during the algorithm design phase. This structure meets the triple requirements of batching assembly tasks, matching operations with parallel machines, and sequencing operations in actual hybrid flow assembly workshops. Extensive data analysis proves that our proposed algorithm effectively solves the scheduling issues in hybrid flow workshops and performs better in solving completion time indicators and stability of non-dominated solution sets than other algorithms.

Prioritizing Energy Performance Improvement Factors for Senior Centers Based on Building Energy Simulation and Economic Feasibility

This study examined energy performance improvement factors by analyzing both energy performance and the economic impacts to reduce energy costs for senior centers. A fact-finding survey was conducted on 20 senior centers in a metropolitan area, identifying key energy improvement factors. Energy simulations of the buildings were performed using ECO2, an officially certified energy assessment program in Korea, comparing the energy requirements before and after the improvements. The energy demand, energy consumption, and floor area were analyzed, with the J, K, and S standard models selected based on the median values of these factors. To assess the impact of the improvements, blower door tests were conducted on two senior centers before and after window upgrades. Based on the ECO2 simulations and the blower door test results, improvement priorities were identified in the following order: windows, exterior walls, boilers, roofs, and doors. Finally, an economic feasibility analysis applied the construction and heating costs to the standard models. Over a 40-year period, only boiler improvements generated a net profit. Without government support, this study recommends prioritizing boiler upgrades when selecting energy performance improvements.

INCREASING THE EFFICIENCY OF THE SEED SHAFT TURBER OF THE LINTER MACHINE

It is noted that the main factor in reducing the quality of the lint is its contamination caused by the increased content of crushed seeds. The latter is explained by the mechanical impact on the seed roller from the rigid elements of the working bodies of the linter machine, such as saws, grates and agitator. The magnitude of this impact largely depends on the density of the seed roller itself. In addition, its high density is the cause of additional energy costs for its rotation. To reduce the dynamic loads on the seeds in the roller, an original design of agitator with elastic inclined blades separated by elastic bushings is proposed. The proposed agitator provides intensive mixing of seeds in the seed roller both in the axial direction and in the direction of its rotation. It is shown that agitator with elastic blades allows to reduce energy costs by 20 % and reduce seed damage by 0.3 %

Optimizing Multi-Channel Green Supply Chain Dynamics with Renewable Energy Integration and Emissions Reduction

In response to the global imperative of mitigating greenhouse gas emissions (GHGs) and the shifting landscape of business models toward multi-channel structures, this study delves into the intricacies of a green supply chain. Operating through both online and traditional channels with uncertain demands, the producer’s distribution strategy prompts an exploration of supply chain dynamics. Utilizing an integer programming model, this study calculates optimal prices, optimizes total profit, and minimizes transportation costs to curtail carbon dioxide emissions, depending on the transportation mode. Additionally, this study incorporates renewable energy sources into the production and transportation processes to further minimize carbon dioxide emissions. The integration of renewable energy not only supports environmental goals, but also contributes to the overall profitability of the supply chain by reducing energy costs. Employing a theoretical technique for linearization, the model, resolved through the Jimenez and TH methods, demonstrates efficacy in reconciling economic and environmental goals. The Jimenez method enables the transformation of fuzzy parameters into deterministic equivalents, allowing for a more reliable optimization during uncertainty, while the TH method provides an interactive fuzzy multi-objective approach, aligning the model’s dual objectives for both economic and environmental goals. Notably, when transportation costs to both markets are equal, the model prioritizes devices with a lower environmental impact, showcasing adaptability. Furthermore, the proposed solution empowers decision makers to influence pricing and enhance the entire supply chain’s profitability. In conclusion, this research offers nuanced insights, strategically aligning economic viability with environmental sustainability in the discourse on green supply chains.

Sustainable Resource Management in an Agro-Industrial Cluster: A Case Study

Introduction: The challenge of water supply and sustainable energy is crucial for addressing long-term clean water and energy challenges. Moreover, there has been an increase in water and air pollution resulting from the community's activities. Objective: The objective of this study is to investigate conservation strategies must be enacted to reduce dependency on clean water and energy supplies. Theoretical Framework: In this topic, the main concepts and theories that underpin the research are presented. Cost reductions, environmental sustainability, and an improved public image for understanding are context of the investigation. Method: The methodology adopted for this research comprises the case study uses a quantitative descriptive method to identify the estimated potential amount of biogas and water quality produced from tapioca and sago industrial wastewater. Data collection was carried out through the specific methods used, such as interviews, questionnaires, observations,and laboratory testing. Results and Discussion: The results obtained revealed energy generation from starch wastewater and recycled water from tapioca wastewater. The wastewater was treated in a biogas reactor as primary treatment to reduce the environmental load and produce biogas simultaneously. The wastewater recycling scheme for the tapioca industry, the quality and quantity of treated water recycled. Research Implications: Biogas production from wastewater reduces energy costs in starch industries. Recycling wastewater saves fresh water daily for starch production. Excess methane can be used for electricity generation or distributed to the community. Implementing anaerobic reactors minimizes environmental pollution. The system boosts sustainability, lowering reliance on external water and energy sources. Originality/Value: This study contributes to the literature by innovative integration of water recycling and biogas generation in starch industries. First application of CoLAR technology for energy self-sufficiency in tapioca and sago sectors. Demonstrates significant cost savings by reusing treated wastewater in production. Offers a scalable model for sustainable water and energy use in agro-industrial clusters. Contributes to reducing environmental impact through waste-to-energy conversion.

Summer Energy Use and Comfort Analysis in Rural Chinese Dwellings: A Case Study of Low-Income Older Populations in Shandong

This paper aims to investigate the indoor environmental conditions and energy use behaviours of older individuals in rural cold climates of China, with a specific focus on cooling practices during the summer months in the Shandong region. This study employs a mixed-method approach, combining quantitative indoor environmental monitoring with qualitative interviews and observations, to explore the relationship between environmental factors, household living conditions, and energy use patterns across five types of elderly households: three generations living together, older people living with grandchildren, older people living with children, older couples living together, and older people living alone. Data collection was conducted over five weeks during the summer of 2023 using HOBO UX100-003 data loggers, while external weather conditions were monitored by the China Meteorological Administration. Face-to-face interviews were conducted to gain deeper insights into daily cooling behaviours and energy use. The results reveal that cooling practices and indoor environmental conditions vary significantly among the different household types. Multigenerational households showed more complex energy use dynamics, with younger family members frequently operating high-energy appliances like air conditioners, while older individuals tended to rely on natural ventilation and electric fans to reduce energy costs. In contrast, older couples and solitary older individuals demonstrated more conservative cooling behaviours, often enduring higher indoor temperatures due to limited financial resources and a desire to minimize energy expenditures. Despite the high energy use intensity in some households, many homes failed to achieve comfortable indoor environments, particularly in dwellings with minimal insulation and older building materials. This study concludes that economic status, household structure, and building characteristics play crucial roles in shaping cooling behaviours and indoor comfort during the summer.

Optimization of PV benefits for Electric Vehicles Charging Station Using a Deterministic Method and Fuzzy Inference System Method

Utilizing renewable energy, specifically Photovoltaic (PV), for Electric Vehicle (EV) charging presents diverse technical and economic opportunities, reflecting a recent trend in innovation and research to address global transportation challenges while highlighting the value of renewable sources. In this context, this paper aims to devise an optimal power management strategy for an EV charging station powered by a PV-based microgrid (MG). The main purpose is to improve the benefits of PV production in recharging EVs to increase self-consumption, decrease dependency on the power grid, and reduce energy costs. Production data from a real-scale MG at the Catholic University in France were used as a case study. This MG comprises a heterogeneous fleet of EVs, PV sources, stationary storage, EV charging stations, and a connection to the power grid. The optimization of PV benefits relies on leveraging the knowledge of EV characteristics to modulate their charge profile. In this context, two methods are used and compared for the modulation of the EVs' charging profile: a deterministic method and a Fuzzy Inference System (FIS)-based method. For each method, different scenarios emerge based on the knowledge or lack thereof of the EVs' characteristics. This methodology permits the optimal distribution of energy among multiple EVs and regulates the necessary power to achieve the desired charge level upon departure. It encourages charging through PV production, resulting in reduced energy costs. The comparison of total costs reveals notable differences between the deterministic and FIS approaches for both slow and fast charging modes. In the case of slow charging, the deterministic approach achieves savings of -863.4 c€/day, whereas the FIS method delivers greater savings of -898.7 c€/day. For fast charging, the deterministic approach yields savings of -524.6 c€/day, while the FIS method results in even higher savings of -540.7 c€/day. These results highlight that the FIS approach enables better cost management, primarily due to more efficient utilization of PV energy. Additionally, simulation results from MATLAB/Simulink confirm the effectiveness of the FIS-based method, which improves PV energy utilization to 98.20%, compared to 81.60% with the deterministic approach. This highlights the superior efficiency of the proposed FIS.

Influence of melatonin on the development of obesity

In recent years, increasing attention of researchers has been paid to the role of melatonin in the regulation of body weight. When analyzing literature data, the problem of the mechanism of action of melatonin on the body and its use in the correction of metabolic disorders is considered. Disruption of circadian rhythms caused by the use of artificial light at night leads to decreased melatonin secretion. This helps increase appetite and reduce energy costs. In addition, melatonin plays a major role in modulating the secretion of adipokines by influencing lipid metabolism. Decreased melatonin production causes insulin resistance and metabolic circadian disorganization, leading to obesity. Assessing the role of melatonin in the development of obesity and its accompanying pathologies is a promising area of scientific research in the field of diagnosis, prevention and treatment of endocrine and cardiovascular pathologies.

A smart home energy management system based on human activity recognition and deep reinforcement learning

Smart home energy management systems (SHEMS) can help users save on energy costs by controlling various household loads in response to environmental changes. However, human activity is closely related to household energy consumption, which can lead to potential energy waste. Additionally, smart homes often feature multiple types of distributed energy resources (DER), and how to leverage their potential for participating in grid scheduling without significantly impacting users remains an unresolved issue. This paper proposes an intelligent home energy management method based on human activity recognition (HAR) and deep reinforcement learning. By using activity recognition results derived from intrusive load monitoring (ILM), we categorize contexts into two groups. The agent employs the soft actor-critic (SAC) algorithm for real-time control. In the first context, the goal is to ensure user satisfaction while reducing energy costs. In the second context, the focus is on controlling DER to participate in grid ancillary services to generate revenue. The proposed agent is trained using a real dataset, and simulations validate the effectiveness of the algorithm in home energy management.

Dynamic characteristics of pumped thermal-liquid air energy storage system: Modeling, analysis, and optimization

Pumped thermal-liquid air energy storage (PTLAES) is a novel energy storage technology that combines pumped thermal- and liquid air energy storage and eliminates the need for cold storage. However, existing studies on this system are all based on steady-state assumption, lacking dynamic analysis and optimization to better understand the system’s performance under cyclic operation. To fill this gap, the mainbody-linearized cyclic dynamic model of the PTLAES system with packed bed thermal energy storage (TES) was first developed. Then, the dynamic characteristics of the baseline system were investigated. Sensitivity analyses were carried out on TES parameters. Minimal values of levelized cost of storage (LCOS) were observed for all parameters in the range of interest. Subsequently, the TES circuit was optimized, and a triple improvement of efficiency and energy density enhancement, discharge stabilization, and cost reduction was achieved. The optimized system's round-trip efficiency and energy density increased from 61.7% to 63.1% and from 141.9 kWh/m³ to 159.2 kWh/m³, and the LCOS decreased from 163.2 $/MWh to 159.4 $/MWh. A power offset ratio lower than 3% was reached, which is the lowest value ever reported in the literature. This study provides reference for future design and operation of the PTLAES system.

Energy Consumption and Energy Costs Based on Time-of- Day Tariffs in Seafood Processing Units in Kerala, India

Energy conservation in seafood processing is crucial due to the highly energy-intensive nature of its main stages such as pre-processing, processing, and storage, which must operate under cold chain conditions. The variability of energy tariffs based on Time of Day (TOD) significantly impacts the economic sustainability of these operations. This study evaluates the electrical energy consumption patterns of seafood processing units in South India, categorized by production capacity. Primary data were collected from 10 seafood processing units, assessing energy consumption during Normal Load Time (NLT), Peak Load Time (PLT), and Off-Peak Load Time (OLT) over a one-year period. Resultsrevealed that smaller capacity units (<30 T/D) accounted for 56% of total production during PLT, despite energy tariffs being highest during this time.These low-capacity units also exhibited higher mean energy consumption and costs compared to larger units, suggesting inefficiencies. The findings highlight the need for optimized energy management strategies, such as rescheduling production to lower-tariff periods and adopting more energy-efficient technologies. Implementing such measures could reduce energy costs and improve the overall profitability of seafood processing operations, particularly for smaller units.

Stochastic Decision-Making Optimization Model for Large Electricity Self-Producers Using Natural Gas in Industrial Processes: An Approach Considering a Regret Cost Function

In the context of high energy costs and energy transition, the optimal use of energy resources for industrial consumption is of fundamental importance. This paper presents a decision-making structure for large consumers with flexibility to manage electricity or natural gas consumption to satisfy the demands of industrial processes. The proposed modelling energy system structure relates monthly medium and hourly short-term decisions to which these agents are subjected, represented by two connected optimization models. In the medium term, the decision occurs under uncertain conditions of energy and natural gas market prices, as well as hydropower generation (self-production). The monthly decision is represented by a risk-constrained optimization model. In the short term, hourly optimization considers the operational flexibility of energy and/or natural gas consumption, subject to the strategy defined in the medium term and mathematically connected by a regret cost function. The model application of a real case of a Brazilian aluminum producer indicates a measured energy cost reduction of USD 3.98 millions over a six-month analysis period.

Water–Energy Nexus-Based Optimization of the Water Supply Infrastructure in a Dryland Urban Setting

Managing water supply systems is essential for developing countries to face climate variability in dryland settings. This is exacerbated by high energy costs for pumping, water losses due to aging infrastructures, and increasing demand driven by population growth. Therefore, optimizing the available resources using a water–energy nexus approach can increase the reliability of the water distribution network by saving energy for distributing the same water. This study proposes a methodology that optimizes the Water Distribution Network (WDN) and its management that can be replicated elsewhere, as it is developed in a data-scarce area. Indeed, this approach shows the gathering of WDN information and a model to save energy by optimizing pump schedules, which guarantee water distribution at minimal operational costs. The approach integrates a genetic algorithm to create pumping patterns and the EPANET hydraulic simulator to test their reliability. The methodology is applied for a water utility in the dryland urban setting of Lodwar, Turkana County, Kenya. The results indicate a potential reduction in energy costs by 50% to 57% without compromising the supply reliability. The findings highlight the potential of WEN-based solutions in enhancing the efficiency and sustainability of data-scarce water utilities in dryland ecosystems.

Theoretical Study of the Influence of Electroconvection on the Efficiency of Pulsed Electric Field (PEF) Modes in ED Desalination

Pulsed electric field (PEF) modes of electrodialysis (ED) are known for their efficiency in mitigating the fouling of ion-exchange membranes. Many authors have also reported the possibility of increasing the mass transfer/desalination rate and reducing energy costs. In the literature, such possibilities were theoretically studied using 1D modeling, which, however, did not consider the effect of electroconvection. In this paper, the analysis of the ED desalination characteristics of PEF modes is carried out based on a 2D mathematical model including the Nernst–Planck–Poisson and Navier–Stokes equations. Three PEF modes are considered: galvanodynamic (pulses of constant electric current alternate with zero current pauses), potentiodynamic (pulses of constant voltage alternate with zero voltage pauses), and mixed galvanopotentiodynamic (pulses of constant voltage alternate with zero current pauses) modes. It is found that at overlimiting currents, in accordance with previous papers, in the range of relatively low frequencies, the mass transfer rate increases and the energy consumption decreases with increasing frequency. However, in the range of high frequencies, the tendency changes to the opposite. Thus, the best characteristics are obtained at a frequency close to 1 Hz. At higher frequencies, the pulse duration is too short, and electroconvective vortices, enhancing mass transfer, do not have time to develop.

Two decades of progressive cost reduction: A paradigm shift for distributed solar photovoltaics and energy efficiency

The increasing deployment of renewable energy resources has led to massive energy cost reductions worldwide in the past decade. The emergence of this cost revolution led electricity consumers to increasingly adopt distributed renewable energy resources to decrease dependence on traditional power grids. This paper applies the integrated resource planning framework, the objective of which is to design a least-cost electricity system by looking at renewable energy resources, efficient appliances, and demand response management strategies to reduce electricity bills. The results show that the commercial entity can save its electricity bill by $0.16 if it installs 6 MW solar PV over the lifetime of the solar PV plants. Besides, it was observed that wind turbines were not economically feasible to install at the site because of low wind resources. Biogas power plant is too expensive mainly because of the cost of fuel (waste). It also shows that by retrofitting 2000 compact fluorescent lamps (CFLs) with light-emitting diodes (LEDs), the company can save $0.15 million. By shifting between 0.5 MW and 1.4 MW of heating and cooling demand to periods of low tariff costs, the company can save $0.013 million annually. The climate transition plan for this company relies on PV, efficiency interventions and demand response. The study also demonstrates that an integrated resource planning framework can be used to plan a mini-grid.

Multi-scale retrofit pathways for improving building performance and energy equity across cities: A UBEM framework

Institutional inequality has created an environment in which our physical surroundings reflect racial and economic biases – causing inequitable thermal comfort and energy consumption across neighborhoods. Just as energy and thermal comfort challenges are unique to each climate and built environment, the solutions to address these issues should be optimized for their context. We introduce a novel integrated data-driven and building energy simulation method that incorporates urban context, satellite imagery, and socioeconomic data to analyze multi-scale retrofit scenarios across disparate communities. We apply this method to case studies in New York City and Los Angeles to analyze energy and thermal comfort inequities across distinct climate zones and urban morphologies. Through parametric analysis, we demonstrate the efficacy of multi-scale retrofits at improving building performance and reducing existing thermal inequities. Our research shows that retrofit effectiveness is influenced by the microclimatic changes induced by the urban context and existing infrastructural inequity. For example, instantaneous building cooling energy demand in a disadvantaged community of Los Angeles is reduced more due to window retrofits (around 30 kWh) than by a greenspace retrofit (around 3 kWh). Greenspace installation dissipates more heat overnight and in the early morning, whereas window retrofits reduce solar heat gain during the day, showing the different heat reduction pathways that retrofits can achieve. Although the window retrofits lead to an order of magnitude higher utility cost savings ($70 savings from windows vs $9 for greenspace and $12 for reduced cars), the district-scale retrofits show promising pathways for reducing the cost burden across all residents. We also analyze pathways for achieving city-specific CO2 reduction targets and find that building-level retrofits are most effective at meeting building performance standards (reducing CO2 by 40%), whereas district-level retrofits should be used to achieve city-scale climate goals because of the sequestered and abated emissions offered by these pathways. This generalizable modeling framework empowers policymakers, urban planners, and building owners around the world to analyze pathways for meeting their climate action plan goals by reducing extreme heat, reducing greenhouse emissions, and reducing energy cost burden while improving environmental justice in their cities.

Performance Comparison of Bio-Inspired Algorithms for Optimizing an ANN-Based MPPT Forecast for PV Systems.

This study compares bio-inspired optimization algorithms for enhancing an ANN-based Maximum Power Point Tracking (MPPT) forecast system under partial shading conditions in photovoltaic systems. Four algorithms-grey wolf optimizer (GWO), particle swarm optimization (PSO), squirrel search algorithm (SSA), and cuckoo search (CS)-were evaluated, with the dataset augmented by perturbations to simulate shading. The standard ANN performed poorly, with 64 neurons in Layer 1 and 32 in Layer 2 (MSE of 159.9437, MAE of 8.0781). Among the optimized approaches, GWO, with 66 neurons in Layer 1 and 100 in Layer 2, achieved the best prediction accuracy (MSE of 11.9487, MAE of 2.4552) and was computationally efficient (execution time of 1198.99 s). PSO, using 98 neurons in Layer 1 and 100 in Layer 2, minimized MAE (2.1679) but had a slightly longer execution time (1417.80 s). SSA, with the same neuron count as GWO, also performed well (MSE 12.1500, MAE 2.7003) and was the fastest (987.45 s). CS, with 84 neurons in Layer 1 and 74 in Layer 2, was less reliable (MSE 33.7767, MAE 3.8547) and slower (1904.01 s). GWO proved to be the best overall, balancing accuracy and speed. Future real-world applications of this methodology include improving energy efficiency in solar farms under variable weather conditions and optimizing the performance of residential solar panels to reduce energy costs. Further optimization developments could address more complex and larger-scale datasets in real-time, such as integrating renewable energy sources into smart grid systems for better energy distribution.