面向可再生能源-加氢站-园区多时间尺度协同运营的分布鲁棒优化策略

doi:10.19595/j.cnki.1000-6753.tces.260289

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摘要

针对可再生能源（RE）出力随机性导致智能电网多主体协同运营经济性下降及可靠性不足的问题,本文提出一种面向RE-加氢站（HRS）-工业园区（IP）系统的日前-日内协同运行分布鲁棒优化（DRO）策略。首先,构建由能源聚合商（EA）主导的协同运营框架,通过价格诱导机制实现异质主体间的能量互补与需求响应衔接。其次,利用Wasserstein距离构建RE预测误差的分布不确定性集,并基于对偶理论将原DRO模型等价转换为易于求解的单层形式。进一步融合Kriging元模型,构建并求解主从博弈-DRO联合决策以确定EA引导电价并最小化系统总成本。最后设计多时间尺度滚动优化,对日前计划进行日内与实时递推修正,以抑制RE波动对能量平衡与电网交互的影响。算例结果表明,所提策略在提升系统对极端场景鲁棒性的同时,显著优化了运营经济性与低碳性能,实现了多主体利益与系统可靠性的平衡。

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关键词 ： RE-HRS-IP协同运营, 分布鲁棒优化, 主从博弈, 多时间尺度滚动优化, 碳减排

Abstract：

Renewable energy (RE) uncertainty, discontinuous hydrogen refueling demand, and production constraints in industrial parks make it difficult to coordinate economic operation, low-carbon transportation, and grid-friendly power exchange in RE-hydrogen refueling station (HRS)-industrial park (IP) systems. A multi-time-scale distributionally robust optimization framework is developed for the coordinated operation of RE stations, an HRS, an IP, a battery swap station, concentrating solar power (CSP), shared energy storage (SES), and the external grid. The purpose is to use price signals and rolling corrections to coordinate heterogeneous production and transportation loads while limiting the operational risks caused by photovoltaic and wind power forecast errors.
The energy aggregator (EA) is modeled as the leader of a Stackelberg game, while RE stations, the IP, and the HRS are modeled as followers. The EA issues differentiated guiding prices for RE trading, hydrogen production, and industrial production, and the followers respond through production scheduling, power-to-hydrogen operation, hydrogen storage, hydrogen heavy truck refueling, electric mining truck battery swapping, and RE output adjustment. A Wasserstein-distance ambiguity set is used to describe the distributional uncertainty of photovoltaic and wind power forecast errors. The original max-min distributionally robust optimization model is reformulated into a single-level tractable form by dual transformation. To reduce repeated calls to follower optimization models during price searching, a Kriging metamodel is introduced to approximate the mapping between price vectors and response/profit values. A multi-time-scale rolling scheme is then constructed: the day-ahead schedule provides a robust baseline, the intraday stage updates hourly operating points with improved forecasts, and the real-time stage performs 15 min corrections using the HRS, CSP, battery swap station, SES, and grid transactions.
A case study is carried out for a typical mining area in Tibet, China. Under the proposed strategy, the total revenue reaches 2517975.95 RMB, with an ore output of 900 t. Compared with the case in which CSP operates only for its own profit, the revenue increases by 1.26%, the grid power fluctuation range decreases by 56.88%, and grid electricity purchase decreases by 49.09%. Compared with independent optimization of all entities, the revenue increases by 2.81%, the grid power fluctuation range decreases by 65.78%, and grid electricity purchase decreases by 75.22%. The coordinated operation also supports low-carbon transportation. For the same daily transport task, replacing diesel trucks with hydrogen heavy trucks reduces carbon emissions by about 3871.8 kgCO?. On the power side, the local RE supply accounts for 90.64% of the total load, and grid-related carbon emissions are reduced from 47.63 tCO? under full grid supply to 4.45 tCO?, corresponding to a 90.66% reduction.
Sensitivity and robustness tests further show the operational adaptability of the framework. When available RE output varies from 50% to 110% of the benchmark, the RE utilization rate under the proposed strategy remains no lower than 95.13%, while full ore production is maintained. In real-time tests with 0%-20% forecast deviations, the cooperation of the HRS, CSP, battery swap station, and SES achieves a real-time RE tracking rate of 99.19%. Compared with the case without CSP real-time adjustment, the grid additional regulation power variance decreases by up to 26.04%, and the regulation power level decreases by up to 29.91%. Out-of-sample tests show that distributionally robust optimization reduces the revenue standard deviation by 60.72% compared with stochastic optimization and decreases the constraint violation rate from 6.80% to 1.10%, while avoiding the over-conservatism of robust optimization. The Kriging-assisted solution reduces high-fidelity model calls from 2000 in direct Stackelberg iteration to 75, 100, and 150 under initial sample sizes of 25, 50, and 100, with speedup ratios of 16.74, 12.76, and 8.60, respectively.
The results indicate that the proposed framework coordinates price-guided demand response, Wasserstein-based uncertainty management, and rolling multi-time-scale execution within a unified RE-HRS-IP operation model. It provides an operational strategy that balances economic performance, out-of-sample robustness, RE accommodation, carbon reduction, and grid interaction stability under coupled production and transportation constraints.

Key words： RE-HRS-IP coordinated distributionally robust optimization Stackelberg game multi-time-scale rolling optimization carbon reduction

收稿日期: 2026-03-04

PACS:

TM732

基金资助:

智能电网国家科技重大专项资助（2024ZD0800600）

通讯作者: 陈红坤男,1967年生,教授,博士生导师,研究方向为新能源交直流电力系统规划、运行与控制,综合能源系统规划与运行,电能质量分析与评估等。E-mail：chkinsz@163.com

作者简介: 陶文强男,1996年生,博士研究生,研究方向综合能源系统规划与运行。E-mail：w.q.tao@whu.edu.cn

引用本文:

陶文强, 陈红坤, 崔世庭, 陈磊. 面向可再生能源-加氢站-园区多时间尺度协同运营的分布鲁棒优化策略[J]. 电工技术学报, 0, (): 289-. Tao Wenqiang, Chen Hongkun, Cui Shiting, Chen Lei. A Distributed Robust Optimization Strategy for Multi-Time-Scale Coordinated Operation of Renewable Energy, Hydrogen Refueling Stations, and Industrial Parks. Transactions of China Electrotechnical Society, 0, (): 289-.

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