Adaptive load management (ALM) in electric power systems

In this work, we propose a multi-layered adaptive load management (ALM) system capable of integrating large-scale demand response in electric power systems efficiently and reliably. Electric power systems can be seen as a composition of multiple subsystems: power producers, load aggregators/utilities, end-users, etc. Focusing on the demand side of the system, we decompose the whole power system into three layers: the primary layer at the lowest level consisting of end-users of electric energy, the secondary layer or load aggregators that aggregate these endusers and provide service to them, and the tertiary level or the system/market operator at the highest level that incorporates and optimizes the objectives of the system as a whole. We pose the ALM problem as the problem of decomposing a complex network system using Lagrange decomposition techniques. Given the recent changes in electric energy systems, we propose that including more information from demand side helps improve the system-wide optimization. This is done by signaling a demand function, i.e. optimal energy use as a function of electricity price in place of a single point of a Lagrange multiplier, from the end-users at the primary layer to the higher layers. We provide a formulation of the system´s decomposition problems of this multi-layered multi-directional decision making and information exchange. Our novel contribution will be showing that exchanging sensitivities of Lagrange coefficients instead of point-wise values of price data is essential for having a converging interactive information exchange within at the rate needed in physical power systems without storage. We identify open questions and plan for our future work.