Distribution System State Estimation with AMI Based on Load Correction Method

Muttaqi, Tazwar; Baldwin, T.L.; Chiu, Steve C.

doi:10.1109/naps46351.2019.9000334

Cited by 2 publications

(2 citation statements)

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“…According to [6][7][8][9][10], provision of optimal operating conditions and a further improvement in CIG performance, for example, maximization of hosting capacity in distribution networks, reduction of network losses, and others, require enhanced state estimation and coordinated operation of various control means. At the same time, even a low level of communication between CIGs allows achieving better control settings and increased performance [11].…”

Section: Related Workmentioning

confidence: 99%

“…An analysis of PV hosting capacity, according to (7), relied on the Monte Carlo method employed for probabilistic modeling of a large number of various scenarios for the installation of future PV plants in the IEEE 34-bus feeder. Each scenario consisted of PV systems of a certain capacity connected to specific nodes of the system.…”

Section: Voltage Flexibility and Hosting Capacity Analysismentioning

confidence: 99%

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Management of Voltage Flexibility from Inverter-Based Distributed Generation Using Multi-Agent Reinforcement Learning

et al. 2021

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The increase in the use of converter-interfaced generators (CIGs) in today’s electrical grids will require these generators both to supply power and participate in voltage control and provision of grid stability. At the same time, new possibilities of secondary QU droop control in power grids with a large proportion of CIGs (PV panels, wind generators, micro-turbines, fuel cells, and others) open new ways for DSO to increase energy flexibility and maximize hosting capacity. This study extends the existing secondary QU droop control models to enhance the efficiency of CIG integration into electrical networks. The paper presents an approach to decentralized control of secondary voltage through converters based on a multi-agent reinforcement learning (MARL) algorithm. A procedure is also proposed for analyzing hosting capacity and voltage flexibility in a power grid in terms of secondary voltage control. The effectiveness of the proposed static MARL control is demonstrated by the example of a modified IEEE 34-bus test feeder containing CIGs. Experiments have shown that the decentralized approach at issue is effective in stabilizing nodal voltage and preventing overcurrent in lines under various heavy load conditions often caused by active power injections from CIGs themselves and power exchange processes within the TSO/DSO market interaction.

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