Electricity Demand Research Articles

The Environmental Stake of Bitcoin Mining: Present and Future Challenges

The environmental impact of Bitcoin mining has raised severe concerns considering the expected growth of 30% by 2030. This study aimed to develop a Life Cycle Assessment model to determine the carbon dioxide equivalent emissions associated with Bitcoin mining, considering material requirements and energy demand. By applying the impact assessment method IPCC 2021 GWP (100 years), the GHG emissions associated with electricity consumption were estimated at 51.7 Mt CO2 eq/year in 2022 and calculated by modelling real national mixes referring to the geographical area where mining takes place, allowing for the determination of the environmental impacts in a site-specific way. The estimated impacts were then adjusted to future energy projections (2030 and 2050), by modelling electricity mixes coherently with the spatial distribution of mining activities, the related national targeted goals, the increasing demand for electricity for hashrate and the capability of the systems to recover the heat generated in the mining phase. Further projections for 2030, based on two extrapolated energy consumption models, were also determined. The outcomes reveal that, in relation to the considered scenarios and their associated assumptions, breakeven points where the increase in energy consumption associated with mining nullifies the increase in the renewable energy share within the energy mix exist. The amount of amine-based sorbents hypothetically needed to capture the total CO2 equivalent emitted directly and indirectly for Bitcoin mining reaches up to almost 12 Bt. Further developments of the present work would rely on more reliable data related to future energy projections and the geographical distribution of miners, as well as an extension of the environmental categories analyzed. The Life Cycle Assessment methodology represents a valid tool to support policies and decision makers.

City-wide Solar Radiation Potential Analysis by Coupling Physical Modelling and Machine Learning

Abstract. Addressing the challenges posed by climate change and meeting urban energy demands is of utmost importance in today's world. Building Integrated Photovoltaics (BIPV) emerges as a crucial solution for energy conservation and carbon emissions reduction in urban environments. However, traditional methods of assessing solar radiation on buildings using physical models are often computationally intensive and time-consuming. This paper introduces a novel hybrid approach that integrates physical model-based solar radiation calculation with machine learning techniques to analyze Solar Radiation Potential (SRP) across city-wide building infrastructure. The proposed approach precisely evaluates the SRP of representative blocks by leveraging computing-intensive physical models integrated with 3D building data. Subsequently, two machine learning models are developed to effectively predict the SRP of building roofs and facades across the entire city. To validate the efficacy of this approach, an experiment was conducted in Shenzhen, China, yielding insightful results. The findings reveal that Shenzhen has a huge potential for BIPV solar power generation, with mean annual total building roof and facade solar radiation values of 9.22*107 kwh and 2.47*108 kwh, respectively. It can be further observed that relying solely on rooftop installations is insufficient to meet electricity demand. This study not only provides an innovative alternative for city-wide SRP estimation by combining physical modeling and machine learning but also offers valuable insights for fostering low-carbon urban environments and informing data-driven and model-driven urban planning and management strategies.

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.

The impact of North American winter weather regimes on electricity load in the central United States

AbstractExtreme wintertime cold in the central United States (US) can drive excessive electricity demand and grid failures, with substantial socioeconomic effects. Predicting cold-induced demand surges is relatively understudied, especially on the subseasonal-to-seasonal (S2S) timescale of 2 weeks to 2 months. North American winter weather regimes are atmospheric tools that are based on persistent atmospheric circulation patterns, and have been linked to potential S2S predictability of extreme cold in the central US. We study the relationship between winter weather regimes and daily peak load across 13 balancing authorities in the Southwest Power Pool. Anomalous ridging across Alaska, the West Coast, and Greenland drive increases in demand and extreme demand risk. Conversely, anomalous troughing across the Arctic and North Pacific reduces extreme demand risk. Thus, weather regimes may not only be an important long-lead predictor for North American electricity load, but potentially a useful tool for end users and stakeholders.

Future Consumption Scenarios of Power Sector in Bangladesh

Future electricity demand is an essential prerequisite for planning the expansion of a power system. The purpose of this paper is to provide a forecast of electricity consumption in Bangladesh. Forecast is done here total as well as sector-wise consumption up to 2040 considering the base year 2009. The time series analysis to find the trend line by the method of Least Squares (LS) is applied in this study. We accumulated data from the annual reports of the Bangladesh Power Development Board for the years 2009 to 2023 and used that for forecasting purposes. For the convenient and policy purposes, the forecast is presented for every five years interval. The findings have significant implications with respect to energy conservation and economic development. As the study differentiated sector-wise projected growth of electricity consumption in Bangladesh, it has implications for the planners related to electricity distribution system. The findings can create awareness among the planners of power system expansion in Bangladesh to meet the continuous future development demand.

Regional Analysis and Evaluation Method for Assessing Potential for Installation of Renewable Energy and Electric Vehicles

Many countries are adopting renewable energy (RE) and electric vehicles (EVs) to achieve net-zero emissions by 2050. The indicators of RE and EV potentials are different. Decision-makers want to introduce RE and EVs; however, they need a method to find suitable areas. In addition, this is required in the time-series analysis to provide a detailed resolution. In this study, we conducted a time-series analysis in Japan to evaluate suitable areas for the combined use of RE and EVs. The results showed the surplus RE areas and shortage RE urban areas. The time-series analysis has quantitatively shown that it is not enough to charge EV batteries using surplus RE. Moreover, a ranking methodology was developed for the evaluation based on electric demand and vehicle numbers. This enables the government’s prioritization of prefectures and the prefectures’ prioritization of municipalities according to their policies.

Multi-objective energy management using a smart charging technique of a microgrid with the charging impact of plug-in hybrid electric vehicles

The Microgrid (MG) concept is being developed to better integrate renewable energy sources and automate distribution networks. Microgrids combine distributed generating units (DGs) and energy storage systems to achieve this. This research paper aims to simultaneously minimize the daily operational cost and net environmental pollution of a small MG system, factoring in the charging demand from Plug-in-Hybrid Electric Vehicles (PHEVs) and consumer load demands. The proposed energy management process not only minimizes operational costs and emissions, but also determines the optimal battery size for the energy storage system. The analysis also explores the importance of two critical variables - the operation and maintenance costs of the DGs, and the total daily cost of the battery energy storage system. The demand for PHEV charging is managed using an intelligent charging approach. Given the complexity of the optimization, a recently developed metaheuristic algorithm, Slime Mould Algorithm (SMA), is applied. The performance of SMA is compared against the Grasshopper Optimization Algorithm and Sine Cosine Algorithm. To solve the multi-objective problem, a weighted sum method maintaining non-dominance and a fuzzy decision-maker technique are employed alongside the suggested algorithms. Three different scenarios verify the proposed method's effectiveness.

Innovative materials integrated with PCM for enhancing photovoltaic panel efficiency: An experimental investigation

The electrical power generated by a photovoltaic panel is crucial due to the high demand for electricity. Operating temperatures above the Standard Test Conditions (STC) negatively impact the output power, which is a significant drawback of this technology. Therefore, it is essential to keep the front and back surface temperatures of the photovoltaic panel as low as possible. Phase Change Material (PCM) is a good passive cooling method, but it has low thermal conductivity. This paper introduces a novel material to address this issue. Iron Filling Waste (IFW) is integrated with PCM to cool the photovoltaic panel. This investigation is presented experimentally with three modules. The highest surface temperature of the reference panel, 78 °C, is recorded for the uncooled module, while with PCM- IFW is 70.5 °C. The comparative analysis shows that the PV module cooled by PCM-IFW achieved an efficiency enhancement and power output increase of 39 % and 33 % respectively, compared to the reference panel without cooling. IFW-PCM achieves an average enhancement in efficiency and output power by 23 % and 11 %, respectively, compared with panels that use PCM only. Also, IFW enhances thermal conductivity without any additional economic cost for the cooling systems.

Assessing hydrodynamic impacts of tidal range energy impoundments in UK coastal waters

Tidal range energy comprises a vast theoretical resource of 9,220 TWh per year, globally, with advantageous characteristics of predictability, generation flexibility and reliability. Approximately 13% of this resource lies within the United Kingdom’s (UK) coastal waters, where it could supply up to 12% of annual electricity demand. Tidal range energy conversion traditionally involves constructing and operating large-scale coastal or offshore impoundments (O10-100 km2), which will redefine near and far-field water levels and flow patterns. The relationship between the scale of the impoundment area and hydrodynamic impact has not been investigated for UK sites. To address this, we develop a two-dimensional (depth-averaged) TELEMAC model of the Irish Sea, and simulate six scenarios involving tidal range schemes of increasing basin area, from 25 to 150 km2, located on the North Wales coast in an open coastal basin setting. Results indicate that far-field (30−150 km) changes to the amplitude of the semi-diurnal (M2) tidal constituent exhibit a linear relationship with impoundment area and volume (correlation coefficient R=0.95 and R=0.96, respectively). The largest impoundment (150 km2) caused far-field changes in maximum surface elevation (2<ηmax<3 cm); near-field surface elevation was reduced (ηmax>3 cm).

Locating the suitable large-scale solar farms in China's deserts with environmental considerations

Desert areas offer rich solar resources and low land use costs, ideal for large-scale new energy development. However, desert ecosystems are fragile, and large-scale photovoltaic (PV) power facilities pose ecological risks. Current assessments of PV plant sites in deserts lack consideration of wind-sand hazards and ecological impacts. In this study, we have developed a new large-scale photovoltaic (PV) site selection model that integrates the analytic hierarchy process with geographic information system technology, and applies it to the desert regions of China. The results show that the potential for large-scale PV power plants in China's deserts is significant, with 69.4 % of the region assessed as medium or higher. The most suitable area is 12.7 × 104 km2 (7.6 % of the overall study area), mainly centered in the Tibetan Plateau's Qaidam Basin Desert and the deserts of northern China, characterized by favorable solar resources, climate, and terrain. Across all regions, gravel deserts are recognized as more suitable for the construction of large-scale PV power projects than sandy deserts. Considering varying PV installation density scenarios with an installed capacity potential of 36.4–84.9 TW and system costs ranging from 10.0 to 33.5 trillion USD, the study estimates an annual solar power generation potential of 47–110 PWh which is 1.7–3.9 times the global electricity demand. Carbon emissions could be reduced by 26.8–62.6 gigatons annually, offsetting 73–170 % of global emissions. Covering just 4.8–11.5 % of China's desert area (8 × 104–19.4 × 104 km2) would meet the projected 2025 electricity needs of the country. This study lays the groundwork for spatial planning and benefit assessment of large-scale PV projects in desert regions, and reduces conflicts between PV plant construction and local ecosystem.

Design of a positive energy district: A Nigerian case study

The potential of possible solutions for the design and implementation of a 50-house Positive Energy District (PED) in Southern Nigeria is presented in this study. Using a transient energy systems simulation software (TRNSYS 18), the energy demand of a typical building is estimated. Different passive design options considering building materials, window sizes, building orientation, roof ventilation are studied to determine the most energy efficient building envelope. TRNSYS and PVSyst (another energy simulation software), are also used to simulate the dynamics of a positive energy building and operation of solar energy systems used for electricity generation, domestic hot water heating and space cooling for Lagos weather conditions. In terms of electric energy balance, the total annual electricity demand is 598,000 kWh while the electricity generated by PV is 728,600 kWh in a year. As regards to site energy balance, annually, the district exports more electricity than it imports. The difference is 110,200 kWh. These results show that in such climatic conditions, despite economic poverty, it is possible to implement the modern PED principles. However, first, it is necessary to reduce energy consumption as much as possible, including solar passive solutions to the building architecture.

Overview of Financial Efficiency in India’s Power Sector

Power is an important tool for economic growth of the country. Power is vital for every form of human activity-domestic, agricultural and industrial infrastructure, such as telecommunication and transport. The demand for electricity in India is enormous and is growing steadily. This growth has been slower than a country’s economic growth. The big challenge of the power industry is to balance the demand and supply of electricity. Financial management involves planning, organizing, directing, and controlling the financial activities of an organization. Its primary goal is to maximize shareholder wealth and ensure the efficient use of resources. Financial efficiency refers to how effectively an organization utilizes its resources to generate revenue or profits. It involves minimizing costs and maximizing output or outcomes relative to inputs. The financial efficiency of India's power sector is a critical aspect of its energy infrastructure, impacting both economic growth and sustainability. The main needs of the study are the source and use of funds of the company and how to evaluate the financial performance of the company. The objective of this study is to critically evaluate and assess the financial performance of Southern Power Distribution Company of Andhra Pradesh State Limited, Tirupati.

Optimal energy generation of hybrid energy systems considering economic and environmental multi-objective functions

This research is dedicated to exploring and identifying the most effective design for an energy source tailored specifically to meet the electricity demands of a residential community. In an era where energy efficiency and sustainability are paramount, this study emphasizes the importance of both technical and economic considerations in energy sourcing. It posits that any viable solution must not only be efficient in its energy production and consumption but also reliable in its delivery and financially feasible for the residents who will depend on it. To address this multifaceted challenge, the study proposes the innovative use of a rotation-invariant coordinate convolutional neural network in conjunction with binary battle royale optimization techniques. These advanced methodologies are selected for their potential to enhance the modelling and optimization processes involved in energy source design. The primary goal of employing these methods is to minimize two critical factors: the net present cost of the energy system and the overall energy cost incurred by the residents. By focusing on these objectives, the research aims to ensure that the proposed energy solutions are not only cost-effective but also sustainable over the long term. To rigorously test the proposed model and evaluate its performance, the research is conducted using the MATLAB platform. The study employs established methodologies and performance metrics to assess the outcomes of the model, ensuring that the findings are both credible and applicable to real-world scenarios. Through comprehensive testing and detailed analysis, this research aims to provide significant insights and actionable recommendations for the optimal design of energy sources in residential areas. By contributing to the ongoing discourse on sustainable energy solutions, the study seeks to inform policymakers, energy planners, and community stakeholders about effective strategies for meeting residential energy demands while promoting environmental sustainability. Ultimately, the findings of this research could play a crucial role in shaping the future of energy sourcing in residential communities, paving the way for more resilient and sustainable energy systems

A perspective on solar railway potential for the UK

AbstractIn the face of climate change challenges, energy demand is still increasing, and renewable energy is becoming increasingly necessary as a sustainable resource. Solar energy stands out as a promising renewable resource, yet its widespread adoption faces challenges, notably the substantial land requirements for photovoltaic (PV) panels. This conflict intensifies as land could be more efficiently utilized for agriculture and development. Conversely, railway infrastructure occupies expansive tracts of land, presenting an opportunity to integrate PV panels without disrupting rail operations. However, a comprehensive assessment of the solar potential along railway tracks on a national scale is lacking for different countries and the UK is one of them. This study addresses this gap by evaluating the feasibility of installing PV panels on railway tracks throughout the UK to generate electricity. Utilizing mathematical models tailored to five distinct solar panel technologies, the study incorporates various factors, including solar radiation levels across different months. Assumptions were made where necessary, with values for certain variables averaged to facilitate calculations. The findings reveal that solar energy harnessed from railways could satisfy up to 8% of the UK's total electricity demand, with a minimum contribution of 0.3%. This study provides valuable insights from a UK‐centric perspective and offers a replicable framework for similar assessments in other countries with extensive railway networks, such as China and India. By leveraging existing infrastructure for renewable energy generation, such initiatives could contribute significantly to global sustainability efforts.

Wavelet CNN‐LSTM time series forecasting of electricity power generation considering biomass thermal systems

AbstractThe use of biomass as a renewable energy source for electricity generation has gained attention due to its sustainability and environmental benefits. However, the intermittent electricity demand poses challenges for optimizing electricity generation in thermal systems. Time series forecasting techniques are crucial in addressing these challenges by providing accurate predictions of biomass availability and electricity generation. Here, wavelet transform is applied for denoising, convolutional neural networks (CNN) are used to extract features of the time series, and long short‐term memory (LSTM) is applied to perform the predictions. The result of the mean absolute percentage error equal to 0.0148 shows that the wavelet CNN‐LSTM is a promising machine‐learning methodology for electricity generation forecasting. Additionally, this paper discusses the importance of model evaluation techniques and validation strategies to assess the performance of forecasting models in real‐world applications. The major contribution of this paper is related to improving forecasting using a hybrid method that outperforms other models based on deep learning. Finally, future research directions and potential advancements in time series forecasting for biomass thermal systems are outlined to foster continued innovation in sustainable energy generation.

Stress–strength reliability for electricity systems based on the Bernstein copula approximation

ABSTRACT Electricity is a critical resource for modern society, and its availability is essential for various aspects of daily life, from powering homes to supporting industrial processes and infrastructure. Ensuring a reliable supply of electricity is vital for economic stability and the well-being of a population. As the world moves toward more sustainable and renewable energy sources, understanding the reliability of these sources compared to traditional fossil fuels is crucial. In this study, we develop a methodology that enables the measurement of stress–strength reliability when electricity consumption is considered as the strength variable, positioned between electricity generation from renewable energy sources and fossil fuels as stress variables. We achieve this using the copula-based reliability with Bernstein copula approximation. The methodology is applied to the U.S. monthly electricity data. The result shows that the current rates of electricity output from fossil fuels and renewables will not be sufficient to meet the demand for electricity.

Application of a GIS-Based Multi-Criteria Decision-Making Approach to the Siting of Ocean Thermal Energy Conversion Power Plants: A Case Study of the Xisha Sea Area, China

In order to achieve the goals of carbon neutrality and reduced carbon emissions, China is increasingly focusing on the development and utilization of renewable energy sources. Among these, ocean thermal energy conversion (OTEC) has the advantages of small periodic fluctuations and large potential reserves, making it an important research field. With the development of the “Maritime Silk Road”, the Xisha Islands in the South China Sea will see a growing demand for electricity, providing the potential for OTEC development in this region. Optimal site selection of OTEC power plants is a prerequisite for developing thermal energy provision, affecting both the construction costs and future benefits of the power plants. This study establishes a scientific evaluation model based on the decision-making frameworks of geographic information systems (GISs) and multi-criteria decision-making (MCDM) methods, specifically the analytic hierarchy process (AHP) for assigning weights, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to reclassify the factors, and weighted linear combination (WLC) to compute the suitability index. In addition to commonly considered factors such as temperature difference and marine usage status, this study innovatively incorporates geological conditions and maximum offshore distances of cold seawater based on cost control. The final evaluation identifies three suitable areas for OTEC development near the Xuande Atoll and the Yongle Atoll in the Xisha Sea Area, providing valuable insights for energy developers and policymakers.

Optimal Placement and Sizing of Distributed Generation in Electrical DC Distribution Networks Using a Stochastic Mixed-Integer LP Model

AbstractThis paper presents a stochastic mixed-integer linear mathematical model for finding the optimal placement and sizing of distributed generation in a DC distribution network, considering the uncertainty of electrical demand and distributed renewable sources. The proposed model accurately represents the original mixed-integer nonlinear model, obtaining a globally optimal solution in less computational time with low errors. The mathematical model allows for considering constraints related to the maximum limits for the penetration of distributed generation, such as those specified by Resolution CREG 174 of 2021. Furthermore, the uncertainties of the electrical demand, wind energy-based distributed generation (DG), and solar energy-based DG are considered in the mathematical models using a two-stage stochastic programming approach. The accuracy and efficiency of the proposed model were tested and validated on a 21-node DC test system from the specialized literature, and the effectiveness and robustness were assessed on a 69-node DC test system. The obtained results show that the proposed stochastic mixed-integer linear mathematical model performs well.

Analysing the impact of diverse factors on electricity generation cost: Insights from Sri Lanka

The primary responsibility of a power system utility is to provide continuous, high-quality electricity at a fair and competitive price. To maintain low generation costs while utilising diverse energy sources, optimisation methodologies are crucial. Water value and generation costs exhibit significant variability on seasonal, daily and timely basis and reflect dynamic interactions between electricity demand and power plant availability. Currently, power system planners often overlook these seasonal and time-of day dependent variations in water value and generation costs. This research paper addresses this gap by analysing how fluctuations in electricity demand and power plant availability affect generation costs in the Sri Lankan power system. It introduces a novel economic dispatch model and presents three methods for estimating water value to calculate seasonal and time-of-day electricity costs. The discussion highlights how these findings can utilise policymaking, particularly in the context of electricity pricing debates and water management for agriculture versus electricity generation, as well as in calculating the water value for each reservoir.

Applying trade mechanisms to quantify dynamic GHG emissions of electricity consumption in an open economy - The case of Switzerland

Quantifying greenhouse gas (GHG) emissions from electricity consumption presents a significant challenge due to the variable nature of the generation mix throughout the year and the complex dynamics of cross-border electricity trade. In this paper, we introduce an innovative methodology designed to accurately capture the hourly GHG emissions linked to electricity imports, and thus electricity consumption. Contrary to conventional approaches, our methodology identifies the relevant generation technologies in exporting countries which are activated for export. This selection is based on (i) the classification of the technologies according to their dispatch cost (merit order), and (ii) considers trade mechanisms from economic theory.We apply this methodology to Switzerland, a country characterized by a low-carbon electricity generation mix, which however heavily relies on imports from neighbouring countries, particularly during the winter months. Over the period from 2017 to 2021, we find that 88 % of Switzerland's domestic electricity demand is met by its own generation—primarily hydro and nuclear—while the remaining 12 % is imported, mainly from fossil-fuel-based sources. Consequently, the average annual GHG emission factor for electricity consumption in Switzerland is 98 g-CO2eq/kWh, with 29 % attributed to domestic generation and 71 % to imports. This figure is 40 % higher than the 70 g-CO2eq/kWh estimated using a conventional approach, which typically underestimates the specific impacts of electricity imports.The disparity between these approaches becomes even more pronounced when considering the seasonal and hourly variations in the electricity mix and the resulting emission factors. These insights are particularly crucial in the context of emerging electricity demands, such as heat pumps and electric vehicles, where accurate GHG accounting can significantly influence policy and investment decisions.