A strategic investment planning model for China's coal and electricity delivery system

Kuby, Michael; Neuman, Susan; Zhang, Chuntai; Cook, Peter; Dadi, Zhou; Friesz, Terry L.; Qingqi, Shi; Shenhuai, Gao; Watanatada, Thawat; Cao, Wei; Sun, Xuhua; Xie, Zhihua

doi:10.1016/0360-5442(93)90075-o

Cited by 14 publications

(8 citation statements)

References 7 publications

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“…The first stage introduces an optimization approach that shows how transmission and pipelines with discrete capacities can be ‘linearized’ without loss of information and accuracy, therefore allowing necessarily complex energy network models to be developed. Although existing models have used a linear and/or piecewise approach previously , we show for the first time the validity of using a linearized approach for large‐scale energy networks; without this validation, using real data, we cannot have confidence in linearized network approaches. The second stage of the new framework uses a ‘linearized’ solution to prime a fully discrete energy network.…”

Section: Introductionmentioning

confidence: 74%

“…This ‘decision making’ modeling typically entails an optimization approach such as mixed integer‐linear programming (MIP). MIP models involving fixed transport infrastructure (e.g., pipelines, transmission lines, railroads) have been applied to an extensive range of energy scenarios including hydrogen production and distribution [e.g., ], CCS networks [e.g., ], coal and electricity transportation [e.g., ], ethanol distribution [e.g., ], oil distribution and offshore EOR [e.g., ], natural gas transmission [e.g., ], and coupled wind energy and electricity transmission [e.g., ]. Transmission lines and pipelines consist of discrete capacities, that is, they can at maximum carry a defined amount of flow (dependent on pressure for CO 2 ).…”

Section: Pipeline Modelingmentioning

confidence: 99%

“…The remaining constraints (6) through (10) are unmodified from SimCCS CAP , and more details can be found in Middleton et al [28]. Constraint (6) ensures that all CO 2 entering a node (via pipeline or from a CO 2 source), leaves that node (via pipeline or stored in a CO 2 reservoir).…”

Section: Mixed Integer-linear Programming Modelmentioning

confidence: 99%

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A new optimization approach to energy network modeling: anthropogenic CO₂capture coupled with enhanced oil recovery

Middleton

2012

Int. J. Energy Res.

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SUMMARYTo meet next generation energy needs such as wind-and solar-generated electricity, enhanced oil recovery (EOR), CO 2 capture and storage (CCS), and biofuels, the US will have to construct tens to hundreds of thousands of kilometers of new transmission lines and pipelines. Energy network models are central to optimizing these energy resources, including how best to produce, transport, and deliver energy-related products such as oil, natural gas, electricity, and CO 2 . Consequently, understanding how to model new transmission lines and pipelines is central to this process. However, current energy models use simplifying assumptions for deploying pipelines and transmission lines, leading to the design of more costly and inefficient energy networks. In this paper, we introduce a two-stage optimization approach for modeling CCS infrastructure. We show how CO 2 pipelines with discrete capacities can be 'linearized' without loss of information and accuracy, therefore allowing necessarily complex energy models to be solved. We demonstrate the new approach by designing a CCS network that collects large volumes of anthropogenic CO 2 (up to 45 million tonnes of CO 2 per year) from ethylene production facilities and delivers the CO 2 to depleted oil fields to stimulate recovery through EOR. Utilization of anthropogenic CO 2 has great potential to jumpstart commercial-scale CCS while simultaneously reducing the carbon footprint of domestic oil production. Model outputs illustrate the engineering challenge and spatial extent of CCS infrastructure, as well as the costs (or profits) of deploying CCS technology. We show that the new linearized approach is able to offer insights that other network approaches cannot reveal and how the approach can change how we develop future energy systems including transporting massive volumes of shale gas and biofuels as well as electricity transmission for wind and solar energy. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.KEY WORDS CO 2 capture and storage (CCS); enhanced oil recovery (EOR); pipeline optimization; energy network modeling; climate change policy;

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Section: Introductionmentioning

confidence: 74%

Section: Pipeline Modelingmentioning

confidence: 99%

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A new optimization approach to energy network modeling: anthropogenic CO₂capture coupled with enhanced oil recovery

Middleton

2012

Int. J. Energy Res.

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“…We consider planning horizons of 15 and 20 years (T = 15,20) as typical in the literature (see, e.g., Kuby et al 1993). Table 5 specifies the model parameters for Pakistan.…”

Section: The Case Of Pakistanmentioning

confidence: 99%

Designing Energy Supply Chains: Dynamic Models for Energy Security and Economic Prosperity

Rafique

Mun

Zhao

2017

Production and Operations Management

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Many developing countries suffer severe energy deficiencies despite their ample reserves of resources – the so‐called predicament of “resource rich, energy poor." A leading driver is the energy‐economy cycle, where poor economic status, inefficient utilization of limited budget, and energy deficiency reinforced each other and led these countries into a spiral of economic downfall. How to turn this cycle around? It is a classic question but not well answered in the energy policy/economics literature and barely studied in the operations management literature. We apply supply chain design and location optimization models to address the unique features of the energy sector in these countries and present a new class of mathematical models for designing coal‐fired energy supply chains. The model captures the interaction among different parts of an integrated energy supply chain, the unique economics of power transmission such as yield losses, the political issues associated with equity, and the dynamic interaction among energy consumption, economy and budget. The model attempts to answer the classic question by determining the optimal and politically feasible ways to build up an energy supply chain under limited budgets for energy security and economic prosperity. Applying the model to Pakistan's recent energy crises, we show that the solutions can significantly outperform the government's plan by reducing the energy gaps faster, boosting the economy stronger with less greenhouse gas emissions. We develop insights as to how an energy supply chain should be built up strategically for developing countries, and how various system parameters may affect the results.

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“…There are a wide variety of transportation models that use path rather than arc formulations. These include the general class of cost-minimizing, system optimal, capacitated traffic assignment models (for example, Dafermos 1971) mentioned above, user-equilibrium traffic assignment models (Wardrop 1952;Beckmann, McGuire, and Winsten 1956;Leblanc, Morlok, and Pierskalla 1975), network design models (Boyce, Farhi, and Weischedel 1973;LeBlanc 1975;Kuby et al 1993), shipper-carrier models (Friesz, Gottfried, and Morlok 1986), facility location problems (Ray 1990;Hodgson 1990;Current, Pirkul, and Rolland 1994), routing models (Cullen, Jarvis, and Ratliff 1981), and transport risk equity models (Gopalan, Batta, and Kanvan 1990). In many if not all of these areas of application, the existence of substantially different path choices are essential to getting worthwhile results.…”

Section: Introductionmentioning

confidence: 99%

A Minimax Method for Finding the k Best “Differentiated” Paths

Kuby

Xu²,

Xie³

1997

Geographical Analysis

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lhis paper presents a new algorithm deterrnining the K-th best path without any circuit between two specified vertices in a connected, simple and nonoriented graph, The rnethod presented here is based on the well-known fact that the minimum set of ring sum of several Euler graphs and a special path between two vertices consists of all paths between the vertices. Lastly, an illu.strative example is giyen and the effLciency of the algorithn is estimated approxirnately.

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A strategic investment planning model for China's coal and electricity delivery system

Cited by 14 publications

References 7 publications

A new optimization approach to energy network modeling: anthropogenic CO₂capture coupled with enhanced oil recovery

A new optimization approach to energy network modeling: anthropogenic CO₂capture coupled with enhanced oil recovery

Designing Energy Supply Chains: Dynamic Models for Energy Security and Economic Prosperity

A Minimax Method for Finding the k Best “Differentiated” Paths

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A strategic investment planning model for China's coal and electricity delivery system

Cited by 14 publications

References 7 publications

A new optimization approach to energy network modeling: anthropogenic CO2capture coupled with enhanced oil recovery

A new optimization approach to energy network modeling: anthropogenic CO2capture coupled with enhanced oil recovery

Designing Energy Supply Chains: Dynamic Models for Energy Security and Economic Prosperity

A Minimax Method for Finding the k Best “Differentiated” Paths

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Product

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A new optimization approach to energy network modeling: anthropogenic CO₂capture coupled with enhanced oil recovery

A new optimization approach to energy network modeling: anthropogenic CO₂capture coupled with enhanced oil recovery