@Article{ee.2026.078284,
AUTHOR = {Meng Li, Mohan Yang, Yang Gao, Xiaokai Su, Hanwen Liang, Song Zhang},
TITLE = {Research on Master-Slave Game Optimization Scheduling Strategy for Virtual Power Plants Incorporating Photovoltaic and Thermal Storage Electric Heating Clusters},
JOURNAL = {Energy Engineering},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/energy/online/detail/26473},
ISSN = {1546-0118},
ABSTRACT = {In order to maximise the potential flexibility of winter electric heating loads, this study proposes a master-slave game-theoretic optimisation scheduling strategy for virtual power plants incorporating a photovoltaic and thermal storage electric heating cluster. This strategy is formulated as a leader-follower game with dynamic clustering. First, a dynamic clustering approach based on the K-means algorithm is employed to analyse the load characteristics of differentiated users, taking into account dynamic factors such as the charging/discharging power of thermal storage devices and variations in indoor temperature. Next, a leader-follower game optimisation model is established in which the VPP acts as the leader coordinating the photovoltaic-integrated thermal storage heating cluster. Given the differing economic objectives of the VPP and its users, the upper-level model aims to maximise the VPP’s operational revenue, while the lower-level model seeks to minimise users’ heating costs. Finally, Karush–Kuhn–Tucker (KKT) conditions are applied to transform the nonlinear, two-level model into a single-level, mixed-integer linear programming (MILP) problem. The results demonstrate that the proposed strategy can effectively meet users’ heating demands while ensuring the economic benefits for both parties, thereby achieving a mutually beneficial outcome for the VPP and electric heating users.},
DOI = {10.32604/ee.2026.078284}
}