Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Liang, Zhirui"'
Extreme events jeopardize power network operations, causing beyond-design failures and massive supply interruptions. Existing market designs fail to internalize and systematically assess the risk of extreme and rare events. Efficiently maintaining th
Externí odkaz:
http://arxiv.org/abs/2408.03409
Information asymmetry between the Distribution System Operator (DSO) and Distributed Energy Resource Aggregators (DERAs) obstructs designing effective incentives for voltage regulation. To capture this effect, we employ a Stackelberg game-theoretic f
Externí odkaz:
http://arxiv.org/abs/2408.02765
This paper introduces Inverse Distributionally Robust Optimization (I-DRO) as a method to infer the conservativeness level of a decision-maker, represented by the size of a Wasserstein metric-based ambiguity set, from the optimal decisions made using
Externí odkaz:
http://arxiv.org/abs/2405.03123
Despite ambitious offshore wind targets in the U.S. and globally, offshore grid planning guidance remains notably scarce, contrasting with well-established frameworks for onshore grids. This gap, alongside the increasing penetration of offshore wind
Externí odkaz:
http://arxiv.org/abs/2311.09563
Autor:
Ferrando, Robert, Pagnier, Laurent, Mieth, Robert, Liang, Zhirui, Dvorkin, Yury, Bienstock, Daniel, Chertkov, Michael
This paper addresses the challenge of efficiently solving the optimal power flow problem in real-time electricity markets. The proposed solution, named Physics-Informed Market-Aware Active Set learning OPF (PIMA-AS-OPF), leverages physical constraint
Externí odkaz:
http://arxiv.org/abs/2304.00062
Autor:
Liang, Zhirui, Dvorkin, Yury
This paper presents a data-driven inverse optimization (IO) approach to recover the marginal offer prices of generators in a wholesale energy market. By leveraging underlying market-clearing processes, we establish a closed-form relationship between
Externí odkaz:
http://arxiv.org/abs/2302.05498
The growing penetration of variable renewable energy sources (VRES) requires additional flexibility reserve to ensure reliable power system operations. Current industry practice typically assumes a certain fraction of the VRES power production foreca
Externí odkaz:
http://arxiv.org/abs/2209.00707
This paper proposes a modified conditional generative adversarial network (cGAN) model to generate net load scenarios for power systems that are statistically credible, conditioned by given labels (e.g., seasons), and, at the same time, "stressful" t
Externí odkaz:
http://arxiv.org/abs/2110.02152
Maintaining the stability of renewable-dominant power systems requires the procurement of virtual inertia services from non-synchronous resources (e.g., batteries, wind turbines) in addition to inertia traditionally provided by synchronous resources
Externí odkaz:
http://arxiv.org/abs/2107.04101
Publikováno v:
In Electric Power Systems Research November 2022 212
