Economic analysis of exergy efficiency based control strategy for geothermal district heating system

Energy Conversion and Management 73 (2013) 1–9

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Energy Conversion and Management journal homepage: www.elsevier.com/locate/enconman

Economic analysis of exergy efficiency based control strategy for geothermal district heating system _ Yabanova b Ali Keçebasß a,⇑, Ismail a b

Department of Energy Systems Engineering, Technology Faculty, Mug˘la Sıtkı Koçman University, Mug˘la, Turkey Department of Electrical and Electronics, Technology Faculty, Afyon Kocatepe University, Afyonkarahisar, Turkey

a r t i c l e

i n f o

Article history: Received 11 December 2012 Accepted 24 March 2013 Available online 7 May 2013 Keywords: District heating Geothermal Exergy Thermal control strategy Economic impact

a b s t r a c t In this study, the exergy efficiency based control strategy (ExEBCS) for exergy efficiency maximization in geothermal district heating systems (GDHSs) is economically evaluated. As a real case study, the Afyon GDHS in the city of Afyonkarahisar/Turkey is considered. Its actual thermal data as of average weekly data are collected in heating seasons during the period 2006–2010 for artificial neural network (ANN) modeling. The ANN modeling of the Afyon GDHS is used as a test system to demonstrate the effectiveness and economic impact of the ExEBCS under various operating conditions. Then, the ExEBCS is evaluated economically in case of application to real Afyon GDHS of the ExEBCS. The results show that the initial cost for the ExEBCS is more expensive than that for the old one by 6.33 kUS$/year as a result of replacing automatic controller. The saving in heat production makes the ExEBCS profitable by up to 7% of annual energy saving as a result of the increase in the heat production by 88% when the control system is operated. This results in a short payback period of 3.8 years. This study confirms that the use of ExEBCS in district heating systems (especially GDHS) is quite suitable. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction District heating systems (DHSs) that serve great numbers of buildings are complex compared to the central heating systems that serve only one building. A DHS includes a heating plant, pipe network, substations, heated buildings and heaters inside the buildings. It provides heat to many buildings during the winter with the objective to maintain comfortable and healthy conditions for the occupants. This is due to the fact that in many countries and regions of the world, they have been successfully installed and operated, resulting in great economic savings [1]. There are many advantages for DHS, including the increased energy and performance efficiencies through implementing advanced equipment and maintaining them professionally, reduced life cycle costs, augmented control over environmental impacts [2]. In addition, high efficiencies of the DHS reduce the emission of combustion products into atmosphere.

Abbreviations: ANN, artificial neural network; CRF, capital recovery factor; DHS, district heating system; ECC, energy consumption cycle; EDC, energy distribution cycle; EPC, energy production cycle; ExEBCS, exergy efficiency based control strategy; GDHS, geothermal district heating system; PID, proportional integral derivative; PSD, proportional sum derivative; PW, present worth; PWF, present worth factor. ⇑ Corresponding author. Tel.: +90 252 2111701/2113150. E-mail address: [email protected] (A. Keçebasß). 0196-8904/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.enconman.2013.03.036

In recent years, such heating systems have received much attention with regard to improving their energy efficiency and equipment operation. It can also lead to inaccurate control of ambient and water temperatures as well as flow rates, and then the buildings of the user are poor heated or over heated and cause energy waste. In the United States, the energy consumption used to buildings constitutes 48% of the total energy use [3]. Many European countries such as Denmark, Russia, and Finland have deployed the district heating system, and the market share is reported to be about 50% [4]. Space and water heating account for 60–80% of the energy consumed in the residential, commercial, institutional, and public administration sectors of Canada [5]. In Turkey, approximately 31% of the total energy is consumed in the residential and commercial buildings, and approximately 85% of the building energy is consumed by space heating systems [6]. Therefore; methods for reducing the energy consumption of the DHSs in the buildings are highly required, and regulations are targeting the improvement of heating systems. One of the methods is that, the amount of energy consumed for space heating can be thereby reduced if the heating system can be automatically controlled according to the ambient temperature. In addition to the modeling of the DHSs, it is important to develop and apply the effective control strategy for efficient operation of the system from the viewpoint of energy efficiency and savings. In many DHSs, most of the adjustments for system control are still performed manually. Manual operation causes low working

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Nomenclature a c ck C C_ _ Ex d1, d2 h i _ m n P P_ Q_ s S T _ W Z_

annual operating hour (h/year) the unit electricity price (US$/kW h) initial investment cost (US$) cost (US$) monetary flow rate (US$/year or US$/h) exergy rate (kW) disturbance specific enthalpy (kJ/kg) interest rate (%) mass flow rate (kg/s) lifetime (year) pressure (kPa) power (kW) rate of heat (kW) specific entropy (kJ/kg K) salvage value temperature (°C or K) work rate (kW) capital cost rate

u w

maintenance factor flow exergy (kJ/kg)

Subscripts add additional d natural direct discharge dest destroyed elec electricity he heat exchanger i, j successive number of elements in inlet k location mp mixing pool out outlet r re-injected geothermal fluid ref reference Tot total w wellhead 0 reference state

Greek symbols e exergy or second law efficiency (%)

efficiency because of oversight in management and imprecise operation, etc. It can also lead to inaccurate control of ambient and water temperatures as well as flow rates, and then the buildings of the user are poor heated or over heated and cause energy waste. Because of the rapidly increasing costs of control strategy, literatures discussing the system controls are very scarce. A literature survey has revealed that, indeed, this perception is quite opportune. Loveday and Virk [7] derived stochastic models which describe the thermal behavior of a full-scale room exposed to the naturally occurring disturbances of climate (temperature, solar irradiance, infiltration), occupancy and appliance usage. Argiriou et al. [8] proposed an ANN controller able to forecast not only the energy demand but also the weather conditions for energy savings in buildings. By using predictive control methods, residential water heating systems [9] and radiant floor heating systems [10] were controlled effectively. Argiriou et al. [11] studied ANN application to hydronic heating of solar building by prediction of outdoor temperature, solar radiation, indoor temperature, and supply temperature for energy savings. Ben-Nakhi and Mahmoud [12] conducted for optimal start of A/C systems employing ANN for predicting end-of-setback moment; in these, the ANN-based predictive control proved accurate and easy to use. The objective of this work is to develop a novel control strategy for an automatic controller to ensure the maximum exergy efficiency of the Afyon GDHS to respond to the variations of the ambient temperature. To the best of the authors’ knowledge, no examination of exergy efficiency based control strategy (ExEBCS) in thermal systems has appeared in the literature. This was the motivation behind the present work. However; the authors undertook a preliminary study [1] on ExEBCS of the Afyon GDHS to identify and test only. Here, we now present an economic analysis of the ExEBCS of the system at various operating conditions. 2. The Afyon GDHS The heat source of the Afyon geothermal district heating system (GDHS) originates from the Ömer-Gecek geothermal field, 15 km northwest of the city of Afyonkarahisar/Turkey. The well head

temperatures of the production wells vary from 93 to 99 °C, while the flow rates of the wells range from 150 to 220 m3/h. The Afyon GDHS was initially designed for 10,000 residences equivalence but today, 4613 residences, covering a total floor-area of 514,634 m2, are heated. Potential of the Afyon GDHS is 48.33 MWt [13–15]. The Afyon GDHS consists mainly of three cycles: (a) energy production cycle (EPC), (b) energy distribution cycle (EDC), and (c) energy consumption cycle (ECC). A schematic of the Afyon GDHS is shown in Fig. 1. For the EPC in this GDHS, geothermal fluid collected from the production wells is sent to the inlet of the mixing pool via a main collector with about 175 kg/s totally. The fluid at an average temperature of about 95 °C is then pumped through the main pipeline to the Afyon GDHS, located in the center of the Afyonkarahisar province. In here, the geothermal fluid is sent to the six heat plate exchangers with a total capacity of about 18.6 MW (16 million kcal/h) in the geo-heat mechanical room of the Afyon GDHS and is cooled to about 45–50 °C. Because the maximum discharge mass flow rate of the residential heating (175 kg/s) is beyond the total re-injection mass flow rate (122.2 kg/s), the remaining fluid is released to the nature direct discharge. For the EDC, the hot water is pumped to the six heat exchangers and then the supply (flow) water is sent to the heat exchangers installed under all the buildings in the zones. The mean supply/return water temperatures of the building cycle are 60/45 °C. Through control valves for flow rate and temperature at the geoheat mechanical room of the Afyon GDHS, the amount of water needed is adjusted and sent to each housing unit and the heat balance of the system is achieved. However, the ECC of the Afyon GDHS was not considered in the analysis. The actual operational data on temperature, pressure and flow rate of the system have been hourly recorded since 2006 by the technical staff based on the state numbers specified in Fig. 1. The pressure and temperature data on the fluids (including hot water and geothermal fluid) have been measured with Bourdon-tube pressure gauges and fluid-expansion thermometers, respectively. The volumetric flow rates of fluids have also been measured by an ultrasonic flow meter.

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Fig. 1. Schematic diagram of the Afyon GDHS.

The geothermal fluid exergy inputs from the production field of the Afyon GDHS are calculated from

3. Mass and exergy analyses The balance equations for mass and exergy can be written for the Afyon GDHS and its components under steady-state steadyflow control volume conditions. Also, these equations were used by some earlier researchers (e.g., [13–17]). For the Afyon GDHS, the mass balance equation is written as follows n X _ w;i;Tot  m _ mp  m _ r m _d¼0 m

ð1Þ

i¼1

_ w;i;Tot is the total mass flow rate at wellhead, m _ r is the flow where m _ mp is the flow rate of the rate of the reinjected geothermal fluid, m _ d is the mass flow remained geothermal fluid in mixing pool, and m rate of the natural direct discharge. The general exergy rate balance can be expressed as

_ heat  Ex _ work þ Ex _ mass;in  Ex _ mass;out ¼ Ex _ dest Ex

ð4Þ

The exergy destructions in the pump, heat exchanger, mixing pool and system itself of the Afyon GDHS are calculated as follows

_ dest;pump ¼ W _ pump  ðEx _ out  Ex _ in Þ Ex

ð5Þ

_ dest;he ¼ Ex _ in  Ex _ out Ex

ð6Þ

_ dest;mp ¼ Ex _ in  Ex _ out Ex _ dest;system ¼ Ex

X

ð7Þ

_ dest;pump þ Ex

X

_ dest;he þ Ex

X

_ dest;pipes Ex

ð8Þ

ð2Þ

The exergy efficiency of the Afyon GDHS can be defined respectively as

ð3Þ

esystem ¼ _ useful;he ¼ 1  Exbrine

and

 X X X T0 _ _ þ _ dest _ in win  _ out wout ¼ Ex Qk  W 1 m m Tk

_ in ¼ Ex _ brine ¼ m _ w;i;Tot ½ðhbrine  h0 Þ  T 0 ðsbrine  s0 Þ Ex

_ Ex

_ dest þ Ex _ r þ Ex _ d þ Ex _ mp Ex Exbrine

ð9Þ

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The exergy efficiency of a heat exchanger is basically defined as

_ m

ðw

w

Þ

cold;in ehe ¼ _cold cold;out mhot ðwhot;out  whot;in Þ

ð10Þ

4. ANN modeling DHS managers are increasingly using ANN for advanced thermal controls in buildings. ANN, which is analogous to the human brain and its learning process, has been successfully applied to non-linear systems or systems with unclear dynamics. In particular, the adaptability of ANN models through a self-tuning process-which is different from mathematical models such as regression models or PID controllers-makes accurate decisions possible without outside expert intervention when unusual perturbations, disturbances, and/or changes in building background conditions occur [18]. By using the ANN model trained with data of the existing system, system model with good accuracy is possible to create. The authors have conducted some preliminary works [19,20] on the ANN modeling from energy and exergy aspects of the Afyon GDHS in Turkey. An ANN modeling, which is developed to train exergy efficiency of the Afyon GDHS, are trained using real average weekly data recorded during heating seasons in the period 2006 and 2010. The data are provided by the technical staff, and remaining data are calculated by means of exergy analysis. The exergy efficiency of the Afyon GDHS is influenced by ambient parameters (such as ambient temperature) and system operating conditions (such as flow rate, temperature, pressure). Hence, the ambient temperature, the well head temperature, the input and output temperatures, flow rates and pressures of the system and its components are used as input parameters for the network. The exergy efficiency for the whole system is calculated mathematically by using input variables. It is used as output parameters of ANN. Input and output parameters used in ANN model for predicting the exergy efficiency of the system is shown in Table 1. Experimental and calculated data set consists of 65 input and 1 output pairs. The data of 122 are splitted into training (70% of data set), validating (15% of data set) and testing (15% of data set) database to obtain a good representation of the situation diversity. The ANN modeling used for the exergy efficiency prediction is prepared in MATLAB program using neural network toolbox. Levenberg–Marquardt backpropagation algorithm (the most widely used method in ANN) optimizes the weight connection by allowing the error to spread from output layers toward the lower layers (the hidden layer and the input layer). The output of the network is compared with the desired output at each presentation and errors are computed. As can be seen in Refs. [19,20], the simulation results proved that ANN model is capable of predicting the exergy efficiency of the Afyon GDHS with a very good accuracy. Thus, the ANN prediction for the exergy efficiency of the Afyon GDHS approximates the behavior of the physical Afyon GDHS model with good accuracy. 5. Exergy efficiency based control strategy for the Afyon GDHS Thermal control strategies are different from the point of view of their costs and feasibility. Optimization of these strategies can yield a variety of answers, depending not only on the objective of the optimization but also on the constraints that define the problem. More specifically, the optimal paths are different when maximization of exergy rather than energy is of interest. For optimal control strategy, the proposed controller enables to control the output volume flow rates of the system according to exergy efficiency (so ambient temperature) in a systematic and stable manner. Here, it is also important to distinguish here

Table 1 Input and output parameters used in ANN model for predicting the exergy efficiency of the Afyon GDHS. Inputs Date timei Tamb,i Ti,j Pi,j _ i;j m

Outputs (days in heating season) (°C) (°C) (kPa) (kg/s)

Exergy efficiency (%)

Note: i = weeks, and j = the geothermal fluid and clean hot water lines as illustrated in Fig. 1.

between ‘‘controller’’ and ‘‘control strategy’’. While a controller closes the loop in the classical sense (i.e. computes the control signal to control a process variable according to a specific control methodology), the control strategy determine show the information available from the process is used to generate the control signal. Obviously the controllers are integrated in the control strategy. In DHSs, the controllers can be categorized into three groups as: Controllers in first type allow optimal heat distribution in a building. This means that a certain objective function related to the thermal energy provided or living discomfort is minimized. Controllers in second type maximize the difference between the useful collected energy and the energy required to transport the working fluid. The controllers in third kind combine first and second types [21]. The second type controllers are responsible for the optimum operation of the pumps. Two sorts of second type controllers are often used in applications. One is the on/off controller. The other is the proportional controller. Variants of proportional controllers exist such as proportional integral derivative (PID) and proportional sum derivative (PSD) controllers [22]. The conventional PID controller is a widely used industrial controller which uses a combination of proportional, integral and derivative action on the control error to form the output of the controller. Increasing the proportional gain decreases the stability margin of the system, increases the frequency of oscillation, and decreases response time. Setting the proportional gain too high can result in unstable system operation, causing specimen damage. Proportional gain is usually set high enough to achieve an appropriate response time while maintaining stable system response. The integral function increases system accuracy by reducing to zero any residual error after the command has expired. A non-zero value for error causes the residual error to be integrated over time until it is large enough to drive the actuator to its final position. The derivative function is the first derivative of the feedback position here. It is used to improve the control loop dynamic stability by reducing the overshoot at higher proportional gain settings. It should point out that the integral action of the PID controller is not in work when the system is in motion. The integral action works only when experiment is stopped and the piston is placed in neutral position. Null shift inevitably exists in servo valve, which will lead to precision degradation. The integral action is mainly used to suppress these bad effects [23]. ANN model that the controller will use for the optimization is the first step in designing the non-linear Afyon GDHS. This model should be as accurate as possible, while being simple enough to allow for repeated calculations during the optimization. The predictive control scheme (namely exergy efficiency based control strategy (ExEBCS)) is applied in the developed ANN model of the real Afyon GDHS. This scheme is prepared in MATLAB program using Simulink, and whole Afyon GDHS simulation model is shown in Fig. 2. As shown in Figs. 1 and 2, the clean hot water exited from the system is pumped to six different zones. As the flow rates of the system are changed manually by valve without controlling of ambient temperatures, the technical managements of the system

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Fig. 2. Simulation model of the ExEBCS for the Afyon GDHS.

encounter some problems in many residences. Since the temperature values of the system cannot be kept under control, only flow rates are controlled by pumps according to the exergy efficiency (so ambient temperature) in the system thanks to this study. Thus, the flow rates of each pumps are controlled via PID based controller. This PID controller algorithm meets objectives such as closedloop stability, adequate performance and robustness by tuning the PID gains to achieve a good balance between performance and robustness. The algorithm designs an initial controller by choosing a bandwidth to achieve that balance, based upon the open-loop frequency response of linearized model. When the response time is interactively changed, bandwidth, or phase margin using the PID controller interface, the algorithm computes new PID gains. The PID controller computes the responses based upon the block diagram of PID based controller, as shown in Fig. 3. Thus, exergy efficiency of the system is kept at a maximum level by PID controller for energy savings. The real flow rate values in the system are used in this study while the limit values of PID controller are determined. In control simulation average weekly values of heating season in the period 2009–2010 for the Afyon GDHS is used and these values are changed 50 s intervals. On the ANN model of the Afyon GDHS, the flow rates in different zones are controlled via PID based controller of each pumps for ExEBCS. The variations of ambient temperature, exergy efficiency and flow rates of the entire zones with this controller during 1500 s time period with 50 s step size for controlled and manual cases of the Afyon GDHS are conceptually illustrated in Fig. 4, as an example case. First of all, it is observed that the exergy efficiency of the Afyon GDHS decreases significantly while increasing the ambient temperature, as expected. Here the exergy efficiency of the Afyon GDHS decreases due to the decrease of the heat losses and the increase of the well head temperatures. As can be seen in Fig. 4, the limit values of flow rate in each zone differ from each other. Thus, the models guarantee that each zone gets the right temperature. It can be seen that the flow rates, passing through the zones to keep exergy efficiency at the highest value, are increased or decreased by means of the pumps in each zones while the exergy efficiency increases. Thus, the PID controller responses

to step changes of the exergy efficiency (so ambient temperature) can be seen in this figure. It is important to understand that these abrupt changes of the exergy efficiency are for testing the dynamic response of the PID controller, and do not necessarily represent changes in a real case. Heat production of the heat exchangers in the Afyon GDHS used the ExEBCS increases by more 13% than that in non-controlled system. Therefore, in compared with manual control, the Afyon GDHS is best controlled by ExEBCS. 6. Economic analysis for the control strategy The control objective of heating management is to regulate the output flow rates of heating systems according to the ambient temperature and consumers’ request. Here, the benefit of the developed strategy is illustrated for the flow rate control to ensure the maximum exergy efficiency of the Afyon GDHS. One of aims during the operation of the system is to maintain the outlet temperature at the specified value to prevent the waste of energy, but the current control strategy for the system is based on the manipulation by the operators/technical staffs manually, resulting in the poor performance for the temperature regulation. Therefore, an advanced control algorithm is applied to the system in order to evaluate the economic performance in this section. The economic performance of a GDHS can be improved enormously if the heat exchangers, pumps, pipe losses are recovered accordingly. However, it is 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 economic optimal set-points. According to this case, it is assumed that only pumps are used for control. In order to calculate annualized cost of the equipment ðC_ k Þ inside the control strategy, the annualized cost method is employed, as presented in Bejan et al. [24], to calculate the capital costs of the system components

C_ k ¼ PWk CRFði; nÞ

ð11Þ

with

PWk ¼ ck  Sk;n PWFði; nÞ Fig. 3. Block diagram of PID based controller for the ExEBCS.

ð12Þ

where ck is the initial investment cost (kUS$), C_ k is the annualized cost (kUS$/year), PW is the amortization cost (present worth) for

6

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Fig. 4. Comparison of non-controlled (manual) and controlled (PID – ExEBCS) cases of the Afyon GDHS according to ambient temperature, exergy efficiency of the system and flow rates of all the zone.

any particular control system component, CRF(i, n) is the capital recovery factor, S is the salvage value and PWF is the present worth factor. A control strategy may have a positive effect on the efficiency and thus cause negative additional electricity consumption. Nevertheless, the control strategy may have a negative effect on the

component lifetime. In control system analysis, the total additional cost rate can be calculated. This quantity is defined as the cost rate due to the additional electricity consumption plus the difference between the current investment cost rate (i.e. considering the current lifetime) and the reference investment cost rate (i.e. evaluated considering the reference lifetime), namely:

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Fig. 5. Architecture of the ExEBCS on the real Afyon GDHS.

C Tot

add

¼ P_ elec celec þ ðC Tot  C ref Þ

ð13Þ

where P_ elec and celec are the annual electricity power and the unit electricity price, respectively.

Table 2 Initial investments and annualized costs of each component used the ExEBCS of the Afyon GDHS.

7. Economic evaluations of the control strategy This section addresses the economic merit of the developed control strategy over the conventional system. For exergy efficiency maximization of the Afyon GDHS, it is assumed that only six pumps are used for control. Architecture of the ExEBCS on the Afyon GDHS is shown in Fig. 5. As the figure, computer, DAQ card, inverter and the equipments of controller, including the installation and commission are replaced and separately installed. As can be seen from figure, exergy efficiency is automatically calculated after the data (flow rates, temperatures, and pressures) collected from the sensor in the Afyon GDHS are recorded on the computer by DAQ card. The subtraction of the calculated exergy efficiency from desired exergy efficiency produces the error signal of PID controller in each zone. PID control signal through the DAQ card is connected the inverter controlled the flow rate of each zone. Thus, the system is controlled via the exergy efficiency based control strategy (ExEBCS) for maximum exergy efficiency. Here, the economic evaluations of the ExEBCS for real Afyon GDHS are discussed according to the following criteria: (i) initial costs, (ii) annual energy saving costs, (iii) payback period, and (iv) technical performance. 7.1. Costs The calculation of the economic indicators such as cost, saving and payback period involves two major cost categories [25]: owing cost and operating cost. Owing costs are comprised of initial costs, salvage value, property taxes, rents and insurance. On the other hand, the annual system expenditures resulting from actual use of the system are referred to as operating costs. Operating costs include costs for electricity and maintenance. For the purpose of evaluating the proposed system, it is assumed that the owing costs include initial (capital) costs only, whereas the operating costs involve costs for electricity and periodic maintenance only.

Owing costs Inverter 1 (90 kW) Inverter 2 (90 kW) Inverter 3 (90 kW) Inverter 4 (75 kW) Inverter 5 (55 kW) Inverter 6 (55 kW) Control system hardware and software (data acquisition cards, computer, programs) The costs of installation Operating costs Electricity consumption Maintenance Total

Initial investments (kUS$)

Annualized costs (kUS$/year)

5.49 5.49 5.49 4.31 3.84 3.84 10.00

0.79 0.79 0.79 0.62 0.55 0.55 1.44

5.00

0.72

– –

0.07 0.38

43.44

6.33

Note: The unit heat consumption sale price of the Afyon GDHS to users is given as 0.08 US$/kW h at Turkey’s 2010 year status.

For each component used control strategy; the initial investments and annualized costs are given in Table 2. Here, economic data are taken from managements of the Afyon GDHS and the producers of controller and other components for the costs. In addition, the salvage cost and annual operating time are assumed as 10% of the system capital cost and 5040 h/year (24 h  210 days for a year), respectively. The maintenance cost is taken into consideration as 6% of the system capital cost for each of the system components whose average expected life (n) is assumed to be 15 years. The interest rate (i), the unit heat consumption sale price and the unit electricity price are taken as 12%, 0.08 US$/kW and 0.2233 US$/ kW h according to Turkey’s 2010 year status, respectively. As can be seen in Table 2, the initial investment cost for the control strategy is more increase than that for the old one by 6.33 kUS$/year as a result of replacing newly automatic controller, including the installation and commission when the proposed control strategy is used. As a result, although the valves controlled manually would have much lower costs than the control of the

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Table 3 Comparison of annual energy saving effect when using the ExEBCS in the Afyon GDHS.

Non-controlled system Controlled system

Maximum exergy efficiency

The heat production (MW-year)

Total energy saving cost (kUS$/year)

Energy-saving rate

– 29% 29.5% 30%

53.57 77.65 92.41 100.48

– 1.93 3.11 3.75

– 3.6 5.8 7.0

Table 4 Technical performance comparison of the ExEBCS for GDHS.

Temperature controlling precision Energy saving effect Payback period Operability Possibility of pipeline blockage Comfort Ease of maintenance Cost

Non-control

ExEBCS

Low N/A N/A Difficult High Low N/A N/A

High High Low Easy Low High Easy Low

efficiency. The pumps of the system are operated at 100% load as the buildings in heating zones are enough heated when ambient temperature decreases. Then, the high heating occurs in buildings as the ambient temperature starts to rise. In this case, the flow rate of the system is decreased as 75% load by technical staffs/operators. But, consumers of the heated buildings complain about heat when the ambient temperature starts to fall again. The time passing to set at 100% load extends due to reaction time of technical staffs/operators. However, the ExEBCS shortens this reaction time of technical staffs/operators. Also, it can be seen from Table 4 that the ExEBCS has obvious advantages. Automatic control of components and process of GDHS will reduce the losses and human involvement and make the system more effective and efficient.

ExEBCS, this could be compensated for by changing system flow rates automatically. 8. Conclusions 7.2. Annual energy saving costs Here demonstrates possible annual energy saving costs when the proposed control strategy is used. The possible annual energy saving from the ExEBCS compared to the conventional system at the exergy efficiency settings of 29%, 29.5% and 30% is shown in Table 3. It can be seen from this table that total annual energy saving cost, when the ExEBCS is applied, comes to 3.75 kUS$/year with an exergy efficiency ratio of 30%, 3.11 kUS$/year for 29.5%, and 1.93 kUS$/year for 29%. The energy saving rate in heat production from the developed control strategy is approximately 7% when the set point exergy efficiency for the controller is 30%. The potential saving for 29.5% and 29% are 5.8% and 3.6%, respectively. In addition, the saving in heat production makes the control strategy profitable by an annual energy saving cost of 3.75 kUS$/year as a result of the increase in the heat production by 88% when the control system is operated at maximum exergy efficiency (30%). 7.3. Payback period An economic effect of the ExEBCS is evaluated by using a payback period that is calculated by comparing the initial cost of installing the ExEBCS with the annual energy cost which is reduced thanks to the application of the ExEBCS. The ExEBCS has a relatively short payback period (less than 5 years) for any of the three set point exergy efficiencies (e.g. 29%, 29.5% and 30%). With regard to the result of the analysis of the payback period, when the maximum exergy efficiency is 30%, this gives the shortest period of 3.8 years, on the other hand, the longest period is 4.2 years in the case of exergy efficiency of 29% in the Afyon GDHS. In other words, the combination of these set point exergy efficiencies during the system operating hours will further shorten the payback period as a result of the considerable increase in heat production. 7.4. Technical performance The flow rates of the current system are changed manually via valves by technical staffs/operators without controlling of ambient temperatures since its inception. Here, the flow rates of each pumps are controlled via PID based controller according to exergy

In this study, exergy efficiency based control strategy (ExEBCS) is proposed to ensure the maximum exergy efficiency by means of the flow rate control of a large-scale Afyon GDHS. ANN model of the Afyon GDHS is used as a test system to demonstrate the effectiveness of the proposed control strategy under various operating conditions for a wide range of parametric uncertainties, ambient temperatures and load disturbances. Then, in case of application to real Afyon GDHS of the ExEBCS, it is evaluated economically. For optimal control strategy, the PID controller of the ExEBCS enables to control the output volume flow rates of the system according to exergy efficiency (so ambient temperature) in a systematic and stable manner. As a result, the following main concluding remarks are drawn from the present study:  By investing in an ExEBCS, the heat production in the Afyon GDHS can increase by 13% compared to the current system (non-controlled system), where no investment is included.  Overall, the heat production in the GDHS containing the ExEBCS is twice as high as the heat production in the un-controlled system.  The ExEBCS is effective and can ensure the stability of the overall system for all admissible uncertainties and ambient temperature changes.  The initial investment cost for the ExEBCS is more expensive than that for the old one by 6.33 kUS$/year as a result of replacing newly automatic controller when the proposed control strategy is used.  The saving in heat production makes the control strategy profitable by an annual energy saving cost of 3.75 kUS$/year as a result of the increase in the heat production by 13% when the control system is operated at maximum exergy efficiency (30%).  By applying the ExEBCS, when the exergy efficiency rates are 29%, 29.5%, and 30%, the annual energy saving cost is reduced by 3.6%, 5.8% and 7%, respectively.  The result of the analysis of the payback period indicates that, when the exergy efficiency is 30% in the Afyon GDHS with maximum exergy efficiency, this gives the shortest payback period of 3.8 years, and the longest period of 4.2 years, applying to an exergy efficiency of 29%.

_ Yabanova / Energy Conversion and Management 73 (2013) 1–9 A. Keçebasß, I.

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