Energy-cost-aware flow shop scheduling considering intermittent renewables, energy storage, and real-time electricity pricing

The industrial sector is one of the major energy consumers that contribute to global climate change. Demand response programs and on-site renewable energy provide great opportunities for the industrial sector to both go green and lower production costs. In this paper, a 2-stage stochastic flow shop scheduling problem is proposed to minimize the total electricity purchase cost. The energy demand of the designed manufacturing system is met by on-site renewables, energy storage, as well as the supply from the power grid. The volatile price, such as day-ahead and real-time pricing, applies to the portion supplied by the power grid. The first stage of the formulated model determines optimal job schedules and minimizes day-ahead purchase commitment cost that considers forecasted renewable generation. The volatility of the real-time electricity price and the variability of renewable generation are considered in the second stage of the model to compensate for errors of the forecasted renewable supply; the model will also minimize the total cost of real-time electricity supplied by the real-time pricing market and maximize the total profit of renewable fed into the grid. Case study results show that cost savings because of on-site renewables are significant. Seasonal cost saving differences are also observed. The cost saving in summer is higher than that in winter with solar and wind supply in the system. Although the battery system also contributes to the cost saving, its effect is not as significant as the renewables.