This study contributes to select the system voltage fluctuation as the optimization objective and uses improved archimedes optimization algorithm (IAOA) to analyze the control parameters for DC microgrid. DC microgrid...
详细信息
This study contributes to select the system voltage fluctuation as the optimization objective and uses improved archimedes optimization algorithm (IAOA) to analyze the control parameters for DC microgrid. DC microgrids containing hybrid energy storage play an important role in energy utilization efficiency, system stability, operating costs, intelligent management and clean energy development. ESS significantly enhances the stability of the DC microgrid by regulating power balance, suppressing voltage fluctuations, and providing rapid power support. Hybrid photovoltaic storage system (HESS) is controlled by low-pass filter and double closed-loop control. The control strategy based on synchronized generator characteristic is adopted for the microgrid inverter. Yet, prior studies are neglecting to involve the control strategy optimization for the microgrid inverter. This study proposes an improved Archimedean optimizationalgorithm control method based on the Levy flight strategy to improve the DC microgrid system stability incorporating HESS. IAOA is used to find the optimal control parameters adapted to the overall system considering the interaction between microgrid inverter and HESS. This study substitutes the optimized control parameters into the model for simulation to verify the effectiveness of DC microgrid optimization. The control strategy based on IAOA optimization reduces the voltage fluctuation amplitude by 1.6 %-6.98 % when the simulated model is disturbed by analyzing with the traditional sag control.
In this paper, aiming at the problems of slow estimation speed and low estimation precision of traditional fractional-order system (FOS) parameter estimation method, an improved archimedes optimization algorithm (IAOA...
详细信息
In this paper, aiming at the problems of slow estimation speed and low estimation precision of traditional fractional-order system (FOS) parameter estimation method, an improved archimedes optimization algorithm (IAOA) is proposed to calculate the optimal value. By establishing the parameter estimation model and the cost function, the parameter estimation problem is formulated as an optimization problem. As opposed to the archimedesoptimizationalgorithm (AOA), the IAOA introduces three improvements: leadership behavior, levy flight behavior and a new adaptive strategy. This paper verifies the performance of the IAOA by selecting 10 classic test functions. IAOA is applied to the parameter estimation problem of fractional-order unified system to verify the accuracy and feasibility of the algorithm. The simulation results prove that the IAOA has better global optimization ability and estimation accuracy than the original algorithm.
More new energy sources have been incorporated into a microgrid model with parameter space growing exponentially, causing optimization scheduling as a nonlinear issue to become more complex and difficult to calculate....
详细信息
More new energy sources have been incorporated into a microgrid model with parameter space growing exponentially, causing optimization scheduling as a nonlinear issue to become more complex and difficult to calculate. This study suggests an improved archimedes optimization algorithm (IAOA) increases optimal performance for the microgrid operations planning issue. A multiobjective function about optimization planning issues is constructed with relevant economic costs and environmental profits for a microgrid community system (MCS). The IAOA is implemented based on the archimedesoptimizationalgorithm (AOA) by adding reverse learning and multi-directing strategies to avoid the local optimum trap when dealing with complicated situations. The experimental results of the suggested approach on the CEC2017 test suite and microgrid operations planning problem are compared to the various algorithms in the identical condition scenarios to evaluate the recommended approach performance. Compared findings reveal that the suggested IAOA outperforms the various algorithms in comparison, practical solution, and high feasibility.
In this paper, an improved archimedes optimization algorithm (IAOA) is proposed to solve the Optimal Power Flow problem (OPF). The purpose of improving this IAOA algorithm is to increase population diversity in AOA, f...
详细信息
In this paper, an improved archimedes optimization algorithm (IAOA) is proposed to solve the Optimal Power Flow problem (OPF). The purpose of improving this IAOA algorithm is to increase population diversity in AOA, further improve the balance between the exploitation and exploration of AOA, and avoid premature convergence problems. The IAOA strategy uses a different approach to build a neighborhood for each object in which neighbor data can be transferred between objects. Dimension learning-based strategy is used for this process. The IAOA and AOA have been examined on the IEEE 30-bus, IEEE 57-bus and 16-bus South Marmara regional transmission systems. The effectiveness of the proposed IAOA and AOA are tested with the standard IEEE 30-bus and IEEE 57 -bus system and the simulation results are compared with different techniques as available published in the literature in recent years. In addition, in this study, an Offshore Wind Farm (OWF) and 16-bus South Marmara transmission system is modeled, and later OWF is integrated into a 16-bus South Marmara transmission system. Afterward, IAOA and other algorithms have tested for minimization of fuel emissions in this transmission system. The obtained simulation results and the comparison with different techniques show that the IAOA provides robustness.
暂无评论