Model predictive control of grid-connected PV power generation system considering optimal MPPT control of PV modules

Because of system constraints caused by the external environment and grid faults,the conventional maximum power point tracking(MPPT)and inverter control methods of a PV power generation system cannot achieve optimal power output.They can also lead to misjudgments and poor dynamic performance.To address these issues,this paper proposes a new MPPT method of PV modules based on model predictive control(MPC)and a finite control set model predictive current control(FCS-MPCC)of an inverter.Using the identification model of PV arrays,the module-based MPC controller is designed,and maximum output power is achieved by coordinating the optimal combination of spectral wavelength and module temperature.An FCS-MPCC algorithm is then designed to predict the inverter current under different voltage vectors,the optimal voltage vector is selected according to the optimal value function,and the corresponding optimal switching state is applied to power semiconductor devices of the inverter.The MPPT performance of the MPC controller and the responses of the inverter under different constraints are verified,and the steady-state and dynamic control effects of the inverter using FCS-MPCC are compared with the traditional feedforward decoupling PI control in Matlab/Simulink.The results show that MPC has better tracking performance under constraints,and the system has faster and more accurate dynamic response and flexibility than conventional PI control.

Short-term prediction of photovoltaic power generation based on LMD-EE-ESN with error correction

Considering the instability of the output power of photovoltaic(PV)generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorological conditions,a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed.Firstly,through the fuzzy clustering processing of meteorological conditions,taking the power curves of PV power generation in sunny,rainy or snowy,cloudy,and changeable weather as the reference,the local mean decomposition(LMD)was carried out respectively,and their energy entropy(EE)was taken as the meteorological characteristics.Then,the historical generation power series was decomposed by LMD algorithm,and the hierarchical prediction of the power curve was realized by echo state network(ESN)prediction algorithm combined with meteorological characteristics.Finally,the iterative error theory was applied to the correction of power prediction results.The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power,and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.

Calculation Method and Analysis for the Annual Power Generation of PV Faades in China

The application of PV facades emerges greatly in recent years and however its calculation methods and analysis remains insufficient under the weather conditions of China. In such demand, this paper investigates PV facade in terms of PV electricity generation in different arrangements and weather conditions of four major cities in China. The calculation models for PV facade are developed and validated by comparing the results with the measured data from the field experiments. A parametric study is carried out to provide a reference for the optimal design of the PV facades. The results show that with various cities, building orientations, building forms, materials and arrangements of PV modules, there is a distinct difference in the electrical output energy of PV facades. Weather conditions nlav a very important role in terms of PV generation nerformance of PV facades.

LCOE Analysis of Solar Tracker Application in China

In China, systemic techno-economic analysis for solar tracker has been absent. To fill the blank, by taking the economic analysis of solar tracker application as the research object and using the LCOE method widely used internationally, the techno-economic analysis model of solar tracker was established according to conditions in China. Influence factors on LCOE were analyzed by using the established model, and the relationship between each cost factor and the cost component of energy leveling of tracker was further studied. In addition, the calculation method of investment payback period based on energy leveling analysis was established, and the influence of various factors on investment payback period was revealed through an example calculation. The research results will help to measure the economy of tracker application more accurately and comprehensively, and promote the popularization and application of solar tracker. The economic analysis model of solar tracker application was established by using LCOE method. The influence factors and cost component of LCOE were analyzed with the model. The payback period of solar tracker investment was also analyzed based on LCOE method.

Analysis of synergistic influence of multi-scale design parameters on nearly-zero energy office blocks performance based on architectural morphological classification and parametric modeling

Design parameters at different scales in the pre-design phase could significantly impact both building energy consumption and photovoltaic(PV)power generation potential.However,existing studies often overlook the synergistic effects of design parameters across multiple scales(block-building-facade scales)when evaluating these aspects.This paper aims to propose a workflow for the assessing building energy consumption and PV power generation potential of office blocks applicable in the pre-schematic design phase considering the synergistic influence of multi-scale design parameters,using building typology and parametric modelling approach.The study proposed a multi-scale design parameter classification system combined with parametric modelling.The study investigated 80 office blocks in Wuhan as the study case,which were classified into array type and enclosed type.Correlation analysis and multiple regression equations were used to quantify the single versus synergistic effects of different scale design parameters.Results suggest that focusing solely on a single scale during the pre-design stage is typically inadequate for understanding building energy potential.In contrast,multi-scale synergistic analysis boosts energy use intensity(EUI)by 7.56%and net energy use intensity(NEUI)by 33.96%.Under multi-scale synergistic conditions,the EUI of array type is more influenced by the building design parameters,while the NEUI is effected by the balance of multi-scales design parameters.While the EUI of enclosed types exhibit balanced effects across multi-scale design parameters,with NEUI results aligning closely with PV power generation potential.Multiple regression equations highlight building density and shape factor as key influencers for both array and enclosure layouts.This study offers designers a flexible and scalable workflow for evaluating building energy consumption and PV power generation potential in the pre-design phase.The findings can guide nearly-zero energy urban block planning to achieve a balance between energy supply and demand.