Grid tariff designs coping with the challenges of electrification and their socio -economic impacts Philipp Andreas Gunkel1 Claire -Marie Bergaentzlé1 Dogan Keles1 Fabian Scheller2 and Henrik

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Grid tariff designs coping with the challenges of

electrification and their socio-economic impacts

Philipp Andreas Gunkel1*, Claire-Marie Bergaentzlé1, Dogan Keles1, Fabian Scheller2 and Henrik

Klinge Jacobsen1

1 Energy Economics and System Analysis, DTU Management, Technical University of Denmark, 2800

Kongens Lyngby, Denmark

2 Institute Zero Carbon (IZEC), Technical University of Applied Sciences Würzburg-Schweinfurt (THWS),

Ignaz-Schön-Straße 11, 97421 Schweinfurt, Germany

* Correspondence: phgu@dtu.dk;

Received: date; Accepted: date; Published: date

Abstract. This paper investigates volumetric grid tariff designs under consideration of different

pricing mechanisms and resulting cost allocation across socio-techno-economic consumer

categories. In a case study of 1.56 million Danish households divided into 90 socio-techno-economic

categories, we compare three alternative grid tariffs and investigate their impact on annual

electricity bills. The results of our design consisting of a time-dependent threshold penalizing

individual peak consumption and a system peak tariff show (a) a range of different allocations that

distribute the burden of additional grid costs across both technologies and (b) strong positive

outcomes, including reduced expenses for lower-income groups and smaller households.

Keywords: electricity grid tariffs; electrification; network cost distribution

1. Introduction

Policy initiatives such as the “Inflation Reduction Act” and the European “Green Deal” aim to

reduce Greenhouse gas emissions with a particular focus on end-consumers (European Commission,

2019; U.S. Government Publishing Office, 2022). The part of the 2021 European Green Deal directed

towards the electrification of heating systems and individual transport poses unprecedented

challenges for European electricity systems. The European Commission targets the installation of 10

million heat pumps within five years until 2026 (European Commission, 2022), representing an

additional 150 TWh of electrified heat demand. The European commission's “Fit for 55” proposed in

2021 effectively ends the sale of CO2-emitting cars by 2035 (McPhie and Parrondo Crespo, 2022).

Around 30 million electric vehicles are expected on European roads by 2030, increasing electricity

demand by 84 TWh per year (European Commission, 2021). Both electric vehicles and heat pumps

will increase demand by 100% or even 200% residential electricity demand in the coming decade

leading to significant challenges to the existing electricity grid infrastructure (Andersen et al., 2021;

Bollerslev et al., 2021; Systems and Group, 2021; Wangsness et al., 2021). European and US

governments are well-engaged in creating incentive instruments for electrified end-use technologies.

Consequently, investments are unavoidable to reinforce existing grid infrastructure and to respond

to the upcoming demand boom (Clastres et al., 2019; Gautier et al., 2021). In particular, distribution

grids face unprecedented challenges from incoming volumes of additional energy and peak effects

driven by electrification. Distribution System Operators (DSOs) have a portfolio of tools to help the

advanced operation of their networks, among which time-based

tariffs offer adequate means to

Time-based tariffs refer to different structures varying in the temporal dimension like Time-of-Use (TOU)

with fixed schedules, coincidental peak pricing and dynamic rates tied to short-term network usage, and hybrid

TOU with the flexibility to invoke critical peak prices during exceptional congestion situations.

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address such generalized congestion problems as a whole (Cappers and Todd-Blick, 2021; European

Commission, 2020). The question is how to design tariffs appropriately.

Recent literature on demand response has shed light on multiple impacts of price signals on

system use and consumer flexible electricity use (Avau et al., 2021; Bergaentzlé et al., 2019; Cambini

and Soroush, 2019; Fausto et al., 2019). Past literature also informs us about the potential risks but

also windfall effects that tariff designs have on different groups of consumers with distinct socio-

economic and technical characteristics stressing the challenges for vulnerable consumers (Azarova et

al., 2018)

The literature on price signals for peak shedding or shaving discusses two main types of grid

tariff designs for households that are either based on unit price differentiation over time or on

introducing a power-based signal (Bjarghov et al., 2022a; Heleno et al., 2020; Hogan and Pope, 2017).

Depending on the chosen scheme, the tariff design will either predominantly target energy use and

support the reduction of system peaks or target power use and support the reduction of individual

peaks (Council of European Energy Regulators, 2020). However, the current growth in electrical uses

suggests better scrutiny of how to synthesize the attributes of both rate-making types. Because tariff

designs affect consumer groups in a non-uniform way, it is also essential to look at the winners and

losers of such a tariff. Additionally, it has to be considered that network usage is non-excludable but

rival good allowing free-riding behavior if not adequately addressed (Abbott, 2001; Rubino, 2017).

This study designs and tests a new tariff design that limits system-wide congestion effects and

effectively apportions system peak costs to the consumption that drives them. The design is

compared to two tariffs targeting peak system reduction or individual peak reduction. In doing so,

this study deconstructs the underlying fundamentals that govern the formation of these two tariffs:

system peak and individual peak. Here, the key fundamentals to consider are the time variable (i.e.

when do we choose to consider a period peak), and the power variable (i.e., above which grid capacity

use do we consider the demand peak).

The first branch of the literature for electricity pricing in households shows that volumetric

grid

tariffs with different time block rates are supportive of better grid usage (Bergaentzlé et al., 2019;

Cambini and Soroush, 2019; Picciariello et al., 2015). The main goals have been to encourage demand

response to reduce or shift peak consumption from the peak towards off-peak periods. In most cases

in Europe, like in Denmark since 2019, or in the U.S., Time-of-Use tariffs setting pre-determined block

rates were implemented (CEER, 2017; NordREG, 2015; TREFOR, 2022; Wangsness et al., 2021). While

this type of signal allows for some degree of load smoothing, it is limited in sending flexibility signals

in response to more critical events.

The shortcoming of fixed yearly schedules is tackled by introducing more dynamism in ToU

setups. The literature mainly refers to critical peak pricing schemes (CPP) as a rate design enabling

extraordinary peak signals to reflect system peak conditions (Faruqui and Sergici, 2013; Frontier

Economics and Sustainability First, 2012). Empirically such tariffs trigger larger load shifts than

simple ToUs due to the larger price spread between time blocks (CPS Energy, 2021a; Dütschke and

Paetz, 2013; Faruqui et al., 2006). Furthermore, they are limited in the number of times they can be

activated (10 to 15 days per year in the U.S. (CPS Energy, 2021b). Although the Critical Peak Pricing

(CPP) tariff brings about more efficient utilization of the grid, increased flexibility, and reduced peak

demand, it also presents a challenge in terms of allocating sunk costs without distinguishing between

different grid users (Council of European Energy Regulators, 2020).

Another branch of rate-making acknowledges the individual contribution to peaks, taking

consumer load patterns and peak behaviors as a point of departure. Individual Peak Pricing (IPP)

charges a higher tariff during peak consumption periods to penalize users with high peak load

Volumetric tariffs are pricing schemes that are based on volumes or quantities consumed over a determined

period of time. In the context of many European system operators they are usually priced in e.g. €/kWh per

hour.

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effects. Usually, this type of tariff applies a surcharge if the power demand is higher than a certain

threshold. Such tariff has been tested or applied in various experiments and cases to constrain peak

demand (Baldick, 2018; Zarnikau, 2014, 2013) but remains a poor indicator to coincide a customer’s

maximum demand with the system or local congestions (Borenstein, 2016; Hogan and Pope, 2017).

Peak-coincident pricing is more suitable for solving congestion problems, associating the peak

rate with consumptions occurring during system congestion hours (MIT Energy Initiative, 2016;

Morell-Dameto et al., 2023). Abdelmotelleb et al. compare the response outcome of four different

network charges, including the peak-coincident charge, showing that this design led to higher system

economic efficiency (Abdelmotteleb et al., 2018). When applied to large industrial consumers with

foreseeable peak load patterns, peak-coincident pricing drives is close to welfare optimizing behavior

(Baldick, 2018). Azarova et al. test such tariff components on households showing that coincidental

peak charges are the main factor for savings due to the random and short-term overlapping usage of

several appliances (Azarova et al., 2018). However, the dataset (765 households) limits the scope of

the analysis and only partially reveals how tariff designs affect categories of households. Peak-

coincident pricing is efficient enough to apportion system costs across users in times of scarcity, but

they do not distinguish between individual load contributions that effectively cause aggregated

scarcity. Furthermore, while literature has covered optimal peak tariffs approximating them to

forward looking long term marginal cost (Morell-Dameto et al., 2023), the cost distributional effects

among consumers and the pathway towards them has not been in detail.

Time-based volumetric tariffs take their point of departure into system conditions reflecting

system peaks, while individual pricing, whether coincidental or not, departs from individual peak

loads. However, existing literature on grid tariffs neglects to investigate the notion of “peak” itself.

To our knowledge, there is no clear definition of what a peak is, or rather when a system or

household's demand is considered in a peak state. The closer a system operates on the technical

boundaries, the likelier it is in a peak state. An aggregated system peak is the sum of all individual

contributions, while some individuals use more capacity than others. Most time-based volumetric

tariffs, however, treat each individual contribution the same. Consequently, the pricing mechanism

in time-based volumetric tariffs treats the potential exclusion of certain grid users due to limited grid

capacity in a uniform manner through marginal pricing. This approach doesn't differentiate

adequately between individuals who contribute significantly to the scarcity and those whose

contributions are comparatively lower.

The notion of using 5% to define system peaks in the U.S. and Europe has been well established

in the policy literature, thanks to the work of Faruqui et al. (Faruqui et al., 2010, 2007). Koranyi

justifies this threshold by explaining that the 5% corresponds to approximately 400 hours during

which 90% of the total installed capacity in the U.S. is utilized (Koranyi, 2011). Past studies also use

peaker capacities on the supply side as a reference to define the number of hours when peak rates

should apply (Milligan et al., 2017). Many time-based volumetric tariffs build upon the load duration

curve as their foundation. They achieve this by designating a subset of hours that surpass a specific

aggregated installed capacity threshold. This subset of hours is used to symbolize the proportion of

annual hours linked with peak periods.

The notion of threshold is also relevant at the individual level. In this case, the underlying

question becomes how to define individual peak usage, which comes down to deciding what

differentiates a “normal" consumption behavior from a peak behavior. Concretely, a consumer's

maximum capacity threshold is limited by her physical capacity connection to the grid. Nevertheless,

suppose each consumer connected to the same line can consume up to the limits of their individual

physical capacity. In that case, it is not true that they can all do so simultaneously. Fausto et al. and

Pérez-Arriaga et al. suggest symmetric pricing varying across consumers dependent on the state of

the grid but also factoring in varying contributions to the aggregated peak (Fausto et al., 2019; Pérez-

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Arriaga et al., 2017). This may lead to a volumetric grid tariff level function that depends on

individual consumption creating in practice hard-to-solve non-linearities

We, therefore, suggest an approximation of this function in the form of a two-step dynamic

approach that moves the pricing of aggregated scarcity and individual contributions to scarcity of

capacity together while adding a dynamic temporal trigger. We introduce an hourly threshold

consumption, which divides the base and peak tariff to reflect different grid capacity allocations per

consumer while fragmenting and approximating the individual contributions to aggregated peaks in

two levels. This study contributes to the state-of-art in questioning the main underlying assumption

for variable ToU and individual peak pricing, which is the definition of peak period and peak level,

respectively. We build on the two classic approaches of time-based volumetric tariffs and individual

peak-coincidental tariffs and simulate different thresholds for aggregate peak periods duration in the

former and individual peak thresholds in the latter. We finally develop a new tariff design at the

crossroad between time-based volumetric and individual peak pricing to reduce both the system

peak and individual peaks. The impact of these tariffs is comprehensively tested on a large sample

of Danish households with various socio-economic characteristics and with or without an electric car

or a heat pump. Our household dataset offers unprecedented detail by covering 1.56 million

households divided into 90 different socio-techno-economic categories, including dwelling type and

area, household income, occupancy, and electric vehicle or heat pump ownership.

Far from making a normative proposal on the qualification of the peak, this study explores in

depth the redistributive effects related to the characterization of the individual peak and the

characterization of the system peak. The results of this study provide a comprehensive overview of

grid tariff designs and their impact on residential network bills, thereby offering system and network

operators and regulators to understand cost-allocative effects. It does not aim to find one preferable

solution but shows a menu of distributional effects of design approaches.

The key contributions of this study are:

 Developing time-varying volumetric grid tariff designs with a differentiated price

mechanism that considers individual contributions to aggregated peaks differently.

 Investigating the impact of advanced electrification technologies, such as electric

vehicles (EVs) and heat pumps, on the distribution of grid costs among households.

 Assessing the distribution of these time and quantity-based tariff designs among

households belonging to different socio-economic groups.

The contributions of this study enable policymakers and network operators to gain a

comprehensive understanding of how the transition from flat volumetric tariffs to time-based tariffs

would impact the network bills of various consumer groups. This understanding empowers us to

develop and propose novel grid tariff designs that address the complex relationship between

individual and aggregated consumption. These designs aim to tackle the challenges associated with

future constraints on network capacities while ensuring complete transparency regarding their

To illustrate the non-linearity of grid tariffs from Fausto et al., consider the following dynamic grid tariff

concept. Instead of relying solely on a fixed value tied to network conditions, e.g. in coincidence pricing,

imagine a scenario where the tariff depends on both the network's state and how much each household

consumes. This perspective leads to a cost formula for households like this:

 

In this equation, both the network's status and individual consumption play a role in the level of the individual

grid tariff level. Due to consumption appearing in both parts of the formula that are multiplied together, the

formula becomes nonlinear. However, it's important to recognize that while this idea is of theoretical nature,

implementing it in the real world poses challenges and is thus not realistic.

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effects on consumers. This study, only focuses on the part of the final retail electricity price of

households that covers the volumetric distribution network tariffs.

This paper is organized as follows. Section 2 presents our methodology, encompassing the three

grid tariff designs tested in this study and mathematical methodology. Section 3 presents the case-

study context, the data and investigated scenarios. Section 4 presents the results which is followed

by the discussion in section 5.. Section 6 concludes and offers policy recommendations.

2. Methodology

This section presents our approach to designing the three grid tariffs focusing on individual

thresholds and system peak triggers and the combination of both peak definitions. Afterwards, we

develop a pricing model for base and peak consumption by utilizing the definitions of these terms

and applying the corresponding grid tariffs to calculate the annual grid bills by consumer groups and

the total revenue of the system operator.

2.1. Grid tariff designs for individual and system peaks

The first design, Individual Peak Pricing (IPP), is presented in section 2.1.1 by defining a threshold

that divides household consumption into base and peak consumption. In contrast, the second design

presented in 2.1.2, Dynamic Critical Peak Pricing (DCPP), penalizes the critical timing of consumption.

The third grid tariff design, Dynamic Critical Individual Peak Pricing (DCIPP) merges both approaches.

2.1.1. Defining individual peak power thresholds for Individual Peak Pricing (IPP)

The threshold divides individual consumption between peak and baseload. The lower the

threshold, the more essential consumption, such as cooking or lighting, is defined as peak and

subsequently subject to high rates. To address the ambiguity surrounding defining individual peaks

and considering the variability in local technical characteristics, this study establishes four thresholds

that penalize various types of consumption and, by extension, different user groups. We use

sensitivity analysis to offer a menu of results to understand the dynamics of this definition on annual

grid bills per consumer.

Figure 1 shows how much hourly household consumption varies between households with and

without EV and heat pumps. While the lowest threshold of 1 kWh/h targets almost all the consumer’s

individual peaks, the range between 1 kWh/h, 1.5 kWh/h and 2 kWh/h targets particular heat pumps

and electric vehicles. Advancing towards a 3 kWh/h threshold excludes most traditional consumer

groups and heat pump users and defines electric vehicle charging as peak consumption in particular.

We aim to observe the allocative effects of different levels by varying the threshold. Utilities can

choose thresholds by allocating the available capacity among consumers.

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Gridtariffdesignscopingwiththechallengesofelectrificationandtheirsocio-economicimpactsPhilippAndreasGunkel1*,Claire-MarieBergaentzlé1,DoganKeles1,FabianScheller2andHenrikKlingeJacobsen11EnergyEconomicsandSystemAnalysis,DTUManagement,TechnicalUniversityofDenmark,2800KongensLyngby,Denmark2InstituteZer...

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Grid tariff designs coping with the challenges of electrification and their socio -economic impacts Philipp Andreas Gunkel1 Claire -Marie Bergaentzlé1 Dogan Keles1 Fabian Scheller2 and Henrik

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