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

Nikita Tomin, Nikolai Voropai, Victor Kurbatsky and Christian Rehtanz
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Nikita Tomin: Melentiev Energy Systems Institute SB RAS, Elecric Power Systems Department, 664033 Irkutsk, Russia
Nikolai Voropai: Melentiev Energy Systems Institute SB RAS, Elecric Power Systems Department, 664033 Irkutsk, Russia
Victor Kurbatsky: Melentiev Energy Systems Institute SB RAS, Elecric Power Systems Department, 664033 Irkutsk, Russia
Christian Rehtanz: Institute of Energy Systems, Energy Efficiency and Energy Economics (ie3), TU Dortmund University, 44227 Dortmund, Germany

Energies, 2021, vol. 14, issue 24, 1-14

Abstract: 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.

Keywords: voltage flexibility; droop control; multiagent reinforcement learning; hosting capacity; active distribution system; microgrid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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