Increasing Electricity Demand Research Articles

Enhancing Renewable Energy Integration and Implementing EV Charging Stations for Sustainable Electricity in Crete’s Supermarket Chain

In current times, sustainability is paramount, and businesses are increasingly adopting renewable energy sources (RESs) and electric vehicle (EV) charging infrastructure to minimise their environmental impact and operational costs. Such a transition can prove challenging to multi-location businesses since each chain store functions under different constraints; therefore, the implementation of a corporate policy requires adaptations. The increased electricity demand associated with EV charging stations and their installation cost could prove to be a significant financial burden. Therefore, this study aims to investigate and develop strategies for effectively incorporating RES and EV charging stations into the operations of a supermarket chain in Crete. Monthly electricity consumption data, parking availability, and premise dimensions were collected for 20 supermarkets under the same brand. To achieve a more tailored approach to custom energy system sizing, the integration of energy storage coupled with a photovoltaic (PV) system was investigated, using the Moth–Flame Optimiser (MFO) to maximise the Net Present Value (NPV) of 20 years. The algorithm managed to locate optimal solutions that yield profitable installations for all supermarkets by installing the necessary number of PV units. Manual exploration around the solutions led to the optimal integration of energy storage systems with a total upfront cost of EUR 856,477.00 and a total profit for the entire brand equal to EUR 6,426,355.14.

Flexible Demand and Its Impacts on Future Utility‐Scale Photovoltaic Integration and Generation Costs: Case Study on the Italian Energy Transition

The energy transition implies an increase in electrical demand, which shall be met primarily by renewable energy sources, potentially raising costs for the system and the community. However, increasing consumption flexibility has the potential to reduce these additional costs by minimizing the risk of curtailment and reducing the need for new storage and grid line capacity. Few studies have been conducted to examine the techno‐economic implications of improving consumption flexibility from an energy system perspective, but none have determined how these gains may affect future solar power integration costs. In this study, it is aimed to fill the gap by assessing the economic effects of flexible demand on solar integration and generation costs. Different scenarios for 2030 and 2040 of the Italian energy system are investigated taking into account future flexible demand availability and geographical distribution. In the results, it is shown that enabling a 12 h flexible demand can lower powerline transport capacity by up to 14%, and storage capacity by up to 25% in the 2040 scenarios. This corresponds to a reduction in photovoltaic (PV) generation costs from 11 to 13% by lowering overall PV integration costs, with peaks of up to 20% in some cases.

Factors that affect the consumption of electricity in refrigerators in the Forastero sector, Latacunga Canton, Belisario Quevedo Parish during the period of January-February 2024

Introduction: the increase in electricity demand on a global scale implies damage to the Earth's biodiversity. Several Latin American countries experienced a significant economic increase, which generated a greater demand for energy.Objective: to determine the factors that affect electricity consumption in the foreign sector, Latacunga canton, Belisario Quevedo parish, during the period from January to February 2024.Method: an observational, descriptive and cross-sectional study was carried out on the factors that affect electricity consumption in the foreign sector, Latacunga canton, Belisario Quevedo parish, during the period from January to February 2024.Results: 87 % of the total sample have only one refrigerator, 50 % open the door more than five times a day, respondents do not spend time performing the corresponding maintenance for their refrigerator, 100 % do not know the energy efficiency rating of their refrigerators, and indicated that the use of technologies such as programming and energy and temperature savings has a high impact on electricity consumption, which is 75 %.Conclusions: the critical importance of energy efficiency in the use of refrigerators is highlighted, and the need to raise awareness among users about practices they can adopt to significantly reduce electricity consumption is underlined

Factors that affect electricity consumption in refrigerators

Introduction: the increase in electricity demand on a global scale implies damage to the Earth's biodiversity. Several Latin American countries had a significant economic increase, which generated a greater demand for energyObjective: to characterize factors that influence the consumption of electric energy in refrigerators.Method: a review of the bibliography available in databases such as SciELO, Scopus and ClinicalKey was carried out, from which a total of 10 related articles were consulted. Empirical methods such as logical history and analysis and synthesis were used.Results: currently, the issue of energy use, especially in relation to household appliances. Refrigerators represent a significant part of electricity consumption in homes. One of the key factors that influence the energy consumption of refrigerators is ambient temperature. Overloading the refrigerator can also significantly affect its energy efficiency. The frequency of opening the door is also an important factor to consider. Preventive maintenance of a refrigerator is recommended to be performed at least once a year. Conclusions: the critical importance of energy efficiency, the importance of education and the promotion of sustainable practices to reduce electricity consumption are highlighted

Increase in Demand for Electricity Due to Rapid Increase in Data Centers to Support AI and Role of V2G in Supporting this Growth

Purpose: The rapid proliferation of artificial intelligence (AI) technologies has led to unprecedented growth in data center infrastructure, significantly increasing global electricity demand. This study examines how Vehicle-to-Grid (V2G) technology can mitigate the energy challenges associated with AI-driven data center expansion. Methodology: This paper employs a literature review of technological advancements, policy frameworks, and case studies to explore the interplay between AI-driven data center growth, electricity consumption, and the potential of V2G technology. Strategic insights are drawn to evaluate V2G’s role in energy management and grid stabilization. Findings: V2G technology provides a promising solution for peak demand management, renewable energy integration, and grid stabilization by leveraging electric vehicles as mobile energy storage units. Key findings highlight V2G's capacity to support sustainable energy practices in data centers, with examples from real-world implementations. Unique Contribution to Theory, Policy, and Practice: This paper contributes to the understanding of V2G’s transformative potential in addressing energy challenges posed by AI-driven data center expansion. It emphasizes the need for collaborative efforts in technological development, policy-making, and adoption strategies to build a resilient, sustainable infrastructure for the future.

PROSES PEMBUATAN PLTB HORIZONTAL

The increase in electrical energy demand occurs due to high population growth, but this is not balanced with the increase in the supply of electrical energy, the installed power capacity remains the same, while community needs continue to increase. PLTMH (Microhydro Power Plant) and PLTB (Wind Power Plant) are power plants with renewable energy sources. This is seen from an economic and security perspective. Because currently fossil energy reserves are decreasing while the need for fuel consumption continues to increase, this has an impact on the energy crisis. PLTB has the main advantage because of its renewable nature. This means that exploitation of this energy source will not reduce wind resources like the use of fossil fuels. Based on considerations for the fulfillment of electrical energy, an economical, safe and reliable generator was built.

Medium-Term Forecasting of Power Consumption by ANN Considering Meteo-Factors

The main task of forecasting power consumption is to analyze objective factors affecting load changes and compute expected schedules of consumption to support energy companies organize the purchase and production of the necessary electricity in the proper volume. However, modern practical requirements for the accuracy of predictive calculations lead to the fact that previously developed methods do not always provide the necessary accuracy of computations. This is because several factors affect the level of power consumption and the accuracy of forecasting, including meteorological, where the growing use of both wind and photovoltaic energy worldwide leads the power systems to become highly dependent on the weather conditions. This study presents the influence of meteorological factors on the medium-term forecast of electricity consumption based on the power system of the Gorno-Badakhshan Autonomous Oblast in the Republic of Tajikistan, where in winter, due to an increase in demand for electricity and a drop in the water level in rivers, to ensure balance, requires additional commissioning of diesel power plants. Thus, it leads to an increase in the costs of electricity production in the region and environmental degradation. To increase the forecasting effectiveness, the authors propose an approach based on the clustering of meteorological conditions, where each cluster has its regression model. The Principal Component Analysis was proposed for aggregating meteorological factors.

Forecasting for electricity demand utilizing enhanced inception-V4 using improved Osprey optimization

This research examines the impact of temperature, relative humidity, and wind speed on the electricity demand. It presents a unique method that combines an Enhanced Inception-V4 model with an Improved Osprey Optimizer to analyze weather-related factors. The combined model, which has been validated from 2003 to 2023, surpasses traditional forecasting techniques and significantly improves prediction accuracy. The Enhanced Inception-V4 model’s ability to process data allows it to identify key factors that influence electricity demand patterns. Meanwhile, the Modified Osprey Optimizer fine-tunes the model’s parameters, ensuring its adaptability to different weather scenarios. The study confirms the reliability of the OOI-Inception-V4 model in forecasting electricity demand and highlights the strong connection between weather conditions and energy usage, especially during extreme weather events. The projected increase in electricity demand from 2024 to 2030 emphasizes the importance of proactive energy policies, infrastructure upgrades, and sustainability initiatives. The research underscores the crucial role of temperature in driving electricity demand, with noticeable variations during winter and summer due to heightened usage of heating and cooling systems. In general, this study emphasizes the significant impact of climate on energy demand and demonstrates the potential of advanced predictive models in enhancing electricity demand forecasting.

Lynn Good’s green dilemma: leading Duke Energy’s transition

Research methodology This case was developed from secondary sources. The secondary sources included news reports, industry reports, company websites, annual reports and company websites. Case overview/synopsis The case discusses the comprehensive renewable energy transition strategy that Lynn J. Good (Good), CEO and President of Duke Energy and the Chairman of its Board, was undertaking. In September 2019, Good revealed a new climate plan aimed at achieving net-zero carbon emissions with zero methane emissions from natural gas operations by 2030 and zero carbon emissions from electricity generation by 2050. Duke Energy is a Fortune 150 company headquartered in Charlotte, North Carolina and one of the major energy generation companies in the USA with two reportable business segments – Electric Utilities and Infrastructure (EU&I) and Gas Utilities and Infrastructure (GU&I). Good targeted interim carbon emission reduction targets of at least 50% from electric generation by 2030, 50% for Scope 2 and some Scope 3 upstream and downstream emissions by 2035 and 80% from electric generation by 2040. To achieve this, she invested in large electric grid upgrades and energy storage, as well as in research on zero-emission power generation technologies including hydrogen and advanced nuclear technologies. She helped update the grid system and improved customer experience. As part of the revamped climate strategy, Good invested in crucial energy infrastructure and improved affordability of electricity for customers, especially in the North Carolina region. Despite her efforts at curbing emissions, Good faced criticism from various quarters. This included criticism of Duke’s carbon-cutting plans in 2022 in its core North and South Carolina service zones. The company also faced charges of environmental pollution. Its 2024 strategy, which aimed to address a projected increase in demand for electricity using natural gas plants capable of running on hydrogen, was also met with skepticism by industry groups, advocates, activists and local governments. Good had to face the conflict between environmental ambitions and technological realities that highlighted the difficulties in transitioning to a cleaner energy future. It remained to be seen whether she would be able to successfully navigate the various hurdles and help Duke Energy reach its 2030 emission targets. Complexity academic level This case was written for use in teaching graduate and postgraduate management courses in entrepreneurship and economics, politics and business environment.

A Tale of Sustainable Energy Transition Under New Fossil Fuel Discoveries: The Case of Senegal (West Africa)

The transition to renewable and sustainable energy sources is critical to solving the environmental and socioeconomic problems associated with the use of fossil fuels. This study uses an interdisciplinary approach to analyze the challenges and prospects of a sustainable energy transition in contexts with the recent discovery and exploitation of fossil resources. We study the case of Senegal from 2000 to 2027 and the role of recent discoveries of natural gas in its energy transition. In 2000, Senegal’s energy mix consisted of about 97% fossil energy and only 3% renewable energy. Since then, the country has developed renewable energy sources, including solar, hydro, and wind power, which currently account for about 30% of the total energy mix. At the same time, Senegal’s population and electricity production have grown significantly, leading to a fivefold increase in per capita energy consumption over the past two decades. Projections based on a long short-term memory model that predicts future electricity demand and energy balance suggest a structural shift in the energy mix, with natural gas, oil, and renewables at 47%, 32%, and 21%, respectively, by 2027. Overall, this study presents a comprehensive analysis that highlights the benefits of strategically using natural gas as a transition energy source in contexts with increased electricity demand and continued development of renewable energy sources.

China's electric vehicles adoption: implications for sustainable electricity, transportation, and net-zero emissions

IntroductionThe rapid adoption of electric vehicles in China is a key strategy for decarbonizing the transportation sector, facilitating the transition to sustainable energy, and meeting the country's net-zero emissions goals. Notwithstanding this, limited research has explored how technological advancement influences electric vehicle adoption in the context of achieving sustainable electricity.MethodsThis study addresses this gap by integrating vehicle range, smart charging infrastructure, and battery electric vehicles into an econometric count framework utilizing countrywide data-driven insights.ResultsThe findings demonstrate that technological advancements-specifically in vehicle range and the expansion of charging infrastructure-are vital solutions in driving battery electric vehicles acceptance. These advancements are essential for revolutionizing the transportation industry and contributing to the country's emissions reduction targets.DiscussionHowever, this presents a dual challenge: balancing increased electricity demand while capitalizing on these technologies to meet net-zero goals. The study highlights critical policy implications, particularly the need to advance electric vehicle material technologies through the use of critical minerals including aluminum and lithium. By prioritizing these materials, producers can improve electric vehicles' efficiency and support the integration of renewable energy sources. The study concludes that incorporating renewable energy solutions, like solar-powered charging stations, is crucial for ensuring sustainable electricity. Policies encouraging public-private partnerships and investments in research on materials and smart charging technologies are crucial for reducing charging times and improving vehicle range. Additionally, fostering public-private collaborations to install smart charging infrastructure equipped with Internet-of-Things technology at parking slots can create a synergistic effect, significantly boosting electric vehicle adoption in China.

Optimizing IoT-driven smart grid stability prediction with dipper throated optimization algorithm for gradient boosting hyperparameters

With the surge in global population and economic expansion, there's been a marked increase in electricity demand. This necessitates the efficient distribution of electricity to both residential and industrial sectors to minimize energy loss. Smart Grids (SG) emerge as a promising solution to reduce power dissipation in distribution networks. The application of machine learning and artificial intelligence in SGs has significantly improved the precision of predicting consumer electricity needs. This paper presents a novel approach to improving the stability prediction of Internet of Things (IOT)-driven SGs using different advanced machine learning models. This study explores multiple advanced machine-learning techniques, including Gradient Boosting (GB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Neural Networks, and the Decision Tree classifier, focusing on the stability prediction of SGs. This study explores the efficiency of hyperparameter-optimized GB models in predicting SG dynamic stability that encompasses the ability of the system to return to a stable operating point following a disturbance. Focusing on various models, it identifies the Dipper Throated Optimization Algorithm DTO+GB model as the standout, exhibiting unparalleled accuracy and reliability across critical performance metrics such as accuracy (99.32 %), sensitivity (99.16 %), and specificity (99.54 %). Diagnostic and regression analyses further emphasize its better predictive power and the need for hyperparameter optimization to improve the model. This paper highlights the capabilities of advanced machine learning algorithms in conjunction with tactical hyperparameter optimization in enhancing SG stability prediction, introducing a new baseline for future technological and methodological developments in this application.

Heuristic method for electric vehicle charging in a Spanish microgrid: Leveraging renewable energy surplus

The transition towards renewable energies has intensified due to the global energy crisis, climate neutrality goals, and the increase in electrical demand. Technologies such as wind and solar have driven this change, although they present challenges such as intermittency and volatility. To maintain grid stability, solutions such as microgrids and energy storage system are required, improving supply quality and energy efficiency. Electric vehicles have gained importance as energy storage system through Vehicle-to-Grid technology. This article describes the implementation of a heuristic method in the microgrid of CEDER-CIEMAT (Center for Renewable Energy Development – Center for Energy, Environmental and Technological Research) in Spain. This microgrid, with a diversity of components and consumption profiles, allows for the acquisition of accurate data in a real-world setting. The heuristic method optimizes the charging of electric vehicles by leveraging the surplus renewable energy from the center, achieving 78 % of charging with surplus power and 96 % with renewable energy. This results in annual savings exceeding 900 €, ensuring sustainable energy use in the microgrid.

Power sector benefits of flexible heat pumps in 2030 scenarios

Heat pumps play a major role in decreasing fossil fuel use in heating. They increase electricity demand, but could also foster the system integration of variable renewable energy sources. We analyze three scenarios for expanding decentralized heat pumps in Germany by 2030, focusing on the role of buffer heat storage. Using an open-source power sector model, we assess costs, capacity investments, and emissions effects. We find that investments in solar photovoltaics can cost-effectively accompany the roll-out of heat pumps in case wind power expansion potentials are limited. Results further show that short-duration heat storage substantially reduces the need for firm capacity and battery storage. Larger heat storage sizes do not substantially change the results. Increasing the number of heat pumps from 1.7 to 10 million units could annually save more than half of Germany’s private and commercial natural gas consumption and around half of households’ building-related CO2 emissions.

Integrating active demand into the distribution system using metaheuristic techniques

AbstractIntegrating non‐conventional renewable energy sources into distribution systems, alongside data science and enabling technological infrastructures, presents significant challenges, particularly in managing active demand. The rapid evolution of the electric energy system and increasing electricity demand highlight the need for reliable tracking and predictive methods to manage Distributed Energy Resources and digital infrastructure. These methods are essential for advancing carbon neutrality, democratizing environmental sustainability, and improving energy efficiency. Effective active demand monitoring requires understanding the transactional system concept, including digital infrastructure and decentralized demand. Although metaheuristic techniques are increasingly important in demand response integration, much research focuses on specific techniques rather than providing a comprehensive view of dynamic transaction integration for active demand. Technological advancements, like smart meters and communication systems, are shifting from basic consumption measurement to active customer participation. This article reviews key concepts in electrical distribution systems, such as active demand, DERs, and transactive systems. It examines prevalent metaheuristic techniques, emphasizing their role in integrating and predicting active demand and DER behaviors. Additionally, the study presents a methodology serving as a roadmap for efficient DER integration and the transition to active demand and transactive electricity systems, addressing gaps in the current literature.

A new beginning? Obstacles and debates at the relaunch of the Argentine Nuclear Program (1999-2015)

In the 1990's, experts predicted that a ‘Nuclear Renaissance’ would take place in the 21st century, bringing nuclear energy back onto public agendas. This forecast was based on the increase in energy consumption in some Asian countries and the rise in oil prices. In this context, Argentine governments tried to relaunch the nuclear program, which had been paralyzed in 1994. After the recovery from the economic crisis of 2001 and the increase in electricity demand, the energy supply became a crucial aspect of public policies. However, it soon became clear that it was not an easy task. Through the analysis of official legislation, institutional sources and interviews, this paper will explore the main milestones of the relaunch of the Argentine Nuclear Program, as well as the limitations of such planning. We propose that after 2003 the government redefined the role of the actors without questioning the institutional panorama inherited from 1994 and paying little attention to the structural transformations that the sector had undergone after the 1990s. Although a nuclear program for the medium and long term was outlined, said planning was based on immediate political considerations rather than a strategic perspective. That’s why certain objectives, such as the installation of a fourth nuclear power plant or the promotion of the local industry, could not be sustained in the long term.

Prediction and policy: Do empirical gross calorific value prediction help reduce coal testing overload?

The gross calorific value (GCV) of coal is pivotal in shaping policies across various sectors of the Indian economy. It plays a crucial role in classification and valuation of coal and is a major factor in determining electricity tariffs charged by thermal power plants. With coal production escalating year-on-year to meet India's increasing electricity demand, there is significant rise in coal testing activities along the pit-to-power supply chain at multiple points and by multiple testing agencies often driven by sector-specific policy requirements. While laboratory testing accurately determines GCV, it is costly and time-consuming due to the reliance on expensive equipment and skilled personnel. Global researchers have previously devised a plethora of empirical formulae predicting GCV based on its correlations with easy-to-measure properties like moisture and ash content. However, the applicability and utility of these formulae to the prevalent policy matrix of coal and power sector remain to be explored. The introduction of independent third-party assessment of coal quality by Coal India Limited in 2016 has generated a vast dataset of coal sample-test results, offering an opportunity to reassess existing empirical formulae, test their alignment with existing policies, and explore possibility of a unified, region-neutral formula for rapid GCV prediction with a special focus on alleviating the current overload in coal testing.

Utilities and the Future Grid

It is widely accepted among energy industry professionals that the industry is undergoing a significant and rapid transformation. There are increasing challenges facing the industry including extreme weather events, widespread aging of the electric grid infrastructure, rapidly increasing electricity demand, as well as the impact of climate change policy initiatives. This has spurred an innovation rebirth in an industry that has traditionally been viewed as outdated, and most importantly inaccessible for participation by average customers. However, as the challenges of the changing industry and effects of climate change are impacting their day‐to‐day lifestyles, energy customers are now demanding more visibility, choices, and flexibility from their energy providers, putting a spotlight on the roles and services of utility companies.

Enhancing Electricity Demand Forecasting Accuracy Through Hybrid Models and Deep Learning Techniques: A Systematic Literature Review

Abstract: This reviewed literature on electricity forecasting covers its history, terminology, and techniques. A systematic review of existing studies highlighted key findings and future research opportunities. Conventional statistical techniques and MLA can predict electricity demand over time with various techniques and forecasting windows tailored to data and problem specifics. Most studies focused on STLF, often without testing techniques on MTLF and LTLF. The key findings include: Many studies (26%) used conventional statistical methods like ARIMA, ARIMAX, and SARIMAX for electricity forecasting, often without benchmarking algorithms. Various factors, such as time, weather, electricity price, population, and economy, influence ELF. Weather parameters were the most commonly used predictors, though performance varied across studies. A global increase in electricity demand has driven numerous studies, though less research has been done in low- and middle-income countries. Deep neural networks like LSTM have been underutilised in electricity forecasting. LSTM's ability to store memory and address the vanishing gradient problem makes it promising for future research, particularly in hybrid models combining CNN and LSTM for forecasting peak load demand based on economic and environmental factors.

Machine Learning Algorithms for Prediction of Boiler Steam Production

The continuous increase in global electricity demand has resulted in boiler power plants becoming a significant energy source. The production of steam is a principal indicator of boiler efficiency, and the accurate prediction of steam production is paramount importance for the enhancement of boiler efficiency and the reduction of operational costs. In this study employs a boiler dataset with a steam production capacity of 420 tons per hour. A total of 25 independent variables were extracted from the original 39 variables through data processing and feature engineering for the purpose of prediction analysis. Subsequently, 8 machine learning models were used for modeling predictions. Grid search cross-validation was employed in order to optimise the performance of the model. The models were analysed and assessed using the Mean Squared Error (MSE) metrics. The results show that random forest achieves the highest accuracy among the 8 single models. Based on 8 models, New Bagging ensemble model is proposed, which combined predictions from 8 single models, demonstrated the optimal overall fit and the lowest MSE, achieved the purpose of the research. The present study demonstrates the ability to analyse and predict complex industrial systems with machine learning algorithms, and provides insights into the use of machine learning algorithms for industrial big data analytics and Industry 4.0. Further work could explore using larger datasets and deep learning to make predictions more accurate.