Grocery Store Flexibility Management Using Model Predictive Control With Neural Networks

As more and more energy is produced from renewable energy sources (RES), the challenge for balancing production and consumption is being shifted to consumers instead of the power grid. This requires new and intelligent ways of flexibility management at individual building and district levels. To this end, this paper presents a model based optimal control (MPC) algorithm embedded with deep neural network for day-ahead consumption and production forecasting. The algorithm is used to optimize a medium-sized grocery store energy consumption located in Finland. System was tested in a simulation tool utilising real-life power measurements from the grocery store. We report a $8.4\%$ reduction in daily peak loads with flexibility provided by a $20$ kWh battery. On the other hand, a significant benefit was not seen in trying to optimize with respect to the energy spot price. We conclude that our approach is able to significantly reduce peak loads in a grocery store without additional operational costs.