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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 renewable-dominant hybrid generation systems (HGSs) are increasingly important to the electric power system worldwide. However, influenced by uncertain meteorological factors, the operational robustness of HGSs must be evaluated to inform the associated decision-making. Additionally, the main factors affecting the HGS's robustness should be urgently identified under deep uncertainties, as this provides valuable guidance for HGS capacity configuration. In this paper, a multivariate stochastic simulation method is developed and used to generate uncertain resource scenarios of runoff, photovoltaic power, and wind power. Subsequently, a long-term stochastic optimization model of the HGS is employed to derive the optimal operating rules. Finally, these operating rules are used to simulate the long-term operation of an HGS, and the results are used to evaluate the HGS's robustness and identify its main sensitivities. A clean energy base located in the Upper Yellow River Basin, China, is selected as a case study. The results show that the HGS achieves greater operational robustness than an individual hydropower system, and the robustness becomes weaker as the total capacity of photovoltaic and wind power increases. Additionally, the operational robustness of the HGS is found to be more sensitive to the total capacity than to the capacity ratio between photovoltaic and wind power. [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]

The serious problem of wind power curtailment in northern China has created a pressing need to enhance the peak-shaving ability of the power system. As the main source of power supply in northern China, combined heat and power (CHP) units have significant potential for peak-shaving. Currently, the Chinese government encourages CHP plants to increase their peak-shaving capacity by installing power-to-heat (P2H) equipment. In addition, the government has implemented auxiliary service market policies to encourage CHP plants to provide peak-shaving services. In order to maximize economic benefits for CHP plants, this paper proposes an optimal configuration method of P2H equipment with the static payback time (SPT) as the objective function. Cost and income models of installing the P2H equipment are constructed by taking into account the auxiliary service market policies. The peak-shaving income model of the CHP plant is derived emphatically as a key part of the proposed method. Finally, the district heating region in Jilin province is used as a case study example. The results show that adding the P2H equipment is significantly effective in improving the peak-shaving ability of CHP units, and investing in heat pumps is more cost-effective than electric boilers. The proposed method can be applied to other northern regions relying on CHP units for central heating, providing a valuable solution to the problem of wind power curtailment in these regions. [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]

The coordinated use of electricity and a heat energy system can effectively improve the energy structure during winter heating in the northern part of China and improve the environmental pollution problem. In this paper, an economic scheduling model of an electric–thermal integrated energy system, including a wind turbine, regenerative electric boiler, solar heat collection system, biomass boiler, ground source heat pump and battery is proposed, and a biomass boiler was selected as the auxiliary heat source of the solar heat collection system. A mixed integer linear programming model was established to take the operating cost of the whole system as the target. A day-ahead optimization scheduling strategy considering the demand side response and improving new energy consumption is proposed. In order to verify the influence of the coordinated utilization of the flexible load and energy storage equipment on the optimal scheduling in the model built, three scenarios were set up. Scenario 3 contains energy storage and a flexible load. Compared with scenario 1, the total cost of scenario 3 was reduced by 51.5%, and the abandonment cost of wind energy was reduced by 43.3%. The use of a flexible load and energy storage can effectively reduce the cost and improve new energy consumption. By increasing the capacity of the energy-storage device, the wind power is completely absorbed, but the operation and maintenance cost is increased, so the capacity of energy storage equipment is allocated reasonably according to the actual situation. [ABSTRACT FROM AUTHOR]

The issue of renewable energy curtailment poses a crucial challenge to its effective utilization. To address this challenge, mitigating the impact of the intermittency and volatility of wind and solar energy is essential. In this context, this paper employs scenario analysis to examine the complementary features of wind and solar hybrid systems. Firstly, the study defines two types of complementary indicators that distinguish between output smoothing and source-load matching. Secondly, a novel method for generating wind and solar output scenarios based on improved Generative Adversarial Networks is presented and compared against the conventional Monte Carlo and Copula function methods. Lastly, the generated wind and solar scenarios are employed to furnish complementary features. The testing results across eight regions indicate the proposed scenario generation method proficiently depicts the historical relevance as well as future uncertainties. This study found that compared to the Copula function method, the root mean square error of the generated data was reduced by 4% and 3.4% for independent and hybrid systems, respectively. Moreover, combining these two resources in most regions showed that the total output smoothness and source-load matching level cannot be enhanced simultaneously. This research will serve as a valuable point of reference for planning and optimizing hybrid systems in China. [ABSTRACT FROM AUTHOR]

With the centralization of wind power development, power-prediction technology based on wind power clusters has become an important means to reduce the volatility of wind power, so a large-scale power-prediction method of wind power clusters is proposed considering the prediction stability. Firstly, the fluctuating features of wind farms are constructed by acquiring statistical features to further build a divided model of wind power clusters using fuzzy clustering algorithm. Then the spatiotemporal features of the data of wind power are obtained using a spatiotemporal attention network to train the prediction model of wind power clusters in a large scale. Finally, the stability of predictive performance of wind power is analyzed using the comprehensive index evaluation system. The results show that the RMSE of wind power prediction is lower than 0.079 at large-scale wind farms based on the prediction method of wind power proposed in this paper using experience based on the data of 159 wind farms in the Nei Monggol Autonomous Region in China and the extreme error is better than 25% for the total capacity of wind farms, which indicates high stability and accuracy. [ABSTRACT FROM AUTHOR]

This paper discusses how the incorporation of high-resolution ground coverage dataset ESA WorldCover into a wind flow field and wake simulation calculation, as well as the use of the coupled wake model for wind farm output simulation, can improve the accuracy of wind resource assessment using engineering examples. In the actual case of grid-connected wind farms in central China, SCADA wind speed data is reconstructed to the free flow wind speed in front of the wind turbine impeller using the transfer function of the nacelle, and the wind farm is modeled using OpenWind software, simulating the wind speed at the height of each wind turbine hub and each wind turbine output. The results show that when other initial data are consistent, using ESA's high-precision land cover dataset WorldCover 10 m to make roughness lengths which improves the wind farm output simulation accuracy by 8.91%, showing that it is worth trying to apply WorldCover 10 m to the wind farm simulation design. At the same time, this case is used to compare and analyze the application of the Eddy-Viscosity wake model and the two coupled wake models based on the Eddy-Viscosity wake model. The results show that the coupled wake model will have higher accuracy than the Deep Array Eddy Viscosity wake model and it is 1.24% more accurate than the Eddy Viscosity wake model, and the ASM Eddy Viscosity wake model is 5.21% more accurate than the Eddy Viscosity wake model. [ABSTRACT FROM AUTHOR]

Distributed energy resources aggregators (DERAs) are permitted to participate in regional wholesale markets in many counties. At present, new market players such as aggregators participate in China's power market transactions. However, studies related to market trading strategy have mostly focused on centralized wind power and PV generation units. Few studies have been conducted on the decision-making strategies for DERAs in China's power market. This paper proposes an auxiliary decision-making model for distributed energy systems to participate in the day-ahead market with more reasonable trading strategies. Firstly, the Gaussian mixture model (GMM) is used to deal with the uncertainties of wind power and photovoltaic (PV) output in the distributed energy system. Secondly, the information gap decision theory (IGDT) is used to deal with the uncertainty of price fluctuations in the spot electricity market. Thirdly, according to the different risk preferences of the DERAs facing market price fluctuation, the robust decision model and opportunity decision-making model in the day-ahead market are constructed, respectively. Finally, to deal with the irrational behavior of the DERAs' perception of "gain" and "loss" with market risks in China's two-tier market environment, the prospect theory and the marine predator's algorithm (MPA) are employed to obtain a day-ahead trading decision scheme for DERA. The analyses show that RDES with robust preference can withstand greater price volatility in the day-ahead market; they will reduce the bidding expectations and increase the system operating cost to improve the achievability of the expected revenue. However, DERAs under the opportunity strategy is more inclined to sell electricity to the market and offset system operating costs with revenue. The proposed model can provide strategic reference for DERAs with different risk preferences to bid in day-ahead market and can improve the level of aggregators' participation in electricity trading. [ABSTRACT FROM AUTHOR]

Industrial development cannot be separated from policy guidance and support. Scientific evaluation and analysis of wind power industrial policies can promote the sustainable and healthy development of the industry, which has great significance to accelerate the green and low-carbon transformation of energy and achieve the double carbon target. In this paper, based on 66 wind power industrial policies from 2010 to 2021 in China, the policy texts were coded and classified into policy tools. By combining the text mining technology with the Policy Modeling Consistency (PMC) index model, 10 groups of evaluation index systems were established to evaluate the industrial policies quantitatively. The results show that the use of the three types of policy tools is unbalanced. Among them, environmental policy tools are used most frequently while supply and demand policy tools are relatively few. According to the PMC score, the overall situation of wind power industrial policy has certain scientific qualities. However, there are few policy texts for ordinary residents in terms of policy recipients, and there are shortcomings in technical support and talent construction. In addition, the three types of policy tools have certain limitations in different aspects. Based on the existing problems, we put forward suggestions for policy improvement. [ABSTRACT FROM AUTHOR]

Due to the insufficient consideration of medium and long-term wind power contract power in short-term dispatch, long-term planning and real-time consumption of wind power cannot be effectively undertaken, resulting in a large amount of abandoned wind power. A way to improve the wind power absorption capacity has become an urgent problem to be studied. According to the characteristics of the market and dispatching in the process of wind-fire integration construction, this paper constructs a wind power consumption model that connects the mid- and long-term transaction power decomposition and short-term dispatch. Considering the unit output characteristics and maintenance, the monthly contract electricity is decomposed into daily electricity, and the nesting of medium and long-term transactions and short-term scheduling is realized; the second stage is a short-term multi-objective optimal scheduling model considering the decomposition of contract electricity and the output of non-bidding units to improve the real-time consumption of wind power. Finally, a province in northwest China is taken as an example to verify the effectiveness of the proposed method. [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]

How to consider both the influence of weather and wind power in the modeling process of probability distribution of wind power forecast error (WPFE), and to emphasize the application value of conditional modeling, is rarely studied at present. This paper proposes a novel method of conditional probability distribution modeling for WPFE. This method uses a proposed MNSGA-II-Kmeans algorithm to perform multi-objective clustering of multi-dimensional influencing factors (MDIF), including weather and wind power. It can maximize the difference between the probability distributions of each MDIF mode's WPFE while clustering, thus ensuring the application value of the conditional modeling way. Based on the clustering results, by using the versatile distribution to simulate the probability distribution of WPFE and the support vector machine to realize the recognition of MDIF modes, the specific conditional probability distribution function of WPFE can be provided to stochastic economic dispatch by identifying the forecast MDIF data. A wind plant of north China with historical data is selected for calculation. The results verify the effectiveness of the proposed method, and by comparison with the non-conditional probability distribution of WPFE that does not consider MDIF, it can effectively increase the wind power consumption of the power system. [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]

Chinese wind power policies have productively promoted the development of wind power and also promoted the process of wind power participation in electricity market transactions. However, with policy emphasis on investment and neglect of utilization, there have been some difficulties in the development of Chinese wind power. To highlight the guiding role of policy and improve the wind power policy system, an analysis of Chinese wind power policies is conducted in this paper. First, aiming at the main components of wind power chain, including wind power construction, grid connection, transmission, and sales, a comprehensive and systematic frame of Chinese wind power policy system is proposed from the three aspects of development planning, administrative management and market transactions. Second, the indicator system of Chinese wind power development effectiveness is extracted, including installed capacity, power generation, power utilization hours, carbon emission reduction, investment, on-grid power price, wind curtailment rate, etc., and the effectiveness of policy implementation is verified. Third, new policy recommendations are proposed from parts of transmission, consumption, and market, for the development of Chinese wind power industry in the 14th Five-Year Plan period and the goal to reach the crest value of carbon dioxide emissions by 2030 and achieve carbon neutrality by 2060. In this paper, advice to supplement and improve the Chinese wind power policy system is provided, which may it promote better development of Chinese wind power in the following decade from the policy point of view. [ABSTRACT FROM AUTHOR]

In China, with the increasing permeability of wind power, the power supply capacity is enough overall, but has shortage in partial-time. During peak hours, the capability of wind power consumption is poor and the power balance becomes more difficult. In order to maximize the utilization of wind power, the net loads are chosen as the response objectives, which contain significant uncertainties and have no probabilistic distribution characteristics. Under the traditional day-ahead power dispatch mode with fixed length time intervals and in the regions with insufficient hydroelectricity, the thermal generators take charge of the peak-load shaving. The frequent adjustments of thermal power output affect the system operation safety, economic benefits, and environmental benefits. Thus, a time-interval-varying optimal power dispatch strategy based on net load time-series characteristics is proposed in this paper. The net loads respond differently to intervals. The length of each time interval is determined based on the net load time-series characteristics analyzed by random matrix theory. The dispatch mode in each time interval is determined according to the characteristic quantification index calculated by the empirical modal decomposition and sample entropy. The proposed strategy and method can extend the continuous and stable operation time of the thermal generators, reduce the coal consumption caused by the ramping operation, and improve the safety, stability, and economy of the system. Furthermore, the proposed dispatch mode is environmentally friendly with reduced environmental cost and increased carbon credits. An actual provincial power grid in northeast China is taken as the example to verify the rationality and effectiveness of the proposed method and strategy. [ABSTRACT FROM AUTHOR]

Huaneng Rudong 300 MW offshore wind farm project is located in eastern China. The wake effect is one of the major concerns for wind farm operators, as all 70 units are plotted in ranks, and the sea surface roughness is low. This paper investigated the wake intensity by combining a field test and a numerical simulation. To carry out further yaw optimization, a Gaussian wake model was adopted. Firstly, a 3D Light Detection and Ranging device (LiDAR) was used to capture the features in both horizontal and vertical directions of the wake. It indicated that Gaussian wake model can precisely predict the characteristics under time average and steady state in the wind farm. The predicted annual energy production (AEP) of the whole wind farm by the Gaussian model is compared with the calculation result of the actuator line (AL)-based LES method, and the difference between the two methods is mostly under 10%. [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]