Online Battery Control in Distribution Grids Using Reinforcement Learning

Contact: Eleni Stai

Background: Electrical grids are characterized by an increasing penetration of uncertain renewable energy sources. However, in electrical grids, the demand should be always in balance with the supply. Therefore, due to the uncertainty of the renewable energy generation, the need of an increasing amount of reserves emerges. However, the reserves are costly and lead to increased operational and investment costs for the power grid. To reduce the amount of reserves, energy storage, such as batteries, can absorb the uncertainty of the renewable energy sources. The batteries can e.g., charge to consume energy when there is over-generation of energy by the renewable energy sources and discharge to provide extra energy when the demand is more than the supply. In order to exploit the full potential of the batteries, it is necessary to optimally control them. Controlling a battery at a distribution network with stochastic resources and storage devices while accounting for operational constraints and system losses involves an AC Optimal Power Flow (AC OPF) [1].

In this project: We will develop an algorithm for controlling batteries in distribution grids with stochastic resources using reinforcement learning techniques. Reinforcement learning (RL) is an area of machine learning that tries to optimize the behaviour of an agent or controller that interacts (takes actions) with its environment so as to maximize some notion of cumulative reward. For a similar application of reinforcement learning, but for an energy management system of a wireless sensor network please see [2].

Project Goals:

  • Develop an algorithm for online battery control in distribution grids with stochastic resources using reinforcement learning
  • Perform simulations and numerical evaluations of the developed algorithm

Student profile


  • Knowledge of Load Flow and Optimal Power Flow
  • Knowledge of machine learning is a plus


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