Energy communities have recently been introduced as new legal actors in the European Union's energy system. This study introduces the first concepts to energy communities’ planning and operation based on the CLUE research project. It introduces the legal framework and identifies open issues for the legal transpositions into national law, as well as arising questions concerning planning and operation.

Today, distribution system hosting capacity studies for distributed solar photovoltaics (PVs) and other distributed energy resources (DERs) are based on worst-case scenarios that assume DERs operate in unmanaged or unconstrained ways. Under the current paradigm, DER hosting capacity can be limited or costly grid upgrades may be required due to potential, but infrequent, worst-case conditions. DER management systems (DERMS) can enable additional DER to be interconnected as a managed resource that is curtailed to avoid violations under the infrequent worst-case conditions. This scheme, referred to as flexible interconnection, allows the interconnection of more DER while resulting in a minimal reduction in productivity to DER owners. This study assesses the techno-economic value of flexible interconnection for PV enabled by DERMS through detailed modelling and analysis of a real North American distribution feeder. It focuses on using DERMS to avoid distribution system upgrades due to thermal overloads and illustrates the economic value of a flexible interconnection arrangement across a range of PV system capacities. Findings indicate that the value of a flexible interconnection arrangement diminishes for PV systems with larger DC/AC ratios and as interconnected PV capacity increases, given increased amounts of curtailment.

It is easy to assess that the task of determining between the electric vehicle (EV) user charging requirements and distribution system operator (DSO) (physical) constraints will be huge, taking into account the climate and EV goals certain governments have set for 2030 (e.g. 10 mn EVs in Germany ≃ ¼ of the cars). For the DSO, there are two possibilities to address loads individually. Either the DSO can route the congestion to each separate charging point with an own communication path or he uses already existing communication paths established by the charging station operators (CSOs). The demand clearing house (DCH) mitigates the situation of actual or anticipated grid congestion between the restrictions set by the DSOs and the effected CSOs using a unified interface. By using the DCH DSOs will be empowered to allocate available flexibility to charging EVs in order to comply with local grid-related restrictions while open up market optimisation to incorporate EV driver's needs. On the foundation of flexible charging, existing grid planning and grid operation procedures can be updated and take limited grid usage into account. By doing this the macroeconomic cost of integrating EVs will be minimised.

In many algorithmic applications in electrical power grids, state estimation (SE) represents the first step of a process chain. In SE, sensor measurements are processed to infer the most probable grid state. Classical methods such as weighted least squares (WLSs) based approaches use statistical methods that can be based on sensor noise and erroneous measurements. With these methods, only point estimates are made, which results in a lack of knowledge about prediction uncertainties. In this study, machine-learning-based methods for determining the actual state of the grid are proposed. Bayesian optimisation is applied to find the optimal hyperparameter configurations for neural networks (NNs) for SE tasks. The application of Bayesian inference using Bayesian NNs is proposed, which allows the prediction of point estimates as well as uncertainty intervals for the system states. The advantages of using Bayesian approaches in comparison to classical SE methods like WLS are shown.

The growing digitalisation, grid complexity and the number of digitally connected devices that communicate with systems in the distribution grid are enabling the continuous development of automation and intelligence, acquisition of data from sensors, meters and devices for monitoring and managing the distribution network, to achieve an enhanced, preventive, resilient and flexible network operation philosophy. This study presents a set of use cases towards the demonstration of the benefits of implementing a platform that collects, aggregates and facilitates horizontal integration and data correlation from various sources, enabling these use cases across the distribution grid. The adequacy analysis of current distribution network architecture considered derived requirements on the characterisation of its evolution taking advantage of key digital technologies, towards the implementation of distributed control and management strategies. It is also presented a benefit analysis of implementing a centralised common data and service platform, i.e. an internet of things (IoT) platform, regarding new functionalities and applications.

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This study presents an open standards-based information system supporting democratisation and consumer empowerment through flexibility activation. This study describes a functional technical reference infrastructure: a secure, standard-based and viable communication backbone for flexibility activation. The infrastructure allows connection, registering, activation and reporting for different types of granular consumer flexibility. The flexibility sources can be directly controllable set points of chargers and stationary batteries, as well as controllable loads. The proposed communication system sees all these flexibility provisions as distributed energy resources in a wider sense, and the architecture allows consumer-level integration of different energy systems. This makes new flexibility sources fully available to the balancing responsible entities in a viable and realistically implementable manner. The proposed reference architecture, as implemented in the FLEXCoop project, relies on established open standards as it is based on the Open Automated Demand Response (OpenADR) and OAuth2/OpenID standards and the corresponding IEC 62746-10 standard, and it covers interfacing towards other relevant standards. The security and access implications are addressed by the OpenID security layer built on top of the OAuth2 and integrated with the OpenADR standard. To address the data protection and privacy aspects, the architecture is designed on the least knowledge principle.

In smart grids different assets and devices need to be able to communicate with each other and preferably with the grid operator as well. There are different technologies available to establish such a communication, with certain advantages and disadvantages over one another. In the research project ‘Smart Grid Demonstrator Sonderbuch’ two different communication technologies are implemented and operated in parallel: wireless broadband LTE and wire-based broadband power line communication (PLC). In this study, an overview of the scope and objectives of the project are presented, from which requirements for the communication infrastructure were derived. Comparisons of the operation of both communication technologies were drawn, regarding their usability and reliability for real-time applications. Overall, broadband PLC shows better performance in terms of network-latency, while LTE proofs to be less susceptible to short-term interruptions resulting in a higher overall reliability.

The power flow in the power distribution system with DERs is complicated to lower a control performance of voltage regulations such as SVRs. DSO acquires a huge amount of data from smart meters and section switches with voltage and current sensors. We have developed the improvement method for voltage regulators by using these sensor data. The developed method improves an estimation function of SVR and accurately regulates voltages in the power distribution system by using the sensor data. However, the estimation function for the voltage regulation requires a heavy calculation load by applying a huge amount of sensor data. Extraction method for the enormous data is necessary to reduce the calculation load and eliminate unnecessary data. We have developed the extraction method based on clustering method and Cluster Validity Index (CVI). The proposed method divides all the sensor data to plural clusters considering each specification of data such as heavy load, light load, etc. Optimum cluster number is automatically determined by CVI evaluating clusters. We verified that the proposed method extracted the efficient data for the voltage regulation from sensor data. The proposed method significantly reduced the constructed data of the database compared with the ideal method.

Local flexibility markets could support the energy transition by providing distribution system operators with a new tool to manage grid congestions caused by the massive expansion of renewable generation. Fast implementation of such smart markets could meet the need for distributed local flexibility and support the integration of new sustainable energy sources. However, the risk of gaming and the difficulty of identifying such strategies hinder the development and implementation of these markets. Therefore, this study proposes a model approach, which leverages the substantial progress in research on reinforcement learning to simulate the strategic bidding of flexibilities in consideration of local grid characteristics.