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\ua0scheduling model is proposed for a prosumer owning\ua0photovoltaic (PV) facility coupled with a Battery Energy\ua0Storage System (BESS). The objective of the model is to\ua0maximize the prosumer’s expected profits. A two-stage\ua0stochastic mixed-integer nonlinear optimization (SMINLP)\ua0approach is used to cope with the parameters’ uncertainties.\ua0Artificial Neural Networks (ANN) are used to forecast themarkets’ prices and the standard scenario reduction\ua0algorithms are applied to handle the computational\ua0tractability of the problem. The model is applied to a case\ua0study using data from the Nordic electricity markets and\ua0historical PV production data from the Chalmers University\ua0of Technology campus, considering a scaled up 5MWp power\ua0capacity. The results show that the proposed approach could\ua0increase the revenue for the prosumer by up to 11.6% as\ua0compared to the case without any strategy. Furthermore, the\ua0sensitivity analysis of BESS’s size on the expected profit shows\ua0that increasing BESS size could lead to an increase in the net\ua0profits