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The pulp and paper industry faces several challenges linked to climate and environmental impact, resource efficiency, rising energy prices, increased competition for biomass resources, and declining demand for traditional printed paper products. However, these challenges also offer strategic opportunities for the industry to develop into a competitive, resource-efficient, and low-carbon industry in line with a biobased economy. Against this background, this paper aims to analyse current energy strategies in the pulp and paper industry in Sweden. Specifically, the paper analyses how companies combine continuous process efficiency to reduce energy costs with activities that could be developed into new energy-related products to increase revenue. Most of the analysed companies work to various degrees with both these strategies, employing methods that include improving energy efficiency, energy security, and energy conversion, as well as developing a wide range of biobased energy products. However, our study indicates that there is an untapped potential associated with energy product development, and we conclude that energy efficiency measures can free up resources, enabling the development of new energy products. Finally, several potential managerial outcomes and implications are outlined. • The paper analyses the energy strategies in the pulp and paper industry in Sweden. • Energy strategies focus both to improve energy efficiency and develop new energy-products. • The companies' energy strategies combined exploitation and exploration. • A number of managerial implications are presented. [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]

This study aims to investigate the factors of renewable energy consumption in the fifth Technology Revolution with specific evidence from ASEAN countries. The study applies an autoregressive distributed lag (ARDL) approach. The paper also uses the unrestricted fixed and random panel data methods. The manuscript identifies significant factors such as technological innovation, governmental policies, public awareness, electricity consumption, country population, foreign direct investment inflows, imports, exports, and economic growth that have influenced the renewable energy sector in these nations. This study also investigates the determinants that influence renewable energy in the context of international integration and the fifth technology revolution in ASEAN countries. The research analyzes data spanning two decades, from 2000 to 2022, through a combination of econometric modeling and machine learning techniques. Preliminary findings indicate that technological innovation, government policies, and public awareness significantly affect renewable energy consumption in these countries. The empirical results offer insights for policymakers and stakeholders in leveraging technological advancements for sustainable energy development. The findings from this study on the determinants of renewable consumption in the context of the Fifth Technology Revolution in ASEAN countries also have several important policy implications for the sustainable development goal. • This study aims to investigate the factors of renewable energy consumption in the fifth Technology Revolution. • The study uses panel data analysis, and the paper identifies significant factors that have influenced the renewable energy sector. [ABSTRACT FROM AUTHOR]

• Recent advances in discovery and performance prediction of energy storage materials relying on ML. • An overview of the current status and dilemmas of ML databases commonly used in energy storage materials. • ML in energy storage material discovery and performance prediction: typical applications and examples. • Dilemmas to be faced in the development of ML-assisted or led energy storage materials, and corresponding prospects. Energy storage material is one of the critical materials in modern life. However, due to the difficulty of material development, the existing mainstream batteries still use the materials system developed decades ago. Machine learning (ML) is rapidly changing the paradigm of energy storage material discovery and performance prediction due to its ability to solve complex problems efficiently and automatically. Various excellent works are constantly emerging in the field of ML assisted or dominated development of energy storage material, such as exploring of new materials, studying of battery performance, investigating of battery aging mechanism. In this paper, we methodically review recent advances in discovery and performance prediction of energy storage materials relying on ML. After a brief introduction to the general workflow of ML, we provide an overview of the current status and dilemmas of ML databases commonly used in energy storage materials. The typical applications and examples of ML to the finding of novel energy storage materials and the performance forecasting of electrode and electrolyte materials. Furthermore, we explore the dilemmas that will be faced in the development of applied ML-assisted or dominated energy storage materials and propose a corresponding outlook. This review systematically summarizes the current development of ML-assisted energy storage materials research, which is expected to point the way for its further development. [ABSTRACT FROM AUTHOR]

• A hierarchical approach is proposed considering quartile method for optimization. • Proposed CEEMDAN-DMD can regress the energy based on pre-set energy proportion. • Proposed hierarchical allocation strategy can be used for severe power volatility. The development of renewable energy dominated power system and the integration of large-scale wind-storage energy system are inevitable trends of the future society. This paper developed a new methodology of hierarchical power allocation strategy considering wind power fluctuation. A novel idea is presented that fully considers the fluctuation and energy proportion. Based on the Quartile Method (QM) and Hampel-Savitzky Golay (HSG) strategy, a layered-based method that considers the volatility interval to effectively overcome the limitations of a single strategy caused by severe volatility interval is proposed. Then, an improved power division method based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Dynamic Mode Decomposition (DMD) is proposed where CEEMDAN is used to decompose the Intrinsic Mode Function (IMF) of the remaining fluctuations. Furthermore, according to the required setting of energy proportion, DMD strategy is used for power fitting of the hybrid energy storage system (HESS). Through experimental verification, the hierarchical strategy proposed in this paper can effectively reduce the probability of severe volatility interval or distortion interval and ensure the sequence in the ideal interval. Moreover, the proposed CEEMDAN-DMD strategy can realize the power division according to the energy proportion demand. In model verification, the volatility improvement ζλ 1 min = 85.4766%. Set the energy demand ≥ 80%, the fitting energy proportion accounts for 87.7533%, which outperforms the traditional power division method. In addition, it is demonstrated that the theoretical method proposed in this paper is reliable and promising. [ABSTRACT FROM AUTHOR]

Abstract Currently, wind and solar energy have not been well utilized in China, and the rate of abandoned wind and solar facilities in China is increasing annually. Renewable portfolio standards (RPS) are an effective system for promoting large-scale renewable energy integration and consumption and realizing the goal of renewable energy development. To clarify the interactive relationship between the different levels and multiple actors in RPS, this paper uses policy network theory to analyze the interactive relationship of stakeholders from the three aspects of network subject, network structure and network interaction. The policy network of the RPS consists of the policy community, professional network, intergovernmental network, producer network and issue network. Under the RPS, to meet their own needs, participants affect the formulation and implementation of the RPS through the actions of confrontation, alliance, competition and cooperation. In different stages of developing the RPS, the openness and integration of policy networks change accordingly. This study overcomes the limitations of past simple analyses of the actor relationship in the policy process and thereby offers certain reference significance. Highlights • The actors of RPS policy networks are analyzed. • This paper analyze the game relationship of the stakeholders from three aspects. • This research reveals the network relationship between multiple stakeholders. • This study breaks through the limitations of past analysis of actor relationship. [ABSTRACT FROM AUTHOR]

This research investigates the relationship between greenhouse gas (GHG) emissions and energy production and consumption patterns in 122 countries over the period from 1990 to 2020. Particular attention is paid to the role of different types of energy, including renewable, hydro and nuclear energy, as well as oil, gas and coal, in shaping GHG emissions. Unlike previous studies, the paper analyzes the full range of energy resources and focuses on different quantiles of data using the quantile-quantile regression (QQR) method. The results of this approach are compared with traditional quantile regression (QR) to assess its validity. Among the findings of this study are: (1) different parts of the energy mix have a heterogeneous impact on GHG emissions in different quantiles; (2) energy consumption patterns play a more significant role in shaping GHG emissions than production patterns; (3) increasing production and consumption of fossil fuels does not necessarily increase GHG emissions; (4) similarly, the development of renewable energy sources is not always accompanied by a reduction in GHG emissions. Our general conclusion is that existing climate policy requires significant diversification to not be focused only on renewable energy, which impact on GHG emissions is not as homogeneous as it usually considered, and which cannot solve the problem of raising emissions solely. Instead of this, it could be more rational to focus on the searching the ways of hydrocarbon industries sustainable development and support them in this journey, especially in the field of Scope 2 and Scope 3 operations management. • Impact of energy consumption and production on GHG emissions were analyzed for 122 countries in 1990–2020. • Quantile and Quantile-on-Quantile regression were used to catch the specifics of energy consumption and production patterns. • Two temporal breakpoints (1999/2000 and 2009/2010) were introduced to catch shifts in variables dependencies. • The influence of fossil fuels on GHG emissions is not homogeneous as it usually considered, the same is fare for renewables. • A promising option to reduce GHG emissions is carbon capture, utilization and storage (CCUS). [ABSTRACT FROM AUTHOR]

The development of renewable energy is the key to tackling climate changes and ensuring energy security. Meanwhile, the process is usually associated with a huge investment and financing gap, especially for transitional countries like China. Hence, developing and innovating the traditional financial system is urgent. However, existing studies have mainly focused on traditional financial perspectives, and few papers have critically examined how digital financial inclusion affects the growth of renewable energy industries. To this end, using Chinese provincial data from 2011 to 2020 and applying Feasible Generalized Least Squares (FGLS) method, this study explores the effect of financial inclusion on renewable energy and its underlying mechanisms from the production and consumption perspectives. It is found that the impact of financial inclusion on the production side of the renewable energy industry is significantly better than on the consumption side, and there is heterogeneity effect in different energy resource varieties, different regional locations, and different financial regulation intensities. The financial inclusion on renewable energy has a significant pulling effect in photovoltaic and wind provinces, but not in large hydropower provinces. Then, its driving effect is particularly pronounced in the northern region. Nonetheless, it is not significant for renewable energy consumption in the southern region. Besides, this positive effect cannot be achieved without stronger financial regulation. The mechanism of action assessment finds that optimizes the consumption pattern, increase the competition of commercial banks, and improve technological innovation all contribute to the positive effect whereas, FDI shows the negative effect. This study provides new evidence on China's achievement of the energy transition, suggesting the need to further improve incentives for FDI firms and to strengthen consumption-side guidance. [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]

Energy consumption has promoted a continuous development for economy and society, but it has also brought serious environmental pollution problems which endanger public health. In order to focus on the impact of PM 2.5 emissions from future energy consumption on human health better, this paper estimated PM 2.5 emissions caused by energy consumption in Beijing in 2020,2025 and 2030 by setting the baseline scenario, emission reduction scenario, intensified emission reduction scenario and the data of the base year 2015, which will be 162.6 thousand tons, 195.9 thousand tons and 235.9 thousand tons in the baseline scenario, 143.1 thousand tons, 151.8 thousand tons and 160.9 thousand tons under the emission reduction scenario, and 132.6 thousand tons, 130.2 thousand tons, 127.9 thousand tons under the intensified emission reduction scenario. Finally the paper measured the number of different health effect ends caused by PM 2.5 emissions and the economic loss. The results showed that there was a rapid increase, a mild increase and a moderate decrease in energy consumption and PM 2.5 emissions in Beijing under the three scenarios from 2015 to 2030. Besides, under the same scenario and the same concentration level baseline, the number of the internal medicine (over the age of 15) was the largest, and the economic loss caused by total death rate was the biggest. This article provides suggestions to reduce PM 2.5 emissions and public health effect ends for Beijing in the future. [ABSTRACT FROM AUTHOR]

Traditional energy planning methods are mostly related to newly-built area. However, in most already-built regions, the existing district energy systems (DESs) need to be ameliorated due to insufficient capacity and low efficiency. This paper investigates the methodology of expansion planning, which can offer planners a viable way to expand existing DESs. The term “expansion” aims to increase the supply capacity of the energy forms, e.g., steam, hot water and electricity, which cannot be accessed from national or urban energy supply network, and meanwhile increase energy utilization efficiency and cut CO 2 emission. Three parts of the methodology, including problem analysis in current DESs, demand side analysis and the existing DES expansion, are introduced in this paper. New concepts of load structure and evaluation system of the whole district are proposed in this methodology. A case analysis of Ningbo Hi-Tech District, China is conducted to shed light on the application of this method. [ABSTRACT FROM AUTHOR]

Under the trend of global low-carbon development, reasonable and accurate prediction of electricity consumption plays an essential role in vigorously adjusting power system structure, promoting electrification, and other energy-saving and emission reduction measures. Considering the development trend of energy consumption, this paper introduces the Logistic model of energy structure into the system structure, and establishes a novel grey prediction model with system structure. According to the division of energy factors with similar attributes, this model seeks the internal relationship of the development of electricity consumption and describes the interaction between related factors and multiple main factors in the form of equations, which makes the model have better applicability and stability. In the validation part, the ten types of energy are divided according to their attributes, and the main factor group and the related factor group are distinguished. The model proposed in this paper is used for simulation and prediction, and is compared with the three types of models (six models). In the two cases, the simulation error of the new model is as low as 3.9790%, and the prediction error is 0.5645%. Compared with other models, the new model has shown good performance in the case of electricity consumption forecasting in China. The effectiveness of the optimization of the model in structure, background, and application is verified. At the same time, based on the analysis and prediction of China's consumption data, this paper gives relevant policy suggestions for developing China's power structure. • A novel model based on energy background (EPMGM(1,M,N)) is established. • The relationship of factors in the system are characterized by the equations. • A Logistic model based on energy structure is introduced to the new model. • China's electricity consumption is effectively predicted by the model. [ABSTRACT FROM AUTHOR]

Even though renewable energy development has gained momentum in China, thermal power generation still accounts for approximately 70% of the county's total power generation serving as the major source of carbon dioxide (CO 2) emissions in China. Facing the challenges of meeting 2030 peak target of CO 2 emission and realizing the coordinated development of thermal power generation in Beijing-Tianjin-Hebei region, this paper applies generalized Divisia Index Method (GDIM) to decompose the dynamics in the relevant CO 2 emission. The effects of five factors including electricity demand, energy consumption, technology, energy efficiency and energy-mix are considered. The decomposition suggests that electricity demand is the primary factor driving the CO 2 emission up, whereas technology effect decreases CO 2 emission the most. Given the significant roles of technology, energy-mix and energy efficiency in CO 2 emissions reduction, seven scenarios are designed to identify the optimal coordinated development pathway for thermal power generation in Beijing-Tianjin-Hebei region. Through upgrading energy structure and/or enhancing energy efficiency, the thermal power generation in Beijing-Tianjin-Hebei region can achieve coordinated development and realize the 2030 peak target under four scenarios. The detailed development pathways for CO 2 emissions and specific policy implications for Beijing, Tianjin and Hebei are provided to further govern CO 2 emissions and maintain sustainable development. • Proposed a five-factor Generalized Divisia Index Model (GDIM) for CO 2 emission analysis. • Analyzed the CO 2 from thermal power generation in China's Beijing-Tianjin-Hebei region. • Electricity demand and technology contributed the most to the positive and negative changes of CO 2 , respectively. • Designed seven thermal generation development scenarios to study the CO 2 change until 2030. • The 2030 CO 2 peak target can be achieved under four scenarios. [ABSTRACT FROM AUTHOR]

• A multi-VPP energy-sharing model is proposed based on GNB theory. • The network transmission cost encourages the consumption of renewable energy nearby. • The non-convex GNB-based model is divided into two sub-problems by transformation. • A distributed method based on the fast-ADMM is developed. • A contributing factor for profit distribution is applied to endorse renewable energy. The benefits of small-scale renewable energy resources are restricted by a feed-in-tariff strategy that works with distribution grids only. Virtual Power Plant (VPP) aggregates diverse distributed energy resources on the demand side for efficient control, which can improve their revenue and encourage the growth of renewable energy. Based on the general Nash bargaining theory, this paper proposes a multi-VPP energy-sharing mechanism. This approach enables VPPs to increase their revenue by engaging in peer-to-peer energy transactions. First, a general Nash bargaining-based model is proposed for multi-VPPs energy sharing considering peer-to-peer energy transactions. The model is then decomposed into two subproblems: minimizing the total cost of the VPP energy-sharing alliance, and the cost allocation of peer-to-peer energy transactions. Considering the privacy protection of VPPs, the first subproblem is solved by a distributed algorithm based on the alternating direction multiplier method. In the second subproblem, due to the introduction of a bargaining factor that is positively related to each VPP's contribution to energy sharing alliance, the cost allocation better reflects their contribution. The numerical test demonstrates the proposed energy-sharing mechanism, with little computational complexity, delivers a cost distribution result that is comparable to the classical Shapley value while promoting the development of renewable energy resources. [ABSTRACT FROM AUTHOR]

Policies targeting energy efficiency and renewable energy are sometimes viewed as more politically feasible than carbon pricing in pursuit of emission reduction goals. This paper assesses underlying drivers of energy policy development. These factors include economic, social, environmental, and institutional variables. The between estimator for panel data is an appropriate method for our focus on some exogenous variables that vary more across countries than over time. We find that larger oil reserves per capita have a negative relationship with renewable energy policy development. Education and political globalisation have strong positive relationships with both energy-efficiency and renewable-energy policy development. These results suggest that greater participation in global political groups can be an indirect approach toward energy policy development, in cases where direct and immediate policies are hard to implement. OECD countries have higher policy scores by 33 and 25 points in energy efficiency and renewable energy respectively. Blinder-Oaxaca decomposition analysis shows that these higher scores are primarily due to social and political institution predictors rather than economics and physical endowments. • Large-n cross country international sample on continuous policy score measurement. • Political globalisation is positively linked with renewables and energy efficiency policies. • Education is positively linked to energy policy development, especially for efficiency. • Higher OECD countries' policy scores are primarily due to social and political predictors. • Higher incomes are unlikely to lead to automatic improvements in energy policies. [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]

Energy consumption of lift systems is one key issue in modern high-rise buildings, drawing property owners' attention and accounting for 6% of total electricity consumption by end-use in Hong Kong. A Benchmarking Energy Performance Indicator (BEPI), , measured in J.kg−1.m−1 and proposed by the first author of this paper for lift installations is mentioned in the Technical Guidelines of the Building Energy Code , as one emerging good engineering practice. BEPI is a fair parameter for comparing energy performance of lifts irrespective of their brand, model, travel, speed, capacity and control. It considers energy performance due to both the motor drive efficiency and traffic control intelligence. However, one main obstacle to make it statutory is the lacking of a generic monitor able to retrieve three sets of real time data (normally unavailable on an existing lift) to compute the BEPI, namely energy consumption, instantaneous car load and traveling distance, and to collect ample data statistically reliable enough to determine an acceptable threshold value. This paper describes the development of a proof-of-concept prototype BEPI monitor by giving full technical details on the rationale and history of development, the hardware components, software framework, and updated numerical results of one implementation inside the headquarters of Electrical & Mechanical Services Department (EMSD) of HKSAR. Experiences in installing and calibrating those external sensors that make the whole system non-disturbing to normal lift operation, as well as the procedures in computing the BEPI, are discussed in greater details. It has been shown that the BEPI is traffic dependent but the weekly average of it is relatively stable, confirming it may be robust enough for benchmarking purpose. The solution described in this paper may help roll out the monitoring of BEPI for all lifts inside and outside Hong Kong. [ABSTRACT FROM AUTHOR]

Abstract The Association of South East Asian Nations (ASEAN) is a diverse region characterized by rapid economic growth, demographic change and urbanization. The dominance of energy supply from non-renewable resources in the region means that the increasing energy demand has implications for energy security, as well as adverse local and global environmental effects. Climatic conditions in the region are favorable for renewable energy (RE) resources, especially but not only wind and solar technologies. ASEAN countries differ strongly in terms of their national policy frameworks and progress in renewable energy development; the overall target of 23% renewables by 2025 is very ambitious under current policy frameworks. This paper identifies a gap between these national policies and local governance, especially in urban areas, which requires attention to ensure future target fulfilment. By employing a new combined correlation and decomposition approach at country and city levels, we investigate the determinants of RE expansion and explore the trend drivers in ASEAN countries from 1995 to 2013. An Impact Matrix is developed to position and interpret the relative push (e.g. policy) and pull (e.g. market) impacts on RE development, and to derive policy recommendations for countries and sectors. The results highlight that urban areas should be the focus of RE policy and governance in addition to rural areas. The tremendous impact of economic growth creates a great impetus for renewable energy development, but urbanization is the second pull for renewable energy extensions. Since the two effects are located in the first quadrant of the Impact Matrix, if strategists affect these factors, they will create the most powerful incentive for renewable energy growth. This confirms that if the strategic aim is to promote renewable energy market development, through policy and governance measures, the focus should be on urban areas, non-electricity sectors and the demand side. [ABSTRACT FROM AUTHOR]

Reusable launchers have been studied under CNES contracts for more than 30 years, with early concepts such as STS-2000 or Oriflamme, more recently with very significant efforts devoted to Liquid Fly Back Boosters as with the Bargouzin project led with Tsniimash, TSTO with the Everest concept studied by Airbus-DS as prime contractor or the RFS Reusable First Stage concept of a large first stage associated to a cryotechnic second stage. These investigations, summarized in the first part of the paper, enabled CNES to identify clearly the technology requirements associated to reusability, as well as cost efficiency through detailed non-recurring costs and mission costs analysis. In parallel, CNES set in place development logic for sub-systems and equipment based on demonstrators, hardware test benches enabling maturation of technologies up to a TRL such that an actual development can be decided with limited risk. This philosophy has been applied so far to a large number of cases, such as TPTech and TPX for Hydrogen turbo pump, GGPX as demonstrator of innovative gas generator, HX demonstrator of modern cryotechnic upper stage with a dozen of different objectives (Thermal Protection, 20K Helium storage, measurements …). This virtuous approach, “learn as you test”, is currently applied in the phased approach towards scaled down reusable booster stage, whose possibility to be used as first stage of a microlaunch vehicle is under investigation. The selected technologies allow paving the way towards reusable booster stages for Ariane 6 evolutions or main reusable stage for a further generation of heavy launchers. The paper describes the logic behind this project, together with the demonstration objectives set for the various sub-systems as well as operations. [ABSTRACT FROM AUTHOR]

Due to the excellent energy-saving and environmental protection features, electric vehicles (EVs) are gaining significant market penetration, especially in densely populated urban areas with systemic air quality problems. What follows is that we will face one of the biggest challenges: how to accurately predict the energy consumption (EC) of electric vehicles to alleviate the 'range anxiety' problem. To address this problem, this paper proposes a hybrid approach using short-trip segment division (S-TSD) algorithm and deep neural network (DNN) for predicting the energy consumption of battery electric vehicles (BEVs) by using real-world driving datasets. In this study, we aim to accurately predict energy consumption at the short-trip level, so we propose a novel S-TSD algorithm to divide the driving process of BEVs into several driving segments and conduct feature extraction for each segment. Then, using the extracted features, we develop a series of DNN models with varying hidden layers to investigate the respective performance for EC predictions. The results show that the optimal number of hidden layers is identified as 5, and the neuron structure for each hidden layer is determined as 13 × 256 , 256 × 512 , 512 × 256 , 256 × 128 , and 128 × 1 , respectively. Two state-of-art models are selected as benchmark methods to compare with the proposed model for the EC prediction tasks. The comparative analyses suggest that the proposed model outperforms the benchmark methods in terms of the four index values of MAE, MAPE, MSE, and RMSE, indicating that the proposed model has a good performance for predicting the energy consumption of BEVs at a short-trip level. • A novel S-TSD algorithm is proposed for feature extraction at a short trip level. • An EC prediction model combined with S-TSD approach is proposed for the first time. • The optimal structure of the DNN-based model is determined for the EC prediction. • The DNN-based proposed model has a good performance for EC prediction of BEVs. [ABSTRACT FROM AUTHOR]

Because industrial sectors in China are energy-intensive, improving energy efficiency and promoting energy savings in these sectors are of great importance for the further sustainable development of China’s economy. Using a proposed meta-frontier data envelopment analysis (DEA), this paper analyzes the total-factor energy efficiency and energy savings potential in China’s provincial industrial sectors for the years 2000–2014 from three perspectives, namely, the technology gap, management, and scale. The results indicate that (1) driven by technological progress, energy efficiency during the sample period was greatly improved, though the expansion of technology gaps between regions and the decline in scale efficiency represented two significant handicaps; (2) the current energy efficiency in China’s industrial sectors remains extremely low, indicating that there is considerable potential for energy efficiency improvement and energy savings; and (3) the sources of energy inefficiency and energy savings potential in China’s industrial sectors exhibit spatially distinct characteristics. Accordingly, it is determined that most of China’s eastern provinces should focus on improving scale and management efficiency, whereas most of its central and western provinces should concentrate on three factors: narrowing the regional technology gap, improving management efficiency, and optimizing the industrial scale. [ABSTRACT FROM AUTHOR]

Abstract: In March 2011, China’s central government set a new challenging target of reducing its energy intensity by 16% during 2011–2015, after it had achieved a reduction of 19.1% during 2006–2010. And this new target was assigned to provincial authorities in August 2011. However, China’s provincial energy-economic developments are unbalanced and different provinces have different key sectors for energy conservation. Most previous studies focused on provincial energy efficiency at the aggregate level, or the three-industry level (or one-digit level). However, whether for policy decision or academic research, it is necessary to further subdivide the sectors. In this paper, we use three indicators (Gini Coefficient, energy consumption share and energy intensity) to compare provincial energy conservation potentials at the two-digit sector level. To our knowledge, this paper is the first one to identify the keys for energy conversation across the 31 provinces×65 sectors. And the results are shown in visualized maps and matrix tables to help identify the key province×sectors for energy conservation easier. This also helps the central and provincial governments to distinguish key sectors when they monitor the energy conservation progress. [Copyright &y& Elsevier]

Abstract: HCCI combustion can substantially reduce NOx and soot emissions of diesel engines. However it is hard for diesel HCCI to obtain the same level of fuel economy as traditional diesel engines. This paper presents a way to improve the fuel economy of diesel HCCI by adopting Integrated Starter Generator (ISG). A set of diesel parallel hybrid power system (in this article, we name it “hybrid engine”) is constructed by installing an ISG motor on a diesel engine, which applies HCCI–CI combined combustion. Based on a lot of investigation on diesel HCCI, this paper is focus on improving HCCI engine performance, especially the fuel economy, by adopting ISG motor assist control. The coordination control strategies of engine and ISG motor in HCCI transient states, HCCI–CI transition, engine quick start and braking energy regeneration are developed. The results of equivalent New European Driving Cycle (NEDC) tests show that, by using ISG assistance, the fuel consumption of the diesel HCCI engine is greatly reduced. Meanwhile the NOx and soot emission are also improved. [Copyright &y& Elsevier]

Rapid vehicle and powertrain development has become essential to for the design and implementation of vehicles that meet and exceed the fuel efficiency, cost, and performance targets expected by today’s consumer while keeping pace with reduced development cycle and more frequent product releases. Recently, advances in large-scale additive manufacturing have provided the means to bridge hardware-in-the-loop (HIL) experimentation and preproduction mule chassis evaluation. This paper details the accelerated development of a printed range-extended electric vehicle (REEV) by Oak Ridge National Laboratory, by paralleling hardware-in-the-loop development of the powertrain with rapid chassis prototyping using big area additive manufacturing (BAAM). BAAM’s ability to accelerate the mule vehicle development from computer-aided design to vehicle build is explored. The use of a hardware-in-the-loop laboratory is described as it is applied to the design of a range-extended electric powertrain to be installed in a printed prototype vehicle. The integration of the powertrain and the opportunities and challenges it presents are described in this work. A comparison of offline simulation, HIL and chassis rolls results is presented to validate the development process. Chassis dynamometer results for battery electric and range extender operation are analyzed to show the benefits of the architecture. [ABSTRACT FROM AUTHOR]

Shipping is a major contributor to global CO 2 emissions. Various operational and technical measures have been proposed to reduce ship emissions. However, these emission reduction measures may not be all economically feasible to implement. Therefore, it is important to rank all these measures and select the most cost-effective measures for emissions reduction. Moreover, there are various uncertainties in evaluating emission reduction measures, such as uncertainties of implementation cost, fuel consumption, abatement potential and fuel price. These uncertainties may significantly influence the ranking of the emission reduction measures, which further result in an inappropriate selection of the measures for implementation. In this paper, a ranking algorithm with a new criterion is proposed to rank all the emission reduction measures by considering the preference between cost and abatement. Furthermore, a ranking under uncertainty method is developed which takes into account various uncertainties of the impact factors. This method can support policy makers in ranking and selecting emission reduction measures more appropriately by better quantifying and reflecting the uncertainties. [ABSTRACT FROM AUTHOR]

Integrated Assessment Models (IAMs) are omnipresent in energy policy analysis. Even though IAMs can successfully handle uncertainty pertinent to energy planning problems, they render multiple variables as outputs of the modelling. Therefore, policy makers are faced with multiple energy development scenarios and goals. Specifically, technical, environmental, and economic aspects are represented by multiple criteria, which, in turn, are related to conflicting objectives. Preferences of decision makers need to be taken into account in order to facilitate effective energy planning. Multi-criteria decision making (MCDM) tools are relevant in aggregating diverse information and thus comparing alternative energy planning options. The paper aims at ranking European Union (EU) energy development scenarios based on several IAMs with respect to multiple criteria. By doing so, we account for uncertainty surrounding policy priorities outside the IAM. In order to follow a sustainable approach, the ranking of policy options is based on EU energy policy priorities: energy efficiency improvements, increased use of renewables, reduction in and low mitigations costs of GHG emission. The ranking of scenarios is based on the estimates rendered by the two advanced IAMs relying on different approaches, namely TIAM and WITCH. The data are fed into the three MCDM techniques: the method of weighted aggregated sum/product assessment (WASPAS), the Additive Ratio Assessment (ARAS) method, and technique for order preference by similarity to ideal solution (TOPSIS). As MCDM techniques allow assigning different importance to objectives, a sensitivity analysis is carried out to check the impact of perturbations in weights upon the final ranking. The rankings provided for the scenarios by different MCDM techniques diverge, first of all, due to the underlying assumptions of IAMs. Results of the analysis provide valuable insights in integrated application of both IAMs and MCDM models for developing energy policy scenarios and decision making in energy sector. [ABSTRACT FROM AUTHOR]

This paper explores the impacts of active suspension with energy regeneration capability on the fuel economy of a power split Hybrid Electric Vehicle (HEV). Models of HEV powertrain and suspension are established and, based on their interactions, unified. The impacts of control parameters on the suspension performance are synthetically analyzed. The analyses form a basis for the design of suspension controller. High-level supervisory controller and low-level sub-controllers for HEV powertrain and suspension are designed. Low-level sub-controllers can ensure the performance of each system, whose control parameters are updated by the supervisory controller. The equivalent consumption minimization strategy (ECMS), superior in maintaining charge sustainability, is improved for better application in the proposed system. Simulation results under different conditions demonstrate that both HEV fuel economy and ride comfort are improved. Comparative analyses of various scenarios validate the positive effect of suspension energy regeneration on the energy conservation performance of power split HEV. Meanwhile, the improvement of HEV fuel economy can be highlighted by applying ECMS. [ABSTRACT FROM AUTHOR]

This paper presents a model predictive power management strategy for a novel anti-idling system, regenerative auxiliary power system (RAPS), designed for service vehicles. RAPS is able to utilize recovered braking energy for electrified auxiliary systems; this feature distinguishes it from its counterparts - auxiliary power unit (APU) and auxiliary battery powered unit (ABP). To efficiently operate the RAPS, a power management strategy is required to coordinate power flow between different energy sources. Thus, a model predictive controller (MPC) is developed to improve the overall efficiency of the RAPS. As an optimization-based approach, the MPC-based power management strategy usually requires the drive cycle or the drivers’ command to be known a priori. However, in this study, an average concept based MPC is developed without such knowledge. MPC parameters are tuned over an urban drive cycle; whereas, the robustness of this MPC is tested under different drive cycles (e.g. highway and combined). Analysis shows that, the presented MPC has a comparable performance as the prescient MPC regarding fuel consumption, which assumes knows the drive cycle beforehand. Meanwhile, with the help of the proposed MPC and RAPS, the service vehicle saves up to 9% of the total fuel consumption. The proposed MPC is independent of powertrain topology such that it can be directly extended to other types of hybrid electric vehicles (HEVs), and it provides a way to apply the MPC even though future driving information is unavailable. [ABSTRACT FROM AUTHOR]

With the increasing negative effects of fossil fuel combustion, many countries have paid more attention on supporting environmentally friendly energy generation, particularly renewable and sustainable energy sources (RES). However, renewables still cannot economically compete with fossil-fuels and are facing new challenges. Appropriate electricity pricing mechanism plays a vital role on mitigating the stress of limited fossil fuels and can promote renewable energy consumption. As one major type of renewable energy, wind power has been globally promoted. In order to promote the application of wind power, appropriate wind power prices (WPP) should be established by considering the local conditions. This paper targets such a field and conducts a bibliometric and network analysis based on the data from Scopus. The results show that the numbers of total related publications are gradually increasing, with the US as the leading country. European countries also have outstanding achievements. Moreover, both the most cited articles and keywords distribution offer future research directions. In general, this study provides valuable insights to both wind power researchers and practitioners. [ABSTRACT FROM AUTHOR]

In this paper a data-validated power-efficiency model of a diesel-powered fuel-cell-based auxiliary power unit (APU) system is used to investigate the various sizes of the power unit and the battery and to evaluate the optimal choices for specified load profiles. The challenge comes from the FCGEN (Fuel Cell-based power GENeration) EU FP7 project, where such an APU was developed. The system consists of a fuel processor, a PEM stack, and a battery providing power for the startup, shutdown, and for covering load transients; however, the developed prototype system is not optimised. Before redesigning it for mass production, the optimal size of the main components needs to be identified to enable the best possible exploitation of the technology. In this work a case-specific load profile was used and a mesh grid of scenario simulations has been performed using various sizes of the fuel cell with fuel processor as a power unit and the batteries of various capacities as an energy storage unit. For this purpose a scalable APU model, including the BoP component consumption, has been developed. Upon the analysis results, the relation for optimal combinations in terms of efficiency and degradation is proposed and the confronted tradeoffs are discussed. [ABSTRACT FROM AUTHOR]

• Two model buildings are specially built for comparative study. • Dynamic monitoring of external insulation for improving thermal environment and energy saving during heating. • The time for EPS building to approach the air temperature set value has no obvious advantage. • The energy saving effect of short-time heating operation of EPS building is very weak. • A dynamic index is proposed to evaluate the improvement effect of external insulation on indoor thermal environment. In this paper, two identical model buildings were built, one of which adopted 38 mm Expanded Polystyrene (EPS) board as the external insulation. Under the same heating set-point temperature, the dynamic change process of indoor thermal environment and heating energy consumption of EPS insulation building and non-insulation Reference building was compared and monitored. Results show that under the same heating set-point temperature, the indoor temperature of the two buildings can quickly approach the set value in about 20 min. However, the temperature rise of the internal surface of the wall lags behind. After several hours, the advantage of the temperature rise of the internal surface of the EPS building gradually appeared. The energy consumption of the two buildings is similar in the initial heating period of 20 min, and the energy saving rate of EPS building increases slowly with the increase of the heating duration. The energy saving rate is about 1%–26% in the range of 0.5–8 h intermittent heating in hot summer and cold winter zone. The change curve of cumulative temperature rise ratio with heating time is proposed to evaluate the dynamic improvement effect of external insulation on indoor thermal environment. This research provides a reference basis for the development of energy conservation. [ABSTRACT FROM AUTHOR]

Unsustainable and uncontrolled development of urbanization, as well as the expansion and increase of environmental challenges, has caused extensive activities in recent years to reduce environmental pollution and efficient use of energy resources, worldwide. One of the appropriate solutions to reduce carbon dioxide (CO 2) emission and the optimal use of energy sources is the construction of an industrial energy park (IEP). Given the pivotal role of energy parks in different energy networks, accurate techno-economic-environmental assessment of energy parks, along with the promoted energy conversion facilities and flexibility options has become a challenging task. To address this open issue, this paper focuses on the coordinated operation of IEPs with a variety of thermal, electrical, and cooling loads to satisfy economic and environmental benefits. The proposed combined cooling, heat, and power (CCHP)-based energy park is equipped with multiple energy conversion facilities, such as absorption and electric chiller, power-to-heat facility, multi-energy storage, as well as demand response (DR) program as the flexibility option to supply energy demands, while has energy exchanging (power and heat exchanged) with the corresponding markets. The main objectives of this paper are to minimize the total operation cost of the energy park and decrease CO 2 emission rates. The coordinated model is exposed to the fluctuation of wind power, electrical and heat loads, and power prices, so the hybrid stochastic/information gap decision theory (IGDT)-based robust approach is used to handle them. The technical implications of using the proposed strategy are: (1) Creating a robust platform for industrial park operators to actively participate in different energy markets and take advantage of existing economic opportunities to meet the required energy demands at the lowest cost; and (2) Encouraging industrial park operators to participate in DR programs to reduce operating costs, increase energy efficiency, and contribute to the development of energy network plans. The proposed stochastic-IGDT multi-objective model is examined on the sample park and numerical results are discussed for different cases. Results reveal that under the proposed coordinated approach, total operation cost and emission pollution are decreased up to 3.1%, and 2.2%, respectively. • Evaluating the operation of the CCHP-based IEP under coordinated strategy • Providing a hybrid IGDT/stochastic approach for operation of the IEP • Developing economic-environmental scheduling for IEP under hybrid approach • Integrating multi-carrier storage, DR, boiler, CHP, chiller, P2H, and RES in IEP [ABSTRACT FROM AUTHOR]

• Ownership structure of small commercial buildings makes energy retrofits challenging. • Retrofit package for Zero Net Energy small offices in Mediterranean climate. • Energy use and occupant comfort evaluated experimentally, side-by-side, in real time. • 65%-68% energy savings for cooling season; 22%-25% for heating season. • Energy savings are achieved while maintaining visual and thermal comfort. Making buildings zero-net energy (ZNE) is one of the major strategies for achieving carbon emission reduction goals. For this strategy to be successful, it entails a very significant reduction in energy use – 50% or more. In small commercial buildings, however, owners and building management teams usually have limited resources for identifying, analyzing, and procuring appropriate retrofit measures for reducing such use. An approach to overcome this limitation is the development of bundles of energy efficiency measures that can be presented to building owners/operators as a comprehensive package. The research presented in this paper focuses on an experimental evaluation of the impacts of a retrofit package developed for small office buildings in California. Performing this evaluation in a full-scale whole-building integrated systems test facility allowed a side-by-side evaluation in real time, against a reference case, of the impact of the retrofit package not only on energy use but also on visual and thermal comfort. The retrofit package evaluated is comprised of a combination of HVAC, lighting (including daylighting), and plug load measures. The evaluation occurred at different times of the year in order to account for seasonal variations in environmental conditions, including solar angles and weather. The experimental facility allowed testing for two different façade orientations: south and west. Results show that the proposed ZNE retrofit package can save significant amounts of energy for small commercial buildings. During cooling-prevalent periods, total energy savings were 65% for south orientation and 68% for west orientation; during heating-prevalent periods total energy savings were 22% for south orientation and 25% for west orientation. Measurements indicate that the ZNE retrofit package resulted in small but not very significant changes in comfort levels for building occupants. [ABSTRACT FROM AUTHOR]

The objective of this paper is to systematically identify and assess critical factors, which foster or hinder the development of bottom-up initiatives in the diffusion of photovoltaics. Bottom-up initiatives are social innovations, which entail civil engagement in energy transition at a local or regional level, and are expected to play a growing role in the governance of local energy systems in Europe. A mixed design methodology is used to identify critical factors and assess their importance. This involves combining an analysis of strengths, weaknesses, opportunities and threats with an analytic hierarchy process. The findings indicate that successful initiatives are those which are able to draw upon substantial local public interest and trust in the new technology, and which manage to combine financial attractiveness with environmental concerns. The results make clear that the political context is also an extremely important success factor. Given the appropriate circumstances, such initiatives may make a significant contribution in the transition to a sustainable energy system, and thus prove useful in reaching European energy targets. [ABSTRACT FROM AUTHOR]

The Swedish energy performance certificates for single-family housing provide rich information on energy consumption and various physical attributes. They also include estimates of the energy conservation potentials resulting from implementing cost-efficient energy saving measures. By matching the certificates issued for single-family houses in 2009 and 2010, with socio-economic data about the residents, local climate data and information about recent improvements of the building we have created a unique database, which can be used to explore a wide variety of questions related to energy consumption and conservation. One aim of this paper is to assess the role for energy consumption played by socio-economic characteristics of the residents as compared to physical attributes of the house. Another is to estimate the influence of housing attributes and climate on the “engineering estimates” of the conservation potentials. Our results show that while the quantitative impact of physical attributes dominate the energy use for heating and cooling, the opposite holds for household electricity. The assessed conservation potential, amounting to 15% of the energy consumption, is significantly related to both the housing attributes and the use of energy. The results also indicate a need to improve the information provided by the performance certificates. [ABSTRACT FROM AUTHOR]

Buildings account for a large share of energy consumption and greenhouse gas emission. Numerous methods have been applied to operate buildings more efficiently. However, the embodied energy and carbon are identified increasingly important in terms of sustainability throughout building life cycle. In traditional building structural design approach, the engineers pay little attention on environmental aspect which has been recognised as one of the most important factors to consider in modern integrated building design approach. The emergence of Semantic Web technologies provides more opportunities to assist structural engineers in improving building sustainability. The study presented in this paper investigates how ontology and Semantic Web rules can be used in a knowledge-based system, to represent information about structural design and sustainability, and to facilitate decision-making in design process by recommending appropriate solutions for different use cases. A prototypical system named OntoSCS (Ontology for Sustainable Concrete Structure), including a Web Ontology Language (OWL) ontology as knowledge base and SWRL rules providing reasoning mechanism, is developed to offer optimised structural design solutions and selections of material suppliers. Embodied energy and CO 2 are used in the system as indicators to evaluate sustainability of structure. Both ontology reasoner and case studies are employed for system validation. [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 spatial distribution of biomass resources is not uniform. At present, the zoning of biomass energy development suitability is primarily based on resource potential, whereas energy demand is ignored as an important factor affecting the degree of local energy self-sufficiency. Under the constraints of high transportation costs, solar energy with low geographical constraints can be a supplementary energy source for biomass despite its instability. Based on the energy utilization potential of solar energy and biomass resources, this paper evaluates the energy surplus factor (ratio of renewable resource potential to energy demand) of each city in the study area, takes meeting the specific energy surplus factor as the construction prerequisites of biomass combined heat and power (CHP) plants, calculates the biomass resource utilization rate and energy surplus factor in the study area under different conditions, and provides zoning suggestions and data reference for the unified planning of biomass CHP plants. The minimum energy surplus factor that is suitable for the development of biomass energy is set as the construction threshold of biomass CHP plants (The construction threshold is the minimum condition for the construction of biomass CHP plants. Only areas where the energy surplus factor reaches the preset construction threshold are allowed to build biomass CHP plants). The results show that the construction threshold is negatively correlated with the utilization rate of biomass resources. When the construction threshold is less than 1.4, the utilization rate of biomass resources decreases rapidly with the increase of the construction threshold; when the construction threshold is greater than 1.4, the utilization rate of biomass resources decreases slowly and tends to be flat. It is suggested that biomass resources should be developed in different regions and in stages, and priority should be given to developing areas with an energy surplus factor greater than 1.4. Areas with an energy surplus factor of 0.3–1.4 are considered secondary development areas, and areas with an energy surplus factor of less than 0.3 are not recommended for development; these can meet 31% and 59% of the total energy demand and reduce 50319380 tons and 180524400 tons carbon emissions so that the limited construction cost can meet higher energy demand and biomass resource utilization. • Priority development areas are identified based on solar and biomass potential. • Energy surplus factor is calculated to determine the development suitability. • Strategies for zoning and phased planning of biomass resources are proposed. [ABSTRACT FROM AUTHOR]

Better digging the sustainable development path of the top five energy consumers (China, the United States, India, Japan and Russia) can serve both domestic economy and global economy to achieve coordinated development with the ever-increasing energy demand. This work has made the following efforts: (1) the Tapio decoupling model was employed to determine the decoupling states between energy consumption and economy; (2) based on the extended Kaya formula and the LMDI method, this work focused on measuring the impact of technological factors and urbanization factor on energy consumption; (3) the LMDI method and the Tapio decoupling model were combined to measure the impact degree of influencing factors on decoupling indicators. The main findings show that from 2001 to 2018: (1) Japan was the country with the most year of strong decoupling, followed by the United States, which indicates that the decoupling status of developed countries was better than that of developing countries. (2) The driving forces behind the energy consumption growth are mainly the effects of R&D scale and urbanization, while the main factors that hinder energy consumption growth were technological factors, including energy intensity effect and R&D efficiency effect. (3) Energy intensity effect was the main driving force for the decoupling process, and the R&D efficiency effect was the biggest obstacle to decoupling energy consumption from economic growth. Finally, this paper proposes that the government can give priority to relying on technological innovation to accelerate the development and promotion of energy-saving and emission-reduction technologies. • Exploring the coordinated development path of energy consumption and economic growth in top five energy consumers. • Extending the traditional LMDI method to measure the impact of technological progress and urbanization on energy consumption. • R&D scale and urbanization promote energy consumption growth, while technological progress inhibits energy consumption growth. • Governments can prioritize technological innovation to speed up energy conservation and emissions reduction. [ABSTRACT FROM AUTHOR]

In order to estimate the remaining driving range (RDR) in electric vehicles, the remaining discharge energy ( E RDE ) of the applied battery system needs to be precisely predicted. Strongly affected by the load profiles, the available E RDE varies largely in real-world applications and requires specific determination. However, the commonly-used direct calculation (DC) method might result in certain energy prediction errors by relating the E RDE directly to the current state of charge (SOC). To enhance the E RDE accuracy, this paper presents a battery energy prediction (EP) method based on the predictive control theory, in which a coupled prediction of future battery state variation, battery model parameter change, and voltage response, is implemented on the E RDE prediction horizon, and the E RDE is subsequently accumulated and real-timely optimized. Three EP approaches with different model parameter updating routes are introduced, and the predictive-adaptive energy prediction (PAEP) method combining the real-time parameter identification and the future parameter prediction offers the best potential. Based on a large-format lithium-ion battery, the performance of different E RDE calculation methods is compared under various dynamic profiles. Results imply that the EP methods provide much better accuracy than the traditional DC method, and the PAEP could reduce the E RDE error by more than 90% and guarantee the relative energy prediction error under 2%, proving as a proper choice in online E RDE prediction. The correlation of SOC estimation and E RDE calculation is then discussed to illustrate the importance of an accurate E RDE method in real-world applications. [ABSTRACT FROM AUTHOR]

Environmental performance (EP) uncertainties span a number of energy technology options, and pose planning risk when the energy system is subject to environmental constraints. This paper presents two approaches to integrating EP uncertainty into the long-term energy planning framework. The methodologies consider stochastic EP metrics across multiple energy technology options, and produce a development strategy that hedges against the risk of exceeding environmental targets. Both methods are compared within a case study of emission-constrained electricity generation planning in British Columbia, Canada. The analysis provides important insight into model formulation and the interactions with concurrent environmental policy uncertainties. EP risk is found to be particularly important in situations where environmental constraints become increasingly stringent. Model results indicate allocation of a modest risk premium in these situations can provide valuable hedging against EP risk. [ABSTRACT FROM AUTHOR]

With the development of energy internet technology, electric-thermal interconnected virtual power plant, a new integrated demand response entity, tends to become an effective solution to solve the high reliability and efficient utilization of distributed energy systems. Based on this, a new optimal dispatching method for electric-thermal interconnected virtual power plants considering market trading mechanism is established, which can effectively solve the operation and market transaction problems of complex energy systems including electricity, thermal, natural gas and other energy sources. In this paper, the structure and market trading mechanism of electric-thermal coupled virtual power plants are firstly studied. Secondly, a two-stage optimization model of virtual power plant is constructed. In the first stage, a multi-bid day-ahead market bidding model considering both economic and social benefits is constructed, which is aiming at formulating the strategy and bidding value of virtual power plant participating in energy market dispatching. In the second stage, the intra-day scheduling model is constructed, which takes the minimum penalty cost including abandoned wind penalty cost and bidding penalty cost as the optimization objective, and the output of virtual power plant is adjusted to ensure the operation efficiency of the system. Finally, an electric-thermal coupled virtual power plant in Tianjin, China, is taken as an example for analysis. The results show that the two-stage optimization model presented in this paper can significantly improve the volatility of renewable energy output and the economy of the system operation under the conditions of ensuring the comprehensive benefits of virtual power plant and participating in energy market bidding efficiently. [ABSTRACT FROM AUTHOR]

Zero-energy buildings have attracted interest worldwide as they consume less energy and can alleviate energy shortages caused by rapid economic development. This paper presents a review of the research and development regarding zero-energy buildings in China. First, it briefly introduces the evolution of the definition of zero-energy buildings and discusses the key influencing factors of such buildings in China. Then, envelope design, selection of mechanical and electrical equipment, and solar energy utilization aspects of zero-energy buildings are examined. Additionally, this paper discusses the development and application of zero-energy buildings in China, including project types, design methods, energy resources allocation, and energy operation management. Finally, this paper analyzes China's policies for zero-energy buildings, including incentive mechanisms, laws and regulations, and problems encountered, and provides suggestions for promoting zero-energy buildings in China. We expect this review to identify gaps and future directions for research and development, which will lead to new technologies, methods, policies, and standards that can be applied to zero-energy buildings. • Research and development of zero energy building in China is comprehensively reviewed. • Influencing factors and relevant zero-energy building design components are examined. • Development and application of zero-energy buildings in China are examined. • Zero energy policies in China are analyzed and suggestions for the promotion of zero-energy buildings are provided. [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]

Under the downward pressure of the economy, China's extensive economic growth that relies on factor input is difficult to sustain. Scientific analysis of the industrial restructuring and energy consumption has become the key to realizing China's economic growth from high-speed to high-quality. Based on the fully-modified least square method and panel vector error correction model, this paper explores the equilibrium relationship and Granger causality among the industrial restructuring, energy consumption and economic growth at aggregated and disaggregated levels. The results show that: (1) The effect of industrial structure rationalization on economic growth is higher than that of industrial structure upgrading. When emphasizing industrial structure upgrade, the more reliable industrial policy to achieve China's sustainable economic growth is to pay attention to the industrial structure rationalization, especially in western China. (2) The impacts of natural gas and renewable energy consumption on economic growth are much smaller than coal and electricity consumption; more energy policies should be designed to encourage clean energy development. • The industrial structure rationalization has highest contribution to the economic growth of eastern China, followed by central and western China. • There exists unidirectional causality from industrial structure upgrading to structure rationalizing in western China and bidirectional causality between them in central China. • Effects of natural gas and renewable energy consumption on economic growth are much smaller than coal and electricity consumption. • Unidirectional long-term causality from renewable energy to economic growth exist in each sub-region. [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]

[Display omitted] • Classifications and strategies of NZEBs were reviewed. • Multi-criteria analysis supports NZEB designing. • Renewable energy supply options for NZEBs are summarized. • Cost-benefit analysis and life cycle assessment needed for criterion evaluation. Decarbonizing the building sector is extremely important to mitigating climate change as the sector contributes 40% of the overall energy consumption and 36% of the total greenhouse gas emissions in the world. Net-zero energy buildings are one of the promising decarbonization attempts due to their potential of decreasing the use of energy and increasing the total share of renewable energy. To achieve a net-zero energy building, it is necessary to decrease the energy demand by applying efficiency enhancement measures and using renewable energy sources. Net-zero energy buildings can be classified into four models (Net-Zero Site Energy buildings, Net-Zero Emissions buildings, Net-Zero Source Energy buildings, and Net-Zero Cost Energy buildings). A variety of technical, financial, and environmental factors should be considered during the decision-making process of net-zero energy building development, justifying the use of multi-criteria decision analysis methods for the design of net-zero energy buildings. This paper also discussed the contributions of renewable energy generation (hydropower, wind energy, solar, heat pumps, and bioenergy) to the development of net-zero energy buildings and reviewed its role in tackling the decarbonization challenge. Cost-benefit analysis and life cycle assessment of net-zero energy building designs and their challenges were reviewed to shape the priorities of future development. It is important to develop a universal decision instrument for optimum design and operation of net-zero energy buildings. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper presents a novel methodology for modeling building energy performance. [•] The novelty lies in the use of lead-lag dynamics to present building energy system. [•] The developed model is simple and accurate with minimal physical parameters. [•] Comparison with benchmarks and measurements shows good results. [Copyright &y& Elsevier]

Abstract: Development and deployment of low-carbon energy technologies has been a national strategy of both the UK and China for a number of years, including the use of renewable generation technologies and the improvement of energy efficiency of operations and activities. The paper addresses several issues of generic importance to a residential microgrid system such as network modelling, advanced control and integration of intelligent monitoring techniques. The system, comprising representative distributed generation technologies of photovoltaics, wind turbines and combined heat and power, has been simulated by PSCAD/EMTDC under different operational scenarios. Studies include the effect of environmental condition changes, control systems and power electronics on wind turbines and PV cells, and the mixture of wind/solar/CHP energy generation under dominance of each technology. The performance and dynamics of the system are examined against symmetrical and asymmetrical electrical faults to seek an optimal isolation and restoration of the distributed generation unit from the connected grid system. Modelling these system interactions has demonstrated the feasibility of the proposed residential microgrid system. [Copyright &y& Elsevier]