Chance-Constrained Optimization-Based Unbalanced Optimal Power Flow for Radial Distribution Networks
Chance-Constrained Optimization-Based Unbalanced Optimal Power Flow for Radial Distribution Networks
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DOI：
10.1109/tpwrd.2013.2259509
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发表时间：
2013-06
影响因子：
4.4
通讯作者：
Yijia Cao;Yi Tan;Canbing Li;Christian Rehtanz
中科院分区：
文献类型：
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作者：
Yijia Cao;Yi Tan;Canbing Li;Christian Rehtanz
最优潮流（OPF）是可再生能源发电配电网主动管理的重要工具。在配电网OPF中，最好将REG视为随机变量。此外，配电网络本质上是不平衡的。因此，本文考虑径向不平衡配电网短期运行时REG的预测误差，建立了基于机会约束优化的多目标OPF模型。该模型使N-1个突发事件下配电线路的期望总有功损耗、期望过载风险和电压违例风险最小化，并使正常状态下的不等式约束满足预定义的概率水平。因此，在随机REG的存在下，盈利能力和安全性可以得到平衡。采用多目标群搜索优化和两点估计方法求解多目标OPF问题。仿真结果表明，随着REG渗透水平的增加，配电网的经济性和事后性能都在下降，且渗透水平的影响大于REG的预测误差。
Optimal power flow (OPF) is an important tool for active management of distribution networks with renewable energy generation (REG). It is better to treat REG as stochastic variables in the distribution network OPF. In addition, distribution networks are unbalanced in nature. Thus, in this paper, a chance constrained optimization-based multiobjective OPF model is formulated to consider the forecast errors of REG in the short-term operation of radial unbalanced distribution networks. In the model, expected total active power losses of distribution lines, expected overload risk and voltage violation risk with respect to N-1 contingencies are minimized, and inequality constraints in the normal state are satisfied with a predefined probability level. Thus, the profitability and security can be balanced in the presence of stochastic REG. The proposed multiobjective OPF problem is solved by the multiobjective group search optimization and the two-point estimate method. Simulation results show that distribution network economy and postcontingency performance deteriorate with increased penetration level of REG, and the penetration level has a greater impact than the forecast errors of REG.