Assessment of distributed photovoltaic hosting capacity in integrated electricity and heat systems considering uncertainty. Issue 3 (28th June 2021)

• Record Type: Journal Article
• Authors: Zhao, Lebing; Wan, Can; Yu, Peng; Wu, Mengjing; Zhao, Shen
• Other Names: Jiang Tao guestEditor.; Bai Linquan guestEditor.; Mu Yunfei guestEditor.; Venayagamoorthy Kumar guestEditor.; Zhang Yingchen guestEditor.; Teng Fei guestEditor.; Chen Peiyuan guestEditor.; Zhong Haiwang guestEditor.; Yao Wei guestEditor.; Wan Can guestEditor.
• Is Part Of: IET energy systems integration. Volume 3:Issue 3(2021)
• Page Start: 317
• Page End: 326
• Publication Date: 2021-06-28
• Subjects: Power resources -- Periodicals; Energy conservation -- Periodicals; Power resources; Energy conservation; Periodicals; 333.79
• DOI: 10.1049/esi2.12029
• Languages: English
• ISSNs: 2516-8401

Abstract

Abstract: Distributed photovoltaics (DPVs) have been widely integrated into power systems due to their abundance, renewability and low cost, while the stochastic nature of DPVs imposes significant influence on the hosting capacity (HC) of DPVs. Here, the integrated electricity and heat system (IEHS) is explored to increase the deployment of DPVs in comparison to the power system by exploiting the interaction between electric and thermal energy. An MILP‐based HC assessment model for DPVs of the IEHS is proposed to efficiently promote the penetration level of DPV generation in distribution networks considering the uncertainty of DPV generation. To reduce the computational burden for HC assessment, the linearisation method combined with the incremental formulations of the big‐M method is developed to simplify the complex non‐linear model of energy devices and networks in IEHS. A scenario‐based uncertainty modelling approach is applied to characterise DPV uncertainty, which improves the accuracy of HC assessment for DPVs. Comprehensive case studies according to a 33‐bus electric network and a 6‐node heat network validate the superiority of the proposed assessment model.