In recent years, the rapid increase in energy demand and the threat of climate change have pushed the energy sector towards electrification. Despite this being the first step to a more sustainable future, a new problem arises with the growing demand: grid congestion. This project explores innovative strategies within energy system design and operational framework to enhance operation efficiency in large urban energy systems. A model was established that allows the optimization and analysis of the performance of a residential energy community with energy systems supplied by renewable sources. The design focuses on the flexibility of the system to operate with an integrated planning framework exploiting batteries, thermal storage and shared Electric Vehicles (EVs). By investigating these technologies' design and operational conditions, the project seeks to optimize the system's functioning and minimize its impact on the electricity grid to mitigate congestion. Focusing on a new residential area in Utrecht (NL), the study investigates the role of flexibility for new capacity connection challenges in a highly populated urban area with a vulnerable infrastructure. This research provides useful insights that can guide the development of more adaptable energy infrastructures capable of meeting the growing demands of modern cities. The findings aim to inform future urban energy areas to facilitate the transition towards a more sustainable residential landscape.

As part of making a power grid simulator, a hardware controller is needed. This thesis specifically describes the design of such a controller. The objective is to design it in such a way that it is self-explanatory and interactive. Therefore, the following user controls and feedback are made possible. A small solar panel makes it possible to provide input for a solar generator in the simulation. A slider gives input for a wind farm in the simulation. This is visualized by adding a small physical turbine to the hardware, going faster or slower based on the input.
A small knob to go back or forward in the simulation, if pressed, pauses or resumes the time.
A RGB LED indicates the situation on the grid. Based on the loading of transmission lines, the color of the LED changes. These user-control actions and feedback from the simulation are processed on an Arduino Mega Rev3 development board. This uses an ATMega2560 microcontroller. To interact with the simulation, bidirectional serial communication is needed between the Arduino and the computer running the simulation with the Godot game engine. These components are implemented on a PCB to finalize the design.

The electrification of all sectors is essential to achieve carbon neutrality by 2050. In terms of mobility, many nations around the globe envisage a zero-emission fleet resulting in a massive deployment of Electric Vehicles (EV). Nevertheless, this goes hand-in-hand with some challenges related to congestion and voltage problems in power grids caused by inadequate grid capacity. When grid reinforcement cannot keep up with the increasing demand for electricity, both in terms of planning and excessive financial means, smarter, more efficient solutions should solve the problem.

This thesis aims at providing an optimal strategy for consumers’ flexibility distribution by smartly charging EVs in Low Voltage (LV) grids. In that way, grid reinforcement should be reduced to a minimum. In order to do so, the main contribution of this work is covered by the development of a smart, optimal charging model that tries to comply with grid constraints including voltage and congestion boundaries. Mathematical (near) real-time optimisation is used together with the receding horizon optimisation principle. The work of this thesis relates to a typical urban LV grid in the Netherlands.

First, the required level of grid reinforcement was investigated by means of quantifying the voltage and congestion problems in the LV grid. This was done by applying uncontrolled charging scenarios up to 2050 for both winter and summer.

After investigating the uncontrolled scenarios, the need for an optimal smart charging strategy became apparent. This was developed afterwards. The results showed that in 2050, 94.5% of line congestion and 100% of transformer congestion and voltage problems could be bypassed with smart charging only. This was achieved by implementing grid constraints at the most vulnerable locations in the power grid.
Consequently, the need for grid reinforcement could be almost completely avoided till at least 2050.

Lastly, this work distinguishes itself from others by extensively reflecting on the integration of three concepts: a novel recently developed tariff structure, the effect of bidirectional charging, as well as the necessity for grid constraints. This allows summarising the benefits for all stakeholders including the distribution system operator, the charge point operator and EV owners. The final conclusion can be made that including these three concepts results in one of the most favourable investigated strategies for all three parties together.

The electrification of modern-day society keeps increasing and the demand for electricity grows along with it. Technologies like EVs and heat pumps are both part of the new types of demand, while PV systems and wind farms are meant to be our main new sources of generation. Contrary to traditional resources connected to the electricity network, most of these resources tend to be placed and operated in a distributed manner, increasing the demand for transport capacity within the LV and MV grid. This demand cannot always be met however, which leads to situations of congestion.

This thesis seeks to reduce this congestion by means of optimising the grid topology in line with the seasonal variations in the supply and demand of electricity. This is done by means of implementing a two-stage reconfiguration algorithm. The benefit of network reconfiguration is that it is a short-term and low-cost solution which can be implemented by the DSO without relying on other external parties.

In the first stage of the reconfiguration algorithm, the positions of the normally open switches within the network are optimised in order to adjust the power flow therein. These optimised positions are subsequently used in the second stage to calculate the network variables. The first stage is implemented as a MILP optimisation in Python, while the second stage consists of a Newton-Raphson calculation in the commercial software PowerFactory. This distinction is made to enhance the accuracy of the final network parameters, while still being able to optimise the switch positions in a deterministic manner.

Rather than day-ahead or real-time reconfiguration, as is often considered in most literature, this thesis focuses on seasonal reconfiguration to accommodate for the manual operation of the switches present within the MV grid in the Netherlands. These manually operated switches severely reduce the frequency by which reconfiguration actions can be performed, but that does not mean that the topology of the grid cannot be enhanced.

The presented reconfiguration algorithm is able to consistently reduce congestion within the analysed network, completely removing it or reducing its severity. It also outperforms two optimisation options in PowerFactory with regards to the objective function value. Those being an iterative exploration of meshes and a genetic algorithm.