Digitalisation enables the clean energy transition but introduces fragmentation across platforms, protocols, and standards. The PARMENIDES project addresses these challenges by introducing EMS4HESS - an ontology-based energy management system for hybrid energy storage systems within renewable energy communities. EMS4HESS leverages the PARMENIDES Energy Community Ontology (PECO) to ensure semantic interoperability across data models and information layers. PECO describes community structures, asset properties, optimisation strategies, and flexibility signals, enabling automated reasoning and adaptive control. The system integrates diverse storage technologies and optimises their operation to improve resilience and sustainability. Real-world pilots in Austria and Sweden validate the approach under contrasting regulatory frameworks. Initial results show significant improvements in self-sufficiency and self-consumption compared to baseline scenarios without HESS, confirming the feasibility of ontology-driven, grid-aware energy management as a scalable solution for the clean energy transition.

This paper presents a reinforcement learning (RL) approach for evaluating and controlling residential building flexibility in response to dynamic electricity pricing and intermit-tent grid requests. To address the challenge of sparse rewards and fluctuating states, a curriculum learning (CL) strategy is integrated with reward shaping, enabling a model-free RL agent to learn complex, multi-objective control behavior. A custom Gymnasium-EnergyPlus environment is developed to simulate thermal dynamics, price signals, and USEF-aligned flexibility requests. The agent is trained through a three-stage curriculum, progressing from comfort maintenance to cost minimization, and finally, to flexibility response. Results show that CL significantly improves training stability and responsiveness compared to a non-curriculum approach, with agents able to balance comfort, cost efficiency, and flexibility provision under varying conditions.

The power distribution grids of today face significant challenges due to the increasing integration of distributed renewable generation capacities and rising electricity demand from electrification of the heating and mobility sectors. Battery energy storage systems (BESS) offer a promising solution to match supply and demand on a distribution grid scale, reducing strain on grid infrastructure and delaying costly expansions. Energy communities have emerged as a crucial mechanism to enhance end-customer engagement and introduce new business models for renewable energy adoption. This paper presents an analysis of the impact of shared community storage systems on key grid parameters. Using a combination of community simulation and optimal resource sizing methodologies, we explore the effects of economically viable community-scale storage systems on multiple key performance indicators for distribution grids. The study results suggest that the application of energy storage solutions can mitigate transformer overloading issues.

This contribution outlines the influencing factors of renewable energy communities (RECs) on the planning and operation of distribution networks by investigating the grid impact of RECs. First, several diverse scenarios are constructed which are optimized, simulated and then undergo an extensive data analysis. Several statistical measures and a clustering algorithm are employed to highlight the outcomes in both community performance and grid impact. Results show, that thesettlement pattern, PhotoVoltaic (PV) and Battery Energy Storage Systems (BESS)installations are the key influencing factors. Further, the grid impact remains limited, yet ambiguous.

The ongoing digitalization of the energy system is a key enabler for the clean energy transition, but it also leads to fragmentation of platforms, protocols, and standards. The PARMENIDES project addresses these challenges by developing an ontology-based Energy Management System for the utilization of Hybrid Energy Storage Systems. This system leverages the so-called PARMENIDES Energy Community Ontology (PECO), which provides a unified vocabulary ensuring semantic interoperability across data models and information layers within Renewable Energy Communities (RECs). PECO formally describes key aspects of energy communities, including methods for excess energy allocation, internal energy pricing mechanisms, and the relationships among community members and their assets. The project demonstrates its solutions in diverse pilot projects in Austria and Sweden. The Austrian pilot emphasizes Renewable Energy Communities with automated asset optimization, while the Swedish pilot focuses on short-term flexibility and innovative control strategies. By integrating various storage technologies and optimizing their use, the project aims to enhance the resilience and sustainability of energy systems, contributing to the broader goals of the clean energy transition.

An accelerated energy transition calls for an efficient development of innovative energy and grid capacity management algorithms. Field tests often play a vital role in evaluating the performance of new approaches before mass rollout, but often require extensive engineering efforts. To speed up even demanding field trials, this work presents a rapid deployment methodology and its implementation. Based on several core principles that target efficient engineering and demonstration, a rapid deployment architecture is derived. The architecture is then implemented in the AIT Rapid Deployment Platform (AIT RDP) and applied in various projects. It is demonstrated that via a combination of broadly available and mature software components as well as few custom modules that bridge needed functionality, a wide range of application scenarios can be covered with minimum efforts. Although ambitious prototypes often need to be integrated in complex environments using a multitude of different communication patterns and protocols, a large share of interface logic can be reused among installations. Hence, development efforts can be considerably reduced leaving more resources for the innovative algorithms under test.

To tackle climate change, large amounts of distributed renewable power generation capacities will be installed over the coming years. At the same time, the electrification of the mobility and heating sectors will increase the amount of flexible loads connected to power grids. Energy communities can promote an increased consumption of locally generated renewable power by providing price incentives for community participants. Therefore, energy communities could help reduce the strain on distribution grids from increased distributed power generation and further electrification of energy consumption. We present a co-simulation approach for energy communities and respective grid infrastructure. Employing this co-simulation approach we evaluate different heuristic control algorithms and their effect on the power distribution grid. Communicating grid congestion to community members can help reduce negative effects on power grids. In the realistic scenarios we present, the flexibility offered by energy communities is not sufficient to avert negative effects completely.

To optimize the operation of a low-voltage distribution grid, battery storage systems can be efficiently managed at an optimal point by using a grid capacity management system. However, this requires more detailed information about the state of a grid than is usually available. This gap can be bridged by installing additional sensors, and the number of sensors can be minimized through innovative state estimation approaches. The presented approach allows the training of Machine Learning (ML) models capable of predicting the grid state when provided with operational data obtained through load flow calculations from the same grid. The models are trained to predict unknown voltage values based on assumed known data. The conducted experiments aimed at identifying the optimal locations for these voltage measurements to determine the ideal placement for sensory equipment in the grid and assess the estimation error. The results revealed that for both grids specific sensor placement can be identified where models perform very well with a low error and demonstrate a high level of prediction accuracy. Moreover, statements about the ideal placement of the sensors were derived from experiments.

While the increasing usage of renewable energy resources is necessary, the unreliablity of these resources will have a negative impact on the energy grid. By creating financial incentives for their members renewable energy communities (RECs) may assist by consuming excess energy locally and thereby maintaining local energy balance. In this paper, the influence of renewable energy communities on the distribution grid is studied and quantified for nine different future scenarios in Austria. This is done by simulation of these nine RECs over one year. The focus is laid on photovoltaic (PV) production and the quantification of self-consumption and consumption within the REC throughout the year using dynamic partitioning. This quantification provides valuable information for the planning of a good composition of a REC, an intuitive assessment of the expected effects and a simple first estimate of suitable overall community battery sizes.

Distributed generation (DG) and electronic-based load units in power distribution grids introduce new challenges for distribution grid operators (DSOs). To maintain stable grid operations these devices often provide grid-supporting functionalities, whose configurations are difficult to monitor for DSOs. Advanced smart grid automation approaches are essential to manage the numerous units that need monitoring with limited sensory equipment. In this work, misconfigurations of control curves at the installation of devices are to be monitored. The grid specifications and operational data of two existing grid segments were used. These data are used to simulate cases in which a device is configured correctly or misconfigured. The simulated grid operational data is then used as training data for Machine Learning (ML) algorithms, which are used to make statements about whether the measured operational data stem from operation under a misconfigured control curve or not. The DSO confirmed the results for one case under scrutiny, for the other no clear statement could be made. The results show that the presented approach can serve the needs of DSOs and offer a viable solution for automating misconfiguration detection in distribution grids, even though further field testing is needed.

This paper proposes a generic, extensible, and scalable definition of hybrid energy storage systems (HESS) and provides a corresponding information model applicable for energy management system (EMS) implementation. Given the need for flexibility in both energy supply and demand due to the energy transition, multiple energy carriers have been coupled, energy storage mediums have been leveraged, and their characteristics have been optimized. EMS are adapting to these developments, which can be facilitated by having common definitions and information models. There are at least two prevailing descriptions of HESS: one based on complementary characteristics, and another based on the constituent energy storage mediums. The proposed definition is an extension and specific application of the concept of “energy hubs” and a clarification of the multiple descriptions of HESS. On a larger scale, this work aims to facilitate the interoperability of various EMS that involve HESS and to provide a foundational resource for projects related to HESS architectures, control, and optimization. This work is a contribution to the development of an open ontology tailored for EMS applications in the context of energy communities with HESS.

PARMENIDES addresses challenges in the energy system by providing interoperable solutions that harness the potential of Hybrid Energy Storage Systems. A key innovation is the PARMENIDES Energy Community Ontology streamlining energy community operations through optimized energy flows and local energy maximization. The project introduces an Energy Management System for Hybrid Energy Storage Systems, utilizing ontology as a knowledge base and for extended information inference. Diverse PARMENIDES use cases cover scenarios ranging from passive energy community participation to fully automated optimization. These use cases vary in automation levels, optimization features, and flexibility strategies. The developed Information and Communication Technology architecture ensures interoperability, reliability, and security. Components include a Grid Capacity System, Grid Monitoring Devices and Smart Meters, an Information and Configuration System as a central repository for knowledge and data and an Energy Management System. Specific instantiations of the architecture will be implemented in the Austrian and Swedish pilots.

The need for a more sustainable energy system is leading to more electric energy generation being connected to low voltage (LV) distribution grids. At the same time, due to accelerated growth in electric mobility and heat pumps, more and more energy consumed by end customers is supplied via LV distribution grids. These developments cause distribution networks to become less predictable and make them subject to much higher changes in supply and demand. Therefore, additional flexibility will be needed to keep such systems operating with high quality of service. The required flexibility could be provided by distributed generators as well as flexible loads. This leads to the question of how these flexibilities can be activated by distribution system operators when needed. A digital interface between distribution system operator and device operator could be implemented to communicate flexibility requirements. In this work, three possible deployment scenarios for such a digital interface are presented. A market review of available standards and commonly implemented communication protocols was conducted to identify potential candidates for the standardization of such an interface.

This paper presents a methodology to optimally share the available grid capacity among customer assets connected within a low voltage distribution grid. Distributed energy resources (DERs) and a new generation of loads such as heat pumps, thermal, hydrogen, electric storages, and vehicles are increasingly being connected to distribution grids. These DERs and loads are intermittent and it is essential to optimally control them for the safe operation of the grid. Additionally, there is increased interest in the local generation, production, trading, and consumption of energy. New regulations to establish local energy communities (LEC) have come to fruition among member nations across Europe. This is to provide a control, market, and legal framework for managing such distributed generators and flexibilities in low and medium-voltage distribution grids and conclusively empower end-users to democratize the energy system. Within a LEC, a local energy market (LEM) is to be implemented. A significant constraint of a LEM or energy accounting system is the grid settlement process. The grid should remain in a steady state when the bids in the market are executed. The methodology discussed in this paper will preemptively stabilize the grid and generate limiting profiles at various locations for individual flexibilities that are part of the local energy market. This is achieved by using an Optimal Capacity Management system which generates limiting profiles at the points of common couplings of various controllable devices in the grid. The controllable devices are required to maintain their active power injection and consumption within the generated limiting profiles to ensure optimum grid level. This will ensure that grid limits are maintained, which are simulated on a test feeder and also applied to a real network model from the Heimschuh pilot site in Styria, Austria.

This paper presents control relationships between the low voltage distribution grid and flexibilities in a peer-to-peer local energy community using a stratified control strategy. With the increase in a diverse set of distributed energy resources and the next generation of loads such as electric storage, vehicles and heat pumps, it is paramount to maintain them optimally to guarantee grid security and supply continuity. Local energy communities are being introduced and gaining traction in recent years to drive the local production, distribution, consumption and trading of energy. The control scheme presented in this paper involves a stratified controller with grid and flexibility layers. The grid controller consists of a three-phase unbalanced optimal power flow using the holomorphic embedding load flow method wrapped around a genetic algorithm and various flexibility controllers, using three-phase unbalanced model predictive control. The control scheme generates active and reactive power set-points at points of common couplings where flexibilities are connected. The grid controller’s optimal power flow can introduce additional grid support functionalities to further increase grid stability. Flexibility controllers are recommended to actively track the obtained set-points from the grid controller, to ensure system-level optimization. Blockchain enables this control scheme by providing appropriate data exchange between the layers. This scheme is applied to a real low voltage rural grid in Austria, and the result analysis is presented.

The European Union's Clean Energy Package introduces two kinds of energy communities, namely the Renewable Energy Community (REC) in the Renewable Energy Directive of 2018 and the Citizen Energy Community (CEC) in the Electricity Directive of 2019. They aim for local improvements of energy efficiency, increasing integration of renewable energy sources, and a reduction of greenhouse gas emissions, to be achieved by jointly producing, temporarily storing, sharing, consuming, and selling locally generated energy. Households and individuals shall thus be enabled to take an active part in the energy transition. When utilizing blockchain technology for the implementation of such energy communities, as proposed in current research projects, a focus must be laid on the technology-inherent area of conflict with privacy issues, especially since data on households' energy consumption count as personal data.

Energy Communities will be an essential element of the future energy system. Especially Renewable Energy Communities are gaining high attention in many European countries and their implementation, characteristics and use cases are elaborated in many research and development activities all around the world. Within the Austrian research project Blockchain Grid, a Blockchain-based Renewable Energy Community is implemented and field-tested in Heimschuh, Styria. It supports different technical applications like self-consumption optimization and peer-to-peer energy trading for customers, and a novel approach for grid capacity management supporting distribution system operators. These use cases have been implemented and validated in simulative studies showing promising potential for total energy costs for energy community members.

This paper aims to present a methodology, which can be used by local energy communities to enable a peer-to-peer energy market. With the increase in distributed energy resources such as photovoltaic systems, micro-wind and a new generation of loads such as electric vehicle and battery storage, it is essential to optimally manage them. Flexibility derived from these devices can be leveraged in an energy market. Currently, the biggest shortcoming of a local energy market is the lack of settlement process to validate the feasibility of market bids. This is to ensure that the bids do not lead to grid violations, ensuring adequate grid security, operational safety, and continuity of supply. The methodology introduced in this paper reconciles the grid and market settlement issue by generating dynamic active, reactive power consumption and in-feed limits at a certain number of controllable buses where flexibilities are located. This incorporates the use of the Newton-Raphson load flow combined with a heuristic optimization solver which is applied to a real low voltage distribution grid which is located in Steiermark, Austria. State and uncontrollable variables for optimization are fed into the blockchain at every sample and are available at all buses. The results obtained from the study shows that the method can successfully eliminate voltage violations for various use-cases.