Development Of Energy Systems Research Articles

Density functional theory study on the mechanism of oxygen reduction reaction on nitrogen-doped graphene with adjacent Mn and Ni sites

The catalysts for oxygen reduction reaction (ORR) are of great significance to the development of electrochemical energy systems. In this paper, the ORR catalytic performance of Mn–Ni dual-metal doped N-coordinated graphene is studied by density functional theory (DFT). The results show that the catalysts with dual-metal active sites are more stable than that with single metal site, and all catalysts show certain catalytic activity. The reaction pathway analyses of catalysts with two different adsorption configurations of O2 show that when O2 is adsorbed on two metal sites, OOH will automatically split into OH and O. When O2 is adsorbed on Mn site alone, the other site does not participate in the adsorption of oxygen-containing intermediates. The thermodynamic calculation results show that the catalytic performance is mainly influenced by the adsorption strength of O, which is controlled by the number of active sites involved in the adsorption, bond length and the number of transferred electrons at the active sites. The G-MnNiN6-3 shows the best catalytic performance with an overpotential of 0.453 V and a reaction energy barrier of 0.19 eV. The results of this study highlight the potential application of Mn–Ni dual-metal doped N-coordinated graphene as a highly efficient non-precious catalyst.

Parametric and sensitive analysis of Pd-Ag membrane reactor performance in biogas reforming to generate decarbonized hydrogen by Computational Fluid Dynamic-Response Surface Methodology

The efficient production of hydrogen from renewable sources is pivotal for the development of sustainable energy systems. Biogas steam reforming (BSR) conducted in palladium-based membrane reactors (MRs) offers a pathway for high-purity H2 generation at lower temperatures compared to traditional processes. In this study, a computational fluid dynamics (CFD) model was developed to simulate the BSR process within a Pd-Ag MR. The CFD model has integrated comprehensive kinetics, mass transport, and hydrodynamics, and its accuracy was confirmed through validation against experimental data, demonstrating substantial agreement. As a novel approach, to optimize the BSR reaction conditions to get the best MR performance, the CFD model was coupled with response surface methodology (RSM), which has been employed to enable the identification of optimal values for key parameters such as reaction temperature, pressure, gas hour space velocity, feed molar ratio, and sweep gas ratio, where the temperature was found to have the most significant impact on the MR’s performance within the examined intervals of input parameters. The objective of RSM method applied to CFD modeling was to maximize with high accuracy CH4 conversion, H2 recovery, and reduce CO2 emissions. RSM models effectively established correlations between input parameters and responses. The determined optimal conditions for the BSR process were a temperature of 683 K, a reaction pressure of 6 bar, a GHSV of 3000 h−1, a H2O/CH4 ratio of 1.34, and a sweep ratio of 9. Under these conditions, the system achieved remarkable results with 100 % CH4 conversion, 43 % H2 recovery, and an 81 % reduction in CO2 emissions. This study underscores the effectiveness of integrating CFD with RSM for the precise modeling and optimization of MRs in BSR reactions. The synergy between these approaches provides a robust framework for advancing the efficiency and sustainability of hydrogen production processes from renewable sources.

PECULIARITIES OF ENERGY TRANSITION IN THE SOUTH CAUCASUS COUNTRIES

The authors have identified the problems and prospects of implementing the energy transition from conventional to renewable energy in the countries of the South Caucasus in the context of the challenges of sustainable development. The authors analyzed the concept of «sustainable energy» with the definition of its basic components (security, quality of life, environment). The issues of energy transition are studied in the context of new geopolitical challenges, as well as the UN Sustainable Development Goals. The authors presented the specifics of the development of energy systems of the countries of the region in the context of both energy security and integration challenges. Through comparative analysis, the authors have identified opportunities for effective implementation of energy transition policies in the countries of the region. The article studies the structure of existing capacities of the power systems of Georgia, Armenia and Azerbaijan, which allowed to determine the share of renewable energy in these countries. A comparative analysis of renewable energy development in the countries of the region has been carried out to determine the possibilities of increasing hydro, solar, wind, geothermal and bioenergy components. The authors also assess a number of major renewable energy projects currently being implemented in the region.

Renewable energy resources utilization planning for sustainable energy system development on a stand-alone island

The potential of energy resources on islands that can be used for self-sufficiency and sustainable energy supply and demand systems has led to a challenge in energy planning in these areas. Due to Iran's dependence on fossil fuels, there are several challenges and concerns related to the reduction of fossil fuel reserves. Therefore, in order to transition from non-renewable to renewable energy sources, the maximum potential of the country should be utilized, which is one of the most promising areas of the islands. As a result, this study aims to provide approaches that reduce dependence on renewable energy sources in a short period of time and to create the conditions for the implementation of a 100 % renewable energy system. First of all, the basic energy supply system for the island in 2020 will be modeled in the EnergyPLAN software. As a next step, two different scenarios are presented for combining new renewable technologies with fossil fuels to meet the demands for electricity, heating, cooling, and transportation in 2030. As a result of this research, it was determined that by utilizing the potential of local renewable resources, the consumption of fossil fuels would be reduced by 36 %. Also, applying policies for the use of renewable resources can reduce carbon dioxide emissions by 0.7 million tons per year. Further, in the technical analysis of the scenarios, it was found that reducing the consumption of fossil fuels in the field of transportation was the most challenging aspect of achieving the goal of 100 % renewable islands.

An artificial intelligence‐based non‐intrusive load monitoring of energy consumption in an electrical energy system using a modified K‐Nearest Neighbour algorithm

AbstractEnergy profligacy and appliance degradation are the apex reasons accounting for the continuous rise in power wastage and high energy bills. The decline in energy conservation and management in residences has been largely attributed to the financial implications of using intrusive methods. This work aimed to resolve the challenges of intrusive load monitoring by introducing artificial intelligence and machine learning to optimise load monitoring. To solve this challenge, a non‐intrusive approach was proposed where modalities for load prediction and classification were achieved with a Bagging regressor and a modified multiclass K‐Nearest Neighbour algorithms. This developed supervised learning models produced a 0.9624 R2 score and 78.24% accuracy for prediction and classification, respectively, when trained and tested on a Dutch Residential Energy Dataset. This work seeks to provide a cost‐effective approach to the optimisation of energy using steady state active power features. Essentially, the adoption of this non‐intrusive technique for load monitoring would effectively aid customers on the distribution network save cost on energy bills, facilitate the detection of faulty appliances, provide recommendations for smart homes and buildings with the required information for efficient decision making and planning of energy needs. In the long term, easing the pressure on power generation to meet demand would translate to reduction in carbon emissions based on a wide‐scale implementation of this proposed system. Hence, these are important parameters in realising the development of smart sustainable cities and sustainable energy systems in this current industrial revolution.

Advancing microgrid efficiency: a study on battery storage systems and wind energy penetration based on a Contracted fitness-dependent optimization algorithm

ABSTRACT This research study presents a novel approach to enhance the efficiency and performance of Battery Energy Storage Systems (BESSs) within microgrids, focusing particularly on the integration of wind energy. The inherent inconsistency and unpredictability of Renewable Energy Resources (RERs) necessitate the development of effective integration solutions to accommodate them. Our article provides a customized iteration of the metaheuristic algorithm referred to as the Contracted fitness-dependent optimizer. The proposed methodology involves the adaptive adjustment of migration rates based on habitat suitability indices, while also considering variations in perturbations. The incorporation of Lévy flight and an elimination phase significantly enhances the algorithm’s efficacy in problem-solving. In order to establish the superiority of our strategy over alternative optimization approaches, we conduct simulations across diverse conditions and subsequently compare the outcomes. It was calculated that the daily scheduling cost was 235.2$ and the daily operational cost was USD 268$. In Scenario 3, the total operating expenses and arranging costs for 1 day, respectively, were determined to be 165$ and 115$. Further, the highest depth of discharge level was around 77%. Moreover, a −9.18 kW and 23.02 kW maximum charging and discharging power adjustment was made. The findings of our study emphasize the economic and operational benefits associated with appropriately sized BESSs within microgrid contexts. These advantages have the potential to enhance battery longevity and promote the development of more sustainable energy systems.

A new frontier of flexible energy devices: Aqueous proton supercapacitors

Aqueous proton supercapacitors are considered as promising energy storage devices for next-generation wearable electronics due to their high energy density, rapid kinetics, long cycles, and reliable safety. As of now, the research for electrochemical proton energy storage entails more holistic considerations. In this review, we provide a timely and comprehensive review of recent advances in electrochemical proton storage. First, we discuss the main explored proton storage electrode materials, including carbon materials, metal oxides, Prussian blue, COFs, and MXenes, from the aspects of structure, preparation strategy, and proton storage mechanism. Furthermore, we systematically focus on their multifunctional applications of proton supercapacitors including flexible and wearable electronics, anti-freezing energy storage system, etc. Finally, the challenges and prospects of flexible proton supercapacitors are outlined. We believe that this review will greatly improve the knowledge of proton energy storage and facilitate the development of advanced electrochemical energy systems.

TransHyDE‐Sys: An Integrated Systemic Approach for Analyzing and Supporting the Transformation of Energy Systems and Hydrogen Infrastructure Development

In addition to the long‐term goal of mitigating climate change, the current geopolitical upheavals heighten the urgency to transform Europe's energy system. This involves expanding renewable energies while managing intermittent electricity generation. Hydrogen is a promising solution to balance generation and demand, simultaneously decarbonizing complex applications. To model the energy system's transformation, the project TransHyDE‐Sys, funded by the German Federal Ministry of Education and Research, takes an integrated approach beyond traditional energy system analysis, incorporating a diverse range of more detailed methods and tools. Herein, TransHyDE‐Sys is situated within the recent policy discussion. It addresses the requirements for energy system modeling to gain insights into transforming the European hydrogen and energy infrastructure. It identifies knowledge gaps in the existing literature on hydrogen infrastructure‐oriented energy system modeling and presents the research approach of TransHyDE‐Sys. TransHyDE‐Sys analyzes the development of hydrogen and energy infrastructures from “the system” and “the stakeholder” perspectives. The integrated modeling landscape captures temporal and spatial interactions among hydrogen, electricity, and natural gas infrastructure, providing comprehensive insights for systemic infrastructure planning. This allows a more accurate representation of the energy system's dynamics and aids in decision‐making for achieving sustainable and efficient hydrogen network development integration.

Energy-related carbon emission reduction pathways in Northwest China towards carbon neutrality goal

The Chinese government has announced ambitious carbon reduction goals to address climate change, however, there has been limited scientific attention to the achievement of deep decarbonization in regions with insufficient economic development. Moreover, the complex impacts of climate change mitigation on energy system, environment and economic development remain unclear in Northwest China. Here we construct a comprehensive evaluation model system to evaluate the effects of different climate mitigation pathways in Qinghai, Ningxia, Gansu and Xinjiang provinces. Results show that the four provinces need to vigorously increase the share of electricity that consumed in final energy to 53.1–60.0% and 52.9–62.0% for 2 °C and 1.5 °C target, respectively, and reduce total energy demand to 11.6–44.4 Mtoe and 8.2–34.1 Mtoe in 2050. Meanwhile, the CO2 emissions for their energy systems will reach peak by 2025 at lower values. Moreover, the synergies of CO2 reduction are most conducive to abating SO2 pollution and also contributing to decreasing PM2.5 concentrations. Additionally, the GDP losses in 2050 fluctuate from 5.0 to 21.7 billion USD in the 2 °C scenario to 11.5–52.7 billion USD in the 1.5 °C scenario. Our study provides practical guidance on achieving carbon reductions in less developed regions of China and lays foundations to promote decarbonization of energy systems in other provinces.

Local sustainable communities: Sector coupling and community optimization in decentralized energy systems

Achieving local sustainable development often depends on consumers' incentives to efficiently utilize energy and resources. In this paper, Local sustainable communities (LSC) are introduced as a combination of sustainable communities and local energy communities to promote local energy and resource utilization. Business models on technology and service provision and those on promoting sustainable resource utilization are developed, which are then applied to a community in Austria. A modeling framework on sector coupling in community operations, that also considers resource utilization is developed to assess the impact of the business models. LSC business models promote participation and sustainable operation in an LSC as 31% of electricity and 34% of heat can be covered by LSC purchase. The implementation of energy recovery business models and the availability of sufficient decentralized technologies have the greatest impact on LSC operations, reducing external electricity grid coverage to 58%. The consideration of resource business models can positively contribute to a local resource utilization efficiency, reducing the water pipeline coverage by 43%. The introduction of an LSC has a positive impact on the United Nations Sustainable Development Goals and can therefore efficiently contribute to the development of a cleaner energy system.

Interaction Between Social Acceptance and Humanistic Green Techno Educational Capability in Supporting Environmentally Friendly Solar Energy Investments in Jember District

The development of a dynamic energy system requires adjustments to energy policies that are in line with and receive strong support from human resources in government and society. One of the most effective and sustainable policies is encouraging investment in environmentally friendly infrastructure, such as renewable energy. The potential of solar energy, which is a renewable energy source, offers a solution to the energy crisis experienced by developing countries. In this research, a new concept is proposed which is known as "the interaction between social acceptance and investment readiness (SONVES) as a mediator of green_ Techno Humanistic Education Capability towards solar PV investment readiness". This aims to determine the effect of increasing social acceptance, technology acceptance, environmental awareness, and economic value which are expected to have a positive effect on investment readiness. This research model uses a quantitative descriptive approach to describe the situation objectively using data in the form of numbers. overall investment readiness.

Increasing Solar cell Efficiency using Quantum Dot Sensitization

This research examines the effectiveness of swarm intelligence approaches in improving the functioning of solar cell hybrid microgrids, specifically focusing on the difficulties caused by the irregularity of renewable energy sources. Analyzed were simulated data that represented the creation of solar cell and solar cell electricity, the status of charge of batteries, and the outputs of swarm optimization. The solar cell power data exhibited oscillations in power output ranging from 85 kW to 150 kW, corresponding to changes in solar cell speed ranging from 6.5 m/s to 9.0 m/s. On the other hand, solar cell power saw a marginal decrease from 95 kW to 88 kW, which may be attributed to variations in solar cell irradiation ranging from 850 W/m² to 780 W/m². The battery's level of charge varied between 70% and 95%, indicating the fluctuating rates of charging and discharging, which ranged from 20 kW to 30 kW and 12 kW to 25 kW, respectively. The swarm optimization rounds showed a decrease in the cost of the optimum solution from 3200 to 2000, and an improvement in the convergence rate from 80% to 100%. The analysis indicated a significant 76.5% surge in solar cell power output at peak periods, whereas there was an 8% decline in solar cell power. The state of charge (SoC) of the battery exhibited an average rise of 35.7%, while swarm optimization demonstrated a 37.5% reduction in cost and a steady 25% improvement in convergence rate. The results emphasize the natural fluctuation of renewable sources and demonstrate the potential of swarm intelligence in improving microgrid operation. These results highlight the significance of adaptive control methods in the management of renewable-based microgrids, providing valuable insights for improving their stability, dependability, and cost-effectiveness. The study's findings have practical applications, highlighting the importance of swarm intelligence in promoting the development of sustainable energy systems in the context of integrating renewable energy sources.

Ultra-short-term solar forecasting with reduced pre-acquired data considering optimal heuristic configurations of deep neural networks

<abstract> <p>Forecasting solar irradiance, particularly Global Horizontal Irradiance (GHI), has drawn much interest recently due to the rising demand for renewable energy sources. Many works have been proposed in the literature to forecast GHI by incorporating weather or environmental variables. Nevertheless, the expensive cost of the weather station hinders obtaining meteorological data, posing challenges in generating accurate forecasting models. Therefore, this work addresses this issue by developing a framework to reliably forecast the values of GHI even if meteorological data are unavailable or unreliable. It achieves this by leveraging lag observations of GHI values and applying feature extraction capabilities of the deep learning models. An ultra-short-term GHI forecast model is proposed using the Convolution Neural Network (CNN) algorithm, considering optimal heuristic configurations. In addition, to assess the efficacy of the proposed model, sensitivity analysis of different input variables of historical GHI observations is examined, and its performance is compared with other commonly used forecasting algorithm models over different forecasting horizons of 5, 15, and 30 minutes. A case study is carried out, and the model is trained and tested utilizing real GHI data from solar data located in Riyadh, Saudi Arabia. Results reveal the importance of employing historical GHI data in providing precise forecasting outcomes. The developed CNN-based model outperformed in ultra-short-term forecasting, showcasing average root mean square error results across different forecasting horizons: 2.262 W/m<sup>2</sup> (5min), 30.569 W/m<sup>2</sup> (15min), and 54.244 W/m<sup>2</sup> (30min) across varied day types. Finally, the findings of this research can permit GHI to be integrated into the power grid and encourage the development of sustainable energy systems.</p> </abstract>

Development of an Integrated Solar-Biomass Energy System for Near-Zero Energy Buildings: A Technical and Environmental Study

The present study aims to satisfy the energy demands of a set of Norwegian residential structures with the least carbon dioxide and most renewable energy. Real-time data on building domestic hot water (DHW), heating, and electricity usage is used to plan and expand the renewable energy supply side. PVT panels provide DHW and electricity in the hybrid solar and biomass energy system. Heat is produced by the digester and heat pump. Also used is a twofold effect absorption refrigeration system for cooling. Rule-based regulation manages heat streams and redirects flows on the supply side. We provide the plant's size and execute the dynamic energy simulation. Electricity and biomass expenses determine building heating. The system is then tuned for operational conditions and compared to the design point. PVT may generate over 80% of annual DHW. Summertime radiation is more intense and can be turned into cooling energy, therefore 64.8% of cooling output comes from it. Digester/CC heats 66.55% of the structure, suggesting designers use biomass in winter due to increased energy costs. A parametric analysis shows that increasing PVT duration and tank size affects efficiency and emissions differently. Cost, efficiency, and emission index at TOPSIS are 9.73 $/hr, 36.8%, and 7.75 kg/MWh, according to optimization findings.

ЕКСПЕРИМЕНТАЛЬНИЙ АНАЛІЗ РОБОТИ СОНЯЧНИХ ПАНЕЛЕЙ ІЗ РІЗНИМИ ХАРАКТЕРИСТИКАМИ

Abstract. The article reviews the current state and prospects for the development of solar energy devices and systems. The analysis of the main directions for improving the operation of solar power plants was carried out: improving the design of solar panels; power optimizer using trackers; a decrease in the temperature of the SP; surface cleaning of SP; options for connecting SPs to each other; SP modulation. A comparison of these directions was performed, and the perspective of the method of reconfiguration of solar panels was shown. The results of an experimental study of solar panels are presented. Suggestions for further research into solar energy systems are given. It was concluded that it is necessary to analyze the work of the joint venture under the same external factors. Keywords: solar energy; solar power plants, solar panel, power optimizer, volt-ampere characteristic, efficiency improvement

EMT Model of 20-MW Wind Generator for Real-time Simulation of HVDC Networks

As modern trends demand efficient methods to massively and promptly exploit clean energy, there is an urgent need of solutions that can help the ambition of accelerating the development and integration of larger scale energy systems with the capacity to extract, store, and utilize huge amounts of renewable energy sources. In view of this, the rated power output of the wind generation technology is expected to significantly increase in the near future to enable multi-gigawatt roll outs, e.g. the European plans for Offshore infrastructure development in the North-Sea. Besides, gigantic energy systems shall transmit the power to onshore interconnected systems via HVDC transmission. This paper introduces an EMT model of a 20 MW wind generation system with the conventional back-to-back converter replaced by a combination of an AC to DC converter and a DC to DC converter. While the need of such systems is acknowledged in the literature, the design and performance analysis of high power implementation of such systems have not been addressed yet. The proposed system operates at a voltage level of 100 kV, which can directly transfer power through HVDC interconnection. The designed control aspects are outlined and the resulting dynamic response obtained from realtime digital simulation (RTDS) are discussed. The presented wind generator concept can help in reducing the size of a planned energy system by requiring lesser system components. Also, the system efficiency can be enhanced by decreasing the number of power conversion stages.

СМАРТ-СЕТИ И ИХ РОЛЬ В УСТОЙЧИВОМ РАЗВИТИИ ЭНЕРГОСИСТЕМ

In the context of global climate change and growing demand for electricity, the tran-sition to sustainable energy systems is a priority for modern societies. Smart grids are an innovative solution that promotes the integration of renewable energy sources, energy efficiency and stability of energy systems. The article discusses the main advantages and opportunities of smart networks, their role in demand management and the development of distributed generation, as well as the challenges associated with ensuring cybersecurity and cost-effectiveness of their implementation. Special attention is paid to the analysis of global trends and prospects for the development of smart grids in the context of sustainable development of energy systems.

Econometric Model of Electric Power Sector’s Development in Uzbekistan

The article is aimed at building an econometric model of sustainable development in the energy system of Uzbekistan. The peculiarities of the organizational and economic mechanism are analyzed. The paper locates the place of the sustainable development mechanism in the system of functioning of an industrial enterprise in today’s digital economy. We analyze the Concept Note on the Provision of Electricity in the Republic of Uzbekistan in 2020-2030 and adopted legislative acts of the country. Factors influencing the implementation of major programs for the development of thermal power plants are identified and recommendations are given for their use for the further progress of the energy industry. The article gives directions for sustainable development of energy system in Uzbekistan in the period of 2022-2026.

Electric power industry in the conditions of foreign companies exit

The relevance of the study is due to the fact that there are problems with supply of foreign raw materials and goods to the Russian market for the uninterrupted operation of the energy system as a whole caused by modern complicated economic conditions and current international situation. Besides, there is need to develop and form new conceptual approaches to the strategy of energy systems development amid forced modernisation of the industry. The conducted research is aimed at identifying and analysing existing and new directions in the formation of these original conceptual approaches in conditions of efficient functioning of the Russian industry. Based on the conducted work, the materials of scientists who have studied this problem were examined, and according to the results of the assessment, the following conclusions were formulated. To begin with, non-standard approaches to the management component of the energy systems development within the frame-work of forced modernisation were named. Finally, the potential possibility of rational use of all factors through the synchronisation of processes in diversification production played the role of socio-economic orientation.

Robust Optimization of Large-Scale Wind–Solar Storage Renewable Energy Systems Considering Hybrid Storage Multi-Energy Synergy

With the rapid integration of renewable energy sources, such as wind and solar, multiple types of energy storage technologies have been widely used to improve renewable energy generation and promote the development of sustainable energy systems. Energy storage can provide fast response and regulation capabilities, but multiple types of energy storage involve different energy conversion relationships. How to fully utilize the advantages of multiple energy storage and coordinate the multi-energy complementarity of multiple energy storage is the key to maintaining a stable operation of the power system. To this end, this paper proposes a robust optimization method for large-scale wind–solar storage systems considering hybrid storage multi-energy synergy. Firstly, the robust operation model of large-scale wind–solar storage systems considering hybrid energy storage is built. Secondly, the column constraint generation (CCG) algorithm is adopted to transform the original problem into a two-stage master problem and sub-problem for solving to obtain the optimal strategy of system operation with robustness. Finally, the validity of the proposed method is verified through case tests. The results show that the proposed method can effectively coordinate the multi-energy complementary and coordinated operation of multiple hybrid energy storage, and the obtained operation strategy of large-scale wind–solar storage systems can well balance the economy and robustness of the system.