Aggregate Peak EV Charging Demand: The Impact of Segmented Network Tariffs

TL;DR

This work tackles the challenge of peak EV charging on distribution networks and evaluates tariff-based demand shaping using real-world data from over 1,200 public charging points in the Netherlands. It combines charging topology, flat versus day-ahead dynamic energy prices, and fixed versus segmented network tariffs within four dispatch strategies to assess implications for aggregate peak demand, using the diversity factor $d = \frac{\max_t p^{\mathrm{single}}_t}{\max_t p^{\mathrm{agg}}_t}$. Key findings show that multi-level segmented tariffs flatten load and reduce peaks, especially with low power thresholds and higher mid-band tariffs, while dynamic energy prices alone can shift peaks and, in some cases, increase aggregate peaks. When dynamic energy prices are paired with segmented tariffs, further reductions in peaks are observed, offering practical guidance for grid operators on tariff design and demand shaping. The study highlights the diversity factor as a core metric for evaluating population-level flexibility and provides data-driven insights for implementing peak-reducing tariffs in distribution networks.

Abstract

Aggregate peak Electric Vehicle (EV) charging demand is a matter of growing concern for network operators as it severely limits the network's capacity, preventing its reliable operation. Various tariff schemes have been proposed to limit peak demand by incentivizing flexible asset users to shift their demand from peak periods. However, fewer studies quantify the effect of these tariff schemes on the aggregate level. In this paper, we compare the effect of a multi-level segmented network tariff with and without dynamic energy prices for individual EV users on the aggregate peak demand. Results based on real charging transactions from over 1200 public charging points in the Netherlands show that the segmented network tariff with flat energy prices results in more diverse load profiles with increasing aggregation, as compared to cost-optimized dispatch based on only dynamic day-ahead energy prices. When paired with dynamic energy prices, the segmented tariff still outperforms only dynamic energy price-based tariffs in reducing peaks. Results show that a balance between power thresholds and price per threshold is crucial in designing a suitable tariff, taking into account the needs of the power network. We also provide valuable insights to network operators by calculating the diversity factor for various peak demands per charging point.

Figures (4)

• Figure 1: Illustration of three-level segmented tariff. The three thresholds of the segmented tariff are denoted by $\bar{p}_{0,1,2}$ along with their respective prices ($\lambda_{0,1,2}$).
• Figure 2: Representative charging profiles illustrating different dispatch strategies for a single EV. The low-price ($\lambda^-$) variants of segmented tariffs are not shown because the results are identical to the high-price ($\lambda^+$) variants for this session.
• Figure 3: Quantile distribution of charging demand for different dispatch strategies across all days in the year 2022 for all sets of CP. Colours indicate the quantiles of charging power for each hour relative to all days in the year. The dashed lines indicate the maximum observed charging power for each hour.
• Figure 4: Distribution of annual maximum power per charging point under different aggregation levels for different dispatch strategies. The diversity factor corresponding to each power level is also indicated on the right axis.