A ranking algorithm to pick up critical scenarios of fluctuation patterns from the uncertain renewable energy production using machine learning

• Main Authors: Dan, Surajit, Singh, Sweta, Aftab, Muhammad Nauman, Shankarrao Bhure, Vipin, Sukumar, R. Sam, Chakrabarti, Prasun, Kumar, R. Senthil, Amarnath Naidu, B, Hati, Ananda, Loganathan, M. K, Shukla, Suyesha, Singh, A. Robert
• Format: Patent
• Language: English
• Published: 18.11.2021
• Subjects: CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTINGELECTRIC POWER; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER; ELECTRICITY; GENERATION; SYSTEMS FOR STORING ELECTRIC ENERGY

A RANKING ALGORITHM TO PICK UP CRITICAL SCENARIOS OF FLUCTUATION PATTERNS FROM THE UNCERTAIN RENEWABLE ENERGY PRODUCTION USING MACHINE LEARNING Abstract Power system functioning is complicated by the increasing prevalence of variable renewable energy generation. Storage units can shift requirements over time as well as adjust for real-time generation-demand discrepancies, increasing system adaptability and reducing renewable energy generation curtailment. This study offers 2 parametric optimization techniques to measure how the energy and energy storage unit power affects renewable energy usage from two perspectives: renewable energy generation curtailment and system adaptability for uncertainty mitigation. In the parametric capacity of the energy storage unit, the two indicators have been represented as multivariate functions. To choose key situations of fluctuation patterns from the uncertainty renewable energy production set, a ranking method is presented. Renewable energy production does not accompany the load in terms of fluctuation. Thus, even though a substantial renewable energy production capacity is built to satisfy demand during specific periods, additional renewable energy generation is curtailed once there is an inadequate load or the transmission line is overloaded, resulting in energy wastage and a poor resource utilization rate. The proposed rating algorithmic approach depending on the severe characteristic of the worst-case scenario to choose only important possibilities of fluctuation patterns from various uncertainties set. The impact of storage units on renewable energy generation curtailment and uncertainty mitigation is reduced using the ranking algorithmic approach.