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With the increasing penetration of renewable energy resources, their variable, intermittent and unpredictable characteristics bring new challenges to the power system. These challenges require micro-meteorological data and techniques to provide more support for the power systems, including planning, dispatching, operation, and so on. This paper aims to provide readers with insights into the effects of micro-meteorology on power systems, as well as the actual improvement brought by micro-meteorology in some power system scenarios. This paper provides a review including the relevant micro-meteorological techniques such as observation, assimilation and numerical techniques, as well as artificial intelligence, presenting a relatively complete overview of the most recent and relevant micro-meteorology-related literature associated with power systems. The impact of micro-meteorology on power systems is analyzed in six different forms of power generation and three typical scenarios of different stages in the power system, as well as integrated energy systems and disaster prevention and reduction. Finally, a case study in China is provided. This case takes wind power prediction as an example in a power system to compare the performance when applying micro-meteorological data or not. The experimental results demonstrated that using the micro-meteorological reanalysis dataset with high spatial--temporal resolution for wind power prediction performed better, verifying the improvement of micro-meteorology to the power system to some extent. [ABSTRACT FROM AUTHOR]

The large-scale centralized development of wind and PV power resources is the key to China's dual carbon targets and clean energy transition. The vast desert–Gobi–wilderness areas in northern and western China will be the best choice for renewable energy development under multiple considerations of resources endowment, land use constraints, technical conditions, and economic level. It is urgent to carry out a quantitative wind and PV resource assessment study in desert–Gobi–wilderness areas. This paper proposed a multi-dimensional assessment method considering the influence of the power grid and transportation infrastructure distributions, which includes three research levels, namely, the technical installed capacity, the development potential, and the development cost. Nine main desert–Gobi–wilderness areas were assessed. The wind and PV technical installed capacities were 0.6 TW and 10.7 TW, and the total development potentials were over 0.12 TW and 1.2 TW, with the full load hours of 2513 and 1759 and the average development costs of 0.28 CNY/kWh and 0.20 CNY/kWh. Finally, this paper proposed the meteorological–electrical division distribution. A case study in the Kubuqi and Qaidam Deserts was carried out on wind–wind and wind–PV collaborative development across different meteorological–electrical divisions, which can reduce by 58% the long-term energy storage capacity and decrease the total system LCOE from 0.488 CNY/kWh to 0.445 CNY/kWh. [ABSTRACT FROM AUTHOR]

Technological innovation is the key to develop wind power and photovoltaic power industries. The feed-in tariff (FIT) policy, as a demand-pull policy, is important to support renewable energy technological innovation. Using the "difference-in-differences" method, this paper investigates the impact of FIT policy of wind power and the impact of the FIT policy designed according to differences in the distribution of resources on wind power technological innovation. The findings show that the FIT policy can drive patenting in wind power technologies during the implementation period, but may play a relatively weak promoting role in technological innovation in the latter term, and the FIT policy designed according to differences in the distribution of resources also stimulates more patent counts. Finally, based on the fixed effect negative binomial regression model, this paper finds that the higher feed-in tariffs can increase the patent counts in photovoltaic power technologies. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid model constituting dynamic smoothing technique and particle swarm optimization techniques to optimally size and control battery energy storage systems for wind energy ramp rate control and power system frequency performance enhancement. In today's modern power system, a high-proportion renewable energy grid is inevitable. This high-proportion renewable energy grid is a power system with abundant integration of renewable energy resources under the presence of energy storage tools. Energy storage tools are integrated into such power systems to balance the fluctuation and intermittence of renewable energy sources. One of the requirements in a high-proportion renewable energy grid is the fractional power balance between generation and load. One of the requirements set by power system regulators is the generation variation between two time points. A power producer is mandated to satisfy the ramp rate requirement set by the grid owner. This paper proposes dynamic smoothing techniques for initial size determination and particle swarm optimization based on optimal sizing and control of battery energy storage systems for ramp rate control and frequency regulation performance of a power system integrated with a large percentage of wind energy systems. Wind energy data taken from Zhangjiakou wind farm in China are used. The results indicate that the battery energy storage system improves the ramp rate characteristics of the wind farm. In addition, the virtual inertia capability of the battery energy storage system enabled the transient and steady-state frequency response of the test power system to improve significantly. [ABSTRACT FROM AUTHOR]

In the context of carbon neutrality target, renewable energy sources have been transforming from "supplementary energy" to "main energy", which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the "resource-environment-demand" comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry. • This study examines the impact of high-dimensional features variables on urban solar energy development. • Over 70% of solar PV installation capacity is concentrated in Northern and Northwest parts of China. • Water resources, solar radiation, population density have significant impact on solar PV distribution. • Solar PV distribution in China should shift from resource orientation to the comprehensive orientation. [ABSTRACT FROM AUTHOR]

With the sharply increased development of variable renewable energy resources (VRERs) in recent years, the hydro-wind-photovoltaic (PV) hybrid system (HWPHS) has the prospective to enhance the grid integration of VRERs. Nevertheless, the intense variation associated with wind and PV generation causes uncertainties in the long-term operation of the HWPHS. To overcome this drawback, this paper develops a novel method to derive adaptive operating rules for a cascade HWPHS. First, a scenario-generating method coupling Kernel density estimation with the copula function is proposed to characterize the wind and PV forecast errors. Second, based on the power generation scenarios, an optimal scheduling model for the cascade HWPHS considering transmission section constraints is proposed to simulate the hydro-wind-PV complementary operation; finally, the long-term operating rules for the cascade HWPHS are extracted by grey relational analysis and BP neural network. As a case study, the HWPHS of the Wu River basin in China is chosen. Results demonstrate that the proposed model can effectively utilize the flexibility of cascade hydropower stations, improve transmission section utilization efficiency, and promote clean energy absorption. [ABSTRACT FROM AUTHOR]

Summary. With the continuous expansion of grid-connected power generation from renewable energy sources, such as wind and light, the forms of power sources are more diversified. The coupling and uncertainty of multiple energy sources will cause significant changes in the characteristics of the power system. Problems like the complexity of the power system lack a scientific and complete explanation. This paper comprehensively considers the regional weather conditions, the uncertainty of the output of wind and solar energy sources, and the time and space complementary characteristics of multiple power sources. Based on the theory of complex adaptive systems, a systematic and holistic optimization planning method is studied. Combining the idea of complementarity because of differences, a multipoint layout planning and design method for multienergy power systems is proposed to give full play to the synergistic effects of natural resources, achieve optimal resource allocation, and provide a theoretical basis for improving the consumption of wind and renewable energy. Taking an actual provincial power system in China as an example, through the comparison of production simulation calculations and multiscenario analysis and planning methods, the proposed method can significantly increase the consumption of wind and solar renewable energy. [ABSTRACT FROM AUTHOR]

Power system planning is essential for ensuring development of a safe and stable power system. Articles on power system planning have been published in recent decades. In this paper, bibliometric analysis is applied to power system planning to identify its basic characteristics, and to summarize the research hotspots and trends. A total of 4562 publications were obtained from the Web of Science during 1971–2020. The 4562 publications cover 8136 keywords, 1526 journals, 107 countries/territories, and 2519 institutions. IEEE Transactions on Power Systems is the key journal in power system planning; USA, China, and England are the countries that dominate publication production. Research hotspots and trends were analyzed using author-keyword co-occurrence analysis and keyword burst detection. Four clusters were identified and topics from Cluster I (central nodes as power system stability and transient stability) and Cluster IV (central nodes as power system simulation and economics) are important for power system planning research. However, their relative popularities have declined over the past two decades. This indicates that Cluster II (central nodes as renewable energy, wind power, and energy storage) and Cluster III (central nodes as distributed generation and smart grids) will be the focus of future research. [ABSTRACT FROM AUTHOR]

Currently, the world is moving rapidly toward carbon neutrality, and renewable energy technology is very important in carbon neutrality. Among various renewable energy technologies, wind power is attracting much attention due to its sustainability, resource abundance, and high applicability. This study analyzed wind power patents from 2010 to 2021 to derive current global originality and convergence information. For IP5 countries, the growth stages of technology and the patent convergence networks were inferred by time series analysis and an association rule mining algorithm, respectively. The results showed that, during the analysis period, about 5000 patents were applied on annual average, and it was analyzed that China held the most patents at 48%, followed by the US, Europe, Korea, and Japan. According to the technology convergence network of patent codes from all the IP5 countries, patents related to 'wind turbines with rotation axes in the wind direction' occupied a very central position, and 'power conversion electric or electronic aspects' and 'integration of patents related to renewable energy sources in buildings with wind power' were found to show high connection strength. By country, it was analyzed that the US and China showed high patent competitiveness onshore and possessed many 'independent power conversion cluster' technologies. The research hypothesis was that technology development trends can be analyzed and the characteristics of each country can be understood through patent analysis in the wind power field. This hypothesis was analyzed through various patent analysis techniques, and this paper has novelty in that it presents the global megatrend in the wind power field through patent analysis and quantitatively presents the current status of technology development in five major countries. [ABSTRACT FROM AUTHOR]

Aiming at the problem of insufficient peak shaving capacity of the power system after large-scale renewable energy such as wind power and photovoltaics is connected to the grid, a new energy-pumped storage combined system low-carbon economic operation strategy is proposed. First, the energy storage characteristics of pumped storage are studied; wind power and photovoltaic output are shifted in time and space; and a new energy-pumped storage combined system model with photovoltaics, wind power, and pumped storage as the main body is established. Second, the carbon trading mechanism was introduced, and the reward and punishment ladder carbon trading cost model was established. Then, via the new energy-pumped storage combined system, the upper optimization target with the smallest investment cost and the lower optimization target with the largest system benefit are established, and a two-layer optimization model solution method based on linearly decreasing inertia weight particle swarm optimization and sequential quadratic programming algorithm is proposed. Finally, via the operating data of a pumped storage power station on an island in northern China, the new energy-pumped storage combined system low-carbon economic operation strategy was simulated and verified. The simulation results show that the low-carbon economic operation strategy of the new energy-pumped storage combined system proposed in this paper can effectively suppress load fluctuations, reduce the impact of wind and wind anti-peak regulation characteristics on the power grid, increase the utilization rate of renewable energy, and improve system economy. [ABSTRACT FROM AUTHOR]

Hydropower, an excellent power source for peak shaving, can respond quickly to load changes, which helps to overcome the anti-peak characteristics of wind and solar power. However, complementary operation with wind and solar power changes the operating boundaries of hydropower stations, making the original operating rules no longer applicable. Considering daily peak-shaving demands, this paper proposes a methodology to develop operating rules for a hydro–wind–solar hybrid system (HWSHS). First, a multi-objective and multi-timescale operation model is developed to ensure energy generation and peak-shaving performance simultaneously. Second, the feasible and reasonable region of available energy-based operating rules is analyzed, and three rule forms are employed to explore the optimal operating rules. Last, NSGA-II is utilized to optimize the rule parameters. A decoupling method is proposed to handle the shape constraints of operating rules, and the multi-timescale simulation method for a HWSHS is developed as the fitness function. In the simulation method, a load reconstruction-based load shedding (LRLS) method is introduced to determine the hourly output of each hydropower station. A forebay elevation check method is performed to obtain the hourly forebay elevation and correct the hourly output. The Beipan HWSHS in Guizhou Province, China, is selected as a case study, and the optimal operating rules are determined. The results also show that (1) the standard operating rules with hedging and packing increase peak-shaving performance by an average of 4.31% and 2.03% compared to the standard operating rules and linear operating rules, respectively; (2) the peak-shaving performance can be significantly increased (6.70%) with a slight decrease (0.37%) in the energy generation through changing the seasonal distribution of water and energy of the cascade hydropower stations; (3) the LRLS method, which makes the interday energy distribution more uniform, increases the peak-shaving performance by an average of 9.17% compared to the traditional load shedding method; (4) the energy generation and peak-shaving performance of the HWSHS are affected by the transmission capacity, and it is necessary to increase the transmission capacity by 25% to 35% of the installed capacity of wind and solar power stations. • A methodology is proposed to develop operating rules considering peak-shaving demands. • A multi-objective and multi-timescale operation model is developed. • The feasible and reasonable region of available energy-based operating rules is analyzed. • A multi-timescale simulation method is designed to simulate the operation of a HWSHS. • The methodology significantly improves the peak-shaving performance. [ABSTRACT FROM AUTHOR]

The rotational inertia and primary frequency response capabilities of power systems are declining as synchronous generators (SGs) are replaced by inverter-based variable renewable energy (VRE) resources. Therefore, when a high VRE proportion is present, it becomes essential to include frequency security constraints in power system planning. Under this circumstance, the decline in SGs would deteriorate the system frequency response performance; thus, inverter-based resources are supposed to provide frequency response support. This paper presents a frequency-constrained stochastic planning method for high VRE share systems. Frequency response support from wind farms is considered in the planning process for the first time, and the unique characteristics of wind power support are addressed. Specifically, the uncertainty of and variations in wind power support capabilities are modeled from the field-measured data, and the optimal combination of various support schemes can be determined by introducing binary variables with novel linear constraints into the planning process. Moreover, the nonlinear frequency constraints are transformed and embedded in the planning model by the proposed computationally efficient adaptive piecewise linearization method. The planning is formulated as a stochastic optimization model to consider the VRE power uncertainty. Finally, case studies on the IEEE 30-bus system validate the proposed method, with results indicating time savings of 22%. Further, compared with the case that only SGs provide frequency support, the results based on the East China system demonstrate that considering wind power support can improve the VRE share from 49.5% to 79.7%. [ABSTRACT FROM AUTHOR]

China and the United States are both major countries in the world in the production of and trade in wind energy products, and they are also important trading partners. Therefore, it is of great practical significance to analyze and evaluate the Sino-US bilateral wind power complementarity for re-examining their respective comparative advantages and re-understanding the benefits that trade liberalization brings to both countries, in an attempt to strengthen the trade relations between China and the United States and enhance trade complementarities in wind energy products under the global circumstances of fierce competition. In this paper, based on the trade data from the UN Comtrade database in 2007-2016, a quantitative analysis was conducted on the complementarity of Sino-US wind energy products, by adopting the quantitative research methods of trade combination degree index (TCD), export similarity index (SI) and trade complementarity index (TCI). The results shows that: i) the total trade volume of wind energy products between China and the United States has increased rapidly, and the trade surplus between China and the United States has been expanding year by year; ii) the overall trade balance of wind energy products trade between the two countries is generally stable, and the dependence of China's wind energy products on the United States is much higher than that of the U.S wind energy products on China; iii) the Sino-US wind energy products between China and the United States are highly competitive in the international market; iv) Sino-US wind energy products have different degrees of complementarity in bilateral trade. Finally, this paper proposed policy implications based on the findings of this study. [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]

The widespread implementation of feed-in tariff (FIT) policies has played a crucial role in fostering the development of wind power, with their positive effects firmly established in numerous studies. However, the impact of regionally differentiated FIT policies on the misallocation of wind power resources remains a topic of contention, with limited research dedicated to this area. This paper aims to address this gap by examining the implications of such policies on the intensive and extensive margins of wind power installed capacity in China, shedding light on the underlying mechanisms driving resource misallocation. Empirical findings indicate that, concerning the intensive margin, the policy amplifies the concentration of wind power investments in regions characterized by abundant wind resources but low electricity demand. These regions present favorable conditions for large-scale wind farms with cost advantages, consequently exacerbating the misallocation of wind power resources. However, on the extensive margin, the policy promotes the likelihood of locating small and medium-sized wind farms in regions with poor wind resources but higher tariff rates, thus partially mitigating resource misallocation. In summary, China's policy hampers wind power investments in regions characterized by high electricity demand but limited wind resources. This suggests that the negative impact on the intensive margin outweighs the positive impact on the extensive margin. The findings of this study bear significant implications for the development of renewable energy support policies, particularly in countries grappling with substantial regional disparities in renewable energy resources. • We cope with the controversial effect of FIT policies on the resource misallocation • We provide novel perspectives as the intensive and extensive margins • China's regionally differentiated FIT policy hampers wind power investments • We provide implications for countries implemented regionally differentiated FIT [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]

The high penetration of renewable energy will substantially change the power system operation. Traditionally, the annual operation of a power system can be represented by some typical operation modes and acts as the basis for the power-system-related analysis. The introduction of highly penetrated renewable energy will make the power system operation mode highly diversified and variable. These modes may not follow traditional empirical patterns. In this paper, we propose a data-driven method based on high-dimensional power system operation data (including power flow, unit generation, and load demand) to identify the pattern of the operation modes and analyze the impact of high renewable penetration. Specifically, the proposed data-driven method is composed of simulation, preprocessing, clustering, dimension reduction, and visualization with the aim to provide an intuitive understanding of the operation mode variety under high renewable penetration. In addition, several indices are introduced to quantify the space dispersion, time variation, and seasonal consistency of operation modes. A case study on actual Qinghai provincial power system in China validates the effectiveness of the proposed data-driven method and indicates that the dispersion and time variation of operation mode will significantly increase in the beginning and then saturate with the increase in renewable penetration level. The operation mode is also less correlated with seasons in renewable energy dominated power system. [ABSTRACT FROM AUTHOR]

Highlights • Novel formulation of unit commitment model for large-scale power system simulation. • Validated with standard test system and applied to real regional systems in China. • Increase the computational speed by more than 20,000 times with ∼1% error. • Can be applied to planning issues, e.g. optimal sizing of wind, solar and hydro. Abstract Deploying high penetration of variable renewables represents a critical pathway for decarbonizing the power sector. Hydro power (including pumped-hydro), batteries, and fast responding thermal units are essential in providing system flexibility at elevated renewable penetration. How to quantify the merit of flexibility from these sources in accommodating variable renewables, and to evaluate the operational costs considering system flexibility constraints have been central challenges for future power system planning. This paper presents an improved linear formulation of the unit commitment model adopting unit grouping techniques to expedite evaluation of the curtailment of renewables and operational costs for large-scale power systems. All decision variables in this formulation are continuous, and all chronological constraints are formulated subsequently. Tested based on actual data from a regional power system in China, the computational speed of the model is more than 20,000 times faster than the rigorous unit commitment model, with less than 1% difference in results. Hourly simulation for an entire year takes less than 3 min. The results demonstrate strong potential to apply the proposed model to long term planning related issues, such as flexibility assessment, wind curtailment analysis, and operational cost evaluation, which could set a methodological foundation for evaluating the optimal combination of wind, solar and hydro investments. [ABSTRACT FROM AUTHOR]

In order to assess the viability of wind energy projects in China, this paper takes a real options approach by accounting for flexibility over deployment of the technology in response to a stochastic non-renewable energy cost. The difference between the latter and the R&D-enhanced cost of renewable energy operation may be thought of as a benefit from using wind. On the other hand, the Chinese government provides feed-in tariffs to encourage further adoption of wind, which should be included as a cost. This work, thus, shows that there is substantial options value to China's wind energy program, which is bolstered if the cost of carbon dioxide emissions is internalized. Moreover, by varying the rate of feed-in tariffs, the model may be used to show plausible ranges for the feed-in tariffs, which could be a useful policy insight. Furthermore, the study hypothesizes that with the likelihood of renewable energy to replace non-renewable energy, wind power in China can potentially reduce CO 2 emissions by approximately 6.4% by 2020 comparing to the 2005 level and highlights the significant role of wind power in China's emission targets and energy security. This is a relevant paper, especially given the importance of a sustainable energy transition for China. More generally, using the real options approach from a policy perspective could provide insights about the viability of renewable energy programs and robustness of feed-in tariffs rates. [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]

• Define and quantify hydropower flexibility in power regulation and frequency response. • Flexibilities are coordinated in day-ahead scheduling of hydro-wind hybrid power system. • Benefits and costs of hydropower providing flexibility. • Suggested schemes weighing benefits and costs under different capacity ratios. Uncertainty and instantaneous volatility of wind power make it crucial to schedule the hydropower scientifically to supply flexibility at multiple timescales in renewable energy hybrid power generation systems (RHPS). However, current day-ahead scheduling strategies do not consider the flexibility of time scales below the minimum scheduling time resolution, resulting in insufficient flexibility in intraday operation, power curtailment and load loss. Therefore, in this paper, day-ahead scheduling model coordinating power regulation flexibility (PRF) at 15 min timescale and frequency response flexibility (FRF) at seconds timescale is proposed for hydro-wind hybrid power generation system (HWHPS). Meanwhile, the ability, benefits, and costs of hydropower on supplying flexibility in HWHPS are studied in detail. First, the demand and supply for PRF and FRF are defined and quantified at different confidence levels considering the regulation characteristics of hydropower units. Then, a heuristic piecewise method is utilized to linearize the nonlinear FRF expression, and a day-ahead scheduling MILP model of the HWHPS is constructed. The MILP model is applied to a HWHPS composed of a hydropower station in southwest China and a virtual wind farm simulated based on the data representative for the hydropower station region. We found that the available hydropower flexibility has potential in reducing the maximum frequency deviation, wind power curtailment and load loss. Meanwhile, hydropower provides flexibility at the cost of hydro energy loss due to increased water consumption, especially for supplying FRF during periods of high wind penetration. Finally, since benefits and costs are affected by different confidence levels, a comprehensive index is proposed to evaluate different capacity ratios and scenarios to suggest confidence levels for day-ahead scheduling. Decision makers can also refer to the flexibility supply capacity of hydropower station to select the connected wind power capacity and system load to ensure comprehensive benefits. The methods, models and conclusions in this study can provide a valuable reference for the planning and dispatching of HWHPS, which contributes to the reliability and stability. [ABSTRACT FROM AUTHOR]

[Display omitted] • A method for spatiotemporal coordination of multienergy systems is proposed. • Virtual energy storage gain is calculated based on spectral analysis. • The hydropower regulation ability affects the virtual energy storage gain. • The optimal coordination range between multiple energy sources is obtained. Utilizing the spatial heterogeneity and climate periodicity of various available renewable energy sources can enhance the multienergy complementarity, which will further reduce the energy storage demand and contributes to the "virtual energy storage gain." In this paper, we propose a spatiotemporal coordination method based on spectral analysis for a wind-PV-hydropower system that targets the maximum virtual energy storage gain. The complementary effect of hydropower on wind and PV power can be seen as changes in the regulation ability resulting from the hydropower construction development as well as a decreased variance in the total system production output. This method is used to determine the optimal coordination distance of multiple energy sources that are matched over different periods representing typical climate variation. A case study in the Yalong River basin in China obtains the best range of hydropower bundling surrounding wind power and PV power stations under different hydropower construction planning scenarios, and it reveals that increased regulation capacity of hydropower will reduce the optimal coordination distance but can achieve more energy balance if both actual storage and virtual storage is accounted for. This approach can realize energy delivery with the optimal coordination distance to meet intensive and efficient development needs, which can provide guidance and support for the planning and construction of wind-PV-hydropower storage systems. [ABSTRACT FROM AUTHOR]

Under China's current Renewable Portfolio Standards (RPS) policy system, the provincial power grid is the assessment subject, and then the assessment is conducted on the user side. However, in the centralized clearing power market, renewable energy (RE) consumption on the user side cannot be accurately identified, so accurate value transmission cannot be formed to guide users to optimize their electricity consumption behavior. To improve this problem, this paper identifies the irreplaceable role of users in RE consumption through time series simulation. We construct a three-layer RPS assessment value transmission model of "power grid company - retailers - users" based on the identified unique contributions of each user. The equilibrium state of multi-market participants is obtained based on the decentralized training framework of reinforcement learning. The following conclusions are drawn through simulation analysis:(1) the user-side RE contribution identification method can effectively identify the user's contribution to RE consumption, which can reduce about 8% or increase about 7.48% of RPS cost allocation according to different contribution degrees; (2) the RPS value allocation mode can optimize users' power consumption behavior, achieve peak shaving and valley filling, promote RE consumption, and reduce about 0.6% of the abandoned wind power; (3) Under the RPS system, the effect of improving the response-ability of the user-side is better than increasing the RPS target and the peak shaving capacity. • Accurately identify the RE consumption of users under the unified clearing mode. • Construct a transmission mode of RPS assessment among grid, retailers, and users. • Construct a distributed training framework of multi-agent. • The research is helpful in promoting the consumption of new energy. [ABSTRACT FROM AUTHOR]

Under China's current Renewable Portfolio Standards (RPS) policy system, the provincial power grid is the assessment subject, and then the assessment is conducted on the user side. However, in the centralized clearing power market, renewable energy (RE) consumption on the user side cannot be accurately identified, so accurate value transmission cannot be formed to guide users to optimize their electricity consumption behavior. To improve this problem, this paper identifies the irreplaceable role of users in RE consumption through time series simulation. We construct a three-layer RPS assessment value transmission model of "power grid company - retailers - users" based on the identified unique contributions of each user. The equilibrium state of multi-market participants is obtained based on the decentralized training framework of reinforcement learning. The following conclusions are drawn through simulation analysis:(1) the user-side RE contribution identification method can effectively identify the user's contribution to RE consumption, which can reduce about 8% or increase about 7.48% of RPS cost allocation according to different contribution degrees; (2) the RPS value allocation mode can optimize users' power consumption behavior, achieve peak shaving and valley filling, promote RE consumption, and reduce about 0.6% of the abandoned wind power; (3) Under the RPS system, the effect of improving the response-ability of the user-side is better than increasing the RPS target and the peak shaving capacity. • Accurately identify the RE consumption of users under the unified clearing mode. • Construct a transmission mode of RPS assessment among grid, retailers, and users. • Construct a distributed training framework of multi-agent. • The research is helpful in promoting the consumption of new energy. [ABSTRACT FROM AUTHOR]

Improving the efficiency of renewable energy and electricity utilization is an urgent problem for China under the objectives of carbon peaking and carbon neutralization. This paper proposes an optimization scheduling method of electric vehicles (EV) combined with wind and photovoltaic power based on the Frank-Copula-GlueCVaR. First, a joint output model based on copula theory was built to describe the correlation between wind and photovoltaic power output. Second, the Frank-Copula-GlueCVaR index was introduced in a novel way. Operators can now predetermine the future wind-photovoltaic joint output range based on this index and according to their risk preferences. Third, an optimal scheduling model aimed at reducing the group charging cost of EVs was proposed, thereby encouraging EV owners to participate in the demand response. Fourth, this paper: proposes the application of a Variant Roth–Serve algorithm; regards the EV group as a multi-intelligent group; and finds the Pareto optimal strategy of the EV group through continuous learning. Finally, case study results are shown to effectively absorb more renewable energy, reduce the consumption cost of the EV group, and suppress the load fluctuation of the whole EV group, which has a practical significance and theoretical value. [ABSTRACT FROM AUTHOR]

• A wind power prediction method based on ensemble learning and hyper-parameters optimization is proposed. • A wind power sequence segmentation method based on the Swing Window is proposed. • The relationships mapping model between error sequence and NWP sequences are built. • A daily rolling error correction strategy is proposed to correct wind power prediction results. With a high proportion of renewable energy injected into the power grid, the accurate wind power prediction of wind farms is key to improving power quality and ensuring the stable operation of the power grid. In this paper, an ensemble learning prediction model considering the rolling error correction strategy used for short-term wind power prediction is proposed. Firstly, the ensemble learning prediction model integrating multiple Gradient Boosting Trees (GBDTs) based on Bayesian optimization is established, and the characteristics of prediction errors are analyzed. To further improve the prediction accuracy, a daily rolling error correction strategy based on the Swing Window segmentation method, Spearman correlation coefficient and Quantile Regression is established to obtain the optimal compensation amount. Finally, the corrected short-term wind power prediction results are obtained. The two-year dataset collected from a regional wind farm in China is used as the benchmark test. Different prediction models and error correction strategies are compared, and the effectiveness of the proposed method is comprehensively evaluated. The results from different seasons show that the proposed prediction method has good stability, and the proposed error correction strategy has good generalization ability. [ABSTRACT FROM AUTHOR]

• Integrating machine learning and vine-copula to catch the complex spatial correlations among the renewable resources. • Utilization of stochastic model predictive control to optimize the long-term hydro-wind-solar operation. • Potential assessment of hydropower to offset the effects of seasonal uncertainties and mismatch between renewable resources and requirements. It is popular to combine the hydropower plant with the wind and solar power plants to supply electricity, and the joint is termed as the hydro-wind-solar renewable energy supply system (RESS). The long-term optimal operation of the RESS is a challenging task, due to the nature of different spatial and temporal variabilities associated with renewable energy resources, and the significant operation uncertainties due to the changing natural environment. To address the task, the stochastic model predictive control (MPC), based on probabilistic forecasting and rolling stochastic optimization, is designed and implemented for the long-term operations of the RESSs in Yunnan province, China. The paper tests out the system efficiencies for different penetration levels of wind and solar power, and finds out that (1) the Long Short-Term Memory performs best among candidate point prediction algorithms; (2) the stochastic MPC considering the correlations among renewable resources helps the RESS operate with a higher efficiency; (3) the large-scale hydropower plant has great potential to offset the effects of seasonal uncertainties and demand-generation mismatch, but cannot completely avoid the electricity shortage enabled by unexpected scenarios of renewable resources. [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]

In recent years, China has successively issued relevant policies to support the development of distributed renewable energy sources such as dispersed wind power (DWP) and distributed photovoltaic (DPV). The capacity of DPV grows fast, but the progress of DWP deployment is limited. In this paper, the improved levelized cost of electricity (LCOE) model and the internal rate of return (IRR) are employed to evaluate the economics of DWP and DPV for case regions. We find that, under current market regime, the IRR of DWP in eastern and central regions is 8.81%–10.34% for the grid side, and which of DPV is 9.08%–12.8% for the user side. When the power market is restructured, the IRR of DWP for industrial and commercial user side increases to 12.73%–14.86%, higher than that the return of DPV. Policy implications on grid-connection, land expropriation, and financial support, planning and approval procedure are proposed to promote the development of DWP. Overall, this paper covers the research gap on the development of DWP in China with a technical/institutional economics perspective and a comparison with DPV. Furthermore, our efforts make a significant contribution to DWP policy by identifying the institutional factors that hinder the development of DWP. • Explore the technical & institutional factors around DWP deployment in China. • DWP and DPV gain comparable economic returns under current planning regime. • DWP can have better economic returns than DPV with electricity market reform. • Identify institutional factors that hinder DWP and propose policy implications. [ABSTRACT FROM AUTHOR]

More and more attention has been given to the role of electric vehicles in reducing the economic cost and carbon emissions of the power system through the vehicle-to-grid (V2G) mode, as well as helping to integrate renewable energy power. The promotion of V2G mode needs to consider the effectiveness of policy costs, so it is urgent to estimate the potential benefits of V2G. In this paper, a multi-regional power dispatch and expansion model is constructed from the perspective of power system by taking China's plan to reach 1.2 billion kW of installed wind and photovoltaic power generation capacity in 2030 as an example, combining with the development planning of electric vehicles and power systems. The results show that by 2030, the implementation of unidirectional V2G and bidirectional V2G in China can reduce the total cost of power system by 2.02% and 2.08%, reduce the annual carbon emissions of the power system by 2.27% and 2.95%, and increase the total proportion of wind and photovoltaic power generation by 1.33% and 1.28% in the power mix. An increasing proportion of electric vehicles participate in V2G increases the proportion of new-installed wind generation and decreases that of photovoltaic power. Due to the high cost of battery degradation and the current power mix dominated by coal-fired generation, additional benefits of implementing bidirectional V2G over unidirectional V2G are limited. • V2G reduces the total power cost, carbon emissions, and renewables' curtailment. • Increasing the proportion of EVs participating in V2G improves potential benefits. • V2G changes the proportion of newly installed wind and solar power generation. • V2G shifts the electricity production from high-cost areas to low-cost areas. • Additional benefits of bidirectional V2G over unidirectional V2G are limited. [ABSTRACT FROM AUTHOR]

Green bonds have increasingly been utilized around the world as a source of financing for renewable energy development, designed with compliance requirements and measurable economic returns to investors, while mitigating climate change. However, the efficacy of green bond arranged in the emerging economies for financing renewable energy assets and how the underlying risks are managed have remained to be explored. The paper aims to examine the evolving green financial system sponsored by both public and private institutions in managing such risks within China's emerging economy. A case study of green financing for a bundle of wind power assets led by a state-owned enterprise (SOE) reveals an alternative approach by structuring public–private collaboration while stipulating market-based financial incentives to institutional stakeholders under a political economy. This institutional consortium is composed of a state development bank, a commercial bank, credit rating agencies, institutional and private investors, regional power purchasers, and carbon trading entities. Financial stakeholders' risk in such emerging sustainable investment is moderated by these participating institutions and structured "upsides" from carbon trading aligned with the framework of green finance and standards for green bond development. The results reveal the potentials of scaling up the development of renewable energy by adequately managing and sharing key risks, while allocating substantial funding into renewable energy projects under such a green financial system that is to be complementary with a scalable post COVID-19 economic recovery. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) and wind power (WT) resources can influence each other in some scenarios, and this influence tends to show that the rise of PV resources may indicate the drop of WT resources, and vice versa. This pattern of PV and WT resources influencing each other is called the complementary characteristics of PV and WT power. The complementary characteristics of the power outputs of different kinds of distributed renewable energy resources (DRERs) and the correlation between DRERs outputs and loads can impact the consumption of DRERs by the loads within the grid, which represents the rate of DRER outputs consumed by loads instead of being reduced. In this regard, this paper investigates a planning strategy for DRERs considering these two factors. An improved co-variance matrix method is applied to generate complementary samples of DRERs and correlated samples of DRERs and loads. The samples generated are used to study the impacts of the degree of correlation between DRERs and loads on the consumption ability of DRERs. The concept of the cluster is introduced as a region including DRERs with complementary characteristics. Based on the cluster partition method and the samples generated, the DRERs planning model is proposed to maximize the profits of different DRER stakeholders. The planning model is transformed into a single objective model through the ideal point method. A Benders decomposition-based method is developed to efficiently solve the proposed model, and an actual network in China is used to illustrate its performance. The results show DRER consumption can be significantly improved by the proposed planning model. [ABSTRACT FROM AUTHOR]