To deal with the rising integration of stochastic renewables and energy intensive distributed energy resources (DER) to the electricity network, alternatives to expensive network reinforcements are increasingly needed. An alternative solution often under consideration is integrating flexibility from the consumer side to system management. However, such a solution needs to be contemplated from different angles before it can be implemented in practice. To this end, this article considers a case study of the Amsterdam ArenA stadium and its surrounding network where flexibility is expected to be available to support the network in the future. The article studies the technical aspects of using this flexibility to determine to what extent, despite the different, orthogonal goals, the available flexibility can be used by various stakeholders in scenarios with a large load from electric vehicle charging points. Furthermore, a legal study is performed to determine the feasibility of the technical solutions proposed by analysing current European Union (EU) and Dutch law and focusing on the current agreements existing between the parties involved. The article shows that flexibility in the network provided by Amsterdam ArenA is able to significantly increase the number of charging points the network can accommodate. Nonetheless, while several uses of flexibility are feasible under current law, the use of flexibility provided by electric vehicles specifically faces several legal challenges in current arrangements.

The need for advanced monitoring and control applications (MCAs) in future distribution networks calls for highly accurate and fully integrated simulation solutions for the purpose of performance assessment of these MCAs. Offline simulation using models of the power system, measurement equipment, local controllers and the information and communication infrastructure do not always resemble reality accurate enough. Therefore, power hardware‐in‐the‐loop (PHIL) simulations are considered a cost‐effective approach to overcome these challenges. This study presents the design of a PHIL‐interface that allows to integrate a physical low‐voltage feeder within a larger real‐time simulated distribution network. Using the combination of the physical network and the larger simulated network, MCAs can be tested for scalability as well as accuracy. A data acquisition and processing system is presented that acquires all the true system states of both the physical feeder and the real‐time simulated network, such that the MCAs can be verified in real‐time. The functioning of the PHIL‐interface is validated using practical results and illustrated using a test case for the performance assessment of a branch current state estimation algorithm.

Along with the emerging development of demand side management applications, it is still a challenge to exploit flexibility realistically to resolve or prevent specific geographical network issues due to limited situational awareness of the (unbalanced low-voltage) network as well as complex time dependent constraints. To overcome these problems, this paper presents a time-horizon three-phase grid-supportive demand side management methodology for low voltage networks by using a universal interface that is established between the demand side management application and the monitoring and network analysis tools of the network operator. Using time-horizon predictions of the system states that the probability of operational limit violations is identified. Since this analysis is computationally intensive, a data driven approach is adopted by using machine learning. Time-horizon flexibility is procured, which effectively prevents operation limit violation from occurring independent of the objective that the demand side management application has. A practical example featuring fair power sharing demonstrates the effectiveness of the presented method for resolving over-voltages and under-voltages. This is followed by conclusions and recommendations for future work.