Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.18290

On Over-Conservatism Under Low-Probability Extreme Events: A KL-Regularized Wasserstein Distributionally Robust Planning Method

Xinda Shen·Yifan Su·Bo Hu·Yanbo Chen·Changzheng Shao·Tao Wu
View original source ↗Read source document ↗Export metadata ↓

Abstract

The increasing penetration of renewable energy makes power systems more vulnerable to low-probability extreme events, elevating system planning to a critical role in ensuring reliability. Distributionally robust optimization (DRO) based on the Wasserstein distance is widely used to address this challenge. However, we find that the conventional Wasserstein ambiguity set tends to excessively amplify the probabilities of low-probability extreme events, leading to over-conservative investment decisions. To mitigate this over-conservatism, this paper proposes a novel ambiguity set that modifies the Wasserstein distance with a Kullback-Leibler (KL) divergence regularization term. By leveraging the KL divergence's sensitivity to relative probability changes, the proposed set effectively curbs the unrealistic magnification of low-probability events. A two-stage distributionally robust planning model built upon this new ambiguity set is developed to optimize the installed capacity of photovoltaic, wind, and energy storage. The complex model is then reformulated as an equivalent exponential cone program with strict theoretical proofs, making it computationally tractable. Numerical results on the PJM 5-bus and IEEE 118-bus systems reveal the over-conservatism of standard Wasserstein DRO under low-probability extreme events and validate the effectiveness and efficiency of the proposed planning model and solution algorithm.

Explore related subjects

eess.SYcs.SYmath.OC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Xinda Shen, Yifan Su, Bo Hu, Yanbo Chen, Changzheng Shao, Tao Wu. 2026-10-13. On Over-Conservatism Under Low-Probability Extreme Events: A KL-Regularized Wasserstein Distributionally Robust Planning Method. https://arxiv.org/abs/2610.18290

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Age of Trust (AoT): A Continuous Verification Framework for Wireless Networks

Zero Trust is a new security vision for 6G networks that emphasises the philosophy of never trust and always verify. However, there is a fundamental trade-off between the wireless transmission efficiency and the trust level, which is reflected by the verification interval and its adaptation strategy. More importantly, the mathematical framework to characterise the trust level of the adaptive verification strategy is still missing. Inspired by this vision, we propose a concept called age of trust (AoT) to capture the characteristics of the trust level degrading over time, with the definition of the time elapsed since the last verification of the target user's trust plus the initial age, which depends on the trust level evaluated at that verification. The higher the trust level, the lower the initial age. To evaluate the trust level in the long term, the average AoT is used. We then investigate how to find a compromise between average AoT and wireless transmission efficiency with limited resources. In particular, we address the bi-objective optimization (BOO) problem between average AoT and throughput over a single link with arbitrary service process, where the identity of the receiver is constantly verified, and we devise a periodic verification scheme and a Q-learning-based scheme for constant process and random process, respectively. We also tackle the BOO problem in a multiple random access scenario, where a trust-enhanced frame-slotted ALOHA is designed. Finally, the numerical results show that our proposals can achieve a fair compromise between trust level and wireless transmission efficiency, and thus have a wide application prospect in various zero-trust architectures.

eess.SY↗
arXiv2026

LiDAL-Assisted RLNC-NOMA in OWC Systems

Optical wireless communication (OWC) is a key enabler for immersive indoor data transmission in future wireless networks. However, managing multi-user interference remains challenging in dense indoor OWC deployments with multiple optical access points (APs) serving multiple users. In this paper, we propose a dual-function OWC system for communication and localization. For data transmission, we design non-orthogonal multiple access (NOMA) combined with random linear network coding (RLNC): NOMA serves multiple users simultaneously via power-domain multiplexing, while RLNC improves decoding robustness under residual noise and processing impairments. The system is assisted by a light detection and localization (LiDAL) module that estimates users' spatio-temporal presence and location to enable dynamic user association. LiDAL also supports channel state information (CSI) estimation in realistic indoor scenarios where CSI may be noisy and/or highly correlated. We evaluate RLNC-NOMA by analyzing the probability of successful decoding and comparing it with conventional NOMA and orthogonal schemes. In addition, we derive the Cramer-Rao lower bound (CRLB) to evaluate the localization accuracy. The results indicate that RLNC-NOMA increases the probability of successful decoding, and that the proposed unbiased location estimator achieves 44% better CSI accuracy than the baseline CSI estimation and improves the overall system performance.

eess.SY↗
arXiv2026

Ensemble-MIX: Enhancing Sample-Efficiency in Multi-Agent RL via Selective Ensemble-Based Exploration

Multi-agent reinforcement learning (MARL) methods have achieved state-of-the-art results on a range of multi-agent tasks. Yet, MARL algorithms typically require significantly more environment interactions than their single-agent counterparts to converge, a problem exacerbated by the difficulty in exploring over a large joint action space and the high variance intrinsic to MARL environments. To tackle these issues, we propose a novel algorithm that combines a decomposed centralized critic with decentralized ensemble learning, incorporating several key contributions. The main component in our scheme is a selective exploration method that leverages ensemble kurtosis. We extend the global decomposed critic with a diversity-regularized ensemble of individual critics and utilize its excess kurtosis to guide exploration toward high-uncertainty states and actions. To improve sample efficiency, we train the centralized critic with a novel truncated variation of the TD($λ$) algorithm, enabling efficient off-policy learning with reduced variance. On the actor side, our suggested algorithm adapts the mixed samples approach to MARL, mixing on-policy and off-policy loss functions for training the actors. This approach balances between stability and efficiency and outperforms purely off-policy learning. The evaluation shows our method outperforms state-of-the-art baselines on standard MARL benchmarks, including a variety of SMAC II maps.

eess.SY↗