Robust optimisation for deciding on real-time flexibility of storage-integrated photovoltaic units controlled by intelligent software agents

The increasing penetration of Renewable Energy Sources (RES), the liberalization of the electricity markets across the world and devices such as smart meters to realize bidirectional communication with centralized computer systems present the end-users of the power system with
new opportunities to decrease their electricity costs or become active electricity market participants. However, the intermittent nature of RES and dynamic electricity prices require tools against uncertainty to protect the end-users from under utilizing their assets. In this work, we examine the
effectiveness of Robust Optimization (RO)in maximizing the economic benefit of the owner of a home battery storage system in the presence of uncertainty in dynamic electricity prices. The advantage of the robust model is that it keeps its linear class, thus it is not too computationally intensive
to be included in the control algorithm of a residential energy storage controller. We examine the effectiveness of the RO model in doing dynamic electricity price arbitrage and in providing fast response to requests from an aggregating entity for power injection or absorption. In the use-case,
the aggregating entity makes requests for flexibility and coordinates 100 such devices using a price signal. The results indicated that the RO approach can be beneficial for a non-conservative agent in the case of low daily price fluctuations. However, if the daily price fluctuations are higher, as in summer, the agent is better off by ignoring uncertainty in the dynamic electricity prices.