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

Abstract

The increasing penetration of Renewable Energy Sources (RES), the liberalization of the electricity markets across the world and devices such as smart meters 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 underutilizing 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. In the use-case, the aggregating entity makes requests for flexibility and coordinates 100 such devices using a price signal, while the storage controller is doing dynamic electricity price arbitrage. The results indicated that the RO approach can be beneficial for a non-conservative agent in the case of low daily price fluctuations, while, in summer, when the price fluctuations are higher, uncertainty can be ignored.