Dispatchable Substation for Operation and Control of Renewable Energy Resources
Source abstract
Abstract
Renewable generation in power systems has proved to be challenging for system operators owing to the increasing levels of penetration. The operation of power systems currently requires additional flexibility and reserves due to the intermittency and unpredictability of renewable generators. However, it is difficult to precisely predict and control the stochastic nature of renewable sources; nevertheless, its capacity continues to increase. To monitor and control renewable generators efficiently, the entire system needs to be established in a hierarchical order. This study proposed the concept of a substation that is uniquely designed for renewable interconnection. The purpose of this substation is simple: to make the renewable generators dispatchable to operators such that each group of renewable generators is sufficiently stable to be considered as conventional generators. For this purpose, methods for sizing and controlling energy storage system are proposed based on forecasts and error distributions.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Grid Energy Management · Microgrid Control and Optimization · Optimal Power Flow Distribution
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electrical Engineering, Korea University, 145 Anam-ro, Songbuk-gu, Seoul 02841, Korea
School of Electrical Engineering, Hanbat National University, Dongseo-daero, Yuseong-gu, Daejeon 34158, Korea
Energy ICT Convergence Research Department, KIER, 152, Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea
Funding
Korea Electric Power Corporation · Korea Institute of Energy Technology Evaluation and Planning
References (26)
Show source-provided references
1. REN21 (2020, August 05). RENEWABLES 2020GLOBAL STATUS REPORT. Available online: https://www.ren21.net/gsr-2020/.
2. Impact of low rotational inertia on power system stability and operation10.3182/20140824-6-ZA-1003.02615 ↗
3. Eto, J.H., Undrill, J., Mackin, P., Daschmans, R., Williams, B., Haney, B., Hunt, R., Ellis, J., Illian, H., and Martinez, C. (2010). Use of Frequency Response Metrics to Assess the Planning and Operating Requirements for Reliable Integration of Variable Renewable Generation, Lawrence Berkeley National Lab. (LBNL). Technical Report.10.2172/1003830 ↗
4. Knowledge Is Power: Efficiently Integrating Wind Energy and Wind Forecasts10.1109/MPE.2013.2277999 ↗
5. Ackermann, T. (2005). Wind Power in Power Systems, John Wiley & Sons.10.1002/0470012684 ↗
6. Chen, Y., and Lin, Y. (2019, January 10–12). Hierarchical Management of Distributed Energy Resources Using Chance-Constrained OPF and Extremum Seeking Control. Proceedings of the 2019 American Control Conference (ACC), Philadelphia, PA, USA.10.23919/ACC.2019.8815192 ↗
7. Rule-Based Control of Battery Energy Storage for Dispatching Intermittent Renewable Sources10.1109/TSTE.2010.2061880 ↗
8. Grid-Level Application of Electrical Energy Storage: Example Use Cases in the United States and China10.1109/MPE.2017.2708860 ↗
9. Yoo, J. (2020). Problems and Solution for ESS Deployment Policy in Korea, National Assembly Research Serviece (NARS). Technical Report.
10. Lorente, J.L., Liu, X.A., Best, R., and Morrow, D.J. (2018, January 4–7). Energy storage allocation in power networks—A state-of-the-art review. Proceedings of the 2018 53rd International Universities Power Engineering Conference (UPEC), Glasgow, UK.10.1109/UPEC.2018.8542106 ↗
11. Alsaidan, I., Gao, W., and Khodaei, A. (2017, January 16–20). Battery energy storage sizing for commercial customers. Proceedings of the 2017 IEEE Power & Energy Society General Meeting, Chicago, IL, USA.10.1109/PESGM.2017.8274380 ↗
12. Design and management of battery-supercapacitor hybrid electrical energy storage systems for regulation services10.1109/TMSCS.2016.2627543 ↗
13. A Probabilistic Method for Energy Storage Sizing Based on Wind Power Forecast Uncertainty10.1109/TPWRS.2010.2089541 ↗
14. Energy Storage Sizing Taking Into Account Forecast Uncertainties and Receding Horizon Operation10.1109/TSTE.2016.2599074 ↗
15. Optimal sizing of energy storage considering the spatial-temporal correlation of wind power forecast errors10.1049/iet-rpg.2018.5438 ↗
16. Cheng, J., Zhang, N., Wang, Y., Kang, C., Zhu, W., Luo, M., and Que, H. (2016, January 16–20). Evaluating the spatial correlations of multi-area load forecasting errors. Proceedings of the 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Beijing, China.10.1109/PMAPS.2016.7764153 ↗
17. Modeling the temporal correlation of hourly day-ahead short-term wind power forecast error for optimal sizing energy storage system10.1016/j.ijepes.2017.12.012 ↗
18. Taming wind power with better forecasts10.1109/MSPEC.2015.7335902 ↗
19. Solar Forecasting: Methods, Challenges, and Performance10.1109/MPE.2015.2461351 ↗
20. Ela, E., Diakov, V., Ibanez, E., and Heaney, M. (2013). Impacts of Variability and Uncertainty in Solar Photovoltaic Generation at Multiple Timescales, Technical Report.10.2172/1081387 ↗
21. Inertial response of variable speed wind turbines10.1016/j.epsr.2005.12.002 ↗
22. (2018). IEEE Standard for Interconnection and Interoperability of Distributed Energy Resources with Associated Electric Power Systems Interfaces, IEEE Standards Coordinating Committee 21. IEEE Std 1547-2018 (Revision of IEEE Std 1547-2003).
23. Australian Energy Market Operator (2019). Technical Integration of Distributed Energy Resources, AEMO.
24. Dynamic Inertia Response Support by Energy Storage System with Renewable Energy Integration Substation10.35833/MPCE.2018.000760 ↗
25. Solcast: Validation of a satellite-derived solar irradiance dataset10.1016/j.solener.2019.07.086 ↗
26. Mongird, K., Viswanathan, V.V., Balducci, P.J., Alam, M.J.E., Fotedar, V., Koritarov, V.S., and Hadjerioua, B. (2019). Energy Storage Technology and Cost Characterization Report, Technical Report.10.2172/1884043 ↗