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.

Domestic hot water heaters are considered to be easily integrated as flexible loads for demand response. While literature grows on reproducible simulation and lab tests, real-world implementation in field tests considering state estimation and demand prediction-based model predictive control approaches is rare. This work reports the findings of a field test with 16 autonomous smart domestic hot water heaters. The heaters were equipped with a retrofittable sensor/actuator setup and a real-time price-driven model predictive control unit, which covers state estimation, demand prediction, and optimization of switching times. With the introduction of generic performance indicators (specific costs and thermal efficiency), the results achieved in the field are compared by simulations to standard control modes (instantaneous heating, hysteresis, night-only switching). To evaluate how model predictive control performance depends on the user demand prediction and state estimation accuracy, simulations assuming perfect predictions and state estimations are conducted based on the data measured in the field. Results prove the feasible benefit of RTP-based model predictive control in the field compared to a hysteresis-based standard control regarding cost reduction and efficiency increase but show a strong dependency on the degree of utilization.

Hot water heat pumps are well suited for demand side management, as the heat pump market faced a rapid growth in the past years with the trend to decentralized domestic hot water use. Sales were accelerated through wants and needs of energy conservation, energy efficiency, and less restrictive rules regarding Legionella. While in literature the model predictive control potential for heat pumps is commonly shown in simulations, the share of experimental studies is relatively low. To this day, experimental studies considering solely domestic hot water use are not available. In this paper, the realistic achievable model predictive control potential of a hot water heat pump is compared to the standard hysteresis control, to provide an experimental proof. We show for the first time, how state-of-the-art approaches (model predictive control, system identification, live state estimation, and demand prediction) can be transferred from electric hot water heaters to hot water heat pumps, combined, and implemented into a real-world hot water heat pump setup. The optimization approach, embedded in a realistic experimental setting, leads to a decrease in electric energy demand and cost per unit electricity by approximately 12% and 14%, respectively. Further, an increase in efficiency by approximately 13% has been achieved.

Industrial demand side management has shown significant potential to increase the efficiency of industrial energy systems via flexibility management by model-driven optimization methods. We propose a grey-box model of an industrial food processing plant. The model relies on physical and process knowledge and mass and energy balances. The model parameters are estimated using a predictive error method. Optimization methods are applied to separately reduce the total energy consumption, total energy costs and the peak electricity demand of the plant. A viable potential for demand side management in the plant is identified by increasing the energy efficiency, shifting cooling power to low price periods or by peak load reduction.

Verbraucherseitige Laststeuerung (Demand Side Management – DSM) wird als ein möglicher Ansatz betrachtet, um die Auswirkungen des Ausbaus von fluktuierenden Erneuerbaren im Stromnetz auszugleichen. Sollen viele verteilte Energiesysteme damit angesprochen werden, stellen zentralistische Ansätze dabei hohe Anforderungen an die Kommunikationsinfrastruktur. Als Alternative wird vielfach eine autonome Laststeuerung (ADSM) mit anreizbasierter Optimierung direkt auf dem Verbrauchergerät betrachtet. Dabei kann die Anreizfunktion mittels unidirektionaler Kommunikation übertragen werden. Am Forschungszentrum Energie der Fachhochschule Vorarlberg wurden in den letzten Jahren Algorithmen und Prototypen für den Einsatz von ADSM auf verschiedensten verteilten Energiespeichern im elektrischen Stromnetz entwickelt. Dabei werden sowohl thermische Energiespeicher (z. B. Haushalts-Warmwasserspeicher) als auch elektrochemische Speicher (z. B. Batteriespeichersysteme oder Elektroautos) betrachtet. Außerdem werden die Auswirkungen solcher Systeme auf das elektrische Verteilnetz untersucht. Dieser Artikel gibt einen Überblick über die entwickelten Methoden und Ergebnisse aus diesem Forschungsfeld mit dem Ziel, ein weitreichendes Verständnis für die Chancen und Grenzen des ADSM zu schaffen.