Incorporating Forecasting and Peer-to-Peer Negotiation Frameworks Into a Distributed Model-Predictive Control Approach for Meshed Electric Networks

Abstract

The continuous integration of renewable energy sources into power networks is causing a paradigm shift in energy generation and distribution with regard to trading and control. The intermittent nature of renewable sources affects the pricing of energy sold or purchased. The networks are subject to operational constraints, voltage limits at each node, rated capacities for the power electronic devices, and current bounds for distribution lines. These economic and technical constraints, coupled with intermittent renewable injection, may pose a threat to system stability and performance. In this article, we propose a novel holistic approach to energy trading composed of a distributed predictive control framework to handle physical interactions, i.e., voltage constraints and power dispatch, together with a negotiation framework to determine pricing policies for energy transactions. We study the effect of forecasting generation and consumption on the overall network’s performance and market behaviors. We provide a rigorous convergence analysis for both the negotiation framework and the distributed control. Finally, we assess the impact of forecasting in the proposed system with the aid of testing scenarios.