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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 2009, the implementation of feed-in tariff (FIT) and attractive public subsidies for onshore wind farms aroused great investment enthusiasm and spurred remarkable development of wind power in China. Meanwhile, rapid learning-by-doing has significantly cut down the cost of wind turbines and the capital cost of wind farms as well. Therefore, it is the right time to examine the appropriateness of the existing FIT policy for wind power in China. In this paper, we employ the analytical framework for levelized cost of electricity (LCOE) to model the generation cost of wind power. Results show that the existing FIT policy is attractive to investors, but serious curtailment and turbine quality issues could make wind power unprofitable. Meanwhile, rapid substantial decreases in the cost of wind power have made it competitive to coal power in 2013, implying that it is possible and necessary to reform the FIT policy for new wind farms. In the future, energy policies for onshore wind power in China could be concentrated on reducing the integration cost, so as to reduce the overall system cost. [ABSTRACT FROM AUTHOR]

Wind energy has been widely used in renewable energy systems. A probabilistic prediction that can provide uncertainty information is the key to solving this problem. In this paper, a short-term direct probabilistic prediction model of wind power is proposed. First, the initial data set is preprocessed by a box plot and gray correlation analysis. Then, a generalized method is proposed to calculate the natural gradient and the improved natural gradient boosting (NGBoost) model is proposed based on this method. Finally, blending fusion is used in order to enhance the learning effect of improved NGBoost. The model is validated with the help of measured data from Dalian Tuoshan wind farm in China. The results show that under the specified confidence, compared with the single NGBoost metamodel and other short-term direct probability prediction models, the model proposed in this paper can reduce the forecast area coverage probability while ensuring a higher average width of prediction intervals, and can be used to build new efficient and intelligent energy power systems. [ABSTRACT FROM AUTHOR]

Wind power technology is developing rapidly in China. Especially in the north of China, decentralized wind power (DWP) has developed rapidly. However, wind curtailment exceeds 4% of the gross generation, which has become an important obstacle restricting the development of DWP in 2019. Technical schemes are effective solutions to promote decentralized wind power consumption (DWPC). Moreover, there are few economic analyses of DWP projects with key technologies. This paper summarizes the typical technical schemes and analyzes the economy of a DWP grid-connected project with typical technical schemes by combining methods of the net present value (NPV) and real options (RO). It appears that the DWP project can be profitable with an NPV of about CNY 4.3721 million and an option value of CNY 16.6022 million in the case of a feed-in tariff of 0.34 CNY/kWh. Feed-in tariff and equipment investment costs are the most relevant parameters for economic feasibility according to the sensitivity analysis. Furthermore, 30.73 million kWh connects to the Mengxi grid each year. The project has an annual maximum CO2 emission reduction of about 31 thousand tons and an SO2 of about 0.1 thousand tons. In addition, it also realizes fine social benefits, including the support of local residents and the improvement of local new urbanization levels. Finally, this paper puts forward policy suggestions for the development of DWP mainly from an economic 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]

Wind power has become the main power generation method in China's clean energy power generation because of its clean and high efficiency, as well as its high power utilization rate. The research on its pricing mechanism has also become the main research focus of the wind power industry. However, wind power pricing is still at the stage of price benchmarking and no market mechanism has been introduced in China. There are still much research on the pricing mechanism of wind power for us to study. In this paper, the Kernel method is used to distribute wind power income. On the basis of the distribution result, considering the contract execution risk of wind power, cooperative game theory and the Shapley value method are used to redistribute the revenue of wind power connected to power grid. Based on the characteristics of alliance members, ANP (Analytic Network Process) was used to modify the apportioned benefits to obtain the benefit distribution method that was more in line with the interest demands of members, and an example was analyzed. The wind power pricing model based on the cooperative game established in this paper can guarantee the smooth operation of the alliance, reach the pareto optimum, and improve the activity of the wind power market. It will effectively shorten the negotiation time, and reduce the transaction cost and the uncertainty of the wind power transaction. [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]

Selecting an appropriate wind farm location must be specific to a particular administrative region, which involves restrictions balance and trade-offs. Multi-criteria decision making (MCDM) and GIS are widely used in wind energy planning, but have failed to achieve the selection of an optimal location and make it difficult to establish a set of independent factors. Fuzzy measurement is an effective method to evaluate intermediate synthesis and calculates the factor weight through fuzzy integrals. In this paper, optimal wind farm location is analyzed through coupling Grid GIS technique with λ fuzzy measure. Dalian City is selected as the study area for proving the feasibility of the proposed method. Typography, meteorological, transmission facilities, biological passage, and infrastructure are taken into the index system. All the indexes are specialized into victor grid cells which are taken as the base wind farm location alternative unit. The results indicate that the Grid GIS based λ fuzzy measure and Choquet fuzzy integral method could effectively deal with the special optimization problem and reflect optimal wind farm locations. [ABSTRACT FROM AUTHOR]

Offshore wind is considered a crucial part in the future energy supply. However, influenced by weather conditions, the maintenance of offshore wind turbine system (OWTs) equipment is challenged by poor accessibility and serious failure consequences. It is necessary to study the optimized strategy of comprehensive maintenance for offshore wind farms, with consideration of the influences of incomplete equipment maintenance, weather accessibility and economic relevance. In this paper, a Monte Carlo algorithm-improved factor is presented to simulate the imperfect preventive maintenance activity, and waiting windows were created to study the accessibility of weather conditions. Based on a rolling horizon approach, an opportunity group maintenance model of an offshore wind farm was proposed. The maintenance correlations between systems and between equipment as well as breakdown losses, maintenance uncertainty, and weather conditions were taken into account in the model, thus realizing coordination of maintenance activities of different systems and different equipment. The proposed model was applied to calculate the maintenance cost of the Dafengtian Offshore Wind Farm in China. Results proved that the proposed model could realize long-term dynamic optimization of offshore wind farm maintenance activities, increase the total availability of the wind power system and reduce total maintenance costs. [ABSTRACT FROM AUTHOR]

Distributed wind power (DWP) needs to be consumed locally under a 110 kV network without reverse power flow in China. To maximize the use of DWP, this paper proposes a novel method for capacity planning of DWP with participation of the energy storage system (ESS) in multiple scenarios by means of a variable-structure copula and optimization theory. First, wind power and local load are predicted at the planning stage by an autoregressive moving average (ARMA) model, then, variable-structure copula models are established based on different time segment strategies to depict the correlation of DWP and load, and the joint typical scenarios of DWP and load are generated by clustering, and a capacity planning model of DWP is proposed considering investment and operation cost, and environmental benefit and line loss cost under typical scenario conditions. Moreover, a collaborative capacity planning model for DWP and ESS is prospectively proposed. Based on the modified IEEE-33 bus system, the results of the case study show that the DWP capacity result is more reasonable after considering the correlation of wind and load by using a variable-structure copula. With consideration of the collaborative planning of DWP and load, the consumption of DWP is further improved, the annual cost of the system is more economical, and the quality of voltage is effectively improved. The study results validate the proposed method and provide effective reference for the planning strategy of DWP. [ABSTRACT FROM AUTHOR]

The rapidly increasing penetration of wind power into sending-side systems makes the wind power curtailment problem more severe. Enhancing the total transfer capability (TTC) of the transmission channel allows more wind power to be delivered to the load center; therefore, the curtailed wind power can be reduced. In this paper, a new method is proposed to enhance TTC, which works by optimizing the day-ahead thermal generation schedules. First, the impact of thermal generation plant/unit commitment on TTC is analyzed. Based on this, the day-ahead thermal generation scheduling rules to enhance TTC are proposed herein, and the corresponding optimization models are established and solved. Then, the optimal day-ahead thermal generation scheduling method to enhance TTC is formed. The proposed method was validated on the large-scale wind power base sending-side system in Gansu Province in China; the results indicate that the proposed method can significantly enhance TTC, and therefore, reduce the curtailed wind power. [ABSTRACT FROM AUTHOR]

The wind–heat conflict and wind power uncertainty are the main factors leading to the phenomenon of wind curtailment during the heating period in the northern region of China. In this paper, a multi-time scale optimal scheduling strategy for combined heat and power system is proposed. Considering the temporal dependence of wind power fluctuation, the intra-day wind power scenario generation method is put forward, and both day-ahead and intra-day optimization scheduling models based on the scenario method are established to maximize the system's revenue. The case analyzes the impacts of the initial heat storage capacity of a heat storage device and different scheduling strategies on system revenue. It is verified that the scheduling strategy can better adapt to wind power uncertainty and improve the absorption capacity of wind power, while ensuring the safety and economical efficiency of system operation. [ABSTRACT FROM AUTHOR]

When the power source of a voltage source converter (VSC) station at the sending end solely depends on wind power generation, the station is operating in an islanding mode. In this case, the power fluctuation of the wind power will be entirely transmitted to the receiving-end grid. A self-regulation scheme of power fluctuation is proposed in this paper to solve this problem. Firstly, we investigated the short-time variability characteristic of the wind power in a multi-terminal direct-current (MTDC) project in China. Then we designed a virtual frequency (VF) control strategy at the VSC station based on the common constant voltage constant frequency (CVCF) control of VSC station. By cooperating with the primary frequency regulation (PFR) control at the wind farms, the self-regulation of active power pooling at the VSC station was realized. The control parameters of VF and PFR control were carefully settled through the steady-state analysis of the MTDC grid. The self-regulation effect had been demonstrated by a twenty-four-hour simulation. The results showed that the proposed scheme could effectively smoothen the power fluctuation. [ABSTRACT FROM AUTHOR]

In order to satisfy the strategic needs of energy sustainable development, renewable energy has developed rapidly and the power systems have been transformed to a new generation of power systems. In the renewable energy power generation technologies, the fastest developing wind power generation are highly intermittent and fluctuating. When high penetration of renewable power connects to the power grid and participates in the system dispatch, there will be more difficulties and challenges in the energy balance control. In this paper, a wind-thermal-nuclear-storage combined time division power dispatch strategy based on numerical characteristics of net load is proposed, where a specific thermal generating mode and an unconventional nuclear generating mode are discussed. In the strategy, the dispatch time division method is introduced in detail and the sample entropy theory is used to calculate the net load complexity. An adaptive thermal generating mode is determined according to the numerical characteristics of the net load. The nuclear generating modes of constant power operation, time division operation, and net load tracking time division operation are compared and analyzed, respectively. Finally, the wind-thermal-nuclear-storage combined time division power dispatch strategy aiming at decreasing the ramping power of thermal generators is achieved, and the increasing of the participation of pumped storage and improving of the continuous and steady operation time of thermal generators are realized. The experiment simulation is developed on an actual provincial power system in the northeast of China. The results verify that the thermal generator ramping power in the case based on SampEn are reduced, and the participation of pumped storage is improved. When both of the thermal generating mode and nuclear generating mode are according to the changing of net loads, the ramping powers of thermal generators are further decreased. [ABSTRACT FROM AUTHOR]

During the heating period in Northeast China, the electricity generated by Combined Heat and Power (CHP) plants has caused a large amount of wind power curtailment. To avoid this curtailment, Heat Storages (HS) and Electric Boilers (EB) are being widely applied to CHP plants. However, the effectiveness of HS and EB on the curtailment of wind power has still not been well evaluated. This paper proposes a new evaluation method consisting of an operation boundary model and an internal coordinated operation model for each flexible CHP plant with HS and EB. Then a system power balance model based on hour-by-hour analysis is established to determine the wind power integration, down-regulation subsidy and the utilization degree of the flexible equipment. The models are validated by using field measured data of a province in China. The results show that the capacity change of the EB has a bigger impact on the down-regulation subsidy than that of the HS, while the EB is more effective when the level of wind power integration is high. [ABSTRACT FROM AUTHOR]

Wind energy is crucial renewable and sustainable resource, which plays a major role in the energy mix in many countries around the world. Accurately forecasting the wind energy is not only important but also challenging in order to schedule the wind power generation and to ensure the security of wind-power integration. In this paper, four kinds of hybrid models based on cyclic exponential adjustment, adaptive coefficient methods and the cuckoo search algorithm are proposed to forecast the wind speed on large-scale wind farms in China. To verify the developed hybrid models' effectiveness, wind-speed data from four sites of Xinjiang Uygur Autonomous Region located in northwest China are collected and analyzed. Multiple criteria are used to quantitatively evaluate the forecasting results. Simulation results indicate that (1) the proposed four hybrid models achieve desirable forecasting accuracy and outperform traditional back-propagating neural network, autoregressive integrated moving average as well as single adaptive coefficient methods, and (2) the parameters of hybrid models optimized by artificial intelligence contribute to higher forecasting accuracy compared with predetermined parameters. [ABSTRACT FROM AUTHOR]

Accurate wind power and wind speed forecasting remains a critical challenge in wind power systems management. This paper proposes an ultra short-time forecasting method based on the Takagi–Sugeno (T–S) fuzzy model for wind power and wind speed. The model does not rely on a large amount of historical data and can obtain accurate forecasting results though efficient linearization. The proposed method employs meteorological measurements as input. Next, the antecedent and the consequent parameters of the forecasting model are identified by the fuzzy c-means clustering algorithm and the recursive least squares method. From these components, the T–S fuzzy model is obtained. Wind farms located in China (Shanxi Province) and in Ireland (County Kerry) are considered as cases with which to validate the proposed forecasting method. The forecasting results are compared with results from the contemporary machine learning-based models including support vector machine (SVM), the combined model of SVM and empirical mode decomposition, and back propagation neural network methods. The results show that the proposed T–S fuzzy model can effectively improve the precision of the short-term wind power forecasting. [ABSTRACT FROM AUTHOR]

The extensive use of wind power can not only reduce dependence on fossil fuels, but also reduce emissions of polluted gases. However, large-scale wind power curtailments often occur in northeast China during the heat supply season, due to the fact that most of electrical demand is covered by the electrical power of the combined heat and power (CHP) during the off-peak hours. At present, for northeast China with heating demand, most of the research only focuses on how to accommodate more wind power on the spot by using one-directional conversion of the electric and thermal energy. But it is still difficult to realize the bi-directional conversion between the electro-gas or electro-thermal energy. In this paper, a combined electro-gas bi-directional conversion system (CEGBCS) is established by adding the power to gas (P2G), fuel cell and heat storage device in CHP system. This CEGBCS can not only realize bi-directional conversion of the electricity and gas, but also decouple the two operation modes of CHP unit, which greatly improve the ability of system to accommodate additional wind power. Finally, the effectiveness of the proposed CEGBCS is verified by comparing with two traditional methods. [ABSTRACT FROM AUTHOR]

To support high-level wind energy utilization, wind power prediction has become a more and more attractive topic. To improve prediction accuracy and flexibility, joint point-interval prediction of wind power via a stepwise procedure is studied in this paper. Firstly, time-information-granularity (TIG) is defined for ultra-short-term wind speed prediction. Hidden features of wind speed in TIGs are extracted via principal component analysis (PCA) and classified via adaptive affinity propagation (ADAP) clustering. Then, Gaussian process regression (GPR) with joint point-interval estimation ability is adopted for stepwise prediction of the wind power, including wind speed prediction and wind turbine power curve (WTPC) modeling. Considering the sequential uncertainties of stepwise prediction, theoretical support for an uncertainty enlargement effect is deduced. Uncertainties' transmission from single-step or receding multi-step wind speed prediction to wind power prediction is explained in detail. After that, normalized indexes for point-interval estimation performance are presented for GPR parameters' optimization via a hybrid particle swarm optimization-differential evolution (PSO-DE) algorithm. K-fold cross validation (K-CV) is used to test the model stability. Moreover, due to the timeliness of data-driven GPR models, an evolutionary prediction mechanism via sliding time window is proposed to guarantee the required accuracy. Finally, measured data from a wind farm in northern China are acquired for validation. From the simulation results, several conclusions can be drawn: the multi-model structure has insignificant advantages for wind speed prediction via GPR; joint point-interval prediction of wind power is realizable and very reasonable; uncertainty enlargement exists for stepwise prediction of wind power while it is more significant after receding multi-step prediction of wind speed; a reasonable quantification mechanism for uncertainty is revealed and validated. [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]

This paper presents a non-linear dynamic model of a combined heat and power (CHP) unit with two-stage bypass for the first time. This model is derived through an analysis of the material and energy balance of the CHP unit. The static parameters are determined via the design data of the CHP unit, and the dynamic parameters refer to model parameters of same type of units in other references. Based on the model, an optimized control scheme for the coordination system of the unit is proposed. This scheme introduces a stair-like feedforward-feedback predictive control algorithm to solve the control problem of large delays in boiler combustion, and integrates decoupling control to reduce the effect of external disturbance on the main steam pressure. Simulation results indicate that the model effectively reflects the dynamics of the CHP unit and can be used for designing and verifying its coordinated control system; the control scheme can achieve decoupling control of the CHP unit; the fluctuation of main steam pressure is considerably reduced; and the adjustment of coal feed flow is stable. In this case, the proposed scheme can guarantee the safe, stable and flexible operation of the unit and lay the foundation for decoupling the heat load-based constraint of CHP units, thereby expanding the access space of wind power in northern China. [ABSTRACT FROM AUTHOR]