2020/07/01 by Malhar Padhee, Padhee, Malhar, Anamitra Pal +1
Computer Science · Economics, Econometrics and Finance · Engineering · Mathematics · #Complex Systems and Time Series Analysis #Energy Load and Power Forecasting #FOS: Electrical engineering #FOS: Mathematics #Optimization and Control (math.OC) #Signal Processing (eess.SP) #Time Series Analysis and Forecasting #eess.SP #electronic engineering #information engineering #math.OC
paper · pdf · doi:10.48550/arxiv.2007.00805
Major flaw found in the results section (Section V) of the paper which needs to be rectified
openalex publication_date 2020/07/01 · openalex created_date 2020/07/10 · arxiv created 2021/05/26 · arxiv updated 2021/05/27 · openalex updated_date 2026/07/28
Power system planning problems become computationally intractable if one accounts for all uncertain operating scenarios. Consequently, one selects a subset of scenarios that are representative of likely/extreme operating conditions, e.g. heavy summer, heavy winter, light summer, and so on. However, such an approach may not be able to accurately capture the dependencies that exist between renewable generation (RG) and system load in RG-rich power systems. This paper proposes the use of fast dynamic time warping (FDTW) and fuzzy c-means++ (FCM++) clustering to account for key statistical properties of load and RG for scenario generation for power system planning problems. Case studies using a U.S. power network, and comparison with existing scenario generation techniques demonstrate the benefits of the proposed approach.