Multiple Power Supply Capacity Planning Research for New Power System Based on Situation Awareness

• Published in: Energies (Basel) Vol. 15; no. 9; p. 3298
• Main Authors: Li, Dahu, Cheng, Xiaoda, Ge, Leijiao, Huang, Wentao, He, Jun, He, Zhongwei
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 30.04.2022
• Subjects: Algorithms; Alternative energy sources; capacity configuration; Carbon; carbon emission; Emissions; Energy policy; Energy resources; Environmental policy; Environmental protection; Flexibility; inertia security region; multi-objective optimization; Multiple objective analysis; Optimization; Pareto optimization; Photovoltaics; Power supply; Relativity; Renewable resources; Security; situation awareness; Situational awareness; Solar energy; Systems stability; Thermal power; Wind power; wind-photovoltaic-thermal power system
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15093298

In the context of new power systems, reasonable capacity optimization of multiple power systems can not only reduce carbon emissions, but also improve system safety and stability. This paper proposes a situation awareness-based capacity optimization strategy for wind-photovoltaic-thermal power systems and establishes a bi-level model for system capacity optimization. The upper-level model considers environmental protection and economy, and carries out multi-objective optimization of the system capacity planning solution with the objectives of minimizing carbon emissions and total system cost over the whole life cycle of the system, further obtaining a set of capacity planning solutions based on the Pareto frontier. A Pareto optimal solution set decision method based on grey relativity analysis is proposed to quantitatively assess the comprehensive economic–environmental properties of the system. The capacity planning solutions obtained from the upper model are used as the input to the lower model. The lower model integrates system stability, environmental protection, and economy and further optimizes the set of capacity planning solutions obtained from the upper model with the objective of maximizing the inertia security region and the best comprehensive economic–environmental properties to obtain the optimal capacity planning scheme. The NSGA-II modified algorithm (improved NSGA-II algorithm based on dominant strength, INSGA2-DS) is used to solve the upper model, and the Cplex solver is called on to solve the lower model. Finally, the modified IEEE-39 node algorithm is used to verify that the optimized capacity planning scheme can effectively improve the system security and stability and reduce the carbon emissions and total system cost throughout the system life cycle.