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Abstract The Sustainable Development Goals and the Paris Agreement, as the two biggest climate action initiatives, address the need to shift towards a fully sustainable energy system. The deployment of renewable energy, especially solar and wind power, decreases carbon dioxide emissions, but presents issues of resource intermittency. In this study, a cost-optimised 100% renewable energy based system is analysed and quantified for the Americas for the reference year 2030 using high spatially and temporally resolved weather data. Several scenarios have been applied, from a decentralised power system towards a fully centralised and interconnected system, taking into account a mix of renewable energy, energy storage and transmission networks. This research aims to evaluate the benefits of an interconnected energy system for the Americas. The levelised cost of electricity (LCOE) is between 48.8 and 59.0 €/MWh depending on the chosen scenario. The results show that the LCOE and total annualised cost drop by 14% and 15%, respectively, in a centralised power system. The optimised utilisation of transmission grids leads to less energy storage requirement. Sector coupling brings further benefits by reducing additional 4% of LCOE, where electricity demand for power, seawater desalination and non-energetic industrial gas sectors have been supplied. A comparison between the interconnected Americas and North and South America individually shows a reduction of 1.6% and 4.0% for the total annual system cost and LCOE. Although the cost of the energy system decreased due to wide grid interconnection, substantial benefits have not been achieved as reported earlier for a Pan-American energy system. A scenario with synthetic natural gas (SNG) trading through a liquefied natural gas value chain has also been presented. The results suggest that local SNG production cost in the assumed consumption centre is almost the same as the cost of imported SNG. Highlights • A 100% renewable energy based system is analysed for the Americas for the year 2030. • Interconnected Americas show slightly more benefit than North and South America. • Long distance transmission lines cannot compete with energy storage technologies. • Renewable energy variability can be resolved at a cost-effective manner. • 100% renewable energy systems are lower in cost than a business-as-usual scenario. [ABSTRACT FROM AUTHOR]

Rapidly evolving renewable energy generation technologies and the ever-increasing scale of renewable energy installations are driving the need for more accurate, faster, and smarter health monitoring methods. Machine learning (ML) has been widely used for defect identification and fault diagnosis (DIFD) in renewable energy systems (RES) due to its excellent data analysis and pattern recognition capabilities. However, there is still a lack of comprehensive and in-depth research to summarize the progress of ML in RES DIFD. Considering their diversity and evolution, this research systematically reviews the application of ML to DIFD in photovoltaic and wind power systems, providing a detailed analysis of key points, including defects and faults in RESs, available data for DIFD, and ML-based solutions. For each ML application, the model and structure, input and output settings, sample size, equipment technology and scale, target defects/faults, performance, and limitations are discussed. In addition, based on the statistics, analysis, and summary of representative works on RES DIFD, several commonly used ML algorithms are compared. The main trends, challenges, and prospects of applying ML to RES DIFD are extracted and presented. This research is of great significance in guiding further improvements of ML techniques to address the problems of RES DIFD. [Display omitted] • Review of papers applying ML techniques to RES DIFD. • Common defects, faults, and their severity in RESs are discussed. • Available data when applying ML to RES DIFD are summarized. • Performance and limitations of various ML applications are investigated. • Challenges and prospects for RES DIFD using ML techniques are presented. [ABSTRACT FROM AUTHOR]

The current paper presents a numerical analysis of a multi-source renewable energy system for building air conditioning aiming at decarbonising the building envelope. The thermal management of the building is pursued through a radiant floor fed by a heat pump and integrated with phase change materials for thermal inertia enhancement. A fan coil is foreseen for humidity control. The heat pump can be fed through three parallel circuits involving different thermal sources: air with an ordinary air-to-water heat exchanger, sun through photovoltaic thermal solar collectors, and ground using shallow ground flat-panel heat exchangers. At need, the ground can be exploited for thermal energy storage when the heat pump is idle. A set of dedicated control rules chooses the optimal source or mix of sources to exploit at any time. Simulations of a reference building, namely a large single-room snack bar, are performed for various plant configurations, hypothesising the building as located in distinct climate zones and characterised by different thermal transmittance. The thermal performance of the building is given in terms of primary energy needs per year and compared to that of an analogous single-source plant. Results show that the proposed system can lead to primary energy savings of up to 16% compared to the corresponding state-of-the-art single-source plant, being more effective if the geothermal field is large enough and the building heating and cooling needs are comparable. The relevance of a proper control algorithm for plant performance optimisation is highlighted: a thermal power-based approach is proposed and successfully tested. • A multi-source renewable energy system for buildings thermal management is proposed. • Air, sun, and ground can be exploited as thermal sources by means of heat exchangers. • A phase change materials integrated radiant floor covers the building thermal needs. • A control algorithm selects the sources to be used for the optimal plant operation. • TRNSYS simulations foresee energy savings up to 16% in different operating conditions. [ABSTRACT FROM AUTHOR]

The performance of microgrid operation requires hierarchical control and estimation schemes that coordinate and monitor the system dynamics within the expected manipulated and control variables. Smart grid technologies possess innovative tools and frameworks to model the dynamic behaviour of microgrids regardless of their types, structures, etc. Various control and estimation technologies are reviewed for developing dynamic models of smart microgrids. The hierarchical system of a microgrid control consists of three architectural layers, primary, secondary and tertiary, which need to be supported by real-time monitoring and measurement environment of the system variables and parameters. Various control and estimation schemes have been devised to handle the dynamic performance of microgrids in the function of control layers requirement. Firstly, control schemes in the innovative grid environment are evaluated to understand the dynamics of the developed technologies. Six control technologies, linear, non-linear, robust, predictive, intelligent and adaptive, are mainly used to model the control design within the layer(s) regardless of the types of microgrids. Secondly, the estimation technologies are evaluated based on the state of variables, locations and modelling of microgrids that can efficiently support the performance of the controllers and operating microgrids. Finally, a future vision for designing hierarchical and architectural control techniques for the optimal operation of intelligent microgrids is also provided. Therefore, this study will serve as a fundamental conceptual framework to select a perfect optimal design modelling strategy and policy-making decisions to control, monitor and protect the innovative electrical network. [Display omitted] • Frameworks for optimal control and monitoring of smart power grids are presented. • Propose novel control structures as a golden opportunity for autonomous power grid development. • Assess the opportunity of modern energy systems for sustainable development goals. • Develop a digital thread framework for digital twin designs of a modern power grid. • Provide challenges and future perspectives for autonomous power grid technologies. [ABSTRACT FROM AUTHOR]

The accurate power generation forecast of multiple renewable energy sources is significant for the power scheduling of renewable energy systems. However, previous studies focused more on the prediction of a single energy source, ignoring the relationship among different energy sources, and failing to predict accurate power generation for all energy sources simultaneously. This paper proposes a hybrid framework for the power generation forecast of multiple renewable energy sources to overcome deficiencies. A Convolutional Neural Network (CNN) is developed to extract the local correlations among multiple energy sources, the Attention-based Long Short-Term Memory (A-LSTM) network is developed to capture the nonlinear time-series characteristics of weather conditions and individual energy, and the Auto-Regression model is applied to extract the linear time-series characteristics of each energy source. The accuracy and practicality of the proposed method are verified by taking a renewable energy system as an example. The results show that the hybrid framework is more accurate than other advanced models, such as artificial neural networks and decision trees. Mean absolute errors of the proposed method are reduced by 13.4%, 22.9%, and 27.1% for solar PV, solar thermal, and wind power compared with A-LSTM. The sensitivity analysis has been conducted to test the effectiveness of each component of the proposed hybrid framework to prove the significance of energy correlation patterns with higher accuracy and stability compared with the other two patterns. [Display omitted] • Propose a hybrid framework for power forecast of multiple renewable energy sources. • Improve forecast accuracy by considering energy correlation patterns. • Sensitivity analysis is conducted to discuss each information pattern. • Method verified by a real renewable energy system case. [ABSTRACT FROM AUTHOR]

The increase in energy demand and reduction in resources for conventional energy production along with various environmental impacts, promote the use of renewable energy for electricity generation and other energy-need applications around the world. Wind power has emerged as the biggest renewable energy source in the world, whose potential, when employed properly, serves to provide the best power output. In order to achieve self-sustenance in energy supply and to match the critical needs of impoverished and developing regions, wind power has proven to be the best solution. However, wind power is intermittent and unstable in nature and hence creates lot of grid integration and power fluctuation issues, which ultimately disturb the stability of the grid. In such cases, energy storage technologies are highly essential and researchers turned their attention to find efficient ways of storing energy to achieve maximum utilization. The use of batteries to store wind energy is very expensive and not practical for wind applications. Compressed Air Energy Storage (CAES) is found to be a viable solution to store energy generated from wind and other renewable energy systems. A detailed review on various aspects of a CAES system has been made and presented in this paper which includes the thermodynamic analysis, modeling and simulation analysis, experimental investigation, various control strategies, some case studies and economic evaluation with the role of energy storage towards smart grid and poly-generation. [ABSTRACT FROM AUTHOR]

To keep temperature rise well below 2 °C, production systems should be synthesised in a way that are regenerative, climate-resilient, and equitable, and to maintain biodiversity to preserve life and meet future human needs with the integrity of nature. In line with this approach, this paper presents a stepwise transition to a regenerative energy system in the EU by 2050, with the aim of going beyond carbon neutrality and achieving annual net negative emissions. A mixed-integer linear programming model is developed with the goal of maximising the Sustainability Net Present Value while achieving a balanced solution between all the three basic sustainability pillars. In order to obtain a regenerative system design, the annual self-sequestration of CO 2 by the Earth was evaluated based on the amount of CO 2 released into the atmosphere and the change in atmospheric CO 2 concentration. The impact of different CO 2 emission reduction scenarios on the atmospheric CO 2 concentration until 2050 was evaluated. The results show that carbon neutrality at the global level could be achieved by 2050 with a 1.63% annual reduction in anthropogenic CO 2 emissions. Moreover, the results indicate that if carbon emission neutrality is achieved at the global level in 2050, it could be achieved in the EU as early as 2039, given the Earth's self-sequestration capacity. To go beyond carbon emissions neutrality at the EU level, electricity demand is expected to nearly double, particularly due to the electrification of road transport, the use of heat pumps for heating residential and the tertiary sectors, 6G and IoT widespread out etc. • A critical review of sustainable heat production technologies. • A MILP model to achieve carbon emissions neutrality or negative net emissions in the EU by 2050. • Cross-sectoral synthesis of regenerative energy systems – beyond sustainability. • Assessment and prediction of Earth's self-sequestration in the synthesis of regenerative energy systems. [ABSTRACT FROM AUTHOR]

To address the demand for vehicles using fuel cell energy with high-performance electrodes, this paper discusses the energy storage model, nano-scale characterization technology, nanoenergy system and the structural design for fuel cell graphene electrodes while giving special attention to three characteristics: electrode conversion efficiency, specific power and cost. The structural stability model, evolutionary mechanism and the construction process of graphene electrodes of fuel cell technology are introduced. By analyzing electron and ion transmission and characterizing the electrode structure, energy system, surface and interface property at the nano-scale level, this paper reveals the intrinsic link between how an energy system is structured and performs. It combines the system model with the application of high-performance electrodes to highlight the compatibility between graphene and the electrode. By addressing progress in the applicability of renewable energy and the sustainability of energy technology during recent years, this paper provides scientific and experimental support for the practicality of fuel cell energy while also addressing bottleneck issues such as conversion efficiency, specific power and the cost of electrodes. [ABSTRACT FROM AUTHOR]

Sustainable energy sources are promising solutions for reducing carbon footprint and environmental impacts within the building sectors. Reducing energy consumption while ensuring thermal comfort for occupants is now an essential task in building climate control. Two possible ways to cut down building energy consumption are implementing renewable and sustainable energy sources, such as solar energy and geothermal energy, and adopting the model predictive control method. In this study, we develop a novel nonlinear model predictive control (NMPC) framework for climate control of buildings with renewable energy systems to minimize electricity costs. A nonlinear dynamic model of the building climate and renewable energy systems, including temperature, humidity, thermal comfort, geothermal heat pump, and solar panels, is first constructed based on mass and energy balance equations. The nonlinear dynamic model is then integrated into the proposed NMPC framework, which iteratively solves a nonlinear programming problem to generate the optimal control inputs which minimize energy consumption and carbon footprints for sustainability. A simulation case study on controlling a building located on the Cornell University campus is conducted to demonstrate the capability of renewable energy sources to reduce building energy consumption using the proposed NMPC framework. The results show the NMPC framework could efficiently minimize total electricity cost and constraint violation for thermal comfort to 12.9% with no more than 0.2 of violation on predicted mean value index in different seasons. Implementing an electricity storage component could reduce the electricity cost by 19%. The results indicate better sustainability for the smart building using sustainable energy sources and the NMPC framework. • A nonlinear MPC framework is proposed for controlling sustainable building climate. • Renewable energy systems are integrated to cut down building energy consumption. • PMV index, temperature, and humidity are controlled for occupant's thermal comfort. • A case study based on a real-world building in New York state is demonstrated. • The proposed framework can efficiently minimize electricity cost and CO 2 footprints. [ABSTRACT FROM AUTHOR]

Abstract: Renewable energy is considered as a solution for mitigating climate change and environmental pollution; however, an important problem of the application of renewable energy systems (RESs) is that the evaluation of the sustainability of these systems is extremely complex. In order to assess the sustainability of renewable energy systems comprehensively, the use of sustainability indicators (SIs) is often necessary. Since sustainability indicators are necessary to reflect various aspects of sustainability, the development of a general sustainability indicator (GSI) including many basic sustainability indicators (BSIs) becomes critical. In this paper, the methods of selection, quantification, evaluation and weighting of the basic indicators as well as the methods of GSI aggregation are reviewed. The advantages and disadvantages of each method are discussed. Based on these discussion and the analysis of the uncertainties of sustainability assessment, an effective framework and its procedures of the development of GSI for renewable energy systems is presented. This GSI is not only able to evaluate all the sustainability criteria of RESs, but also can provide numerical results of sustainability assessment for different objective systems. The proposed framework in this study can be used as a guidance of the development of sustainability indicator for various renewable energy systems. [Copyright &y& Elsevier]

Abstract: The share of electricity produced from renewable energy is constantly increasing in Germany and worldwide. The transformation to an electricity system based on renewable sources is characterised by an increasing need for balancing power in order to compensate power supply from fluctuating sources, such as solar or wind. Biomass, more precisely energy from biogas, has the potential to generate electricity flexible on-demand. A demand-driven biogas production is vital for balancing power generation and can generally be achieved by biogas storing or flexible biogas production concepts. This study analyses and reviews both concepts regarding their ability to facilitate a biogas supply for short-term and long-term balancing power generation. Results show that a demand-driven biogas supply based on a biogas storing concept is, due to the fast availability of biogas (i.e. biomethane), suitable for the generation of positive secondary and tertiary balancing power. Whereas, long-term balancing power can be provided by flexible biogas production as well as by biomethane, which was injected and stored in the natural gas grid. Basically all reviewed biogas supply concepts that facilitate a shutdown of electricity generation by storing or stopping the biogas production can additionally provide negative balancing power. [Copyright &y& Elsevier]

This study was conducted to ascertain how the use of recycled materials instead of virgin materials affect the installation of renewable energy systems for the energy transition of buildings. LCA was used to estimate the life cycle energy use and GHG emissions of building and renewable energy systems with and without using recycled materials. For the case study, a public building and four representative photovoltaic (PV) systems were selected. National recycling standards and processes were considered for suggested recycled materials. In general, replacing virgin material with recycled counterpart reduced embodied GHG emissions more than embodied energy. However, due to high carbon intensity of operation energy and building materials without available counterparts, using recycled materials reduced the life cycle energy use and GHG emissions of the case building by 4.9% and 3.3%. Among four PV systems, using recycled materials was most significant in the single crystalline-silicon (sC-Si) PV system, 44.5% reduction of energy use and 41.3% reduction of GHG emissions. Therefore, when recycled materials are used for achieving the energy transition of the case building with the sC-Si PV system, the PV system requirements could be reduced by a maximum of 9.6% for energy payback and 4.9% for GHG payback. This study demonstrated that the use of recycled materials is effective at reducing the embodied energy and GHG emissions of PV systems as well as buildings. To achieve carbon-neutral buildings, particularly, the use of recycled materials based on a thorough LCA should be considered in addition to renewable energy system. • Analyzing recycled materials' impact on building energy conservation and transition. • Life cycle assessment for building and PV system was conducted through a case study. • National recycling standard and process were applied for practical recycled material. • Impact of recycled materials was determined by carbon intensity of material and grid. • This study can help to consider an important factor for a carbon-neutral buildings. [ABSTRACT FROM AUTHOR]

In this paper, the contemporary development in multiple input dc-dc converters are identified and examined. The quest to mitigate the difficulties associated with employing renewables in distribution systems and electric vehicles (EVs) has yielded many new converter topologies. These new topologies have easier control, lower parts count, are cheaper and are worthy alternatives to the typical series or parallel connection of converters. The converters are identified by three divisions that bother on the isolation between the respective ports. The electrically connected converters do not have isolation between the ports, and thus, a dc link connects the ports. Electromagnetically connected converters use a dc-link to connect input ports, but the input ports and output port are isolated. In magnetically connected converters, input ports are separated by multiple winding transformer, just as the output port is isolated from the input ports by the winding. The formation, structure, characteristics, operation, merits and demerits of the converters will be presented. Thereafter, comparisons will be done based on the distinct features of the converters. This review identifies that converter properties depend on the specific application requirement and thus, no converter fulfills all demands in the industry. Prospective future research trends are suggested. This work aims to update on research done during the time gap since the last comprehensive reviews. Image 1 • An introduction to the role of multi-input dc-dc converter in HEVs and RE systems. • Existing multi-input dc-dc converters are critiqued and discussed. • Industry applications, challenges and future research trends are highlighted. [ABSTRACT FROM AUTHOR]

Global greenhouse gas emissions from energy production were approximately 40% higher in 2017 than in 2000 (International Energy Agency, 2018), and ambient particulate matter – one of the byproducts from fossil fuel combustion of most concern for public health – is now the fifth largest contributor to global disease (Cohen et al., 2017). Achieving the climate targets of the Paris Agreement and the Sustainable Development Goals requires better accounting for climate and health costs in energy planning. This paper quantifies trade-offs between selected energy infrastructure, climate, and health costs when meeting future electricity demand by increasing the share of renewable energy, with a focus on variable renewable energy (VRE; here: wind and solar photovoltaic power). Using a spatially and temporally resolved approach, we analyzed three scenarios for year 2030 for Northeast Brazil, characterized by 30%, 45%, and 70% of VRE (the latter corresponds to 100% renewable energy). We find that accounting for the health impacts from electricity generation is sufficient to economically justify deep decarbonization of Northeast Brazil's power sector. Full decarbonization is economically justified when the carbon price exceeds $20/tonne CO 2 , which is less than Brazil's country-level social cost of carbon and only 4.8% of the global social cost of carbon. Our study shows that regional climate and health costs from electricity generation alone can be greater than the additional infrastructure costs of decarbonization. Our results highlight how systematically accounting for health and climate costs in energy planning would economically justify the decarbonization of energy systems. • We quantify trade-offs between selected infrastructure, climate, and health costs. • We simulate scenarios with up to 100% renewable energy in Northeast Brazil. • We model the power system, particulate matter dispersion, and health impacts. • Country-level social costs of carbon and health costs justify deep decarbonization. • Health and climate costs should be systematically incorporated in energy planning. Theme: Techno-socio-economic and sustainability aspects of renewable energy systems. [ABSTRACT FROM AUTHOR]