Short answer

When designing small-scale solar power plants with ORC technology, utilize dynamic simulation to test various solar field sizes and solar fractions, and factor in potential government incentives for a realistic economic assessment.

Field: Modelling
Source: American Journal of Engineering and Applied Sciences (2016)
Method: Dynamic Simulation
Evidence: Strong effect

Dynamic simulation of a small-scale solar Organic Rankine Cycle (ORC) plant, incorporating thermal storage and auxiliary heating, can identify optimal configurations for economic viability. This modelling research insight is drawn from a 2016 study published in American Journal of Engineering and Applied Sciences. Using Dynamic simulation, researchers explored how this design variable affects real-world outcomes. The key design takeaway: When designing small-scale solar power plants with ORC technology, utilize dynamic simulation to test various solar field sizes and solar fractions, and factor in potential government incentives for a realistic economic assessment.

Study

ModellingHigh ImpactStrong effect

Dynamic Simulation of a Small-Scale Solar ORC Plant Optimizes Payback Period

Dynamic simulation of a small-scale solar Organic Rankine Cycle (ORC) plant, incorporating thermal storage and auxiliary heating, can identify optimal configurations for economic viability.

American Journal of Engineering and Applied Sciences · 2016

Key Findings

01The optimal solar field area for the best payback period was found to be approximately 200 m².
02A solar fraction of about 75% yielded the most favorable economic results.
03The system's economic competitiveness is highly dependent on the availability of incentives for renewable energy applications.

Application

Design takeaway

How to apply

Use simulation software like TRNSYS to model your proposed solar power system, varying key parameters such as collector area and storage capacity, and analyze the resulting energy output and estimated payback period.

Project actions

01Clearly define the scope of your simulation, including all components and their interactions.
02Justify the choice of simulation software and any specific models used.
03Present simulation results clearly, using graphs and tables to illustrate performance and economic outcomes.

Method & Evidence

AimWhat is the optimal thermoeconomic configuration for a small-scale solar ORC power plant, considering dynamic weather and load conditions?

MethodDynamic Simulation

ProcedureA TRNSYS simulation environment was used to model a 10 kW e ORC plant integrated with novel flat-plate evacuated solar collectors, a diathermic oil thermal storage tank, and an auxiliary gas-fired burner. The simulation included dynamic models for solar collectors and ORC performance maps, alongside tools for energy and economic parameter calculation.

ContextRenewable energy systems, small-scale power generation, building integrated energy systems

Variables

IV["Solar field area","Solar fraction","Auxiliary burner operation"]

DV["Payback period","Energy output (electrical and thermal)","System efficiency"]

CV["ORC plant capacity (10 kW e)","Collector type (flat-plate evacuated)","Diathermic oil properties","Ambient temperature"]

Strengths & Limitations

Strengths

+Comprehensive dynamic simulation of an integrated system.
+Inclusion of thermal storage and auxiliary heating for realistic operation.
+Thermoeconomic analysis to assess economic viability.

Limitations

The accuracy of the simulation is dependent on the quality of the input data and the underlying models used. Real-world performance may vary due to unforeseen operational factors.

Reliability & validity

The reliability of the simulation depends on the accuracy of the TRNSYS models and input data. Validity is supported by the inclusion of key system components and operational strategies, though real-world validation would be required.

Think critically

To what extent can simulation results be generalized to different geographical locations with varying solar irradiance and weather patterns?

Design Principles

"Optimize system performance and economic viability through dynamic simulation of integrated renewable energy components under variable conditions."

This research demonstrates the power of dynamic simulation in optimizing complex renewable energy systems. By modeling the interplay of solar collectors, thermal storage, and an ORC, designers can predict performance under varying conditions and pinpoint configurations that maximize energy utilization and minimize payback periods.

What This Means for Your Design

Using computer simulations, researchers found that a specific size of solar panels (around 200 m²) and using solar power for 75% of the time gave the best financial return for a small solar power system. However, the system only becomes affordable if the government offers financial help.

How to use in your project

1.Reference this study when discussing the use of dynamic simulation for optimizing energy systems.
2.Use the findings on optimal solar fraction and field size as a benchmark for your own design considerations.

Add to My Project

Quick Cite

Paragraph starter

The dynamic simulation of a small-scale solar Organic Rankine Cycle (ORC) plant, as demonstrated by Buonomano et al. (2016), highlights the critical role of modeling in optimizing system design. Their work utilized TRNSYS to identify that a solar field area of approximately 200 m² and a solar fraction of 75% yielded the most favorable economic payback period. This underscores the importance of detailed simulation in balancing energy generation with economic viability, particularly when considering the impact of external incentives on renewable energy projects.

Source

American Journal of Engineering and Applied Sciences

A Novel Prototype of a Small-Scale Solar Power Plant: dynamic Simulation and Thermoeconomic Analysis

journal · 2016

View source

Questions About This Research

What does the research say about dynamic simulation of a small-scale solar orc plant optimizes payback period?: When designing small-scale solar power plants with ORC technology, utilize dynamic simulation to test various solar field sizes and solar fractions, and factor in potential government incentives for a realistic economic assessment. Evidence: American Journal of Engineering and Applied Sciences (2016).
Why does "Dynamic Simulation of a Small-Scale Solar ORC Plant Optimizes Payback Period" matter for design?: This research demonstrates the power of dynamic simulation in optimizing complex renewable energy systems. By modeling the interplay of solar collectors, thermal storage, and an ORC, designers can predict performance under varying conditions and pinpoint configurations that maximize energy utilization and minimize payback periods.
How can designers apply this research?: When designing small-scale solar power plants with ORC technology, utilize dynamic simulation to test various solar field sizes and solar fractions, and factor in potential government incentives for a realistic economic assessment.
What were the main findings?: The optimal solar field area for the best payback period was found to be approximately 200 m².. A solar fraction of about 75% yielded the most favorable economic results.. The system's economic competitiveness is highly dependent on the availability of incentives for renewable energy applications.
What research method was used?: Dynamic Simulation.
How strong is the evidence?: Evidence strength is rated Strong effect, based on a 2016 journal from American Journal of Engineering and Applied Sciences.
What should I do differently in my next project?: Use simulation software like TRNSYS to model your proposed solar power system, varying key parameters such as collector area and storage capacity, and analyze the resulting energy output and estimated payback period.
What are the limitations?: The simulation relies on manufacturer-provided ORC performance maps, and the economic analysis is contingent on the availability and level of external incentives.