Optimal operation of new coastal power systems with seawater desalination based on grey wolf optimization - Gao Yujie; Yang Hao; Zhou Bowen; Chen Xinyi; Hu Zhijun (Energy Reports, 2023)
Seawater desalination is one of the effective means to efficiently consume renewable energy and to improve the flexibility of system control in the new power system. In the optimal operation of new power systems, seawater desalination is usually considered as an entirety, and a motor equivalent model is used to simulate the overall external characteristics of seawater desalination. The characteristics of multiple sets of motors with multiple equipment processes inside the seawater desalination are neglected, which makes operation control characteristics inaccurate. In order to solve the above-mentioned problems, this paper firstly studies a seawater desalination system composed of 12 motors, and introduces the specific parameters of these 12 motors in detail. Secondly, after comprehensively considering the constraints of water flow, water pressure, and other factors, three control strategies are proposed for the 12 motors, respectively. Then, a detailed mathematical model of seawater desalination units and an optimal operation model of a new power system with seawater desalination are established. The optimization model aims at the minimum operating cost of seawater desalination, and considers the constraints of practical problems comprehensively such as motor power requirements and water storage capacity. Finally, the grey wolf optimization algorithm (GWO) is used in this paper to solve the optimization problem. The simulation results show that the model proposed in this paper can precisely control each unit in the seawater desalination system and improve the economic benefits of the system. Compared with the Particle Swarm Optimization algorithm (PSO) and the Moth-flame Optimization algorithm (MFO), the algorithm used in this paper can find the global optimal solution, and has both faster convergence speed and wider practicability.