Optimal unit sizing of cogeneration systems under the toleration for shortage of energy supply

Gamou, Satoshi; Yokoyama, Ryohei; Ito, Koichi

doi:10.1002/(sici)1099-114x(200001)24:1<61::aid-er571>3.0.co;2-q

Cited by 9 publications

(6 citation statements)

References 4 publications

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“…Once identified the stochastic behaviour of the energy loads, the authors proposed a hierarchical optimization method, with an upper level where equipment capacities and maximum contract utility are optimized (by nonlinear programming) and a lower level where the optimal energy flow rates and on/off status of equipment are optimized (by mixed integer linear programming routines coupled with sensitivity analysis and enumeration methods). These results are interesting since, in absence of data, in a previous paper [16] the same authors had hypothesised the energy loads to follow a binomial distribution. The results presented by Gamou et al [15] inspired several other works, related with combined heat, cooling and power planning under uncertainty.…”

Section: Introductionmentioning

confidence: 70%

On the Reliability of Optimization Results for Trigeneration Systems in Buildings, in the Presence of Price Uncertainties and Erroneous Load Estimation

et al. 2016

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Cogeneration and trigeneration plants are widely recognized as promising technologies for increasing energy efficiency in buildings. However, their overall potential is scarcely exploited, due to the difficulties in achieving economic viability and the risk of investment related to uncertainties in future energy loads and prices. Several stochastic optimization models have been proposed in the literature to account for uncertainties, but these instruments share in a common reliance on user-defined probability functions for each stochastic parameter. Being such functions hard to predict, in this paper an analysis of the influence of erroneous estimation of the uncertain energy loads and prices on the optimal plant design and operation is proposed. With reference to a hotel building, a number of realistic scenarios is developed, exploring all the most frequent errors occurring in the estimation of energy loads and prices. Then, profit-oriented optimizations are performed for the examined scenarios, by means of a deterministic mixed integer linear programming algorithm. From a comparison between the achieved results, it emerges that: (i) the plant profitability is prevalently influenced by the average "spark-spread" (i.e., ratio between electricity and fuel price) and, secondarily, from the shape of the daily price profiles; (ii) the "optimal sizes" of the main components are scarcely influenced by the daily load profiles, while they are more strictly related with the average "power to heat" and "power to cooling" ratios of the building.

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Section: Introductionmentioning

confidence: 70%

On the Reliability of Optimization Results for Trigeneration Systems in Buildings, in the Presence of Price Uncertainties and Erroneous Load Estimation

et al. 2016

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“…The dynamic behavior and optimization of these systems are important with respect to achieving better performance and lower emissions as indicated in the studies by Ganou et al . and Yoru et al . .…”

Section: Introductionmentioning

confidence: 88%

“…According to IEA [16], the expected reduction as a result of using cogeneration and trigeneration systems may reach 950 Mt annually by 2030. The dynamic behavior and optimization of these systems are important with respect to achieving better performance and lower emissions as indicated in the studies by Ganou et al [17] and Yoru et al [18].…”

Section: Introductionmentioning

confidence: 99%

Renewable-energy-based multigeneration systems

Dinçer

Zamfirescu

2012

Int. J. Energy Res.

168

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SUMMARY This article develops the concept of renewable‐energy‐based multigeneration options for producing a number of outputs, such as power, heat, hot water, cooling, hydrogen, fresh water, and so forth; and discusses their benefits. Such multigeneration options obviously lead to improved system performance and reduced environmental impacts. First, single‐generation (power generation only) and cogeneration systems are compared with respect to the energy utilization efficiency, exergy efficiency, green house gases (GHG) mitigation, and payback period. It is found that the cogeneration increases GHG mitigation about 2 to 4 times, whereas the payback time decreases about 2.8 times with respect to the single‐generation case. Exergy efficiency is found to be between about 55% and 65%, depending on the degree of cogeneration. The exergy efficiency shows the maximum values for certain source temperatures. For the case studied here, the optimum source temperature is taken as 200°C for analysis purposes. The results show that multigeneration of energy systems helps increase both energy and exergy efficiencies, reduce cost and environmental impact, and increase sustainability. Copyright © 2012 John Wiley & Sons, Ltd.

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“…In terms of the more complex energy systems, a co-optimization method was presented to determine the optimal operation strategy along with the optimal system configurations, indicating that the new structured CCHP system performs much better than the conventional separate system in the annual cost of the CCHP [19]. When taking the influence of the shortage of energy supplies into consideration, the results showed that the economy of the system would be improved if we design the system considering tolerance for energy supply shortages [20].…”

Section: Introductionmentioning

confidence: 99%

Optimal Capacity Configuration for Energy Hubs Considering Part-Load Characteristics of Generation Units

Deng

Jing

et al. 2017

Energies

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Abstract:The simulation model is one of the key points affecting the optimal planning and operation of energy hubs (EHs). Since treating the efficiencies of generation units as constants would significantly simplify the calculation, only a simplified model is investigated in most research works. In this paper, aiming at optimizing the capacity configuration of an EH, we present a part-load characteristics-based (PLCB) model, in which the efficiencies of generation units will change with the fluctuating load. Based on the PLCB model, the accuracy of the EH model can be improved. Furthermore, a two-stage planning method is proposed to solve the optimal capacity configuration problem of the EH. Group Search Optimizer (GSO) is used to determine the optimal size in the first stage, and a mathematical programming method is applied to obtain the optimal operation of the EH in the second stage. Comparative studies using the PLCB model and the simplified model are performed to examine the impacts of equipment part-load characteristics on the sizing results. Simulation results indicate that the proposed model appears to have a better economic performance than the simplified model.

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Optimal unit sizing of cogeneration systems under the toleration for shortage of energy supply

Cited by 9 publications

References 4 publications

On the Reliability of Optimization Results for Trigeneration Systems in Buildings, in the Presence of Price Uncertainties and Erroneous Load Estimation

On the Reliability of Optimization Results for Trigeneration Systems in Buildings, in the Presence of Price Uncertainties and Erroneous Load Estimation

Renewable-energy-based multigeneration systems

Optimal Capacity Configuration for Energy Hubs Considering Part-Load Characteristics of Generation Units

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