需給不確実性下での統合エネルギーシステムの二段階数量・品質協調最適化

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🔬研究者:Provides a novel bi-level optimization framework integrating carbon and exergy flows, useful for advancing energy system modeling.

🏢実務担当者:Offers a method for optimizing IES operations to reduce carbon emissions and improve energy quality, applicable for energy utilities.

🏛政策担当者:Highlights the importance of carbon allocation and exergy efficiency in energy planning, informing policy for low-carbon transitions.

With large-scale renewable energy integration and intensifying multi-energy coupling, day-ahead scheduling of integrated energy systems (IES) faces challenges from renewable generation fluctuations, load uncertainty, limited demand-side flexibility, and energy quantity–quality mismatches. Conventional methods focus primarily on energy quantity balance and cannot characterize energy transmission, carbon allocation, and exergy loss or capture the differentiated responsibilities of energy stations in quantity–quality coordinated operation. To address these issues, this paper proposes a bi-level quantity–quality coordinated optimization method for IES under supply–demand uncertainty. First, a unified steady-state energy–carbon–exergy flow model is developed for electricity–heat–gas coupled systems to jointly characterize transmission losses, carbon transfer, and energy quality degradation. Second, considering stochastic fluctuations of wind turbine (WT), photovoltaic (PV), and electric, thermal, and gas loads, quantity–quality-oriented supply–demand scenarios are constructed to identify critical operational boundaries with equivalent total energy supply but distinct energy-grade mismatch risks. An integrated demand response (IDR) model is introduced to adjust electric, thermal, and gas loads, enhancing demand-side flexibility and source–load matching. Accordingly, a bi-level optimization framework is established: the upper-level coordinates network losses, carbon emissions, and exergy efficiency, whereas the lower level minimizes the generalized operating costs of energy stations. A Nash game-based exergy-loss pricing mechanism is incorporated into the lower-level model to allocate differentiated exergy-loss responsibilities among energy stations. Case studies demonstrate that the proposed method coordinates economic performance, low-carbon operation, and energy quality utilization, enhances multi-station complementarity, and improves IES quantity–quality matching under supply–demand uncertainty.

• Research Square https://doi.org/10.22541/authorea.15007853/v1first seen 2026-08-27 04:41:28 · last seen 2026-09-08 04:21:52