Network-Constrained Unit Commitment Based on Reserve Models Fully Considering the Stochastic Characteristics of Wind Power

Energies

Dahai You¹  Gang Wang¹  Qi Zou¹  Zhe Zhang¹  Li Dai¹  Hengwei Liu¹ 

[1] State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, No. 1037, Luoyu Road, Wuhan 430074, China;

关键词: network-constrained unit commitment;    wind power;    stochastic characteristics;    reserve models;    network congestion;   

DOI  :  10.3390/en11020435

来源: DOAJ

【 摘 要 】

The existing optimization approaches regarding network-constrained unit commitment with large wind power integration face great difficulties in reconciling the two crucial but contradictory objectives: computational efficiency and the economy of the solutions. This paper proposes a new network-constrained unit commitment approach, which aims to better achieve these two objectives, by introducing newly proposed reserve models and simplified network constraints. This approach constructs the reserve models based on a sufficiently large number of stochastic wind power scenarios to fully and accurately capture the stochastic characteristics of wind power. These reserve models are directly incorporated into the traditional unit commitment formulation to simultaneously optimize the on/off decision variables and system reserve levels, therefore, this approach can comprehensively evaluate the costs and benefits of the scheduled reserves and thus produce very economical schedule. Meanwhile, these reserve models bring in very little computational burden because they simply consist of a small number of continuous variables and linear constraints. Besides, this approach can evaluate the impact of network congestion on the schedule by just introducing a small number of network constraints that are closely related to network congestion, i.e., the simplified network constraints, and thus concurrently ensures its high computational efficiency. Numerical results show that the proposed approach can produce more economical schedule than stochastic approach and deterministic approach but has similar computational efficiency as the deterministic approach.

【 授权许可】

Unknown