Peak-Aware Online Economic Dispatching for Microgrids
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...
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| Published in | IEEE transactions on smart grid Vol. 9; no. 1; pp. 323 - 335 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
01.01.2018
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1949-3053 1949-3061 |
| DOI | 10.1109/TSG.2016.2551282 |
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| 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. |
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| AbstractList | 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. |
| Author | Hajiesmaili, Mohammad H. Minghua Chen Ying Zhang Sinan Cai Qi Zhu |
| Author_xml | – sequence: 1 surname: Ying Zhang fullname: Ying Zhang email: zy013@ie.cuhk.edu.hk organization: Dept. of Inf. Eng., Chinese Univ. of Hong Kong, Hong Kong, China – sequence: 2 givenname: Mohammad H. surname: Hajiesmaili fullname: Hajiesmaili, Mohammad H. organization: Dept. of Inf. Eng., Chinese Univ. of Hong Kong, Hong Kong, China – sequence: 3 surname: Sinan Cai fullname: Sinan Cai organization: Sch. of Electr. Eng., Xi'an Jiao Tong Univ., Xi'an, China – sequence: 4 surname: Minghua Chen fullname: Minghua Chen organization: Dept. of Inf. Eng., Chinese Univ. of Hong Kong, Hong Kong, China – sequence: 5 surname: Qi Zhu fullname: Qi Zhu organization: Dept. of Electr. & Comput. Eng., Univ. of California at Riverside, Riverside, CA, USA |
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| SubjectTerms | Algorithm design and analysis Dispatching economic dispatching Economics Generators Microgrids online algorithm peak-aware scheduling Pricing Uncertainty |
| Title | Peak-Aware Online Economic Dispatching for Microgrids |
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