A multi-stage stochastic model for production planning using onsite renewable generation with prosumer approach
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This thesis researches on finding a production plan that minimizes the cost of a manufacturing system facing uncertainties on the demand of its final products over a horizon of multiple periods and considering adoption of renewable power as an energy prosumer (i.e. consumer and seller). Researched energy sources are wind turbines and solar photovoltaics coupled with energy storage systems (i.e. batteries). Renewable generation varies because of daily changes in wind speed and weather conditions. To account for the uncertainty on products demand and power supply, a multi-stage stochastic programing model is proposed. First-stage decision variables are the size of the renewable generation technologies, capacity of the batteries, and amount of production for the first set of periods. Second-stage recourse actions to cope with the uncertainty include: (1) storing final products in inventory or purchasing from vendors, as needed, (2) using battery to discharge or store energy and (3) purchasing/selling energy to/from the grid. In the second-stage, a new production decision for the second set of periods is also determined considering the inventory levels, production and purchasing costs. The third-stage includes deciding again on the best recourse actions to the second-stage decision. The model is implemented using the scenario-tree approach, and it is solved under two operation strategies: (1) factory and warehouse consolidated in Amarillo and (2) factory in Amarillo and warehouse in Phoenix. Numerical experiments show that a prosumer microgrid model is cost-effective (annual cost $7,052,410, levelized cost of electricity (LCOE) $37/MWh) if compared to an island microgrid model (annual cost $15,150,000, LCOE $70/MWh). Due to high battery costs, the prosumer option reduces amount of battery capacity adopted and purchases some energy to the grid to save cost.