Thermodynamic pre-determination of power generation potential in geothermal low-temperature applications
© Öhman and Lundqvist; licensee Springer 2014
Received: 6 October 2013
Accepted: 10 March 2014
Published: 26 April 2014
Small capacity, low temperature, geothermal heat sources provide significant opportunities for distributed, small scale power generation. Project definitions and pre-dimensioning however require advanced thermodynamic engineering at a cost independent of project size. In order to reduce this cost, a new method has been developed to allow basic level engineers to perform pre-optimisation of thermodynamic potential as well as expected performance using available power plant technology at such pre-optimized conditions.
By reducing the complexity of second-law computations, a simple two-dimensional diagram is shown representing the dimensioning criteria required for maximum power generation using the particular heat source and sink while considering expected power plant performance, using systems and components on the market.
By sensitivity analysis the combination of thermodynamic analysis and real world data correlations was simplified and arranged for pre-dimensioning of business cases.
Optimal pre-dimensioning of power generation system for any geothermal heat source, with a defined heat sink, can be determined without advanced thermodynamic expertise. This reduces the cost for business case proposals, pre-dimensioning and tender specifications of small-scale power generation systems for low temperature heat sources. The intended implication of this work is to increase the use of low-temperature geothermal wells for distributed power generation.
Well over 1,000 scientific works are published yearly on findings from research on Low Temperature Power Cycles (LTPC). Advanced cycle configurations are thoroughly investigated (Ho et. al ), as well as transcritical solutions (Vèles et. al ). Internal fluid selections for very specific geothermal applications (Bu et. al ) as well as general use of zeotropic mixtures (Heberle et. al ; Aghahosseini and Dincer ) constantly improve our knowledge. Yet, dissemination of the knowledge is slow and few scientific articles are published on results from real, operating field units. Technical developments of LTPCs benefit greatly from the findings available in research. Site developers of geothermal wells however require references to real systems. The few reports of real, operating system performances therefore need to be carefully assessed and generalized in order to provide guidance to what a practitioner could achieve with a particular geothermal well.
Conventional second law analysis of power generation potential in low-temperature applications is vital, as well as a non-biased approach, to understand any particular case of LTPC application. As long as power plants of sufficient sizes are considered, cost of expert engineering is not prohibitive to projects. This workload is however very much similar regardless if a 50- or 0.5-MWe plant is considered. Obviously, the relative cost will then rise significantly for small-scale power plants and could prohibit the use of such.
The function of is determined by ending the summation in Equation 1 at . This also gives a relation of ηc, Il(ψ U ), where ψ U is the utilization of the available heat transport from source to sink by a reversible power cycle.
Though the calculations of Integrated Local Carnot Efficiency is relatively simple and straight-forward (see Equation 1), it would still require the local engineer to set up a model in a suitable software by himself or purchase a commercial software, in order to perform his investigation. Making software is prone to cause errors and purchasing commercial software is likely to be non-attractive from a cost perspective.
Small-scale power plants, using low-temperature heat, LTPCs, have entered the market in a number of formats. Öhman and Lundqvist () investigated a large sample of operating plants as well as commercially available, standardized LTPCs. It showed that performance of all plants is a function of the ratio of utilized to potential heat transport from heat source to heat sink.
With this knowledge, the need to simulate a particular thermal process, such as Organic Rankine cycles (ORC; Schuster et al. ), Kalina cycle (Walraven et al. ), Trilateral Flash cycle (TFC; Fischer ) or any other cycle becomes unnecessary for pre-optimizing plants and business cases.
In order to offer a correct and cost-efficient tool, a graphic model was therefore developed in order to allow the simplest possible access to Equation 1. Combined with a calculated absolute maximum rate of heat exchange from the heat source , any practitioner is able to present a scientific best estimate of the maximum expected power output and the corresponding required utilization of available heat exchange between source and sink. This allows for estimating size and cost of the major cost items as well as the expected sales volume of electric power.
The authors' intention is to enhance the use of small-scale LTPCs by offering this simplified analysis procedure.
The Integrated Local Carnot Efficiency model is dependent on a number of parameters making a detailed graphic version multidimensional and too complicated. Therefore, a reduction of dependent parameters was performed by analysing the model sensitivity to each parameter.
If ψ U is further increased, the power cycle has to perform heat pump duty and ηc, Il will eventually become negative.
As explained in detail in the reference, Equation5is derived from the theoretical assumption of equalising the source and sink temperature by using a reversible power cycle to transport the heat. As the Curzon-Ahlborn efficiency is known at TCA, the latter can be expressed analytically.
Note that Equation 7 is an empirical correlation based on a large amount of data.
Since Equation 1 suggests some degree of numerical error, a computational investigation was performed. In varying T1 from 85 to 1,000°C with αrat ranging from 0.5 to 1.5, a total sensitivity of 10− 5 was discovered. Thus, the absolute level of temperature was determined as irrelevant for the domain studied. Temperature ratios, according to Equation 6, were investigated separately.
Heat source and heat sink streams
A computational analysis was performed showing zero dependency of absolute level of α1 in Equation 1 using constant αrat and variation in α1 of 1 to 10. As long as αrat is kept constant the absolute level of α1 is therefore considered as irrelevant to Equation 1.
The absolute level was therefore considered as irrelevant to the results of Equation 1.
Note that this is only true for combinations of source and sink with constant apparent heat capacities.
In order to further reduce the number of parameters, αrat was investigated by a similar computational analysis. The result can be seen in Figure 2, showing a negligible sensitivity of αrat at Trat = 0.7 while at 0.8 and 0.9, no significant difference at all was observed.
From this, Equation 1 could be considered as independent of αrat within the studied domain of Trat > 0.7 Note that this is the interesting domain for applications using low-temperature heat sources. For solar heated power cycles and combustion heated power cycles, Equation1has to be calculated numerically due to the lower levels of Trat.
The user has to calculate numerically according to Equation 4, based on his local application data. Then to calculate the expected net power output, , using real equipment the user only need to identify the utilization in Figure 3, giving maximum Ωspec, a, and from there use Equation 12 to get the answer.
Examples of calculation
Stepwise calculation results
ψ U max
Ω spec, a
Assuming that the river water and brine has an equal specific heat capacity of 4,180 J/kg K, as well as a density of 1, 000 kg/m3, the inverse apparent heat capacity of the heat source stream, α1, can be determined to 2.87 K/MW, according to Equation 9.
As the flow of river water is unknown, three flow rates are investigated, 150, 300 and 450 m3/h. This corresponds to α2 of 5.74, 2.87 and 1.91 K/MW and αrat of 2, 1 and 0.67 respectively, as of Equation 10.
With the known data can be calculated according to Equation 4. Optimum utilization can be identified in Figure 3 and net power output, is calculated according to Equation 1. On top of that, we can calculate the maximum rate of heat transfer for the heat exchangers in the heat source, , using Equation 2.
With this information, the engineer can create preliminary business cases by making estimations on yearly operating times, price of electric power, investment level, maintenance costs, interest rates, etcetera, in a conventional and simple manner.
Note that this information is of importance also in writing tenders for suppliers to make quotations on improving the quality of the quotes received considerably.
Results and discussion
This article shows that in low-temperature geothermal applications, the expected power output of any combination of finite heat source and finite heat sink can be estimated by two unique parameters. One, the ratio of entry temperatures in source and sink, defines the particular site or application. The second, utilization ψ U , is the ratio of chosen, real heat transport from the heat source to the theoretical heat transport from the heat source if a reversible power cycle is assumed. By introducing the grouped term, application-specific work ratio or Ωspec, a(ψ U ), the calculation of expected power output is simplified to the degree of a graph shown in Figure 3.
Ωspec, a(ψ U ) is defined by Equation 13 and ψ U by Equation 2. Ωspec, a(ψ U ) indicates the ratio of expected net power out, at the particular utilization, to , the rate of heat transfer from the heat source at the utilization of 1. Irreversibilities in real LTPCs are accounted for by the fraction of Carnot (FoC), as in Equation 7.
Figure 3 can be used to determine expected maximum power generation of any real LTPC as a function of the utilization ratio, see Equation 2. This is done by multiplying Ωspec, a(ψ U ) with of Equation 4. This calculation is fully compatible to first- and second-law requirements while being simple to perform without expert thermodynamic knowledge. It is however limited to heat sources and heat sinks with constant apparent heat capacity. The calculation takes scientific correlation of practically achieved performances into account.
The simplified procedure to determine expected max power generation is as follows:
Determine the maximum allowed rate of heat transfer from the heat source, assuming no losses, as of Equation 4 (requires source and sink temperature and flow stream information of the particular application).
Determine the utilization providing the maximum power efficiency using Figure 3
Calculate the expected power generation using Equation 12. Note that cases may occur when the optimal ψ U may not be technically or economically feasible. In such case, the closest available ψ U should be used and the corresponding Ωspec, a(ψ U ).
In the examples provided the reader may follow the procedure using real data.
One could argue that conventional exergy loss analysis or entropy generation analysis would offer better accuracy and more information. The authors' opinion is that, however, such detailed analysis is better suited in a later stage of a project, allowing detail optimization and creation of high-resolution performance validation points.
As is seen in Figure 3, the optimal level of ψ U is found in a narrow range. The explanation for this is likely to be found in two fundamental properties: the shape of the curves in Figure 1 in combination with the exponential form of Equation 7. A detailed analysis of this behaviour is beyond the scope of this article but would be of interest for a deeper understanding.
The conclusion of this investigation is that a simplified model is suitable to determine expected power output from any combination of finite heat source and finite heat sink using any type of real low-temperature power cycle.
Furthermore, this simplified model can be reduced to only require the input data of flow rate, heat capacity and entry temperature of the heat sink and heat source.
LTPC: A`bbreviation for Low Temperature driven Power Cycle
WHR: Abbreviation for Waste Heat Recovery
η th : Thermal efficiency. Ratio of work to heat drawn from heat source (%)
ηc, Il: Integrated Local Carnot Efficiency (%)
ηc, l: Local Carnot Efficiency (%)
η CA : Curzon-Ahlborn Efficiency (%)
: Rate of heat transfer from source at ψ U = 1 (W)
: Rate of heat transfer from source (W)
T CA : Common source and sink exit temperature with reversible process (K)
ψ U : Utilization of reversibly available heat transfer from source to sink by means of a reversible power cycle (−)
T1: Heat source entry temperature Brine temp (K)
T2: Heat sink entry temperature (K)
α1: Inverse of apparent heat capacity of the source stream (K/MW)
α2: Inverse of the apparent heat capacity of the sink stream (K/MW)
FoC: FractionOfCarnot Measure of irreversibility
: Net Power Out (W)
Ω spec ,α : Application specific work ratio. ( to ) (−)
T rat : Ratio of heat sink and heat source entry temperatures (−)
a rat : Ratio of the inverse apparent heat capacities of the streams (−)
aNotably, this term is identical to the Curzon-Ahlborn efficiency, or endo-reversible efficiency, ηCA (Curzon and Ahlborn ), when all of the potential heat transfer from source to sink is utilized. However, in real applications, all heat transfer cannot be utilized; therefore, integrated local Carnot efficiency is required for the analysis. A further development was made by (Wu and Kiang ) explaining the Curzon-Ahlborn power rate with simplified irreversibility and also using all of the potential heat transfer between a finite heat source and a finite heat sink.
HÖ has a 23-year experience of introducing novel thermodynamic technology to existing industrial enterprises. Furthermore, HÖ is a PhD student at the Royal Institute of Technology, KTH, Stockholm. PL is professor in Applied Thermodynamics and Refrigeration, Department of Energy Technology at the Royal Institute of Technology, KTH, Stockholm. PL has 25 years of experience from analysis and development of thermodynamic cycles for energy conversion. PL is member of the Swedish Academy for engineering sciences and President for commission E2, Heat Pumps and Heat Recovery of the IIR, International Institute of Refrigeration.
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