Reliable and flexible steam and power system design

Sun, Li; Liu, Chang

doi:10.1016/j.applthermaleng.2014.11.076

Cited by 29 publications

(9 citation statements)

References 36 publications

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“…This approach used combined chance-constrained programming and k -out-of- n system modelling. Meanwhile, Sun and Liu [8] developed a mixed-integer linear programming (MILP) model for designing steam and power systems considering reliability and uncertainty. The proposed MILP model [8] incorporated the configuration of the system (e.g., equipment capacity, efficiency, failure) as well as operational scheduling when computing the optimized cost (i.e., minimum).…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, Sun and Liu [8] developed a mixed-integer linear programming (MILP) model for designing steam and power systems considering reliability and uncertainty. The proposed MILP model [8] incorporated the configuration of the system (e.g., equipment capacity, efficiency, failure) as well as operational scheduling when computing the optimized cost (i.e., minimum). Based on [8], the results show that redundancy based on spare capacity and auxiliary equipment will provide more reliability and flexibility in utility systems.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed MILP model [8] incorporated the configuration of the system (e.g., equipment capacity, efficiency, failure) as well as operational scheduling when computing the optimized cost (i.e., minimum). Based on [8], the results show that redundancy based on spare capacity and auxiliary equipment will provide more reliability and flexibility in utility systems. Later, a multi-objective optimization model was developed by Rad et al.…”

Section: Introductionmentioning

confidence: 99%

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Design, optimisation and reliability allocation for energy systems based on equipment function and operating capacity

Andiappan

Benjamin

Tan

et al. 2019

Heliyon

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Designers of energy systems often face challenges in balancing the trade-off between cost and reliability. In literature, several papers have presented mathematical models for optimizing the reliability and cost of energy systems. However, the previous models only addressed reliability implicitly, i.e., based on availability and maintenance planning. Others focused on allocation of reliability based on individual equipment requirements via non-linear models that require high computational effort. This work proposes a novel mixed-integer linear programming (MILP) model that combines the use of both input-output (I-O) modelling and linearized parallel system reliability expressions. The proposed MILP model can optimize the design and reliability of energy systems based on equipment function and operating capacity. The model allocates equipment with sufficient reliability to meet system functional requirements and determines the required capacity. A simple pedagogical example is presented in this work to illustrate the features of proposed MILP model. The MILP model is then applied to a polygeneration case study consisting of two scenarios. In the first scenario, the polygeneration system was optimized based on specified reliability requirements. The technologies chosen for Scenario 1 were the CHP module, reverse osmosis unit and vapour compression chiller. The total annualized cost (TAC) for Scenario 1 was 53.3 US$ million/year. In the second scenario, the minimum reliability level for heat production was increased. The corresponding results indicated that an additional auxiliary boiler must be operated to meet the new requirements. The resulting TAC for the Scenario 2 was 5.3% higher than in the first scenario.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design, optimisation and reliability allocation for energy systems based on equipment function and operating capacity

Andiappan

Benjamin

Tan

et al. 2019

Heliyon

View full text Add to dashboard Cite

show abstract

“…The model is then used to optimise the structure, size and operations of a tri-generation system. More recently, Sun and Liu (2015) proposed an approach based on simultaneously modelling and optimising the structure and operation of a steam and power plant system with Markov Chain reliability analysis. A multi-period stochastic model is proposed considering compensation costs and penalty costs to obtain both system configuration with spare equipment (and spare capacities) and operating scheduling specification for equipment failures and process demand fluctuations.…”

Section: Reliability Aspects In Synthesis Of Energy Systemsmentioning

confidence: 99%

State-Of-The-Art Review of Mathematical Optimisation Approaches for Synthesis of Energy Systems

Andiappan

2017

Process Integr Optim Sustain

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An energy system is a crucial component in fulfilling the energy requirements of a given industrial process. If not designed appropriately, energy systems may not be able to perform designated operations in an optimised manner. Mathematical optimisation approaches have had a long history in addressing the synthesis of energy systems. Mathematical optimisation approaches are part of a larger domain known as process systems engineering (PSE). The main objective of this review is to provide a state-of-the-art overview of the mathematical optimisation approaches developed, particularly those developed for synthesis of energy systems, including the handling of uncertainty and the optimisation of multiple objectives. Subsequently, the synthesis of energy systems is further discussed on specific areas such as reliability, operability, flexibility and retrofit and eco-industrial parks. Following this, an overall analysis of the contributions in these areas is provided. Finally, future research directions are identified at the end of this review.

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“…Flexibility in the power plants operation is characterized by the necessity of frequent load changes, the need to work outside the nominal operating conditions, and the need of more frequent power units start-ups as well as shortening the time of start-up, shutdown or changing a partial load. The necessary flexibility can be achieved with acceptable impacts on component life, efficiency, and emissions but it needs continuous monitoring and controlling of basic operating parameters [38][39][40][41][42][43][44][45][46][47]. In Figure 4, the forecasted electricity generation (2012-2040) based on renewable energy sources is presented.…”

mentioning

confidence: 99%

Introductory Chapter: New Trends and Recent Developments for Thermal Power Plants

Madejski¹

2018

Thermal Power Plants - New Trends and Recent Developments

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Reliable and flexible steam and power system design

Cited by 29 publications

References 36 publications

Design, optimisation and reliability allocation for energy systems based on equipment function and operating capacity

Design, optimisation and reliability allocation for energy systems based on equipment function and operating capacity

State-Of-The-Art Review of Mathematical Optimisation Approaches for Synthesis of Energy Systems

Introductory Chapter: New Trends and Recent Developments for Thermal Power Plants

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