Energetic and economic evaluations of geothermal district heating systems by using ANN

Energy Policy 56 (2013) 558–567

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Energetic and economic evaluations of geothermal district heating systems by using ANN Ali Kec- ebas- a,n, Mehmet Ali Alkan b, _Ismail Yabanova c, Mehmet Yumurtacı d a

Department of Energy Systems Engineering, Technology Faculty, Mug˘ la Sıtkı Koc- man University, Mug˘ la, Turkey ˘ Sıtkı Koc- man University, Mug˘ la, Turkey Department of Installation Technology, Ula Ali Koc- man Vocational High School, Mugla c Department of Electrical and Electronics, Technology Faculty, Afyon Kocatepe University, Afyonkarahisar, Turkey d Department of Electricity, Technical Education Faculty, Afyon Kocatepe University, Afyonkarahisar, Turkey b

H I G H L I G H T S c c c c c

Each energetic and economic evaluation of the AGDHS is investigated by using ANN. Actual thermal data are collected for the heating seasons in the period 2006–2010. ANN is to quickly predict the behavior of the physical AGDHS with good accuracy. Information about the best design and most profitable oper/ating of the system is provided. Influences of the PWF, ambient temperature and flow rate on the costs are shown.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 23 July 2012 Accepted 17 January 2013 Available online 12 February 2013

This paper proposes an artificial neural network (ANN) technique as a new approach to evaluate the energy input, losses, output, efficiency, and economic optimization of a geothermal district heating system (GDHS). By using ANN, an energetic analysis is evaluated on the Afyon geothermal district heating system (AGDHS) located in the city of Afyonkarahisar, Turkey. Promising results are obtained about the economic evaluation of that system. This has been used to determine if the existing system is operating at its optimal level, and will provide information about the optimal design and profitable operation of the system. The results of the study show that the ANN model used for the prediction of the energy performance of the AGDHS has good statistical performance values: a correlation coefficient of 0.9983 with minimum RMS and MAPE values. The total cost for the AGDHS is profitable when the PWF is higher than 7.9. However, the PWF of the AGDHS was found to be 1.43 for the given values. As a result, while installing a GDHS, one should take into account the influences of the PWF, ambient temperature and flow rate on the total costs of the system in any location where it is to be established. Crown Copyright & 2013 Published by Elsevier Ltd. All rights reserved.

Keywords: Geothermal energy District heating Life cycle cost

1. Introduction Within the focus of decreasing climate change, we have to start reducing greenhouse gas emissions where they originate. This requires searching for new engineering and scientific solutions in the field of thermal and process engineering. One of the possibilities for reducing environmental pollution and lower energy consumption is the use of district heating systems (DHSs) in urban settlements. In order for district energy to become a serious alternative to existing or future individual heating systems, it must provide significant benefits to both the community in which it is operated and the consumer who purchases

n

Corresponding author. Tel.: þ90 252 2111701; fax: þ 90 252 2113150. E-mail address: [email protected] (A. Kec- ebas-).

energy from the system. In addition, it must provide major social benefits if federal, state, or local governments are to offer the financial or institutional support that is required for its successful development (Lienau, 2000). In the United States, the energy consumption used by buildings constitutes 48% of the total energy use (Pasztory et al., 2011). Many European countries such as Denmark, Russia, and Finland have deployed district heating systems, and the market share is reported to be about 50% (DHCCC, 2005). Space and water heating account for 60–80% of the energy consumed in the residential, commercial, institutional, and public administration sectors of Canada (Cuddihy et al., 2005). In Turkey, approximately 31% of the total energy is consumed in residential and commercial buildings, and approximately 85% of this building energy is consumed by space heating systems (Ozkan and Onan, 2011). Thus, DHSs offer the possible benefits of using local resources such as low cost waste heat from

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Nomenclature c C E E_ F g h i & N o p Q_ t ttao &

unit cost ($/kW or $/MWh) total cost ($) fixed costs ($) energy rate (kW) operational costs ($) inflation rate (%) specific enthalpy (kJ/kg) interest rate (%) mass flow rate (kg/s) lifetime (year) output value pattern heat rate (kW) target value total annual operating time (h) work rate, power (kW)

Greek symbols

Z

energy or first law efficiency (%)

Subscripts d dr E&C exp hc he

natural direct discharge drilling equipment and construction exploration heat consumption sale price heat exchanger

industries, from the combustion of low quality fuels such as municipal solid waste, and from combining heating and power plants. This contributes not only to a more competitive energy supply, but also to a lower environmental impact due to the avoidance of the emissions associated with the use of other fuels. Several DHSs are in widespread use in the Turkey and in the cold areas of other countries. These systems are composed of a central heat source, circulating pumps, piping systems, and accessory facilities to distribute the heat generated in a centralized location according to residential and commercial requirements (Difs et al., 2009). There are several types of heating system in current use: boiler systems using different kinds of fuel; heat pump systems with different sources of energy, such as air, ground, water, or solar energy; combined heating and power systems; and direct-fired absorption unit systems (Sadohara and Ojima, 1991). Because different heating systems are associated with different effects on the economics, environment, and energy technology, the choice of the type of heating system is of great importance to decision makers and managers (Wei et al., 2010). Many researchers have made various economic analyses of DHSs. Bowitz and Trong (2001) developed criteria using DHS projects as case studies for cost benefit analyses, emphasizing both the economic and environmental costs. Dzenajaviciene et al. (2007) presented an economic analysis of heat power generation costs for various technological solutions and capacities suitable for consumers in small towns. Some researchers have also been concerned the economic aspects of the heat production facilities themselves. Lahdelma and Hakonen (2003) and Rong and Lahdelma (2007) planned the cost-efficient operation of a combined heat and power (CHP) system using an optimization model

in mp O&M out per r sal t Tot w 0

559

inlet mixing pool operation and maintenance outlet permitting re-injected geothermal fluid salvage transmission total well reference state

Abbreviations AGDHS ANN CHP DHS ECC EDC EPC GDHS GPP LCC MAPE MLP PWF RMS R2 TMREI

Afyon geothermal district heating system artificial neural network combined heat and power district heating system energy consumption cycle energy distribution cycle energy production cycle geothermal district heating system geothermal power plant life cycle cost mean absolute percentage error multi-layer perceptron present worth factor root-mean square correlation coefficient Turkish Mineral Research and Exploration Institute

based on hourly load forecasts. They modeled the hourly CHP operation as a linear programming problem. Wei et al. (2010) evaluated seven DHSs in China using a fuzzy comprehensive evaluation method, in which the economics, environment, and energy technology factors were taken into account synthetically. They concluded that CHP is the best choice for all systems, and that the gas-fired boiler system is the best fossil-fed solution among the coal- and oil-fired ones for heating purposes. Over the past two decades, DHSs have become ever more important in the heating of houses and will become even more important in the years to come. Furthermore, DHSs will have a central role in the energy system of the future, based on renewable energy (Lund et al., 2010). For that, the DHSs should be rethink the way district energy is produced and distributed to endusers since policies on energy conservation pose stringent requirements in the building energy sector (Bloomquist, 2001). In this regard, geothermal energy appears to be a potential solution and a key tool. A geothermal district heating system (GDHS) can be provided on a building-by-building basis or through a district heating network that supplies the needs of multiple consumers by means of an underground piping network connected to one or multiple wells or downhole heat exchangers. The development of GDHS, led by Iceland, has been one of the fastest growing fields of geothermal heating, and now accounts for over 75% of all space heating provided from geothermal resources worldwide (Lund et al., 2005). Besides, geothermal directheat utilization capacity nearly doubled from 2000 to 2005; an increase of 13 GW, with at least 13 new countries using geothermal heat for the first time (Bertani, 2005). There are also other countries, e.g., China, Japan, and Turkey, using geothermal heating.

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The first geothermal researches and investigations in Turkey were started by the Turkish Mineral Research and Exploration Institute (TMREI) in the 1960s. Since then, about 170 geothermal fields have been discovered by TMREI, where 95% of them are low– medium enthalpy fields, which are suitable mostly for direct-use applications. Turkey has significant potential for geothermal energy production, possessing one-eighth of the world’s total geothermal potential. Much of this potential is of relatively low enthalpy that is not suitable for electricity production but still useful for direct heating applications. Out of Turkey’s total geothermal potential, around 95% is appropriate for thermal use (temperature less than 150 1C) and the remainder for electricity production (temperature higher than 150 1C) (Erdogdu, 2009). The main utilization of geothermal energy in Turkey, however, is in domestic heating, greenhouses, spas, and thermal resorts. The overall geothermal heat usage of Turkey is about 2084 MWt (36,885.9 TJ/y), of which 219 MWt (2417 TJ/y) is for individual space heating, 792 MWt (7386.4 TJ/y) for district heating, 483 MWt (9138 TJ/y) for greenhouse heating, 552 MWt (17,408 TJ/y) for bathing and swimming, and 38 MWt (536.5 TJ/y) for geothermal heat pumps. At present, the amount of geothermal heating is equivalent to supplying energy to 201,000 residences. It is projected that, by the years 2013 and 2020, the total installed capacity will increase to 4000 MWt (500,000 residence equivalent) and 8300 MWt (1,250,000 residence equivalent) for space heating, respectively (Mertoglu et al., 2010). Studies conducted on the economic analysis of GDHSs include the following publications. Erdogmus et al. (2006) evaluated the Balcova–Narlidere GDHS from an economic perspective for the profitability of the investment by using the internal rate of return method. Oktay and Dincer (2009) presented an application of an exergoeconomic model, which included both exergy and cost accounting analyses for a GDHS in Balikesir/Turkey. They applied the cost balance equation to each component of the system and to each junction and solved a set of equations to calculate the unit costs of the various exergies. They obtained the lost cost of each component of the system. Some configurations for the GDHS were also considered and compared in the analysis, which used the appropriate exergy and cost balance equations. Hepbasli (2010) reviewed the GDHSs in terms of three aspects, namely the energetic, the exergetic, and the exergoeconomic analyses and assessments. Kec- ebas- (2011) performed an exergy and exergoeconomic analysis of the Afyon GDHS through its thermodynamic performances and thermoeconomic assessments. The definitions of the various relations that are required in order to determine the energetic and economic performances and to improve system operation and the interaction of the various parameters on the energetic and economic performances of a GDHS are fairly complex. Therefore, approximate methods can be proposed to define the energetic and economic performances of a GDHS. Approximate methods, including heuristic approaches and artificial neural networks (ANNs), to solve these problems instead of using traditional optimization methods such as linear programming, Lagrangian relaxation, quadratic programming, etc., have been proposed. Heuristic methods can be seen as simple procedures that rapidly provide satisfactory, but not necessarily optimal, solutions to large instances of complex problems (Gendreau and Potvin, 2010). Even so, the complexity of the problems to be solved can be so high that even heuristic methods are not able to obtain accurate solutions in reasonable runtimes (Alba, 2005). Thus, more recently, ANNs have drawn some attention as a modeling and predicting technique, although the concept of an ANN had been discovered about 50 years ago. They have a certain capacity to map linear and non-linear dependencies in the data without using any preconceptions, and thus to solve complex problems. The benefits of ANNs are their non-linearity, flexibility, speed, simplicity, and

capacity for adaptive learning. For the simulation and monitoring of a modern DHS, models using an ANN have turned out to be very suitable, especially for existing operating DHSs for which no physical model exists (De et al., 2007). These models are data driven, adaptive, fast in response, and have good accuracy if they are trained with proper data. These models using ANNs can be used for the real-time simulation, monitoring, prediction of degradation and impending fault, training of DHS operators, decision making support for DHS maintenance, etc. In addition, ANNs have been shown to be good candidates for fault diagnosis, process identification, and the modeling of nonlinear systems in the energy field (Kalogirou, 2000; Cziesla and Tsatsaronis, 2002; Dotzauer, 2002; Popescu et al., 2009; Fast and Palme, 2010; Arslan and Yetik, 2011; Caner et al., 2011; Kec- ebas- and Yabanova, 2012). In the present paper, not only energetic but also economic evaluations of a GDHS will be investigated by using an ANN. An energetic analysis will be evaluated on the Afyon geothermal district heating system (AGDHS) located in the city of Afyonkarahisar, Turkey. Therefore, an ANN model will be developed in order to estimate the energy input, losses, output, and efficiency of the AGDHS, using actual data obtained as the average weekly values for the heating seasons in the period 2006–2010 from (AGDHS, 2011). In the economic evaluation, an economic analysis is performed depending on the life cycle cost analysis. This has been used to determine if the existing system is operating at its optimal level, and will provide information about the optimal design and profitable operation of the system. In addition, the ANN model developed has been found to be very useful for the training of personnel and for condition monitoring of GDHSs.

2. Case study: The Afyon GDHS 2.1. Definition of the case study The geothermal district heating system which is here investigated is located in inland north-western Anatolia. In 1994, the Afyon geothermal district heating system (AGDHS) was founded to provide residential heating in the city of Afyonkarahisar, Turkey. Today, 4613 residences, covering a total floor-area of 514,634 m2, are heated by this AGDHS. The geothermal fluid of ¨ mer–Gecek geothermal field. It is this AGDHS is fed from the O ¨ located near the Afyon–Kutahya highway 15 km to the northwest of Afyonkarahisar. It covers a total area of about 16 km2. Twentysix geothermal production and reinjection wells have been drilled up to now. Only 14 of these geothermal production and reinjection wells have been monitored by technical staff. However, seven geothermal production and reinjection wells are nowadays used by the AGDHS. The data regarding these wells, including their depth, production type, temperature, and discharge rate, is in (Kec- ebas- , 2011; Kec- ebas- et al., 2011a). As shown in Fig. 1, the AGDHS consists mainly of three cycles: (i) the energy production cycle (EPC) or geothermal well loop, (ii) the energy distribution cycle (EDC) or district heating distribution network, and (iii) the energy consumption cycle (ECC). Geothermal fluid is sent to the primary plate-type heat exchanger (between the geothermal fluid and the district heating water) and is cooled to about 45–50 1C, as its heat is transferred to the district heating water. The temperatures obtained during the operation of the AGDHS are, on average, 96/47 1C for the district heating distribution network (or EDC) and 60/45 1C for the building circuit (or ECC). For the study presented in the present paper, the pressure and temperature data of the fluids (including the geothermal and the district heating water) were measured with bourdon-tube pressure gauges and fluid-expansion thermometers, respectively.

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Fig. 1. Schematic diagram of the AGDHS.

In addition, the volumetric flow rates of fluids were measured by flow meters and ultrasonic flow meters.

2.2. Energy analysis The two general balance equations, namely mass and energy, are employed to find the heat input and the energy efficiencies for a general steady-state, steady-flow process. In general, the mass P P _ in ¼ _ out . For the balance equation can be expressed as m m AGDHS, it is written as " # n X _ r m _ d ¼0 _ w m _ mp m ð1Þ m i¼1

_ is the mass flow rate, the subscript in stands for inlet, where m _ w is the mass flow rate at wellhead, n and out for outlet. Besides, m _ mp is the flow is the amount of geothermal production wells, m _ r is the rate of the remained geothermal fluid in mixing pool, m _ d is the mass flow rate of the reinjected geothermal fluid and m flow rate of the natural direct discharge. Neglecting changes in kinetic and potential energies with no heat P _ in  or work transfers, the general energy balance, Q_ þ m P _ _ out  hout , can be simplified to flow enthalpies only hin ¼ W þ m

(Ozgener et al., 2006a): X X _ in  hin ¼ _ out  hout m m

ð2Þ

where Q_ ¼ Q_ net,in ¼ Q_ in Q_ out is the rate of net heat input, _ ¼W _ net,out ¼ W _ out W _ in is the rate of net work output, and h is W the specific enthalpy. The geothermal fluid (brine) energy inputs from the production field of the AGDHS are calculated from the following equation: _ w  ðhbrine h0 Þ E_ brine ¼ m

ð3Þ

The energy efficiency of the AGDHS is calculated from E_

E_

Zsystem ¼ _output ¼ useful,he E input E_ input

ð4Þ

where the subscript output refers to product or desired value, and the subscript input refers to given or used. 2.3. ANN modeling Artificial neural network (ANN) models may be used as alternative methods in engineering analysis and predictions. ANNs imitate the learning process of a biological brain. The neural

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network, through the learning process, understands the underlying functional relationships in the considered data and these relationships are then stored as inter-neuron connection strengths or synaptic weights (Haykin, 1994). The network usually consists of an input layer, some hidden layers, and an output layer. This network structure is also referred to as a multi-layer perceptron (MLP). In one simple form, for example, each neuron is connected to all other neurons of a previous layer through adaptable weights. Knowledge is usually stored as a set of connection weights. Training is the process of modifying the connection weights in some systematic fashion, using a suitable learning method. The network uses a learning mode, in which the input is presented to the network along with the desired output and the weights are adjusted as the network attempts to reproduce the desired output. The weights after training contain meaningful information, whereas before training they were random and had no meaning (Kalogirou, 2000; Kalogirou and Bojic, 2000). There are different learning algorithms. A popular algorithm is the back-propagation algorithm, which has different variants. Back-propagation training algorithms such as gradient descent and gradient descent with momentum are often too slow for practical problems because they require slow learning rates for stable learning. In addition, success in the algorithms depends on some user-dependent parameters, such as the learning rate and the momentum constant. Faster algorithms such as the conjugate gradient, quasi-Newton, and Levenberg– Marquardt algorithms use standard numerical optimization techniques. An ANN with the back-propagation algorithm learns by changing the weights, and these changes are then stored as knowledge. The performance of ANN-based prediction is evaluated by a regression analysis between the network outputs and the experimental values. The most common criterion used for measuring network performance is referred to as root mean squared (RMS) (Mohanraj et al., 2008) and is defined by 2 RMS ¼ 4ð1=pÞ

X

31=2 9t j oj 9

25

ð5Þ

j

2.4. Life cycle cost analysis The initial and operating costs are the main factors that influence the economics of a DHS. The calculated initial cost includes the costs of the whole heat source system, indoor and outdoor piping systems, and the terminal facilities, as well as the construction of the heat source (Wei et al., 2010). The costs of exploration, permitting, drilling, equipment-construction, and transmission are included in the initial cost of a GDHS. The operational and maintenance costs consist of the costs of the needed personnel, electricity, tap water, chemicals (an inhibitor and other chemicals used to prevent corrosion and to clean the heat exchangers), maintenance, marketing, and rent of the facilities. In the life cycle cost (LCC) analysis, the total annual cost of any GDHS is generally expressed as C Tot ¼ cexp þcper þ cdr þ cE&C þ ct þ cO&M csal chc

In addition, the correlation coefficient (R ) and mean absolute percentage error (MAPE) are, respectively, expressed by "P # 2 j ðt j oj Þ 2 R ¼ 1 P ð6Þ 2 j ðoj Þ ot  100 o

ð7Þ

where t is the target value, o is the output value, and p is the pattern (Sozen and Arcaklıoglu, 2007). The input and output layer are normalized so that they will lie within either the range (1, 1) or (0, 1). More details on ANNs together with a review of applications can be found in (Kalogirou, 1999, 2000, 2001). In the present paper, a three-layer feed-forward network with sigmoid hidden neurons and linear output neurons is used as the network. For training this network, the Levenberg–Marquardt back-propagation training algorithm, which obtains the fastest

ð8Þ

where cexp is the exploration cost, cper is the permitting cost, cdr is the drilling cost, cE&C is the equipment-construction cost, ct is the transmission cost, cO&M is the operation-maintenance cost, csal is the salvage cost, and chc is the sales price of heat consumption. The unit costs for both a geothermal power plant (GPP) and a GDHS are given in Table 1. For many thermal systems, the amount of energy that must be purchased in order to operate the equipment does not change significantly from year to year. In this case, the LCC can be calculated using the method presented in (Duffie and Beckman, 2006) where the LCC is considered to be the sum of two terms. As seen in the equation below, the first term is proportional to the initial costs of the system, referred to as fixed costs, (E), and the second term is proportional to the first year’s operating costs, referred to as the operational costs and denoted by (F). C Tot ¼ E þ PWF  F

2

MAPE ¼

learning with high accuracy (as mentioned by Arslan and Yetik, 2011), with three neurons in the software MATLAB is used.

ð9Þ

where PWF is the present worth factor, the costs during the lifetime of the system in terms of their present-day values, according to the concept of LCC (Hasan, 1999; Uyguno˘glu and Kec- ebas- , 2011; Kec- ebas- et al., 2011b). The PWF depends on the inflation rate, g, and the interest rate, i, and is adjusted for inflation as follows. 8 < 1ig ; i 4g þg n i ¼ ð10Þ : gi ; i o g 1þi and then 8 < 1ð1 þn in ÞN i PWF ¼ : ð1 þiÞ1

; ia g

ð11Þ

; i¼g

where i* is the interest rate adjusted for inflation rate and N is the lifetime of system.

Table 1 Unitary costs for GPP and GDHS. Factors affecting costs

cexp $/kW

cper $/kW

cdr $/kW

cE&C $/kW

ct $/kW

cO&M $/MWh

Cost range for GPP in 2005a Cost range for GDHS in 2010b

100–250 150

10–50 10

500–1000 500

1300–2000 1000

15–250 240

10–35 35

a b

From Hance (2005). From ETSAP (2010).

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Following the above discussion, the total annual cost of the AGDHS now can be re-written as C Tot ¼ ðcexp þcper þcdr þ cE&C þ ct csal Þ þ PWF  t tao ðcO&M chc Þ

ð12Þ

where ttao is total annual operating time (h). For the AGDHS from Table 1, the related costs of cexp, cper, cdr, cE&C, ct and cO&M are taken as 150 US$/kW, 100 US$/kW, 500 US$/kW, 1000 US$/kW, 240 US$/kW and 35 US$/MWh per year, respectively. The inflation (g) and interest (i) rates are respectively taken as 10.45% and 5.75% according to Turkey’s current status. The operating lifetime of the AGDHS has been assumed as 30 years. The salvage cost and the unit heat consumption sale price are assumed to be 10% of the equipment-construction cost and 80 US$/MWh, respectively. Finally, assuming a total annual operating time of 5040 h/y and using the cost parameters shown in Table 1, the total annual cost of the AGDHS is therefore calculated as C Tot ¼ 1800  E_ output PWF  226:8  E_ output

ð13Þ

Fig. 3. Comparison of ANN predicted and calculated energy efficiencies during heating seasons in the period from 2006 to 2010.

3. Results and discussion In this study, energetic and economic evaluations have been accounted for in an ANN modeling aiming to simulate and evaluate the operation of the studied GDHS based on its life cycle costs (LCCs). As a comprehensive case study, the Afyon geothermal district heating system (AGDHS) was described and investigated from the energetic performance and economic points of view by using an ANN. The ANN model, which was developed to learn the energy inputs, losses, outputs, and efficiency of the GDHS installed in the city of Afyonkarahisar (located in the inside Aegean Region of Turkey) was trained. For training and testing, the energy input, losses, output, and efficiency values, which were calculated by an energy analysis using the average weekly temperature, pressure, and flow rate data at each reference point of the AGDHS (see Fig. 1) in the heating seasons of 2006–2010, were used. In order to develop the ANN model for this AGDHS, the available data set obtained from the experimental observations and energy analysis results were divided into training and testing sets. The data set consisted of 93 input and seven output pairs. Here, 86 of the data pairs were used for training the ANN, 18 for validation, and the remaining 18 for testing the performance of the ANN (these were not used for training or validating the ANN). The architecture of the ANN used for this AGDHS is schematically illustrated in Fig. 2. For predicting the energy input, losses, output, and efficiency values in the future, time (as weekly), temperature, pressure and flow rate are used as the input values for the ANN model. The comparison of the calculated and the predicted energy efficiency values of the AGDHS in the heating seasons of 2006–2010 in the training set is shown in Fig. 3. As can also be seen from the figure,

Total energy input Data timei

Natural direct dicharge

Temperatureamb,i

Pipeline loss

Temperaturei,j

Thermal reinjection

Pressurei,j

Mixingp

Flow rate i,j

All heat exchangers Energy efficiency

Input Layer

Hidden Layer

Output Layer

Fig. 2. Architecture of ANN used for this study.

Fig. 4. Variation of ANN predicted energy rates for the AGDHS and its components during heating seasons in the period from 2006 to 2010.

the trends of the calculated and predicted values are very close to each other and the future values are predicted with a high degree of accuracy. According to the results obtained, the respective RMS, R2, and MAPE values for training are 0.02577, 0.9976, and 0.0033; however for the test, these values are 0.03728, 0.9983, and 0.0046, respectively. The simulation results proved that the ANN model is capable of predicting the energy input, losses, output, and efficiency values of the AGDHS with a very good accuracy. Thus, the ANN model for the energy input, losses, output, and efficiency values of the AGDHS approximates the behavior of the physical AGDHS model with good accuracy. Besides, it can be seen from the figure that all the observed efficiency changes over time. This situation is related to the ambient temperature (restricted dead state) change. The energy input, losses, and output values predicted by the ANN model according to time are given in Fig. 4. It is clearly observed that the used geothermal fluid, after being circulated through the heat exchangers, is discharged into the river. It is extremely important to save such high amounts of energy by re-injecting the fluid back to the well. In addition, large energy losses occur in the mixing pool in each period. It is obvious that the performance could be improved enormously if the heat exchangers, reinjection, pipeline losses, etc., were recovered accordingly and used in the system. Thus, an economic savings in the AGDHS could be made. From the figure, the variation in the total energy input and all heat exchangers in the

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Fig. 5. Variation of total costs for the AGDHS during heating seasons in the period from 2006 to 2010.

AGDHS was from 75.84 to 91.60 MW and from 30.16 to 30.85 MW, respectively, during 2006–2010. On average over this period, the thermal re-injection, the mixing pool losses, and the natural direct discharge are predicted to be respectively 26.62%, 24.03% and 11.33% of the total energy input, while the pipeline losses of the system are 2.64%, and the heat exchangers gain 35.39%. Also, this study presented an application of an economic analysis (namely an LCC), through energy and cost accounting analyses, to the ANN model of the AGDHS for the entire system. The idea behind the economics part was to always have two values of the production cost (actual and predicted values), thus allowing for instance an economic evaluation of system degradation. This is possible since the ANN models always predict the performance of a healthy GDHS and any economic calculations based on these predictions will indicate what the production cost should be. In Fig. 5, the predicted total cost values of the AGDHS in the heating seasons 2006–2010 are shown. As can be seen from the figure, the total costs vary from 44.468 to 45.496 million US$ with the change in heating seasons. As the initial costs are taken into account at all times, the total cost is too high. But, here, it is important to note that the total costs change over time. The total costs of the AGDHS increase by an increase in performance at the beginning and end of the heating season due to the higher ambient temperatures, while they fall in the middle of winter. The reason for this is non-rational heat consumption through eliminating the overheating of all buildings in the zones, especially at the beginning and end of the heating season. The operating costs of the GDHS are strongly affected by dynamic disturbances evolving at different time scales. These include input flows, product demands, energy prices, weather conditions, etc. In order to manage these disturbances, the operational decisions are decomposed in a hierarchical manner. The top decision-making level is the supervisory or economic optimization layer which adjusts the set-points as low-frequency disturbances evolve in time. The lower decision making level is the control level that rejects high-frequency disturbances in order to keep the process close to the economically optimal set-points. As can be seen in Fig. 6, the total costs, dependent upon the ambient temperatures, are given as the predicted values for the system. The results briefly show that the total costs, especially the energy output, increase due to the decrease in the heat losses and the increase of the well-head temperatures while the ambient temperatures change from 0 1C to 24 1C. It can be understood from this that there are influences of the ambient temperature on the total costs of the system in the location where it will be established, while setting up a GDHS. The ambient temperature and the economic costs are the major factors which need to be considered while planning the setup of a GDHS.

Fig. 6. Effect of ambient temperatures on total costs of the AGDHS.

Fig. 7. Effect of flow rates on total costs of the AGDHS according to ambient temperature.

Many GDHSs such as the AGDHS do not have automatic temperature or flow rate control systems for the ambient temperature. As the flow rates of the systems are changed manually without controlling for the ambient temperatures, the technical management of a system faces some problems, especially economic ones. As an example, the effect of the flow rate on the total costs of the AGDHS (according to the ambient temperature) is shown in Fig. 7, and has similar characteristics to the ambient temperatures (see Fig. 6). From this figure, it can be seen that the total costs decrease with an increasing flow rate of the system while the ambient temperature decreases. As is known, one of the most important parameters which affect the optimum point is the PWF. Especially for the countries with a fluctuating economy, the PWF makes the optimum point change on a large scale, and hence so do the benefits. The PWF is the value by which the future cash flow is gathered in order to obtain the current present value of the project. The PWF factors are calculated using the inflation rate, the interest rate, and the lifetime. In the economic model process, the data predicted by means of the ANN model and the PWF are used in the cost equation. As can be seen in Table 2, several economic values are obtained after taking different PWF values into consideration. From Table 2, the total cost of the AGDHS for lower PWFs is not beneficial for all ambient temperatures. For example, if the

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Table 2 Total costs of the AGDHS for different PWFs (million US$). Ambient temperature

0 4 8 12 16 20 24

PWF 1

3

5

7

9

11

13

15

47.450 47.452 47.453 47.456 47.478 47.530 47.584

33.769 33.770 33.771 33.773 33.788 33.825 33.864

20.087 20.088 20.089 20.090 20.099 20.121 20.144

6.406 6.407 6.407 6.407 6.410 6.417 6.424

 7.275  7.275  7.275  7.276  7.279  7.287  7.296

 20.956  20.957  20.957  20.959  20.968  20.991  21.015

 34.637  34.639  34.639  34.642  34.658  34.695  34.735

 48.318  48.321  48.322  48.325  48.347  48.400  48.455

Table 3 Results of the other studies related to effect of total energy output (useful energy) on total cost of GDHSs. GDHSs

Location/ country

Number of dwellings heated/projected capacity

Afyon

Dead state or ambient temperature (1C)

Afyonkarahisar/ 4159/10000 Turkey Afyon Afyonkarahisar/ 4613/10000 Turkey Balcova Izmir/Turkey 6849/20000

13.1

Balcova Izmir/Turkey

6849/20000

11.4

Bigadic

2200/3000

11.0

Dikili

Balıkesir/ Turkey Izmir/Turkey

 /4000

9.2 2.3

1.9

Date of data used

Total energy Energy output/ input (kW) useful energy (kW)

Energy efficiency (%)

Reference

Total cost of GDHSs (million US$)

January 8, 2009 January 20, 2010 January 1, 2003 January 2, 2004

82828.12

21879.30

37.59

32.256

89381.20

31154.00

34.86

62665.11

23557.06

37.60

94670.00

40100.00

42.36

December 2006 Project data January 20, 2007

25809.00

10423.00

40.00

72084.96

28989.92

40.21

33837.00

11466.00

33.89

Kec- ebas- , (2011) Kec- ebas(2013) Ozgener et al. (2004) Ozgener et al. (2006b) Oktay et al. (2008) Kalinci et al. (2008) Oktay and Dincer (2008) Ozgener et al. (2005a) Ozgener et al. (2005b)

Edremit Balıkesir/ Turkey

1650/7500

13.4

Gonen

Balıkesir/ Turkey

3400/4500

6.0

February 1, 2004

28027.8

12868.90

45.91

Salihli

Manisa/Turkey

5470/20000

2.9

February 1, 2004

18426.52

10226.83

55.50

ambient temperature is 0 1C or 4 1C, the benefit from the AGDHS would be 48.318 or 48.321 million US$, respectively. The most profitable case is obtained with a benefit of 48.455 million US$ when the ambient temperature is taken to be 24 1C. Thus, its total cost is profitable when the PWF is higher than 7.9. However, the PWF of the AGDHS is found to be 1.43 for the given values. From the results of these economical values, it can be seen that the optimum AGDHS case is not the one that can get the maximum benefit. Finally, with an decrease of system lifetime and the inflation rate, and with an increase of the discount rate, the PWF decreases, which finally results in a decrease of the total costs of the AGDHS. This most important reason is the technical difficulties. The results of other studies related to the effect of the total energy output (useful energy) on the total cost of GDHSs are summarized in Table 3. As can be seen from this table, for only GDHSs, the most economical case is the Salihli GDHS, which has the highest energy efficiency, while the Balcova GDHS has the highest total cost due to its capacity and energy efficiency. It can be understood that the effect on the total cost of dead state/ ambient temperature is great (e.g., for Afyon and Balcova GDHSs). Thus, the total cost increases with a decrease in the ambient temperature. In addition, the results of Kec- ebas- (2013) are in harmony with those obtained by Kalinci et al. (2008) at temperatures close to each other (e.g., 1.9 1C and 2.3 1C) in Turkey.

45.930 34.730 59.118

15.366 42.739 16.904

18.972

15.077

Such a thermal and economic optimization process, combining the ANN with an energetic and economic analysis, will be useful in the thermal engineering field. Especially, by predicting with good accuracy the energy input, losses, output, and efficiency, as well as providing an accurate economic evaluation, a monitoring system for the GDHS, the degradation of its performance, and its economic prospects can be implemented.

4. Conclusions Whether the system is owned by a public utility or a user, such as a multi-building location, it has economic and environmental benefits depending somewhat on the particular application. Political feasibility must be considered, particularly if a municipality or governmental body is considering a GDHS installation. Historically, successful GDHSs have had the political backing and support of the community. The system design parameters, working conditions, different temperatures, etc., have a great effect on the energy input, losses, output, and efficiency of a geothermal district heating system. Initially, in this paper, an ANN was used for the analysis of the energy input, losses, output, and efficiency of an AGDHS as a case study, using the actual data obtained as average weekly values over the heating seasons of 2006–2010. Promising results were obtained about the economic evaluation

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of that system. Then, an economic analysis was performed using the LCC model of analysis. This was then used to determine if the existing system is operating at its optimal level, and provides information about the optimal design and profitable operation of the system. In this study, the results showed that the ANN model used for the prediction of the energy performance of the AGDHS has good statistical performance values: A correlation coefficient of 0.9983, and minimal RMS and MAPE values. In the economic model process, the data obtained by means of the ANN model were used in the cost equation. Therefore, several economic values were obtained after taking the different PWF values, ambient temperatures, and flow rates into consideration. The total cost for the AGDHS is profitable when the PWF is higher than 7.9. However, the PWF of the AGDHS was found to be 1.43 for the given values. From the results of these economical values, it can be seen that the optimum AGDHS case is not the one that can get the maximum profit. Furthermore, while installing a GDHS, one should take into account the influences of the ambient temperature and the flow rates on the total costs of the system in the location where it will be established. Finally, the most important advantage of the ANN is that it can be used for the design and optimization of new, novel, or existing operations as well as for more complicated systems, which are similar to the studied AGDHS. The applied results are expected to guide the designers, engineers, and policy makers for a better implementation of GDHSs, and to be very useful for the training of personnel and for the condition monitoring of an AGDHS.

Acknowledgments The authors would like to thank for the support provided by the Afyonkarahisar Geothermal, Inc.

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