Increasing Electricity Demand Research Articles

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.

Well-to-Wheel emissions of electric vehicles in Türkiye and emission mitigation by renewable penetration

Automotive technology is currently on the verge of transitioning to electric vehicles, and both the impact of these vehicles on electricity consumption and their environmental performance are the focus of significant interest. In this paper, the emission impacts based on the use of electric vehicles are analyzed using the Well-to-Wheel methodology. In order to evaluate electric vehicle emissions, emissions based on electricity generation and consumption are first determined. Emission intensities are found in gCO2e/kWh for electricity consumption and gCO2e/km for battery electric vehicles. Well-to-wheel emissions are found 172.2 and 89.1 gCO2e/km for conventional and electric vehicles, respectively. With the renewable penetration in electricity generation, the emission intensity will be lower, which means that the emissions of electric vehicles will be further reduced. Thus, a renewable energy supported charging station is simulated and it is shown that emission intensities can be reduced by 60 %. It is concluded that Türkiye should focus primarily on renewable energy sources and then nuclear energy in order to meet the increasing electricity demand and reduce emission intensities.

Ice source heat pump system for energy supply via gas pipelines – Part1: Performance analysis in residential units

This study evaluates the performance of heat pumps using water or ice as the heat source, with energy transferred via repurposed gas delivery pipes. The proposed system offers significant benefits for urban energy networks, particularly in densely populated areas. The article is divided into two main sections: the first compares the thermal efficiency of the proposed system for residential heating against similar heat pumps. The second section examines the implementation, energy transfer capacity, and water requirements of using a gas pipeline network. The study models various heating systems, including electric-resistance, air-source, water-source, ground-source, and ice-source heat pumps, in Nottingham throughout the year. The results show that switching from gas to heat pumps significantly increases electricity demand, especially during peak periods. Specifically, electric-resistance heating, air-source, water-source, and ice-source heat pumps increase peak electricity consumption by factors of 7.5, 3.2, 2.5, and 2.7, respectively, compared to a gas boiler. Additionally, electricity consumption exceeds the current peak for 89 %, 47 %, 35 %, and 39 % of the hours in the year for each respective system. These findings emphasize the proposed system's role in managing peak electricity demand and enhancing generation and transmission capacity post-natural gas phase-out.

Temperature Compensation Model for Monitoring Sensor in Steel Industry Load Management

The iron ore industry faces increasing electricity demand due to industrialization, making effective management of electricity demand crucial. This study proposes a temperature compensation model using Support Vector Regression (SVR), aiming to enhance the accuracy of sensors in monitoring electricity demand. An experiment is conducted to assess the impact of temperature on sensor measurements, and a modified Whale Optimization Algorithm is employed to correct the sensor outputs. The proposed model is compared with both PSO-SVR and unimproved WOA-SVR. Results show that the proposed model significantly improves accuracy, achieving a determination coefficient of 0.7882 and a relative standard deviation of the error square sum of 4.6412%. The results of this study not only enhance power demand management in iron mining but also hold potential applications across various industries.

Flexible emulation of the climate warming cooling feedback to globally assess the maladaptation implications of future air conditioning use

Abstract Rising affluence and a warming climate mean that the demand for air conditioning (AC) is rising rapidly, as society adapts to climate extremes. Here we present findings from a new methodological framework to flexibly couple and emulate these growing demands into a global integrated assessment model (IAM), subsequently representing the positive feedbacks between rising temperatures, growth in cooling demand, and carbon emissions. In assessing global and regional climate change impacts on cooling energy demand, the emulator incorporates climate model uncertainties and can explore behavioural and adaptation-related assumptions on setpoint temperature and access to cooling. It is also agnostic to the emissions and climate warming trajectory, enabling the IAM to run new policy-relevant scenarios (Current Policies, 2 °C and 1.5 °C) with climate impacts that do not follow Representative Concentration Pathways. We find that climate model uncertainty has a significant effect, more than doubling the increase in electricity demand, when comparing the 95th percentile cases to the median of the climate model ensemble. Residential AC cooling energy demands are expected to increase by 150% by 2050 whilst providing universal access to AC would result in the order of a 400% increase. Depending on the region, under current policies and limited mitigation, climate change could bring in the order of 10%–20% higher cooling-related electricity demands by 2050, and approximately 50% by 2100. Set point temperature has an important moderating role—increasing internal set-point from 23 °C to 26 °C, approximately halves the growth in electricity demand, for the majority of scenarios and regions. This effect is so strong that the change in set point temperature to both residential and commercial sectors outweighs the growth in demand that would occur by providing universal access to AC by 2050 to the 40% of the global population who would otherwise not afford it.

Economy and energy flexibility optimization of the photovoltaic heat pump system with thermal energy storage

Photovoltaic systems have been widely applied in building energy systems. However, with the sharp increase in demand of electricity in buildings during heating and cooling seasons, the uncertainty of photovoltaic generation further exacerbates the peak-valley difference in electricity use. To improve the economy and energy flexibility of buildings in hot summer and cold winter zones of China, a rule-based operation strategy was proposed for the photovoltaic heat pump with thermal energy storage system to optimize the size of the thermal energy storage system and system operation, with aims of minimizing the total annual cost of the system and maximizing the self-consumption rate of the photovoltaic generation. The effects of grid export power limits, grid import power limits, feed-in tariffs, and PV generation on the system operation optimization results were also investigated. The results indicated that by integrating the thermal energy storage system into the photovoltaic heat pump system, the self-consumption rate of the photovoltaic generation was reduced by 2.39 %, the total annual cost of the system was decreased by 6.61 %, and the payback period of the thermal energy storage system was 1.31 years. The optimum size of the thermal energy storage system and the self-consumption rate of the photovoltaic generation decreased with the increasing grid export power limits and grid import power limits. In contrast, higher grid export power limits, grid import power limits, feed-in tariffs, and PV generation all resulted in a lower total annual cost. This study provided a systematic design and operation optimization method for building energy systems.

ЗБІЛЬШЕННЯ ПРОПУСКНОЇ СПРОМОЖНОСТІ МАГІСТРАЛЬНИХ МЕРЕЖ УКРАЇНИ

In connection with the projected significant increase in electricity demand by 2030, as well as for the purpose of devel-oping the electricity system and effective interaction of the IPS of Ukraine with the energy systems of the European Un-ion, the article considers the need and feasibility of converting the Ukrainian power grids from 330 kV to 400 kV, com-paring the technical characteristics of grids of different voltage classes in terms of reliability, stability and efficiency of such grids. The paper provides an applied analysis of the efficiency of switching 330 kV rated voltage networks to 400 kV voltage class by the criterion of increasing the capacity of electrical equipment and reducing total power losses in the network. On the example of the Dnipro region of the IPS of Ukraine, the complex of computer programs designed for the design of power grids of power systems " Thesaurus" was used to model and analyze the modes of operation of the power grid at 330 kV and 400 kV. As a result, it was found that an increase in the voltage level will lead to a de-crease in the load of certain line sections by 17 %, while the value of total network losses will decrease by 10.8 %. Ref. 4, fig. 3.

Optimizing and Exploring Untapped Micro-Hydro Hybrid Systems: a Multi-Objective Approach for Crystal Lake as a Large-Scale Energy Storage Solution

Increasing electricity demand and concerns about climate change and fossil fuel consumption have highlighted the importance of renewable energy resources and storage systems. This paper proposes a method for exploring untapped pumped hydro storage potentials to accommodate intermittent renewable energy generation profiles. Hourly measured data from 2022 in Benzie County, Michigan, United States, were gathered for system sizing and a thorough, realistic analysis. By employing the multi-objective grey wolf optimization algorithm, we formulated optimal sizing and energy-management strategies for three different scenarios. Unlike similar studies, the 3rd with triple objective functions (OFs) scenario aims to maximize both reliability and ecological OFs while minimizing the cost OF. It has shown promising results with multiple solutions, considering economic, environmental, and reliability factors. A case study conducted in Crystal Lake, Michigan, revealed that although Crystal Lake would function only as a micro-hydro power facility, it is a promising and huge storage unit with a substantial storage capacity of around 14.9734GWh. The system investigated is significant in the USA due to its rapid deployment capabilities, minimal construction requirements, and ease of integration with the distribution grid. The fuzzy logic method was employed to identify the best non-dominant solution among the other solutions. These outcomes include a notably low levelized cost of energy at 0.046147$/kWh, a robust index of reliability of 99.705%, and a significant reduction in CO2 emissions amounting to 7.9142×103 tons/year, when considering the triple OFs. The paper’s methodology provides valuable insights for regions aiming to utilize renewable energy from untapped storage sources.

A jövőbeli erőművi portfólió elemzés költség- és kibocsátás minimum optimalizálás szempontjából

The article examines the expected composition of the power plant portfolio in Hungary by 2030. The indicators considered are the life-cycle unit costs (LCOE) and the life-cycle specific carbon dioxide emissions (LCA(CO2)) of the power plant types. The minimum of these two indicators, as objective functions, is determined by a linear programming method for the power plant portfolio. The results show that the LCOE minimum for the power plant portfolios in 2030 is worse in absolute terms and better in specific terms than in 2021. In both absolute and specific terms, the LCA(CO2) minimum is more favourable in 2021. These results are met under the thirty and twenty-five percent electricity import scenarios. With twenty percent imports, the absolute values are worse and the specific values are better for both indicators. On the other hand, the results of the calculations for 2030 fall short of the 2030 Agenda of the Institute for a Green Transition. This is due to the delay in commissioning a new nuclear power plant and the transformation of industry with increasing electricity demand. For the portfolios under review, a minimum of thirty percent of domestic generation from renewable sources is met. This contributes significantly to the European Union's ambition for the sector to be net greenhouse gas-free by 2050.

Utilizing multilayer perceptron for machine learning diagnosis in phase change material‐based thermal management systems

AbstractElectric vehicles encounter significant challenges in colder climates due to reduced battery efficiency at low temperatures and increased electricity demand for cabin heating, which impacts vehicle propulsion. This study aims to address these challenges by implementing a thermal management system utilizing Phase Change Materials (PCMs) and validating the performance of a Multilayer Perceptron (MLP) model in predicting PCMs behavior and battery temperature distributions. The study employs an MLP model trained with 160 samples of diverse heat inputs, including pulsating, constant, wiener, discharging, and random temperatures. The model uses these temperatures as inputs and liquid fractions as target values. Performance evaluation is conducted using the MATLAB platform and is benchmarked against existing approaches, such as Long Short‐term Memory (LSTM), spatiotemporal convolutional neural network (CNN), and pooled CNN‐LSTM. The MLP model's accuracy in predicting PCMs phase transitions is validated by comparing predicted liquid fractions with numerically obtained values. Additionally, this study forecasts temperature distributions within a standard battery pack under various discharge scenarios, considering the performance of commercial lithium‐ion batteries. The proposed MLP model demonstrates high efficacy, achieving a correlation of up to 0.999 and root mean squared error below 0.013 compared with numerical results.

Can investors benefit from the early retirement of coal plants: A plant-level analysis of Chinese-sponsored coal stations in Vietnam and Pakistan

The global energy sector's shift towards decarbonization necessitates the early decommissioning of coal-fired power plants (CFPPs), requiring novel financing strategies. While research on financing CFPP retirement is extensive for developed countries, it remains nascent for developing nations, particularly in Asia, which holds 76% of the world's coal capacity. Initiatives like the Asian Development Bank's Energy Transition Mechanism and the Just Energy Transition Partnership reflect the growing focus on Asian CFPP retirement. However, challenges such as increasing electricity demand, the youth of Asian CFPPs, and dominant Chinese sponsorship complicate retirement efforts. This study evaluates the enterprise values of six Chinese-backed CFPPs in Vietnam and Pakistan commissioned between 2010 and 2023 and capacities ranging from 600 to 1320 MW under three financing models and future geoeconomic scenarios impacting CFPP cash flows. Findings indicate that refinancing can bolster enterprise values, supporting early retirement. Younger plants, with more operational years to forgo, are especially suitable for early retirement. Combining refinancing with early retirement and renewable energy investments can significantly enhance enterprise values, advocating for the expedited retirement of CFPPs.

Support for Advanced Nuclear Power Growing in the United States

Global clean energy and decarbonization goals are forcing governments around the world to take a hard look at their energy resource portfolios and future plans for meeting increasing electricity demand. While the last several years have seen a significant uptick in the deployment of solar, both terrestrial and offshore wind, and battery storage here in the United States, nuclear energy is once again being considered a critical energy generation source. Nuclear power, and in particular small modular nuclear reactors, are being considered as carbon‐free power generation sources to strategically meet new loads.

Towards Automated Model Selection for Wind Speed and Solar Irradiance Forecasting.

Given the recent increase in demand for electricity, it is necessary for renewable energy sources (RESs) to be widely integrated into power networks, with the two most commonly adopted alternatives being solar and wind power. Nonetheless, there is a significant amount of variation in wind speed and solar irradiance, on both a seasonal and a daily basis, an issue that, in turn, causes a large degree of variation in the amount of solar and wind energy produced. Therefore, RES technology integration into electricity networks is challenging. Accurate forecasting of solar irradiance and wind speed is crucial for the efficient operation of renewable energy power plants, guaranteeing the electricity supply at the most competitive price and preserving the dependability and security of electrical networks. In this research, a variety of different models were evaluated to predict medium-term (24 h ahead) wind speed and solar irradiance based on real-time measurement data relevant to the island of Crete, Greece. Illustrating several preprocessing steps and exploring a collection of "classical" and deep learning algorithms, this analysis highlights their conceptual design and rationale as time series predictors. Concluding the analysis, it discusses the importance of the "features" (intended as "time steps"), showing how it is possible to pinpoint the specific time of the day that most influences the forecast. Aside from producing the most accurate model for the case under examination, the necessity of performing extensive model searches in similar studies is highlighted by the current work.

Estimating and forecasting suppressed electricity demand in Ghana under climate change, the informal economy and sector inefficiencies

Suppressed demand arises from inadequate energy access, resulting in unmet basic needs. Therefore, this study investigates the impact of the informal economy, rising temperatures, and electricity transmission losses on suppressed demand in Ghana from 2000 to 2020, using a quantile autoregressive distributed lag (QARDL) approach. The study forecasts suppressed demand using Shared Socioeconomic Pathway (SSP) scenarios, offering insights for energy system planning. The results indicate that all the variables significantly affect suppressed demand in the mid-quantiles. Notably, transmission losses and growth of informal economy variables significantly impact suppressed demand within the 50th to 75th quantiles but have minimal impact before the 50th and after the 75th quantiles in the long run. Additionally, rising temperatures substantially increase suppressed demand by increasing electricity demand for cooling. All future scenarios project this growth trend will continue through 2050, albeit at varying rates. In the business-as-usual (BAU) case, suppressed demand is expected to steadily increase from 1782 MW in 2020 to 8636 MW in 2050. This trajectory aligns well with historical growth trends, which saw suppressed demand increase from 659 GWh to 1782 GWh between 2000 and 2020. SSP scenarios suggest that suppressed demand could grow substantially through 2050, driven by high losses and informal sector growth. Despite sustainable development narratives like SSP1, suppressed demand remains high without major grid and governance improvements. Comparing the results with past studies shows that our findings align with previous research but provide more nuanced insights by incorporating the effects of the informal economy and using advanced forecasting techniques. Practical policy implications include investing in green infrastructure, upgrading grid infrastructure, and formalising the informal economy to alleviate suppressed demand. These actions are critical for sustainable energy access and meeting future electricity needs effectively.

Determining the Location of Solar Power Plant in Indonesia Using Fuzzy-AHP TOPSIS

Solar is a promising renewable energy source for Indonesia's increasing electricity demand, which grows at the rate of 3.3% annually. However, high investment costs and unclear policies hinder Solar Power Plant (SPP) development. Considering the potential for growth in energy demand and the low long-term operational costs, it is imperative to foster SPP expansion in Indonesia. This study aims to identify location criteria and potential SPP development sites in Indonesia. We employ Multi-Criteria Decision-Making (MCDM) combining Fuzzy-AHP and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodologies to assign criteria weights and prioritize alternative SPP locations. Results show that, from all respondents using the geometric mean in the Fuzzy Analytic Hierarchy Process (AHP), it is found that the Economic criteria give the highest weight at 31%, and subcriteria of Land Availability, Peak Sun Hours, Geographic Location, Distance from Transportation Networks, Construction Costs, and Government Regulations contribute significantly. The ranking of alternative solutions indicates that South Sumatra Province holds the highest priority for SPP development with a 0.572 score, closely followed by North Sumatra at 0.571