An essential method for assessing the effectiveness of microgrid (MG) operations and sizing is economic analysis. The most cost-effective operation and sizing of an MG necessitate the use
Particle Swarm Optimization – Model Predictive Control for Microgrid Energy Management Abstract: Microgrid is becoming the most attractive solution for integrating distributed
Raghavan, A., Maan, P. & Shenoy, A. K. B. Optimization of day-ahead energy storage system scheduling in microgrid using genetic algorithm and particle swarm optimization. IEEE Access 8, 173068
This paper investigates a method applying constrained multi-swarm particle swarm optimization without velocity-based model predictive control to optimize the operation cost in small scale
In this study, the Pareto optimal solution theory is adopted to solve the multi-objective optimal scheduling problem of microgrids; the traditional particle swarm and improved particle swarm algorithms are used as the
In this study, we propose a multi-objective particle swarm algorithm-based optimal scheduling method for household microgrids. A household microgrid optimization model is formulated, taking into account time-sharing tariffs and users'' travel
Microgrids have attracted more and more attention due to their low cost, low voltage, and low pollution. The goal of microgrid development is not only to ensure The traditional particle
To solve this model, dynamic guiding chaotic search particle swarm optimization is adopted and three scenarios including basic scenario, energy storage participation and demand response
The particle swarm optimization (PSO) method, with the background given in, is proposed as an optimal strategy to manage microgrids with hybrid renewable energy sources (HRESs) while considering microgrid
The simulation of the optimization effect of the conventional particle swarm algorithm and the modified particle swarm algorithm on the microgrid were carried out, respectively, in MATLAB, which verifies the advantage of the modified particle swarm algorithm on the optimization of microgrids.
In this study, the Pareto optimal solution theory is adopted to solve the multi-objective optimal scheduling problem of microgrids; the traditional particle swarm and improved particle swarm algorithms are used as the intelligent optimization algorithms; and the data of a power grid in East China are used as the simulation data.
The modified particle swarm algorithm sets up an external repository in order to filter and store the particles that meet the requirements. The particles in the repository determine the particle swarm moving state, and the addition and deletion of particles in the repository are accomplished by the adaptive grid method.
From the above simulation results, it can be understood that the modified particle swarm algorithm obtained through the introduction of variable inertia weight and learning factors has a higher utilization rate of external storage libraries and a better global convergence.
A modified particle swarm optimization algorithm tailored to address a batch-processing machine scheduling problem characterized by arbitrary release times and non-identical job sizes is introduced 38. Novel machine learning methodologies are applied for fault diagnosis and optimization 39, 40, 41.
The initial and final values of the inertia weight of the modified particle swarm algorithm are set to 0.9 and 0.2, respectively, and the larger initial value in the early stage facilitates the global search, while the weight gradually decrease as the number of iterations increases, which is convenient for the convergence of the particle swarm.
