This study focuses on analysing the most energy efficient utility system supply structure in terms of carbon emissions, primary energy efficiency and energy costs. In the German food processing industry, the state-of-the-art technologies in the utility supply structure are a gas fired steam boiler for steam generation and ammonia chillers for chilled water generation.Low investment costs and its durability are attractive for industrial production sites. But, given the ongoing energy transition to renewable energy, opportunities to reduce emissions will become increasingly important. There are other energy supply options, such as Combined Heat and Power and Heat Pumps, that need to compete against the conventional energy supply systems. In the short-term, countries with presently high electricity Grid Emissions Factors (GEF) such as Germany and the USA, the use of decentralised CHP results in savings of primary energy and emissions. This option is less attractive for countries with already low GEF such as Norway. It is also less attractive in the long-term for countries like Germany as the on-going energy transition towards renewables is anticipated to decrease the current GEF by 50% in 2030. In these cases of low GEF, HP solutions provide the lowest emissions and highest primary energy efficiency. HighlightsAssessment of supply chains from energy source to final consumption Quantified environmental impact of process heat supply technology Analysed outlook for industrial energy supply structures in different countries With future low emissions electricity, heat pumps are a key energy supply solution

This paper investigates the use of renewable energies to supply hotels in island regions. The aim is to evaluate the effect of weather and occupancy fluctuations on the sensitivity of investment criteria. The sensitivity of the chosen energy system is examined using a Monte Carlo simulation considering stochastic weather data, occupancy rates and energy needs. For this purpose, algorithms based on measured data are developed and applied to a case study on the Canary Islands.The results underline that electricity use in hotels is by far the largest contributor to their overall energy cost. For the invested hotel on the Canary Islands, the optimal share of renewable electricity generation is found to be 63 %, split into 67 % photovoltaic and 33 % wind power. Furthermore, a battery is used to balance the differences between day and night. It is found, that the results are sensitive to weather fluctuations as well as economic parameters to about the same degree. The results underline the risk caused by using reference time series for designing energy systems. The Monte Carlo method helps to define the mean of the annuity more precisely and to rate the risk of fluctuating weather and occupancy better.

This study applies a Total Site Heat Integration approach in conjunction with a detailed process and utility model, to develop an innovative ultra-low energy milk powder plant design. The basis for the analysis is a state-of-the-art modern milk powder plant that requires 5,265 MJ/t p of fuel and 210.5 kWh/t p (58.5 MJ e /t p ) of electricity. The model of the modern milk powder plant was validated against industrial data and changes to process and/or utility systems are targeted and implemented into the model to understand the impacts on thermal and electrical demands and emissions. Results show that seven significant changes are beneficial: (1) pre-concentration of milk to 30 % using reverse osmosis, (2) a two-stage intermediate concentrate (30 %) homogenisation to enable high solids (60 %) spray drying, (3) an ultra-low energy Mechanical Vapour Recompression

Prerequisite for system efficiency towards an industrial energy transition is the reducing of energy demand on the process level. In typical manufacturing systems with machine tools and washing machines, the proper design of intelligent standby control and heat pump storage system (HPS) represent high efficiency. The integration of HPS is complicated due to high non-continuity, especially when implementing a standby control system. Our approach aims at designing one single HPS for multiple heat sources and sinks. Robust design should consider the various influencing material flow system factors. For the generation of stochastic heating and cooling demand sum curves, 512 Design of Experiments-based material flow simulations for each of three standby scenarios have been conducted. These curves serve as input data for HPS sizing and d yn amic thermal system simulation. The combined integration of an HPS and a practical standby control system offers the best compromise in terms of system efficiency with significantly lower investment costs and only slightly lower energy savings than ideal standby operation. Compared to the initial state, the electrical energy demand ofthe machines can be reduced by 27 % and both the heating (83 %) and cooling (48 % ) demand can be efficiently covered by HPs.

For increased total site heat integration, the optimal sizing and robust operation of a heat recovery loop (HRL) are prerequisites for economic efficiency. However, sizing based on one representative time series, not considering the variability of process streams due to their discontinuous operation, often leads to oversizing. The sensitive evaluation of the performance of an HRL by Monte Carlo (MC) simulation requires sufficient historical data and performance models. Stochastic time series are generated by distribution functions of measured data. With these inputs, one can then model and reliably assess the benefits of installing a new HRL. A key element of the HRL is a stratified heat storage tank. Validation tests of a stratified tank (ST) showed sufficient accuracy with acceptable simulation time for the variable layer height (VLH) multi-node (MN) modelling approach. The results of the MC simulation of the HRL system show only minor yield losses in terms of heat recovery rate (HRR) for smaller tanks. In this way, costs due to oversizing equipment can be reduced by better understanding the energy-capital trade-off.