To demonstrate the framework’s capabilities, we present some examples of the usage of \(eta\_utility\). The live energy forecasting example illustrates capabilities of the connectors module. The cyber-physical production system example uses the framework to optimize the operation of an industrial cleaning machine. The code for these examples is included in the \(eta\_utility\) software repository. The repository also contains some additional example code and documentation.
We also describe other applications which were implemented using the framework. These applications have been published elsewhere; thus, we only provide summaries.
Live energy forecasting
The forecasting example illustrates the usage of the connectors module. Forecasts of energy consumption or energy prices can be an essential element of the rolling horizon energy optimization. They provide the information required for subsequent optimization steps. Chang (2021) applies the connectors module to deploy an energy forecasting model on edge devices. The forecasting model, published in Dietrich et al. (2021), is a 100 s forecast of the electric load of a grinding machine in the ETA research factory based on a keras deep learning model. The forecast can be used for peak shaving or energy-optimal process scheduling. Input data for the forecast consists of nine signals corresponding to the total electric load, and the electric load of sub-components of the grinding machine. The data is a consecutive sequence of 100 s with a frequency of 1 Hz.
For deployment of the model, (Chang 2021) implements a loop of reading data, model inference, and publishing the forecast and executes it with a frequency of 1 Hz. An OPC UA server on the grinding machine’s PLC and a Modbus TCP server on the energy metering device are the data sources.
The connectors module is used to connect to the data sources. The direct connection to the production machine’s PLC and sensor gateway is advantageous due to the flexibility it provides. It facilitates data processing, integration with the energy-optimization and fast development times. Other solutions, for instance, using Telegraf to read OPC UA and Modbus TCP data directly to InfluxDB offer less flexibility and introduce additional points of failure.
Cyber-physical production system for demand response
The second use case takes advantage of the entire \(eta\_utility\) framework for energy-flexible operation of an aqueous cleaning machine (Grosch et al. 2022). The aim is to execute DR measures on the cleaning machine MAFAC KEA in the ETA research factory by controlling its tank heater. Accordingly, the machine is extended to a cyber-physical production system that includes:
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the physical machine and its automation program (implemented as an eta_x environment with a connectors instance),
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an OPC UA server on the machine’s PLC to publish current operation steps, conditions and energy consumption data (which we connect to with the connectors instance),
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a dynamic multi-physics simulation model of the machine (implemented as an eta_x environment with a simulators instance) and
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the DR service (agent) to control the machine that receives external energy prices for its optimization (implemented using the timeseries module).
The \(eta\_utility\) framework manages the interaction between these components.
In Grosch et al. (2022), the DR service and the simulation model are executed on a PC connected to the machine’s PLC via Ethernet. The \(eta\_utility\) framework is used as a cyber-physical interface to connect different hierarchy levels of the Reference Architecture Model Industry 4.0 (DIN 2016): The machine modules controlled by the automation program and executed on the PLC are located on the field or control device level. The DR service and the simulation are located on the station level.
The authors of Grosch et al. (2022) use an automation data model for the hierarchical connection between the machine automation and the DR service. The automation data model consists of the automation data specification and the automation data dictionary (Grosch et al. 2022): The automation data specification is located on the machine’s PLC and its implementation leads to a hierarchically structured OPC UA server. The automation data dictionary is implemented using the LiveConnect class which is configured using a JSON file. It includes
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the name and the IP address of the OPC UA server(s),
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the user name and password to establish a connection,
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Node descriptions and data types for variables used by the DR service and
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a mapping between the Nodes and equivalent variables of the DR service.
The DR service fetches external energy prices to determine the state of the tank heater. It is switched on when the energy price is below 100 €/MWh and switched off when the price is above 100 €/MWh, as shown in Fig. 2. The DR service also interacts with the simulation model of the cleaning machine which simulates the thermal and electrical behavior of the machine. A forecast of the tank heater state from the simulation model guarantees that temperature limits are not exceeded by DR measures. Overall, the field test shows a 19 % decrease in energy costs. The controller and the simulation model performed as expected and set safety values were obeyed.
Other applications
\(eta\_utility\) permits comfortable implementation of various use cases, leading to substantially reduced time to obtain research results for energy-optimized factory operations. In the following we present a selection of additional use cases built on the functionality of \(eta\_utility\).
Low-cost energy monitoring based on offline trained prediction models
In order to reduce the costs of monitoring energy flows in the factory, Hybrid Virtual Energy Metering Points (VMPs) can be used. VMPs are offline trained models that predict the energy consumption of energy consumers like industrial production machines. They are set up empirically by correlating a temporary power consumption measurement with machine-internal process and state signals (Sossenheimer et al. 2020). Further research shows how other data sources can be used for training and deploying VMPs in case of insufficient machine data availability (Sossenheimer et al. 2021). The overall goal of using VMPs is to save costs by deploying trained black-box machine learning models to predict the energy consumption instead of installing physical metering devices. The connectors module is used to read the necessary machine data to the VMPs and the servers module to publish the predicted power consumption via OPC UA.
Optimized control of a central cooling system
In Weigold et al. (2021), the eta_x module is used and the DRL algorithm PPO is successfully applied to a simulation of an industrial cooling supply system. Significant reductions in electricity costs by 3 % to 17 % as well as reductions in CO\(_2\) emissions by 2 % to 11 % are achieved. The DRL-based control strategy is interpreted and three main reasons for the performance increase are identified. The DRL controller reduces energy cost by utilizing the storage capacity of the cooling system and moving electricity demand to times of lower prices. Additionally, the DRL-based control strategy for cooling towers (CT) as well as compression chillers (CC) reduces electricity costs and wear-related costs alike (compare Fig. 3). To achieve these results, the cost function to be minimized was designed as a weighted sum of temperature restriction cost (\(C_T\)), energy cost (\(C_E\)), switching cost (\(C_S\)) and other cost (\(C_O\)), each multiplied by individual weights (w):
$$\begin{aligned} \begin{aligned} C = \mathrm {w_T C_T+w_E C_E+w_S C_S+w_O C_O}. \end{aligned} \end{aligned}$$
(1)
Comparative study of algorithms for optimized control of energy supply systems
In Kohne et al. (2020), the eta_x module is used to obtain a standardized comparative study of different controllers (rule-, model- and data-based) for optimized operation strategies by connecting them to dynamic simulation models of two industrial energy supply systems of varying complexity. The first energy supply system consists of a heating, gas and electricity grid which are supplied by a combined heat and power unit, a gas boiler and an immersion heater. In the second energy supply system, a cooling grid with a cooling tower, a compression chiller and a heat pump between heating and cooling grid are added. The rule-based controller activates or deactivates the respective energy converters based on the temperatures in the top and bottom of the thermal storages depending on a priority list. The objective function of the model-based controller (mixed-integer linear programming (MILP)) is explained by Eq. (2), which contains costs for gas (\(C_G\)) and electricity (\(C_{El}\)) as well as taxes (\(C_P\)) on procured energy and charges for peak loads. Additionally, non-direct costs for switching (\(C_S\)) are added. As not every optimization run might result in feasible solutions due to grid constraints, infinite sinks and sources (\(C_{SS}\)) are modeled to ensure system stability of the optimization process, resulting in
$$\begin{aligned} \begin{aligned} \mathrm {min}\ C = \mathrm {C_{G}+C_{El} + C_{P} + C_{S} + C_{SS}.} \end{aligned} \end{aligned}$$
(2)
The cost function of the data-based DRL controller is designed as a sum of weighted terms, similar to Eq. (1) with costs for energy, switching, temperature limits and other cost (Panten 2019). The results indicate that controllers based on DRL and MILP have significant potential to reduce energy-related costs of up to 50 % for less complex (Fig. 4) and around 6 % for more complex systems.