MINLP optimisation model for increased power production in small-scale CHP plants

Savola, Tuula; Fogelholm, Carl-Johan

doi:10.1016/j.applthermaleng.2006.05.002

Cited by 49 publications

(13 citation statements)

References 24 publications

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“…Along with system simulation and economic analysis, mathematical optimization was applied to gas turbine power cycles [14][15][16][17][18]. For the optimization, several algorithms were selected depending on the type of optimization, e.g., the mixed integer linear programming (MINLP), genetic algorithm (GA), simplex method, and sequential quadratic programming (SQP).…”

Section: Simple Cyclementioning

confidence: 99%

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Life-Cycle Cost Minimization of Gas Turbine Power Cycles for Distributed Power Generation Using Sequential Quadratic Programming Method

et al. 2018

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The life-cycle cost reduction of medium-class gas turbine power plants was investigated using the mathematical optimization technique. Three different types of gas turbine power cycles-a simple cycle, a regenerative cycle, and a combined cycle-were examined, and their optimal design conditions were determined using the sequential quadratic programming (SQP) technique. As a modeling reference, the Siemens SGT-700 gas turbine was chosen and its technical data were used for system simulation and validation. Through optimization using the SQP method, the overall costs of the simple cycle, regenerative cycle, and combined cycle were reduced by 7.4%, 12.0%, and 3.9%, respectively, compared to the cost of the base cases. To examine the effect of economic parameters on the optimal design condition and cost, different values of fuel costs, interest rates, and discount rates were applied to the cost calculation, and the optimization results were analyzed and compared. The values were chosen to reflect different countries' economic situations: South Korea, China, India, and Indonesia. For South Korea and China, the optimal design condition is proposed near the upper bound of the variation range, implying that the efficiency improvement plays an important role in cost reduction. For India and Indonesia, the optimal condition is proposed in the middle of the variation ranges. Even for India and Indonesia, the fuel cost has the largest contribution to the total cost, accounting for more than 60%.Energies 2018, 11, 3511 2 of 21As an alternative to large-scale, centralized power plants, small-scale power plants located at or near the consumer sites can supply electricity; these small power generators are called distributed (or decentralized) power generation (DPG). The use of DPG is increasing; it was responsible for 21% of the global electricity generation in 2000, and it is projected to account for approximately 40% of the global electricity increase in 2020 [5].Among several available technologies such as photovoltaic panels, wind turbines, internal combustion engines, and fuel cells [6,7], gas turbines were recognized as the most attractive option from the technological and economic standpoints [5]. However, power generation using small-or medium-sized gas turbines still remains expensive compared to the large-scale power generators [8] due to the relatively higher investment costs and lower electrical efficiencies. Therefore, reducing the operating costs of gas turbines by means of the mathematical optimization is crucial, particularly in distributed power generation areas. As shown in Table 1, several commercial gas turbine products exist that are suitable for use in large factories and urban buildings [8][9][10][11].

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Section: Simple Cyclementioning

confidence: 99%

“…Natural gas prices fluctuate considerably year to year; the effect of the fuel cost on the overall cost was investigated in this study, and is discussed in Section 4.5. The nominal specific capital investment (Z speci f ic ), expressed in USD/kW, was calculated using Equation (15). The overall cost rate ( .…”

Section: Calculation Of Life-cycle Costmentioning

confidence: 99%

Life-Cycle Cost Minimization of Gas Turbine Power Cycles for Distributed Power Generation Using Sequential Quadratic Programming Method

et al. 2018

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“…That is, they are typically based on nominal or estimated values for all the input data considered in the analysis 1359 [5,[12][13][14][15][16][17]. This means that the key parameters that influence the optimization task are assumed to be perfectly known in advance, so the only situation assessed in the study is the most likely one.…”

Section: Introductionmentioning

confidence: 99%

Economic performance optimization of an absorption cooling system under uncertainty

Gebreslassie

Guillén‐Gosálbez

Jiménez

et al. 2009

Applied Thermal Engineering

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a b s t r a c tMany of the strategies devised so far to address the optimization of energy systems are deterministic approaches that rely on estimated data. However, in real world applications there are many sources of uncertainty that introduce variability into the decision-making problem. Within this general context, we propose a novel approach to address the design of absorption cooling systems under uncertainty in the energy cost. As opposed to other approaches that optimize the expected performance of the system as a single objective, in our method the design task is formulated as a stochastic bi-criteria non-linear optimization problem that simultaneously accounts for the minimization of the expected total cost and the financial risk associated with the investment. The latter criterion is measured by the downside risk, which avoids the need to define binary variables thus improving the computational performance of the model. The capabilities of the proposed modeling framework and solution strategy are illustrated in a case study problem that addresses the design of a typical absorption cooling system. Numerical results demonstrate that the method presented allows to manage the risk level effectively by varying the area of the heat exchangers of the absorption cycle. Specifically, our strategy allows identifying the optimal values of the operating and design variables of the cycle that make it less sensitive to fluctuations in the energy price, thus improving its robustness in the face of uncertainty.

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“…The relatively new technology of the PEMFC lacks of liability due to limited operational testing for a significant amount of time but o ers great advantages such as low acoustic noise and no vibration [361]. [238,281] Design and development of domestic trigeneration systems Savola et al (2005Savola et al ( , 2007 [315,316] Small scale CHP model development and MINLP formulation maximizing production objectives Wu et al (2006) [361] Review on Combined cooling, heat and power cogeneration systems Onovwiona et al (2006) [263] Technology review on heat and power cogeneration systems Videla et al (2006) [348] Modeling and dynamic simulation of small scale internal combustion based CHP systems Cockroft et al (2006) [78] Assessment of power sources for cogeneration Dorer et al (2009) [92] Energy, environmental and performance studies on PEMFC, SOFC, Stirling engine and internal combustion engine based residential cogeneration Collazos et al (2009) [79] Optimal control with mixed integer formulation and stochastic elements for micro generation in domestic applications Konstantinidis et al…”

Section: Domestic Chp Modelmentioning

confidence: 99%

Model-based multi-parametric programming strategies towards the integration of design, control and operational optimization

Diangelakis

Pistikopoulos

2017

Computer Aided Chemical Engineering

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MINLP optimisation model for increased power production in small-scale CHP plants

Cited by 49 publications

References 24 publications

Life-Cycle Cost Minimization of Gas Turbine Power Cycles for Distributed Power Generation Using Sequential Quadratic Programming Method

Life-Cycle Cost Minimization of Gas Turbine Power Cycles for Distributed Power Generation Using Sequential Quadratic Programming Method

Economic performance optimization of an absorption cooling system under uncertainty

Model-based multi-parametric programming strategies towards the integration of design, control and operational optimization

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