Abstract
This paper proposes a two-stage data-driven unit commitment dispatching scheme, which coordinates the multi-function demand response of commercial air conditioning aggregators with thermal units to encounter the variations of load and wind power. In the proposed dispatching scheme, the flexible ramping capability of the thermal units is reflected in the coupling constraints between the ramping capacity and reserve capacity. To improve the flexibility of the whole system, multi-function demand response of commercial air conditioning aggregators is incorporated into the scheduling in both stages. In the day-ahead stage, load-shifting demand response reduces the ramping requirements by reshaping the load profile. In the real-time stage, reserve-capacity demand response releases the ramping capability by providing reserve for the rebalance of wind power fluctuation, which is represented by a data-driven uncertainty set. Since the dispatching scheme is formulated as a min–max-min problem, an accelerating solution method is developed to improve the computational efficiency, which combines the column-and-constraints generation algorithm and the specific property of the model. The simulation results tested on the modified IEEE 118-bus system and Henan power grid in China validate the effectiveness of the dispatching scheme and solution method in increasing flexible ramping capability, reducing wind power curtailment and load shedding, improving economic efficiency, and accelerating computation.
Original language | English |
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Article number | 106790 |
Journal | International Journal of Electrical Power and Energy Systems |
Volume | 129 |
Number of pages | 12 |
ISSN | 0142-0615 |
DOIs | |
Publication status | Published - Jul 2021 |
Bibliographical note
Funding Information:This work was supported by Key-Area Research and Development Program of Guangdong Province under Grant no. 2019B111109002 and the National Natural Science Foundation of China (51707077).
Publisher Copyright:
© 2021 Elsevier Ltd
Keywords
- Accelerating solution method
- Data-driven uncertainty set
- Flexible ramping capability
- Load-shifting demand response
- Reserve-capacity demand response
- Stochastic unit commitment