Due to the increasing penetration of distributed energy resources, congestion problems are already emerging in Dutch distribution grids. The available flexibility of assets in the built environment could have the potential to reduce congestion if prosumers are properly incentivized by distribution grid operators (DSOs). However, it is not yet clear what (combinations of) flexibility a ctivation mechanisms will be effective for congestion management in Dutch Distribution grids. To shed light on this issue, the GO-e consortium aims at performing large-scale agent-based simulations of up to 120 low-voltage networks and a large variety of possible instruments and scenarios. For this reason, we developed a novel scalable time-discrete simulation framework for distributed agent-based simulations of energy systems. We demonstrate the framework on a case-study in which we assess the effectiveness of a dynamic bandwidth tariff instrument on overloading problems in a low-voltage network containing solar panels, batteries, and heat pumps. It was shown that a dynamic bandwidth tariff can successfully resolve forecasted congestion if the associated costs are high enough compared to the day-ahead prices. However, the resulting load shifting can cause new congestion intra-day aswell.

To guarantee a successful deployment of a droop-based control strategy to mitigate overvoltage problems caused by solar photovoltaic (PV) generation, Distribution System Operators (DSOs) will need to estimate the amount of active power curtailed by the PV inverters for billing purposes. This paper provides a structural elaboration on the development of data-driven approaches in Python to estimate the PV curtailed power as a provision of voltage support services by residential users using droop-based voltage control strategies. The use of the total input data, available for a DSO, would be impractical for an all-regression approach for the estimation of the PV curtailed power. Since in the majority of the data no active power is curtailed, the data-driven models would in this case partly be trained and fitted for situations where there is no active power curtailment. The regression models for the curtailed power prediction are therefore preceded by a classification model. The developed combined classification-regression model was able to estimate the PV curtailed power with an error of less than 4%, for test data from the network on which the model was trained.

In this paper, a co-simulation framework is presented to assess the impact on the distribution network of provision of support services (i.e. voltage support) by smart residential users. Such users are capable of providing flexibility by increasing/decreasing generation/consumption controlling the operation of an available set of flexible assets. The control of assets such as PV systems, electrical vehicles (EVs), heat storage and micro combined heat and power (mCHP) units is done by a Customer Energy Manager (CEM) after receiving flexibility requests as a result of an Aggregator-Distribution System Operator (DSO) interaction. In the presented framework, the distribution system is modeled in OpenDSS while the aggregator-flexible asset interaction, including the market-clearing procedure, is modeled using the Energy System Simulator (ESSIM). Results from several simulated scenarios are presented. According to the presented results, in summer, where over-voltage issues are expected due to the high PV penetration, a solution rate of 90% is estimated. For winter, in which under-voltage issues are more predominant, the solution rate is found to be around 70%.

High penetration of renewable energy sources brings both opportunities and challenges for Smart Grid operation. Due to their high contribution to energy consumption, aggregated load flexibility of small residential and service sector consumers has a potential to address the intermittency challenge of distributed generation. Predicting aggregated load flexibility of this consumer sector involves access to sensitive smart meter data, raising data collection and sharing concerns. Federated Learning, a decentralized machine learning technique that uses data distributed on user devices to construct an aggregated, global model, offers potential solutions to tackling this challenge. This paper explores the potential of using Federated Learning for flexibility prediction in Smart Grids through an analysis of its opportunities and implications for different stakeholders involved, as well as the challenges faced. The analysis shows that Federated Learning is a promising approach for building privacy-preserving energy portfolios of aggregated demand data.

Currently, the majority of the available test feeder models are based on the North American type of distribution systems, leaving an absence of representative feeder models for the European (and Dutch) types of distribution systems. Therefore, a standardized set of power flow models for typical Dutch LV distribution networks is developed using real networks and operational data provided by a Dutch DSO. These network models are used to assess if typical Dutch LV distribution networks are ready to operate with large penetration of low-carbon energy technologies (e.g. PV systems, electric heat pumps, EVs). According to the obtained results, simulations of all the modelled networks showed the same behaviour for the power flow at the head of the distribution system and the voltage magnitude level. The changes in voltage magnitude however is dependent on the size of the network, resulting in larger changes in the larger networks. While the voltage magnitude level of the smaller networks stays within the ± 10% range, the larger networks encounter voltage magnitude violations. Thus, it can be concluded that the larger networks are not ready (under the current conditions) for large penetrations of low-carbon energy technologies, while the smaller networks appear to be ready.