Addressing grid congestion in the Dutch distribution grid requires price-based solutions, such as scarcity reflective grid tariffs. One proposed grid tariff structure is the Time-of-Use (ToU) contracted power tariff for large-scale electricity consumers, where power can be contracted hourly at varying prices to incentivize lower electricity usage during peak periods. However, it is unclear whether this tariff meaningfully reduces congestion. This study investigates: “How can Time-of-Use contracted power tariffs contribute to reducing grid congestion and improving efficient grid usage in the Dutch distribution grid, given modeled large-scale electricity user behavior?”.

To answer this question a demand response estimation model was created. Literature was first reviewed to understand consumer behavior in response to price signals, revealing existing models are insufficiently scalable or detailed to analyze grid tariffs on a physical network. A new model was thus created, combining elements from prior literature and an interview with a large-scale electricity consumer. The model assumes cost-minimizing behavior, constrained by consumer-specific historical consumption patterns. The redistribution of a daily amount of energy is optimized considering commodity costs, grid tariffs and penalties for deviations from the reference profile. Penalties are based on price elasticities of electricity demand. Network capacity, electricity prices and total demand were treated as exogenous variables.

The ToU contracted power tariff was evaluated using system efficiency as a guiding principle, operationalized through peak reduction and an adjusted load factor. The adjusted load factor measures the ratio of average load to system peaks. A medium-voltage network segment in the Maasvlakte was modeled to assess the effects of the ToU contracted power tariff on these performance indicators.

The results have shown that the ToU contracted power tariff, as a complement to ToU volumetric and peak tariffs, leads to only a marginal amount of additional peak reduction. The maximum measured reduction is 0,008 MW, and occurs under moderate assumed consumer flexibility. This reduces the system peak from the modeled network from 12.660 MW to 12.652 MW. Also, the ToU contracted power tariff does not consistently improve the adjusted load factor, thus indicating limited incentive for more efficient grid usage.

The structure of the tariff explains its limited effectiveness. The tariff primarily incentivizes load shifting rather than peak reduction. If consumption remains below contracted capacity, additional load does not increase costs. Only the timing of contracted capacity affects pricing. Thus, the load tends to shift across hours rather than reducing peaks, which can lead to lower adjusted load factors.

In conclusion, the ToU contracted power tariff is not a reliable solution for reducing congestion or improving system efficiency. The complementary value to the proposed ToU volumetric and peak tariff is limited, and its complexity may hinder implementation. Also, given that peaks occur infrequently, applying ToU tariffs to an entire year offers limited benefits. Future congestion management should focus on tariffs that directly influence locational, temporal and peak-driven aspects of congestion. Future research into consumer-specific price elasticities could improve on the realism of the created model, and improve future analyses.