The growing integration of renewable energy sources presents significant challenges for the reliability of the energy infrastructures in The Netherlands. To address these challenges, dynamic electricity pricing has been proposed as a potential solution to align the electricity demand with the variable supply. While dynamic pricing can incentivize the shifting of flexible loads, its impact on the low-voltage grid remains under-investigated. This thesis explores the impact of dynamic pricing on grid congestion, especially in combination with EV charging and PV systems.


It is hypothesized that while dynamic pricing can effectively shift electricity consumption, it may also lead to concentrated periods of high demand. When consumers synchronize their electricity usage during low-price times, grid congestion can occur.
The study creates a simulation model using Python and PowerFactory to analyse the effects of dynamic pricing on the low-voltage grid. The simulations are based on real-world data from 2023, making use of hourly intervals of loads, EV charging profiles, and solar energy generation across three representative weeks. The model evaluates the voltage levels and power flows to assess the potential for grid congestion under various scenarios of EV and PV adoption.

The results indicate that dynamic pricing can lead to concentrated EV-charging loads, resulting in voltage dips and power spikes. These findings show that the current low-voltage grid may not be equipped to handle the increased demand and variability caused by dynamic pricing schemes. Thus, while dynamic pricing can offer a promising tool for demand-side management, additional measures are necessary to ensure grid stability and to avoid future congestion issues.

In conclusion, the study underscores the dual nature of dynamic pricing as both a valuable tool and a potential risk for the low-voltage grid. Both the voltage and power limits of the grid were found to be causes of congestion, highlighting the need to investigate both. Future research should focus on optimizing dynamic pricing schemes and developing robust grid infrastructure to fully leverage the benefits of renewable energy integration while ensuring grid reliability and resilience.

As more and more solar panels are installed on households, a problem arises when the power from the solar panels fluctuating throughout the day due to clouds passing over the solar panels. This in turn causes voltage flickers in the household, which can be visible/irritable to the human eye and damage appliances in the household. These voltage flickers can exceed limits set by the IEC standard 61000-3-7 or the visible flicker threshold on the Low Voltage (LV) network set by Qualtech [1]. This problem can be solved by making use of an Energy Storage System (ESS) which delivers active power in order to reduce and avoid the voltage fluctuations above the visible flicker threshold. In this report, a novel control algorithm is designed and implemented in two different ways to control the active power dispatch of the ESS. The main goal for the control algorithm is to reduce or avoid any visible voltage flickers from occurring and minimizing energy usage asmuch as possible in order to open the possibility for a Supercapacitor Energy Storage System (SESS) application in the future. For this, a power to energy ratio of higher than 70 is needed to achieve this. The primary control algorithm makes use of a moving average with a weight distribution that is optimised for smoothness and accuracy. Furthermore, extra layers of control was added to the control algorithm tominimize energy usage. The first implementation of the control algorithm, called power control, makes use of the Photovoltaic (PV) system output power to dispatch the appropriate amount of power from the ESS. The second implementation, called voltage control, makes use of the measured voltage at Point of Common Coupling (PCC) to dispatch the ESS power. Simulation results using pre-existing PV system data showed that the power control implementation was not able to fully eradicate all of the measured visible and annoying voltage flickers. The voltage control implementation was able to do so within the window of operation. Furthermore, simulation results showed that the energy usage from the control algorithm with a power control implementation uses 87% percent less energy than a control algorithm using conventional moving average. The energy usage from the control algorithm with a voltage control implementation uses 95% less energy than the conventional moving average control algorithm. Furthermore, the power to energy ratio of the control algorithm with a power control implementation was around 98.9 and with a voltage control implementation the power to energy ratio was around 190.2. This shows a clear implementation of a SESS in the future. Two different experimental setups were built and commissioned. The first setup having the Battery Energy Storage System (BESS) connected via an inverter on the same Alternating Current (AC) bus with the PV system. The second setup has the BESS connected on the same Direct Current (DC) bus with the PV system which is then connected to an inverter. Experiment results showed that the DC connected ESS experiment setup operating in power control mode had the best performance in terms of avoiding any visible voltage flickers from occurring. While the AC experimental setup and mode of operation (voltage and power control mode) did not operate properly due to the slow inverter response time. In terms of energy usage, both experimental setups had very low ESS energy usage. Although the results showed that power to energy ratio from both experimental setups using both power and voltage control did not exceed the set goal of 70 due to the limitations of both experimental setups. Finally, a conclusion based on the results is given with future work and as well as recommendations for the continuation on this research topic.