Chongqing University Researchers Improve Microgrid Scheduling with Quantum Particle Swarm Optimization

Researchers from Chongqing University of Posts and Telecommunications have developed an optimal scheduling model for microgrids using an improved quantum particle swarm optimization algorithm. The model considers power grid operating costs and environmental governance costs, and uses a quantum particle swarm optimization method to optimize these factors. The improved algorithm has a higher degree of optimization accuracy and a faster convergence rate. The model can reduce users’ electricity costs and environmental pollution, and improve the operation of microgrids. The research also suggests future studies should focus on improving the complementary scheduling of micro power systems.

What is the Optimal Scheduling of Microgrid Based on Improved Quantum Particle Swarm Optimization Algorithm?

The research conducted by Fengyi Liu and Pan Duan from Chongqing University of Posts and Telecommunications in China focuses on the optimal scheduling of microgrid using an improved quantum particle swarm optimization algorithm. The large-scale integration of new energy into the power grid has put the safety and reliability of the power grid to the test. The optimized configuration of micro power systems is a key element of intelligent power systems, playing a crucial role in reducing energy consumption and environmental pollution.

The researchers propose a power grid optimization scheduling model that comprehensively considers the issues of power grid operating costs and environmental governance costs. The model uses a quantum particle swarm optimization method to optimize the objective function with the lowest system operating cost and the lowest environmental governance cost. To improve the search ability of the algorithm and eliminate the problem of easily getting stuck in local optima, the Levy flight strategy is introduced and the variable weight method is used to update the particle factor to improve the optimization ability of the algorithm.

The simulation results show that the improved quantum particle swarm optimization algorithm has strong optimization ability and the scheduling model proposed in this paper can achieve good scheduling results in different scheduling tasks. The improved particle swarm algorithm, in comparison to its predecessor, boasts a greater degree of optimization accuracy, a swifter convergence rate, and the capability to avoid the algorithm’s descent into the local optimal solution at a later stage of the process.

How Does the Proposed Model Impact Users and the Environment?

The proposed model can effectively reduce users’ electricity costs and environmental pollution and promote the optimized operation of microgrids. The optimal scheduling of microgrid is to properly distribute the output energy of DG and coordinate the transmission energy of microgrid and main network under the premise of complying with all the constraints of the system so as to achieve multiple goals such as reducing operating costs, reducing pollutants, improving stability, and increasing power generation benefits.

For consumers, optimized scheduling of microgrids can significantly reduce their electricity bills. On the power supply side, through the optimal deployment of microgrids, we can enhance the balance of the grid, reduce energy consumption and environmental damage in the power manufacturing process. Therefore, it is of great practical value to optimize the deployment of microgrid.

What are the Current Research Areas in New Energy?

At present, there are three main aspects of research on new energy: predicting the power generation of new energy, constructing scheduling models to optimize scheduling, and the development of intelligent algorithms. However, in practice, the prediction accuracy of these methods cannot meet the scheduling requirements of the system. Many scholars have conducted specific research on the scheduling model and strategy of distributed energy uncertainty.

How Does the Research Address Environmental Pollution?

The research addresses environmental pollution by proposing a power system model with interactive optimal scheduling considering virtual power plants. The model takes operating economic benefits and peak shaving and valley filling effects as optimization objectives. In order to achieve efficient consumption of new energy and reduce system carbon emissions, a new dual-layer low-carbon optimal scheduling model of power system based on carbon emission theory and carbon tax as demand response incentive signal is established.

What are the Future Directions for Research in this Field?

The research suggests that future studies should focus on improving the complementary scheduling of micro power systems. As for the complementary optimal scheduling of large-scale renewable energy grid connection, there are few studies. The researchers suggest using stochastic programming methods to explore the complementary scheduling strategies of wind power generation and pumped storage in isolated island environments. The cooperation between pumped storage power station and wind farm can greatly reduce the negative impact of wind power output randomness on power grid operation.