| Work Detail |
Chinese scientists propose a new method of optimizing energy trading between interconnected microgrids and the main grid. The new approach uses particle swarm optimization algorithms and gravitational search with Nash Negotiation.
An international group of scientists has developed a novel algorithm to optimize energy trading in cooperative renewable energy microgrids. The method uses particle swarm optimization (PSO) and gravitational search algorithms (GSA) with Nash Negotiation.
“Inspired by social behavior, PSO emphasizes local search and solution refinement, while GSA, inspired by the law of gravity, emphasizes global exploration,” the researchers explain. “The combination of these algorithms aims to achieve a balance between exploration and exploitation, allowing for efficient and robust energy exchange strategies.”
The group calibrated some parameters of this hybrid model, after preliminary tests. Next, they added the Generalized Nash Bargaining (GNB) technique, which helps balance the interests of different microgrids and facilitates fair results in energy trading.
Following its development, the novel algorithm was implemented in MATLAB software, where it was asked to solve an energy cost minimization trading problem. This simulation was carried out with four interconnected microgrids with different generation sources: one with only photovoltaic energy, another with only wind energy, another with both renewable energy sources and another without neither. All of these system configurations were tested with different power prices and intricate load profiles.
Furthermore, the optimization solution for microgrid trading was compared with a base case scenario. Each of these four microgrids could only trade with the public network in the base scenario.
According to the results, the total monthly cost of the four microgrids was $94,551 without energy trading. However, using the PSO-GSA model with Nash Negotiation in a scenario where microgrids can trade with each other has reduced their total bill to $60,720.
“It can be said that the cost of energy has been reduced sufficiently thanks to the introduction of cooperative energy trading in microgrids,” the scientists say. “For example, in the base case, microgrid 1 has to pay a bill of $749.10 (for 24 hours) for energy purchased from the main grid; However, after cooperative trading, the invoice amount has been reduced to $500.25.”
In addition, the academic group has compared the novel optimization with four other metaheuristic algorithms under the same data and conditions. While the novel PSO-GSA has an energy cost of $60,720, GWO (Grey Wolf Optimizer) achieved $66,582. MPA (Marine Predator Algorithm) obtained $64,166; PSO only $60,925; ETSO (Enhanced Transient Search Optimizer) achieved $61,122; and GSA only $60,994.
“The proposed hybrid PSO-GSA algorithm outperforms other optimization approaches in terms of its convergence characteristics and its ability to minimize energy costs,” the academics conclude.
The results of the simulations were presented in “ Optimal energy trading in cooperative microgrids considering hybrid renewable energy systems,” published in the Alexandria Engineering Journal . The research team consisted of scientists from Huazhong University of Science and Technology in China, COMSATS University in Islamabad, Pakistan, Taibah University in Saudi Arabia, and Ain Shams and Future Universities in Egypt. |
