Deployment of distributed generation, environmental concerns and shrinking of the conventional energy resources the energy suppliers are exploiting options of the renewable energy sources. These sources are often characterized by highly intermittent nature compared to the conventional ones such oil and natural gas. For the regions where governmental policies favor the use of renewables the electricity generated in times of for example strong winds might cause significant surplus of the electricity availability. In the liberalized energy markets for which the price of electricity tend to express the true cost of generation this situation might cause a decrease of the energy cost for the final user.
On the other hand, shortage of electricity availability might drive significant increase in the electricity cost for example during very cold winter day. In both cases, the electricity demand must always match the supply, otherwise the grid infrastructure is stressed, very often causing expensive failures. Therefore, it is of interest to the supply-side of the grid to better match potential demand.
Traditionally, general assumption was that the demand is inelastic in a short-term. Largely because the electricity consumer was not getting any incentive signals, which would impose a change of its consumption pattern when needed. In recent times however, smart grid technologies and liberalization of energy markets provide new ways of communicating the signals, both for dispatchable (the user is given direct signals to change the load) and non-dispatchable (the user decides whether to change the load) strategies (NERC 2007). The signals can take a form of financial incentives and different pricing signals therefore it is important the plant can somehow use this information in the daily operations such as production planning and scheduling.
Among the identified technologies for supporting an active shaping of the energy use patterns is Demand-side Management. It consists of two strategies: Energy Efficiency and Demand-Side Response (iDSR). The latter activities are defined as a temporary change in electricity consumption in response to market or reliability conditions. Industrial Demand-Side Response can be considered as one of the most cost-effective technologies to support the new challenges in meeting the demand-supply balance by reducing the peak consumption in the electrical grid, as it involves no physical equipment investments. This is even more critical for plants running on low capacity due to the economic crisis as this creates a favorable environment for shifting the production to hours of lower electricity prices, especially for energy-intensive plants.
For many years now the industrial sector accounts for the most energy use globally. According to IEA projection, in 2014 industrial delivered global energy end-use will account to more than 50% , with transportation, residential and commercial sharing the rest (IEA 2013). Therefore if applicable, Demand-side Response activities in this sector could provide beneficial influence on the demand-supply balance. Among the industries, as shown for an example of Germany in a national grid study (DENA 2011), the steel industry is the most energy-intensive. When investigating DSR of industry, it is important to consider the technical potential of response capabilities, as pointed out by Paulus and Borggrefe (2011), not only the total consumption of the process. Ideal industrial plant should have large electric equipment that operates with a consistent workload and a degree of process flexibility. Which is the case for stainless-steel plants that are melting scrap metal in highly energy-intensive Electric-Arc Furnaces.
In recent years there have been studies carried out to investigate and quantify potential benefits of iDSR. Its economic potential as well as some other benefits are reported by literature (NECR 2007, CRA 2005, ALCOA 2009), selected positive influences of the technology are as follows:
Plant level: Direct cost savings on electricity bill
Grid level: Increase of reliability benefits, e.g. reduction of outages cost
Grid level: Reduction of expensive peak load hours in short-term
Environment: Potential emission savings by reducing grid’s peak generation (only for regions with high-emitting peak generation)
Environment: Potential emission savings by enabling employment of larger renewable capacities
Market: Market-wide wholesale electricity price reduction in long-term
Market: Market performance benefits, e.g. mitigating suppliers’ ability to raise prices significantly above production costs
Even though the iDSR technology is recognized as beneficial for both the power supplier side and for the energy-intensive industry, it should be noted that its impact will not replace the long-term deficits or surplus of electricity in the regional grid, for example in case of many days of no wind in regions highly penetrated by wind-farm generated electricity.
In both situations of electricity surplus or deficit in the supply region, the industry should have a mean to react, turning the potential threat of higher electricity bill into an opportunity of lowering the raw material cost. This is especially valid for energy-intensive process industries, where the energy accounts even up to 60% of the total production cost. That fact calls for efficient methods to align the energy costs with production planning. Changes in the prices of energy might highly affect profitability, as shown on a stainless-steel case in Hadera et al. (2014a). In order to be able to respond and change the consumption pattern a certain flexibility of the process is required. If assumed that the plant’s goal is to deliver the same amount of final product over certain time horizon the production schedule can be augmented only when process-specific constraints are satisfied and at the same time the plant faces certain under-utilization of production capacity. In recent years, due to the economic crisis and decreased steel-product orders many of the energy-intensive industries are challenged with significant decrease of the capacity utilization. As shown in Figure 1, the US based energy-intensive primary metal sector went down by nearly 20% in recent years compared to 1990’s (BGFRS 2013). This potentially creates flexibility to optimally shift production to times when the electricity consumption is cheaper.
This blog post is based on a publication to be submitted to Computers and Chemical Engineering:
H. Hadera, I. Harjunkoski, G. Sand, I. E. Grossmann, S. Engell, 2014b, Optimal Steel Plant Scheduling Under Realistic Time-Varying Electricity Cost
NERC, 2007, Data Collection for Demand-Side Management for Quantifying its Influence on Reliability, http://www.nerc.com/docs/pc/drdtf/NERC_DSMTF_Report_040308.pdf, accessed 02.02.2014
ALCOA, 2009, Providing reliability services through demand response: A preliminary evaluation of the demand response capabilities of Alcoa Inc
BGFRS – Board of Governors of Federal Reserve System, 2013, Industrial Production and Capacity Utilization, http://www.federalreserve.gov, accessed 02.02.2014
CRA (Charles River Associates), 2005, Primer on demand-side management, The World Bank
DENA, 2011, Dena Grid Study II: Integration of Renewable Energy Sources in the German Power Supply System from 2015 – 2020 with an Outlook to 2025, Deutsche Energie-Agentur GmbH (dena)
M. Paulus, F. Borggrefe, 2011, The potential of demand-side management in energy-intensive industries for electricity markets in Germany, Applied Energy, 88, 2, pp. 432-441
H. Hadera, I. Harjunkoski, I. E Grossmann, G. Sand, S. Engell, 2014a, Steel production scheduling optimization under time-sensitive electricity cost, Computer Aided Chemical Engineering, Elsevier, 33, 373-278