Sustainable Energy Systems Research Articles

Quantum long short-term memory (QLSTM) vs. classical LSTM in time series forecasting: a comparative study in solar power forecasting

Accurate solar power forecasting is pivotal for the global transition towards sustainable energy systems. This study conducts a meticulous comparison between Quantum Long Short-Term Memory (QLSTM) and classical Long Short-Term Memory (LSTM) models for solar power production forecasting. The primary objective is to evaluate the potential advantages of QLSTMs, leveraging their exponential representational capabilities, in capturing the intricate spatiotemporal patterns inherent in renewable energy data. Through controlled experiments on real-world photovoltaic datasets, our findings reveal promising improvements offered by QLSTMs, including accelerated training convergence and substantially reduced test loss within the initial epoch compared to classical LSTMs. These empirical results demonstrate QLSTM’s potential to swiftly assimilate complex time series relationships, enabled by quantum phenomena like superposition. However, realizing QLSTM’s full capabilities necessitates further research into model validation across diverse conditions, systematic hyperparameter optimization, hardware noise resilience, and applications to correlated renewable forecasting problems. With continued progress, quantum machine learning can offer a paradigm shift in renewable energy time series prediction, potentially ushering in an era of unprecedented accuracy and reliability in solar power forecasting worldwide. This pioneering work provides initial evidence substantiating quantum advantages over classical LSTM models while acknowledging present limitations. Through rigorous benchmarking grounded in real-world data, our study illustrates a promising trajectory for quantum learning in renewable forecasting.

FACTS: Present and Future

Flexible AC Transmission Systems (FACTS) are pivotal in modernizing power systems, enhancing their stability, controllability, and efficiency. Currently, FACTS devices such as Static VAR Compensators (SVCs), Static Synchronous Compensators (STATCOMs), and Unified Power Flow Controllers (UPFCs) are employed to manage power flow, mitigate system instabilities, and improve voltage regulation across transmission networks. These technologies address present challenges, including the integration of renewable energy sources, reduction of transmission losses, and enhancement of system reliability in the face of fluctuating power demands. However, the growing complexity of power grids, driven by the increasing penetration of intermittent renewable energy, electric vehicles, and distributed generation, necessitates advanced and scalable solutions. The future of FACTS lies in the development of more sophisticated, adaptive, and intelligent devices that leverage real-time data analytics, artificial intelligence, and machine learning to optimize power flow dynamically. Future advancements are expected to focus on enhancing the interoperability of FACTS with smart grid technologies, improving the resilience of power systems against cyber-physical threats, and facilitating the transition towards more decentralized and sustainable energy systems. Moreover, the integration of energy storage with FACTS devices could revolutionize their functionality, offering not only reactive power compensation but also energy balancing capabilities. This paper explores the current applications of FACTS and envisions their future role in addressing the evolving challenges of global power systems, emphasizing the importance of innovation and strategic investment in the ongoing transformation of electrical networks.

Circular Economy Models in Renewable Energy: Technological Innovations and Business Viability

The transitions from fossil-based energy to renewable energy are deeply connected with circular economy solutions for business development and innovation. This paper aims to evaluate to what extent innovation in renewable energy can be used to advance circular economy strategies, with emphasis on the business aspect. Using a combination of qualitative and quantitative analysis, the research analyzes case contributions of a number of RES projects that apply CE principles. It also focuses on the main issues which specialists encounter while implementing these models and considers their economic feasibility. The results show that circular economy principles present renewable energy firms with tremendous potential for enhancing resource use efficiency and financial performance. This research provides practical recommendations aimed at the stakeholders in terms of constructing a more sustainable energy system. In doing so this research adds novelty by identifying the technological factors as advocates for implementing circular economy models to support business outcomes within the renewable energy industry.

Blocked force characterization and transposition at secondary connections of a compressor

Applications in sustainable energy systems, like heat pumps, are encountering emerging challenges, among which acoustic comfort stands out as a critical factor in shaping the end-user experience. Early integration of vibroacoustic into each industrial design phase ensures the prevention and prediction of potential noise issues, thus avoid costly design modification loops. BDR Thermea and CETIM, in a collaborative project, have implemented new methodologies for designing acoustically optimized products, highlighting the importance of considering vibroacoustic design at each design stage of new products developments. In this study, structure-borne noise characterization methods are applied to a heat pump compressor using the ISO 21955 and ISO 20270 standards. The forces transmitted at its suction and discharge ports were studied through component based TPA for the intrinsic characterization of the compressor. Once obtained, these forces were transposed to a new receiving structure. The results allowed a comparison of the outcomes from both approaches and highlighted the importance of considering the influence of the stiffness of the connections at the compressor ports.

Potential use of district heating networks and the prospects for the advancements within urban areas of Nottingham as a case study

Buildings in urban areas significantly contribute to global energy consumption, necessitating solutions to reduce energy demands and mitigate environmental impacts. This study investigates district heating networks in urban areas, using Nottingham as a case study. A literature review addresses key barriers to decarbonising building energy in the UK, focusing on: (1) Information, engagement, and behaviour, (2) Strategy, policy, and regulation, (3) Infrastructure, (4) Supply chain and skills, and (5) Distribution of costs and impacts. The study examines the network's location, current processes, energy performance, and potential future impacts. Heat mapping and energy flow assessments identify areas for energy savings, guiding system optimisation. The findings suggest that increasing the energy output from the heat station to the network can enhance performance. The analysis highlights the potential of a life cycle perspective for district heating networks, proposing a framework that considers all stages from material sourcing to system utilisation. This approach helps assess environmental impacts, including greenhouse gas emissions and resource depletion, identifying opportunities to improve resource efficiency and reduce environmental impacts. By evaluating current sustainability goals and regulatory compliance, the study provides insights into the environmental and social implications of the district heating network's operations, pinpointing areas for improvement. It emphasises the need for continuous optimisation and innovation throughout the network's life cycle. The potential of a full life cycle assessment (LCA) approach is highlighted to evaluate four main future advancements: (1) heating network with added energy storage, (2) advanced system operations through model predictive control (MPC) for demand prediction, (3) general network improvements, and (4) network expansion. Overall, this study aims to enhance the sustainability, efficiency, and resilience of district heating networks, contributing to the transition towards sustainable energy systems while ensuring environmental and economic viability over time.

Socio economic perspectives on fusion power for a sustainable future energy system

In the global effort towards the mitigation of climate change, low-carbon electricity generating technologies play a pivotal role. Nuclear fusion stands as a highly promising option for carbon-free electricity generation in a sustainable, reliable, affordable and socially acceptable future energy system. The EUROfusion work package “Socio Economic Studies” (WPSES) on fusion conducts research aimed at identifying social and economic conditions that can effectively support fusion deployment in future energy markets. Placing nuclear fusion in the broader context of energy and climate issues, the SES research explores fusion as an energy technology contributing to sustainable energy production for the future society. Because of the cross-cutting nature of the research, SES studies can provide decision-makers with scientific evidence relevant for shaping appropriate strategies in favour of fusion. This paper aims to offer the scientific community a thorough overview of EUROfusion SES research, addressing a gap in the current literature.

Integrating renewable energy technologies in green ships for mobile hydrogen, electricity, and freshwater generation

This research investigates a sustainable and renewable mobile energy system designed for electricity production, hydrogen generation, and seawater desalination. It targets critical energy and freshwater needs during disaster scenarios, particularly in island nations and coastal cities where infrastructure may be compromised. The system integrates advanced technologies, including wind turbines, photovoltaic panels, solar collectors, and reverse osmosis units, all mounted on a green ship. Simulations using Engineering Equation Solver software indicate that in July, the system can produce 2,201,865 MJ of electricity, 7252 kg of hydrogen, and 3456 tons of fresh and hot water. This output can power 1072 electric vehicles and 1284 hydrogen-powered vehicles while supplying cold water to 57,600 people and hot water to 5760 people. By relying on renewable energy, the system prevents approximately 43,543 kg of carbon emissions in July. The total monthly economic value is estimated at $47,241.36, demonstrating its potential as a sustainable solution for disaster response and coastal communities.

Low carbon solar-based sustainable energy system planning for residential buildings

Demand-side policy support has a significant role in energy systems. System profitability will widely hinge on planning and programming under conventional systems with no support. On the other hand, in smart grids, buildings are no longer passive consumers, but they can be used for demand-side energy management. In this study, two energy systems are assumed for an on-grid smart building. The power grid and PV panels are the first system’s electricity suppliers, and the thermal load is fulfilled by an electric boiler, Solar Collector (SC), and thermal storage in this system. The second energy system, instead of an electric boiler, is equipped with a gas boiler. As a consequence of investigating this system as an energy hub, the best combination and optimized size of each design are achieved using the Particle Swarm Optimization heuristic method (PSO) via MATLAB software based on technical and economic benchmarks. The payback period for the system equipped with a gas boiler is equal to 3.3 years, compared to 4.3 years of the electric boiler system payback and their net present value is 4633.9$ and 3909.5$ respectively. The results of optimization inditated that the PV panels should be 15 m2 in size, the solar collector should have a size of 2.68 m2, and the gas boiler should be 3.09 kW in capacity. The optimization results indicate that the solar collector is not beneficial in the system with the gas boiler, so all of the thermal loads have been provided with a gas boiler in this system.

System friendliness of distributed resources in sustainable energy systems

The increasing share of volatile renewable energy generation in current and future energy systems which does not follow the demand, makes it necessary to shift the synchronization tasks to the system and demand level. The intended demand side consumption management is usually incentivized via financial mechanisms. However, the optimum design of related incentives is still unclear. It is usually discussed under the term “system friendliness” which in turn still lacks a broadly accepted definition. In this article we introduce a new technical definition of “system friendliness” and develop comprehensive and holistic related indicators. For that, we introduce the burden concept for energy systems which represents the amount of technical infrastructure required for stable operation. We use the concept of a hypothetical system energy storage to derive indicators that are capable of quantifying the system friendliness of a given point-of-interest. The developed indicators are independent of regulations or market environments which enables comparability of results between studies and different systems. In a case study we demonstrate our newly developed methodology and exemplary examine decentral district energy storages. Our findings demonstrate that today’s regulatory environments that usually support residential self-consumption maximization are counterproductive. Instead, we show that dynamic end-user prices coupled with dynamic feed in tariffs are able to incentivize an almost 100% system friendly behavior of distributed prosumers.

Design of sustainable integrated energy systems for green ports – selection and sizing

Abstract Design of sustainable and integrated energy systems for marine port is a complex problem, involving multiple conflicting objectives like energy efficiency and minimum losses, cost-effectiveness, and avoiding excessive cycling and degradation. This paper addresses the sizing of integrated and sustainable port energy systems with renewable energy sources and energy storage systems based on two different methods. Both developed methods start by selecting design sets representing the gradual expansion of renewable sources and storage to the maximum size, limited by various constraints. Each design variant is evaluated based on the energy demands of the port, historical prices, and site energy production capacity. The first method, developed by ITK Engineering GmbH, uses a conventional deterministic forecasting approach with detailed annual data. The latter, developed by NTNU, employs several 24-hour data sets to capture annual energy dynamics. Key performance indicators for both methods are defined based on the total cost, with the benchmark cost obtained using the first (deterministic) method with 100% grid-supplied energy. Two virtual ports of different sizes (Port A and B) are established in two electric markets (1 and 2) for verification and benchmarking. The paper then discusses the influence of uncertainty in 24-hour data sets, used by the second (stochastic) method, for all cases.

Leverages of energy transition at home: a critical discussion of barriers and future trends

Abstract The transition of the residential sector to a more sustainable energy system is regarded as a crucial action. Thus, significant efforts have been spent to promote deep energy retrofitting of existing inefficient assets. In addition, in more recent times, academics have begun to consider the potential for behavioral change among households to generate energy savings for the sector. This has led to the emergence of a growing body of literature aimed at understanding how to mobilize energy demand reduction potential that can be prompted by households’ behavioral change. Based on findings from a systematic literature review by the Authors, the paper tries to speculate upon four main dichotomic approaches that are retrieved within. These include behavioral-driven versus technological-driven approaches, awareness-raising versus rewarding or incentive-based systems, structural measures such as home retrofitting or the shift to clean energy, and provisional measures such as subsidies and tax rebates for vulnerable consumers. The paper also considers individual/customized measures versus collective policies towards energy transition. Based on that, common grounds are sought to advance effective strategies enabling energy transition of the residential sector. The drivers and limitations of the various approaches are also discussed in this context.

Energy scheduling strategy for energy hubs using reinforcement learning approach

The rapid integration of Renewable Energy Sources (RES) into energy systems is critical for achieving a fossil fuel-free future. However, it also introduces significant challenges, such as increased system complexity and uncertainties in energy output. To overcome these challenges, this paper presents a model-free Deep Reinforcement Learning (DRL) method to energy hub scheduling using state-of-the-art algorithms, specifically Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO). Breaking away from traditional energy management systems that typically use discrete action spaces, this approach employs a multi-dimensional, continuous action space, providing a more accurate framework for decision-making. Furthermore, the current study adopts a comprehensive perspective by considering not only energy supply and operational costs, but also the often-overlooked impact of emitted emissions in the objective function. The results yield a Pareto space encompassing various criteria, offering a diverse scheduling framework that balances economic feasibility with environmental sustainability. By presenting a range of optimal solutions, this study contributes to the development of more resilient and sustainable energy systems.

Energy Transition Analysis and Climate Action Strategy in the Power Sector: A Case Study of the Java-Bali System

Abstract The transformation of societies over time has led to a marked shift in energy consumption patterns. In the face of rapid industrial growth and a burgeoning population, Indonesia confronts the multifaceted challenge of achieving a balance among energy trilemma: energy security, economic affordability, and environmental sustainability. This study assesses the feasibility of low-carbon energy systems in Indonesia, evaluating current policies and proposing a roadmap for net-zero emissions. Utilizing the Low Emissions Analysis Platform (LEAP) and the Next Energy Modeling System for Optimization (NEMO), the study explores multiple scenarios—namely, Business-as-Usual (BAU), Carbon Capture and Storage Mitigation (CCSM), and New and Renewable Energy Mitigation (NREM). Of these, the NREM scenario, emphasizing the integration of innovative and renewable energy sources, achieves the most substantial reduction in greenhouse gas emissions while maintaining economic viability. This strategy advocates for the comprehensive adoption of new and renewable energy, aiming to approximate zero emissions by 2060. The peak direct cost of production for this scenario is estimated at $270.8 billion. However, each scenario presents its own set of technological, economic, and policy-related challenges, necessitating strategic solutions for a successful transition to sustainable energy systems.

Statistical characterization of electricity use profile: Leveraging data analytics for stochastic simulation in a smart campus

On the path to energy transition, advanced metering infrastructures have been installed in distribution systems to support sustainability goals, generating a substantial volume of electricity consumption data that are essential for planning and management studies. Additionally, given the stochastic nature of electricity consumption, understanding and quantifying statistical properties such as data distribution, normality, stationarity, and autocorrelation are crucial for the development of more sustainable systems and the enhancement of building performance. In this context, this paper presents a statistical methodology for assessing key aspects of electricity consumption in buildings on a smart campus, which is an initiative originated on university campuses that integrates sustainable energy systems, efficient electrical infrastructure, and data-driven technologies to establish a sustainable learning environment. Using 28 months of electricity consumption data from a Brazilian smart campus, Electricity Use Profile models are developed and several hypothesis tests and probability distribution fittings are conducted to extract statistical features from the models of 128 buildings. The results indicate that each building exhibits unique statistical properties that cannot be generalized, emphasizing the need for data analysis for each building before using the data in decision-making processes.

Research Progress of Battery Thermal Management Systems with Minichannels

With the developing technology, energy storage systems, especially lithium-ion batteries (LiBs), play a critical role in electric vehicles and renewable energy applications. However, the performance and life of batteries are significantly affected by their operating temperatures. In this context, battery thermal management systems (BTMS) are of vital importance to ensure temperature control of batteries and to create a safe operating environment. BTMSs are divided into main two groups active and passive which require and does not require extra energy consumption, respectively. In this review, the basic principles, design criteria and application areas of battery thermal management systems are examined. First of all, the components of BTMS, passive and active cooling methods, heat dissipation and temperature monitoring techniques are detailed. In addition, the effects of different BTMS approaches on efficiency and performance are compared. The analysis of existing studies in the literature reveals the positive contributions of BTMS on battery life, charge-discharge efficiency and safety. In addition, future research areas and development opportunities are also highlighted. In conclusion, an effective thermal management system is a critical factor in the development of battery technology and has great potential in terms of sustainability of energy systems.

Contractual challenges in renewable energy projects: The role of ADR in managing risks and disputes

Renewable energy projects are increasingly pivotal in the global transition to sustainable energy systems. However, these projects face unique contractual challenges that can lead to disputes and risks, impacting project timelines and costs. This paper explores the critical role of Alternative Dispute Resolution (ADR) mechanisms in managing and mitigating these challenges. The study highlights common contractual issues encountered in renewable energy projects, including ambiguities in contract terms, scope changes, and performance obligations. It examines how ADR methods, such as mediation, arbitration, and adjudication, offer effective solutions for resolving disputes and minimizing disruptions. By analyzing case studies and industry practices, the paper demonstrates the efficacy of ADR in addressing specific issues such as delays, cost overruns, and compliance failures. The findings underscore the importance of incorporating ADR clauses into contract frameworks to enhance dispute resolution processes and risk management strategies. The paper concludes with recommendations for integrating ADR into contractual agreements and emphasizes the need for stakeholders to proactively address potential disputes to ensure project success.

Prediction Model for Electricity Energy Consumption and Pricing Using Xgboosts With L1 Regularization

The increasing global demand for electricity, driven by rapid industrialization, urbanization, and digitalization, necessitates more accurate and efficient predictive models for electricity generation and consumption. Traditional statistical techniques often fall short in incorporating the complex environmental factors influencing energy demand. This study presents an advanced machine learning model, leveraging XGBoost with L1 regularization, to improve the accuracy of electricity consumption and generation predictions. By integrating comprehensive datasets, including economic indicators and weather variables, the model addresses the critical gaps in existing forecasting methods. The research involved rigorous data pre-processing, feature engineering, and model validation using metrics such as RMSE, MAPE, and MAE. The model achieves a RMSE of 2.212 and MAPE of 2.393%, indicating good predictive performance. Effective data cleaning, feature engineering, and the application of PCA contributed to this result. The results demonstrate significant improvements in prediction accuracy, highlighting the potential of the proposed model to enhance energy management practices, optimize grid operations, and support the integration of renewable energy sources. This study provides a robust framework for future research and practical applications, contributing to the advancement of sustainable energy systems and informed policy-making. The findings underscore the importance of integrating diverse data sources and sophisticated machine-learning techniques to meet the growing challenges in the energy sector.

Nickel-copper alloying arrays realizing efficient co-electrosynthesis of adipic acid and hydrogen

Constructing electrocatalytic overall reaction technology to couple the electrosynthesis of adipic acid with energy-saving hydrogen production is of significant for sustainable energy systems. However, the development of highly-active bifunctional electrocatalysts remains a challenge. Herein, 3D hierarchical nickel-copper alloying arrays with dendritic morphology are manufactured by a simple electrodeposition process, standing for the excellent bifunctional electrocatalyst towards the co-production of adipic acid and H2 from cyclohexanone and water. The membrane-free flow electrolyzer of Cu0.81Ni0.19/NF shows the superior electrooxidation performance of ketone-alcohol (KA) oil with high faradaic efficiencies of over 90% for adipic acid and H2, robust stability over 200 h as well as a high yield of 0.6 mmol h−1 for adipic acid at 100 mA cm−2. In-situ spectroscopy indicates the Cu0.81Ni0.19 alloy contributes to forming more active NiOOH species to involve in the conversion of cyclohexanone to adipic acid, while the proposed reaction pathway undergoes the 2-hydroxycyclohexanone and 2,7-oxepanedione intermediates. Moreover, the theoretical calculations confirm that the optimal electronic interaction, boosted reaction kinetics as well as improved adsorption free energy of reaction intermediates, synergistically endows Cu0.81Ni0.19 alloy with superior bifunctional performance.

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.

Exploring novel nanostructural architecture of doubly doped ceria (Ce0.85Sr0.075Sm0.075O2-δ) and barium cerate (BaCeO3) as electrolytes for low-temperature solid oxide fuel cells

This study presents the development of a composite electrolyte for use in low-temperature (300–500 °C) Solid oxide Fuel Cells, focusing on a novel nanostructural design. The electrolyte comprises co-doped ceria (Ce0.85Sr0.075Sm0.075O2-δ, DCO) and barium cerate (BaCeO3, BCO), integrated into a unique nanostructural architecture. Initially, the constituent phases of the composite DCO and BCO are synthesized using a soft-chemical route separately, followed by their integration through liquid mixing technique in various compositions (90:10, 80:20, 70:30, 60:40, 50:50) of DCO:BCO in weight ratio. The composite structure is confirmed by X-ray diffraction, and refined the structural data by Rietveld refinement indicating a structural agreement with cubic and fluorite phases of DCO and BCO, respectively. Scanning Electron microscopy (SEM) and Energy Dispersive X-ray Analysis (EDAX) reveal a uniform distribution of two phases with fine size distribution below 50 nm. Transmission Electron microscopy (TEM) images illustrate the core-shell-like integration of the two phases. High-resolution TEM images indicate that the core consists of the DCO phase, while the shell is composed of the BCO phase. The microstructure of sintered pellets exhibits gas-tight densification, with grains dominated by the DCO phase and BCO phase dispersed along grain boundaries. The ionic conductivity of composite systems is extracted from Electrochemical impedance spectroscopy (EIS) studies, showing that DCO-BCO-40 exhibits the highest conductivity 1.132 × 10−3 Scm−1 @400 °C, which is higher than pristine DCO (0.34 × 10−4 Scm−1 @400 °C). Single cells fabricated using DCO-BCO-40 electrolytes deliver a power output of 280 mW cm−2 whereas pristine DCO showed 279 mW @500 °C. This study exhibits the potential of the core-shell-like nanostructural architecture of electrolytes in advancing Low-temperature solid oxide Fuel cell (LT-SOFC) technology and facilitating the transition toward sustainable energy systems.