# On the means, costs, and system-level impacts of 24/7 carbon-free energy
procurement

Iegor Riepin<EMAIL_ADDRESS>Tom Brown<EMAIL_ADDRESS>Department of Digital Transformation in Energy Systems, TU Berlin, Germany

###### Abstract

A growing number of public and private energy buyers are interested in 24/7
carbon-free energy (CFE) procurement, which means that every kilowatt-hour of
electricity consumption is met by carbon-free sources at all times. It has the
potential to overcome the limitations of established procurement schemes, such
as the temporal mismatch between clean electricity supply and buyers’ demand
that is inherent to “volumetric” matching. Yet it is unclear how 24/7 CFE
procurement affects the rest of the power system, and whether this effect is
consistent across regional contexts and different levels of system cleanness.
We use a mathematical model to systematically examine different designs,
optimal procurement strategies, costs, and impacts of the 24/7 CFE matching,
both for participating buyers and for regions where voluntary procurement
occurs. We examine mechanisms driving system-level emissions reduction and how
they vary across regions and over time. Our results indicate that clean energy
procurement commitments have consistent beneficial effects on participants and
the electricity system. Even as grids become cleaner over time, the hourly
matching strategy contributes significantly to system-level emissions
reduction. In addition, voluntary commitments to 24/7 CFE have a further
transformative effect on electricity systems through accelerated innovation
and early deployment of advanced energy technologies.

###### keywords:

24/7 carbon-free electricity, Clean electricity procurement, Energy modelling,
Renewable energy

††journal: TBA

SSupplementary References elsarticle-num

###### Contents

  1. 1 Introduction
  2. 2 Methods and experimental design
  3. 3 Results
    1. 3.1 Base scenario
    2. 3.2 Technology access
    3. 3.3 Diverse grid locations
    4. 3.4 Advancing five years forward
    5. 3.5 Understanding the mechanism of 24/7 CFE procurement in grid decarbonisation
  4. 4 Discussion
  5. 5 Conclusion and Policy Implications
  6. S1 Technology assumptions
  7. S2 Background system assumptions

##  1 Introduction

Climate change is driving a global effort to rapidly decarbonise electricity
systems across the globe. The European Union (European Union (EU)) has
committed to achieving net-zero emissions by 2050, and the European Commission
has adopted the European Climate Law with a target of reducing net greenhouse
gas emissions by at least 55% by 2030, compared to 1990 levels [1]. National
climate initiatives are gaining ground across the globe, including the United
States with the Biden administration’s goal of reducing emissions by 50-52% by
2030, compared to 2005 levels [2], and China’s commitment to have CO2
emissions peak before 2030 and achieve carbon neutrality before 2060 [3].

Many public and private energy buyers have joined this effort by purchasing
clean energy to demonstrate their sustainability credentials. The established
certification schemes for voluntary clean energy procurement are the
Guarantees of Origin (Guarantees of Origin (GO) s) in Europe and the Renewable
Energy Certificates (Renewable Energy Certificate (REC) s) in the United
States. A common feature of these schemes is that renewable energy credits are
“unbundled” from megawatt-hours of energy and can be traded numerous times on
specialized markets before being retired. The owner gets to claim
environmental benefits when “retiring” (i.e. using) the certificate. The
unbundled renewable energy credits, however, have been criticized for not
ensuring a physical match between renewable energy generation and consumption
[4, 5, 6], and for absence of evidence that such schemes drive build-out of
additional renewable energy capacity [7, 8].

Some corporate and industrial (Corporate and Industry (sectors) (C&I)) buyers
recongnised the problems associated with the unbundled certificates and turned
towards Power Purchase Agreements (Power Purchase Agreement (PPA) s). When
signing a PPA, energy buyers pledge to buy both the energy and the
environmental credits “bundled” to megawatt-hours. Market-driven procurement
of renewable energy has become one of the major drivers of the energy
transition: PPA s are expected to account for one-fifth of utility solar PV
and wind capacity expansion in 2023–2024, and almost twice as much (36%) when
China is excluded [9]. In some countries, such as Spain and Sweden, market-
driven procurement of renewable energy capacity surpasses policy-driven
capacity expansion [9].

The key problem – When C&I consumers purchase renewable credits, either via
unbundled schemes or via bundled PPA s, renewable energy supply is typically
matched over a long period of time with buyers’ energy demand. As an example,
more than 400 members of the RE100 group [6] have committed to purchasing
renewable energy that matches 100% of their electricity consumption on an
annual basis. Even the “best case” of 100% annual matching commitments, where
energy buyers sign PPA s with renewable energy generators located within a
local grid, has a major problem: the temporal mismatch between renewable
energy supply and electricity demand. In hours when wind and solar generation
is low, energy buyers have to depend on carbon-emitting technologies, like
coal and gas, available on local markets. As a result, the 100% annual
renewable matching approach does not entirely eliminate carbon emissions from
the energy buyer’s portfolio. Furthermore, the 100% annual matching approach
keeps electricity buyers exposed to price volatility, as the price of
electricity on the wholesale markets can vary significantly from hour to hour.

One proposed solution: 24/7 CFE – There is growing interest from the leaders
in the clean energy procurement to move beyond the 100% annual matching
approach. One approach is called 24/7 Carbon-Free Energy (Carbon-Free
Electricity (CFE)) procurement, which aims to match clean energy supply with
electricity demand on an hourly basis. Thus, 24/7 CFE procurement has the
potential to eliminate all greenhouse gas emissions associated with
electricity use of the participating companies. Furthermore, 24/7 CFE
procurement is focused on carbon-free rather than renewable energy, which
opens up opportunities for a broad range of advanced energy technologies, such
as long-duration energy storage (Long Duration Energy Storage (LDES)) or new
clean firm power generators that are needed for deep decarbonisation of energy
systems.

Google led the 24/7 movement in 2020 by announcing its commitment to cover the
demand at all its data centres and campuses worldwide using the 24/7 CFE
approach by 2030 [10]. Shortly after, Google published a policy roadmap on
achieving the goal [11]. Also, Microsoft has committed to the 100/100/0 by
2030 goal (i.e. “100% of electricity consumption, 100% of the time, matched by
zero carbon energy purchases” [12]. The list of entities that have committed
to 24/7 CFE procurement includes also utilities, such as Peninsula Clean
Energy (a community choice aggregator in California) committed to 24/7 CFE by
2025 [13], and even cities like Des Moines, Iowa U.S., with a commitment by
2035 [14]. In 2021, an international group of energy suppliers, electricity
buyers, and Non-Governmental Organization (NGO) s launched the 24/7 Carbon-
free Energy Compact [15]. With over 135 signatories, the group aims to
“develop and scale high-impact technologies, energy policies, procurement
practices, aand solutions to make 24/7 Carbon-Free Energy achievable for all”.

Existing literature on 24/7 CFE – The growing interest in the means, costs and
the system-level impacts of hourly CFE procurement has created a need for a
quantitative analysis of the approach. There is relatively little research on
the system-level impacts of 24/7 CFE procurement; and it is all fairly recent.
The first to incorporate it into the electricity system planning model were Xu
et al. (2022) [16].111A working paper updates a prior report published in
November 2021 investigating the system-level impacts of 24/7 CFE in two
regions of the U.S. – California and PJM [17]. The authors study the system-
level impacts of 24/7 CFE procurement based on an example of California in the
year 2030. In November 2022, IEA also released a model-based study on the 24/7
CFE, focusing on India and Indonesia [18]. In early 2023, Peninsula Clean
Energy published an own white paper introducing their modeling tool—MATCH
(Matching Around-the-Clock Hourly energy)—and the results of the modeling
indicating feasibility of the company’s “24/7 renewable energy by 2025” goal
[19].

The first studies indicate that voluntary 24/7 CFE commitments reduce the
emissions of participating buyers as well as emissions in the electricity
grids they operate. What has been missing is a comprehensive analysis of 24/7
CFE procurement over multiple regions and years, since electricity system
characteristics, National Climate and Energy Plans (National Energy and
Climate Plan (NECP) s), renewable resource quality, and the cost of clean
energy vary significantly across regions and over time. Furthermore, it is
unclear whether decarbonisation effect of 24/7 CFE procurement remains
relevant when electricity grids are progressively decarbonised, as is the case
in many countries and jurisdictions, or if the 24/7 CFE procurement is only
impactful in the early stages of the energy transition.

Contribution – The novelty of this study is to go beyond case studies and show
in a systematic way, across many regions and levels of energy transition, how
voluntary commitments to 24/7 CFE procurement impact participating buyers and
regions where voluntary procurement occurs. We explore different designs,
optimal procurement strategies, costs, benefits and system-level impacts of
the 24/7 CFE matching approach. Our analysis focuses on four regions within
the European energy system with unique characteristics, and we vary the
analysis years to track the evolution of the energy system. Additionally, we
examine the underlying mechanisms that drive the system-level reduction of CO2
emissions, and examine how they perform over time and across regions. This
work is a follow-up to the earlier public report by the authors [20] with an
updated model, updated system-wide assumptions, and additional analysis of the
results.

The remainder of the paper is structured as follows: Section 2 introduces a
mathematical model of clean energy procurement strategies and gives a brief
summary of the methodology, sources of model input data, and the experimental
setup. The results are presented and discussed in Section 3 and put in
perspective in Section 4. The work is concluded in Section 5. Supplementary
Information provides further details about model assumptions and
parametrisation of the background energy system.

##  2 Methods and experimental design

Optimisation model – The simulations were carried out with the modified
version of the open European power system model PyPSA-Eur, built using the
open framework PyPSA [21]. The objective of the model is to minimise the
annualised system-wide costs of meeting energy demand within the modelled
system, while adhering to all relevant engineering, reliability, and policy
constraints. We use a brownfield investment approach, which means that we keep
the existing power generation and storage assets in the model and only
optimise the capacity expansion of new assets. The optimal solution of the
model includes the location and capacity of new power generation and storage
assets, as well as the hourly dispatch of all existing and new assets,
including the linear optimal power flow on the transmission network for the
planning year. This is standard for the PyPSA-Eur model [22] and the long-term
energy planning models of this class [23, 24].

Clean energy procurement model – We introduce new model components
(parameters, variables, constraints) into the PyPSA-Eur model to represent
voluntary clean energy procurement. By incorporating these into the
mathematical problem, we model a situation where a fraction of electricity
demand in a particular bidding zone is voluntarily committed to procuring
clean energy with a desired strategy. The fraction is set at 10% of the total
C&I electricity demand in a particular bidding zone, hereinafter referred to
as “participating consumers”. The model co-optimises procurement and
operational decisions of the participating consumers to meet their electricity
demand in accordance with the procurement strategy they have chosen.

We model and compare two procurement strategies:

  * 1.

100% annual matching: participating buyers meet their energy demand on an
annual volumetric basis with additional renewable energy procurement;

  * 2.

24/7 hourly matching: participating buyers optimise their investment and
operational decisions mixing procurement of additional generation and storage
resources and electricity imports so that CFE supply meets electricity demand
24/7 throughout the year with the desired quality score.

  * 3.

For benchmarking, we also model a reference case: participating buyers have no
voluntary environmental commitments and purchase all their electricity from
the local grid.

100% annual matching – The annual volumetric renewable matching strategy is
modelled with a constraint (1). More formally, the sum of all dispatch
$g_{r,t}$ of contracted renewable generators $r\in RES$ over the year $t\in T$
is equal to the annual electricity demand of participating consumers:

$\sum_{r\in RES,t\in T}g_{r,t}=\sum_{t\in T}d_{t}$ (1)

The contracted renewable generators must be new, i.e., they must be additional
to the system. Procured generators can be sited only in the local bidding
zone; thus, we model the “best case” of the annual matching strategy.

24/7 CFE hourly matching – The hourly matching strategy is modelled with a
constraint (2). The 24/7 CFE procurement framework is based on the
methodologies and metrics paper by Google (2021) [25]. The framework considers
various CFE supply options for the participating consumers, including
procurement of their own generation resources and storage assets, and
electricity imports from the local grid.

More formally, the hourly generation from the procured CFE generators $r\in
CFE$, discharge and charge from the procured storage technologies $s\in STO$,
plus imports of electricity from the regional grid $im_{t}$ multiplied by the
grid’s hourly CFE factor $CFE_{t}$ minus the excess of the CFE supply must be
higher or equal than a certain CFE score $x$ multiplied by the total load of
participating consumers:

$\begin{split}\sum_{r\in CFE,t\in T}g_{r,t}&+\sum_{s\in STO,t\in
T}\left(\bar{g}_{s,t}-\underline{g}_{s,t}\right)\\\ &-\sum_{t\in
T}ex_{t}+\sum_{t\in T}CFE_{t}\cdot im_{t}\geq x\cdot\sum_{t\in
T}d_{t}\end{split}$ (2)

where CFE score $x$[%] measures the degree to which hourly electricity
consumption is matched with carbon-free electricity generation. Equation (2)
thus allows for controlling the quality score of the 24/7 CFE procurement by
adjusting the parameter $x$. The best quality score—100% CFE—means that every
kilowatt-hour of electricity consumption is met by carbon-free sources at all
times.

Similarly to the hourly matching strategy, the contracted generators must be
additional to the system and can be sited only in the local bidding zone.

Note that if total electricity generation of assets procured by participating
consumers exceeds demand in a given hour, the “excess” carbon-free electricity
is not counted toward a CFE score. Here we assume that the excess can either
be curtailed or sold to the regional electricity market at wholesale market
prices. We set a constraint on the total amount of excess generation sold to
the regional grid, setting the limit to 20% of the total annual demand of
participating consumers.222In Discussion, we explain the rationale behind this
limit and discuss the implications of this assumption. The wholesale market
prices are derived from dual variables of a nodal energy balance constraint.

The CFE factor of the regional grid ($CFE_{t}$) can be seen as the percentage
of clean electricity in each MWh of imported electricity to supply demand of
participating consumers in a given hour. To compute $CFE_{t}$, we consider
both the hourly electricity mix in the local bidding zone and the
emissionality of imported electricity from the neighbouring zones. The
methodology to calculate the grid CFE factor is described in the prior work of
the authors [20].

Note that in equation (2), $CFE_{t}$ is affected by additional CFE resources
procured by the participating consumers. This introduces a nonconvex term to
the optimisation problem. The nonconvexity can be avoided by treating the grid
CFE factor as a parameter that is iteratively updated (starting with
$CFE_{t}=0\,~{}\forall t$). We find that one forward pass (i.e. 2 iterations)
yields very good convergence.

Model scope – Geographically, the model encompasses the entire European
Network for Transmission System Operators for Electricity (ENTSO-E) area,
which covers the entire European electricity system.333Islands that are not
connected to the main European system, such as Malta, Crete and Cyprus, are
excluded from the model. In this study, electricity demand, supply and power
transmission infrastructure (alternating current lines at and above 220 kV
voltage level and all high voltage direct current lines) are clustered to 37
individual bidding zones [26]. Thus, each zone represents a country; some
countries straddling different synchronous areas are split into individual
bidding zones, such as DK1 (West) and DK2 (East).

Participating consumers can be located in either of the four selected zones:
Ireland, Denmark (zone DK1), Germany, or Poland. The comparison of results
across these zones allows us to generalize the implications of voluntary clean
energy procurement, taking into account factors such as electricity demand,
weather, availability of renewable resources, existing electricity generation
capacity mix, and national energy policies, among other factors.

We model two individual years: 2025 and 2030. From a modelling perspective, a
five-year step changes many system parameters. In particular, technology costs
decline as a result of economies of scale and incremental innovation, NECP s
become tighter, CO2 prices increase, and some legacy power plants go out of
business. The problem is solved with a temporal resolution of 2920 snapshots,
representing a 3-hourly average of the hourly time-series data.

Carbon-free energy technologies in scope – Depending on the scenario,
participating consumers can choose from a variety of energy technologies
available on the European market. By grouping technologies according to their
maturity levels and availability on the market, we formulate three scenarios:

  * 1.

Palette 1 – technologies that are commercially available today: onshore wind,
utility scale solar photovoltaics (Solar photovoltaics (PV)), and battery
storage;

  * 2.

Palette 2 – all above plus a long-duration energy storage (LDES) that is
expected to be commercially available in the near future;

  * 3.

Palette 3 – all above plus promising advanced technologies that are in
prototype stage today, but are expected to be commercially available in the
future: (i) Allam cycle power plant with Carbon Capture and Sequestration
(CCS) 444Allam cycle is a natural gas power plant with up to 100% of carbon
capture and sequestration to represent a technology option with higher
operational expences (Operational expenditures (OPEX)), and (ii) “an advanced
dispatchable generator”, a stand-in for clean firm technologies with higher
capital expences (Capital expenditures (CAPEX)), such as advanced geothermal
or nuclear systems.

Technology assumptions – Cost and other assumptions for energy technologies
available for participating consumers were collected primarily from the Danish
Energy Agency [27] for the respective years and are provided in [Table S1].
For the LDES, we assume an underground hydrogen storage in salt caverns with
an electolyzer and a fuel cell for hydrogen conversion. Data for advanced
clean firm technologies is less reliable due to technological uncertainty and
lack of commercial experience; therefore, use our own indicative assumptions.

Other assumptions – Other model inputs and key background system assumptions
are provided in [S2]. For a full list of technology assumptions, see the
GitHub repository [28].

##  3 Results

Throughout the analysis, we examine how voluntary clean electricity
procurement strategies affect both buyers and the electricity system as a
whole. From the C&I buyers’ perspective, we examine optimal procurement
strategies (i.e., a mix of locally procured energy generation and storage
assets and electricity imports), the average cost of procured electricity and
the carbon emissions associated with electricity usage, also known as Scope 2
emissions [29]. From a system perspective, we analyze how voluntary clean
energy procurement impacts CO2 emissions in the region and the “demand pull”
for advanced energy technologies.

Our experimental setup incorporates a wide range of scenarios, allowing us to
generalise our results beyond individual assumptions. For convenience, we
start with a base scenario presented in Section 3.1. The base scenario
reflects a case when 10% (on volumetric basis) of C&I consumers in Ireland in
2025 are committed to procuring additional clean energy with a desired
strategy. The participating consumers can attain the 24/7 CFE procurement
targets by co-optimizing local procurement using mature technologies,
designated as palette 1, and by importing electricity from the local grid.
Afterward, we delve deeper into the scenario space, altering selected
parameters to explore various alternatives and address the following
questions:

  1. 1.

Section 3.2: What if a wider palette of carbon-free technologies were
available to participating buyers?

  2. 2.

Section 3.3: What if participating buyers are located in areas of the European
electricity grid that differ significantly from the base scenario?

  3. 3.

Section 3.4: What are the implications if we move the timeframe ahead five
years to 2030?

  4. 4.

Finally, in Section 3.5, we get a closer look at how 24/7 CFE commitments can
help decarbonize local grids and reveal the individual mechanisms involved.

Considering the large scenario space, this work presents results for a
selection of scenario branches that generalise the system impacts of the 24/7
carbon-free energy procurement. We publish all code to reproduce the
experiments under open license alongside the paper. See Data and Code
Availability.

###  3.1 Base scenario

######  Result 1.

Through 24/7 CFE procurement, participating consumers can eliminate carbon
emissions associated with their electricity consumption and help to
decarbonise the entire electricity system.

######  Result 2.

90%–95% 24/7 CFE targets have only a small cost premium over 100% annual
renewable matching. 100% CFE target can be achieved with solar PV, wind and
battery storage, but at a high cost premium for participating consumers.

Figure 1 shows the fraction of hourly demand met with CFE depending on the
procurement policy that participating consumers follow. We contrast two main
cases, one where participants commit to 100% annual renewable matching policy
(hereinafter – “100% RES”) and another case where the consumers commit to 24/7
CFE hourly matching policy (hereinafter – “24/7 CFE”) with various CFE targets
in a range from 80% to 100%. In the reference case, participating consumers
cover their load purely with grid purchases without any policy regarding the
origin of electricity.

Figure 1: Fraction of hourly demand met with carbon-free electricity.

In the reference case, only 54% of demand is met with CFE. 100% RES—the best
case for the annual renewable matching policy—results in a 83% fraction.
Consequently, CFE targets beyond 85% yield higher share of hourly demand met
with CFE than 100% RES. Another notable trend in Figure 1 is that
participating consumers rely more on procured resources and less on grid
imports with higher CFE targets. When targets approach 100%, participating
consumers cannot rely on electricity imports from the grid, since the grid
supply mix contains some carbon content in almost all hours.

Figure 2(a) shows how choice of a procurement policy affects the average
emissions rate of participating consumers. The average emissions rate is
derived by dividing the total CO2 emissions linked to the electricity
consumption of participating consumers by the total kilowatt-hours (kWh)
consumed. Already in 2025, Ireland has a moderately clean electricity system:
the emission rate is at 177 gCO2/kWh in the reference case. If participating
consumers follow 100% RES policy, the emission rate is reduced to 60 gCO2/kWh.
This yields a considerable emission rate reduction compared to the reference
case. The 24/7 CFE procurement with targets above 85% helps the participating
consumers to reduce emissions further beyond the treshhold set by the 100%
RES. At the 100% CFE target, participating consumers attain a zero emission
rate associated with their electricity consumption, as they perfectly align
demand and CFE supply on an hourly basis.

Voluntary procurement of clean electricity also helps decarbonise the
electricity system as a whole. We explore this effect by plotting CO2
emissions in the local region of the participating consumers, i.e. Ireland
(Figure 2(b)). Without voluntary procurement, the model estimates Irish power
sector carbon emissions to be at the level of 4.2 MtCO2/a.555For comparison,
seai.ie reports this value to be at 10.3 MtCO2 in 2021. A strong decreasing
trend is expected, since the Irish government has set ambitious targets to
achieve a low-carbon energy system [30]. The modelling results indicate that a
commitment to 100% 24/7 CFE in 2025 from 10% of the C&I electricity demand
could yield a reduction in Irish emissions by 0.6 MtCO2/a, in contrast to a
scenario absent voluntary procurement. This reduction equates to 14% of the
total emissions from the Irish power sector. 100% RES can also facilitate
system-level CO2 emissions reductions (as depicted by the left bar in Figure
2(b)); however, 24/7 CFE attains superior emissions reductions when the CFE
target surpasses 85%.

(a) Average emissions rate of participating consumers.

(b) CO2 emissions in the local region (Ireland).

Figure 2: Impact of clean electricity procurement commitments on emissions of
participating consumers (top panel) and the system-level emissions in a local
region (bottom panel).

(a) Portfolio capacity procured by participating consumers.

(b) Cost breakdown of a procurement policy.

Figure 3: Optimal capacity portfolios (top panel) and costs breakdown (bottom
panel) per procurement strategy.

The following analysis unveils the modelled cost-optimal procurement
strategies for each procurement policy (Figure 3(a)). In Ireland, 10% of the
C&I sector’s demand yields a load of 220 MW. The findings indicate that, to
cover this load adhering to a 100% RES policy, participating consumers procure
a combined capacity of 1.3 GW from onshore wind and solar PV generators.
Ensuring demand is matched with CFE on an hourly basis requires a considerably
more substantial portfolio of wind and solar PV, up to 4.0 GW of combined
capacity to reach the 100% CFE target. Batteries are integrated into the cost-
optimal portfolio mix when CFE targets exceed 85%. It is noteworthy that, for
an 80% CFE target, participating consumers procure less capacity than under a
100% RES policy, as they rely more on grid imports.

The breakdown of costs associated with a procurement policy that participating
consumers choose is shown in Figure 3(b). Note that revenues from selling the
excess electricity to the regional grid at market prices can be treated as
“negative costs” and subtracted from the net procurement cost. The results
show that a CFE targets of 80%–95% can be achieved at a small cost premium to
100% RES policy with solar, wind and batteries. However, what stands out in
the plot is the rapid increase of procurement costs for high 24/7 CFE targets.
For example, 98% CFE target has cost premium of 54% over 100% the annual
matching policy; while the last 2% of hourly matching more than doubles the
costs.

###  3.2 Technology access

######  Result 3.

24/7 CFE procurement creates an early market for advanced energy technologies
the system will need later: long-duration energy storage and clean firm
generators.

######  Result 4.

The cost premium of 24/7 procurement with high CFE targets may substantially
decrease if advanced energy technologies are available for commercial scale-
up.

The cost premium of 24/7 CFE procurement presented in Section 3.1 is driven by
the variability of renewable power supply. Indeed, in the periods when not
much wind or solar is available, matching every kWh of electricity consumption
with carbon-free electricity on hourly basis is not an easy task. Battery
storage technology is cost-optimal for shifting surplus power supply by a
couple of hours. However, bridging extended periods of low wind and sun with
battery storage is expensive. Also, the results above show that 24/7
participating consumers rely little on grid supply to reach CFE targets of
98%–100%, since a local grid’s electricity mix almost always includes some
fossil-fueled generation.

The results in Figure 4 reflect a case when 24/7 CFE participants have access
to a wider palette of technologies, including LDES and advanced clean firm
generators, such as advanced geothermal systems, advanced nuclear or Allam
cycle generators with CCS.

Figure 4(a) (left panel) shows the breakdown of costs for a case when
participating consumers have an access to palette 2 technologies, i.e., long-
duration storage complements onshore wind, solar PV and battery storage. Here
we assume an underground hydrogen storage in salt caverns with energy storage
cost of 2.5 €/kWh [Table S1]. An LDES system helps to bridge hours with no
renewable feed-in. As a result, the required portfolio of renewable capacity
and procurement costs are reduced, especially for high CFE targets. Thus, 100%
24/7 CFE policy has a cost premium of 42% above 100% RES, which is
significantly lower than without LDES.

Figure 4(b) (right panel) depicts the technological palette 3 scenario,
wherein participating consumers additionally gain access to clean firm
generation technologies. The Allam cycle turbine with CCS now enters into the
optimal procurement strategy. The cost-optimal share of to clean firm
technology increases as CFE targets rise. The clean dispatchable generator
helps to fill in the gaps in variable renewable generation to match demand and
CFE supply on an hourly basis. The cost premium is further reduced: 100% 24/7
CFE costs only 15% above the 100% RES.

(a) Cost breakdown of a procurement policy (w/ LDES).

(b) Costs of a procurement policy
(w/ LDES and advanced technologies).

Figure 4: The breakdown of costs per procurement policy if participating
consumers have an access to a wider palette of technologies: w/ LDES (left
panel); w/ LDES and advanced clean firm generators (right panel).

###  3.3 Diverse grid locations

######  Result 5.

Despite the inherent variability in regional characteristics—ranging from
renewable resources and national policies to degrees of interconnection—the
model shows a consistent effects of 24/7 clean energy procurement on both
participating buyers and overarching system-level impacts across all analyzed
regions.

While the primary focus has been a base scenario exemplifying Ireland,
analogous modelling can be applied to diverse regions within the European
electricity grid, each exhibiting unique characteristics influenced by local
resources, renewable potentials, NECP s, the degree of interconnections, and
other factors.

Figure 5 depicts outcomes for the selected regions of Germany and Poland.
Germany is selected due to its extensive interconnections with neighbouring
regions with relatively clean grids, such as France and Denmark, and its
status as the largest electricity consumer in Europe. In contrast, Poland has
a smaller electricity demand. Moreover, the Polish energy system typically has
higher carbon intensity, largely due to its historical dependence on coal
resources. The participation rate is maintained at 10% of the C&I sector in
both regions, aligning with the base scenario.666A 10% participation rate
within the C&I sector corresponds to loads of 3.8 GW in Germany and 1.1 GW in
Poland. Despite regional variances, the dynamics and system impacts of clean
energy procurement, as observed in the base scenario, exhibit similarities.

German consumers experience lower average emissions rates, attributed to a
cleaner grid, in contrast to those in Poland. In the reference cases, emission
values stand at 240 gCO2/kWh and 549 gCO2/kWh for Germany and Poland,
respectively.777For context, the emission intensity of electricity generation
in 2021 was 402 gCO2/kWh for Germany and 750 gCO2/kWh for Poland [31]. Echoing
the Irish scenario, both Figure 5(a) and Figure 5(b) demonstrate that in both
contexts, participating consumers attain reduced emissions rates with 24/7 CFE
procurement compared to 100% RES, given sufficiently strict CFE targets.

The impact of 24/7 hourly CFE procurement on system-level CO2 emissions is
notable in both Germany and Poland, as depicted in Figure 5(c) and Figure
5(d). In the absence of any procurement strategy, carbon emissions from the
power sectors of Germany and Poland are projected to be 118.8 MtCO2 and 83.8
MtCO2 in 2025, respectively.888Despite Germany consuming more electricity, the
similar scale of power sector carbon emissions between the two countries can
be attributed to Poland’s significantly higher emission intensity in its
electricity mix. By engaging in voluntary clean energy procurement, C&I
consumers can drive notable decarbonisation in the local regions, exceeding
the benchmarks set by NECP s. For instance, in Germany, even a 10%
participation rate under a 100% RES policy can diminish national emissions by
11.1 MtCO2 annually; remarkably, the 24/7 CFE approach can amplify this impact
to up to 14 MtCO2/a, given a CFE target of 100%.

The optimal capacities of energy technologies and associated costs are
illustrated in Figure 5(e) and Figure 5(f). Similar to the Irish scenario,
participating consumers complement renewable energy procurement with
electricity imports from the grid to meet lower CFE targets. For the
realization of strict CFE targets, both the Allam cycle with CCS and advanced
clean firm generators are incorporated into the technology mix. This entry of
two advanced technologies into the optimal solution space diverges from the
Irish findings, where solely the OPEX-heavy Allam cycle turbine is deemed
cost-optimal. The difference is likely attributable to Germany and Poland
being more extensively interconnected with the broader European electricity
grid compared to Ireland. Better interconnection allows exhausting all
arbitrage opportunities across temporal and spatial dimensions, and maximising
the dispatch value derived from an advanced dispatchable generator with high
CAPEX. Mirroring the findings from Ireland, the cost premium of 24/7 CFE
matching gets minimal when these advanced technologies are accessible.

(a) Average emissions rate of participating consumers (Germany).

(b) Average emissions rate of participating consumers (Poland).

(c) CO2 emissions in the local region (Germany).

(d) CO2 emissions in the local region (Poland).

(e) Cost breakdown of a procurement policy (Germany).

(f) Cost breakdown of a procurement policy (Poland).

Figure 5: Selected results for scenarios when participating consumers are
located in Germany (left) and Poland (right); all plots are for technological
palette 3.

###  3.4 Advancing five years forward

######  Result 6.

Over time, participating consumers reap benefits from the gradual decline in
clean technology costs and a progressively cleaner state of the electricity
grids, enhancing the affordability of 24/7 CFE procurement.

######  Result 7.

Voluntary commitments to 24/7 CFE procurement retain their significance in
enhancing system value, even as grids become cleaner. Analogous to the base
scenario, hourly matching commitments facilitate a more profound
decarbonisation compared to 100% annual renewable matching, provided that CFE
targets surpass a particular threshold.

Having explored the methods, costs, and impacts of clean electricity
procurement in the European electricity system for the year 2025 in preceding
sections, this section delves into the projected results and implications of
these strategies for 2030. Referencing back to the Methods and experimental
design section, the key implications of advancing five years forward are
cleaner electricity grids and decreased costs for energy technologies. Figure
6 illustrates the outcomes for the base scenario transitioned to 2030,
maintaining all other parameters constant.

Analyzing Figure 6(a), which presents the fraction of hourly demand met by
clean electricity under each procurement policy in 2030, two key insights
emerge when compared to the base scenario. Firstly, 72% of demand is met by
CFE in the reference case, marking an 18% increase from 2025, indicating an
enhancement in the cleanliness of the background electricity grid.
Consequently, lower CFE targets (like 80%) are met with higher share of
electricity imports. Secondly, participating consumers depend on their own
procurement of CFE resources and storage, limiting imports from the background
grid to achieve strict CFE targets. This effect stays consistent with the base
scenario.

A cleaner background grid results in lower average emissions rates for
participating consumers. Figure 6(b) shows that the emissions rate for
consumers purchasing only grid electricity decreases from 240 gCO2/kWh in 2025
to 107 gCO2/kWh in 2030. The hourly matching policy enables the attainment of
zero emissions linked to C&I participants’ consumption. It is interesting to
note that a CFE target of 90% yields lower average emissions than a 100%
annual matching policy, even though these policies are equally costly (see
Figure 6(d)–Figure 6(f)).

In cleaner systems, both 100% RES and 24/7 CFE policies sustain their
beneficial impact on system-level emissions. As observed in Figure 6(c), 24/7
CFE with a sufficiently high CFE target, outperforms 100% RES policy in the
decarbonisation impact: with 10% participation rate, hourly matching reduces
local emissions by up to 0.4 Mt$\text{CO}_{2}$/a, in contrast to the 0.2
Mt$\text{CO}_{2}$/a reduction achieved by annual matching. The 0.2
Mt$\text{CO}_{2}$/a differential equates to approximately 8% of Irish power
sector emissions. The mechanisms driving this impact are detailed in Section
3.5.

As illustrated in Figure 6(d)–Figure 6(f), the cost breakdown of the
procurement policy reveals a decline in 24/7 CFE costs across all
technological pallets relative to 2025. Participating consumers benefit from
lower prices for clean energy technologies and progressively cleaner
electricity grids. Remarkably, these participants not only achieve zero
emissions associated with their consumption and exert a notable impact on
system-level $\text{CO}_{2}$ emissions but also do so while encountering
smaller cost premiums, enhancing the accessibility of the voluntary clean
energy procurement goals.

(a) Fraction of hourly demand met with carbon-free electricity.

(b) Average emissions rate of participating consumers.

(c) CO2 emissions in the local region (Ireland).

(d) Cost breakdown of a procurement policy: technological palette 1.

(e) Cost breakdown of a procurement policy: technological palette 2.

(f) Cost breakdown of a procurement policy: technological palette 3.

Figure 6: Results for the scenario of Ireland 2030; 10% participation rate.
Figures 6(a)–6(d) display the technological palette 1 scenario.

###  3.5 Understanding the mechanism of 24/7 CFE procurement in grid
decarbonisation

######  Result 8.

24/7 CFE procurement mitigates system-level CO2
emissions through two distinct mechanisms: “profile” and “volume”. The
mechanisms origin from distinct aspects of the interplay between the 24/7 CFE
procurement and the background electricity grids.

######  Result 9.

The disparity in decarbonisation outcomes between 24/7 CFE hourly and 100%
annual renewable matching policies becomes increasingly pronounced as local
grids transition to cleaner states. This phenomenon underscores the
effectiveness of 24/7 CFE procurement in supporting system decarbonisation in
the context of evolving national energy and climate policies.

In the previous sections, we demonstrated that 24/7 clean energy procurement
results in a substantial reduction of CO2 emissions in local electricity grids
(Figures 2(b), 5(c), 5(d) and 6(c)). In this section, we examine the
underlying mechanisms that drive these observed reductions, so as to better
understand how clean energy procurement and emissions reduction interrelate.

As originally identified by Xu et al. (2021) [17], two mechanisms are
responsible for reducing system-wide emissions:

First, a profile mechanism: participants doing 24/7 hourly matching procure
clean energy resources that better match their demand patterns. As certain
consumers align their demand with CFE supply on hourly basis, the need for
dispatchable generation that can firm intermittent renewable supply is lower
in the rest of the system. This mechanism can reduce the utilisation of
fossil-based generators, such as Open Cycle Gas Turbine (OCGT) power plants,
which typically ramp up when wind and solar resources are scarce.

Second, a volume mechanism: even the cost-optimal procurement strategy for
24/7 hourly matching might include some amount of excess clean energy. If
total CFE generation of assets procured by 24/7 participants exceeds demand in
a given hour, the “excess CFE” is not counted toward a CFE target; however, it
is clean electricity that can potentially be stored (using batteries or LDES),
or sold to the regional grid. As for the latter, excess CFE might displace
emitting grid generators or reduce the need to import electricity from
neighbouring areas.

Several factors influence the degree to which profile and volume mechanisms
contribute to decarbonising the local grids, such as the composition of the
24/7 portfolio, the volume of excess generation sold to the grid, and the
electricity generation (and import) mix in the local zone.

We conduct an experiment to isolate the decarbonisation effects of the volume
and profile mechanisms. In the experiment, we reran the optimization while
fixing an excess electricity sold to the grid to zero (NB excess is still
possible, but must be either stored or curtailed). Such a model setup includes
only a profile decarbonisation mechanism. We attribute the difference between
emissions reduction in the experiment model setup and the base scenario to the
volume mechanism. As a result, the reduction of emissions in a local zone can
be attributed either to excess electricity sold to the grid (volume effect) or
to better alignment of CFE generation with demand (profile effect).

Figure 7(a) illustrates the reduction in local zone emissions attributed to
each of the two decarbonisation mechanisms. In the plot, the x-axis shows the
different states of electricity grids in which C&I consumers participate in
24/7 procurement. The eight ’background grids’ are generated from the
combination of modelled wildcards: four zones and two years.

Figure 7(a) shows that the impact of 24/7 CFE procurement in absolute terms is
proportional to how clean the background grid on average is. Thus, the largest
impact is at 571.6 kgCO2/a·MWh-1 takes place in the Poland 2025 zone, and the
lowest one is 3.7 kgCO2/a·MWh-1 in Denmark 2030 (NB values for 10% of C&I
sector participation rate with CFE 100% target). It is noteworthy that from
2025 to 2030, the decarbonisation effect of voluntary clean electricity
procurement decreases over time in all zones since electricity grids become
cleaner as a result of national climate policy programs and decomissioning of
emitting power plants. A comparison of the volume and profile mechanisms
reveals that both contribute to the decarbonization of the local grids. In
particular, the profile mechanism contributes significantly to overall
emissions reductions, with a major share across all zones regardless of their
configuration.

(a) Emissions reduction in a local zone with profile and volume mechanisms
isolated. Data is normalised per MWh of demand participating in 24/7
procurement.

(b) Percentage of emissions reductions in a local zone as a result of a
procurement policy compared with the no procurement policy case.

Figure 7:  System-level emission reduction: comparison of procurement policies
and isolation of decarbonisation mechanisms. Figure 7(a) shows breakdown of
emissions reduction in a local zone if 10% of C&I load follows 24/7 hourly
procurement with CFE 100% target. The profile are volume mechanisms are
isolated. Figure 7(b) compares reductions in annual zone emissions in percent
points achieved through 100% annual renewable matching and 100% 24/7 CFE
matching; no procurement policy is assumed in the counterfactual scenario.

Additional insights into decarbonisation mechanisms can be gained by examining
the emission reductions achieved through 100% RES and 100% 24/7 CFE matching
policies. Figure 7(b) compares the decarbonisation impact within a local zone
of the two procurement policies in relative terms (percent point p.a.), with a
counterfactual scenario where no consumers engage in voluntary clean
electricity procurement. The figure demonstrates that, even though the
absolute impact of procurement policies diminishes over time, the relative
impact of 24/7 CFE on system-level emissions increases over time when compared
to 100% RES. This phenomenon is attributable to the self-cannibalisation
effect of wind and solar that influences the value of additional renewable
capacity. This is relevant in regions where governments adhere to ambitious
NECP targets, like Ireland and Germany (NB Denmark is an outlier with an
exceptional RES target exceeding 100% of national electricity demand for
2030). The profile mechanism maintains its relevancy even when renewable
energy generators undergo self-cannibalization. The volume mechanism retains,
or even amplifies, its relevance, by leveraging a diversified portfolio of
procured resources by 24/7 participants, including batteries, long-term energy
storage, and advanced clean firm technologies. Consequently, clean electricity
is not only available but can also be sold to the background grid during
periods when system-wide wind and solar generation is scarce.

##  4 Discussion

Our results in perspective – Our findings agree with the existing model-based
studies on clean energy procurement [16, 18, 19], as well as with the general
understanding of the energy research community that “100% renewable energy is
not enough” [32] in a context of electricity consumers claiming environmental
sustainability. Specifically, we show that through hourly matching of demand
with clean electricity, electricity consumers can both completely negate their
carbon emissions and contribute to broader system-wide decarbonisation. 100%
CFE matching is viable with commercially available technologies, albeit incurs
a cost premium for participating consumers. The cost premium can be
substantially reduced if the advanced technologies, such as long-duration
energy storage and clean firm generators are available, or if consumers opt
for participating in the 24/7 CFE procurement with quality scores ranging from
90% to 95% instead of an absolute 100%.

Another important finding of this work that confirms prevailing understanding
is that 24/7 CFE matching creates an early market for advanced energy
technologies. Further research is needed to better understand how 24/7 CFE
commitments push advanced energy technologies along their learning curves, and
to estimate the potential economic impact on global decarbonisation costs.

According to a comparison of the results over the modeled regions and time
periods, voluntary clean energy procurement commitments appear to have
consistent effects on both participating buyers and system-level
decarbonisation. Based on these findings, this work can provide a basis for
generalising the implications of clean energy procurement strategies, while
taking into account regional differences in electricity demand, renewable
resource availability, legacy power plant fleets, degrees of interconnection,
and energy policies, among other factors.

A deeper look at the role of voluntary clean energy procurement commitments in
grid decarbonisation reveals that the hourly matching strategy retains its
significance in reducing system-level emissions, even as grids become cleaner
over time. We examined two distinct mechanisms through which 24/7 CFE
procurement facilitates system decarbonization: “profile” and “volume”. As
companies adapt their procurement strategies, it is important to understand
how these mechanisms perform across different grid locations and years.
Interestingly, the disparity between the annual (“volumetric”) matching with
renewable energy and 24/7 CFE hourly matching strategies in decarbonisation
outcomes becomes ever more evident as electricity grids become cleaner. This
phenomenon illustrates the effectiveness of 24/7 CFE procurement in supporting
system decarbonisation within the context of evolving national energy and
climate policies.

Features of 24/7 CFE not covered in this work – Some positive features of 24/7
CFE procurement fall beyond the scope of this manuscript.

From a system-wide perspective, voluntary commitments to 24/7 CFE reduce the
need for flexibility in background electricity systems. There will be less
investment in gas-fired peaker generators, as well as less need for battery
storage elsewhere in the system. This effect has been explored by Xu et al.
(2022) and Riepin & Brown (2022) [20, 17]

From an electricity buyer perspective, 24/7 CFE procurement can provide a
hedge against volatile wholesale market prices. Since 100% CFE matching
implies covering the entire electricity demand with clean energy from procured
generators via power purchase agreements and operating storage, the buyer is
not exposed to the wholesale market price risk (see Figure 1). More
information on the price hedge effect is provided by the 24/7 CFE Energy
Compact [15].

Critical appraisal – This work has simplifications and limitations that should
be acknowledged.

First, only the electricity sector is included in the mathematical model of
the European energy system, which is justified by the research focus. However,
it is important to acknowledge that the electricity sector is not an isolated
system, but is increasingly becoming linked with other energy sectors, such as
heat and transport. Brown et al. (2018) discuss the synergies of sector
coupling in the integrated European energy system [33].

Second, consumers participating in clean energy procurement are modelled as
one entity. The real-world situation may involve a number of individual
consumers participating in a voluntary clean energy procurement scheme. Our
assumption is that all consumers that commit to 24/7 matching, form alliances,
and enter into contracts with carbon-free energy generators so that their
aggregated consumption can be matched with clean generation on an hourly basis
to achieve a given CFE target. In reality, participating consumers can pursue
hourly matching strategies independently based on their own load profiles. See
Xu & Jenkins (2022) investigating this case [34].

Third, we assume that the participating consumers have a flat demand profile
(i.e. baseload). As a matter of fact, consumer demand profiles vary based on
their business activities. It has been shown by Riepin & Brown (2022) that the
shape of consumption profiles affects the cost-optimal technology mix required
for achieving a certain CFE target; however, the shape of the profile has
relatively little impact on procurement costs, emissionality of the portfolio,
and system-level impacts of 24/7 procurement [20].

Fourth, participating consumers have an inflexible demand, i.e., no demand-
side management is considered. In reality, many C&I consumers have some degree
of demand flexibility, which they can use to adjust their consumption as
needed based on grid signals, such as the wholesale market price and the
average emission rate of the local electricity mix. Moreover, some consumers,
such as data centers and cloud computing providers, can shift their load also
in space, i.e., to shift computing jobs and associated power loads across data
center locations [35]. In the recent work, Riepin & Brown (2023) explore how
space-time load-shifting flexibility can be used to meet high 24/7 CFE
targets, as well as what potential benefits it may offer to participating
energy buyers and to the rest of the energy system [36].

One of the simplifications of mathematical models implementing 24/7 CFE
procurement is how the excess carbon-free electricity is handled. The “excess
CFE” can be defined as the total amount of carbon-free electricity supply
exceeding the demand of the participating consumers in a given period of time.
Here we assume that the excess can either be curtailed or sold to the regional
electricity market at wholesale market prices, until the excess constraint is
reached. The constraint restricts the volume of excess electricity sold to the
regional grid, ensuring it does not surpass 20% of the annual demand from
participating consumers. The problem with the uncostrained export of excess
CFE to the regional market could be a situation where excess electricity from
the renewable generators procured by 24/7 consumers displaces electricity from
other renewable generators in the market, which in turn may challenge
additionality of the 24/7 procurement; and if system-wide renewable expansion
is constrained, the cost-optimal solution may be to use the excess clean
electricity to “relax” the constraint, increasing the renewable portfolio of
participating consumers. Similar “spillover effect” problem has been observed
and discussed by Xu et al. (2022) [16]. In their white paper, Peninsula Clean
Energy team also discuss the problems and implications of the excess supply.
Citing their work: “We conclude that excess supply is a necessary aspect of a
time-coincident renewable portfolio in the range of likely market conditions
in California today” [19]. Both studies perform sensitivity analyses on the
excess supply and discuss the implications on their findings [16, 19].

Is 24/7 CFE the right strategy? – Last but not least, it is worth mentioning
an ongoing discussion whether 24/7 CFE is the right strategy for companies to
reduce their carbon emissions. Some proponents of alternative strategies
contend that 24/7 CFE, or “temporal matching”, may not represent the most
effective approach to emissions reduction. This argument is based on the
rationale that optimising procurement and deployment of carbon-free generators
and batteries to cover consumption 24/7 with clean electricity is a different
strategy to optimising the assets specifically to displace “as much emissions
as possible”.

One of the alternative strategies could be that companies, in their pursuit to
reduce system-level carbon emissions, focus on system-wide impact as measured
via short-run marginal emissions accounting (“emissions matching”). Xu et al.
(2023) bring this discussion from the realm of arguments into quantitative
analysis, concluding that “emissions matching strategies have zero or near-
zero long-run impact on system-level CO2 emissions” [37]. The reason for this,
citing the authors, is that voluntary carbon-free energy procurement under
this strategy almost exclusively displaces other carbon-free energy rather
than fossil fuels.

Another alternative strategy could be that companies do annual matching with
“overprocurement”, i.e., covering their own consumption on an annual
volumentric basis plus procuring some more clean electricity on top. Such
strategy could be implemented at low cost, e.g., with solar PV. However, as
shown in Section 3.5, this strategy would not be as effective in reducing
system-level emissions as 24/7 CFE matching, especially in the context of
evolving national energy and climate policies. Further, such a strategy would
not promote innovation and pull forward in time research and investment
decisions in advanced energy technologies, such as long-duration energy
storage and clean firm generators.

More work—encompassing the collective contributions of the research community,
NGO s, policy-makers, and corporate energy buyers—is needed to deepen our
understanding of the implications, challenges, and opportunities inherent in
various clean energy accounting approaches and procurement strategies, guiding
us all towards net-zero sustainable energy systems.

##  5 Conclusion and Policy Implications

In this work, we systematically examined the means, costs, and the system-
level impacts of the 24/7 carbon-free energy matching. We find that 24/7
matching is a viable and effective strategy for electricity buyers aiming to
eliminate their own carbon footprint and contribute to wider system
decarbonisation. Voluntary commitments to the hourly matching strategy have a
further transformative effect on electricity systems through accelerated
innovation and early deployment of advanced energy technologies. The evidence
holds across geographical locations and is robust to sensitivity analyses.
Even as grids become cleaner over time, the 24/7 matching strategy retains its
significance in reducing system-level emissions, which is essential in light
of evolving national energy and climate policies.

## Acknowledgements

We thank the following people for fruitful discussions on the 24/7 carbon-free
energy procurement and the art of energy system modelling: Fabian Neumann,
Elisabeth Zeyen, Devon Swezey, Brian Denvir, Marc Oman, Jesse Jenkins and
research staff of the Zero carbon Energy systems Research and Optimization
Laboratory (ZERO Lab) at Princeton University.

Iegor Riepin was supported by the research grant from Google LLC.

This work is a follow-up to the prior public report by the authors [20] with
an updated model, updated system-wide assumptions, and additional analysis of
the results.

## Author Contributions

Iegor Riepin: Conceptualization – Data curation – Formal Analysis –
Investigation – Methodology – Software – Validation – Visualization – Writing
- original draft

Tom Brown: Conceptualization – Methodology – Software – Supervision – Writing
- review & editing

## Data and Code Availability

The simulations were carried out with PyPSA — an open-source software
framework for simulating and optimising modern energy systems [21].

All code, input data and results are published under an open license. The code
to reproduce the experiments is available at GitHub [28].

## Glossary

CAPEX

    Capital expenditures
CCS

    Carbon Capture and Sequestration
CFE

    Carbon-Free Electricity
C&I

    Corporate and Industry (sectors)
ENTSO-E

    European Network for Transmission System Operators for Electricity
EU

    European Union
GO

    Guarantees of Origin
LDES

    Long Duration Energy Storage
NECP

    National Energy and Climate Plan
NGO

    Non-Governmental Organization
OCGT

    Open Cycle Gas Turbine
OPEX

    Operational expenditures
PPA

    Power Purchase Agreement
PV

    Solar photovoltaics
REC

    Renewable Energy Certificate

## References

  * [1] European Commission, The European Climate Law (2021).
URL https://climate.ec.europa.eu/eu-action/european-climate-law_en

  * [2] The White House, FACT SHEET: President Biden to Catalyze Global Climate Action through the Major Economies Forum on Energy and Climate (Apr. 2023).
URL https://www.whitehouse.gov/briefing-room/statements-
releases/2023/04/20/fact-sheet-president-biden-to-catalyze-global-climate-
action-through-the-major-economies-forum-on-energy-and-climate/

  * [3] IEA, An energy sector roadmap to carbon neutrality in China – Analysis, Tech. rep., International Energy Agency (Sep. 2021).
URL https://www.iea.org/reports/an-energy-sector-roadmap-to-carbon-neutrality-
in-china

  * [4] S&P Global, Problematic corporate purchases of clean energy credits threaten net zero goals (May 2021).
URL https://www.spglobal.com/esg/insights/problematic-corporate-purchases-of-
clean-energy-credits-threaten-net-zero-goals

  * [5] H. Böck, How Iceland sold the same Green Electricity twice (May 2023).
URL https://industrydecarbonization.com/news/how-iceland-sold-the-same-green-
electricity-twice.html

  * [6] RE100, Progress and Insights Annual Report, Tech. rep., RE100 Climate Group (2020).
URL https://www.there100.org/growing-renewable-power-companies-seizing-
leadership-opportunities

  * [7] A. Bjørn, S. M. Lloyd, M. Brander, H. D. Matthews, Renewable energy certificates threaten the integrity of corporate science-based targets, Nature Climate Change 12 (6) (2022) 539–546.  doi:10.1038/s41558-022-01379-5.
URL https://www.nature.com/articles/s41558-022-01379-5

  * [8] M. Gillenwater, X. Lu, M. Fischlein, Additionality of wind energy investments in the U.S. voluntary green power market, Renewable Energy 63 (2014) 452–457.  doi:10.1016/j.renene.2013.10.003.
URL https://www.sciencedirect.com/science/article/pii/S0960148113005338

  * [9] IEA, Are market forces overtaking policy measures as the driving force behind wind and solar PV? – Renewable Energy Market Update, Tech. rep., International Energy Agency (Jun. 2023).
URL https://www.iea.org/reports/renewable-energy-market-update-june-2023/are-
market-forces-overtaking-policy-measures-as-the-driving-force-behind-wind-and-
solar-pv

  * [10] Google LLC, 24/7 by 2030: Realizing a Carbon-free Future (2020).
URL https://www.gstatic.com/gumdrop/sustainability/247-carbon-free-energy.pdf

  * [11] Google LLC, A Policy Roadmap for 24/7 Carbon-Free Energy (2022).
URL https://www.gstatic.com/gumdrop/sustainability/policy-roadmap-carbon-free-
energy.pdf

  * [12] Microsoft, Made to measure: Sustainability commitment progress and updates (Jul. 2021).
URL https://blogs.microsoft.com/blog/2021/07/14/made-to-measure-
sustainability-commitment-progress-and-updates/

  * [13] Peninsula Clean Energy, Our path to 24/7 renewable power by 2025 (Dec. 2021).
URL https://www.peninsulacleanenergy.com/our-path-to-24-7-renewable-power-
by-2025/

  * [14] Iowa Environmental Council, History made as Des Moines adopts 24/7 carbon-free energy resolution, becoming first in nation - Iowa Environmental Council (Jan. 2021).
URL https://www.iaenvironment.org/newsroom/energy-news/history-made-as-des-
moines-adopts-247-carbonfree-energy-resolution-becoming-first-in-nation-

  * [15] 24/7 CFE Energy Compact, 24/7 Carbon-Free Energy: Methods, Impact & Benefits.
URL https://gocarbonfree247.com/

  * [16] Q. Xu, A. Manocha, N. Patankar, J. Jenkins, System-Level Impacts of 24/7 Carbon-Free Electricity Procurement (Oct. 2022).  doi:10.2139/ssrn.4248431.
URL https://papers.ssrn.com/abstract=4248431

  * [17] Q. Xu, A. Manocha, N. Patankar, J. D. Jenkins, (report) System-level Impacts of 24/7 Carbon-free Electricity Procurement, Tech. rep. (Nov. 2021).
URL https://zenodo.org/record/6229426

  * [18] IEA, Advancing Decarbonisation through Clean Electricity Procurement, Tech. rep., International Energy Agency (Nov. 2022).
URL https://www.iea.org/reports/advancing-decarbonisation-through-clean-
electricity-procurement

  * [19] Peninsula Clean Energy, Advanced Modeling Tool Demonstrates Cost-Effective 24/7 Renewable Power Strategy, Tech. rep. (Jan. 2023).
URL https://www.peninsulacleanenergy.com/advanced-modeling-tool-demonstrates-
cost-effective-24-7-renewable-power-strategy/

  * [20] I. Riepin, T. Brown, System-level impacts of 24/7 carbon-free electricity procurement in Europe, Tech. rep., Zenodo (Oct. 2022).  doi:10.5281/zenodo.7180098.
URL https://zenodo.org/record/7180098

  * [21] T. Brown, J. Hörsch, D. Schlachtberger, PyPSA: Python for Power System Analysis, Journal of Open Research Software 6 (1) (2018) 4.  doi:10.5334/jors.188.
URL https://openresearchsoftware.metajnl.com/articles/10.5334/jors.188

  * [22] J. Hörsch, F. Hofmann, D. Schlachtberger, T. Brown, PyPSA-Eur: An open optimisation model of the European transmission system, Energy Strategy Reviews 22 (2018) 207–215.  doi:10.1016/j.esr.2018.08.012.
URL https://www.sciencedirect.com/science/article/pii/S2211467X18300804

  * [23] J. Jenkins, N. Sepulveda, D. Mallapragada, N. Patankar, A. Schwartz, J. Schwartz, S. Chakrabarti, Q. Xu, J. Morris, N. Sepulveda, GenX (Apr. 2022).  doi:10.5281/zenodo.6229425.
URL https://github.com/GenXProject/GenX

  * [24] M. Howells, H. Rogner, N. Strachan, C. Heaps, H. Huntington, S. Kypreos, A. Hughes, S. Silveira, J. DeCarolis, M. Bazillian, A. Roehrl, OSeMOSYS: The Open Source Energy Modeling System: An introduction to its ethos, structure and development, Energy Policy 39 (10) (2011) 5850–5870.  doi:10.1016/j.enpol.2011.06.033.
URL https://www.sciencedirect.com/science/article/pii/S0301421511004897

  * [25] Google LLC, 24/7 Carbon-Free Energy: Methodologies and Metrics (2021).
URL https://www.gstatic.com/gumdrop/sustainability/24x7-carbon-free-energy-
methodologies-metrics.pdf

  * [26] PyPSA-Eur documentation, Spatial resolution — PyPSA-Eur.
URL https://pypsa-eur.readthedocs.io/en/latest/spatial_resolution.html

  * [27] Technology Data for Generation of Electricity and District Heating, Tech. rep., Danish Energy Agency (Aug. 2016).
URL https://ens.dk/en/our-services/projections-and-models/technology-data

  * [28] I. Riepin, Brown, Tom, Code for 24/7 CFE procurement In Europe (2023).
URL https://github.com/Irieo/247-procurement-paper

  * [29] Greenhouse Gas Protocol, GHG Protocol: Scope 2 Guidance.
URL https://ghgprotocol.org/scope-2-guidance

  * [30] SEAI, CO2 emissions report (2020).
URL https://www.seai.ie/data-and-insights/seai-statistics/key-
publications/co2-emissions-report/

  * [31] European Environment Agency, Greenhouse gas emission intensity of electricity generation in Europe (2023).
URL https://www.eea.europa.eu/ims/greenhouse-gas-emission-intensity-of-1

  * [32] J. A. de Chalendar, S. M. Benson, Why 100% Renewable Energy Is Not Enough, Joule 3 (6) (2019) 1389–1393.  doi:10.1016/j.joule.2019.05.002.
URL https://www.cell.com/joule/abstract/S2542-4351(19)30214-4

  * [33] T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner, Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system, Energy 160 (2018) 720–739.  doi:10.1016/j.energy.2018.06.222.
URL https://www.sciencedirect.com/science/article/pii/S036054421831288X

  * [34] Xu, Qingyu, Jenkins, Jesse, Electricity System and Market Impacts of Time-based Attribute Trading and 24/7 Carbon-free Electricity Procurement, Tech. rep., Princeton University (Sep. 2022).
URL https://zenodo.org/records/7082212

  * [35] Ross Koningstein, We now do more computing where there’s cleaner energy (May 2021).
URL https://blog.google/outreach-initiatives/sustainability/carbon-aware-
computing-location/

  * [36] Riepin, Iegor, Brown, Tom, The value of space-time load-shifting flexibility for 24/7 carbon-free electricity procurement, Tech. rep., Zenodo (Jul. 2023).
URL https://zenodo.org/records/8185850

  * [37] Xu, Qingyu, RIcks, WIlson, Manocha, Aneesha, Patankar, Neha, Jenkins, Jesse, Working Paper: ”System-level Impacts of Voluntary Carbon-free Electricity Procurement Strategies” (Sep. 2023).  doi:10.5281/zenodo.8325964.
URL https://zenodo.org/records/8325964

  * [38] P. Srivastav, M. Schenkel, G. U. R. Mir, T. Berg, M. Staats, Carbon Capture, Utilisation, and Storage : Decarbonisation Pathways for Singapore’s Energy and Chemicals Sectors, Tech. rep., Navigant Netherlands B.V.
URL https://file.go.gov.sg/carbon-capture-utilisation-and-storage-
decarbonisation-pathway-for-singapore-energy-and-chemical-sectors-pdf.pdf

  * [39] Larson, Eric, Net-zero America: Potential pathways, infrastructure, and impacts., Tech. rep., Princeton University (Oct. 2021).
URL https://netzeroamerica.princeton.edu/the-report

  * [40] F. Hofmann, J. Hampp, F. Neumann, T. Brown, J. Hörsch, Atlite: A Lightweight Python Package for Calculating Renewable Power Potentials and Time Series (Jun. 2021).  doi:10.21105/joss.03294.
URL https://github.com/PyPSA/atlite

  * [41] Open Power System Data – A platform for open data of the European power system.
URL https://open-power-system-data.org/

  * [42] Powerplantmatching: Set of tools to combine multiple power plant databases, PyPSA (Oct. 2023).
URL https://github.com/PyPSA/powerplantmatching

  * [43] Beyond Fossil Fuels.
URL https://beyondfossilfuels.org/

  * [44] World Nuclear Association - World Nuclear Association.
URL https://world-nuclear.org/

## Supplementary Information

##  S1 Technology assumptions

Cost and other assumptions for energy technologies available for participating
consumers were collected primarily from the Danish Energy Agency [27]. These
assumptions are listed in Table S1. A full list of technology assumptions,
including the data for energy technologies in the background energy system, is
available via the reproducible scientific workflow in the GitHub repository
[28].

Technology | Year |  | CAPEX
---
(overnight cost)
| FOM
---
[%/year]
| VOM
---
[Eur/MWh]
| Efficiency
---
[per unit]
| Lifetime
---
[years]
Source
Utility solar PV | 2025 | 612 €/kW | 1.7 | 0.01 | - | 37.5 | [27]
| 2030 | 492 €/kW | 2.0 | 0.01 | - | 40.0 | [27]
Onshore wind | 2025 | 1077 €/kW | 1.2 | 0.015 | - | 28.5 | [27]
| 2030 | 1035 €/kW | 1.2 | 0.015 | - | 30 | [27]
Battery storage | 2025 | 187 €/kWh | - | - | - | 22.5 | [27]
| 2030 | 142 €/kWh | - | - | - | 25 | [27]
Battery inverter | 2025 | 215 €/kW | 0.3 | - | 0.96 | 10 | [27]
| 2030 | 160 €/kW | 0.3 | - | 0.96 | 10 | [27]
Hydrogen storage1 | 2025 | 2.5 €/kWh${}_{\text{H}_{2}}$ | - | - | - | 100 | [27]
| 2030 | 2.0 €/kWh${}_{\text{H}_{2}}$ | - | - | - | 100 | [27]
Hydrogen electrolysis | 2025 | 550 €/kW${}_{\text{el}}$ | 2.0 | - | 0.67 | 27.5 | [27]
| 2030 | 450 €/kW${}_{\text{el}}$ | 2.0 | - | 0.68 | 30 | [27]
Hydrogen fuel cell | 2025 | 1200 €/kW | 5.0 | - | 0.50 | 10.0 | [27]
| 2030 | 1100 €/kW | 5.0 | - | 0.50 | 10.0 | [27]
Allam cycle generator2 | 2025 | 2760 €/kW | 14.8 | 3.2 | 0.54 | 30 | [38, 39]
| 2030 | 2600 €/kW | 14.8 | 3.2 | 0.54 | 30 | [38, 39]
Clean firm generator3 | 2025 | 10000 €/kW | 0 | 0 | 1.00 | 30.0 | [-]
| 2030 | 10000 €/kW | 0 | 0 | 1.00 | 30.0 | [-]

  * Notes: All costs are in 2020 euros; 1assumed to be underground hydrogen storage in salt caverns; 2costs also include estimate of 40 €/tCO2for carbon transport and sequestration; 3a stand-in for clean dispatchable technologies, such as advanced geothermal or advanced nuclear systems; own indicative assumptions.

  * CAPEX = capital expenditure; FOM = fixed operations and maintenance costs; VOM = variable operations and maintenance costs.

Table S1: Technology assumptions.

##  S2 Background system assumptions

The model performs a perfect-foresight optimisation of investment and power
dispatch decisions to meet electricity demand of the 24/7 consumers, as well
as the demand of other consumers in the European electricity system for 2025
or 2030.

In all the modelled countries, renewable generation must meet the political
targets as defined in the NECP s or by more recent national policy targets
(such as the Easter package in Germany). In countries that not have a target
for 2025, a linear increase from targets between 2020 and 2030 is assumed. The
assumed renewable targets are available in the config file of the scientific
workflow [28].

We assume 2013 to be a representative climate year for renewable generation.
The renewable feed-in profiles are computed with the atlite package [40].

The country demand profiles for 2025 and 2030 are assumed to be the same as in
2013. Time-series data for electricity demand is based on the Open Power
System Data project [41].

The existing power plant fleet data is based on the powerplantmatching package
[42]. We consider national policies and decommissioning plans for coal and
nuclear power plants based on data from the “beyondfossilfuels.org” and the
“world-nuclear.org” projects [43, 44].

We assume price for EU ETS allowances to be 80 €/tCO2and 130 €/tCO2for 2025
and 2030, accordingly. The price for natural gas is assumed to be 35 €/MWh.