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In the challenge of achieving environmental sustainability, industrial production plants, as large contributors to the overall energy demand of a country, are prime candidates for applying energy efficiency measures. A modelling approach using cubes is used to decompose a production facility into manageable modules. All aspects of the facility are considered, classified into the building, energy system, production and logistics. This approach leads to specific challenges for building performance simulations since all parts of the facility are highly interconnected. To meet this challenge, models for the building, thermal zones, energy converters and energy grids are presented and the interfaces to the production and logistics equipment are illustrated. The advantages and limitations of the chosen approach are discussed. In an example implementation, the feasibility of the approach and models is shown. Different scenarios are simulated to highlight the models and the results are compared.

Demand Response can be seen as one effective way to harmonize demand and supply in order to achieve high self-coverage of energy consumption by means of renewable energy sources. This paper presents two different simulation-based concepts to integrate demand-response strategies into energy management systems in the customer domain of the Smart Grid. The first approach is a Model Predictive Control of the heating and cooling system of a low-energy office building. The second concept aims at industrial Demand Side Management by integrating energy use optimization into industrial automation systems. Both approaches are targeted at day-ahead planning. Furthermore, insights gained into the implications of the concepts onto the design of the model, simulation and optimization will be discussed. While both approaches share a similar architecture, different modelling and simulation approaches were required by the use cases.

To assess cost, time investment, energy consumption and carbon emission of manufacturing on a per-piece basis, a bottom-up approach for aggregating a real-time product footprint is proposed. This method allows the evaluation of the environmental impact of a batch or even single product using monitoring or simulation data. To analyze the infrastructure, the production plant is decomposed into modules that are in relation to each other via inputs and outputs. Distinguishing between modules for production, logistics, energy system, buildings and auxiliary systems, the different approaches for distributing resource consumption between the products are presented. Special attention is paid to typical scenarios that occur in production plants and problems that may arise from them. For example, the incorporation of standby-, setup-and ramp-up times, the energy consumption of the administration and the allocation of different products and by-products manufactured at a machine are taken into account.

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This paper presents an approach for interdisciplinary optimization of energy efficiency in production plants. Domain-specific areas of action are discussed as well as the integration into a dynamic co-simulation that helps predicting the impact and financial benefit of selected energy saving measures by comparing and quantifying different scenarios. This should help giving incentives and creating impulses for strategic investment decisions. In a comprehensive methodological approach, optimization potential of both the production process itself as well as the production infrastructure is combined. The technical implementation involves several simulation environments and a framework for synchronization and data exchange in terms of co-simulation. The paper concludes with a discussion of some exemplary simulation results. I. MOTIVATIONThe manufacturing industry in Austria is responsible for nearly 33% of the national energy consumption and therefore is one of the biggest consumers besides private households and transport [1]. While in the latter two sectors energy efficiency is promoted very widely, the focus in planning of production facilities is more on flexibility and extensibility [2].[3] estimates the potential savings -depending on the industrial sector -at 35% to 60% of the energy consumption, this means significant financial savings for energy intensive industries. Despite this fact, so far only a fraction of the manufacturing companies made an effort to install energy saving measures in their production [2]. Due to economic and social conditions, many companies focus more and more on the CO 2 emission of their production facilities, which extends the tension field between time, quality and costs regarding strategic decisions.The funding of the necessary investment capital and the lack of reliable planning and optimization tools are substantial obstacles for companies to set energy saving measures in their own facilities [4], [5]. The goal of the presented research is to provide a simulation-based tool that shows the impact regarding energy flows and financial benefits of energy saving measures, and thereby incentives investment decisions. II. METHODOLOGICAL APPROACHIn order to perform a comprehensive optimization of all aspects of the system manufacturing facility and its implications on each other, an interdisciplinary approach was chosen that combines energy optimization of the production process itself (e.g. described in [6], [7], [8] for different types of production facilities) as well as the production infrastructure. For this, the main areas of action within the facility were identified, shown in Fig. 1. First the individual optimization fields process, machine, production system, energy system and building were analyzed separately for their potential savings. In a next step the connections and implications between these subregions were recorded in detail in order to understand their relations and interdependencies. This made it possible to identify the implications of small changes 2013 UKSim 15th...

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