Concentrating Solar Power Commercial Application Study

2009 ◽  
Author(s):  
none,
Keyword(s):  
Solar Power ◽  
Commercial Application ◽  
Concentrating Solar Power ◽  
Application Study

Post subsidy conditions: Evaluating the techno-economic performance of concentrating solar power in Spain

Solar Energy ◽  
2021 ◽  
Vol 218 ◽  
pp. 571-586
Author(s):  
Pierre-Antoine Parent ◽  
Pegah Mirzania ◽  
Nazmiye Balta-Ozkan ◽  
Peter King
Keyword(s):  
Economic Performance ◽  
Solar Power ◽  
Concentrating Solar Power

scholarly journals Profit-Based Unit Commitment for a GENCO Equipped with Compressed Air Energy Storage and Concentrating Solar Power Units

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 576
Author(s):  
Mostafa Nasouri Gilvaei ◽  
Mahmood Hosseini Imani ◽  
Mojtaba Jabbari Ghadi ◽  
Li Li ◽  
Anahita Golrang
Keyword(s):  
Energy Storage ◽  
Solar Power ◽  
Unit Commitment ◽  
Thermal Power ◽  
System Structure ◽  
Compressed Air ◽  
Mixed Integer ◽  
Compressed Air Energy Storage ◽  
Concentrating Solar Power ◽  
Unit Commitment Problem

With the advent of restructuring in the power industry, the conventional unit commitment problem in power systems, involving the minimization of operation costs in a traditional vertically integrated system structure, has been transformed to the profit-based unit commitment (PBUC) approach, whereby generation companies (GENCOs) perform scheduling of the available production units with the aim of profit maximization. Generally, a GENCO solves the PBUC problem for participation in the day-ahead market (DAM) through determining the commitment and scheduling of fossil-fuel-based units to maximize their own profit according to a set of forecasted price and load data. This study presents a methodology to achieve optimal offering curves for a price-taker GENCO owning compressed air energy storage (CAES) and concentrating solar power (CSP) units, in addition to conventional thermal power plants. Various technical and physical constraints regarding the generation units are considered in the provided model. The proposed framework is mathematically described as a mixed-integer linear programming (MILP) problem, which is solved by using commercial software packages. Meanwhile, several cases are analyzed to evaluate the impacts of CAES and CSP units on the optimal solution of the PBUC problem. The achieved results demonstrate that incorporating the CAES and CSP units into the self-scheduling problem faced by the GENCO would increase its profitability in the DAM to a great extent.

scholarly journals Hybrid Forecasting Methodology for Wind Power-Photovoltaic-Concentrating Solar Power Generation Clustered Renewable Energy Systems

2021 ◽  
Vol 13 (12) ◽  
pp. 6681
Author(s):  
Simian Pang ◽  
Zixuan Zheng ◽  
Fan Luo ◽  
Xianyong Xiao ◽  
Lanlan Xu
Keyword(s):  
Power Generation ◽  
Renewable Energy ◽  
Wind Power ◽  
Solar Power ◽  
Prediction Method ◽  
Random Fluctuation ◽  
Concentrating Solar Power ◽  
Short Term ◽  
Hybrid Forecasting ◽  
Energy Cluster

Forecasting of large-scale renewable energy clusters composed of wind power generation, photovoltaic and concentrating solar power (CSP) generation encounters complex uncertainties due to spatial scale dispersion and time scale random fluctuation. In response to this, a short-term forecasting method is proposed to improve the hybrid forecasting accuracy of multiple generation types in the same region. It is formed through training the long short-term memory (LSTM) network using spatial panel data. Historical power data and meteorological data for CSP plant, wind farm and photovoltaic (PV) plant are included in the dataset. Based on the data set, the correlation between these three types of power generation is proved by Pearson coefficient, and the feasibility of improving the forecasting ability through the hybrid renewable energy clusters is analyzed. Moreover, cases study indicates that the uncertainty of renewable energy cluster power tends to weaken due to partial controllability of CSP generation. Compared with the traditional prediction method, the hybrid prediction method has better prediction accuracy in the real case of renewable energy cluster in Northwest China.

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