In recent years, electricity costs in the Australian National Electric Market (NEM) have increased primarily due to the network costs associated with grid infrastructure upgrade and operation. Distributed power generation through small-scale concentrated solar power (CSP) can mitigate these network costs if deployed at critical points in the network where low capacity transmission lines most constrain transmission. This study identified cost-effective configurations of small-scale CSP plants, from 10 to 50 MWe, via levelised cost of electricity (LCOE) by adjusting the solar field layout and size, tower height, receiver dimensions and storage capacity. The model was implemented in SolarTherm, and parametric sweeps were carried out by varying solar multiple, storage capacity, and receiver size and tower height to determine the effect of these parameters in the LCOE. Also, two material options were considered for tower construction: reinforced concrete and steel truss. Results showed that the highest capacity factor is found at a 3.4 solar multiple and 15–16 hours of storage capacity at all CSP sizes, achieving an LCOE between 13.7–16.7 ¢USD/kWh depending on CSP size and assuming a 1.5 MW/m2 peak heat flux. Besides, if peak flux is reduced to 0.85 MW/m2, then LCOE ranges between 20.5–26.2 ¢USD/kWh. The use of steel towers is suitable for plant scales below 20 MWe, reducing the LCOE by 2.5–4.2% compared to concrete towers. Meanwhile, concrete construction is the best option for 30–50 MWe.

1.
AECOM
,
“Australia's off-grid clean energy market
,”
Tech. Rep. (Australian Renewable Energy Agency
,
2014
).
2.
ACCC
,
“Retail electricity pricing inquiry — final report
,”
Tech. Rep. (Australian Competition & Consumer Commission
,
2018
).
3.
J.
Holland
and Associates,
“Australian concentrating solar thermal roadmap
,”
Tech. Rep. (Australian Renewable Energy National Agency
,
2019
).
4.
K.
Reddy
,
K.
Kumar
, and
V. A.
Devaraj
,
“Feasibility analysis of megawatt scale solar thermal power plants
,”
Journal of Renewable and Sustainable Energy
4
,
063111
(
2012
).
5.
V.
Balaji
and
H.
Gurgenci
,
“Search for optimum renewable mix for australian off-grid power generation
,”
Energy
175
,
1234
1245
(
2019
).
6.
P.
Scott
,
A. d. l. C.
Alonso
,
J. T.
Hinkley
, and
J.
Pye
, “Solartherm: A flexible modelica-based simulator for csp systems,” in
AIP Conference Proceedings
, Vol.
1850
(
AIP Publishing
,
2017
) p.
160026
.
7.
M.
Meybodi
and
A.
Beath
,
“Impact of cost uncertainties and solar data variations on the economics of central receiver solar power plants: An australian case study
,”
Renewable energy
93
,
510
524
(
2016
).
8.
L. L.
Vant-Hull
and
M. E.
Izygon
,
“Guideline to central receiver system heliostat field optimization
,”
Advances in solar energy
15
,
1
42
(
2003
).
9.
B. L.
Kistler
, “A user's manual for delsol3: a computer code for calculating the optical performance and optimal system design for solar thermal central receiver plants,”
Tech. Rep
. (
Sandia National Labs
.,
Livermore, CA (USA
),
1986
).
10.
J.
Coventry
and
J.
Pye
, “
Heliostat cost reduction–where to now?
Energy Procedia
49
,
60
70
(
2014
).
11.
J.
Peterseim
,
S.
White
, and
U.
Hellwig
, “Novel solar tower structure to lower plant cost and construction risk,” in
AIP Conference Proceedings
, Vol.
1734
(
AIP Publishing LLC
,
2016
) p.
070025
.
This content is only available via PDF.