Abstract
Energy-intensive industries can take advantage of process flexibility to reduce operating costs by optimal scheduling of production tasks. In this study, we develop an MILP formulation to extend a continuous-time model with energy-awareness to optimize the daily production schedules and the electricity purchase including the load commitment problem. The sources of electricity that are considered are purchase on volatile markets, time-of-use and base load contracts, as well as onsite generation. The possibility to sell electricity back to the grid is also included. The model is applied to the melt shop section of a stainless steel plant. Due to the large-scale nature of the combinatorial problem, we propose a bi-level heuristic algorithm to tackle instances of industrial size. Case studies show that the potential impact of high prices in the day-ahead markets of electricity can be mitigated by jointly optimizing the production schedule and the associated net electricity consumption cost.
Highlights
In many countries, renewable energy sources contribute a significant share of the overall electric power consumption and due to the volatility of their availability and their privileged role on the market, this may cause high fluctuations of the energy cost for the final user
In this study we develop a Mixed-Integer Linear Programing (MILP) formulation to extend a continuous-time model with energy-awareness to optimize the daily production schedules and the electricity purchase including the load commitment problem
This implies the need of proper day-to-day scheduling and planning of plant operations, and for making use of incentive and price based schemes, such as for example intra-day or day-ahead spot market pricing since changes in the prices of energy might significantly affect the profitability as shown for a stainless-steel production plant in Hadera et al (2014)
Summary
Renewable energy sources contribute a significant share of the overall electric power consumption and due to the volatility of their availability and their privileged role on the market, this may cause high fluctuations of the energy cost for the final user. Smart grid technologies and the liberalization of the energy markets provided new ways of communicating such signals, both for dispatchable loads (the user is given direct signals to change the consumption) and for non-dispatchable loads (the user decides whether to change the consumption) (NERC 2007) The latter signals are considered in this work in the form of financial incentives and different pricing contracts. The capacity utilization of the US-based energy-intensive primary metal sector went down by nearly 20% in recent years compared to 1990’s (BGFRS 2013) This creates a potential to optimally shift the production to times when the consumption of electricity is cheaper. While the iDSR technology is recognized as beneficial for both the power supplier side and for the energy-intensive industry, it should be noted that it cannot compensate long-term deficits or surplus of electricity generation in regional grids
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