Practical Considerations of DER Coordination with Distributed Optimal Power Flow

TL;DR

This paper evaluates the practical implementation of distributed optimal power flow for DER coordination using low-power edge devices, analyzing computation, convergence, precision, and communication in various network scenarios.

Contribution

It demonstrates the feasibility and challenges of deploying DOPF on Raspberry Pi-like hardware, providing insights into computation, convergence, and communication trade-offs.

Findings

Edge computing devices can execute parts of DOPF with acceptable computation times.

Network congestion affects the convergence speed of DOPF solutions.

Trade-offs exist between solution accuracy and iteration count.

Abstract

The coordination of prosumer-owned, behind-the-meter distributed energy resources (DER) can be achieved using a multiperiod, distributed optimal power flow (DOPF), which satisfies network constraints and preserves the privacy of prosumers. To solve the problem in a distributed fashion, it is decomposed and solved using the alternating direction method of multipliers (ADMM), which may require many iterations between prosumers and the central entity (i.e., an aggregator). Furthermore, the computational burden is shared among the agents with different processing capacities. Therefore, computational constraints and communication requirements may make the DOPF infeasible or impractical. In this paper, part of the DOPF (some of the prosumer subproblems) is executed on a Raspberry Pi-based hardware prototype, which emulates a low processing power, edge computing device. Four important aspects are analyzed using test cases of different complexities. The first is the computation cost of executing the subproblems in the edge computing device. The second is the algorithm operation on congested electrical networks, which impacts the convergence speed of DOPF solutions. Third, the precision of the computed solution, including the trade-off between solution quality and the number of iterations, is examined. Fourth, the communication requirements for implementation across different communication networks are investigated. The above metrics are analyzed in four scenarios involving 26-bus and 51-bus networks.