基于两阶段混合优化算法的电氢热一体化园区容量配置方法

Capacity configuration methodology for integrated electricity-hydrogen-thermal parks based on a two-stage hybrid optimization algorithm

摘要

为实现风光制氢系统的最优经济性规划,构建了电-氢-热多元储能耦合的容量优化模型,该模型以净现值最大化为目标,综合考虑设备投资、运行约束及氧气销售与碳减排等副产品收益,并将储热收益纳入全生命周期经济评估体系;针对模型的高维非线性特性,提出一种基于遗传算法和序列二次规划算法的两阶段混合优化算法(Genetic Algorithm-Sequential Quadratic Programming,GA-SQP),第一阶段利用遗传算法进行全局探索,第二阶段采用序列二次规划进行局部精细化求解,有效兼顾全局最优性与求解效率。以西北某地区风光数据为基础建立离网型风光制氢园区,采用全年8 760 h逐时仿真,优化后得到的最佳配置为电解槽35 MW、储氢容量555 kg、电化学储能10 MW·h、储热装置5 MW·h,该方案年氢气产量达2 182.70 t,总减碳43 654 t,平准化制氢成本降至15.42元/kg,较对比模型降低了3.80%,且与传统遗传算法相比,计算时间缩短46.80%,这验证了所提模型与算法的有效性,为可再生能源制氢系统规划决策提供了理论依据与实际应用方法。

Abstract

To achieve optimal economic planning for a wind-solar hydrogen production system, this paper develops a capacity optimization model that couples electricity-hydrogen-thermal multi-energy storage. The model aims to maximize the net present value, comprehensively considering equipment investment, operational constraints, and by-product revenues such as oxygen sales and carbon emission reduction, while incorporating thermal storage benefits into the full lifecycle economic assessment framework. To address the model's high-dimensional nonlinear characteristics, a two-stage hybrid optimization algorithm based on the Genetic Algorithm and Sequential Quadratic Programming (GA-SQP) is proposed. In the first stage, the genetic algorithm is employed for global exploration, and in the second stage, sequential quadratic programming is applied for refined local solution, effectively balancing global optimality and computational efficiency. An off-grid wind-solar hydrogen production park is established based on the wind and solar resource data from a region in Northwest China, with year-round hourly simulations over 8 760 hours. The optimized configuration yields an electrolyzer capacity of 35 MW, hydrogen storage capacity of 555 kg, electrochemical energy storage of 10 MW·h, and thermal storage device of 5 MW·h. Under this scheme, the annual hydrogen production reaches 2 182.70 t, with total carbon emission reduction of 43 654 t, and the levelized cost of hydrogen is reduced to 15.42 CNY/kg, representing a 3.80% decrease compared to the benchmark model. Moreover, the computation time is shortened by 46.80% relative to the conventional genetic algorithm, which validates the effectiveness of the proposed model and algorithm, and provides both a theoretical foundation and practical implementation methodology for planning decision-making in renewable-energy-based hydrogen production systems.

关键词

电氢协同 / 容量配置 / 遗传算法 / 序列二次规划算法 / 碳减排

Key words

electricity-hydrogen synergy / capacity configuration / genetic algorithm / sequential quadratic programming algorithm / carbon emission reduction

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基金资助

国家重点研发计划项目(2022YFB3807500),国家自然科学基金项目(52207217)