基于双层优化的微电网系统规划设计方法

规划设计是微电网系统核心技术体系之一。从分布式电源的综合优化(组合优化、容量优化)和分布式电源间的调度优化两个方面对其展开研究。根据分布式电源特性,提出了适用于并网型微电网系统和独立型微电网系统的双层优化规划设计模型。上层优化采用基于NSGA-II的多目标遗传算法计算系统最优配置;下层优化采用混合整数线性规划算法(MILP)计算系统最优运行方案。运用所建立模型,分别针对并网型和独立型微电网系统作了案例计算,验证了所提方法的正确性。     

绿色电火花成型加工多目标工艺参数优化

为实现电火花成型加工的绿色制造,在保证加工效率和加工质量的基础上尽可能减少能耗和污染物排放,采用正交试验与非支配排序遗传算法(NSGA-Ⅱ)对加工参数进行多目标优化。选择脉冲电流(I)、周率(T)及效率(η)3个工艺参数作为因变量,表面粗糙度(Ra)、能耗(EEV)和环境污染物(EEC)作为响应值,对SKD11进行电火花成型加工试验。通过回归分析验证工艺参数与响应之间所建模型的正确性,并利用信噪比分析获得影响能耗和污染物排放的主要因素。得出了加工工艺参数与加工效果之间的回归关系,并通过NSGA-Ⅱ算法对其进行优化得到Pareto前沿。Ra、EEV和EEC预测结果的平均相对误差分别为6.46%、10.45%、9.58%,表明优化结果准确有效,对今后的研究以及企业的绿色加工具有一定参考意义。     

基于非支配排序遗传算法的振动主动控制优化方法

非支配排序遗传算法是一种基于Pareto前沿的低计算需求、有精英策略、约束处理简单的多目标优化算法。该算法能够搜索到Pareto解集,由专家根据具体要求进行客观的筛选,根据各个目标函数不同的加权进行合理选择最优解,满足了不同研究目的对同一系统的不同要求,这是单目标优化算法不可比拟的。以结构振动系统的结构振动能量和系统控制能量作为多目标优化函数,建立了振动主动控制系统的控制增益以及传感器和作动器位置、数量和长度的多目标优化配置数学模型,首次利用非支配排序遗传算法作为优化策略,并以悬臂梁作为算例,进行了仿真实验,验证了该方法的有效性和可行性。     

Modeling and multi-objective optimization of a stand-alone photovoltaic-wind turbine-hydrogen-battery hybrid energy system based on hysteresis band

Hybrid renewable energy system (HRES) can provide power without emission for off-grid areas. Due to intermittency of renewable energy, energy storage system (ESS) is essential for reliable power supply, while its cost is still relatively high. Appropriate power management strategy (PMS) can help to delay the degradation of energy storage devices and reduce the system cost. In this study, power management strategy and configuration optimization of the system are focused and the study includes three main contributions. First, mathematical models of the system, including photovoltaics (PVs), wind turbines (WTs), batteries, fuel cells (FCs), electrolyzers (ELZs), and hydrogen tanks are developed. The degradation of fuel cells and electrolyzers is considered in the modeling process. Second, power management strategy considering hysteresis band is employed to control energy flow to delay fuel cell and electrolyzer degradation. Third, a multi-objective optimization function including the system net annual value (NAV), loss of power supply probability (LPSP) and excess energy (E_excess) is established. Non-dominating Sorting Genetic Algorithm II (NSGA-II) is used to solve objective function. The results demonstrate that using hysteresis band help improve the system performance and reduce the cost. In addition, by setting the goal of excess energy, system reliability is well preserved with a LPSP as low as 0.92%. Compared with other optimization algorithms such as MOEA/D, NSGA-II has a smaller SI value of 422.10 and a larger DI value of 830.78, therefore the Pareto solution obtained by NSGA-II has a more uniform distribution and larger coverage.     

Multi-objective optimization of gradient porosity of gas diffusion layer and operation parameters in PEMFC based on recombination optimization compromise strategy

Proton exchange membrane fuel cell (PEMFC) is one of the most promising power energy sources in the world, and its mechanism research has become the main starting point to improve the comprehensive performance of fuel cells. The gas diffusion layer (GDL) of a proton exchange membrane fuel cell has a significant impact on the overall performance of the cell as an important component in supporting the catalytic layer, collecting the current, conducting the gas and discharging the reaction product water. In this paper, a three-dimensional two-phase isothermal fuel cell model is established based on COMSOL, the gradient porosity of the GDL, thickness of the GDL, operating voltage and working pressure of proton exchange membrane fuel cell are analyzed, the consistency problem of fuel cell performance improvement and life extension that is easily overlooked in numerous studies is found. On this basis, a neural network proxy model is constructed through a large amount of data, and a multi-objective genetic optimization algorithm based on the compromise strategy of recombination optimization is proposed to optimize the uniformity of fuel cell power and oxygen molar concentration distribution, which improves the performance of the fuel cell by 1.45% compared with the power increase when it is not optimized. At the same time, the uniformity of oxygen distribution is improved 10.28%, which makes the oxygen distribution more uniform, prolongs the life of the fuel cell, and fills the gap in the optimization direction of the comprehensive performance of the fuel cell.     

Multiobjective trajectory optimization of the wall-building robot based on RBF–NSGA-II in an uncertain viscoelastic contact environment

To solve the problem of poor masonry quality of traditional wall-building robots in an uncertain viscoelastic contact environment while reducing energy consumption, reducing contact forces with the environment, and improving work efficiency and smoothness, a segmented multiobjective trajectory optimization method is proposed based on radial basis function (RBF) and nondominated sorting genetic algorithm II (NSGA-II). The method divides the motion trajectory into the free motion segment and the masonry segment. In the masonry segment, the compensation variable is introduced at the brick-stopping position, and the values of design variables are obtained by Latin hypercube sampling. The relationship between the objective functions and the design variables is established by using an RBF substitution model. The optimal design is carried out by the NSGA-II, and the compromise solution is obtained by using the technique for order preference by similarity to an ideal solution algorithm. On this basis, a multiobjective trajectory optimization method based on seven times nonuniform B-spline curves is proposed for the free motion segment. According to the performance indicators, such as operation efficiency, trajectory smoothness, and energy consumption, the compromise solution is again sought and obtained. Finally, the proposed trajectory optimization method is compared with the standard gate-shaped trajectory planning method. The results show that after trajectory optimization, the masonry efficiency of the wall-building robot is improved by 28.36%, and the energy consumption and trajectory smoothness are reduced by 28.68% and 93.81%, respectively. At the same time, the contact force with the environment is reduced by 12.26%, and the masonry error is reduced from 2.67 to 0.13 mm. These results can contribute to the construction of walls and improve the masonry quality of bricks while considering other performance indicators.