A Privacy-preserving Decentralized Algorithm for Distribution Locational Marginal Prices

A major challenge in today's electricity system is the management of flexibilities offered by new usages, such as smart home appliances or electric vehicles. By incentivizing energy consumption profiles of individuals, demand response seeks to adjust the power demand to the supply, for increased grid stability and better integration of renewable energies. This optimization of flexibility is typically managed by Load Aggregators, independent entities which aggregate and optimize numerous flexibility providers. The consideration of the underlying distribution network constraints, which couple the different actors, leads to a complex multi-agent problem. To address it, we propose a new decentralized algorithm that solves a convex relaxation of the classical Alternative Current Optimal Power Flow (ACOPF) problem, and which relies on local information only. Each computational step is performed in a privacy-preserving manner, and system-wide coordination is achieved via node-specific distribution locational marginal prices (DLMPs). We demonstrate the efficiency of our approach on a 15-bus radial distribution network.