Modelling of concentrating solar power plant for power system reliability studies

Abstract

Growing share of concentrating solar power (CSP) plants in power systems creates the need for including these renewable sources in power system reliability studies. As such studies analyse the grid on a global scale and start to be performed increasingly by Monte Carlo simulations, modelling of CSP production has to be reasonably simplified in order to reduce the calculation time. The model simplification also concerns the minimisation of the required specific knowledge and input data which enables power system engineers to evaluate the impact of CSP without substantial expertise in its underlying physics and by focusing on the key design parameters. There is a large variety of accurate CSP simulation programs and models, and yet neither of them offers the required simplicity. This study addresses this gap by proposing reduced models for predicting energy output of parabolic trough and central receiver systems. The calculation procedure and its underlying assumptions are presented. The adequacy of the models is demonstrated by comparing their predictions with that of the System Advisor Model for different case scenarios.

Potential Assessment of Modelling Performance of a Central Tower Receiver Power Plant Using Intelligent Control Technique

“…The solar thermal power (P th,tr ) reflected by the entire heliostat field into the tower receiver equals to the sum of the field efficiency of each heliostat (η fild ), heliostat area (A hs ) and the reflected solar radiation by each heliostat [19].…”

“…For example, one of the famous programs is the System Advisor Model (SAM) software, which was developed by NREL to simulate the output power of RES . SAM software needs more than 90 input parameters to describe an individual CSP technology [19]. Therefore, SAM and other software may not be appropriate to assess the power system reliability by using MC method because of the increased uncertainty in simulation results.…”

“…Therefore, SAM and other software may not be appropriate to assess the power system reliability by using MC method because of the increased uncertainty in simulation results. The increased uncertainty may arise from the following reasons: complex operating condition, several required input data, need more experience and knowledge, and long computational time [19].…”

Research on the Novel Non-Contact Power Supply System Tuning Methodology based on Fuzzy Neural Network Theory under the Random Load Condition

“…Inverter load in order to adapt to variable frequency power supply system simulation test comprehensive electrical characteristics of load demand, in addition to the load components and the cooling efficiency and so on to take the necessary measures, variable frequency simulation load control should be designed as a closed loop automatic control, load loading should be the general load automatic management system setting load value, power factor and feedback parameters such as accuracy, load protection value, and automatic feedback and correction loading amount, to meet the schedule requirements [29]. To make the electricity equipment better work, need to provide them with different frequency and voltage of power supply.…”

Inter-hour direct normal irradiance forecast with multiple data types and time-series

“…Solar power significantly depends on the solar irradiance, such as the global horizontal irradiance (GHI) for PV plants and the direct normal irradiance (DNI) for CST plants [6], and thus the solar power output forecast can be transformed into a solar irradiance (GHI or DNI) forecast [7][8][9]. In this study, we focus on a DNI forecast.…”