An interval full-infinite mixed-integer programming method for planning municipal energy systems – A case study of Beijing

Zhu, Yong; Huang, Guohe; Li, Y. P.; He, Li; Zhang, X.X.

doi:10.1016/j.apenergy.2011.01.058

Cited by 61 publications

(25 citation statements)

References 45 publications

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“…District scale models have been the dominant choice of spatial optimisation since at least the year 2000 [179][180][181][182][183][184][185][186][187][188][189] whilst others have focused on building stock models [190][191][192][193]. It is reflective of the recent advances in both programming software and computational hardware that some researchers have increasingly pushed from district scales towards urban scale models [194][195][196][197][198][199][200][201].…”

Section: ] Citesmentioning

confidence: 99%

A review of urban energy system models: Approaches, challenges and opportunities

Keirstead

Jennings

Sivakumar

2012

Renewable and Sustainable Energy Reviews

529

238

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Energy use in cities has attracted significant research in recent years. However such a broad topic inevitably results in number of alternative interpretations of the problem domain and the modelling tools used in its study. This paper seeks to pull together these strands by proposing a theoretical definition of an urban energy system model and then evaluating the state of current practice. Drawing on a review of 219 papers, five key areas of practice were identified -technology design, building design, urban climate, systems design, and policy assessment -each with distinct and incomplete interpretations of the problem domain. We also highlight a sixth field, land use and transportation modelling, which has direct relevance to the use of energy in cities but has been somewhat overlooked by the literature to date.Despite their diversity, these approaches to urban energy system modelling share four common challenges in understanding model complexity, data quality and uncertainty, model integration, and policy relevance. We then examine the opportunities for improving current practice in urban energy systems * Corresponding author Email addresses: j.keirstead@imperial.ac.uk (James Keirstead), m.jennings09@imperial.ac.uk (Mark Jennings), a.sivakumar@imperial.ac.uk (Aruna Sivakumar) Preprint submitted to Renewable and Sustainable Energy ReviewsOctober 5, 2011modelling, focusing on the potential of sensitivity analysis and cloud computing, data collection and integration techniques, and the use of activity-based modelling as an integrating framework. The results indicate that there is significant potential for urban energy systems modelling to move beyond single disciplinary approaches towards a sophisticated integrated perspective that more fully captures the theoretical intricacy of urban energy systems.

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Section: ] Citesmentioning

confidence: 99%

A review of urban energy system models: Approaches, challenges and opportunities

Keirstead

Jennings

Sivakumar

2012

Renewable and Sustainable Energy Reviews

529

238

View full text Add to dashboard Cite

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“…FP methods can effectively reflect uncertainties expressed as fuzzy sets; however, they are infeasible to treat uncertainties presented as interval values without knowing their membership functions. Interval-parameter programming (IPP) is effective for handling uncertainties in objective function and constraints, since interval numbers are acceptable as its uncertain inputs [37,38]. Therefore, one potential approach for accounting for fuzzy and interval uncertainties is to combine the IPP with FP; this leads to an interval-fuzzy linear programming (IFLP) model as follows:…”

Section: Methodsmentioning

confidence: 99%

Interval-fuzzy municipal-scale energy model for identification of optimal strategies for energy management – A case study of Tianjin, China

Chen

Huang

2016

Renewable Energy

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a b s t r a c tAn interval-fuzzy municipal-scale energy model (IF-MEM) is developed for energy systems planning. IF-MEM can effectively tackle uncertainties presented in terms of fuzzy sets and discrete intervals. IF-MEM is applied to a real case of energy systems planning in the City of Tianjin, which is the economic and industrial center of northern China. IF-MEM allows in-depth analyses for tradeoffs among economic objective, satisfaction degree, and constraint-violation risk. The results indicate that reasonable solutions have been generated. The results suggest that: (i) coal would be the dominant sources in city's energy systems due to the increasing energy demand; however, its consumption ratio would decrease over the planning horizon due to requirements of environment protection and sustainable development; (ii) clean (e.g., natural gas, liquefied petroleum gas) and renewable energies (e.g., wind power, solar power) would be vigorously encouraged during the planning periods; (iii) coal-fired technology (i.e., coal-fired power and heat) would be the major contributor for the city's atmosphere pollution; (iv) the pollutant emissions would be decreased over the planning horizon due to the restriction of fossil fuels and the development of renewable energy. Promoting the utilization of renewable energy sources is crucial for supporting pollutant emission reduction and solving energy shortage crisis.

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“…The model sought to identify the least costly pathway in securing electricity supply taking into account many possible life cycle events in capacity planning through the construction, retrofitting and eventual decommissioning. Also based in China, a study by Zhu et al [9] applied an MILP model for planning municipal energy systems in Beijing.…”

Section: Introductionmentioning

confidence: 99%

A multi-period mixed-integer linear optimisation of future electricity supply considering life cycle costs and environmental impacts

et al. 2014

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An MILP model developed for life cycle optimisation of electricity supply up to 2060. The model can optimise on costs and a number of life cycle environmental impacts. Optimising on costs alone leads to suboptimal solutions. Optimising on GWP reduces both costs and other environmental impacts. Multi-objective optimisation crucial for identifying sustainable electricity futures. a b s t r a c tA multi-period mixed-integer linear programming model has been developed to help explore future pathways for electricity supply where costs and carbon reduction are a priority. The model follows a life cycle approach and can optimise on costs and on a number of environmental objectives. To illustrate the application, the model has been optimised on two objectives: whole system costs and global warming potential (GWP) using the UK as an example. Four different scenarios have been considered up to 2060, each assuming different electricity demand and carbon reduction targets. When optimising on system costs, they range from £156.6 bn for the least carbon-constrained scenario with moderate increase in electricity demand to £269.9 bn for the scenario with high demand and requiring 100% decarbonisation of electricity supply by 2035. In optimisation on GWP, negative carbon emissions are achieved in all scenarios, ranging from À0.5 to À1.28 Gt CO 2 eq. over the period, owing to biomass carbon capture and storage. Optimising on the GWP also reduces significantly other environmental impacts at costs comparable to optimised costs. This research shows that meeting carbon targets will require careful planning and consideration of objectives other than costs alone to ensure that optimal rather than suboptimal solutions are found for a more sustainable electricity supply.

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An interval full-infinite mixed-integer programming method for planning municipal energy systems – A case study of Beijing

Cited by 61 publications

References 45 publications

A review of urban energy system models: Approaches, challenges and opportunities

A review of urban energy system models: Approaches, challenges and opportunities

Interval-fuzzy municipal-scale energy model for identification of optimal strategies for energy management – A case study of Tianjin, China

A multi-period mixed-integer linear optimisation of future electricity supply considering life cycle costs and environmental impacts

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