Line Upgrade Deferral Scenarios for Distributed Renewable Energy Resources

Distributed Energy Investment as an Alternative to Distribution Upgrades in Rural Areas

Scenarios for Distributed Energy Investment as an Alternative to
Distribution Line Upgrades in Rural Areas
By Iain Sanders, Sustainable Innovative Solutions Limited, and Alister Gardiner, Industrial
Research Limited

Abstract
In this paper, we identify and evaluate various ‘best-case scenarios’ for investing in decentralised
micro-generation from a utility-driven, distribution network perspective. A distribution line experiencing
significant over-capacity from increasing customer demand is used to determine the Net Present
Value (NPV) of five different modular, distributed energy systems: (1) hydroelectric power (HEP) with
diesel genset (DGN) support; (2) wind turbine generation (WTG) with DGN support; (3) photovoltaics
(PVS) with DGN support; (4) solar hot water (SHW) with DGN support; and (5) DGN by itself, as a
reference case.

The study considers the value of distributed energy (DE) in deferring or eliminating distribution line
energy- / capacity-based upgrades. The basic principle applied in this study is that the distributed
generation installed consists of a combination of fuel-based (DGN) generation and intermittent
renewable energy (RE) to ensure that “normal” supply reliability can be delivered at all times,
irrespective of RE availability. Typical RE supply profiles are used to indicate the likely mix of RE and
DGN supply throughout the year on a continuous half-hourly basis. The scale and format of the
particular technologies is not specified, instead these are simply identified as opportunity costs.

As a typical case study involving real “industry” data, the NPV of DE as a line upgrade deferral option
was compared with a “business as usual” scenario for a rural distribution line in the Eastland Networks
Limited (ENL) region of the north island of New Zealand. For the data presented in this report, the
annual energy demand growth rate was exaggerated and extended over a 20-year timeframe to
emphasize the potential contribution that DE could have on the energy / capacity supply mix for
regions of high growth. The net results were almost always in favour of DE line upgrade deferral (as
opposed to a “business as usual” network management arrangement) under the conditions assumed
for this study. No attempt was made to account for any contributions of heat generated by the fuel
based (diesel assumed) generation. Combined Heat and Power (CHP) would add substantial value by
providing additional end use energy from the fuel resource.

Introduction
Over the last nine years, Industrial Research has evaluated a wide range of resource opportunities for
adopting Renewable Distributed Energy (RDE) technologies in New Zealand. The objective has been
to evaluate and demonstrate the techno-economic viability of micro- (less than 100kW capacity), mini-
(between 100kW and 1000kW capacity) and small-scale (between 1MW and 10MW capacity) RDE
systems in New Zealand. In the process specialised tools and methodologies have been developed to
fulfil this purpose. (Unless ‘scale’ is specifically mentioned, the term ‘small’ will refer to anything from
micro-scale to small-scale inclusive).

This research into distributed energy-based systems has been motivated by the promise of more
efficient energy utilisation and the opportunity for capturing local renewable energy resources with
minimal use of additional infrastructure. Specific network benefits are possible through:

1. Local generation solutions relieving distribution network capacity while maintaining utilisation (fig.1).
2. Technology that will provide alternatives to uneconomic network sections.
3. Creating the means for large numbers of small distributed generators to export aggregated

Dr. Iain Sanders Sustainable Innovative Solutions Limited Page 1 of 25


electricity from otherwise uneconomic network assets to different network users (see figure 2).
4. Ability to track slow growth in demand with small matching incremental steps in generation, thus
avoiding or delaying major upgrades.

2 0 0 3 L o a d D u r a t io n C u r v e fo r L y n d o n L in e

100

90

80
8 0 k W M A X IM U M S T A N D A R D O P E R A T IN G C A P A C IT Y T H R E S H O L D

70
Firm DE 6 0 k W B A S E - C A S E U S E D F O R L O A D D U R A T IO N P R O J E C T IO N S
Capacity (kW)

60

50

40

30
Primary objective for DE to
20 meet peak load requirements
10

0
1133

1360

1586

1813

2039

2266

2492

2719

2945

3172

3398

3625

3851

4078

4304

4531

4757

4984

5210

5437

5663

5890

6116

6343

6569

6796

7022

7249

7475

7702

7928

8155

8381

8608
227

454

680

907
1

C u m m u la t iv e H o u r s o f t h e Y e a r

MainPower Lyndon
(ML) line

Predicted Load
Duration Curve

Typical
Example
What is Line
Upgrade Deferral?
Figure 1: Local generation solutions to relieve peak distribution network capacity

T o d a y 's T o m o r r o w 's
C e n tr a l U tility D is tr ib u te d U tility ?
C e n tr a l G e n e r a tio n C e n tr a l G e n e r a t io n

W in d

R em o te
G en set L oads

PV
F u e l C e ll
B a tte r y
C u sto m er
C u sto m ers E f f ic ie n c y
M ic r o tu r b in e
1
Can Costly Upgrades Be Prevented?
© 2 0 0 2 D i s t r i b u te d U t il it y A s s o c i a t e s

Figure 2: Redesigning Distribution Networks Around Locally Available Distributed Energy Resources

Local distributed energy provides significant benefits to various stakeholders:

1. Support adoption of environmentally friendly energy supply alternatives;
2. Provide supplementary revenue for farmers – other network customers;
3. Reduce burden of long-term infrastructure upgrades on network customers;



4. Reduce risk of failure of overloaded transmission and distribution lines, and potentially
increase system security;
5. Promote supply energy-efficiency;
6. Only invest in what is required using modular distributed energy (DE) technologies; and,
7. Potentially provide additional revenue / savings for network operators.

Using integrated distributed energy technologies, some technologies may be owned and controlled by
the networks, and some technologies may be owned and controlled by the customers. These potential
network benefits contrast with the more popular view that distributed generation threatens the
traditional electricity supply infrastructure by taking away energy delivery but not alleviating capacity
demands. Note that the network benefits are case specific, and are primarily based on demand growth
scenarios. Previous work by Industrial Research has identified few if any benefits accruing to
distribution networks from distributed generation in regions with static or declining load.

In the main, small-scale technology developers have been preoccupied with reducing the costs of their
own particular product in the high volume micro- / mini-scale embedded generation marketplace.
Unfortunately, no single technology can yet provide the quality of service delivered by the distribution
network, at the distribution network price. For example, a wind generator cannot guarantee firm
capacity, so the network must provide this; and, while a diesel genset can deliver capacity the cost of
energy from a diesel genset is generally too high, so it is relegated to a standby function. This paper
evaluates the ability of combinations of local resources to deliver matching energy and firm capacity to
complement grid based electricity services, and the value accrued from offsetting investment costs
associated with local growth.

Background of Research
Industrial Research Limited (IRL) has worked with Eastland Networks Limited (ENL) support to
evaluate the potential economic impact of Distributed Energy Resources (DERs) on the East Coast
potion of their distribution network (see figure 3). This was chosen as typical of rural network asset

Eastland Network
TE ARAROA
INPORT

RUATORIA
INPORT

Te Puia is
fed from
Tokomaru
Bay
50/11kV line

TOKOMARU
BAY INPORT

FOCUS TOLAGA BAY
INPORT

Main Case Study – a
section of the Eastland
Network was chosen –
The Ruatoria 11kV
Feeder from the
GISBORNE Ruatoria 50/11kV
INPORT
Substation
Figure 3: East Coast Portion of Eastland Networks Limited



conditions and costs, because of the limited availability of detailed asset valuation. The East Coast
portion of the Eastland Network stretches from Gisborne in the south to Hick’s Bay in the north. A
single 50kV subtransmission line carries electricity from Gisborne up the coast to four substations at: 1.
Tolaga Bay, 2. Tokomaru Bay, 3. Ruatoria, and 4. Te Araroa (see figure 3).

These four substations deliver power to the communities on the East Coast via 14 11kV feeders (see
figure 4). The 11kV feeders distribute electricity to the communities and individuals in the region.

TOLAGA B AY TOKOMARU BAY RUATORIA TE ARAROA
SUBSTATION SUBSTATION SUBSTATION SUBSTATION

FROM 4 TH SUBSTATION:
FEEDERS
L, M & N

FROM 3 RD SUBSTATION:
FEEDERS
H, I, J & K

FROM 2 ND SUBSTATION:
FEEDERS
E, F & G

FROM 1 ST SUBSTATION:
FEEDERS
A, B, C & D

Figure 4: Eastland Network’s East Coast feeders and substations

Motivation for the Research
It is getting harder for electricity distribution networks to cover their O&M and replacement costs on
infrastructure for the following reasons:

1. Increasing or remote rural population hot spots putting pressure (often seasonal) on existing
rural networks (although this reason is not particularly relevant to the East Coast region);
2. Most rural network infrastructure is old, nearing the end of its normal life, making O&M costly
and in need of replacement; and,
3. Routine preventive O&M is less affordable, resulting in more severe and costly failures when
they happen.

New Zealand is rich in alternative energy resources which could make a substantial contribution
towards meeting the country’s future energy demand through DE grid-support projects. At present
however, these generation technologies are hard to justify on a purely user “demand side” basis. If
treated as a “supply side” asset, (as they potentially are via the right to connect) the economic case
can improve dramatically. There is substantial potential for DE technologies to reduce peak demand
and hence extend the life of New Zealand’s ageing network infrastructure. These opportunities may be
extended in the future to automatic islanding and self-healing interactive micro-grids delivering higher
reliability at lower service costs. Furthermore, local communities are keen to develop natural



resources for long-term sustainable development or as part of locally-sponsored sustainability
initiatives and programs. Providing that the regulatory and market environment is adapted to recognize
the benefits, these technologies will transform many aspects of network power in the future.

Methodology of the Study
In this study, three load growth scenarios representing a 1.5%, 5% and 10% increase in peak load per
year for 20 years were selected and used to demonstrate the potential value from deferring
infrastructure line upgrades, by using supplementary distributed energy to provide the peak load
shortfall whenever the physical limit of the distribution line was exceeded. The peak load shortfall was
calculated both as a capacity shortfall (in kW) and an energy shortfall requirement (in kWh), so that
the line upgrade deferral value (of investment in network infrastructure capacity to meet the peak load
shortfall) could be measured as a Net Present Value (NPV) marginal distribution capacity cost
(MDCC) in $/kW/year (known as the capacity-valuation method), and as a NPV marginal distribution
energy cost (MDEC) in $/kWh (known as the energy-valuation method).

The peak load capacity / energy shortfall requirement was determined by selecting an appropriate
capacity threshold (i.e. physical upper limit of feeder capacity supply) for the distribution feeder
meeting the demand. In this report, we cover the 10% annual load growth scenario, and show how
distributed energy can be used to reliably meet the capacity / energy shortfall resulting from demand
outstripping the capability of a network feeder to supply all the capacity / energy required.

Capacity / energy shortfalls from surplus demand were addressed by a combination of renewable (in
this case: hydroelectric, wind, photovoltaic and solar hot water) and fuel-driven (in this case diesel)
distributed energy. Ratios of 80%/20%, 50%/50% and 20%/80% RE/DGN were used, and these ratios
represent the proportion of capacity delivered by the RE and DGN components when 100% of the RE
capacity is available. If the peak capacity shortfall is 100kW for example, for a 50%/50% WTG/DGN
system, 50kW of WTG is the maximum capacity contribution from the wind (and the assumed name
plate sizing of the turbine) with the peak capacity shortfall balance of 50kW met by the diesel genset.

The capacity and energy shortfall requirements were calculated on a half-hourly basis over a 20-year
period for each of the load growth scenarios. These figures were converted into monetary values
using the network asset valuation reports to derive an annual financial contribution requirement to
operate, maintain and replace the existing feeder. The annual financial contribution to line upgrade /
replacement was discounted to provide the NPV marginal distribution cost introduced previously.

The total capacity and energy benefit derived from the various combinations of distributed energy (DE)
used, covered: (a) line upgrade deferral using RE and DGN; (b) peak distribution capacity shortfall
(wholesale) energy contributions from RE and DGN; (c) off-peak (wholesale) energy contributions
from RE (don’t want to waste non-peak RE available); (d) transmission peak load reduction at the grid
exit point (GXP) from RE and DGN capacity contributions. These benefits were offset by the capital
and O&M costs (including fuel costs) associated with using different DE combinations, and the loss in
network energy distribution revenue caused by using local DE to meet the demand instead of energy
imported from the GXP. The net benefit / cost was derived from the difference between these amounts,
and the return on investment (ROI) was derived from the ratio of these amounts. The economic
assumptions are summarized below in table 1:
Table 1: Key Economic Assumptions for DE Benefits and Fuel Costs
Line Upgrade Deferral Value (Capacity-valuation method, MDCC) $99.37/kW/Year
Line Upgrade Deferral Value (Energy-valuation method, MDEC) $0.0807/kWh
GXP Transmission Savings Value $50.62/kW/Year
Energy Wholesale Price $0.1289/kWh
Energy Distribution Revenue Loss $0.0687/kWh
Diesel Fuel Price and Annual Increase Range $1-$3.00/litre, 2-10%/Year increase



Ruatoria Feeder Case Study
The Ruatoria feeder (see figure 4) on the Ruatoria substation (see figure 3) was selected for this study
(see figure 5). This feeder was selected because it demonstrated an annual increase of peak capacity,
and detailed asset management information along with half-hourly demand information was available.

Figure 5: Ruatoria Feeder Half-hourly Capacity Profiles for 2001-2003

Load Profile History and Projections
The local load profiles are used to enable prediction of DE capacity support opportunities. For three
years in succession, 2001 to 2003, the average peak capacity growth rate was 1.5% / year. To better

Comparison of Peak Load Growth Scenarios and Relationship to the Capacity Threshold for Grid-
Support on the Ruatoria Feeder

9,000

8,000

7,000

6,000
Total Load (kW)

PEAK LOAD Threshold
5,000 DE REQUIREMENT
1.75% / Yr
FOR 10%/YR GROWTH
4,000 5% / Yr
10% / Yr
3,000 PEAK LOAD
DE REQUIREMENT
2,000 FOR 5%/YR GROWTH

1,000 PEAK LOAD DE
REQUIREMENT
FOR 1.75%/YR
0 GROWTH
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Time in Years

Figure 6: Ruatoria Load Growth Scenarios Adopted



illustrate the potential impact of DE on line upgrade deferral for the more general case, growth
projections of: 1.75, 5 and 10% / year were investigated. A peak capacity threshold of 1,600kW was
selected to illustrate the methodology used, although the exact capacity constraints were not identified.
The capacity threshold represents the capacity above which the feeder is overloaded due to voltage
drop, overheating or overloading etc. (see figure 6). We have assumed that the demand profile and
load factor do not change over this growth period. Detailed analysis presented here gives the results
obtained for the 10% load growth scenario to emphasize the potential impacts of DE technologies.

Assessing Costs of Network Upgrades
In order to derive the benefit available from installing DE to meet the peak load requirement when the
threshold capacity of the Ruatoria Feeder is surpassed (1,600kW for the purposes of this case study),
it is necessary to calculate the line upgrade deferral value of the feeder. The line upgrade deferral
value is calculated by combining the direct and indirect annual O&M costs of the feeder with the
hypothetical cost of reinvestment once existing infrastructure is replaced. The following information
has been provided by ENL, based upon ENL network data and ENL assumptions made.
Direct & Indirect Annual O&M Costs (Derived from ENL Asset
Management Plan and ENL Asset Accounting Spreadsheets)
Direct Costs = $935 / km / Year
Indirect Costs = $66 / Connection / Year
FEEDER Length (km) No. of Connections Annual O&M Costs
Ruatoria: H. Ruatoria 20.000 333 $40,678.00

Estimated cost of reinvestment once existing infrastructure is
replaced (as assessed by ENL)
Annual reinvestment cost = {ODRC x 2} / 40 (lifetime)
ODRC = Optimized Depreciation Replacement Cost
Total Return = Annual O&M Costs + Annual Reinvestment
FEEDER Reinvestment / Yr. Annual O&M Costs Total Annual Required
Ruatoria: H. Ruatoria $33,350 $40,678.00 $74,028.00

The total annual investment required to maintain and upgrade the Ruatoria Feeder has been
calculated to be $74,028. This value was converted into NPV $/kW/year and $/kWh/year amounts,
corresponding to the annual energy and capacity demand forecasts predicted over a 20-year
timeframe for a 10% annual growth rate. The marginal distribution capacity and energy costs (MDCC
and MDEC) are summarized below in table 2:

Table 2: Key Parameters and Assumptions for Marginal Distribution Capacity and Energy Costs
Parameters Value
Feeder Capacity Threshold, C(T) 1,600kW
Note 1
Network Finite Planning Horizon, n 40 Years
Note 2
(Max.) Network Investment Deferral Period, D(t) 20 Years
Unity Cost of Capital (Borrowing), r 10%
Inflation Rate Net of Technology Progress, i 3%
Baseline Diesel Fuel Price and annual increase $1.00/litre (Yr-0), 2%/yr inc.
Capacity Deferred by D(t) Years 6,473kW
NPV Marginal Distribution Cost (MDC) $739,063.44
NPV MDCC / kW / Year $99.37 / kW / Year
NPV MDEC / kWh $0.0807 / kWh
Note 3
Net Present Cost of Feeder Distribution / Customer / Day $0.609
Note 1: Planning Horizon = Furthest extent of asset investment (max. possible in the model is 100 years).
Note 2: Deferral Time = Duration of DE project (1 to 30 years (max.) possible).
Note 3: This cost does not include the share of the 50kV subtransmission which is included in the total distribution cost. Because our DE
options do not effect this cost component, it has not been used in the comparisons.



Assessing Costs of Network Upgrades
The values in the list above for the net present value marginal distribution capacity and energy cost
(NPV MDCC and NPV MDEC): $99.37/kW/year and $0.0807/kWh/year, compare reasonably well with
data from other sources and represent the value per unit capacity and energy per year of deferring
distribution network upgrade by a year via reliable DE peak capacity / energy network-support. There
are however, costs to the network from introducing reliable DE, and these will reduce the net benefits
of line upgrade deferral. The assumed DE capital and operating (fuel and maintenance) costs are
summarized in figure 7 below and table 2 above. The costs of DE necessary to match the system
capacity shortfall were derived from the total capacity shortfall using figure 7.

The quality of results delivered with the model are dependent on the level of detail provided by the
load profile projections, RE supply profiles and other input parameters required. The higher the detail,
the better the accuracy of the costs predicted. In this study the renewable energy (RE) and diesel
genset (DGN) supply curves were derived from half-hourly time sequence data derived for a complete
year. These curves are used to establish the amount of DGN and RE generation capacity required to
maintain supply service as the demand grows, without network capacity extensions.

That is, local DE supply was used to support the capacity / energy shortfall when capacity / energy
demand surpassed the Ruatoria Feeder’s threshold value as identified in table 2. RE was always the
preferred local DE supply option selected to make up for the peak load shortfall, with the fuel-driven
DGN making up the balance. The capital costs assumed for the individual modular units used in the
five DE scenarios selected:

(1) hydroelectric power (HEP) with diesel genset (DGN) support;
(2) wind turbine generation (WTG) with DGN support;
(3) photovoltaics (PVS) with DGN support;
(4) solar hot water (SHW) with DGN support; and
(5) DGN by itself, are shown in figure 7.

Hydro & Wind Capital Cost: O&M = 2% of Capital Cost / Year
PV & SHW Capital Cost: O&M = 0.5% of Capital Cost / Year
Diesel Genset Capital Cost: O&M = 5% of Capital Cost / Year

$100,000

Hydro
Wind
Capital Cost, $/kW

$10,000 Photovoltaic
Solar Hot Water
Genset
Log. (Photovoltaic)
Log. (Hydro)
$1,000 Log. (Wind)
Log. (Solar Hot Water)
Log. (Genset)

$100
0 1 10 100 1,000 10,000
Size, kW

Figure 7: Assumed DE Capital Costs

The modular capacity profiles of the RE resources used in this study (the four RE resources used in



the five DE scenarios mentioned previously) are shown in the following graphs. With the exception of
HEP, the supply profiles (see figures 10-12) were adjusted by scaling to the peak capacity shortfall.
The HEP profiles were derived differently, because the volume of water available in the flow-of-river
resource was fixed (se figure 8).

1/2 Hourly Water Flow (in cubic metres per second) for an
Actual River for Day 1 to 182 of a Normal Calendar Year

1
8
15
22
29
36
43
50
57 1,200
64
71
78 1,000
85

Flow (m3/s)
92
99 800
D
ay 106
113
of 120
600
Ye 127
ar 134 400
/.. 141
. 148 200
155
162
0
169

23:00
21:30
20:00
176

18:30
0.00-200.00 200.00-400.00

17:00
15:30
14:00
12:30
11:00
9:30
8:00
6:30
5:00
3:30

400.00-600.00 600.00-800.00
2:00
0:30

800.00-1000.00 1000.00-1200.00 Time of Day

Figure 8: First Six Months’ Half-Hourly Flow-of-River Hydro Resource Used in the Study

The HEP supply factor corresponding to the flow-of-river resource used (see figure 8) was related to
the HEP turbine capacity rating (see figure 9).

HEP Supply Curve

1.000

0.900
(Average Delivered/Turbine Rating)

0.800

0.700
Supply Factor

0.600

0.500

0.400

0.300

0.200

0.100

0.000
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Percentage of Max. Capacity Available

Figure 9: HEP Supply Factor for Flow-of-River Used

The HEP supply curve in figure 9 shows the relationship between the average capacity delivered to
the actual turbine capacity rating, based upon the maximum capacity available for extracting from the



river. If for example, the river can deliver a maximum capacity of 10MW for 2 weeks of the year, and
the HEP turbine rating is 1MW, the average HEP supply capacity available is 0.6 x 1MW = 600kW.

The first six months’ WTG supply profile for a 1MW turbine is shown in figure 10 below. This profile
assumes an average annual wind speed of 6m/s.

1/2 Hourly Load Profile for 1 MW WTG for Day 1 to 182
of a Normal Calendar Year (Annual Wind Speed = 6m/s)

1
8
15
22
29
36
43
50
57 1100
64
71 1000
78 900

Capacity (kW)
85
92 800
99 700
D
ay 106
600
of 113
120 500
Ye 127
ar 134
400
/ .. 141 300
.
148 200
155 100
162
0
169

23:00
21:30
20:00
176

18:30
0-100 100-200 200-300 300-400

17:00
15:30
14:00
12:30
11:00
9:30
8:00
6:30
5:00
3:30

400-500 500-600 600-700 700-800
2:00
0:30

800-900 900-1000 1000-1100 Time of Day

Figure 10: WTG First Six Months’ Half-Hourly Profile

1/2 Hourly Load Profile for 1 kW PV System for Day 1 to 182
of a Normal Calendar Year

1
8
15
22
29
36
43
50
57 1.00
64
71 0.90
78
Capacity (kW)

0.80
85
92 0.70
99
D 0.60
ay 106
113
of 120
0.50
Y 0.40
ea 127
r/ 134 0.30
... 141
148 0.20
155 0.10
162
0.00
169
23:00
21:30
20:00

176
18:30

0.00-0.10 0.10-0.20 0.20-0.30 0.30-0.40
17:00
15:30
14:00
12:30
11:00
9:30
8:00
6:30
5:00
3:30

0.40-0.50 0.50-0.60 0.60-0.70 0.70-0.80
2:00
0:30

0.80-0.90 0.90-1.00 Time of Day

Figure 11: PVS First Six Months’ Half-Hourly Profile

The first six months’ PVS half-hourly profile for a 1kW system is shown in figure 11. The profile used
was derived from best fit solar data for the East Coast region and also applied to produce the SHW


profile in figure 12.

The solar hot water profile is significantly different to the PVS profile because it is assumed that
electrical water heating (the load that is replaced by SHW) takes place during the off-peak period:
11pm to 7am (used in ripple-relay control of domestic water cylinders in many parts of New Zealand).
This would not be the case for all time periods in the year, but is an adequate approximation because
it would certainly be the case during peak load periods.

Heating Profiles Contributed by a 4m2 Solar Hot Water System

0-0.5 0.5-1
0:00
1:00
2:00
3:00 1-1.5 1.5-2
4:00
5:00
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00 2
Time of Day 14:00

kW Electrical Hot
Water Equivalent
15:00 1.5
16:00
17:00 1
18:00
0.5
19:00
20:00 0
21:00 D
N ec
O o
22:00 S e ct v
A p
J ug
J ul
23:00 M un
Ap ay
M r
F ar Month of Year
J an eb

Figure 12: SHW Contribution to Electrical Heating Demand

It is worth mentioning at this point the quite negative impact that SHW investment has on network
costs. SHW results in lower energy sales for the same network capacity requirements. This can be
substantial since water heating represents 25-35% of residential energy demand.

As identified in the introduction, a basic principle for the analysis is that DE must provide the same
level of reliability as network supply. Thus is achieved by matching the capacity requirement one for
one with fueled generation (DGN), irrespective of the level of intermittent renewable technology
installed.

The five DE scenarios selected: (1) hydroelectric power (HEP) with diesel genset (DGN) support; (2)
wind turbine generation (WTG) with DGN support; (3) photovoltaics (PVS) with DGN support; (4) solar
hot water (SHW) with DGN support; and (5) DGN by itself; matched continuous half-hourly energy
demand with the supply available from each of these scenarios. The net cost-benefit of each of these
DE scenarios on the Ruatoria Feeder for line upgrade deferral was determined. The following DE
costs were included: capital investments, maintenance, operating costs and fuel costs etc. to calculate
the net present value (NPV) of both the renewable and fuel-based DE options.

The following DE benefits were included (as introduced in table 1) to calculate the NPV of both the
renewable and fuel-based DE options.

• line upgrade deferral: $99.37/kW/Year or $0.0807/kWh/Year;
• transmission savings: $50.62/kW/Year peak GXP load reduction;



• grid-supporting energy production: $0.1289/kWh; and,
• non-grid-supporting energy production ($0.1289/kWh/Year)
less loss of distribution earnings ($0.0687/kWh/Year) to the network from using local energy.

NPVs were calculated over a 20-year project lifecycle, assuming a 10% utility cost of capital interest
rate, and a 3% inflation rate net of technology progress. The RE-DGN mix was fixed at the following
peak capacity (name plate rating) ratios in this study: 1. 20%/80% DGN/RE, 2. 50%/50% DGN/RE, 3.
80%/20% DGN/RE, and 100% DGN (scenario five). The RE generation capacity was selected to
ensure it met 20, 50 or 80% of the peak load shortfall when delivering 100% of its name plate capacity
rating (sizing).

Alternative Line Upgrade Deferral Methodologies
Two different line upgrade deferral valuation methodologies were used in this study to calculate the
NPV derived from DE technologies: capacity and energy. Both methods calculated the network
capacity requirements from DE for every half-hour over a 20-year period.

Method 1: capacity valuation – values the line assets or any alternative generation (DE) options –
based upon the peak (maximum) capacity delivered by the assets each year; and, method 2: energy
valuation – values the line assets or any alternative generation (DE) options – based upon the total
(sum) energy delivered by the assets each year. The net lifetime benefit from each method for line
upgrade deferral is compared in figure 13.

Comparison of Net Benefit from Capacity-Driven vs. Energy-Driven Line Upgrade Deferral

$8,000,000

$7,000,000
Relates to energy
supplied only
during peak
$6,000,000
periods related
to grid-support
$5,000,000
NPV for Lifetime Benefit

$4,000,000 Distribution
feeder peak load Peak
Disc. Distribution (kWh)
Peak
reduction period kWh Disc. Grid-Supporting Energy (kWh)
$3,000,000 period kW
corresponding to energy Disc. Trans. Saving (kW)
capacity
GXP peak load valuation Disc. Upgrade Deferral (kW)
$2,000,000 valuation
reduction for line
for line
upgrade upgrade
$1,000,000 deferral
deferral

$0 Distribution
kW-Focus kWh-Focus revenue lost
-$1,000,000
Line upgrade deferral value Line upgrade deferral value
based on kW capacity valuation based on kWh energy valuation
-$2,000,000
methodology methodology
Figure 13: Comparison of Capacity Valuation (Method 1) and Energy Valuation (Method 2) for Line Upgrade Deferral

Figure 13 shows that the dominant value from DE in this situation is for line upgrade deferral,
representing 78% for capacity valuation and 85% for energy valuation. The difference in value is
attributed to the difference in impact of NPV discounting on the variation in energy and capacity
benefits during the 20-year line upgrade deferral project lifetime. A substantial increase in energy
value from line upgrade deferral in latter years is not offset by NPV discounting to the same degree as
the capacity value. The NPV of capacity-driven line upgrade deferral benefits is greater in the early
years, while the NPV of energy-driven line upgrade deferral is greater in the latter years. Overall, the
energy NPV over the 20-year project lifetime is greater than the capacity NPV.



It has been observed that method 1: capacity valuation, is more beneficial to fuel-driven DE systems
with low capital costs (i.e. DGN by itself), and method 2: energy valuation is more beneficial to RE-
driven DE systems with unpredictable delivery of capacity needs. Capacity valuation benefits fuel-
driven DE systems more because energy is only provided to supply capacity needs for peak load
reduction. Peak load reduction may typically involve long narrow (sharp) spikes with little energy
content. This scenario is ideal for network companies operating diesel gensets for only a few hours of
the year. Energy valuation is better for customer-driven renewable energy contributions that cannot be
switched on and off “on tap” like a standby generator. Energy valuation notes and values the
aggregate contribution of individual RE options over the year when capacity-support from a particular
RE technology may vary anywhere between 0 and 100% for different (peak load) time periods.

Results from the Ruatoria Feeder Case Study
A comparison of the RE-DGN cost-benefits is given in figure 14 below, with the NPV benefits shown in
blue besides the NPV costs shown in red for each scenario investigated. The bar chart shows the RE
costs / benefits on top (brighter colours) of the DGN costs / benefits (lighter colours).
Renewable Benefit
– Cost of
distribution Comparison of Net-Benefits and Net-Costs from Various RE-Genset Combinations
revenue Renewable Cost
loss $10,000,000
$9,000,000

$8,000,000
NPV of Benefit / Cost

$7,000,000

$6,000,000

$5,000,000

$4,000,000

$3,000,000

$2,000,000

$1,000,000
$0
Genset Benefit
it

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G

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20

50

80
G

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20
20

50

80

Genset Cost RE-Genset Combination

Figure 14: RE-DGN Cost-Benefit Analysis

Figure 14 shows that for this baseline case of low fuel inflation (2%/year), the greater the DGN
component in the total RE-DGN mix, the lower the NPV lifecycle cost of the system installed. Only two
DE systems are shown to make a net loss: the 20%/80% DGN/PVS and the 50%/50% DGN/PVS, due
to the large capital costs incurred with installing the PVS system (see figure 7). Figure 15 compares
the annualized Return on Investment (ROI) from having invested in the different RE-DGN
combinations shown in figure 14 above.

Figure 15 shows that under current costs for operating gensets in New Zealand (assuming a
$1.00/litre price for diesel (or any other fuel producing the same electrical output), increasing at an
average fixed rate of 2% per year), scenario 5: DGN by itself (0%/100% RE/DGN) represents the most
beneficial option for DE line upgrade deferral on the Ruatoria Feeder.

While renewable DE without carbon credits appears to be less attractive than diesel generation,
distributed renewable energy (RDE) coupled with firm capacity from fuel-based generation such as
diesel gensets (DGN) still offers a substantial strategic benefit over conventional expansion of the



network supply system capacity in the demand growth scenario. Comparison of the difference in NPV
between any combination of DGN/RE and the base-case of DGN alone (last 2 columns of figure 14)
shows the cost of adopting various strategic options to increase the amount of renewable electricity by
this approach. From figure 15 it can be seen that the value of the contribution of RE to the total DE
contribution varies with different technologies as shown in figure 16. Both the costs and the benefits of
the RE portion of the investment are plotted against the percentage of renewables present. This data
is taken from the RE only portion of the bar graphs in figure 14.

Annualised Return on Investment (ROI) from Investing in Different RE-Genset Combinations

10.0%

9.0%

8.0%
ROI / Yr, Annualised over 20 Years (%)

7.0%

6.0%
Net Benefit
5.0%

4.0%

3.0%
Net Cost
2.0%

1.0%

0.0%
fit fit fit fit fit fit fit fit fit fit fit fit fit
ne ne ne ne ne ne ne ne ne ne ne ne ne
-1.0% Be Be Be Be Be Be Be Be Be Be Be Be Be
o o o
dr ind PV HW dr ind PV HW dr ind PV HW en
Hy W 80 0S Hy 0W 50 0S Hy 0W 20 0S 0G
80 80 n/ /8 50 /5 n/ /5 20 /2 n/ /2 10
n/ -2.0% en/ Ge en n/ en Ge en n/ en Ge en
Ge 0G 20 0G Ge 0G 50 0G Ge 0G 80 0G
20 2 2 50 5 5 80 8 8
RE-Genset Combination

Figure 15: RE-DGN Annualized ROI

Net RE Cost-Benefit from Different Renewable Ratios in the Genset-Renewable DE Mix

$9,000,000

$8,000,000
Net Cost-Benefit in NPV (Over 20 Years)

Benefit
$7,000,000

Cost Hydro-Benefit
$6,000,000
Wind-Benefit
PV-Benefit
$5,000,000
SHW-Benefit
Hydro-Cost
$4,000,000
Wind-Cost
PV-Cost
$3,000,000
SHW-Cost

$2,000,000

$1,000,000

$0
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Percentage of Renewable Present

Figure 16: RE-only Cost-Benefit Analysis for Various RE-DGN ratios

In every case bar one (HEP-DGN benefit scenario), the net benefit / cost increases when the RE
component increases. Net benefit increases because more energy is delivered, and the average



capacity supplied is increased. The net cost increases because the size of the HEP, WTG, PVS or
SHW system increases, and the capital cost is directly proportional to the name plate capacity rating
of each RE system. However, overall, the % ROI reduces for a larger investment in renewables
because small scale renewables at present show a lower ROI than diesel gensets. This is the cost
one must pay if wishing to maintain or increase the renewable component of a strategic DG policy.

The HEP-DGN benefit scenario in figure 16 is unusual, in that at about 50%/50% HEP/DGN the return
becomes negative (cost is greater than the benefit). The explanation for this is tied to the HEP supply
factor graph given in figure 9. The larger HEP system in figure 16 has a lower supply factor (see figure
9), implying that less peak capacity is available to match the peak demand required for line upgrade
deferral.

Net DE [RE+Genset] Cost-Benefit from Different Renewable Ratios in the Genset-Renewable DE Mix

$11,000,000

$10,000,000
Benefit
Net Cost-Benefit in NPV (Over 20 Years)

$9,000,000

$8,000,000
Cost
Hydro-DE Cost
$7,000,000 Wind-DE Cost
PV-DE Cost
$6,000,000
SHW-DE Cost
$5,000,000 Hydro-Benefit
Wind-Benefit
$4,000,000
PV-Benefit
$3,000,000 SHW-Benefit

$2,000,000

$1,000,000

$0
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Percentage of Renewable Present

Figure 17: Combined RE-DGN Cost-Benefit Analysis for Various RE-DGN ratios

The PV scenarios show negative financial benefit for all ratios, as do the SHW scenarios. Only the
wind scenarios show a positive financial benefit for all levels of penetration. The negative SHW benefit
may appear odd. This analysis however, is only for the benefit of avoiding network upgrades. The
energy value to the owner is not included. Hence, because SHW is assumed to offset a controlled
load (electric hot water storage heating) this analysis illustrates a very important outcome: SHW
investment will not reduce the need for network investment in regions of demand growth.

Figure 17 contains the combined RE-DGN cost-benefits derived for the different ratios examined. The
DGN cost / benefit component makes up the balance of the RE results given in figure 16 and has the
effect of smoothing out the NPV change between different percentages of RE in the combined RE-
DGN system.

The NPV of energy delivered for line upgrade deferral (refer to figure 13 for comparison with the
capacity valuation method), transmission savings at the Grid Exit Point (GXP), wholesale energy sold
(providing grid-support) and loss of distribution earnings, were calculated and compared for each year,
using 100% DGN as the base case (scenario 5). Figure18 shows the NPV energy valuation time-
series for line upgrade deferral for scenario 5: 100%/0% DGN/RE with a 10% / year peak load growth
scenario.

The other DE systems with a RE component: scenarios 1-4, also include a NPV of surplus wholesale



energy sold and loss of distribution earnings not related to line upgrade deferral. Surplus wholesale
renewable energy sold corresponds to the energy produced by the RE system throughout the year
that doesn’t correspond to the peak load reduction periods mentioned in figure 6. Furthermore, this
energy corresponds to a loss in revenue to the network company that would have delivered the same
amount of energy to the end-user from the Grid-Exit-Point (GXP) instead of through locally available
renewable distributed energy. This is the component of renewable energy produced during non-
peaking times, which it is assumed in this study does not attract any line charge and therefore
replaces energy which would otherwise be conveyed over the network for a fee.

Line Upgrade Deferral Met By 100% Genset and 0% Renewable (RE)

$1,600,000
$1,500,000
$1,400,000
$1,300,000
Net Present Value of Net Annual DE Benefit

$1,200,000
$1,100,000
$1,000,000
$900,000
Gen Distribution Loss
$800,000
Gen Grid-Supporting Energy
$700,000
Gen Trans. Saving
$600,000
Gen Upgrade Deferral
$500,000
$400,000
Peak period
$300,000 kWh energy
$200,000 valuation for
$100,000 line upgrade
deferral
$0
-$100,000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

-$200,000
No. of Years Line Upgrade Deferred

Figure 18: 100%/0% DGN/RE Net Benefit Per Year Over Lifetime

Differences Between Capacity Valuation and Energy Valuation Methods
Despite greater discounting of long-term capacity / energy benefits, the overall (summation) financial
benefit of the discounted kWh energy valuation methodology (see figure 13) was greater than the
financial benefit derived from the discounted kW capacity valuation methodology. This implies
minimum-cost (financial outlay) for network-operated DE with capacity-driven valuation (for example:
large-scale DE capacity installation with Orion Networks); and, maximum-benefit for customer-
operated DE with energy-driven valuation (for example: small-scale DE energy installation with Orion
Networks). In reality there is an optimum between the two approaches used: network cost-reduction
versus customer value-creation, because smaller systems are more capital intensive (greater cost per
unit kW / kWh supplied) and costly to operate and maintain per kW / kWh supplied.

Time-Series DGN-RE Scenarios for the 50%/50% DGN/RE Ratio Mix
A comparison of different renewable (RE) to fuel-driven (DGN) ratios was made based upon the actual
kW sizing (name plate) of each individual RE and DGN system. The annual installation of DE capacity
over the 20-year lifecycle selected, matched the shortfall in the distribution system capacity for the
Ruatoria Feeder. Note that these graphs give the grid-support capacity value from the various energy
production components, not the value of energy itself. This study examined the results for: 100%/0%,
80%/20%, 50%/50% and 20%/80% DGN/RE ratios. The results for the 50%/50% DGN/RE ratio mix
are presented below for discussion.



Energy and Capacity Contributions of the 50%/50% DGN/RE Ratio Mix
The following graphs (figures 19 to 22) represent the energy and capacity (grid-support in equivalent
kWh supplied) contributions provided by the renewable and non-renewable (i.e. DGN) components of
the 50%/50% DGN/RE ratio mix for grid-support. In every case, DGN provides the energy and
capacity shortfall not available from the RE-component (HEP, WTG, PV and SHW). These graphs can
be compared with the Net Present Values (NPV) of the overall DE (DGN+RE) benefits delivered per
year for each of the four 50%/50% DGN/RE ratio scenarios examined in the next section (see figures
23 to 26).

Energy Supplied from Various Sources using HEP

40,000,000

35,000,000

30,000,000

25,000,000
kWh / Year

RE Non Grid-Support kWh
Genset Grid-Support kWh
20,000,000
RE Grid-Support kWh
Grid Supplied kWh
15,000,000

10,000,000

5,000,000

0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Year

Figure 19: 50%/50% DGN-HEP Grid-Support and Additional Energy Contributions in kWh / Year

Energy Supplied from Various Sources using WTG

35,000,000

30,000,000

25,000,000
kWh / Year

20,000,000 RE Non Grid-Support kWh
RE Grid-Support kWh
15,000,000 Grid Supplied kWh

10,000,000

5,000,000

0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Year

Figure 20: 50%/50% DGN-WTG Grid-Support and Additional Energy Contributions in kWh / Year



Figures 19 to 22 show that non grid-support energy contributions are significantly higher with HEP
(figure 19) than with the other RE resources (figures 20-22). The non grid-support energy contributions
represent the surplus energy supplied by the alternative RE resources that do not contribute to peak
load reduction: i.e. do not reduce the peak load required from the grid to meet demand.

The non grid-support energy contributions from the WTG and SHW components are almost identical,
despite the fact that SHW grid-support is negligible when compared directly with that supplied by the
WTG. Although the non grid-support energy supplied by PV is negligible, PV’s overall contribution is
greater than SHW when grid-support is taken into consideration. Grid-support energy is valued

Energy Supplied from Various Sources using PV

35,000,000

30,000,000

25,000,000
kWh / Year

RE Grid-Support kWh

10,000,000

5,000,000

0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Year

Figure 21: 50%/50% DGN-PV Grid-Support and Additional Energy Contributions in kWh / Year

Energy Supplied from Various Sources using SHW

35,000,000

30,000,000

25,000,000
kWh / Year

RE Grid-Support kWh

10,000,000

5,000,000

0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Year

Figure 22: 50%/50% DGN-SHW Grid-Support and Additional Energy Contributions in kWh / Year



substantially higher than non grid-support energy, as illustrated by comparing the NPV of energy
supplied by PV (figure 25) and SHW (figure 26).

Despite the fact that grid-support and non grid-support energy contributions from PV are less than
those from WTG, there is still a significant contribution from PV, due to the close match between the
solar energy profile supplied and the peak load of the Ruatoria feeder needing to be reduced. The
SHW scenario does not support peak load reduction on the Ruatoria feeder, because solar energy is
stored as hot water to substitute electrical heating of water cylinder at night. There is a poor correlation
between the profile for night-rate water heating and the Ruatoria peak load reduction required in the
middle of the day. SHW replaces night-rate water heating and does not offset the daily peak load.

Valuation of the 50%/50% DGN/RE Ratio Mix
A summary of the results obtained for the 50%/50% DGN/RE Ratio are shown in figures 23 to 26.

Line Upgrade Deferral Met By 50% Diesel Genset and 50% HEP

$1,600,000
$1,500,000
$1,400,000
$1,300,000

$1,200,000
$1,100,000
RE Non-Grid-Supporting Energy
$1,000,000
$900,000 RE Distribution Loss
$800,000 Gen Grid-Supporting Energy
$700,000 RE Grid-Supporting Energy
$600,000 Gen Trans. Saving

$500,000 RE Trans. Saving
$400,000
RE Upgrade Deferral
$300,000
$200,000
$100,000
$0
-$100,000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

-$200,000

Figure 23: 50%/50% DGN-HEP Analysis

In each scenario, the NPV of the net annual DE benefit remains constant, because the capacity and
energy requirement that has to be met by DE for line upgrade deferral to take place has been defined
as the same for all scenarios. However, as can been seen from figures 23 to 26, the contribution to the
net benefit varies, based upon the year and the RE-DGN mix selected. The only exception to this rule,
is provided by the non grid-supporting RE contributions, as these are surplus to requirement for line
upgrade deferral, and do not cost anymore to produce – unlike diesel fuel which is only used to meet
RE capacity-support shortfalls for line upgrade deferral. HEP provides the most additional non grid-
supporting energy (figure 23), followed by WTG (figure 24), then SHW (figure 26) and finally PV (figure
25). The reason why PV provides so little non grid-supporting energy, is because most of the energy
produced by PV is actually grid-supporting: the PV supply profile closely matches the Ruatoria
demand profile and the periods when peak load reduction is required.

The break down of components contributing to the Net Present Value of the diesel fuel and renewable
energy mix in figures 23 to 26 is as follows: NPV of DE benefits [DGN+RE] = [DGN+RE] line upgrade
deferral providing capacity-support, plus [DGN+RE] grid-supporting energy corresponding to line



upgrade deferral capacity-support periods, plus [RE] non-grid-supporting energy produced outside
line upgrade deferral capacity-support periods, plus [DGN+RE] transmission saving from GXP peak
load reduction from capacity-support, minus [DGN+RE] distribution losses in revenue to the lines
company, from less energy distributed by the lines company to the end-user from the GXP, because
the energy is supplied locally by distributed generation [DGN+RE] instead.

Line Upgrade Deferral Met By 50% Genset and 50% WTG DE

$1,800,000
$1,700,000
$1,600,000
$1,500,000
$1,400,000

$1,300,000
$1,200,000 RE Non-Grid-Supporting Energy
$1,100,000 Gen Distribution Loss
$1,000,000 RE Distribution Loss
$500,000 Gen Upgrade Deferral
$400,000 RE Upgrade Deferral
$300,000
$200,000
$100,000
$0
-$100,000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
-$200,000

Figure 24: 50%/50% DGN-WTG Analysis

Line Upgrade Deferral Met By 50% Diesel Genset and 50% PV

$1,600,000
$1,500,000
$1,400,000
$1,300,000

$1,200,000
$1,100,000
$1,000,000

$400,000
RE Upgrade Deferral
$300,000
$200,000
$100,000
$0
-$100,000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

-$200,000

Figure 25: 50%/50% DGN-PVS Analysis



Line Upgrade Deferral Met By 50% Diesel Genset and 50% SHW

$1,600,000
$1,500,000
$1,400,000
$1,300,000

$1,200,000
$1,100,000
$1,000,000

$400,000
RE Upgrade Deferral
$300,000
$200,000
$100,000
$0
-$100,000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

-$200,000

Figure 26: 50%/50% DGN-SHW Analysis

To summarize, the analysis shows several interesting features and trends:

• High non grid-supporting RE from HEP (figure 19) in early years;
• Absence of non grid-supporting energy from PV (fig 21) and a poor return due to high capital costs;
• Low overall grid-support from SHW (figure 22) due to mismatch between SHW off-peak power
storage (substituting electrical night-rate water heating) and the peak daily Ruatoria load.

The Influence of Carbon Tax and Fuel Costs on Diesel Price
The base case selected for DGN operation assumed an annual increase of 2% per year in the price of
diesel, with a starting price of: $1.00 / litre. More dramatic (exaggerated) fuel price increases of: 5 and
10% per year are included to evaluate the impact of scarcity of fuel at some future date, and / or the
gradual introduction of carbon pricing. Initial fuel starting prices of $1.50 and $2.00 per litre are also
considered to account for an abrupt change. These fuel pricing scenarios are included to compare the
impact of substantial fuel price increases on the overall profitability of the various RE-DGN scenarios
investigated.

The influence of increasing fuel prices on the annual ROI of the different RE-DGN system
combinations is compared in figures 27 to 30 below. Negative annual ROIs occur when the diesel fuel
price becomes prohibitively expensive, and fuel costs over-ride the benefits provided by other factors.

Figures 27 to 30 show the influence of fuel price on increasing the percentage of DGN in the total RE-
DGN mix from 20% to 100%. Figures 27 and 28 show that a diesel price is reached when it is no
longer profitable to increase the diesel component in the RE-DGN system. In figures 27 and 28, the
optimum DGN percentage shifts from 100 to 80% once the base line diesel price and / or annual
diesel price increase reaches a certain value. For a diesel price of $1.00/litre, this value is given by an
annual increase of 10% in price; for a diesel price of $1.50/litre, this value is given by an annual
increase of 5% in price; and, for a diesel price of $2.00/litre, this value is given by an annual increase



of 2% in price. In other words, as the base line price increases, the annual increase in price allowed
diminishes. In figure 29, no optimum is reached with the PVS-DGN system, simply because the
profitability of the system is heavily dependent on the DGN component (more so than for the other RE
systems) – see figure 14 for a comparison.

Influence of Hydro on Rising Diesel Prices Optimum ROI for
$1.00 & 2%/yr inc.
$1.50 & 2%/yr inc.
10% $1.00 & 5%/yr inc.

8%
Annual Return on Investment (ROI)

Hydro ($1.00@2%/yr)
6%
Hydro ($1.50@2%/yr)
Hydro ($2.00@2%/yr)
Hydro ($1.00@5%/yr)
4% Hydro ($1.50@5%/yr)
Hydro ($2.00@5%/yr)
Hydro ($1.00@10%/yr)
Hydro ($1.50@10%/yr)
2%
Hydro ($2.00@10%/yr)

Optimum ROI for
0% $2.00 & 2%/yr inc.
$1.50 & 5%/yr inc.
$2.00 & 5%/yr inc.
$1.00 & 10%/yr inc.
-2%
$1.50 & 10%/yr inc.
20% 30% 40% 50% 60% 70% 80% 90% 100%
$2.00 & 10%/yr inc.
Percentage of Capacity Supplied by Diesel

Figure 27: Influence of increasing fuel prices on the annual ROI of HEP-DGN systems

Influence of Wind on Rising Diesel Prices Optimum ROI for
$1.00 & 2%/yr inc.
10% $1.50 & 2%/yr inc.
$1.00 & 5%/yr inc.

8%

Wind ($1.00@2%/yr)
6%
Wind ($1.50@2%/yr)
Wind ($2.00@2%/yr)
Wind ($1.00@5%/yr)
4% Wind ($1.50@5%/yr)
Wind ($2.00@5%/yr)
Wind ($1.00@10%/yr)
Wind ($1.50@10%/yr)
2%
Wind ($2.00@10%/yr)

Optimum ROI for
0% $2.00 & 2%/yr inc.
$1.50 & 5%/yr inc.
$2.00 & 5%/yr inc.
$1.00 & 10%/yr inc.
-2%
$1.50 & 10%/yr inc.
20% 30% 40% 50% 60% 70% 80% 90% 100%
$2.00 & 10%/yr inc.

Figure 28: Influence of increasing fuel prices on the annual ROI of WTG-DGN systems

Figure 30 also shows that a diesel price is reached when it is no longer profitable to increase the
diesel component in the RE-DGN system. The optimum DGN percentage shifts from 100 to 80% once
the base line diesel price and / or annual diesel price increase reaches a certain value. For a diesel
price of $1.50/litre, this value is given by an annual increase of 10% in price; and also, for a diesel
price of $2.00/litre, this value is given by an annual increase of 10% in price.



Not that the above trends are based on varying only fuel prices. It can be expected that if PV costs
were progressively reduced over the 20 years, savings anticipated internationally would have the
effect of pushing the middle of the PV curve (figure 29) upwards into a positive ROI. This analysis has
not been carried out as it is beyond the scope of the present study.

Influence of PV on Rising Diesel Prices

10% Optimum ROI for
all scenarios

8%

6%
PV ($1.00@2%/yr)
PV ($1.50@2%/yr)
PV ($2.00@2%/yr)
4%
PV ($1.00@5%/yr)
PV ($1.50@5%/yr)
PV ($2.00@5%/yr)
2%
PV ($1.00@10%/yr)
PV ($1.50@10%/yr)
PV ($2.00@10%/yr)
0%

-2%

-4%
20% 30% 40% 50% 60% 70% 80% 90% 100%


Figure 29: Influence of increasing fuel prices on the annual ROI of PVS-DGN systems

Optimum ROI for
Influence of SHW on Rising Diesel Prices
$1.00 & 2%/yr inc.
$1.50 & 2%/yr inc.
10% $1.00 & 5%/yr inc.
$2.00 & 2%/yr inc.
$1.50 & 5%/yr inc.
8%
$2.00 & 5%/yr inc.
$1.00 & 10%/yr inc.

SHW ($1.00@2%/yr)
6%
SHW ($1.50@2%/yr)
SHW ($2.00@2%/yr)
SHW ($1.00@5%/yr)
4% SHW ($1.50@5%/yr)
SHW ($2.00@5%/yr)
SHW ($1.00@10%/yr)
SHW ($1.50@10%/yr)
2%
SHW ($2.00@10%/yr)

0%

Optimum ROI for
$1.50 & 10%/yr inc.
-2% $2.00 & 10%/yr inc.
20% 30% 40% 50% 60% 70% 80% 90% 100%


Figure 30: Influence of increasing fuel prices on the annual ROI of SHW-DGN systems

Summarizing
Using ODRC asset valuation, this report presents two alternative methods for deriving the Net Present
Value of capacity grid-support for distribution networks, using (a) capacity and (b) energy to calculate
the benefit of line upgrade deferral from various distributed energy options. These results are



summarized below for the four DE combinations studied: 100%/0%, 80%/20%, 50%/50% and
20%/80% DGN/RE.

Table 3: Capacity-based Line Upgrade Investment Deferral Methodology for Grid Supply-Support
%RE 0% RE (DGN-Only) 20% RE 50% RE 80% RE
Capacity Max $ Max $ Max $ Max $
MW MW MW MW
DGN+HEP 6.473 $739,051 2.2 $391,995 2.520 $470,418 2.202 $444,703
DGN+WTG 6.473 $739,051 1.242 $165,492 2.438 $281,188 2.916 $333,953
DGN+PV 6.473 $739,051 0.694 $124,050 1.649 $243,390 2.24 $307,870
DGN+SHW 6.473 $739,051 0.604 $25,623 0.805 $30,382 0.837 $31,212

The values in table 3 for capacity-based line upgrade deferral, show the maximum capacity-support
provided (and the NPV associated with providing capacity-support over 20 years) from: (a) diesel
genset by itself (0% RE); (b) the 20% RE component by itself; (c) the 50% RE component by itself;
and, (d) the 80% RE component by itself. The balance of capacity required for scenarios (b) to (d), is
provided by diesel, and is equivalent to the difference between what the diesel genset delivers by itself
in scenario (a), and what the RE component of the total DE mix provides in scenarios (b) to (d).

Table 4: Energy-based Line Upgrade Investment Deferral Methodology for Grid Support-Support
%RE 0% RE (DGN-Only) 20% RE 50% RE 80% RE
Energy Total $ Total $ Total $ Total $
MWh MWh MWh MWh
DGN+HEP 73,265.4 $739,063 30,987.3 $333,557 35,504.5 $382,461 31,604.5 $343,186
DGN+WTG 73,265.4 $739,063 16,223.2 $169,292 29,046.4 $295,473 34,287.5 $347,788
DGN+PV 73,265.4 $739,063 10,015.0 $108,746 21,682.3 $227,611 28,193.6 $292,370
DGN+SHW 73,265.4 $739,063 5,151.4 $44,758 6,240.1 $53,405 6,427.6 $54,924

The values in table 4 for energy-based line upgrade deferral, show the total energy-support provided
(and the NPV associated with providing energy-support over 20 years) from: (a) diesel genset by itself
(0% RE); (b) the 20% RE component by itself; (c) the 50% RE component by itself; and, (d) the 80%
RE component by itself. The balance of energy required to meet the supply shortfall for scenarios (b)
to (d), is provided by diesel, and is equivalent to the difference between what the diesel genset
delivers by itself in scenario (a), and what the RE component of the total DE mix provides in scenarios
(b) to (d).

Conclusions
Future fuel price volatility and uncertainty with availability of supply and global warming taxation
indicates a preference to at least combine diesel generation with a renewable component to minimize
risk. Some scenarios, e.g. hydro and wind, indicate that the best annual ROI includes a 20-80%
renewable energy component.

This analysis demonstrates that the accumulated benefits of localized distributed energy (kWh) and
capacity (kW) support exceed the costs for the case study developed for Eastland Networks’ Ruatoria
Feeder in the East Coast region. The main analysis was based on a 10% demand growth rate to
accentuate the effects, but lower growth rates exhibit similar trends.

An investment strategy to replace line capacity upgrades with hybrid DE also offers a trade-off
between direct ROI and intermittent renewable energy. Net benefits and costs will vary with differing
stakeholder / user-operator perspectives. It all depends upon who is responsible for the investment
and who benefits from the revenue streams generated. There are at least nine financial options



available for operating DE in New Zealand, depending on who the stakeholders are. The nine financial
options (in no particular order) are:

1. standalone DE (where no grid is available, or to completely replace the existing grid);
2. DE consumer retail energy savings by reducing demand for grid electricity;
3. Customer-generator DE (wholesale) energy supply to an energy retailer;
4. DE grid-support for a downgraded network segment (e.g. single-phase feeder support for a
downgraded three-phase feeder);
5. DE grid-support to defer network expansion / upgrades;
6. DE wholesale energy spot price contributions;
7. DE Grid Exit Point (GXP) peak (transmission) demand reduction;
8. DE backup / UPS for high-risk power failure applications; and
9. DE reduction of distribution losses.

The real challenge however, is finding a way to concentrate the multiple stakeholder benefits for an
option into a single revenue stream for a single stakeholder, so that the DE investment is cost-
effective. This analysis shows that for load growth scenarios, distribution networks could contend for
this position, and should seriously consider a DE investment encouragement strategy, in regions of
high load growth, whether or not they are allowed ownership under market rules.

Moving Forward
This research demonstrates quite clearly that there is an economic opportunity for distribution
networks with capacity constraints and increasing customer demand to investigate DE line upgrade
deferral. We encourage collaborative research and development amongst appropriate distribution
networks with complimentary interests. Furthermore, we recommend that the Electricity Commission
and Transpower work more closely with distribution networks and energy retailers to standardize such
proceedings and establish industrial best practice. Finally, we believe that a regulatory framework
should be developed which encourages a decentralized approach to infrastructure development. At
present the regulatory and market structures support central generation.

Much more detail is available from the analysis than has been presented in this report. Further
implications could be drawn from the case study results or the methodologies could be easily applied
to other case studies. In particular, the uptake of combinations of rooftop PV and storage systems in
the urban and residential environment as an alternative to grid capacity growth could and should be
investigated with urgency. The methodology applied is considered to be robust and thorough, and is
based on ODRC (Optimized Depreciated Replacement Cost) data.

Acknowledgements
This work has been completed with the financial support of the Foundation for Research, Science and
Technology (FRST). Industrial Research is very grateful for the support provided by Eastland
Networks Limited in producing this paper.


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