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Journal article(2026)
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Chuyi Li, Kedi Zheng, Pedro P. Vergara, Hongye Guo, Mohammad Shahidehpour, Ning Zhang
Addressing uncertainty is essential in power systems with high levels of renewable energy penetration. Distributed energy resources (DERs), due to their partially controllable nature, are a major source of uncertainty. However, due to their large numbers and complex correlations, their aggregated uncertainty is highly complex. This paper aims to track how the uncertainty is modeled from individual DER prediction errors to the aggregated-level. By enforcing a specified confidence level, the aggregated-level probabilistic flexibility boundary is formulated as a Minkowski sum under joint chance constraints (JCCs). Despite the inherent intractability of this problem, we establish an equivalent representation that allows for an effective approximation using the proposed quantile cube approximation method. An iterative algorithm is also developed to enhance computational efficiency in implementing the method. Numerical tests demonstrate that the proposed method effectively reduces the conservativeness of the aggregated confidence boundary and the computation time at the same time.
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Addressing uncertainty is essential in power systems with high levels of renewable energy penetration. Distributed energy resources (DERs), due to their partially controllable nature, are a major source of uncertainty. However, due to their large numbers and complex correlations, their aggregated uncertainty is highly complex. This paper aims to track how the uncertainty is modeled from individual DER prediction errors to the aggregated-level. By enforcing a specified confidence level, the aggregated-level probabilistic flexibility boundary is formulated as a Minkowski sum under joint chance constraints (JCCs). Despite the inherent intractability of this problem, we establish an equivalent representation that allows for an effective approximation using the proposed quantile cube approximation method. An iterative algorithm is also developed to enhance computational efficiency in implementing the method. Numerical tests demonstrate that the proposed method effectively reduces the conservativeness of the aggregated confidence boundary and the computation time at the same time.
The switch to renewable power generation is promoted aggressively by government policies, growing investments, consumer preferences, and many other factors. However, high renewable penetration can impose significant challenges to designing and employing measures that enhance power grid resilience. Resilience has been posed as a requirement of increased criticality following severe phenomena and events (Texas freeze, California wildfires, India heatwaves, cyberattacks on power plants etc.) that go beyond electrical grid reliability. Dependence of renewables on climate and weather conditions and reliance on information and communication technologies complicate the challenge of accounting for them within grid resilience frameworks. Specifically, the asynchronous and limited-inertia characteristics of inverter-based resources can severely degrade the grid dynamic performance and shrink stability regions. Also, stochastic and intermittent nature of renewables requires the availability and fast response of flexibility resources and increases the computational complexity of decision-making problems, which will make methods for grid resilience even more challenging. Extensive behind-the-meter distributed energy resources further alter the behaviour of both distribution systems and transmission systems. Therefore, this Special Issue aims to address these challenges and key technologies for facilitating grid resilience in the pathway of grid decarbonization, with specific focus on operational and structural resilience of power grids.
...
The switch to renewable power generation is promoted aggressively by government policies, growing investments, consumer preferences, and many other factors. However, high renewable penetration can impose significant challenges to designing and employing measures that enhance power grid resilience. Resilience has been posed as a requirement of increased criticality following severe phenomena and events (Texas freeze, California wildfires, India heatwaves, cyberattacks on power plants etc.) that go beyond electrical grid reliability. Dependence of renewables on climate and weather conditions and reliance on information and communication technologies complicate the challenge of accounting for them within grid resilience frameworks. Specifically, the asynchronous and limited-inertia characteristics of inverter-based resources can severely degrade the grid dynamic performance and shrink stability regions. Also, stochastic and intermittent nature of renewables requires the availability and fast response of flexibility resources and increases the computational complexity of decision-making problems, which will make methods for grid resilience even more challenging. Extensive behind-the-meter distributed energy resources further alter the behaviour of both distribution systems and transmission systems. Therefore, this Special Issue aims to address these challenges and key technologies for facilitating grid resilience in the pathway of grid decarbonization, with specific focus on operational and structural resilience of power grids.
