Peak-aware online economic dispatching for microgrids

Ying Zhang, Mohammad H. Hajiesmaili, Sinan Cai, Minghua Chen, Qi Zhu

Research output: Contribution to journalArticlepeer-review

51 Scopus citations

Abstract

By employing local renewable energy sources and power generation units while connected to the central grid, microgrid can usher in great benefits in terms of cost efficiency, power reliability, and environmental awareness. Economic dispatching is a central problem in microgrid operation, which aims at effectively scheduling various energy sources to minimize the operating cost while satisfying the electricity demand. Designing intelligent economic dispatching strategies for microgrids; however, it is drastically different from that for conventional central grids due to two unique challenges. First, the demand and renewable generation uncertainty emphasizes the need for online algorithms. Second, the widely-adopted peak-based pricing scheme brings out the need for new peak-aware strategy design. In this paper, we tackle these critical challenges and devise peak-aware online economic dispatching algorithms. We prove that our deterministic and randomized algorithms achieve the best possible competitive ratios 2 - β and e/(e - 1 + β) in the fast responding generator scenario, where β ∈ [0, 1] is the ratio between the minimum grid spot price and the local-generation price. By extensive empirical evaluations using real-world traces, we show that our online algorithms achieve near offline-optimal performance. In a representative scenario, our algorithm achieves 17.5% and 9.24% cost reduction as compared with the case without local generation units and the case using peak-oblivious algorithms, respectively.

Original languageEnglish (US)
Pages (from-to)323-335
Number of pages13
JournalIEEE Transactions on Smart Grid
Volume9
Issue number1
DOIs
StatePublished - Jan 2018

Funding

Manuscript received October 8, 2015; revised February 21, 2016; accepted March 18, 2016. Date of publication April 6, 2016; date of current version December 21, 2017. This work was supported in part by the National Basic Research Program of China under Project 2013CB336700, and in part by the University Grants Committee of the Hong Kong Special Administrative Region, China, through the Theme-Based Research Scheme under Project T23-407/13-N and the General Research Grant 14201014. Paper no. TSG-01302-2015.

Keywords

  • Economic dispatching
  • Microgrids
  • Online algorithm
  • Peak-aware scheduling

ASJC Scopus subject areas

  • General Computer Science

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