Data-driven Distributionally Adjustable Robust Chance-constrained DG Capacity Assessment

Moving away from fossil fuels towards renewable sources requires system operators to determine the capacity of distribution systems to safely accommodate green and distributed generation(DG).However,the DG capacity of a distribution system is often underestimated due to either overly conservative electrical demand and DG output uncertainty modelling or neglecting the recourse capability of the available components.To improve the accuracy of DG capacity assessment,this paper proposes a distributionally adjustable robust chance-constrained approach that utilises uncertainty information to reduce the conservativeness of conventional robust approaches.The proposed approach also enables fast-acting devices such as inverters to adjust to the real-time realisation of uncertainty using the adjustable robust counterpart methodology.To achieve a tractable formulation,we first define uncertain chance constraints through distributionally robust conditional value-at-risk(CVaR),which is then reformulated into convex quadratic constraints.We subsequently solve the resulting large-scale,yet convex,model in a distributed fashion using the alternating direction method of multipliers(ADMM).Through numerical simulations,we demonstrate that the proposed approach outperforms the adjustable robust and conventional distributionally robust approaches by up to 15%and 40%,respectively,in terms of total installed DG capacity.

Cluster voltage control method for “Whole County” distributed photovoltaics based on improved differential evolution algorithm

China is vigorously promoting the “whole county promotion” of distributed photovoltaics (DPVs). However, the high penetration rate of DPVs has brought problems such as voltage violation and power quality degradation to the distribution network, seriously affecting the safety and reliability of the power system. The traditional centralized control method of the distribution network has the problem of low efficiency, which is not practical enough in engineering practice. To address the problems, this paper proposes a cluster voltage control method for distributed photovoltaic grid-connected distribution network. First, it partitions the distribution network into clusters, and different clusters exchange terminal voltage information through a “virtual slack bus.” Then, in each cluster, based on the control strategy of “reactive power compensation first, active power curtailment later,” it employs an improved differential evolution (IDE) algorithm based on Cauchy disturbance to control the voltage. Simulation results in two different distribution systems show that the proposed method not only greatly improves the operational efficiency of the algorithm but also effectively controls the voltage of the distribution network, and maximizes the consumption capacity of DPVs based on qualified voltage.

Capacity Reliability of Signalized Intersections with Mixed Traffic Conditions

The reliability of capacity of signalized intersections in mixed traffic conditions involving vehicles, bicycles, and pedestrians was investigated to complete the conventional, deterministic capacity calculations. Simulations using VISSIM provided estimates of capacity distributions, and demonstrated the effects of the analysis intervals on the distributions. With the random vehicle arrivals taken into account, a capacity reliability assessment method was given as a function. Assessments were also performed regarding the effects of the conflicting pedestrian and bicycle volumes on capacity reliability. The simulation indicates that the pedestrians and bicycles result in greater random fluctuations of exclusive turning lane capacities, but have less effect on the variability of shared lane capacities. Normal distributions can be used to model the capacities for intervals not less than 10 rain. At higher vehicular volumes, the capacity reliability is more sensitive to the mean and standard deviation of the pedestrian and bicycle volumes.

Establishment of a Hybrid Rainfall-Runoff Model for Use in the Noah LSM

There is an increasing trend to incorporate the basin hydrological model into the traditional land surface model （LSM） to improve the description of hydrological processes in them. For incorporating with the Noah LSM, a new rainfall-runoff model named XXT （the first X stands for Xinanjiang, the second X stands for hybrid, and T stands for TOPMODEL） was developed and presented in this study, based on the soil moisture storage capacity distribution curve （SMSCC）, some essential modules of the Xinanjiang model, together with the simple model framework of the TOPMODEL （a topography based hydrological model）. The innovation of XXT is that the water table is incorporated into SMSCC and it connects the surface runoff production with base flow production. This improves the description of the dynamically varying saturated areas that produce runoff and also captures the physical underground water level. XXT was tested in a small-scale watershed Youshuijie （946 km2） and a large-scale watershed Yinglouxia （10009 km2） in China. The results show that XXT has better performance against the TOPMODEL and the Xinanjiang model for the two watersheds in both the calibration period and the validation period in terms of the Nash-Sutcliffe efficiency. Moreover, XXT captures the largest peak flow well for both the small： and large-scale watersheds during the validation period, while the TOPMODEL produces significant overestimates or underestimates, so does the Xinanjiang model.