Load increase response window identification and low-carbon regulation for grid-interactive salt lake chemical parks under process feasibility constraints

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:プロセス制約下での需要応答と再エネ統合の最適化手法に関心のある研究者に有用。

🏢実務担当者:化学・素材産業のサステナビリティ担当者が、再エネ受入と生産制約の両立によるコスト削減と炭素削減の可能性を検討する際に参考になる。

🏛政策担当者:産業部門の需要応答を促進する政策設計や、系統運用ルールの柔軟化を検討する際のエビデンスを提供。

The increasing penetration of renewable energy has intensified the pressure on power grids to accommodate renewable generation, while the production processes of energy intensive loads are subject to stringent process rigidity constraints, posing substantial challenges to source–load interactive scheduling. To address this issue, this paper investigates typical energy intensive loads in a salt lake chemical park and proposes a method for load increase response window identification and low-carbon regulation under process feasibility constraints. First, based on the material transfer relationships in potassium, lithium, and magnesium coproduction, a load increase potential assessment model is developed to characterize the formation and interprocess transfer of adjustable load potential by jointly considering process feasibility and distribution network access margins. Second, a feasibility discrimination model for load increase response windows is established, in which shift rhythm adaptation, material buffer consistency, and renewable power matching are incorporated into a unified evaluation framework. An improved method integrating the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and multi-criteria optimization and compromise solution (VIKOR) is then used to identify suitable response windows under surplus renewable power conditions. Finally, a low-carbon regulation model is formulated to coordinate renewable energy accommodation with coproduction process constraints, and an adaptive large neighborhood search (ALNS) algorithm embedded with process feasibility reconstruction is designed to solve the model. In three representative seasonal scenarios, each spanning 24 h and based on case inputs jointly constructed from archived desensitized operating sequences, engineering constraints, and standardized scenario parameters, the proposed method achieved renewable energy accommodation of 10.9–14.0 MWh, incremental net benefits of CNY 66,400–89,100, and net carbon reductions of approximately 3.0–3.2 tCO 2 , while satisfying all process and distribution network constraints. Tests using 30 paired random seeds further showed that the improved ALNS converged faster and exhibited lower variability than Basic ALNS. An extended analysis over 7 days further revealed the tradeoff among renewable energy accommodation, inventory accumulation, and economic returns. These results indicate that, under the operating conditions and parameter ranges considered for the park, the proposed method can coordinate renewable energy accommodation, production feasibility, economic performance, and carbon mitigation.

• openalex https://doi.org/10.3389/fenrg.2026.1922129first seen 2026-10-01 04:54:12