Growing Energy Demand Research Articles

Multi-Objective Optimal Scheduling for Microgrids—Improved Goose Algorithm

Against the background of the dual challenges of global energy demand growth and environmental protection, this paper focuses on the study of microgrid optimization and scheduling technology and constructs a smart microgrid system integrating energy production, storage, conversion, and distribution. By integrating high-precision load forecasting, dynamic power allocation algorithms, and intelligent control technologies, a microgrid scheduling model is proposed. This model simultaneously considers environmental protection and economic efficiency, aiming to achieve the optimal allocation of energy resources and maintain a dynamic balance between supply and demand. The goose optimization algorithm (GO) is innovatively introduced and improved, enhancing the algorithm’s ability to use global search and local fine search in complex optimization problems by simulating the social aggregation of the goose flock, the adaptive monitoring mechanism, and the improved algorithm, which effectively avoids the problem of the local optimal solution. Meanwhile, the combination of super-Latin stereo sampling and the K-means clustering algorithm improves the data processing efficiency and model accuracy. The results demonstrate that the proposed model and algorithm effectively reduce the operating costs of microgrids and mitigate environmental pollution. Using the improved goose algorithm (IGO), the combined operating and environmental costs are reduced by 16.15%, confirming the model’s effectiveness and superiority.

Power Grid Renovation: A Comprehensive Review of Technical Challenges and Innovations for Medium Voltage Cable Replacement

The rapid growth of electrical energy demands raises the need for the modernization of distribution grids. Medium-voltage (MV) aged cables are infrastructures facing significant challenges that can compromise the security of supply and reduce the reliability of power grids. To address the challenges, there is a growing interest in optimizing cable replacement and management strategies. This comprehensive review focuses on the technical challenges and innovations associated with MV cable replacement, highlighting defect detection, lifetime estimation, reliability assessment, and management strategies. Various methods for detecting and monitoring cable defects and discussing their advantages and limitations are surveyed. Moreover, different models and techniques for estimating the remaining useful life of MV cables are explored, emphasizing the importance of accurate predictions for assessing cable reliability and optimizing replacement schedules. Furthermore, emerging technologies that enhance cable management strategies are also highlighted. This review provides insights and recommendations for future research and development, paving the way for the sustainable evolution of power grids.

A resilient biofuel supply chain design based on Paulownia and Jatropha under uncertainty considering the water-energy nexus

The growth in global population, pollution, and energy demand, in addition to dwindling fossil fuel reserves, are driving scientists and industries toward adopting biomass-based products over non-renewable energy sources. Moreover, the decline of water resources and the importance of conserving water have induced governments to start initiatives to control the consumption of water, especially in the agricultural and industrial sectors. Therefore, the water-energy nexus is a significant issue that has drawn the interest of researchers in recent years. A bi-objective two-stage stochastic multi-product optimization model is presented for designing a resilient second-generation biofuel supply chain under uncertainty, considering the water-energy nexus. In this supply chain, biofuel is yielded from inedible plants Paulownia and Jatropha. Various resilience strategies are adopted to cope with potential disruptions to the supply chain, including multi-sourcing, capacity expansion, imports, multiple transportation modes, and lateral transshipment. The objectives of the model are twofold: maximizing profit and minimizing water consumption. This is achieved by making some key decisions including: facility location, managing the flows of raw materials and finished products among networks, determining production volumes, setting inventory levels, allocating land for biomass crop cultivation, planning production and storage capacities, scaling capacity expansions, the number of vehicles to rent or purchase, and optimizing the amounts of raw materials to import and biomass to export. An actual case study is provided to demonstrate the real-world applicability of the approach, while multiple sensitivity analyses are performed on the problem's main parameters. The study concludes by providing valuable managerial insights that focus on the cost savings and profit increases achievable through resilience strategies. The analysis considers different scenarios and explores effective methods for handling disruptions, emphasizing water resource conservation achieved by integrating the water-energy nexus within the biofuel supply chain.

Role of phase change materials and digital twin technology in thermal energy storage system: A review

The exponential growth in energy consumption and demand, along with the depletion of natural resources, is exerting a catastrophic impact on global ecosystems. Recent advances in research and development have focused on the distribution of renewable energy sources and the reduction of traditional energy usage as strategies to address pressing environmental concerns, such as climate change and global warming. Moreover, there is an urgent need for appropriate technologies that can enhance the thermal performance of buildings, given the rapid increase in global cooling and heating demands. This study examines the role of phase change materials (PCMs) and digital twin (DT) technology in thermal energy storage (TES), drawing on an analysis of 89 research articles sourced from multiple databases and references. The findings demonstrate that TES systems optimized through meticulous selection of PCMs can effectively meet thermal comfort requirements. Integrating DT technology with building systems allows for the analysis of cooling effects and optimization of energy demand through DT models of smart buildings. The present study provides a comprehensive overview of the different PCMs used in cooling applications and explores the implementation of DT technologies within building systems. In addition, practical applications of DT technologies for TES systems are presented, providing insights into their potential for enhancing energy efficiency in building systems.

Overview of Typical Projects for Geological Storage of CO2 in Offshore Saline Aquifers

With the continuous growth of global energy demand, greenhouse gas emissions are also rising, leading to serious challenges posed by climate change. Carbon Capture, Utilization, and Storage (CCUS) technology is considered one of the key pathways to mitigate climate change. Among the CCUS technologies, CO2 storage in offshore saline aquifers has gained significant attention in recent years. This paper conducts an in-depth analysis of the Sleipner and Snøhvit projects in Norway and the Tomakomai project in Japan, exploring key issues related to the application, geological characteristics, injection strategies, monitoring systems, and simulation methods of CO2 storage in offshore saline aquifers. This study finds that CO2 storage in offshore saline aquifers has high safety and storage potential but faces several challenges in practical applications, such as geological reservoir characteristics, technological innovation, operational costs, and social acceptance. Therefore, it is necessary to further strengthen technological innovation and policy support to promote the development and application of CO2 storage in offshore saline aquifers. This study provides valuable experiences and insights for similar projects worldwide, contributing to the sustainable development of CO2 storage in offshore saline aquifers and making a greater contribution to achieving global net-zero emission targets.

Processable solid-solid full-phase change composites with novel elevated enthalpy from synergistic crystallization facilitated on hydrogen bonding interactions

Given the growing energy demands, organic phase change materials (PCMs) have been widely explored, with leakage and high phase change enthalpy being primary concerns for practical storage applications. In this study, micro-crosslinked PCMs were prepared using polyethylene glycol (PEG), poly (butylene terephthalate) (PBT), trimesic acid (TA) and adipic acid (AA) through simple esterification and transesterification, firstly. To prevent the leakage of myristic acid (MA) during its solid-liquid phase change, micro-crosslinked PCMs were then used as encapsulation materials and melting-blended with MA to produce solid-solid full-phase change composites (FPCCs). The resulting FPCCs showed superior processability due to the micro-crosslinked structure of encapsulation materials and plasticization effect of MA with low molecular weight. All FPCCs exhibited a single phase change temperature despite the significant difference in phase change temperatures between the micro-crosslinked PCMs and MA. Notably, the actual enthalpy of all FPCCs was higher than their theoretical enthalpy, with FPCCs-3 showing a 12.5 % increase over its theoretical value. The onset and final decomposition temperatures of MA in FPCCs were enhanced by at least 34.6 % and 10.6 %, respectively, compared to those of pure MA. The hydrogen bonding interactions between the carboxyl groups of MA and oxygen atoms of PEG segments are responsible for these extraordinary properties.

Examining the nexus between carbon dioxide emissions, economic growth, fossil fuel energy use, urbanization and renewable energy towards achieving environmental sustainability in India

Purpose Global urbanization has accelerated due to the persistent trend of rural-to-urban migration in search of better prospects and livelihoods, which has had serious negative effects on the environment, especially in rapidly developing economies. Hence, the purpose of the study is to analyse the relationship between urbanization, economic growth, consumption of renewable energy and carbon emissions with careful examination, particularly in the context of India, where urban population growth has skyrocketed. Design/methodology/approach This study uses econometric methods like Granger causality analysis and the ARDL bound tests, to analyse the intricate relationships between the selected time series variables for India from 1970 to 2022. Findings This research highlights the difficult task of striking a balance between economic development and environmental preservation by emphasizing the crucial role that urbanization and economic expansion play in causing carbon emissions. India’s urbanization trajectory presents a significant policy problem that calls for a move towards renewable energy sources to successfully decrease carbon emissions. Moreover, this research indicates a two-way causal relationship between economic growth, urbanization and carbon emissions, pointing to the intricate interactions between these variables during the developmental stage. Research limitations/implications Despite India’s per capita emissions remaining below the global average, this study highlights the mounting policy challenge of balancing economic development with environmental sustainability as urbanization persists. The paper emphasizes the need for India to invest in renewable energy capacity to replace non-renewable sources and mitigate the carbon footprint of its growing energy demands. Collaborative efforts between India and the developed world to facilitate access to clean energy technologies are crucial for India to achieve sustainable growth in the long run. Originality/value To the best of the authors’ knowledge, existing literature predominantly focuses on investigating the relationship between renewable energy and economic growth, with only a limited number of studies exploring the impact on sustainable development to attain carbon neutrality. Furthermore, these studies have not considered the role of urbanization and non-renewable energy in addressing the challenge of sustainability issues in an emerging country like India. Hence, this study is a comprehensive study that addresses the research gap in these directions.

Predicting CO2 and H2 Solubility in Pure Water and Various Aqueous Systems: Implication for CO2–EOR, Carbon Capture and Sequestration, Natural Hydrogen Production and Underground Hydrogen Storage

The growing energy demand and the need for climate mitigation strategies have spurred interest in the application of CO2–enhanced oil recovery (CO2–EOR) and carbon capture, utilization, and storage (CCUS). Furthermore, natural hydrogen (H2) production and underground hydrogen storage (UHS) in geological media have emerged as promising technologies for cleaner energy and achieving net–zero emissions. However, selecting a suitable geological storage medium is complex, as it depends on the physicochemical and petrophysical characteristics of the host rock. Solubility is a key factor affecting the above–mentioned processes, and it is critical to understand phase distribution and estimating trapping capacities. This paper conducts a succinct review of predictive techniques and present novel simple and non–iterative predictive models for swift and reliable prediction of solubility behaviors in CO2–brine and H2–brine systems under varying conditions of pressure, temperature, and salinity (T–P–m salts), which are crucial for many geological and energy–related applications. The proposed models predict CO2 solubility in CO2 + H2O and CO2 + brine systems containing mixed salts and various single salt systems (Na+, K+, Ca2+, Mg2+, Cl−, SO42−) under typical geological conditions (273.15–523.15 K, 0–71 MPa), as well as H2 solubility in H2 + H2O and H2 + brine systems containing NaCl (273.15–630 K, 0–101 MPa). The proposed models are validated against experimental data, with average absolute errors for CO2 solubility in pure water and brine ranging between 8.19 and 8.80% and for H2 solubility in pure water and brine between 4.03 and 9.91%, respectively. These results demonstrate that the models can accurately predict solubility over a wide range of conditions while remaining computationally efficient compared to traditional models. Importantly, the proposed models can reproduce abrupt variations in phase composition during phase transitions and account for the influence of different ions on CO2 solubility. The solubility models accurately capture the salting–out (SO) characteristics of CO2 and H2 gas in various types of salt systems which are consistent with previous studies. The simplified solubility models for CO2 and H2 presented in this study offer significant advantages over conventional approaches, including computational efficiency and accuracy across a wide range of geological conditions. The explicit, derivative–continuous nature of these models eliminates the need for iterative algorithms, making them suitable for integration into large–scale multiphase flow simulations. This work contributes to the field by offering reliable tools for modeling solubility in various subsurface energy and environmental–related applications, facilitating their application in energy transition strategies aimed at reducing carbon emissions.

Management of diverse smart homes: Coordination among controllable loads, EVs, and PVs

Growing energy demand, driven by urbanization, transportation electrification, and smart home devices, highlights the significance of residential electricity consumption. However, controllable loads, including HVAC systems, water heaters, electric vehicles (EVs), and industrial processes, can be managed to balance electricity supply and demand. Therefore, this study addresses the escalating energy demand in residential buildings including the integration of electric vehicles (EVs) and renewable energy technologies. It focuses on controllable loads, which offer flexibility that can be managed to balance supply and demand through demand response programs. First, the controllable loads are categorized as HVAC (air conditioners, electric heaters, and electric heat pumps), uninterruptable (washing machines, dishwashers, and electric cookers), interruptible (water heater), and EVs. Then, this study proposes a detailed clustering approach for homes, categorizing them into six clusters to capture diverse consumption patterns, including scenarios such as work-from-home setups. The analysis includes examining the adaptability of these home clusters and controllable load groups under different EV charging levels (level 1 and level 2) and coordinating them with PV systems. The study evaluates the coordination of each controllable load group with EVs and PVs, aiming to optimize overall energy usage and enhance renewable resource integration. Parameters such as energy management, operation of controllable equipment, and indoor temperature are analyzed to provide insights for homeowners and policymakers to make informed decisions about load management and energy resource utilization in residential settings. Simulation results show that with level 1 chargers, type 2 homes achieve higher average EV avoidance (81.94 %) and lower PV consumption (31.22 %), while with level 2 chargers, all homes reach 100 % EV avoidance.

Optimal Design of an Off-Grid Solar Energy System Integrated with a Diesel Generator for Urban Areas in Pakistan

The growing energy demand in Pakistan, coupled with the challenges posed by reliance on imported fossil fuels, necessitates the exploration of alternative energy solutions. This study presents the design and techno-economic analysis of an off-grid hybrid energy system tailored for a residential neighborhood in Karachi, Pakistan. The system integrates individual solar energy solutions for seven housles, supplemented by a shared diesel generator to ensure a reliable power supply, particularly during load-shedding and grid outages. The study utilizes HOMER Pro software to model and optimize various configurations, taking into account local solar insolation levels and seasonal variability in energy demands. The optimized system includes photovoltaic (PV) panels, battery storage, and a shared diesel generator with individual connections and meters, with each house receiving a customized solution based on its specific energy requirements, ranging from 15 kWh/day to 45 kWh/day. The shared diesel generator is designed to reduce the need for large battery banks, thereby minimizing both capital and operational costs. The results demonstrate a cost-effective solution with a Levelized Cost of Energy (LCOE) of $0.2959/kWh and a Net Present Cost (NPC) of $15,562.55 over a 25-year period. This research highlights the potential for implementing such systems in urban areas of Pakistan, offering a sustainable, reliable, and economically viable alternative to conventional energy sources.

Multi-parametric analysis for mixed integer linear programming: An application to transmission upgrade and congestion management

Upgrading the capacity of existing transmission lines is essential for meeting the growing energy demands, facilitating the integration of renewable energy, and ensuring the security of the transmission system. This study focuses on the selection of lines whose capacities and by how much should be expanded from the perspective of the Independent System Operators (ISOs) to minimize the total system cost. We employ advanced multi-parametric programming and an enhanced branch-and-bound algorithm to address complex mixed-integer linear programming (MILP) problems, considering multi-period time constraints and physical limitations of generators and transmission lines. To characterize the various decisions in transmission expansion, we model the increased capacity of existing lines as parameters within a specified range. This study first relaxes the binary variables to continuous variables and applies the Lagrange method and Karush-Kuhn-Tucker (KKT) conditions to obtain optimal solutions and identify critical regions associated with active and inactive constraints. Moreover, we extend the traditional branch-and-bound (B&B) method by determining the problem’s upper and lower bounds at each node of the B&B decision tree, helping to manage computational challenges in large-scale MILP problems. We compare the difference between the upper and lower bounds to obtain an approximate optimal solution within the decision-makers’ tolerable error range. In addition, the first derivative of the objective function on the parameters of each line is used to inform the selection of lines for easing congestion and maximizing social welfare. Finally, the capacity upgrades are selected by weighing the reductions in system costs against the expense of upgrading line capacities. The findings are supported by numerical simulations and provide transmission-line planners with decision-making guidance.

Empirical insights into India's energy demand: unveiling elasticity dynamics for strategic policy initiatives.

Policymakers and stakeholders can use insights from price elasticity and income elasticities of energy demand to devise targeted interventions that promote energy efficiency, reduce reliance on fossil fuels, and drive the transition to more sustainable and resilient energy systems. This study seeks to examine how changes in income elasticity and price elasticity affect energy consumption over time using the ARDL model and a time-varying parameter technique, state-space Kalman filter. The study's findings reveal that energy prices have a negative and significant impact on energy consumption, whereas GDP per capita and population growth have apositive and significant impact on energy consumption in both the short and long run. The findings underline the importance of economic growth and increasing population in driving growing energy demand in India, which is consistent with the country's energy-intensive development aspirations. The time-varying analysis shows that the price elasticity of energy consumption follows a U-shaped pattern across time. However, from the onset of the new millennium decade, the elasticity of energy consumption with regard to its price has continually decreased, reaching - 0.54 by 2020. Furthermore, the findings imply that the income elasticity of India's energy demand has stabilized around unity, indicating that the country is currently near the middle of the energy intensity-GDP inverted-U trajectory. Given these evolving elasticities, policymakers should take a flexible and forward-thinking strategy to achieve optimal energy consumption levels.

Decomposition analysis of renewable energy demand and coupling effect between renewable energy and energy demand: Evidence from China

Driving up demand for renewable energy is key to China's response to the energy crisis and to controlling carbon emissions. Several studies have been conducted to explore the factors affecting renewable energy demand. However, the literature on the importance of technological innovation in fossil energy production in affecting renewable energy demand is scant. Therefore, it is essential to explore the impact of technological innovations in fossil energy production on renewable energy demand, as well as to reveal the dynamic penetration mechanism of renewable energy in the field of energy demand, to provide policy guidelines for China to promote its energy revolution. This study decomposes renewable energy demand into 10 drivers with the extended IDA-PDA (Index Decomposition Analysis and Production-theory Decomposition Analysis) model. Then the dynamic penetration mechanism of renewable energy on the energy demand side is revealed by combining the coupled model and the decomposition results. The results show that the investment intensity effects and energy restructuring contributed the most to renewable energy demand growth, contributing 60.73 % and 79.97 % respectively. Conversely, technological advances in fossil energy combustion made the largest negative cumulative contribution to renewable energy demand growth, with a cumulative negative contribution of −39.57 %. The drivers of renewable energy demand vary considerably across types, regions and resource endowment levels, which means that regions should develop differentiated development strategies based on their own characteristics. In addition, the coupling effect between renewable energy demand and total energy demand is weakening, with market and policy effects becoming the drivers for augmenting the response of renewable energy to total energy demand. The findings of this study provide clear pathways for expanding renewable energy demand, offering important insights for the government to balance between promoting capital-driven interests and policy-driven development.

Atomic catalysis meets heterostructure synergy: Unveiling the trifunctional efficacy of transition Metal@WS2/ReSe2

Integrating and advancing renewable energy storage and conversion technologies such as water splitting and rechargeable air-based batteries face significant challenges in finding appropriate catalysts that offer both high activity and low cost. This paper successfully designed efficient, cost-effective, and eco-friendly catalysts that meet the requirements for hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and oxygen reduction reaction (ORR) catalytic activity by employing two-dimensional (2D) transition metal dichalcogenides (TMDs) interface engineering combined with a single-atom doping strategy. First, the heterostructure was constructed, and its optimal layer spacing (3.13 Å) was determined by calculating the most negative binding energy. These materials were theoretically evaluated using density functional theory (DFT), demonstrating that transition metal (TM) can all be firmly attached to the S1 site of the WS2/ReSe2 Z-scheme heterostructure with favorable binding energies and excellent electronic properties. By comparing the catalysts doped with different metals (Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, and Zn), the three functional catalysts with the best performance were finally selected: Mn@WS2/ReSe2, Ni@WS2/ReSe2, and Cu@WS2/ReSe2. In particular, Mn@WS2/ReSe2 exhibited exceptional promise as multifunctional catalysts, with overpotentials for the HER/OER/ORR of 0.05/0.29/0.35 V, respectively. The superior catalytic performance of this WS2/ReSe2 Z-scheme heterostructure was attributed to the introduction of TM atoms and the synergistic interaction between rhenium and the TM atoms. This interaction facilitated more efficient electron transfer between the catalyst and the reactants, enhancing the overall catalytic activity. This research is a successful attempt to combine interface engineering with single-atom catalysis (SACs). It provided valuable insights into the design of next-generation multifunctional electrocatalysts, offering a novel approach to address the growing energy demands in an environmentally benign and economically viable way.

Triplet-Sensitized Switching of High-Energy-Density Norbornadienes for Molecular Solar Thermal Energy Storage with Visible Light.

Norbornadiene-based photoswitches have emerged as promising candidates for harnessing and storing solar energy, holding great promise as a viable solution to meet the growing energy demands. Despite their potential, the effectiveness of their direct photochemical conversion into the resulting quadricyclanes has room for improvement owing to (i) moderate quantum yields, (ii) poor overlap with the solar spectrum and (iii) photochemical back reactions. Herein, we present an approach to enhance the performance of such molecular solar thermal energy storage (MOST) systems through the triplet-sensitized conversion of aryl-substituted norbornadienes. Our study combines deep spectroscopic analyses, irradiation experiments, and quantum mechanical calculations to elucidate the energy transfer mechanism and inherent advantages of the resulting MOST systems. We demonstrate remarkable quantum yields using readily available sensitizers under both LED and solar light irradiation, significantly surpassing those achieved through direct excitation with photons of higher energy. In contrast to the conventional approach, light-induced back reactions of the high-energy products do not play any role, allowing quantitative switching within minutes. These results not only underscore the potential of triplet-sensitized MOST systems to leverage the high energy storage capabilities of multistate photoswitches but they might also stimulate the broader usage of sensitization strategies in photochemical energy conversion.

Techno-economic analysis of hybrid renewable energy systems for cost reduction and reliability improvement using dwarf mongoose optimization algorithm

The global energy crisis, particularly in isolated and remote regions, has increased interest in renewable energy sources (RESs) to meet growing energy demands. Integrating RESs with energy storage systems offers a promising solution to mitigate fluctuations and intermittency, but concerns about cost and reliability remain. This study explores the optimal design of various microgrid configurations, combining photovoltaic (PV), wind turbine (WT), battery energy storage system (BESS), and diesel generator (DG) systems for Najran city, Saudi Arabia, via real-world meteorological and load demand data. The Dwarf Mongoose Optimization Algorithm (DMOA), alongside the salp swarm algorithm (SSA) and whale optimization algorithm (WOA), was applied to minimize the levelized cost of energy (LCOE) while improving system reliability. The results demonstrate that the PV/BESS configuration, although cost-effective with an LCOE of 0.038 USD/kWh, fail to meet reliability constraints with a loss of power supply probability (LPSP) of 0.679. In contrast, the PV, WT, BESS, and DG configurations achieved an LPSP of 1.9 × 10^--8% with an LCOE of 0.199 USD/kWh, offering a robust and reliable solution for the region's energy needs. This paper presents a novel application of the DMOA for optimizing hybrid renewable energy systems, demonstrating its effectiveness in achieving a balance between cost and reliability. This strategy provides a viable approach for sustainable energy planning in similar regions facing energy challenges.

Applying machine learning for biomass gasification prediction: enhancing efficiency and sustainability

Abstract In the contemporary era, marked by the increasing significance of sustainable energy sources, biomass gasification emerges as a highly promising technology for converting organic materials into valuable fuel, offering an environmentally friendly approach that not only mitigates waste but also addresses the growing energy demands. However, the effectiveness of biomass gasification is intricately tied to its predictability and efficiency, presenting a substantial challenge in achieving optimal operational parameters for this complex process. It is at this precise juncture that machine learning assumes a pivotal role, initiating a transformative paradigm shift in the approach to biomass gasification. This article delves into the convergence of machine learning and the prediction of biomass gasification and introduces two innovative hybrid models that amalgamate the Support Vector Regression (SVR) algorithm with Coot Optimization Algorithm (COA) and Walrus Optimization Algorithm (WaOA). These models harness nearby biomass data to forecast the elemental compositions of CH4 and C2Hn, thereby enhancing the precision and practicality of biomass gasification predictions, offering potential solutions to the intricate challenges within the domain. The SVWO model (SVR optimized with WaOA) is an effective tool for predicting these elemental compositions. SVWO exhibited outstanding performance with notable R 2 values of 0.992 for CH4 and 0.994 for C2Hn, emphasizing its exceptional accuracy. Additionally, the minimal RMSE values of 0.317 for CH4 and 0.136 for C2Hn underscore the precision of SVWO. This accuracy in SVWO’s predictions affirms its suitability for practical, real-world applications.

Mathematical modeling of Ethiopia's energy demand by sectors and energy types, with forecasts for the next 30 years

Due to the scarcity of economic resources, it is vital to optimize everything so that the supply and demand lines intersect in an optimized quantity, with no shortage or surplus of a provided item or service. Energy supply contains both surplus and shortage, thus estimating the amount of projected energy demand is a key work that must be completed. The objective of this paper is generating a mathematical model based on the actual data for thirty years forecasting. To create a mathematical model using actual data from the last fifteen years, a model that can represent the past trend and be used for future forecasting. This study provides a general overview of Ethiopia's current energy requirement with different energy type as well as sector-specific energy demand and estimates for different economic growth scenarios up to 2052. This model was created using a linear regression polynomial fit through Origin graphic and analysis software, and an econometric model was also applied. GDP was utilized as an independent variable in the economic model to determine the trend of energy consumption. Another input-output model is also used for multilinear regression to evaluate the change of four variables, GDP, population growth, urbanization growth rate, and general inflation rate, which was quantitatively linked with total energy requirement using Weka software. The mathematical model developed through linear and multilinear regression has been validated by using a different assumption on GDP growth based on past growth rate as low, medium and high growth rate and using the mathematical formula to generate an energy demand trend that can be compared with the actual trend; as a result, all the mathematical model that are generated has been found to be valid for the purpose of the intended work. Based on the generated mathematical model and different GDP growth rate scenario as low, medium, business as usual (BAU) and high a future energy demand was forecasted up to 2052 for thirty years. The model's results can help energy planners ensure that the country's supply capacity keeps up with predicted energy demand growth.

Attention Based Energy Demand Forecasting in Smart Grid Environments

The smart grid is a crucial aspect of the modern energy landscape, providing a reliable, efficient, and sustainable way of meeting the growing energy demands. However, the vast amounts of data generated by smart grid technology necessitate the development of advanced data processing and analysis techniques. In this paper, we propose an attention-based time series workflow that combines dilated convolution and attention mechanisms for time series forecasting in smart grid applications. This workflow extracts temporal features from time series data using dilated convolutions and emphasizes significant temporal points in the hidden states using attention mechanisms. Experimental evaluations showed up to an 8% better performance for energy demand forecasting compared to commonly used deep learning-based methods. Our workflow achieved this gain by requiring 1/3 of the training time other models took. We also improved performance by 42% in various domains, demonstrating the adaptability of our approach across different areas. This study may assist researchers in constructing accurate forecasting models for smart grid environments. Furthermore, it highlights that the attention-based approach can be employed to promote sustainable energy and optimize smart grid environments.

Multi-energy synergistic planning of distributed energy supply system: Wind-solar-hydrogen coupling energy supply

This study is to improve the efficiency of energy utilization with the continuous growth of global energy demand and the increasingly severe environmental problem. A hydrogen-containing distributed energy supply system is proposed, which includes a hydrogen energy subsystem. The main driver of the system is renewable energy, while hydrogen energy is used as a carrier to assist the flow of energy. This study proposes an improved following electric load (FEL) strategy based on a comprehensive consideration of energy interactions within the system, and designs an optimal configuration model aimed at improving its economic, environmental and energy benefits. In addition, an improved rime algorithm (IRIME) is proposed, whose convergence performance and solution stability are greatly improved. A multi-objective optimization algorithm with better convergence performance, multi-objective improved rime algorithm (MO-IRIME), is proposed for solving the optimal configuration model to obtain the optimal capacity of the equipment inside hydrogen-containing distributed energy supply system. The total annual cost is reduced by 44.80 %, the primary energy consumption by 68.47 % and the pollutant gas emissions by 75.79 % compared to the reference system. In addition, the way of interaction and coordination of multiple energy sources in the daily cycle is analyzed.