Two-stage stochastic aggregate production planning models with renewable energy prosumers considering multiple facilities and hourly time of use
Islam, Sayed Rezwanul
This thesis work presents a two-stage stochastic aggregate production planning model to determine the optimum renewable energy capacity, production plan, machine and workforce levels that minimize the operational cost of a production system consisting of multiple facilities operating in different geographic locations. The model considers uncertainty on the demand of products, machine and labor capacities, and on the renewable energy supply under a horizon of twelve months. The goal of this work is to evaluate the feasibility of decarbonizing the manufacturing, transportation and warehouse operations by adopting wind turbine and solar photovoltaics coupled with battery storage (BS) assuming the facilities are energy prosumers. In the model, the first-stage decisions are the sitting and sizing of the renewable generation technologies, the capacity of the BS, amount of product to produce, hours of labor to keep, hire or layoff, and regular, overtime, and idle machine hours to use for the entire planning horizon. Second-stage recourse actions include storing product in inventory, subcontracting or backorder it, buying energy, selling renewable energy to the main grid, and using BS to respond to variations in wind profile and weather conditions. Climate analytics performed in six U.S. cities permits to estimate the capacity factors of the renewables and test their feasibility of adoption. Numerical experiments performed on three model instances: island microgrid (IM), energy prosumer with and without time-of-use (TOU) tariffs, show favorable levelized costs of energy in $50-$100 per MWh. The instances are relevant to manufacturing companies and the society since they replace the usage of fossil fuels and accelerate eco-friendly operations to achieve net-zero carbon manufacturing operations.
stochastic programming, renewable energy
Islam, S. R. (2021). Two-stage stochastic aggregate production planning models with renewable energy prosumers considering multiple facilities and hourly time of use (Unpublished thesis). Texas State University, San Marcos, Texas.