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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]

Recently, with the development of new energy technologies, all-electric ships (AESs) with hybrid energy storage system (HESS) are becoming a promising solution to reduce fuel consumption and emissions. However, the high maneuverability of ships during the actual navigation places higher performance requirements on the HESS, which presents a nonlinear and multi-objective challenge for the HESS design. Therefore, it is necessary to consider the coupling between HESS sizing and energy management strategy (EMS). In this paper, a self-adaptive joint optimization framework (SJOF) for marine HESS design considering load fluctuation characteristics is proposed, which can find the optimal decision solution with excellent system economic and battery life performance for AES HESS design. Based on the rain flow counting (RFC) method, a multi-objective joint optimization method considering life cycle cost (LCC) and battery degradation index (BDI) is introduced into SJOF. Besides, a novel EMS including the self-adaptive segmentation mechanism (SSM) and power allocation is proposed, which can achieve the most efficient energy scheduling. • A self-adaptive working condition classification approach is proposed. • A power allocation strategy with optimal filter matching is proposed. • A reconstruction method for obtaining optimal solution set is proposed. [ABSTRACT FROM AUTHOR]

The rapid development of renewable energy sources (RESs) facilitates the coordinated operation of different energy sources to hedge against the uncertain and non-dispatchable nature of RESs. In this paper, we propose an effective approach for ultra-short-term optimal operation of a photovoltaic-energy storage hybrid generation system (PV-ES HGS) under forecast uncertainty. First, a generic approach for modelling forecast uncertainty is designed to capture PV output characteristics in the form of scenarios. Then, stochastic model predictive control (SMPC) is utilized in the coordinated operation of PV-ES HGS for load shifting under forecast uncertainty. Finally, a PV-ES power station located in Northeast China is selected as a case study to validate the feasibility and effectiveness of the proposed method in detail. The results show that: (1) The forecast uncertainty modelling method, which incorporates adaptive kernel density estimation (KDE) and two-layer nested copula, could capture the characteristics of PV output effectively. (2) The proposed sampling procedure ensures the representativeness and temporal dependence of scenarios extracted from the joint predictive density, leading to stability and efficiency in solving the stochastic optimization problems. (3) The SMPC-based coordinated operation strategy exhibits significant superiority in load shifting performance and resistance to risks. The standard deviation of residual load (SDORL) is reduced by 17.5% on average compared to the conventional scheme. Thus, this work provides operators with guidance in the ultra-short-term optimal operation of a PV-ES HGS under forecast uncertainty. [Display omitted] • Integration of adaptive KDE and two-layer copula to model PV forecast uncertainty. • Proposal of a representative scenario generation method incorporating LH and CD. • Utilization of scenario-based SMPC to optimize ultra-short-term PV-ES HGS operation. • Identification of trade-off between load shifting performance and operational risks. [ABSTRACT FROM AUTHOR]

With the increasing penetration of electric vehicles into the existing power networks, the development of proper energy management tools become essential to mitigate the damaging effects of emerging high-power charging modes. To facilitate the management of vehicles, they can be gathered into clusters in order to aggregate their charging profiles. This paradigm is called cluster of vehicles and can be forward developed by gathering a group of parking, thus forming a parking cluster where the characteristics of different parking lots are aggregated. In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The new proposal is casted as a game-based framework which seeks for the equilibrium between the cluster coordinator and parking lots, instead of conventional tools where the pricing strategy is decided by a central entity disregarding the interests of users. The developed model is established as a bi-level optimization problem, which is further linearized to be tractable and solvable by off-the-shelf solvers. The implications of vehicle-to-grid are also studied by fully modelling bi-directional power flows in chargers. In addition, two uncertainty-aware strategies are proposed to cope with uncertain energy margins in parking lots. A case study is conducted and various results are analysed and discussed. It is shown that enabling vehicle-to-grid characteristics discloses significant economic benefits for users and the cluster coordinator. In addition, vehicle-to-grid impacts notably on the risk-averse character of the uncertainty-aware strategies proposed. The developed pricing mechanism is also compared with the conventional pricing policies based on key of repartitions, showing that the new proposal is able to reduce the cost for users, avoiding to directly translate the energy cost to charging points. • A new game-based pricing mechanism for parking clusters is developed. • Two uncertainty-aware strategies are proposed and compared. • Vehicle-to-grid helps to improve the economy of the cluster notably. • The impact of uncertainties strongly depends on vehicle-to-grid availability. • The new proposal reduces the cost of users compared with other approaches. [ABSTRACT FROM AUTHOR]

• The method to effectively capture the key information of faults and improved model efficiency. • The method based on meta-learning represents an innovative path for the inverter output decision-making. • The method can quickly and accurately fit the mapping between features and decisions. With the increase of energy demand and the continuous development of renewable energy technology, active distribution networks have become increasingly important. However, the introduction of a large amount of renewable energy has made the structure of ADN increasingly complex and fragile, and emergency fault caused by emergencies may often occur. Voltage control in the emergency fault event is particularly important. In this context, this paper presents a meta-learning based voltage control strategy for renewable energy integrated active distribution network. A general regression neural network is first applied to extract features from the operation data. Then, the local cross-channel interaction network is adopted to capture targeted information that is most related to emergency fault from the features and induce knowledge transfer to update the voltage control strategy. This allows the proposed strategy to make optimal decisions quickly when only limited data are available under an emergency fault that has never occurred. Comparison results based on a 69-bus distribution network validate the effectiveness and robustness of the proposed strategy. [ABSTRACT FROM AUTHOR]

• BRICS countries exhibit expansionary strategies in solar and wind power. • DE countries exhibit conservative strategies in solar and wind power. • The solar and wind power markets in BRICS countries are capital-oriented. • BRICS countries should prevent the new energy technology dilemma. Carbon neutrality is proposed by many countries for responding to climate change and energy transformation. Developing new energy is crucial to achieving carbon neutrality goals. In the context of carbon neutrality, this paper analyzes the factor structure and market orientation of the new energy market in BRICS countries (including the Federative Republic of Brazil, Russian Federation, the Republic of India, People's Republic of China, and the Republic of South Africa) and developed European (DE) countries in recent years. The primary conclusions are as follows: (1) In wind and solar energy markets, the BRICS countries exhibit an elastic substitution effect in production, indicating they have chosen the expansionary strategy with unbalanced factors. Through the contribution analysis, we find the development of wind and solar energy in BRICS countries is capital-oriented. However, the DE countries have opted for a conservative strategy with balanced factors, in which the wind and solar energy markets need promotional measures for capital investment and technological progress. (2) In the biomass energy market, both the BRICS countries and DE countries adopt the expansionary strategy. Besides, the development of the biomass energy market is technology-oriented except for India. (3) For the nuclear energy market, the BRICS countries and DE countries choose the conservative strategy and expansionary strategy, respectively. However, due to safety concerns, the expansion of nuclear energy in all countries has slowed. (4) Although a capital-oriented strategy can quickly promote the development of the new energy market, this practice compromises the local welfare and the benefits of the new energy technology progress to varying extents. The core insight of this paper is that BRICS countries should avoid the new energy capital trap and prevent the new energy technology dilemma. For the BRICS countries, policymakers should further evaluate the balance between capital-oriented interest and technology-oriented development. Also, achieving carbon neutrality is more difficult for the BRICS countries than DE countries. Hence, the BRICS countries should further refine their energy strategies for the long term. [ABSTRACT FROM AUTHOR]

• Study of microgrid with energy-stored quasi-Z-source cascaded H-bridge multilevel inverter and PV system. • Development of energy management system based on model predictive control (MPC-EMS). • Results compared with proportional sharing algorithm based on SOC level (SOC-EMS). • Best results obtained from MPC-EMS. This paper presents a new energy management system (EMS) based on model predictive control (MPC) for a microgrid with solar photovoltaic (PV) power plants and a quasi-Z-source cascaded H-bridge multilevel inverter that integrates an energy storage system (ES-qZS-CHBMLI). The system comprises three modules, each with a PV power plant, quasi-impedance network, battery energy storage system (BESS), and voltage source inverter (VSI). Traditional EMS methods focus on distributing the power among the BESSs to balance their state of charge (SOC), operating in charging or discharging mode. The proposed MPC-EMS carries out a multi-objective control for an ES-qZS-CHBMLI topology, which allows an optimized BESS power distribution while meeting the system operator requirements. It prioritizes the charge of the BESS with the lowest SOC and the discharge of the BESS with the highest SOC. Thus, both modes can coexist simultaneously, while ensuring decoupled power control. The MPC-EMS proposed herein is compared with a proportional sharing algorithm based on SOC (SOC-EMS) that pursues the same objectives. The simulation results show an improvement in the control of the power delivered to the grid. The Integral Time Absolute Error, ITAE, achieved with the MPC-EMS for the active and reactive power is 20 % and 4 %, respectively, lower than that obtained with the SOC-EMS. A 1,3 % higher charge for the BESS with the lowest SOC is also registered. Furthermore, an experimental setup based on an OPAL RT-4510 unit and a dSPACE MicroLabBox prototyping unit is implemented to validate the simulation results. [ABSTRACT FROM AUTHOR]

Towards the global endeavor of clean energy transition, there is a rapid development of distributed energy resources installed in the premises of residential or commercial users, enabling them to act as flexible energy prosumers. Empowering prosumers is envisioned as a catalytic development for modern energy economies, with recent research, as well as innovation and policy actions, pointing to the promising direction of decentralized energy markets, where active energy prosumers exchange energy in a decentralized fashion. Despite the vast amount of recent research on prosumer-centric peer-to-peer (p2p) energy markets, only a small subset of studies accounts for managing the inherent uncertainty of prosumers' flexible demands. In this paper, we consider the problem of controlling the decisions of energy prosumers' within a p2p exchange network. The multi-bilateral economic dispatch is formulated as an optimal control problem. The proposed solution is based on a direct lookahead policy, effectively addressing the issues of dimensionality and local constraint satisfaction. Experimental simulations demonstrate the method's efficiency and the system's behavior. The proposed formulation and method is shown to effectively address the operation of p2p markets under uncertainty, closely tracking the performance of the (full information) optimal-in-hindsight benchmark. • A p2p economic dispatch problem is considered, taking into account the system's uncertainty. • The problem is formulated as a multi-agent optimal control problem. • A machine learning algorithm is used to predict the system's optimal dual variables. • The ADMM algorithm is combined with the machine-learning predicted duals to obtain an efficient control policy. • The proposed policy respects the prosumers' local constraints while achieving a near-optimal dispatch. [ABSTRACT FROM AUTHOR]

• A co-simulation platform for Hybrid Electric Vehicles is developed for virtual assessment of Energy Management, performance, CO 2 & pollutant emissions. • CO 2 & pollutant emissions models are based on convolutional neural network for accurate transient state prediction. • A continuous deep reinforcement learning based EMS of HEV is proposed. • DDQL is trained with random generated driving cycles based on Markov Chain approach from real recordings. • Cumulative d error for predicted pollutants emissions is below 8.5% while HEV simulation is running up to 10 times faster than real time in a standard laptop. The Energy Management Strategy (EMS) in an HEV is the key for improving fuel economy and simultaneously reducing pollutant emissions. This paper presents a methodology for developing hybrid models that enable EMS testing as well as the evaluation of fuel consumption, CO 2 and pollutant emissions (CO, NO x and THC). In this context, pollutant emissions are hard to quantify with static models such as the well-known map-based approach which is mainly due to the pronounced impact of transient effects. The novelty of this paper primarily comes from the characterization of pollutant emissions through Convolutional Neural Networks (CNN), providing high accuracy for both, instantaneous and cumulative values. The input parameters are classical Internal Combustion Engine (ICE) measurements such as engine speed, air mass flow, torque and exhaust temperature. The proposed CNNs are reduced to a minimum for low complexity and fast computability. These models are developed with experimental data from chassis dyno testing of a conventional turbo-charged gasoline engine vehicle. The pollutant emission models are used in conjunction with physical models of the remaining powertrain allowing for real time simulations of the complete HEV vehicle. The Double Deep-Q learning algorithm is proposed for the EMS and compared to the Dynamic programming (DP) solution. The introduced methodology is developed in a co-simulation framework between MATLAB-Simulink and AMESIM. The resulting model runs between 8 and 10 times faster than real time in an off-the-shelf PC. This provides the capability for developing models suitable for HIL (hardware-in-the-loop) and SIL (software-in-the-loop) applications. The final error in predicted CO 2 remains below 2.5% while the final cumulative error for pollutants is below 8.5% in the case of CO and HC emissions. [ABSTRACT FROM AUTHOR]

With the development of distributed energy resources, the peer-to-peer (P2P) transactive energy market has emerged as an effective method for consuming the excess renewable energy on the end-user side. In the current research on transactive markets, distributed energy storages (DESs) have been widely applied in energy trading for transaction flexibility and system management. These small-scale energy storages have achieved a considerable momentum that, if properly harnessed, may unleash their potential for prosumers' utility maximization, reserves for renewable energy uncertainty, and transmission congestion relief. The present work reviews distributed energy storage in the transactive market, classifying and analyzing 120 papers according to their applications, algorithms, and adopted policies. This study first identifies DES functions in wholesale and transactive markets and then provides the mathematical models of DESs in various transactive market types in detail. This paper then focuses on the pricing mechanism for the DESs' contributions in the P2P transactive energy market. A transactive energy market mechanism for accommodating DESs is proposed based on the Vickrey–Clarke–Groves framework to meet incentive compatibility. The energy policies related to the promotion of DESs in electricity markets are listed and compared. Furthermore, this review highlights the possible questions and future development opportunities according to the analyzed studies. With the roles and relationships among aggregators, DES owners, and power grid determined, the benefit of participating P2P transactive market can be further improved. [Display omitted] • We analyze the application of distributed energy storage in electricity markets. • We focus on reviewing distributed energy storages in transactive energy market. • We propose a Vickrey–Clarke–Groves mechanism for accommodating energy storages. • The future development and possible questions are discussed in detail. [ABSTRACT FROM AUTHOR]

With the proposed concept of energy blockchain, peer-to-peer (P2P) energy trading between prosumers is regarded as a potential way for future power market development. However, existing P2P energy trading often has problems such as low trading efficiency and significant communication losses. This paper presents an energy trading strategy in the blockchain environment to solve the problems of P2P energy trading. First, the type of market is determined based on energy supply and demand. Then, a Stackelberg game is used to establish a two-layer model of the leader (upper) and follower (lower) to determine the price. In addition, we propose an energy trading method based on feasible regions. The trading strategy considers the supply and demand of the market, provides an accurate energy transfer signal, and can help energy blockchain achieve better "self-sufficiency". A case study proves the effectiveness of the strategy. Compared with the existing methods, the strategy proposed in this paper considers the market type and can promote the coordination and complementation of energy in the microgrid, and improve trading efficiency and reduce communication loss without sacrificing privacy. This study demonstrates that P2P power trading has brought benefits to prosumers, has promoted the development of the energy market, and has shown the massive potential of energy blockchain. • A P2P energy trading model and a consensus algorithm in the blockchain are proposed. • An interactive pricing-and-matching P2P energy trading strategy is proposed. • A novel P2P energy trading pricing model based on game theory is proposed. • A P2P energy trading matching method based on the feasible region is proposed. • Results show that the strategy proposed in this paper can optimize the microgrid. [ABSTRACT FROM AUTHOR]

• The analysis framework of a digital business model of PV fishery is constructed. • Multiple value propositions in the digital business model of PV fishery to create value for stakeholders. • The digital business model of PV fishery improves the operational efficiency of PV power generation. • The digital PV fishery increases the diversification of revenue sources of PV agricultural company. A business model that combines solar photovoltaic and fishery plays a role to expand distributed renewable energy and improve rural economy development. However, the business model of solar photovoltaic fishery is encountering growing difficulties with revenues decreasing due to changes of policy such as withdrawal of photovoltaic subsidies and land control. The spreading of digitization in photovoltaic industry and rural areas provides new opportunities for innovation of traditional solar photovoltaic fishery. This paper focuses on multiple value propositions to discuss a digital business model based of solar photovoltaic fishery on a case with a local Chinese photovoltaic agricultural company. The study results show that the digital business model of solar photovoltaic fishery improves the operational efficiency of solar photovoltaic power generation, economic benefits of aquaculture, and the diversification of revenue sources of solar photovoltaic agricultural company and leasing company. The transformation of business model from traditional solar photovoltaic power generation and fishery business to digital service platforms can not only accelerate expansion of distributed renewable energy and be potential deployed globally but also assist photovoltaic agricultural company to effectively respond to policy change. Finally, this paper proposes that supportive policy may focus on advocating system coupling to expedite the development of distributed renewable energies based on digital technology, and digital photovoltaic combined with rural energy market and financial, which will advance development of renewable energy electrification and promote rural economic growth. [ABSTRACT FROM AUTHOR]

• Site suitability assessments are at the heart of renewable energy planning. • A collaborative approach enables developing a fit for purpose decision-support tool. • Stakeholder-driven site suitability criteria supports transparent decisions. • The tool supports informed discussions and fosters consensus on deployment areas. • Data and technological limitations signal action areas for advancing practice. Competing land-use demands associated with planning for renewables is resulting in growing social and political challenges. Spatial decision-support tools are being developed and applied to tackle some of these challenges by facilitating systematic identification of suitable development sites based on technical, environmental and socio-economic criteria. Nevertheless, decision-support tools are often developed with limited input from stakeholders affecting participative and accountable planning and decision-making. This paper aims to address this shortcoming and foster greater transparency in the development of support tools for renewable energy planning, and thus in siting decisions. It present and discusses a stakeholder-driven approach to the co-creation of an online decision-support tool that attends to planning policy, technical considerations, environmental protection and social concerns influencing renewable energy planning decisions in Ireland. This bottom-up approach, easily transferable to other jurisdictions, fosters proactive expert input, awareness raising and greater comprehensibility and acceptance of siting decisions. It also enables developing an interface that responds to users' needs. The paper contributes to advancing renewable energy planning practice through effective and meaningful stakeholder engagement that supports transparent and accountable decisions from the outset. It also highlights ongoing data and technological shortcomings affecting the integration and operationalisation of stakeholders demands and needs, which signal priority areas of action to further advance the development of renewable energy decision-support tools. [ABSTRACT FROM AUTHOR]

IETB framework for green clean hydrogen energy development in the low-carbon transition. [Display omitted] • Most of the countries agree to consider renewable energy-based hydrogen generation as their long-term strategy. • Primary driving reasons for the growth of hydrogen energy in BRICS-US are economic, institutional and behavioural factors. • Key aspects of hydrogen are reviewed i.e. production cost, application, production and delivery in BRICS-US. • This study focuses on the qualitative inflection point through IETB framework in the development of hydrogen energy. Carbon dioxide (CO 2) levels in the atmosphere have grown rapidly (by 50% since the 1750 industrial revolution) due to natural processes, the use of fossil fuels, deforestation, and other factors. Carbon dioxide contributes to global warming, which causes climate change and extreme weather events. Because of technological advancement and use, the world's energy consumption has skyrocketed. The novelty of the study as it focuses on the present state of carbon emissions (in terms of sectors and fuel) as a result of current hydrogen production projects, applications, cost, and delivery, as well as government policy and structural changes in the BRICS-US (Brazil, Russia, India, China, South Africa, and the United States) relationship throughout time. A conceptual model has been established for multi-dimensional debate for logical comparison analysis in institution-economic-technology-behavior (IETB). This paper has three main findings. First, in terms of horizontal comparison, each country has its advantages but distinct underlying driving causes. Second, in terms of vertical comparison, each country's hydrogen energy development is still in the quantitative stage, with the qualitative inflection point still to be reached. Third, in terms of a strategic goal, producing hydrogen energy benefits the energy transition from a primary mission aspect. However, studies have revealed that China is the leader in CO 2 emissions, followed by the US. China is the largest producer and consumer of hydrogen (24 million tonnes). Even though the world's largest green hydrogen plant is being developed in California (United States) (11,000 kg of green hydrogen per day and 3.8 million kilograms per year). This study highlights potential future development despite existing challenges and opens doors for enlightening the core researchers for establishing programs in transition (emerging and frontier) economies to strengthen state energy, efficiency, and conservation policies. [ABSTRACT FROM AUTHOR]

[Display omitted] • A directly solar-driven power-to-methane system is proposed. • The detailed distributed parameter method is used to model the system. • A comprehensive thermodynamic and economic analysis is performed. • Energy and exergy efficiencies are 9.88% and 11.08% with 914.51 MWh/y SNG yield. • Unit cost is 186.57 $/MWh in 2020 and will be reduced to 91.51 $/MWh in 2050. The transformation of solar energy into easily transportable and storable fuel brings a prospective solution to the issue caused by its intermittency and non-dispatching. Relying on well-established infrastructures and mature technologies for application, methane can be an attractive energy carrier for improving the penetration of solar energy in the future energy structure. To evaluate the feasibility of solar-to-methane from technical and economic views, this paper proposes a directly solar-driven power-to-methane system integrating photovoltaic plant, solid oxide electrolysis cell, methanation reactor, and membrane module. The detailed distributed parameter method is employed for thermodynamically modeling the core components of the system to consider the profiles of current density, temperatures, and composition concentrations. A comprehensive thermodynamic and economic analysis is further conducted for the system, and the results indicate that the power-to-methane efficiency, total energy efficiency, and total exergy efficiency of the system can reach 68.41%, 9.88%, and 11.08%, respectively. With the annual average solar radiation of 557.43 W/m2, the system achieves an annual synthetic natural gas yield of 914.51 MWh/y. A high total product unit cost of 186.57 $/MWh is obtained in 2020, whereas it is predicted to decrease to 91.51 $/MWh (about 51% reduction) in a future scenario with a payback period of 4.66 years due to the significant decrement of the photovoltaic investment cost. The present work reveals the engineering realizability and economic viability of solar-to-methane and provides a competitive option for the development of solar energy. [ABSTRACT FROM AUTHOR]

• Spatial pattern and evolution trends of four kinds of residential energy consumption in China are investigated. • Spatial difference in the distribution of gravity centers of residential energy consumption is depicted. • Standard deviational ellipse demonstrates the dynamic evolution patterns of residential energy consumption. • Coupling coordination degree between residential energy consumption and the economic indicator is investigated. • Electricity should be taken into further consideration in the development of residential energy improvement. The residential energy consumption in China shows diversity and complexity in space. This paper aims to investigate the spatial pattern and evolution trends of four kinds of residential energy consumption (electricity, coal, oil, and natural gas) through gravity center modeling and standard deviational ellipse modeling. Further, their relations to economic growth are probed by using the coupling and coordination degree model. Panel data at the provincial level in China are collected from 2000 to 2019. Our results show that the most drastic movement trend of gravity centers is coal, natural gas, followed by oil, whereas electricity moved slightly in space. Further, the standard deviational ellipse of electricity exhibits relatively stable patterns than the other three residential energy consumption during the study period. In addition, the coupling coordination degree between residential energy consumption and the economic growth is examined, and the results indicate that the total residential electricity consumption shows the highest coupling coordination degree, followed by residential gas, oil, and coal. It demonstrates lower coupling coordination degrees in rural areas than in urban areas in the total and sectoral residential energy consumption. In sum, several policy implications can be demonstrated based on our findings, i.e., the development of electricity consumption in the residential energy structure may improve the energy stability in space in China, and ensuring access to electricity should be taken into further consideration in the development of residential energy improvement to achieve a better coordination level to economic development. [ABSTRACT FROM AUTHOR]

• The total-factor carbon performance (TFCP) of 72 economies during 2000–2018 is calculated to indicate low carbon development. • The mediation analysis and the moderation analysis are conducted, and the robustness of the results is checked by the Sobel Test and the Bootstrap Test. • Clean energy use is the mediator of the positive impact of national affluence and R&D on TFCP. • Clean energy use mitigates the negative impact of industrialization on TFCP. The world seeks for a more sustainable and low carbon development (LCD) and the application of clean energy forms a critical solution in this process. However, there are few studies about how clean energy utilization works as a mediator between national income level and LCD as well as between R&D and LCD or a moderator adjusting carbon emission activities in the world's low-carbon development. We hypothesize that clean energy use is a mediating variable for national income levels and R&D investment and positively affect low-carbon development. In contrast, clean energy acts as a moderating variable that mitigates the negative impact of industrialization on low-carbon development. To test the hypothesis, this study uses panel data of 72 economies from 2000 to 2019. Total-factor carbon performance (TFCP) is calculated by fixed-effects stochastic frontier analysis and indicates LCD level. The hypothesis is supported by the results of mediation and moderation analysis. The Sobel mediation test and Bootstrap Test are applied to verify the robustness of the results. This paper deeply reveals the importance of clean energy utilization as a mediator and a moderator. Under the pressure of industrialization and increasing energy need for economic growth, clean energy should have a larger share. Investments and R&D should pay more attention to clean energy development. [ABSTRACT FROM AUTHOR]

Integrated community energy systems (ICESs) are a modern development of local energy systems by integrating distributed energy resources and local communities. Cost allocation is one of the key issues affecting the success of ICESs. Costs should be allocated to those who cause them, and benefits to those who make the investments. A well-designed cost allocation approach will therefore contribute to a successful implementation and sustainable development of ICESs. This paper presents a general framework for designing cost allocation schemes in ICESs. Various cost allocation methods are proposed to compute the energy bills for local community members in an ICES. In addition, the cost reflectiveness of different cost allocation methods has been computed based on a case study of an ICES to gain insights into how well the costs are allocated. Next to this, the same is also done for the cost predictability to investigate how the energy costs would change in the long term. The results showed that methods with a single energy charging component perform the best in terms of the two criteria. Our assessment can facilitate local community members in selecting a method that satisfies their requirements. Overall, this research contributes to a successful implementation of cost allocation in an ICES. • A framework is designed for cost allocation in ICESs. • Various methods are proposed for cost allocation in ICESs. • Cost allocation methods are assessed by cost reflectiveness and predictability. [ABSTRACT FROM AUTHOR]

• Discussed cross-building energy prediction in a limited-data context. • Compared different methods in a same case via a competition. • Identified hybrid strategies for hybrid building energy prediction models. • Discussed the shortcomings and suggestions for the data preparation process. • Highlighted the importance of data selection for accurate cross-building prediction. With the evolution of new energy and carbon trading systems, it is important to accurately predict building energy consumption to help energy arrangements. Additionally, the widespread use of smart meters has introduced a new data context for building energy prediction. Building energy prediction techniques need improvement but the ideas of various new prediction methods are still on the way and have not yet been compared and tested side-by-side in the reported studies. Thus, we held a competition called 'Energy Detective'. To investigate the status quo of the current prediction techniques, we designed a representative prediction case: cross-building prediction with limited physical parameters and historical data. A total of 195 participants formed 89 teams to participate in the competition. This paper describes the models presented in the competition. By analysing the methods and results, we identified strategies for the future development of energy prediction in hybrid modelling and data-driven modelling. For hybrid modelling, we discuss the basic strategies for hybrid models and suggest that more hybrid models can be developed by combining a wide variety of individual models in sequence or parallel or via feedback methods to achieve accurate and interpretable models. For data-driven modelling, we analyse and discuss the areas of improvement for the current data-driven workflow and suggest that processes other than model application are also important and should be carefully considered. Considering the increasing amount of data available for prediction, we discuss the shortcomings and suggestions for improving the current data preparation process. We recommend comprehensive consideration of the anomaly types in data pre-processing and a focus on feature engineering for higher accuracy and model interpretability, while emphasising the vital role of data selection in cross-building energy prediction. [ABSTRACT FROM AUTHOR]

• It evaluates the effect of China's ETS on non-fossil energy development. • It analyzes ETS's effects from energy type, pilot, and pilot-energy type aspects. • The ETS has the most significant promoting effect on hydropower and photovoltaics. • Carbon price plays a positive role in moderating the effect of China's ETS. Emissions Trading Scheme (ETS) can internalize the environmental costs of fossil energy consumption and encourage enterprises to consume non-fossil energy, which is crucial for People's Republic of China (PRC) to achieve the contribution target of the Paris Agreement cost-effectively. However, whether the ETS in PRC can promote non-fossil energy development remains to be answered. This paper uses a difference-in-differences model to evaluate the effect of the ETS on non-fossil energy development in PRC. Using provincial monthly panel data from January 2004 to December 2019, we find that (1) the ETS has significantly promoted non-fossil energy development in PRC, increasing the monthly average share of non-fossil energy power generation in total power generation by 2.326 percentage points, which has been verified by a series of robustness tests. (2) The effect of the ETS on non-fossil energy development is heterogeneous by energy type, pilot region, and pilot-energy type. The ETS has significantly increased the share of hydropower and photovoltaic power generation, and has significantly promoted non-fossil energy development in Guangdong; meanwhile, the ETS has significantly increased the share of hydropower and nuclear power in Guangdong, and the share of wind power and photovoltaic power in Hubei. (3) The higher the carbon price, the greater the role of the ETS in promoting non fossil energy development. [ABSTRACT FROM AUTHOR]

[Display omitted] • LSTM integrated with Robust Local Mean Decomposition for half-hour solar prediction. • RLMD-LSTM benchmarked against RLMD-MARS, RLMD-SVR, RLMD- Persistence. • RLMD-LSTM evaluated at four solar-rich cities in Vietnam. • RLMD-LSTM outperforms comparative models at Vietnam's studied sites. Data-intelligent algorithms tailored for short-term energy forecasting can generate meaningful information on the future variability of solar energy developments. Traditional forecasting methods find it relatively difficult to obtain a reliable solar energy monitoring system because of the inherent nonlinearities in solar radiation and the related atmospheric input variables to any forecasting system. This paper proposes a new artificial intelligence-based hybrid model by employing the robust version of local mean decomposition (RLMD) and Long Short-term Memory (LSTM) network denoted as RLMD-LSTM. The objective model (i.e., RLMD-LSTM) is built near real-time, half-hourly ground-based solar radiation dataset for the solar rich, metropolitan study sites in Vietnam with all of the forecasting results being benchmarked through classical modelling approaches (i.e., Support Vector Regression SVR, Long Short-term Memory LSTM, Multivariate Adaptive Regression Spline MARS, Persistence) as well as the other alternative hybrid methods (i.e., RLMD-MARS, RLMD-Persistence and RLMD-SVR). Verified by statistical metrics and visual infographics, the present results demonstrate that the proposed model can generate satisfactory predictions, outperforming several counterpart methods. The predictive performance is stable for all study sites that the root-mean-square error remained profoundly lower for RLMD-LSTM (19–20%) compared with RLMD-MARS (20–21%), RLMD-SVR (29–35%), RLMD- Persistence (29–51%), LSTM (25–48%), MARS (21–51%) and SVR (23–85%), Persistence (29–51%). The Legates and McCabe's Index, yielding a value of approximately 0.7988–0.9256 for RLMD-LSTM compared with 0.765–0.8142, 0.4917–0.5711, 0.6900–0.7482, 0.6914–0.7646, 0.4349–0.7170 respectively, for the RLMD-MARS, RLMD-SVR, RLMD-Persistence, LSTM, MARS, SVR, Persistence models, also confirms the outstanding performance of RLMD-LSTM model. Accordingly, the study ascertains that the newly designed approach can be a potential candidate for real-time energy management, renewable energy integration into a power grid and other decisions to optimise the overall system's scheduling and performance. [ABSTRACT FROM AUTHOR]