Planning of energy system management and GHG-emission control in the Municipality of Beijing—An inexact-dynamic stochastic programming model
Introduction
With economic development, population growth and urban expansion, concerns over increasing energy price, exacerbating power shortage and changing climatic conditions are emerging within municipal energy management systems. These issues are highly interrelated, not only among each other but also with a variety of social, economic, political, environmental and technical factors (Frei et al., 2003). To address such complexities, municipal energy systems planning models (MEMs) are desired.
In the past decades, a number of studies were conducted for planning energy systems and managing GHG emission at a municipal level. Kambo et al. (1991) developed a municipal energy systems planning model for the City of Delhi in India. Haurie (2001) presented a MARKAL-LITE model for municipal energy systems planning in Geneva. Richter and Hamacher (2002) built an integrated model for energy and environment management in Augsburg, Germany. Li et al. (2004) proposed a municipal energy systems planning model for the City of Hohhot, China. Kaewniyompanit et al. (2006) conducted a modeling study that focused on power supply variations in Japan. Lin et al. (2008) developed a municipal energy systems planning model for the Toronto–Niagara Region, Canada. Cai et al. (2008) reported an energy systems (EIA, 2001) planning model for GHG-emission management and climate-change impact analysis in the Region of Kitchener–Waterloo, Canada. Lin and Huang (2009) developed an energy systems planning model for the City of Waterloo, Canada.
The previous studies emphasized on the planning of either individual energy sectors or general energy systems. Studies on individual energy sectors, however, could hardly reflect the complicated interactions among energy supply, conversion and demand; therefore, they could hardly provide sound bases for supporting comprehensive energy systems planning and environmental management. On the other hand, studies on general energy systems would face difficulties in addressing uncertainties that presented in various forms (Muela et al., 2007). Such uncertainties could be expressed as interval numbers or probabilistic distributions. Effectively addressing these uncertainties would be critical for analyzing system reliability, assessing associated risks and supporting the formulation of sound management policies (Lin and Huang, 2008). Moreover, dynamics of system-capacity expansions under uncertainty were complicated in municipal energy management systems. Such dynamic issues were not effectively addressed in the previous studies.
Therefore, the objective of this study is to develop a dynamic interval-parameter stochastic municipal energy systems planning model (DITS-MEM) for supporting energy systems planning and environmental management under uncertainty. The development of DITS-MEM necessitates sub-tasks including: (1) development of a municipal energy systems planning model through optimizing energy supply, processing and demand activities and regulating the associated greenhouse-gas (GHG) emissions; (2) incorporation of mixed-integer and interval-parameter linear programming (ILP) techniques into MEM to formulate a dynamic interval-parameter municipal energy systems planning model (DI-MEM) for dealing with dynamics of capacity-expansion issues and uncertainties presented as interval values; (3) integration of a two-stage stochastic programming (TSP) method into DI-MEM to formulate DITS-MEM for addressing random GHG-emission reduction targets and (4) application of the developed DITS-MEM to the Municipality of Beijing, China, for demonstrating its capability in providing decision bases for supporting energy systems planning and GHG-emission management under uncertainty.
Section snippets
Development of DITS-MEM
Urban and municipal energy systems may be occasionally considered equivalent to each other. In fact, they are different in terms of spatial and technological considerations. Spatially, urban energy systems are usually limited to urban zones in a municipality or region; rural communities that consume energy in a different way are excluded. Meanwhile, municipal energy systems are often bound with an area covering the entire municipal jurisdiction, which may contains both urban and rural
Overview of the study system
China is the world's most populous country with a rapidly growing economy that has led to sharp increases of energy demands. Production and consumption of coal are the highest in the world. Rising oil import has made China a significant factor in world oil markets (Huang et al., 2006). Beijing, as the political, economic and cultural center of China, is one of the largest cities in the world. According to Beijing Statistics Bureau (2006), total energy consumption amounted to 39.13 and 47.08
Conclusions
A dynamic inexact two-stage stochastic municipal energy systems planning model (DITS-MEM) was developed for supporting GHG-emission management and energy systems planning under uncertainty. Through integrating mixed-integer, interval-parameter and two-stage programming techniques, the developed DITS-MEM can address dynamics of capacity-expansion issues, uncertainties presented as interval values, and emission reduction scenarios associated with different levels of economic implications. The
Acknowledgments
This research was supported by the Major State Basic Research Development Program of MOST (2005CB724200), Environment Canada and the Natural Science and Engineering Research Council of Canada.
References (22)
- et al.
Dynamic formulation of a top-down and bottom-up merging energy-policy model
Energy Policy
(2003) - et al.
Capacity planning for an integrated waste management system under uncertainty: a North American case study
Waste Management & Research
(1997) - et al.
An integrated numerical and physical modeling system for an enhanced insitu bioremediation process
Environmental Pollution
(2006) - et al.
Linear goal programming model for urban energy-economy-environment interaction
Energy Build
(1991) - et al.
IFTEM: an interval-fuzzy two-stage stochastic optimization model for regional energy systems planning under uncertainty
Energy Policy
(2009) - et al.
Fuzzy possibilistic model for medium-term power generation planning with environmental criteria
Energy Policy
(2007) - Beijing Statistics Bureau, 2006. Beijing energy supply and consumption in 1978–2004 (in Chinese). Beijing,...
- BMDRC, 2006. Beijing Municipality's eleventh-five-year electricity development plan (in Chinese). Beijing Municipal...
- et al.
Development of an optimization model for energy systems planning in the Region of Waterloo
International Journal of Energy Research
(2008) - et al.
A two-stage stochastic programming with recourse model for determining robust planting plans in horticulture
Journal of the Operational Research Society
(2000)
Cited by (58)
Power distribution system planning framework (A comprehensive review)
2023, Energy Strategy ReviewsReviewing local and integrated energy system models: insights into flexibility and robustness challenges
2022, Applied EnergyCitation Excerpt :Sensitivity analyses were the second most common option, used by a quarter of studies and applied to cost assumptions [48,79,90,143,153], technology availability [117,122,139,154,155], or both [58,93,103,132,156]. Beyond these, more sophisticated mathematical formulations such as interval linear programming or chance-constrained programming can also be used to generate more resilient solutions [56,68–70,80,83], but are significantly rarer due to the required effort. The stochastic sampling of input data, for example generation profiles for solar and wind [106,157] or load and EV profiles [106,114] represents an alternative approach to consider uncertainty regarding parameter values.
A Multi-Stochastic SMR Siting Model Applied to the Province of Saskatchewan, Canada: Emphasis on Technological Competition and Policy Impacts
2022, Resources, Conservation and Recycling