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In the context of large-scale wind power access to the power system, it is urgent to explore new probabilistic supply–demand analysis methods. This paper proposes a wind power stochastic and extreme scenario generation method considering wind power–temperature correlations and carries out probabilistic supply–demand balance analysis based on it. Firstly, the influence of temperature on wind power output is analyzed via Pearson coefficient to obtain the correlation between wind power and temperature. Secondly, based on the historical wind power curve, a large number of wind power output scenarios are randomly generated while fully preserving its characteristics, and probabilistic supply–demand analysis is carried out. Thirdly, for the extreme case of continuous multi-day extreme heat without wind, extreme scenarios are selected from the generated scenarios for supply–demand balance analysis. Finally, a practical example in a province in central-eastern China is used to verify the effectiveness of the proposed method. The results indicate that the scenario generation method can effectively capture the historical wind power characteristics and can be better applied to the diversified supply and demand balance analysis to obtain more accurate analysis results. [ABSTRACT FROM AUTHOR]

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]

Energy distribution justice is of primary concern within the energy justice framework and it is crucial to increase public acceptance of offshore wind energy and further advance its development. The rapid development of offshore wind energy in China has inevitably impacted the livelihoods of coastal vulnerable groups (CVGs) engaged in fisheries and tourism in the coastal zone. While current policies often compensate for livelihood losses through cash payments, the fiscal strain caused by COVID-19 renders this approach unsustainable. Consequently, this research pioneers the exploration of Chinese tourist groups' landscape preferences towards offshore wind farms (OWFs). This study proposes a new approach to enhance OWF landscapes for tourism development, thereby balancing the distribution of costs and benefits between CVGs and tourists. The research focuses on Beihai City in the Beibu Gulf Economic Region, utilizing a combination of Q-methodology and choice experiments that incorporates cut-offs. Answers to eighty Q-methodology questionnaires and 1324 choice experiment questionnaires are obtained. The findings indicate that this region can achieve energy distribution justice by compensating for the livelihood losses of CVGs through tourism. Contrary to traditional assumptions about wind farm noise preferences, Chinese tourists prefer proximity to OWFs, as an appropriate coastal acoustics landscape can enhance their tourism experience. In light of these findings, this paper presents policy recommendations towards energy distribution justice. [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 joint probability density function of wind speed and wind direction serves as the mathematical basis for directional wind energy assessment. In this study, a nonparametric joint probability estimation system for wind velocity and direction based on copulas is proposed and empirically investigated in Inner Mongolia, China. Optimal bandwidth algorithms and transformation techniques are used to determine the nonparametric copula method. Various parameter copula models and models without considering dependency relationships are introduced and compared with this approach. The results indicate a significant advantage of employing the nonparametric copula model for fitting joint probability distributions of both wind speed and wind direction, as well as conducting correlation analyses. By utilizing the proposed KDE-COP-CV model, it becomes possible to accurately and reliably analyze how wind power density fluctuates in relation to wind direction. This study reveals the researched region possesses abundant wind resources, with the highest wind power density being highly dependent on wind direction at maximum speeds. Wind resources in selected regions of Inner Mongolia are predominantly concentrated in the northwest and west directions. These findings can contribute to improving the accuracy of micro-siting for wind farms, as well as optimizing the design and capacity of wind turbine generators. [ABSTRACT FROM AUTHOR]

The development of renewable energy and the construction of a comprehensive energy system with multiple complementary energy sources have gradually become the main direction of China's energy development. As the penetration rate of renewable energy increases, the intermittent and fluctuating output of wind and solar power has a more significant impact on the system. This article conducts research on the optimization configuration of integrated energy system (IES) considering photovoltaic output uncertainty under a ladder carbon trading mechanism. Firstly, a two-stage distributed robust optimization (DRO) configuration model for integrated energy system is established. In detail, a deterministic model aimed at minimizing investment costs is given in the first stage and an uncertainty model aimed at minimizing operating costs in the probability distribution of the worst scenario is built in the second stage. Then, a data-driven distributed robust optimization method is adopted to deal with the uncertainty of photovoltaic output using MATLAB software (R2020A). Finally, the column and constraint generation (C&CG) algorithm is used to solve the problem, and the optimal investment capacity and cost results of the integrated energy system considering demand response under a ladder carbon trading mechanism are obtained. Through analysis, the proposed method achieves a 5.54% reduction in carbon emission costs while maintaining nearly unchanged investment costs, thus balancing economic and environmental benefits. Additionally, the CCG algorithm can effectively improve computational efficiency and guarantee the optimality of the solution. [ABSTRACT FROM AUTHOR]

In China's mountainous coastal terrain, storms can badly damage low-rise buildings. At present, it is not clear how the relative position of buildings and mountains affects the surface of low-rise buildings. The study compared these results with the wind pressure distribution without the surrounding environment. The distribution of wind pressure in different hillside landforms is examined through a wind tunnel experiment, which is also compared with the distribution in an open environment. The study examined the fluctuating coefficient as the distance between the building and the hillside changed, specifically for wind blowing at a 0° angle. The investigation examined the power spectrum and wind pressure probability distribution while considering the proximity of the building to an adjacent hill. The findings indicated that as the distance between the slope and the mountain increases, the fluctuating wind pressure coefficient continues to increase, and the contour lines of the wind pressure distribution are relatively denser compared to where there is a mountain. The maximum value of the fluctuating wind pressure coefficient is 0.22, which appears at the windward roof. The roof's wind pressure coefficient fluctuated and gradually increased until it reached its peak, unaffected by the surroundings. The wind pressure on the leeward side exhibited Gaussian characteristics in its probability distribution. [ABSTRACT FROM AUTHOR]

This study addresses the integral role of typical wind power generation curves in the analysis of power system flexibility planning. A novel method is introduced for extracting these curves, integrating an enhanced K-means clustering algorithm with advanced optimization techniques. The process commences with thorough data cleaning, filtering, and smoothing. Subsequently, the refined K-means algorithm, augmented by the Pearson correlation coefficient and a greedy algorithm, clusters the wind power curves. The optimal number of clusters is ascertained through the silhouette coefficient. The final stage employs particle swarm and whale optimization algorithms for the extraction of quintessential wind power output curves, essential for flexibility planning in power systems. This methodology is validated through a case study involving wind power output data from a new energy-rich provincial power grid in North China, spanning from 1 January 2019, to 31 December 2022. The resultant curves proficiently mirror wind power fluctuations, thereby laying a foundational framework for power system flexibility planning analysis. [ABSTRACT FROM AUTHOR]

To date, few studies have addressed the charging and discharging schedules of electric vehicle battery-swapping stations in China's isolated microgrids. Given that battery-swapping is expected to become increasingly widespread, this study innovatively considered distributed power sources, such as wind power and photovoltaic power, to analyze battery-swapping station operation and isolated microgrid operation schedules. Considering the needs of potential future communities that might depend on electric vehicle battery-swapping stations, two scenarios were analyzed, and the most effective integer planning method was adopted. The international algorithm software program YALMIP + CPLEX was used to address the problem, and simulation results proved that the proposed model and its solution method could effectively affect the safe operation and scheduling of islanded microgrid battery-swapping stations and reduce the cost of islanded microgrid operation, with significant advantages. [ABSTRACT FROM AUTHOR]