Distributed Model Predictive Control for Smart Energy Systems

Abstract

Abstract-Integration of a large number of flexible consumers in a Smart Grid requires a scalable power balancing strategy. We formulate the control problem as an optimization problem to be solved repeatedly by the aggregator in a Model Predictive Control (MPC) framework. To solve the large-scale control problem in real-time requires decomposition methods. We propose a decomposition method based on Douglas-Rachford splitting to solve this large-scale control problem. The method decomposes the problem into smaller subproblems that can be solved in parallel, e.g. locally by each unit connected to an aggregator. The total power consumption is controlled through a negotiation procedure between all cooperating units and an aggregator that coordinates the overall objective. For large-scale systems this method is faster than solving the original problem and can be distributed to include an arbitrary number of units. We show how different aggregator objectives are implemented and provide simulations of the controller including the computational performance.

A cyber-physical systems perspective on decentralized energy management

“…This choice is often justified because of the required balance between energy management in the future and adequate response time to resolve problems at hand. However, in [64] and [157] a different approach is taken by utilizing dual decomposition methods to distribute the optimization workload. In particular the alternating direction method of multipliers [16] is applied, for which adequate solutions can be found in a reasonable number of iterations.…”

“…Support of multiple commodities within these double-sided auctions is not straightforward [143]. Other approaches [64,157] create a new planning each interval and could thereby incorporate these requirements, at the cost of requiring significant computation power.…”

Performance Assessment of Black Box Capacity Forecasting for Multi-Market Trade Application

“…It was shown that distribution systems not only reduce communication requirements, but also allow scalability and reduce computation times. Further, in [3] and [4], distributed control approaches are combined with model predictive control (MPC) techniques to offer flexibility for multiple smart grid services, namely trading on multiple wholesale energy markets. Here, the response of large VPPs comprised of many small devices to price incentives is predicted using historic load behavior and forecasted weather data.…”

Distributed Model Predictive Control of linear discrete-time systems with local and global constraints

“…However, the optimality of the overall system is unclear as they are not explicitly pursued. In addition, these approaches require direct communications among all systems that are coupled by (3) which, even for a system of moderate size, is a strong requirement (Halvgaard, Vandenberghe, Poulsen, Madsen, & Jorgensen, 2016;Low & Lapsley, 1999;Spudić, Conte, Baotić, & Morari, 2015).…”