Multiobjective Transmission Network Planning considering the Uncertainty and Correlation of Wind Power

In order to consider the uncertainty and correlation of wind power in multiobjective transmission network expansion planning (TNEP), this paper presents an extended point-estimation method to calculate the probabilistic power flow, based on which the correlative power outputs of wind farm are sampled and the uncertain multiobjective transmission network planning model is transformed into a solvable deterministic model. A modified epsilon multiobjective evolutionary algorithm is used to solve the above model and a well-distributed Pareto front is achieved, and then the final planning scheme can be obtained from the set of nondominated solutions by a fuzzy satisfied method. The proposed method only needs the first four statistical moments and correlation coefficients of the output power of wind farms as input information; the modeling of wind power is more precise by considering the correlation between wind farms, and it can be easily combined with the multiobjective transmission network planning model. Besides, as the self-adaptive probabilities of crossover and mutation are adopted, the global search capabilities of the proposed algorithm can be significantly improved while the probability of being stuck in the local optimum is effectively reduced. The accuracy and efficiency of the proposed method are validated by IEEE 24 as well as a real system

Papel de la bahía de Jiaozhou como una fuente/depósito de CO2 durante un ciclo estacional

The seasonal evolution of dissolved inorganic carbon (DIC) and CO2 air-sea fluxes in the Jiaozhou Bay was investigated by means of a data set from four cruises covering a seasonal cycle during 2003 and 2004. The results revealed that DIC had no obvious seasonal variation, with an average concentration of 2035 µmol kg-1 C in surface water. However, the sea surface partial pressure of CO2 changed with the season. pCO2 was 695 µatm in July and 317 µatm in February. Using the gas exchange coefficient calculated with Wanninkhof’s model, it was concluded that the Jiaozhou Bay was a source of atmospheric CO2 in spring, summer, and autumn, whereas it was a sink in winter. The Jiaozhou Bay released 2.60 x 1011 mmol C to the atmosphere in spring, 6.18 x 1011 mmol C in summer, and 3.01 x 1011 mmol C in autumn, whereas it absorbed 5.32 x 1010 mmol C from the atmosphere in winter. A total of 1.13 x 1012 mmol C was released to the atmosphere over one year. The behaviour as a carbon source/sink obviously varied in the different regions of the Jiaozhou Bay. In February, the inner bay was a carbon sink, while the bay mouth and the outer bay were carbon sources. In June and July, the inner and outer bay were carbon sources, but the strength was different, increasing from the inner to the outer bay. In November, the inner bay was a carbon source, but the bay mouth was a carbon sink. The outer bay was a weaker CO2 source. These changes are controlled by many factors, the most important being temperature and phytoplankton. Water temperature in particular was the main factor controlling the carbon dioxide system and the behaviour of the Jiaozhou Bay as a carbon source/sink. The Jiaozhou Bay is a carbon dioxide source when the water temperature is higher than 6.6°C. Otherwise, it is a carbon sink. Phytoplankton is another controlling factor that may play an important role in behaviour as a carbon source or sink in regions where the source or sink nature is weaker.La evolución estacional del carbono inorgánico disuelto (DIC) y el intercambio de flujos de CO2 aire-mar en la bahía de Jiaozhou han sido investigados a partir de datos obtenidos en 4 campañas oceanográficas que cubren un ciclo estacional entre 2003 y 2004. Los resultados muestran que el DIC no presenta una clara variación estacional con una concentración promedio de 2035 μmol kg-1 C en el agua de superficie. No obstante la presión parcial de CO2 en el agua superficial cambiaba con la estación. La PCO2 era de 695 μatm en Julio y 317 μatm en febrero. Utilizando el coeficiente de intercambio de gases calculado con el modelo de Wanninkhof concluíamos que la bahía de Jiaozhou era una fuente de CO2 a la atmósfera en primavera, verano y otoño, mientras que era un depósito de CO2 en invierno. La bahía proporcionaba 2.60 × 1011 mmol C a la atmósfera en primavera, 6.18 × 1011 mmol C en verano, y 3.01 × 1011 mmol C in otoño, mientras absorbia 5.32 × 1010 mmol C desde la atmósfera en invierno. Un total de 1.13 × 1012 mmol C eran liberados a la atmósfera durante un año. El comportamiento como fuente/depósito de carbono, obviamente era diferente en las distintas regiones de la bahía de Jiaozhou. En Febrero, la parte interior de la bahía era un depósito para el carbono, mientras que la desembocadura y la parte exterior actuaba como fuente de carbono. En Junio y Julio, las partes interna y externa de la bahía eran fuentes de carbono, pero la intensidad era diferente, incrementando desde la parte interior a la exterior de la bahía. En Noviembre, la parte interior de la bahía era fuente de carbono, pero la desembocadura de la bahía se comportaba como depósito de carbono. El exterior de la bahía era una fuente poco importante de CO2. Estos cambios están controlados por muchos factores, siendo los mas importantes la temperatura y el fitoplancton. Especialmente, la temperatura del agua era el factor principal en el control del dióxido de carbono en el sistema y del comportamiento de la bahía de Jiaozhou como fuente/depósito de carbono. La bahía de Jiaozhou es una fuente de dióxido de carbono cuando la temperatura del agua es mas alta que 6.6ºC. Si no es así es un depósito de carbono. El fitoplancton es el otro factor de control que puede jugar un papel importante en el comportamiento como fuente o depósito de carbono en regiones donde el carácter de fuente o depósito es debil.

Dynamic multi-dimensional scaling of 30+ year evolution of Chinese urban systems: patterns and performance

Understanding the co-evolution and organizational dynamics of urban properties (i.e., urban scaling) is the science base for pursuing synergies toward sustainable cities and society. The generalization of urban scaling theory yet requires more studies from various developmental regimes and across time. Here, we extend the universality proposition by exploring the evolution of longitudinal and transversal scaling of Chinese urban attributes between 1987 and 2018 using a global artificial impervious area (GAIA) remotely sensed dataset, harmonized night light data (NTL), and socioeconomic data, and revealed agreements and disagreements with theories. The superlinear relationship of urban area and population often considered as an indicator of wasting land resources (challenging the universality theory β  = 2/3), is in fact the powerful impetus (capital raising) behind the concurrent superlinear expansion of socio-economic metabolisms (e.g., GDP, total wage) in a rapidly urbanizing country that has not yet reached equilibrium. Similarly, infrastructural variables associated with public services, such as hospitals and educational institutions, exhibited some deviations as well and were scaled linearly. However, the temporal narrowing of spatial deviations, such as the decline in urban land diseconomies of scale and the stabilization of economic output, clearly indicates the Chinese government's effort in charting urban systems toward balanced and sustainable development across the country. More importantly, the transversal sublinear scaling of areal-based socio-economic variables was inconsistent with the theoretical concept of increasing returns to scale, thus validating the view that a single measurement cannot unravel the intricate web of diverse urban attributes and urbanization. Our dynamic urban scaling analysis across space and through time in China provides new insights into the evolving nexus of urbanization, socioeconomic development, and national policies

Development of a constraint non-causal wave energy control algorithm based on artificial intelligence

The real-time implementation of wave energy control leads to non-causality as the wave load that comes in the next few seconds is used to optimize the control command. The present work tackles non-causality through online forecasting of future wave force using artificial intelligence technique. The past free surface elevation is used to forecast the incoming wave load. A feedforward artificial neural network is developed for the forecasting, which learns to establish the intrinsic link between past free surface elevation and future wave force through machine learning algorithm. With the implementation of the developed online wave force prediction algorithm, a real-time discrete control algorithm taking constraint on response amplitude into account is developed and implemented to a bi-oscillator wave energy converter in the present research. The dynamic response and the wave power extraction are simulated using a state-space hydrodynamic model. It is shown that the developed real-time control algorithm enhances the power capture substantially whereas the motion of the system is hardly increased. The prediction error effect on power extraction is investigated. The reduction of power extraction is mainly caused by phase error, whilst the amplitude error has minimal influence. A link between the power capture efficiency and the constraint on control is also identified

Simulation Study on neutrino nucleus cross section measurement in Segmented Detector at Spallation Neutron Source

Knowledge of $\nu_e$-$\mathrm{Fe}/\mathrm{Pb}$ differential cross sections for $\nu_e$ energy below several tens of MeV scale is believed to be crucial in understanding Supernova physics. In a segmented detector at Spallation Neutrino Source, $\nu_e$ energy reconstructed from the electron range measurement is strongly affected because of both multiple scattering and electromagnetic showers occurring along the electron passage in target materials. In order to estimate the effect, a simulation study has been performed with a cube block model assuming a perfect tracking precision. The distortion of energy spectrum is observed to be proportional to the atomic number of target material. Feasibility of unfolding the distorted $\nu_e$ energy spectrum is studied for both Fe and Pb cases. Evaluation of statistical accuracy attainable is therefore provided for a segmented detector.Comment: 6 pages, 6 figures, submitted to Chinese Physics

Plaquette Singlet Transition, Magnetic Barocaloric Effect, and Spin Supersolidity in the Shastry-Sutherland Model

Inspired by recent experimental measurements [Guo \textit{et al.}, Phys. Rev. Lett.~\textbf{124}, 206602 (2020); Jim\'enez \textit{et al.}, Nature \textbf{592}, 370 (2021)] on frustrated quantum magnet SrCu$_2$(BO$_3$)$_2$ under combined pressure and magnetic fields, we study the related spin-$1/2$ Shastry-Sutherland (SS) model using state-of-the-art tensor network methods. By calculating thermodynamics, correlations and susceptibilities, we find, in zero magnetic field, not only a line of first-order plaquette-singlet (PS) to dimer-singlet phase transition ending with a critical point, but also signatures of the ordered PS transition with its critical endpoint terminating on this first-order line. Moreover, we uncover prominent magnetic barocaloric responses, a novel type of quantum correlation induced cooling effect, in the strongly fluctuating supercritical regime. Under finite fields, we identify a quantum phase transition from the PS phase to the spin supersolid phase that breaks simultaneously lattice translational and spin rotational symmetries. The present findings on the SS model are accessible in current experiments and would shed new light on exotic critical and supercritical phenomena in archetypal frustrated quantum magnets.Comment: Close to the published version. 7 pages, 4 figures (SM 9 pages, 12 figures

Ruthenium atomically dispersed in carbon outperforms platinum toward hydrogen evolution in alkaline media.

Hydrogen evolution reaction is an important process in electrochemical energy technologies. Herein, ruthenium and nitrogen codoped carbon nanowires are prepared as effective hydrogen evolution catalysts. The catalytic performance is markedly better than that of commercial platinum catalyst, with an overpotential of only -12 mV to reach the current density of 10 mV cm-2 in 1 M KOH and -47 mV in 0.1 M KOH. Comparisons with control experiments suggest that the remarkable activity is mainly ascribed to individual ruthenium atoms embedded within the carbon matrix, with minimal contributions from ruthenium nanoparticles. Consistent results are obtained in first-principles calculations, where RuCxNy moieties are found to show a much lower hydrogen binding energy than ruthenium nanoparticles, and a lower kinetic barrier for water dissociation than platinum. Among these, RuC2N2 stands out as the most active catalytic center, where both ruthenium and adjacent carbon atoms are the possible active sites