Gunn Larsen

Distributed Control of the Power Supply-Demand Balance

This paper aims to achieve a balance of power in a group of prosumers, based on a price mechanism, i.e., to steer the difference between the total production and consumption of power to zero. We first set the information network topology such that the prosumers exchange price (power) information with their neighbors according to a chosen information network topology. Based on the exchanged information and the prosumers own measured power demand, each prosumer uses a local control strategy to turn on and off its power generator to cooperatively achieve the global balance. More specifically, the local control strategy results from a distributed model predictive control method based on dual decomposition and sub-gradient iterations. The method achieves a unique dynamic price signal for each prosumer. Simulation results with realistic data validate the method.

Distributed MPC Applied to a Network of Households With Micro-CHP and Heat Storage

This paper considers heat and power production from micro Combined Heat and Power systems and heat storage in a network of households. The goal is to balance the local heat demand and supply in combination with balancing the power supply and demand in the network. The on-off decisions of the local generators are done completely distributed based on local information and information exchange with a few neighbors in the network. This is achieved by using an information sharing model with a distributed model predictive control method based on dual-decomposition and sub-gradient iterations. Because of the binary nature of the decisions, a mixed integer quadratic problem is solved at each agent. The approach is tested with simulation, using realistic heat and power demand patterns. We conclude that the distributed control approach is suitable for embedding the distributed generation at a household level.

Distributed MPC for controlling μ-CHPs in a network

This paper describes a dynamic price mechanism to coordinate electricity generation from micro Combined Heat and Power (μ-CHP) systems in a network of households. The control is done on household level in a completely distributed manner. Distributed Model Predictive control is applied to the network of households with μ-CHP installed. Each house has a unique demand pattern based on realistic data. Information from a few neighbors are taken into account in the local optimal control problems. Desired behavior for the network model in the distributed MPC approach is showed by simulation.

Distributed Control in a Network of Households with

Abstract This is an application of a dynamic price mechanism to distributed optimization of a network of houses which are both producers and consumers of electricity. One possibility for domestic generation is the Micro Combined Heat Power system ( μ CHP). We use a pricing mechanism based on dual decomposition, that was combined with feedback controllers in Rantzer (2009), and apply it to the modeling of the electrical power grid. In the end we perform a numerical test with realistic electricity demand patterns obtained from real data.