modeling dispatchability potential of csp in south africa

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Transcript modeling dispatchability potential of csp in south africa

CSP energy systems modelling in STERG

Paul Gauché

SA Energy Modelling Colloquium 31 July 2012

Fakulteit Ingenieurswese

Faculty of Engineering

Agenda

• Introduction to STERG • Why we do CSP systems modelling • How we do plant and systems modelling • What we can do and don’t/won’t do • How we can collaborate 2

STERG INTRODUCTION

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STERG fits in here

Stellenbosch University Engineering Mechanical Engineering • • •

STERG

NEW: Eskom chair Sasol researcher DST/NRF spoke DST/NRF CRSES (Renewable Centre) 4

STERG research structure

Sciences STERG Holistic/Multidisciplinary Research Social & Political Engineering Economic Sciences SWH, Process Heat, Desalination etc.

SUNSTEL

Stellenbosch University Solar Thermal Electricity Project (Primary projects: SUNSPOT, LFR) System R&D (Modelling, Techno-economic, Resources, etc) 5 Solar Resource Measure & R&D Component R&D: Eg. Dry Cooling Component R&D: Eg. Thermal Storage Component R&D: Eg. Heliostats, Receivers

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Technology focus areas for R&D (and modelling)

11+ Projects from distribution to system to components focused on SUNSPOT

Experimental foundation

18 m tower 7 Solar resource station

WHY WE DO CSP SYSTEMS MODELLING

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SA background

• • • SA learned good lessons in last 15 years Struggle to bring IPP’s and renewables onto grid Introduced the Integrated resource plan, a robust planning process as law 2012 2013 2014 201x Tender year IRP 2010 IRP horizon

| Basic IRP timeline structure |

20 years CSP Allocation

IRP 2 Tender: 200 MW Total: 1,000 MW Tender: 100 MW? Total: 1,000 MW Tender: 100 MW? Total: >1,000 MW?

IRP 3 On-going…

IRP summary

Capacity Electricity produced 10

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Background

• • •

CSP Status

Just entering growth phase of tech lifecycle Largely unknown in SA (no plant experience) ~1% of installed capacity by 2030 (IRP) • • •

CSP Need

LTMS, IEA, (Eskom) see CSP as foundation post fossil Climate change & fossil resources suggest crisis Large wind and PV allocation in IRP require 100% capacity backup  not accounted for GAP Sources: Grobbelaar, S., A road map for CSP industry development in South Africa: current policy gaps and recommended next steps for developing a competitive CSP industry, Essay, University of Cambridge, 2011.

IRP2010. 2011. Integrated Resource Plan for Electricity 2010-2030. Government Gazette, Republic of South Africa, 6 May, 2011.

Winkler (ed) 2007. Long Term Mitigation Scenarios: Technical Report. Prepared by the Energy Research Centre for Department of Environment Affairs and Tourism, Pretoria, October 2007.

Wind and solar in symphony

(Denholm & Mehos - NREL) ?

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SA background

• CSP potential has been investigated by Fluri (short term) and Meyer & van Niekerk (longer term) 13 • • • Short term multi-constraint potential (500GWe+) vastly exceeds current or future electricity needs IRP 2010/11 allocates generously to renewables but not CSP – we see this as risk for baseload or peaking.

This work extends previous work to explore full potential

Rutledge coal model

• • Based on Hubbert peak model – finite resources follow a normal distribution production curve.

It works very well. Would have forecast British coal depletion to within months 100 years earlier.

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South African coal

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South African coal

Source

Mohr & Evans (2009) Rutledge (2011)

Peak year (and peak production)

2012 (258 Mt/y) Similar to others but prefers not to comment due to peak year volatility 2007

90% year (and/or total cumulative extraction)

18.6 Gt 2048 (18 Gt) Patzek & Croft (2010) Hartnady (2010) & (2012) 2020 (284 Mt) 2012/2013 (254.3 Mt/yr) (478.6 EJ calculated as 17.15 Gt) 23 Gt 18.675 Gt

What are these models saying?

Peak coal: Now – 2020 Then it’s downhill to about mid century

Other resources (worldwide)

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Conventional uranium: ~2065 Other conventional and unconventional fuels also limited

Wind, water and solar

• Note: 2030 IRP annual power need =~ 500 TWh • The wind resource is about 80 TWh • Hydro is not a major source in SA • Wave and ocean current is for the future • Solar resource is immense and vastly exceeds future needs • Both are intermittent and a problem • This concludes the major energy sources 18

Making sense of it all

CSP no storage 900 CSP w Storage 900 PV 900 Wind 80 2030 energy needs Coal 300 TWh CCGT 10 Hydro 15 ~500 TWh Nuclear 77 TWh

Intermittent Baseload

CSP Future >> 900 OCGT 10

Dispatch

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HOW WE DO CSP SYSTEMS MODELLING

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Introduction

• • Dispatchability = storage + low inertia = CSP value prop 20 MWe Gemasolar plant demonstrated 24h full load 21

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Method: Plant

• • • Based on the Gemasolar plant Approximated optical performance + Chambadal Novikov engine (modified Carnot) + inertia capacitance + storage capacitance Model validated using • • eSolar measured data (Gauché et al. SolarPACES 2011) NREL predicted annual electricity generation for this plant (110 vs. 115 GWh/yr)

Item

Country, Region Location Land area Solar resource Electricity Generation Cost O&M jobs Heliostat aperture area Number of heliostats Heliostat size Tower height Heat transfer fluid Receiver outlet / inlet temperature Turbine capacity (gross) Cooling Storage 37°33 ′

Value

Spain, Seville Andalucía 44.95

″ North, 5°19 ′ West 49.39

″ 195 Ha 2,172 kWh/m 2 /yr 110 GWh/yr (planned) 230,000,000 Euro 45 304,750 m 2 2,650 120 m 2 140 m Molten salt 565 °C / 290 °C 19.9MWe

Wet 2 tank, 15 hours

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Method: Plant

• • • • Heliostat field 1 0,8 0,6 0,4 0,2 0 0 y = 0,4254x 6 - 1,148x 5 + 0,3507x 4 + 0,755x 3 R² = 0,9998 - 0,5918x 2 + 0,0816x + 0,832 0,2 0,4 0,6 0,8 1

Zenith Angle [radians]

1,2 1,4 Receiver balance 𝑛 (1 − 𝛼) 𝑖𝑛 = 𝜎𝜀 𝑟 𝐴 𝑖 𝐹 𝑖 𝑖=1 Inertia & storage model Heat engine 𝜂 𝑡ℎ = 1 − 𝑇 𝐿 𝑇 𝐻 & 𝑇 𝑟𝑖 4 −𝑇 𝑎 4 𝑊 = 𝜂 𝑡ℎ 𝑄 + ℎ𝐴 𝑟 𝑜𝑢𝑡 𝑟 − 𝑇 𝑎 + 𝑜𝑢𝑡 1,6

Method: Spatial solar and weather data

• • Plant model only requires 3 parameters for each hour for dry cooled plant (DNI, Tamb, wind) Grid of points for all South Africa: • 0.375 ° increments latitude and longitude • 823 points in the boundaries of SA 24

823 Grid points (uniform / unbiased) Pretoria Johannesburg Bloemfontein Durban Cape Town

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Method: Spatial solar and weather data

• • • • • • • Plant model only requires 3 parameters for each hour for dry cooled plant (DNI, Tamb, wind) Grid of points for all South Africa: • 0.375 ° increments latitude and longitude • 823 points in the boundaries of SA Helioclim-3 data derived from Meteosat Real 2005 data (not TMY) Point validation of wind and ambient temperature using SA weather data.

Sensitivity analysis to DNI, Tamb, wind showed strong sensitivity to DNI and very weak sensitivity to wind and Tamb.

Helioclim DNI data has issues. The method is still demonstrable. 26

Method: The spatial analysis

• • • 823 grid points * 3 parameters * 8760 hours = 21.6 million inputs 1 output parameter (power) = 7.2 million outputs Proxy for testing dispatchability • Run plant as-is (generates power when it can) • • • Half size power block (emulates half the 823 plants attempting to run at any 1 time) Quarter size power block (emulates quarter of 823 plants attempting to run at any 1 time) Some other combinations were tried 27

Results and analysis: Time plots

8 January days 8 June days 28

Results and analysis: Time plots

29 Data anomaly 1 out of 4 plants running at a time practically demonstrates baseload

Results and analysis: Spatial

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What we can do and don’t/won’t do

• • • STERG centric (CRSES to some degree, but not SI) Can do in future • Through partnerships: Real and TMY solar, wind* and weather data – multi year • • • CSP, PV & wind spatial and time modelling GIS modelling for multi-criteria spatial type analysis Develop and improve underlying technology models Don’t / won’t do (as far as I can tell) • • ERC-like TIMES modelling (stochastic, complex multi-criteria systems considerations) Climate and climate change models • Anything in the policy or social space 31

Areas for collaboration

• • Collective database of • • • • • • • Discount rate sets for RE technologies (scenarios) Capacity and capacity factor scenario sets for all options Technology models Conventional resource estimation scenarios (fossil and fissile) Common solar, wind and weather data sets (real and TMY) Demand profiles at least to hourly demand (historical and forecast) Other… For IRP • • • Set of assumptions on demand per year and finer resolution Recognition of non electric energy needs that transition to electricity – particularly transport Other… 32

Thank you!

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