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In response to the obstacles posed by finite resources and environmental issues, Sarawak has transitioned its focus towards sustainable and green energy to establish a resilient and eco-friendly energy landscape. Generally, Sarawak is blessed with abundant green energy resources; however, constructive green energy integration methods were absent. Proper green energy integration is necessary due to its intermittent properties and geographical dispersion. Therefore, the paper proposes a novel methodology using Geographical Information System tools incorporating geographical databases, fuzzy logic operations and Traveling Salesman Problem-Binary Integer Programming algorithms to integrate green energies focusing on optimal power line routing design. The methodology begins with clustering the green energies based on geographical divisions. Then, it considers three influential factors (distance, elevation difference, and average ground flash density) and transforms them into matrix data for each cluster. Fuzzy logic operations optimize the trade-off among these factors and form fuzzy values. Following this, the Traveling Salesman Problem-Binary Integer Programming formulation creates pairs of green energy, distance vectors, equality constraints, and binary bounds. The optimization process constructs and eliminates multiple subtours to generate an optimal single loop with minimal value, representing the optimal power line routing design. Rigorous analyses, comparison, and validation demonstrate that the proposed method consistently outperforms ordinary Traveling Salesman Problem-Binary Integer Programming, ranking top 1 across all clusters. Further evaluation against the state-of-the-art fuzzy Traveling Salesman Problem algorithms reveals that the proposed model secures the lowest fuzzy values with extremely low computation times across all clusters. Furthermore, the paper presents an innovative way to consider the ground flash density factor in green energy integration. The comprehensive algorithms and coding provided are valuable assets for researchers and investors to explore and conduct in-depth research on this prospect. Finally, this paper provides valuable directions for regional development, especially in harnessing and integrating green energies to boost economic and state infrastructure. • Innovative and novel methodology for optimal green energy integration. • Clustering green energy locations with optimal power line routing design. • Incorporation of ground flash density for enhanced green energy efficiency. • Proposed methods VS ordinary TSP-BIP and other fuzzy TSP algorithms. • Valuable complete algorithms for research, investment, and regional development. [ABSTRACT FROM AUTHOR]

Residential consumers play an important role in the sustainable transition of the energy system by leveraging their household loads for demand response (DR). This paper aims to analyze the enrollment rates of residential consumers within DR through an agent-based model (ABM). Both economic and noneconomic (social/behavioral) parameters that influence the consumer enrollment in DR are considered. An energy management model, a home energy management system (HEMS), is used to identify the potential economic savings of consumers enrolling in DR. Consumers are randomly assigned to different neighborhoods and have different social relationships (e.g., friends, neighbors), which, in turn, influences their decision-making in the ABM. The results of this paper highlight the indirect relationship of expected annual savings and direct relationship of the share of consumers having electric vehicles (EV), photovoltaics (PV), and battery energy storage systems (BESSs) on the DR enrollment rates. Based on the enrollment rates, the maximum energy savings were obtained in April and the minimum during the last quarter of the year. Monte Carlo analysis is employed to handle the randomness associated with different variable selections, which provides a ± 10 % variation of consumer enrollment rate in DR. The results of this study have practical implications for energy flexibility in the residential sector. • Introduced an agent-based model to simulate residential consumer enrollment in DR. • Utilized economic and noneconomic factors to identify consumer decision-making to enroll. • Introduced different enrollment contract types in DR for consumers. • Analyzed different parameters influencing consumer enrollment rates. [ABSTRACT FROM AUTHOR]

The motivation of this paper is to investigate the resistance of countries' energy research and development (RD&D) expenditures to random shocks. The analysis includes the five countries (France, Germany, Japan, the United States, and the United Kingdom) that are the biggest investors in RD&D in fossil fuels, renewables, energy efficiency, and nuclear energy. Thus, economies will be able to take precautions against global shocks in producing sustainable energy policies. To achieve this aim, this paper uses the artificial neural network approach, which uses a distributed computing model that can store and generalize data after a learning period. Empirical results widely depend on countries and distinct energy technology policy fields. The key findings provide evidence that RD&D expenditures, except for renewables, do not tend to mean revert. The economies of Japan, Germany, and the United States should make more renewable investments to reduce the resistance of renewable energy sources to shocks and thus produce policies for both environmental sustainability and energy security. The economies of France and the United Kingdom can ensure energy security by continuing their sustainable energy policies. • Applied SDG policies focused on energy technology RD&D are presented. • The stochastic characteristics of energy technology investments are examined. • Carbon-reducing policies are recommended for the top five economies. • Energy technologies are perused through artificial intelligence approach. [ABSTRACT FROM AUTHOR]

In recent decades, the use of fossil fuels has had a harsh impact on the climate, and the frequency and intensity of various extreme weather events have increased, which has triggered an increased the use of renewable energy sources and reflection on technological advances. Many countries are trying to promote renewable energy to reduce their impact on climate change, but the impact of national energy policies in the climate risk framework remains poorly understood. Based on the panel data of 84 developed/developing countries around the world from 2006 to 2019, this paper studies and analyzes the impact of renewable energy use on climate risk. The study found that the increase in the proportion of renewable energy use can reduce the climate risk, and this result is still robust after undergoing robustness tests such as changing variables, samples, and research methods. The findings offer an important solution for European countries, where climate risks are most acute. The heterogeneity analysis shows that the higher the level of economic development, population density and the lower the level of incorruptibility, the weaker the inhibitory effect of renewable energy use on climate risk. The mechanism analysis also shows that the increase in the proportion of renewable energy use can reduce climate risk by reducing carbon dioxide (CO 2), methane (CH 4), nitrogen monoxide (NO), PM2.5 emissions, and the proportion of fossil fuel use, but it does not reduce climate risk by increasing biodiversity. In particular, we find that technological progress has not played a role in the use of renewable energy to reduce climate risk, breaking the stereotype. The policy recommendation of this paper is that under the increasing pressure of climate change, the transition to clean energy is an indispensable option, and the need to accelerate the energy transition becomes more urgent. There is a greater need to further protect biodiversity and enhance technological progress. • The increase in the proportion of renewable energy use can reduce the climate risk. • The mechanism is to reduce CO 2 , CH 4 , NO, PM2.5 emissions and fossil fuels use. • The use of renewable energy cannot reduce climate risks by increasing biodiversity. • The impact of renewable energy use on climate risk is heterogeneous. • Technological progress has not played a role in the use of renewable energy to reduce climate risk. [ABSTRACT FROM AUTHOR]

Timely monitoring of abnormal vibration of machinery in highly harsh environments is essential to ensure the safe operation of mechanical systems. This paper uses a self-charging power module to harvest energy to provide sustainable power for monitoring multidirectional ultra-low frequency mechanical vibrations. The power generation unit consists mainly of a spherical electromagnetic triboelectric hybrid nanogenerator (SETE-HNG). It is equipped with advanced sensing functions to support deep learning to identify signals with different directions of vibration, different frequencies, and different amplitudes of vibration, thereby enhancing the high-precision perception function. The accuracy of the prediction results is as high as 98.6622%, 100%, and 99.3333%, respectively. A Power Management Circuitry (PMC) has been meticulously crafted to maximize the utilization of vibrational energy. It efficiently charges a 40 mAh lithium polymer battery to 3.3 V in just 26 min, all without the requirement of an external power source. This advancement facilitates self-powered Global Positioning System (GPS) tracking of vibrational signals. Moreover, the stored energy is harnessed to energize a microcontroller and a low-power Bluetooth module. This enables real-time monitoring of mechanical vibrations via a mobile phone. The design presented in this paper is a testament to the potential of self-powered multidirectional mechanical vibration monitoring, contributing significantly to the safety and efficiency of mechanical systems. The design in this paper enables self-powered monitoring of abnormal vibrations in mechanical systems in extremely harsh environments, thus ensuring the safe operation of equipment. [Display omitted] • Large-scale vibration energy harvesting provides an innovative and efficient method. • SETE-HNG has multidirectional ultra-low frequency mechanical vibration monitoring and energy harvesting functions. • A power management circuit is designed to store energy efficiently. [ABSTRACT FROM AUTHOR]

As power systems around the world are rapidly evolving to achieve decarbonization objectives, it is crucial that power system planners and operators use appropriate models and tools to analyze and address the associated challenges. This paper provides a detailed overview of the properties of power market models in the context of the clean energy transition. We review common power market model methodologies, their readiness for low- and zero‑carbon grids, and new power market trends. Based on the review, we suggest model improvements and new designs to increase modeling capabilities for future grids. The paper highlights key modeling concepts related to power system flexibility, with a particular focus on hydropower and energy storage, as well as the representation of grid services, price formation, temporal structure, and the importance of uncertainty. We find that a changing resource mix, market restructuring, and growing price uncertainty require more precise modeling techniques to adequately capture the new technology constraints and the dynamics of future power markets. In particular, models must adequately represent resource opportunity costs, multi-horizon flexibility, and energy storage capabilities across the full range of grid services. Moreover, at the system level, it is increasingly important to consider sub-hourly time resolution, enhanced uncertainty representation, and introduce co-optimization for dual market clearing of energy and grid services. Likewise, models should capture interdependencies between multiple energy carriers and demand sectors. • Review methodologies and assumptions commonly used in power market models. • Identify model design features critical to analyzing the clean energy transition. • Survey current state-of-the-art in modeling low-carbon power markets. • Identify key model improvements needed for future deep decarbonization scenarios. • Highlight importance of tailoring tools for specific power market applications. [ABSTRACT FROM AUTHOR]

Under the goal of global carbon reduction, hydropower-wind-photovoltaic complementary operation (HWPCO) in the clean energy base (CEB) has become the key to achieving a high-quality clean energy supply. However, stochastic wind power and photovoltaic need the help of adjustable hydropower to get onto the grid in the HWPCO process, which makes the conflict between different power generation entities more and more complex. This paper takes Yalong River CEB as the research object and sets up the separate operation scenario and complementary operation scenario of hydropower stations and wind-photovoltaic plants. Meanwhile, the corresponding day-ahead schedule model considering the stability of the power system is established. On this basis, this paper analyzes changes in profit and loss indexes of each power generation entity and clarifies the profit and loss relationship of different power generation entities in the CEB. Further, based on the model group for quantifying contributions and the compensation electricity contribution value, this paper proposes the benefit compensation mechanism between the cascade hydropower station and wind-photovoltaic plants and the benefit compensation mechanism between each hydropower station within the cascade hydropower station. The main results of the research are as follows: (1) after HWPCO, the total revenue of wind-photovoltaic plants increases by 61.48 million RMB (8.61 million dollars), and the total revenue of the cascade hydropower station decreases by 10.77 million RMB (1.51 million dollars), which means wind-photovoltaic plants are the beneficiary and the cascade hydropower station is the loss. (2) after the benefit compensation, the total revenue of wind-photovoltaic plants increases by 20.97 million RMB (2.94 million dollars), and the total revenue of the cascade hydropower station increases by 29.74 million RMB (4.16 million dollars) compared with the hydropower-wind-photovoltaic separate operation (HWPSO). By the benefit compensation mechanism proposed in this paper, the benefit of each power generation entity can reach a relative balance in the Yalong River clean energy base. • Quantify the profit and loss relationship between different power generation entities in the HWPCO process. • Establish the model group for quantifying contributions between different power sources. • The incremental benefit of HWPHS is reasonably distributed. [ABSTRACT FROM AUTHOR]

In response to Japan's ambitious pursuit of carbon neutrality by 2050, this paper investigates the potential for tidal power generation in the Seto Inland Sea (SIS), Japan, utilising high-resolution ocean modelling and an analytic hierarchy process (AHP) combined with GIS-based spatial analysis. The analysis incorporates a 100 m resolution tidal current and water depth layers from the ocean model. The criteria used for evaluation were divided into main criteria, namely, proximity to existing infrastructure, wave height, and shipping density, and subcriteria, namely, distance from ports, power stations, highly populated areas, and coastlines. The identified optimal locations include the Naruto Strait, Akashi Strait, Matsushima Island, Hanaguri Strait, Funaori Strait, Taizaki Island, Kurushima Strait, Obatake Strait, and Tsuwaji Strait. With a focused analysis of the Obatake Strait in Yamaguchi Prefecture, analytical assessments of potential tidal power were conducted by considering two tidal turbines, Openhydro and Voith. The results indicate a power supply for approximately 100,000 households and a reduction of approximately 198,000 t-CO 2 per year in the most optimistic scenario. In a less optimistic scenario, with the installation of only 10% of the maximum capacity, a power supply for approximately 10,000 households and a reduction of approximately 19,800 t-CO 2 per year are observed. As Japan targets carbon neutrality by 2050, this research offers vital and practical insights for policymakers, energy planners, and environmentalists, identifying the Seto Inland Sea as a strategic area for tidal power development, aligning with Japan's commitment to a sustainable and resilient energy landscape. • High-resolution ocean model used with GIS for optimal site selection of tidal power • The Seto Inland Sea is vital for tidal power with optimal sites equals to 1.18 km2. • Tidal power quantification is based on the dominant tidal velocity • 10% of tidal power in Obatake Strait supplies 60% of Yanai City households • 10% of tidal power in Obatake Strait resulted in 19,800 t-CO 2 reduction per year [ABSTRACT FROM AUTHOR]

An understanding of the spatial evolution of global household energy and its determinants is needed to achieve global access to clean cooking energy before 2030. Based on a novel global household energy database, we adopt the log t regression test to identify the global trend and find the potential convergent clubs. Besides, we utilize Random Forest method and Ordered Probit model to investigate the drivers affecting the club convergence. As a result: (1) The global sample exhibits divergence from 2000 to 2020; (2) The 191 countries and regions can be classified into 3 convergent clubs, characterized by significant disparities in both the mean and growth rate of their access to clean energy; (3) Per capita GDP, urbanization level, infrastructure development, and educational attainment are identified as the most important factors influencing the club convergence; (4) Countries with a higher level of socio-economic development generally have a higher proportion of residents using clean fuels, with gas energy playing a pivotal role in driving this transformation. This paper adds more evidence to understand the disparity of global household cooking energy use, we propose that economic and infrastructure development, education attainment, and international cooperation are favorable. • Convergence analysis of country-level clean cooking is conducted. • 191 countries are classified into 3 convergent clubs by access to clean cooking. • Drivers that affect the formation of club convergence are investigated. • Policy suggestions to reduce the gap of SDG 7 are proposed. [ABSTRACT FROM AUTHOR]

The evolution of distributed building energy systems fuels the growing demand for sustainable energy solutions. In this paper, Proton Exchange Membrane Fuel Cell (PEMFC) and Air Source Heat Pump (ASHP) were integrated to form PEMFC-ASHP-CHP systems in three combination methods, i.e., Direct Combination (DC), Parallel Combination (PC), Series Combination (SC). And compared the energy management strategies and power balance of the system. To further improve reliability and flexibility, a diversion PEMFC-ASHP-CHP system was proposed by combining PC and SC's system advantages. Additionally, an iterative algorithm addressed the mismatch between power supply and demand. An empirical formula was proposed to improve iterative convergence speed for practical control situations. The coefficients were optimized using six metaheuristic algorithms, and the outcomes were summarized into an optimized operational plane to enhance the system control response speed further. The results show that the PC method performs better than DC and SC. It achieves a power consumption reduction of 52.8% and a COP improvement of 111.4% compared with the ASHP system. Meanwhile, the diversion system can more effectively utilize waste heat from the PEMFC and further improve the system's performance. The Queuing Search Algorithm (QSA) demonstrates superior accuracy for coefficient optimization. By using the established empirical formula, the convergence speeds of global and local iterative methods are improved by 26.10% and 41.78%, respectively, compared to the direct iterative algorithm. Ultimately, the optimized operational plane can achieve a maximum 37.16% hydrogen consumption reduction compared to the unoptimized system. • Compared performance of three PEMFC-ASHP integration methods for CHP systems. • Proposed a diversion system to solve low evaporation temperature operational issues. • Developed an algorithm to balance power supply & demand and reduce energy waste. • Reducing hydrogen consumption by up to 37.16%. • QSA derives two empirical formulas which improve iteration speeds by 26.10% and 41.78% seperately. [ABSTRACT FROM AUTHOR]

The coupling of multimedia materials and energy with sustainable energy penetration in large-scale industrial refineries significantly lowers energy consumption and greenhouse gas (GHG) emissions. This paper presents a sustainable retrofitting framework for coupled production materials and steam systems (CPMSS) utilising two-stage stochastic programming (TSSP). A novel sustainable energy-integrated CPMSS (SEICPMSS) model that includes wind, solar, and hydrogen energy and considers multiple operating modes and hydrogen consumption of the process units was established. First-principles models of wind turbines, solar thermal collectors, and proton exchange membrane electrolysers were adopted to sustainably retrofit the CPMSS. Latin hypercube sampling and Gaussian mixture model methods were applied to classify high volumes of uncertain wind speed and solar radiation data. A SEICPMSS optimisation model formulated as a mixed-integer nonlinear programming problem was developed by considering the investment costs, carbon taxes, and operational costs. Finally, a series of case studies from the industrial refinery's CPMSS were conducted to illustrate the effectiveness of the proposed method. The optimisation results indicate that the TSSP method can reduce operating costs by 5.3355 × 108 CNY/year and decrease GHG emissions by 7.4104 × 106 t/year compared to the traditional CPMSS without the integration of sustainable energy systems. • A novel sustainable retrofitting model with multiple sustainable energy is developed. • TSSP optimisation frameworks are proposed to handle multiple uncertainties. • The optimisation model is formulated as an MINLP problem considering carbon tax. • Effectiveness of the proposed method is demonstrated by case studies of an industrial refinery. [ABSTRACT FROM AUTHOR]

The surging adoption of electric vehicles (EVs) poses significant challenges for distribution networks (DNs) due to EV charging impact. This paper presents a multi-objective optimization (MOO) model that coordinates EV charging in DNs, aiming to address the interests of different stakeholders, such as the distribution network operator (DNO) and EV owners and achieve a balanced outcome. Specifically, our model's objectives include minimizing the operation costs for the DNO, power loss in the DN, and EV owners' charging expenses, emphasizing the delicate trade-off between these objectives. An innovative improved-augmented epsilon-constrained (I-AUGMENCON) method is proposed to tackle the complex trade-offs by solving the MOO effectively and efficiently. A case study on an modified IEEE 33-bus DN attests to the strategy's efficacy, showcasing a range of solutions that coordinate DNO's costs, DN power loss, and EV charging costs. Furthermore, our I-AUGMENCON outperforms other prevalent MOO solution methods, such as the weighted-sum and Non-dominated Sorting Genetic Algorithm II (NSGA-II), in determining non-dominated solutions and obtaining a Pareto-efficient solution set for the MOO to characterize an effective trade-off between three key objectives. Our model coordinates EV charging to optimize economic and technical objectives, reducing power losses from 6% to around 2% and enhancing voltage stability. By balancing cost savings with power quality, the strategy improves operational efficiency and grid resilience, marking a significant advancement in complex distribution network management. • Enhance multi-objective optimization model clarity for better trade-off visualization in EV charging. • Optimizes interactions between EV owners and DNOs to foster sustainable energy practices. • Improve the AUGMECON method surpassing state-of-the-art methods in efficiency and solution quality. [ABSTRACT FROM AUTHOR]

Protonic ceramic electrochemical cells (PCECs) are promising energy storage technologies due to their high performance and ability to convert CO2 into value-added chemicals. However, there are many fundamental aspects of this technology that require investigation to better understand its interactions and opportunities. Thus, this paper focuses on the development of a steady-state PCEC model, encompassing intricate details of reactive porous-media transport, elementary catalytic chemistry, and electrochemistry within unit cells. The model is calibrated and validated using experimental data. The model presents the rate of methane production at different temperatures and revealed that efficient PCEC for co-electrolysis of CO 2 and H 2 O is operated in the temperature range of 420–470 °C and the optimum being 450 °C at the rate of 0.0391 moldm−3 min−1. It provides insights into the effects of overpotentials on cell performance. At a current density of 3000 A/m2 the ohmic, concentration, and activation overpotentials amount to 0.65, 0.004, and 0.19 V, respectively. The results obtained from the model highlight the potential of PCECs as efficient CO 2 sinks for decarbonization purposes and as a means of methane production. Furthermore, the model's findings offer valuable guidance for the design, selection of stacks, and choice of building materials in PCEC systems. The potential applications of PCECs as real-life fuel utilization technologies are justified with improved material development to reduce cell overpotentials, opening doors for scale-up and eventual commercialization. These findings contribute to the development of sustainable energy systems and advance the pursuit of decarbonization goals. • A protonic ceramic electrolysis cell (PCEC) model for co-electrolysis of CO 2 and H 2 O is developed. • Model calibration and validation are performed for the PCEC • The PCEC is best operated in the temperature range of 400–500 °C for Sabatier reaction. • The optimum temperature for methane production is 450 °C at the rate of 0.0391 moldm−3 min−1. • The effects gas composition and temperature on cell performance are investigated. [ABSTRACT FROM AUTHOR]

Seasonal volatility data is often disturbed by uncertain external shocks, making accurate forecasting particularly strenuous. This paper proposes a progressive adaptive prediction framework of data preprocessing, feature recognition, and seasonal prediction, namely SAWBO-TNGBM (1,1) model. Specifically, the seasonal full information variable weight weakening buffering operator is employed to effectively smooth the nonlinear fluctuation data. Furthermore, the grey Bernoulli model is extended by considering the time-varying effect, and Grey Wolf Optimization algorithm improves the overall prediction efficiency. Necessarily, the Convertibility , Unbiasedness, and Recursiveness are fully derived and proven, which undoubtedly improves the reliability and the ability to capture seasonal information. Empirically, from a data-driven perspective, US seasonal clean energy net generations with diverse fluctuating characteristics are utilized to validate the predicted performance, including quarterly series (waste, geotherm) and monthly series (nuclear, wood). Results obtained from comprehensive experimental comparative analyses show that the fitting ability of the SAWBO-TNGBM (1,1) model exceeds that of other models, demonstrating its flexibility, universality, and high precision. Lastly, innovative robustness testing and extended analysis ensure that the novel model provides an effective tool for seasonal forecasting in clean energy generation. • Seasonal prediction framework handles seasonality, nonlinearity, and abrupt changes. • A novel seasonal grey model with SAWBO and time-varying effect is proposed. • GWO algorithm confirms a solution for capturing optimal nonlinear parameters. • An effective tool for seasonal clean energy net power generations forecasting is provided. [ABSTRACT FROM AUTHOR]

• Decoupling of GDP from all energy sources under multiple principles are examined. • World economy achieved weak energy decoupling in most years during 2000–2011. • World economy coupled with coal use and tended to couple with cleaner energy use. • None of studied economies achieved long-term GDP-embodied energy decoupling. • Decoupling delusions occurred when only considering direct and total energy use. Decoupling indicators are widely used to understand links between economic growth and energy use. However, traditional decoupling analyses mostly focus on domestic energy consumption (i.e., the production-based principle) and neglect off-site energy use across global supply chains to satisfy an economy's final consumption (i.e., the consumption-based principle). Moreover, analyses for total primary energy conceal an economy's preference for different energy sources. Therefore, this paper evaluates decoupling states of GDP from all types of primary energy use under consumption-based principle, for world economy and eight typical economies during 2000–2011. Regarding total primary energy, world economy witnessed weak decoupling in most years, and most economies studied (e.g., USA, Japan and China) achieved decoupling initially but performed negative decoupling finally. For EU, USA, Japan, Russia and India, production-based decoupling performances were generally better than consumption-based ones. Decoupling phenomena detected under production-based principle even became coupling or negative decoupling under consumption-based principle in some cases. As for each energy source, world economy decoupled from oil use, but still coupled with coal use, and gradually showed a trend to couple with natural gas and renewables use. Different energy sources showed distinct decoupling degrees from GDP, affected by individual embodied energy requirement structure. This paper uncovers potential energy decoupling delusions to deepen the understanding of relationships between energy use and economic growth. [ABSTRACT FROM AUTHOR]

The need to develop green and smart transport solutions for NetZero cities to reduce carbon emissions through the use of clean energy is driving innovation in cities around the world. A result of this trend is a rise in micro-mobility solutions such as e-scooters in cities around the globe. Nottingham (UK) is one of the cities that conducted an e-scooter pilot scheme permitting the rental of e-scooters to travel around the city in a bid to encourage more sustainable personal transport use. However, to ensure pedestrian safety, e-scooters are required to be ridden on the road network among cars. Hence, giving rise to two potential risks for e-scooter users: the air quality that they breathe and the physical risk of being near cars, whose drivers may be unfamiliar with seeing e-scooters on the road. This study seeks to explore this interaction using a mixed methods approach to explore the experiences of e-scooter riders in respect to their physical safety and exposure to air pollution. The research makes use of two quantitative surveys an international e-scooter user survey n = 801 and a survey of UK car drivers n = 92, focussed qualitative e-scooter rider interviews and quantitative in-depth road data collection trials comprising of air quality particulate sensing, video capturing around the rider and GPS tracking. The in-depth road data was analysed using an AI approach utilising the ASPS approach, the automated sensor and signal processing approach, implemented for image and signal processing to detect the existence of cars alongside the pollution readings. The findings show that e-scooter riders are typically aware of physical dangers to their safety from other road users, as well as how their presence among pedestrians can impact on more vulnerable users; however, they were unaware of the prevalence and effects of air pollution on them whilst riding. The study highlights the need for a multifaceted approach to improvements in safety for micro-mobility users, predominately considering suitable infrastructure to sperate them from motor vehicles and pedestrians but also the need to consider the proximity to emission emitting vehicles, developing infrastructure in green spaces to address these air pollution levels. [Display omitted] • Electric and clean energy transportation technologies are key in future NetZero cities. • This paper uses a mixed methods approach including Artificial Intelligence to address risks to e-scooter users. • Risk to e-scooter users include air pollution from fossil-fuel cars and physical risk. • The results indicate a better strategy is required to ensure the safety of e-scooter users. • The improved strategy could include training, dedicated spaces, improved visibility, and the use of helmets. [ABSTRACT FROM AUTHOR]

The need to develop green and smart transport solutions for NetZero cities to reduce carbon emissions through the use of clean energy is driving innovation in cities around the world. A result of this trend is a rise in micro-mobility solutions such as e-scooters in cities around the globe. Nottingham (UK) is one of the cities that conducted an e-scooter pilot scheme permitting the rental of e-scooters to travel around the city in a bid to encourage more sustainable personal transport use. However, to ensure pedestrian safety, e-scooters are required to be ridden on the road network among cars. Hence, giving rise to two potential risks for e-scooter users: the air quality that they breathe and the physical risk of being near cars, whose drivers may be unfamiliar with seeing e-scooters on the road. This study seeks to explore this interaction using a mixed methods approach to explore the experiences of e-scooter riders in respect to their physical safety and exposure to air pollution. The research makes use of two quantitative surveys an international e-scooter user survey n = 801 and a survey of UK car drivers n = 92, focussed qualitative e-scooter rider interviews and quantitative in-depth road data collection trials comprising of air quality particulate sensing, video capturing around the rider and GPS tracking. The in-depth road data was analysed using an AI approach utilising the ASPS approach, the automated sensor and signal processing approach, implemented for image and signal processing to detect the existence of cars alongside the pollution readings. The findings show that e-scooter riders are typically aware of physical dangers to their safety from other road users, as well as how their presence among pedestrians can impact on more vulnerable users; however, they were unaware of the prevalence and effects of air pollution on them whilst riding. The study highlights the need for a multifaceted approach to improvements in safety for micro-mobility users, predominately considering suitable infrastructure to sperate them from motor vehicles and pedestrians but also the need to consider the proximity to emission emitting vehicles, developing infrastructure in green spaces to address these air pollution levels. [Display omitted] • Electric and clean energy transportation technologies are key in future NetZero cities. • This paper uses a mixed methods approach including Artificial Intelligence to address risks to e-scooter users. • Risk to e-scooter users include air pollution from fossil-fuel cars and physical risk. • The results indicate a better strategy is required to ensure the safety of e-scooter users. • The improved strategy could include training, dedicated spaces, improved visibility, and the use of helmets. [ABSTRACT FROM AUTHOR]

The novel energy storage serves as a crucial infrastructure and a key enabling technology in shaping a new power system and advancing the green and low-carbon energy transformation. It stands as a vital support for achieving the dual-carbon strategy. In response to the high cost of deploying distributed energy storage and the potential for fraudulent behaviors in transactions, this research proposes an optimized operational strategy for fraudulent balancing in electricity trading considering the neighborhood scene public energy storage, taking into account potential fraudulent behaviors by market participants. Firstly, this research establishes a comprehensive framework for the joint operation of neighborhood scene public energy storage and multiple microgrids (MMG), developing the operational cost models for both MMG and public energy storage unit (PESU). Secondly, drawing upon the Nash bargaining theory, a cost minimization model is formulated to guide the collaborative operation of MMG and PESU. In addition, a transaction model is constructed to maximize profit distribution while considering fraudulent behaviors among market members. Finally, design a numerical example and solve it using the alternating direction method of multipliers (ADMM), the calculations suggest that the implementation of peer-to-peer (P2P) energy trading can increase the overall benefit by 65.45%, while the incorporation of PESU can boost the overall benefit by 49.93%, the fraudulent balancing model guarantees a sense of fairness in energy transactions. Addressing the potential obstacles to model expansion, further strategies involving the method of secondary distribution and setting an initial quotation range have been proposed to balance the fraudulent benefits among various entities and enhance participant enthusiasm. The fraudulent balancing strategy presented in this paper facilitates the achievement of fraud-balanced trading and maximization of profits among multi-energy entities, demonstrating considerable economic efficacy and high potential for widespread adoption. • Puting forth an innovative model for public energy storage suitable for neighborhood scenarios. • Demonstrating the fraudulent balancing point based on market fraudulent behaviors. • Putingforth an optimizing operational strategy for fraudulent balancing in energy cooperation. [ABSTRACT FROM AUTHOR]

Hydrogen technologies have gained momentum in recent years in the context of achieving fully renewable energy systems. Apart from the ability of electrolyzers and fuel cells to consume and generate green energy, the latest research on their technical characteristics also promotes them as providers of balancing services in both power and gas (either hydrogen or natural gas) systems. The balancing services provision is a highly uncertain process and is not scheduled in advance, however, due to signed contracts and rules, the provision of these services to the TSO is not considered as a deviation from the agreed schedule. Contrary to battery storage, whose operation only affects the power system power flows, the operation of power-to-hydrogen and hydrogen-to-power units in one system (e.g. power system) has an impact on the other system (e.g. gas) as well. Because of that, the unscheduled balancing services provision to one TSO will cause an imbalance towards the TSO in the other system. This impact remains unexplored in the available literature. In this paper, we propose a model of a PV-battery-hydrogen power plant participating in both the power and gas (hydrogen) system markets while acting as a provider of balancing services and the responsible party of its own balancing group in both systems as well. We analyze how considering the influence that energy-conversion units simultaneously have on both observed systems affects the realized profit of the power plant and we show that neglecting this impact increases the total imbalance costs of the power plant, and consequently reduces its overall profit. • Operational model of a hybrid power plant connecting power and gas systems. • Hybrid power plant providing balancing services to power and gas TSOs. • Impact of balancing services provision on power flows in both systems. • A larger balancing group enables easier imbalance resolving. • Considering balancing services' impact on power flows increases profitability. [ABSTRACT FROM AUTHOR]

Renewable Energy Communities (RECs) are being deployed all around the World as a technically feasible solution to decreasing users' dependence on fossil fuels. Several demonstration facilities have shown their potential to provide final consumers with clean energy and all associated environmental benefits. However, the economic evaluation of these systems as a whole set is more complex than evaluating generation technologies individually, which can be considered a barrier, and it may be more complicated to calculate its uncertainty with precision. This paper deals with this challenge and adapts a model for the evaluation of the global Levelized Cost of Electricity (LCOE) of a polygeneration microgrid to the characteristics of a typical REC, allowing the assessment of the distribution of the LCOE depending on the uncertainty of the input parameters. Thanks to its simple analytical formulation, the proposed model, that can be used for any combination of technologies (both renewable and conventional), provides relevant information on uncertainty propagation in a symbolic way that avoids the need to run numerical simulations or make assumptions on the distribution of the random input parameters. A case study has been presented, considering a typical small electrical REC with photovoltaic plants and micro wind turbines. Although the model can be defined to any market, as a representative example, it has been evaluated according to the current Spanish and Italian regulations, which are analyzed in depth with reference to the scientific literature. Results show that uncertainties in parameter estimates give rise to a very large scatter in the LCOE, pointing out a set of quantities whose role is crucial for a reliable estimate, among which electricity purchase and selling prices, yearly power load, and self-consumption / virtually-shared energy rates stand out. [Display omitted] • The global LCOE for a typical REC has been defined. • The uncertainty propagation in the LCOE formulation has been studied. • The proposed model has been applied to an electrical polygeneration REC. • As an example, the Spanish and Italian regulatory frameworks have been considered. • The LCOE of RECs is mainly sensitive to the electricity price and the power load. [ABSTRACT FROM AUTHOR]

The sustainable energies take increasing proportion in the power systems due to the "net-zero emission" goal, and the future trend is to make the new type power systems operate safely and stably while maintaining low carbon and economic efficiency. This paper proposes a novel smart generation system (SGS) architecture and a smart generation system consensus (SGSC) algorithm from the perspective of automatic generation control. The SGS is based on stratified sequencing method and multi-agent theory, which describes the dynamic and optimal generation dispatch of the units as a hierarchical decentralized multiple objectives programming problem. Further, the SGSC is proposed based on distributed Newton algorithm and leaderless consensus algorithm to solve the security performance degradation caused by decentralization. All the units are configured in parallel with the designed communication topology to achieve dispatch decentralization, thus ensuring low-carbon, economical and safe operation of the new type power system. The effectiveness of the proposed algorithm is verified on a multi-layer, multi-zone smart generation system model with a high proportion of sustainable energies from macro and micro aspects. Different control methods are also used for performance comparison on the two-area load frequency control model with lower regulation costs and lower carbon emissions achievement. • A decentralized control Architecture of autonomous SGS is developed. • A novel SGSC algorithm is proposed for dynamic generation command optimization of AGC. • The proposed algorithm not only minimizes regulation costs and carbon emissions but also ensures the stability of system operations, even during communication failures. [ABSTRACT FROM AUTHOR]

Distributed photovoltaic energy storage systems (DPVES) offer a proactive means of harnessing green energy to drive the decarbonization efforts of China's manufacturing sector. Capacity planning for these systems in manufacturing enterprises requires additional consideration such as carbon price and load management. This paper proposed a triple-layer optimization model for DPVES capacity configuration in the manufacturing sector using a chemical fibre manufacturing enterprise for demonstration. Refined photovoltaic generation and energy storage lifetime models were used. Beyond the considerations of electricity prices and meteorological conditions, we further studied the influence of carbon price and user load management on system capacity configuration and associated economic feasibility. Firstly, without considering carbon, minimizing user costs requires maximizing PV capacity up to the area limit while adjusting the ES to its optimal capacity and power. The optimal DPVES annually reduces the grid electricity consumption and carbon emissions, resulting in a 12.73% annual cost reduction. When considering the costs of carbon emissions, the carbon reduction contributed by DPVES can reduce the annual costs, making the whole system more economically feasible. However, the presence of substantial carbon emissions costs diminishes the economic feasibility of the ES, leading to a reduction of 24.51% in the optimal capacity configuration. Finally, user load management can further reduce system costs because it replaces some of the functions of energy storage. This results in a decrease of over 39% in the optimal energy storage capacity and a further reduction in related costs. Additionally, we found that load management by enterprises is more effective during the low carbon price than the high carbon price, implying that companies should implement load management as soon as possible. • Established a triple-layer optimization model for capacity configuration of distributed photovoltaic energy storage systems • The annual cost can be reduced by about 12.73% through capacity and power configuration optimziation • High carbon prices may reduce the economic viability of the energy storage system, causing reduction in its optimal capacity • Load management can further reduce the energy storage capacity demand by 39%, bringing economic benefits especially in low carbon prices [ABSTRACT FROM AUTHOR]

China's energy transformation has been accompanied by potential energy security issues. However, the influence degree and driving mechanism behind this phenomenon remain unclear. In this paper, we developed a complex network-based technical resilience assessment framework and quantified both the historical trajectory and the prospective pathway of energy technical resilience in China. We proposed to reveal the relationship between cleaner energy supply and technical resilience by defining three energy technology resilience indicators: energy robustness, energy recovery rate, and energy performance loss. The results showed a consistent decline in energy technical resilience within China, predominantly characterized by an average diminution of 6.83% in energy robustness from 2010 to 2018. Taking carbon neutrality as a target, China's energy system transformation, characterized by increasing clean energy supply, leads to a significant improvement of 7.73% in energy robustness from 2020 to 2060. Conversely, the nationally determined contribution scenario exhibited negligible changes (±0.57%) in energy robustness, while a decline of 2.21% was noted under the business as usual scenario. Furthermore, energy robustness exhibited increments of 12.07%, 2.3%, and 4.17% for every 10% rise in the share of clean energy supply during 2020–2060, demonstrating greater resilience for cleaner energy systems against disruptions, including energy import interruptions, malicious attacks, and random failures. It has been concluded that enhancing the supply of clean energy can significantly contribute to bolstering China's energy technical resilience, while simultaneously addressing climate change concerns. • Network analysis is used to assess China's energy technical resilience. • Energy technical resilience is evaluated regarding robustness, recovery rate, and performance loss. • Energy system robustness significantly increases under carbon neutrality scenario. • Increasing clean energy supply strengthens resilience against shocks. [ABSTRACT FROM AUTHOR]

Climate change is driving the adoption of sustainable energy, with low-cost solar photovoltaics and wind power at the forefront. However, land-constrained regions and islands have a limited onshore renewable energy potential. Wave power may prove useful for such regions, supported by growing literature in the field. This study delves into wave power's techno-economic potential, addressing a gap in previous assessments focused solely on theoretical or technical prospects. Utilising hourly wave data and a wave energy converter manufacturer's power matrix, global wave electricity yield is estimated. Considering projected costs, levelised cost of electricity is used to gauge economic viability. Although wave power is currently expensive, the results suggest that it could become cost-competitive with offshore wind power in the 2030s, with levelised cost of electricity below 70 €/MWh by 2035 in areas with good wave energy resources. Finally, the paper contributes openly accessible, hourly capacity factor data of global wave power generation, empowering further energy system modelling research. This study paves the way for informed decision-making on wave power's role in a diversified, sustainable energy future. • Global-local wave power potential is estimated. • Economic potential estimation is presented with projection until 2050. • Point absorbers have a wide operating range leading to high yields and low costs. • Wave power is a feasible alternative for power supply in coastal regions and islands. • Energy systems can benefit from the complementarity of wave power and solar PV. [ABSTRACT FROM AUTHOR]

• H-RE-CCHP system is formed by integrating hydrogen device, renewable energy and CCHP. • System's capacity planning and operation are studied under source-load uncertainties. • Distributionally robust optimization model with cost and carbon targets is proposed. • The proposed model can resist multiple uncertainties and reduce conservatism. • The formed system can achieve notable environmental benefit by increasing less costs. Hydrogen is one type of clean renewable energy. By introducing hydrogen production, storage and conversion devices into the combined cooling, heating and power (CCHP) system to replace its fossil fuel components, the hydrogen-involved total renewable energy CCHP (H-RE-CCHP) system is formed, thus bringing an effective way to promote energy transition towards low carbon. However, the conflict between economy and environment, and interference of source-load uncertainties hinder the low-cost construction as well as economic, stable, and low-carbon operation of H-RE-CCHP. In view of this, this paper proposes a multi-objective distributionally robust optimization (DRO) model for H-RE-CCHP planning. Firstly, a moment-based ambiguity set is constructed to define the possible range for the joint distribution of solar irradiation and multiple loads. Secondly, considering the "worst-case" distribution within the ambiguity set, the economic and environmental objective functions are presented for minimizing system construction, maintenance and operation costs while reducing carbon emission. Furthermore, the sizing- and operation-related constraints are formulated according to such principles as maintaining multi-energy balance when the forecasting errors of uncertainties occur. Finally, by applying strong duality and augmented ε-constraint for model solving, Pareto optimal solution set is obtained, and from which final planning and operation scheme is selected according to decision preference and interactions between objectives. Simulations mainly verify: 1) due to considering multiple objectives, H-RE-CCHP planning and operation reach the reasonable trade-off between economy and environment, that is, carbon emission/economic cost can be reduced by 99.81 %/66.70 % comparing with single economic/environmental objective; 2) with applying DRO in resisting uncertainties, 100 % renewable energy accommodation is achieved while meeting stable H-RE-CCHP operation; etc. Accordingly, this paper provides an effective approach for renewable energy system planning, thus contributing to the low carbon development of economic society. [ABSTRACT FROM AUTHOR]

• Parameters in a common accident are predicted by data- and model-driven approaches. • Data-driven gated recurrent unit network is used for autoregressive prediction. • Model-driven RELAP5 simulation code is used to predict unmeasured parameters. • The methods and predicted results are compared on six aspects. • The comparison results show the characteristics of the two approaches. Nuclear energy plays a crucial role in mitigating climate change as a near carbon-free and clean energy source. Predicting operating parameters is key to the digitalization and intellectualization of nuclear power plants, improving energy efficiency and reducing costs. Parameter prediction methods mainly consist of model-driven and data-driven approaches, and a comparative study is necessary to select the appropriate prediction method or combine them. In this paper, the gated recurrent unit network and the thermal–hydraulic program RELAP5 are chosen as representative data-driven and model-driven approaches to be used for predicting parameters for a specific operating condition to assess the characteristics and capabilities of each. The chosen experiment is a steam generator tube rupture accident which is important to pressurized water reactors. In this paper the modelling process and the prediction results for both approaches are given. The correlation coefficient for dome pressure, dome temperature, and primary outlet temperature of both approaches is greater than 0.993, except that the correlation coefficient of downcomer temperature is 0.83865 using RELAP5 and 0.99392 using gated recurrent unit network. Then the two approaches are compared from six perspectives of practical application, including accuracy (gated recurrent unit network and RELAP5 received scores of 8 and 6, respectively.), data requirements (7 and 5, respectively), model building algorithm (7 and 5, respectively), prediction speed (10 and 6, respectively), expertise requirements (6 and 6, respectively), and interpretability (5 and 9, respectively). It shows that model-driven and data-driven approaches both require specialist knowledge and appropriate data for modelling process. The data-driven gated recurrent unit network exhibits better accuracy and higher speed. The model-driven RELAP5 has better interpretability compared to the black-box gated recurrent unit network. It is suggested that hybrid approaches of model-driven and data-driven approaches could better deal with the prediction problems. [ABSTRACT FROM AUTHOR]

• An online platform for dynamic lifecycle analysis is presented. • An energy-retrofitting use case in a multi-family residential building is examined. • The primary energy needs, carbon emissions, and lifecycle costs are analyzed. • The retrofitting use case can lead to a 95 % reduction of CO2 eq emissions. • For 25 years of operation, a total of 515 k€ can be saved. Building stock retrofitting is essential to achieve the ambitious sustainability goals of the building sector due to its high energy consumption rates. The evaluation of the various building interventions shall be holistically assessed in terms of environmental and costing impact. The aim of this paper is twofold: First, it presents the innovative characteristics of a developed online tool (Virtual intEgrated platfoRm on LIfe cycle AnalYsis - VERIFY) able to perform dynamic life cycle analysis and global warming impact assessments by capitalizing on the well-known LCA and LCC methodologies, applicable in the case of building renovation. VERIFY is able to analyse dynamic life cycle inventories that consider the temporal profiles of energy consumption, and the time-dependent temperature changes, while being also interoperable in terms of exchanging data with other available energy simulation engines, or even using real-time monitoring data from sensors, processing any data time granulation. Second, the paper evaluates, from a life cycle perspective, the impact of specific energy retrofitting measures, meeting the Passive House Standard, for the case of a multi-family residential building in Athens, Greece. The proposed energy-retrofitting scenario examines actions related to the deep retrofitting of the building envelope and the upgrade of the thermal components as well as to the incorporation of clean electricity generation based on renewable energy systems; all aiming to drastically reduce the environmental impact of the building, rendering it almost near zero energy. Through the planned infrastructure installations, the primary energy needs and CO 2eq emissions were reduced by 91 % and by 95 % respectively, while for a building operational lifespan of 25 years, savings up to 515 k€ compared to the baseline scenario, can be achieved. [ABSTRACT FROM AUTHOR]

This paper explores the viability of utilizing agricultural biomass-based energy potential, employing mathematical, engineering, and economic modeling techniques. Moreover, the potential of a biogas-based co-digestion (CD) system, integrating its techno-economic performance and environmental sustainability in terms of electricity generation, has also been studied. In this investigation, the categorization of 25 different plant species into two groups: arable field crops (AFCs) and horticultural plants (HPs), was performed. Data was collected during the 2021‐–2022 cropping season in Bangladesh from various sources, including literature reviews, governmental, and non-governmental organizations. The findings revealed that the available agricultural biomass residues, totaling 1,02,585.75 KT, have the capacity to generate 1,33,815 million m3/year of biogas. This energy potential corresponds to 291,125.85 TJ/year or 9231.60 MW of electricity, which can fulfill 88% of the national total energy demand. In terms of levelized cost, the proposed approach is more competitive and shows a greater promise compared to other technologies. Furthermore, it demonstrates environmental friendliness by reducing CO 2 emissions by 156 tons at a cost of $7/ton while earning $1092 annually from the potential carbon-credit market. This approach presents a potential solution to address Bangladesh's energy crisis. The payback period of the system ranged from 2.93 to 3.75 years, with and without the inclusion of a slurry, respectively. The recommended methods hold significant promise for meeting national energy demands. A case study was provided as a proof-of-concept (PoC) to validate the approach. This study is the first of its kind, providing valuable insights into the renewable energy potential in Bangladesh. The results will assist policymakers in formulating sustainable energy policies. • Mathematics, engineering, economics and environmental modeling explored renewable energy. • Techno-economics and environmental sustainability validated the proposed methodology. • Bioenergy-based electricity could supply 88% of the national energy demand of Bangladesh. • A case study was provided as a proof-of-concept (PoC) to validate the approach. [ABSTRACT FROM AUTHOR]

Hydrogen is among the energy carriers, which are most often considered for bringing about a sustainable energy system. Yet, so far, hydrogen has not delivered as an energy carrier. The core objective of this paper is to provide a comprehensive analysis of the state-of-the-art and the future prospects of green hydrogen in the European energy system from economic, energetic and CO 2 emissions point-of-view. The analysis shows that there are some increasing opportunities for hydrogen use in industry and in the transport sector, when electrification is not possible or is too expensive, as well as a storage in the European electricity system. However, a hydrogen-based energy system will remain a vision at least over the next decades. The major reason for this is the unfavorable economics mostly due to high investment costs in the whole supply chain. In addition, the overall efficiencies in the hydrogen chains are moderate in general. The full environmental benignity of hydrogen as an energy carrier is only provided when renewable energy sources are used for hydrogen production. However, in Europe, the potentials for green hydrogen are very limited due to the insufficient expansion of renewable electricity generation. For this reason, many European countries are considering options for green hydrogen import. The future of hydrogen is highly dependent on the supporting policy framework. To reduce the risk in the investment in hydrogen infrastructure, as well as to justify the promotion of green hydrogen, it is very important that Europe works out a very clear and realistic long-term implementation strategy. • Hydrogen might have potential in the European energy system, yet a hydrogen-based energy system stays visionary. • Green hydrogen from renewable electricity via electrolyzers is key for environmental benefits and decarbonization. • Hydrogen should be prioritized for uses where electrification is not feasible. [ABSTRACT FROM AUTHOR]

This paper introduces a continuous 24/7 power purchase agreement (PPA) designed for contracting photovoltaic (PV) generation within sustainable plus energy neighbourhoods (SPENs) or local energy communities, aiming to ensure a stable economic revenue stream for community stakeholders. The PPA involves the sale of solar PV generation, auctioned at a fixed strike price, to an external off-taker. Employing statistical prediction tools such as long short-term memory and auto-regressive modelling, the proposed framework allows hour-to-hour power delivery commitments between seller and buyer, accurately estimating the agreed-upon volume of renewable energy to be exchanged. A fixed PPA price is negotiated utilizing Nash Bargaining Theory, aiming to optimize revenue for the SPEN while minimizing procurement costs for the buyer, thus achieving an economic equilibrium that mitigates the long-term price risk prevalent in wholesale energy markets. Additionally, the proposed methodology includes utilization of a battery energy storage system (BESS) to store excess power or address supply–demand contractual disparities during periods of low PV generation. Simulation results obtained under varying climatic conditions and energy market dynamics across different countries, demonstrate that the proposed PPA framework, by combining risk assessment strategies and statistical learning methods, can effectively reduce associated financial risks while maximizing payoff for the community. • A novel VaR and RNN-based approach for determining energy trading volumes in long-term forward contracts is proposed. • A cooperative bargaining solution for optimal pricing in long-term PPAs is outlined. • BESS flexibility in mitigating PV uncertainties for hourly base-load PPAs across diverse European conditions is studied. [ABSTRACT FROM AUTHOR]

The paper deals with the energy transition, focusing on the decisions to be taken about the change in the energy conversion capacity of specified portion of the universe, such as industrial or domestic districts, regions, countries, etc. Conversion capacity is intended here as the set of all energy conversion and storage units characterized by type, size and number, and interacting with the natural gas and electricity grids. The goal is to find guidelines for new installations, obtained by simulations of a model able to describe properly the behavior of each energy conversion technology and the interaction with grids. The model is based on a traditional MILNP approach, but contains unique features in the literature, which allow to obtain results of general validity for applications to very different geographical areas, or to countries including very different regions that transmit energy with each other or export/import it from abroad. The main novelty of this work is to identify the best planning for the decarbonisation of energy demand sectors, according to the criterion of minimum cost, both at global and at regional level. The results applied to the case of Italy allow identifying clearly the energy demand sectors to which new installations should be applied first to minimize the cost for the total energy system, and the units that must be foreseen to satisfy these energy demands, i.e. a precise strategy for the energy transition towards a 100% renewable system. • A multi-regional model is built to find the most convenient energy transition path. • General guidelines for Western countries are derived from the Italian case study. • Replacement of boilers with heat pumps allows increasing RES share from 20 to 30%. • For RES share >60%, PtG is the low-carbon strategy for high temperature heat demand. • Transportation is the last energy sector that should be decarbonized. [ABSTRACT FROM AUTHOR]

Wind power serves as a clean and sustainable form of energy. However, its generation is fraught with variability and uncertainty, owing to the stochastic and dynamic characteristics of wind. Accurate forecasting of wind power is indispensable for the efficient planning, operation, and grid integration of wind energy systems. In this paper, we introduce a novel forecasting method termed Temporal Collaborative Attention (TCOAT). This data-driven approach is designed to capture both temporal and spatial dependencies in wind power generation data, as well as discern long-term and short-term patterns. Utilizing attention mechanisms, TCOAT dynamically adjusts the weights of each input variable and time step based on their contextual relevance for forecasting. Furthermore, the method employs collaborative attention units to assimilate directional and global information from the input data. It also explicitly models the interactions and correlations among different variables or time steps through the use of self-attention and cross-attention mechanisms. To integrate long-term and short-term information effectively, TCOAT incorporates a temporal fusion layer that employs concatenation and mapping operations, along with hierarchical feature extraction and aggregation. We validate the efficacy of TCOAT through extensive experiments on a real-world wind power generation dataset from Greece and compare its performance against twenty-two state-of-the-art methods. Experimental results demonstrate that TCOAT outperforms existing methods in terms of both accuracy and robustness in wind power forecasting. Moreover, we conduct a generality study on an additional real-world dataset from a different climate condition and wind power characteristics. The results show that TCOAT can achieve comparable or better performance than the state-of-the-art methods, confirming the generalization ability of TCOAT. • Novel deep learning model for wind power forecasting • Temporal collaborative attention and fusion for wind data • Superior performance on real-world wind power dataset • Attention maps for interpretability and end-to-end training [ABSTRACT FROM AUTHOR]

The utilization of Vehicle-to-Grid (V2G) technology, which facilitates the integration of electric vehicles (EVs) into the power grid, is promising for energy management systems. Among them, electric taxi (ET) is an important part of EVs. The relatively stable and predictable operating patterns of the ET fleets could have a scale effect, effectively managing load demand and supply with V2G technology. This paper investigates the potential and flexibility of V2G technology for urban power system based on real-world ET trajectory data. To evaluate the flexibility of V2G more precisely, a general framework is proposed to infer vehicle charging, energy consumption, and reconstruction of the State of Charge (SoC) for ETs. The V2G potential estimation model is established for each ET, followed by a bottom-up agent-based model to simulate flexibility changes in the system during various V2G events. The proposed methodology is tested on 19,900 ETs in Shenzhen over a month to explore the potential and flexibility of V2G technology. The results show that the V2G system could supply at least 50 MW for 1 h, 30 MW for 2 h, and 20 MW for 3 h during peak periods, while simultaneously recovering within 2–3 h, without disrupting the regular operations of ETs. The great potential of ETs to supply additional energy during peak periods and recover energy during valley periods is highlighted, which could lead to a more efficient and sustainable management of energy resources. • Real trajectory data with 19,900 ETs is used for V2G potential and resilience analysis. • A method infers charging patterns, energy use, and reconstructs State of Charge is proposed. • A general method framework for modeling ET-V2G system on supply side is proposed. • An agent-based model is introduced for ET-V2G system modeling and simulation. • ET-V2G system can supply up to 60 MW and recover within 2–3 h during peak periods. [ABSTRACT FROM AUTHOR]

The economic assessment of low-carbon energy options is the primary step towards the design of policy portfolios to foster the green energy economy. However, today these assessments often fall short of including important determinants of the overall cost-benefit balance of such options by not including indirect costs and benefits, even though these can be game-changing. This is often due to the lack of adequate methodologies. The purpose of this paper is to provide a comprehensive account of the key methodological challenges to the assessment of the multiple impacts of energy options, and an initial menu of potential solutions to address these challenges. The paper first provides evidence for the importance of the multiple impacts of energy actions in the assessment of low-carbon options. The paper identifies a few key challenges to the evaluation of the co-impacts of low-carbon options and demonstrates that these are more complex for co-impacts than for the direct ones. Such challenges include several layers of additionality, high context dependency, and accounting for distributional effects. The paper continues by identifying the key challenges to the aggregation of multiple impacts including the risks of overcounting while taking into account the multitude of interactions among the various co-impacts. The paper proposes an analytical framework that can help address these and frame a systematic assessment of the multiple impacts. [ABSTRACT FROM AUTHOR]

Energy efficiency policies play a key role in the transformation to a ‘green energy economy’. In this paper, we take stock of the impacts of the existing energy efficiency policy instruments in Germany and review the energy, environmental and socioeconomic impacts of the country’s latest energy efficiency and climate strategies for the year 2020. We find evidence supporting the findings of other studies that enhanced green energy policies will trigger tangible economic benefits in terms of GDP growth and new jobs even in the short term. Whereas policy makers have already acknowledged and implemented this conclusion in the case of renewable energies, our paper shows that striving for more ambitious energy efficiency policies represents a similar win–win strategy, which should be exploited to a much larger extent. [ABSTRACT FROM AUTHOR]

This paper seeks to better understand how one plausible development in a green energy economy transition of the transport sector can be governed: a breakthrough of battery-electric vehicles (BEV). Drawing on recent results and lessons from BEV studies at local, national and regional scales, the paper presents two alternative scenarios of BEV uptake until 2030 – one incremental growth scenario and one breakthrough scenario. It then draws on the multilevel perspective (MLP) on socio-technical systems as an approach to identify the governance implications of the breakthrough scenario. Based on a characterisation of barriers and drivers at landscape, regime and niche levels, it identifies governance interventions to enable a BEV breakthrough. The results point towards a multidimensional governance approach that includes conventional policy instruments such as durable incentive policies, with a predictable mechanism for adjustment and phase-out, and mechanisms for mobilising investment finance for fast and super-fast charging and home charging along public roads. In addition, more innovation-systems oriented governance is required, such as familiarisation and experience building to ease cognitive barriers and build knowledge for both consumers and businesses, and supporting structural and technological change within automotive industries. [ABSTRACT FROM AUTHOR]

This paper aims at assessing the value of load shifting and demand side flexibility for improving electric grid system operations. In particular, this study investigates to what extent residential heat pumps participating in load shifting can contribute to reducing operational costs and CO 2 emissions associated with electric power generation and how home owners with heat pump systems can be best motivated to achieve these flexibility benefits. Residential heat pumps, when intelligently orchestrated in their operation, can lower operational costs and CO 2 emissions by performing load shifting in order to reduce curtailment of electricity from renewable energy sources and improve the efficiency of dispatchable power plants. In order to study the interaction, both the electricity generation system and residences with heat pumps are modeled. In a first step, an integrated modeling approach is presented which represents the idealized case where the electrical grid operation in terms of unit commitment and dispatch is concurrently optimized with that of a large number of residential heat pumps located in homes designed to low-energy design standards. While this joint optimization approach does not lend itself for real-time implementation, it serves as an upper bound for the achievable operational cost savings. The main focus of this paper is to assess to what extent load shifting incentives are able to achieve the aforementioned savings potential. Two types of incentives are studied: direct load control and dynamic time-of-use pricing. Since both the electricity generation supply system and the residential building stock with heat pumps had been modeled for the joint optimization, the performance of both load shifting incentives can be compared by separately assessing the supply and demand side. Superior performance is noted for the direct-load control scenario, achieving 60–90% of the cost savings attained in the jointly optimized best-case scenario. In dynamic time-of-use pricing, poor performance in terms of reduced cost and emissions is noted when the heat pumps response is not taken into account. When the heat pumps response is taken into account, dynamic time-of-use pricing performs better. However, both dynamic time-of-use pricing schemes show inferior performance at high levels of residential heat pump penetration. [ABSTRACT FROM AUTHOR]

• The concepts of energy harvesting from flow-induced vibrations are classified. • The methods and principles of flow-induced vibrations energy harvesting are summarized. • The application of energy capture devices and commercially available devices are discussed. • The prospects of the research and application of flow-induced vibrations energy harvesting are presented. In this paper, the currently popular flow-induced vibrations energy harvesting technologies are reviewed, including numerical and experimental endeavors, and some existing or proposed energy capture concepts and devices are discussed. The energy harvesting mechanism and current research progress of four types of flow-induced vibrations, such as vortex-induced vibrations, galloping, flutter and buffeting, are introduced. To enhance the performance of the harvesters and broaden the operating range, the researchers have proposed various mechanical designs, methods of the structures' surfaces optimization and concepts with incorporated magnets for multistability. The paper summarizes the works led to the current wind energy and hydro energy harvesters based on the principle of flow-induced vibrations, including bladeless generator Vortex Bladeless, University of Michigan vortex-induced vibrations aquatic clean energy, Australian BPS company's airfoil tidal energy capture device bioSTREAM, and others. This shows the gradual progress and maturity of the flow-induced vibrations energy harvesters. The article concludes with a discussion on the current problems in the area of the flow-induced vibration energy capture and the challenges faced. [ABSTRACT FROM AUTHOR]

Managers in electric utilities face the disruption of their conventional business model of selling electricity per kilowatt-hour for invariant prices. However, the forthcoming widespread uptake of sustainable energy technologies – such as rooftop solar, batteries, heat pumps and electric vehicles – by residential customers also represents a chance for local utilities to diversify their service portfolio. To appropriately market these technologies to households, utilities need data on consumers. In this paper, we present a novel data-driven service bundle recommendation model incorporating technologies and tariffs for residential customers based on individual household data. We validate the model in a case study and quantify the utility of sharing different levels of household data. We find substantial synergies of flexible sustainable technologies and time-varying tariffs, leading to higher cost reductions for customers than tariff-switching alone that can be recommended based on easy-to-obtain data. This demonstrates a large potential for energy service bundle marketing by local utilities. The presented Machine Learning recommendation models enable more reliable recommendations than a naive benchmark. Our research thus demonstrates the potential of data-driven utility marketing strategies that focus on service bundling and the integration of customers' energy consumption data. [ABSTRACT FROM AUTHOR]

• Four environmental indicators suitable for use in energy system models are proposed (85) • The indicators allow direct comparison of decarbonisation potential of renewables (84) • Gearbox double-fed induction generators are the preferred form of wind turbine (80) • Cadmium telluride photovoltaics exhibit best overall raw materials outcomes (78) Raw materials and their related environmental impacts will play a key role in the implementation of renewable energy infrastructures for decarbonization. Despite the growing amount of data quantifying raw materials for energy production technologies, few examples of these data sources are being included in current energy system models. Accordingly, this paper introduces possible pathways for integrating material-specific life cycle assessment outputs and material metabolism indicators into energy system models so that raw material requirements, and their associated impacts, can be accounted for. The paper discusses the availability of life cycle inventories, impact assessment methods and important output indicators. The material metabolism indicators most relevant to the current policy debate surrounding the European Green Deal–namely, material supply risk and contribution of recycled materials to total supply–are also discussed alongside the value of adding this information to energy system models. A methodology for using data from both approaches is offered and operationalised using four sub-technologies of both wind turbines and solar photovoltaic panels as case studies. The results show that considerable variation exists between and within the two groups for all indicators. The technologies with the lowest global warming potential, cumulative energy demand and supply risk are turbines with gearbox double-fed induction generators and cadmium telluride photovoltaics. Furthermore, wind turbines exhibit significantly higher recycling rates than photovoltaics. Ultimately, the integration of such methodologies into energy system models could greatly increase the awareness of raw material issues and guide policies that maximise compatibilities between resource availability and cleaner energy systems. [ABSTRACT FROM AUTHOR]

• Environmental impacts of hydropower plants in Europe is investigated by LCA analysis. • Impacts are estimated by the ReCiPe 2016, Impact 2002+, and Eco-points 97 methods. • Hydropower plants of alpine regions are found least hazardous to the environment. • Plants of non-alpine areas are 10 times responsible for climate change than alpines. Hydropower is the widely used source of clean energy which includes some hazardous emissions that affect human health, ecosystems, and resources. However, in spite of an enormous amount of hydropower generation in Europe, no research has been carried out in evaluating the hazardous emissions from the plants located in alpine and non-alpine areas. Therefore, this paper will analyse the comparative environmental impacts of hydropower plants in alpine and non-alpine areas of Europe by a systematic life-cycle assessment (LCA) approach. The impacts are estimated by the ReCiPe 2016, Impact 2002+, Eco-points 97 methods under a number of effect-assessing indicators such as global warming, ozone formation, eco-toxicity, water consumption, acidification, eutrophication, ionizing radiation, carcinogenic radiation, ozone depletion, and land use. Moreover, the fossil-fuel-based power consumptions and the greenhouse-gas emissions in the life-cycle of hydropower plants in both locations are estimated using the Cumulative Energy Demand (CED) and the Intergovernmental Panel on Climate Change (IPCC) methods, respectively. The outcomes reveal that hydropower plants of alpine regions offer a better environmental profile for the global-warming indicator (2.97 × 10 - 5 kg CO 2 -eq/MJ) than non-alpine plants (3.92 × 10 - 4 kg CO 2 -eq/MJ), but the effects are nearly identical for the other indicators. Overall, the hydropower plants of non-alpine regions are responsible for climate change with a rate 10 times as high as for alpine ones. The findings of this research will play a pivotal role in promoting sustainable production of hydropower, especially the full potentials of the alpine region, and thus leading towards environmentally friendly clean renewable electricity generation. [ABSTRACT FROM AUTHOR]

Highlights • Quantitative complementarity evaluation indices for combined power generation. • Evaluation indices are presented from two perspectives: variability and ramp. • Typical day screening method for multi-dimension correlated meteorological data. • Complementarity can be improved by changing the ratio of solar and wind power. Abstract Complementarity between wind power, photovoltaic, and hydropower is of great importance for the optimal planning and operation of a combined power system. However, less attention has been paid to quantify the level of complementarity of wind power, photovoltaic and hydropower. Therefore, this paper proposes a complementarity evaluation method for wind power, photovoltaic and hydropower by thoroughly examining the fluctuation of the independent and combined power generation. To begin with, a series of evaluation indices are presented from two perspectives: random fluctuation between adjacent time slots and ramp within continuous time windows. Secondly, a method for screening the typical meteorological day is proposed by combining t-distributed stochastic neighbor embedding with K-means clustering algorithm to improve the evaluation of daily complementarity. This method can deal with the feature extraction of high-dimension and correlated data sequences. A case study is conducted for the validation of the proposed method, and the evaluation result of the proposed method was compared with that of the Kendall' rank correlation coefficient based on the Gaussian copula function. It is found that the proposed method has higher accuracy because it describes the complementarity from two aspects including fluctuation and ramp. It is also found from the study case that the optimum complementarity level for a certain case can be achieved by changing the ratio of photovoltaic and wind power. This work will provide reference for the optimization of power grid dispatch and power supply planning of combined power stations. [ABSTRACT FROM AUTHOR]

Highlights • We propose a cost-effective energy management algorithm for PV-storage in the context of a shared community. • Uncertainties related with electricity demand and solar power generation are used in the decision-making process. • The impact of energy management is included for net present value (NPV) calculation of each system design. • An approach to identify the optimal storage sizing using NPV is presented and applied to each house of the community. Abstract The aim of this paper is to propose a new energy management framework and storage sizing for a community composed of multiple houses and distributed solar generation. Uncertainties associated with solar generation and electricity demand are included to make the mathematical models more realistic, and as a result, provide more accurate control strategies to manage storage devices utilization. To evaluate that, a multi-stage stochastic program model designed to minimize community electricity purchase cost per day is used to support decision-making by creating control policies for energy management. Two different strategies are created to represent the interest of a single household (the individual energy management - IEM) and households that share their assets with the community (shared energy management - SEM). Our strategies consider time-of-use rates (ToU), load and resource variation during different seasons, with their distinct days of the year, to calculate net present value (NPV) associated with the energy savings. IEM and SEM are then used in a framework designed to establish the requirement of optimal energy storage size for each house of the community based on NPV values. The results of this study for an analysis considering a community with five houses show that the proposed SEM strategy reduces the overall electricity purchase costs for a summer day up to 11% and 3% compared with heuristic and IEM control respectively. Moreover, our results suggest that the application of the methodology increases peak energy savings up to 17%, scales up solar generation usage up to 23%, and the optimal storage size obtained in the shared community case reduces up to 50%. [ABSTRACT FROM AUTHOR]

Highlights • A Markov chain prediction method is embodied in dynamic CGE model. • Energy consumption intensity target shall be achieved under scenario shock. • Carbon emission intensity declined within scenario implement. • Electricity generation used gas plays a pivotal role in optimizing structure. Abstract The global 2030 agenda for promoting clean energy development explicitly calls for the implementation of official policy related to sustainable development. China pledged to formulate an energy structure that focuses on non-fossil energy and natural gas towards 2030. Natural gas is therefore entering a new critical and potentially advantageous development stage. Optimizing the utilization structure in a scientific manner can be regarded as the significant means to increase natural gas consumption in order to achieve the targets upon 2020 and 2030. In this study, what ultimately matters from a sustainable lens is not whether the future natural gas consumption increases but rather whether the restricted and limited proven reserves should be arranged and utilized to reach the best effectiveness on economy-energy-environment system to further ensure sustainable development. Herein, with aims to offering operationally actionable-related insights on the optimal natural gas consumption pathways for policy governors, this paper examined the multiple effectiveness of a spectrum of representing indicators selected from the economy-energy-environment system in China under natural gas consumption structure constraint policy scenarios using a dynamic CGE approach and Markov Chains forecasting technique. Consequently, this study recommends one optimal natural gas consumption structure scenario to policy governors given its comprehensive effectiveness in promoting sustainable development in China according to horizontal and vertical comparative analyses. [ABSTRACT FROM AUTHOR]

Hydrogen (H 2) can be a critical factor in achieving a more sustainable society reducing carbon emissions worldwide in the long run. However, there are some challenges to be solved yet. One of these challenges is that, right now, the economical competitiveness of electrolysis production strongly depends on the Renewable Energy Sources (RES) availability. This paper analyzes the economic and environmental implications of complementing the power supply of a H 2 production plant that usually operates using only RES with grid electricity during the time RES are not available. For performing this analysis, we proposed an assessment model that incorporates Alkaline (ALK) and PEM electrolyzer technology features (such as the low dynamic load range of ALK electrolyzers, which is usually ignored when modeling H 2 production). Moreover, we introduce a new metric, named the LCOH&E, which captures the existing trade-off between reducing the Levelized Cost Of Hydrogen (LCOH) with more H 2 production using grid electricity and increasing CO 2 emissions by producing more H 2 with grid electricity. We illustrate the proposed assessment model with a case study based on real data from Chile. Our results show that the optimal fraction of grid electricity to be used in producing H 2 may be different when minimizing the LCOH and when minimizing the LCOH&E because of the LCOH-CO 2 emissions' trade off. Moreover, it is remarkable that using only 10% of grid electricity in an ALK electrolyzer, it is possible to increase the H 2 production in 25.7%, without significantly increasing the CO 2 emission level. • This paper analyzes the value of adding grid electricity to a pure RES H 2 production plant. • The Levelized Cost of Hydrogen and CO 2 emissions are obtained for Alkaline and PEM electrolyzers. • We combine the Levelized Cost of H 2 and the CO 2 emissions in a single cost function to optimize. • The assessment model incorporates crucial ALK and PEM technology features of the H 2 production. • Using only 10% of grid electricity in an ALK electrolyzer, H 2 production can increase 25.7%. [ABSTRACT FROM AUTHOR]

To meet carbon emission reduction goals in line with the Paris agreement, planning resilient and sustainable energy systems has never been more important. In the building sector, particularly, strategic urban energy planning engenders large optimization problems across multiple spatiotemporal scales leading to necessary system scope simplifications. This has resulted in disconnected system scales, namely, building occupants (bottom layer) and smart-city energy networks (top layer). This paper intends on bridging these disjointed scales to secure both resilient and more energy-efficient urban planning. To assess the aggregated impact of user behavior stochasticities on optimal urban energy planning, a stochastic energy community sizing and operation problem is designed, encompassing multi-level utilities founded on energy hub concepts for improved energy and carbon emission efficiencies. The problem is solved through an organic spatial problem distribution suitable for field deployment, validated by a proof of concept. We examine uncertainty factors affecting urban energy planning through a local sensitivity analysis, namely, economic, climate, and occupant-behavior uncertainties. Founded on this modeling setup, an energy community of 41 Dutch residential buildings is optimally designed using historical measurements. Results disclose a fast-converging distributed stochastic problem, showcasing boilers as the preferred heating utility. Distributed renewable energy and storage systems are identified as unprofitable for the community. Occupant behavior is particularly exposed as the leading uncertainty factor impacting energy community planning. This demonstrates the relevance and value of our approach in connecting occupants to cities for improved, and more resilient, urban energy planning strategies. • Stochastic optimization for resilient planning of energy communities. • Uncertainty factor impact assessment on optimal system design through local sensitivity analysis. • Scalable urban planning, and control-oriented real-world deployment readiness through a distributed optimization approach. • Case study investigation encompassing 41 Dutch residential buildings. • Results reveal occupant behavior to be the leading uncertainty factor affecting the system design, behind climate and economic conditions. [ABSTRACT FROM AUTHOR]

[Display omitted] • Varied reactor patterns to deconvolute synergy between pinewood and polycarbonate. • Feedstocks co-pyrolyzed at 900 °C used for syngas and char in fixed bed reactor. • Enhanced yields of syngas by 19%, H 2 by 33%, CO by 26% and energy efficiency by 7% • Pinewood char catalytically improved polycarbonate decomposition. • Pinewood enhanced polycarbonate pyrolysis & polycarbonate was neutral to pinewood. Utilization of biomass and plastic wastes has drawn growing attention in recent years as it offers promising pathway for simultaneous energy production and waste disposal. In this paper, the co-pyrolysis of pinewood and polycarbonate was performed in a fixed bed reactor with focus on determining the extent of synergistic effects and unraveling the underlying synergistic mechanism. The extent of synergistic effects was quantified via direct comparison of the results from co-pyrolysis of pinewood-polycarbonate mixture with the weighted average values from individual components pyrolysis. The observed synergistic effects were further deconvolved into different component interactions (such as volatiles-volatiles interactions, volatiles-feedstocks interaction, and catalytic effects of chars) using tailored feedstock configurations to gain more insights into the synergistic mechanism. The results showed that co-pyrolysis enhancedH 2 by 33%, CO by 26%, and total syngas yields by 19% as compared to their weighted values from individual pyrolysis. Co-pyrolysis also exhibited superiority in energy recovery as the overall energy efficiency was noticeably promoted from 42.9% to 48.6%. Deconvolution of synergistic effects revealed that volatiles-volatiles interactions were the primary contributor for the enhancement of H 2 and CO. Examination of volatiles-feedstock interactions indicated that the highly reactive pinewood volatiles facilitated polycarbonate degradation, while polycarbonate volatiles were relatively stable and unable to promote pinewood decomposition. Furthermore, pinewood char catalytically enhanced polycarbonate degradation. This paper provides a deconvolved understanding of synergistic effects in co-pyrolysis of lignocellulosic biomass and polycarbonate wastes, which is very helpful in designing clean and efficient energy recovery systems from these waste resources. [ABSTRACT FROM AUTHOR]

Energy harvesting is a promising technique that can help produce renewable and clean energy and improve sustainability of infrastructure. The objective of this paper is to review available energy harvesting techniques that are used for roadway and bridge for different applications, including photovoltaic cell, solar collector, geothermal, thermoelectric, electromagnetic, and piezoelectric systems. The energy harvested can produce electric energy, provide heating or cooling, ice melting, power wireless sensors, and monitor structure conditions. Each energy harvesting technology is examined in depth including working principles, application examples, prototype developments, and major findings reported in the literature. The review summarized previous study efforts on energy harvesting system in the areas of material design, system optimization, theoretical analysis, laboratory testing, field experiment, and numerical modeling. Different energy-harvesting technologies were compared in terms of power output, cost-effectiveness, technology readiness level, advantages and disadvantages, support from government and industry. Finally, future research recommendations of energy harvesting in roadway and bridge were proposed. [ABSTRACT FROM AUTHOR]

Energy sectors of all the countries around the world are faced with a big challenge: the energy transition under the imperative of continuous reduction of carbon dioxide emissions. The energy transition and the sustainability constraints pose a significant challenge for engineers, researcher and scientists developing new and clean energy-efficient technologies. Energy production and transformations, along with its storage, distribution and consumption, are achieved by utilising new and clean energy technologies, with the goal of the continuous increase of energy efficiency, the growth of renewable energy sources utilisation, the uninterrupted switch from fossil fuels to alternative energy sources, and the continued reduction of CO2 emissions. This editorial paper addresses the latest progress and the findings in research and developments in the sustainable energy technologies, energy and environment systems from selected papers of the conference on Sustainable Development of Energy, Water and Environment Systems held in 2019. [ABSTRACT FROM AUTHOR]

The United Nations agenda for sustainable development by the year 2030 proposes 17 sustainable development goals which include access to affordable, reliable and clean energy, sustained economic growth with full productive employment and, urgent action to mitigate environmental degradation. Planning for sustainable development requires integrating conflicting criteria on economy, energy, environment and social aspects. In this paper, we introduce a Weighted Goal Programming model involving criteria on the economic development (GDP), the electricity consumption, the greenhouse gas emissions, and the total number of employees to determine optimal labor allocation across various economic sectors. The proposed model is validated with data from the six members of the Gulf Cooperation Council (namely Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates). The results of the model aim to provide empirical evidence and insights to decision makers and policy analysts in developing optimal strategies able to simultaneously satisfy energy demand, economic growth, labor development and reduction in greenhouse gas emissions to achieve sustainability targets by the year 2030. [ABSTRACT FROM AUTHOR]