SAM-Based Performance Modeling of a 50 MWe Solar Thermal Power Plant | Power for Future

Dinesh Kumar Sharma (u. a.)

ISBN 10: 6209239676 / ISBN 13: 9786209239670

Published by LAP LAMBERT Academic Publishing, 2026

About this Item

SAM-Based Performance Modeling of a 50 MWe Solar Thermal Power Plant | Power for Future | Dinesh Kumar Sharma (u. a.) | Taschenbuch | Englisch | 2026 | LAP LAMBERT Academic Publishing | EAN 9786209239670 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.

Seller Inventory # 134526663

Bibliographic Details

• Title: SAM-Based Performance Modeling of a 50 MWe Solar Thermal Power Plant | Power for Future
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2026
• Language: English
• ISBN 10: 6209239676
• ISBN 13: 9786209239670
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 7D millimeters 220 millimeters width by 150 millimeters height by 7 millimeters depth
• Weight: 185 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

India's tropical geographical location offers immense potential for solar energy utilization. With rapid growth in population and GDP, the country's energy demand is increasing continuously, while excessive reliance on fossil fuels is accelerating greenhouse gas emissions and global warming. In this context, adopting solar energy has become a national necessity rather than a choice. This book presents the performance estimation and modeling of a 50 MWe Concentrated Parabolic Trough Solar Thermal Power Plant using the System Advisor Model (SAM) software. Emphasis is placed on virtual techno-economic analysis to optimize plant design while saving time and cost. SAM integrates key inputs such as site-specific meteorological data, solar field configuration, collectors, receivers, power cycle, thermal storage, system costs, and financial parameters to simulate plant performance. The simulated results are validated against actual plant data. The model predicts an annual electricity generation of 97.24 million kWh compared to 97.42 million kWh from the actual plant under identical conditions, showing a close agreement with only 0.18% deviation.