论文标题
需求空间中可持续电力系统的灵活性表征：数据驱动的反优化方法
Flexibility Characterization of Sustainable Power Systems in Demand Space: A Data-Driven Inverse Optimization Approach
论文作者
论文摘要
可再生能源的渗透加深了挑战电源系统操作员如何应对其相关的可变性和不确定性。电力系统中存在的可分配资产的固有灵活性（通常被刻在特征）中对于应对这一挑战至关重要。明确的灵活性表征的几项建议集中于定义可行区域，该区域可以在发电或不确定性空间中确保操作。这些方法的主要缺点是在有多个不确定参数时很难可视化该可行性区域。此外，这些方法集中在系统操作约束上，并且经常忽略可再生生成和需求变异性的固有耦合（例如，空间相关）的影响。为了应对这些挑战，我们提出了一个新型的数据驱动的逆优化框架，以及其几何直觉在需求空间中的灵活性表征灵活性。该方法使用多面体不确定性集捕获了多站点可再生产生和负载的空间相关性。此外，该框架向需求空间中电力系统的可行性区域投射了不确定性，这也称为可加载能力集。提出的逆优化方案将重新铸造作为线性优化问题，用于推断系统的灵活性适当性，从负荷集组中提出了足够的功能。
The deepening of the penetration of renewable energy is challenging how power system operators cope with their associated variability and uncertainty. The inherent flexibility of dispathchable assets present in power systems, which is often ill-characterized, is essential in addressing this challenge. Several proposals for explicit flexibility characterization focus on defining a feasible region that secures operations either in generation or uncertainty spaces. The main drawback of these approaches is the difficulty in visualizing this feasibility region when there are multiple uncertain parameters. Moreover, these approaches focus on system operational constraints and often neglect the impact of inherent couplings (e.g., spatial correlation) of renewable generation and demand variability. To address these challenges, we propose a novel data-driven inverse optimization framework for flexibility characterization of power systems in the demand space along with its geometric intuition. The approach captures the spatial correlation of multi-site renewable generation and load using polyhedral uncertainty sets. Moreover, the framework projects the uncertainty on the feasibility region of power systems in the demand space, which are also called loadability sets. The proposed inverse optimization scheme, recast as a linear optimization problem, is used to infer system flexibility adequacy from loadability sets.
