Sustainable Development Of Energy Systems Research Articles

Green Electrolytes for Aqueous Ion Batteries: Towards High‐Energy and Low‐Temperature Applications

AbstractAqueous battery systems are increasingly recognized for their potential as environmentally friendly next‐generation energy storage solutions. However, their commercialization faces challenges due to the need for electrolytes that can operate stably at high voltages and in low‐temperatures. Traditional approaches to address these issues often involve materials that compromise the green nature. This review highlights the importance of developing environmentally friendly materials to improve the performance of aqueous electrolytes under high voltage in different types of aqueous electrolytes such as water‐in‐salt, molecular crowding electrolytes, eutectic electrolytes and cosolvents. In addition, we review advances in different types of aqueous electrolytes focused on using sustainable materials to achieve stable electrolytes at low‐temperature by suppressing water crystallization and lowering the freezing point. By integrating these innovations, we envision a future where aqueous batteries offer both high performance and eco‐friendliness, contributing significantly to the development of sustainable energy systems.

Operational greenhouse gas emissions of various energy carriers for building heating

The decarbonisation of the building heating sector requires a shift from decentralised fossil fuel heating appliances to systems converting energy carriers with low greenhouse gas (GHG) emissions. However, for certain energy carriers, a considerable portion of GHG emissions arises upstream during production, processing and transportation, rather than during energy conversion. Accurately quantifying these indirect GHG emissions typically requires life cycle assessments, which are often resource-intensive and impractical during the early stages of energy system design. This study introduces operational GHG emissions as a pragmatic metric for the preliminary assessment of energy carrier environmental impact in building heating applications. These operational GHG emissions include both direct CO2 emissions and indirect CO2, CH4 and N2O emissions. Based on a comprehensive literature analysis, average estimates are proposed for the operational GHG emissions of various energy carriers within a European context, including natural gas, oil, coal and wood, as well as the average European and Belgian electricity grid, and hydrogen from various production methods. The findings underscore the significant contribution of indirect GHG emissions, as the selection of the energy carrier with the lowest environmental impact hinges on whether direct emissions alone or the broader operational GHG emissions are considered. By integrating operational GHG emissions into the early design stages of energy systems, stakeholders can make more informed decisions about which energy systems warrant further investigation, thereby facilitating more sustainable energy system development from the outset.

Using Urban Building Energy Models for the Development of Sustainable Island Energy Systems

This study evaluates the use of City Energy Analyst, an urban building energy modelling tool, to design zero-carbon energy communities in low-industry isolated island settings. The research aims to test the effectiveness of the software during the development of sustainable energy systems in isolated microgrids and compares it with the widely used tool EnergyPLAN. The goal of the study focused on making a community self-sustainable, considering the rooftop area available in the populated settlements to install photovoltaic systems and distributed storage capacity. With this purpose in mind, the evaluated tool estimated the energy consumption of each building and the respective total annual consumption of Corvo Island, a location that is naturally isolated and dependent on fossil fuels. The results demonstrated that City Energy Analyst is an innovative tool to estimate energy consumption and potential energy generation of photovoltaic systems in a remote location, providing additional features to a traditional model and motivating further development of the associated plug-in. However, it requires initial time-consuming efforts to build a reliable model. As a complement, EnergyPLAN can be used to enhance the design, with the integration of the local existing and potential generation sources and to confirm the stability of the overall energy system. This tool introduced additional wind capacity and centralized storage into the model, testing the balance of the system. Therefore, the study proposes a framework combining the strengths of both tools to measure island energy systems, as they can complement each other, to build a strong analysis model.

Efficient Energy Storage via Methane Production Using Protonic Ceramic Electrochemical Cells

Protonic ceramic electrochemical cells (PCECs) are promising energy storage technologies due to their high performance and ability to convert CO2 into value-added chemicals. However, there are many fundamental aspects of this technology that require investigation to better understand its interactions and opportunities. Thus, this paper focuses on the development of a steady-state PCEC model, encompassing intricate details of reactive porous-media transport, elementary catalytic chemistry, and electrochemistry within unit cells. The model is calibrated and validated using experimental data. The model presents the rate of methane production at different temperatures and revealed that efficient PCEC for co-electrolysis of CO2 and H2O is operated in the temperature range of 420–470 °C and the optimum being 450 °C at the rate of 0.0391 moldm−3 min−1. It provides insights into the effects of overpotentials on cell performance. At a current density of 3000 A/m2 the ohmic, concentration, and activation overpotentials amount to 0.65, 0.004, and 0.19 V, respectively. The results obtained from the model highlight the potential of PCECs as efficient CO2 sinks for decarbonization purposes and as a means of methane production. Furthermore, the model's findings offer valuable guidance for the design, selection of stacks, and choice of building materials in PCEC systems. The potential applications of PCECs as real-life fuel utilization technologies are justified with improved material development to reduce cell overpotentials, opening doors for scale-up and eventual commercialization. These findings contribute to the development of sustainable energy systems and advance the pursuit of decarbonization goals.

Application of Fuzzy Control and Neural Network Control in the Commercial Development of Sustainable Energy System

Sustainable energy systems (SESs) occupy a prominent position in the modern global energy landscape. The purpose of this study is to explore the application of fuzzy control and neural network control in photovoltaic systems to improve the power generation efficiency and stability of the system. By establishing the mathematical model of a photovoltaic system, the nonlinear and uncertain characteristics of photovoltaic system are considered. Fuzzy control and neural network control are used to control the system, and their performance is verified by experiments. The experimental results show that under the conditions of low light and moderate temperature, the fuzzy neural network control achieves a 3.33% improvement in power generation efficiency compared with the single control strategy. Meanwhile, the system can still maintain relatively stable operation under different environmental conditions under this comprehensive control. This shows that fuzzy neural network control has significant advantages in improving power generation efficiency and provides beneficial technical support and guidance for the commercial development of SESs.

Exploring the effects of TiO2${\rm TiO}_2$–Ag/water hybrid nanofluid on MHD flow over a permeable exponentially stretching–shrinking sheet with radiative heat transfer and slip conditions

AbstractThe objective of this article is to study the importance of velocity and thermal slip in the exponentially stretching or shrinking sheet with the megnetohydrodynamic (MHD) hybrid nanofluid flow along with magnetic field, radiative, heat generation effects. This study has practical importance in enhancing heat transfer efficiency, improving energy conversion and optimizing thermal management systems. The findings can also contribute to the development of sustainable energy systems and aid in assessing the stability and safety of nanofluid applications. The governing partial differential equations are transformed into nonlinear ordinary differential equations using similarity transformation. The ODEs are solved using bvp4c solver in MATLAB. The dual solutions are achieved for specific range of the stretching or shrinking parameter and mass suction parameter. The first solution is physically significant after considering stability analysis. The hybrid nanofluid has numerous real‐life and industrial applications, such as microelectronics, manufacturing, naval structures, nuclear system cooling, biomedical, and drug reduction. The skin‐friction increases but Nusselt number decreases over the stretching sheet whereas the opposite effect shows over shrinking sheet for the both solution with the increasing velocity slip parameter. On the other hand, The Nusselt number decreases over the shrinking or stretching sheet for both solutions with the increasing in the thermal slip parameter. The skin friction increases over shrinking sheet but decreases over stretching sheet for the first solution with increasing silver volume fraction parameter and porosity parameter (). Enhancing magnetic field parameter leads to the skin friction increases over the shrinking sheet but decreases over stretching sheet but Nusselt number shows opposite trend. The Nusselt number decreases over shrinking sheet but increases over stretching sheet for the first solution with increasing porosity parameter (). The Nusselt number decreases over shrinking or stretching sheet as the increases heat generation () and variable thermal conductivity parameter () but Nusselt number shows opposite trend as the increases radiative parameter ().

Development of multi-input multi-output converter for decarbonization energy system

Decarbonization energy systems include a range of renewable energy sources, such as solar, wind, hydro, and geothermal. These sources generate electricity with minimal carbon emissions and can be used in various applications, including power generation, heating, and transportation. Therefore, the use of a multi-input multi-output (MIMO) converter is becoming increasingly important in decarbonization energy systems. This is because the integration of multiple renewable energy sources, requires a power converter that can manage multiple inputs and outputs efficiently. This paper presents the development of a MIMO converter using a single-phase matrix converter for decarbonization energy systems. The proposed converter is capable of integrating multiple renewable energy sources and can be used in a microgrid application, thus removing the need of multiple converters. The design and analysis of the converter are presented, including the selection of power switches and control strategy. The performance of the converter is evaluated through experimental results, demonstrating its effectiveness in managing multiple inputs and outputs using a single power converter circuit. The results indicate that the proposed converter is a promising solution for decarbonization energy systems, contributing to the development of sustainable energy systems.

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.

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.

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.

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>

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.

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.

The Optimization of Steam Generation in a Biomass-Fired Micro-Cogeneration Prototype Operating on a Modified Rankine Cycle

According to the United Nations, one of the sustainable development goals is to ensure access to affordable, reliable, sustainable, and modern energy for all. Among other options, these goals can be achieved by developing and introducing micro-scale combined heat and power systems powered by renewable energy sources, including solar and biomass energy. Considering renewable energy-powered cogeneration technologies, the most promising are steam/vapor turbines, Stirling engines, and thermoelectric generators. This paper focuses on the selected operational aspects and retrofitting optimization of the prototypical micro-cogeneration system powered by a biomass-fired batch boiler and operating according to the modified Rankine cycle. The existing installation was tested, and the amount of energy transferred from the oil to the condensate and steam and the efficiency of the evaporator and the superheater were determined. A retrofitting optimization aimed at maximizing the piston engine’s power output was conducted based on the results. In particular, it was shown that the system’s power output might be as high as 9 kWe. Moreover, the analyzed system featured a high energy utilization factor of 97.9% at optimal operating conditions. In general, it was shown that the micro-scale steam Rankine system may successfully serve as an alternative technology for micro- and distributed cogeneration systems. As a technology supplied with renewable biomass energy and operating on a cheap and environmentally friendly working medium (water), it fits very well into the idea of sustainable energy system development.

Sustainable development pathways for energies in Yangtze River Delta urban agglomeration

The sustainable development of urban agglomerations plays a pivotal role in national and global efforts to reduce emissions. By focusing on the efficient exchange and optimization of energy consumption across various sectors, the sustainable development of energy systems within urban agglomerations can be achieved. However, the overall impact of the cross-sector energy optimization and complementarity has not been quantitatively analyzed. Here, we focused on the Yangtze River Delta (YRD) urban agglomeration in China and proposed an optimization framework for energy, environment, and economy. The framework considered four sectors: transportation sector, power sector, industry sector, and building sector, in order to determine the most sustainable development pathway for the urban agglomeration. The optimization model considers total costs and greenhouse gas emissions reduction as the objectives and utilizes technologies as constraints to optimize the pathway. We found that this optimization strategy resulted in a 53.1 billion tons increase in CO2 emissions reduction in the region. The results of emission reduction varied across sectors, ranging from 4.5 to 22.2 billion tons CO2 equivalent, and across cities, ranging from 7.1 to 4688.1 Mt. The results suggest that the core cities in the urban agglomeration can take on a leadership role. By promoting cross-sector collaboration and implementing energy recycling, the energy efficiency of surrounding cities can be greatly improved, leading to the sustainable development of the urban agglomeration.

A multidimensional factorial enviro-economic model: Approaches of retrospective decomposition and prospective projection for energy systems

Understanding the patterns of energy usage through simulation and prediction from multiple perspectives is crucial for alleviating energy-environmental conflicts and achieving sustainable energy system development. Despite their importance, multidimensional interactions among the production, demand, and supply sides have not been systematically characterized, thus representing a significant research gap. This paper aims to address this gap by developing a a multidimensional factorial enviro-economic (MFEE) model, which offers an in-depth investigation of Guangdong's energy system. This model, designed for retrospective decomposition (1997–2017) and prospective projections (2035), uniquely links historical and future analysis from systematic and sectoral perspectives through structural decomposition analysis, sectoral evolution analysis, and the Biproportional Scaling RAS method. Our findings reveal that the final demand level and Research & development (R & D) are the principal drivers of increased energy usage. We also found that the wholesale and retail sector have the most significant impact on demand and input sides, accounting for over 70 %. Furthermore, the interaction of key sectors from the production-based perspective notably influences system symbiosis. To effectively manage these dynamics, our study recommends regulating and controlling measures for the circulation sector that consider the benefits of both the energy sector and the accommodation and catering sector. This comprehensive analysis, therefore, offers scientifically-supported guidance for sustainable energy system development.

Research on the Dynamic Characteristics of Photovoltaic Power Production and Sales Based on Game Theory

This paper mainly studies the dynamic characteristics of production and sales of distributed photovoltaic power. This is beneficial for the construction and development of a green power system, and it promotes the sustainable development of the social economy. First, the production and sales laws of the photovoltaic power are analyzed, and the trade process between photovoltaic power producers (PVPs) and photovoltaic power demanders (PVDs) is modeled as a game. Different from the existing relevant literature, two kinds of payoff bimatrices are provided, which correspond to the PVD market and PVP market, respectively. Then, the dynamic characteristics of the model are revealed by using the semitensor product method. The results present a more reliable theoretical basis for the sustainable development of the green electricity market. Finally, through an illustrative example, it can be seen that the strategies of all participants are constantly changing to obtain more profits rather than stable at a traditional Nash equilibrium point. It is worth pointing out that the method and results are applicable to other distributed low-carbon energies, contributing to the development of sustainable energy systems.

Numerical and thermal analysis of a caloric refrigeration device operating near room temperature

The application of external stimuli such as the magnetic and electric field in magnetocaloric and electrocaloric materials, and stress and pressure in elastocaloric and barocaloric materials give rise to a new generation of a refrigeration technology based on caloric materials which are considered an emerging alternative to classical refrigeration. Active caloric regenerator (ACR) made in parallel plates is studied under a large number of materials with Comsol multiphysics for a 2D numerical model. In this work, we compare various types of caloric materials, in terms of their thermodynamic properties, working mechanisms, and potential applications as solid refrigerant on caloric refrigeration devices. For this purpose, the energy equation, Navier-Stocks equation, and continuity equation are considered to study the heat transfer phenomena in refrigerator. The water was used as a carrier fluid to transport the thermal energy from the solid refrigerants to heat exchanger. This study is performed at velocity 0.06 m/s and the frequency 2 Hz at room temperature. Among them, Gadolinium show the best results in term temperature span, coefficient of performance, and the cooling power, higher than every other caloric materials, conferring to magnetocaloric cooling globally the most promising system. Our analysis provides insights into the selection and optimization of caloric materials for caloric refrigeration, which can contribute to the development of sustainable energy systems.

Sustainable Intelligent Energy Management System for Microgrid Using Multi-Agent Systems: A Case Study

In this paper, a sustainable, intelligent energy management system for a microgrid based on a multi-agent system (MAS) is studied. The system is designed to address the challenges posed by the intermittence of renewable energy sources. Also, the system optimizes the use of available AC–DC renewable energy sources by utilizing load flexibility and the complementarity of renewable sources. To evaluate the effectiveness of this proposed multi-agent framework, a co-simulation using MATLAB and JADE platforms is conducted for a microgrid connected to the main grid. The results show that the proposed energy sharing system achieved over 82.34% of energy savings. This innovative solution has the potential to reduce the need for energy storage and improve energy efficiency while also reducing CO2 emissions. It offers a promising sustainable development solution for managing and controlling MG. The proposed system contributes to the development of sustainable energy systems based on artificial intelligence to meet the global goals.