Stochastic Operation Scheduling Model for a Swedish Prosumer with PV and BESS in Nordic Day-Ahead Electricity Market

In this paper, an optimal stochastic operation scheduling model is proposed for a prosumer owning photovoltaic (PV) facility coupled with a Battery Energy Storage System (BESS). The objective of the model is to maximize the prosumer's expected profits. A two-stage stochastic mixed-integer nonlinear optimization (SMINLP) approach is used to cope with the parameters' uncertainties. Artificial Neural Networks (ANN) are used to forecast the markets' prices and the standard scenario reduction algorithms are applied to handle the computational tractability of the problem. The model is applied to a case study using data from the Nordic electricity markets and historical PV production data from the Chalmers University of Technology campus, considering a scaled up 5MWp power capacity. The results show that the proposed approach could increase the revenue for the prosumer by up to 11.6% as compared to the case without any strategy. Furthermore, the sensitivity analysis of BESS's size on the expected profit shows that increasing BESS size could lead to an increase in the net profits.