PHIL implementation of a decentralized online OPF for active distribution grids

This paper demonstrates a Power Hardware-in-the-Loop (PHIL) implementation of a decentralized optimal power flow (D-OPF) algorithm embedded into the operations of two microgrids connected by a tie line. To integrate the static behavior of the optimization model, a two layer control architecture is introduced. Underneath the dispatch commands from the D-OPF, a primary control scheme provides instantaneous reaction to the load dynamics. This setup is tested in the PHIL environment of the CoSES Lab in TU Munich. In the experiment, the two microgrids cooperatively optimize their operation through an ADMM based unbalanced D-OPF. The operations is then benchmarked against the exclusive use of primary control, without D-OPF. The decentralized approach outperforms, but also shows minor inefficiencies of integrating optimization methods into the real-time operation of the system.<br>

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PHIL implementation of a decentralized online OPF for active distribution grids

10.36227/techrxiv.17065193.v1 ◽

2021 ◽

Author(s):

Martin Cornejo ◽

Anurag Mohapatra ◽

Soner Candas ◽

Vedran S. Peric

Keyword(s):

Power Flow ◽

Optimal Power Flow ◽

Optimization Methods ◽

Primary Control ◽

Time Operation ◽

Control Scheme ◽

Real Time Operation ◽

Distribution Grids ◽

Tie Line ◽

Layer Control

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Power System Real Time Operation based on Security Constrained Optimal Power Flow and Distributed Processing

2007 IEEE Lausanne Power Tech ◽

10.1109/pct.2007.4538445 ◽

2007 ◽

Cited By ~ 4

Author(s):

Carmen L. T. Borges ◽

Juliana M. T. Alves

Keyword(s):

Power System ◽

Real Time ◽

Power Flow ◽

Optimal Power Flow ◽

Distributed Processing ◽

Optimal Power ◽

Time Operation ◽

Real Time Operation

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State-Aware Stochastic Optimal Power Flow

Sustainability ◽

10.3390/su13147577 ◽

2021 ◽

Vol 13 (14) ◽

pp. 7577

Author(s):

Parikshit Pareek ◽

Hung D. Nguyen

Keyword(s):

Real Time ◽

Power Flow ◽

Optimal Power Flow ◽

Variable Load ◽

Point Change ◽

Optimal Power ◽

Time Operation ◽

Generation Cost ◽

Real Time Operation ◽

Bus System

The increase in distributed generation (DG) and variable load mandates system operators to perform decision-making considering uncertainties. This paper introduces a novel state-aware stochastic optimal power flow (SA-SOPF) problem formulation. The proposed SA-SOPF has objective to find a day-ahead base-solution that minimizes the generation cost and expectation of deviations in generation and node voltage set-points during real-time operation. We formulate SA-SOPF for a given affine policy and employ Gaussian process learning to obtain a distributionally robust (DR) affine policy for generation and voltage set-point change in real-time. In simulations, the GP-based affine policy has shown distributional robustness over three different uncertainty distributions for IEEE 14-bus system. The results also depict that the proposed SA-OPF formulation can reduce the expectation in voltage and generation deviation more than 60% in real-time operation with an additional day-ahead scheduling cost of 4.68% only for 14-bus system. For, in a 30-bus system, the reduction in generation and voltage deviation, the expectation is achieved to be greater than 90% for 1.195% extra generation cost. These results are strong indicators of possibility of achieving the day-ahead solution which lead to lower real-time deviation with minimal cost increase.

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