Low-carbon economic optimization strategy for industrial loads in parks considering source-load-price multivariate uncertainty

IF 3.3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Electric Power Systems Research Pub Date : 2024-11-06 DOI:10.1016/j.epsr.2024.111200

Yuxiu Zang , Jia Cui , Shunjiang Wang , Yan Zhao , Weichun Ge , Chaoran Li

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引用次数: 0

Abstract

A low-carbon economic optimization method is proposed for industrial loads in parks considering multivariate uncertainty. Model of dynamic load node carbon intensity is proposed considering the uncertainty of clean energy and dynamic conventional units carbon intensity based on system currents. The robustness is improved based on multi-interval uncertainty set. The gap is filled by hybrid copula model with Generalized Autoregressive Conditional Heteroskedasticity (GARCH) prices in the uncertainty model. The GARCH is used to built the marginal distribution function of prices, and a hybrid copula model is proposed to obtain the joint probability density function based on the weights calculated by Euclidean distance. The LNCI probability distributions of the regulation potential of loads are proposed with the virtual energy storage and the virtual power model to improve the descriptive ability of uncertainty based on the expansion and contraction functions. Finally, the typical scenarios are extracted based on Monte Carlo and Wasserstein measures. The Column sum Constraint Generation algorithm and strong duality theory are used to get the robust-stochastic optimization. The method proposed in the paper has a positive effect on enterprises to rationalize their production schedules, reduce electricity and carbon emissions costs and increase the revenue from participating in grid regulation.

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考虑源-荷-价多元不确定性的园区工业负荷低碳经济优化策略

针对园区工业负荷，提出了一种考虑多变量不确定性的低碳经济优化方法。考虑到清洁能源的不确定性和基于系统电流的动态常规机组碳强度，提出了动态负荷节点碳强度模型。基于多区间不确定性集提高了鲁棒性。不确定性模型中的广义自回归条件异方差（GARCH）价格混合 copula 模型填补了这一空白。利用 GARCH 建立价格的边际分布函数，并提出混合 copula 模型，根据欧氏距离计算的权重获得联合概率密度函数。结合虚拟储能和虚拟电力模型，提出了负荷调节潜力的 LNCI 概率分布，以提高基于扩展和收缩函数的不确定性描述能力。最后，基于蒙特卡洛和瓦瑟斯坦测量法提取典型情景。利用列和约束生成算法和强对偶理论得到鲁棒-随机优化。本文提出的方法对企业合理安排生产计划、降低电力和碳排放成本、增加参与电网调节的收益具有积极作用。

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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来源期刊

Electric Power Systems Research 工程技术-工程：电子与电气

CiteScore

7.50

自引率

17.90%

发文量

963

审稿时长

3.8 months

期刊介绍： Electric Power Systems Research is an international medium for the publication of original papers concerned with the generation, transmission, distribution and utilization of electrical energy. The journal aims at presenting important results of work in this field, whether in the form of applied research, development of new procedures or components, orginal application of existing knowledge or new designapproaches. The scope of Electric Power Systems Research is broad, encompassing all aspects of electric power systems. The following list of topics is not intended to be exhaustive, but rather to indicate topics that fall within the journal purview. • Generation techniques ranging from advances in conventional electromechanical methods, through nuclear power generation, to renewable energy generation. • Transmission, spanning the broad area from UHV (ac and dc) to network operation and protection, line routing and design. • Substation work: equipment design, protection and control systems. • Distribution techniques, equipment development, and smart grids. • The utilization area from energy efficiency to distributed load levelling techniques. • Systems studies including control techniques, planning, optimization methods, stability, security assessment and insulation coordination.