基于Karush-Kuhn-Tucker条件的混合储能系统最优功率分配
Optimal Power Sharing for Hybrid Energy Storage Systems Based on Karush-Kuhn-Tucker Conditions
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中文总结 AI 辅助
针对混合储能系统，提出基于KKT条件解析验证的功率分配控制器，以最小化损耗，计算高效，适用于实时应用。
中文摘要 AI 辅助
可再生能源发电厂日益被期望向电网提供辅助服务。然而，太阳能光伏等能源的波动性要求额外的运行灵活性来提供这些服务。在此背景下，混合储能系统（HESS）提供了一种合适的解决方案，因为可以应用和利用具有互补特性的不同技术来分担功率需求并提高整体性能。然而，适当的功率分配需要对储能元件进行详细建模。这一方面在文献中几乎没有被研究，大多数模型考虑恒定效率。此外，大多数公式基于优化问题，这些优化问题在计算上过于繁重，无法实时求解。本文提出了一种控制器，以最小化HESS的损耗，考虑了每种储能技术的详细功率相关效率曲线。功率分配方法基于Karush-Kuhn-Tucker（KKT）条件的解析验证，这使得它在计算上高效，适合实际部署。所提出的控制器应用于一个测试案例，该案例包括一个10 MW光伏电站，配备基于锂离子电池和氧化还原液流电池的HESS，每个电池的功率为2.5 MW，容量为5 MWh。主要贡献通过MATLAB/Simulink中进行的数值模拟得到验证，而在OPAL-RT中部署的实时实现则证明了该算法在实时应用中的可行性。
英文摘要
Renewable energy power plants are increasingly expected to provide ancillary services to the grid. Yet, the variability of sources such as solar photovoltaics requires additional operational flexibility to deliver these services. In this context, hybrid energy storage systems (HESSs) offer an appropriate solution, because different technologies with complementary characteristics can be applied and leveraged to share the power demand and improve the overall performance. Yet, a proper power sharing requires a detailed model of the storage elements. This aspect has barely been studied in the literature, where most models consider constant efficiencies. Moreover, most formulations are based on optimisation problems that are computationally too demanding to be solved in real time. In this paper, a controller is proposed to minimise the losses of a HESS considering detailed power-dependent efficiency curves of each storage technology. The power sharing method is based on the analytical verification of the Karush-Kuhn-Tucker (KKT) conditions, which makes it computationally efficient and suitable for real-world deployment. The proposed controller is applied to a test case consisting of a 10 MW PV power plant with a HESS based on a lithium-ion battery and a redox-flow battery, each with 2.5 MW power and 5 MWh capacity. The main contributions are verified via numerical simulations performed in MATLAB/Simulink, whereas a real-time implementation deployed in OPAL-RT demonstrates the viability of the algorithm for real-time applications.
