1. Distributed Consensus-Based Coordination of Flexible Demand and Energy Storage Resources
- Author
-
Jing Li, Goran Strbac, Yujian Ye, Dimitrios Papadaskalopoulos, and Engineering & Physical Science Research Council (EPSRC)
- Subjects
Technology ,Mathematical optimization ,Energy storage ,Economics ,Computer science ,020209 energy ,Reliability (computer networking) ,media_common.quotation_subject ,distributed coordination ,Energy Engineering and Power Technology ,Initialization ,02 engineering and technology ,Adaptability ,distributed energy resources ,CENTRALIZED CONTROL ,Distributed power generation ,Engineering ,MICROGRIDS ,DISPATCH ,Consensus ,Robustness (computer science) ,Convergence (routing) ,MANAGEMENT ,0202 electrical engineering, electronic engineering, information engineering ,Consensus-based algorithms ,ALGORITHM ,Coordination game ,Electrical and Electronic Engineering ,media_common ,Science & Technology ,Energy ,flexible demand ,business.industry ,SCHEME ,Engineering, Electrical & Electronic ,Indexes ,Generators ,0906 Electrical and Electronic Engineering ,Privacy ,Distributed generation ,Convergence ,business - Abstract
Distributed, consensus-based algorithms have emerged as a promising approach for the coordination of distributed energy resources (DER) due to their communication, computation, privacy and reliability advantages over centralized approaches. However, state-of-the-art consensus-based algorithms address the DER coordination problem in independent time periods and therefore are inherently unable to capture the time-coupling operating characteristics of flexible demand (FD) and energy storage (ES) resources. This paper demonstrates that state-of-the-art algorithms fail to converge when these time-coupling characteristics are considered. In order to address this fundamental limitation, a novel consensus-based algorithm is proposed which includes additional consensus variables. These variables express relative maximum power limits imposed on the FD and ES resources which effectively mitigate the concentration of the FD and ES responses at the same time periods and steer the consensual outcome to a feasible and optimal solution. The convergence and optimality of the proposed algorithm are theoretically proven while case studies numerically demonstrate its convergence, optimality, robustness to initialization and information loss, and plug-and-play adaptability.
- Published
- 2021