Project 2.1
Cost-Benefit Framework: Secondary
Benefits and Ancillary Services
MIKE QUASHIE AND GEZA JOOS (MCGILL UNIVERSITY)
www.smart-microgrid.ca
Presentation outline
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Problem Identification
Proposed Solution
Methodology
Results and Conclusion
Future work and collaboration
Problem Identification
• Microgrids are often touted as a technology that can improve local
system reliability and aid in the integration of renewable energy
resources. However, to facilitate the additional control and operating
modes associated with a Microgrid, additional equipment is needed.
– The cost associated with this infrastructure can be quantified once the necessary
elements are identified.
– Contrarily, there may be certain costs, such as those associated with changes to
operating protocol, training and new safety requirements, which may be more
difficult to translate into a dollar figure.
– For utilities and business owners, this analysis is required to develop the business
case for a given technology.
• Many of the benefits of a Microgrid are also not tangible in the
Canadian context and consequently require additional considerations
to monetize.
• How can the assumptions made in the cost-benefit framework be
tested?
Solution
• Previous work done in collaboration with CanmetEnergy and
project 1.4, identified benefits of Microgrids and how the
benefits impacts stakeholders.
• Benefits Identified:
Technical: Network efficiency improvement(losses reduction), reliability improvement
Economic: cost of energy
Environmental: GHC emission reduction
• The natural step that follows is to developed a
methodology to optimize the identified benefits.
Historical data of wind speed and solar irradiance were
obtained from CWEEDS through interaction with
CANMET ENERGY.
Methodology
• The work done over the year is
proposes a generalized methodology to
determine the optimal configuration of
microgrids that maximizes its benefits.
HOURLY SOLAR OUTPUT
0.06
0.09
0.05
0.08
0.07
0.06
0.05
0.04
0
Winter
Spring
Summer
Fall
5
10
15
Hours
20
25
HOURLY OUTPUT IN PER UNIT (P.U)
HOURLY OUTPUT IN PER UNIT (P. U)
HOURLY WIND POWER OUTPUT
0.1
Winter
Spring
Summer
Fall
0.04
0.03
0.02
0.01
0
0
5
10
15
HOURS
20
25
Methodology
• The technique proposed herein, incorporates a
probabilistic modeling of variable loads and
distributed generators into a deterministic power
flow problem to solve an optimization problem
𝑝
min 𝐹 =
ML t, n, µ, σ + GCC t, n + GHC(t, n)
(1)
𝑛=1
Subject to:
𝑃𝐺,𝑗 − 𝑃𝐿𝑜𝑠𝑠,𝑗 t, n, µ, σ + 𝑃𝑊,𝑗 t, n, µ, σ + 𝑃𝑆,𝑗 t, n, µ, σ = 𝑃𝐿𝑜𝑎𝑑,𝑗 t, n
𝑃𝑊,𝑗 t, n, µ, σ + 𝑃𝑆,𝑗 t, n, µ, σ ≤ 𝑃𝑝𝑒𝑛,𝑗 × 𝑃𝐿𝑜𝑎𝑑,𝑗 t, n
(2)
(3)
• The losses (PLOSS) from the power flow problem
is monetized and supplied to the objective
function as one of its input multiple cost
functions
ML t, n, µ, σ = 𝑃𝐿𝑂𝑆𝑆 t, n, µ, σ × CP
(4)
Methodology
• For GCC:
𝐺𝐶𝐶 𝑡, 𝑛 = 𝑃𝐺 ∗ 𝐶𝑃 + 𝑃𝑊,𝑗 t, n, µ, σ ∗ 𝐶𝑃𝑊 + t, n, µ, σ ∗ 𝐶𝑃𝑃𝑉 (5)
𝐶𝑅𝐹 =
𝑟(𝑟+1)𝑦
𝑟(𝑟+1)𝑦 −1
(6)
(7)
𝐴𝐶 = 𝐶𝑅𝐹 × (𝐶𝐶 − 𝑑𝑖𝑠𝑐𝑜𝑢𝑛𝑡𝑒𝑑 𝑆𝑉)
𝐴𝑉𝐶 =
𝐴𝐶($/𝑦𝑒𝑎𝑟)
𝐴𝑛𝑛𝑢𝑎𝑙 𝐸𝑛𝑒𝑟𝑔𝑦 (𝐾𝑊ℎ/𝑦𝑒𝑎𝑟)
(8)
• For GHC:
𝐸𝐶 = 𝑃𝐺 × 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛
𝑡𝑜𝑛𝑠
𝑘𝑤ℎ
× 𝑐𝑎𝑟𝑏𝑜𝑛 𝑡𝑎𝑥
$
𝑡𝑜𝑛𝑠
(21)
• The optimization problem then concatenates the
monetized losses, monetized emission and the cost
components of generation into a single objective and
then minimizes it.
Test System
Peak load of
12.775MW.
The optimal configuration was also
found to be 1.2 MW of wind turbine
on node 7 and 30 KW solar panel on
node 4.
Figure 1. Cigre's North American medium Voltage DistributionNetwork
Benchmark with DG connected to operate as Microgrid .(CIGRE TF C6.04.02,
“Benchmark systems for network integration of renewable and distributed energy resources,” technical
brochure, version 21, pp25-39, August 2010.)
RESULTS
COST OF ENERGY TO THE DISTRIBUTION SYSTEM OPERATOR
0.06
A 16.1 percent decrease
in the average cost of
energy for the optimal
case is observed in
comparison to the base
case
0.058
COST OF ENERGY(S/KWh)
0.056
0.054
0.052
0.05
0.048
0.046
0.044
0.042
0.04
Base Case
Optimal Case
0
10
WINTER
20
30
SPRING
40
50
HOURS
60
SUMMER
70
80
90
100
FALL
Figure 2. Comparison of Hourly Total Cost of Energy to the Distributed
Network Operator for a Year (each 24hr represents a Season)
Results
Average Losses(MWper hr)
Seasons Winter Spring
Summer
Fall
Annual(MWh)
0.6743
243.1123
0.6605
238.1372
0.6082
218.4336
0.6078
218.3088
I. Base Case(No DG)
0.6757 0.6743
0.6770
II. Solar Only
0.6620 0.6605
0.6631
III. Wind Only
0.6037 0.5992
0.6160
IV. Wind and Solar
0.6035 0.5988
0.6155
TABLE I. LOSSES IN THE POWER NETWORK FOR VARIOUS SCENARIOS
The efficiency of the
network is also
improved by 10
percent through loss
minimization for the
optimal case in
comparison to the
base case.
Conclusion
• Application of the methodology to the microgrid planning process
resulted in significant decrease in the cost of energy to the
distribution network operator.
• A significant improvement in the network’s efficiency (loss
minimization) as well as reduction in CO2 emissions is also
observed in applying the methodology to the microgrid planning
process.
• The economic, technical and environmental benefits of the
microgid which are maximized through the use of the proposed
methodology help offset the cost associated with microgrid’s
implementation, building a better business case for microgrid
advancement
• Results of the study was present at Power and Energy Society
general meeting 2013 in Vancouver.
Future work
• Develop a framework for implementing and
quantifying ancillary services .
• Application of the methodology to Microgrid
demonstrations in existing and potential
Microgrids (commercial, industrial and
remote community settings).
Potential collaboration with project 2.4 and 1.4
• Future collaboration with 3.1 to have an
accurate estimate of ICT infrastruture in
Microgrids