Simultaneous Placements of TCSC and VSCDG for Congestion Management

Relieving congestion is a major technical issue in power transmission systems. In this paper, simultaneous placement of Thyristor controlled series capacitors (TCSC) and Voltage source converter based distribution generators (VSC-DG) are used for relieving congestion, minimize system cost and maintain the system in secure state. The system has been analyzed in three stages: (i) first stage, the congestions are made in different transmission network by overload and line outages, (ii) second stage, relieving congestion of transmission lines by using TCSC and VSCDG, where these devices are placed simultaneously at their optimal locations, (iii) comparative cost analysis is done between normal and congested systems. The proposed approach is tested on the 62-bus Indian power system network. The optimal solution of the test systems are obtained by genetic algorithm method and its solutions are compared with primal linear programming. All the results are validated through Power World Simulator and GA toolbox in MATLAB.

Congestion Graphs for Automated Time Predictions

Time prediction is an essential component of decision making in various Artificial Intelligence application areas, including transportation systems, healthcare, and manufacturing. Predictions are required for efficient resource allocation and scheduling, optimized routing, and temporal action planning. In this work, we focus on time prediction in congested systems, where entities share scarce resources. To achieve accurate and explainable time prediction in this setting, features describing system congestion (e.g., workload and resource availability), must be considered. These features are typically gathered using process knowledge, (i.e., insights on the interplay of a system’s entities). Such knowledge is expensive to gather and may be completely unavailable. In order to automatically extract such features from data without prior process knowledge, we propose the model of congestion graphs, which are grounded in queueing theory. We show how congestion graphs are mined from raw event data using queueing theory based assumptions on the information contained in these logs. We evaluate our approach on two real-world datasets from healthcare systems where scarce resources prevail: an emergency department and an outpatient cancer clinic. Our experimental results show that using automatic generation of congestion features, we get an up to 23% improvement in terms of relative error in time prediction, compared to common baseline methods. We also detail how congestion graphs can be used to explain delays in the system.

Pricing in M/M/1 queues when cost of waiting in queue differs from cost of waiting in service

Service providers can adjust the entrance price to the state of the demand in real life service systems where the customers' decision to receive the service, is based on this price, state of demand and other system parameters. We analyzed service provider's short and long term pricing problems in unobservable M/M/1 queues having the rational customers, where, for customers, the unit cost of waiting in the queue is higher than unit cost of waiting in the service. We showed that waiting in the queue has a clear negative effect on customers’ utilities, hence the service provider's price values. We also showed that, in the short term, monopolistic pricing is optimal for congested systems with high server utilization levels, whereas in the long term, market capturing pricing is more profitable.