An optimization-model-based interactive decision support system for regional energy management systems planning under uncertainty

Cai, Yanpeng; Huang, Guohe; Lin, Q.G.; Nie, Xianghui; Tan, Qian

doi:10.1016/j.eswa.2008.02.036

Cited by 114 publications

(58 citation statements)

References 38 publications

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“…Many of existing work focus on single objective optimization, involving an economic requirement to minimize the overall cost of energy generation [14], [15], [16], [17]. The underpinning research addresses individual systems or technologies in the domain of district energy optimization (for example, boilers, CHP, district heating networks, biomass energy…etc.).…”

Section: Related Workmentioning

confidence: 99%

An Analytical Optimization Model for Holistic Multiobjective District Energy Management - A Case Study Approach

Jayan¹,

Li²,

Rezgui³

et al. 2016

IJMO

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Abstract-Efficient management during the operational phase of district energy systems has become increasingly complex due to the various static and dynamic factors involved. Existing deterministic algorithms which are largely based on human experience acquired from specific domains, normally fail to consider the overall efficiency of district energy systems in a holistic way. This paper looks into taking a black box approach by using genetic algorithms (GA) to solve a multiobjectiveoptimization problem conforming to economic, environmental and efficiency standards. This holistic optimization model, takes into account both heat and electricity demand profiles, and was applied in Ebbw Vale district, in Wales. The model helps compute optimized daily schedules for the generation mix in the district and different operational strategies are analyzed using deterministic and genetic algorithm (GA) based combined optimization methods. The results evidence that GA can be used to define an optimum strategy behind heat production leading to an increase in profit by 32% and reduction in CO 2 emissions by 36% in the 24 hour period analyzed. This research fits in well with future district energy systems which give priority to integrated and systematic management.Index Terms-Analytical model, district energy management, energy efficiency, genetic algorithms, multiobjective optimization.

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Section: Related Workmentioning

confidence: 99%

An Analytical Optimization Model for Holistic Multiobjective District Energy Management - A Case Study Approach

Jayan¹,

Li²,

Rezgui³

et al. 2016

IJMO

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“…In order to diminish the risks, this work upgrades the previous model by adding two gray parameters. According to the studies of Karmakar and Mujumdar (2006), Cai et al (2009), the upgraded model has changed its attribute from deterministic into inexact by considering some parameters as gray interval instead of deterministic real numbers. The gray interval is an inexact continuous variable that is a value within the lower and upper bounds, as expressed as the following:…”

Section: Hauling Situationmentioning

confidence: 99%

Planning of methane emission control from hoggery using an inexact two-stage optimization model

Chen

2014

Journal of the Air & Waste Management Association

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This study aims to develop an inexact two-stage optimization model to gather manure distributed over the southwest Taiwan and convert it into bioenergy. In the method, local optimization of each hauling zone is performed first using a gray mixed-integer programming model. Then, the hauling zones are prioritized by its performance on four gray scenarios. Although the biogas yield and the manure generation rate are ambiguous, one can easily evaluate his opportunity and risk by gray interval, which is a group of values within the lower and upper bounds. The analyses reveal that the biogas yield dominates the profit in this project, and it leads to the failure of the project when the biogas yield is below the level of 0.2 m 3 kg À1. With the goal of reducing 45% of methane emissions from pig farms, seven hauling zones are required to be developed. The farmers living in these zones from the project get carbon credits ranging from 478 to 3269 ton CO 2eq per year, and the investors own the carbon credits in the range of 3264-11820 ton CO 2eq per year. Through the carbon trading, both the investors and pig farmers are able to make profits by trading their carbon credits.Implications: Biogas recovered from hoggery can be used as a bioenergy source and mitigate the atmospheric greenhouse effect and global warming. This research develops an inexact two-stage optimization model to evaluate the potential of gathering manure for biogas and converting it into bioenergy. The analyses reveal that the biogas yield dominates the profit in this project, and it leads to the failure of the project when the biogas yield is below the level of 0.2 m 3 kg À1 . This study has provided a useful reference for the management of biogas production and carbon trading from hoggery for bioenergy.

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“…At the same time, a large number of inexact programming methods were successfully used for managing municipal solid waste, water resources, air quality as well as energy resource allocation problems [10,12,18,19,33,. However, few studies focused on uncertainties existed in low-carbon energy system management [11,21].…”

Section: Introductionmentioning

confidence: 99%

“…However, a great number of system parameters (such as coal properties, power-generation demand, and facility capacity, as well as their interactions) may appear uncertain and be presented in interval, possibilistic and probabilistic formats. These uncertainties may not only be complicated by the interactions of multiple sectors and processes, but also could be affected by associated economic and environmental implications, leading to a variety of complexities in relevant decision-making processes [10][11][12][13][14][15][16][17][18][19]. Consequently, effective systems analysis methods are desired for supporting the planning of coupled coal and power systems with CO 2 emissions mitigation management under uncertainty.…”

Section: Introductionmentioning

confidence: 99%

An Inexact Mix-Integer Two-Stage Linear Programming Model for Supporting the Management of a Low-Carbon Energy System in China

et al. 2011

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Abstract:In view of the great contribution of coal-fired units to CO 2 emissions, the coupled coal and power system with consideration of CO 2 mitigation is a typical sub-system of the highly emitting Chinese energy system for low-carbon studies. In this study, an inexact mix-integer two-stage programming (IMITSP) model for the management of low-carbon energy systems was developed based on the integration of multiple inexact programming techniques. Uncertainties and complexities related to the carbon mitigation issues in the coupled coal and power system can be effectively reflected and dealt with in this model. An optimal CO 2 mitigation strategy associated with stochastic power-generation demand under specific CO 2 mitigation targets could be obtained. Dynamic analysis of capacity expansion, facility improvement, coal selection, as well as coal blending within a multi-period and multi-option context could be facilitated. The developed IMITSP model was applied to a semi-hypothetical case of long-term coupled management of coal and power within a low-carbon energy system in north China. The generated decision alternatives could help decision makers identify desired strategies related to coal production and allocation, CO 2 emission mitigation, as well as facility capacity upgrade and expansion OPEN ACCESSEnergies 2011, 4 1658 under various social-economic, ecological, environmental and system-reliability constraints. It could also provide interval solutions with a minimized system cost, a maximized system reliability and a maximized power-generation demand security. Moreover, the developed model could provide an in-depth insight into various CO 2 mitigation technologies and the associated environmental and economic implications under a given reduction target. Tradeoffs among system costs, energy security and CO 2 emission reduction could be analyzed.

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An optimization-model-based interactive decision support system for regional energy management systems planning under uncertainty

Cited by 114 publications

References 38 publications

An Analytical Optimization Model for Holistic Multiobjective District Energy Management - A Case Study Approach

An Analytical Optimization Model for Holistic Multiobjective District Energy Management - A Case Study Approach

Planning of methane emission control from hoggery using an inexact two-stage optimization model

An Inexact Mix-Integer Two-Stage Linear Programming Model for Supporting the Management of a Low-Carbon Energy System in China

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