A Distributed Demand Side Energy Management Algorithm for Smart Grid

Min-fan He, Fu-xing Zhang, Yong Huang, Jian Chen, Jue Wang and Rui Wang
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Min-fan He: School of Mathematics and Big Data, Foshan University, Foshan 528000, China
Fu-xing Zhang: State Grid Key Laboratory of Information & Network Security, Global Energy Interconnection Research Institute, Beijing 102211, China
Yong Huang: School of Mathematics and Big Data, Foshan University, Foshan 528000, China
Jian Chen: School of Mathematics and Big Data, Foshan University, Foshan 528000, China
Jue Wang: School of Physics & information technology, Shaanxi Normal University, Xi’an, 710119 China
Rui Wang: National University of Defense Technology, Changsha, 410073, China

Energies, 2019, vol. 12, issue 3, 1-19

Abstract: This paper proposes a model predictive control (MPC) framework-based distributed demand side energy management method (denoted as DMPC) for users and utilities in a smart grid. The users are equipped with renewable energy resources (RESs), energy storage system (ESSs) and different types of smart loads. With the proposed method, each user finds an optimal operation routine in response to the varying electricity prices according to his/her own preference individually, for example, the power reduction of flexible loads, the start time of shift-able loads, the operation power of schedulable loads, and the charge/discharge routine of the ESSs. Moreover, in the method a penalty term is used to avoid large fluctuation of the user’s operation routines in two consecutive iteration steps. In addition, unlike traditional energy management methods which neglect the forecast errors, the proposed DMPC method can adapt the operation routine to newly updated data. The DMPC is compared with a frequently used method, namely, a day-ahead programming-based method (denoted as DDA). Simulation results demonstrate the efficiency and flexibility of the DMPC over the DDA method.

Keywords: model predictive control (MPC); smart grid; demand side management (DSM); distributed optimization; game theory (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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