A Novel Two-Stage Scheduling Approach for a Hybrid Floating Photovoltaic-Battery-Hydropower Plant Considering Uncertainties

作     者：Xengyang Chierthaichingpangxao Kittaya Somsai Sengthavy Phommixay Mouhamadou Thiam Edjadessamam Akoro Eric Mpapa Xengyang Chierthaichingpangxao;Kittaya Somsai;Sengthavy Phommixay;Mouhamadou Thiam;Edjadessamam Akoro;Eric Mpapa

作者机构：Department of Electrical Engineering Faculty of Industry and Technology Rajamangala University of Technology Isan Sakon Nakhon Thailand Engineering Department Namtheun 2 Power Company Vientiane Laos Laboratoire des Sciences et Techniques de l’Eau et de l’Environnement Ecole Polytechnique de Thiès Thiès Sénégal Department of Applied Physics Gaston Berger University Saint-Louis Senegal Engineering and Technology of Dakar (MIT) Higher Institute of Management Dakar Senegal 

出 版 物：《Journal of Power and Energy Engineering》 (电力能源（英文）)

年 卷 期：2023年第11卷第7期

页      面：1-20页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Distributionally Robust Chance-Constrained Robust-Stochastic Optimization Double-Layer Particle Swarm Optimization Floating PV Uncertainties Operational Cost 

摘      要：The application of floating photovoltaics (PVs) in hydropower plants has gained increasing interest in forming hybrid energy systems (HESs). It enhances the operational benefits of the existing hydropower plants. However, uncertainties of PV and load powers can present great challenges to scheduling HESs. To address these uncertainties, this paper proposes a novel two-stage optimization approach that combines distributionally robust chance-constrained (DRCC) and robust-stochastic optimization (RSO) approaches to minimize the operational cost of an HES. In the first stage, the scheduling of each device is obtained via the DRCC approach considering the PV power and load forecast errors. The second stage provides a robust near real time energy dispatch according to different scenarios of PV power and load demand. The solution of the RSO problem is obtained via a novel double-layer particle swarm optimization algorithm. The performance of the proposed approach is compared to the traditional stochastic and robust-stochastic approaches. Simulation results de- monstrate the superiority of the proposed two-stage approach and its solution method in terms of operational cost and execution time.