A data‐driven network optimisation approach to coordinated control of distributed photovoltaic systems and smart buildings in distribution systems. Issue 3 (6th June 2021)

• Record Type: Journal Article
• Authors: Bai, Linquan; Xue, Yaosuo; Xu, Guanglin; Dong, Jin; Olama, Mohammed M.; Kuruganti, Teja
• 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: 285
• Page End: 294
• Publication Date: 2021-06-06
• Subjects: Power resources -- Periodicals; Energy conservation -- Periodicals; Power resources; Energy conservation; Periodicals; 333.79
• DOI: 10.1049/esi2.12025
• Languages: English
• ISSNs: 2516-8401

Abstract

Abstract: The increasing integration of distributed energy resources, including demand‐side resources and distributed photovoltaics (PVs), into distribution systems has resulted in more complicated power system operation. A data‐driven network optimisation approach is proposed to coordinate the control of distributed PVs and smart buildings in distribution networks considering the uncertainties of solar power, outdoor temperature and heat gain associated with building thermal dynamics. These uncertain parameters have a significant impact on the operation and control of distributed PVs and smart buildings, bringing challenges to the distribution system operation. In the proposed data‐driven distributionally robust optimisation (DRO) approach, the Wasserstein ball is used to construct an ambiguity set for the uncertain parameters, which does not require the probability distributions to be known. Furthermore, a conditional value‐at‐risk is incorporated into the Wasserstein‐based DRO model and converted into a computationally tractable mixed‐integer convex optimisation problem. Benchmarked with robust optimisation and chance‐constrained programming, the proposed data‐driven model can give a less conservative robust solution.