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With global climate change posing a major threat to human society, a growing number of countries have taken "carbon-neutral" as a national strategy and proposed a vision of carbon-free future. As an important supplement to traditional fossil energy, renewable energy is the main force to reduce the use of high-carbon energy and carbon dioxide emissions, which will become the trend of social development in the future. Finding the optimal renewable energy source is of particular significance for achieving the net zero emissions. However, the existing evaluation methods of renewable energy sources have obvious shortcomings. In terms of weight calculation methods, such as the randomness of the subjective method is strong and the index weights do not reflect the small changes of the evaluation matrix, which affect the reliability and accuracy of the evaluation result. The existing ranking methods can only achieve the complete ranking of the different objects, but cannot classify the renewable energy technical alternatives into different grades. Given this background, this paper proposes a novel evaluation method for renewable energy plans based on improved sparrow search algorithm and projection pursuit model. Firstly, this paper improves the traditional sparrow search algorithm from three aspects: population initialization, population update and population variation. Then, the projection pursuit model is constructed, and the improved sparrow search algorithm is applied to optimize the projection target to find the optimal projection direction, so as to determine the weight values of each evaluation index. Finally, the weighted rank-sum ratio method is used to select the best renewable energy technical plan, which can not only realize the complete ranking of different plans, but also classify the technical plans into different levels. Based on the actual renewable energy development data from a province in China, experiments were carried out to investigate the effectiveness of the proposed method. Experimental results show that the proposed method performs better than some existing evaluation methods of renewable energy technical plans. [ABSTRACT FROM AUTHOR]

A hybrid integrated energy system (HIES) optimal scheduling model is developed by considering energy storage (ES), solar thermal (ST), wind turbine (WT), demand response (DR), and carbon capture and storage (CCS) to minimize the economic cost and pollutant gas emission; A multi-population dung beetle optimizer based on Q-learning (MODBO-QL) is designed to solve the HIES optimal scheduling model. In MODBO-QL, three subpopulations are constructed first: Global search subpopulation (GSS), Local search subpopulation (LSS), and Balanced search subpopulation (BSS). Second, the search and evolution strategies corresponding to the population characteristics are given. The GSS and LSS perform global search and local search, respectively. At the same time, the BSS is responsible for randomly exploring or exploiting the solution to increase the diversity of the population. Finally, the number of populations is dynamically and adaptively adjusted using Q-learning to enhance information exchange between different sub-populations and improve solution performance. An optimal dispatch experiment was conducted using three typical seasonal load data and renewable energy forecast data in a region of China. The experimental results show that compared with the original system, the three typical quarters' average economic costs of the dispatch results obtained by the method in this paper are reduced by 8.7%, 20.99%, and 3.97%, and carbon emissions are reduced by 617.35t, 284t, and 19.5t, respectively; MODBO-QL performs better compared to MOPSO, MOWOA, and MODBO. [ABSTRACT FROM AUTHOR]

As the penetration of renewable energy sources (RES) significantly increases, their inherent uncertainty has brought about a paradigm shift in distribution system planning and operation. The existing planning methodologies usually consider the uncertainty during the investment and day-ahead stages while neglecting the interactive power fluctuations during the intraday stage, resulting in planning schemes with a risk of inadequate regional stability. In light of this, this paper proposes a novel adaptive robust distribution system planning approach that integrates local uncertainty management (LUM) to provide a comprehensive and refined consideration of uncertainty. First, we introduce the concept of LUM that can effectively address the deviations between predicted and actual power of RES and load. Second, we propose the adaptive robust planning model for the distribution system, in which the investment and day-ahead operation are considered in the first stage, while LUM is considered in the second stage to maintain the local stability of interactive power. The proposed model can actively manage uncertainties of RES and load demand, reaching the optimal balance between the investment cost, operational cost, and LUM cost. Finally, by employing the column-and-constraint generation (C&CG), the adaptive robust planning model is decoupled into a master min problem and a slaver max-min problem, after which the max-min problem is converted into a single mixed-integer linear programming (MILP) problem through the strong duality theory and big-M method. The effectiveness of the proposed model is demonstrated using a real-world distribution system located in Fujian Province, China. The simulation results indicate that the proposed model achieves a quantitative decrease of approximately 10% in total cost compared to deterministic planning. Sensitivity analysis is also conducted to reveal the trade-off between economic cost and robustness level. • Local uncertainty management (LUM) is incorporated into distribution system planning. • A three-stage framework is developed for the planning problem incorporating LUM. • An adaptive robust distribution system planning model incorporating LUM is proposed. [ABSTRACT FROM AUTHOR]

Future energy system will feature in a high-share of renewable energies (REs), which poses huge challenges to obtain full utilization of renewable power generation. To solve the problem, this paper presents a joint-operation two-stage mixed integer linear programming model to coordinate the power system and train transportation system by optimizing the logistics of mobile batteries and real-time charge/discharge in cities. The full/empty batteries are transported through the train transportation system between the load side and the renewable energy station, which improves renewable energy penetration, economics, and mobilities. The first-stage model deals with the preliminary logistic optimization of batteries including routes, time and capacities of transportation, and the second-stage model optimizes real-time schedule of charge/discharge of batteries. The aim of the proposed model is to minimize the transportation cost, maximize the utilization rate of renewable energy in an energy system and smooth the daily load curve. The proposed model considers technical constraints such as railway transportation capacity, load demand satisfaction and renewable energy consumption in the power system. The optimal logistics plan and real-time charging and discharging plan can be obtained for both full and empty battery transportation. The validity of the method is verified with real data from Northeast China and Northern China, including railway routes, renewable energy output, and load profiles. The results show that the renewable energy penetration rate increases from 84.4% to 95.3% in Northeast China and from 42.7% to 78.2% in Northern China under the 2050 scenario with the proposed method. The peak-to-valley difference in the daily load curve decreases by 84% and 100% in Northeast China and Northern China, respectively. Meanwhile, the case study shows that the costs of battery transportation decrease from 0.398 CNY/kWh and 0.377 CNY/kWh to 0.252 CNY/kWh and 0.254 CNY/kWh when energy density of battery increases from 0.170 kWh/kg to 0.250 kWh/kg. This indicates that mobile energy storage has great economics. Moreover, renewable energy penetration rate is increased and peak-to-valley difference of daily load curve is decreased with no additional transmission lines. The model moves renewable energy from renewable energy power plants to end users. [Display omitted] • MESS aims to solve uneven spatio-temporal distribution of renewable energy and load. • A framework for joint operation of power system and transportation system is proposed. • The model is validated based on real data from Northeast and North China. [ABSTRACT FROM AUTHOR]

Large-scale renewable integration presents an effective way to decarbonize power grids, but carries increased risk of supply shortfalls owing to its volatility and uncertainty. Storage is a promising option to improve the generation adequacy of renewable. Thus, capacity credit assessment of renewable and storage is crucial in ensuring adequate generation capacity to meet loads. However, efficiently and accurately assessing capacity credit of these resources is challenging due to temporal dependencies in the operational time series (net loads and conventional generation units) and the need for extensive operation simulations. This paper develops a comprehensive multi-time-scale assessment framework integrating analytical and simulation methods to calculate the capacity credit of renewable and storage, thus capturing temporal features of these time series. Then, an interval-based strategy is proposed to simulate system operations, incorporating demand response in key scenarios. Furthermore, partitioning around medoids clustering and parallel computing techniques are employed to greatly accelerate the numerous operations for capacity credit assessment. The proposed method is validated using the RTS-79 system and a provincial real-world power grid in China. The results indicate that the developed framework can achieve efficient and refined capacity credit assessment and thus evaluate the impact of storage on the capacity credit. • Propose a novel method to quantify temporal features of operational time series for capacity credit (CC) assessment. • Develop a multi-time-scale CC assessment framework with an interval-based strategy to capture temporal features. • Utilize partitioning around medoids clustering and parallel computing to greatly enhance calculation efficiency. [ABSTRACT FROM AUTHOR]

As a renewable energy source, wind power has received growing attentions, but mostly been utilized in wind power farms or high-rise buildings where the wind with high speed is available. Few studies focus on the micro-wind utilization in low-rise buildings due to the bottleneck that the wind speed cannot meet the minimal speed requirement of small wind turbines. This paper aims to identify the optimal building design which could enable the harvesting of the maximal micro-wind power around low-rise residential buildings. Based on the comparison among different building shapes and the computational fluid dynamics (CFD) analysis via software Phoenics , this paper identified that the building shape of “composite prism” could enable the harvesting of the most micro-wind power. The identified building shape was then tested in a simulated environment of a residential community in Pingtan Island, China. The local wind conditions, long-term community planning, and the requirement of comfort level were all considered in the simulation model. The result shows that the potential of utilizing wind energy in low-rise residential buildings is huge by adopting the proposed building shape of “composite prism”. The finding has significant implications for renewable energy utilization in built environment. [ABSTRACT FROM AUTHOR]

Factors influencing the development of renewable energy have emerged as a hot topic of policy discussion in recent years. However, the direct impact of natural resource dependence on renewable energy has been overlooked, especially in emerging economies. Using Chinese province data from 2005 to 2019, this study employs a nonlinear panel model and a Quantile Regression with Nonadditive Fixed Effects for Panel Data (QRPD) model to examine the impact of natural resource dependence on renewable energy development and explores the great importance of government policy support. The study indicates that the influence of natural resource dependence on renewable energy development supports the "conditional resource curse" hypothesis, showing a U-shaped nonlinear relationship. The effect of natural resource dependence and government policy support on the development of renewable energy shows a dynamic change trend. In the early stage of renewable energy development, policy support is crucial, while during the growing and mature stages, natural resource dependence exerts a stronger driving force. The results of the mechanism test indicate that natural resource dependence can boost renewable energy development through green technology innovation and environmental regulation channels. This paper's conclusion provides a theoretical foundation for fostering the sustainable development of the renewable energy industry and is vital to China's efforts to reach peak carbon and carbon neutrality. • Nonlinear panel and QRPD models are employed. • Impact of natural resource dependence (NRD) on renewable energy development is revealed. • Dynamic effect of NRD and government policy support is discussed. • NRD has a negative and then positive impact on renewable energy development. • Pathways to improve renewable energy development through NRD are identified. [ABSTRACT FROM AUTHOR]

Transnational power cooperation contributes to promoting the large-scale use of renewable energies in Asia, but how to develop the structure of power transmission network deserves careful investigation. This paper thoroughly investigates the structure of power investment and transmission network in China and neighbouring countries, aiming to promote the large-scale use of renewable energies around Asia. Various power-generation and power-transmission technologies are considered in detail, and transnational power interconnection risk cost and carbon emission cost are embedded into a bottom-up optimization model. The future power-generation structures and networks installed under various policy scenarios are analysed in detail, and the corresponding economic, carbon emission reduction and renewable energy consumption benefits are evaluated. The results show that power transmission between China's internal regions and neighbouring countries can save $56.7 billion for the entire power system through the large-scale grid integration of renewable energy. The installed capacities of wind power, solar power, and hydropower renewable energy power generation could be increased by 141 GW, 163 GW, and 58 GW, respectively. The construction of transnational transmission lines from Central Asia to Northwest China, Mongolia to North China, the Russian Far East to Northeast China, and Northeast China to South Korea could effectively promote the utilization of renewable energy in the Russian Far East, Mongolia and Central Asia and further reduce carbon emissions in China, Japan and South Korea. • It examines how to construct a power investment and transmission network in Asia. • Various technologies, risks and costs are considered based on bottom-up model. • Huge benefits exist for power transmission between China and neighbouring countries. • Various power-generation structures and networks installed are analysed in detail. • Suggestions about power investment and transmission are given for Asian countries. [ABSTRACT FROM AUTHOR]

Renewable energy is an effective solution to alleviate the energy crisis and achieve sustainable development, and is gradually growing into an important energy source to support economic and social development. Different renewable energy technical plans have different characteristics under multiple criteria. Therefore, before the further exploitation of renewable energy sources, it is of great significance to evaluate the comprehensive performance of different plans and then determine the best renewable energy sources. Given this background, this paper proposes a novel evaluation method for renewable energy technical plans based on improved rough set theory and grey cloud model. Firstly, based on the normalized evaluation data, self-organizing map (SOM) clustering algorithm and system clustering analysis are adopted to discretize the evaluation attributes indexes and decision attributes indexes, respectively. Next, the improved rough set conditional information entropy method is used to calculate the index weights. Finally, the grey cloud model is employed to determine the grade classification of each evaluated plan. At the same time, each grade is quantified by assigning a corresponding score, and the comprehensive performances of renewable energy plans can be calculated, and then the ranking of different plans is achieved. Based on the actual renewable energy development data from a province in China, experiments were carried out to evaluate the performance of the proposed method. The results show that the proposed method performs better than some evaluation methods for renewable energy technical plans. [Display omitted] • Propose a novel evaluation method for renewable energy technical plans. • Use self-organizing map clustering algorithm to discretize the attributes indexes. • Apply improved rough set theory to determine the index weights. • Achieve the complete ranking of plans, and classify the plans into different levels. • Compared with the existing evaluation method, the proposed method performs better. [ABSTRACT FROM AUTHOR]

Multiple geographically dispersed data center buildings have been increasingly positioned in renewable-rich areas due to their high energy consumption. This paper proposes an optimal multi-energy portfolio for zero carbon data center buildings in the presence of proactive demand response programs. In the data center buildings, the complementarities of hydro-solar-wind hybrid renewable energy sources (RESs) are fully exploited via multi-energy conversion and storage devices, which are modeled as 100% renewable energy hubs for zero carbon multi-energy supplies. In order to exploit the hub-internal operational dispatchability and flexibility, a comprehensive energy consumption model of data center buildings is proposed to facilitate the demand response in terms of available serves, while the variable speed pumped storage (VSPS) is modeled to capture its dynamic operational characteristics. The energy‑carbon production, conversion, storage, and consumption are formulated as an energy‑carbon matrix, which are solved via a two-stage method to obtain the optimal portfolio. Case studies on southwest China data center buildings are implemented to demonstrate the effectiveness and superiority of the proposed methodology on cost-effective accommodation of RESs. Simulations results show that the system portfolio cost can be reduced by at most 17.8% with a higher operational flexibility. • A cost-effective data center building is proposed to accommodate zero carbon targets. • Hydro-solar-wind 100% renewables are formulated for multi-energy supplies. • VSPS and data center energy consumption are modeled to exploit inherent flexibility. • The energy-carbon production, conversion, storage, and consumption are optimized. [ABSTRACT FROM AUTHOR]

Abstract: With the economic development, the demond for energy is growing in China, and the traditional non-renewable energy makes the energy transformation is imminent.In this paper, based on the fully understanding of our existing energy structure, and from the perspective of supply and demand, the author analysis the changes in the structure of future energy and gives the suggestion.The paper argues that China''s energy structure will gradually turn to be comprehensive and diverse, but the cost and market constraints will obstruct the promotion of renewable energy sources.Therefore, the traditional high-carbon energy is still the main form of domestic energy consumptionin a very long period of time.Only by improving technology and using of the macro-control can reduce the costs and regulate the import and export, which can achieve sustainable development of changing from a high-carbon energy to low carbon. [Copyright &y& Elsevier]

The rapid growth of Chinese renewable energy innovation has shown great potential to improve total-factor carbon productivity (TFCP), but it still lacks sufficient literature understanding of the detailed impact and mechanisms. This paper empirically tests the influence mechanism, effect characteristics, and causal configurations based on a sample of 30 provinces from 2006 to 2017. We reveal optimizing the energy structure is the crucial intermediate channel for renewable energy technology innovation improving the TFCP, but it is not the unique mechanism. Since renewable energy technology innovation could directly improve the efficiency of the full industrial chain of electricity, the remaining channel can be summarized as the power operating efficiency improvement. From the heterogeneous analysis, the technological innovations in wind and solar energy display relatively larger contributions to the growth of total-factor carbon productivity. The threshold effect analysis further indicates the larger the coal consumption, the larger probability for renewable energy innovation to play a role in improving TFCP. Eventually, we observed four causal configurations of renewable energy technology innovation to determine the TFCP. It suggests renewable energy innovation cannot affect TFCP independently and often acts with other factors, eventually explaining the actual levels of regional TFCP. [Display omitted] • Renewable energy innovation can be a positive force in low-carbon transition by rising the total-factor carbon productivity. • Optimizing the energy mix is a channel through which renewable energy innovation contributes to the low-carbon transition. • The remaining channel can be summarized as the power operating efficiency improvement. • If there is a large coal consumption level gap, renewable energy innovation can profoundly impact on the low-carbon transition. • There are four causal configurations for renewable energy innovation to play a role. [ABSTRACT FROM AUTHOR]

• Proposing a new robust co-planning of AC/DC hybrid transmission network and energy storage. • Embedding BFM within the proposed co-planning model. • Using data-adaptive robust optimization to cope with uncertainties. • Validating the effectiveness of the proposed method in practical power system. This paper proposes a robust co-planning model of hybrid AC/DC transmission network and energy storage with the penetration of renewable energy to promote the accommodation of renewable energy and to avoid investment redundancy. The energy storage configured in the power grid can improve the power flow distribution and alleviate transmission congestion, postponing the investment of new devices. A deterministic co-planning model is firstly developed considering voltage fluctuation, reactive power flow and the flexibility of voltage source converter based high voltage direct current (VSC-HVDC). To address the solving complexity caused by the non-convexity of the model, second-order cone programming (SOCP) is applied to transform the proposed model into a convex problem. To cope with the uncertainty of renewable energy, the robust co-planning formulation is established, where the data-adaptive uncertainty set with the extreme scenario method is introduced to describe renewable generation uncertainty. The column-and-constraint generation (C&CG) algorithm is adopted to decompose the robust co-planning problem into a master problem and several slave problems, which reduces the calculation scale and accelerates the solving process. Two case studies on a modified Garver's 6-bus system and a practical Jiangxi province power system in China are carried out to verify the effectiveness and superiority of the proposed robust co-planning model. [ABSTRACT FROM AUTHOR]

Both air pollutant and carbon emissions are serious problems in China, particularly the economic and pollution intensive urban agglomerations. Therefore, this paper selected the most severely polluted Jing-Jin-Ji region (Beijing, Tianjin and Hebei urban clusters) to study the potential of mitigating both carbon and air pollutant emission. The Computable General Equilibrium (CGE) model was applied and six scenarios were constructed by considering policies of environmental tax and renewable energy. Business-as-usual (BaU) scenario assumed no tax and fixed renewable energy to 2002 level, whereas other scenarios assumed that intensified renewable energy (RE) and tax on sulfur dioxide (SO 2 ) (TAX1 and TAX2) were implemented either alone or combined. The results indicated that both RE and SO 2 tax could contribute to regional low-carbon development and air pollutant reduction, and the reduction is about 10–40% depending on scenarios. However, Renewable energy is always more effective in reducing CO 2 and air pollutant emissions than TAX1 but less effective than TAX2 for Hebei. From the sectoral perspective, power sector would exhibit both the largest emissions and reduction potentials in all three Cities, accounting for about 21%, 31% and 28% of total CO 2 emissions respectively for Beijing, Tianjin and Hebei, in 2020. Considering different policies, it is more effective for Hebei to apply renewable energy to reduce its power sector emissions in the long term, and tax policy is more effective for emission reduction from other sectors in Tianjin. Further analysis on the economic impact of mitigation policy revealed that Tianjin and Beijing would suffer relatively greater negative impacts than Hebei in terms of loss in total GDP, while developing renewable energy could help to reduce such negative impacts. It is suggested that the policymakers need to take into accounts the regional economic situation and natural resource potential when they formulate low-carbon policies. [ABSTRACT FROM AUTHOR]

Building heating energy consumption accounts for about 19.12% of total energy consumption in China in 2014. With the development of building energy-saving technology, renewable energy heating systems, such as GSHP (ground source heat pump), solar energy, air source heat pump, show high energy efficiency and low emissions, which are gradually replacing traditional heating systems. However, due to the restriction of existing productivity and environment conditions, the capital investment of such renewable heating systems is often much higher than traditional ones. As a result, the advantage of renewable heating system is hard to be realized, since its application is highly limited by economic factors. In this paper, traditional thermal-economical evaluation method is modified and improved for feasibility analysis of renewable heating system. Moreover, an illustrative example, which combines solar heating and air-source heat pump together, in Sichuan is given to show the preliminary application of the proposed method. The results indicate that the modified thermal-economical evaluation method can assess the feasibility of renewable heating systems more comprehensively, since it takes the indirect economic effects caused by energy saving and environment protection into consideration, which can balance the huge capital investment of such systems. For the studied case, solar collector combined with air-source heat pump is more preferable than traditional systems. This work is important for feasibility analysis and design optimization of building heating system with renewable energy. [ABSTRACT FROM AUTHOR]

Wind curtailment is currently one of the major challenges in China’s renewable energy development. Technical solutions to wind curtailment could include more flexible power sources, storage and grid facilities. These, however, are long-term solutions due to physical, economic and institutional constraints. We argue that an effective short-term solution could be the full utilization of the technical potential of the existing power system, particularly by using China’s dominant coal-fired power capacity to provide peak regulation services for the priority dispatch of wind power. This paper assesses the economic and environmental effects of this approach. To this end, four scenarios are developed: Scenario I—full wind curtailment and average dispatch of coal-fired power, Scenario II—no wind curtailment and average dispatch of coal-fired power, Scenario III— no wind curtailment and optimal dispatch of coal-fired power, and Scenario IV —partial wind curtailment and optimal dispatch of coal-fired power. The economic and environmental effects under the four scenarios are then computed using the GAMS program based on the load curve on a typical spring day for a coal-fired power plant and a wind farm in China’s northeastern Harbin City. The results of our research show that whilst Scenario IV has the best economic effects, Scenarios III and IV have the best environmental effects. Scenario IV is considered the optimal solution as more economic benefits could be realized at the expense of smaller wind curtailment. Drawing on these results, it is recommended to amend the Renewable Energy Law to allow a small proportion of renewable energy curtailment; to enhance the flexibility of the existing coal-fired power units; to advance energy efficient power dispatch approach; and to incentivise coal-fired power to provide peak regulation services for renewable energy. [ABSTRACT FROM AUTHOR]

In this paper, an improved hybrid MADM method based on interval Decision Making Trial and Evaluation Laboratory (DEMATEL) and interval regret theory are put forward to evaluate five renewable energy alternatives for Fujian province, China. Firstly, the volatility of the evaluation information and the subjectivity of experts or stakeholders are taken into account. Second, political factors are further considered based on existing research. Third, the influence of the subjective initiative of decision makers on the decision result has been paid attention to. Furthermore, the result of the analysis shows that the extended regret theory method should set different parameters to reflect the preferences of decision makers. The ranking results of renewable energy are different with different parameter settings. For example, when α = 0.1 , δ = 0.1 , the priority of clean energy in Fujian Province is hydropower, followed by wind energy, solar energy, geothermal energy, and biomass energy. Sensitivity analysis indicates that the model possesses good serviceability. Based on considering different parameter settings, the Fujian government should invest, in order of priority, in these renewable energy sources to obtain benefits. And the projects related to these renewable energy sources should be evaluated. [ABSTRACT FROM AUTHOR]

• A novel fractional grey Chebyshev polynomial Bernoulli model is proposed. • The structure of the NGBM(1,1,Tn) model is variable. • Comparative study shows that the new model is superior to other five grey models. Accurate mid-to-long term China's renewable energy forecasting is becoming more and more important for integrating renewable energy systems with smart grid and energy strategic planning. For this purpose, this paper proposes a novel fractional structural adaptive grey Chebyshev polynomial Bernoulli model. The new model is based on NGBM(1,1) model to describe nonlinear phenomena. The proposed adjacent accumulation operator can balance old and new information accumulation with less fluctuation. The introduction of the Chebyshev polynomial enables the model to adaptively adjust the structure of the model, which reduces the difficulty of the modeler's operation from the point of view of the expert system. Meanwhile, an improved Grey Wolf optimization algorithm (IGWO) was used to optimize the parameters. Four real cases of renewable energy in China, including production and consumption are used for verification. In addition, Monte Carlo simulation and probability density analysis illustrate the robustness and accuracy of the proposed model. Finally, the development of the four cases in the next 5 years is predicted. [ABSTRACT FROM AUTHOR]

• The data envelopment analysis is introduced to renewable energy development. • The method has two stages: comprehensive evaluation and structure optimization. • Indexes from different aspects are quantified via intuitionistic fuzzy numbers. • The comprehensive performances are determined based on the relative efficiencies. • The proportions of renewable energy are optimized based on the whole efficiency. Different kinds of renewable energy resources have developed rapidly. For renewable energy planning, it is meaningful to assess the comprehensive performances of different schemes and then to determine the optimal design of the energy structure. This paper presents a two-stage method for the comprehensive evaluation and structure optimization of renewable energy plans. First, in the evaluation stage, multiple indexes from different aspects are taken into consideration, of which each qualitative index will be converted quantitatively, using the intuitionistic fuzzy number to describe the fuzziness and ambiguity in a qualitative index. Then, the superefficiency data envelopment analysis model is used to determine the comprehensive performances of different plans based on the concept of relative efficiency. Next, in the optimization stage, an optimal model combining multiple renewable energy resources is established based on the relative efficiency results of the evaluation process. This model aims at the maximum efficiency as a whole and can be used to optimize the proportions of different renewable energy resources. Finally, a real case of renewable energy development from a province in China is given to demonstrate the feasibility and effectiveness of the proposed method in this paper. The results show that it can overcome the shortcomings of the traditional basic model, obtain more objective evaluation results and provide beneficial references for the strategy making of renewable energy development. [ABSTRACT FROM AUTHOR]

Renewable energy has a critical role in limiting the greenhouse gas (GHG) emissions. This paper assesses the implication of aligning renewable energy deployment target with national emission reduction target for mitigation cost. The assessment methodology uses Asia-Pacific Integrated Assessment/computable general equilibrium (AIM/CGE) model to determine the mitigation cost in terms of GDP and welfare loss under alternative renewable targets in different climate-constrained scenarios. A range of country-specific emission constraints is taken to address the uncertainties related to global emission pathway and emission entitlement scheme. Comparative results show that China needs to increase its share of non-fossil fuel significantly in the primary energy mix to achieve the stringent emission reduction target compared to India. The mitigation cost in terms of economic and welfare loss can be reduced by increasing the penetration of the renewable energy to achieve the same emission reduction target. The modeling results show that coordinated national climate and renewable energy policies help to achieve the GHG emission reduction target in an efficient and cost-effective manner. [ABSTRACT FROM AUTHOR]

• Inequality of renewable technology innovation is evaluated by Gini coefficients. • Contributors to renewable innovation inequality are decomposed by Shapley method. • Variables of regional attributes, policies and official characteristics are proposed. • Knowledge stock and R&D expenditure are the greatest contributors. • Energy-related experience and tenure of officials may be potential contributors. Regional inequality of technology innovation associated with renewables may lead to insufficient innovation growth over the long-term, which impedes the deployment of renewable energy and slows down low-carbon transition. This paper aims to explore the major contributors to regional inequality of technology innovation in renewable energy by taking China as a representative example. Three groups of variables including regional attributes, supporting policies, and local official characteristics are proposed as potential explanatory factors. A set of panel data covering both the photovoltaic and wind energy sectors in 31 Chinese provinces from 2008 to 2017 are collected for the empirical study. Gini coefficient is used to quantify the inequalities of renewable technology innovation, while Shapley decomposition method is applied to evaluate the contribution of each significant factors. The results show that the overall Gini coefficients of photovoltaic and wind technology innovation in China are 0.65 and 0.62, respectively, indicating medium-level regional technology innovation inequality in both sectors. Knowledge stock (46.44%), R&D expenditure (36.56%), green fixed industrial investment (13.53%), electricity consumption (1.33%), and the energy-related experience of local officials (0.05%) are significant contributors to regional inequality of photovoltaic innovation. Knowledge stock (45.20%), R&D expenditure (37.90%), electricity consumption (8.63%), feed-in tariffs (1.02%), and the tenure of local officials (0.18%) are significant contributors to regional inequality of wind innovation. Therefore, eliminating regional inequality of technology innovation in renewables should start with balancing regional R&D expenditure and knowledge stock. [ABSTRACT FROM AUTHOR]

Abstract: China has set ambitious goals to increase the use of renewable energy. Developing renewables in rural areas is also one of the most important energy strategies. This paper examines rural social acceptance of renewable energy deployment taking Shandong as a case study via a field questionnaire survey. Theory of planned behavior is adopted to establish an analytical framework, and a logit model is used to examine possible determinants of local social acceptance. The results show that rural residents are generally supportive renewable energy development given its positive impacts on environment. A stated willingness to pay more for renewable electricity is taken as a variable representing an individual’s behavioral intention. The probability of occurrence of positive intention is found to increase with household income, individual knowledge level and belief about costs of renewable energy use but decrease with individual age. Residents with higher level of income are more likely to be willing to pay more for green electricity, so are the younger people. Enhancive knowledge and understanding about renewable energy (for instance, the cost) would be conducive to win public acceptance of renewable energy deployment. [Copyright &y& Elsevier]

Abstract: Click In the background of global financial crisis and abnormal climate, low-carbon economy has become an optimal choice for the sustainable development of all countries. The biomass based power generation industry is an important way of developing low-carbon economy as well as a way of improving the energy structure of China. As a renewable energy, biomass power has a rapid growth in the last few decades in China. This industry faces opportunities as well as challenges for development. Using SWOT analysis method, this paper reviews and assesses the factors of strengths, weaknesses, opportunities, and threats of biomass power generation industry of China. Meanwhile, according to the weaknesses and threats of this industry, it comes up with corresponding suggestions for future work, substantial work is still required for China to catch up with developed countries. Finally, study indicated that the usage of biomass to generate power has great potential for development in China. [Copyright &y& Elsevier]

Abstract: The purpose of this paper is to investigate the reasons for ever growing energy consumption in buildings and to give enhanced supervision strategies for reduction of building energy consumption compared to regular ones. A case study of Ningbo city was used to make a detail analysis. Several factors were determined as the reasons for ever growing building energy consumption including climate change, household electricity load increase, the growth of real estate, fast-growing household electrical appliances, high energy consumption in existing buildings, changes in industrial structure and the lack of enhanced government supervision. Then the discussion of suitable countermeasures shows that only enhanced supervision strategies are currently applicable. Finally, it is concluded that enhanced government supervision strategies, including the establishment of a strict control system for new built buildings through information integration and encryption, establishing an energy efficiency supervision system of large-scale public building and a carrot-and-stick approach with added expert checklist for the building application of renewable energy, showed great advantages in promoting building energy efficiency in Ningbo, compared with other cities. These supervision strategies are applicable in other cities as they are in the similar situations in the enforcement of building energy efficiency. [Copyright &y& Elsevier]

Abstract: Wind power has become one of the fastest growing new energy in China, which has important market prospects and research value. This paper analyses the regional distribution of wind power installed capacity and its characteristics in China by the method of cluster analysis. With the Panel Data of 13 selected provinces from 2004 to 2008, it also analyses the impact factors of China''s wind-generated electricity. The results show that the regional distribution of wind power investment basically corresponds to the wind resources distribution, but the Xinjiang region has not been developed effectively: investment in wind power significantly affects the level of wind-generated electricity; wind electricity presents reverse distribution compared to the electricity consumption. Therefore, policies such as overall planning, oversea markets expanding should be applied proactively in China''s wind power development. [Copyright &y& Elsevier]

Abstract: Energy and environmental problems have been the hot issues nowadays. Energy is not only the material base of economic development, social improvement and enhancement of people''s living standard, but also the significant factor which influences human being''s living environment. A scientific and practical energy planning is the important guarantee to realize the country''s future energy development strategy. This paper analyzed the current situation of renewable energy source, the developing strategy and the relevant policies, and made up suggestions about how to develop renewable energy source better. That will contribute to the renewable energy research approach in the future [Copyright &y& Elsevier]

Abstract: Power crisis today contains not only the phenomenal of power storage, but also the problem of environment pollution caused by electric power industry. China has experienced fast development in the past 30 years and correspondingly, people''s living level is higher than the past. As a result, with the fast development of economy, the people''s demand on energy and power is more and larger than the past. In practice, although the burden of power supply has released in certain degree via building more power stations, power crisis is still in existence because of the flaw in electricity industry structure and technique aspects. In the paper, we analyze national stage of China in electricity industry aspect, and then we provide two typical cases about Power Crisis and coping strategies. Finally, we present relevant policies and strategies to tackle with the corresponding problem of China power crisis. [Copyright &y& Elsevier]

Abstract: Renewable energy resources have a positive effect on energy supply security as well as environmental protection. By analyzing the situation of the world and China''s solar industry, the paper indicates there is of great potential in China''s solar industry. However, there are capital, technology, market and policy obstacles in the development of solar industry. So we need not only the regulation of the market, but also the support of government policy, particularly economic incentives to promote solar industry in China. [Copyright &y& Elsevier]

Abstract: Energy consumption of rural residents involves many aspects such as energy security, environmental effect, the new rural construction and so on. Based on the analysis of the present situation of renewable energy consumption by rural residents in daily life, this paper estimates the carbon emissions of renewable energy consumption by rural residents in daily life from 1998-2007, and evaluates the environmental effect of the renewable energy consumption in daily life in rural areas. The conclusion shows: in the course of energy consumption by rural residents in daily life, the main body of carbon emissions is caused by the traditional usage of straws, firewood and the other biomass energy in China, and gives relevant policy suggestion accordingly. [Copyright &y& Elsevier]

• China's Nationally Determined Contributions do not comply with the Paris Agreement. • Overall coal usage needs to decrease by 60% (2 °C) or even 100% (1.5 °C) by 2050. • 100% Renewable energies are cost-efficient to decarbonize the electricity sector. • Sector coupling increases the electricity demand by 100% (2 °C) to 400% (1.5 °C). • Incentives for local actors are needed for a sustainable low-carbon transformation. Growing prosperity among its population and an inherent increasing demand for energy complicate China's target of combating climate change, while maintaining its economic growth. This paper, therefore, describes three potential decarbonization pathways to analyze different effects for the electricity, transport, heating, and industrial sectors until 2050. Using an enhanced version of the multi-sectoral, open-source Global Energy System Model, enables us to assess the impact of different CO 2 budgets on the upcoming energy system transformation. A detailed provincial resolution allows for the implementation of regional characteristics and disparities within China. Conclusively, we complement the model-based analysis with a quantitative assessment of current barriers for the needed transformation. Results indicate that overall energy system CO 2 emissions and in particular coal usage have to be reduced drastically to meet (inter-) national climate targets. Specifically, coal consumption has to decrease by around 60% in 2050 compared to 2015. The current Nationally Determined Contributions proposed by the Chinese government of peaking emissions in 2030 are, therefore, not sufficient to comply with a global CO 2 budget in line with the Paris Agreement. Renewable energies, in particular photovoltaics and onshore wind, profit from decreasing costs and can provide a more sustainable and cheaper energy source. Furthermore, increased stakeholder interactions and incentives are needed to mitigate the resistance of local actors against a low-carbon transformation. [ABSTRACT FROM AUTHOR]