Reduce Energy Costs Research Articles

Structural insulation panel system (SIP) as an alternative for cooling energy saving, decarbonization, and energy cost reduction in hot and dry climates. A simulation-based approach

ABSTRACT During the last decades, the growing urban prospects represent a challenging condition to develop climate change adaptation solutions before the urban overheating effects and its challenges to the environment, human health, and building energy consumption, which is critical in hot arid climates with record-breaking heatwaves where society is turning to air conditioning instead of energy-efficient building envelopes. The primary aim of this study was to examine the influence of the Structural Insulation Panel (SIP) system heat transfer in energy-related aspects and contrast it with those of conventional construction systems. Therefore, a comparative study was carried out in low-income dwellings to measure the thermal transmittance of the building envelope and assess the thermal energy simulation allocated in Köppen-Geiger´s hot desert climate, characterized by typical maximum temperatures of 45°C in the summer season. The investigation ascertained the levels of energy consumption, the capacity of the air conditioning system, the associated energy costs, and the potential decrease in carbon footprint. The study shows SIP as a viable construction system alternative, the evidence shows a 20% heat gain reduction, the Heating, Ventilation, and Air Conditioning (HVAC) system capacity was reduced by 38%, and energy cost by 27%. The system also contributes to the decarbonization and carbon footprint by 20%. Likewise, to reduce the heat flow, a crucial element is evident, since transmittance in thermal bridges significantly affects the heat transfer in these low thermal conductivity systems with structural reinforcement elements.

Application of supervised and unsupervised learning for enhancing energy efficiency and thermal comfort in air conditioning scheduling under uncertain and dynamic environments

Air conditioning (AC) plays a major role in building energy management because it generally requires a large amount of energy to maintain indoor thermal comfort. The main objective of this study is to develop a novel method for scheduling AC operations to minimize energy costs and ensure the thermal comfort of occupants under uncertainty. The key challenge is the uncertainty and variability in time-series data and their serial dependence in determining AC operation. To address this challenge, we propose an optimization-informed learning approach that integrates unsupervised and supervised learning techniques with a stochastic optimization model. This method derives energy-efficient and thermal comfort-aware AC operation schedules through a comprehensive interpretation of uncertainties and variabilities in time-series data. Numerical experimental results demonstrate that the proposed approach can reduce energy costs by up to 15.6% and decrease thermal comfort violations by up to 63.6% compared to the Deep Q-learning method, while also reducing energy costs by 1.8% and decreasing thermal comfort violations by 37.5% compared to the forecast data-driven AC scheduling method.

Techno-economic feasibility analysis and optimisation of on/off-grid wind-biogas-CHP hybrid energy system for the electrification of university campus: A case study

This paper provides a comprehensive feasibility analysis of a hybrid energy system with different configurations to meet electricity and thermal load demand at the University of Southampton campus. The suggested hybrid energy system (HES) comprises wind turbine, biogas generator, battery, CHP natural gas-generator, thermal load controller, boiler, and converter and is simulated in Homer Pro software. In addition, a comparative analysis between stand-alone and on-grid HES is presented. The results indicate that the grid-connected HES is significantly more cost-effective, with a 45% reduction in cost of energy (COE) and %15.5 decrease in net present cost (NPC) compared to the off-grid system, which amounts to 0.03359 $/kWh and 467M$, respectively. The grid-connected HES is not only more eco-friendly in terms of greenhouse gas emissions (GHG) and produces 19% less emissions annually compared to the grid-independent system, but it also effectively achieves a positive return on investment (ROI) of %45 with 2.3 years of payback time. Considering the university’s total emissions of 38.56 kT CO2e, the proposed on-grid hybrid system has the potential to lessen GHG emissions by 91.2%.

OptiPower AI: A deep reinforcement learning framework for intelligent cluster energy management and V2X optimization in industrial applications

Peer-to-peer (P2P) energy trading has emerged as a practical solution for efficient energy management, particularly with the rising affordability of renewable energy and electric vehicles (EVs). This paper introduces the Optimal Power Artificial Intelligence (OptiPower AI) algorithm, a significant advancement in Intelligent Cluster Energy Management (ICEM). OptiPower AI combines Double Deep Q-Network (DDQN)-based Deep Reinforcement Learning (DRL), Vehicle-to-Everything (V2X) technology, and P2P energy trading to optimize energy distribution among clusters of prosumers and consumers. The system efficiently manages Renewable Energy Sources (RES) and EVs, achieving a 19.18% reduction in energy costs and a 50.02% decrease in average energy prices across V2X and P2P scenarios.OptiPower AI uses DRL to dynamically allocate energy and implement real-time pricing, enhancing energy efficiency and user satisfaction. Simulations based on meteorological data from Tunisia validate the system’s ability to improve thermal comfort, increase energy savings, and lower costs. The model’s parameters enable accurate forecasting and allocation, showcasing OptiPower AI’s reliability in variable demand conditions. This work advances the application of DRL in decentralized, sustainable P2P energy management systems for industrial clusters, addressing critical challenges in energy distribution, efficiency, and cost reduction.

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.

Influence of geometric and operational variables on indirect evaporative cooling using cyclone as heat exchanger

With the increasing environmental impacts of human activities and the demand for new air cooling alternatives, indirect evaporative cooling (IEC) emerges as a sustainable alternative. However, there is a gap in the literature regarding examining cyclone configurations in this context. Despite being good separator devices, cyclones can be used as heat exchangers, enhancing convective heat transfer through turbulence. This opens the possibility of developing improved and economically accessible geometries of IEC systems, especially beneficial for hot and arid regions, and communities with limited resources. This study aimed to investigate the performance of 25 geometric configurations of cyclones used as indirect evaporative heat exchangers. The researchers employed an experimental approach by subjecting cyclones covered with a wet cotton fabric to IEC tests under various geometric and operating conditions, followed by statistical analyses to identify relationships between the variables. The results showed significant thermal variation in the cyclones, with a considerable impact on all variables, particularly the cyclone’s total length, relative humidity, air temperature, and flow rate. The thermal efficiency of the cyclones varied widely, with total length and flow rate being the most significant factors. Additionally, an analysis of the Euler values revealed the importance of the cyclone geometry in selecting the one that reduces energy costs. Among the cyclones tested, the configuration with the optimal geometric design (Cyclone 8) achieved the highest thermal performance and lowest Euler number. The findings of this study could help developing more efficient and sustainable IEC systems, helping to reduce energy consumption and environmental impacts.

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.

System design of a novel open-air brayton cycle integrating direct air capture

The Direct Air Capture (DAC) technology is essential for achieving carbon neutrality, as it enables processes with net-negative CO2 emissions. However, its widespread commercialization faces significant challenges due to high energy requirements. Numerous attempts have been made to address this issue through thermal integration, yet the fundamental challenge of the high cost associated with extracting large volumes of low-concentration CO2 from ambient air remains unresolved. In this study, the integration of Open-Air Brayton Cycle (OABC) as a solution to enhance overall system utilization by simultaneously utilizing large volumes of ambient air is introduced. Various OABC coupled temperature swing adsorption based DAC system layouts are analyzed while considering different regeneration temperatures, and the results revealed the optimal configurations that significantly reduce energy cost per captured unit of CO2 with high purity and recovery. By combining an equilibrium short-cut model for temperature swing adsorption with process simulation methodologies, this research proposes the concept of “energy cost”—a metric that represents the amount of CO2 captured against the energy penalty incurred by integrating DAC with OABC systems. The findings demonstrate that combining DAC and OABC systems could yield high purity and recovery rates of CO2 through strategic thermal management and advanced adsorbent usage, offering a synergistic approach to carbon capture from an energy consumption perspective.

Low corrosive dual desiccant air conditioning: Antimicrobial activity, performance characteristics, and economic valuation with traditional VCRS

Liquid desiccant dehumidification systems (LDDS) serve as an energy-efficient alternative to conventional vapor compression refrigeration (VCR) systems, providing enhanced control over both temperature and humidity levels. However, the broader implementation of LDDS is limited due to the corrosive effects of the liquid desiccants on metal components. In response, this study introduces an economical blend of potassium formate (HCOOK) and magnesium chloride (MgCl2) designed to minimize corrosion while maintaining energy efficiency and performance. Selection of mixture ratio and solution concentration and determination of thermo-physical properties were conducted by initial lab-scale tests. Significant bactericidal effects against E. coli and S. Aureus were observed. The feasibility and effectiveness of the developed solution were experimentally tested by a comparative investigation in a large-scale solar-integrated liquid desiccant dehumidification system against standard HCOOK (63.5 wt%). The cyclic performance of the novel desiccant solution was examined under varying operational conditions for tropical climates. Results demonstrated that the corrosion levels of the new mixture are comparable to those observed with a standard 63.5 wt% HCOOK solution, while the dehumidification efficiency showed significant improvements, with moisture removal and effectiveness enhanced by 8.7–14.3 % and 5.8–15.2 %, respectively. Significant regeneration occurred between 45 °C and 55 °C. Regeneration efficiency improved by a maximum of 12.5 % relative to the standard HCOOK solution. Notably, the deployment of a solar-assisted regeneration system led to energy cost reductions by 22.8 % compared to traditional VCR systems, achieving a return on investment within 1.8 years.

Dynamic relationships between inflation, industrial production, and freight rates in the EU: A wavelet coherence and causality analysis

This study investigates the impact of maritime transport costs on macroeconomic indicators in the European Union (EU) economy. Using data for the period between June 2002 and April 2024, the dynamic relationships between the Baltic Dry Index, the Baltic Dirty Tanker Index, and the Baltic Clean Tanker Index and EU inflation and industrial production are analyzed using wavelet coherence analysis and the Breitung-Candelon frequency domain causality test. The results show that maritime transport costs significantly impact EU inflation and industrial production, especially during periods of economic crisis and shocks. The study suggests that the EU needs to strengthen its supply chain management, diversify its logistics infrastructure, and develop strategies to reduce energy costs. Proactive and sustainable policy recommendations for the EU are also presented to increase the predictability of maritime transport costs. Our study emphasizes that the EU needs to make structural investments in energy and logistics to ensure price stability. It suggests that the EU should monitor and integrate transport costs into economic policies.

Multi-line hybrid flow-shop scheduling problem with energy considerations

This article introduces a novel scheduling problem consisting of a multi-line hybrid flow-shop with energy considerations. The scheduling problem aims at optimising energy cost under time of use pricing structure with respect to production and energy-efficiency constraints. A 0–1 integer linear program based on a time-indexed formulation is proposed and allows to consider of variable power profiles for operations. Subsequently, a multi-start iterated local search-based heuristic is developed in order to address the resolution of large-scale instances. The performance of the proposed approaches is then assessed on randomly generated instances of various scales. Numerical experiments on an industrial case study also illustrate the economic benefits of considering a time of use pricing scheme with over 20% reduction in energy cost. Furthermore, it is demonstrated the importance of considering the system as a whole when considering energy in such a multi-line shop floor by examining three distinct optimisation strategies. Indeed, the benefits of a multi-line optimisation strategy compared to a sequential one are around 24% on average.

Study on two-stage robust optimal configuration of integrated energy system considering CCUS and electric hydrogen production

Under the background of complementary and cascade utilization of multiple types of energy, how to carry out the capacity planning of economic, efficient, and low-carbon integrated energy system to meet the diversified needs of cooling, heating, electricity, and hydrogen loads is a challenging problem. This study conducted a two-stage robust optimization (RO) configuration of integrated energy system considering Carbon Capture Utilization and Storage (CCUS) and electric hydrogen production. In order to adapt the configuration scheme to the potential extreme operational scenarios, the uncertainty sets of new energy and load are constructed based on Latin hypercubic sampling. The efficacy is validated through the design of several scenarios and a comparison with alternative methods. The results indicate: 1) Strengthening of carbon emission constraints facilitates the cleaner and electrified transformation of energy. In the shift from “low carbon” to “zero carbon” scenario, the proportion of new energy capacity in power generation equipment increases from 45.1% to 79.8%; the proportion of electric chillers in cooling equipment increases from 1.3% to 7.2%, and reaches 60% under “zero carbon” scenario. 2) With a 36% reduction in the investment cost of new energy, the spatiotemporal transfer value of energy is realized, which increases the investment capacity of energy storage by about 7 times. 3) The configuration of hydrogen production and storage equipment to meet hydrogen demand reduces costs by 19% compared to direct purchasing in “low carbon” scenario. 4) Compared to other methods, the proposed method in this study is able to reduce the solution time and configuration cost.

Regulation mechanism of optimal bubble diameter for ozone wastewater treatment

Efforts have been made to enhance ozone utilization efficiency and reduce energy costs in ozone-aerated wastewater treatment. Microbubbles, characterized by their large interfacial area and efficient gas-liquid mass transfer, are extensively used to boost ozone utilization. However, generating microbubbles demands significant energy, and their actual efficiency requires careful evaluation due to the absence of universal strategies for designing microbubbles with optimal performance. This study established an integrated mathematical model for ozone oxidation in wastewater treatment, based on comprehensive ozone reaction kinetics, microbubble mass transfer control theory, and mass conservation equations for gas-liquid phase components. Simulation results showed a pollutant removal rate with a prediction error of less than 20%, confirming the model's reliability. Further analysis revealed that although the initial mass transfer rate of 0.5 mm bubbles is lower than that of 0.1 mm bubbles, the total mass transfer quantity is reduced by only 8.7%. This suggests that an optimal bubble diameter can balance mass transfer efficiency and energy consumption. Based on this finding, we integrated ozone production and bubble generation energy consumption to develop a regulation mechanism for optimizing bubble diameter, minimizing total energy consumption while meeting target removal rates. Results indicate that the optimal bubble diameter is closely related to water depth: as depth increases, the optimal bubble diameter also increases. At a depth of 2.1 meters, the optimized bubble diameter reduces energy consumption by 33.5% and 16.2% compared to millimeter-sized and 100-micron bubbles, respectively. Sensitivity analysis shows that total energy consumption is more sensitive to changes in specific ozone energy consumption, while variations in bubble generation energy remain relatively stable. These results underscore the feasibility of using the proposed model to guide energy-efficient bubble size selection.

Development of sustainable self-healing engineered cementitious composites incorporating maximum amounts of natural pozzolan

In light of the scarcity of the most commonly used supplementary cementitious materials in Engineered Cementitious Composites (ECC), there is a pressing need to explore alternative materials to replace them. This paper considers the influence of integrating high amounts of natural Pozzolan (NP) on the sound and self-healing characteristics of ECC, including compressive and flexural strengths, deflections, cracking behavior, ultrasonic pulse velocity, rapid chloride permeability, and microstructure both before and after pre-cracking and recovery periods. NP contents at Portland cement (PC) ratios of 1.2, 1.5, 1.8, and 2.2 were explored, along with a combination of (50 % NP + 50 % slag)-to-PC ratio of 2.2. The results obtained indicate that superior self-healing properties can be achieved with the use of NP instead of slag, particularly at higher NP/PC ratios. Furthermore, the ductility of ECC significantly increased with the inclusion of NP, reaching more than twice the deflection capacity of the control specimens at various ages. The results of this research confirm the formation of additional C-A-S-H-type healing products as the outcome of the NP incorporation on the self-healing ability of ECC. In addition, the life cycle assessment analysis (LCA) highlights the significant reductions in energy consumption, CO2 emissions, and cost of ECC mixtures prepared with increased inclusion of NP, up to a 2.2 NP/PC ratio.

Optimizing Consumer Adoption: Strategic Approaches to Increase Utilization of Solar Energy from Owned Solar Panels

The adoption of solar energy through solar panels presents a significant opportunity for consumers to reduce energy costs and contribute to environmental sustainability. However, maximizing the utilization of this energy resource remains challenging. This study explores strategic approaches to optimize consumer adoption and increase the utilization of energy generated by owned solar panels. This study focuses on the key factors influencing consumer behavior, the economic benefits of solar energy, technological innovations, policy support, and the role of public awareness campaigns in fostering greater use of solar energy systems. This research paper delves into the strategic approaches necessary to optimize consumer adoption and enhance the utilization of energy generated by privately owned solar panels. The study conducts a comprehensive examination of critical factors that influence consumer behavior, economic advantages, technological advancements, policy support, and public awareness initiatives in promoting increased use of solar energy systems. By analyzing these key elements, the research aims to provide a holistic understanding of the current landscape and potential avenues for improvement in the solar energy sector. The investigation addresses several significant barriers to adoption that have hindered the widespread implementation of solar energy systems. These obstacles include high installation costs, which can be prohibitive for many consumers, insufficient awareness about the benefits and feasibility of solar energy, technological complexities that may deter potential adopters, and inconsistent regulatory frameworks that create uncertainty in the market. By identifying and exploring these challenges, the study lays the groundwork for developing targeted solutions to overcome these hurdles. In response to the identified barriers, the paper proposes a multifaceted approach to augment adoption and utilization of solar energy systems. One key strategy involves the implementation of financial incentives, such as tax credits, rebates, or low-interest loans, to make solar installations more affordable and attractive to a broader range of consumers. These economic measures aim to reduce the initial financial burden and accelerate the return on investment for homeowners and businesses considering solar energy adoption. Additionally, the research emphasizes the importance of technological innovations in energy storage and smart grid systems. These advancements play a crucial role in addressing the intermittent nature of solar energy production and enhancing the overall efficiency and reliability of solar power systems. By improving energy storage capabilities and integrating solar systems with smart grid technologies, consumers can maximize the utilization of their generated energy and potentially contribute excess power back to the grid. The paper also highlights the need for supportive policies and regulatory reforms to create a favorable environment for solar energy adoption. This includes streamlining permitting processes, establishing clear guidelines for grid integration, and implementing net metering policies that fairly compensate solar energy producers. Such policy measures can provide the necessary framework to encourage investment in solar technologies and ensure long-term stability in the market. Furthermore, the research underscores the significance of public education initiatives in promoting solar energy adoption. These efforts aim to increase awareness about the benefits of solar power, dispel common misconceptions, and provide consumers with accurate information to make informed decisions. By empowering individuals with knowledge about solar energy systems, their environmental impact, and potential cost savings, these initiatives can help overcome the barrier of insufficient awareness and drive greater adoption rates. The study also explores the potential for community-based solar projects and innovative financing models, such as solar leasing or power purchase agreements, to make solar energy more accessible to a wider range of consumers. These alternative approaches can help overcome financial barriers and allow individuals who may not have suitable roof space or homeownership to participate in the solar energy transition. By proposing and analyzing these multifaceted approaches, the research aims to provide a comprehensive strategy for increasing solar energy adoption and utilization. The ultimate goal is to assist consumers in reducing their energy costs while simultaneously contributing to environmental sustainability and energy security. The paper argues that by implementing a combination of financial incentives, technological innovations, supportive policies, and educational initiatives, it is possible to create a more favorable ecosystem for solar energy adoption and accelerate the transition towards a cleaner, more sustainable energy future. In conclusion, this research paper offers valuable insights into the complex landscape of solar energy adoption and utilization. By addressing key barriers and proposing targeted strategies, it provides a roadmap for policymakers, industry stakeholders, and consumers to work together in harnessing the full potential of solar energy. The findings and recommendations presented in this study have the potential to inform future policy decisions, guide technological developments, and empower consumers to embrace solar energy as a viable and beneficial alternative to traditional energy sources.

A large-scale study of the impact of node behavior on loosely coupled data dissemination: The case of the Distributed Arctic Observatory

A Cyber-Physical System (CPS) deployed in remote and resource-constrained environments faces multiple challenges. It has, no or limited: network coverage, possibility of energy replenishment, physical access by humans.Cyber-physical nodes deployed to observe and interact with the Arctic tundra face these challenges. They are subject to environmental factors such as avalanches, low temperatures, snow, ice, water and wild animals. Without energy supply infrastructures and humans available, nodes must achieve long operational lifetime from a single battery charge. They must be extremely energy-efficient. To reduce energy costs and increase their energy efficiency, cyber-physical nodes sleep most of the time, and avoid to communicate when they are unreachable.But, a CPS needs to disseminate data between the nodes for multiple purposes including data reporting to a back-end service, resilient operations, safe-keeping of observational data, and propagating nodes updates. Loosely-coupled data dissemination policies offer this possibility [1]. Although, investigations should be made on their applicability to large-scale CPS.In this paper, we evaluate and discuss the efficiency in energy, time and number of successful delivery of four data dissemination policies proposed in [1]. This evaluation is based on flow-level simulations. We study small and large-scale CPS, and evaluate the effects of the number of nodes and the size of the disseminated data on the nodes energy consumption and the dissemination's delivery success. To mitigate negative effects raised on large-scale CPS and large disseminated data sizes, different strategies are proposed and evaluated. We show that energy saving strategies do not always imply energy efficiency, and better data dissemination often comes at a cost. This last result highlights the importance of simulation prior to real CPS deployments in constrained environments.

The role of long-term hydrogen storage in decarbonizing remote communities in Canada: An optimization framework with economic, environmental and social objectives

Many small Canadian communities lack access to electricity grids, relying instead on costly and polluting diesel generators, despite the local availability of renewable energies like solar and wind. The intermittent nature of these sources limits reliable power supply; thus, hydrogen is proposed as a cost-effective and eco-friendly long-term energy storage solution. However, it remains uncertain whether hydrogen storage can significantly contribute to a 100% renewable energy system (100RES), given the diverse characteristics of these communities. Additionally, the potential for fully renewable infrastructure to reduce costs, mitigate adverse environmental impacts, and enhance social impact is still unclear. A multi-period optimization model that balances economic, environmental, and social objectives to determine the optimal configuration of 100RESs for isolated communities is introduced and utilized to evaluate hydrogen as an energy storage solution to seasonal fluctuations. By identifying the best combinations of technologies tailored to local conditions and priorities, this study offers valuable insights for policymakers, supporting the transition to sustainable energy and achieving national climate goals. The results demonstrate that hydrogen could serve as an excellent long-term energy storage option to address energy shortages during the winter. Different combinations and sizes of energy generation and storage technologies are selected based on the characteristics of each community. For instance, a community in the northern territories with high wind speeds, low solar radiation, extremely low temperatures, and limited biomass resources should optimally rely on wind turbines to meet 80.7% of its total energy demand, resulting in a 62.0% cost reduction and a 49.5% decrease in environmental impact compared to the existing diesel-based system. By 2050, all communities are projected to reduce energy costs per capita, with northern territories achieving 33% and coastal areas achieving 55% cost reductions, eventually leading to the utilization of hydrogen as the main energy storage medium.

Optimal control framework for cost-effective, intelligent, renewable energy-driven communities in Norway, enhanced by ANN and grey wolf method

The present work introduces a novel yet smart energy system to decarbonize the energy mix, provide cost-effective green energy, and help to achieve sustainable energy production/storage/usage. Smart hydronic units and multiple controllers are the backbone of this idea, which aims to monitor energy conversion among components, the grid, and users through an innovative rule-based framework. This clever integration results in smaller components, bidirectional grid connection, and increased penetration of renewable energy sources into the local energy network. The system is driven by photovoltaic thermal panels and a biomass heater integrated with a heat pump and internal combustion engine. The TRNSYS-MATLAB framework evaluates the system's practicality from all aspects. Multi-objective optimization based on the grey wolf approach equipped with artificial intelligence is added to find the most optimal state. Compared to the conventional system in Norway (hydropower + wind), the proposed smart integration achieves up to 55 % and 5.4 % reduction in energy costs and emission index. In particular, the carbon emission index is lowered to 36.3 g/kWh, and the energy cost is decreased to 38.4 $/MWh. Also, the parametric analysis indicates a conflictive change among key metrics when altering a variable, necessitating the multi-objective optimization to achieve a balancing trade-off. The optimization reduces the emission index, investment cost, and energy cost by about 1.8 g/kWh (5 %), 156,000 $/year (2.5 %), and 1.5 $/MWh (4 %) while improving the efficiency by about 1.7 % compared to the design condition. The suggested system generates 17,830 MW/year of clean energy and can mitigate carbon dioxide emissions by 552,000 kg per year compared to the Norwegian energy mix.

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.