Date of Award


Degree Name

Doctor of Philosophy


Electrical and Computer Engineering

First Advisor

Asrari, Asash


In this research, the problem of real-time congestion management in a modern distribution system with massive active elements such as electric vehicles (EVs), distributed energy resources (DERs), and demand response (DR) is investigated. A novel hierarchical operation and management framework is proposed that can take advantage of the demand side contribution to manage the real-time congestion. There are five main steps in this framework as 1) the aggregators send their demand to the microgrid operators (MGOs), 2) the MGOs send their demand to the distribution system operator (DSO), 3) the DSO detects the congestions and calls the engaged MGOs to reduce their demand, 4) the MGOs update the electricity price to motivate the aggregators to reduce the overall demand, and 5) the DSO dispatches the system according to the finalized demand. The proposed framework is validated on two modified IEEE unbalanced test systems. The results illustrate two congestion cases at t=8:45 am and t=9:30 am in the modified IEEE 13-bus test system, which needs 363kW and 286 kW load reductions, respectively, to be fully addressed. MG#1 and MG#2 are engaged to maintain the 363 kW reduction at t=8:45, and MG#3 and MG#4 are called to reduce their demands by 386 kW at t=9:30 am. The overall interactions can relieve the congested branches. The DSO’s calculations show three congestions at t=1 pm, t=3 pm, and t=9 pm on the IEEE 123-bus test system. These congestion cases can be alleviated by reducing 809 kW, 1177 kW, and 497 kW from the corresponding MGs at t=1 pm, t=3 pm, and t=9 pm, respectively. The second part of the simulation results demonstrates that the proposed real-time data estimator (RDE) can reduce the DSO’s miss-detected congestion cases due to the uncertain data. There are two miss-detected congestions in the IEEE 13-bus test system at t=1:15 pm and t=1:30 pm that can be filtered for t=1:15 pm and minored for t=1:30 pm using the RDE. The proposed RDE can also reduce the miss-detected congestions from 18 cases to four cases in the IEEE 123-bus test system. As a result, the RDE can minimize the extra costs due to the uncertain data. The overall results validate that the proposed framework can adaptively manage real-time congestions in distribution systems.




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