Supply–Demand Dynamics Quantification and Distributionally Robust Scheduling for Renewable-Integrated Power Systems with Flexibility Constraints

Jiaji Liang, Jinniu Miao (), Lei Sun, Liqian Zhao, Jingyang Wu, Peng Du, Ge Cao and Wei Zhao
Additional contact information
Jiaji Liang: China Oil & Gas Piping Network Corporation, Beijing 102206, China
Jinniu Miao: China Petroleum Pipeline Engineering Corporation, Langfang 065000, China
Lei Sun: SINO-PIPELINE International Company Limited Myanmar-China Oil & Gas Pipeline Project, Beijing 102206, China
Liqian Zhao: China Petroleum Pipeline Engineering Corporation, Langfang 065000, China
Jingyang Wu: China Oil & Gas Piping Network Corporation, Beijing 102206, China
Peng Du: China Petroleum Pipeline Engineering Corporation, Langfang 065000, China
Ge Cao: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Wei Zhao: China Petroleum Pipeline Engineering Corporation, Langfang 065000, China

Energies, 2025, vol. 18, issue 5, 1-23

Abstract: The growing penetration of renewable energy sources (RES) has exacerbated operational flexibility deficiencies in modern power systems under time-varying conditions. To address the limitations of existing flexibility management approaches, which often exhibit excessive conservatism or risk exposure in managing supply–demand uncertainties, this study introduces a data-driven distributionally robust optimization (DRO) framework for power system scheduling. The methodology comprises three key phases: First, a meteorologically aware uncertainty characterization model is developed using Copula theory, explicitly capturing spatiotemporal correlations in wind and PV power outputs. System flexibility requirements are quantified through integrated scenario-interval analysis, augmented by flexibility adjustment factors (FAFs) that mathematically describe heterogeneous resource participation in multi-scale flexibility provision. These innovations facilitate the formulation of physics-informed flexibility equilibrium constraints. Second, a two-stage DRO model is established, incorporating demand-side resources such as electric vehicle fleets as flexibility providers. The optimization objective aims to minimize total operational costs, encompassing resource activation expenses and flexibility deficit penalties. To strike a balance between robustness and reduced conservatism, polyhedral ambiguity sets bounded by generalized moment constraints are employed, leveraging Wasserstein metric-based probability density regularization to diminish the probabilities of extreme scenarios. Third, the bilevel optimization structure is transformed into a solvable mixed-integer programming problem using a zero-sum game equivalence. This problem is subsequently solved using an enhanced column-and-constraint generation (C&CG) algorithm with adaptive cut generation. Finally, simulation results demonstrate that the proposed model positively impacts the flexibility margin and economy of the power system, compared to traditional uncertainty models.

Keywords: distributionally robust optimization (DRO); renewable energy integration flexibility; flexibility constraint; demand-side resource; Copula theory (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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