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首页> 外文期刊>Energy Conversion & Management >Designing a standalone wind-diesel-CAES hybrid energy system by using a scenario-based bi-level programming method
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Designing a standalone wind-diesel-CAES hybrid energy system by using a scenario-based bi-level programming method

机译：通过使用基于场景的双级编程方法设计独立风力柴油 - CAES混合能量系统

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摘要

Compressed air energy storage (CAES) systems are promising for the application of a standalone hybrid system. This study adopts a scenario-based bi-level programming method to design a standalone hybrid system that mainly contains wind turbines, diesel generators (DGs), and a CAES system. The demand response is considered as a deferrable load. The uncertainties on the wind power outputs and load demand are modeled using scenario generation and reduction techniques. The generated scenarios are used in a bi-level programming model for designing the hybrid energy system (HES). The model is composed of an outer planning layer and an inner operation layer. The outer layer optimizes the size of each component in the HES using a quantum particle swarm optimization (QPSO) method with the objective of minimizing daily total costs including daily investment costs and daily operating costs. On the other hand, the inner layer optimizes the operational strategies of the HES using a sequential quadratic programming method with the objective of minimizing the total operating costs, including the generation and emission costs of the DGs and the degradation cost of the CAES system. The well-established HES tool HOMER is used to validate the results obtained by the developed and adopted models. The results indicate that 1) the QPSO method performs better than the particle swarm optimization and genetic algorithm methods. 2) The results obtained by the scenario-based bi-level programming method have an average similarity of approximately 97%, which is very high compared to that of the results obtained by HOMER.
机译：压缩空气储能（CAES）系统是对独立混合系统的应用的希望。本研究采用了一种基于场景的双级编程方法来设计一个主要包含风力涡轮机，柴油发电机（DGS）和CAES系统的独立混合动力系统。需求响应被视为可推迟的负载。风电输出和负载需求的不确定性使用场景生成和减少技术进行建模。生成的方案用于设计混合能量系统（HES）的双级编程模型。该模型由外部规划层和内部操作层组成。外层使用量子粒子群优化（QPSO）方法优化HER中每个组分的尺寸，其目的是最小化每日总成本，包括每日投资成本和日常运营成本。另一方面，内层使用顺序二次编程方法优化HER的操作策略，其目的是最小化总运营成本，包括DG的发射和发射成本以及CAES系统的降级成本。良好的HES工具HOMER用于验证由开发和采用的模型获得的结果。结果表明，1）QPSO方法比粒子群优化和遗传算法方法更好。 2）通过基于场景的双级编程方法获得的结果具有约97％的平均相似性，与本垒打获得的结果相比非常高。

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