A dynamic model to optimize municipal electric power systems by considering carbon emission trading under uncertainty. (August 2015)
- Record Type:
- Journal Article
- Title:
- A dynamic model to optimize municipal electric power systems by considering carbon emission trading under uncertainty. (August 2015)
- Main Title:
- A dynamic model to optimize municipal electric power systems by considering carbon emission trading under uncertainty
- Authors:
- Zhu, Y.
Li, Y.P.
Huang, G.H.
Fan, Y.R.
Nie, S. - Abstract:
- Abstract: In this study, a FFSP (full-infinite fuzzy stochastic programming) method is developed for planning MEPS (municipal electric power systems) associated with GHG (greenhouse gas) control under uncertainty. FFSP can deal with multiple uncertainties presented in terms of fuzzy sets, functional intervals, and random variables. FFSP is also applied to a case study of Beijing for managing MEPS, and reducing the GHG emission through introducing the EU ETS (European Union greenhouse gas emission trading scheme). The results indicate that reasonable solutions have been generated, which can be used for generating schemes of energy resources, electricity production/allocation, and capacity expansion under various economic costs and GHG reduction requirements. The case study demonstrates that FFSP can increase the abilities of reflecting complexities for dynamics of capacity expansion and interaction of multiple uncertainties in MEPS. The results allow in-depth analyses of trade-offs between GHG mitigation and economic objective as well as those between system cost and decision makers' satisfaction degree. Besides, this study can also provide an example to help China construct domestic carbon trading market at municipal scale for addressing the challenges of global climate change. Highlights: A dynamic optimization (FFSP) method is developed for tackling uncertainties. FFSP is applied to planning MEPS (municipal electric power systems) of Beijing. CET (Carbon emission trading)Abstract: In this study, a FFSP (full-infinite fuzzy stochastic programming) method is developed for planning MEPS (municipal electric power systems) associated with GHG (greenhouse gas) control under uncertainty. FFSP can deal with multiple uncertainties presented in terms of fuzzy sets, functional intervals, and random variables. FFSP is also applied to a case study of Beijing for managing MEPS, and reducing the GHG emission through introducing the EU ETS (European Union greenhouse gas emission trading scheme). The results indicate that reasonable solutions have been generated, which can be used for generating schemes of energy resources, electricity production/allocation, and capacity expansion under various economic costs and GHG reduction requirements. The case study demonstrates that FFSP can increase the abilities of reflecting complexities for dynamics of capacity expansion and interaction of multiple uncertainties in MEPS. The results allow in-depth analyses of trade-offs between GHG mitigation and economic objective as well as those between system cost and decision makers' satisfaction degree. Besides, this study can also provide an example to help China construct domestic carbon trading market at municipal scale for addressing the challenges of global climate change. Highlights: A dynamic optimization (FFSP) method is developed for tackling uncertainties. FFSP is applied to planning MEPS (municipal electric power systems) of Beijing. CET (Carbon emission trading) is introduced into MEPS for mitigating CO2 emissions. Trade-offs occur between system cost and satisfaction degree under uncertainties. Results can provide an example to construct domestic CET market in China. … (more)
- Is Part Of:
- Energy. Volume 88(2015)
- Journal:
- Energy
- Issue:
- Volume 88(2015)
- Issue Display:
- Volume 88, Issue 2015 (2015)
- Year:
- 2015
- Volume:
- 88
- Issue:
- 2015
- Issue Sort Value:
- 2015-0088-2015-0000
- Page Start:
- 636
- Page End:
- 649
- Publication Date:
- 2015-08
- Subjects:
- Carbon trading -- Electric power systems -- Fuzzy sets -- Optimization -- Stochastic programming -- Uncertainty
Power resources -- Periodicals
Power (Mechanics) -- Periodicals
Energy consumption -- Periodicals
333.7905 - Journal URLs:
- http://www.elsevier.com/journals ↗
- DOI:
- 10.1016/j.energy.2015.05.106 ↗
- Languages:
- English
- ISSNs:
- 0360-5442
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3747.445000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 8426.xml