Distributed Capacity Allocation of Shared Energy Storage Using Online Convex Optimization

Kan Xie, Weifeng Zhong, Weijun Li and Yinhao Zhu
Additional contact information
Kan Xie: Key Laboratory of Ministry of Education, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Weifeng Zhong: Key Laboratory of Ministry of Education, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Weijun Li: Key Laboratory of Ministry of Education, School of Automation, Guangdong University of Technology, Guangzhou 510006, China
Yinhao Zhu: School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China

Energies, 2019, vol. 12, issue 9, 1-15

Abstract: This paper studies capacity allocation of an energy storage (ES) device which is shared by multiple homes in smart grid. Given a time-of-use (TOU) tariff, homes use the ES to shift loads from peak periods to off-peak periods, reducing electricity bills. In the proposed ES sharing model, the ES capacity has to be allocated to homes before the homes’ load data is completely known. To this end, an online ES capacity allocation algorithm is developed based on the online convex optimization framework. Under the online algorithm, the complex allocation problem can be solved round by round: at each round, the algorithm observes current system states and predicts a decision for the next round. The proposed algorithm is able to minimize homes’ costs by learning from home load data in a serial fashion. It is proven that the online algorithm can ensure zero average regret and long-term budget balance of homes. Further, a distributed implementation of the online algorithm is proposed based on alternating direction method of multipliers framework. In the distributed implementation, the one-round system problem is decomposed into multiple subproblems that can be solved by homes locally, so that an individual home does not need to send its private load data to any other. In simulation, actual home load data and a TOU tariff of the United States are used. Results show that the proposed online approach leads to the lowest home costs, compared to other benchmark approaches.

Keywords: capacity allocation; energy storage sharing; online convex optimization; smart home (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 complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/9/1642/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/9/1642/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:9:p:1642-:d:227110

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().