基于EPSILON约束法的配电自动化设备多目标优化布点模型

通过在配电网中配置自动化设备可以有效限制故障带来的负面影响，从而提高供电可靠性，减少停电损失，但采用全覆盖式的布点方案会极大程度地加剧电力公司的资金压力。为了合理地配置各类自动化设备，最大程度地发挥资金效能，提出了配电自动化设备的多目标优化布点模型。首先，分析了各类设备布点情况与负荷停电时间之间的关系，进而获得了供电可靠性与停电损失关于设备布点情况的表达式。然后，以各类设备的全生命周期费用作为约束，搭建了基于混合整数线性规划的配电自动化设备多目标优化布点模型，并通过数学求解器配合EPSILON约束法对其进行求解，以有限的资金获得最大的供电可靠性以及最小的停电损失。最后，基于IEEE RBTS-BUS4的算例结果表明，所提出的模型可以精确有效地对配电自动化设备进行优化布点，并直观地反映供电可靠性与停电损失之间的关系，为电网规划人员提供了辅助材料。

Multi-objective layout optimization model of distribution automation equipment based on the EPSILON constraint method

The deployment of automation equipment in the distribution network can effectively limit the negative impact of failures, thereby improving power supply reliability and reducing power outage losses. However, a full coverage scheme would greatly aggravate the financial pressure on power companies. In order to rationally configure various types of automation equipment and maximize the effectiveness of funds, a multi-objective optimization model of distribution automation equipment is proposed. First, the relationship between the distribution of various equipment and the time of load outage is analyzed, and then the expression of the reliability of power supply and the loss of power outage on the distribution of equipment is obtained. Secondly, with the life cycle cost of various types of equipment as constraints, a multi-objective optimization model of distribution automation equipment based on mixed integer linear programming is established. A mathematical solver is used in conjunction with the EPSILON constraint method for analysis, trying to use limited funds to obtain the greatest power supply reliability and the smallest power outage loss. Finally, the results of calculation examples based on IEEE RBTS-BUS4 show that the proposed model can accurately and effectively optimize distribution automation equipment, intuitively reflect the relationship between power supply reliability and power outage losses, and provide power grid planners with supplementary materials. This work is supported by the National Natural Science Foundation of China (No. 51777067).