考虑极端情况的岛屿微电网分布式鲁棒优化配置方法

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-06-13 DOI:10.1016/j.egyai.2024.100389
Qingzhu Zhang, Yunfei Mu, Hongjie Jia, Xiaodan Yu, Kai Hou
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引用次数: 0

摘要

海洋气候条件复杂多变。在风能和太阳能接入比例较高的情况下,岛屿微电网能源来源的不确定性大大增加,这对岛屿微电网容量配置的经济可行性和可靠性都提出了挑战。针对这一问题,本文提出了一种考虑风能和太阳能极端情况的岛屿微电网分布式鲁棒优化(DRO)方法。首先,为了解决在复杂的海岛气候条件下确定风能和太阳能概率分布函数的难题,本文采用了条件生成对抗网络(CGAN)来生成风能和太阳能条件的情景集。然后,通过将 k-means 聚类与极端情景选择方法相结合,从生成的情景集中选择典型情景和极端情景,形成海岛微电网 DRO 模型的情景集。在此基础上,以投资成本、运行和维护成本、燃料采购成本、风能和太阳能削减惩罚成本以及负荷损失惩罚成本之和最小化为目标,构建了基于多个离散情景的 DRO 模型。该模型受到设备运行和电力平衡约束,并使用列和约束生成(CCG)算法求解。最后,通过典型实例验证了本文方法在平衡岛屿微电网配置方案的经济可行性和鲁棒性,以及减少风能和太阳能削减和负荷损失方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Distributionally robust optimization configuration method for island microgrid considering extreme scenarios

The marine climate conditions are intricate and variable. In scenarios characterized by high proportions of wind and solar energy access, the uncertainty regarding the energy sources for island microgrid is significantly exacerbated, presenting challenges to both the economic viability and reliability of the capacity configuration for island microgrids. To address this issue, this paper proposes a distributionally robust optimization (DRO) method for island microgrids, considering extreme scenarios of wind and solar conditions. Firstly, to address the challenge of determining the probability distribution functions of wind and solar in complex island climates, a conditional generative adversarial network (CGAN) is employed to generate a scenario set for wind and solar conditions. Then, by combining k-means clustering with an extreme scenario selection method, typical scenarios and extreme scenarios are selected from the generated scenario set, forming the scenario set for the DRO model of island microgrids. On this basis, a DRO model based on multiple discrete scenarios is constructed with the objective of minimizing the sum of investment costs, operation and maintenance costs, fuel purchase costs, penalty costs of wind and solar curtailment, and penalty costs of load loss. The model is subjected to equipment operation and power balance constraints, and solved using the columns and constraints generation (CCG) algorithm. Finally, through typical examples, the effectiveness of this paper’s method in balancing the economic viability and robustness of the configuration scheme for the island microgrid, as well as reducing wind and solar curtailment and load loss, is verified.

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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
期刊最新文献
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