A Novel Storage Energy Dispatch Improvement Model Based on Chameleon Swarm Algorithm

With the rapid advancement of renewable energy sources including wind and photovoltaic power generation, integration has become a big challenge due to nature features of renewable energies. Combining the energy storage units into renewable energy system is able to make the power output more reliable. However, it brings much economic difficulty to battery power charging and discharging due to the volatility of electricity prices. Considering minimizing the operating cost to achieve economic optimization, in this paper, a novel energy dispatch model based on chameleon swarm algorithm (CSA) is established. Firstly, we convert the complicated chance constraints into a set of deterministic inequalities. Subsequently, a new objective function is formulated with the aim of minimizing the operating costs of the system. Finally, the program is established based on CSA. By adjusting the ratio of fuel cells, batteries, and grid-connected electricity sales, the operating cost is minimized. Program results are compared with commonly used particle swarm optimization algorithms (PSO), genetic algorithms (GA), and artificial bee colony algorithms (ABC). The case study shows that the optimal power allocation determined by the CSA algorithm proposed in this paper effectively reduce the total system cost by 0.6%-3.4% compared with other methods.