Design and Valuation of High-Capacity HVDC Macrogrid...
Transcript of Design and Valuation of High-Capacity HVDC Macrogrid...
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Abstract-- This work explored the benefits of increasing
transmission capacity between the US Eastern and Western
Interconnections under a high-renewables future. Given the
existing “seam” between the two interconnections, a co-optimized
infrastructure planning model was developed to assess tradeoffs
between investments in cross-seam HVDC transmission, AC & DC
transmission needs within each interconnection, generation
investment costs, and operational costs, at different renewable
penetration levels. The model allows existing and candidate
generation resources for deliverable planning reserves to be
shared throughout the interconnections, a feature which drives
identification of least-cost investments. This work is performed
using an industry-vetted 169-bus representation of the North
American power grid. Results from this analysis indicate that,
under high wind/solar growth scenarios, the cost of a macrogrid
HVDC transmission is outweighed by the generation-related
savings they produce. The presence of other benefits related to
grid reliability, resilience, and adaptability, suggest that cross-
seam transmission is highly attractive infrastructure.
Index Terms- HVDC, transmission/generation planning
I. NOMENCLATURE
INDEXES 𝑦/𝑁𝑦 Year/Planning horizon
𝑔/𝑁𝑔 Area/Total number of areas
𝑠/𝑁𝑠 Block/Total number of blocks ℎ/𝑁ℎ Single/Total number of generation technologies 𝑡/𝑁𝑡 Single/Total number of transmission technologies
SETS 𝐸 Set of energy blocks 𝑃 Set of non-coincident peak-load blocks 𝐺 Set of reserve sharing groups (RSG) 𝑀 Set of HVDC transmission lines
DECISION VARIABLES 𝑃 Generation dispatch 𝐶𝑁𝑒𝑤 New generation capacity 𝐶𝑅𝑒𝑡 Retired generation capacity 𝐶 Cumulative generation capacity 𝑇𝐶𝑁𝑒𝑤 New transmission capacity 𝑇𝐶 Cumulative transmission capacity 𝑅𝑈 Regulation reserve up
This work was supported in part by the U.S. Department of Energy, the
National Science Foundation (Award 1069283), and the ISU Electric Power Research Center.
A. L. Figueroa-Acevedo was with Iowa State University and is now with Policy Studies at the Midcontinent Independent System Operator, Eagan, MN USA (e-mail: [email protected]).
J. McCalley, A. Jahanbani-Ardakani, and A. Venkatraman are with the Electrical and Computer Engineering Department, Iowa State University, Ames, IA USA (email: [email protected], [email protected], [email protected]).
H. Nosair was with Iowa State University and is now with the New York Independent System Operator, Rensselaer, NY 12144 USA (email: [email protected]).
𝑅𝐷 Regulation reserve down 𝐶𝑅 Contingency reserve 𝜃𝑖 Voltage phase angle (sending) 𝜃𝑖 Voltage phase angle (receiving) 𝑓 Branch flow
PARAMETERS 𝐶𝐴𝑃𝐸𝑋 Generation investment cost in $/MW 𝐼𝐶 Transmission investment cost in $/MW 𝐹𝑂𝑀 Fixed operation and maintenance cost in $/MW-yr 𝑉𝑂𝑀 Variable operation and maintenance cost in $/MWh 𝐻𝑅 Weighted-average heat rate in MBTU/MWh 𝐹𝐶 Fuel cost in $/MBTU ∆𝑠 Block duration in hours 휀 Discount factor 𝑟 Real discount rate 𝐷 Demand in MW 𝐶𝐸𝑥𝑖𝑠𝑡 Existing generation capacity in MW 𝐶𝐹 Capacity factor 𝐶𝑉 Capacity value using deterministic approach 𝛼 Coefficient to scale regulation up requirements 𝛽 Coefficient to scale regulation down requirements 𝛿 Largest credible contingency 𝑟𝑟1−𝑚𝑖𝑛 Ramp rate in MW/min 𝑋 Reactance in per unit (100 MVA base) 𝑇𝑀𝑎𝑥 Transmission investment cap 𝐹𝑂𝑅 Forced outage rate 𝐷𝐸𝐶 Decommissioning cost of generation in $/MW 𝜋𝑟𝑢 Regulation up reserve scaling factor in $/MWh 𝜋𝑟𝑑 Regulation down reserve scaling factor in $/MWh 𝜋𝑐𝑟 Contingency reserve scaling factor in $/MWh
II. INTRODUCTION
HE Federal Energy Regulatory Commission (FERC)
defines “interregional transmission facility” in its Order
1000 [1] as a transmission facility that is physically located in
two or more transmission planning regions. FERC currently
identifies 14 planning regions that cover most of the continental
US. The coordination of markets through Regional
Transmission Operators (RTOs), motivated by FERC Order
1000, has been central in identifying interregional transmission
investment opportunities with high economic, reliability, and
policy value. In addition, there are independent merchant
transmission developers who have proposed interregional
Aaron Bloom was with the National Renewable Energy Laboratory and is now with the Product R&D at NextEra Analytics, St. Paul, MN ([email protected]).
D. Osborn is retired from the Midcontinent ISO, Eagan, MN (email: [email protected]).
Jay Caspary and Harvey Scribner are with the Research, Development & Tariff Services at the Southwest Power Pool, Little Rock, AR USA (email: [email protected]; [email protected]).
James Okullo and Jordan Bakke are with Policy Studies at the Midcontinent Independent System Operator, Eagan, MN USA (email: [email protected]; [email protected]).
Design and Valuation of High-Capacity HVDC
Macrogrid Transmission for the Continental US Armando L. Figueroa-Acevedo, Member, IEEE, Ali Jahanbani-Ardakani, Hussam Nosair, Abhinav
Venkatraman, James D. McCalley, Fellow, IEEE, Aaron Bloom, Dale Osborn, Sr. Member, IEEE,
Jay Caspary, Member, IEEE, James Okullo, Jordan Bakke, and Harvey Scribner, Member, IEEE
T
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transmission projects [2]-[7]. Most interregional projects
considered to date have been internal to either the Eastern
Interconnection (EI) or to the Western Interconnection (WI). In
this work, we consider interregional transmission expansion
between the EI and WI.
In 1967, the EI and WI were interconnected with 230 kV AC
lines in Montana, the Dakotas, and Nebraska, but separations
occurred frequently, and the connections were permanently
opened in the 1970’s [8]. Although [9] and [10] reported studies
of promising cross-seam AC interconnections, subsequent
interconnections between the EI and WI have been limited to
high-voltage direct-current (HVDC) back-to-back (B2B)
connections. Recent studies have suggested that increasing
transmission capacity between the EI and WI creates significant
economic value related to renewable energy integration [11]-
[13]. The work reported in this paper quantifies benefits
associated with high capacity HVDC cross-seam transmission
expansion, establishing such an investment as being a clearly
attractive grid development for reducing cost while increasing
the long-term integrity of the electric infrastructure. This paper
is organized as follows. Section III identifies the drivers of
interregional transmission development. In Section IV the co-
optimization model developed in this work is described. Section
V presents the assumptions and the design process adopted. The
major findings related to the benefits of increasing transmission
capacity between the EI and WI are presented in Section VI,
including results of a sensitivity assessment under future
uncertainties. Section VII concludes.
III. DRIVERS OF INTERREGIONAL TRANSMISSION
The benefits of interregional transmission include access of
load centers to higher quality renewable resources,
interregional sharing of the most economic energy resources,
increased interregional sharing of operational and planning
reserves, and increased interconnectedness and deliverability
with consequential effects on reliability, resilience, and
adaptability. These are described in what follows.
A. Resource quality
Access to high-quality renewables reduces the levelized cost
of electricity and facilitates the implementation of cost-
effective renewable energy policies. This is reflected in the
most recent RTO generation interconnection queues. For
example, on the EI side, the Midcontinent Independent System
Operator (MISO) interconnection queue includes 42 GW of
wind projects and 38 GW of solar projects [14]. Likewise, wind
and solar projects in the Southwest Power Pool (SPP)
generation interconnection queue account for nearly 100% of
the requests [15]. On the WI side, the California Independent
System Operator (CAISO) current interconnection queue
includes 18 GW of wind and solar projects [16]. This trend is
expected to continue as technological advances in wind and
solar resources evolve and their economics become
increasingly attractive.
B. Interregional sharing on a diurnal basis of the most
economic energy resources
Daily diversity in peaking time refers to the extent to which
different regions peak at different times over each 24 hour
period (largely due to time zone differences), thus enabling
increased use of the most efficient units between regions and
particularly across interconnections. Additional diurnal benefits
of interregional transmission include expanded geographical
coverage for frequency regulation and therefore reduced
variability in solar and wind due to geo-smoothing.
Furthermore, increased duration of access to solar resources,
due to the integration between east and west coast systems,
reduces Eastern and Central US end-of-day ramping
requirements relative to what these regions see in isolation. The
daily benefits also include primary frequency response sharing,
resulting in improved reliability performance and cost reduction
if such markets are developed.
C. Interregional sharing of peaking capacity
Weather patterns and time-zone differences are among the
principal sources of annual peak load diversity in continent-
wide interconnections [17]. When the peak load time of two or
more regions differs, and the spare capacity of one region
relative to one or more other regions is large enough, increasing
transmission capacity between them creates value by allowing
two or more balancing authorities (BAs) to maximize the use of
local generation resources during the annual peak load
conditions of each respective BA. In [18], the minimum load
diversity between the US EI and WI was estimated to be 30 GW
when neglecting transmission constraints, based on 7 years of
historical load data. With the addition of frequency response
sharing, energy arbitrage and wind diversity, the resulting
benefit-to-cost ratio of a conceptual HVDC macro-grid overlay
was 1.25. A different study [19] used two years of synchronized
hourly load data and showed that load diversity creates savings
when combined with other value drivers (e.g. renewable energy
and operating reserves sharing). However, additional value
drivers such as the sharing of frequency response obligations
[20], integration of renewables [21], and sharing of regulation
and other intra-hour reserves (e.g., load following) [22],
increases the economic and reliability value of a well-designed
interregional HVDC transmission infrastructure.
D. HVDC technological capabilities
Although the majority of interregional transmission projects
across the US remain AC, HVDC technology has been the
preferred option worldwide when considering interconnections
between asynchronous systems [23]-[25]. Its controllability and
black-start capabilities reduce the risk of cascading outages and
facilitate the sharing of resources in continent-wide
interconnections. Additionally, HVDC offers the ability to
hedge against financial risks (e.g., reduction in market price
volatility), extreme weather events and fuel unavailability risks
(e.g., Polar Vortex, Hurricane Katrina). Furthermore, the
power transfer capability of HVDC is independent of distance,
power can be scheduled from source-to-sink without causing
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loop flow in the underlying AC system, and power angle
limitations can be mitigated [18]. Finally, the HVDC
technology offers long-distance transmission at less cost than
AC solutions while offering unique ways to provide additional
voltage and frequency control capabilities.
E. Increases interconnectedness and deliverability
Deliverability between two regions is considered here to be
the ability of the system to deliver generation in one region to
the load of the other region while maintaining adequate levels
of reliability. Although previous research has investigated the
benefits of increasing deliverability for renewable integration in
the US, most have focused on either the EI side or the WI side
[26], [27]. Previous work on interregional transmission at the
national level is limited, and the application of co-optimization
techniques has been applied only in a few studies. A
transportation model for transmission was used in [21] to design
an HVDC infrastructure for the continental US. Results from
this work showed that high capacity HVDC transmission
expansion between the EI and WI is attractive. The National
Renewable Energy Laboratory (NREL) Renewable Electricity
Futures study (RE Futures) [28] showed that approximately 35
GW of transmission expansion between the EI and WI could
potentially facilitate 80% renewable generation in the US when
electrification in the transportation infrastructure is considered,
and it was found in the work of [12] that 42 GW of cross-seam
transmission is an optimal value under a high inland wind
scenario. The work reported in this paper further contributes to
the literature by explicitly accounting for capacity sharing
between the EI and WI in a co-optimization framework. From
a long-term capacity expansion planning modeling perspective,
this work extends that of [12] and [18] through improved data,
more rigorous modeling, and expanded sensitivity work, all
completed under the oversight of a project advisory board
comprised of representatives from more than 30 utilities and
organizations within the energy industry [29].
IV. CO-OPTIMIZED EXPANSION PLANNING MODEL
The problem is formulated as a co-optimized expansion
planning (CEP) linear programming model (LP), where the
NPV of two different yet interdependent infrastructures (e.g.,
transmission and generation) are simultaneously minimized
within one optimization formulation as shown in (1)-(21). We
describe this model in what follows.
Min CAPEX(y, g, h)𝐶𝑦𝑔ℎ𝑁𝑒𝑤+FOM(g, h)𝐶𝑦𝑔ℎ
𝑇𝑜𝑡 +𝑇𝐶 (t, k)𝑇𝑦𝑡𝑁𝑒𝑤
+𝑃𝑦𝑔ℎ𝑠[𝐹𝐶(𝑦, 𝑔, ℎ, 𝑠) ∙ 𝐻𝑅(ℎ) + 𝑉𝑂𝑀(ℎ)] + 𝜋𝑟𝑢 ∙ 𝑅𝑈𝑦𝑔ℎ𝑠 ∙[𝐹𝐶(𝑦, 𝑔, ℎ, 𝑠)𝐻𝑅(ℎ)] + 𝜋𝑟𝑑 ∙ 𝑅𝐷𝑦𝑔ℎ𝑠 ∙ [𝐹𝐶(𝑦, 𝑔, ℎ, 𝑠) ∙𝐻𝑅(ℎ)] + 𝜋𝑐𝑟 ∙ 𝐶𝑅𝑦𝑔ℎ𝑠 ∙ [𝐹𝐶(𝑦, 𝑔, ℎ, 𝑠) ∙ 𝐻𝑅(ℎ)] +(𝑉𝑂𝐿𝐿)𝑈𝐸𝑦𝑘 + DEC(y, g, h)𝐶𝑦𝑔ℎ
𝑅𝑒𝑡 (1)
Subject to:
∑ ∑ 𝑃𝑦𝑔ℎ𝑠𝑁ℎℎ=1
𝑁𝑔𝑔=1 ± ∑ 𝑓𝑦𝑡
𝑁𝑡𝑡=1 + 𝑈𝐸𝑦𝑠 = 𝐷(𝑦, 𝑔, 𝑠) ∀𝑠 ∈ 𝐸 (2)
∑ 𝑃𝑦𝑔ℎ𝑠𝑁ℎℎ=1 ± ∑ 𝑓𝑦𝑡
𝑁𝑡𝑡=1 + 𝑈𝐸𝑦𝑘 = 𝐷(𝑦, 𝐺, 𝑠) × (1 +
𝑃𝑅𝑀) ∀𝑠 ∈ 𝑃 (3) 𝐶𝑦𝑔ℎ
𝑇𝑜𝑡 = 𝐶𝑦𝑔ℎ0 + 𝐶𝑦𝑔ℎ
𝑁𝑒𝑤 − 𝐶𝑦𝑔ℎ𝑅𝑒𝑡 (4)
𝑃𝑦𝑔ℎ𝑠 + 𝑅𝑈𝑦𝑔ℎ𝑠 + 𝐶𝑅𝑦𝑔ℎ𝑠 ≤ 𝐶𝑦𝑔ℎT𝑜𝑡 ∀ 𝑠 ∈ 𝐸 (5)
𝑃𝑦𝑔ℎ𝑠 ≤ 𝐶𝑉(𝐺, ℎ) × 𝐶𝑦𝑔ℎ𝑇𝑜𝑡 ∀ 𝑠 ∈ 𝑃 (6)
𝑃𝑦𝑔ℎ𝑠 − 𝑅𝐷𝑦𝑔ℎ𝑠 ≥ 𝑃𝑚𝑖𝑛(𝑔, ℎ, 𝑠) ∀ 𝑠 ∈ 𝐸 (7)
∑ ∑ 𝑅𝑈𝑦𝑠𝑔ℎ𝑁ℎℎ
𝐺𝑔=1 ≥ 𝛼 ∗ 𝜎𝑁𝐿−𝑢𝑝
1𝑚𝑖𝑛 ∀ 𝑔 ∈ 𝑁𝑔 (8)
∑ ∑ 𝑅𝐷𝑦𝑠𝑔ℎ𝑁ℎℎ
𝐺𝑔=1 ≥ 𝛽 ∗ 𝜎𝑁𝐿−𝑑𝑜𝑤𝑛
1𝑚𝑖𝑛 ∀ 𝑔 ∈ 𝑁𝑔 (9)
∑ ∑ 𝐶𝑅𝑦𝑠𝑔ℎ𝑁ℎℎ
𝐺𝑔=1 ≥ 𝛿 ∗ 𝐷(𝑦, 𝑔, 𝑠) ∀ 𝑔 ∈ 𝑁𝑔 (10)
𝑅𝑈𝑦𝑔ℎ𝑠 ≤ 𝑟𝑟ℎ1𝑚𝑖𝑛𝐶𝑦𝑔ℎ (11)
𝑅𝐷𝑦𝑔ℎ𝑠 ≤ 𝑟𝑟ℎ1𝑚𝑖𝑛𝐶𝑦𝑔ℎ (12)
𝐶𝑅𝑦𝑔ℎ𝑠 ≤ 𝑟𝑟ℎ10𝑚𝑖𝑛𝐶𝑦𝑔ℎ (13)
𝜃𝑦𝑠𝑔𝑖 − 𝜃𝑦𝑠𝑔𝑗 = 𝑋𝑡(𝐵𝑦𝑠𝑡) (14)
𝑇𝑦𝑘𝑡 = 𝑇𝑦𝑘𝑡0 + ∑ 𝑇𝑦𝑘𝑡
𝑁𝑒𝑤𝑁𝑘𝑘=1 (15)
−𝑇𝑦𝑘𝑡 ≤ 𝐵𝑦𝑠𝑘𝑡 ≤ 𝑇𝑦𝑘𝑡 (16)
𝑇𝑦𝑘𝑡𝑁𝑒𝑤 ≤ 𝑇𝑦𝑘𝑡
𝑀𝑎𝑥 (17)
𝑇𝑦𝑘1𝑡 = 𝑇𝑦𝑘2𝑡 = . . . 𝑇𝑦𝑘𝑀𝑡 (18)
𝐶𝑦𝑔ℎ𝑁𝑒𝑤 ≤ 𝐶𝑦𝑔𝑡
𝑀𝑎𝑥 (19)
𝐶𝑦𝑔ℎ𝑁𝑒𝑤, 𝐶𝑦𝑔ℎ
𝑅𝑒𝑡 , 𝑃𝑦𝑔ℎ𝑠 , 𝑇𝑦𝑘𝑡𝑁𝑒𝑤 , 𝑅𝑈𝑦𝑔ℎ𝑠 , 𝑅𝐷𝑦𝑔ℎ𝑠 , 𝐶𝑅𝑦𝑔ℎ𝑠 ≥ 0 (20)
The objective (1) of our least-cost co-optimized generation
and transmission model (CGT-Plan) is to minimize the net-
present value (NPV) of generation investments, transmission
investments, generation retirements, the production cost of
generation resources, the cost of providing regulation up,
regulation down, and contingency reserves, the operations and
maintenance costs of new and existing generation resources,
and the value of loss of load. The objective function is refined
to account for technology maturation rates and regional cost
multipliers as presented in [30]. The capital expenditure
(CAPEX) is used for new generation resources to represent the
total cost required to achieve commercial operation. The
retirement decision is driven by the capacity factor and FOM:
the model retires a generation technology in any year where it
does not provide operating reserves, planning reserves or
energy, an indication that its FOM exceeds its economic benefit.
The CEP formulation addresses end-effects by including 20
years of operation beyond the planning horizon. This accounts
for life-time operational costs of technologies invested in the
later years of the planning horizon. A discount factor is applied
to each term in the objective function to account for the time
value of money.
The objective function is evaluated subject to nodal power
balance constraints for the energy blocks (2), nodal power
balance constraints for the peak-load blocks (3), cumulative
generation capacity (4), generation dispatch upper limits for the
energy blocks (5), generation dispatch upper limits for the peak-
load blocks (6), and generation minimum stable level
constraints (7). Equation (3) guarantees that capacity is allowed
to be exchanged and delivered using existing and new
transmission. We use the term “PRM deliverability” to indicate
the extent to which transmission is available to deliver
generation capacity necessary to meet requirements associated
with planning reserve margins (PRM). To account for the non-
coincident peak loads, historical load data is used to
parameterize the right-hand-side of (3). For each region k, its
peak block is represented by its own peak scaled to account for
its required margin (e.g., 1.15) while the rest of the regions j≠k
are characterized by an expected load level at the time region k
is peaking. The sum over all j≠k of the differences between the
region j peak and the region j expected load level during the
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region k peak is defined as the annual load diversity for region
k. The annual load diversity of any two or more regions was
determined using multiple years of historical load data [18]. The
capacity value (CV) in (6) accounts for the availability of
variable resources during peak load times. The effective load
carrying capability (ELCC) was used to characterize the CV of
peaking regions [34], [35]. The CV of non-peaking (assisting)
regions is computed as the average available power during the
top ten net-load peak times of the peaking month. In this
approach, CVs computed (based on ELCC) are considered to
conservatively represent renewable generation levels for the
peaking region k, while the CVs computed (based on average
of top-ten net-load peak times) are considered to be slightly less
conservative for assisting regions j≠k. This results in a balance
of conservative need in the peaking region with credible ability
in the assisting regions. In order to account for the effects of
renewables on the short-term operation of a power system, a set
of operating reserve constraints are defined as a function of net-
load variability. Equations (8)-(13) show the regulation up,
down, and contingency reserve requirements. The capability of
a generation technology to ramp up/down in providing
regulation reserves is modeled as a function of the net-load
variability, represented by the standard deviation σ of 1-minute
changes in net load. Coefficients α and β are parameters used to
scale σ; they are estimated using hourly wind, solar and load
delta profiles, such that the 99th percentile of deltas comply with
NERC’s CPS-2 standard. The implication of (8) and (9) is that
as wind and solar investments increase, so do the net-load
variability. Regulation reserve requirements increase as a
function of the net-load variability and are only provided by
thermal and hydro technologies. Coefficient δ in (10) represents
the largest credible contingency as a percentage of the demand.
The rest of the constraints include the DC power flow and
thermal limit constraints for existing and new transmission
(14)-(17). Equation (18) is included to address the need for the
design to be self-contingent; it requires all HVDC candidate
segments have equal capacities. For a given total transfer level,
requiring equal capacities minimizes pre-contingency
curtailment to satisfy N-1 contingency criteria, consistent with
the rule of three [11]. Equation (19) is included to limit the
generation investments as a function of population density, land
availability, and resource potential. Finally, (20) defines all
decision variables to be non-negative. This mathematical
formulation was implemented using GAMS and solved using
CPLEX. The code was validated by comparing its results to
that of similar codes obtained from developers at other
institutions. The results of these efforts were favorable and can
be found in [30].
V. DESIGN PROCESS
The formulation presented in the previous section was
implemented using a model of the North American electric
power grid, as part of the Interconnection Seam Study [29].
This section describes the assumptions underlying the base
conditions used in the study.
Hourly generation, hydro, and load data corresponding to
year 2024 were obtained from NREL, based on [36] and [37].
The existing generators are energy-limited by their capacity
factor and capacity-limited by their forced outage rate (FOR).
The investment and operational cost data for new generation
resources were gathered from the 2017 annual technology
baseline (ATB) [31]. Maturation rates for candidate generation
technologies were gathered from NRELs 2017 annual
technology baseline [31] and the regional multipliers from [32].
The transmission regional multipliers were adapted from [33].
An additional cost was included in the CAPEX of candidate
wind and solar generation technologies to account for the
transmission necessary to connect resources located in remote
locations. The cost of each transmission spur line was
calculated based on the distance to the closest bus, kV level,
terrain type, and base cost data, gathered from [13] and [38].
For the WI side, Kron reduction [39] was used to create a
reduced network equivalent of a 2026 power flow case obtained
from the Transmission Expansion Planning Policy Committee
(TEPPC) of the Western Electricity Coordinating Council
(WECC). A total of 101 buses, including 7 buses for modeling
existing back-to-back (B2B) DC ties to the EI, were selected
and preserved, retaining most key paths in the WECC region as
defined in the notes for the TEPPC 2026 power flow case [40].
Kron reduction distributes current injections of an eliminated
bus to the remaining (preserved) buses; we refer to the factors
computed for this purpose as the fractional mapping of the
eliminated bus. The fractional mapping was used to relocate
load of eliminated buses in fractions, whereas the highest
fraction was selected to relocate generation of eliminated buses
integrally so as to retain the identity of individual generation
units. The EI model, developed by engineers from the
Midcontinent Independent System Operator (MISO), used 61
buses, with 7 buses for modeling existing B2B DC ties to the
WI. MISO used the software Transmission Adequacy and
Reliability Assessment (TARA) [41] to calculate transfer limits
between connected buses under N-1 conditions. The equivalent
impedances for all lines within the EI were estimated based on
knowledge of voltage level connecting each pair of regions,
distance, and transfer limit. Figure 1 shows the 169-bus
representation of the model used in this work.
Interregional transmission candidate lines were assumed to be
overhead, including high-voltage AC and HVDC technologies.
Base cost data of transmission was gathered from [13] and [38].
The base costs per mile for transmission were escalated from
the 2014 values to 2024 using an inflation rate of 2%. Natural
gas, oil, and coal fuel prices are consistent with forecasted
values in [42]. Based on review of governmental documents
and previous studies, a 5.7%/year real discount rate was
assumed with 2%/year inflation. A 3%/year increase in
distributed generation was adopted.
A. Selection of typical operating blocks
Raw data was obtained in the form of 8760-hour time
series of wind, solar, hydro, and load for a highly granular set
of points characterizing the entire US. In order to capture the
diurnal diversity of wind, solar, hydro and load, all time-series
were referenced to a common time. An operating block is a T-
hour operating condition that represents T similar 1-hour
5
operating conditions; the operating conditions need not be (and
typically are not) sequential. A total of 19 blocks were
developed to represent each year. A 5-block representation of a
typical 24-hour period was used for the production cost model
(e.g. the energy blocks). This approach is an adaptation from
[43]. Each energy block was characterized by the average over
the hours represented by the block of load, wind, solar, and
hydro production levels, and a capacity addition above net-load
to account for operating reserve products (regulation up,
regulation down, and contingency). Three seasons were defined
to capture the annual variation of wind, solar, hydro and load:
winter (November, December, January, February), summer
(May, June, July, August), and shoulder (March, April,
September, October). Finally, four 1-hour duration blocks were
defined to represent the conditions throughout the model
corresponding to the peak load of each of four reserve sharing
groups (RSGs). The four RSGs were defined based on time-
zones. These blocks are characterized by the peak-load of the
RSG that is peaking and an expected load for all others RSGs
that are not peaking as presented in Section IV. A 15% planning
reserve margin above peak was enforced on each peak-load
block.
B. Description of designs and conditions
In order to quantify the benefits of a conceptual HVDC
macrogrid overlay, a co-optimized plan was developed for a
benchmark case called Design 1, where it was assumed the
existing B2B ties remain as currently existing so that the EI-WI
(“cross-seam”) transmission capacity is maintained fixed at
1.31GW. Three other designs were studied:
Designs 2a and 2b both enabled economically optimal
unconstrained growth of transmission capacity at the
existing B2B ties. Whereas this was the only cross-seam
transmission growth allowed in Design 2a, Design 2b also
allowed cross-seam transmission growth via three East-
West HVDC lines, constrained to have equal capacities,
with terminals located at interior points of the two
respective interconnections, and terminal locations chosen
to maximize cross-seam transmission value.
Design 3 allowed HVDC growth only in 15 segments of an
HVDC macrogrid network, with segment capacities
constrained to be equal. This design did not allow
additional growth of transmission at the existing B2B ties.
All designs allowed unconstrained growth of generation and
AC transmission, with two exceptions: (a) generation growth
for the entire model over the 15-year period was capped at 600
GW to recognize regulatory process and construction limits on
what could be physically built in 15 years; (b) AC transmission
expansion could only occur on existing circuits. The planning
horizon was 2024-2038. The four transmission designs were
studied across two different renewable penetration levels: 50%
and 40% (by energy, including wind, solar, and hydro). The
generation mix of the 50% case was obtained without RPS
constraints enforced and with an escalating emissions price of
$3/tonne/year, starting from $3/tonne in 2024 and growing to
$45/tonne in 2038. This approach, referred to as the base
condition, was driven by the study Technical Review
Committee (TRC) as a proxy for potential growth in wind and
solar. The TRC viewed that projections for wind and solar
installations have historically been conservative, and the
inclusion of an emission price is an objective method to
increase the amount of wind and solar on a system in light of
uncertainty in traditional forecasts for deployment. The
generation mix of the 40% case was obtained with constraints
associated with existing (as of 2016) renewable portfolio
standards (RPS) enforced, and no emissions price – we refer to
this case as “current policy.” Results for the base condition and
the current policy are provided for Designs 1 and 3; results for
sensitivities are provided for Design 3; results for Designs 2a
and 2b may be obtained in [19].
VI. ECONOMIC VALUE OF HVDC MACROGRID
INFRASTRUCTURE
The benefits of Design 3 are shown in Table 1, and Fig. 2
illustrates the cumulative transmission and generation additions
for Design 3 under base assumptions. The economic benefits
were calculated as the difference (the “delta”) in the total 15-yr
NPV between Designs 1 and 3 in terms of (1) the following
objective function non-transmission components: generation
investment cost, O&M costs (fuel cost, FOM and VOM cost,
regulation-up and –down reserve cost), emission cost, and
contingency reserve cost; and (2) the line investment cost (AC
and DC). All non-transmission deltas were summed and divided
by the transmission delta to get the B/C ratio.
Table 1: Benefits of increasing transmission capacity between the EI and
WI for 50% renewable case.
Objective Function Design 1 Design 3 Delta
Line Investment (B$) 61.21 80.10 18.89
Generation Investment (B$) 704.03 700.51 -3.52
O&M (B$) 1336.36 1300.70 -35.66
Emission Cost (B$) 171.10 162.50 -8.60
15-yr B/C Ratio - - 2.52
Figure 1: Cumulative transmission, wind and solar addition from 2024 to
2038for the macrogrid design (Design 3) for 50% renewable case
A. Discussion
There are four significant observations to make relative to
this design, as follows:
1. Figure 2 indicates the locations for the highest quality wind
(Midwest) and solar (South) resources in the US. The
implication is that a renewable-rich future is selected in this
study because of its economics. That is, existing and expected
future technology costs indicate that the most economically
attractive new energy investments are wind and solar; natural
gas is also part of the resource mix, providing operational
6
flexibility, and, depending on emission price, some energy.
2. Although it is possible, even likely, that transmission
expansion to the Electric Reliability Council of Texas
(ERCOT) may offer significant benefits to EI, WI, and ERCOT,
these potential benefits were not studied in this work in order to
limit its scope to that achievable within the time and with the
resources available.
3. All cross-seam transmission (existing and added) is HVDC.
Cross-seam AC transmission was not considered in our work
because it would synchronize what are now two asynchronous
grids, and therefore it may not offer the same flexibility of
choice in capacity and location as compared to use of HVDC.
A study to investigate the value of a hybrid HVDC/Ultra-High
Voltage AC solution could provide insight for a co-optimized
transmission and generation investment strategy.
4. The reduction in fuel cost is the dominant benefit for Design
3. However, this benefit alone is not sufficient to justify the
macrogrid overlay and underlying infrastructure. Savings in
regulation-up and down, contingency reserves, and
displacement of generation capacity required to meet future
PRM requirements also contribute.
5. Table 2 shows a comparison of performance between
Designs and 3. Although the differences in total generation
expansion between both designs are small, the difference in
creditable capacity, which is the sum of capacity available to
meet peak per (21), is significant. This is important because it
reflects the enhanced ability of Design 3 to benefit from load
diversity between regions during non-coincident peak load
times.
Table 2: Differences in generation and transmission investments between
Design 1 and Design 3, for the 50% renewable case
Metric (GW) Design 1 Design 3 Delta
Invested gen
(wind, solar, gas)
600
(386/172/36)
600
(392/169/38)
0
(7/-6/1)
Retired generation 240 294 54
2038 creditable capacity 838.5 794.1 -44.4
Invested AC transmission 228.9 195.1 -33.8
Invested DC transmission 0 125.8 125.8
To identify the effect of cross-seam transmission on the
location of generation investments, each interconnection was
divided into three subregions, as shown in Fig. 1. Figure 3
shows, for each subregion and for each interconnection, the
change in generation investments in Design 3 relative to those
of Design 1. This comparison indicates that cross-seam
transmission tends to move wind resources eastward and solar
resources westwards, linking load in each interconnection to the
most economically desirable resources. Finally, the breakdown
of generation technologies by fuel type for each interconnection
is included in Table 3. The 2038 infrastructure achieves a 50%
renewable generation (including hydro).
Figure 2: Difference in generation expansion between Design 1 and the
macrogrid design (Design 3) for the 50% renewable case
B. Robustness testing
The following sensitivities were performed to test the
robustness of each design to variation in a single assumption
relative to the base condition:
1) Low gas price: The gas price was changed from one that
began at 4.5 $/MBTU in 2024 and ended at 5.4 $/MBTU
in 2038 to one that began at 3.5$/MBTU and ended at
4.0$/MBTU. These prices are national averages; the
influence of spatial variation on gas prices was modeled.
2) Enforced RPS: State RPS constraints were enforced; but
the escalating emission price remained; this resulted in a
2038 renewable penetration level of 40%
3) No emission price: The emission price was zeroed; this
resulted in a 2038 renewable penetration level of 38%.
4) No sharing: Each RSG was required to satisfy its own
PRM.
Results of these sensitivities, together with those of the base
condition and the current policy condition, are shown in Fig. 4,
which portrays for each case both cross-seam investment
amount (bars) and B/C ratio (curve). We make the following
observations from this figure:
B/C ratio tracks cross-seam transmission capacity: The
conditions resulting in the highest cross-seam transmission
capacity are the conditions having the highest B/C ratio.
Base condition is best: The base condition (50% renewable
case) was chosen under two criteria: (a) it must be credible;
(b) it should maximize B/C ratio. These criteria were used
perceiving that studying other conditions would only be of
value if such a chosen base condition provides an
acceptable B/C ratio. It did, thereby justifying our
sensitivity studies.
All sensitivities invest > 10 GW cross-seam transmission:
This observation shows that even under low transmission-
friendly conditions, optimal investment plans still include
cross-seam transmission growth by a factor exceeding 7.
The no-sharing sensitivity has B/C < 1.0: This shows that
sharing planning and operating reserves is an important
value driver for cross-seam transmission. Because the
industry currently requires reserve requirements to be
satisfied intra-regionally, the benefits of reserve sharing
can be obtained only if the industry is willing to relax this
requirement.
The other four sensitivities have B/C > 1.0: This shows that
under conditions associated with a high-renewable future
greater than 40%, cross-seam transmission pays for itself,
based only on a 15-year period to assess savings generated
by generation investments and operational efficiencies.
(50)
(25)
-
25
50
Net-
Invest
ed
Gen
era
tio
n
Ca
pa
cit
y (
GW
) WIND SOLAR GAS
7
In addition, there are several benefits not included in our
B/C ratios, will likely cause them to be significantly higher
if they were included, as follows.
o Based on annual production costs observed in our study,
we estimate that operational savings beyond the 15-year
period to be between $1B and $4B/year;
o HVDC enables reliability improvements through
improved frequency response [12] and voltage control
[44];
o Increased interregional transmission enhances the ability
to minimize the cost of major disturbances resulting in
loss of regional generation capacity [45].
Figure 3: Incremental transmission between the EI and WI under
different future assumptions
VII. CONCLUSIONS
This paper provides an economic valuation of HVDC high
capacity transmission for the continental US, focusing
particularly on a macrogrid design. Under a high renewable
future, the design produces economic savings over a 15-year
period that exceed its investment cost; these savings result from
access to cost-effective renewables, decreased energy costs, and
less expensive flexibility services (regulating, contingency, and
planning reserves). Other potential benefits not quantified in
this study, that would require additional analysis, are obtained
at no cost; these benefits include enhanced reliability in terms
of frequency response and voltage control, and increased
resilience against large-scale disruptions and adaptability to
changes in policy and/or technology, both of the latter resulting
from increased interconnectedness. There are ongoing efforts to
address technological issues associated with design and
operation of high-capacity HVDC overlays, coordinated by the
IEEE Power Engineering Task Force on Ultra-Wide Area
HVDC Overlay Studies. Perhaps the greatest challenge lies in
the formation of appropriate policy to guide macrogrid
development, implementation, and operation. An initial effort
in this direction was taken via the Transgrid-X2030 Symposium
[46] from which resulted a steering committee to pursue follow-
on work. Much work remains in this arena; this paper
establishes that such effort is justified by the benefits a
macrogrid would deliver.
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IX. BIOGRAPHIES
Armando L. Figueroa-Acevedo (M’2008) received his BS and MS degrees in
electrical engineering from the Polytechnic University of Puerto Rico and the University of Puerto Rico, Mayagüez, in 2009, and ‘13, respectively, and Ph.D.
degree in wind, energy, science and policy and electrical engineering from Iowa State University in ’17.
Ali Jahanbani Ardakani received his BS and MS degrees in EE from
Amirkabir University in 2005 and 2008 and his Ph.D. degree from McGill University in 2014.
Hussam Nosair received his BS in EE and MS in Process Control from University of Alberta, Edmonton, AB in 2006 and 2009, respectively. He
received his PhD in EE from McGill University, Montreal, QC in 2016. He was
a postdoctoral research associate at Iowa State University, Ames, IA from 2016 to 2017, before joining the New York Independent System Operator, Rensselaer, NY as a research engineer.
Abhinav Venkatraman received his BE degree in Electrical and Electronics
Engineering from Anna U., India in 2014 and his MS degree in EE from Iowa State University in 2016. He is a Ph.D. student in EE at Iowa State University.
James McCalley (F’2003) received the BS, MS, and Ph.D. degrees from
Georgia Tech, all in EE. He is a Distinguished Professor and London Professor of Power Systems Engineering at Iowa State University. He was a transmission planning engineer at Pacific Gas & Electric 1985-1990.
Aaron Bloom received his BA in Political Science from Michigan State
University and his Masters in Public Administration from The Ohio State
University.
Dale Osborn (M’1964) received B. Sc. and M.S. degrees in electrical
engineering from the University of Nebraska, Lincoln, NE, USA. He was a Consulting Advisor in the Regulatory and Economic Studies group at the
Midcontinent Independent System Operator, Eagan, MN. He is now retired. Jay
Caspary received his BS in EE with an emphasis in Power Systems from the
University of Illinois – Urbana / Champaign in 1981. He is the Director –
Research, Development & Tariff Services at Southwest Power Pool in Little Rock, Arkansas with over 35 years of industry experience. He served as a Sr.
Policy Advisory for the Office of Electricity at the U.S. Department of Energy in support of grid modernization and research priorities in 2012-2013.
James Okullo received his BS in Electrical and Computer Engineering from
Seattle University, an MS in Electrical Engineering from the University of Washington, and an MS in Industrial Engineering from Purdue University. He is a Policy Studies Engineer at the Midcontinent ISO.
Jordan Bakke received his BS in electrical engineering from North Dakota
State University in 2009, and Masters of Business Administration from the
University of St. Thomas in 2014. He is the manager of Policy Studies at the Midcontinent ISO.
Harvey Scribner (SM’2003) received his BS in EE from Texas Tech University, Lubbock, Texas in 1987, MS in Management and Human Relations
from Abilene Christian University, Abilene, Texas in 1994. He is a Lead
Engineer in Research, Development & Tariff Services at the Southwest Power Pool in Little Rock, Arkansas with over 30 years of industry experience. He is a registered Professional Engineer in the State of Texas.