Search Results (567)

Green technologies are strongly present in the energy mixes of countries around the world. In addition to the need to reduce the extraction of non-renewable raw materials and the harmful environmental impact associated with energy production, the trend towards renewable energy development should also be linked to the need to minimize energy poverty stemming from high electricity prices and the need to increase the energy efficiency of existing solutions. These issues formed the basis for the study’s objective, which was to examine the regulatory framework for the development of Poland’s energy system, with particular emphasis on sustainable development. A particularly important aspect of the study was the exploration of the market for green technologies introduced into the energy system in Poland, with a primary focus on solutions dedicated to small, individual consumers (households). The cognitive value of the study and its original character is created by the cognitive aspect in terms of the interests and consumer preferences of households in this area, motivated by economic considerations related to the energy efficiency aspect of RES solutions. In this regard, there is a relatively limited number of current studies conducted for the reference country (Poland), justifying the choice of the research topic and theme. For the purposes of the study, a literature review, as well as legal standards and industry reports, was conducted. A practical study was conducted based on the results of surveys conducted by selected companies involved in the sale and installation of heating solutions. Detailed research was supported by statistical instruments using PQstat software version 1.8.4.164. Key findings confirm significant household interest in green electricity production technologies, which enable improved energy efficiency of home energy installations. Importantly, the potential for lower electricity bills, which can be attributed to low system maintenance costs and the ability to manage consumption, is a factor in choosing renewable energy solutions. Current interest in renewable energy solutions focuses on heat pumps, photovoltaics, and energy storage. Renewable energy users are interested in integrating renewable energy technology solutions into energy production and management to optimize energy consumption costs and increase household energy independence. Full article

Volatility in the modern world and electricity Day-Ahead Markets (DAMs) usually makes long-term historical data irrelevant or even detrimental for accurate forecasting. This study directly addresses this challenge by proposing a novel forecasting paradigm centered on extremely short training windows, ranging from 7 to 90 days, to maximize responsiveness to recent market dynamics. This volatility-driven approach intentionally creates a data-scarce environment where the suitability of deep learning models is limited. Building on the hypothesis that shallow machine learning models, and more specifically boosting trees, are better adapted to this reality, we evaluate four models, namely LSTM with feed-forward error correction, XGBoost, LightGBM, and CatBoost, across three European energy markets (Greece, Belgium, Ireland) using feature sets derived from ENTSO-E forecast data. Results consistently demonstrate that LightGBM provides superior forecasting accuracy and robustness, particularly when trained on 45–60 day windows, which strike an optimal balance between temporal relevance and learning depth. Furthermore, a stronger capability in detecting seasonal effects and peak price events is exhibited. These findings validate that a short-window training strategy, combined with computationally efficient shallow models, is a highly effective and practical approach for navigating the volatility and data constraints of modern DAM forecasting. Full article

The dynamic development of electromobility and tightening emissions regulations are making electric light commercial vehicles an increasingly important element of modern urban transport. The purpose of this article is to analyze and compare selected models of electric light commercial vehicles available on the market in terms of four key operational parameters: range, charging time, payload, and energy consumption. These parameters directly impact the efficiency of vehicle operation in real-world conditions, especially in last-mile transport. The study employed a multi-criteria decision method (MCDM), which evaluated 10 alternatives and objectively assigned criterion weights using the CRITIC method, which takes into account data variability and correlations between criteria. The article presents the interdependencies between these factors, emphasizing the need to find a compromise between maximum range and usable payload, as well as the impact of charging time on vehicle operational availability. The analysis aims to identify design and technological solutions that contribute most to improving the efficiency of electric light commercial vehicles in urban and suburban applications. Full article

The transition toward decentralized energy systems has amplified interest in peer-to-peer electricity trading. However, research on prosumer behavior in such markets remains fragmented, hindered by a lack of benchmarkable experimental infrastructure. Addressing this gap, the LabChain system was developed—a modular, interactive prototype designed to study human behavior in synthetic P2P electricity markets under controlled laboratory conditions. This system integrates real-world technologies, such as blockchain-based transaction backends, flexibility market interfaces, and asset control tools, allowing fine-grained observation of strategic and perceptual dimensions of prosumer activity. The research followed an iterative design approach to develop the infrastructure for experimental energy economics research, and to assess its effectiveness in aligning participant experience with design intentions. Based on the meta-requirements generality, affordance-centric design, and technological grounding, 13 detailed peer-to-peer market, software, and system requirements that allow for system evaluation were developed. As a proof of concept, seven participants simulated prosumer behavior over a week through interaction with the system. Their interaction with the system was analyzed through simulation data and focus group interviews, using a modified thematic content analysis with a hybrid inductive–deductive coding approach. The main achievements are (i) the design and implementation of the LabChain system as a modular infrastructure for P2P electricity market experiments, (ii) the development of an associated experimental workflow and research design, and (iii) its demonstration through an illustrative, proof-of-concept evaluation based on thematic content analysis of a single focus group session focusing on interaction and perceptions. The behavioral results from an initial session are limited, exploratory, and demonstrative in nature and should be interpreted as illustrative only. They nevertheless revealed tension between system flexibility and cognitive usability: while the system supports diverse strategies and market roles, limitations in interface clarity and information feedback constrain strategic engagement. Full article

Modern smart cities face increasing pressure to invest in sustainable and reliable energy systems while navigating uncertainties arising from fluctuating market conditions, evolving technology landscapes, and diverse expert opinions. Traditional multi-criteria decision-making (MCDM) approaches often fail to fully represent these uncertainties as they typically rely on crisp inputs, lack temporal memory, and do not explicitly account for stochastic variability. To address these limitations, this study introduces a novel Stochastic Fractional Fuzzy Tensor (SFFT)-based Group Decision-Making framework. The proposed approach integrates three dimensions of uncertainty within a unified mathematical structure: fuzzy representation of subjective expert assessments, fractional temporal operators (Caputo derivative, $\alpha =0.85$) to model the influence of historical evaluations, and stochastic diffusion terms ($\sigma =0.05$) to capture real-world volatility. A complete decision algorithm is developed and applied to a realistic smart city renewable energy selection problem involving six alternatives and six criteria evaluated by three experts. The SFFT-based evaluation identified Geothermal Energy as the optimal choice with a score of 0.798, followed by Offshore Wind (0.722) and Waste-to-Hydrogen (0.713). Comparative evaluation against benchmark MCDM methods—TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), VIKOR (VIšekriterijumsko KOmpromisno Rangiranje), and WSM (Weighted Sum Model)—demonstrates that the SFFT approach yields more robust and stable rankings, particularly under uncertainty and model perturbations. Extensive sensitivity analysis confirms high resilience of the top-ranked alternative, with Geothermal retaining the first position in 82.4% of 5000 Monte Carlo simulations under simultaneous variations in weights, memory parameter ($\alpha \in [0.25,0.95]$), and noise intensity ($\sigma \in [0.01,0.10]$). This research provides a realistic, mathematically grounded, and decision-maker-friendly tool for strategic planning in uncertain, dynamic urban environments, with strong potential for deployment in wider engineering, management, and policy applications. Full article

Following the World Health Organization’s 2023 declaration, which ended the global health emergency, energy policy shifted from a short-term crisis response to a structural recovery focused on renewable energy. However, the current literature remains fragmented, often overlooking the realities of implementation in the Global South and failing to integrate diverse policy instruments. This study examines post-pandemic renewable recovery strategies to categorize instruments, evaluate effectiveness, and identify critical implementation gaps. An integrative review was conducted, combining bibliometric mapping of 113 documents (n = 113) and systematic thematic synthesis of 42 studies (n = 42), utilizing the SPIDER and PRISMA protocols. Policy instruments were classified into five groups: Recovery (REC), Fiscal/Financial (FISC), Regulatory (REG), Energy Efficiency (EE), and Social and Information (SOC), revealing a “Global North-South Asymmetry”, where advanced economies leverage fiscal–regulatory coupling while emerging markets face administrative bottlenecks. Findings identify coordination failures, such as missequencing, and propose a “Cascading Policy Logic” that prioritizes de-risking before mandatory standardization. This research bridges the evidence gap by validating the need for informal sector mechanisms and equity safeguards in developing nations. Ultimately, this review provides a strategic framework for policymakers to transition from a reactive stimulus to durable, socially legitimate decarbonization pathways beyond 2025. Full article

The construction sector has a very high share in solving the energy demand of the world and global warming problems. Therefore, it had to increase studies on building materials-based heat storage and thermal insulation. Foam concrete is one of them, but its thermal and mechanical properties need to be improved. So, in this study, calcined marl was used as a replacement material to evaluate its thermal performance in the production of foam mortars. The aims of this study are to determine the physical, mechanical, and thermal properties of foam mortars produced with blended cements containing calcined marl at 0, 10, 30, and 50% ratios and to obtain novel and optimum design data for the foam concrete market. In conclusion, the optimum calcined marl replacement ratio is up to 30% in terms of both thermal performance and mechanical properties of foam mortars. Due to calcined marl, this study presents a foam mortar design with economic and low-carbon. And, thanks to the mixed designs of foam mortars prepared with blended cement containing novel calcined marl additive, it is observed that they improve the thermal insulation and heat storage ability of foam mortars and provide sufficient strength. Full article

The growing share of variable renewable energy sources in power systems is increasing the need for short-term operational flexibility—particularly from large industrial electricity consumers. This study proposes a practical, two-stage optimization framework to unlock this flexibility in cement manufacturing and support participation in electricity balancing markets. In Stage 1, a mixed-integer linear programming model minimizes electricity procurement costs by optimally scheduling the raw milling subsystem, subject to technical and operational constraints. In Stage 2, a flexibility assessment model identifies and evaluates profitable deviations from this baseline, targeting participation in Spain’s manual Frequency Restoration Reserve market. The methodology is validated through a real-world case study at a Spanish cement plant, incorporating photovoltaic (PV) generation and battery energy storage systems (BESS). The results show that flexibility services can yield monthly revenues of up to €800, with limited disruption to production processes. Additionally, combined PV + BESS configurations achieve electricity cost reductions and investment paybacks as short as six years. The proposed framework offers a replicable pathway for integrating demand-side flexibility into energy-intensive industries—enhancing grid resilience, economic performance, and decarbonization efforts. Full article

Virtual power plants (VPPs) provide essential regulation capabilities by aggregating diverse distributed energy resources (DERs). Accurately assessing the value of VPPs from the grid’s side is essential for improving market mechanism design and, in turn, encouraging participation of VPPs. However, existing assessment methods neglect the refined evaluations integrating Automatic Generation Control (AGC)-based operational simulations derived from economic dispatch results, thereby failing to comprehensively capture the multi-dimensional benefits VPPs contribute to the grid. To bridge this gap, this study proposes a multi-dimensional benefit assessment method of VPPs and a simulation method from the grid’s perspective. First, the environmental, security, and economic benefits of VPPs are characterized. A decoupled quantitative assessment framework based on the Vickrey-Clarke-Groves (VCG) mechanism is then established to evaluate these benefits by analyzing system cost variations induced by VPP aggregation. Next, the method of actual operation simulation based on scheduling outcomes is discussed. The corresponding system operation costs are obtained under various scenarios. Case studies utilizing real-world data from a provincial power grid in China analyzed the benefits of VPPs across multiple scenarios defined by varying renewable energy penetration rates, aggregation sizes, and output stability. Notably, the value of the VPP differs significantly across renewable energy penetration levels. Under high penetration, its value increases by 18.5% compared with the low-penetration case, and the value of security and ancillary services accounts for the largest share (50.3%), a component frequently overlooked in existing literature. These findings offer valuable insights for optimizing electricity market mechanisms. Full article

Adaptive facades represent the result of a complex combination of innovative technologies, components, and materials, as well as mechanical, electronic, or digital technologies from sectors outside the construction world (technology transfer), which require a constant multidisciplinary systemic approach. Unlike traditional envelopes, adaptive facades integrate aesthetics, functionality, and energy performance within a single system. This field of research has long been the subject of study by important institutions and research groups that have identified the macro-categories of adaptive envelopes that cover the largest share of the market and have defined the first ISO standards related to dynamic shading, chromogenic envelopes, and active ventilated facades. From the state-of-the-art analysis, adaptive facade systems exhibit short response times, measurable in seconds or minutes, while medium- to long-term adaptability remains underexplored. The objective of this study is to address this gap by considering durability and circularity. Analysis of a database of 329 building envelopes reveals a predominance of short-term strategies within the environmental domain, while long-term strategies focus on material durability and resilience through system regeneration and reuse. These strategies allow for maintaining energy performance by reducing degradation. Ongoing research integrates these strategies with reusability and circularity, extending the perspective beyond the building’s service life to support sustainable lifecycle approaches. Full article

Sustainable utilization of energy depends on the establishment of an advanced energy system. As the world’s largest consumer and importer of energy, China’s progress in this field has attracted considerable attention. This study seeks to address the limitations of most existing research, which largely remains at a qualitative level, by expanding perspectives and methodologies. Utilizing think-tank research approaches and indicator system evaluation methods, it quantitatively evaluates the development level of new energy systems across thirty provincial-level administrative regions in China from 2011 to 2023. Machine learning methods were applied to empirically analyze the driving mechanisms of “new” factors through the construction of a random forest model. The results reveal that: (1) China’s new energy system exhibited an overall positive development trend, albeit at a relatively slow pace and with notable spatial disparities. The development levels of the three core objectives followed a gradient pattern, showing marked improvements after the implementation of China’s supply-side structural reform policies. (2) Innovation funding and high-level labor input served as the dominant driving forces for development, while factors such as the scale of the technology market, the proportion of the tertiary sector, and environmental regulation investment played supplementary roles, with regional variations observed. Full article

Reducing energy intensity is a critical measure for promoting sustainable industrial development and achieving high-quality economic growth. Based on enterprise survey data provided by the World Bank from 2011 to 2013, this paper empirically examines the impact and underlying mechanisms of technological innovation capability on the energy intensity of manufacturing enterprises in China and India. The study finds that an improvement in an enterprise’s technological innovation capability can significantly reduce its energy intensity. Specifically, when an enterprise’s technological innovation capability increases from the 25th to the 75th percentile of its distribution, its energy intensity decreases by an average of 11.22%. This conclusion remains robust after a series of robustness checks. Heterogeneity analysis reveals that the energy-saving effect of technological innovation is significant only in the Chinese sample, but not in the Indian sample. Furthermore, this effect is more pronounced in larger, more capital-intensive enterprises. Further mechanism analysis indicates that improvements in production efficiency and enhancements in operational flexibility are effective mediating channels through which technological innovation reduces energy intensity. This study provides micro-level evidence for the theoretical proposition of a green transition driven by technological innovation in the context of emerging markets. It also offers policy implications for China and India to formulate differentiated energy conservation and emission reduction policies and to promote the green upgrading of their manufacturing sectors. Full article

This article introduces a cost-effective, IoT-enabled flexible energy management system (EMS) for residential photovoltaic (PV) microgrids with battery storage, implemented on an ESP32 microcontroller. The proposed system achieves indirect control over commercial household inverters by altering wattmeter readings and utilizing Modbus communication, thereby avoiding expensive hardware modifications. A significant contribution of this work is enabling the injection of energy from the Battery Energy Storage System (BESS) into the grid, a capability often restricted by commercial inverters. Real-world experimentation validated robust performance of the proposed system, demonstrating its ability to dynamically manage energy flows, achieve minimal tracking errors, and optimize energy usage in response to both flexibility market signals and electricity prices. This approach provides a practical and accessible solution for prosumers to actively participate in energy trading and flexibility markets using widely available technology. Full article

The global transition toward sustainable energy has significantly accelerated the deployment of solar power systems. Yet, the inherent variability of solar energy continues to present considerable challenges in ensuring its stable and efficient integration into modern power grids. As the demand for clean and dependable energy sources intensifies, the integration of artificial intelligence (AI) with solar systems, particularly those coupled with energy storage, has emerged as a promising and increasingly vital solution. It explores the practical applications of machine learning (ML), deep learning (DL), fuzzy logic, and emerging generative AI models, focusing on their roles in areas such as solar irradiance forecasting, energy management, fault detection, and overall operational optimisation. Alongside these advancements, the review also addresses persistent challenges, including data limitations, difficulties in model generalization, and the integration of AI in real-time control scenarios. We included peer-reviewed journal articles published between 2015 and 2025 that apply AI methods to PV + ESS, with empirical evaluation. We excluded studies lacking evaluation against baselines or those focusing solely on PV or ESS in isolation. We searched IEEE Xplore, Scopus, Web of Science, and Google Scholar up to 1 July 2025. Two reviewers independently screened titles/abstracts and full texts; disagreements were resolved via discussion. Risk of bias was assessed with a custom tool evaluating validation method, dataset partitioning, baseline comparison, overfitting risk, and reporting clarity. Results were synthesized narratively by grouping AI techniques (forecasting, MPPT/control, dispatch, data augmentation). We screened 412 records and included 67 studies published between 2018 and 2025, following a documented PRISMA process. The review revealed that AI-driven techniques significantly enhance performance in solar + battery energy storage system (BESS) applications. In solar irradiance and PV output forecasting, deep learning models in particular, long short-term memory (LSTM) and hybrid convolutional neural network–LSTM (CNN–LSTM) architectures repeatedly outperform conventional statistical methods, obtaining significantly lower Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and higher R-squared. Smarter energy dispatch and market-based storage decisions are made possible by reinforcement learning and deep reinforcement learning frameworks, which increase economic returns and lower curtailment risks. Furthermore, hybrid metaheuristic–AI optimisation improves control tuning and system sizing with increased efficiency and convergence. In conclusion, AI enables transformative gains in forecasting, dispatch, and optimisation for solar-BESSs. Future efforts should focus on explainable, robust AI models, standardized benchmark datasets, and real-world pilot deployments to ensure scalability, reliability, and stakeholder trust. Full article

The rapid adoption of electric vehicles (BEVs) has increased the need to understand how fast-charging strategies influence long-distance travel times under real-world conditions. While most manufacturers specify maximum charging power and standardized driving ranges, these figures often fail to reflect actual highway operation, particularly in adverse weather. This study addresses this gap by analyzing the fast-charging behaviour, net battery capacity and highway energy consumption of 62 EVs from different market segments. Charging power curves were obtained experimentally at high-power DC stations, with data recorded through both the charging infrastructure and the vehicles’ battery management systems. Tests were conducted, under optimal conditions, between 10% and 90% state of charge (SoC), with additional sessions performed under both cold and preconditioned battery conditions to show thermal effects on the batteries’ fast-charging capabilities. Real-world highway consumption values were applied to simulate 1000 km journeys at 120 km/h under cold (−10 °C, cabin heating) and mild (23 °C, no AC) weather scenarios. An optimization model was developed to minimize total trip time by adjusting the number and duration of charging stops, including a 5 min detour for each charging session. Results show that the optimal charging cutoff point consistently emerges around 59% SoC, with a typical deviation of 10, regardless of ambient temperature. Charging beyond 70% SoC is generally inefficient unless dictated by charging station availability. The optimal strategy involves increasing the number of shorter stops—typically every 2–3 h of driving—thereby reducing total trip. Full article