A systematic decision analysis approach to design biomass combined heat and power systems

Ling, Wen; Andiappan, Viknesh; Wan, Yoke Kin; Ng, Denny K. S.

doi:10.1016/j.cherd.2018.07.016

Cited by 10 publications

(7 citation statements)

References 32 publications

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“…3.2) for maximizing the production. Overall, the sugarcane plant model (1)- (20) comprises mixed-integer linear equality and inequality constraints.…”

Section: Sugarcane Plantwide Modelingmentioning

confidence: 99%

“…The methods and tools developed in Process Systems Engineering (PSE) can be a great help in this context. Systematic optimization methods based on so-called process superstructure models have proven particularly valuable for the synthesis and design of energy systems, including bioenergy production systems; see the recent survey and methodological papers [15][16][17][18][19][20]. A superstructure model describes a set of candidate process pathways, both the process units and the interconnections; then, the optimizer is in charge of se-lecting the best possible pathways in the sense of one or several criteria, for instance maximizing economic performance or minimizing environmental impact.…”

Section: Introductionmentioning

confidence: 99%

“…In order to de-risk the solutions one may also want to account for potential shortfalls [23,24] or regret [25] alongside the other design criteria, for instance using a multiobjective optimization approach. Such superstructure optimization models for risk-conscious decision-making under uncertainty are well developed and have been used for decision-making in various (bio)energy sectors, including CHP systems [20,26,27], distributed energy systems [28], and bioethanol supply-chains [29,30].…”

Section: Introductionmentioning

confidence: 99%

“…Parameter values in the plantwide model (1)-(20), derived from the Open Sugarcane Process Simulation Platform[36].…”

mentioning

confidence: 99%

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Risk-conscious optimization model to support bioenergy investments in the Brazilian sugarcane industry

et al. 2020

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The past decades have seen a diversification of the sugarcane industry with the emergence of new technology to produce bioenergy from by-product and waste process streams. Given Brazil's ambitious goal of reducing greenhouse gas emissions by over 40% below 2005 levels by 2030, it is of paramount importance to develop reliable decision-making systems in order to stimulate investment in these low-carbon technologies. This paper seeks to develop a more accurate optimization model to inform risk-conscious investment decisions for bioenergy generation capacity in sugarcane mills. The main objective is for the model to enable a better understanding of how Brazilian government policies, such as the electricity price in the regulated market, may impact these investments, by taking into account the uncertainty in sugar, ethanol and spot electricity markets and the interdependency between production and investment decisions in terms of saleable product mix. The proposed methodology combines portfolio optimization theory with superstructure process modeling and it relies on simple surrogates derived from a detailed sugarcane plant simulator to retain computational tractability and enable scenario analysis. The case study of an existing sugarcane plant is used to demonstrate the methodology and illustrate how the model can assist decision-makers. In all of the scenarios assessed, the model recommends investment in extra bioelectricity capacity via the anaerobic digestion of vinasse but advises against investment in second-generation ethanol production via the hydrolysis of surplus bagasse. Furthermore, the decision to upgrade the cogeneration system with a condensation turbine is highly sensitive to the electricity price practiced in the regulated market, capacity constraints on the sugar-ethanol mix, and the accepted level of risk. Another key insight drawn from the case study is that recent market conditions have favored a production focused on the sugar business, making it challenging for policy-makers to create attractive scenarios for biofuels. Long-term electricity contracting appears to be the main hedging strategy for de-risking other products and investments in the sugarcane business, provided it is priced adequately.

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“…3.2) for maximizing the production. Overall, the sugarcane plant model (1)- (20) comprises mixed-integer linear equality and inequality constraints.…”

Section: Sugarcane Plantwide Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Parameter values in the plantwide model (1)-(20), derived from the Open Sugarcane Process Simulation Platform[36].…”

mentioning

confidence: 99%

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Risk-conscious optimization model to support bioenergy investments in the Brazilian sugarcane industry

et al. 2020

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“…Biomass cogeneration is a hot topic studied by scholars at home and abroad. Andiappan, et al designed a system decision analysis method of a biomass cogeneration system [31]. Tagliaferri et al analyzed and evaluated the life cycle of biomass cogeneration power plants in the UK through a case study.…”

Section: Cogeneration System and Biomass Cogenerationmentioning

confidence: 99%

An Economic and Technology Analysis of a New High-Efficiency Biomass Cogeneration System: A Case Study in DC County, China

Huang

Yan

Song³

et al. 2020

Energies

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Biomass is the fourth largest energy source in the world; it is easy to store and can be converted into various kinds of renewable energies. The biomass cogeneration system is an important way to utilize biomass energy, especially in northern China. At present, there are many problems in biomass power plants in China, such as high latent heat loss of chimney and cooling towers, low power generation efficiency, and thermal efficiency. In order to solve this problem, this paper introduces low vacuum circulating water heating technology in the biomass cogeneration system, and expounds the differences between China and Western countries in biomass power plants. Based on this background, the technology is redesigned and reformed to make it more suitable for the biomass fuel varieties in the power plant location, and realize the localization of technology and the expansion of scale. The application of this improved technology in China’s biomass cogeneration project is analyzed. Based on the biomass cogeneration project in the DC County of China, the analysis confirms that the designed low vacuum circulating water heating technology is suitable for biomass power generation projects with agricultural and forestry wastes as raw materials, and its application can greatly improve the heat utilization efficiency of the whole cogeneration system. At the same time, in order to estimate the possibility of profitable investment when the key financial parameters change, the financial risk is analyzed. The results show that the probability of 90% net present value (NPV) in 15 years is between 355.28 million RMB and 623.96 million RMB, and the internal rate of return can reach 17.7%.

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Disjunctive fuzzy optimisation of electricity generation by biomass power producers in a feed‐in tariff programme

Nair

Andiappan

Foo

et al. 2022

Asia-Pacific J Chem Eng

Self Cite

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The global drive to achieve the climate change mitigation targets set during the Paris Agreement and COP26 necessitates sustainable electricity generation from renewable energy sources. However, renewable energy systems face economic challenges due to unattractive feed-in-tariffs (FiT). This paper presents a mathematical programming model to optimise the participation and electricity generation by biomass power plants in a FiT programme. A disjunctive fuzzy optimisation approach is adopted in this work due to the presence of several biomass power plants, thus resulting in a multi-objective optimisation.The newly developed mixed-integer linear programming (MILP) model maximises the overall satisfaction extent of biomass power plants in power generation. The optimisation model ensures the satisfaction of all biomass power plants' technical and economic constraints. A base case study of seven power plants utilising various palm biomass is used to demonstrate the disjunctive fuzzy optimisation approach for the MILP model. This is followed by a scenario investigating the impact of demand variation. The optimisation of the case studies favours the utilisation of empty fruit bunch (EFB), palm oil mill effluent (POME), and palm mesocarp fibre (PMF) for sustainable electricity generation. Palm kernel shell (PKS) is the least preferred palm biomass due to its lowest net availability of useful energy from biomass combustion. This work provides a decision tool for the selection of biomass type during electricity generation. The MILP model in this work may be readily extended for any number of power plants, handling a variety of biomass feedstocks.

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A systematic decision analysis approach to design biomass combined heat and power systems

Cited by 10 publications

References 32 publications

Risk-conscious optimization model to support bioenergy investments in the Brazilian sugarcane industry

Risk-conscious optimization model to support bioenergy investments in the Brazilian sugarcane industry

An Economic and Technology Analysis of a New High-Efficiency Biomass Cogeneration System: A Case Study in DC County, China

Disjunctive fuzzy optimisation of electricity generation by biomass power producers in a feed‐in tariff programme

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