Due to increases in energy prices in recent years and the threat of global warming, closely related to energy use, energy issues have been brought to the fore worldwide. Furthermore, due to increased globalisation,  industries are facing greater competition that is pressing companies to reduce their costs. Energy efficiency is therefore an essential task for the future as it has a significant impact on both the environment and business  profits. Different means exist to accomplish a reduction in energy costs, for example, investments in energy efficiency and load shaping. To analyse the results of such changes and provide support when choosing between different measures, different modelling tools can be used. This paper describes Energy Systems Optimisation (ESO) and Discrete Event Simulation (DES) tools and illustrates how an ESO tool and a DES tool can be applied in combination using two simplified case studies. The focus will be on the interactivity analyses between methods and how they provide the user with additional information regarding energy efficiency measures. In the first case, results from the ESO model are simulated to investigate whether the results can be applied in reality. In the second case, the results from the DES model from three different investment alternatives are used as input data to the ESO model to investigate which alternative gives the maximum process utilisation, lowest environmental impact and lowest system costs. Using these tools together improves overall process utilisation and helps provide the analysis with more information than if the tools are used separately.

To face global competition, and also reduce environmental and climate impact, industry-wide changes are needed, especially regarding energy use, which is closely related to global warming. Energy efficiency is therefore an essential task for the future as it has a significant impact on both business profits and the environment. For the analysis of possible changes in industrial production processes, and to choose what changes should be made, various modelling tools can be used as a decision support. This paper uses two types of energy analysis tool: Discrete Event Simulation (DES) and Energy Systems Optimisation (ESO). The aim of this study is to describe how a DES and an ESO tool can be combined. A comprehensive five-step approach is proposed for reducing system costs and making a more robust production system. A case study representing a new investment in part of a Swedish iron foundry is also included to illustrate the method's use. The method described in this paper is based on the use of the DES program QUEST and the ESO tool reMIND. The method combination itself is generic, i.e. other similar programs can be used as well with some adjustments and adaptations.

The results from the case study show that when different boundary conditions are used the result obtained from the simulation tools is not optimum, in other words, the result shows only a feasible solution and not the best way to run the factory. It is therefore important to use the optimisation tool in such cases in order to obtain the optimum operating strategy. By using the optimisation tool a substantial amount of resources can be saved. The results also show that the combination of optimisation and simulation tools is useful to provide very detailed information about how the system works and to predict system behaviour as well as to minimise the system cost.

In recent years, there has been a worldwide focus on the issue of energy because of increased energy prices and the threat of increasing global warming. Furthermore, industries are facing greater competition as a result of increasing globalisation, which is forcing companies to reduce their expenses. Reducing the use of energy is therefore an essential task for the future as it has a positive impact on both the environment and the profits of any business. Reductions in energy demand can be accomplished by different means, such as investments in energy-efficient processes or load management. Analytical tools may be used to support the decision-making process, when choosing between a number of measures, and analysing the results can help to choose which changes should be made.

This paper studies two types of energy analysis tool: energy systems optimisation (ESO) and discrete event simulation (DES). The aim of this paper is to describe a method where a DES and an ESO tool are combined in order to study the potential energy and resource reduction in complex industrial energy systems. A case study representing a part of a dairy is also included to illustrate the use of the method. The system modelled includes a process where the durability or longevity of milk increases from a few days to 28 days by using steam injection.

The results from the case study show that the dairy has much higher potential production capacity than realised today. This also means that there is a potential to reduce the operation hours from a three-shift to a two-shift operation to meet the existing weekly demand. The analysis also shows that there are large potential reductions in both energy and other resources. The largest potential reductions are primarily from electricity and water. The combination of tools increases the reliability of the analysis and facilitates decision making in an industrial site.

Due to increased globalisation, industries are facing greater competition that is pressing companies into decreasing their expenses in order to increase their profits. As regards Swedish industry, it has been faced with substantial increases in energy prices in recent years. Barriers to energy efficiency such as imperfect information inhibit investments in energy efficiency measures, energy audits being one means of reducing barriers and overcoming imperfect information. However, an evaluation of such energy audits in Sweden reveals that it is chiefly low-cost measures that are undertaken as a result of an audit. Moreover, these audits often tend to focus on support processes such as ventilation, lighting, air compressors etc., while measures impacting production processes are often not as extensively covered, which underlines the need for further support in addition to energy audits. Decision support is practised in a variety of different disciplines such as optimization and simulation and the aim of this paper is to explore whether investment decision support practices may be used successfully towards small and medium-sized manufacturers in Sweden when complex production-related investment decisions are taken. The optimization results from the different cases, involving a foundry’s investment in a new melting unit, indicate that with no electricity price fluctuations over the day, the investment seems sound as it lowers the overall energy costs. However, with fluctuating electricity prices, there are no large differences in energy costs between the option of retaining the existing five melting furnaces at the foundry and investing in a twin furnace and removing the holding furnaces – which was the initial investment plan for the foundry in the study. It would not have been possible to achieve this outcome without the use of investment decision support such as MIND. One of the main conclusions in this paper is that investment decision support, when strategic investment decisions are to be taken, may be a means of emphasising energy efficiency for energy-intensive SMEs beyond the level of traditional energy auditing.

The increased competition in the global market-place is forcing industrial manufacturers to develop their production systems by increasing flexibility, improving quality and lowering production costs. With the help of simulation techniques, the understanding of manufacturing systems can be enhanced and alternative solutions can be tested. Simulation has, therefore, played an important role in industrial development in recent years. At the same time, energy-related costs have been neglected by Swedish industry due to historically low energy costs in Sweden, in comparison with such costs in other European countries. The developments in the energy market, with uncertainty concerning future prices, have increased the need for energy efficiency. The research described in this paper focuses on methodologies developed to enhance the efficient analyses of energy systems in manufacturing plants by using discrete event simulation. The focus is on electricity use. The paper briefly presents the main features of the methodologies and describes the results from four case studies carried out in the Swedish foundry industry. The methodology improves efficiency by identifying those processes that are important, the activities that must be undertaken and the types of analyses that can be undertaken.

Investments of different kinds are vital for industries to stay competitive. However, there are several issues that need to be considered before investing, e.g. the timing and size of the investment. In this paper a methodology is presented for analysing investments form the point of view of optimal size and timing. The energy systems optimization tool reMIND is used as the basis of the modelling, and has been used in several industrial energy systems studies for various purposes. reMIND is based on Mixed Integer Linear Programming (MILP) and has been further developed to consider investments of different kinds. The different constraints needed to model the investment properly are presented together with the variables included in the objective function. A simple case study is also included to illustrate how the method is used. The results from the case study show that the timing and size of the different investments change, depending on the size of the proposed increase in production rate.

There are many different aspects a production-planning model has to be able to handle to make a model adequate for the purpose. One aspect is the handling of start-ups and shutdowns for different processes. The production plan is likely to be changed when considering, for example, a cost connected to the start-up and/or shutdown of processes. Besides costs associated with start-ups and shutdowns, waste may be produced during the start-up and shutdown. However, there is also the possibility of carrying out soft start-ups and shutdowns or limiting the number of start-ups and shutdowns. Thus, start-ups and shutdowns have to be handled in an adequate way in models to produce reliable and accurate results. In optimisation tools, this may be dealt with by introducing certain constraints, including integers. In this paper, the implementation of alternative ways to consider start-ups and shutdowns are presented. This is done in the energy system optimisation tool reMIND, which deals with Mixed Integer Linear Programming (MILP) problems. The purpose of this paper is to show four alternatives to consider start-ups and shutdowns in optimisation models. This involves, in total, almost 50 constraints. Also, a simple dairy case study is included in the paper to visualise the effect of implementing the different alternatives to shutdowns.