Reduce Energy Costs Research Articles

Optimised rule-based peak shaving and its impact on annual energy cost reduction

This paper presents an optimised rule-based peak-shaving method which is tested on an educational institute to know its impact on annual energy cost reduction. In recent years, energy management in low-voltage distribution systems has drawn more attention. Peak shaving is an important energy management method which helps consumers and grid operators. Peak shaving reduces voltage drop and voltage-rise issues, ratings of the different components and energy consumption costs and power losses. In this work, some of the most effective peak-shaving techniques to handle the problem of peak shaving and a few real-world implementations are discussed. Furthermore, an optimised rule-based peak-shaving (RBPS) method, which considers grid import and export power limits, is presented. The impact of this peak-shaving method, on a case study of an educational institute with a photovoltaic (PV)-battery energy storage system (BESS) based on its net load data over an annum for annual operating energy cost reduction, is presented. The results indicate that the total cost reduction for a year is 68.48% using PV and BESS with the peak-shaving application.

A Novel Method for Optimising Energy Efficiency in High-Performance Computing Systems

This paper introduces a new method to promote energy efficiency in computing systems. The rapidly growing demand for computational power in high-performance computing (HPC) systems is accompanied by a significant increase in energy consumption. This research investigates the many ways to minimise energy consumption in HPC systems without sacrificing computational performance. Building upon previous research we combined and refined ideas to develop an optimised approach. Our method utilises a tri-modular framework incorporating an Energy-Efficient Hardware Design, Resource Management, and Optimisation, and Server Virtualisation. The first module employs a method of designing smart energy-efficient hardware. This method incorporates leakage reduction techniques such as clock gating and sleep states. The second module uses a technique focused on optimising server software. This method is based on Dynamic Voltage and frequency scaling (DVFS) for power management in data centres. The third module is based on server virtualisation and employs software to create multiple virtual machines on a single physical server allowing for a significant reduction in energy and hardware costs. The methods used in these three modules are systematically integrated to produce a more efficient consumption of electricity. This will allow for the computing system to minimise energy consumption without compromising computational power.

Enhancing Smart Grid Efficiency through Machine Learning-Based Renewable Energy Optimization

Managing renewable energy in smart grids poses a significant challenge due to the inherent uncertainty and variability of energy sources like solar and wind power. To address this issue, we propose a novel approach that leverages the strengths of both Extreme Learning Machine (ELM) and Particle Swarm Optimization (PSO) algorithms. Our method utilizes ELM to model and predict renewable energy generation, enabling more accurate forecasting and planning. Meanwhile, PSO optimizes the parameters of the ELM algorithm, ensuring optimal performance and efficiency. We evaluated our approach using a dataset of solar energy production and compared its performance to existing optimization techniques. The results show that our ELM-PSO approach significantly improves the accuracy of renewable energy predictions and reduces energy costs in smart grids. The implications of our research are far-reaching, as our approach can be applied to various renewable energy systems, including wind turbines, solar panels, and hydroelectric power plants. By enhancing the efficiency and reliability of renewable energy utilization, we can create a more sustainable and resilient energy future.

METHODOLOGICAL APPROACH TO ASSESSING THE IMPACT OF ENERGY EFFICIENCY ON THE FINANCIAL PERFORMANCE OF A GAS COMPANY

Currently, the use of energy-saving technologies that reduce costs and improve the production process is the most important factor in the development of the gas industry. The relevance of the research topic is due to the fact that in order to resolve issues related to reducing energy costs in the gas sector of the Russian economy, optimal use of non-renewable energy resources, and reducing technological losses, an integrated approach to assessing energy efficiency is required, as well as a methodology for assessing the impact of energy efficiency indicators on the financial performance of a company. The purpose of this article is to develop a methodological approach to assessing the impact of the level of energy efficiency on the financial performance of a gas company. The research methodology is based on correlation and regression analysis tools. As part of the study, a system of energy efficiency indicators for a gas company's activities has been formed and, using correlation and regression analysis tools, a methodology for assessing the impact of the level of energy efficiency on the financial performance of a gas company is proposed. An algorithm for correlation and regression analysis is presented, which makes it possible to formalize a step-by-step assessment of the impact of the level of energy efficiency on the financial performance of a gas company. The proposed methodological approach allows us to identify correlation-regression dependencies between the company’s key performance indicators (sales revenue and net profit) and the proposed energy efficiency indicators, determine the most significant indicators that affect the company’s performance indicators and determine the equation for the relationship between the company’s performance and energy efficiency indicators. Testing of the proposed approach using the example of the gas company PJSC Gazprom made it possible to quantify how changes in the energy intensity of gas production and the energy intensity of gas transportation will affect changes in sales revenue and net profit of the company. It has been established that the energy efficiency indicator such as the specific energy intensity of gas transportation has the greatest impact on the financial performance of the company. Regression equations have been established to describe the nature of the relationship between energy efficiency indicators and sales revenue and net profit of a gas company.

Application of multi-objective neural network algorithm in industrial polymerization reactors for reducing energy cost and maximising productivity

Optimization on an industrial scale is a complex task that involves fine-tuning the performance of large-scale systems and applications to make them more efficient and effective. This process can be challenging due to the increasing volume of work, growing system complexity, and the need to maintain optimal performance. Due to the significant power required for compression and the high costs of reactant materials, optimizing low-density polyethylene (LDPE) production to provide maximum productivity with a reduction of energy cost is required. However, it is not a simple process because the optimization problem of the LDPE tubular reactor consists of conflicting objective functions. Multi-objective neural network algorithm (MONNA) is a metaheuristic optimization method that provides a versatile and robust approach for solving complex, contradictory targets and diverse optimization problems that do not rely on specific mathematical properties of the problem. It is inspired by the structure and information-processing capabilities of biological neural networks. MONNA iteratively proposes solutions, evaluates its performance, and adjusts its approach based on feedback, which avoids complex mathematical formulations. In this work, we implement Multi-objective optimization neural network algorithm (MONNA) in LDPE tubular reactor for maximising productivity, conversion and minimising energy costs with three scenario of problem optimization, i.e. maximising productivity and reducing energy cost for the first problem (P1); increasing conversion and reducing energy costs for the second problem (P2); and increasing productivity and reducing by-products for the third problem (P3). The results show that the highest productivity, highest conversion, and lowest energy are 545.1 mil. RM/year, 0.314, and 0.672 mil. RM/year. The extreme points in the Pareto Front (PF) for various bi-objective situations provide practitioners with helpful information for selecting the best trade-off for the operational strategy. According to their preferences, decision-makers can use the resulting Pareto to decide on the most acceptable alternative. The decision variable plots show that both initiators in the reacting zone highly affected the optimal solution with the opposite action.

Ceramic materials of low-temperature sintering from natural and technogenic rocks of the Tuva Republic

Abroad, they are actively engaged in “cold” sintering of materials, including ceramic, which is a mechanically and thermally conditioned mass transfer process that allows for low-temperature integration of various materials. The paper presents the results of research on low-temperature sintering and optimization of firing regimes for ceramic materials by introducing a salt complex with a liquidus temperature of 825оC into a charge based on natural and man-made rocks of the Republic of Tyva. The optimal concentrations of salt-containing additives in the ceramic mass, which affect the roasting properties of the resulting materials, have been determined. It was found that the introduction of a complex of salts into the ceramic charge promotes earlier sintering of the shard and the production of high-quality wall ceramics at lower temperatures, which leads to a reduction in energy costs during firing of products.

Cascading valorization of defatted rice bran for lactic acid fermentation and biogas production

This study investigated the integrated valorization of defatted rice bran (DRB) by converting it into lactic acid (LA) and subsequently utilizing the residues from LA production for biomethane generation through anaerobic digestion (AD). Processing 480 kg of DRB resulted in the production of 70 L of pure LA and generated significant waste streams, primarily consisting of 572 kg of decanted hydrolysate pellet (Pellet DEC) and 220 kg of microfiltration retentate (Retentate MF). Exceptionally high methane yields of 374‒434 LN kgVS-1 were observed for residues from LA fermentation in biochemical methane potential tests, indicating their high potential for biogas production. During long-term semi-continuous AD, varying organic loading rates (OLRs) from 0.5‒2.5 kgVS m-3 d⁻¹ demonstrated feedstock- and OLR-dependent methane production. Reactor failure at higher OLRs was attributed to the accumulation of total ammoniacal nitrogen (TAN). The co-digestion of Pellet DEC and Retentate MF proved to be more resilient, with OLRs up to 2 kgVS m-3 d-1, mitigating TAN inhibition. Methane yields, ranging from 265‒334 LN kgVS-1 before reaching inhibitory OLR levels, were higher than those found in the literature. Process integration has emerged as a promising approach because the biogas generated from residues could effectively offset the energy demands of LA production. Supported by life cycle assessment, the integrated processes showed a 67% lower environmental impact at the midpoint and a 71% lower environmental impact at the endpoint, along with an 80% reduction in energy costs compared to the standalone LA production. Results proved a significant enhancement of the sustainability and economic viability of this integrated biorefinery approach.

Flow field characteristics and drag reduction performance of high–low velocity stripes on the biomimetic imbricated fish scale surfaces

AbstractImproving energy efficiency and cost reduction is a perennial challenge in engineering. Natural biological systems have evolved unique functional surfaces or special physiological functions over centuries to adapt to their complex environments. Among these biological wonders, fish, one of the oldest vertebrate groups, has garnered significant attention due to its exceptional fluid dynamics capabilities. Researchers are actively exploring the potential of fish skin's distinctive structural and material characteristics in reducing resistance. In this study, models of biomimetic imbricated fish scale are established, and the evolution characteristics of the flow field and drag reduction performance on these bionic surfaces are investigated. The results showed a close relationship between the high–low velocity stripes generated and the fluid motion by the imbricated fish scale surface. The stripes' prominence increases with the spacing of the adjacent scales and tilt angle of the fish scale, and the velocity amplitude of the stripes decreases as the exposed length of the imbricated fish scale surface increases. Moreover, the biomimetic imbricated fish scale surface can decrease the velocity gradient and thereby reduce the wall shear stress. The insights gained from the fish skin‐inspired imbricated fish surface provide valuable perspectives for an in‐depth analysis of fish hydrodynamics and offer fresh inspiration for drag reduction and antifouling strategies in engineering applications.

Evaluation of environmental and economic performance of terminal equipment considering alternative fuels

Container-terminal equipment is the main source of emissions at ports, and the environmental and economic impacts of alternative fuels on them have not been sufficiently investigated. In this study, a novel framework for quantitative evaluation of environmental and economic performances is constructed by considering four dimensions: various fuel pathways, full fuel lifecycles, fuel preparation sources, and economic policies. A case study is conducted through empirical data from Qingdao Container Terminal, and the combined impacts of the five sensitive factors at different periods are studied in depth. The result shows that liquefied natural gas, electricity, and diesel-electric hybrid offer substantial overall benefits. Owing to energy transformation, technological progress, and cost reduction, hydrogen and electricity may emerge as the most advantageous energy sources. Policies are crucial in reducing emissions by port enterprises, and the government should improve emission regulations, stabilize incentive policies, and promote the use of new energy.

Energy Intervention Model in Public Education Institutions that Contribute to Sustainable Development

Sustainable development is a global policy that requires the collective effort of the actors present in each territory. In this sense, an energy renewal intervention model is presented at the Juan XXIII Educational Institution in the city of Monteria, Córdoba, Colombia, which results from alliances between international, national, and regional actors, becoming a reference that could serve as a basis. To be replicated in other institutions with characteristics similar to those described in this case. The model generally describes the entire process carried out in the intervention and focuses on the benefits generated for the educational community. Among the main results, the increase in thermal, lighting, and acoustic comfort of the educational community stands out, according to a survey and semi-structured interviews carried out. A fact that could be attributed to the perception of increased comfort in the community is the increase in the student population in 2022, going from 1,478 in 2019 to 1,909 in 2022, with a growth of approximately 29%. Energy renovation also resulted in the improvement of the indoor climate of the classrooms (from 35°C to 27°C), the improvement in the physical infrastructure of the institution, the integration of photovoltaic solar energy, and the subsequent reduction of energy cost.

Intermittent Light Scheduling for Energy Cost Reduction in Vertical Farming

Intermittent Light Scheduling for Energy Cost Reduction in Vertical Farming

Energy management of hybrid microgrids – A comparative study with hydroplus and methanol based fuel cells

Energy management is essential for the efficient operation of microgrids with reduced energy costs and minimized emissions. Energy management of PV/battery/fuel cell/diesel generator-based microgrid to minimize the operations cost considering battery degradation and emissions for a fully functional microgrid existing in the campus of Sultan Qaboos University, Oman, is presented in this work. A microgrid with a state-of-the-art hydroplus fuel cell without the necessity for hydrogen storage is presented in this study with experimentally obtained parameters. Also, a comparison of operations cost with microgrids using two different technologies of PEM fuel cells, one with hydroplus fuel cell and the second with the methanol fuel cell which requires provision for hydrogen storage is performed with three different cases; the scheduled, grid-tied, and islanded with different scenarios under grid-tied mode. The analysis proved that using a hydroplus fuel cell instead of a methanol fuel cell with hydrogen storage reduces the cost of the daily operation by 6.9% in the scheduled mode and 18.2% in the islanded mode. In the grid-tied mode three different grid limits, 20 kW, 15 kW, and 10 kW are considered. The analysis showed no reduction, 1.3% and 5.9% reduction in the operations cost respectively. The results obtained are highly promising to be applied in microgrids where conventional fuel cells are currently employed. The new technology of fuel cells introduced in this study, possesses the advantages of near zero emissions and reduced operations costs besides avoiding the perilousness of hydrogen storage.

Developing a Typology Based on Energy Practices and Environmental Attitudes

The European Union is increasingly directing efforts and resources toward promoting the adoption of energy saving measures among citizens, but the effectiveness of these efforts cannot be ensured if the heterogeneity of citizens is neglected. This paper assesses whether Greek citizens present heterogeneity in terms of their energy behavior and investigates whether they can be classified based on the practices they apply to reduce energy costs and their environmental attitudes. To that end, the study collected a representative citizen sample, and k-means cluster analysis was performed. Results indicated significant heterogeneity and four clusters were identified: ‘Extreme heat savers and pro-environmental food consumers’ saved on heating but neglected practices regarding lighting, hot water use, and transport; ‘Heat savers and environmentally unaware’ applied energy saving measures only to lower heating costs; ‘Environmentally aware energy savers’ were the most engaged segment in the application of energy saving measures driven by pro-environmental attitudes; and ‘Mindful resources and transport users’ did not apply any measures for heating despite having positive attitudes in terms of electricity, water and transport use. Therefore, it is necessary to train citizens on pro-environmental practices, and in doing so, the recorded heterogeneity can guide the design of differentiated and effective approaches.

Technoeconomic analysis of solar PV electrification to remote areas of Dera Ghazi Khan: A case study

With the increase in population at an immense rate, electricity demand is growing exponentially. Researchers and policymakers are seeking alternating means of power generation to meet the load demand. These resources should be cost-effective, environmentally friendly and least carbon emissions. To mitigate the load demand Renewable Energy Sources (RES) are integrated into electrical networks. In this work, an off-grid solar photovoltaic (PV) system is designed for rural areas of Dera Ghazi Khan (DG Khan), Pakistan. These areas often lack access to reliable grid power. Installing PV systems in these areas can help provide a reliable source of electricity, reduce reliance on fossil fuels, and improve living conditions. This case study is simulated using PVsyst 7.2. of Roonghan village such as hospitals, shops, and residential houses for techno-economic analysis of off-grid solar PV. The economic viability shows that installing off-grid solar PV in DG Khan is much cheaper. The electricity taken from the grid that's almost 80.01%is expensive. This case study helps other researchers and policymakers to mitigate the electricity requirement of remote areas located far away from the national grid. To install a transmission line, having cost $26.98 corer but using an off-grid to provide electricity to the same area is $2.16 corer. Additionally, it integrates Battery Energy Storage (BES) with Renewable Energy Sources (RES) to achieve a 5 % annual energy cost reduction and enhanced self-sufficiency, filling a gap in existing literature.

Investigating the Influence of Medium Size and Ratio on Grinding Characteristics

This study explores the effect of steel ball size and proportion on mineral grinding characteristics using Discrete Element Method (DEM) simulations. Based on batch grinding kinetics, this paper analyzes the contact behavior during grinding, discussing particle breakage conditions and critical breakage energy. The results indicate that while increasing the size of the steel balls leads to higher collision energy, the collision probability decreases significantly; the opposite is true for smaller steel balls. Simulation results with different ball size combinations show that increasing the proportion of smaller balls does not significantly change the collision energy but greatly increases the collision probability, providing a basis for optimizing ball size distribution to improve grinding performance. Furthermore, appropriately increasing the proportion of smaller balls can reduce fluctuations in grinding energy consumption, thereby enhancing collision energy and collision probability while reducing energy costs. Liner wear results demonstrate that larger ball sizes increase liner wear, but different ball size combinations can effectively distribute the forces on the liner, reducing wear.

Economic Scheduling Strategy for Multi-Energy-Integrated Highway Service Centers Considering Carbon Trading and Critical Peak Pricing Mechanism

This study proposes an optimized economic scheduling strategy for multi-energy-integrated highway service centers (MEIHSCs) within a 24 h operational timeframe. With the imperative of carbon peaking and carbon neutrality, highway areas are increasingly incorporating renewable energy systems, such as photovoltaic arrays, to capitalize on abundant resources along highways. Considering the diverse load demands of new energy vehicles and the mismatch between energy supply and demand on the highway, MEIHSCs must adapt to these trends by establishing integrated networks for electricity, natural gas, and hydrogen refueling. However, there is a lack of coordination between equipment switching and the phases of low electricity prices and peak renewable energy periods. To address this challenge and improve economic efficiency, this study proposes an economic dispatch strategy that combines economic incentives based on carbon trading and critical peak pricing mechanisms. This strategy aims to maximize economic benefits while fully meeting the load demands of new energy vehicles. Case studies indicate that operating costs are reduced by 28.04% compared to strategies without new energy installations, and by 47.85% compared to strategies without optimization. The results demonstrate that this integrated and optimized strategy significantly reduces energy costs and enhances economic benefits in highway service centers.

Research on the Design of Green Roofs for Office Buildings in Xuzhou Based on Building Energy Consumption Evaluation

The roof is the part of a building that is exposed to solar radiation for the longest period, making green roofs particularly effective in reducing air conditioning energy consumption during the summer. This study aims to assess the advantages of modular green roofs in terms of energy savings and cost reduction during the summer in Xuzhou. By conducting field measurements and surveys under both air-conditioned and non-air-conditioned conditions and utilizing building energy simulation tools, the performance of green roofs with different parameters was compared. Using EnergyPlus, factors such as soil thickness, thermal conductivity, and leaf area index were simulated. The results indicated that green roofs have superior thermal performance in summer, with the daily cooling load per unit area for top-floor rooms being 1.05 kWh/m2, 0.21 kWh/m2 lower than that for bare roofs, achieving an energy saving rate of 16.7%. It is recommended that soil thickness not exceed 0.3 m and insulation thickness not exceed 0.05 m or be set to 0 m. Take building no. 2 of the Xuzhou material market as an example: with the optimized green roof, the energy saving rate increased to 27.0%, which is 12.4% higher than that of the original green roof. The suggested cost for modular green roofs is 204 RMB/m2.

ENERGY EFFICIENCY AS A KEY FACTOR IN ENHANCING THE POTENTIAL AND COMPETITIVENESS OF ENTERPRISES IN THE CONSTRUCTION INDUSTRY THROUGH THE APPLICATION OF GLOBAL PRACTICES

The article substantiates the importance of energy efficiency as a key factor in enhancing the potential and competitiveness of enterprises in Ukraine's construction industry through the application of global practices. The article demonstrates how the implementation of advanced energy-saving technologies and renewable energy sources, which are widely used at the international level, allows enterprises to reduce energy costs, increase environmental responsibility, and strengthen market positions. It is proven that the integration of global practices in energy efficiency, including the modernization of production processes and the implementation of automated energy management systems, significantly enhances the economic efficiency of enterprises. The example of «DZBM» LLC, which reduces production costs and generates additional income from selling electricity at the «green tariff» through the construction of a mini-CHP plant running on biofuel, confirms the effectiveness of this approach. The relevance of the topic lies in the need to adapt global energy efficiency practices in Ukraine's construction industry, which is particularly important in the context of rising energy costs and economic instability. The article lays the foundation for further research in this area and proposes ways to optimize energy use to enhance the competitiveness of enterprises.

Multi-objective optimization of combined heat and power system integrated with multi-energy storage systems for rural communities

Renewable-based combined heat and power system (CHPS) benefit communities by enhancing energy resilience and reducing energy costs and emissions. Considering its reliability issues, previous studies integrated two or more energy sources or employed energy storage systems, compromising emission reduction or imposing an economic burden. To address this challenge, this study introduces three energy system schemes (ESSs) based on renewable, non-renewable, and hybrid energy resources. Each ESS includes various feasible configurations integrated with energy storage systems, such as batteries and hydrogen, resulting in 22 configurations modeled, optimized, and systematically investigated using HOMER software, considering energy-economic-environmental (3E) criteria to select the optimal system configuration (OSC). The introduced CHPS configurations are presented in a practical case study of a remote community in Pakistan. Results showcased that OSC belongs to a hybrid ESS with the following characteristics: (a) COE (0.127 $/kWh) and NPC (0.192 M$) are minimum among other configurations, (b) capacity shortage and unmet electric load are 0.21 % and 0.14 %, and (c) CO2 is reduced by 67.49 % compared to the base case. Furthermore, the 3E effect of adding a thermal load controller and batteries to the OSC is explored. The 3E results are inclusively compared with the existing literature, revealing that OSC is economical, reliable, and eco-friendly.

Genetic modification optimization technique: A neural network multi-objective energy management approach

In this study, a Neural Network-Enhanced Gene Modification Optimization Technique was introduced for multi-objective energy resource management. Addressing the need for sustainable energy solutions, this technique integrated neural network models as fitness functions, representing an advancement in artificial intelligence-driven optimization. Data collected in the European Union covered greenhouse gas emissions, energy consumption by sources, energy imports, and Levelized Cost of Energy. Since different configurations of energy consumption by sources lead to varying greenhouse gas emissions, costs, and imports, neural network prediction models were used to project the effect of new energy combinations on these variables. The projections were then fed into the gene modification optimization process to identify optimal configurations. Over 28 generations, simulations demonstrated a 46 percent reduction in energy costs and a 9 percent decrease in emissions. Human bias and subjectivity were mitigated by automating parameter settings, enhancing the objectivity of results. Benchmarking against traditional methods, such as Euclidean Distance, validated the superior performance of this approach. Furthermore, the technique's ability to visualize chromosomes and gene values offered clarity in optimization processes. These results suggest significant advancements in the energy sector and potential applications in other industries, contributing to the global effort to combat climate change.