Abstract
In this paper, a Reinforcement Learning (RL)-based approach to optimally dispatch PV inverters in unbalanced distribution systems is presented. The proposed approach exploits a decentralized architecture in which PV inverters are operated by agents that perform all computational processes locally; while communicating with a central agent to guarantee voltage magnitude regulation within the distribution system. The dispatch problem of PV inverters is modeled as a Markov Decision Process (MDP), enabling the use of RL algorithms. A rolling horizon strategy is used to avoid the computational burden usually associated with continuous state and action spaces, coupled with a computationally efficient learning algorithm to model the action-value function for each PV inverter. The effectiveness of the proposed decentralized RL approach is compared with the optimal solution provided by a centralized nonlinear programming (NLP) formulation. Results showed that within several executions, the proposed approach converges either to the optimal solution or to solutions with a PV curtailment excess of less than 2.5% while still enforcing voltage magnitude regulation.
Original language | English |
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Article number | 107628 |
Pages (from-to) | 1-13 |
Number of pages | 13 |
Journal | International Journal of Electrical Power and Energy Systems |
Volume | 136 |
DOIs | |
Publication status | Published - 2022 |
Keywords
- Distribution systems
- Optimal dispatch
- PV systems
- Reinforcement Learning
- Voltage regulation