Online energy generation scheduling for microgrids with intermittent energy sources and co-generation

  • Authors:

  • Lian Lu;Jinlong Tu;Chi-Kin Chau;Minghua Chen;Xiaojun Lin

  • Affiliations:

  • Information Engineering Dept., The Chinese Univ. of Hong Kong, Hong Kong, Hong Kong;Information Engineering Dept., The Chinese Univ. of Hong Kong, Hong Kong, Hong Kong;Masdar Institute of Science and Technology, Masdar City, Uae;Information Engineering Dept., The Chinese Univ. of Hong Kong, Hong Kong, Hong Kong;School of Electrical and Computer Engineering, Purdue Univ., West Lafayette, USA

  • Venue:
  • Proceedings of the ACM SIGMETRICS/international conference on Measurement and modeling of computer systems
  • Year:
  • 2013

Quantified Score

Hi-index 0.00

Visualization

Abstract

Microgrids represent an emerging paradigm of future electric power systems that can utilize both distributed and centralized generations. Two recent trends in microgrids are the integration of local renewable energy sources (such as wind farms) and the use of co-generation (i.e., to supply both electricity and heat). However, these trends also bring unprecedented challenges to the design of intelligent control strategies for microgrids. Traditional generation scheduling paradigms rely on perfect prediction of future electricity supply and demand. They are no longer applicable to microgrids with unpredictable renewable energy supply and with co-generation (that needs to consider both electricity and heat demand). In this paper, we study online algorithms for the microgrid generation scheduling problem with intermittent renewable energy sources and co-generation, with the goal of maximizing the cost-savings with local generation. Based on the insights from the structure of the offline optimal solution, we propose a class of competitive online algorithms, called CHASE (Competitive Heuristic Algorithm for Scheduling Energy-generation), that track the offline optimal in an online fashion. Under typical settings, we show that CHASE achieves the best competitive ratio among all deterministic online algorithms, and the ratio is no larger than a small constant 3. We also extend our algorithms to intelligently leverage on limited prediction of the future, such as near-term demand or wind forecast. By extensive empirical evaluations using real-world traces, we show that our proposed algorithms can achieve near offline-optimal performance. In a representative scenario, CHASE leads to around 20% cost reduction with no future look-ahead, and the cost reduction increases with the future look-ahead window.