The DC Optimal Power Flow (DC-OPF) problem is a widely-studied topic in the field of power systems. A solution to the problem consists of minimizing the running costs of the power system, through defining the optimal operating state for each entity in the system, while adhering to a set of physical constraints. A lot of research has been conducted on decentralized and distributed solutions to this problem, which, when compared to centralized solutions, offer benefits such as adaptability, reliability and scalability. Nevertheless, most of these solutions have only been evaluated through simulations, while physical applications of these algorithms introduce new challenges, such as noise, delays, and the regulation of physical variables like voltage and current. In this thesis, we focus on a decentralized DC-OPF algorithm based on the Consensus and Innovation method, where system entities utilize their physical measurements and communicate with their neighbouring entities in order to reach consensus on a solution. While previous implementations of the algorithm were tested on simulated environments, this thesis explores and proves the effectiveness of the algorithm implemented for a real DC unipolar microgrid, consisting of power supplies, loads and a Power Circuit Board attached to each, where each device's behavior is governed by its own, exclusive entity. The newly-introduced challenges of a physical environment are accounted for, and any negative effects of them are mitigated as much as possible. Furthermore, the algorithm is successfully modified and extended to handle region DC-OPF, something that has not been attempted before, where a single entity of the algorithm could be responsible for many devices in the network. Finally, it is known that the original algorithm has not been experimented on before on scenarios of islanding and de-islanding, which are of importance in OPF, because a power fault may occur and one area may wish to isolate itself from the faulty area, or perhaps a distributed energy resource should only be connected to the network during certain periods, e.g., during sunlight for solar panels. Hence, this thesis also proves the effectivess of the algorithm on scenarios of islanding and de-islanding, in an innovation site called the Green Village, where technologies in the field of sustainable energy provision are tested and applied in a real-life environment.