Connecting distributed generation (DG) to weak distribution networks can often cause voltage rise problems. Traditionally, these voltage rise problems have been mitigated by passive methods such as reinforcing the network. This can, however, lead to high connection costs of DG. The connection costs can in many cases be lowered if active voltage control methods are used instead of the passive approach. In this paper, two coordinated voltage control algorithms suitable for usage in distribution networks including several distributed energy resources are proposed and studied. The first algorithm uses control rules to determine its control actions and the second algorithm utilizes optimization. The operation of the implemented algorithms is, at first, studied using time domain simulations. Thereafter, the network effects and costs of both algorithms are compared using statistical distribution network planning and also practical implementation issues are discussed.

Congestion management is one of the core enablers of smart distribution systems where distributed energy resources are utilised in network control to enable cost-effective network interconnection of distributed generation (DG) and better utilisation of network assets. The primary aim of congestion management is to prevent voltage violations and network overloading. Congestion management algorithms can also be used to optimise the network state. This study proposes a hierarchical and distributed congestion management concept for future distribution networks having large-scale DG and other controllable resources in MV and LV networks. The control concept aims at operating the network at minimum costs while retaining an acceptable network state. The hierarchy consists of three levels: primary controllers operate based on local measurements, secondary control optimises the set points of the primary controllers in real-time and tertiary control utilises load and production forecasts as its inputs and realises network reconfiguration algorithm and connection to the market. Primary controllers are located at the connection point of the controllable resource, secondary controllers at primary and secondary substations and tertiary control at the control centre. Hence, the control is spatially distributed and operates in different time frames.

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A driving force for the realization of a sustainable energy supply in Europe is the integration of distributed, renewable energy resources. Due to their dynamic and stochastic generation behaviour, utilities and network operators are confronted with a more complex operation of the underlying distribution grids. Additionally, due to the higher flexibility on the consumer side through partly controllable loads, ongoing changes of regulatory rules, technology developments, and the liberalization of energy markets, the system's operation needs adaptation. Sophisticated design approaches together with proper operational concepts and intelligent automation provide the basis to turn the existing power system into an intelligent entity, a so-called smart grid. While reaping the benefits that come along with those intelligent behaviours, it is expected that the system-level testing will play a significantly larger role in the development of future solutions and technologies. Proper validation approaches, concepts, and corresponding tools are partly missing until now. This paper addresses these issues by discussing the progress in the integrated Pan-European research infrastructure project ERIGrid where proper validation methods and tools are currently being developed for validating smart grid systems and solutions.

This paper presents a coalitional game for value sharing in energy communities (ECs). It is proved that the game is super-additive, and the grand coalition effectively increases the global payoff. It is also proved that the model is balanced and thus, it has a nonempty core. This means there always exists at least one value sharing mechanism that makes the grand coalition stable. Therefore, prosumers will always achieve lower bills if they join to form larger ECs. A counterexample is presented to demonstrate that the game is not convex and value sharing based on Shapley values does not necessarily ensure the stability of the coalition. To find a stabilizing value sharing mechanism that belongs to the core of the game, the worst-case excess minimization concept is applied. In this concept, however, size of the optimization problem increases exponentially with respect to the number of members in EC. To make the problem computationally tractable, the idea of clustering members based on their generation/load profiles and considering the same profile and share for members in the same cluster is proposed here. K-means algorithm is used for clustering prosumers' profiles. This way, the problem would have several redundant constraints that can be removed. The redundant constraints are identified and removed via the generalized Llewellyn's rules. Finally, value sharing in an apartment building in the southern part of Finland in the metropolitan area is studied to demonstrate effectiveness of the method.