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Due to the energy transition, the future electric power system will face further challenges that affect the functionality of the electricity grid and therefore the security of supply. For this reason, this article examines the future frequency stabilisation in a 100% renewable electric power system. A focus is set on the provision of inertia and frequency containment reserve. Today, the frequency stabilisation in most power systems is based on synchronous generators. By using grid-forming frequency converters, a large potential of alternative frequency stabilisation reserves can be tapped. Consequently, frequency stabilisation is not a problem of existing capacities but whether and how these are utilised. Therefore, in this paper, a collaborative approach to realise frequency stabilisation is proposed. By distributing the required inertia and frequency containment reserve across all technologies that are able to provide it, the relative contribution of each individual provider is low. To cover the need for frequency containment reserve, each capable technology would have to provide less than 1% of its rated power. The inertia demand can be covered by the available capacities at a coverage ratio of 171% (excluding wind power) to 217% (all capacities). As a result, it is proposed that provision of frequency stabilisation is made mandatory for all capable technologies. The joint provision distributes the burden of frequency stabilisation across many participants and hence increases redundancy. It ensures the stability of future electricity grids, and at the same time, it reduces the technological and economic effort. The findings are presented for the example of the German electricity grid. [ABSTRACT FROM AUTHOR]

Due to the complexity and technicality of named entity recognition (NER) in the power grid field, existing methods are ineffective at identifying specialized terms in power grid operation record texts. Therefore, this paper proposes a Chinese power violation description entity recognition model based on word-character fusion and multi-head attention mechanisms. The model first utilizes a collected power grid domain corpus to train a Word2Vec model, which produces static word vector representations. These static word vectors are then integrated with the dynamic character vector features of the input text generated by the BERT model, thereby mitigating the impact of segmentation errors on the NER model and enhancing the model's ability to identify entity boundaries. The combined vectors are subsequently input into a BiGRU model for learning contextual features. The output from the BiGRU layer is then passed to an attention mechanism layer to obtain enhanced semantic features, which highlight key semantics and improve the model's contextual understanding ability. Finally, the CRF layer decodes the output to generate the globally optimal label sequence with the highest probability. Experimental results on the constructed power grid field operation violation description dataset demonstrate that the proposed NER model outperforms the traditional BERT-BiLSTM-CRF model, with an average improvement of 1.58% in precision, recall, and F1-score. This demonstrates the effectiveness of the model design and further enhances the accuracy of entity recognition in the power grid domain. [ABSTRACT FROM AUTHOR]

This study proposes a multi-energy complementary heating system that uses solar energy combined with biomass energy as the main heat source, with electricity as an auxiliary heat source. The system aims to tackle the low efficiency, high energy consumption, and pollution associated with traditional heating methods in rural southern Xinjiang, enhancing performance and productivity. It is designed to operate in five modes based on the region's climate and building heat load requirements. An experimental platform was set up in eight rural households in Tumushuk City, Xinjiang, where winter heating tests were conducted. The goal of this study was to analyze the economic and environmental benefits of the system. The results showed that the energy utilization efficiencies of the five modes were 56.84%, 74.34%, 70.1%, 63.13%, and 59.68%. The corresponding CO2 emissions were 3.56 kg/d, 45.09 kg/d, 105.75 kg/d, 30.97 kg/d, and 76.79 kg/d. The environmental and economic costs for each mode were 0.0493 USD/d, 0.6398 USD/d, 1.5029 USD/d, 0.4384 USD/d, and 1.0905 USD/d. It is clear that as an auxiliary heat source, biomass energy is more beneficial than electricity. All five modes maintained indoor temperatures of 18 °C or higher, meeting winter heating needs in cold regions. The results of this study provide important data support for the promotion and application of solar and biomass heating systems in the rural areas of southern Xinjiang and also provide valuable references for solving the problem of decentralized heating in rural areas. [ABSTRACT FROM AUTHOR]

Accurate electricity demand forecasting is critical for improving energy efficiency, maintaining grid stability, reducing operational costs, and promoting sustainability. This study presents a novel hybrid forecasting model that integrates Long Short-Term Memory (LSTM) networks and Prophet models, leveraging their complementary strengths through a dynamic weighted ensemble methodology. The LSTM component captures nonlinear dependencies and long-term temporal patterns, while Prophet models seasonal trends and event-driven fluctuations. The hybrid model was evaluated using a comprehensive dataset of hourly electricity consumption from Ontario, Canada, achieving a Root Mean Square Error (RMSE) of 65.34, Mean Absolute Percentage Error (MAPE) of 7.3%, and an R2 of 0.98. These results demonstrate significant improvements over standalone LSTM, Prophet, and other State-of-the-Art methods, highlighting the hybrid model's adaptability and superior accuracy. This study underscores the practical implications of the hybrid approach, particularly in energy grid management and resource optimization, setting a new benchmark for time series forecasting in the energy sector. [ABSTRACT FROM AUTHOR]

The elevated penetration of renewable energy has seen a significant increase in the integration of inverter-based resources (IBRs) into the electricity network. According to various industrial standards on interconnection and interoperability, IBRs should be able to withstand variability in grid conditions. Positive sequence voltage-oriented control (PSVOC) with a feed-forward decoupling approach is often adopted to ensure closed-loop control of inverters. However, the dynamic response of this control scheme deteriorates during fluctuations in the grid voltage due to the sensitivity of proportional–integral controllers, the presence of the direct- and quadrature-axis voltage terms in the cross-coupling, and predefined saturation limits. As such, a twin delayed deep deterministic policy gradient-based voltage-oriented control (TD3VOC) is formulated and trained to provide effective current control of inverter-based resources under various dynamic conditions of the grid through transfer learning. The actor–critic-based reinforcement learning agent is designed and trained using the model-free Markov decision process through interaction with a grid-connected photovoltaic inverter environment developed in MATLAB/Simulink® 2023b. Using the standard PSVOC method results in inverter input voltage overshoots of up to 2.50 p.u., with post-fault current restoration times of as high as 0.55 s during asymmetrical faults. The designed TD3VOC technique confines the DC link voltage overshoot to 1.05 p.u. and achieves a low current recovery duration of 0.01 s after fault clearance. In the event of a severe symmetric fault, the conventional control method is unable to restore the inverter operation, leading to integral-time absolute errors of 0.60 and 0.32 for the currents of the d and q axes, respectively. The newly proposed agent-based control strategy restricts cumulative errors to 0.03 and 0.09 for the d and q axes, respectively, thus improving inverter regulation. The results indicate the superior performance of the proposed control scheme in maintaining the stability of the inverter DC link bus voltage, reducing post-fault system recovery time, and limiting negative sequence currents during severe asymmetrical and symmetrical grid faults compared with the conventional PSVOC approach. [ABSTRACT FROM AUTHOR]

In grid intelligent inspection systems, automatic registration of infrared and visible light images in power scenes is a crucial research technology. Since there are obvious differences in key attributes between visible and infrared images, direct alignment is often difficult to achieve the expected results. To overcome the high difficulty of aligning infrared and visible light images, an image alignment method is proposed in this paper. First, we use the Sobel operator to extract the edge information of the image pair. Second, the feature points in the edges are recognised by a curvature scale space (CSS) corner detector. Third, the Histogram of Orientation Gradients (HOG) is extracted as the gradient distribution characteristics of the feature points, which are normalised with the Scale Invariant Feature Transform (SIFT) algorithm to form feature descriptors. Finally, initial matching and accurate matching are achieved by the improved fast approximate nearest-neighbour matching method and adaptive thresholding, respectively. Experiments show that this method can robustly match the feature points of image pairs under rotation, scale, and viewpoint differences, and achieves excellent matching results. [ABSTRACT FROM AUTHOR]

Given that existing methods for recommending electricity sale packages primarily consider scenarios where customers are familiar with all package attributes, they overlook psychological factors, attribute correlations, and the determination of attribute weights during decision-making. To address these limitations, this paper proposes a recommendation method for electricity sale package based on prospect strengths and weaknesses degree and the Choquet integral. The details are as follows: Firstly, a label system for evaluating electricity sale packages and customer clustering is utilized to identify similar customers to a new customer. Secondly, a set of similar customers is identified, and N similar customers are selected as experimental customers. Their decision-making information is aggregated using the Choquet integral to construct a customer group decision-making matrix. Next, to account for customers' psychological risk preferences, the Prospect Theory is integrated into the preference difference function of the classical Superiority–Inferiority Ranking method, resulting in the degree of prospect strengths and weaknesses. Building on this, and addressing the challenges of attribute correlation and weight determination, a model is constructed using the Choquet integral and comprehensive attribute weights. This model ranks electricity sale packages based on the degree of prospect strengths and weaknesses, capturing the differences between various schemes through the prospect strengths and weaknesses flow. The ranking of packages for recommendation is then derived from this flow. Finally, a case analysis is conducted with customers in a western Zhejiang region in China to verify the accuracy and effectiveness of the proposed recommendation method. [ABSTRACT FROM AUTHOR]

Short-Term Load Forecasting (STLF) is essential for the efficient management of power systems, as it improves forecasting accuracy while optimizing power scheduling efficiency. Despite significant recent advancements in STLF models, forecasting accuracy in high-volatility regions remains a key challenge. To address this issue, this paper introduces a hybrid load forecasting model that integrates the Long Short-Term Memory Network (LSTM) with the Stochastic Configuration Network (SCN). We first verify the Universal Approximation Property of SCN through experiments on two regression datasets. Subsequently, we reconstruct the features and input them into the LSTM for feature extraction. These extracted feature vectors are then used as inputs for SCN-based STLF. Finally, we evaluate the performance of the LSTM-SCN model against other baseline models using the Australian Electricity Load dataset. We also select five high-volatility regions in the test set to validate the LSTM-SCN model's advantages in such scenarios. The results show that the LSTM-SCN model achieved an RMSE of 56.970, MAE of 43.033, and MAPE of 0.492% on the test set. Compared to the next best model, the LSTM-SCN model reduced errors by 6.016, 8.846, and 0.053% for RMSE, MAE, and MAPE, respectively. Additionally, the model consistently outperformed across all five high-volatility regions analyzed. These findings highlight its contribution to improved power system management, particularly in challenging high-volatility scenarios. [ABSTRACT FROM AUTHOR]

This study evaluates the impact of foreign direct investment (FDI), per capita GDP, renewable energy consumption, and urbanization on India's C O 2 emissions over the period 1990–2023. In the context of rapid economic growth and urbanization, India faces major challenges related to environmental sustainability. Using the ARDL (Autoregressive Distributed Lag) model and the Environmental Kuznets Curve (EKC), this research analyzes the complex relationships between these factors and C O 2 emissions. The results highlight the existence of an N-shaped EKC curve with two inflection points at GDP values. This study highlights the essential role of renewable energy consumption in reducing emissions and improving access to electricity in promoting sustainable development. The findings provide valuable insights into economic and energy policies, highlighting the need to balance economic growth with environmental protection. [ABSTRACT FROM AUTHOR]

Energy-intensive enterprises (EIEs), as vital demand-side flexibility resources, can significantly enhance the power system's ability to regulate demand by participating in demand response (DR). This helps alleviate supply pressures during tight demand–supply conditions, ensuring the system's safe and stable operation. However, due to the current level of electricity management in EIEs, their participation in demand response has disrupted the continuity of production to some extent, which may hinder the sustainability of demand-side management mechanisms. To address this issue, this paper proposes a two-stage distributionally robust optimization (DRO) model for managing production electricity in EIEs, considering multiple uncertainties. First, a production electricity load model based on the state task network (STN) is developed, reflecting the characteristics of industrial production lines. Next, a two-stage DRO model for day-ahead and intra-day electricity management is formulated, integrating an uncertainty set for distributed generation output based on the Wasserstein distance and probabilistic constraints for the day-ahead DR capacity. Finally, a cement plant in western China is used as a case study to validate the effectiveness of the proposed model. The results show that the proposed model effectively guides EIE in participating in DR while optimizing electricity costs, enabling cost savings of up to 27.7%. [ABSTRACT FROM AUTHOR]

The strong development of distributed energy sources has become one of the most important measures for low-carbon development worldwide. With a significant quantity of photovoltaic (PV) power generation being integrated to the grid, accurate and efficient prediction of PV power generation is an essential guarantee for the security and stability of the electricity grid. Due to the shortage of data from PV stations and the influence of weather, it is difficult to obtain satisfactory performance for accurate PV power prediction. In this regard, we present a PV power forecasting model based on a Fourier graph neural network (FourierGNN). Firstly, the hypervariable graph is constructed by considering the electricity and weather data of neighbouring PV plants as nodes, respectively. The hypervariance graph is then transformed in Fourier space to capture the spatio-temporal dependence among the nodes via the discrete Fourier transform. The multilayer Fourier graph operator (FGO) can be further exploited for spatio-temporal dependence information. Experiments carried out at six photovoltaic plants show that the presented approach enables the optimal performance to be obtained by adequately exploiting the spatio-temporal information. [ABSTRACT FROM AUTHOR]

Domestic electrical fires can occur for a variety of reasons, including faulty wiring and plugs, overloaded circuits, and malfunctioning electrical appliances. In this article, the circumstances of domestic electrical fire injuries between 2011 and 2022 in the county of Merseyside in Northwestern England were examined in order to inform fire prevention activities. Householder carelessness appeared to be less of a factor in electrical fire injury compared to other types of fire injury such as cooking or smoking fire injury. Faulty electricity supplies were the main cause of electrical fire injuries. Male residents were slightly more likely to sustain injury in an electrical fire in comparison to females (1.25 to 1). Those aged 75+ appeared to be more at risk of electrical fire injuries compared to other age groups. [ABSTRACT FROM AUTHOR]

In the quest for sustainable development, the energy sector must address the three pillars of sustainability: economic, environmental, and social. However, compared to the other pillars, research on social sustainability has been relatively limited. A systematic literature review was conducted covering 143 peer-reviewed articles (after initial screening) to explore the current state and opportunities in the social sustainability assessment of electricity generating systems. The contributions of this research are two-fold: first is the analysis of the elements of social sustainability assessment, particularly the explicit and implicit definitions of social sustainability as well as the roles and nature of involvement of stakeholders. Second is the analysis of the intersections among framework elements. The insights provided serve as a valuable resource in conducting social sustainability assessments of electricity generating systems as well as inform researchers and stakeholders concerning future research directions. [ABSTRACT FROM AUTHOR]

The transformation of energy systems toward renewable energy sources necessitates a responsible approach to understanding and mitigating their impact on the natural environment. While previous research has primarily focused on operational aspects, a more holistic approach—such as life cycle assessment (LCA)—is crucial, as it encompasses energy installations' production, operation, and disposal phases. This review article aims to fill this gap by comprehensively analysing the geothermal energy sector, covering electricity generation, central heating, and hot water preparation. This study emphasises the need for rigorous life cycle and water footprint assessments to understand geothermal systems' sustainability better. Key conclusions are drawn from an extensive literature review, highlighting the sector's challenges and opportunities. Furthermore, this article identifies innovations and research directions essential for advancing geothermal energy as an integral component of the renewable energy industry, underscoring its potential to contribute to global sustainability goals. [ABSTRACT FROM AUTHOR]

The worldwide promotion of carbon-neutral policies is leading to a continuously growing percentage of electricity being derived from renewable energy, which makes it feasible to design power systems composed of 100% renewable energy in the future. The question of how to realize stable transmission for 100% renewable energy-integrating grids under different operating conditions needs to receive more attention. Voltage source converter-based high-voltage direct current (VSC-HVDC) technology is one of the prospective solutions for large-scale renewable energy integration due to its unique dominance in areas such as independent reactivity and active control. In this study, we design a novel, 100% renewable energy system through grid integration via a VSC-HVDC system structure and a control strategy. Unlike in other research, a mixed control strategy based on grid-forming control (PSL) and grid-following control (GFL) is developed to realize smooth switching in order to ensure secure transmission and consistent operation when the operating conditions of the 100% renewable energy-integrating grid changes. The simulation results indicate that the proposed system structure and control could stabilize renewable energy transmission under normal operation conditions and provide necessary grid support under different system disturbances. [ABSTRACT FROM AUTHOR]

The anthropic causes of climate change are well known, but the influence of climate change on society needs to be better estimated. This study estimates the impact of climate change on energy consumption on small tropical islands using monthly temperatures and energy production/consumption statistics during the last decades. Here, we show, using energy, meteorological, demographic, and economic datasets, as well as statistical correlations, that energy consumption is sensitive to (i) cyclonic activity and (ii) temperature warming. On small tropical islands, increased electricity consumption correlates with temperatures rising above 26 °C in relation to air conditioner electricity consumption. On La Réunion Island, a +1 °C increase is expected to cause an electricity production of 1.5 MWh/inhabitant per year, representing a growth of 3.2%. Considering that non-renewable sources are primarily used to produce electricity, this feedback contributed significantly (i.e., 2000 to 4000 TWh) to the greenhouse gas increase caused by climate warming over the last decades on tropical islands. Demographic and wealth variations, as well as socio-economic crises, also have a significant impact on energy consumption (2 kWh for 1000 inhabitants, 0.008 GWh/inhabitant growth for a 10,000 GDP/inhabitant growth, and a 0.2 GWh/inhabitant decrease during COVID-19, for annual consumption, respectively) and must be taken into account for decadal variation analysis. The relationship between climate change and energy consumption in tropical areas should be better integrated into climatic scenarios to adapt building isolation and energy production. [ABSTRACT FROM AUTHOR]

Owing to the high tidal range, the Severn Estuary and Bristol Channel have the potential to generate huge amounts of renewable electricity for the UK. During the flood tide, the surging water travels upstream to form the Severn Bore. This study explores the dynamics of the Severn Bore through hydrodynamic modelling, analyzing how the tidal amplitude, mean water level, and river discharge affect the bore's intensity, reach, and sustainability. The Delft3D simulations show that the downstream tidal amplitude plays a critical role. Tides with an amplitude of less than 6 m will lead to the disappearance of the Severn Bore. The mean water level also significantly influences the bore's propagation, with a 1.5 m drop resulting in a 15 km retreat of the bore. A high river discharge rate weakens the bore's intensity and reduces its reach. These findings underscore the need for careful planning in tidal energy development within the Severn Estuary. Excessive exploitation of tidal energy can be detrimental to the Severn Bore and the ecological function of the estuary. [ABSTRACT FROM AUTHOR]

Existing short-circuit calculation methods for distribution networks with renewable energy sources ignore the fluctuation of renewable sources and cannot reflect the impact of renewable sources and load changes on short-circuit current in real time at all times of the day and in extreme scenarios. A real-time short-circuit current calculation method is proposed to take into account the stochastic nature of distributed generators (DGs) and electricity loads. Firstly, the continuous power flow of distribution networks is calculated based on the real-time renewable energy output and electricity loads. And then, equivalent DG models with low-voltage ride through (LVRT) strategies are substituted into the iterative calculation method to obtain the short-circuit currents of all main branches in real time. The effects of different renewable energy output curves on distribution network short-circuit currents are quantitatively analyzed during the fluctuation in distributed power output, which can provide an important basis for the setting calculation of distribution network relay protection and the study of new principles of protection. [ABSTRACT FROM AUTHOR]

Non-intrusive load disaggregation is a technique that monitors the total electrical load of an entire building or household. It uses a single power metering device to measure the total load. Then, it employs algorithms to break it down into the individual usage of different electrical devices. To address issues in load disaggregation models such as long training times, feature interference caused by the activation of other loads, and accuracy deficiencies caused by behavioral interference from users' electricity usage habits, this paper proposes a VMD–Nyströmformer–BiTCN network architecture. The variational mode decomposition (VMD) filters the raw power data, reducing errors caused by noise and enhancing the accuracy of decomposing the load. A deep learning network utilizes a modified attention model, Nyströmformer, to reduce feature entanglement and accuracy degradation caused by habitual behavior interference during load disaggregation, while ensuring precise accuracy and improving network operational speed. The training network uses a bidirectional temporal convolutional network (BiTCN) and incorporates a residual network to expand the receptive field, allowing it to receive longer load sequence data and acquire more effective load information, thereby improving the disaggregation effectiveness for target appliances. [ABSTRACT FROM AUTHOR]

The rapid expansion of renewable energy sources has led to increased instability in the power grid of Jeju Island, leading to the implementation of the plus demand response (DR) system, which aims to boost electricity consumption during curtailment periods. However, the frequency of curtailment owing to the increased utilization of renewable energy is outpacing the implementation of plus DR, highlighting the need for additional resources, such as energy storage systems (ESS). High initial investment costs have been the primary hindrance to the adoption of ESS by DR-participating companies but have not been fully considered in earlier studies on DR incentive determination. Therefore, this study proposes an algorithm for calculating appropriate incentives for plus DR participation considering the investment costs required for ESS. Based on actual load data, incentives are determined using an iterative mixed-integer programming (MIP) optimization method that progressively adjusts the incentive level to address the overall nonlinearity arising from both the multiplication of variables and the nonlinear characteristics of the internal rate of return (IRR), ensuring that the target IRR is achieved. A case study on the impact of factors such as IRR, ESS costs, and fluctuations in electricity rates on incentive calculations demonstrated that plus DR incentives required to achieve IRR targets of 5%, 10%, and 15% have increased linearly from 142.2 KRW/kWh to 363.0 KRW/kWh, confirming that the appropriate incentive level can be effectively determined based on ESS investment costs and target IRR. This result could help promote ESS adoption among DR companies and plus DR participation, thereby enhancing power grid stability. [ABSTRACT FROM AUTHOR]

The energy transition and sustainability are the critical challenges of our time, and cities are at the forefront of this transformation. As key players, these urban centers are pioneering innovative strategies to reduce carbon emissions, enhance energy efficiency, and promote renewable energy sources. Central to the transition is the role of electricity, which acts as a cornerstone in the "cocktail" of sustainable solutions. The development of an efficient and resilient "Electricity City Grid" is essential to support this shift. This article explores the target functions and challenges associated with designing and operating the Smart Electricity City Grid of the future. It delves into the infrastructural costs required for this transition and examines the economic and technical hurdles that must be overcome. Finally, the article looks ahead to the future of research in this field, highlighting the areas that will be crucial for the continued evolution and success of smart urban grids. Through this comprehensive analysis, we aim to suggest a roadmap for cities striving to achieve sustainability through advanced electrical infrastructure. [ABSTRACT FROM AUTHOR]

To mitigate environmental emissions in the industrial nanosilica sector and promote its sustainable development, the life cycle assessment (LCA) method is employed to evaluate the environmental impacts throughout the life cycle of industrial precipitated nanosilica. This LCA spans from the acquisition and transportation of raw materials to the production of nanosilica. By identifying the critical contributing factors, effective optimization strategies have been proposed to enhance the environmental performance of the nanosilica life cycle. The effects of electricity, alkalis, acids, and steam on the life cycle emission factors of nanosilica were examined. The results indicate that substituting traditional coal power and steam with cleaner alternatives like wind energy, hydroelectric power, and solar power (both photovoltaic and thermal), as well as biogas steam, can lead to a significant reduction in the life cycle emission factors of nanosilica, ranging from 50% to 90%. Notably, the types of acids and alkalis used only significantly reduce certain environmental factors. These findings provide valuable theoretical insights and practical guidance for the industrial nanosilica sector, particularly in the areas of energy conservation, emission reduction, and the transition towards a lower-carbon economy. [ABSTRACT FROM AUTHOR]

Previous studies have identified the importance of access to electricity and the socio-economic environment for poverty reduction, but the comprehensive interplay and synergistic relationships between them remain unclear. Using data collected from Myanmar, this study establishes and tests conceptual models to explore the paths from access to electricity and socio-economic environment to capability and income poverty reduction. The results of structural equation modeling show that capability poverty reduction acts as a complete mediator between access to electricity and income poverty reduction, and plays a partial mediating role between socio-economic environment and income poverty reduction. Analysis of fuzzy-set qualitative comparative analysis demonstrates that lack of electricity is a key barrier impeding poverty alleviation efforts. Moreover, five effective configurations of poverty reduction factors are identified for Myanmar by considering the heterogeneity of different regions. This indicates that individual factors could not ensure a highly effective poverty reduction and different factors need to be appropriately configured for achieving the synergistic effects. These findings contribute to practical insights into poverty reduction policy making and sustainable development for developing countries. [ABSTRACT FROM AUTHOR]

In the context of achieving the two-carbon target, this study utilized a wastewater treatment plant in Shenyang City as a case study to accurately calculate indirect emissions related to energy and chemical consumption within the energy-intensive wastewater treatment industry. Sumo software was employed for precise mathematical modeling. Considering the operational characteristics of wastewater treatment plants in cold regions, this study innovatively divided the annual operation cycle into two periods, namely normal temperature and low temperature, and determined the optimal operational parameters under a low-carbon mode. The results indicate that precise regulation of dissolved oxygen concentration to 0.5–1.5 mg/L (normal temperature period) and 1–2 mg/L (low temperature period) can significantly reduce carbon emissions related to electricity consumption by 13,781.9 t CO2-eq. From the perspective of chemical consumption, adjusting the dosage of polyaluminum chloride (PAC) to 75% and sodium acetate to 70% during the normal temperature period can lead to a reduction in indirect carbon emissions of 1614.4 t CO2-eq compared to the same period last year. During the low-temperature period, by reducing the dosage of polyaluminum chloride to 80% and sodium acetate to 75%, the indirect carbon emissions can be reduced by 1557.3 t CO2-eq compared to the corresponding period last year. After optimization, USD 1.49 million can be saved. This study simulated the operation conditions of cold-region urban wastewater treatment plants at different times to effectively control carbon emissions resulting from energy and chemical consumption in wastewater treatment. This result can provide innovative ideas for energy saving and carbon reduction in cold-region wastewater treatment plants. [ABSTRACT FROM AUTHOR]

In the realm of smart grids, machine learning (ML) detectors—both binary (or supervised) and anomaly (or unsupervised)—have proven effective in detecting electricity theft (ET). However, binary detectors are designed for specific attacks, making their performance unpredictable against new attacks. Anomaly detectors, conversely, are trained on benign data and identify deviations from benign patterns as anomalies, but their performance is highly sensitive to the selected threshold values. Additionally, ML detectors are vulnerable to evasion attacks, where attackers make minimal changes to malicious samples to evade detection. To address these limitations, we introduce a hybrid anomaly detector that combines a Deep Auto-Encoder (DAE) with a One-Class Support Vector Machine (OCSVM). This detector not only enhances classification performance but also mitigates the threshold sensitivity of the DAE. Furthermore, we evaluate the vulnerability of this detector to benchmark evasion attacks. Lastly, we propose an accurate and robust cluster-based DAE+OCSVM ET anomaly detector, trained using Explainable Artificial Intelligence (XAI) explanations generated by the Shapley Additive Explanations (SHAP) method on consumption readings. Our experimental results demonstrate that the proposed XAI-based detector achieves superior classification performance and exhibits enhanced robustness against various evasion attacks, including gradient-based and optimization-based methods, under a black-box threat model. [ABSTRACT FROM AUTHOR]

The deployment of photovoltaic (PV) systems on rooftops in urban environments may encounter shading on the PV collectors from surrounding walls, acting adversely on the generated electricity of the PV systems. Most studies on the shading of PV systems do not deal analytically with the shading losses of PV collectors affected by walls, fences, and obscuring objects. The present article mathematically formulates shadow expressions for wall and inter-row shading on PV collectors and calculates the percentage of annual shading losses affected by wall heights and azimuth angles, the distances of walls to collectors, and the length and azimuth angles of collectors. This study indicates that the shading losses increase for shorter distances from the collector to the walls and for higher walls. Shading losses may reach 7 percent for wall heights of 4 m and at a distance of 2 m from the collectors to a wall. The results indicate that wall shading dominates inter-row shading. [ABSTRACT FROM AUTHOR]

Transitioning to low-carbon energy systems is crucial for sustainable development, particularly in oil-rich developing countries (ORDCs) that face intertwined economic and environmental challenges. This review uses the PRISMA methodology to systematically assess the current state and prospects of low-carbon thermal electricity generation and utilization technologies in ORDCs. The study emphasizes clean thermal technologies such as biogas, biofuels, biomass, hydrogen, and geothermal energy, focusing on solutions that are technically feasible, economically viable, and efficient in combustion processes. These nations face significant challenges, including heavy reliance on fossil fuels, transmission losses, and financial constraints, making energy diversification urgent. The global shift towards renewable energy and the need to mitigate climate change presents an opportunity to adopt low-carbon solutions that align with Sustainable Development Goals related to energy access, economic growth, and climate action. This review aims to (1) evaluate the current state of low-carbon thermal electricity technologies, (2) analyze the technical and economic challenges related to combustion processes and energy efficiency, and (3) provide recommendations for research and policy initiatives to advance the transition toward sustainable thermal energy systems in ORDCs. The review highlights practical approaches for diversifying energy sources in these nations, focusing on overcoming existing barriers and supporting the implementation of clean thermal technologies. [ABSTRACT FROM AUTHOR]

Research on White Etching Cracks (WEC) in multiple bearing applications has identified various drivers that cause them. Lubricants and electricity combined with contact mechanics have been proven to catalyze WEC significantly. However, none of these factors solely cause WEC on its own; instead, combinations of factors discretize whether WEC appears or not. Hence, the WEC phenomenon appears to be multidimensional, making WEC still unpredictable. The current paper is about a systematic study using a Deep Grove Ball Bearing test rig to investigate how lubricant chemicals, combined with electricity and variations in oil flow and pressure, lead to WEC formation. It becomes evident that even under critical conditions for WEC formation, increasing oil flow and decreasing contact pressure can prevent WEC. [ABSTRACT FROM AUTHOR]

In the field of renewable energies, the logistical intricacies of production, as well as the use and storage of photovoltaic energy, have become critical issues. In addition to sheer quantity, the stability of this type of energy is a crucial factor in ensuring the reliability and consistency of power generation. This paper defines Solar Irradiance Stability Factors (SISFs) as indicators complementing electricity production. When planning solar energy production in each geographical area, both the quantity and stability of solar irradiance are necessary for exploitation and determining the quality of solar irradiance. While the average production of solar energy per unit area in each time interval is a widely used parameter in daily practice, the observation of the amplitude of solar irradiance and its influence on energy production in the observed time interval is currently still rare. The SISFs defined in this article are new metrics that mainly depend on the meteorological variability in an area, and the observed time intervals should be in the range of seconds, minutes, or even hours. Larger time intervals are not helpful for the stability of solar irradiance in energy production and logistics from the source to the destination. They provide a complementary and more accurate measure of how suitable a particular geographical area is for producing solar energy. [ABSTRACT FROM AUTHOR]

A reduction in greenhouse gases has become an inescapable requirement. An effective scenario for achieving carbon neutrality is to develop a hydrogen economy. Its success, however, requires strict control of the different processes involved in planned hydrogen chains. The energy chain considered in this paper is a stationary application which involves the production of hydrogen by electrolysis (a power-to gas process) and its combustion in gas turbine combined cycles to generate electricity (a gas-to-power process). In such applications, the need is twofold: (i) to control the risk of explosive atmospheres by performing safe gas detection in the presence of hydrogen and (ii) to secure the reliability of all chain processes using hydrogen-rich gases by achieving reliable analyses of these gases. This paper is dedicated to the development of hydrogen energy to decarbonize the thermal production of electricity. We will first describe the hydrogen chain that would best suit the power generation sector. Then, we will highlight the properties of hydrogen that are critical for its reliable operation. Finally, we will review the sensing technologies suitable for hydrogen-containing fuels. This review paper was published as part of a Joint Industrial Project (JIP) aimed at enabling the safe and reliable deployment of hydrogen energy. [ABSTRACT FROM AUTHOR]

A MEMS piezoresistive sensor for measuring accelerations greater than 100,000 g (about 106 m/s2) is described in this work. To enhance the performance of the sensor, specifically widening its measurement range and natural frequency, a cross-beam construction with a center block was devised, and a Wheatstone bridge was formed by placing four piezoresistors at the ends of the fixed beams to convert acceleration into electricity. The location of the varistor was determined using the finite element approach, which yielded the optimal sensitivity. Additionally, a reliable Pt-Ti-Pt-Au electrode was designed to solve the issue of the electrode failing under high impact and enhancing the stability of the ohmic contact. The accelerometer was fabricated using MEMS technology, and the experiment with a Hopkinson pressure bar and hammering was conducted, and the bias stability was measured. It had a sensitivity of 1.06 μV/g with good linearity. The simulated natural frequency was 633 kHz The test result revealed that the accelerometer can successfully measure an acceleration of 100,000 g. [ABSTRACT FROM AUTHOR]

Wind energy is a clean energy source that is characterised by significant uncertainty. The electricity generated from wind power also exhibits strong unpredictability, which when integrated can have a substantial impact on the security of the power grid. In the context of integrating wind power into the grid, accurate prediction of wind power generation is crucial in order to minimise damage to the grid system. This paper proposes a novel composite model (MLL-MPFLA) that combines a multilayer perceptron (MLP) and an LSTM-based encoder–decoder network for short-term prediction of wind power generation. In this model, the MLP first extracts multidimensional features from wind power data. Subsequently, an LSTM-based encoder-decoder network explores the temporal characteristics of the data in depth, combining multidimensional features and temporal features for effective prediction. During decoding, an improved focused linear attention mechanism called multi-point focused linear attention is employed. This mechanism enhances prediction accuracy by weighting predictions from different subspaces. A comparative analysis against the MLP, LSTM, LSTM–Attention–LSTM, LSTM–Self_Attention–LSTM, and CNN–LSTM–Attention models demonstrates that the proposed MLL-MPFLA model outperforms the others in terms of MAE, RMSE, MAPE, and R 2 , thereby validating its predictive performance. [ABSTRACT FROM AUTHOR]

Predicting the need for modeling and solutions is one of the largest difficulties in the electricity system. The static-constrained solution, which is not always powerful, is provided by the Gradient Method Power Flow (GMPF). Another benefit of using both dynamic and transient restrictions is that GMPF will increase transient stability against faults. The system is observed under contingency situations using the Dynamic Stability for Constrained Gradient Method Power Flow (DSCGMPF). The population optimization technique is the foundation of a recent algorithm called Training Learning Based Optimization (TLBO). The TLBO-based approach for obtaining DSCGMPF is implemented in this work. The total system losses and the cost of the individual generators have been optimized. Analysis of the stability limits under contingency conditions has been conducted as well. To illustrate the suggested approaches, a Standard 3 machine 5-bus system is simulated using the MATLAB 2022B platform. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of determining the optimal capacity of concentrated solar power (CSP) plants, especially in the context of hybrid solar power plants. This work presents an innovative analytical approach to optimizing the capacity of concentrated solar plants. The proposed method is based on the use of additional non-dimensional parameters, in particular, the design factor and the solar multiple factor. This paper presents a mathematical optimization model that focuses on the capacity of concentrated solar power plants where thermal storage plays a key role in the energy source. The analytical approach provides a more complete understanding of the design process for hybrid power plants. In addition, the use of additional factors and the combination of the proposed method with existing numerical methods allows for more refined optimization, which allows for the more accurate selection of the capacity for specific geographical conditions. Importantly, the proposed method significantly increases the speed of computation compared to that of traditional numerical methods. Finally, the authors present the results of the analysis of the proposed system of equations for calculating the levelized cost of electricity (LCOE) for hybrid solar power plants. The nonlinearity of the LCOE on the main calculation parameters is shown. [ABSTRACT FROM AUTHOR]

Australia aims to achieve net zero emissions by 2050, with an interim target of reducing emissions to 43% below 2005 levels by 2030. Electrification of industry processes currently reliant on fossil fuels is a necessary step to achieve these emission reduction goals. This study investigates electrification of steam generation relevant to major industrial operations in the southwest of Western Australia using different renewable energy input levels. The designed system incorporates thermal storage to ensure continuous steam generation. The optimized technology mix, including wind, PV, and concentrated solar thermal (CST) systems for each renewable energy input target, is presented. The optimization process also identifies optimal locations for new renewable energy plants. In summary, the optimization tends towards favouring the development of large CST plants near a demand point. This avoids the use of the transmission network by direct use of the CST system for heating of the storage media, to address the costs and efficiency reductions arising from electrical heating, but the scope of CST use is expected to be limited by site constraints. The levelized cost of heat (LCOH) for the studied renewable energy input targets (i.e., 30–90%) ranges from 15.34 to 36.92 AUD/GJ. This is promising for the 30% renewable energy target, as future natural gas prices in Western Australia are likely to match or exceed the expected LCOH. Cost reductions for renewable generation and storage technologies with further implementation at a large scale in the future may result in more competitive LCOH at higher decarbonization levels, but it is likely that additional technologies will be required for cost competitiveness at very high decarbonization levels. [ABSTRACT FROM AUTHOR]

The Italian industrial sector contains 22% of the final energy demand due to the poor energy performance of manufacturing buildings. This proposed study aimed to evaluate retrofit interventions for existing industrial buildings integrating photovoltaic solutions into the external envelope to improve both the environmental sustainability and the facade performance. The methodology is based on an innovative procedure including BIM and generative design tools. Starting from the Revit model of a representative case study, interoperability with energy analysis plugins via Grasshopper were exploited to optimize the differently oriented facade layout of photovoltaic modules to maximize the electricity production. In the case of comparable facade sizes, the building orientation had a minor impact on the results, although a southern exposure was preferable. The optimized configuration involved the installation of PV panels with a tilt angle ranging from −35° to −75°. The best compromise solution between the panel surface area and energy production during the summer solstice involves 466 m2 of PV modules. The design-optioneering approach was used to define possible alternatives to be explored for the possible installation of solar shading systems on existing windows. In this case, the impact on visual comfort within the working environment was chosen as a reference parameter, along with the value of the indoor air temperature. A decrease in this parameter equal to 0.46 was registered for the solution with horizontal (or nearly horizontal) shaders and a spacing ranging between 0.2 and 0.4. The integration of the BIM environment with generative design tools effectively assists decision-making processes for the selection of technological solutions in the building sector. [ABSTRACT FROM AUTHOR]

Background: Pressure is growing in North America for heavy-duty, long-haul trucking to reduce greenhouse gas (GHG) emissions, ultimately to zero. With freight volumes rising, improvement depends on zero-emissions technologies, e.g., battery electric vehicles (BEVs) and fuel cell electric vehicles (FCEVs). However, emissions reductions are constrained by technological and commercial realities. BEVs and FCEVs are expensive. Further, BEVs depend on existing electricity grids and FCEVs rely on steam–methane reforming (SMR) or electrolysis using existing grids to produce hydrogen. Methods: This study assembles publicly available data from reputable sources to estimate breakeven vehicle purchase prices under various conditions to match conventional (diesel) truck prices. It also estimates GHG emissions reductions. Results: BEVs face numerous obstacles, including (1) limited range; (2) heavy batteries and reduced cargo capacity; (3) long recharging time; and (4) uncertain hours-of-service (HOS) implications. On the other hand, FCEVs face two primary obstacles: (1) cost and availability of hydrogen and (2) cost of fuel cells. Conclusions: In estimating emissions reductions and economic feasibility of BEVs and FCEVs versus diesel trucks, the primary contributions of this study involve its consideration of vehicle prices, carbon taxes, and electricity grid capacity constraints and demand fees. As electricity grids reduce their emissions intensity, grid congestion and capacity constraints, opportunities arise for BEVs. On the other hand, rising electricity demand fees benefit FCEVs, with SMR-produced hydrogen a logical starting point. Further, carbon taxation appears to be less important than other factors in the transition to zero-emission trucking. [ABSTRACT FROM AUTHOR]

The marine parasitic nematode Anisakis simplex has a complex life cycle including marine mammals (mainly cetaceans) as definitive hosts, invertebrates (e.g., copepods and euphausiids) as the first paratenic hosts, and a wide range of fish species acting as second paratenic hosts. During the life cycle, the larva encounters a wide range of biotic (host immune factors and microelectric activity) and abiotic conditions (temperature and pH). We hypothesized that these factors may explain the differential behaviour of the nematode, recognized during the different life cycle stages. In this study, third-stage larvae (L3) of A. simplex were isolated from freshly caught Atlantic herring (Clupea harengus) from FAO zone 27. We exposed nematodes to different pH values (pH 2 to 9) at different temperature levels (4, 14, 21 and 37 °C), electric currents (6 mA, 12 mA, 18 mA) and different concentrations of fish immune cells. The nematode larvae exhibited significantly differential behaviour (stretched non-aggregated, spiral non-aggregated and aggregated) and activity levels when exposed to the different physicochemical conditions. We recorded negative correlations between activity and pH (maximum at pH 2) and positive correlations between activity and temperature (maximum at 37 °C). The nematode larvae were affected when exposed to electricity and fish immune cells. Electric currents at 6 mA induced minor changes, but at 12 mA and 18 mA, the majority or all nematode larvae aggregated and rolled up into spirals. Exposure to leukocytes, isolated from rainbow trout head kidney and spleen, induced a similar concentration-dependent spiralling process in larvae. We discuss these behavioural patterns of A. simplex as adaptations to conditions encountered by the worm larvae during the different stages of their complex life cycle. [ABSTRACT FROM AUTHOR]

The noticeable dynamic development of electromobility poses new challenges for the energy industry and users of electric vehicles. One of these challenges is coping with the change in the way we refuel. In the case of electric vehicles, the batteries can also be charged via home electrical installations. The presence of a new, non-linear load with significant power may affect the quality parameters of electricity in this installation and therefore indirectly affect the operation of other loads. In order to investigate the possible impact of the electric vehicle charging process on these parameters, a number of measurements were carried out. This paper presents the results of observing voltage distortions in the installation as a result of the harmonics of the current supplying the AC/DC converter in the vehicle. The test results confirm the compliance of the voltage with the requirements of the standards; however, the large share of current harmonics requires the analysis of the anti-shock protections existing in the installation to ensure that they are effective when currents at higher frequencies flow. The research results may be a guide for designers and users of home electrical installations. [ABSTRACT FROM AUTHOR]

With the explosive development of power edge equipment and the continuous improvement in power inspection performance, the requirements of substations and terminal equipment, such as drones with limited resources, cannot meet the strict delay and energy consumption requirements. This paper proposes an adaptive partitioning strategy for heterogeneous substation inspection systems. First, a layer delay prediction model and layer energy consumption prediction model are established on each heterogeneous node, and nonlinear characteristics related to delay and energy consumption are trained. On this basis, a deep neural network (DNN) hybrid partitioning strategy is proposed. The DNN task is divided into synchronous cooperative reasoning between terminal devices and multi-heterogeneous edge nodes. The experimental results show that the average absolute percentage error (MAPE) of the delay model was reduced by 31.49% on average. On drones and mobile edge nodes, the energy consumption model MAPE reduced the average by 21.92%, and the DNN end-to-end latency was reduced by 31.48%. The total cost of the system was reduced and the efficiency of UAV inspection was improved. [ABSTRACT FROM AUTHOR]

Power consumers can obtain authoritative green environmental value certification through green electricity trading, which plays an important role in improving the production competitiveness of enterprises, especially for international product trade affairs. However, the credibility of green electricity transactions faces serious challenges in the enterprise green authentication affairs, especially the user's identity authentication, the traceability of green electricity transactions, and the standardization of green electricity transactions. Aiming to solve the certification and traceability problem of tradable green certificates, this paper proposes an integrated green certificate trading protocol, which solves its double-trading problem and helps to improve the credibility of renewable energy use. The main contribution is providing a solution based on the consortium blockchain technology to solve the main challenges mentioned above. The main solved scheme designs a series of protocols, which includes a purchase protocol, payment protocol, and non-transferable protocol. The whole process ensures the credibility, traceability, and non-transferability of green certificate trading. Multiple verification measures are adopted to address security and privacy challenges in green certificate management. Through security analysis, the protocol effectively defends against attacks such as double payments, transaction rollback, and transaction replays while ensuring users' privacy. [ABSTRACT FROM AUTHOR]

Understanding electrical load profiles and detecting anomaly behaviors are important to the smart grid system. However, current load identification and anomaly analysis are based on static analysis, and less consideration is given to anomaly findings under load change conditions. This paper proposes a deep variational autoencoder network (DVAE) for load profiles, along with anomaly analysis services, and introduces auto-time series data updating strategies based on sliding window adjustment. DVAE can help reconstruct the load curve and measure the difference between the original and the newer curve, whose measurement indicators include reconstruction probability and Pearson similarity. Meanwhile, the design of the sliding window strategy updates the data and DVAE model in a time-series manner. Experiments were carried out based on datasets from the U.S. Department of Energy and from Southeast China. The results showed that the proposed services could result in a 5% improvement in the AUC value, which helps to identify the anomaly behavior. [ABSTRACT FROM AUTHOR]

In the context of achieving carbon peak and carbon neutrality goals, the power industry has become a key and challenging place to promote the green and low-carbon transformation of the economy and society. We selected green power generation enterprises and thermal power generation enterprises in the power industry as the research objects and applied relevant theories such as game theory and low-carbon economy theory to analyze the low-carbon transformation path of electricity under the "dual carbon" goal. We quantitatively analyzed the competition and cooperation relationship between green power and thermal power. Based on mutual benefit preferences, a competitive game model was constructed, and the optimal competitive equilibrium electricity quantity and price of both parties were discussed in different scenarios. The master–slave game problem was transformed into a double-layer game model and solved using the GA algorithm. We draw the following conclusions: (1) When green power enterprises and thermal power enterprises compete to achieve equilibrium, a high degree of altruistic willingness is a necessary condition for both parties to maximize their own utility. (2) A high-level mutually beneficial relationship is a key factor in effectively improving overall profits. (3) In the long-term mutually beneficial competitive relationship between green power and thermal power, by quantitatively adjusting the internal and external factors that affect system evolution, the quantitative adjustment of the feasible domain boundaries of the evolution of the mutually beneficial competitive relationship can be achieved, thereby influencing the existing transformation of the competitive relationship to evolve toward the desired direction. [ABSTRACT FROM AUTHOR]

This study integrates genetic algorithms with simulation programs, applying the genetic algorithm's (GA) fitness calculation within the simulation to reduce complexity and significantly improve the efficiency of the optimization process. Additionally, the simulation introduces the concept of "Field Leveling" (FL), utilizing a push–pull strategy to explore more space for absorbing and utilizing unnecessary spillage for energy generation, thereby maximizing electricity production and ensuring optimal reservoir scheduling. Two methods are provided, namely the field-leveling genetic algorithms GAFL1 and GAFL2. GAFL1 involves only pushing and does not include a push–pull process; thus, it cannot optimize spillage. On the other hand, GAFL2 implements a complete push–pull strategy, continuously exploring additional space to absorb and utilize unnecessary spillage. Both GAFL1 and GAFL2 achieved reasonable results; specifically, compared to SQP, GAFL1 improved firm yield by 8.3%, spillage increased by 2.2 times, and total energy decreased by 1.2%. GAFL2, building on the basis of GAFL1, effectively reduces spillage under all hydrological conditions without affecting the highest priority of stable output. However, the impact of reducing spillage on energy generation is not consistent; in wet and dry years, reducing spillage increases energy generation. However, in normal years, a reduction in spillage corresponds with decreased energy generation. [ABSTRACT FROM AUTHOR]

Accurate energy consumption prediction is crucial for addressing energy scheduling problems. Traditional machine learning models often struggle with small-scale datasets and nonlinear data patterns. To address these challenges, this paper proposes a hybrid grey model based on stacked LSTM layers. This approach leverages neural network structures to enhance feature learning and harnesses the strengths of grey models in handling small-scale data. The model is trained using the Adam algorithm with parameter optimization facilitated by the grid search algorithm. We use the latest annual data on coal, electricity, and gasoline consumption in Henan Province as the application background. The model's performance is evaluated against nine machine learning models and fifteen grey models based on four performance metrics. Our results show that the proposed model achieves the smallest prediction errors across all four metrics (RMSE, MAE, MAPE, TIC, U1, U2) compared with other 15 grey system models and 9 machine learning models during the testing phase, indicating higher prediction accuracy and stronger generalization performance. Additionally, the study investigates the impact of different LSTM layers on the model's prediction performance, concluding that while increasing the number of layers initially improves prediction performance, too many layers lead to overfitting. [ABSTRACT FROM AUTHOR]

Microhydraulic turbines offer a promising solution for decentralized energy production, suitable for both grid-connected and standalone applications, due to their compactness and high efficiency. This paper introduces a control approach for such systems employing microhydraulic turbines as distributed generators (DGs), utilizing six-phase induction generators for electricity production. This study emphasizes control strategies for both grid-connected and standalone modes utilizing proportional-integral (PI) controllers. An integrated energy storage system based on Li-Ion battery technology is also implemented to store the excess energy and compensate for production deficits to meet demand. The results obtained using MATLAB/Simulink demonstrate efficient and reliable power management among production sources, the grid and the local load, highlighting the unique contribution of employing a six-phase induction generator with the energy storage system. [ABSTRACT FROM AUTHOR]

The transition towards energy efficiency measures and green energy sources is strongly fostered by the European Union. Italy is among the EU countries that have heavily invested in renewable energy sources, more than doubling their share in gross final energy consumption. In particular, biogas has a pivotal role in the generation of electricity and can also be upgraded into biomethane, with a higher and more stable energy content. In this study, the sustainability of the supply chain of electricity from biogas in Italy has been thoroughly analyzed in the broader framework of the ARCADIA (Life Cycle Approach in Public Procurement and Italian LCA Database for Resource Efficiency) project. The environmental assessment, carried out by means of Life Cycle Assessment (LCA), provides a two-fold perspective. Firstly, it allows us to identify the main hotspots of the investigated system, such as the cultivation of dedicated crops, and to provide useful insights for improving environmental performance. Furthermore, a focus on the modeling of the dataset related to the production of electricity from biogas within the Italian electricity mix represents a step ahead in the LCA research, filling the lack of site-specific databases for reliable LCA results. [ABSTRACT FROM AUTHOR]

Featured Application: Featured Application: This research offers a practical methodology for designing biogas plants, improving renewable energy production, and promoting sustainable agricultural practices. This paper aims to enhance the efficiency of biogas plants for the production of electrical and thermal energy by optimizing substrate selection and digester heating techniques. The study presents a comprehensive project of a biogas plant, including all necessary installations and a detailed methodology for equipment selection. Specific substrates used include animal flour, corn silage, and molasses, each chosen for their high dry matter content and biogas production efficiency. The digester heating technique was analyzed to maintain optimal fermentation temperatures, ensuring a stable and efficient biogas production process. The projected daily biogas production is estimated to be 5688 m3. The analysis shows that maintaining a constant digester temperature significantly improves biogas yield. Seasonal variations in thermal energy requirements were identified, highlighting the need for effective insulation and heating mechanisms, particularly during colder months. Additionally, the implementation of advanced heating techniques demonstrated a reduction in overall energy consumption for maintaining the desired digester temperature. This research provides valuable insights for the design and operation of efficient biogas plants. By optimizing substrate mixtures and digester heating, the study contributes to sustainable energy production, reduced greenhouse gas emissions, and enhanced biogas plant efficiency, supporting both environmental protection and economic development. [ABSTRACT FROM AUTHOR]

Widespread adoption of residential heat pumps is predicted to create challenges for national and local electricity systems. Flexible operation of heat pumps could help smooth peak demand and better utilise renewables. Achieving these benefits involves many stakeholders from the heat pump and electricity sectors with different perspectives and expectations. This work brought together 52 experts from different parts of the UK system to discuss and debate the role of heat pump flexibility in a decarbonised electricity system in 2035. A co-production research model was adopted, designed to integrate diverse forms of knowledge and perspectives in the co-production of knowledge on heat pump flexibility. A series of participatory activities were undertaken including a one-day workshop. Elements of a common vision emerged, such as the anticipated widespread flexible operation of heat pumps as the cheapest way of running a heat pump and the likelihood of a highly automated and remote-controlled manner of operation. Disagreements and unknowns also emerged. This work aims to support stakeholders in planning for the social, technical and economic aspects of flexible heat pump operation in their own organisations. [ABSTRACT FROM AUTHOR]

The need for renewable energy is regarded as a major component in the move towards achieving sustainable development. Using a large sample of 177 countries over the period 1990 to 2020, this research explores the impact of the most significant drivers of renewable energy. Findings from this work contribute to the literature by identifying the most significant drivers of renewable energy deployment and their different responses in developed and developing economies. Empirical results suggest that GDP, oil price, access to electricity, and CO2 and methane emissions are significant determinants of renewables both as a share in energy consumption and as a share in electricity production. Additionally, trade is found to be a significant driver for the share of renewables in total energy consumption but not for the share of renewables in the electricity production model. Finally, our findings indicate that the factors influencing the development of renewables vary significantly between developed and developing countries, necessitating distinct approaches for each group of countries. These results can play a significant role from a policy perspective in designing and implementing specific policies to increase renewable energy deployment. [ABSTRACT FROM AUTHOR]