Plug and Play Load Control

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While traditionally, power supply matches the fixed demand at any given time, modernized grids can leverage the flexibility of demand for utilizing resources more efficiently, often referred to as demand-side management. In particular electric vehicles represent not only a large load, but offer significant flexibility and the compelling capability of storing and providing energy for the grid. In addition, many traditional household loads can be deferred depending on the grid load. For example, a costumer may want the vehicle to be charged or dishes to be washed by a certain time, regardless of when the power is provided. This represents an enormous new potential for balancing a grid that relies on highly uncertain renewable energy.

We focus on three main challenges in this problem: 1) Load connections vary due to their utilization by the costumer, who is generally not willing to compromise on the connection times; 2) Grid safety has to be guaranteed, e.g. lines cannot be overloaded; 3) The optimization of large populations of distributed loads has to be performed efficiently.

We have developed a new approach for optimizing load profiles to reduce peak power in a distribution grid that can deal with varying load connections on the fly in a collaboration with UC Berkeley. More details can be found in the references below.

a) Schematic representation of loads plugging in and out of the distribution network. b) Cumulative real power in a simulated 55-bus distribution grid for the uncontrolled and controlled system, showing that load shifting can reduce peak power by more than 40%, while grid constraints (voltage bounds) are satisfied at all times.
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Publications & Talks

S. Bansal, M. N. Zeilinger and C. J. Tomlin. Plug-and-play model predictive control for electric vehicle charging and voltage control in smart grids. Proc. IEEE Conf. on Decision and Control, 2016, pages 5894-5900.

C. Le Floch, S. Bansal, C. J. Tomlin, S. Moura and M. N. Zeilinger. Plug-and-play model predictive control for load shaping and voltage control in smart grids. IEEE Transactions on Smart Grid, 2017.

M. Zellner, T. T. De Rubira, G. Hug, and M. N. Zeilinger. Distributed Differentially Private Model Predictive Control for Energy Storage. IFAC World Congress, 2017.

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