Performance characterization of a power generation unit–organic Rankine cycle system based on the efficiencies of the system components

Energy Conversion and Management 105 (2015) 480–487

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Performance characterization of a power generation unit–organic Rankine cycle system based on the efficiencies of the system components Alta Knizley ⇑, Pedro J. Mago, James Tobermann, Harrison R. Warren Department of Mechanical Engineering, Mississippi State University, USA

a r t i c l e

i n f o

Article history: Received 6 July 2015 Accepted 3 August 2015

Keywords: Combined heat and power system CHP Organic Rankine cycle

a b s t r a c t This paper analyzes the potential of using the waste heat from a power generation unit to generate additional electricity using an organic Rankine cycle to reduce operational cost, primary energy consumption, and carbon dioxide emissions in different locations in the U.S. The power generation unit–organic Rankine cycle system is compared with a conventional system in terms of operational cost, primary energy consumption, and carbon dioxide emissions reduction. A parameter (Rmin), which is based on system efficiencies, is established to determine when the proposed power generation unit–organic Rankine cycle system would potentially provide savings versus the conventional system in which electricity is purchased from the utility grid. The effect on the Rmin parameter with variation of each system efficiency is also analyzed in this paper. Results indicated that savings in one parameter, such as primary energy consumption, did not imply savings in the other two parameters. Savings in the three parameters (operational cost, primary energy consumption, and carbon dioxide emissions) varied widely based on location due to prices of natural gas and electricity, source-to-site conversion factors, and carbon dioxide emissions conversion factors for electricity and natural gas. Variations in each system efficiency affected Rmin, but varying the power generation unit efficiency had the most dramatic effect in the overall savings potential from the proposed system. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction A combined heat and power (CHP) system simultaneously generates on-site electricity and provides useful heat by utilizing waste heat from a power generation unit (PGU) [1]. CHP systems can enhance energy production efficiency and energy sustainability by reducing grid dependency, often yielding cost savings in the process [2]. CHP systems can potentially provide significant savings over separate heating and power (SHP) systems not only in terms of operational cost and primary energy consumption (PEC) [3], but also in terms of carbon dioxide emissions (CDE) [4]. Knizley and Mago [5] have also explored the performance of CHP systems with dual power generation units for various benchmark buildings and determined that CHP system performance can vary widely among different facilities. An organic Rankine cycle (ORC) can be incorporated into a CHP system to allow for increased potential reductions in operational cost, PEC, and CDE when compared to separate heat and power ⇑ Corresponding author at: Box 9552, Mississippi State, MS 39762, USA. E-mail address: [email protected] (A. Knizley). http://dx.doi.org/10.1016/j.enconman.2015.08.010 0196-8904/Ó 2015 Elsevier Ltd. All rights reserved.

(SHP) systems [6]. An ORC utilizes an organic working fluid. The evaporation of the organic fluid takes place at a lower temperature than does the evaporation of water in conventional Rankine cycles [7]. The evaporation temperature is a key factor in the efficiency of an ORC because it allows the ORC to recover heat from relatively low-temperature sources [8], such as geothermal heating, biomass combustion systems, solar energy systems [9], and industrial waste heat. ORCs can be a highly efficient method for converting low-grade thermal energy into electricity [8]. As demonstrated by Yamamoto et al. [10], ORCs not only possess a significant advantage over conventional Rankine cycles in terms of energy efficiency, but are also an attractive choice because they do not emit pollutants such as CO, CO2, or NOx. Research has indicated that ORCs can provide considerable economic benefits over more conventional means of recovering waste heat. For example, Heberle and Brüggemann [11] have presented a thermo-economic analysis of the ORC and illustrated its economic advantages, particularly when combined with a geothermal energy source. Law et al. have performed a comparative study between a high-temperature heat pump and an ORC using theoretical models. They found the ORC yielded higher cost savings as well as higher

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Nomenclature CHP ORC PGU Epgu—ORC Epgu EORC EHP Eb

combined heat and power organic rankine cycle power generation unit power generated by the PGU–ORC configuration electricity generated by the power generation unit electricity generated by the ORC electricity required to operate the heat pump electricity required by the building for lights and other equipment except the heat pump. Em total electricity registered at the electric meter fuel energy supplied to the PGU F pgu gpgu efficiency of the PGU Q pgu heat that can be recovered from the PGU gHRS efficiency of the heat recovery system n factor that accounts for PGU energy losses Qb thermal load of the building COP heat pump coefficient of performance Costpgu—ORC cost to operate the PGU–ORC system Costf cost of the fuel greenhouse gas reductions. Additionally, they completed a sensitivity analysis based on utility cost trends that suggests the ORC will be much more profitable than the heat pump in future years [12]. Significant theoretical and experimental analysis of the efficiency of ORCs has taken place in recent years. It has been shown by researchers such as Hung [13], Liu et al. [14], and Saleh et al. [15] that the efficiency of operating a given ORC can vary greatly depending on the thermodynamic properties of the working fluid as well as the cycle operating conditions. Hung et al. have investigated numerous refrigerants and examined their effectiveness for recovering low- and high-temperature waste heat [16]. Research has also taken place toward the end of developing an optimum design criterion for ORCs. For example, Hettiarachchi et al. utilize a loss function comprising the ratio of total heat exchanger area to net power output in order to select the optimum working fluid for a given application [17]. Dai et al. explored a parametric optimization of ORC performance based on the thermodynamic properties for various working fluids and verified that cycles with organic working fluids are significantly more efficient for converting waste heat into useable energy than those with water as the working fluid [18]. Additionally, work has been done toward predicting the behavior and potential cost savings of ORCs. Zhang et al. have proposed a generalized predictive control algorithm that outputs a set of variable adjustments to optimize ORC behavior for a given facility [19]. Zhang et al. have also demonstrated the use of a constrained model predictive control (MPC) strategy to enhance the efficiency of ORC systems [20]. Wei et al. have demonstrated the benefits of maximizing the usage of exhaust heat and keeping the degree of cooling at the condenser outlet within an optimal range for the chosen working fluid, as well as examined the negative effects of high ambient temperature on output net power and efficiency [21]. The principal thermal energy source examined in this paper is waste heat from power generation units (PGUs) in both the residential and commercial sectors. In this paper, the authors seek to develop a metric which can be utilized to estimate quickly whether operational cost savings can be achieved from an ORC and how much savings ought to be expected. Additionally, while many predictive strategies rely upon predictive algorithms and locationspecific factors such as facility and climate conditions, the tool proposed in this paper relies upon a relatively simple equation and is dependent only on the efficiencies of the system components and

CostCV cost to operate the building for the conventional case CDEpgu—ORC carbon dioxide emissions (CDE) associated with the PGU–ORC system CDEf CDE factor for the system CDECV CDE connected with the conventional case PEC pgu—ORC primary energy consumption (PEC) for the PGU–ORC system PEC f PEC factor for the system PEC CV PEC resulting from operating the building for the conventional case Rcost cost ratio defined as Coste =Costf RCO2 CDE ratio defined as CDEe =CDEf RPEC PEC ratio defined as PEC e =PEC f Rmin minimum Rcost that provides cost savings for the PGU–ORC system versus the conventional system Redcost cost reduction defined in Eq. (15) RedPEC PEC reduction defined in Eq. (16) CDE reduction defined in Eq. (17) RedCDE

local prices of electricity and natural gas, allowing for simpler and more universal application. 2. Analysis This section presents the model used to evaluate the performance of the proposed PGU–ORC system, shown in Fig. 1, and the conventional system, shown in Fig. 2. 2.1. Combined PGU–ORC The power generated by the PGU–ORC configuration, Epgu–ORC, can be expressed as:

PGU E pgu E pgu-ORC

HRS Qpgu E ORC

Eb

Building

E HP

ORC

HP

Qb

Fig. 1. Schematic of the PGU–ORC model.

Em

Eb

Building

E HP HP

Qb

Fig. 2. Schematic for the conventional case.

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Epgu—ORC ¼ Epgu þ EORC ¼ EHP þ Eb

ð1Þ

where Epgu is the electricity generated by the PGU, EORC is the electricity generated by the ORC, EHP is the electricity required to operate the heat pump, and Eb is the electricity required by the building for lights and other equipment except the heat pump. The electricity generated by the PGU is

Epgu ¼ F pgu gpgu

ð2Þ

where Fpgu is the fuel energy supplied to the PGU and gpgu is the efficiency of the PGU. The heat that can be recovered from the PGU is given by

Q pgu ¼ F pgu ð1  gpgu ÞngHRS

ð3Þ

where gHRS represents the efficiency of the heat recovery system, and n represents a factor that accounts for PGU energy losses. The electricity generated by the ORC is

EORC ¼ Q pgu gORC Table 1 Parameters used to determine the minimum cost ratio, Rmin.

gpgu gHRS

0.25 0.80 0.95 0.20 2.74

n

gORC Rmin

ð4Þ

where gORC is the efficiency of the ORC. The efficiency of the ORC depends on the operating conditions, high and low cycle pressures, and the type of organic fluid used in the system. The electricity required by the heat pump is given by

EHP ¼

Qb COP

ð5Þ

Table 2 Cost of electricity [22] and natural gas [23] and Rcost for the residential and commercial sector. Electricity ($/kW h)

Natural gas ($/kW h)

Rcost

Rcost

State

Residential

Commercial

Residential

Commercial

Residential

Commercial

Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

0.1188 0.2059 0.1253 0.1017 0.1767 0.1309 0.1945 0.1363 0.1217 0.1196 0.1255 0.3842 0.1063 0.1162 0.1167 0.127 0.1274 0.1035 0.101 0.1531 0.1377 0.1627 0.1511 0.1306 0.1163 0.1234 0.1082 0.1225 0.1265 0.1723 0.1652 0.1363 0.2031 0.1129 0.1096 0.1344 0.105 0.1083 0.1396 0.1585 0.1255 0.1158 0.1076 0.12 0.1161 0.1793 0.1198 0.0896 0.0938 0.1453 0.1127

0.1098 0.1796 0.1076 0.0842 0.1789 0.1071 0.1496 0.1035 0.1163 0.0981 0.1044 0.3479 0.0826 0.0877 0.0988 0.0971 0.1059 0.0947 0.0938 0.1191 0.1105 0.1463 0.1106 0.101 0.1095 0.1026 0.0962 0.0957 0.1014 0.1395 0.1358 0.1147 0.1673 0.09 0.089 0.0996 0.0887 0.0882 0.0949 0.1354 0.1023 0.091 0.1076 0.082 0.0889 0.1473 0.0847 0.0793 0.0772 0.1127 0.0901

0.0526 0.0275 0.0512 0.0384 0.0297 0.0270 0.0460 0.0495 0.0393 0.0595 0.0527 0.1718 0.0268 0.0268 0.0290 0.0307 0.0329 0.0331 0.0375 0.0518 0.0395 0.0430 0.0353 0.0260 0.0312 0.0398 0.0262 0.0282 0.0329 0.0446 0.0360 0.0282 0.0421 0.0396 0.0241 0.0322 0.0361 0.0365 0.0390 0.0464 0.0431 0.0273 0.0323 0.0343 0.0283 0.0544 0.0404 0.0386 0.0352 0.0301 0.0274

0.0408 0.0263 0.0304 0.0260 0.0229 0.0246 0.0273 0.0432 0.0364 0.0338 0.0316 0.1528 0.0239 0.0252 0.0250 0.0232 0.0287 0.0269 0.0274 0.0397 0.0325 0.0347 0.0271 0.0207 0.0240 0.0310 0.0259 0.0201 0.0241 0.0388 0.0276 0.0205 0.0255 0.0280 0.0196 0.0231 0.0291 0.0290 0.0333 0.0400 0.0282 0.0210 0.0272 0.0215 0.0227 0.0393 0.0285 0.0319 0.0305 0.0239 0.0218

2.26 7.48 2.45 2.65 5.95 4.85 4.22 2.75 3.10 2.01 2.38 2.24 3.96 4.33 4.02 4.13 3.87 3.13 2.69 2.96 3.48 3.79 4.28 5.02 3.73 3.10 4.13 4.34 3.84 3.86 4.58 4.83 4.82 2.85 4.54 4.17 2.91 2.97 3.58 3.41 2.91 4.25 3.33 3.50 4.11 3.30 2.97 2.32 2.67 4.82 4.12

2.69 4.40 4.09 2.77 6.89 4.35 5.48 2.39 3.20 2.90 3.30 2.28 3.46 3.47 3.96 4.19 3.70 3.52 3.42 3.00 3.40 4.22 4.08 4.87 4.57 3.31 3.71 4.76 4.20 3.59 4.92 5.59 6.57 3.21 4.53 4.31 3.05 3.05 2.85 3.39 3.63 4.34 3.96 3.81 3.91 3.75 2.97 2.49 2.53 4.73 4.13

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where Qb is the thermal load of the building and COP represents the heat pump coefficient of performance. Substituting Eqs. (2)–(5) into Eq. (1), the fuel energy that needs to be supplied to the PGU can be expressed as:

F pgu ¼

Eb þ ðQ b =COPÞ gpgu þ ð1  gpgu ÞngHRS gORC

ð6Þ

The cost to operate the PGU–ORC system can be expressed as:

Cost pguORC ¼ F pgu Cost f

ð7Þ

where Costf is the cost of the fuel. Similarly, the carbon dioxide emissions (CDE) associated with the PGU–ORC system can be expressed as:

CDEpguORC ¼ F pgu CDEf

ð8Þ

where CDEf is the CDE factor for the system. The primary energy consumption (PEC) for the PGU–ORC system can be expressed as:

PEC pguORC ¼ F pgu PEC f

ð9Þ

Table 3 Source-to-site conversion factors for electricity [24] and natural gas [25].

AK AL AR AZ CA CO CT DC DE FL GA HI IA ID IL IN KS KY LA MA MD ME MI MN MO MS MT NC ND NE NH NJ NM NV NY OH OK OR PA RI SC SD TN TX UT VA VT WA WI WV WY

Electricity

Natural gas

2.9 3.18 3.32 3.06 2.45 3.32 3.24 5.28 3.48 3.05 3.2 3.36 3.52 1.51 3.5 3.38 3.75 3.35 3.23 3.06 3.25 2.97 3.2 3.53 3.31 3.14 2.91 3.13 3.68 3.61 3.13 3.13 3.27 2.92 2.91 3.2 3.03 1.76 3.25 2.73 3.17 2.55 3.13 3.16 3.35 3.3 2.96 1.81 3.52 3.06 3.6

1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047 1.047

where PECf is the PEC factor for the system. 2.2. Conventional system For the conventional system, the electricity needed by the facility, including the electricity required for the heat pump, is imported from the grid (see Fig. 2). Therefore, the total electricity registered at the electric meter is given by:

Em ¼ Eb þ EHP

ð10Þ

The cost to operate the building for the conventional case is

CostCV ¼ Em Cost e

ð11Þ

where Coste is the cost of the electricity. Similarly, the CDE connected with the conventional case is

CDECV ¼ Em CDEe

ð12Þ

where CDEe is the CDE factor associated with the electricity. Additionally, the PEC resulting from operating the building for the conventional case is

PEC CV ¼ Em PEC e

ð13Þ

where PECe is the PEC factor associated with the electricity. 2.3. Systems comparison By defining Rcost = Coste/Costf and setting Eq. (11) equal to Eq. (7), the minimum Rcost that provides cost savings for the PGU–ORC system versus the conventional system can be determined as

Rmin ¼

1

ð14Þ

gpgu þ ngHRS gORC  gpgu ngHRS gORC

Table 4 Emission conversion factors for electricity and natural gas for the various eGRID regions [26,27]. eGRID subregion

eGRID subregion name

Electricity (kg/ kW h)

Natural gas (kg/ kW h)

RCO2

AKGD AKMS

ASCC Alaska Grid ASCC Miscellaneous ERCOT All FRCC All HICC Miscellaneous HICC Oahu MRO East MRO West NPCC Long Island NPCC New England NPCC NYC/ Westchester NPCC Upstate NY RFC East RFC Michigan RFC West SERC Midwest SERC Mississippi Valley SERC South SERC Tennessee Valley SERC Virginia/ Carolina SPP North SPP South WECC California WECC Northwest WECC Rockies WECC Southwest

0.57 0.203

0.181 0.181

3.15 1.12

0.553 0.543 0.603

0.181 0.181 0.181

3.06 3.00 3.33

0.736 0.731 0.697 0.606 0.328

0.181 0.181 0.181 0.181 0.181

4.07 4.04 3.85 3.35 1.81

0.282

0.181

1.56

0.248 0.454 0.739 0.682 0.821 0.467

0.181 0.181 0.181 0.181 0.181 0.181

1.37 2.51 4.08 3.77 4.54 2.58

0.614 0.63

0.181 0.181

3.39 3.48

0.487

0.181

2.69

0.816 0.717 0.277 0.382 0.86 0.534

0.181 0.181 0.181 0.181 0.181 0.181

4.51 3.96 1.53 2.11 4.75 2.95

ERCT FRCC HIMS HIOA MROE MROW NYLI NEWE NYCW NYUP RFCE RFCM RFCW SRMW SRMV SRSO SRTV SRVC SPNO SPSO CAMX NWPP RMPA AZNM

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Eq. (14) clearly indicates that the value of Rmin only depends on the efficiencies of the system components and not on the building or climate conditions. By defining RCO2 = CDEe/CDEf and setting Eq. (12) equal to Eq. (8), the minimum RCO2 that provides savings over the conventional case is the same as that presented in Eq. (14). Defining RPEC = PECe/PECf and setting Eq. (13) equal to Eq. (9) also yields the same result presented in Eq. (14). The cost reduction is given by

Redcost

  Cost CV  CostORC ¼ Cost CV

¼1

1 Rcost ðgpgu þ ngHRS gORC  ngHRS gORC gpgu Þ

¼1

Rmin Rcost

The PEC reduction can be estimated using Eq. (15) by changing Rcost by RPEC as

RedPEC ¼ 1 

Rmin RPEC

ð16Þ

Similarly, the CDE reduction can be estimated using Eq. (15) by changing Rcost by RCDE as

RedCDE ¼ 1 

Rmin RCDE

ð17Þ

3. Results 3.1. Potential reductions in cost, PEC, and CDE

ð15Þ In this section, the potential savings in terms of operational cost, primary energy consumption, and carbon dioxide emissions

Table 5 Potential cost reduction for each state based on the values presented in Tables 1 and 2.

Table 6 Potential primary energy consumption reduction for each state based on the values presented in Tables 1 and 3.

State

Reduction residential (%)

Reduction commercial (%)

State

PEC savings (%)

Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

21.72 63.28 12.20 3.74 53.83 43.33 34.97 0.20 11.25 36.65 15.43 22.81 30.64 36.55 31.62 33.51 29.03 12.12 1.99 7.07 21.11 27.47 35.85 45.32 26.40 11.39 33.51 36.75 28.45 28.82 40.08 43.09 43.00 3.63 39.49 34.18 5.64 7.53 23.34 19.53 5.76 35.33 17.46 21.53 33.11 16.71 7.46 18.25 3.06 43.05 33.31

2.02 37.63 32.89 0.88 60.14 36.83 49.88 14.79 14.12 5.28 16.73 20.66 20.57 20.92 30.62 34.46 25.66 21.96 19.69 8.42 19.22 34.84 32.61 43.62 39.84 17.00 25.96 42.27 34.60 23.54 44.13 50.90 58.17 14.51 39.42 36.28 9.94 9.83 3.69 18.85 24.35 36.73 30.65 27.83 29.72 26.74 7.58 10.53 8.57 41.87 33.43

AK AL AR AZ CA CO CT DC DE FL GA HI IA ID IL IN KS KY LA MA MD ME MI MN MO MS MT NC ND NE NH NJ NM NV NY OH OK OR PA RI SC SD TN TX UT VA VT WA WI WV WY

0.82 9.56 13.37 6.01 17.39 13.37 11.23 45.53 17.35 5.70 10.12 14.40 18.29 90.47 17.83 14.91 23.30 14.15 10.96 6.01 11.50 3.16 10.12 18.52 13.11 8.40 1.16 8.11 21.84 20.33 8.11 8.11 12.05 1.50 1.16 10.12 5.08 63.42 11.50 5.35 9.27 12.79 8.11 8.98 14.15 12.85 2.83 58.90 18.29 6.01 20.11

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Table 7 Potential emissions savings for the different eGRID regions based on the values presented in Tables 1 and 4. eGRID subregion

eGRID subregion name

CDE savings (%)

AKGD AKMS ERCT FRCC HIMS HIOA MROE MROW NYLI NEWE NYCW NYUP RFCE RFCM RFCW SRMW SRMV SRSO SRTV SRVC SPNO SPSO CAMX NWPP RMPA AZNM

ASCC Alaska Grid ASCC Miscellaneous ERCOT All FRCC All HICC Miscellaneous HICC Oahu MRO East MRO West NPCC Long Island NPCC New England NPCC NYC/Westchester NPCC Upstate NY RFC East RFC Michigan RFC West SERC Midwest SERC Mississippi Valley SERC South SERC Tennessee Valley SERC Virginia/Carolina SPP North SPP South WECC California WECC Northwest WECC Rockies WECC Southwest

12.77 144.93 10.09 8.43 17.54 32.44 31.98 28.66 17.95 51.59 76.31 100.49 9.52 32.72 27.10 39.44 6.47 19.02 21.08 2.10 39.07 30.65 79.50 30.16 42.19 6.89

are evaluated for different US locations. For the initial analysis, the values used for the efficiency of the PGU, the heat recovery system efficiency, the loss factor, and the efficiency of the ORC to determine Rmin (Eq. (14)) are presented in Table 1. The cost of electricity and natural gas for the residential and commercial sector for several US locations are presented in Table 2. This table also shows the Rcost values for the residential prices and commercial prices. Table 3 presents the source-to-site energy conversion factors for electricity and natural gas [24–25]. The carbon dioxide emission conversion factors for electricity and natural gas for the different eGRID regions in the US are presented in Table 4. For the values presented in Table 1, Rmin was determined as 2.74. With this value and the Rcost values for the residential and commercial sectors in the US presented in Table 2, the potential of the proposed PGU–ORC configuration for reducing the operational cost can be estimated. These values are presented in Table 5. In this table, negative values mean that the proposed PGU–ORC does not provide any cost savings in comparison with the conventional systems. It is also important to highlight that only 9 states showed no potential cost savings in the residential sector. On the other hand, only 5 states showed no potential cost savings in the commercial sector. For example, for the State of Arizona, the proposed system increases the cost by 12.2% for the residential sector. However, it reduces the cost in the commercial sector by about 32.89%. The maximum cost reduction in the residential sector was obtained in Alaska, 63.28%, and the maximum cost reduction in the commercial sector was obtained in California, 60.14%. Similarly, with the established value for the Rmin associated with the system and the source-to-site conversion factors and CDE factors presented in Tables 3 and 4, the potential reductions in PEC and CDE can be projected as shown in Tables 6 and 7. In terms of PEC, only 6 states showed no potential PEC savings, while only 9 out of 26 eGrid regions showed no potential CDE savings. The maximum reduction of PEC was obtained for DC, 45.3%, while the maximum reduction of CDE was obtained for the WECC Rockies eGrid subregion, 42.19%. Fig. 3 presents a comparison of the potential reductions in operational cost, PEC, and CDE for three states, California, Illinois, and

Fig. 3. CHP–ORC system performance for various states.

Hawaii. The parameters displayed on the plot are reductions; accordingly, positive values indicate savings, and negative values indicate increases in the performance parameters. As Fig. 3 demonstrates, a reduction in one performance parameter does not guarantee reduction of other performance parameters. This figure also highlights the area-dependent nature of the proposed PGU– ORC system performance; potential reductions can vary widely from state to state depending on cost, source-to-site energy conversion factors, and CDE conversion factors for electricity and natural gas. For example, in California, operational cost savings are expected, but PEC and CDE will increase. By contrast, in Hawaii, the PGU–ORC system increases operational costs but reduces PEC and CDE. As another example, the system provides potential reductions in all of the performance parameters in Illinois. Therefore, it can be concluded that the proposed system needs to be carefully analyzed for each location to estimate the potential reductions in operational cost, PEC, and CDE. 3.2. Effect of varying the system parameters on the Rmin value This section examines the relationships between the various system components and Rmin. The effect of the efficiencies of the PGU, HRS, and ORC, as well as the effect of the factor that accounts for losses, on Rmin is presented in Figs. 4–7. Since Rmin must be less than Rcost (or RCO2 or RPEC) to produce savings, reducing the value of Rmin through efficiency metrics can lead to greater cost savings with a PGU–ORC system.

Fig. 4. Effect of varying the PGU efficiency on Rmin.

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Fig. 5. Effect of varying the ORC efficiency on Rmin.

a PGU efficiency of 10%, the Rmin value is about 4.2 while for PGU efficiency of 30% Rmin value is about 2.5. Fig. 5 shows the effect of the ORC efficiency on Rmin. This figure demonstrates that increasing the efficiency of the ORC decreases the Rmin value. However, this effect is less pronounced than that of the PGU efficiency. Thus, the efficiency of the ORC is a lower priority than that of the PGU for maximizing potential PGU–ORC savings over a conventional system. Figs. 6 and 7 show the effect of the HRS efficiency and the factor that accounts for losses, respectively, on Rmin. The results in both figures indicate that increasing both parameters decreases the Rmin value. However, both parameters have little influence on the value of Rmin. Therefore, as shown through the examination of Figs. 4–7, the efficiency of the PGU is the metric that has the greatest potential to affect cost, PEC, and CDE savings of the PGU–ORC system over a conventional system. While the ORC efficiency, HRS efficiency, and loss factor all have an impact on PGU–ORC performance, the more significant component is that of the PGU efficiency.

4. Conclusion

Fig. 6. Effect of varying the efficiency of the heat recovery system on Rmin.

Fig. 7. Effect of varying the parameter that accounts for the losses of the heat recovery system on Rmin.

Fig. 4 illustrates the effect of the PGU efficiency on Rmin. As shown in Fig. 4, the efficiency of the PGU has a significant impact on Rmin. Increasing the efficiency of the PGU decreases the Rmin value, implying that potential savings capabilities could be increased with a more efficient PGU. The value of Rmin can span a relatively wide range depending on the efficiency of the PGU. For

This paper analyzed the potential of using the waste heat from a PGU to generate additional electricity using an ORC to reduce operational cost, PEC, and CDE in different locations in the U.S. The PGU–ORC system was compared with a conventional system in terms of operational cost, PEC, and CDE reduction. A parameter, Rmin, based on system efficiencies, was established to determine when the proposed PGU–ORC system would potentially provide savings versus the conventional system in which electricity is purchased from the utility grid. When Rmin is significantly less than Rcost, RCO2 , or RPEC, then potential savings in cost, carbon dioxide emissions, or primary energy consumption, respectively, can be obtained by utilizing the PGU–ORC over a conventional system. The results of the simulation suggested, as expected, that for some locations, the PGU–ORC system would be quite effective in terms of operational cost, PEC, and CDE reductions; however, for some locations, the PGU–ORC system would not be effective, particularly in the realm of CDE reduction. As Rmin depends on the system components and their efficiencies, varying the system parameters can have a significant effect on Rmin, in turn affecting the results of the simulation. Varying the efficiency of the PGU has the most dramatic effect on Rmin. Therefore, this parameter has to be carefully considered when estimating the potential savings from the proposed system. Variations in the ORC and HRS efficiencies and the parameter that accounts for losses all have a far less pronounced effect on Rmin. The maximum cost reduction in the residential sector was obtained in Alaska, 63.28%, and the maximum cost reduction in the commercial sector was obtained in California, 60.14%. The maximum reduction of PEC was obtained for DC, 45.3%, while the maximum reduction of CDE was obtained for the WECC Rockies eGrid subregion, 42.19%. Overall, the results indicated that this metric can be used as a screening tool to quickly predict the potential for operational cost, PEC, and CDE reductions associated with a given system with reasonable accuracy. If the proposed methodology predicts potential reductions in the desired parameter, the PGU–ORC system should be carefully explored as an option. On the other hand, if the proposed methodology does not predict potential reductions in the desired parameter, the PGU–ORC system will not yield reductions, and other alternatives should be considered. The results presented in this paper were obtained using natural gas to power the proposed system. However, the proposed methodology can be used to investigate if another fuel would provide more benefits that natural gas in terms of operational cost, PEC, and CDE.

A. Knizley et al. / Energy Conversion and Management 105 (2015) 480–487

References [1] Mago PJ, Chamra LM, Hueffed A. A review on energy, economical, and environmental benefits of the use of CHP systems for small commercial buildings for the North American climate. Int J Energy Res 2009;33 (14):1252–65. [2] Rosen Ma, Le MN, Dincer I. Efficiency analysis of a cogeneration and district energy system. Appl Therm Eng 2005;25(1):147–59. [3] Knizley A, Mago PJ, Smith AD. Evaluation of the performance of combined cooling, heating, and power systems with dual power generation units. Energy Policy 2014;66:654–65. [4] Knizley A, Mago PJ. Potential emissions reductions from the use of dualcombined heat and power systems in different climate conditions. Int J Ambient Energy 2014;35(1):20–9. [5] Knizley A, Mago PJ. Evaluation of combined heat and power (CHP) systems performance with dual power generation units for different building configurations. Int J Energy Res 2013;37(12):1529–38. [6] Mago PJ, Hueffed A, Chamra LM. Analysis and optimization of the use of CHP– ORC systems for small commercial buildings. Energy Build 2010;42(9):1491–8. [7] Auld A, Berson A, Hogg S. Organic Rankine cycles in waste heat recovery: a comparative study. Int J Low-Carbon Technol 2013;8(suppl 1):i9–i18. [8] Mikielewicz D, Mikielewicz J. On the efficient use of a low temperature heat source by the organic Rankine cycle. Arch Thermodyn 2013;34(3):61–73. [9] Delgado Torres A, Garcia-Rodriguez L. Analysis and optimization of the lowtemperature solar organic Rankine cycle (ORC). Energy Convers Manag 2010;51(12):2846–56. [10] Yamamoto T, Furuhata T, Arai N, Mori K. Design and testing of the organic Rankine cycle. Energy 2001;26(3):239–51. [11] Heberle F, Brüggemann D. Thermoeconomic analysis of hybrid power plant concepts for geothermal combined heat and power generation. Energies 2014;7(7):4482–97. [12] Law R, Harvey A, Reay D. Techno-economic comparison of a high-temperature heat pump and an organic Rankine cycle machine for low-grade waste heat recovery in UK industry. Int J Low-Carbon Technol 2013;8(suppl 1):i47–54. [13] Hung T-C. Waste heat recovery of organic Rankine cycle using dry fluids. Energy Convers Manag 2001;42(5):539–53.

487

[14] Liu B-T, Chien K-H, Wang C-C. Effect of working fluids on organic Rankine cycle for waste heat recovery. Energy 2004;29(8):1207–17. [15] Saleh B, Koglbauer G, Wendland M, Fischer J. Working fluids for lowtemperature organic Rankine cycles. Energy 2007;32(7):1210–21. [16] Hung TC, Shai TY, Wang SK. A review of organic rankine cycles (ORCs) for the recovery of low-grade waste heat. Energy 1997;22(7):661–7. [17] Madhawa Hettiarachchi HD, Golubovic M, Worek WM, Ikegami Y. Optimum design criteria for an Organic Rankine cycle using low-temperature geothermal heat sources. Energy 2007;32(9):1698–706. [18] Dai Y, Wang J, Gao L. Parametric optimization and comparative study of organic Rankine cycle (ORC) for low grade waste heat recovery. Energy Convers Manag 2009;50(3):576–82. [19] Zhang J, Li Y, Wang Z, Hou G. Dynamic characteristics and predictive control for organic Rankine cycle. 7th IEEE Conf Ind Electron Appl 2012;state 1:281–4. [20] Zhang J, Zhou Y, Gao S, Hou G. Constrained predictive control based on state space model of Organic Rankine Cycle system for waste heat recovery. 24th Chin Control Decision Conf 2012:230–4. [21] Wei D, Lu X, Lu Z, Gu J. Performance analysis and optimization of organic Rankine cycle (ORC) for waste heat recovery. Energy Convers Manag 2007;48 (4):1113–9. [22] EIA – electricity data. Available: http://www.eia.gov/electricity/monthly/epm_ table_grapher.cfm?t=epmt_5_6_a [accessed: 14.10.14]. [23] Average commercial price. Available: http://www.eia.gov/dnav/ng/ng_ pri_sum_a_EPG0_PCS_DMcf_a.htm [accessed: 11.11.14]. [24] Final Report v1 –0008ENERGYEMISSIONFACTORSRESCONSUMPTION.pdf. Available: http://www.aga.org/SiteCollectionDocuments/KnowledgeCenter/ OpsEng/CodesStandards/0008ENERGYEMISSIONFACTORSRESCONSUMPTION. pdf [accessed: 12.11.14]. [25] Methodology for incorporating source energy use – site_source.pdf. Available: http://www.energystar.gov/ia/business/evaluate_performance/site_source. pdf [accessed: 12.11.14]. [26] eGRID 9th edition version 1.0 summary tables for year 2010 data – eGRID_9th_edition_V1-0_year_2010_Summary_Tables.pdf. Available: http:// www.epa.gov/cleanenergy/ documents/egridzips/eGRID_9th_edition_V1-0_ year_2010_Summary_Tables.pdf [accessed: 12.11.14]. [27] U.S. Environmental Protection Agency. Clean energy calculations and references.