The study presents the application of Siemens distribution network algorithms in look-ahead mode to a real-world network that frequently experiences violations mainly caused by renewable generation. The goal of the approach is to detect the future problems several hours ahead and to calculate the amount of flexibility to be provided by the regional market for the problem resolution. The work has been conducted within the German research project enera in cooperation with several partners.

The integration of battery energy storage system (BESS) solutions, particularly those connected to the medium-voltage (MV) and low-voltage (LV) networks, can significantly increase the flexibility of distribution network operation. Compared with conventional reinforcement solutions, BESS can increase the network capacity to integrate renewable energy sources and provide grid support functionalities for congestion management, Volt/Var support and backup power supply. This study evaluates the impact of BESS in the expansion of distribution networks considering two complementary business cases: one where the distribution system operator (DSO) operates the BESS and another where the consumers in a self-consumption scheme own the BESS. A technical-economic analysis for a 20-year planning horizon is performed for different MV networks to evaluate the effectiveness of BESS to eliminate branch's congestion and to improve the continuity of supply, which is measured by the reduction on the expected energy not distributed (EEND) index. Simulation results for the Portuguese reality have shown that while owning a BESS is an economically viable option for the DSO to handle congestions in the MV network, the cost-effective alternative to reduce the EEND is to foster distributed storage coupled with local production.

Low-voltage distribution networks are facing the increasing penetration of electric vehicles, which can cause congestion in the form of undervoltage and overcurrent events. A possible mitigation strategy is asking residential users to reduce their power consumption below a given threshold if congestion risk is forecasted. Although active power reduction of loads does not necessarily entail direct economic losses to the residential customer, unlike the case of PV generation, this type of congestion mitigation method needs to be adjusted to be acceptable for the public, as it might affect user comfort. This paper presents a mitigation method in which several residential users accept to reduce their power demand in exchange for economic benefits. The solution is based on a set of contractual agreements that can be stipulated between residential users and their system operators. The contractual terms limit the time and duration of power reduction, in order not to compromise the comfort of the users. The fairness of different sets of contractual criteria is explored. In particular, it is assessed how the selection of the contractual agreement affects customers, depending on their locations.

The use of decentralised energy resources (DER) in private households will change the load and feed-in behaviour in the future and thus influence the power flow at the low-voltage level. Therefore, grid planning, especially at a low-voltage level, must be adapted. Hence, new approaches for modelling the residual load of a household, with regard to different portfolios of DER, must be addressed. For this, an existing mathematical optimisation model to optimise both, a household's investment decision in DER and their operating times, is extended using machine learning (ML) approaches. Several methods of ML have been tested, however, the highest solution quality was achieved using neural networks. Following this, the prediction of the investment in DER by classification algorithms with convolutional neural networks shows the highest agreement with the deterministic approach. In the case of the prediction of DER operating times and residual loads based on them, recurrent neural networks with long short-term memory are used. Finally, this study indicates that the resulting predicted time series of residual load can be used for power flow calculations in the low-voltage grid. This allows grid calculations to be performed depending on existing or future political and market conditions.

Flexibility tools like demand response, distributed storage and integration of other energy networks are all key aspects of efficient utilisation of the electrical grid. This study presents a complete model for the reliability-based planning of district integrated energy systems (IESs) considering distributed energy storage and integrated demand response. First, an optimal planning model of district IES is proposed to determine the type and capacity of new facilities to be invested over the planning horizon, for minimising total investment and operation costs. Furthermore, a joint N−1 and N−k reliability criterion is introduced to construct the complete reliability-based planning model for economical and reliable planning solutions. The proposed model is solved via a heuristic algorithm, by iteratively solving a base-case master problem and two operation subproblems to check N−1 and N−k criteria. Numerical case studies demonstrate the effectiveness of the proposed reliability-based planning model and methodology. Results show that the model can promote the flexibility of distribution systems while ensuring reliability and economy.