Distributed Optimization in Low Voltage Distribution Networks via Broadcast Signals †

With the development of distributed energy resources, the low voltage distribution network (LVDN) is supposed to be the integrator of small distributed energy sources. This makes the users in LVDNs multifarious, which leads to more complex modeling. Additionally, data acquisition could be tricky due to rising privacy concerns. These impose severe demands on control schemes in LVDNs that the classical centralized control might not be able to fulfill. To tackle this, a model-free control approach with distributed decision-making architecture is proposed in this paper. Employing statistical methods and game theory, individual users in LVDNs achieve local optimum autonomously. Comparing to conventional approaches applied in LVDNs, the proposed approach is able to achieve active control with less communication burden and computational resources. The paper proves the convergence to the Nash Equilibrium (NE) and uses player compatible relations to form the specific equilibrium. A variant of the log-linear trial and error learning process is applied in a novel “suggest-convince” mechanism to implement the proposed approach. In the case study, a 103 nodes test network based on a real Belgian semiurban LVDN is illustrated. The proposed approach is validated and analyzed with practical load profiles on the 103 nodes network. In addition to that, centralized control is implemented as a benchmark to show the performance of the proposed approach by comparing it with the classical optimization result. The results demonstrate that the proposed approach is able to achieve player compatible equilibrium in an expected way, resulting in a good approximation to the local optimum.