A5032781285- Power Systems and Renewable Energy
- Microgrid Control and Optimization
- Energy Load and Power Forecasting
- Electric Vehicles and Infrastructure
- Smart Grid Energy Management
- Machine Learning and ELM
- Electric Power System Optimization
- Frequency Control in Power Systems
Shanxi University
2022-2023
Electricity price forecasting is a crucial aspect of spot trading in the electricity market and optimal scheduling microgrids. However, stochastic periodic nature sequences often results low accuracy forecasting. To address this issue, study proposes quadratic hybrid decomposition method based on ensemble empirical modal (EEMD) wavelet packet (WPD), along with deep extreme learning machine (DELM) optimized by THPO algorithm to enhance prediction. overcome problem optimization falling into...
To address the problem of low-carbon, optimal operation AC–DC hybrid microgrids, a carbon trading mechanism is introduced and impact multiple uncertainties on system optimization considered. Firstly, two-layer model with comprehensive economy microgrid as upper layer respective AC DC sub-microgrids lower established demand-side response introduced, based which uncertainty scenery load simulated using multiscenario analysis method. Then, baseline method used to allocate emission allowances...
Currently, the integration of new energy sources into power system poses a significant challenge to frequency stability. To address issue capacity sizing when utilizing storage battery systems assist grid in control, optimal allocation model is proposed for primary regulation storage. Due requirement large number actual parameters model, simulation constructed based on characteristics control provide necessary parameters. Subsequently, modulation output established by considering basic...
In response to the insufficient accuracy of load forecasting in power system and wide range intervals, a combined short-term model considering interval construction historical data is proposed. First, are decomposed into relatively stable subsequences using extreme-point symmetric mode decomposition (ESMD), adaptive dispersion entropy (DE) C–C algorithm proposed recombine similar subsequences. Then, periodicity correlation analysis used determine input set each reconstructed component,...