三峡大学学报（自然科学版）

针对高比例新能源并网引发的电力系统暂态失稳风险,提出了一种融合机会约束理论与动态参数优化的预防控制策略.首先分析新能源出力的随机性与波动性,分别构建风力和光伏出力的概率密度函数;然后,基于机会约束理论将暂态稳定、电压越限等安全运行约束转化为概率形式,进而构建暂态稳定约束最优潮流(transient stability constrains optimal power flow,TSCOPF~)模型,并推导出各约束的确定性等价不等式,以此量化系统的运行风险;在此基础上,设计自适应粒子群优化算法(adaptive particle swarm optimization,APSO,)结合动态惯性权重调整与暂态稳定梯度反馈机制,实现在约束可行域内的高效求解;最后在改进的IEEE₃₉节点系统进行仿真,结果表明:较标准PSO与混沌PSO算法,本文所提方法的总运行成本分别降低了5.1%和1.7%,为高比例新能源电力系统的预防控制提供了兼具鲁棒性与实时性的解决方案.

In view of the risk of transient instability of power system caused by the high proportion of new energy grid, a preventive control strategy that integrates the theory of opportunity constraint and dynamic parameter optimization is proposed in this paper. Firstly, the randomness and fluctuation of the output for new energy are analyzed, and the probability density functions of wind and photovoltaic output are constructed respectively. Then, the safe operation constraints such as transient stability and voltage overrun are transformed into probabilistic forms based on the chance constraint theory. Moreover, the model of transient stability constrained optimal power flow(TSCOPF) is constructed, and the deterministic equivalent inequality of each constraint is deduced to quantify the operation risk of the system. On this basis, an algorithm of adaptive particle swarm optimization(APSO) is designed, which combined with dynamic inertia weight adjustment and transient stable gradient feedback mechanism to achieve the efficient solution in the constraint feasible domain. Finally, the simulations in the improved IEEE 39-node system show that the total operating cost of the proposed method is reduced by 5.1% and 1.7% respectively compared with the algorithms of standard PSO and chaotic PSO, which provides a robust and real-time solution for the prevention and control of power systems with a high proportion of new energy.