Optimal day‐ahead operation of user‐level integrated energy system considering dynamic behaviour of heat loads and customers' heat satisfaction. Issue 3 (17th May 2019)

• Record Type: Journal Article
• Authors: Lv, Jiawei; Zhang, Shenxi; Cheng, Haozhong; Fang, Sidun
• Is Part Of: IET smart grid. Volume 2:Issue 3(2019)
• Page Start: 320
• Page End: 326
• Publication Date: 2019-05-17
• Subjects: power generation scheduling -- quadratic programming -- power distribution economics -- power generation economics -- nonlinear programming -- optimisation -- power markets; user‐level integrated energy system -- dynamic behaviour -- heat loads -- customers -- novel optimal day‐ahead operation method -- energy hub -- dynamic model -- optimal day‐ahead operation model -- total cost -- energy purchase -- energy converters -- standard modelling method; B0260 Optimisation techniques -- B8110B Power system management, operation and economics -- B8120J Distribution networks -- C1180 Optimisation techniques; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2019.0065
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : This study proposes a novel optimal day‐ahead operation method for the user‐level integrated energy system (IES), which can take into account the dynamic behaviour of heat loads and customers' heat satisfaction. First, based on the energy hub, the dynamic model of heat loads is added into IES. Additionally, the customers' heat satisfaction model is constructed by the variance of water temperature and punishment coefficient. Then, the optimal day‐ahead operation model aimed at minimising the total cost is proposed. Constraints of energy purchase, operation of energy converters, loads, temperature and the variation of temperature are all contained. Piecewise linearisation and a standard modelling method are utilised to simplify the initial non‐convex and non‐linear problem to a quadratic programming problem. Finally, case studies are carried out on a practical calculation example. Results indicate that the total cost can be reduced by 5.6% when considering the dynamic behaviour of heat loads. Also, customers' heat satisfaction exerts a remarkable influence on the total cost.