Improvement of the Open Circuit Voltage of CZTSe Thin-Film Solar Cells by Surface Sulfurization Using SnS

The objective of this study is to find an effective method to improve Voc without Jsc loss for Cu2ZnSnSe4 （CZTSe） thin film solar cells, which have been fabricated by the one step co-evaporation technique. Surface sulfurization of CZTSe thin films is carried out by using one technique that does not utilize toxic H2S gas; a sequential evaporation of SnS after CZTSe deposition and the annealing of CZTSe thin films in selenium vapor. A Cu2ZnSn（S, Se）4 （CZTSSe） thin layer is grown on the surface of the CZTSe thin film after the annealing. The conversion efficiency of the completed device is improved due to the enhancement of Voc, which could be attributed to the formation of a hole-recombination barrier at the surface or the passivation of the surface and grain boundary by S incorporation.

Effects of Doping Trace Sm^(3+) and Gd^(3+) on Luminescent Character of Y_2O_2S∶Eu^(3+)

After trace Sm^3＋ ions and Gd^3＋ ions doping, the emission intensity of red phosphors Y2O2S： Eu^3 ＋ was enhanced and the voltage character （relation between emission intensity and excitation voltage） was improved while the other properties of physics and chemistry were not changed. The origins of enhancement and improvement are discussed. Probably the distortion and the defect of crystals are decreased by the substitution of Gd^3＋ for Y^3＋ instead of Eu^3＋ for Y^3＋ , and thus the Eu^3＋ crystal field is improved, and radiationless process and energy loss resulted from crystal defect are weakened, which leads to increased luminescence intensity and voltage character improvement. The overlapping fluorescent spectra of Y2O2S： Sm^3＋ emission and Y2O2S：Eu^3＋ excitation as well as Eu^3 ＋ excitation spectra transitions spectra lead to energy transfer from Sm^3 ＋ sensitization of Sm^3＋ ions fectively. containing Sm^3＋ excitation the possibility of resonance ions to Eu^3＋ ions, and the to Eu^3＋ ions is achieved effectively.

Topology-independent end-to-end learning model for improving the voltage profile in microgrids-integrated power distribution networks

With multiple microgrids(MGs)integrated into power distribution networks in a distributed manner,the penetration of renewable energy like photovoltaic(PV)power generation surges.However,the operation of power distribution networks is challenged by the issues of multiple power flow directions and voltage security.Accordingly,an efficient voltage control strategy is needed to ensure voltage security against ever-changing operating conditions,especially when the network topology information is absent or inaccurate.In this paper,we propose a novel data-driven voltage profile improvement model,denoted as system-wide composite adaptive network(SCAN),which depends on operational data instead of network topology details in the context of power distribution networks integrated with multiple MGs.Unlike existing studies that realize topology identification and decisionmaking optimization in sequence,the proposed end-to-end model determines the optimal voltage control decisions in one shot.More specifically,the proposed model consists of four modules,Pre-training Network and modified interior point methods with adversarial networks(Modified IPMAN)as core modules,and discriminator generative adversarial network(Dis-GAN)and Volt convolutional neural network(Volt-CNN)as ancillary modules.In particular,the generator in SCAN is trained by the core modules in sequence so as to form an end-to-end mode from data to decision.Numerical experiments based on IEEE 33-bus and 123-bus systems have validated the effectiveness and efficiency of the proposed method.

Application of Grade Algorithm Based Approach along with PV Analysis for Enhancement of Power System Performance

This paper presents an application of GRADE Algorithm based approach along with PV analysis to solve multi objective optimization problem of minimizing real power losses, improving the voltage profile and hence enhancing the performance of power system. GRADE Algorithm is a hybrid technique combining genetic and differential evolution algorithms. Control variables considered are Generator bus voltages, MVAR at capacitor banks, transformer tap settings and reactive power generation at generator buses. The optimal values of the control variables are obtained by solving the multi objective optimization problem using GRADE Algorithm programmed using M coding in MATLAB platform. With the optimal setting for the control variables, Newton Raphson based power flow is performed for two test systems, viz, IEEE 30 bus system and IEEE 57 bus system for three loading conditions. Minimization of Real power loss and improvement of voltage profile obtained are compared with the results obtained using firefly and particle swarm optimization (PSO) techniques. Improvement of Loadability margin is established through PV curve plotted using continuation power flow with the real power load at the most affected bus as the bifurcation parameter. The simulated output shows improved results when compared to that of firefly and PSO techniques, in term of convergence time, reduction of real power loss, improvement of voltage profile and enhancement of loadability margin.

Integration of PV system with SMES based on model predictive control for utility grid reliability improvement

This paper describes the integration of a photovoltaic (PV) renewable energy source with a superconducting magnetic energy storage (SMES) system. The integrated system can improve the voltage stability of the utility grid and achieve power leveling. The control schemes employ model predictive control (MPC), which has gained significant attention in recent years because of its advantages such as fast response and simple implementation. The PV system provides maximum power at various irradiation levels using the incremental conductance technique (INC). The interfaced grid side converter of the SMES can control the grid voltage by regulating its injected reactive power to the grid, while the charge and discharge operation of the SMES coil can be managed by the system operator to inject/absorb active power to/from the grid to achieve the power leveling strategy. Simulation results based on MATLAB/Simulink® software prove the fast response of the system control objectives in tracking the setpoints at different loading scenarios and PV irradiance levels, while the SMES injects/absorbs active and reactive power to/from the grid during various events to improve the voltage response and achieve power leveling strategy.