A Two-Stage Clustering-Based Approach for Assessing Peak Shifting and Valley Filling Potential in Industrial Electric Loads

• Published in: 2023 International Conference on Neuromorphic Computing (ICNC) pp. 527 - 531
• Main Authors: Lu, Feng, Zhou, Xianfu, Jiang, Zhong, Dong, Hanyu, Xu, Jun, Xing, Yi, Wang, Ke, Zheng, Songsong
• Format: Conference Proceeding
• Language: English
• Published: IEEE 15-12-2023
• Subjects: Carbon neutral; cluster analysis; Demand side management; Electric potential; Electricity; Neuromorphic engineering; peak shifting and valley filling; potential assessment; Statistical analysis; Urban areas
• DOI: 10.1109/ICNC59488.2023.10462796

With the advancement of the carbon peaking and carbon neutrality goals, there has been significant development in new energy generation. Due to the intermittency and randomness of new energy generation, it has become more challenging to maintain power balance within the electrical system. Despite significant attention to demand-side management, there remains a lack of effective methods to assess the peak shifting and valley filling potential in a specific region. This paper proposes an industrial electricity load peak shifting and valley filling potential assessment method based on two-stage clustering, aiming to evaluate the potential of industrial users in a particular region. The method relies solely on users' historical electricity consumption data to evaluate their electricity usage patterns and regularities and subsequently calculates the potential factor for each user. Therefore, this method is universally applicable for assessing any region's peak shifting and valley filling, the method is applied to assess the peak shifting and valley filling potential in a certain prefecture-level city in China, demonstrating its practical value.