Simultaneous design and operation optimization of renewable combined cooling heating and power systems

Liu, Zuming; Lim, Mei Qi; Kraft, Markus; Wang, Xiaonan

doi:10.1002/aic.17039

Cited by 14 publications

(3 citation statements)

References 48 publications

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“…In order to determine the optimal operation strategy for the system, Feng et al investigated the impact of the operation strategy on the system performance based on two different chiller configuration schemes [20]. Hawkes and Liu et al used a linear programming approach to achieve optimization of the system's operating strategy and equipment configuration [21,22]. As for the IESs with more equipment, due to the large number of decision variables involved, which increases the difficulty of solving the model, intelligent optimization algorithms, such as particle swarm optimization (PSO) and non-dominated sorting genetic algorithm (NSGA), are usually used for optimization and determining solutions [23].…”

Section: Introductionmentioning

confidence: 99%

Study of Key Parameters and Uncertainties Based on Integrated Energy Systems Coupled with Renewable Energy Sources

Liu,

Ji,

Guo

et al. 2023

Sustainability

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The extensive research and application of integrated energy systems (IES) coupled with renewable energy sources have played a pivotal role in alleviating the problems of fossil energy shortage and promoting sustainability to a certain extent. However, the uncertainty of photovoltaic (PV) and wind power in IES increases the difficulty of maintaining stable system operation, posing a challenge to long-term sustainability. In addition, the capacity configuration of each device in IES and the operation strategy under different conditions will also significantly impact the operation cost and expected results of the system, influencing its overall sustainability. To address the above problems, this paper establishes an optimization model based on linear programming to optimize the equipment capacity and operation strategy of IES coupled with PV and wind power with the minimum total annual cost as the objective function, thereby promoting economic sustainability. Moreover, an integrated assessment framework, including economic, energy efficiency, and environmental aspects, is constructed to provide a comprehensive assessment of the operation of IES, ensuring a holistic view of sustainability. Finally, taking the IES of an industrial park in Xi’an, China, as the specific case, sensitivity analysis is used to explore the impact of a variety of critical parameters on the equipment capacity and operating strategy. Additionally, the Monte Carlo method is used to explore the impact of source-load uncertainty on the performance of the IES. The results show that the facilitating or constraining relationship between renewable energy access and the cascading utilization of combined heat and power generation (CHP) energy depends on the relative magnitude of the user load thermoelectric ratio to the prime mover thermoelectric ratio. To cope with the negative impact of source-load uncertainty on the stable operation of the IES, the capacities of the electric chiller and absorption chiller should be increased by 4.0% and 5.8%, respectively. It is worth noting that the increase in the penetration rate of renewable energy has not changed the system’s dependence on the grid.

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Section: Introductionmentioning

confidence: 99%

Study of Key Parameters and Uncertainties Based on Integrated Energy Systems Coupled with Renewable Energy Sources

Liu,

Ji,

Guo

et al. 2023

Sustainability

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“…14 Multiobjective approaches have been applied quite broadly in chemical process systems research. 15 With respect to sustainability, it is most common to analyze the tradeoffs between a single environmental and a single economic objective, with example applications in plant design, [16][17][18] supply chain management, 19,20 and process operations, 21,22 to name a few.…”

Section: Introductionmentioning

confidence: 99%

Sustainable decision making for chemical process systems via dimensionality reduction of many objective problems

Russell

Allman

2022

AIChE Journal

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Recent global events and the rise of sustainable investing have made clear that the chemical and energy industry must consider sustainability goals beyond profit maximization to remain competitive. Multiobjective optimization provides an ideal framework for analyzing sustainability tradeoffs, but when four or more objectives are considered, the ability to rigorously solve problems and interpret results is lost. This necessitates an approach to systematically reduce the dimensionality of many objective problems to three or fewer objectives. In this work, an algorithm to group objectives based on their correlating nature a priori to solving the full space problem is proposed. It utilizes community detection on a novel weighted objective correlation graph to identify two or three groups of correlated objectives. Results from three representative case studies demonstrate that objective groupings obtained from this algorithm minimize the amount of tradeoff information lost and outperform intuitive groupings by economics or the environment.

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“…Mishra et al used the complex proportional assessment (COPRAS) method to evaluate the sustainability of the bioenergy production process for objectives from four dimensions (i.e., environmental, economic, technological, and social). Liu et al applied TOPSIS to determine the most desirable solution in designing a renewable combined cooling, heating, and power system. Recently, Lin et al extended the ELimination Et Choice Translating Reality (ELECTRE) method to develop a prioritization framework for selecting the most suitable hydrogen production pathway.…”

Section: Introductionmentioning

confidence: 99%

Preference Ranking on the Basis of Ideal-Average Distance Method for Multi-Criteria Decision-Making

Wang

Rangaiah

Wang

2021

Ind. Eng. Chem. Res.

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Over the past 2 decades, numerous papers have been published on multi-objective optimization (MOO) in chemical engineering. For conflicting objectives, MOO produces a set of non-dominated optimal solutions that are equally good. Only one of these optimal solutions has to be selected for implementation. Ranking of non-dominated solutions and this selection have been discussed, mostly in the area of multi-criteria decision-making or -analysis (MCDM/MCDA). There is limited research in this area for chemical engineering applications. This paper proposes a novel MCDM method, the PROBID (preference ranking on the basis of ideal-average distance) method, and its simpler variant, the sPROBID. The PROBID method comprehensively considers a spectrum of ideal solutions and the average solution to determine the performance score of each optimal solution. The sPROBID method takes into consideration only the first and last quarters of ideal solutions. Comparative sensitivity analysis is performed, and both the methods are seen to be sensitive to changes in the weights of objectives, which is desirable. Then, the ranking consistency of the PROBID and sPROBID is tested and compared with 7 popular/recent MCDM methods, for 12 scenarios on 3 different data sets from the literature; these scenarios include the linear transformation of measurement unit, equivalent objective formulation, and addition/deletion of optimal solutions in the data set. The results of these tests show that the PROBID method is more consistent and robust compared to the other MCDM methods tested. Although sPROBID is less robust than PROBID, it outperforms four of the seven MCDM methods in terms of ranking consistency.

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Simultaneous design and operation optimization of renewable combined cooling heating and power systems

Cited by 14 publications

References 48 publications

Study of Key Parameters and Uncertainties Based on Integrated Energy Systems Coupled with Renewable Energy Sources

Study of Key Parameters and Uncertainties Based on Integrated Energy Systems Coupled with Renewable Energy Sources

Sustainable decision making for chemical process systems via dimensionality reduction of many objective problems

Preference Ranking on the Basis of Ideal-Average Distance Method for Multi-Criteria Decision-Making

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