(426a) Optimal Scheduling of a Steel Plant Under Complex Electricity Price Structures

In the coming decades, the energy sector in Europe will face a fundamental shift towards renewable electricity generation, mostly from wind power. Wind feed-in is generally unresponsive to systems needs and associated to a high level of uncertainty. It will have price effects in the spot market and will lead to a growing demand for positive and negative balancing power (Paulus & Borggrefe, 2011). One way to provide the required stability and flexibility to the power system is through Demand Side Management (DSM), which can be categorized as either reducing energy consumption, or rescheduling and shifting energy demand to a later time. Large-scale, energy intensive processes, such as the melt shop of a steel plant, may play an important role in this context and the question that arises in the scientific community is how to balance electricity supply and availability against the background of profitable conditions for industry.

In the industrial DSM or Demand Response (DR) grid-consumer interface, the electricity provider gives economic incentives to the industry to alter their electricity usage behavior. In the price-based program, customers respond to the electricity price structure (e.g. day-ahead market) with voluntary changes in their timing of electricity usage, taking advantage of low-price periods and avoiding production in high-priced periods. In the incentive-based program, customers can obtain electricity at a reduced cost, if electricity consumption is contracted ahead of time (e.g. 1-2 days in advance). The consumption curve then has to be tracked as closely as possible to avoid penalties for over- and under-consumption.

Electric arc furnaces in steel manufacturing can provide significant amounts of positive reserve capacity by decreasing demand when the electricity system falls short of providing sufficient capacity. The challenge is to take the complex production constraints (Harjunkoski & Grossmann, 2001) into account so that the plant’s participation in the spot market does not lead to possible disruptions in the process.

Nolde & Morari (2010) and Häit & Artigues (2011) proposed continuous-time formulations for electrical load tracking of a steel plant. The problem consists on scheduling the production tasks such that energy consumption is kept as close as possible to a pre-specified load curve to avoid penalties from the electricity provider. Power consumption of tasks are given parameters but their processing times are allowed to vary between minimum and maximum values to provide schedule flexibility.

Castro et al. (2009) addressed the problem of deriving a minimum cost schedule for a cement plant. A deterministic price profile was given together with hard constraints on maximum power consumption over certain periods of time. Resource-Task Network (RTN) discrete and continuous-time formulations were tested, with the former exhibiting a better performance. It was later combined with the continuous-time model within a rolling-horizon algorithm to guarantee the generation of practical schedules (Castro et al. 2011). Mitra et al. (2012) also looked at optimal production planning based on predefined hourly prices with a discrete-time deterministic model. Industrial case studies on air separation units and cement plants were tackled. Vujanic et al. (2012) proposed a method for obtaining flexible schedules using robust optimization so that delaying the execution of a job does not cause infeasibility problems. The aim was for this flexibility to be sold on the markets for ancillary services.

In this work, we consider the melt shop of a steel plant with four stages, two parallel units per stage, fixed processing times and power consumption values. Transportation times between stages must lie below enforced upper bounds to avoid excessive cooling of steel heats. This poses interesting constraints that can be modeled approximately, in the context of a RTN process model with multiple location states for the different heats, or rigorously, by defining a considerably simpler RTN model coupled with logic constraints. One major goal will be to show that the selection of the process model is an important decision with respect to computation performance of the resulting MILP, which relies on a discrete-time representation. The other novel aspect concerns the incorporation of penalties for under- and overconsumption on both power and energy so that deviations from a pre-contracted load curve for electricity can be accounted for. Overall, we study the impact of fluctuating energy prices on the scheduling of operations and the economic benefits that can be obtained from the plant’s participation in the price and incentive based Industrial Demand Side Management programs.

References:

-Castro, P.M.; Harjunkoski, I.; Grossmann, I.E. (2009). New Continuous-Time Scheduling Formulation for Continuous Plants under Variable Electricity Cost. Ind. Eng. Chem. Res. 48, 6701.

-Castro, P.M.; Harjunkoski, I.; Grossmann, I.E. (2011). Optimal scheduling of continuous plants with energy constraints. Comput. Chem. Eng. 35, 372.

-Häit, A.; Artigues, C. (2011). On electrical load tracking scheduling for a steel plant. Comput. Chem. Eng. 35, 3044.

-Harjunkoski, I.; Grossmann, I.E. (2001). A Decomposition Approach for the Scheduling of a Steel Plant Production. Comput. Chem. Eng, 25, 1647.

-Mitra, S.; Grossmann, I.E.; Pinto, J.M.; Arora, N. (2012). Optimal production planning under time-sensitive electricity prices for continuous power-intensive processes. Comput. Chem. Eng. 38, 171.

-Nolde, K.; Morari, M. (2010). Electrical load tracking scheduling of a steel plant. Comput. Chem. Eng. 34, 1899.

-Paulus, M.; Borggrefe, F. (2011). The potential of demand-side management in energy-intensive industries for electricity markets in Germany. Applied Energy, 88, 432.

-Vujanic, R.; Mariéthoz, S.; Goulart, P.; Morari, M. (2012). Robust Integer Optimization and Scheduling Problems for Large Electricity Consumers. 2012 American Control Conference, Montreal, Canada, pp. 3108-3113.