采用混合智能算法的含风电电力系统多目标优化调度

为了增强含风电电力系统的安全性和稳定性，提出一种计及运行风险及备用成本的含风电电力系统环境经济调度新模型。在目标函数中加入了系统运行风险指标和正、负旋转备用成本；增加了系统可靠性约束条件，确保了较低的系统运行风险，并同时获取正、负旋转备用量。采用一种新型高效的场景生成技术来描述风电功率的随机性。基于花授粉算法及差分进化算法提出一种具有时变模糊选择机制的多目标优化算法。将所提模型及求解方法在具有一个并网风电场的4机组系统中进行仿真。分析了各参数变化对系统运行的影响，并与其他两种启发式智能算法进行比较，验证了所提模型及算法的可行性和有效性。

Multi-objective optimal scheduling of wind integrated power systems with hybrid intelligent algorithm

In order to enhance the security and stability of power system with wind power, a new environmental and economic dispatch model of power system with wind power considering operation risk and reserve cost is proposed. In the objective function, the system operation risk index and positive and negative spinning reserve cost are joined, and the system reliability constraints are increased, which ensure the system run with the lower risk, and also obtain positive and negative spinning reserve amount. A new and efficient scene generation technique is used to describe the randomness of wind power. A multi-objective optimization algorithm containing time-varying fuzzy selection mechanism based on the flower pollination algorithm and differential evolution algorithm is proposed. The proposed model and solution method are simulated in a 4-unit system with a grid connected wind farm and compared with the other two heuristic algorithms. The effects of various parameters on the system operation are analyzed. The feasibility and effectiveness of the proposed model and algorithm are verified.