期刊
IEEE ACCESS
卷 9, 期 -, 页码 122322-122334出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3109108
关键词
Batteries; Dams; Optimization; Real-time systems; Uncertainty; Energy resources; Solid modeling; Battery storage; distribution-level market; flexibility; multi-stage models; uncertainty
资金
- European Union's Horizon 2020 Research and Innovation Program through the FLEXGRID Project [863876]
Modern power systems are shifting towards distributed energy resources (DERs) and renewable energy sources (RES), posing challenges to system operators. The distribution-level flexibility market (DLFM) is considered a viable solution to integrating high shares of RES. Different market clearing sequences and optimization methods can impact providers offering flexibility and market operators procuring it.
Modern power systems are experiencing a paradigm shift toward distributed energy resources (DERs) and an accelerated penetration of the renewable energy sources (RES). Intermittent and distributed RES pose serious challenges to the system operators in terms of the increased flexibility requirements. Besides the technical flexibility, achieved through, e.g. storage devices, the market flexibility is also important as it enables rewarding the flexibility providers at an appropriate time-scale. Distribution-level flexibility market (DLFM) is considered as one of the viable solutions to successfully integrate high shares of RES and promote an active role of electricity consumers. This paper defines and analyzes two DLFM setups where a distributed flexibility source, i.e. battery energy storage, can bid in addition to the existing markets (day-ahead, intraday and balancing). The main difference between the observed DLFM setups is their clearing time. One clears before the day-ahead market, while the other one in between the day-ahead and the intraday markets. Uncertainty in the intraday market is addressed using robust optimization, while stochastic optimization deals with the DLFMs and the day-ahead market. This results in a four-stage model, which is reduced to a three-stage model because the balancing market follows directly from the previous market position realizations. In the presented case study, we analyze how different market clearing sequences affect both the player providing flexibility and the market operator procuring it.
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