Cryogenic-Energy-Storage-Based Optimized Green Growth of an Integrated and Sustainable Energy System

Abstract

The advancement of using the cryogenic energy storage (CES) system has enabled efficient utilization of abandoned wind and solar energy, and the system can be dispatched in the peak hours of regional power load demand to release energy. It can fill the demand gap, which is conducive to the peak regulation of the power system and can further promote the rapid development of new energy. This study optimizes the various types of energy complementary to the CES system using hybrid gravitational search algorithm-local search optimization (hGSA-LS). First, the mathematical model of the energy storage system (ESS) including the CES system is briefly described. Second, an economic scheduling optimization model of the IES is constructed by minimizing the operating cost of the system. Third, the hGSA-LS methods to solve the optimization problem are proposed. Simulations show that the hGSA-LS methodology is more efficient. The simulation results verify the feasibility of CES compared with traditional systems in terms of economic benefits, new energy consumption rate, primary energy saving rate, and carbon emissions under different fluctuations in energy prices. Optimization of the system operation using the proposed hGSA-LS algorithm takes 5.87 s; however, the GA, PSO, and GSA require 12.56, 10.33, and 7.95 s, respectively. Thus, the hGSA-LS algorithm shows a comparatively better performance than GA, PSO, and GSA in terms of time.