Mathematical model of flexible multi-energy industrial prosumer under uncertainty

In traditional power systems production always followed consumption, while nowadays consumers are incentivized to take active part in electricity markets. In energy intensive industry large portion of the product cost goes to energy expenses. Thus, optimizing operations based on market signals can create substantial benefits for industrial prosumers. Industries with more than one energy input vector, e.g. electricity and gas, both being bought from their respective day ahead markets are investigated in this paper. The paper introduces enthalpy modeling versus conventional mass flow which increases the scheduling efficiency. Proposed optimization model is based on stochastic mixed integer linear programming where prices of electricity are treated as stochastic process as oppose to deterministic approach usually used. Goal of optimization is to reduce overall energy cost. Also, it must provide bidding strategy for both day-ahead markets. Idea is to reduce market variability by proper device scheduling, utilizing flexibility between energy vectors and behind the meter production of electricity.