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Uncertain optimization decision of interruptible load in Demand Response program

机译：需求响应程序中可中断负载的不确定优化决策

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In the Smart Grid, Demand Response (DR) programs have been developed rapidly, which change the fixed mindsets of satisfying the growing power demand merely by the development of power supply side, and utilize the demand side resources as alternative energy sources. Nevertheless, in application of DR programs, there are many uncertain factors which cannot be reflected in conventional optimization approaches. In this paper, the interruptible load optimization is carried out considering the uncertainties of customer response and total interruptible capacity requirement. The probability distribution of customer response can be deduced according to historical data. So the expected value of settling accounts as compensation or penalty to customers is calculated and minimized as one of the objective functions. The sum of variances is minimized as another objective function. The uncertainty of total interruptible capacity requirement is considered in the constraints, which is described as the confidence level. This optimization problem is called chance constrained programming because of the random variables in the constraints, which can be transformed to its deterministic equivalents. Example analysis demonstrates that the proposed optimization method can consider the coordination of economy and reliability in interrupting the customer loads and satisfy the confidence level.
机译：在智能电网中，需求响应（DR）计划得到了迅速发展，它仅通过电源供应方的发展就改变了满足不断增长的电力需求的固定观念，并利用需求方资源作为替代能源。然而，在DR程序的应用中，存在许多不确定因素，而这些因素是常规优化方法无法反映的。在本文中，考虑了客户响应和总可中断容量需求的不确定性，对可中断负载进行了优化。客户响应的概率分布可以根据历史数据推导出。因此，作为对客户的补偿或罚款的结算帐户的期望值被计算并最小化为目标函数之一。方差之和被最小化为另一个目标函数。约束中考虑了总可中断容量需求的不确定性，这被描述为置信度。由于约束中的随机变量，这种优化问题称为机会约束编程，可以将其转换为确定性等效项。实例分析表明，所提出的优化方法可以在中断客户负荷时考虑经济性和可靠性的协调，并满足置信度。

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