Open Access

Renewable energy communities (REC) are legal entities defined by the European Clean Energy Package to own and share energy resources. A common goal of RECs is to have a high self-consumption. This work focuses on how self-consumption can be increased by means of intelligent control of flexibilities based on a use case featuring a community battery energy storage (CBES). Many simulation studies for the control and optimization of distributed energy resources (DER) including community storage exist, focusing on various aspects, however, often neglecting important effects for a real implementation due to simplified assumptions: (A) The control or optimization routine is run open loop. Thereby, the influence of prediction and modeling errors is ignored, so that the results obtained represent only an upper limit for the effectiveness. (B) The availability of data is not considered appropriately which might render certain control approaches unpractical. In practice, this might be a limiting factor for forecasting methods and control methods in general. Especially, as RECs are of a distributed nature and the implementation of potentially costly data acquisition and information infrastructure could limit the economic rentability significantly. We investigate a realistic use case, constructed based on the current Austrian legal definition of RECs. Here, recent smart meter data of the REC participants, as it is made available by the distribution system operator (DSO) for settlement purposes. We utilize this data for control. Our realistic simulation model consists of several residential loads, a community PV system, two private PV systems and a CBES which is controlled to boost the RECs self-consumption. The model was implemented within the MOSAIK co-simulation framework. Due to the enforced modularity of this framework, the controller is integrated into the simulation model in a closed loop manner, addressing limitation (A) of similar studies. In addition, the data exchange between the different actors, such as the grid operator and the REC operator, is modeled, which clearly addresses limitation (B). A promising method for the control problem of the described REC is model predictive control (MPC) and its solution by means of mixed integer linear programming (MILP). To control the community storage system using MPC, an accurate prediction of the residual load is required. This forecast is created using a machine learning model (LSTM model), which is pre-trained on synthetic load profiles to improve the prediction with limited data availability. Results show that MPC can handle the model and prediction uncertainties generally well. Nevertheless, the consideration of forecasting and modelling uncertainties in the simulation shows that the achievable self-consumption rates can be significantly reduced. Utilizing simplified forecasting and considering modelling uncertainty reduces the self-consumption by up to 14.5% points in the use case considered. These losses can, however, be contained by accurate prediction methods. With the consideration of modelling uncertainties for the CBES model alone, we find that a discrepancy between real storage capacity and controller model capacity of 20% reduces the self-consumption by 3.9% points. As the CBES can be considered a comparably “easy to model” component, this highlights the need for closed loop simulation in the controller development for RECs.

Grey Box models provide an important approach for control analysis in the Heating, Ventilation and Air Conditioning (HVAC) sector. Grey Box models consist of physical models where parameters are estimated from data. Due to the vast amount of component models that can be found in literature, the question arises, which component models perform best on a given system or dataset? This question is investigated systematically using a test case system with real operational data. The test case system consists of a HVAC system containing an energy recovery unit (ER), a heating coil (HC) and a cooling coil (CC). For each component, several suitable model variants from the literature are adapted appropriately and implemented. Four model variants are implemented for the ER and five model variants each for the HC and CC. Further, three global optimization algorithms and four local optimization algorithms to solve the nonlinear least squares system identification are implemented, leading to a total of 700 combinations. The comparison of all variants shows that the global optimization algorithms do not provide significantly better solutions. Their runtimes are significantly higher. Analysis of the models shows a dependency of the model accuracy on the number of total parameters.

To keep energy systems in an optimal state it is necessary to ensure proper maintenance and optimized operation. Both of these goals can be achieved with implementation of predictive maintenance. Based on sensor data, it is a perfect option to fulfil diagnostics and prognostic tasks, and optimize long time operation of the power plant. Prediction of failures and outlier detection methods are the basis of predictive maintenance. Several outlier and failure detection algorithms have been presented so far, but still have limitations concerning implementation. We propose a predictive maintenance approach for a water treatment system of a power plant of BERTSCHEnergy. We investigate methods for failure detection and find that only a combination of data-driven and knowledge-based leads to the desired result.