Energy management strategy of active distribution network with integrated distributed wind power and smart buildings. Issue 12 (18th August 2020)

• Record Type: Journal Article
• Authors: Li, Zening; Su, Su; Zhao, Yuming; Jin, Xiaolong; Chen, Houhe; Li, Yujing; Zhang, Renzun
• Is Part Of: IET renewable power generation. Volume 14:Issue 12(2020)
• Page Start: 2255
• Page End: 2267
• Publication Date: 2020-08-18
• Subjects: building management systems -- wind power -- HVAC -- energy management systems -- decision making -- smart power grids -- distribution networks -- power consumption -- analytic hierarchy process; energy consumption prediction model -- smart buildings -- multiobjective ADN model -- energy management strategy -- active distribution network -- integrated distributed wind power -- thermal storage characteristics -- resistor‐capacitor model -- analytic hierarchy process -- power grid -- second‐order conic relaxation -- heating ventilation air‐conditioning systems -- HVAC systems; Renewable energy sources -- Periodicals; 333.79405
• DOI: 10.1049/iet-rpg.2020.0049
• Languages: English
• ISSNs: 1752-1416

Abstract

Abstract : Aiming to achieve the flexible operation of distribution network (ADN), an energy management strategy of ADN with integrated distributed wind power and smart buildings is proposed in this study. First, based on the thermal storage characteristics of buildings, the resistor–capacitor model is used to develop an energy consumption prediction model of smart buildings. Second, the smart buildings are modelled as flexible resources of ADN. A multi‐objective ADN model is further formulated based on analytic hierarchy process (AHP) by comprehensively considering constraints of power grid and smart buildings. Then, the unified model of ADN is transformed by second‐order conic relaxation to guarantee the global optimal solution. Finally, the scheduling results for the unified model of ADN under different control methods for heating ventilation and air‐conditioning (HVAC) systems are compared in the winter heating scenario. In addition, the impact of the demand response capability for smart buildings on the economic and secure operation of the ADN is further analysed. Numerical studies demonstrate that the proposed method can increase the penetration of local wind power, reduce peak‐valley load difference and network loss of the grid while ensuring the comfort temperature of customers, and further achieve the flexible operation of ADN.