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Load Shedding Optimization Considering Consumer Appliance Prioritization Using Genetic Algorithm for Real-time Application

机译：负载脱落优化考虑使用遗传算法进行实时应用的消费设备优先级

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In the event of energy supply-demand imbalance caused by deficiency in energy generation, Distribution Utilities (DUs) implement load shedding methods to avoid system damages. In the current distribution set-up, consumers experience unscheduled or scheduled total blackouts with no control over which appliances to spare. This paper introduces a novel method to implement automated load shedding, which considers appliance activities and priority levels as predefined by the consumers, in a smart distribution system. The proposed method utilizes the information from the distributed appliance controllers which are assumed to have power monitoring and direct load control capabilities with bidirectional communication. Since consumer appliance switching is binary in nature, Genetic Algorithm (GA) is used to perform the optimization which is to allocate the available power supply to as many appliances as possible considering the consumer-defined appliance priority levels. With the limited power supply, consumer power allocation is determined by executing two GA processes, in each appliance controller and in the central station, respectively. The GA process in each appliance controller allocates the available supply capacity to the enrolled appliances to determine their switching on or off considering their priority levels. In order to avoid repeated switching of particular appliances, ‘fairness’ of switching implementations is judged by a proposed criterion. The remaining unallocated supply capacity is collected and optimally redistributed by GA in the central station. The case study results showed that the proposed method ensures optimum power utilization to avoid total blackouts with fast convergence signifying a promising capability for real-time applications. Furthermore, the proposed method is able to involve consumers in deciding which appliances to deload through their priority level inputs.
机译：在能量发电缺陷引起的能量供给需求不平衡的情况下，分配公用事业（DUS）实施负载脱落方法以避免系统损害。在当前的分布设置中，消费者经历了未经核化的或计划的全天空停电，无需控制哪些设备备用。本文介绍了一种实施自动负载脱落的新方法，将设备活动和优先级视为消费者预定义的智能分配系统。该提出的方法利用来自分布式设备控制器的信息，该信息被假设具有具有双向通信的功率监视和直接负载控制能力。由于消费设备切换本质上是二进制的，因此遗传算法（GA）用于执行优化，该优化是考虑到消费者定义的设备优先级，尽可能地将可用电源分配给尽可能多的设备。利用有限的电源，通过分别在每个设备控制器和中央站中执行两个GA进程来确定消费者功率分配。每个设备控制器中的GA进程将可用的供应容量分配给注册设备，以确定其优先级的开启或关闭。为了避免重复切换特定设备，通过提出的标准判断切换实现的“公平”。剩余的未分配的供应能力被收集并在中央站的GA最佳地重新分配。案例研究结果表明，该方法确保了最佳的电力利用，以避免快速收敛的全部停电，从而引起实时应用的有希望的能力。此外，所提出的方法能够涉及消费者决定通过其优先级输入来删除哪些设备。

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