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@article{Teng:2017:10.1109/TASE.2016.2629479,
author = {Teng, F and Strbac, G},
doi = {10.1109/TASE.2016.2629479},
journal = {IEEE Transactions on Automation Science and Engineering},
pages = {451--470},
title = {Full stochastic scheduling for low-carbon electricity systems},
url = {http://dx.doi.org/10.1109/TASE.2016.2629479},
volume = {14},
year = {2017}
}
TY - JOUR
AB - High penetration of renewable generation will increase the requirement for both operating reserve and frequency response, due to its variability, uncertainty and limited inertia capability. Although the importance of optimal scheduling of operating reserve has been widely studied, the scheduling of frequency response has not yet been fully investigated. In this context, this paper proposes a computationally-efficient mixed integer linear programming formulation for a full stochastic scheduling model that simultaneously optimizes energy production, operating reserve, frequency response and under-frequency load shedding. By using value of lost load as the single security measure, the model optimally balances the cost associated with the provision of various ancillary services against the benefit of reduced cost of load curtailment. The proposed model is applied in a 2030 GB system to demonstrate its effectiveness. Impact of installed capacity of wind generation and setting of value of lost load are also analysed. Note to Practitioners— One of the obstacles for large scale deployment of wind generation is the challenges it imposes on the efficient operation of the electricity system. This paper presents a full stochastic scheduling model. The long-term uncertainty driven by wind forecasting errors and short-term uncertainty driven by generation outages are modelled by using scenario tree and capacity outage probability table, respectively. The model leads to significant operation cost saving within reasonable computational time. The proposed model could be applied in real large-scale power systems to support the cost-effective integration of wind generation.
AU - Teng,F
AU - Strbac,G
DO - 10.1109/TASE.2016.2629479
EP - 470
PY - 2017///
SN - 1558-3783
SP - 451
TI - Full stochastic scheduling for low-carbon electricity systems
T2 - IEEE Transactions on Automation Science and Engineering
UR - http://dx.doi.org/10.1109/TASE.2016.2629479
UR - http://hdl.handle.net/10044/1/42552
VL - 14
ER -