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This paper aims to present an approach for the planning of carbon low heat supply in a future district heating system based on open data for German cities with existing district heating networks. One focus is on the integration of industrial waste heat and the uncertainty of future waste heat sources as well as restrictions on the use of biomass. For that purpose, knowledge about the energy demand is necessary. In a first step it is shown how the demand around a heating network is estimated with spatial data and a load profile is generated. Local available heat sources are examined according to their suitability and their kind of integration in the heating network. As heat production from different units are optimised, the development of a simulation model will be presented. The simulation is based on the optimisation of the operational costs of the used technologies for heating supply. Different scenarios covering various technologies and economic assumptions are applied. The results show the levelized costs of heating as well as the ecological performance. A sensitivity analysis shows the importance of uncertainties for the economic assumptions. The results showing levelized costs of heating as well as the ecological performance underlining the advantage of excess heat integration.

In the fight against anthropogenic climate change, the benefit of the integration of fluctuating renewable energies (wind and photovoltaics) into the electricity grid is a widely proved concept. At the same time, a fluctuating and decentralised supply of energy, especially at lower voltage levels, leads to a local discrepancy in the power balance between generation and consumption. A possible solution in connection with demand side management is the grid-oriented flexibilisation of energy demand. The present study shows how the use of an innovative hybrid-redundant high-temperature heat system (combined heat and power (CHP), power-to-heat system (PtH), gas boiler) can contribute to a flexibilisation of the electrical energy demand of plastics processing companies. In this context, the flexibilisation potential of a company is to be understood as the grid-related change of the energy supply through a change of the energy sources within the framework of the process heat supply. For this purpose, an omniscient control algorithm is developed that specifies the schedule of the individual system components. A sensitivity analysis is used to test the functionality of the control algorithm. Determination of the electrical flexibilisation potential is carried out via a comprehensive simulation study using Monte Carlo methods. For this purpose, the residual load curves of four characteristic distribution grids with a high share of renewable energies as well as heat load profiles of injection moulding machines are taken into consideration. A frequency distribution provides information on the electrical flexibilisation potential to be expected depending on the various combinations. The evaluation is carried out using a specially introduced logic, which identifies grid-relevant changes in the company's power consumption as flexibilisation potential based on a reference load curve. The results show that a reliable energy supply for production is possible despite flexibilisation. Depending on the grid under consideration, there are differences in the exploitation of the potential, which essentially depends on the installed renewable capacity. Depending on the scenario under consideration, an average of up to 1486 kWhel can be shifted in a positive direction and 1199 kWhel in a negative direction.

The following paper describes an economical, multiple model predictive control (EMMPC) for an air conditioning system of a confectionery manufacturer in Germany. The application consists of a packaging hall for chocolate bars, in which a new local conveyor belt air conditioning system is used and thus the temperature and humidity limits in the hall can be significantly extended. The EMMPC calculates the optimum energy or cost humidity and temperature set points in the hall. For this purpose, time-discrete state space models and an economic objective function with which it is possible to react to flexible electricity prices in a cost-optimised manner are created. A possible future electricity price model for Germany with a flexible Renewable Energies levy (EEG levy) was used as a flexible electricity price. The flexibility potential is determined by variable temperature and humidity limits in the hall, which are oriented towards the comfort field for easily working persons, and the building mass. The building mass of the created room model is used as a thermal energy store. Considering the electricity price and weather forecasts as well as an internal, production plan-dependent load forecasts, the model predictive controller directly controls the heating and cooling register and the humidifier of the air conditioning system.

Prerequisite for an efficient cooling energy system is the knowledge and optimal combination of different operating conditions of individual compression and free cooling chillers. The performance of cooling systems depends on their part-load performance and their condensing temperature, which are often not continuously measured. Recorded energy data remain unused, and manufacturers’ data differ from the real performance. For this purpose, manufacturer and real data are combined and continuously adapted to form part-load chiller models. This study applied a predictive optimization algorithm to calculate the optimal operating conditions of multiple chillers. A sprinkler tank offers the opportunity to store cold-water for later utilization. This potential is used to show the load shifting potential of the cooling system by using a variable electricity price as an input variable to the optimization. The set points from the optimization have been continuously adjusted throughout a dynamic simulation. A case study of a plastic processing company evaluates different scenarios against the status quo. Applying an optimal chiller sequencing and charging strategy of a sprinkler tank leads to electrical energy savings of up to 43%. Purchasing electricity on the EPEX SPOT market leads to additional costs savings of up to 17%. The total energy savings highly depend on the weather conditions and the prediction horizon.

Renewable energy and storage systems are widely discussed to minimise the impact of global warming. In addition to the temporal resolution of simulation tools, also the chosen input data might have a strong impact on the performance of renewable energy systems, and energy storage systems in particular. This study analyses the impact of probabilistic weather data on the design of renewable energy systems. The main objective is hereby the determination of the robustness of a recently state-of-the-art design process of a 100% renewable energy and storage system with varying probabilistic input data. The island of La Gomera, Canary Islands, is taken as a case study. Although all analysed systems show some variance in their results, the combination of vehicle-to-grid and power-to-hydrogen shows the best economic performance. Hereby, small island energy systems depending heavily on wind energy show higher variations than those with high shares of solar energy. This analysis illustrates clearly that the choice of one historical reference year is not suitable to determine the expected performance of an energy system. To learn about their sensitivity, synthetic probabilistic inputs as applied in this study are a good way to determine both the expected mean values and their variance.

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.