Energy communities (ECs) play a key role in enabling local demand shifting and enhancing self-sufficiency, as energy systems transition toward decentralized structures with high shares of renewable generation. To optimally operate them, accurate short-term load forecasting is essential, particularly for implementing demand-side management strategies. With the recent rise of deep learning methods, data-driven forecasting has gained significant attention, however, its role in optimizing energy communities remains insufficiently explored under varying conditions. Therefore, this study evaluates the effectiveness of state-of-the-art deep learning models—including Long Short-Term Memory (LSTM), extended LSTM (xLSTM), and Transformer architectures—compared to traditional benchmarks such as K-Nearest Neighbors (KNN) and persistence prediction, across different community sizes, historical data availability, and model complexity. Additionally, we assess the benefits of transfer learning using publicly available synthetic load profiles. On average, transfer learning improves the normalized mean absolute error by 1.97 percentage points when only two months of training data are available. Interestingly, for less than six months of training data, simple persistence predictions outperform deep learning architectures in forecast accuracy, indicating their suitability in data-scarce scenarios. The practical value of improved forecasting is demonstrated using model predictive control for ECs with a shared battery energy storage system. For an energy community with 50 households, the most accurate deep learning model achieves an average reduction in financial energy costs of 8.06%. Notably, the simple persistence and KNN approaches yield cost savings that are not statistically significantly different to deep learning methods (paired t-test, all p > 0.99). All implementations are publicly available to facilitate reproducibility. These findings offer actionable insights for ECs, and they highlight when the additional complexity of deep learning is warranted by performance gains.

The increasing penetration of volatile renewables and growing electricity demand pose several challenges for power systems. Simultaneously, flexible devices-so called distributed energy resources (DER)-are becoming more widespread, making them attractive for providing ancillary services. The flexibility of a single device can be represented by a set of reference power profiles, and the flexibility of multiple devices by the summation of individual flexibility sets. However, set addition, also known as the Minkowski sum, is usually computationally intractable. This has led to the development of various approximation methods in the literature. The current study improves upon our previously published vertex-based inner approximation, by extending it to more general storage devices and hierarchical aggregation settings. We validate the efficacy and accuracy of the proposed method through case studies using real data and provide the source code of the algorithm as a Python package that enables the (dis-)aggregation of various flexible devices in real-world scenarios.

EU regulations get stricter from 2028 on by imposing net-zero energy building (NZEB) standards on new residential buildings including on-site renewable energy integration. Heat pumps (HP) using thermal building mass, and Model Predictive Control (MPC) provide a viable solution to this problem. However, the MPC potential in NZEBs considering the impact on indoor comfort have not yet been investigated comprehensively. Therefore, we present a co-simulative approach combining MPC optimization and IDA ICE building simulation. The demand response (DR) potential of a ground-source HP and the long-term indoor comfort in an NZEB located in Vorarlberg, Austria over a one year period are investigated. Optimization is performed using Mixed-Integer Linear Programming (MILP) based on a simplified RC model. The HP in the building simulation is controlled by power signals obtained from the optimization. The investigation shows reductions in electricity costs of up to 49% for the HP and up to 5% for the building, as well as increases in PV self-consumption and the self-sufficiency ratio by up to 4% pt., respectively, in two distinct optimization scenarios. Consequently, the grid consumption decreased by up to 5%. Moreover, compared to the reference PI controller, the MPC scenarios enhanced indoor comfort by reducing room temperature fluctuations and lowering the average percentage of people dissatisfied by 1% pt., resulting in more stable indoor conditions. Especially precooling strategies mitigated overheating risks in summer and ensured indoor comfort according to EN 16798-1 class II standards.

According to a conservative estimate, a 1% reduction in forecast error for a 10 GW energy utility can save up to $ 1.6 million annually. In our context, achieving precise forecasts of future power consumption is crucial for operating flexible energy assets using model predictive control approaches. Specifically, this work focuses on the load profile forecast of a first-year energy community with the common practical challenge of limited historical data availability. We propose to pretrain the load prediction models with open-access synthetic load profiles using transfer learning techniques to tackle this challenge. Results show that this approach improves both, the training stability and prediction error. In a test case with 74 households, the prediction mean squared error (MSE) decreased from 0.34 to 0.13, showing transfer learning based on synthetic load profiles to be a viable approach to compensate for a lack of historic data.

The food industry faces significant challenges in managing operational costs due to its high energy intensity and rising energy prices. Industrial food-processing facilities, with substantial thermal capacities and large demands for cooling and heating, offer promising opportunities for demand response (DR) strategies. This study explores the application of deep reinforcement learning (RL) as an innovative, data-driven approach for DR in the food industry. By leveraging the adaptive, self-learning capabilities of RL, energy costs in the investigated plant are effectively decreased. The RL algorithm was compared with the well-established optimization method Mixed Integer Linear Programming (MILP), and both were benchmarked against a reference scenario without DR. The two optimization strategies demonstrate cost savings of 17.57% and 18.65% for RL and MILP, respectively. Although RL is slightly less efficient in cost reduction, it significantly outperforms in computational speed, being approximately 20 times faster. During operation, RL only needs 2ms per optimization compared to 19s for MILP, making it a promising optimization tool for edge computing. Moreover, while MILP’s computation time increases considerably with the number of binary variables, RL efficiently learns dynamic system behavior and scales to more complex systems without significant performance degradation. These results highlight that deep RL, when applied to DR, offers substantial cost savings and computational efficiency, with broad applicability to energy management in various applications.

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.

Demand side management holds potential for improving energy efficiency and cutting energy consumption within the food industry. This research introduces a demand response approach tailored for an industrial food processing facility, utilizing a chilled water buffer as active thermal energy storage and the plant building as passive thermal energy storage. The plant building and production process are modeled using transient thermal energy balances and the demand side management problem is formulated as a linear program. Model predictive control is employed to manage uncertainties in the optimization process. A simulated case study of an Austrian food processing plant shows reductions in electrical power consumption by up to 18%, electricity costs by up to 24%, and peak load by up to 36% in three distinct optimization scenarios. Simple prediction approaches via averaging historical data already lead to nearly optimal results concerning energy consumption and cost reduction. Highly accurate predictions are necessary for peak load reduction, as considering the simple prediction method only roughly a third of the potential reductions are achieved.

Optimal scheduling of electric vehicle charging is a non-trivial problem associated with multiple sources of uncertainties. These uncertainties are often neglected in demand-side management studies assuming perfect predictions, which in practice is unrealistic. In this paper, we propose a model predictive control framework for scheduling the charging of residential electric vehicles to account for the uncertainties associated. The evaluations are performed considering a decentralized demand-side management algorithm proposed in the literature, which aims to exploit the flexibility of electric vehicles to fill the valleys in the demand curve in order to flatten the aggregated load. The performance of the method is evaluated in response to the uncertainty in the non-elastic load, aggregated electric vehicle demand, and electric vehicle user behavior. The results show that the variance in the demand is reduced by a factor of 4.8 in the proposed model predictive-based method in the presence of all three uncertainties considered relative to the uncontrolled charging. Under perfect prediction, the reduction is a factor of 7.5, thereby indicating that the method is a viable solution against uncertainties. Moreover, the study provides an overview of the degree of overestimations in the desired outcomes realized under the assumptions of perfect predictions for the different uncertain parameters, demonstrating that the most significant impact arises from uncertainty in mobility usage.

The increasing penetration of volatile renewables combined with increasing demands poses a challenge to modern power grids. Furthermore, distributed energy resources and flexible devices (electric vehicles, PV generation, ...) are becoming more widespread, making their aggregate usage for ancillary services interesting. However, accurately quantifying the aggregate flexibility of numerous flexible devices is known to be limited by the curse of dimensionality, i.e., it does not scale well computationally. This has led to the development of various approximation algorithms. In this study, we improve upon our previously proposed vertex-based inner approximation, extending it to more general storage devices. We demonstrate the efficacy and accuracy of the proposed method in a case study comparing our approach with an exact centralized control framework, where the flexibility of numerous electric vehicles is combined to reduce the peak load in a residential area.

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 accel-erated 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 com-monly shown in simulations, the share of experimental studies is relatively low. To this day, experimen-tal 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 hys-teresis 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 predic-tion) can be transferred from electric hot water heaters to hot water heat pumps, combined, and imple-mented 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.(c) 2023 Published by Elsevier B.V.

Activation of heat pump flexibilities is a viable solution to support balancing the grid via Demand Side Management measures and fulfill the need for flexibility options. Aggregators as interface between prosumers, distribution system operators and balance responsible parties face the challenge due to data privacy and technical restrictions to transform prosumer information into aggregated available flexibility to enable trading thereof. Thereby, literature lacks a generic, applicable and widely accepted flexibility estimation method for heat pumps, which incorporates reduced sensor and system information, system- and demand-dependent behaviour. In this paper, we adapt and extend a method from literature, by incorporating domain knowledge to overcome reduced sensor and system information. We apply data of five real-world heat pump systems, distinguish operation modes, estimate power and energy flexibility of each single heat pump system, proof transferability of the method, and aggregate the flexibilities available to showcase a small HP pool as a proof of concept.

PV hosting capacity of a distribution grid can differ depending on the method used for its quantification. Even different settings adopted in a particular method can lead to different hosting capacity values. Those differences have not been well contrasted in literature, limiting the understanding of the attributes and drawbacks of available quantification methods. In this paper, the hosting capacity of the IEEE 69-bus distribution grid is quantified using four methods: violation-mitigation-based, stochastic, optimal power flow, and continuation power flow. Multiple settings are adopted in each method to assess the effects on the resulting hosting capacity. Quantitative comparisons of the resulting hosting capacities at a grid and node level are carried out. A better understanding of the quantified hosting capacity resulting from different methods showcases their point of improvement and facilitates the election of the quantification method.

Hosting capacity knowledge is of great importance for distribution utilities to assess the amount of PV capacity possible to accommodate without troubling the operation of the grid. In this paper, a novel method to quantify the hosting capacity of low voltage grids is presented. The method starts considering a state of fully exploited building rooftop solar potential. A downward process is proposed-from the starting state with expected violations on the grid operation to a state with no violations. In this process, the installed PV capacity is progressively reduced. The reductions are made sequentially and selectively aiming to mitigate specific violations: nodes overvoltage, lines overcurrent and transformer overloading. Evaluated on real data of fourteen low voltage grids from Austria, the method proposed exhibits benefits in terms of higher hosting capacities and lower computational costs compared to stochastic methods. Furthermore, it also quantifies hosting capacity expansions achievable by overcoming the effect of the violations. The usage of a potential different from solar rooftops is also presented, demonstrating that a user-defined potential allows to quantify the hosting capacity in a more general setting with the method proposed.

Increasing electric vehicle penetration leads to undesirable peaks in power if no proper coordination in charging is implemented. We tested the feasibility of electric vehicles acting as flexible demands responding to power signals to minimize the system peaks. The proposed hierarchical autonomous demand side management algorithm is formulated as an optimal power tracking problem. The distribution grid operator determines a power signal for filling the valleys in the non-electric vehicle load profile using the electric vehicle demand flexibility and sends it to all electric vehicle controllers. After receiving the control signal, each electric vehicle controller re-scales it to the expected individual electric vehicle energy demand and determines the optimal charging schedule to track the re-scaled signal. No information concerning the electric vehicles are reported back to the utility, hence the approach can be implemented using unidirectional communication with reduced infrastructural requirements. The achieved results show that the optimal power tracking approach has the potential to eliminate additional peak demands induced by electric vehicle charging and performs comparably to its central implementation. The reduced complexity and computational overhead permits also convenient deployment in practice.

PV hosting capacity provides utilities the knowledge of the maximum amount of solar installations possible to accommodate in low voltage grids such that no operational problems arise. As the quantification of the hosting capacity requires data collection, grid modelling, and often time-consuming simulations, simplified estimations for large-scale applications are of interest. In this paper, Bayesian statistical inference is applied to estimate the hosting capacities of more than 5000 real feeders in Austria. The results show that the hosting capacity of 95% of the total feeders can be estimated with a mean error below 20% by only having knowledge of a random sample of 5%. Moreover, the hosting capacity estimation at a regional level shows a maximum error below 9%, also relying on a random sample of 5% of the total feeders. Furthermore, the approach proposed provides a methodology to assess new parameters aiming to improve the accuracy of the hosting capacity estimation at a feeder level.

Charging scheduling algorithms play a vital role in diminishing the negative consequences on electricity networks from the widespread adaptation of electro-mobility. Therefore, there is a growing interest in a pragmatic solution that requires only modest resources. To reach this goal, we propose a decentralized, IEC charging standard compliant, two-layer charging scheduling algorithm, which only requires unidirectional communication and reduced computing capabilities. The objective of the algorithm proposed is to achieve valley filling by exploiting the flexibility of electric vehicles through optimal tracking of a target signal. The IEC standard compliant, semi-continuous charging characteristic is attained with a mixed-integer linear formulation. Different formulations of the problem by forming vehicle groups and randomization in charging events are examined. The results show that the IEC 61851-compliant formulation with a semi-continuous charging characteristic for the proposed method fails to perform as good as the variable charging rate formulation, which has a 2.8 and 3.9-fold deviation in the variance of the total demand relative to the variable charging rate at 50% and 100% penetration rates, respectively. Nevertheless, the inclusion of randomization and grouping improves the performance of the IEC standard-compliant formulation. Considering four groups, the variance in demand of semi-continuous charging formulation at 50% penetration is nearly equal to that of the variable charging rate proofing the viable potential of the technically feasible solution proposed.

Due to the global competition in manufacturing, flexibility to provide for individually customized products is considered an important selling point. Constantly changing manufacturing processes face higher production costs than well known reoccurring schedules. To lower these costs in general, we propose a model predictive control concept to reduce manufacturing energy costs in particular, using an existing digital twin to estimate the load of the different manufacturing steps. Based on a mixed integer linear programming formulation of the battery-supported manufacturing process, the system makes optimum use of the on-site photovoltaic generation by production scheduling and adaptive battery control. A simulation study considering a time of use and a real-time pricing scenario provides a proof of concept.

The impact of global warming and climate change has forced countries to introduce strict policies and decarbonization goals toward sustainable development. To achieve the decarbonization of the economy, a substantial increase of renewable energy sources is required to meed energy demand and to transition away from fossil fuels. However, renewables are sensitive to environmental conditions, which may lead to imbalances between energy supply and demand. Battery energy storage systems are gaining more attention for balancing energy systems in existing grid networks at various levels such as bulk power management, transmission and distribution, and for end-users. Integrating battery energy storage systems with renewables can also solve reliability issues related to transient energy production and be used as a buffer source for electrical vehicle fast charging. Despite these advantages, batteries are still expensive and typically built for a single application – either for an energy- or power-dense application – which limits economic feasibility and flexibility. This paper presents a theoretical approach of a hybrid energy storage system that utilizes both energy- and power-dense batteries serving multiple grid applications. The proposed system will employ second use electrical vehicle batteries in order to maximise the potential of battery waste. The approach is based on a survey of battery modelling techniques and control methods. It was found that equivalent circuit models as well as unified control methods are best suited for modelling hybrid energy storages for grid applications. This approach for hybrid modelling is intended to help accelerate the renewable energy transition by providing reliable energy storage.

In the regime of incentive-based autonomous demand response, time dependent prices are typically used to serve as signals from a system operator to consumers. However, this approach has been shown to be problematic from various perspectives. We clarify these shortcomings in a geometric way and thereby motivate the use of power signals instead of price signals. The main contribution of this paper consists of demonstrating in a standard setting that power tracking signals can control flexibilities more efficiently than real-time price signals. For comparison by simulation, German renewable energy production and German standard load profiles are used for daily production and demand profiles, respectively. As for flexibility, an energy storage system with realistic efficiencies is considered. Most critically, the new approach is able to induce consumptions on the demand side that real-time pricing is unable to induce. Moreover, the pricing approach is outperformed with regards to imbalance energy, peak consumption, storage variation, and storage losses without the need for additional communication or computation efforts. It is further shown that the advantages of the optimal power tracking approach compared to the pricing approach increase with the extent of the flexibility. The results indicate that autonomous flexibility control by optimal power tracking is able to integrate renewable energy production efficiently, has additional benefits, and the potential for enhancements. The latter include data uncertainties, systems of flexibilities, and economic implementation.