计及电能质量影响的智能配电网孤岛划分策略
发布时间：2019-02-16 03:44
【摘要】：现有智能配电网孤岛划分研究主要从功率平衡的角度出发,对电网安全约束考虑不充分,未考虑电能质量的影响,难以保证孤岛安全可靠运行。提出一种计及电能质量影响的智能配电网孤岛划分策略。故障后,首先计及联络开关进行网络连通性分析,进行初始孤岛划分。然后,对每个初始孤岛,建立切负荷-网络重构双层优化模型,并采用微分进化算法和混合粒子群算法求解,以降低搜索空间维数。具体地,在切负荷优化模型中,在允许频偏范围内充分发挥DG的调频特性,以减少负荷切除量;在网络重构优化模型中,建立包含频率约束、负序及谐波不确定性约束的电能质量综合约束集。提出基于序列二次规划法的配电网柔性潮流算法计算频率偏移条件下的孤岛潮流,引入点估计法进行负序、谐波不确定性分析,以改善孤岛的电能质量,提高其运行可靠性。最后,基于实际复杂配电网算例,验证了该策略的有效性和优越性。
[Abstract]:The existing research on islanding of smart distribution network is mainly from the point of view of power balance. It is difficult to ensure the safe and reliable operation of the island because of the insufficient consideration of security constraints and the influence of power quality. An islanding strategy for intelligent distribution network is proposed, which takes into account the influence of power quality. After the fault, the network connectivity is analyzed with the contact switch, and the initial island partition is carried out. Then, for each initial isolated island, a double layer optimization model of shedding load network reconstruction is established and solved by using differential evolution algorithm and hybrid particle swarm optimization algorithm to reduce the dimension of search space. Specifically, in the load shedding optimization model, the frequency modulation characteristic of DG is brought into full play in the range of allowable frequency offset to reduce the load removal. In the optimization model of network reconfiguration, a comprehensive constraint set of power quality including frequency constraint, negative sequence constraint and harmonic uncertainty constraint is established. A flexible power flow algorithm based on sequential quadratic programming is proposed to calculate the islanding power flow under frequency offset condition. The point estimation method is introduced to analyze the negative sequence and harmonic uncertainty in order to improve the power quality of the isolated island and improve its operational reliability. Finally, the effectiveness and superiority of the strategy are verified based on a practical example of complex distribution network.
【作者单位】： 武汉大学电气工程学院;太阳能高效利用湖北省协同创新中心;华中电力调控分中心;
【基金】：国家自然科学基金项目(51177111) 太阳能高效利用湖北省协同创新中心重大专项(HBSZD2014003)资助
【分类号】：TM732
[Abstract]:The existing research on islanding of smart distribution network is mainly from the point of view of power balance. It is difficult to ensure the safe and reliable operation of the island because of the insufficient consideration of security constraints and the influence of power quality. An islanding strategy for intelligent distribution network is proposed, which takes into account the influence of power quality. After the fault, the network connectivity is analyzed with the contact switch, and the initial island partition is carried out. Then, for each initial isolated island, a double layer optimization model of shedding load network reconstruction is established and solved by using differential evolution algorithm and hybrid particle swarm optimization algorithm to reduce the dimension of search space. Specifically, in the load shedding optimization model, the frequency modulation characteristic of DG is brought into full play in the range of allowable frequency offset to reduce the load removal. In the optimization model of network reconfiguration, a comprehensive constraint set of power quality including frequency constraint, negative sequence constraint and harmonic uncertainty constraint is established. A flexible power flow algorithm based on sequential quadratic programming is proposed to calculate the islanding power flow under frequency offset condition. The point estimation method is introduced to analyze the negative sequence and harmonic uncertainty in order to improve the power quality of the isolated island and improve its operational reliability. Finally, the effectiveness and superiority of the strategy are verified based on a practical example of complex distribution network.
【作者单位】： 武汉大学电气工程学院;太阳能高效利用湖北省协同创新中心;华中电力调控分中心;
【基金】：国家自然科学基金项目(51177111) 太阳能高效利用湖北省协同创新中心重大专项(HBSZD2014003)资助
【分类号】：TM732
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