考虑风电不确定度的风-火-水-气-核-抽水蓄能多源协同旋转备用优化

A Multi-Source Coordinated Spinning Reserve Model Considering Wind Power Uncertainty

随着风电并网渗透率的快速增长,系统的随机波动性显著增强。为合理地协调旋转备用配置效益和风险的矛盾,首先以多面体不确定集刻画风电不确定性,构建了计及风电出力误差概率的期望弃风和期望切负荷模型。在综合考虑风电、火电、水电等多类型电源机组运行特性的基础上,以发电效益与潜在期望风险为备用目标,建立了考虑风电不确定度的风-火-水-气-核-抽水蓄能多类型电源机组协同调度的旋转备用优化模型,并提出了一种自适应遗传算法与分枝切割算法相结合的双层优化算法以求解所建模型。算例结果表明,所提备用方法能自适应地寻找风电最优不确定度,合理地权衡备用策略的经济效益和风险成本,同时所建模型能适应在不同风电预测精度、不同切负荷成本系数以及不同风电调峰特性下的旋转备用优化配置,实现综合效益最优。

With rapid growth of wind power penetration, randomness and volatility of power system are significantly enhanced, possibly leading to growing contradiction between benefit and risk of spinning reserve. In this paper, uncertainty of wind power is described with polyhedron uncertainty set instead of conventional box uncertainty set, and an expected risk model based on probability of wind power is constructed. In order to reasonably make a trade-off between benefit and risk in spinning reserve optimization, an uncertainty-set optimization model of wind power with cooperatively scheduling of multi-power units is established by evaluating potential risk of wind power uncertainty. Besides, a two-level optimization method combining adaptive genetic algorithm and branch cut algorithm is proposed to solve the MINLP model. Results indicate that, the proposed method can adaptively search for the optimal uncertainty-set of wind power, able to reasonably balance the benefit and risk of spinning reserve. The proposed model is adaptable to different wind power prediction accuracy, different load costing coefficients and different wind power inversion characteristics, so as to achieve optimal comprehensive cost.