Cities worldwide are stepping up efforts to reshape their infrastructure to ensure a carbon-neutral and sustainable future, leading to the rapid electrification of transportation systems. The electricity demand of this sector, particularly that of high-speed railways, is increasing. Application of the existing infrastructures of railway stations and available land along rail lines for photovoltaic (PV) electricity generation has the potential to power high-speed bullet trains with renewable energy and supply surplus electricity to surrounding users. In this work, a methodology based on a geographic information system was established to evaluate the PV potential along rail lines and on the roofs of train stations. The Beijing-Shanghai high-speed railway (HSR) was used as a case study. Its total PV potential reached 5.65 GW (of which the station potential accounted for 264 MW, approximately 4.68%, of the total potential), with a lifelong generation capacity of 155 TWh, which corresponds to approximately 12% of the total new installed capacity of China in 2020. Although electricity prices and solar resources differed along the railway line, all PV systems were profitable. Moreover, a comparison between the electricity consumption and generation shows that the PV-PHSR system can cover most of the electricity demand of the Beijing-Shanghai HSR without a storage system. This concept can be further expanded to other rail lines and stations. Within the context of global carbon peaks and carbon neutrality, the integration of PV and railway systems should be promoted.

Aiming at the problem of transmission line congestion caused by large-scale wind power integration into the system, this paper proposes a Static Synchronous Series Compensator (SSSC) location and capacity determination method based on Particle Swarm Optimization (PSO). The SSSC steady-state power injection model is established, and the power flow calculation method with SSSC is deduced. In terms of site selection, a wind power scenario generation method is proposed. Based on multiple scenarios, an optimization model is established with the minimum transmission congestion index as the objective function to determine the optimal installation location and configuration capacity of SSSC. In terms of capacity selection, according to a specific scenario, a reactive power optimization model with SSSC is established with the minimum system network loss as the objective function, and the real-time operation control parameters of SSSC are determined. The calculation example results show that the PSO has good convergence and stable optimization results, and SSSC can effectively reduce the line blocking degree and system network loss.

Aiming at the randomness of the wind speed change, the wind turbine torque disturbance, high-frequency unmodeled uncertainties in wind power generation system, use loop-shaping algorithm on system design of robust controller after forming. In this paper, the local linear model for the wind energy conversion system is established. Through the design and simulation of robust control toolbox in matlab and using Hankel singular value to reduce the order. Simulation results show that using robust control toolbox functions the simplified controller design is able to replace the original controller well.

Aims The response pattern of terrestrial soil respiration to warming during non-growing seasons is a poorly understood phenomenon, though many believe that these warming effects are potentially significant. This study was conducted in a semiarid temperate steppe to examine the effects of warming during the non-growing seasons on soil respiration and the underlying mechanisms associated therewith. Methods This experiment was conducted in a semiarid temperate grassland and included 10 paired control and experimental plots. Experimental warming was achieved with open top chambers (OTCs) in October 2014. Soil respiration, soil temperature and soil moisture were measured several times monthly from November 2014 to April 2015 and from November 2015 to April 2016. Microbial biomass carbon (MBC), microbial biomass nitrogen (MBN) and available nitrogen content of soil were measured from 0 to 20 cm soil depth. Repeated measurement ANOVAs and paired-sample t tests were conducted to document the effect of warming, and the interactions between warming and time on the above variables. Simple regressions were employed to detect the underlying causality for the observed effects. Important Findings Soil respiration rate was 0.24 mu mol m(-2) s(-1) in the control plots during the non-growing seasons, which was roughly 14.4% of total soil carbon flux observed during growing seasons. Across the two non-growing seasons, warming treatment significantly increased soil temperature and soil respiration by 1.48 degrees C (P < 0.001) and 42.1% (P < 0.01), respectively, when compared with control plots. Warming slightly, but did not significantly decrease soil moisture by 0.66% in the non-growing seasons from 2015 to 2016. In the nongrowing seasons 2015-16, experimental warming significantly elevated MBC and MBN by 19.72% and 20.99% (both P < 0.05), respectively. In addition, soil respiration responses to warming were regulated by changes in soil temperate, MBC and MBN. These findings indicate that changes in non-growing season soil respiration impact other components in the carbon cycle. Additionally, these findings facilitate projections regarding climate change-terrestrial carbon cycling.

The increasing integration of wind power generation brings more uncertainty into the power system. Since the correlation may have a notable influence on the power system, the output powers of wind farms are generally considered as correlated random variables in uncertainty analysis. In this paper, the C-vine pair copula theory is introduced to describe the complicated dependence of multidimensional wind power injection, and samples obeying this dependence structure are generated. Monte Carlo simulation is performed to analyze the small signal stability of a test system. The probabilistic stability under different correlation models and different operating conditions scenarios is investigated. The results indicate that the probabilistic small signal stability analysis adopting pair copula model is more accurate and stable than other dependence models under different conditions.

Metros are critical infrastructure in big cities and evaluation of their safe operation is of increasing importance. To make a reasonable safety evaluation for the metro during operation, this paper establishes a rational safety evaluation model based on long-term monitoring data of Shanghai Metro Line 2. Four evaluation indicators, i.e., absolute settlement, relative curvature, deformation rate and curvature radius, are adopted. Analytic hierarchy process (AHP) and entropy method are combined to determine the weights of the indicators. The risk level values at different mileage are calculated and five danger levels are defined accordingly to determine the safety state of Shanghai Metro Line 2, i.e., safe, relatively safe, critical, relatively dangerous, and dangerous. Safety evaluation of Shanghai Metro Line 2 shows that: 83.81% areas of Shanghai Metro Line 2 are in safe, relatively safe and critical states, while 15.63% and 0.57% areas are in relatively dangerous and dangerous states, respectively; the parts of Shanghai Metro Line 2 where the risk level value exceeds the critical value are mainly distributed around the mileage at 6.0–7.5 km and 8.5–11.0 km, and the risk level value peaks around the mileage at 7.3 km, in which high attention should be attached and relevant protective measures be taken; the sections with the high risk level value coincide with the distinctly deforming areas of the metro, indicating that this evaluation method is valid.

Fetal heart rate (FHR) monitoring is a widely used method for fetal health assessment. At present, most of the FHR data are recorded on the cardiotocogram (CTG) paper. Based on the morphological shape of the FHR in the CTG image, pathological patterns are diagnosed by obstetricians with the experience. However, this method lacks a unified evaluation standard. Consequently, it is necessary to construct a computer-assisted diagnosis method. Thus, in this paper, considering that the sinusoidal FHR (SFHR) pattern indicates severe conditions such as fetal hypoxia, the SFHR pattern is modeled in CTG image format based on the characteristics of periodicity and fractal dimension. Meanwhile, an improved linear SVM with parameters optimization is designed to ensure the full recognition of the SFHR. In addition, the linear model is further optimized by the fluctuation limit to reduce the misrecognition. Finally, based on the real FHR data source, the simulation shows that the model has a good performance and the classification accuracy is more than 97%.

This study proposes a novel robust transmission-constrained unit commitment model with adjustable conservatism (denoted as RAC-TCUC). Ellipsoidal uncertainty set (EUS) is adopted in this model to well fit the spatial-temporal correlated wind power. The affine policy is utilised in the generation dispatch process of the model for the sake of computational tractability. To reduce the conservatism, a novel criterion for budget value selection of the EUS is presented. Moreover, this study discusses a crucial, yet barely addressed issue in the literature: the feasibility of the solution against the realisation of uncertainties beyond the prescribed EUS. To prove the validity of the criterion, an analytical relationship between the budget value of the EUS and the actual probability of solution's feasibility against all possible scenarios of uncertainties is presented. Finally, the testing results demonstrate the effectiveness and economic benefits of the proposed method.

Traditional dispatch methods based on day-ahead point forecast of wind power do not consider the uncertainty appropriately. As a result, the potential of accommodating wind power cannot be fully exploited. This paper proposes the robust unit commitment to improve the admissible region of wind power. The valid admissible region of wind generation is constructed based on the available wind generation interval. An affinely adjustable robust unit commitment model is constructed considering the electro-thermal coupling of the cogeneration systems. The proposed model seeks to maximize the admissible region and guarantees the feasibility within the admissible region. The proposed model can be transformed into the mixed integer linear programming. Finally,the proposed method is compared with the traditional dispatch method and validated using the modified IEEE 39-bus system. The simulation results verify the effectiveness of the proposed method.

鲁棒优化是解决大规模新能源接入后电力系统调度的重要手段.相比于基于场景的随机规划、带有风险约束的机组组合等,鲁棒机组组合的结果往往偏于保守.鲁棒优化的保守性直接受到不确定集合的影响.研究了风电预测误差时间相关特点,提出了基于自相关性的时间相关性约束.并利用不确定集合的离散性特点,将该约束近似简化为可以用于实际鲁棒优化问题的线性约束.在列与限制生成(C&CG)算法的基础上,改进了Bender's分解后子问题的求解算法,提出了一种适合离散型不确定集合的鲁棒优化求解方法.最后,以真实风电数据进行了大量的仿真实验.结果表明,提出的算法能够在不影响机组组合可靠性的前提下,降低鲁棒优化保守性.

With the rapid development of HVDC power transmission,the stability of AC/DC hybrid power grid is becoming more and more prominent,and it is a new way to realize the asynchronous interconnection of bulk grid with synchronous operation through HVDC.After the asynchronous operation of the bulk grid,the transient stability is improved,especially for power angle stability caused by HVDC pole blocking faults,but the transient voltage stability risks still remain in the asynchronous operation.According to actual large AC/DC power system,the paper establishes the equivalent models of the AC/DC hybrid power grid with synchronous and asynchronous operation,analyzes the risk of the transient voltage instability in both the synchronous and asynchronous operation caused by AC faults and DC faults through theoretical analysis and simulation calculation,and discusses the factors influencing the transient voltage stability of the AC/DC hybrid power grid.The results show that the asynchronous operation of the bulk grid can effectively avoid large area voltage instability,but there may still exist local power angle instability or the voltage instability in the weak network frame or with uneven power flow distribution.

A new compact dual polarized co-axial antenna with gain greater than 8.0dBi is presented operated in 0.69GHz-0.96GHz/1.71GHz-2.69GHz. The size of the antenna is 286 × 286 × 81mm 3 . Its reflection coefficient and the cross-polarization isolation are less than -10.5dB (-12dB) and higher than 15dB (20dB) in low (high) frequency respectively. Measured results are agree well with the simulated one, verifying the availability of this antenna design.

With a large scale of integrated wind power,the dispatch scheme of power systems should cope with the uncertainty of wind power carefully.Considering the spatiotemporal correlation of wind power,an affinely adjustable robust unit commitment was proposed.Ellipsoidal uncertainty set was adopted to well represent the spatiotemporal correlation of wind power.The robust unit commitment model was built using affine policies and was transformed into mixed integer second order cone programming.Based on the relationship between the parameter of the uncertainty set and probabilistic guarantees on the feasibility,the parameter of the uncertainty set was tuned appropriately to avoid over-conservatism and strike the balance between economy and robustness against stochastic wind power.Finally,the impact of spatiotemporal correlation of wind power on system operation was investigated on the IEEE 118-bus system.The proposed method is compared with tradition unit commitment with reserves and simulation results verify the robustness and economy of the proposed dispatch scheme.

Photovoltaic (PV) generation is increasingly popular in power systems. The nonlinear dependence associated with a large number of distributed PV sources adds the complexity to construct an accurate probability model and negatively affects confidence levels and reliability, thereby resulting in a more challenging operation of the systems. Most probability models have many restrictions when constructing multiple PV sources with complex dependence. This paper proposes a versatile probability model of PV generation on the basis of pair copula construction. In order to tackle the computational burden required to construct pair copula in high-dimensional cases, a systematic simplification technique is utilized that can significantly reduce the computational effort while preserving satisfactory precision. The proposed method can simplify the modeling procedure and provide a flexible and optimal probability model for the PV generation with complex dependence. The proposed model is tested using a set of historical data from colocated PV sites. It is then applied to the probabilistic load flow (PLF) study of the IEEE 118-bus system. The results demonstrate the effectiveness and accuracy of the proposed model.

Probabilistic load flow (PLF) is a powerful and widely used tool to address the random nature of renewable energy resources for the planning and operation of power systems. In this study, a new approach using a combination of the multiple integral method (MIM) and the cumulant method (CM) is proposed for PLF studies. The PLF problem with high dimensionality is decomposed into a bi-level problem and solved by MIM and CM to reduce the computational burden significantly while achieving satisfactory accuracy. The proposed method is applied to the IEEE 118-bus system and is compared with CM and point estimate method. Simulation results show that the proposed method achieves great computational efficiency and accuracy.

The increasing integration of wind power generation brings more uncertainty into the power system. Since the correlation may have a notable influence on the power system, the output powers of wind farms are generally considered as correlated random variables in uncertainty analysis. In this paper, the pair copula theory is introduced to describe the complicated dependence of multidimensional wind power injection, and samples obeying this dependence structure are generated. Monte Carlo simulation is performed to analyze the small signal stability of a test system. The probabilistic stability under different correlation models is investigated. The results indicate that the probabilistic small signal analysis adopting pair copula model is more accurate and stable than other dependence models under different operating conditions.