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Software helps shift production to times when energy is cheaper

Shifting production in energy-intensive industries to times when energy is cheaper can result in significant savings. How can the many variables, constraints and industry-specific aspects be taken into account to produce an optimized model?

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Flexible production saves energy costs

Many energy-intensive industries such as steel, pulp and paper, and cement face the challenge of how to deal with the effect of increasing and fluctuating energy prices on daily production operations. New collaboration schemes offered to these industries through intelligent and flexible electricity networks (smart grids) significantly reduce total production costs by optimally timing electricity consumption. The main concern is how to make the production process flexible enough that a company can buy electricity when it is cheap – and even sell it back to the grid during peak hours. At perhaps a hundred times the purchase price.

Need for new approaches

Replacing traditional stable and controllable energy sources with fluctuating renewable sources means energy supply and price can no longer be taken for granted. Because of this, market tools for purchasing and selling electricity have become almost a necessity for large consumers. Since the price of electricity has a direct impact on production cost, large consumers have also started to consider including energy forecasts in their production planning. This concept, coupled with energy efficiency, is called demand-side management.

In contrast to energy efficiency strategies, which aim to produce the same using less energy, demand-side response focuses on profitable time-shifting of the load. In practice, this means that an industrial plant needs to adapt production according to the energy cost situation. If future electricity pricing information is available – and this discussion will assume it is – many processes can take it into account in short-term planning or scheduling.
In contrast to energy efficiency strategies, which aim to produce the same using less energy, demand-side response focuses on profitable shifting of the load in time.
Energy demand-side management concept and the motivation for having it

Energy management solution

ABB already offers a solution for optimizing the energy portfolio for a given production plan: cpmPlus Energy Manager has been available for more than a decade and covers energy conversion (eg, fuel to energy), purchasing from various markets and also some production planning decisions – especially for continuous processes. The solution has been installed by many types of customers – including pulp and paper, metals and mining – as a part of ABB’s collaborative production management (CPM) solution and has demonstrated significant benefits.

The following graph shows Energy Manager solution for a thermomechanical pulp (TMP) mill, with the production lines displayed in the upper diagram and the fiber storage tank level in the middle diagram. A mathematical optimization is used to simultaneously consider all energy-consuming and energy-producing units together with the option of purchasing from or selling the energy to the grid based on current prices. The electricity consumption of the three TMP lines is shown in the bar graph in the lower part of the graph and the varying electricity price is indicated by the yellow line.

This example illustrates how a CPM solution can collect and connect information from various sources and generate the most cost-efficient production strategies, while also taking into account electricity costs. As the solution also includes other production units, it decides when to run which production line, taking into account, for example, total downstream steam demand, the capacity and cost of alternative steam sources, the production plan of the paper machines, and the minimum and maximum production limits of each refiner line.
Energy management solution for a TMP mill. All lines are stopped during the highest electricity price peak.

Here, at least a partly prespecified production schedule is assumed

Here, the production schedule and the electricity purchase strategy are simultaneously or iteratively optimized

Holistic optimization

The TMP example assumes that there exists at least a partly prespecified production schedule. The scientific challenge arises in simultaneously optimizing the production schedule and the electricity purchase strategy. The main idea is to optimally schedule production while considering aspects from the control, scheduling and supply chain layers. Mixed-integer linear programming (MILP) techniques represent a very promising way to arrive at holistic optimization solutions to problems like this that have partly competing targets. MILP solvers have improved significantly and can now solve problems that are several magnitudes larger than those of a decade ago.

The problem of simultaneously optimizing energy management aspects and production planning needs has still not been completely solved and researchers are currently trying to find ways to handle this in realistic production environments. Nevertheless, ABB has collaborated with a steel company on this topic to come up with some feasible concepts that are currently being tested live in production.
The scientific challenge arises in simultaneously optimizing the production schedule and the electricity purchase strategy.

Flexibility is key

The complexity of production scheduling is increasing in industries outside of papermaking too, mainly due to smaller and more customized orders. Production plants now have to be agile and flexible to respond to short-term changes. These industries also face the complexity arising from variable, but potentially more affordable, electricity pricing on an hourly basis in the day-ahead market. Consequently, combined energy and production planning processes must always be well integrated with real-time data.

Having a full offering of process and grid automation, ABB has the tools to realize a proper matching of supply and demand using internal buffers in the process and production load shifting for a wide range of industries.


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