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Two-stage optimisation of hybrid solar power plants

机译:混合太阳能发电厂的两阶段优化

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Hybrid solar power plants which combine concentrated solar power (CSP) and photovoltaic (PV) systems with thermal energy storage (TES) have the potential to provide cost competitive and dispatchable renewable energy. The integration of energy storage gives dispatchability to the variable renewable generation while the combination of different generation technologies can reduce the costs. However, the design of reliable and cost competitive hybrid solar power plants requires the careful balancing of trade-offs between financial and technical performance. This is made more complicated by the dependence on a larger number of parameters compared to conventional plants and due to the integration of TES which requires that the operational profile is optimised for every design. This contribution presents a two-stage, multi-objective optimisation framework which combines multi-objective linear programming methods for the operational optimisation with multi-objective genetic algorithms for the design optimisation. The operational optimisation which is performed for every design point needs to be performed with linear programming methods. Here an automated scalarisation method is developed for the linear programming method which enables the multi-objective optimisation of the operational profile. This enables the evaluation of the trade-offs between financial and technical performance in both the design and operational optimisation, which is required to design reliable and cost competitive sustainable energy systems. The two-stage multi-objective optimisation is applied to analyse and improve the design of the hybrid solar power plant Atacama-1. It is demonstrated that balancing the trade-off between financial and technical performance is key to increase the competitiveness of solar energy and that it is possible to simultaneously increase dispatchability and decrease the levelised cost of energy. This shows that the operational and design optimisations have to be directly linked in order to exploit the synergies of hybrid systems. Thus the optimisation framework presented in this study can improve the decision making in the design of hybrid solar power plants.
机译:将集中式太阳能(CSP)和光伏(PV)系统与热能存储(TES)结合在一起的混合太阳能发电厂有潜力提供具有成本竞争力的可调度可再生能源。能量存储的集成使可变的可再生能源发电具有可调度性,而不同发电技术的结合可以降低成本。但是,设计可靠且具有成本竞争力的混合太阳能发电厂需要在财务和技术性能之间进行权衡取舍。与传统工厂相比,由于依赖大量参数,并且由于TES的集成,TES的集成使情况变得更加复杂,而TES的集成要求针对每种设计优化操作配置文件。该贡献提出了一个两阶段,多目标优化框架,该框架结合了用于操作优化的多目标线性规划方法和用于优化设计的多目标遗传算法。需要使用线性编程方法来执行针对每个设计点的操作优化。在此,针对线性编程方法开发了一种自动标量方法,该方法可以对运行曲线进行多目标优化。这样就可以评估设计和运营优化中财务和技术性能之间的权衡,这是设计可靠和成本竞争力的可持续能源系统所必需的。采用两阶段多目标优化方法,对混合型太阳能发电厂阿​​塔卡马-1号进行了分析和改进。事实证明,在财务和技术绩效之间进行权衡取舍是提高太阳能竞争力的关键,并且可以同时提高可调度性和降低能源的平准化成本。这表明必须将操作和设计优化直接联系起来,才能利用混合系统的协同作用。因此,本研究提出的优化框架可以改善混合太阳能发电厂的设计决策。

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