Utilising Geothermal Energy in Pohang · utilising the geothermal energy for either electricity...

465.420 Geothermal Energy, Fall 2016

SNU Geothermal Student Conference 2016

Final Report

Utilising Geothermal Energy in Pohang

A Comparison of District Heating Production

and Electricity Production

Department of Energy Resources Engineering

Mohammad Abdulhadi, 2016-82543

Dong Young Yoon, 2010-12369

Mathias Calloe Gjoel, 2016-81144


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State of originality

“We hereby declare that this report is our own work and that it contains, to the best of our

knowledge and belief, neither material previously published or written by another person nor

material that, to a substantial extent, has been submitted for another course, except where due

acknowledgment is made in the report.”


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Contents

1 Introduction ...................................................................................................................................... - 4 -

2 Electricity Production ....................................................................................................................... - 5 -

2.1 Mechanism ................................................................................................................................ - 5 -

2.2 Energy Conversion Technologies .............................................................................................. - 5 -

2.2.1 Binary Steam ...................................................................................................................... - 5 -

2.2.2 Dry Steam ........................................................................................................................... - 6 -

2.2.3 Flash Steam ......................................................................................................................... - 6 -

2.2.4 Flash Binary Combined Cycle ............................................................................................ - 6 -

2.3 Factors Affecting the Power Conversion Efficiency ................................................................. - 7 -

2.4 Efficiency of Geothermal Power Plant ...................................................................................... - 7 -

3 District Heating Production .............................................................................................................. - 9 -

3.1 Heat Exchanger Analysis ........................................................................................................... - 9 -

3.2 Transmission Network Analysis .............................................................................................. - 10 -

3.2.1 Pipe Materials ................................................................................................................... - 11 -

3.2.2 Pipe Size ........................................................................................................................... - 11 -

3.2.3 Effectiveness of Transmission Network ........................................................................... - 13 -

4 Cost Analysis .................................................................................................................................. - 15 -

4.1 Electricity Production .............................................................................................................. - 15 -

4.2 District Heating Production ..................................................................................................... - 15 -

4.2.1 Heat Exchanger ................................................................................................................. - 15 -

4.2.2 Transmission Network ...................................................................................................... - 15 -

5 Results ............................................................................................................................................ - 18 -

5.1 Temperature Dependent Efficiency ......................................................................................... - 18 -

5.2 Efficiency Adjusted for Cost ................................................................................................... - 18 -

6 Conclusion ...................................................................................................................................... - 20 -

References ......................................................................................................................................... - 21 -

Appendix A - MatLab Model ............................................................................................................ - 22 -


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Abstract This report is composed as the term project for the course Geothermal Energy at Seoul National University. The project revolves around the Korean EGS project in Pohang and analyses the potential in utilising the geothermal energy for either electricity production or for district heating production. The purpose of the study is to compare the efficiency, both technological and economic, for the two applications and to investigate what the motivation for choosing one application over the other should be. The analysis of the electricity production, is based on the system intended to be installed at the production site in Pohang, Korea and the analysis of the district heating production, including an analysis of the heat exchanger and the transmission network, is based on the system currently used at the deep geothermal power plant in Rittershoffen, France [1]. A mathematical model is used to compare the two technologies. The outcome of said model determines the threshold above which electricity production is more efficient than district heating production in Pohang. As the brine extraction temperature is constant at 180°C, the model finds that the main deciding factor is the length of the transmission system in the district heating system. Assuming equal demand for electric and thermal energy, the point at which electricity production is more efficient than district heating production is 35 km; i.e., if the demand requires more than 35 km transportation of the energy, the most efficient solution is electricity production and vice versa, if the demand lies within a total distance of 35 km, district heating production will be the most efficient.


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1 Introduction The global energy sector is currently undergoing a major transition. The issues regarding climate change calls for a paradigm shift in the way we, as human beings, produce and consume energy. It seems to be widely appreciated that the conventional way of energy production from fossil fuels only will take us so far. The implementation of the many various renewable technologies in a well-functioning energy system is far less straight-forward than the implementation of CHP-plants and gas turbines etc. Most renewable production technologies have two things in common; they are based on non-fossil fuelled production and their production is fluctuating by nature. This fluctuating production makes it difficult to meet the demand whilst maintaining an acceptable security of supply. This is exactly the reason why geothermal energy production has its merit in the future energy sector as the geothermal power plants can produce energy all year around at a somewhat constant supply and thereby function as a reliable base load. This report revolves around the geothermal EGS project in Pohang. It will analyse the possibilities of producing electricity and district heating from the energy extracted from the well. The purpose of this study, is to compare the effectiveness of the different utilisations of geothermal energy. The district heating system analysed in this project, is the one currently used in Rittershoffen in France to supply heat to a biorefinery nearby. The analysis of the district heating system will include an analysis of the heat exchanger and the heat loss in the pipes in the transmission system. The electricity system analysed, will be the one intended to be installed at the site in Pohang. These two systems will be compared in terms of energy efficiency and lifetime expenditures.


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2 Electricity Production This chapter analyses the electricity production system intended to be installed at the EGS site in Pohang, Korea and from that, a temperature dependent efficiency of utilising geothermal energy in electricity production is determined. 2.1 Mechanism Like all conventional thermal power plants, geothermal power plant uses a heat source to transform the liquid to steam while the source here comes from heat flow contained within the Earth core where the cool water seeps into Earth's crust (reservoirs) then it heats up and rises either naturally or artificially to the surface as steam or hot water. The produced steam drives the turbine mechanically which rotates the generator and finally produces the electricity passing through different phases of energy conversion.

Figure 1:Illustration of the energy conversion stages when using geothermal energy.

2.2 Energy Conversion Technologies There are several types of geothermal power plants which rely upon the use of conversion technology in order to turn the hydrothermal resources into electricity. Binary cycle is planning to use in Pohang due to its properties which match the reservoir parameters. While, there are other methods for energy conversion such as dry/flash steam and flash binary combined cycle. 2.2.1 Binary Steam Binary cycle power stations are the most recent development technology, and can accept fluid temperatures as low as 74°C and as high as 177°C. Currently, two types of geothermal resources can be used in binary cycle power plants to generate electricity: enhanced geothermal systems (EGS) and low-temperature or co-produced resources. For the previous reasons, it is planned to be used in Pohang geothermal power plant with expected reservoir temperature around 180°C. In the binary process, the geothermal fluid, which can be either hot water, steam, or a mixture of both, heats another working fluid usually has an organic compound such as isopentane (with boiling point of 28°C) or Pentane (36.1°C) therefore, it boils at a lower temperature than water. The two liquids are kept completely separate through the use of a heat exchanger which transfer heat energy from the geothermal water to the working fluid. When the working fluid heated up it vaporizes into a gas (like steam). Thus, the force of the expanding gas turns the turbines that drive the generators producing electricity. Furthermore, 100 percent of the geothermal water can be injected back into the system through a closed loop because the water never flashes in air cooled binary plant. This leads to reducing already low emissions to near zero. Both Organic Rankine and Kalina cycles can be used but in case of the geothermal fluid temperature ≤ 180°C, the ORC system is considered more economical due to that reason they use it Pohang geothermal power plant. The thermal efficiency of this type station is typically about 10 to 13 percent.


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Figure 2: Schematic diagram of a Binary Steam Geothermal power plant [2].

2.2.2 Dry Steam Dry steam power plant is the simplest and oldest conversion technology have been used to extract the geothermal energy which use the flow of steam directly from the underground reservoir into the turbine to drive the electrical generator. Nevertheless, getting a pure natural steam requires a geothermal reservoir with very high enthalpy which are rather rare. 2.2.3 Flash Steam Flash steam power plants including single, double and triple flash technologies are the most common used technology globally where a mixture of liquid water and steam is produced from the wells with temperature range of 177°C to 260°C. At a flash steam facility, being the hot water in the deep earth is under high pressure keeps the liquid from boiling but once this hot water moves from deeper in the earth to shallower levels, it quickly loses pressure, boils and flashes to steam. Then the steam is separated from the liquid in a surface vessel (steam separator) and used to turn the turbine-generator shaft producing the electricity. In case of dry/flash steam power plant, after the thermal energy has been used to turn the turbine, spent steam is condensed back to be a liquid again and re-injected into the ground where it is reused in the geothermal system, prolonging the lifetime of a geothermal plant and making it a sustainable resource. Electricity is then transported by transmission lines into the regional grid. 2.2.4 Flash Binary Combined Cycle It is a combination of flash and binary technology, which has been used effectively to take advantages of both technologies. In this type of plant, the flashed steam is first converted to electricity with a steam turbine, and the low-pressure steam exiting the backpressure turbine is condensed in a binary system. This allows for the effective use of air cooling towers with flash applications and takes advantage of the binary process. The flash/binary system has a higher efficiency where the well-fields produce high pressure steam. A power plant typically requires 6 to 9 months to build once the construction process begins. However, when the time needed for exploration, discovery, permitting, and other hurdles is taken into account, the entire geothermal development process can last anywhere from three to seven years or more [2]. Capital costs for the construction of geothermal power plants are much higher than for large coal-fired


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plants or new natural gas turbine technologies. But geothermal plants have reasonable operation and maintenance costs and no fuel costs. Though more expensive than wind and solar power in most cases but Geothermal plants can operate 24/7, which increases their value from a reliability point-of-view, unlike some intermittent renewable sources such as solar and wind. 2.3 Factors Affecting the Power Conversion Efficiency During the geothermal energy extraction process, the extracted liquid or steam passes through many different pieces of equipment on its way to the power station. Thus, it loses some energy which is no longer used to produce power. For example, in liquid dominated systems, the produced two-phase geothermal fluid loses a significant amount of heat when separating steam from water, because only the separated steam is used for generation unless there is another separator or binary plant installed. Non-condensable gases Geothermal fluid which contains NCG lowers the power efficiency because it decreases the specific expansion work in the turbine and has adverse effect on the performance of a turbine. An NCG content of 1 percent by weight reduces the output power by 0.59 percent in comparison with steam without NCG [3]. Auxiliary power consumption Cooling the steam as it leaves the turbine is necessary in order to raise the power conversion efficiency. Thus, an auxiliary power consumption of the equipment is existing and subtracted from the gross electrical output of the geothermal plant, such as gas extractors, cooling pumps and fans for the condenser in the dry type cooling tower which consumes twice as much electricity than the water-cooled system which depends on the availability of the water. Furthermore, an extraction and reinjection pumps might need in some geothermal power plant [3]. Heat losses in the pipes Geothermal fluid also loses heat in the pipes, where the size of the losses depending on the pipe insulator, the length of pipe, and the ambient temperature. However, in some cases it is possible to consider the heat loss in the pipe as relatively negligible. The turbine efficiency Once the steam reaches the power station it passes through the turbine that drive the generator. The turbine efficiency drops due to deviation from isentropic behaviour and the presence of moisture in the turbine during the steam expansion process. The Baumann rule shows that the presence of 1 percent average moisture causes a drop of about 1 percent turbine efficiency [3]. The generator efficiency In general, the generator efficiency is relative to the power capacity but comparing a range of generator efficiencies from different manufacturers clarifies that the generator efficiency of geothermal power plant ranges from 95.7 to 98.7 percent [3]. 2.4 Efficiency of Geothermal Power Plant Efficiency is an important measure of power generating facility performance but the main target of getting higher efficiency can be changed slightly depends on the thermal power plant type conventional or renewable. For conventional power plant, getting higher efficiency aims to avoid climate change, health problems, and ecosystem damage which means less burned fuel used per output and fewer emissions. Unlike geothermal and other renewables, fossil fuel use is not sustainable even if managed properly and used efficiently. While, for geothermal power plant, in contrast to fossil fuel use, efficiency is basically an economic concern and confined primarily to land use, not climate change, health and conservation issues [2]. Geothermal power plants have lower efficiency relative to other thermal conventional power plants. However, each geothermal power plant has its own efficiency which is affected by many parameters


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including the power plant energy conversion technology, size, gas content, parasitic load, ambient conditions in addition to the reservoir parameters (temperature and fluid flow rate) [3]. In this project two models are used to predict the efficiency of the electric generation power plant using binary system in Pohang which are introduced by Dickson & Fanelli and DiPippo [3]. These models provide the net power generation and thus dividing them by the maximum extracted heat energy from the geothermal reservoir yields the efficiency of electricity generation. The Dickson & Fanelli model determines the efficiency as,

𝜂1 =𝑊

𝑊𝑚𝑎𝑥=

0.18 𝑇𝐻 − 10

278 (1)

and the DiPippo model determines the efficiency as,

𝜂2 =𝑊

𝑊𝑚𝑎𝑥= 2.47

(𝑇0 − 𝑇𝐻)(𝑇𝐶 − 𝑇𝐻)

𝑇𝐻 𝑐𝑃 (𝑇0 − 𝑇𝐻) (2)

from which the mean efficiency will determine the approximated efficiency of electricity generation as,

𝜂𝑃𝑜ℎ𝑎𝑛𝑔 =𝜂1 + 𝜂2

2 (3)

where W is the approximate net electric power generation (kWe), ṁ is the total mass flow rate (kg/s), 𝑊𝑚𝑎𝑥 is the maximum available thermal power (kJ/s), TH is the inlet temperature (℃), T0 is the ambient temperature of 20℃, TC is the outlet temperature (℃) and cp is the specific heat capacity (kJ/kg K). This efficiency from Equation (3 will be used to determine the temperature dependent efficiency.


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3 District Heating Production This chapter analyses the district heating system at the ECOGI EGS project in Rittershoffen, France and from that, a temperature dependent efficiency of utilising geothermal energy in district heating is found. The project in Rittershoffen is chosen to be the case study for this part of the project as this, being inaugurated in June 2016 [1], is assumed to represent the most modern and developed technology in the field of converting the geothermal energy to district heating. This is an important aspect when analysing the feasibility of the applications and efficiencies for a technology still so relative early in its development and therefore, presumably, in the very early stages of the learning curve. There are many aspects to take into consideration when designing a district heating system, especially when the heat source is a geothermal well. The general idea would be to design a heat exchanger that can convert the energy extracted from the underground into energy which then can be used in a district heating transmission system. This heat exchanger is essential as the brine used in the well is unfit to be used as the working fluid in the transmission system as it is too harsh [4] on the components in the district heating network where the working fluid has been optimised for higher efficiency by minimising the risk of corrosion and coatings [5]. 3.1 Heat Exchanger Analysis When the design process of the heat exchanger used at the ECOGI EGS project in Rittershoffen was first initiated, three general requirements were defined for eventual manufacturers to evaluate their design on. The three technological requirements or parameters were, briefly listed, high temperature and pressure resistance, easy cleaning operation and a small pinch between the two thermal loops [4]. The best overall solution for the specific project turned out to be a shell-and-tube heat exchanger solution; more specifically an AEL [6] shell-and-tube heat exchanger with straight tubes and no tubes in the windows. To furthermore enhance the thermal power of the system, different scenarios were analysed and the final installation was constructed with twelve of these AEL exchangers connected in series. In Figure 3, the typical shell-and-tube heat exchanger design and the technological improvement of having no tubes in the windows are illustrated. This design lowers the risk of malfunction due to vibrations in the shell. The additional baffles in the design will also mitigate with the vibration problem as the high-pressure fluid flows through the tubes and the shell.

Figure 3: Illustration of the AEL shell-and-tube heat exchanger and the "no tubes in windows" design implemented in the

Rittershoffen project [7].

To determine the effectiveness of a complex heat exchanger as the one used in Rittershoffen, many technical specifications should be known before determining the exact effectiveness. However, in this project, a simplified approach is used as many of these specifications are impossible to obtain. The effectiveness determination in this project is based on the data in Table 1, and therefore neglects technological properties such as surface area and thermal resistance which are typically essential in a heat exchanger analysis.


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Table 1: Data from the Rittershoffen ECOGI EGS project with twelve shell-and-tube heat exchangers in series [4].

The heat transfer in the heat exchanger system is described as,

𝑞 = �̇� 𝑐𝑝 Δ𝑇 , (4)

where 𝑞 is the heat transfer, �̇� is the mass flow, cp is the specific heat capacity and Δ𝑇 is the temperature difference. Equation (4 determines the heat transfer for both the brine and for the secondary fluid under the assumption of no heat loss so that,

𝑞𝑏𝑟𝑖𝑛𝑒 = 𝑞𝑠𝑒𝑐𝑜𝑛𝑑𝑎𝑟𝑦 . (5)

The effectiveness of the heat exchanger is described by,

𝜖 =𝑞

𝑞𝑚𝑎𝑥 , (6)

where 𝑞𝑚𝑎𝑥 is determined as,

𝑞𝑚𝑎𝑥 = 𝐶𝑚𝑖𝑛 Δ𝑇𝑚𝑎𝑥 . (7)

Here, the 𝐶𝑚𝑖𝑛 is determined by the fluid that has the minimum value of heat capacity and Δ𝑇𝑚𝑎𝑥 is determined as the temperature difference between the inlet temperature of the brine and the inlet temperature of the secondary fluid; that is, the maximum temperature difference in the system. The effectiveness of the heat exchanger varies with the inlet temperature of the brine extracted from the geothermal well. This temperature dependent effectiveness can be found by constructing a model based on Equation (4, where Δ𝑇 is the temperature difference between the inlet and outlet temperature of the brine. The model is set to determine the increase in effectiveness as the inlet brine temperature increases one degree. This model is constructed in MatLab and can be reviewed in Appendix A. This effectiveness of the heat exchanger is only one part of the district heating system. The next section determines the efficiency of the transmission network based on the Rittershoffen project so the overall efficiency of utilising geothermal energy for district heating production can be found. 3.2 Transmission Network Analysis The source of geothermal fluid for a direct use application is often located some distance away from the user. This means that the fluid transport through the pipeline is required. The cost of transmission lines and the distribution networks in direct-use projects is significant. In this part the efficiency of the pipeline is determined. To calculate the efficiency and cost of the pipe some variables including pipe size, material of carrier and insulation should be determined. For this project, reasonable assumptions of these variables based on previous case from other sites and data from Pohang EGS site are made.


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3.2.1 Pipe Materials There are various types of piping materials but carbon steel is now the most widely used material for geothermal transmission lines and distribution networks; especially if the fluid temperature is above 100°C [8]. Figure 4 and Figure 5 introduce the temperature limitations for the different materials and breakdown of the total piping by type of closed system.

Figure 4: Maximum service temperature for pipe materials [9].

Figure 5: Distribution of piping used in closed loop geothermal systems [9].

In Pohang, the temperature of the water is about 180° C which is lower than the maximum service temperature for the steel pipe. As seen in Figure 5, asbestos cement (AC) and fiberglass reinforced plastic (FRP) were also commonly used but is used these days because of environment problems. For these reasons, the carrier pipe is set to be steel. Carbon steel piping can be insulated with polyurethane foam, rock wool, or fiberglass. The important thing is that pipe material does not have significant effect on heat loss. The flow rate is more important factor of heat loss the heat loss is higher than as greater flows [8]. In this project, one of the mostly used material rock wool is chosen as there are some data on it and it was proven to have lower temperature drop through the pipeline in Iceland case (less than 2°C drop through 27 km from Nesjavellir power plant to Reykjavik) [10]. 3.2.2 Pipe Size The size of piping is determined using,

𝐷2 =4�̇�

𝜌𝑣𝜋 , (8)

where 𝐷 is pipe diameter (m), �̇� is mass flow rate (kg/s), ρ is density of fluid (kg/m3), 𝑣 is velocity of the fluid (m/s). Velocity of the fluid is designed by concerning the allowable pressure loss through the pipe. In this project, there was a lack of data, so it is assumed to have the same velocity of Iceland case. From the data of Iceland case, the velocity is then


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𝑣 =�̇�

𝜌 𝜋𝐷2

4

=400 𝑘𝑔/𝑠

960𝑘𝑔𝑚3 ∗ 𝜋 ∗

(0.576 𝑚)2

4

= 1.599𝑚

𝑠 . (9)

From this result, the diameter of the pipe is determined using,

𝐷 = √4𝑚 ̇

𝜌𝑣𝜋= √

4 ∗ 40 𝑘𝑔/𝑠

960 ∗ 𝜋 ∗ 1.599 𝑚/𝑠= 0.182 𝑚 . (10)

Insulation thickness can be determined by using chart from Korea Energy Management Corporation about rock wool insulation in Figure 6.

Figure 6: Rock wool insulation thickness for heat transmission pipe [11]. In this project the effect of temperature change will be observed so the regression of temperature and insulation thickness is used to determine insulation thickness by different temperature. Data when nominal pipe size is 8B is used to make a regression.

Figure 7: Illustration of the tendency of how the insulation thickness changes with the fluid temperature inside the pipe.


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For example, if the temperature is 170°C using the regression equation the insulation thickness is set to be about 93 mm.

𝐼𝑛𝑠𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑇ℎ𝑖𝑐𝑘𝑛𝑒𝑠𝑠 = 61.803 ∗ ln(170) − 224.07 = 93.34 𝑚𝑚 (11)

Considering former information, Figure 8 shows the final design of transmission pipe for the temperature 170℃.

Figure 8: Design of transmission pipe with inner diameter of 182 mm and outer diameter of 368.68 mm.

3.2.3 Effectiveness of Transmission Network To calculate effectiveness, the amount of heat loss through the pipeline is needed.

Figure 9: One pipe in the ground.

The concept of thermal resistance in Equations (12 and (13 are used for determining the heat loss.

𝑅𝑖𝑛𝑠𝑢𝑙𝑎𝑡𝑖𝑜𝑛 =ln (

𝑟0𝑟𝑖

)

2 𝜋 𝑘𝑖 (12)

and

𝑅𝑔𝑟𝑜𝑢𝑛𝑑 =

ln (𝐻𝑟0

+ √𝐻2

𝑟0− 1)

2 𝜋 𝑘𝑔 .

(13)

So, the heat loss becomes,

𝑞𝑙𝑜𝑠𝑠 =𝑇1 − 𝑇0

𝑅𝑡𝑜𝑡𝑎𝑙∗ 𝐿 =

𝑇1 − 𝑇0

ln (𝑟0𝑟𝑖

)

2 𝜋 𝑘𝑖+

ln (𝐻𝑟0

+ √𝐻2

𝑟0− 1)

2 𝜋 𝑘𝑔

∗ 𝐿

(14)


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where L is the length of the pipe. The relative error in the heat loss for district heating pipes typically less than 1 percent [8]. After determining the heat loss, the efficiency of the pipe transmission can be found by,

𝜖 =𝑞𝑖𝑛 − 𝑞𝑙𝑜𝑠𝑠

𝑞𝑖𝑛 , (15)

where 𝑞𝑖𝑛 (W) is the heat flow entering the pipe. It is the energy coming out of heat exchanger and is calculated above. 𝑞𝑙𝑜𝑠𝑠 (W) is total heat loss from the pipe so it is calculated by Equation (14 multiplying the distance of the pipe. The relation change in effectiveness with the inlet temperature of the brine is determined by the MatLab model, as for the temperature dependent heat exchanger effectiveness.


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4 Cost Analysis In this Chapter, the total cost of the two ways of utilising the geothermal energy is determined. By taking the difference in both investment and operation & management (O&M) costs into account when comparing the efficiencies, the comparison will enable a conclusion on the most efficient production technology. First the electricity production system is analysed following an analysis of the district heating production system. Both analyses will determine the total cost throughout the entire lifetime of the system, which is assumed to be 30 years for both. 4.1 Electricity Production The marginal investment cost per installed kW capacity is 2,800 € [11] and as the current pilot project in Pohang intends to reach a capacity of 1.2 MW, the total investment cost of the electricity production is 3,360,000 €. The total O&M cost throughout the lifetime is assumed to be 3.5 percent of the investment cost, that is 117,600 €, resulting in a total cost of the electricity production system of 3,477,600 € over the course of its 30-year lifetime. This will be the cost used in the comparison of the two technologies in Chapter 5.

𝑇𝑜𝑡𝑎𝑙 𝐶𝑜𝑠𝑡𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 = 3,477,600 € (16)

4.2 District Heating Production To determine the total cost of the district heating production system, both the cost of the heat exchangers and the cost of the transmission system is needed. 4.2.1 Heat Exchanger The investment costs per heat exchanger is 127,000 € [4] resulting in a total investment cost of the heat exchangers of 1,524,000 €. The annual O&M cost is assumed to be 1 percent of the investment cost [14], that is 15,240 €, where the present value of the total O&M cost throughout the lifetime is determined from the annuity equation,

𝑃𝑉𝑂&𝑀 = 𝑂&𝑀𝐻𝐸 (1 − (1 + 𝑟)−𝑛

𝑟) (1 + 𝑟)

= 15,240 € (1 − (1 + 0.04)−30

0.04) (1 + 0.04)

= 274,070 €

(17)

So, the total cost of the heat exchangers, including the investment and O&M costs, throughout the entire lifetime of 30 years is 1,798,100 €. This cost will be added to the cost of the transmission network to determine the total cost of the district heating production system.

𝑇𝑜𝑡𝑎𝑙 𝐶𝑜𝑠𝑡𝐻𝑒𝑎𝑡 𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒𝑟 = 1,798,100 € (18)

4.2.2 Transmission Network The exact initial cost for pipeline construction is difficult to assume because it differs a lot by situation. Some of the cost associated with geothermal distribution piping in the context of the applications is evaluated in Figure 10 [9].


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Figure 10: Base Case Cost Summary ($/ft.) – Ductile Iron piping [13].

Figure 11:United States consumer price index.

The cost for installation of insulated piping were broken down into 11 categories; saw cutting of existing pavement, removal of pavement, hauling of pipe, trenching and backfill, pipe material, bedding, installation and connection of piping, valves, fittings, traffic control, and paving. This report is from 1996 so the consumer price index (CPI) should be considered to use from 2016. Figure 11 show the change of CPI of United States. From this graph, each value of 1996 and 2015 is 156.9 and 237.017. To verify whether this method can be applied, first the 1996 steel pipe price is compared with the 2011 steel pipe price [13] as there was no data for the 2016 price. In 2011, the 8” steel pipe price per feet was $56.19~$66.70 and from the data in the report steel pipe price was $43.48 considering CPI to convert to 2011 price.

𝑃𝑟𝑖𝑐𝑒2011 = 𝑃𝑟𝑖𝑐𝑒1996 𝐶𝑃𝐼(2011)

𝐶𝑃𝐼(1996)

= 43.48 ∗224.939

156.9

= 62.21

(19)

Figure 12: Comparing steel pipe price at 2011 (calculated from CPI and real).


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The material for piping is steel piping and rock wool insulation, so the rock wool insulation price should also be considered. The rock wool insulation price from Alibaba is about $3 per feet so converting to 1996 price it is about $2. The total material pipe price is similar $43.39 + $2 = $45.39 so the Figure 10 data is used. To apply in this project, interpolation of outer diameter size is used. Figure 13 shows the tendency of total price. By increasing pipe size from the trend line or price for this project can be calculated.

Figure 13: Tendency of how the total price for initial construction varies with pipe size. Total cost for pipe construction is can be predicted by,

𝐶𝑜𝑠𝑡𝑃𝑖𝑝𝑒 = 11.321 ∗ 𝐷𝑝𝑖𝑝𝑒 + 34.879 (20)

where 𝐷𝑝𝑖𝑝𝑒 is the pipe diameter in inches. The final price for the piping is determined by,

𝐶𝑜𝑠𝑡𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛 = 𝐶𝑜𝑠𝑡𝑃𝑖𝑝𝑒 ∗𝐶𝑃𝐼(2011)

𝐶𝑃𝐼(1996)∗ 3.281

m

ft∗ 0.9334

€

$∗ 𝐿𝑁𝑒𝑡𝑤𝑜𝑟𝑘

= (11.321 ∗ 𝐷𝑝𝑖𝑝𝑒 + 34.879) ∗ 4.626 𝐿𝑁𝑒𝑡𝑤𝑜𝑟𝑘 (21)

where 𝐿𝑁𝑒𝑡𝑤𝑜𝑟𝑘 is the length of the transmission network. The O&M cost for the transmission network are assumed to be 1 percent of the investment cost, as was the case for the heat exchangers. When the O&M cost is adjusted with the annuity equation, the expression for the total cost of the transmission network including investment and O&M cost, is,

𝑂&𝑀𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛 = 0.01 𝐶𝑜𝑠𝑡𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛 1 − (1 + 0.04)−30

0.04 (1 + 0.04) (22)

This O&M cost is then added to the investment cost of the transmission network yielding the total cost of the transmission network.

𝑇𝑜𝑡𝑎𝑙 𝐶𝑜𝑠𝑡𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛 = 𝐶𝑜𝑠𝑡𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛 + 𝑂&𝑀𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛 (23)

The expression for the total cost of the district heating system throughout its 30-year lifetime is then,

𝑇𝑜𝑡𝑎𝑙 𝐶𝑜𝑠𝑡𝐷𝑖𝑠𝑡𝑟𝑖𝑐𝑡 𝐻𝑒𝑎𝑡𝑖𝑛𝑔 = 𝑇𝑜𝑡𝑎𝑙 𝐶𝑜𝑠𝑡𝐻𝑒𝑎𝑡 𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒𝑟 + 𝑇𝑜𝑡𝑎𝑙 𝐶𝑜𝑠𝑡𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛

(24)


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5 Results In this Chapter, the results of the temperature dependent efficiencies found in Chapters 2 and 3 are first presented and then the efficiencies are adjusted for the total cost of the two technologies found in Chapter 4. 5.1 Temperature Dependent Efficiency To determine the overall temperature dependent efficiency of the electricity production, the model determines the marginal increase in overall efficiency by each 1°C increase in inlet brine temperature. The resulting plot of how the electricity production efficiency varies with the inlet temperature of the brine is illustrated in Figure 14 which shows that the relation between mean efficiency of electricity production and brine temperature is close to linear. To determine the overall temperature dependent efficiency of the district heating system, the heat exchanger effectiveness and the transmission network efficiency are combined for every inlet temperature of the brine, between 70° C and 400° C. The resulting plot of how the district heating production efficiency varies with the inlet temperature of the brine is also illustrated in Figure 7 which shows that as the inlet temperature of the brine increases, so does the efficiency. In this illustration, the difference in efficiency at different lengths of the transmission network is also shown. A significant observation for the 15-km situation, is that unless the brine temperature is, at least, 80°C, the effectiveness is very low and drops to below zero within a few degrees. In order for the effectiveness to exceed 0.80, the temperature should be higher than 100°C and if the brine temperature is above 200°C, the effectiveness begins to stall. Furthermore, it can be seen from the illustration that as the length of the transmission network increases, the effectiveness decreases due to the heat loss in the pipes.

Figure 14: Illustration of how the overall electricity production efficiency and the overall district heating efficiency varies

with the inlet temperature of the brine and how the efficiency of the district heating production varies a great deal with

length of transmission network as illustrated by the four graphs for 25 km, 35 km, 45 km and 55 km respectively.

5.2 Efficiency Adjusted for Cost To adjust the efficiencies presented in Figure 14, the functions are divided by their respective costs, resulting in the graphs in Figure 15. The figure shows the efficiencies of district heating systems with four different lengths of transmission networks. The vertical, dotted line marks the brine temperature at Pohang illustrating the threshold for utilising the geothermal energy for electricity or district heating production. From the figure, it can be concluded that if the demand for electricity and district heating is assumed to be equal in the Pohang area, the limiting variable for installing district heating production is the length of the transmission network. If the demand lies within a range of 35 km, the most efficient production technology, in terms of both energy and cost efficiency, would be district heating; otherwise the most efficient production technology is electricity production.


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Figure 15: Illustration of the comparison between electricity production and district heating production when adjusted for

their respective lifetime cost. The figure shows the efficiencies of district heating systems with four different lengths of

transmission networks. The vertical, dotted line marks the brine temperature at Pohang illustrating the threshold for utilising

the geothermal energy for electricity or district heating production.

This model determines, 35 km of transmission network as the limit below which district heating production is more efficient than electricity production but an essential assumption in the model is that the demand for thermal and electric energy is equal. The EGS project in Pohang is going to utilise the geothermal energy for electricity production and based on this model, the reason for choosing to do so is that the demand for heating in the Pohang area, requires more than 35 km of transmission. To improve the model, the income from selling the energy could be included in the economic analysis. This would make the economic analysis not only a cost analysis but a net benefit analysis. Typically, the price of thermal energy is different from the price of electric energy and this improved economic analysis would implement this yielding a more complete and reliable model. This could, however, not be done in this context, as the potential price of energy in both technologies is unknown.


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6 Conclusion To accommodate the fluctuating nature of most renewable technologies that are currently playing the major role in the transition within the global energy sector, geothermal energy could be used as a base load. This report has analysed the two different utilisations of the geothermal energy, that is electricity production and district heating production, in terms of energy efficiency and lifetime expenditures. The analysis was carried out by composing a MatLab model determining a temperature dependent efficiency that, for every extraction temperature of the geothermal brine, determines an efficiency. The result of this, for both electricity production and district heating production, is illustrated in Figure 14. In order to compare the two technologies, their respective lifetime cost was determined and the temperature efficiencies were adjusted by these costs, resulting in the relation illustrated in Figure 15. This comparison shows, that the most efficient utilisation of the geothermal energy in Pohang, where the temperature of the extracted brine is approximately 180° C, mainly depends on the transmission network in the district heating system. Assuming equal demand for district heating and electricity, district heating production is the most efficient technology, in terms of both energy and cost efficiency, if the demand lies within a range of 35 km. If the energy demand lies more than 35 km away from the production site, the most efficient production technology in Pohang is electricity production.


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References [1] És, Roquette and Caisse des Dépôtes. June 7 2016. Press Release: Inauguration of the First Deep

Geothermal Plant for Industrial Use in Rittershoffen, France. [2] Alyssa Kagel. 2008. The State of Geothermal Technology Part II: Surface Technology. [3] Hyungsul Moon, Sadiq J. Zarrouk. 2012. Efficiency of geothermal power plants: a worldwide

review. [4] Ravier, Huttenloch, Scheiber, Perrot, and Sioly. 2016. Design, Manufacturing and Commissioning

of the ECOGI’s Heat Exchangers at Rittershoffen (France): A Case Study. Strasbourg, France: European Geothermal Congress 2016.

[5] Danfoss. n.d.. 8 Steps - Control of Heating Systems. [6] Tubular Exchanger Manufacturers Association (TEMA). 2007. Standards of the Tubular Exchanger

Manufacturers Association. Tubular Exchanger Manufacturers Association Inc. [7] Mukherjee, R. 1998. Effectively Design Shell-and-Tube Heat Exchangers. American Institute of

Chemical Engineers. [8] Lund, John W. 2004. Geothermal direct-heat utilization. Technika Poszukiwań Geologicznych

43.4: 19-33. [9] Rafferty, Kevin D.. 1998. Piping. Geo-Heat Center, Klamath Falls, OR. [10] Unnarsdóttir. 2013. Heat loss of geothermal water though a pipeline. [11] 에너지관리공단, 배관설계 및 관리 [12] Konstantinos Vatopoulos, David Andrews, Johan Carlsson, Ioulia Papaioannou, Ghassan

Zubi (2012) Study on the state of play of energy efficiency of heat and electricity production technologies.

[13] Brismet. 2011. Welded Stainless Steel Pipe Price. [14] IEA. 2013. Energy Technology Systems Analysis Programme, District Heating.


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Appendix A - MatLab Model %% Temperature-Dependent District Heating Production Efficiency % The known properties of the project in Rittershoffen are: m_secondary = 61.739; % kg/s (245 ton/hr) m_brine = 69.299; % kg/s (275 ton/hr) c_p_secondary = 4676; % J/kg*K c_p_brine = 4170; % J/kg*K T_brine_out = 69.5; % degC T_secondary_in = 65; % degC T_secondary_out = 160.6; % degC % The temperature dependent effectiveness is determined by first finding % the C-values for both fluids so the C_min value can be identified. C_brine = m_brine*c_p_brine; % W/K C_secondary = m_secondary*c_p_secondary; % W/K if C_brine < C_secondary C_min = C_brine; fprint= ('C_brine is C_min'); elseif C_secondary < C_brine C_min = C_secondary; fprint= ('C_secondary is C_min'); end % Now the for-loop procedure will find the effectiveness of the heat % exchanger for every inlet brine temperature between 70 degC and 400 degC. T_H = 70; % Initial inlet temperature of 70 degC for n = 1:330 T_H(n+1) = T_H(n)+1; % degC (70 to 400 degC) q_brine(n+1) = m_brine*c_p_brine*(T_H(n)-T_brine_out); % W dT_max(n+1) = T_H(n)-T_secondary_in; % K q_max(n+1) = C_min*dT_max(n); % W q(n+1) = q_brine(n); % W e_HE(n+1) = q(n)/q_max(n); end % The transmission efficiency is now determined, also by a for-loop % procedure. First the properties inthe network are defined as: Distance_1 = 25000; % m (25 km) Distance_2 = 35000; % m (35 km) Distance_3 = 45000; % m (45 km) Distance_4 = 55000; % m (55 km) Distance_Rittershoffen = 15000; % m (15 km) Pipe_thick = 0.182; % Steel pipe thickness in m K_i = 0.5; % W/m^2 degC K_g = 1.0; % W/m^2/ degC H_depth = 1.2; % Depth of pipes in m T_0 = 20; % Average ambient temperature in degC for n = 1:330 T_H(n+1) = T_H(n)+1; Insul_thick(n+1) = (61.803*log(T_H(n))-224.07)/1000; Radius_pipe(n+1) = Pipe_thick/2; Radius_insul(n+1) = Radius_pipe(n)+Insul_thick(n); R_insul(n+1) = log(Radius_insul(n)/Radius_pipe(n))/(2*pi*K_i); R_ground(n+1) = log(H_depth/Radius_insul(n) + sqrt((H_depth^2/... R_insul(n))-1))/(2*pi*K_g);


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Q_tloss(n+1) = (T_H(n)-T_0)/(R_insul(n)+R_ground(n))*(Distance_1); e_T_1(n+1) = (q(n)-Q_tloss(n))/q(n); end for n = 1:330 T_H(n+1) = T_H(n)+1; Insul_thick(n+1) = (61.803*log(T_H(n))-224.07)/1000; Radius_pipe(n+1) = Pipe_thick/2; Radius_insul(n+1) = Radius_pipe(n)+Insul_thick(n); R_insul(n+1) = log(Radius_insul(n)/Radius_pipe(n))/(2*pi*K_i); R_ground(n+1) = log(H_depth/Radius_insul(n) + sqrt((H_depth^2/... R_insul(n))-1))/(2*pi*K_g); Q_tloss(n+1) = (T_H(n)-T_0)/(R_insul(n)+R_ground(n))*(Distance_2); e_T_2(n+1) = (q(n)-Q_tloss(n))/q(n); end for n = 1:330 T_H(n+1) = T_H(n)+1; Insul_thick(n+1) = (61.803*log(T_H(n))-224.07)/1000; Radius_pipe(n+1) = Pipe_thick/2; Radius_insul(n+1) = Radius_pipe(n)+Insul_thick(n); R_insul(n+1) = log(Radius_insul(n)/Radius_pipe(n))/(2*pi*K_i); R_ground(n+1) = log(H_depth/Radius_insul(n) + sqrt((H_depth^2/... R_insul(n))-1))/(2*pi*K_g); Q_tloss(n+1) = (T_H(n)-T_0)/(R_insul(n)+R_ground(n))*(Distance_3); e_T_3(n+1) = (q(n)-Q_tloss(n))/q(n); end for n = 1:330 T_H(n+1) = T_H(n)+1; Insul_thick(n+1) = (61.803*log(T_H(n))-224.07)/1000; Radius_pipe(n+1) = Pipe_thick/2; Radius_insul(n+1) = Radius_pipe(n)+Insul_thick(n); R_insul(n+1) = log(Radius_insul(n)/Radius_pipe(n))/(2*pi*K_i); R_ground(n+1) = log(H_depth/Radius_insul(n) + sqrt((H_depth^2/... R_insul(n))-1))/(2*pi*K_g); Q_tloss(n+1) = (T_H(n)-T_0)/(R_insul(n)+R_ground(n))*(Distance_4); e_T_4(n+1) = (q(n)-Q_tloss(n))/q(n); end for n = 1:330 T_H(n+1) = T_H(n)+1; Insul_thick(n+1) = (61.803*log(T_H(n))-224.07)/1000; Radius_pipe(n+1) = Pipe_thick/2; Radius_insul(n+1) = Radius_pipe(n)+Insul_thick(n); R_insul(n+1) = log(Radius_insul(n)/Radius_pipe(n))/(2*pi*K_i); R_ground(n+1) = log(H_depth/Radius_insul(n) + sqrt((H_depth^2/... R_insul(n))-1))/(2*pi*K_g); Q_tloss(n+1) = (T_H(n)-T_0)/(R_insul(n)+R_ground(n))*(Distance_Rittershoffen); e_T_Rittershoffen(n+1)= (q(n)-Q_tloss(n))/q(n); end % As both the efficiency of the heat exchanger and the transmission network % is temperature dependent, the overall temperature dependent efficiency of % the district heating system is: e_DH_1 = e_HE.*e_T_1; e_DH_2 = e_HE.*e_T_2;


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e_DH_3 = e_HE.*e_T_3; e_DH_4 = e_HE.*e_T_4; % The efficiency of the system in Rittershoffen is determined at a brine % inlet temperature of 165 degC: e_HE_Rittershoffen = e_HE(165-70); e_T_Rittershoffen = e_T_Rittershoffen(165-70); e_Rittershoffen = e_HE_Rittershoffen*e_T_Rittershoffen; % The efficiency for DH is illustrated as temperature dependent: plot(T_H,e_DH_1,'b-','Linewidth',2),hold on plot(T_H,e_DH_2,'b-','Linewidth',2),hold on plot(T_H,e_DH_3,'b-','Linewidth',2),hold on plot(T_H,e_DH_4,'b-','Linewidth',2) xlabel('Brine Inlet Temperature [^oC]') ylabel('District Heating Production Efficiency') legend('District Heating','Location','southeast') title('District Heating Production') axis([0 460 0 0.9]) set(gca,'ytick',[0:0.1:1]), set(gca,'xtick',[0:50:400]) hold off text(410,0.84,num2str('25 km')) text(410,0.77,num2str('35 km')) text(410,0.70,num2str('45 km')) text(410,0.64,num2str('55 km')) %% Temperature-Dependent Electricity Production Efficiency % The known properties of the Pohang project are: m_brine_el = 40; % kg/s c_p_brine_el = 4.189; % kJ/kg*K T_brine_in_el = 180; % degC T_brine_out_el = 60; % degC delta_T_brine = T_brine_in_el-T_brine_out_el; % degC W_Pohang = 1.2*10^3; % kW T_0 = 20; % degC % Now the iteration procedure will find the efficiency for every % inlet brine temperature between 70 degC and 400 degC. T_H=70; % Initial inlet temperature of 70 degC for n = 1:330 T_H(n+1) = T_H(n)+1; % K (70 to 400 degC) e_EL_low(n+1) = 2.47*((T_0-T_H(n))*(T_brine_out_el-T_H(n)))/... ((T_H(n)+273.15)*c_p_brine_el*(T_0+T_H(n)+2*273.15)); e_EL_high(n+1) = (0.18*T_H(n)-10)/278; end e_EL = (e_EL_low+e_EL_high)/2; e_Pohang_Low = e_EL_low(180-70); e_Pohang_High = e_EL_high(180-70); e_Pohang = e_EL(180-70); % The efficiency for EL is illustrated: plot(T_H,e_EL,'r-','Linewidth',2),hold on plot(T_H,e_EL_low,'r--',T_H,e_EL_high,'r--','Linewidth',1) xlabel('Brine Inlet Temperature [^oC]') ylabel('Electricity Production Efficiency')


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legend('Electricity','Min & Max Electricity','Location','southeast') title('Electricity Production') axis([0 400 0 0.22]) set(gca,'ytick',[0:0.02:1]), set(gca,'xtick',[0:50:400]) hold off %% Economic Analysis of the District Heating Network % The objective here is to determine the total cost of the heat % exchanger throughout the entire lifetime. This way it's % effectiveness can be adjusted for the cost of the project and % thereby compared to the electricity production. % The life time is assumed to be 30 years: t_HE = 30; % years % Now total costs for the heat exchanger in € are determined by using an % appropriate discount rate in determining the present value of the future % marginal costs: r_DH = 0.04; % Discount rate of 4% % The investment cost of heat exchangers is: Investment_HE = 12*127000; % € % The annual OM costs of the heat exchangers are assumed to be 1% of the % investment cost on an annual basis: % from http://iea-etsap.org/E-TechDS/PDF/E16_DistrHeat_EA_Final_Jan2013_GSOK.pdf Total_OM_HE = Investment_HE*0.01; % € % Present Value of OM cost of the heat exchangers over 30 years: PV_OM_HE = Total_OM_HE*((1-(1+r_DH)^-t_HE)/(r_DH))*(1+r_DH); % € % The total cost of the heat exchanger over the lifetime is: Total_Cost_HE = Investment_HE+PV_OM_HE; % € % Investment cost of transmission by varying distance is: Distance_1 = 25; % m (25 km) Distance_2 = 35; % m (35 km) Distance_3 = 45; % m (45 km) Distance_4 = 55; % m (55 km) Investment_T_1 = 303.06*3281*Distance_1*0.9334; % € Investment_T_2 = 303.06*3281*Distance_2*0.9334; % € Investment_T_3 = 303.06*3281*Distance_3*0.9334; % € Investment_T_4 = 303.06*3281*Distance_4*0.9334; % € % The OM costs of the transmission system by varying distance: Total_OM_T_1 = Investment_T_1*0.01; % € Total_OM_T_2 = Investment_T_2*0.01; % € Total_OM_T_3 = Investment_T_3*0.01; % € Total_OM_T_4 = Investment_T_4*0.01; % € % Present Value of OM cost of the transmission by varying distance over % 30 years lifetime: PV_OM_T_1 = Total_OM_T_1*((1-(1+r_DH)^-t_HE)/(r_DH))*(1+r_DH); % € PV_OM_T_2 = Total_OM_T_2*((1-(1+r_DH)^-t_HE)/(r_DH))*(1+r_DH); % € PV_OM_T_3 = Total_OM_T_3*((1-(1+r_DH)^-t_HE)/(r_DH))*(1+r_DH); % € PV_OM_T_4 = Total_OM_T_4*((1-(1+r_DH)^-t_HE)/(r_DH))*(1+r_DH); % €


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% The total cost of the transmission network by varying distance is: Total_Cost_T_1 = Investment_T_1 + PV_OM_T_1; Total_Cost_T_2 = Investment_T_2 + PV_OM_T_2; Total_Cost_T_3 = Investment_T_3 + PV_OM_T_3; Total_Cost_T_4 = Investment_T_4 + PV_OM_T_4; % The present value of the total cost of DH by varying distance is then: Total_Cost_DH_1 = Total_Cost_HE + Total_Cost_T_1; Total_Cost_DH_2 = Total_Cost_HE + Total_Cost_T_2; Total_Cost_DH_3 = Total_Cost_HE + Total_Cost_T_3; Total_Cost_DH_4 = Total_Cost_HE + Total_Cost_T_4; %% Economic Analysis of the Electricity Production % The objective here is to determined the total cost of the % electricity production system throughout the entire lifetime. % This way it's effectiveness can be adjusted for the cost of the % project and thereby compared to the electricity production. % The life time is assumed to be 30 years t_EL = 30; % years % Discount rate is: r_EL = 0.04; % Discount rate of 4% % The investment cost is: Investment_EL_Marginal = 2800; % €/kW Production_Pohang_kW = 1200; % kW Investment_EL = Investment_EL_Marginal*Production_Pohang_kW; % € % The O&M costs of the electricity production are determined to be 3.5 % of % the investment cost: Total_OM_EL = Investment_EL*0.035; % € % The present value of the total cost of DH is then: Total_Cost_EL = Investment_EL + Total_OM_EL; % € %% Effectiveness and Total Cost Curve % It is assumed that the total cost, that is the investment % and the O&M costs are the same for every scenario of inlet % brine temperature. Therefore, the effectiveness can be adjusted % for the investment cost by: % EL efficiency by cost: y_EL = e_EL/Total_Cost_EL; y_EL_min = e_EL_low/Total_Cost_EL; y_EL_max = e_EL_high/Total_Cost_EL; % DH efficiency by cost by varying distance: y_DH_1 = e_DH_1/Total_Cost_DH_1; y_DH_2 = e_DH_2/Total_Cost_DH_2; y_DH_3 = e_DH_3/Total_Cost_DH_3; y_DH_4 = e_DH_4/Total_Cost_DH_4; % In the plot of the above ratio, the x-value is the brine inlet % temperature: x = T_H;


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% Plot the graphs for both electricity and district heating: plot(x,y_EL,'r','Linewidth',2), hold on plot(x,y_DH_1,'b-','Linewidth',1), hold on plot(x,y_DH_2,'b-','Linewidth',1), hold on plot(x,y_DH_3,'b-','Linewidth',1), hold on plot(x,y_DH_4,'b-','Linewidth',1), hold on plot([180 180], [0 5*10^-8],'k--') %plot(x,y_EL_min,'r--',x,y_EL_max,'r--','Linewidth',1) xlabel('Brine Inlet Temperature [^oC]') ylabel('Efficiency / Total Cost') axis([0 450 0 3.5*10^-8]) set(gca,'xtick',[0:50:400]) legend('Electricity','District Heating','Location','southeast') text(405,2.9*10^-8,num2str('25 km')) text(405,1.9*10^-8,num2str('35 km')) text(405,1.4*10^-8,num2str('45 km')) text(405,1.05*10^-8,num2str('55 km')) legend('boxoff') hold off

Utilising Geothermal Energy in Pohang · utilising the geothermal energy for either electricity...

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