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A case study of local integrated resource planning
Energy 28 (2003) 711–720 www.elsevier.com/locate/energy
A case study of local integrated resource planning Arif S. Malik a,∗, Cecilio U. Sumaoy b a
College of Engineering, PO Box 33, Sultan Qaboos University, Al-Khod 123, Oman b Cagayan Electric Power and Light Company, Cagayan de Oro City, Philippines Received 8 July 2001
Abstract This paper reports the results of a local integrated resource planning (LIRP) study conducted for the Philippines. Based on the investment plans for serving loads at distribution feeders our key finding is that distributed resources (DR) are cost effective alternatives to supply capacity expansion for Cagayan Electric Power and Light Co. (CEPALCO), an investor-owned power distribution utility company operating in the Southern Philippines. The total cost savings for the selected feeder on which LIRP was performed are about 19 million Pesos (about half a million US dollars, 1998) over a 10-year period. 2003 Elsevier Science Ltd. All rights reserved.
1. Introduction Local integrated resource planning (LIRP) is obviously relevant to a regulated integrated utility that is obligated to serve its ratepayers reliability at the least cost. The recent trend of electricity market reforms heightens the importance of LIRP because, unlike generation whose investment decisions become decentralized, transmission (or distribution) investments continue to be the decision of a regulated wire company. Even when deciding transmission investment (e.g., California), the independent market operator (ISO) continues to rely on LIRP to find the leastcost expansion plan. The LIRP, which integrates distributed resources (DR), promises major benefits for a distribution utility, mainly in the form of deferral of major investments related to distribution system expansion—from the substations to the customer service lines. In some cases, this reduces distribution utility expenditures by two- to ten-fold [1,2]. In the face of competition, transmission and distribution (T&D) facility construction has ∗
Corresponding author. Tel.: +968-515-390; fax: +968-513-416. E-mail address: [email protected] (A.S. Malik).
0360-5442/03/$ - see front matter 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0360-5442(03)00003-3
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recently become capital-intensive due to the utilities’ effort to upgrade their services to retain customers in a competitive environment. On the other hand, T&D system load factors are low, [3,4] which means under-utilization of these resources. Therefore, the need for the LIRP is iminent under this utility environment. Other benefits have not been widely recognized, including reduced vulnerability to conflicts over T&D sites, reduced risk in a time of rapid industry change, and an opportunity to gain valuable experience with distributed generation and storage resources [2]. Although the concept of IRP was introduced in most developing countries more than a decade ago, only a few utilities in these countries have ever developed a comprehensive system plan based on IRP. Coincidentally, privatization and deregulation are also being pushed through in most of these countries requiring modification of the existing IRP approaches to fit the new power utility business environment. The objective of this paper is to demonstrate the applicability of LIRP in an investor-owned power distribution utility company, CEPALCO, operating in the southern Philippines. CEPALCO’s 15-year expansion plan is used as the reference distribution investment plan. The tasks that involve load forecasting, evaluation of existing and candidate facilities and the optimization works were beyond the scope of this study. From the investment plan, feeder specific costs are calculated to select the most expensive feeder to operate in the distribution system. LIRP then revolves around this selected feeder where DR are evaluated, screened and selected on the basis of the feeder’s avoided costs. The impact of each resource option to the feeder reliability, losses, and voltage profile is assessed. Later, the benefit to the utility is evaluated and the conclusions are drawn. 2. Feeder avoided costs The marginal feeder capacity cost (MFCC) is realized by the deferral of major load-driven investments in the distribution level [5,6]. MFCC for eight feeders of the distribution system are calculated and shown in Table 1. On the basis of MFCC Tagoloan 1, a 34.5 kV Feeder has been Table 1 CEPALCO feeders’ total avoidable costs Feeder
⌬kW
Load factor
⌬kWh
MFCC SMCCa (PP/kW) (PP/kW)
SMECa FSMC (PP) (PP/kWh)
AIC (PP/kWh)
Tagoloan 1 Camaman-an 3 Camaman-an 2 Carmen 1 Camaman-an 1 Tagoloan 2 Carmen 2 Carmen 3
2,296 2,600 919 337 1,528 619 495 2,200
62.68% 67.68% 61.05% 65.23% 47.23% 72.21% 65.23% 65.23%
12,606,156 15,415,305 4,915,389 1,925,602 6,322,396 3,914,865 2,828,406 12,570,695
5,642 3,840 3,656 3,230 3,167 3,104 2,974 1,029
7.794 7.794 7.794 7.794 7.794 7.794 7.794 7.794
2.05 1.95 2.00 1.95 2.15 1.89 1.94 1.88
a
13,354.85 13,354.85 13,354.85 13,354.85 13,354.85 13,354.85 13,354.85 13,354.85
158,891,146 184,632,402 60,417,375 23,068,538 83,464,261 45,581,947 33,741,666 145,172,316
Present worth value of annual marginal capacity cost and marginal energy cost of 2173.44 PP/kW and 1.2684 PP/kWh, respectively; discount rate of 10% and ten years study horizon.
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selected for LIRP. The supply-side marginal capacity cost (SMCC) a1d supply-side marginal energy cost (SMEC) are fixed to the marginal supplier of CEPALCO. The two power suppliers to CEPALCO are investigated to determine which source supplies for the system incremental demand. MINERGY, an IPP with 18 MW capacity gas-turbines, supplies around 20% of the total system requirements while the remainder is supplied by the state-owned National Power Corporation (NPC). MINERGY is operated as a base-load plant on a 25-year firm power purchase contract. Based on this operational constraint, the NPC is identified as CEPALCO’s marginal power supplier such that the supply level marginal capacity costs (SMCC) and energy costs (SMEC) are pegged to NPC’s power rates. The current capacity charge of NPC is PP 2,173.44/kW per year while the total energy charge is PP 1.2684/kWh as of March 1998. Total avoidable cost is the sum of supply and distribution level avoidable costs and is hereby called feeder specific marginal cost (FSMC). These costs need to be converted into a common reference point since the MFCC is a present worth value, while SMEC and SMCC are annual values.
3. Screening of distributed resources The term “distributed resources” includes modular power technologies called distributed generation (DG) and non-generating demand side management (DSM) measures, such as energy efficiency improvements, which reduce the load at the distribution level of the T&D grid. A DSM or DG option is preliminarily screened by comparing its conservation or generation costs to the feeder average incremental costs (AIC). Mathematically, AIC ⫽ FSMC ⫻ CRF / ⌬kWh, PP / kWh,
(1)
where CRF=capital recovery factor; ⌬kWh=annual incremental feeder energy demand. The AIC for each feeder is calculated and given in Table 1. The conservation costs of a DSM option (CDSM) is the incremental costs required to conserve a kWh of energy with DSM while the generation costs of a DG (CDG) include the option’s capital and operating costs. Mathematically, CDSM ⫽ PCOSTDSM ⫻ CRF / ⌬kWhDSM, PP / kWh,
(2)
CDG ⫽ PCOSTDG ⫻ CRF / ⌬kWhDG, PP / kWh,
(3)
where PCOST=present value of all costs for DSM or DG; ⌬kWhDSM=energy savings by DSM resource; and ⌬kWhDG=energy generation of DG resource. DSM and DG resource options pass this preliminary screening if their respective CDSM and CDG are less than the feeder’s AIC. 3.1. DG resource options During the energy crisis felt throughout the Philippines in the early 1990s, several customers installed diesel generation sets in their facilities. Sizes of these gensets range from a few kW to 5000 kW. As of 1997 at least 39 MW of these gensets are installed throughout CEPALCO’s distribution system. These gensets, almost all of which are operational, remain idle for most of the time of the year [7]. The capital cost of these assets is already sunk. Considering only the fuel and O&M costs
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(assumed to be 10% of fuel cost), the cost of generated energy of these gensets is found to be PP 1.994/kWh based on an average fuel rate of 0.2414 l/kWh and at current fuel price of PP 7.16 per liter. This generation cost is lower than the PP 2.05/kWh AIC of the selected feeder. However, this option is only marginally feasible considering that the generation cost will surpass the feeder’s AIC if the fuel price increases by just 5%. Nevertheless, the resource option is taken for further integration into LIRP. 3.2. DSM resource options The DSM options considered here are not technology-specific but rather taken as a DSM program (for example, a lighting efficiency program may be composed of several efficient lighting technologies). The conservation capital costs of commercial lighting and cooling efficiency programs are given in Table 2. These costs are based on the consultant’s estimates [8] and are adjusted here to reflect the current 40 Philippine pesos to a US dollar (1998) conversion rate. 3.3. Potential for capacity reduction Three diesel gensets of a total 2500 kW capacity (2×700 kW and 1×1250 kW unit capacity) at a commercial mall, connected to the study feeder, is considered for DG option. The capacity reduction due to energy conservation potential is based on the expected energy conservation potential of commercial customers [8] with the application of the feeder average load factor. Potential capacity reductions due to energy efficiency resources and capacity generation by selected distributed resources are shown in Table 2.
4. Resources’ impact on planning criteria A distribution load flow is run for each year in the study horizon where impact on reliability, line losses, and voltage profile, at a selected node, is established. This is depicted in Fig. 1 and detailed calculations can be seen in [9].
Table 2 Energy conservation potential of selected DR Distributed resources
Generation/conservation cost End-use share of (PP/kWh) total consumption
Standby gensets 1.994 Cooling efficiency program 1.940 Lighting efficiency 1.323 program
– 30.0 % 23.0 %
Energy conservation potential
Potential capacity (kW)
– 39.0 % 32.2 %
2,500 287 258
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Fig. 1. Flowchart for the evaluation of reliability, voltage, and financial impact of DSM/DG.
4.1. Resources’ impact on reliability The results from load flow analysis under the feeder’s average conditions indicate that DR’s reliability effect is negligible. This is because the system average interruption duration index (SAIDI) and system average interruption frequency index (SAIFI) largely depend on outage restoration that reflects the utility operation practices, rather than line loadings. 4.2. Resources’ impact on load profile and losses Due to the high operating cost, the diesel gensets operating time is made limited to clip the peak to the desired level. The DG impact on system and feeder hourly load is shown in Figs 2 and 3, respectively. Due to diversity between the system and feeder peaks, the genset is able to clip only 1600 kW to the system compared to its 2500 kW impact on the feeder peak (capacity clipping to system is only 66% of the gensets’ capacity). In a similar manner, lighting and cooling energy efficiency programs’ impact on feeder and system peaks is evaluated and the results are shown in Table 3. The operating times for these resources are based on typical operating times found in RMI survey [8] on commercial customers. As shown, lighting efficiency has a very minimal impact on the system peak (6%) compared to the diesel genset (66%). On the contrary, the cooling efficiency has maximum impact on the
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Fig. 2. Impact of 2500 kW gensets on the system’s hourly load profile.
Fig. 3. Impact of 2500 kW gensets on the feeder’s hourly load profile.
Table 3 Impact of resources on feeder and system peak loads Demand-side resource
Capacity (kW)
Impact on feeder peak (kW)
Impact on system peak (kW)
Impact on feeder peak Impact on system peak in percent of total in percent of total distributed capacity distributed capacity (%) (%)
Diesel genset Lighting efficiency Cooling efficiency Total
2500 258 287 3045
2500 258 191 2777
1640 15 287 1942
100 100 67 91
66 6 100 64
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Table 4 Distributed resources’ impact on line loss reduction Year
With gensets
1999 2004 2008 Total
With cooling
With lighting
kWh
PP
kWh
PP
kWh
PP
481,465 438,857 454,084 4,659,637
907,562 827,246 855,949 8,783,427
294,377 302,139 291,056 3,034,219
554,901 569,533 548,641 5,719,509
139,519 154,821 143,626 1,502,849
262,993 291,837 270,736 2,832,874
system peak than on the feeder (impact on the feeder is only 67% of the capacity reduction potential). This is because the system peak occurs at 1500 h where the cooling requirement is high while the feeder peak occurs at around 1900 h where the lighting load is at maximum. Based on the results of load flow the impact of each DR on feeder line losses is quantified and the savings in annual line losses are presented in Table 4. It is observed that even if the cooling efficiency program’s estimated capacity reduction on the feeder is only around 8% of the standby genset, the saved energy losses of the former are around 65% of the latter. This is so because of the limitation imposed on the diesel genset operation. 4.3. Resources’ impact on feeder voltage profile The voltage problem is almost always experienced at the most remote point in the distribution system due to high voltage drops along the line. In this study, the resource’s impact on the feeder voltage is measured based on the amount of voltage it is able to raise at the identified end-point. As expected, the resource which is able to generate or reduce the largest capacity potential causes the highest rise in the end-point voltage. The average per unit voltage raised by each demandside option is shown in Table 5 for three representative years with average over 10 years. 4.4. Impact on the utility’s financial status Under the utility’s perspective, the costs involved for a DSM and DG resource option j are COSTj ⫽ ICj ⫹ FCj ⫹ OMj ⫹ OHj ⫹ LRj,
(4)
where IC=first cost (initial investment); FC=fuel cost in the case of the DG option; OM=operation Table 5 Impact of DR on rise in per unit voltage at the feeder’s end point for selected years Resource
1999
2004
2008
Ave.
Standby genset Lighting efficiency Cooling efficiency
0.019 pu 0.003 pu 0.003 pu
0.008 pu 0.002 pu 0.002 pu
0.007 pu 0.004 pu 0.004 pu
0.0106 pu 0.0023 pu 0.0027 pu
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and maintenance cost; OH=administrative and other costs associated with the DSM or DG program; LR=lost revenue caused by the DSM option=(UCSE⫺CPE)×⌬kWhDSM; CPE=energy rate per kWh of the supplier; UCSE=utility’s cost of sold energy; and the benefits for the resource option j are BENEFITj ⫽ ACj ⫹ AEj ⫹ ALj ⫹ AENSj
(5)
where AC=avoided capacity cost; AE=avoided energy cost; AL=avoided cost of line losses=CPE×⌬LOSS; AENS1=avoided cost of energy not served=(UCSE⫺CPE)×⌬ENS; ⌬LOSS=change in line losses due to DSM or DG; ⌬ENS=change in energy not served due to the DSM or DG. The overbars in Eqs (4) and (5) represent that all stream of costs and benefits are expressed in their present worth values. The annual stream of benefits for a DSM or a DG is calculated mainly from the avoided costs. The avoided costs of energy not served may not be confused with the lost revenue. It is in fact a credit given to DR for reliability improvement in terms of less frequency of outages—hence reduction in energy not served quantity due to improved load profile. Administrative cost is pegged to be 25% of annual equipment costs while customer incentives are designed based on equal sharing of net benefits. The standby genset, as a DG resource, connected directly to the distribution system adds to the system capacity and does not cause lost revenue to the utility and no bill reduction is expected for the participating customer. As such, the customers are allocated 50% of the net benefits (avoided costs) profited by the utility company. On the other hand, when the benefits are accrued solely by the customers, as in the form of reduced billing in case of DSM programs, the utility is allocated 50% of the benefits in addition to the utility’s avoided costs. This scheme is adjudged to be fair considering that the customers do not spend any amount in the program implementation aside from its normal operating costs before the implementation of the project. The present worth values of these streams of costs and benefits are presented in Tables 6 and 7, respectively. Table 8 provides the summary of net benefits. The costs for efficiency programs exceed the benefits accrued by the utility company when the savings derived from the reduction of line losses and energy not served are ignored. Lighting efficiency program is only marginally beneficial with and without line losses and ENS reduction. On the other hand, using the standby genset as DR shows a substantial net present value. Table 6 Utility’s present worth of costs of demand-side resource options Demand-side resource
Capital cost (PP)
Genset—DG – Lighting—DSM 11,760,074 Cooling—DSM 19,231,555
1
Fuel cost (PP)
O&M cost Lost revenue Customer Admin. cost Total (PP) (PP) (PP) incentive (PP) (PP)
29,801,763 – –
2,980,176 – – 10,337,533 – 11,527,932
8,471,724 – –
Note that from the society’s point of view the cost of energy not served could be quite large.
8,195,485 2,940,018 4,807,889
49,449,147 25,037,625 35,567,375
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Table 7 Utility’s present worth of benefits of demand-side resource options Dem.-side Res.
Avoided capacity Avoided energy (PP) (PP)
Genset—DG 36,053,504 Lighting— 1,648,018 DSM Cooling— 4,915,870 DSM
Share of benefits (PP)
Total w/o ENS & Losses (PP)
Total w/ ENS & Losses (PP)
21,867,367 11,278,302
– 12,906,951
57,920,871 25,833,271
65,379,315 27,983,337
12,577,033
14,393,226
31,886,129
35,966,687
Table 8 Summary of Utility’s net present worth of demand-side resource options Dem.-side Res.
TOTAL w/o ENS & Losses (PP)
TOTAL w/ ENS & Losses (PP)
Genset—DG Lighting—DSM Cooling—DSM Total
8,471,724 795,646 ⫺3,681,246 5,586,124
15,930,168 2,945,712 399,312 19,275,192
5. Conclusion The paper has presented the results of an LIRP conducted for a distribution company operating in the Southern Philippines. Three distributed resources i.e., diesel gensets, lighting, and cooling efficiency programs of about 3 MW capacity were identified as suitable candidates for LIRP. The impact of DR on the selected feeder—based on the highest marginal capacity cost—was evaluated in terms of capacity reduction, reliability, losses, and voltage profile. The net present value of these resources is also worked out and it is found that the cooling efficiency program is only marginally beneficial. The diesel gensets whose capital costs are already sunk are the most attractive option the utility can pursue. For the selected feeder with 2500 kW of peak-capacity reduction these gensets are worth 16 million pesos. The environmental impact due to DR is not evaluated in this study. Acknowledgements The authors are grateful to the Journal’s referees for suggested improvements. References [1] Hoff T. Identifying distributed generation and demand side management investment opportunities. The Energy J 1996;17(4):89–105.
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[2] Lenssen N. Local integrated resource planning: a new tool for a competitive era. The Electricity J 1996;9(6):26–36. [3] Blecker D. Integrated targeted area resource planning (ITARP): a model for transmission and distribution planning in the deregulated utility. Middleton, WI: MSB Energy Associates, 1996. [4] Letendre S, Byrne J, Wang YD. The distributed utility concept: toward a sustainable electric utility sector. Newark, DE: Center for Energy and Environment Policy, University of Delaware, 1996. [5] Heffener G, Woo CK, Horii B, Lloyd-Zannetti D. Variations in area- and time-specific marginal capacity costs of electricity distribution. IEEE Trans Power Systems 1998;13(2):560–7. [6] Woo CK, Orans R, Horii B, Pupp R, Heffner G. Area and time-specific marginal capacity costs of electricity distribution benefits. Energy 1994;19(12):1213–8. [7] CEPALCO. Demand-Side Management Plan. Cagayan de Oro City, Philippines: Cagayan Electric Power and Light Co., Inc., 1997. [8] RMI, Feasibility study on an energy efficiency program based on demand-side management in the Philippines, Final Report. Pasig City, Philippines: Resource Management International, Inc., Ortigas Center, 1996. [9] Sumaoy CU. Prioritization of demand-side resource options in distribution system local integrated resource planning, AIT MSc.Thesis No. ET-98-27, Bangkok: Asian Institute of Technology, 1998.
A case study of local integrated resource planning Arif S. Malik a,∗, Cecilio U. Sumaoy b a
College of Engineering, PO Box 33, Sultan Qaboos University, Al-Khod 123, Oman b Cagayan Electric Power and Light Company, Cagayan de Oro City, Philippines Received 8 July 2001
Abstract This paper reports the results of a local integrated resource planning (LIRP) study conducted for the Philippines. Based on the investment plans for serving loads at distribution feeders our key finding is that distributed resources (DR) are cost effective alternatives to supply capacity expansion for Cagayan Electric Power and Light Co. (CEPALCO), an investor-owned power distribution utility company operating in the Southern Philippines. The total cost savings for the selected feeder on which LIRP was performed are about 19 million Pesos (about half a million US dollars, 1998) over a 10-year period. 2003 Elsevier Science Ltd. All rights reserved.
1. Introduction Local integrated resource planning (LIRP) is obviously relevant to a regulated integrated utility that is obligated to serve its ratepayers reliability at the least cost. The recent trend of electricity market reforms heightens the importance of LIRP because, unlike generation whose investment decisions become decentralized, transmission (or distribution) investments continue to be the decision of a regulated wire company. Even when deciding transmission investment (e.g., California), the independent market operator (ISO) continues to rely on LIRP to find the leastcost expansion plan. The LIRP, which integrates distributed resources (DR), promises major benefits for a distribution utility, mainly in the form of deferral of major investments related to distribution system expansion—from the substations to the customer service lines. In some cases, this reduces distribution utility expenditures by two- to ten-fold [1,2]. In the face of competition, transmission and distribution (T&D) facility construction has ∗
Corresponding author. Tel.: +968-515-390; fax: +968-513-416. E-mail address: [email protected] (A.S. Malik).
0360-5442/03/$ - see front matter 2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S0360-5442(03)00003-3
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A.S. Malik, C.U. Sumaoy / Energy 28 (2003) 711–720
recently become capital-intensive due to the utilities’ effort to upgrade their services to retain customers in a competitive environment. On the other hand, T&D system load factors are low, [3,4] which means under-utilization of these resources. Therefore, the need for the LIRP is iminent under this utility environment. Other benefits have not been widely recognized, including reduced vulnerability to conflicts over T&D sites, reduced risk in a time of rapid industry change, and an opportunity to gain valuable experience with distributed generation and storage resources [2]. Although the concept of IRP was introduced in most developing countries more than a decade ago, only a few utilities in these countries have ever developed a comprehensive system plan based on IRP. Coincidentally, privatization and deregulation are also being pushed through in most of these countries requiring modification of the existing IRP approaches to fit the new power utility business environment. The objective of this paper is to demonstrate the applicability of LIRP in an investor-owned power distribution utility company, CEPALCO, operating in the southern Philippines. CEPALCO’s 15-year expansion plan is used as the reference distribution investment plan. The tasks that involve load forecasting, evaluation of existing and candidate facilities and the optimization works were beyond the scope of this study. From the investment plan, feeder specific costs are calculated to select the most expensive feeder to operate in the distribution system. LIRP then revolves around this selected feeder where DR are evaluated, screened and selected on the basis of the feeder’s avoided costs. The impact of each resource option to the feeder reliability, losses, and voltage profile is assessed. Later, the benefit to the utility is evaluated and the conclusions are drawn. 2. Feeder avoided costs The marginal feeder capacity cost (MFCC) is realized by the deferral of major load-driven investments in the distribution level [5,6]. MFCC for eight feeders of the distribution system are calculated and shown in Table 1. On the basis of MFCC Tagoloan 1, a 34.5 kV Feeder has been Table 1 CEPALCO feeders’ total avoidable costs Feeder
⌬kW
Load factor
⌬kWh
MFCC SMCCa (PP/kW) (PP/kW)
SMECa FSMC (PP) (PP/kWh)
AIC (PP/kWh)
Tagoloan 1 Camaman-an 3 Camaman-an 2 Carmen 1 Camaman-an 1 Tagoloan 2 Carmen 2 Carmen 3
2,296 2,600 919 337 1,528 619 495 2,200
62.68% 67.68% 61.05% 65.23% 47.23% 72.21% 65.23% 65.23%
12,606,156 15,415,305 4,915,389 1,925,602 6,322,396 3,914,865 2,828,406 12,570,695
5,642 3,840 3,656 3,230 3,167 3,104 2,974 1,029
7.794 7.794 7.794 7.794 7.794 7.794 7.794 7.794
2.05 1.95 2.00 1.95 2.15 1.89 1.94 1.88
a
13,354.85 13,354.85 13,354.85 13,354.85 13,354.85 13,354.85 13,354.85 13,354.85
158,891,146 184,632,402 60,417,375 23,068,538 83,464,261 45,581,947 33,741,666 145,172,316
Present worth value of annual marginal capacity cost and marginal energy cost of 2173.44 PP/kW and 1.2684 PP/kWh, respectively; discount rate of 10% and ten years study horizon.
A.S. Malik, C.U. Sumaoy / Energy 28 (2003) 711–720
713
selected for LIRP. The supply-side marginal capacity cost (SMCC) a1d supply-side marginal energy cost (SMEC) are fixed to the marginal supplier of CEPALCO. The two power suppliers to CEPALCO are investigated to determine which source supplies for the system incremental demand. MINERGY, an IPP with 18 MW capacity gas-turbines, supplies around 20% of the total system requirements while the remainder is supplied by the state-owned National Power Corporation (NPC). MINERGY is operated as a base-load plant on a 25-year firm power purchase contract. Based on this operational constraint, the NPC is identified as CEPALCO’s marginal power supplier such that the supply level marginal capacity costs (SMCC) and energy costs (SMEC) are pegged to NPC’s power rates. The current capacity charge of NPC is PP 2,173.44/kW per year while the total energy charge is PP 1.2684/kWh as of March 1998. Total avoidable cost is the sum of supply and distribution level avoidable costs and is hereby called feeder specific marginal cost (FSMC). These costs need to be converted into a common reference point since the MFCC is a present worth value, while SMEC and SMCC are annual values.
3. Screening of distributed resources The term “distributed resources” includes modular power technologies called distributed generation (DG) and non-generating demand side management (DSM) measures, such as energy efficiency improvements, which reduce the load at the distribution level of the T&D grid. A DSM or DG option is preliminarily screened by comparing its conservation or generation costs to the feeder average incremental costs (AIC). Mathematically, AIC ⫽ FSMC ⫻ CRF / ⌬kWh, PP / kWh,
(1)
where CRF=capital recovery factor; ⌬kWh=annual incremental feeder energy demand. The AIC for each feeder is calculated and given in Table 1. The conservation costs of a DSM option (CDSM) is the incremental costs required to conserve a kWh of energy with DSM while the generation costs of a DG (CDG) include the option’s capital and operating costs. Mathematically, CDSM ⫽ PCOSTDSM ⫻ CRF / ⌬kWhDSM, PP / kWh,
(2)
CDG ⫽ PCOSTDG ⫻ CRF / ⌬kWhDG, PP / kWh,
(3)
where PCOST=present value of all costs for DSM or DG; ⌬kWhDSM=energy savings by DSM resource; and ⌬kWhDG=energy generation of DG resource. DSM and DG resource options pass this preliminary screening if their respective CDSM and CDG are less than the feeder’s AIC. 3.1. DG resource options During the energy crisis felt throughout the Philippines in the early 1990s, several customers installed diesel generation sets in their facilities. Sizes of these gensets range from a few kW to 5000 kW. As of 1997 at least 39 MW of these gensets are installed throughout CEPALCO’s distribution system. These gensets, almost all of which are operational, remain idle for most of the time of the year [7]. The capital cost of these assets is already sunk. Considering only the fuel and O&M costs
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(assumed to be 10% of fuel cost), the cost of generated energy of these gensets is found to be PP 1.994/kWh based on an average fuel rate of 0.2414 l/kWh and at current fuel price of PP 7.16 per liter. This generation cost is lower than the PP 2.05/kWh AIC of the selected feeder. However, this option is only marginally feasible considering that the generation cost will surpass the feeder’s AIC if the fuel price increases by just 5%. Nevertheless, the resource option is taken for further integration into LIRP. 3.2. DSM resource options The DSM options considered here are not technology-specific but rather taken as a DSM program (for example, a lighting efficiency program may be composed of several efficient lighting technologies). The conservation capital costs of commercial lighting and cooling efficiency programs are given in Table 2. These costs are based on the consultant’s estimates [8] and are adjusted here to reflect the current 40 Philippine pesos to a US dollar (1998) conversion rate. 3.3. Potential for capacity reduction Three diesel gensets of a total 2500 kW capacity (2×700 kW and 1×1250 kW unit capacity) at a commercial mall, connected to the study feeder, is considered for DG option. The capacity reduction due to energy conservation potential is based on the expected energy conservation potential of commercial customers [8] with the application of the feeder average load factor. Potential capacity reductions due to energy efficiency resources and capacity generation by selected distributed resources are shown in Table 2.
4. Resources’ impact on planning criteria A distribution load flow is run for each year in the study horizon where impact on reliability, line losses, and voltage profile, at a selected node, is established. This is depicted in Fig. 1 and detailed calculations can be seen in [9].
Table 2 Energy conservation potential of selected DR Distributed resources
Generation/conservation cost End-use share of (PP/kWh) total consumption
Standby gensets 1.994 Cooling efficiency program 1.940 Lighting efficiency 1.323 program
– 30.0 % 23.0 %
Energy conservation potential
Potential capacity (kW)
– 39.0 % 32.2 %
2,500 287 258
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Fig. 1. Flowchart for the evaluation of reliability, voltage, and financial impact of DSM/DG.
4.1. Resources’ impact on reliability The results from load flow analysis under the feeder’s average conditions indicate that DR’s reliability effect is negligible. This is because the system average interruption duration index (SAIDI) and system average interruption frequency index (SAIFI) largely depend on outage restoration that reflects the utility operation practices, rather than line loadings. 4.2. Resources’ impact on load profile and losses Due to the high operating cost, the diesel gensets operating time is made limited to clip the peak to the desired level. The DG impact on system and feeder hourly load is shown in Figs 2 and 3, respectively. Due to diversity between the system and feeder peaks, the genset is able to clip only 1600 kW to the system compared to its 2500 kW impact on the feeder peak (capacity clipping to system is only 66% of the gensets’ capacity). In a similar manner, lighting and cooling energy efficiency programs’ impact on feeder and system peaks is evaluated and the results are shown in Table 3. The operating times for these resources are based on typical operating times found in RMI survey [8] on commercial customers. As shown, lighting efficiency has a very minimal impact on the system peak (6%) compared to the diesel genset (66%). On the contrary, the cooling efficiency has maximum impact on the
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Fig. 2. Impact of 2500 kW gensets on the system’s hourly load profile.
Fig. 3. Impact of 2500 kW gensets on the feeder’s hourly load profile.
Table 3 Impact of resources on feeder and system peak loads Demand-side resource
Capacity (kW)
Impact on feeder peak (kW)
Impact on system peak (kW)
Impact on feeder peak Impact on system peak in percent of total in percent of total distributed capacity distributed capacity (%) (%)
Diesel genset Lighting efficiency Cooling efficiency Total
2500 258 287 3045
2500 258 191 2777
1640 15 287 1942
100 100 67 91
66 6 100 64
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Table 4 Distributed resources’ impact on line loss reduction Year
With gensets
1999 2004 2008 Total
With cooling
With lighting
kWh
PP
kWh
PP
kWh
PP
481,465 438,857 454,084 4,659,637
907,562 827,246 855,949 8,783,427
294,377 302,139 291,056 3,034,219
554,901 569,533 548,641 5,719,509
139,519 154,821 143,626 1,502,849
262,993 291,837 270,736 2,832,874
system peak than on the feeder (impact on the feeder is only 67% of the capacity reduction potential). This is because the system peak occurs at 1500 h where the cooling requirement is high while the feeder peak occurs at around 1900 h where the lighting load is at maximum. Based on the results of load flow the impact of each DR on feeder line losses is quantified and the savings in annual line losses are presented in Table 4. It is observed that even if the cooling efficiency program’s estimated capacity reduction on the feeder is only around 8% of the standby genset, the saved energy losses of the former are around 65% of the latter. This is so because of the limitation imposed on the diesel genset operation. 4.3. Resources’ impact on feeder voltage profile The voltage problem is almost always experienced at the most remote point in the distribution system due to high voltage drops along the line. In this study, the resource’s impact on the feeder voltage is measured based on the amount of voltage it is able to raise at the identified end-point. As expected, the resource which is able to generate or reduce the largest capacity potential causes the highest rise in the end-point voltage. The average per unit voltage raised by each demandside option is shown in Table 5 for three representative years with average over 10 years. 4.4. Impact on the utility’s financial status Under the utility’s perspective, the costs involved for a DSM and DG resource option j are COSTj ⫽ ICj ⫹ FCj ⫹ OMj ⫹ OHj ⫹ LRj,
(4)
where IC=first cost (initial investment); FC=fuel cost in the case of the DG option; OM=operation Table 5 Impact of DR on rise in per unit voltage at the feeder’s end point for selected years Resource
1999
2004
2008
Ave.
Standby genset Lighting efficiency Cooling efficiency
0.019 pu 0.003 pu 0.003 pu
0.008 pu 0.002 pu 0.002 pu
0.007 pu 0.004 pu 0.004 pu
0.0106 pu 0.0023 pu 0.0027 pu
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and maintenance cost; OH=administrative and other costs associated with the DSM or DG program; LR=lost revenue caused by the DSM option=(UCSE⫺CPE)×⌬kWhDSM; CPE=energy rate per kWh of the supplier; UCSE=utility’s cost of sold energy; and the benefits for the resource option j are BENEFITj ⫽ ACj ⫹ AEj ⫹ ALj ⫹ AENSj
(5)
where AC=avoided capacity cost; AE=avoided energy cost; AL=avoided cost of line losses=CPE×⌬LOSS; AENS1=avoided cost of energy not served=(UCSE⫺CPE)×⌬ENS; ⌬LOSS=change in line losses due to DSM or DG; ⌬ENS=change in energy not served due to the DSM or DG. The overbars in Eqs (4) and (5) represent that all stream of costs and benefits are expressed in their present worth values. The annual stream of benefits for a DSM or a DG is calculated mainly from the avoided costs. The avoided costs of energy not served may not be confused with the lost revenue. It is in fact a credit given to DR for reliability improvement in terms of less frequency of outages—hence reduction in energy not served quantity due to improved load profile. Administrative cost is pegged to be 25% of annual equipment costs while customer incentives are designed based on equal sharing of net benefits. The standby genset, as a DG resource, connected directly to the distribution system adds to the system capacity and does not cause lost revenue to the utility and no bill reduction is expected for the participating customer. As such, the customers are allocated 50% of the net benefits (avoided costs) profited by the utility company. On the other hand, when the benefits are accrued solely by the customers, as in the form of reduced billing in case of DSM programs, the utility is allocated 50% of the benefits in addition to the utility’s avoided costs. This scheme is adjudged to be fair considering that the customers do not spend any amount in the program implementation aside from its normal operating costs before the implementation of the project. The present worth values of these streams of costs and benefits are presented in Tables 6 and 7, respectively. Table 8 provides the summary of net benefits. The costs for efficiency programs exceed the benefits accrued by the utility company when the savings derived from the reduction of line losses and energy not served are ignored. Lighting efficiency program is only marginally beneficial with and without line losses and ENS reduction. On the other hand, using the standby genset as DR shows a substantial net present value. Table 6 Utility’s present worth of costs of demand-side resource options Demand-side resource
Capital cost (PP)
Genset—DG – Lighting—DSM 11,760,074 Cooling—DSM 19,231,555
1
Fuel cost (PP)
O&M cost Lost revenue Customer Admin. cost Total (PP) (PP) (PP) incentive (PP) (PP)
29,801,763 – –
2,980,176 – – 10,337,533 – 11,527,932
8,471,724 – –
Note that from the society’s point of view the cost of energy not served could be quite large.
8,195,485 2,940,018 4,807,889
49,449,147 25,037,625 35,567,375
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Table 7 Utility’s present worth of benefits of demand-side resource options Dem.-side Res.
Avoided capacity Avoided energy (PP) (PP)
Genset—DG 36,053,504 Lighting— 1,648,018 DSM Cooling— 4,915,870 DSM
Share of benefits (PP)
Total w/o ENS & Losses (PP)
Total w/ ENS & Losses (PP)
21,867,367 11,278,302
– 12,906,951
57,920,871 25,833,271
65,379,315 27,983,337
12,577,033
14,393,226
31,886,129
35,966,687
Table 8 Summary of Utility’s net present worth of demand-side resource options Dem.-side Res.
TOTAL w/o ENS & Losses (PP)
TOTAL w/ ENS & Losses (PP)
Genset—DG Lighting—DSM Cooling—DSM Total
8,471,724 795,646 ⫺3,681,246 5,586,124
15,930,168 2,945,712 399,312 19,275,192
5. Conclusion The paper has presented the results of an LIRP conducted for a distribution company operating in the Southern Philippines. Three distributed resources i.e., diesel gensets, lighting, and cooling efficiency programs of about 3 MW capacity were identified as suitable candidates for LIRP. The impact of DR on the selected feeder—based on the highest marginal capacity cost—was evaluated in terms of capacity reduction, reliability, losses, and voltage profile. The net present value of these resources is also worked out and it is found that the cooling efficiency program is only marginally beneficial. The diesel gensets whose capital costs are already sunk are the most attractive option the utility can pursue. For the selected feeder with 2500 kW of peak-capacity reduction these gensets are worth 16 million pesos. The environmental impact due to DR is not evaluated in this study. Acknowledgements The authors are grateful to the Journal’s referees for suggested improvements. References [1] Hoff T. Identifying distributed generation and demand side management investment opportunities. The Energy J 1996;17(4):89–105.
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[2] Lenssen N. Local integrated resource planning: a new tool for a competitive era. The Electricity J 1996;9(6):26–36. [3] Blecker D. Integrated targeted area resource planning (ITARP): a model for transmission and distribution planning in the deregulated utility. Middleton, WI: MSB Energy Associates, 1996. [4] Letendre S, Byrne J, Wang YD. The distributed utility concept: toward a sustainable electric utility sector. Newark, DE: Center for Energy and Environment Policy, University of Delaware, 1996. [5] Heffener G, Woo CK, Horii B, Lloyd-Zannetti D. Variations in area- and time-specific marginal capacity costs of electricity distribution. IEEE Trans Power Systems 1998;13(2):560–7. [6] Woo CK, Orans R, Horii B, Pupp R, Heffner G. Area and time-specific marginal capacity costs of electricity distribution benefits. Energy 1994;19(12):1213–8. [7] CEPALCO. Demand-Side Management Plan. Cagayan de Oro City, Philippines: Cagayan Electric Power and Light Co., Inc., 1997. [8] RMI, Feasibility study on an energy efficiency program based on demand-side management in the Philippines, Final Report. Pasig City, Philippines: Resource Management International, Inc., Ortigas Center, 1996. [9] Sumaoy CU. Prioritization of demand-side resource options in distribution system local integrated resource planning, AIT MSc.Thesis No. ET-98-27, Bangkok: Asian Institute of Technology, 1998.