This document summarizes ongoing macroeconomic modeling work at University College London's Energy Institute. It describes the UK Times Model energy systems model and efforts to link it with a macroeconomic stand-alone model and a computable general equilibrium model. The macro-stand alone model allows exploring the impact of different capital-energy substitution elasticities. Preliminary results show low GDP impacts of climate policies but higher total system costs. Future work includes further comparison of macro modeling approaches and linking the models to assess whole energy-economy impacts of policies.
How do changes to future technology and fuel developments affect the optimal ...IEA-ETSAP
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How do changes to future technology and fuel developments affect the optimal ...IEA-ETSAP
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heating decarbonisation pathway?
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District heating potential in the Italian NECP: assessment through a new resi...IEA-ETSAP
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Planning a reliable power system with a high share of renewables in France by...IEA-ETSAP
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District heating potential in the Italian NECP: assessment through a new resi...IEA-ETSAP
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Planning a reliable power system with a high share of renewables in France by...IEA-ETSAP
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Electricity Demand Side Management and End-use efficiencyeecfncci
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Electricity Demand Side Management (DSM) and End-use Efficiencyeecfncci
This presentation explains the concept of Electical Demand Side Management and shows how to implement it in industries. It was prepared for energy auditor training in Nepal in the context of GIZ/NEEP programme. For further information go to EEC webpage: http://www.eec-fncci.org
#تواصل_تطوير
المحاضرة رقم 179
الدكتور / علي المراكبي
استشاري الهندسة الميكانيكية وخبير ترشيد الطاقة
بعنوان
"اقتصاديات إنتاج الكهرباء بالتكنولوجيات المختلفة"
يوم الإثنين 24 أكتوبر 2022
الثامنة مساء توقيت القاهرة
التاسعة مساء توقيت مكة المكرمة
و الحضور عبر تطبيق زووم
https://us02web.zoom.us/meeting/register/tZUoc-quqTorGtB0hRPR6oljN7DKOanGdx1f
علما ان هناك بث مباشر للمحاضرة على القنوات الخاصة بجمعية المهندسين المصريين
ونأمل أن نوفق في تقديم ما ينفع المهندس ومهمة الهندسة في عالمنا العربي
والله الموفق
للتواصل مع إدارة المبادرة عبر قناة التليجرام
https://t.me/EEAKSA
ومتابعة المبادرة والبث المباشر عبر نوافذنا المختلفة
رابط اللينكدان والمكتبة الالكترونية
https://www.linkedin.com/company/eeaksa-egyptian-engineers-association/
رابط قناة التويتر
https://twitter.com/eeaksa
رابط قناة الفيسبوك
https://www.facebook.com/EEAKSA
رابط قناة اليوتيوب
https://www.youtube.com/user/EEAchannal
رابط التسجيل العام للمحاضرات
https://forms.gle/vVmw7L187tiATRPw9
ملحوظة : توجد شهادات حضور مجانية لمن يسجل فى رابط التقيم اخر المحاضرة.
This paper develops a cost model for onshore wind farms in the U.S.. This model is then used to analyze the influence of different designs and economic parameters on the cost of a wind farm. A response surface based cost model is developed using Extended Radial Basis Functions (E-RBF). The E-RBF ap- proach, a combination of radial and non-radial basis functions, can provide the designer with significant flexibility and freedom in the metamodeling process. The E-RBF based cost model is composed of three parts that can estimate (i) the installation cost, (ii) the annual Operation and Maintenance (O&M) cost, and (iii) the total annual cost of a wind farm. The input param- eters for the E-RBF based cost model include the rotor diameter of a wind turbine,the number of wind turbines in a wind farm, the construction labor cost, the management labor cost and the technician labor cost. The accuracy of the model is favorably explored through comparison with pertinent real world data. It is found that the cost of a wind farm is appreciably sensitive to
the rotor diameter and the number of wind turbines for a given desirable total power output.
A New Solution to Improve Power Quality of Renewable Energy Sources Smart Gri...iosrjce
This particular article reveals a prototyped interface current control protocol which is ideal for
multilevel converters and it’s utilization with a three-phase cascaded H-bridge inverter. This kind of
administration approach utilizes a discrete-time type of the device to estimate the longer term benefit from the
current for many voltage vectors, as well as decides on the vector which in turn decreases an expense purpose.
A result of the multitude of voltage vectors obtainable in a multilevel inverter, numerous computations are
expected, producing challenging execution with this strategy in a typical control program. A new improved
strategic approach with the demonstration using physical framework as well as Matlab system significantly
decreases the number of computations without influencing the actual system’s effectiveness is suggested.
Experimental outcomes intended for five-level inverters confirm the suggested strategy. Additionally, author
considered the socio-environmental effects occurring due to carbon foot printing as per KYOTO protocol and
demonstrates the implementation of prototype carbon foot printing control sub-protocol for minimization of
carbon foot printing occurring due to microbial fuel cell micro-grid setup. Hence this will help to manage
attitudinal goals to improve electricity resource quality and efficiency.
Study of the Pipeline Network Planned in the Humber Region of the UK, Xiaobo Luo, University of Hull. Presented at CO2 Properties and EoS for Pipeline Engineering, 11th November 2014
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This second webinar was held on Friday 25th April, for anyone who wasn't able to join us for the previous webinar held on Thursday 20th March.
This webinar presented the findings of a study to assess the economic viability of natural gas combined-cycle power plants with CO2 capture and storage (NGCC-CCS) in climate change mitigation strategies, emphasising the use of renewable energy and natural gas for electric power generation. In this study, the cost of NGCC-CCS was compared on a level playing field to those of intermittent renewable energy systems (IRES) and energy storage technologies as a means of reducing power sector greenhouse gas emissions. Specifically, the levelised cost of electricity (LCOE) of NGCC-CCS was compared to that of offshore wind, photovoltaic systems, and concentrated solar power (CSP) together with pumped hydro storage (PHS), compressed air energy storage (CAES), and Li-ion, ZEBRA and Zn-Br battery storage systems. The cost of NGCC-CCS as a backup technology in conjunction with IRES also was assessed.
At this webinar, Machteld van den Broek, senior researcher at the Utrecht University, presented the findings of the study. Her expertise is energy systems modelling and CCS. Among others, she is involved in the CATO-2 programme, the second Dutch national research programme on CCS. During the webinar Professor Edward Rubin from Carnegie Mellon University and co-author of this study, will assist during the Q&A session. Niels Berghout, from Utrecht University also contributed to this study.
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Ongoing Macro-Stand Alone and CGE modelling approaches at UCL Energy Institute
1. Ongoing Macro-stand alone and CGE modelling
approaches at UCL Energy Institute
Dr. Matthew Winning
Energy Institute, University College London
ETSAP Workshop, Copenhagen
19 Nov 2014
2. Contents
1. UK Times Model (UKTM)
2. Macro Stand-Alone
3. Capital-energy substitution
4. UKTM-MSA scenarios, issues and results
5. GTAP-UCL
6. Conclusions and extensions
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
3. UKTM
Energy Systems model
Bottom-up with energy technology choice
Partial equilibrium, least-cost optimisation
Exogenous energy demands
New features
Non-CO2 greenhouse gases
Non-energy mitigation options
Energy storage and other energy infrastructures
New time slices (4 intra-day x 4 seasonal)
Updated industry and residential sectors
Development process
Transparency at the forefront of development (data, assumptions, structure is clear and traceable,
full replicability of results, comprehensive QA processes)
Full sectoral data update & 2010 base-year recalibration
User constraints categorized & explicit
UKTM will be fully open-source from January 2015
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
4. MARKAL-Macro in UK
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
Strachan and Kannan (2008), Hybrid modelling of long-
term carbon reduction scenarios for the UK, Energy
Economics (30), 2947 – 2963
Strachan, Pye and Hughes (2008), The Role of
International Drivers on UK scenarios of a low-carbon
Society, Climate Policy, 8:sup1, 125 – 139
Results: loss of GDP in UK ranging from 0.3% to 1.5% by
2050
5. Motivation
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
Q/ Is it worthwhile linking full-blown CGE model to
TIMES for overall impacts i.e. marginal benefit of using
TIMES-MSA vs CGE for overall economic impacts?
Q/ TIMES-MSA vs. CGE parameterisation of elasticity of
substitution between capital and energy
6. TIMES-MSA
(Kypreos and Lehtila, 2013)
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
• A single-agent, single-sector neoclassical optimal
growth general equilibrium model
• Cumulative and discounted utility maximisation of a
representative consumer-producer agent
• Production function is energy and a capital/labour
composite
• GDP comprises of consumption, investment and
energy system costs
• Explore MSA parameter sensitivities
• e.g. elasticity of substitution between
capital/labour composite and energy
• E.g. capital to GDP ratio in the UK
7. TIMES-MSA
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
UKTM
ENERGY SOURCES
TECHNOLOGY CHARACTERISTICS
ENVIRONMENTAL CONSTRAINTS
& POLICIES
TECHNOLOGY MIX
FUEL MIX
EMISSIONS SOURCES & LEVELS
FUEL & EMISSION MARGINAL COSTS
RANKING OF MITIGATION OPTIONS
MACRO
LABOUR
GDP
CONSUMPTION
CAPITAL INVESTMENT
USEFUL ENERGY
SERVICES
ENERGY
PAYMENTS
8. TIMES-MSA
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
Parameter Description Default Value
TM_ARBM Arbitrary multiplier for the last period replication 1
TM_DEFVAL(item) Default values for regional Macro constants
TM_DEFVAL(ESC) 1.03
TM_DEPR(r) Depreciation rate (percentage) 5.00
TM_DMTOL(r) Lower bound factor for the demand variables 0.50
TM_ESUB(r) Elasticity of substitution 0.25
TM_GDP0(r) GDP in the first period
TM_GR(r,y) Projected annual GDP growth in per cent
TM_IVETOL(r) Investment and energy cost upper bound tolerance 0.50
TM_KGDP(r) Initial capital to GDP ratio 2.50
TM_KPVS(r) Initial capital value share in all production factors 0.25
TM_SCALE_CST Scaling factor for cost units 0.00
TM_SCALE_NRG Scaling factor for the demand units 1.00
TM_SCALE_UTIL Scaling factor for the utility function 0.00
TM_QFAC(r) Switch for market penetration penalty function * 0.00
9. TIMES-MSA
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
Parameter Description Default Value
TM_ARBM Arbitrary multiplier for the last period replication 1
TM_DEFVAL(item) Default values for regional Macro constants
TM_DEFVAL(ESC) 1.03
TM_DEPR(r) Depreciation rate (percentage) 5.00
TM_DMTOL(r) Lower bound factor for the demand variables 0.50
TM_ESUB(r) Elasticity of substitution 0.25
TM_GDP0(r) GDP in the first period
TM_GR(r,y) Projected annual GDP growth in per cent
TM_IVETOL(r) Investment and energy cost upper bound tolerance 0.50
TM_KGDP(r) Initial capital to GDP ratio 2.50
TM_KPVS(r) Initial capital value share in all production factors 0.25
TM_SCALE_CST Scaling factor for cost units 0.00
TM_SCALE_NRG Scaling factor for the demand units 1.00
TM_SCALE_UTIL Scaling factor for the utility function 0.00
TM_QFAC(r) Switch for market penetration penalty function * 0.00
10. TIMES-MSA
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
Parameter Description Default Value
TM_ARBM Arbitrary multiplier for the last period replication 1
TM_DEFVAL(item) Default values for regional Macro constants
TM_DEFVAL(ESC) 1.03
TM_DEPR(r) Depreciation rate (percentage) 5.00
TM_DMTOL(r) Lower bound factor for the demand variables 0.50
TM_ESUB(r) Elasticity of substitution 0.25
TM_GDP0(r) GDP in the first period
TM_GR(r,y) Projected annual GDP growth in per cent
TM_IVETOL(r) Investment and energy cost upper bound tolerance 0.50
TM_KGDP(r) Initial capital to GDP ratio 2.50
TM_KPVS(r) Initial capital value share in all production factors 0.25
TM_SCALE_CST Scaling factor for cost units 0.00
TM_SCALE_NRG Scaling factor for the demand units 1.00
TM_SCALE_UTIL Scaling factor for the utility function 0.00
TM_QFAC(r) Switch for market penetration penalty function * 0.00
11. Capital-energy substitution
Both cross-price substitution and Morishima elasticity of
substitution
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
SR LR SR LR
Morishima 0.41 – 0.44 1.05 – 1.07 0.14 – 0.17 0.77 – 0.80
Cross-price 0.22 – 0.38 0.36 – 0.52 0.17 – 0.34 0.31 – 0.48
North America Europe
(M.J. Koeste et al, 2008)
12. Capital-energy substitution
Both cross-price substitution and Morishima elasticity of
substitution
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
SR LR SR LR
Morishima 0.41 – 0.44 1.05 – 1.07 0.14 – 0.17 0.77 – 0.80
Cross-price 0.22 – 0.38 0.36 – 0.52 0.17 – 0.34 0.31 – 0.48
North America Europe
(M.J. Koeste et al, 2008)
13. UKTM-MSA results
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
Total Energy
5400
5600
5800
6000
6200
6400
6600
6800
7000
UKTM_BASE UKTM_BASE_MSA UKTM_LowGHG_Int2 UKTM_LowGHG_MSA_Int2
Total energy 2040 (PJ)
14. UKTM-MSA results
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
Base MSA
ALL BIO ALL COALS ALL ELECTRICITY
ALL GAS ALL HYDROGEN ALL MANFUELS
ALL OIL PRODUCTS ALL OTHER RNW
LowGHG-MSA
ALL BIO ALL COALS ALL ELECTRICITY ALL GAS
ALL HYDROGEN ALL MANFUELS ALL OIL PRODUCTS ALL OTHER RNW
Final energy by fuel
15. UKTM-MSA results
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
Base
FUEL TECHS AGR FUEL TECHS ELC
FUEL TECHS HYG FUEL TECHS INDUS
FUEL TECHS PRC FUEL TECHS RES
FUEL TECHS SERV FUEL TECHS TRA
FUEL TECHS UPSTREAMLowGHG
Base MSA
LowGHG-
MSA
16. UKTM-MSA results
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
Total Emissions
-100000
0
100000
200000
300000
400000
500000
600000
700000
BASE MSA_035 LowGHG_MSA_Int2
GHG emissions 2040 (kT)
EMIS GHG UPS
EMIS GHG TRA
EMIS GHG SER
EMIS GHG RES
EMIS GHG PRC
EMIS GHG NEU
EMIS GHG IND
EMIS GHG HYG
EMIS GHG ELC
EMIS GHG AGR
17. UKTM-MSA results
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
Total System Costs
8500000
8800000
9100000
9400000
9700000
10000000
10300000
Total System Cost
18. UKTM-MSA results
Scenario 2020 2030 2040
LowGHG 0.29 - 0.67 - 0.73
LowGHG (esub 0.35) 0.21 - 0.68 - 0.72
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
GDP loss from baseline
19. UKTM-MSA results
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
Macro variables: Consumption (£bn)
2020 2025 2030 2035 2040
UKTM_BASE_MSA 1458 1646 1866 2147 2607
UKTM_BASE_MSA_
035 1461 1649 1868 2148 2606
UKTM_LowGHG_M
SA 1463 1646 1861 2136 2589
UKTM_LowGHG_M
SA_035 1463 1648 1861 2137 2590
20. UKTM-MSA results
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
Macro variables
300.00
350.00
400.00
450.00
500.00
2020 2025 2030 2035 2040
Energy System costs
UKTM_BASE_MSA_2050 UKTM_BASE_MSA_035 UKTM_LowGHG_MSA_2050_Int2 UKTM_LowGHG_Int2_MSA_035
21. UKTM-MSA results
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
Macro variables
280.00
300.00
320.00
340.00
360.00
380.00
400.00
420.00
2015 2020 2025 2030 2035
Investment (£bn)
UKTM_BASE_MSA_2050 UKTM_BASE_MSA_035 UKTM_LowGHG_MSA_2050_Int2 UKTM_LowGHG_Int2_MSA_035
22. UKTM-MSA issues
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
• NLP solver MINOS vs. CONOPT
• Lower bound on demand required
• Demand marginals volatility – further analysis
required
23. GTAP-UCL
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
• Based on GTAP-E production structure
• GTAP8 database based on Social Accounting
Matrix and trade data
• 129 regions, 57 economic sectors, 5 factors of
production
• MCP formulation solved in GAMS
• Currently standardised methodology of the
disaggregation of electricity sector as part of
ADVANCE FP7 project
24. Conclusions and future work
Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
• Comparison of capital/energy substitution in
MSA vs. CGE models
• Linking UKTM with GTAP-UCL
• Application of MSA to European Times Model
• WholeSEM – link GTAP-UCL and/or UKTM-
MSA with UK land-water models such as
FORESEER at Cambridge
25. Ongoing Macro-stand alone and CGE modelling approaches at UCL Energy Institute
IEA-ETSAP Copenhagen November 2014
Thank you for listening
m.winning@ucl.ac.uk
Questions please!