How grid reinforcement costs differ by the income of electric vehicle users

The increasing adoption of electric vehicles (EVs), driven by stricter carbon emission regulations, presents a challenge to the stability of electricity distribution grids. A significant concern is the potential for peak demand during periods of widespread EV charging, particularly in the evenings. This peak demand can lead to grid overloads and necessitate costly infrastructure upgrades. The extent of these upgrades is not uniform across all areas; it varies significantly based on factors such as the number of EVs, the types of EVs (and their associated energy consumption), and the charging behavior of EV users. These factors are strongly correlated with socioeconomic attributes, most notably household income. Higher-income households tend to adopt EVs at a higher rate, frequently own more expensive and larger vehicles with greater energy demands, and exhibit charging patterns that contribute more to peak demand. Consequently, grid operators may be forced to make disproportionately large investments in grid infrastructure in wealthier neighborhoods. The costs of these upgrades are typically borne by all consumers through increased electricity prices. This scenario creates a significant problem of equity: higher-income neighborhoods drive up costs that are then shared across all income levels, disproportionately impacting lower-income households. This becomes particularly critical given the rising cost of electricity, increasing the risk of energy poverty even for middle-class households. The clean energy transition, therefore, risks exacerbating existing inequalities. This paper aims to quantify these cost differences in grid reinforcement between lower- and higher-income neighborhoods, and to propose policy recommendations to mitigate potential energy inequities.

Existing research on the impact of EVs on distribution grids often models homogeneous EV adoption and usage patterns, neglecting the influence of socioeconomic factors. This simplification can lead to significant inaccuracies. While studies have shown that plug-in hybrid electric vehicle (PHEV) penetration levels between 10% and 30% can cause voltage imbalances and power losses, few have explicitly incorporated socioeconomic variables like income, age, gender, occupation, or education level into their load assessments. However, there is substantial evidence linking higher household income to a greater likelihood of EV ownership and the choice of more energy-intensive vehicle models. Studies across various regions confirm this correlation, noting that higher-income households tend to own more expensive and larger vehicles, resulting in increased charging loads and greater strain on the grid. Driving patterns also vary with socioeconomic status, influencing EV charging patterns and peak load profiles. While a few studies have begun to incorporate socioeconomic factors into load assessments, none have provided quantifiable estimations of the resulting distribution grid reinforcement needs or costs. This paper addresses this gap by focusing on household income as a key socioeconomic factor, offering a more nuanced perspective on the issue of energy equity in the context of EV adoption and grid infrastructure investment.

This study employs a power flow analysis using real trip data to compare grid reinforcement costs between above-average and below-average income neighborhoods (defined as a 50:50 split of households by income). The analysis uses representative distribution grids in urban, suburban, and rural settings to capture variations in grid structure and load capacity. The model accounts for differences in EV adoption rates, model choices, and driving patterns between income groups. Real-life driving data from the German Mobility Panel was used to create representative driving and charging profiles for each income group. The study uses a time-inhomogeneous Markov chain simulation to generate synthetic trips reflecting the differing travel behaviors of high- and low-income households. Factors such as trip frequency, trip duration, and arrival times are differentiated by income group. The model also incorporates the probability of EV charging, influenced by the state of charge (SOC) of the vehicle. The charging probability is modeled using a previously validated inverse s-shaped relationship between SOC and charging initiation. Household electricity load profiles were generated using empirical sampling from the Load Profile Generator, a widely used and validated tool that creates realistic synthetic profiles based on household characteristics. These household loads are combined with EV charging loads to calculate the overall electricity demand at each grid node. Power flow simulations, using the Newton-Raphson method in the MATPOWER package in MATLAB, were used to identify grid overloads. The simulations were conducted in 5-min intervals for the month of December, focusing on a typical week to represent a challenging period for electricity usage. Grid reinforcement costs were then calculated to address the identified overloads, considering costs for line reinforcement and transformer upgrades based on German estimates. The impacts of EV adoption, model choice, and driving patterns on grid reinforcement costs are analyzed separately using a ceteris paribus approach.

The simulations reveal substantial differences in grid reinforcement costs between higher- and lower-income neighborhoods. The number of grid overloads in higher-income neighborhoods significantly exceeded those in lower-income neighborhoods, by over 12-fold on average across the different grid types (rural, suburban, urban). The rural grid exhibited the lowest overall resilience, but even there, grid reinforcement costs were 50% higher in the higher-income neighborhood. More striking disparities were observed in the suburban and urban grids, with cost differences reaching approximately 3300% and 480%, respectively. Extrapolating these findings to the EU, the potential cost asymmetry could amount to €14 billion. A detailed breakdown of the cost asymmetries, analyzing the individual contributions of EV adoption, model choice, and driving patterns, showed that differences in driving patterns have the most significant impact on the cost disparity, while the effect of model choice is relatively smaller. Even if EV adoption rates were equal across income groups, significant additional reinforcement costs would still persist for higher-income neighborhoods. The current practice of allocating grid reinforcement costs through across-the-board electricity price increases is highlighted as inequitable, as higher-income neighborhoods disproportionately drive up these costs while consuming only slightly more electricity overall. This is further complicated by the usual structure of electricity tariffs, which include a fixed base rate in addition to a per-kWh charge, diminishing the relative cost contribution of higher-income households.

This study underscores the importance of incorporating socioeconomic factors into grid planning models to accurately assess and equitably allocate the costs of grid reinforcement needed to support EV adoption. The significant cost asymmetries revealed highlight the potential for the clean energy transition to exacerbate existing social inequalities if not addressed with appropriate policy measures. The findings suggest that the current approach of covering grid reinforcement costs through across-the-board price increases is inequitable, particularly given the disproportionate contribution of higher-income neighborhoods to these costs. The analysis provides strong support for implementing dynamic electricity pricing mechanisms, such as time-of-use tariffs or load-based pricing, to better reflect the actual grid impact of different consumption patterns. Furthermore, income-dependent EV subsidies and measures to improve charging station access in lower-income areas could help mitigate the observed cost disparities. These measures would work to make EV adoption and use more equitable across different income groups, while also potentially mitigating peak demand issues.

This research quantifies the significant cost asymmetries in grid reinforcement associated with EV adoption across different income levels. The current system of cost allocation via uniform electricity price increases is shown to be inequitable. The findings strongly advocate for policy interventions such as time-of-use or load-based pricing, targeted subsidies, and improved charging infrastructure access to promote energy equity in the transition to electric mobility. Future research could explore the effectiveness of various policy interventions in different contexts and investigate the interaction between grid planning, energy equity, and other socioeconomic factors in greater depth.

The study focuses specifically on Germany, using data from the German Mobility Panel and German grid infrastructure. The findings might not directly generalize to other countries with different electricity pricing structures, grid characteristics, or EV adoption patterns. The model makes certain assumptions about EV charging behavior and household electricity consumption patterns, which might slightly deviate from real-world scenarios. Further research is needed to validate these assumptions in other contexts and to explore the long-term impacts of different policy interventions.