4.7 Article

Optimal design and operation method of integrated energy system based on stochastic scenario iteration and energy storage attribute evaluation

期刊

SUSTAINABLE CITIES AND SOCIETY
卷 95, 期 -, 页码 -

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ELSEVIER
DOI: 10.1016/j.scs.2023.104585

关键词

Integrated energy system; Capacity design; Real time dispatch; Stochastic scenario; Attribute evaluation

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In this study, an optimal design and operation method for integrated energy systems is proposed based on double loop optimization and stochastic scenario iteration. The accuracy and reliability of the design and operation are improved by completing initial parameters and constraints. The proposed methods optimize component capacity and energy attribute in the outer loop and real-time energy dispatch in the inner loop to improve the system's overall performance.
Integrated energy system servicing buildings brings economic, energy, and environmental benefits, which relies on advanced operation and design methods. However, incomplete and inaccurate initial information leads to significant design errors. In this study, an attribute evaluation method of the stored energy is given, and an optimal design and operation method based on double loop optimization and stochastic scenario iteration is proposed. By completing initial parameters and constraints, the accuracy and reliability of the design and operation of integrated energy system are improved. Component capacity and energy attribute are optimized in the outer loop, in which various annual operation scenario is continuously updated to enhance the adaptability to the uncertain customer demand and stochastic renewable energy. Real time energy dispatch is optimized in the inner loop, in which the benefits from the charging and discharging of storage component are calculated in time through these attributes to improve the overall performance of the system. A business vacation resort with variable demands and resources is created as potential project of integrated energy system. Four cases are simulated to compare different system design methods. The proposed methods decreased the difference between the actual and predicted performance from 11.72% to 0.90%; furthermore, the consumption, cost, and emissions were reduced by 1.41%, 3.86%, and 1.51%, respectively, with an average reduction of 2.26%.

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