Statistical machine learning model for capacitor planning considering uncertainties in photovoltaic power

• Published in: Protection and control of modern power systems Vol. 7; no. 1
• Main Author: Fu, Xueqian
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.12.2022; Power System Protection and Control Press
• Subjects: Accuracy; Algorithms; Alternative energy sources; Artificial intelligence; Electric power distribution; Electrical Machines and Networks; Energy; Energy storage; Energy Systems; Load distribution; Machine learning; Markov chains; Mathematical programming; Maximum likelihood estimators; New-Generation Artificial Intelligence Techniques Applications on Smart Distribution Network Planning and Dispatching; Optimization; Original Research; Photovoltaics; Planning; Power Electronics; Power flow; Probability distribution; Renewable and Green Energy; Renewable resources; Response surface methodology; Smart grid; Statistical analysis; Thermal analysis; Uncertainty; Statistical machine learning; Uncertainty; Renewable energy; Stochastic programming
• ISSN: 2367-2617; 2367-0983; 2367-0983
• DOI: 10.1186/s41601-022-00228-z

New energy integration and flexible demand response make smart grid operation scenarios complex and changeable, which bring challenges to network planning. If every possible scenario is considered, the solution to the planning can become extremely time-consuming and difficult. This paper introduces statistical machine learning (SML) techniques to carry out multi-scenario based probabilistic power flow calculations and describes their application to the stochastic planning of distribution networks. The proposed SML includes linear regression, probability distribution, Markov chain, isoprobabilistic transformation, maximum likelihood estimator, stochastic response surface and center point method. Based on the above SML model, capricious weather, photovoltaic power generation, thermal load, power flow and uncertainty programming are simulated. Taking a 33-bus distribution system as an example, this paper compares the stochastic planning model based on SML with the traditional models published in the literature. The results verify that the proposed model greatly improves planning performance while meeting accuracy requirements. The case study also considers a realistic power distribution system operating under stressed conditions.