ArticlePDF Available

Abstract and Figures

Using electricity for heating can contribute to decarbonization and provide flexibility to integrate variable renewable energy. We analyze the case of electric storage heaters in German 2030 scenarios with an open-source electricity sector model. We find that flexible electric heaters generally increase the use of generation technologies with low variable costs, which are not necessarily renewables. Yet making customary night-time storage heaters temporally more flexible offers only moderate benefits because renewable availability during daytime is limited in the heating season. Respective investment costs accordingly have to be very low in order to realize total system cost benefits. As storage heaters feature only short-term heat storage, they also cannot reconcile the seasonal mismatch of heat demand in winter and high renewable availability in summer. Future research should evaluate the benefits of longer-term heat storage.
Content may be subject to copyright.
Contents lists available at ScienceDirect
Applied Energy
journal homepage: www.elsevier.com/locate/apenergy
Flexible electricity use for heating in markets with renewable energy
Wolf-Peter Schill, Alexander Zerrahn
German Institute for Economic Research (DIW Berlin), Department of Energy, Transportation, Environment, Germany
HIGHLIGHTS
Electric heating can contribute to decarbonization and provide flexibility for renewable integration.
Analysis of electric storage heaters for German 2030 scenarios with open-source electricity sector model.
Temporally flexible charging of storage heaters provides only small benefits.
Electric storage heaters not suited to align seasonal mismatch between renewables and heat demand.
Flexible power-to-heat generally fosters use of generation technologies with low variable costs.
ARTICLE INFO
Keywords:
Power-to-heat
Electric heating
Renewable energy integration
Energy storage
Demand-side management
Decarbonization
Power system model
ABSTRACT
Using electricity for heating can contribute to decarbonization and provide flexibility to integrate variable re-
newable energy. We analyze the case of electric storage heaters in German 2030 scenarios with an open-source
electricity sector model. We find that flexible electric heaters generally increase the use of generation tech-
nologies with low variable costs, which are not necessarily renewables. Yet making customary night-time storage
heaters temporally more flexible offers only moderate benefits because renewable availability during daytime is
limited in the heating season. Respective investment costs accordingly have to be very low in order to realize
total system cost benefits. As storage heaters feature only short-term heat storage, they also cannot reconcile the
seasonal mismatch of heat demand in winter and high renewable availability in summer. Future research should
evaluate the benefits of longer-term heat storage.
1. Introduction
Mitigating climate change demands decarbonizing energy supply,
and renewable electricity sources play an essential role [1]. In Ger-
many, often considered a frontrunner in the transition to renewables,
they supplied nearly 38% of gross electricity demand in 2018, up from
around 6% in 2000 [2]. For 2030, current legislation foresees a growth
to at least 50%, and the German government’s 2019 climate package
targets an even faster growth to 65% by 2030.
1
At the same time,
decarbonization must go beyond current electricity use. By 2017, more
than a quarter of gross energy consumption and 14% of greenhouse gas
emissions stemmed from space heating [3]. In Germany and many other
countries, substantial efforts are required to reach medium- and long-
term climate policy goals. One option is the use of renewable electricity
in the heating and transportation sectors, often referred to as sector
coupling or electrification. In its latest report on limiting global
warming to 1.5 degrees Celsius, the IPCC also puts an emphasis on such
electrification of end energy use [1].
While electricity generation from wind and solar PV is largely
carbon neutral, it comes with two peculiarities: its supply has virtually
zero variable costs, and it cannot be dispatched at full discretion.
Renewables’ natural variability calls for flexibility within the electricity
sector to efficiently use available low-cost renewables. One option is on
the demand side of the electricity market: electricity demand, which
has been largely inelastic in the past, could be shifted to hours with
high renewable supply and avoid hours with scarce supply.
Against this background, this paper is motivated by the twin chal-
lenges of decarbonizing space heating supply and providing flexibility
for the integration of renewable electricity. Specifically, we apply an
open-source electricity sector model to a German 2030 setting to ana-
lyze the system effects of flexibilizing electric heating in Germany.
Current electric night-time storage heaters demand electricity at night –
https://doi.org/10.1016/j.apenergy.2020.114571
Received 8 November 2019; Received in revised form 6 January 2020; Accepted 26 January 2020
Corresponding author at: Address: DIW Berlin, Mohrenstr. 58, 10117 Berlin, Germany.
E-mail address: azerrahn@diw.de (A. Zerrahn).
1
See https://www.bundesregierung.de/resource/blob/975226/1679914/e01d6bd855f09bf05cf7498e06d0a3ff/2019–10-09-klima-massnahmen-data.pdf (ac-
cessed December 22, 2019).
Applied Energy 266 (2020) 114571
0306-2619/ © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/BY/4.0/).
T
when other demand and wholesale prices were, historically, low – and
convert it to heat. This heat is stored and released again during the day
to meet the households’ space heat demand. An upgrade can render
electric storage heaters more flexible such that they can demand elec-
tricity not only at night-time, but at each point in time and thus respond
more flexibly to variable renewable energy supply. Across a range of
scenarios, we analyze the effects on system costs, renewable energy
integration, and emissions. In doing so, we also disentangle the drivers
of cost savings.
Previous research has addressed various aspects of flexible electric
heating in renewables-dominated electricity systems [4]. We contribute
to the literature in several ways: first, we provide evidence on system
effects of electric thermal storage heaters; past contributions on this
technology are scarce and findings are mixed. Second, previous papers
largely refrain from explicitly disentangling drivers of cost savings; a
point that we analyze in depth. Specifically, we explicitly compare
model outcomes for different assumptions on the flexibility of electric
heating technologies, while most analyses in the literature generally
assume flexible operations. Third, we offer a comprehensive formula-
tion for modeling a range of power-to-heat technologies in an open-
source framework. Finally, we provide evidence on the role of flexible
electric heating in future decarbonization scenarios in Germany.
Our analysis illustrates that additional power system flexibility re-
lated to electric heating generally increases the use of generation
technologies with low variable costs, but not necessarily renewables.
Results further show that overall cost savings of making customary
night-time storage heaters more flexible are rather moderate.
Accordingly, upgrades must come at very low costs to be economical.
This is driven by mismatching patterns of heat demand and renewable
supply in Germany, which serves as an example for temperate coun-
tries, and a lack of seasonal storage capabilities of electric heaters.
During summer and spring, the value of additional flexibility is modest
because absolute heat demand is low, and electric heaters cannot
benefit from high renewable electricity supply during daytime. During
the heating season in winter and fall, the absolute value of additional
flexibility is also modest because there is relatively low supply of low-
cost renewable electricity during daytime. Even in case of substantially
higher shares of renewable energy sources than today, flexible electric
heaters would still mainly charge at night-time. Yet additional demand-
side flexibility proves more valuable when the merit order is steeper, for
instance in case of a coal phase-out or higher CO
2
prices.
The remainder of this paper is structured as follows: Section 2 gives
an overview of the related literature. Section 3 presents the model.
Section 4 exhibits data and scenarios. We describe and interpret results
in Section 5.Section 6 sets our findings into perspective and outlines
avenues for future research. The final Section 7 concludes.
2. Related literature
The literature highlights a vital role for power-to-heat technologies
to decarbonize energy systems beyond current electricity use. Based on
a comprehensive model of a future German electricity and heating
system which was first introduced by Henning and Palzer [5], Palzer
and Henning [6] put forward that a complete renewable supply is not
more costly than the existing energy system. Comparing cost-effec-
tiveness across sectors, Merkel et al. [7] highlight that the heat sector
plays a prominent role in efficient and ambitious German dec-
arbonization pathways to 2050. Kiviluoma and Meibom [8] provide a
similar result for Finland, and Connolly et al. [9] draw an analogous
conclusion in their analysis of a complete decarbonization of the Eur-
opean energy system, comprising electricity, heat, and mobility. They
identify a central role for heating electrification as low-cost option with
a high impact for reducing emissions.
Yet such decarbonization requires that electricity for space heating
is increasingly generated by renewable energy sources. In many coun-
tries, these are predominantly variable wind power and solar
photovoltaics (PV) because potentials for hydro power or bioenergy are
limited. Thus, the temporally flexible use of variable renewable energy
gains relevance. Systematically reviewing model-based studies, Bloess
et al. [4] provide a focused overview on flexibility potentials of power-
to-heat technologies in energy systems.
Specifically for heat pumps, previous research yields rich evidence
on the benefits of their flexible operation in power systems with high
shares of renewable energy. In a stream of papers for a Belgian appli-
cation, Patteeuw and co-authors identify reduced total system costs
[10], CO
2
abatement costs, necessary peak load capacities [11], and
curtailment [12] compared to an inflexible heat pump operation, and
reduced emissions compared to a baseline with natural gas-based
heating [13]. For Denmark, Hedegaard and Balyk [14] conclude that
flexible operation of residential heat pumps saves on system costs
through both arbitrage gains from shifting power-to-heat electricity
demand to low-price hours with high renewable supply and a reduced
need for investments into peak generation capacity. In their analysis,
heat storage plays a vital role. Kiviluoma and Meibom [8] derive a
comparable result for Finland. In contrast, Hedegaard and Münster [15]
highlight the potential of flexible heat pumps to integrate wind energy
and thus reduce CO
2
emissions and total system costs even without
making use of flexibility from heat storage. For Germany, Bloess [16]
similarly finds that heat pumps play a large role in future scenarios with
large shares of renewables, while heat storage is of minor importance.
The literature provides comparable evidence on the benefits of flexible
heat pumps also for other countries such as China [17], Germany [18],
the UK [19], and the US [20].
Yet the role of other power-to-heat technologies, specifically electric
storage heaters, is less well understood. While Dodds [19] highlights,
for the UK, that electric night-time storage heaters continue to play a
role, evidence on power system effects is scarce and mixed. Barton et al.
[21] emphasize that their flexible operation can reduce electricity peak
load and smooth the electricity demand profile. For China, Chen et al.
[17] conclude that they do not help to mitigate emissions to a great
extent due to the relatively low efficiency compared to heat pumps;
considerably, this result is based on the assumption of a high share of
coal power in the electricity mix. For the PJM system in the US, Pensini
et al. [22] stress that flexible decentral electric storage heaters can
greatly reduce curtailment, but centralized heat pumps with heat sto-
rage would be more cost effective. For the Finnish housing stock, Rasku
and Kiviluoma [23] find that electric storage heaters can become more
beneficial than energy efficiency improvements from a system cost
perspective when assuming very high shares of variable renewables; yet
from a house owner’s perspective, the opposite may be true.
3. Model
To analyze the electricity sector effects of electric storage heaters,
we augment the open-source electricity sector model DIETER by a
power-to-heat module. DIETER is a dispatch and investment model
with a long-run equilibrium perspective that minimizes the total cost of
electricity generation for one year in hourly resolution. See Zerrahn and
Schill [24] for an introduction to the basic model version.
3.1. DIETER
The model’s objective function covers operational costs, which
comprise, among others, fuel and CO
2
emission costs, and annualized
investment costs of electricity generation and storage technologies. A
market clearing condition, also referred to as energy balance, ensures
that electricity supply satisfies inelastic electricity demand in each
hour. Generation technologies comprise thermal generators, such as
coal- and natural gas-fired plants, and the renewable technologies
bioenergy, run-of-river hydro, onshore and offshore wind, and solar PV.
Flexibility options to temporally align supply and demand include dif-
ferent types of energy storage, several demand-side management (DSM)
W.-P. Schill and A. Zerrahn Applied Energy 266 (2020) 114571
2
options, differentiated by load shedding and load shifting, as well as the
curtailment of renewables. Further constraints ensure that hourly
generation by a technology does not exceed installed capacities and that
installed capacities do not exceed the potential of a technology.
Moreover, the model features intertemporal restrictions for storage and
DSM operations as well as constraints related to the provision and ac-
tivation of balancing reserves.
Model inputs comprise costs and availabilities of technologies as
well as hourly demand and renewables feed-in profiles. Endogenous
variables are investments into generation and flexibility technologies
and their hourly use, including the provision of balancing reserves.
Model outputs cover the total cost of providing electricity, installed
capacity, the hourly dispatch of all technologies, and various derived
indicators on the utilization of different technologies.
The model version used here focuses on the German electricity
sector and abstracts from an explicit spatial resolution as well as
modeling interactions with neighboring countries. The model assumes
perfect foresight and is solved once for an entire year. DIETER is im-
plemented in the General Algebraic Modeling System (GAMS). Code,
data, and a comprehensive model documentation are available open-
source under a permissive license.
2
3.2. Representation of the residential power-to-heat segment in DIETER
For this analysis, we augment the DIETER version presented in [24]
by a representation of the residential power-to-heat segment, featuring
direct resistive heaters, electric storage heaters, and water-based
heating systems. In the latter case, ground-sourced or air-sourced heat
pumps or fossil-fueled boilers with an auxiliary electric heating rod
supply heat to a buffer storage. In this paper, our main focus in on
electric storage heaters. For brevity, we denote them by NETS (night-
time electric thermal storage heaters) for customary devices that charge
electricity inflexibly during the night, and SETS (smart electric thermal
storage heaters) for upgraded devices that can charge flexibly around
the clock. The residential heat module also features the provision of
domestic hot water (DHW), either from the buffer storage, from a se-
parate module complementing SETS, or from direct resistive heaters.
All electric space heating and DHW technologies, except direct resistive
heating, can also provide secondary and tertiary balancing reserves,
both positive and negative.
In Appendix A, we present the model equations relating to re-
sidential heat. They interact with the overarching electricity sector
model at three instances: first, electricity demand by heating technol-
ogies enters the electricity balance of DIETER; second, reserve provision
by heating technologies enters the reserves balance of DIETER; and
third, costs of fossil fuel consumption for hybrid heating technologies
enter the objective function. Investment costs of power-to-heat options
do not enter the objective function of the model version used here, as
we vary their capacities exogenously. Yet investments related to SETS
are considered when evaluating overall results.
4. Data and scenarios
We carry out our analysis for the year 2030. This time horizon al-
lows for a range of plausible scenarios with different assumptions on
costs and availabilities of various technologies. At the same time, 2030
is still close enough to plausibly abstract from major uncertainties with
respect to technology developments, breakthroughs of alternative
sector coupling options or costs.
4.1. Input data
As for input data, we lean on the EU Reference Scenario 2016 de-
veloped by Capros et al. [25]; the respective dataset is provided by
[26]. For the power plant portfolio, we take the figures as given in the
Reference Scenario whenever possible. For some technologies, we
adopt further assumptions, preferably from other established studies, to
align them with the technology types in our model.
3
4.1.1. Capacities of power plants and flexibility options
We adopt assumptions on lower capacity bounds for solar PV, wind
power, and pumped hydro storage as well as assumptions on upper
capacity bounds for fossil-fueled plants, bioenergy, and run-of-river
hydro, guided by the Reference Scenario. Appendix B provides further
details on the derivation of the input data. Fig. 1 shows the specific
assumptions.
Concerning flexibility options, we assume 6.5 GW of pumped hydro
storage power capacities, with an energy capacity of 45.5 GWh. This
figure leans on the pumped hydro capacity installed in Germany in
2018. We assume no further flexibility options such as batteries or
demand-side management. If a scenario allows for demand-side man-
agement, we base maximum capacities for three load shedding and five
load shifting technologies to potentials derived by Frontier and Formaet
[27], Gils [28], and Klobasa [29].
4.1.2. Electricity demand and renewable generation time series
Time series inputs follow German data from the default base year
2016 [30], taken from the Open Power System Data platform [31]. For
renewable infeed, we take hourly capacity factors, defined as actual
hourly generation of onshore wind, offshore wind, and solar PV, di-
vided by the historical capacity of the respective technology. Hourly
load time series include electricity demand by existing night-time
electric thermal storage heaters. Total annual demand is around
490 Terawatt hours (TWh) of which about 2.5%, or 11.5 TWh, accrue
from NETS.
For the SETS upgrade case, we construct a synthetic demand profile
of NETS, subtract it from the demand time series, and allocate the
11.5 TWh of heating electricity demand to SETS. To this end, we
transform the yearly space heating energy provided by existing night-
time storage heaters to an electricity consumption pattern covering
night-time hours between 10 p.m. and 6 a.m. Accordingly, the elec-
tricity consumed by NETS in any such night-time period equals the
heating demand of the respective subsequent day. Within contiguous
hours of each night, we assume a uniform distribution of heating en-
ergy.
Hourly demand for the provision of balancing reserves also follows
German data from the base year, differentiated into primary, secondary,
and tertiary reserves, each both positive and negative [32]. Likewise,
hourly activation of secondary and tertiary reserves follows the actual
German pattern from the base year [33]; for primary reserves, we as-
sume a flat hourly activation of 5%.
4.1.3. Cost assumptions, fuel and CO
2
prices
Assumptions on fuel prices follow the Ten Year Network
Development Plan (TYNDP) 2016 [34,35], scenario “Vision 3”
(Table 1). The CO
2
price of 33.33 euros per ton follows the EU Re-
ference Scenario. Additionally, we explore a scenario with a high CO
2
price of 71 euros per ton according to the TYNDP 2016. We draw on
Schröder et al. [36] for figures on thermal efficiency, overnight in-
vestment costs, and the technical lifetime of plants, and Kunz et al. [37]
for the carbon content of fossil fuels. For overnight investment costs,
efficiency, and technical lifetime of storage technologies, we draw on
2
We use DIETER version 1.3.0 for this paper. See http://www.diw.de/dieter
for a complete documentation of the model, all input data, and the executable
code files.
3
We provide spreadsheets containing the input data for this paper under
http://www.diw.de/dieter.
W.-P. Schill and A. Zerrahn Applied Energy 266 (2020) 114571
3
Pape et al. [38] and Agora Energiewende [39], complemented by own
assumptions on annual fixed costs. Marginal generation, fixed, and
overnight investment costs for DSM technologies follow Frontier and
Formaet [27].
4.1.4. Power-to-heat technologies
We assume that a large share of the presently existing fleet of cus-
tomary night-time electric thermal storage heaters devices is still pre-
sent in Germany by 2030.
4
Turning these NETS into more flexible SETS
requires respective control and communication interfaces. According to
figures provided by a leading manufacturer, we make the following
default assumptions: 270 euros per SETS unit for a communication
module plus 140 euros per flat for a gateway.
5
We further assume an
average flat size of 150 square meters and 32 kWh storage capacity per
SETS device. With a ten years depreciation period and an interest rate
of 4%, the upgrade cost annuity amounts to about 1.13 euros per kWh.
With respect to SETS dimensioning, we assume their maximum
hourly heat output per square meter to cover the hour with the highest
heating load of the year, i.e., we do not assume backup heating options.
SETS’ electric power rating per square meter is then set to be twice as
high, and SETS’ energy storage capacity in turn is eight times the
maximum hourly electric power consumption. That is, SETS have a
storage capacity of eight hours. These parameter choices are guided by
technical data sheets of typical SETS devices sold in Germany by 2018.
6
We further assume a static heat release of 2.5% of stored heating energy
per hour. SETS domestic hot water devices (SETS-DHW) are also
parameterized such that their maximum hot water output covers the
hour with the highest demand of the year. Electric power rating of
SETS-DHW devices is equal to maximum hourly DHW output, and en-
ergy storage capacity covers 2.2 hours of the maximum hourly electric
power consumption.
Ground-sourced or air-sourced heat pumps supply heat to a buffer
storage, covering three hours of maximum hourly heating load. We set
the maximum hourly heat output capacity to cover the hour with the
highest heating load of the year, including domestic hot water. We also
take into account the coefficient of performance (COP) such that the
respective electric power rating is accordingly lower.
7
4.1.5. Heat demand
To adequately represent the German residential building stock, we
assume twelve building archetypes: six for one-family homes and six for
multi-family buildings, differentiated by building age and corre-
sponding energy efficiency levels. The archetypes are defined by RWTH
Aachen University, based on the findings of two research projects.
8
A
thermal simulation model calculates the annual energy demand per
square meter. Taking into account the German targets for energy effi-
ciency improvements of the building stock, we next determine a pro-
jection of the total square meters, and thus total annual energy demand,
for each building archetype in 2030. We assume that the share of
electric storage heaters in total square meters of the respective building
archetypes does not change by 2030. Beyond SETS, we assume a certain
share of the residential floor area to be equipped with heat pumps, with
Fig. 1. Assumptions on upper and lower bounds for the generation portfolio. Dispatchable thermal and run-of-river generation capacity (black) can be installed up to
the specified upper bound; in contrast, variable renewable and pumped hydro storage capacity (green) face a lower installation bound
Table 1
Fuel and CO
2
price assumptions for 2030.
Unit Price assumption
Lignite Euros per MWh
th
3.96
Hard coal Euros per MWh
th
10.08
Natural gas Euros per MWh
th
26.03
Oil Euros per MWh
th
41.65
CO
2
certificates Euros per ton 33.33 (71.00 in alternative scenario)
4
Heitkoetter et al. [40] make a similar assumption.
5
Information provided by the manufacturer GlenDimplex in the context of
the EU Horizon 2020 project RealValue.
6
Specifically, the parameter choices are guided by the device “Quantum
heater” by the manufacturer GlenDimplex as well as additional information
provided in the context of the EU Horizon 2020 project RealValue. Also com-
pare the manufacturer’s product website https://www.glendimplexireland.
com/brands/dimplex/domestic-heating-systems/quantum-off-peak-heaters
(footnote continued)
(accessed November 08, 2019).
7
For heat pump dimensioning, we assume a time-constant source tempera-
ture of 10 °C for ground-sourced heat pumps and minus 5 °C for air-sourced heat
pumps. The actual COP entering the model varies with outside air tempera-
tures. See Appendix A.3 for further information. The additional scenarios in
Appendix A.4 feature fossil-fueled boilers with an auxiliary electric heating rod
that supply heat to a buffer storage, which we also dimension to cover three
hours of maximum heating load. We define their power rating for maximum
hourly electricity demand such that they could supply the heat load in the hour
of highest demand without making use of the buffer storage.
8
EU projects EPISCOPE (Monitor Progress Towards Climate Targets in
European Housing Stocks) and TABULA (Typology Approach for Building Stock
Energy Assessment; see Loga et al. [58]). See Appendix A.3 for more informa-
tion.
W.-P. Schill and A. Zerrahn Applied Energy 266 (2020) 114571
4
an equal split between ground-sourced and air-sourced devices. This
share is present in all scenarios. Table 2 shows the central parameters.
Hourly heat demand profiles for each archetype are based on a
building simulation model, taking into account the behavior of re-
sidents and inner loads.
9
Fig. 2 exemplarily shows hourly space heating
demand profiles for a full year for three one-family home archetypes.
The typical heating period is clearly visible.
Domestic hot water demand is modeled separately from heating
energy demand, mainly depending on the assumed number of residents
in each apartment or building. DHW profiles are assumed not to vary
between housing types; they are derived from the Swiss SIA 2024
standard [42].
4.2. Scenarios
The capacity bounds of the generation portfolio shown in Fig. 1
serve as a reference for the model runs: we set the capacity assumptions
for thermal plants as upper bounds for investments and the capacity
assumptions for renewable plants and storage as lower bounds for in-
vestments. Accordingly, our scenarios are generally in line with the
German energy and climate policy targets, while still leaning on the
established European Reference Scenario. Beyond pumped hydro sto-
rage, the model can also invest in lithium-ion batteries and, in one
scenario, into DSM.
We apply the model to a range of scenarios. In each scenario, we
compare two cases: a NETS baseline with inflexible electric night-time
storage heaters and a SETS upgrade scenario in which the entire NETS
fleet is upgraded to more flexible SETS. Considering Germany’s energy
and climate policy, we consider the upgrade of existing night-time
electric thermal storage heaters to be the most plausible market for
installing SETS for several reasons: first, SETS are unlikely to replace
centralized heating technologies such as district heating systems.
Second, it is unlikely that SETS are installed in buildings in which a
water-based heating system already exists, taking into account in-
stallation and operating costs as well as thermal comfort. If existing
water-based heating systems, powered by fossil-fueled boilers in 2018,
were to be replaced by power-to-heat options, it appears more likely
that they will be converted to heat pumps, which require considerably
less electricity. For the same reason, third, new future dwellings are also
more likely to be equipped with heat pumps or some centralized
heating system.
In all scenarios, we abstract from endogenous investments into SETS
or other electricity-based heating systems. Instead, we vary their
presence exogenously while their hourly use is determined en-
dogenously in the model.
10
This allows to readily identify effects of
more flexible electric heaters and their drivers within the electricity
sector. Thus, assumptions on future investment or upgrade costs for
various heating systems in different building types, which are both
uncertain and idiosyncratic, are not required. Accordingly, such costs
are also not part of the objective function. Nonetheless, we consider the
costs for upgrading NETS to SETS when comparing overall model re-
sults.
Table 3 lists our central scenarios. Beyond the central SETS upgrade
scenario, two scenarios explore the effect of competing flexibility or
power-to-heat options: demand-side management and a greater share of
heat pumps. Three additional scenarios implement more ambitious
environmental policies: a higher CO
2
price, a higher share of renew-
ables in electricity generation, and a coal phase-out. The coal phase-out
scenario leans on the generation capacity reduction path proposed by
the Commission on Growth, Structural Change and Employment in
February 2019. The additional natural gas OCGT capacities reflect the
backup capacities currently contracted in the German “grid reserve”
between 2018 and 2021.
11
We also calculate an alternative counterfactual baseline with direct
electric heaters in place instead of the actual NETS fleet. While this
comparison is not realistic for Germany, it illustrates the benefits of
flexible power-to-heat operations more prominently. It also connects to
other country studies such as Pensini et al. [22] or Rasku and Kiviluoma
[23] in which direct electric heaters are considered to be more relevant.
In Appendix D, we show results of further scenarios that vary the share
of SETS and other power-to-heat technologies.
5. Results
We examine how a more flexible use of electricity for heating affects
costs, investments into different generation capacities, their dispatch,
CO
2
emissions, the provision of balancing reserves, and wholesale
electricity prices. In doing so, we also investigate important drivers of
different effects.
Table 2
Building archetypes and their heating energy demand satisfied by electric heating.
Description Annual heating energy demand Floorarea with NETS/SETS Floor area with heat pumps
[kWh/m
2
] [million m
2
] [million m
2
]
b1 One-family house very high energy demand 276 7.15 2.47
b2 Multi-family house 223 3.58 2.22
b3 One-family house high energy demand 203 12.50 4.31
b4 Multi-family house 164 6.75 4.18
b5 One-family house medium energy demand 153 16.49 7.57
b6 Multi-family house 130 5.74 0
b7 One-family house low energy demand 112 6.87 32.23
b8 Multi-family house 103 0.98 3.35
b9 One-family house very low energy demand 66 0 103.88
b10 Multi-family house 51 0 28.96
b11 One-family house passive house 15 0 127.67
b12 Multi-family house 11 0 37.15
9
The building simulation is provided by RWTH Aachen, using the open-
source model TEASER (Tool for Energy Analysis and Simulation for Efficient
Retrofit, https://github.com/RWTH-EBC/TEASER)[41]. The simulation is
carried out for an eastern German location; sensitivities for other locations
show negligible differences. See Appendix A.3 for more detailed information.
10
We aim to shed light on the effects of more flexible electric heaters within
the power system. In order to study an optimal configuration of the overall
heating system, a much broader representation of the heating sector would be
required. This would also have to include district heating, combined heat and
power, the market for fossil heating fuels, and energy efficiency measures.
11
The Commission on Growth, Structural Change and Employment (often
referred to as Coal Commission) is composed of researchers, civil society re-
presentatives, and politicians. During 2018 and 2019, they developed a sche-
dule to phase out electricity generation from lignite and hard coal in Germany
until 2038. See https://www.kommission-wsb.de/ for further information (in
German) and [43] for the final report.
W.-P. Schill and A. Zerrahn Applied Energy 266 (2020) 114571
5
5.1. Electricity sector costs and total system costs
Total system costs are calculated as overall costs of providing
electricity within one year, consisting of investment and dispatch costs.
They are given as the sum of electricity sector costs, i.e., the value of the
objective function plus SETS investment costs.
12
In the central SETS upgrade scenario, where SETS fully replace the
existing NETS fleet, their temporally more flexible electricity demand
enables yearly electricity sector costs savings of around 20 euros per
unit. This corresponds to about 0.15% of electricity sector costs, or
about 50 million euros in absolute terms. Yet considering SETS in-
vestments of around 36 euros per unit, total system costs increase by
nearly 16 euros per unit (Fig. 3). Thus, the investment costs for making
SETS more flexible exceed the electricity sector benefits of flexibility.
If competing flexibility options are available, the cost-benefit tra-
deoff from flexibilizing NETS worsens further. Electricity sector benefits
are lower and, accordingly, total system costs increase by around 17
euros per unit in the DSM breakthrough scenario and around 22 euros
per unit in the heat pump breakthrough scenario. Also in the scenario
with a higher CO
2
price, the upgrade costs exceed the flexibility ben-
efits, yet by only around 7 euros per unit. Benefits over-compensate
costs only in the 65% renewables and coal phase-out scenarios, with
total system cost savings of about 19 and 22 euros per unit, respec-
tively.
Four implications emerge. First, investments into SETS flexibility
are less valuable to the electricity sector if it features more other
sources of flexibility such as DSM or heat pumps. These flexibility
options compete with SETS for for low-cost renewable electricity in
periods of high renewable availability. Accordingly, the marginal ben-
efits of additional flexibility diminish. Second, the flexibility of SETS, in
turn, proves more valuable if there are more variable renewable energy
sources in the system. The share of renewable energy sources is 59% in
the high CO
2
price scenario and 65% in both the 65% renewables and
coal phase-out scenarios, compared to 52% in the basic SETS upgrade
scenario. Third, the flexibility of SETS proves more valuable if the merit
order is steeper, as comparing the 65% renewables and the coal phase-
out scenarios shows. While the renewable shares are equal, the mar-
ginal costs of the remaining conventional natural gas generators are
higher in the coal phase-out scenario. Accordingly, more flexible elec-
tricity demand can gain a somewhat larger advantage of directing de-
mand to hours with low-cost generation. Fourth, improving the char-
ging patterns of night-time storage heaters through upgrading them to
SETS does not necessarily lead to overall efficiency gains, depending on
the configuration of the electricity sector.
13
To capture uncertainty in future cost developments, we vary the
default cost assumption for upgrading NETS to SETS by halving or
doubling it. Except for the heat pump breakthrough scenario, total
system costs decrease at least slightly if SETS upgrade costs are only
half the default assumption (Fig. 4). Conversely, the costs of respective
investments exceed the electricity sector benefits in all scenarios under
the assumption of double upgrade costs. Therefore, low upgrade costs
Fig. 2. Hourly heating demand time series for one year for three different building archetypes.
Table 3
Central scenarios.
Scenario Alternative assumption Rationale
SETS upgrade Basic scenario with central assumptions
DSM breakthrough Demand-side management available Competing flexibility option on the demand side
Heat pump breakthrough 10% of residential space heating provided by ground-sourced and air-sourced heat
pumps each
Increased roll-out of competing power-to-heat technology
High CO
2
price CO
2
price of 71 euros per ton according to the TYNDP 2016 More stringent climate policy scenario
65% renewables Renewables supply at least 65% of electricity demand Implements higher target for 2030 laid out in the German
2018 government coalition agreement
Coal phase-out Maximum lignite and hard coal capacities reduced to 9 GW and 8 GW, additional 6.6
GW OCGT capacities, at least 65% renewables in electricity demand
More progressive energy and climate policy scenario in line
with current German policy goals
12
SETS investment costs have to be added because they are not included in
the objective function, compare Sections 3.1 and 3.2.
13
In a sensitivity with a pure dispatch model, turning NETS into more flexible
SETS would be even less attractive. Here, total system costs would increase by
about 25 euros per unit in the central SETS upgrade scenario. Intuitively, more
flexible demand enables a greater use of low-cost generation technologies, but a
pure dispatch model does not allow for adjusting the generation portfolio ac-
cordingly.
W.-P. Schill and A. Zerrahn Applied Energy 266 (2020) 114571
6
are a vital condition for enabling total system costs savings from
making electric storage heaters more flexible.
When we compare SETS against the hypothetical baseline scenario
with fully inflexible direct resistive heaters instead of NETS, electricity
sector effects are considerably more pronounced. Benefits to the elec-
tricity sector amount to about 112 euros per unit – compared to about
20 euros per unit when compared to the NETS reference. In absolute
terms, this corresponds to 280 million euros, or 0.8% of electricity
sector costs. Yet investment costs for replacing direct resistive heaters
with SETS would also be higher, so total system cost effect would de-
pend on respective investment cost assumptions.
14
Albeit direct re-
sistive heating is not a practically relevant reference case for Germany,
it provides a general insight. Demand patterns of existing night-time
storage heaters in Germany are already well aligned with periods of low
wholesale electricity prices. In historic markets, in which price patterns
were generally demand-driven, low electricity prices occurred at night.
In the future, rising shares of variable wind and solar PV energy add
supply-driven price variability, not necessary related to the time of day.
However, this shows that the general pattern of low night-time elec-
tricity prices remains relevant during the heating season.
5.2. Investment, dispatch, and CO
2
emissions
Next, we investigate investment and dispatch effects as drivers of
total system cost changes. Flexibilizing NETS leads to only minor ad-
justments in the power plant fleet in the central SETS upgrade scenario.
Notably, the additional flexibility related to SETS allows reducing the
electrical storage capacity in the system by 250 MW (Fig. 5). A similar
finding holds also for the other scenarios.
15
Sizeable additional re-
newable investments are only triggered if we assume a high CO
2
price.
Under higher carbon prices, SETS flexibility allows integrating addi-
tional 3.1 GW of photovoltaics and 0.8 GW of wind power, which goes
along with an increasing share of renewables.
The impacts of SETS on generation capacities go along with
Fig. 3. Effects on electricity sector costs, SETS investment costs, and resulting total system cost effects per SETS unit from upgrading NETS to SETS in the central
scenarios.
Fig. 4. Specific total system cost savings per SETS unit in the central scenarios for different investment cost assumptions for upgrading NETS to SETS.
14
Compare also O’Dwyer et al. [44], Section 7.1, for complementary illus-
trations for different countries with varying cost assumptions.
15
This is an instance of a more general finding: additional flexibility can
strongly mitigate electrical storage requirements as long as the share of variable
renewable energy sources is well below 100% (for a more generic analysis, see
[45].
W.-P. Schill and A. Zerrahn Applied Energy 266 (2020) 114571
7
corresponding changes in annual electricity provision when comparing
the SETS upgrade case with the NETS baseline for the central scenarios
(Fig. 6). SETS help to make better use of available generation resources
through intertemporal arbitrage. On the one hand, this lowers genera-
tion from electrical storage in all scenarios. On the other, SETS crowd
out technologies with high marginal costs and help to integrate more
electricity from generators with low marginal costs. However, this only
slightly increases the use of renewables in the central SETS upgrade,
DSM breakthrough, and heat pump breakthrough scenarios because
renewable surpluses in the respective NETS baselines are already very
low. So there is hardly any potential for integrating additional renew-
able energy through increased demand-side flexibility. Instead, SETS
displace natural gas and integrate more electricity from coal plants. The
same also holds for the coal phase-out scenario, and partly for the 65%
renewables scenario, but less pronounced.
16
Only when assuming a high CO
2
price of 71 euros per ton, SETS help
to integrate variable renewables to a sizeable extent. Renewables re-
place around 4 GWh of electricity generation by fossil plants, which
corresponds to about half of SETS electricity demand. The renewable
share accordingly increases from 57.8% to 58.7% in this scenario. Thus,
the shape of the merit order determines which technologies benefit
from additional flexibility. With a high CO
2
price, the absolute ad-
vantage in marginal costs of renewables compared to fossil-fueled
technologies is greater, and it is optimal to invest into additional re-
newables that can be more easily integrated by flexibility from SETS,
despite higher fixed costs. For the default CO
2
price, the absolute ad-
vantage of renewables in marginal costs hardly justifies further in-
vestments into renewables, even though more flexibility from SETS is
available.
The changing dispatch pattern has implications for CO
2
emissions.
Independent of SETS, emissions are lowest in the high CO
2
price and
coal phase-out scenarios (Fig. 7). In all scenarios with baseline as-
sumptions on the CO
2
price, SETS trigger additional electricity gen-
eration from coal, and CO
2
emissions of the electricity sector accord-
ingly increase. In the central SETS upgrade scenario, emissions grow by
about 0.3 Megatons (0.13%). SETS help to decrease CO
2
emissions only
in the high CO
2
price scenario, by around 2.9 Megatons (2.4%).
Fig. 5. Differences in installed generation and storage capacities compared to the respective NETS baselines.
Fig. 6. Differences in annual electricity generation compared to the respective NETS baselines.
16
While the renewables share in electricity demand is at 65% in the NETS
baseline and SETS upgrade cases, electricity generation from renewables
slightly increases in the 65% renewables and coal phase-out scenarios. There
are two indirect explanations: (i) reduced provision of balancing reserves by
renewables, and (ii) possible over-heating of SETS (compare equation (1) in
Appendix A.1).
W.-P. Schill and A. Zerrahn Applied Energy 266 (2020) 114571
8
5.3. Drivers of system cost savings
To disentangle dispatch and investment effects on the reduction of
total system costs, we devise a “waterfall” separation. We first run the
baseline specification with NETS, fix all generation capacities to their
optimal values, and then re-run the model in a pure dispatch mode with
SETS to isolate the system value of SETS arbitrage. Next, we allow re-
serves provision by SETS to pin down the reserves value. Finally, we
carry out the full-fledged investment run to infer the capacity-related
value of SETS. The latter reflects the value of an adjusted power plant
portfolio, which also includes additional dispatch changes.
17
Fig. 8
shows the results.
Three quarters of electricity sector cost savings arise from arbitrage,
that is, the temporally more flexible demand opposed to NETS. The
reserves value, in turn, is negligible despite the fact that SETS con-
siderably contribute to the provision of balancing reserves. In the
central SETS upgrade scenario, they provide over 13% of all positive
secondary positive reserves and over 10% of all secondary negative
reserves.
18
However, this is hardly valuable in the electricity sector
because other technologies can provide reserves at similar costs, for
instance thermal and renewable plants or electricity storage.
19
Finally,
around one quarter of the electricity sector cost savings stems from the
capacity, or portfolio, value attributable to SETS.
5.4. Wholesale electricity prices
If SETS substitute NETS, this has an impact on wholesale electricity
prices. In the model, they are given as the marginal on the electricity
market balance. In the NETS baseline, the unweighted mean electricity
price is around 60 euros per MWh (Fig. 9). The mean price for NETS
electricity is about 53 euros per MWh, around 12% below the system
mean price. The mean price for SETS electricity in the central SETS
upgrade scenario is about 48 euros per MWh, 20% below the system
mean price. This reflects the greater flexibility of SETS to better sche-
dule consumption to low-price hours, i.e., hours with higher availability
of generation technologies with low marginal costs. In line with that,
the mean electricity price for inflexible direct resistive heating in the
counterfactual baseline is markedly higher, at 71 euros per MWh, more
than 18% above the system mean price.
If competing flexibility options are available, average prices for
SETS electricity demand are slightly higher. This “cannibalization” in-
creases the mean price for SETS to around 49 euros per MWh in the
DSM breakthrough scenario. Conversely, the electricity price advantage
of SETS is more pronounced in the 65% renewables and coal phase-out
scenarios, both in absolute and relative terms. With more renewables,
the temporal flexibility of SETS allows to make better use of low-price
periods compared to the respective NETS baselines.
The price advantage of SETS is also reflected in the annual heating
electricity bill of households, which can be obtained by summing up all
hourly electricity payments for residential space heating and DHW and
subtracting revenues from the provision of balancing reserves.
20
In the
central SETS substitution scenario, the annual heating electricity bill for
SETS is 9.35 euros per square meter, compared to 10.21 euros per
square meter in the NETS baseline. Analogous to the mean heating
electricity prices, the reduction in the electricity bill is lower if there is
more competing flexibility in the electricity sector, and it is larger if the
share of renewable energy sources increases.
5.5. Why are electricity sector cost effects of flexible storage heaters not
more beneficial?
While SETS flexibility helps to make use of cheaper generation re-
sources, total system cost effects are not necessarily beneficial and in
any case rather moderate. This is due to the temporal pattern of elec-
tricity demand for heating. Fig. 10 shows the daily distribution of
heating electricity demand, averaged over the year, for the NETS
baseline, the central SETS upgrade scenario, and the 65% renewables
scenarios. The curves largely follow a diurnal pattern. By default, NETS
charge only at night-time (dotted line), but also SETS charge more than
three quarters of their annual electricity demand at night in the central
SETS upgrade scenario (solid black line). Thus, except for a kink around
noon, the charging pattern is comparable under basic assumptions al-
beit SETS electricity demand is temporally more flexible. In turn, in the
65% renewables scenario, less than 60% of charging occurs at night
(solid gray line). Due to a greater share of renewables, especially solar
Fig. 7. CO
2
emissions in the NETS baseline and SETS upgrade cases in the central scenarios.
17
This type of portfolio-oriented capacity value should not be confused with
the narrower, peak-oriented capacity value definition typically used in relia-
bility studies.
18
Positive reserves are activated when supply is lower than demand in the
electricity sector. In case of SETS, they reduce their scheduled demand.
19
This finding holds in a robustness check in which we restrict reserve pro-
vision by variable renewables; while reserve provision shares of SETS are
somewhat greater, the system value is almost identical.
20
That is, this “heating bill” only includes wholesale electricity expenditures
and no other retail price components.
W.-P. Schill and A. Zerrahn Applied Energy 266 (2020) 114571
9
Fig. 8. Waterfall separation of SETS system values in the central SETS upgrade scenario.
Fig. 9. Unweighted average wholesale electricity prices and prices of heating electricity consumption of NETS, SETS, and inflexible direct resistive heaters.
Fig. 10. Average daily charging pattern of NETS and SETS.
W.-P. Schill and A. Zerrahn Applied Energy 266 (2020) 114571
10
PV, more charging is shifted to daytime.
Almost 80% of annual heating demand arises in winter of fall.
During the heating season, prices are, on average, still absolutely lowest
at night in the central SETS upgrade scenario (Fig. 11). Therefore, SETS
have an incentive to mainly charge at night and their flexibility does
not offer a substantial price advantage compared to NETS. Conversely,
PV feed-in is highest during summer and spring days around noon, and
prices are lower than at night-time. However, only about 20% of
heating demand falls into that seasons, and SETS heat storage capacity
does not allow for seasonal storage. These special characteristics of
electricity demand for heat render benefits rather moderate.
21
If the renewable share rises to 65%, daily price patterns change
(Fig. 12). While average prices are still absolutely lowest at night, the
PV dip is more pronounced also in winter. Accordingly, more charging
occurs during daytime and the temporal flexibility of SETS proves more
valuable to the electricity sector.
6. Discussion of limitations and scope for future research
The model we use in this paper is subject to several limitations re-
levant for interpreting results. First, we analyze the German electricity
sector in isolation without explicitly taking into account exchange with
neighboring countries. Spatial balancing with other European countries
would provide additional flexibility to the German electricity sector.
Moreover, we do not incorporate further potential flexibility options
such as electric vehicles or power-to-X, for instance hydrogen.
Assuming that these technologies can be operated in a sufficiently
flexible way, we tend to under-estimate the supply of flexibility of
electric storage heaters and thus over-estimate its value. The results of
the DSM and heat pump breakthrough scenarios point into this direc-
tion. This conclusion is not unanimous though. It is also conceivable
that a future electric vehicle fleet charges predominantly in an in-
flexible, user-driven fashion[47]. Likewise, power-to-X could constitute
a rather inflexible base load. Future research is needed to assess the
interplay between electric heating and spatial balancing with other
countries as well as interactions with other – more or less flexible –
sector coupling technologies.
Second, we do not incorporate the electricity network and thus
network congestion. Especially in regions with high demand or high
renewable supply, temporal demand-side flexibility could prove more
valuable to the electricity sector. In this regard, our results could under-
estimate the demand for flexibility and thus its value. However, in a
study on heat pumps, Felten et al. [46] conclude that locally differ-
entiated prices only have a modest beneficial effect on the electricity
system while entailing large distributional repercussions. Future re-
search could provide more evidence on the spatial dimension of tem-
poral (demand-side) flexibility.
22
Third, we do not take into account all conceivable power-to-heat
options. Based on this analysis, especially those technologies that come
with longer-term heat storage are likely to provide a greater benefit to
the electricity sector. They are better able to align the mismatching
temporal long-term patterns of renewable electricity supply and heat
demand. McKenna et al. [49] analyze seasonal heat storage on a re-
sidential district level and provide a literature overview of studies on
the building and district levels. Whether and under which conditions
the electricity sector benefit exceeds their respective investment re-
quires further research.
We also assume that the demand side faces wholesale real-time
electricity prices and thus abstract from a range of regulatory price
components. Residential retail prices are normally time-invariant and
include a range of taxes and surcharges, for instance, to finance the
electricity network or renewable support schemes. However, this
common simplification helps to isolate relevant tradeoffs within an
optimal power system. Future research could identify how incentives
and behavior on the demand side depend on the design of regulated
price components, for instance, whether they are energy-based or ca-
pacity-based. This could also incur a specific focus on prosumage, that
is, the self-consumption of solar electricity.
Finally, and related, we tacitly assume that households are able and
willing to allow a flexible use of their electric storage heaters. As Boait
et al. [50] and Darby [51] conclude from field trials with smart electric
thermal storage devices in several countries, critical success factors for
a demand-response system comprise a well-designed interface and ef-
fective user activation. Households may also be unwilling to change the
status quo of their heating system [52] or shy away from upfront in-
vestments for potential later savings [53]. An obstacle for the realiza-
tion of system-friendly behavior by households may be their concern
about data protection and security [54]. Broberg and Persson [55] as
well as Wilson et al. [56] raise strong concerns about the unwillingness
of households to cede autonomy and accept remote control of parts of
Fig. 11. Seasonal average hourly electricity prices in the central SETS upgrade scenario.
21
Felten et al. [46] provide comparable evidence for the case of heat pumps
in Germany.
22
Runge et al. [48] devise an analysis for different electric fuels that sheds
some light on the impact of locally differentiated prices on electricity demand
of this sector coupling option.
W.-P. Schill and A. Zerrahn Applied Energy 266 (2020) 114571
11
their electricity use. However, both large-scale empirical evidence on
acceptance and the incorporation of such “soft” factors into numerical
models is missing.
7. Conclusions
Decarbonizing the energy system requires a shift to renewable en-
ergy sources, not only for current electricity uses. Electric storage
heaters for space heating are one option for the flexible use of renew-
able electricity from wind and photovoltaics in the heating sector.
These devices convert electricity to heat that can be stored and released
when needed. Using an open-source electricity sector model, we ana-
lyze electricity sector effects in a German 2030 setting if customary
night-time storage heaters are upgraded to flexibly charge electricity
around the clock. Beyond evidence on electric storage heaters, results
also provide more general insights on residential demand-side flex-
ibility in renewables-based electricity markets.
First, temporal flexibility on the demand side is agnostic about the
electricity it helps to integrate. It benefits generation technologies with
low marginal costs. This is also the main channel for the (moderate)
electricity sector benefits from upgrading electric storage heaters; the
benefit from adjustments in the generation portfolio is lower, the
benefit from providing reserves negligible. Which generation technol-
ogies benefit most depends on the shape of the merit order. Beyond
renewables, this may be also coal or natural gas. For flexibility options
to trigger a further expansion of renewables, other measures may be
required like, for instance, higher CO
2
prices.
Second, low investment costs for upgrading customary night-time
storage heaters are vital for savings in total system costs. Unless the
required upgrade investments are very low, they may exceed the ben-
efits to the electricity sector. To this end, further cost reductions in
information and communication technology as well as stable regulatory
conditions that enable profitable business models would be favorable.
Third, overall cost savings are moderate because the temporal pat-
terns of renewable availability and heat demand are not well aligned in
Germany, as an example of temperate climate countries. During the
heating season in fall and winter, when energy demand is high, elec-
tricity wholesale prices are likely to be lowest at night-time, even for a
renewables penetration above 50%. More flexible electric heaters thus
do not gain a large advantage compared to customary night-time sto-
rage heaters. Only if the share of renewables increases to 65%, low-
price phases more frequently occur at daytime, and flexible electricity
demand for heating gains a larger advantage. Thus, temporal flexibility
for electric heating appliances appears to be less valuable to the power
system in the medium run than other demand-side flexibility options
such as electric vehicles, which have a less seasonal demand pattern.
Those may provide short-term flexibility also during periods of the year
in which it is more beneficial, for instance, during summertime with
high diurnal PV supply. Alternatively, power-to-heat technologies with
a long-term heat storage may help to exploit high availability of re-
newables outside the heating season.
Electric storage heaters entail several further drawbacks not ex-
plicitly analyzed in this paper. Compared to heat pumps, they come
with a relatively low electrical efficiency. Especially in the long-run, the
level of electricity consumption is likely to become a more critical
factor when it comes to a comprehensive decarbonization of energy
supply based on renewable energy sources. Also, their heat storage
capacity is not sufficient to bridge longer periods of low electricity
supply and high electricity demand. In this respect, accelerated building
retrofitting toward greater energy efficiency and heat pumps appear to
be a more promising option. However, if upgrades can be realized at
very low costs, flexible electric storage heaters may also play a small yet
beneficial role in decarbonizing the energy system.
CRediT authorship contribution statement
Wolf-Peter Schill: Conceptualization, Methodology, Project ad-
ministration, Software. Alexander Zerrahn: Conceptualization,
Methodology, Software, Visualization.
Acknowledgments
We thank Ciara O’Dwyer, Claudia Kemfert, Karsten Neuhoff, Nils
May, participants of the 7th International Ruhr Energy Conference
2018 in Essen and the 11
th
IEWT (Internationale Energiewirtschaftstagung,
International Energy Economics Conference) 2019 in Vienna for valu-
able comments on earlier drafts as well as Simon Schnier for research
assistance. We also thank Claus Michelsen for projections of the future
German building stock and Henryk Wolisz of RWTH Aachen University
for providing input data on buildings’ heat demand. This work was
carried out within the EU Horizon 2020 project “Realising Value from
Electricity Markets with Local Smart Electric Thermal Storage
Technology (RealValue)“, grant agreement No. 646166. All remaining
errors are ours.
Declarations of Competing Interest
None.
Fig. 12. Seasonal average hourly electricity prices in the 65% renewables scenario.
W.-P. Schill and A. Zerrahn Applied Energy 266 (2020) 114571
12
Role of the funding source
The funding source had no involvement in study design, collection,
analysis and interpretation of data, in the writing of the report, and in
the decision to submit the article.
Appendix A. Model formulation
In this section of the appendix, we document the equations that extend the power sector model DIETER with an electric space heating module.
A.1. Heating technologies
Let indices
h
denote the hours of the year and
bB
the building archetypes. For power-to-heat technologies. Let
dir
,
, and
=hp hp{ , }
hp as gs
denote the (singleton) sets of direct, SETS, and heat pump heating technologies,
elec
and
=gas oil{ , }
fos
the (singleton) sets of electric and fossil
heating technologies. Accordingly
= ×
hy elec fos
denotes the set of hybrid heating technologies that combine a fossil fuel and an auxiliary electric
heating rod, and
=
sto hp hy
the set of all technologies that feed to a hot water buffer tank Finally, let
=ch ch dir sets hp hy
denote all theoretically available (combinations) of heating technologies.
Hence, a dwelling may be heated by a stand-alone technology or a (hybrid) water-based heating technology, possibly combining two sources that
feed to a hot water buffer tank. Among the hybrid technologies, we only consider the combinations of a heat pump or a fossil boiler with an auxiliary
electric heating rod.
23
The general formulation would allow for further combinations. To address the heating technologies covered in the application,
we define the binary parameter
={0, 1}
b ch
sto
,
. It is equal to zero if the respective water-based heating technology
ch
is not in place in building type
b
.
It is equal to one if the heating technology is in place in that building type. Analogous parameters
b ch
dir
,
,
b ch
elec
,
,
b ch
hp
,
, and
b ch
sets
,
indicate whether direct
resistive heating, an auxiliary electric heating rod, heat pumps or SETS are present in building archetype
b
. For instance, if
=0
b sets
sets
2,
, then building
archetype 2 is not equipped with SETS; or if
=1
b hp elec
sto
6, _
gs
, then some proportion of building archetype 6 is equipped with ground-sourced heat
pumps with an auxiliary electric boiler.
The specific proportion of the floor area of a building type equipped with the respective heating technology follows an exogenous assumption. It
is contained in the hourly heat demand parameters that are derived separately for all building type-heating technology combinations at hand. On
that note, a proportion of a building type can be equipped with one heating technology, and another proportion of the same building type with
another heating technology.
A.2. Heat energy balance
The heating energy balance (1) prescribes that, for each hour, heat output by the respective technologies installed in the building archetypes must
satisfy residential heat demand.
+ + +H H H H d b ch h[ (1 ) ] , ,
b ch
dir
b ch h
dir
b ch
sto
b ch h
sto out
b ch
sets
b ch h
sets out sets
b ch h
sets l
b ch h
h
,, , ,, ,
,,, ,
,
, ,
,, ,
(1)
where hourly heating demand
db ch h
h
, ,
can be met by direct resistive electric heating,
Hb ch h
dir
, ,
, heat output from water-based storage heaters,
Hb ch h
sto out
, ,
,
,
or SETS,
Hb ch h
sets out
, ,
,
, in case the respective technology exists in
b
. The hourly heating demand enters the model as data and is specified for each
technology-building combination. For SETS, static heat losses
1sets
also contribute to residential heating, yet in an uncontrolled fashion. To give
the model leeway of tolerating over-heating, we set up the heating energy balance as inequality. Accordingly, we assume that residents either
tolerate such over-heating or cause heat losses by opening the window. Throughout the paper, capital Roman letters denote variables and lower case
letters parameters.
A.3. SETS
Eq. (2a) links the energy level of the SETS heat storage,
Hb ch h
sets l
, ,
,
, in each hour to the storage level in the previous period – deteriorated by static
efficiency losses – the heat output, and the intake of electricity,
Eb ch h
sets
, ,
, corrected by activated balancing reserves.
= +
+
+ +
H H E H RP RP b ch h, ,
b ch h
sets l sets
b ch h
sets l
b ch h
sets
b ch h
sets out
r
r b ch
sets
r h
act
r
r b ch
sets
r h
act sets
, ,
,
, , 1
,
, , , ,
,
, , , , , ,
(2a)
where
+
RPr b ch
sets
, ,
is the endogenously determined hourly provision of positive balancing reserves,
+
r
, by SETS and
+
r h
act
,
the exogenous hourly share
of reserves activated following actual data from the base year. Analogously,
r
represents negative reserve qualities. For positive balancing reserves,
SETS reduce their electricity demand to a lower level than initially scheduled; for negative balancing reserves, SETS increase their electricity demand
beyond the original schedule.
Four constraints take account of SETS’ capacity limits. The power rating,
nb ch
sets in
,
,
, restricts hourly SETS electricity demand, also taking account of
the provision of negative reserves (2b). The SETS heating power capacity,
nb ch
sets out
,
,
, restricts hourly SETS heat output (2c). If SETS provide positive
reserves, the reserve provision may be no larger than the hourly scheduled electricity intake (2d). Finally, the SETS storage energy level may not
exceed its energy capacity,
nb ch
sets e
,
,
(2e).
+E RP n b ch h, ,
b ch h
sets
r
r b ch
sets
b ch
sets in sets
, , , , ,
,
(2b)
H n b ch h, ,
b ch h
sets out
b ch
sets out sets
, ,
,
,
,
(2c)
23
The combination of heat pumps with auxiliary electric heating rods is implemented in the model formulation, but not used in the present analysis.
W.-P. Schill and A. Zerrahn Applied Energy 266 (2020) 114571
13
+
+
RP E b ch h, ,
r
r b ch
sets
b ch h
sets sets
, ,
, ,
(2d)
H n b ch h, ,
b ch h
sets lev
b ch
sets e sets
, ,
,
,
,
(2e)
A.4. Direct resistive heaters
Alternatively to SETS, residential heat may be provided by direct resistive heaters. Their heat output is part of the heat energy balance (1) above.
Their electricity input enters the energy balance of the electricity sector in the same hour (not shown here).
A.5. Water-based storage heating: heat pumps
Heat pumps convert electricity input,
Eb ch h
hp
, ,
, to heat output to the water storage tank,
Hb ch h
hp
, ,
. This conversion is subject to the coefficient of
performance (COP).
+°
cop temp C
temp temp b ch h
273. 15
, ,
b ch h hp dyn b ch
sink
b ch
sink
b ch h
source
hp
, ,
,,
, , ,
(3a)
The COP relates the sink temperature,
tempb ch
sink
,
, to the source temperature,
tempb ch h
source
, ,
, both in degrees Celsius. It is augmented by the efficiency of
the heat pump,
hp dyn,
. For ground-sourced heat pumps, we assume a time-constant source temperature, and a time-varying source temperature for
air-sourced heat pumps. The time series of the air temperature enters the model as data. As for SETS, the electricity demand is netted by the
activation of balancing reserves.
=
+
+ +
H E RP RP cop b ch h, ,
b ch h
hp
b ch h
hp
r
r b ch
hp
r h
act
r
r b ch
hp
r h
act
b ch hp
, , , ,
, , , , , ,,
(3b)
Heat pump electricity demand is restricted by the electrical power rating,
nb ch
hp in
,
,
,(3c) as well as a required minimum scheduled electricity demand
in case of positive reserve provision (3d).
+E RP n b ch h, ,
b ch h
hp
r
r b ch
hp
b ch
hp in hp
, , , , ,
,
(3c)
+
+
RP E b ch h, ,
r
r b ch
hp
b ch h
hp hp
, ,
, ,
(3d)
A.6. Water-based storage heating: auxiliary electric heating rods
Water-based storage heating systems may complementarily be powered by an auxiliary electric heating rod, for which analogous equations as for
heat pumps apply. Specifically, the heat output to the hot water storage tank,
Hb ch h
elec
, ,
, equals the electricity intake in the same hour,
Eb ch h
elec
, ,
, corrected
by activated reserves.
=
+
+ +
H E RP RP b ch h, ,
b ch h
elec
b ch h
elec
r
r b ch
elec
r h
act
r
r b ch
elec
r h
act elec
, , , ,
, , ,
, , ,
(4a)
Eqs. (4b) and (4c) restrict the maximum and minimum electricity demand according to the power rating,
nb ch
elec
,
, and the provision of reserves,
respectively.
+E RP n b ch h, ,
b ch h
elec
r
r b ch
elec
b ch
elec elec
, , , , ,
(4b)
+
+
RP E b ch h, ,
r
r b ch
elec
b ch h
elec elec
, ,
, ,
(4c)
A.7. Water-based storage heating: storage tank
The heat supply of heat pumps, electric heating rods, and fossil boilers feeds to the hot water storage tank. Its energy level in each hour,
Hb ch h
sto l
, ,
,
, is
determined by the level in the previous hour – corrected by static efficiency losses,
heat sto,
– plus the net of heat inputs by the technologies that feed
to the heat storage and the heat output for space heating,
Hb ch h
sto out
, ,
,
, and domestic hot water,
Hb ch h
DHW out
, ,
,
.
Hb ch h
fos
, ,
denotes the heat input from fossil-fueled
boilers to the hot water heat storage tank. (5a). The storage energy capacity,
nb ch
sto e
,
,
, restricts the maximum storage energy level (5b).
= + + +H H H H H H H b ch h, ,
b ch h
sto l heat sto
b ch h
sto l
b ch
hp
b ch h
hp
b ch
elec b ch h
elec
b ch
fos
b ch h
fos
b ch h
sto out
b ch h
DHW out sto
, ,
_,
, , 1
,
,, , , , , , , , , ,
,
, ,
,
(5a)
H n b ch h, ,
b ch h
sto l
b ch
sto e sto
, ,
,
,
,
(5b)
A.8. Domestic hot water
In each hour, domestic hot water (DHW) demand,
db ch h
DHW
, ,
, must be satisfied from the heating system’s buffer storage, a direct hot water provision
element complementing direct resistive space heaters,
Hb ch h
DHWdir
, ,
, or an auxiliary hot water storage tank complementing SETS,
Hb ch h
DHWsets out
, ,
,
. We refer to
W.-P. Schill and A. Zerrahn Applied Energy 266 (2020) 114571
14
the latter as DHW-SETS in the following.
24
+ + =H H H d b ch h, ,
b ch
dir
b ch h
DHWdir
b ch
sto
b ch h
DHW out
b ch
sets
b ch h
DHWsets out
b ch h
DHW
,
, ,
,, ,
,
,, ,
,
, ,
(6a)
where heat output,
Hb ch h
DHWdir
, ,
, equals the required electricity demand and enters the electricity energy balance in the respective hour (not shown
here). The energy level of the auxiliary DHW tank complementing SETS space heating,
Hb ch h
DHWsets l
, ,
,
, is subject to the following intertemporal equation:
= +
+
+ +
H H E H RP RP b ch h, ,
b ch h
DHWsets l DHWsets
b ch h
DHWsets l
b ch h
DHWsets
b ch h
DHWsets out
r
r b ch
DHWsets
r h
act
r
r b ch
DHWsets
r h
act sets
, ,
,
, , 1
,
, , , ,
,
, , , , , ,
(6b)
which links the storage level in each period to the level in the previous period – corrected by static efficiency losses,
DHWsets
– plus the net of
energy inflow,
Eb ch h
DHWsets
, ,
, and outflows, also accounting for reserves provision. Electricity inflows (6c) and the tank’s energy level (6e) are restricted by
the the respective capacities,
nb ch
DHWsets in
,
,
and
nb ch
DHWsets l
,
,
, respectively, also taking balancing reserves provision into account (6d).
+E RP n b h h, c ,
b ch h
DHWsets
r
r b ch
DHWsets
b ch
DHWsets in sets
, , , , ,
,
(6c)
+
+
RP E b h h, c ,
r
r b ch
DHWsets
b ch h
DHWsets sets
, , , ,
(6d)
H n b h h, c ,
b ch h
DHWsets l
b ch
DHWsets l sets
, ,
,
,
,
(6e)
Appendix B. More information on the generation capacity assumptions
Numbers on solids-fired thermal plants specified in the Reference Scenario [25,26] are only given as aggregate figure. To differentiate between
lignite and hard coal, we assume a split according to the 2030 scenario Vision 3 (“National Green Transition”) of the Ten Year Network Development
Plan (TYNDP) 2016 [34,35]. We attribute natural gas-fired capacities evenly to combined cycle gas turbines (CCGT) and open cycle gas turbines
(OCGT). For the split between onshore and offshore wind, we assume about 18% offshore and about 82% onshore. This follows the most recent
proposal for the central scenario B from the German Network Development Plan for 2030 [57]. Lastly, we summarize the remaining, minor tech-
nologies “other renewables,” “hydrogen plants,” and “geothermal heat” as the type “other” for our model application.
Appendix C. More information on heating demand
Hourly time series of space heat and DHW demand per square meter enter the model as data, differentiated between twelve building archetypes.
Further exogenous inputs comprise the electric power rating of heating technologies, their storage energy capacity, the heat output capacity, and the
static and dynamic efficiency, which is given as the coefficient of performance (COP) for heat pumps. For ground-sourced heat pumps, the COP is
constant; for air-sourced heat pumps, it varies hourly over the year, depending on the outdoor air temperature, which also enters the model as input
data in line with the test reference year assumptions of the heating profiles.
Hourly outputs comprise the electricity demand of residential power-to-heat options, their heat and DHW output, the provision and activation of
balancing reserves, and the heating electricity price. Derived indicators encompass, among others, yearly heating costs, average electricity prices as
well as revenues from providing reserves.
Hourly heating energy demand profiles were calculated by RWTH Aachen University within the EU Horizon 2020 research project RealValue and
then serve as input parameters for the power sector model DIETER. To this end, twelve building archetypes were defined to adequately represent the
large and heterogeneous German residential building stock. The definition of archetypes is based on results of two European research projects: (i)
EPISCOPE, Monitor Progress Towards Climate Targets in European Housing Stocks; and (ii) TABULA, Typology Approach for Building Stock Energy
Assessment; also compare Loga et al. [58]. For modern and future buildings, not covered by the projects, relevant characteristics were selected based
on the current German Energy Saving Ordinance (EnEV) [59]and other sources.
The twelve archetypes are differentiated by two building sizes (one-family houses, OFH, and multi-family house, MFH) and six different vintage
classes: buildings with very high energy demand (VHED), built before 1957; buildings with high energy demand (HED), built in the period
1958–1978; buildings with medium energy demand (MED), built in the period 1979–1994; buildings with low energy demand (LED), built in the
period 1995–2009; buildings with very low energy demand (VLED), built in the period 2010–2019; and passive houses (PH), built after 2019. The
share of each building type in the year of analysis, 2030, is based on an own forward projection of depreciation and renovation rates, guided by
general trends and reflecting the ambitious targets for energy efficiency improvements by the German government.
To derive the hourly heating demand profile, the open-source thermal building model TEASER (Tool for Energy Analysis and Simulation for
Efficient Retrofit) was used [41]. Each archetype was modeled separately, drawing on the publicly available AixLib Library [60]. The thermal
building model features resistances and capacities to take into account heat flows and thermal inertia of physical components. Heat flows inside
the building and toward the ambience were modeled considering heat conduction, convection, and radiation effects. Internal loads were no
endogenous part of the simulations. Based on the Swiss SIA 2024 standard [42], they were subtracted from hourly heating energy demand
profiles after the simulation. Indoor temperatures reflect a daily set temperature of 22 °C, based on the current standards DIN EN 15251 and
DIN EN ISO 7730. To reflect actual heating behavior in Germany, a reduction of night-time indoor temperatures to 18 °C was allowed between
10 p.m. and 5 a.m. A German test reference year (TRY) approach was employed to ensure representative environmental boundary conditions
for building simulations, based on weather data calibrated to a central eastern German region. Hourly heat energy demand profiles are defined
per square meter. For aggregation to the national level, all values were multiplied with the overall square meters in the respective building
class.
Domestic hot water demand in buildings is generally not correlated with the building’s size, year of construction or standard of energy efficiency.
24
In contrast to SETS used for space heating, DHW-SETS store thermal energy directly in the water for domestic use and not in a solid thermal storage medium.
W.-P. Schill and A. Zerrahn Applied Energy 266 (2020) 114571
15
Therefore, DHW demand was modeled separately, depending on the assumed number of residents in each apartment or building. Its hourly profile
was also derived from the Swiss SIA 2024 standard [42].
Appendix D. Further scenarios with different shares of SETS and other power-to-heat technologies
In this section, we show results of further scenarios that vary the share of SETS and other power-to-heat technologies. Beyond the complete
upgrade of NETS to SETS, we provide intermediate cases in which only 25%, 50%, and 75% of the current German NETS fleet is substituted. Going
beyond full upgrades of existing NETS, one scenario exemplarily assumes a SETS capacity double the size of the former NETS fleet. Finally, we devise
a scenario in which hybrid electric-natural gas boilers replace existing NETS. Table 4 gives an overview.
If SETS substitute only a part of the current NETS fleet, electricity sector costs, i.e., not accounting for SETS investments, decrease with a diminishing
marginal rate: if SETS replace 25% of the current NETS capacity, they are lower by 0.045%; if SETS replace 50% of NETS, they are lower by 0.083%; by
0.116% for 75%, and and by 0.145% for 100% NETS upgrades. Fig. 13 plots this convex curve (solid line) against a hypothetical linear decrease (dotted line).
Table 4
Further scenarios with alternative assumptions on heating technologies.
Scenario Alternative assumption Rationale
25%, 50%, 75% SETS upgrade Only a share of existing NETS is upgraded to SETS Explore electricity sector effects for lower SETS penetration
Double SETS Double SETS capacities compared to upgrade case Explore electricity sector effects of SETS roll-out beyond upgrade of
existing NETS
Hybrid substitution NETS fleet substituted by hybrid electric-natural gas boilers instead
of SETS
Explore electricity sector effects of competing power-to-heat technology
Heat pump substitution NETS fleet substituted by heat pumps instead of SETS Explore electricity sector effects of competing power-to-heat technology
Fig. 13. Specific electricity sector cost savings per SETS unit in case of partial upgrades of NETS to SETS.
Fig. 14. Electricity sector cost effects for further scenarios with alternative assumptions on heating technologies.
W.-P. Schill and A. Zerrahn Applied Energy 266 (2020) 114571
16
Accordingly, SETS compete against themselves. More precisely, each additional SETS unit not only comes with a decreasing marginal flexibility
benefit, but also decreases the average benefits of already existing SETS units. This illustrates a more general point: the more competing sources of
flexibility there are in the electricity system, the lower is the value of additional flexibility. The effect parallels the finding for the scenarios with DSM
or heat pumps as competing flexibility options.
If we increase the share of the residential floor area heated by SETS beyond upgrading the existing NETS fleet, electricity sector costs no longer
decrease (as in the basic SETS upgrade scenario), but rise by around 1.5% (Fig. 14). This is driven by additional electricity demand of storage heaters,
which is here twice as large as for the initial NETS fleet. Accounting for SETS investments, the effect of total system costs would be more pronounced.
To allow for better comparison, we assume that the additional SETS replace natural gas-based heating systems and include according fuel cost
savings in the calculation. Even then, the overall cost effect is still positive. This finding is in line with our assumption that SETS are unlikely to
become a widespread heating option beyond the NETS replacement market.
Finally, if we assume that hybrid electric-natural gas heating systems or heat pumps replace NETS, electricity sector costs decrease by a greater
extent than if NETS are upgraded to SETS. This cost advantage is particularly pronounced in the heat pump substitution scenario with a cost decrease
of −1.0%, reflecting the more efficient electricity use of heat pumps compared to SETS. In the hybrid substitution scenario, the pure electricity sector
cost effect is even larger, but savings drop to −0.3% if we also consider additional natural gas expenditures for hybrid heating systems.
While these sensitivities provide complementary insights, more detailed calculations on relative advantages of specific heating technologies
would also have to consider the full costs of respective installations. This analysis is out of the scope of this work and left for future research.
References
[1] IPCC. Summary for Policymakers. In: Global warming of 1.5°C. An IPCC Special
Report on the impacts of global warming of 1.5°C above pre-industrial levels and
related global greenhouse gas emission pathways, in the context of strengthening
the global response to the threat of climate change, sustainable development, and
efforts to eradicate poverty [V. Masson-Delmotte, P. Zhai, H. O. Pörtner, D. Roberts,
J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors,
J. B. R. Matthews, Y. Chen, X. Zhou, M. I. Gomis, E. Lonnoy, T. Maycock, M. Tignor,
T. Waterfield (eds.)]. In press. https://www.ipcc.ch/site/assets/uploads/sites/2/
2019/05/SR15_SPM_version_report_LR.pdf [accessed February 03, 2020].
[2] BMWi. Zeitreihen zur Entwicklung der erneuerbaren Energien in Deutschland.
Federal Ministry for Economic Affairs and Energy (BMWi); 2019. https://www.
erneuerbare-energien.de/EE/Redaktion/DE/Downloads/zeitreihen-zur-
entwicklung-der-erneuerbaren-energien-in-deutschland-1990-2018.pdf;jsessionid=
2208E0A2E2596A02D5AF2102C5AF055B?__blob=publicationFile&v=22 [ac-
cessed November 08, 2019].
[3] BMWi. Zahlen und Fakten – Energiedaten. Federal Ministry for Economic Affairs
and Energy (BMWi); 2019 https://www.bmwi.de/Redaktion/DE/Artikel/Energie/
energiedaten-gesamtausgabe.html [accessed November 08, 2019].
[4] Bloess A, Schill W-P, Zerrahn A. Power-to-heat for renewable energy integration: a
review of technologies, modeling approaches, and flexibility potentials. Appl
Energy 2018;212:1611–26. https://doi.org/10.1016/j.apenergy.2017.12.073.
[5] Henning H-M, Palzer A. A comprehensive model for the German electricity and heat
sector in a future energy system with a dominant contribution from renewable
energy technologies – part I: methodology. Renew Sustain Energy Rev
2014;30:1003–18. https://doi.org/10.1016/j.rser.2013.09.012.
[6] Palzer A, Henning H-M. A comprehensive model for the German electricity and heat
sector in a future energy system with a dominant contribution from renewable
energy technologies – part II: results. Renew Sustain Energy Rev 2014;30:1019–34.
https://doi.org/10.1016/j.rser.2013.11.032.
[7] Merkel E, McKenna R, Fehrenbach D, Fichtner W. A model-based assessment of
climate and energy targets for the German residential heat system. J Clean Prod
2017;142:3151–73. https://doi.org/10.1016/j.jclepro.2016.10.153.
[8] Kiviluoma J, Meibom P. Influence of wind power, plug-in electric vehicles, and heat
storages on power system investments. Energy 2010;35(3):1244–55. https://doi.
org/10.1016/j.energy.2009.11.004.
[9] Connolly D, Lund H, Mathiesen BV. Smart energy Europe: The technical and eco-
nomic impact of one potential 100% renewable energy scenario for the European
Union. Renew Sustain Energy Rev 2016;60:1634–53. https://doi.org/10.1016/j.
rser.2016.02.025.
[10] Patteeuw D, Bruninx K, Arteconi A, Delarue E, D’haeseleer W, Helsen L. Integrated
modeling of active demand response with electric heating systems coupled to
thermal energy storage systems. Appl Energy 2015;151:306–19. https://doi.org/10.
1016/j.apenergy.2015.04.014.
[11] Patteeuw D, Reynders G, Bruninx K, Protopapadaki C, Delarue E, D’haeseleer W,
et al. CO
2
-abatement cost of residential heat pumps with active demand response:
demand-and supply-side effects. Appl Energy 156 2015:490–501. https://doi.org/
10.1016/j.apenergy.2015.07.038.
[12] Arteconi A, Patteeuw D, Bruninx K, Delarue E, D’haeseleer W, Helsen L. Active
demand response with electric heating systems: impact of market penetration. Appl
Energy 2016;177:636–48. https://doi.org/10.1016/j.apenergy.2016.05.146.
[13] Patteeuw D, Helsen L. Combined design and control optimization of residential
heating systems in a smart-grid context. Energy Build 2016;133:640–57. https://
doi.org/10.1016/j.enbuild.2016.09.030.
[14] Hedegaard K, Balyk O. Energy system investment model incorporating heat pumps
with thermal storage in buildings and buffer tanks. Energy 2013;63:356–65.
https://doi.org/10.1016/j.energy.2013.09.061.
[15] Hedegaard K, Münster M. Influence of individual heat pumps on wind power in-
tegration – energy system investments and operation. Energy Convers Manage
2013;75:673–84. https://doi.org/10.1016/j.enconman.2013.08.015.
[16] Bloess A. Impacts of heat sector transformation on Germany’s power system through
increased use of power-to-heat. Appl Energy 2019;239:560–80. https://doi.org/10.
1016/j.apenergy.2019.01.101.
[17] Chen X, Lu X, McElroy MB, Nielsen CP, Kang C. Synergies of wind power and
electrified space heating: case study for Beijing. Environ Sci Technol
2014;48:2016–24. https://doi.org/10.1021/es405653x.
[18] Papaefthymiou G, Hasche B, Nabe C. Potential of heat pumps for demand side
management and wind power integration in the German electricity market. IEEE
Trans Sustain Energy 2012;3(4):636–42. https://doi.org/10.1109/TSTE.2012.
2202132.
[19] Dodds PE. Integrating housing stock and energy system models as a strategy to
improve heat decarbonisation assessments. Appl Energy 2014;132:358–69. https://
doi.org/10.1016/j.apenergy.2014.06.079.
[20] Waite M, Modi V. Potential for increased wind-generated electricity utilization
using heat pumps in urban areas. Appl Energy 2014;135:634–42. https://doi.org/
10.1016/j.apenergy.2014.04.059.
[21] Barton J, Huang S, Infield D, Leach M, Ogunkunle D, Torriti J, et al. The evolution
of electricity demand and the role for demand side participation, in buildings and
transport. Energy Pol 2013;52:85–102. https://doi.org/10.1016/j.enpol.2012.08.
040.
[22] Pensini A, Rasmussen CN, Kempton W. Economic analysis of using excess renewable
electricity to displace heating fuels. Appl Energy 2014;131:530–43. https://doi.
org/10.1016/j.apenergy.2014.04.111.
[23] Rasku T, Kiviluoma J. A comparison ofwidespread flexible residential electric
heating and energy efficiency in a future nordic power system. Energies 2019;12(5).
https://doi.org/10.3390/en12010005.
[24] Zerrahn A, Schill W-P. Long-run power storage requirements for high shares of
renewables: review and a new model. Renew Sustain Energy Rev 2017;79:1518–34.
https://doi.org/10.1016/j.rser.2016.11.098.
[25] Capros P, De Vita A, Tasios N, Siskos P, Kannavou M, Petropoulos A, et al. EU
Reference Scenario 2016 – Energy, transport and GHG emsissions trends to 2050.
Study prepared for the European Commission, Directorate-General for Energy,
Directorate-General for Climate Action and Directorate-General for Mobility and
Transport; 2016 https://ec.europa.eu/energy/sites/ener/files/documents/
20160713%20draft_publication_REF2016_v13.pdf [accessed February 03, 2020].
[26] E3M Lab. EU28: Reference scenario(REF2016), Summary report. E3M Lab, National
Technical University of Athens; 2016. https://data.europa.eu/euodp/data/dataset/
energy-modelling [accessed November 08, 2019].
[27] Frontier Economics and Formaet. Strommarkt in Deutschland – Gewährleistet das
derzeitige Marktdesign Versorgungssicherheit? Bericht für das Bundesministerium
für Wirtschaft und Energie (BMWi); 2014. https://www.bmwi.de/Redaktion/DE/
Publikationen/Studien/strommarkt-in-deutschland-gewaehrleistung-das-
derzeitige-marktdesign-versorgungssicherheit.pdf?__blob=publicationFile&v=5
[accessed November 08, 2019].
[28] Gils HC. Assessment of the theoretical demand response potential in Europe. Energy
2014;67:1–18. https://doi.org/10.1016/j.energy.2014.02.019.
[29] Klobasa, M. Dynamische Simulation eines Lastmanagements und Integration von
Windenergie in ein Elektrizitätsnetz auf Landesebene unter regelungstechnischen
und Kostengesichtspunkten. Ph.D. Thesis, ETH Zürich; 2007 https://www.research-
collection.ethz.ch/handle/20.500.11850/150146 [accessed November 08, 2019].
[30] OPSD. Data Package Time series. Version 2018-03-13. Open Power System Data
(OPSD); 2018 https://data.open-power-system-data.org/time_series/2018-03-13
(Primary data from various sources, for a complete list see URL) [accessed
November 19, 2018].
[31] Wiese F, Schlecht I, Bunke W-D, Gerbaulet C, Hirth L, Jahn M, et al. Open power
system data – frictionless data for electricity system modelling. Appl Energy
2019;236:401–9. https://doi.org/10.1016/j.apenergy.2018.11.097.
[32] regelleistung.net. Daten zur Regelenergie; 2018. https://www.regelleistung.net/
ext/data/ [accessed November 19, 2018].
[33] regelleistung.net. Ausschreibungssuche; 2018. https://www.regelleistung.net/ext/
tender/ [accessed November 19, 2018].
[34] ENTSO-E. TYNDP 2016 Scenario development report. European Network of
W.-P. Schill and A. Zerrahn Applied Energy 266 (2020) 114571
17
Transmission System Operators (ENTSO-E); 2015. https://docstore.entsoe.eu/
Documents/TYNDP%20documents/TYNDP2018/Scenario_Report_2018_Final.pdf
[accessed February 03, 2020].
[35] ENTSO-E. TYNDP 2016 market modelling data. European Network of Transmission
System Operators (ENTSO-E); 2015 http://tyndp.entsoe.eu/reference/#downloads
[accessed April 20, 2018].
[36] Schröder A., Kunz F., Meiss J., Mendelevitch R., von Hirschhausen C. Current and
prospective costs of electricity generation until 2050. DIW Data Documentation
2013;68 https://www.diw.de/documents/publikationen/73/diw_01.c.424566.de/
diw_datadoc_2013-068.pdf [accessed November 08, 2019].
[37] Kunz F., Kendziorski M., Schill W.-P., Weibezahn J., Zepter J., von Hirschhausen C.,
Hauser P., Zech M., Möst D., Heidari S., Felten B., Weber C. Electricity, heat, and
gas sector data for modeling the German system. DIW Data Documentation
2017;92. https://www.diw.de/documents/publikationen/73/diw_01.c.574130.de/
diw_datadoc_2017-092.pdf [accessed November 08, 2019].
[38] Pape C., Gerhard N., Härtel P., Scholz A., Schwinn R., Drees T., Maaz A., Sprey J.,
Breuer C., Moser A., Sailer F., Reuter S., Müller T. Roadmap Speicher. Bestimmung
des Speicherbedarfs in Deutschland im europäischen Kontext und Ableitung von
technisch-ökonomischen sowie rechtlichen Handlungsempfehlungen für die
Speicherförderung. Endbericht. Kassel, Aachen, Würzburg; 2014 http://www.fvee.
de/fileadmin/publikationen/Politische_Papiere_FVEE/14.IWES_Roadmap-
Speicher/14_IWES-etal_Roadmap_Speicher_Langfassung.pdf [accessed November
08, 2019].
[39] Agora Energiewende. Stromspeicher in der Energiewende: Untersuchung zum
Bedarf an neuen Stromspeichern in Deutschland für den Erzeugungsausgleich,
Systemdienstleistungen und im Verteilnetz. Agora Energiewende. Berlin; 2014
https://www.agora-energiewende.de/fileadmin2/Projekte/2013/speicher-in-der-
energiewende/Agora_Speicherstudie_Web.pdf [accessed November 08, 2019].
[40] Heitkoetter W, Medjroubi W, Vogt T, Agert C. Regionalised heat demand and
power-to-heat capacities in Germany – an open dataset for assessing renewable
energy integration. Appl Energy 2019;114161. https://doi.org/10.1016/j.
apenergy.2019.114161.
[41] Remmen P, Lauster M, Mans M, Fuchs M, Osterhage T, Müller D. TEASER: an open
tool for urban energy modelling of building stocks. J Build Perform Simul
2017;11(1):84–9. https://doi.org/10.1080/19401493.2017.1283539.
[42] SIA. Merkblatt 2024: Standard-Nutzungsbedingungen für die Energie- und
Gebäudetechnik. Swiss society of engineers and architects (SIA). Zurich
(Switzerland); 2006.
[43] BMWi. Kommission “Wachstum, Strukturwandel und Beschäftigung“ –
Abschlussbericht. Kommission “Wachstum, Strukturwandel und Beschäftigung“,
published by the Federal Ministry for Economic Affairs and Energy (BMWi); 2019
https://www.bmwi.de/Redaktion/DE/Downloads/A/abschlussbericht-
kommission-wachstum-strukturwandel-und-beschaeftigung.pdf?__blob=
publicationFile [accessed November 08, 2019].
[44] O’Dwyer C., Anwar M., Dillon J., Bakhtvar M., Buttitta G., Andrade Cabrera C.,
et al. H2020 REALVALUE: D3.6 Cost benefit analysis of SETS and alternative local
small-scale storage options. Project Report. 30 May 2018; 2018.
[45] Zerrahn A, Schill W-P, Kemfert C. On the economics of electrical storage for variable
renewable energy sources. Euro Econ Rev 2018;108:259–79. https://doi.org/10.
1016/j.euroecorev.2018.07.004.
[46] Felten B, Raasch J, Weber C. Photovoltaics and heat pumps – Limitations of local
pricing mechanisms. Energy Econ 2018;71:383–402. https://doi.org/10.1016/j.
eneco.2017.12.032.
[47] Schill W-P, Gerbaulet C. Power system impacts of electric vehicles in Germany:
charging with coal or renewables. Appl Energy 2015;156:185–96. https://doi.org/
10.1016/j.apenergy.2015.07.012.
[48] Runge P, Sölch C, Albert J, Wasserscheid P, Zöttl G, Grimm V. Economic compar-
ison of different electric fuels for energy scenarios in 2035. Appl Energy
2019;233–234:1078–93. https://doi.org/10.1016/j.apenergy.2018.10.023.
[49] McKenna R, Fehrenbach D, Merkel E. The role of seasonal thermal energy storage in
increasing renewable heating shares: a techno-economic analysis for a typical re-
sidential district. Energy Build 2019;187:38–49. https://doi.org/10.1016/j.enbuild.
2019.01.044.
[50] Boait PJ, Snape JR, Darby SJ, Hamilton J, Morris RJR. Making legacy thermal
storage heating fit for the smart grid. Energy Build 2017;138:630–40. https://doi.
org/10.1016/j.enbuild.2016.12.053.
[51] Darby SJ. Smart electric storage heating and potential for residential demand re-
sponse. Energy Effi 2018;11:67–77. https://doi.org/10.1007/s12053-017-9550-3.
[52] Michelsen CC, Madlener R. Homeowners’ preferences for adopting innovative re-
sidential heating systems: a discrete choice analysis for Germany. Energy Econ
2012;34:1271–83. https://doi.org/10.1016/j.eneco.2012.06.009.
[53] Hlavinka AN, Mhelde JW, Dharmasena S, Holland C. Forecasting the adoption of
residential ductless heat pumps. Energy Econ 2016;54:60–7. https://doi.org/10.
1016/j.eneco.2015.11.020.
[54] Michaels L, Parag Y. Motivations and barriers to integrating ‘prosuming’ services
into the future decentralized electricity grid: findings from Israel. Energy Res Social
Sci 2016;21:70–83. https://doi.org/10.1016/j.erss.2016.06.023.
[55] Broberg T, Persson L. Is our everyday comfort for sale? Preferences for demand
management on the electricity market. Energy Econ 2016;54:24–32. https://doi.
org/10.1016/j.eneco.2015.11.005.
[56] Wilson C, Hargreaves T, Hauxwell-Baldwin R. Benefits and risks of smart home
technologies. Energy Pol 2017;103:72–83. https://doi.org/10.1016/j.enpol.2016.
12.047.
[57] 50Hertz Transmission, Amprion, TenneT TSO, and TransnetBW. Szenariorahmen
für den Netzentwicklungsplan Strom 2030 (Version 2019) – Entwurf der
Übertragungsnetzbetreiber; 2018 https://www.netzentwicklungsplan.de/sites/
default/files/paragraphs-files/NEP_2030_V2019_1_Entwurf_Teil1_1.pdf [February
03, 2020].
[58] Loga T, Calisti J, Stein B. TABULA – WebTool: building typologies; 2016 http://
webtool.building-typology.eu/ [accessed November 08, 2019].
[59] BMUB. The German. Energy Saving Ordinance (EnEV). Federal Ministry for the
Environment Nature Conservation, Building and Nuclear Safety. BMUB; 2016.
[60] Müller D, Lauster M, Constantin A, Fuchs M, Remmen P. AixLib - An Open-Source
Modelica Library within the IEA-EBC Annex 60 Framework. In: Proceedings of
BauSIM, Dresden, Germany; 2016.
W.-P. Schill and A. Zerrahn Applied Energy 266 (2020) 114571
18
... Facilitate RES integration [53,56,57,74,[284][285][286][287] Peak-demand shaving [249,[288][289][290][291][292][293] Real-time balancing/ operation [294][295][296][297][298] Operation cost reduction of energy/power system [16,57,299,300] [16,178,314,315] Regarding the thermal dynamic model, TRNSYS is widely used in many research studies [316]. This is open-source software that can simulate transients of both electrical and heat sectors of DHS. ...
... In the DHS problem, the energy markets, especially the electricity market, are modeled mathematically by GAMS [284]. This software is well-fitted to model market trading floors with price uncertainty. ...
Article
Increasing the penetration of Renewable Energy Sources (RES), e.g. wind and solar, intermittency and volatility of the supply-side are increasing in power systems worldwide. Therefore, the power systems need alternative forms of flexibility potentials to hedge against the intermittent power. District Heating Systems (DHS), especially Heat Pump Systems (HPS), show true potentials to provide demand-side flexibility for power systems. This paper aims to survey the literature on the applications of DHS in power system flexibility. In this way, first of all, the basic structure of the DHS, including the heat resources, thermal units, and thermal storage, is surveyed to give insight into the DHS problem. Afterward, classifying flexibility potentials of the DHS, the conventional and advanced control strategies of the DHS are reviewed comprehensively to investigate the role of heat controllers on power system flexibility. To study the compatibility of the controllers to flexible energy markets, the roles of different control schemes in successive trading floors of the electricity markets, from the energy market to ancillary service markets, are investigated. Finally, the optimization solutions, including mathematical and heuristic approaches, with software tools are reviewed to give readers general insight into the way a DHS problem is modeled and solved.
... Other studies do consider changing demand, but still assume it to be an exogenous factor, focusing their analysis on the supply side. Often these studies also only account for changes in electricity demand from decarbonizing one specific sector, like space heating [15] or transport [16,17], some have a more comprehensive perspective and include hydrogen in their supply-side analysis [18,19]. ...
Preprint
Full-text available
Supply and demand for electricity are central to the decarbonisation of the energy system. To replace fossil fuels, supply of electricity must shift to wind and solar, but due to their variability, fully renewable supply poses a challenge. On the other hand, additional demand for electricity arises to cut emissions in the heating, transport, or industry sector. We analyze how additional demand from these sectors can be flexible to support the integration of fluctuating renewables on the supply-side. The analysis builds on a macro-energy system model with an extensive scope to cover all sectors and high spatio-temporal detail to capture the variability of renewables. Results show that flexible electrification can efficiently provide a major share of system flexibility if incentivized by regulation. Especially electricity demand for the production of hydrogen is flexible, if hydrogen pipelines and storages are deployed to match production with final consumption.
... For example, in the case of thermostatically controlled loads, some of the parameters needed are outside temperature, isolation characteristics and thermal inertia of the building [81,82]. And even with some level of aggregation (few buildings) these aggregated parameters are still needed [83,84]. Many of these detailed models for specific appliances are listed in Refs. ...
Article
Full-text available
Demand response (DR) is expected to play a major role in integrating large shares of variable renewable energy (VRE) sources in power systems. For example, DR can increase or decrease consumption depending on the VRE availability, and use generating and network assets more efficiently. Detailed DR models are usually very complex, hence, unsuitable for large-scale energy models, where simplicity and linearity are key elements to keep a reasonable computational performance. In contrast, aggregated DR models are usually too simplistic and therefore conclusions derived from them may be misleading. This paper focuses on classifying and modelling DR in large-scale models. The first part of the paper classifies different DR services, and provides an overview of benefits and challenges. The second part presents mathematical formulations for different types of DR ranging from curtailment and ideal shifting, to shifting including saturation and immediate load recovery. Here, we suggest a collection of linear constraints that are appropriate for large-scale power systems and integrated energy system models, but sufficiently sophisticated to capture the key effects of DR in the energy system. We also propose a mixed-integer programming formulation for load shifting that guarantees immediate load recovery, and its linear relaxation better approximates the exact solution compared with previous models.
... Based on this, China proposed an electricity substitution strategy in 2013 to make use of different types of electricity production methods that are safe and convenient as well as clean and efficient [6,7]. Electric power substitution underway throughout the country, evidenced in electric heating [8], electric vehicles [9], port shore power [10], etc. After years of development, China has made some palpable achievements. ...
Article
Full-text available
An electricity substitution strategy that replaces fossil fuels such as coal and oil with electricity in end-use energy consumption, can effectively contribute to an energy transition and the early achievement of carbon peaking and carbon neutrality targets. As the benefits of electricity substitution are not synchronized across China’s regions, this paper uses a three-stage data envelopment analysis (DEA) model to measure the efficiency of electric energy substitution in 30 provinces of China in 2017. The results show that both environmental factors and random errors have significant effects on energy efficiency. After eliminating these influences, the efficiency of electrical energy substitution among regions presented the following pattern: “high in the east and low in the west”. According to the evaluation results, this paper proposes corresponding suggestions for the development of electrical energy substitution.
Article
A novel hybrid 4D bio‐inspired optimization combined with an Artificial Neural Network (ANN) is proposed for finding optimal design and operational variables for a fuel cell dynamic model. In addition, a novel classification of fuel cells is proposed which can help designers and decision‐makers to set operational and contractual parameters for their desired purposes. The present article proposes three general clusters of proton exchange membrane fuel cells (PEMFCs), which help designers to choose design parameters for a vast range of applications from hydrogen vehicles to combined power generation plants. After investigating PEMFC design parameters followed by sensitivity analysis, several correlations between PEMFCs design parameters are found. For instance, the correlation between fuel cell active area and membrane thickness for optimized fuel cell units can help designers to ensure that their design is optimized. An ANN model is developed which can be used by others to ascertain in what category their fuel cell stack is.
Article
Full-text available
To decarbonize the economy, many governments have set targets for the use of renewable energy sources. These are often formulated as relative shares of electricity demand or supply. Implementing respective constraints in energy models is a surprisingly delicate issue. They may cause a modeling artifact of excessive electricity storage use. We introduce this phenomenon as “unintended storage cycling”, which can be detected in case of simultaneous storage charging and discharging. In this paper, we provide an analytical representation of different approaches for implementing minimum renewable share constraints in energy models, and show how these may lead to unintended storage cycling. Using a parsimonious optimization model, we quantify related distortions of optimal dispatch and investment decisions as well as market prices, and identify important drivers of the phenomenon. Finally, we provide recommendations on how to avoid the distorting effects of unintended storage cycling in energy modeling.
Article
Full-text available
Model-based scenario analyses of future energy systems often come to deviating results and conclusions when different models are used. This may be caused by heterogeneous input data and by inherent differences in model formulations. The representation of technologies for the conversion, storage, use, and transport of energy is usually stylized in comprehensive system models in order to limit the size of the mathematical problem, and may substantially differ between models. This paper presents a systematic comparison of nine power sector models with sector coupling. We analyze the impact of differences in the representation of technologies, optimization approaches, and further model features on model outcomes. The comparison uses fully harmonized input data and highly simplified system configurations to isolate and quantify model-specific effects. We identify structural differences in terms of the optimization approach between the models. Furthermore, we find substantial differences in technology modeling primarily for battery electric vehicles, reservoir hydro power, power transmission, and demand response. These depend largely on the specific focus of the models. In model analyses where these technologies are a relevant factor, it is therefore important to be aware of potential effects of the chosen modeling approach. For the detailed analysis of the effect of individual differences in technology modeling and model features, the chosen approach of highly simplified test cases is suitable, as it allows to isolate the effects of model-specific differences on results. However, it strongly limits the model’s degrees of freedom, which reduces its suitability for the evaluation of fundamentally different modeling approaches.
Article
In the current research, 4E analysis and multi-criteria optimization are applied to the poly generation unit for power, heating, refrigeration, and freshwater generation. This system consists of a solid oxide fuel cell (SOFC), multi-effect thermal vapor desalination (MED-TVC), an organic system with ejector refrigeration (OSER), a heat recovery steam generator (HRSG) and a domestic hot water generator. The mathematical simulation is applied to assess the performance of the plant at design conditions and the genetic algorithm finds the optimum operating point with two different scenarios. Parametric analysis and multi-objective optimization are carried out. Findings represent that the developed plant can provide 257.65 kW power, 12.13 kW, 7.44 kW cooling and heating load, and 0.04 kg/s freshwater with a total cost rate of 10.62 $/h. In this case, the plant energy and exergy efficiency is 73.9% and 71.35% respectively. The results of multi-objective optimization show that these values can be improved to 79% and 73.9% respectively. In addition, the plant cost can be reached to 10.07 $/h in this condition.
Article
The rising share of Variable Renewable Energy Sources (VRES) in the electricity generation mix leads to new challenges for the whole energy system. It especially raises technological issues to handle variability and to match electricity load with supply at all times. This study introduces a new methodology to quantify the relevance of different electricity storage technologies, based on a time scale analysis. It additionally provides an understanding of how electricity storages work in combination to handle variable load and intermittent generation. First, we set up a simple model of variable production, fluctuating over a single time-scale. This analysis provides figures of merit for electricity storage and curtailment. Second, we simulate the collaboration and competition behavior of various storages with a dual time-scale signal. Then, results are compared with the optimization of an energy system with real variable electricity supply and consumption time-series. We eventually highlight the trade-off mechanisms between the storage efficiency and its investment cost.
Chapter
Power-to-heat storage is an interesting option in energy systems with high shares of fluctuating electricity that exceed the electricity demand, while insufficient alternative energy sources with low exergy content are available to meet the thermal energy demand. The basic idea is to stabilize the system by adding flexibility on the demand side, the thermal power allows the load profile to change without disadvantages for the demand side. This chapter gives an overview of power-to-heat storage systems for residential heating, typical storage materials and an outlook on the application for process heat applications.
Technical Report
Full-text available
Realising Value from Electricity Markets with Local Smart Electric Thermal Storage Technology (RealValue): The purpose of this report is to present the findings of Task 3.6 “Perform Cost Benefit Analysis of SETS and Alternative Local Small-Scale Storage Options”. Using the techno-economic models developed in Tasks 3.2 – 3.5, and the various system scenarios from Task 3.1, this Task explores the overall relative cost-reduction benefits that SETS brings to European combined energy system planning and operation. The analysis is completed for a number of European countries (Germany, UK, Ireland, Finland and Latvia), while considering the large number of credible uncertainties facing the European energy system over the coming decades.
Book
Full-text available
This data documentation describes a data set of the German electricity, heat, and natural gas sectors compiled within the research project ‘LKD-EU’ (Long-term planning and short-term optimization of the German electricity system within the European framework: Further devel-opment of methods and models to analyze the electricity system including the heat and gas sector). The project is a joined effort by the German Institute for Economic Research (DIW Berlin), the Workgroup for Infrastructure Policy (WIP) at Technische Universität Berlin (TUB), the Chair of Energy Economics (EE2) at Technische Universität Dresden (TUD), and the House of Energy Markets & Finance at University of Duisburg-Essen. The project was funded by the German Federal Ministry for Economic Affairs and Energy through the grant ‘LKD-EU’, FKZ 03ET4028A.
Article
Full-text available
European renewable energy developments have so far focussed on electricity generation, with relatively modest progress in renewable heating. Partly this is due to the temporal mismatch between solar irradiation availability and residential heating demand profiles. Seasonal thermal energy storage (STES) has been proven in several pilot projects and is market ready, albeit not currently economical. This paper sets out to assess the potential contribution of STES to increasing the renewable heating fraction in residential buildings. An existing mixed integer linear program (MILP) is extended to consider STES and applied to optimize the energy supply system for a typical residential district with efficient new-build apartment buildings, in the context of five contrasting scenarios. Achieving 100% renewable heat supply requires significant capacities of seasonal storages and is associated with substantially (14%) higher cost than in the reference scenario. To achieve a 60% renewable heat supply fraction under today's framework conditions, the cost increase compared to the reference scenario is only marginal (1%). The results in three future scenarios reflecting possible conditions in 2030 demonstrate that even higher levels of renewable heat supply could soon become economical. Overall the recommendation is to aim for renewable heat supply levels of around 60–80% combined with demand side measures such as improved insulation. Further work should focus on more systematically exploring the relationship between the grid renewable electricity fraction, available solar collector area and the optimal renewable heat integration strategy.
Preprint
Full-text available
The quality of electricity system modelling heavily depends on the input data used. Although a lot of data is publicly available, it is often dispersed, tedious to process and partly contains errors. We argue that a central provision of input data for modelling has the character of a public good: it reduces overall societal costs for quantitative energy research as redundant work is avoided, and it improves transparency and reproducibility in electricity system modelling. This paper describes the Open Power System Data platform that aims at realising the efficiency and quality gains of centralised data provision by collecting, checking, processing, aggregating, documenting and publishing data required by most modellers. We conclude that the platform can provide substantial benefits to the modelling community, and that it can also contribute to improving the quality of original data published by respective providers.
Article
Full-text available
Integrating ever-increasing amounts of variable renewable energy (VRE) into the power system could benefit from harnessing widespread residential demand-side management. This paper presents case studies on the potential benefits of power-to-heat (P2H) flexibility and energy efficiency improvements in a hypothetical future Finnish detached housing stock in the year 2030, both as a part of the larger Nordic power system and in an isolated Finnish power system. The housing stock was depicted using two archetype houses modeled using a simple lumped capacitance approach, integrally optimized as a part of a stochastic linear programming unit commitment model of the power system. With sufficient amounts of VRE, residential P2H with thermal storage was found to yield more system cost savings than simple energy efficiency improvements. However, energy efficiency improvements remained more beneficial for house owners, as excessive use of residential P2H for assisting the power system could result in increased heating costs.
Article
Full-text available
The use of renewable energy sources is a major strategy to mitigate climate change. Yet Sinn (2017) argues that excessive electrical storage requirements could limit the further expansion of variable wind and solar energy. We question, and alter, strong implicit assumptions of Sinn's approach and find that storage needs are considerably lower, up to two orders of magnitude. First, we move away from corner solutions by allowing for combinations of storage and renewable curtailment. Second, we specify a parsimonious model to derive first-best outcomes. We conclude that electrical storage is unlikely to limit the transition to renewable energy.
Article
Higher shares of fluctuating generation from renewable energy sources in the power system lead to an increase in grid balancing demand. One approach for avoiding curtailment of renewable energies is to use excess electricity feed-in for heating applications. To assess in which regions power-to-heat technologies can contribute to renewable energy integration, detailed data on the spatial distribution of the heat demand are needed. We determine the overall heat load in the residential building sector and the share covered by electric heating technologies for each administrative district in Germany, with a temporal resolution of 15 min. Using a special evaluation of German census data, we defined 729 building categories and assigned individual heat demand values. Furthermore, heating types and different classes of installed heating capacity were defined. Our analysis showed that the share of small-scale single-storey heating and large-scale central heating is higher in cities, whereas there is more medium-scale central heating in rural areas. This results from the different shares of single and multi-family houses in the respective regions. To determine the electrically-covered heat demand, we took into account heat pumps and resistive heating technologies. All results, as well as the developed code, are published under open source licenses and can thus also be used by other researchers for the assessment of power-to-heat for renewable energy integration.
Article
Electric fuels (e-fuels) enable CO2-neutral mobility and are therefore an alternative to battery-powered electric vehicles. This paper compares the cost-effectiveness of Fischer-Tropsch diesel, methanol and Liquid Organic Hydrogen Carriers. The production costs of those fuels are to a large part driven by the energy-intensive electrolytic hydrogen production. In this paper, we apply a multi-level electricity market model to calculate future hourly electricity prices for various electricity market designs in Germany for the year 2035. We then assess the economic efficiency of the different fuels under various future market conditions. In particular, we use the electricity price vectors derived from an electricity market model calibrated for 2035 as an input for a mathematical model of the entire process chain from hydrogen production and chemical bonding to the energetic utilization of the fuels in a vehicle. Within this model, we perform a sensitivity analysis, which quantifies the impact of various parameters on the fuel production cost. Most importantly, we consider prices resulting from own model calculations for different energy market designs, the investment cost for the electrolysis systems and the carbon dioxide purchase price. The results suggest that the use of hydrogen, which is temporarily bound to Liquid Organic Hydrogen Carriers, is a favorable alternative to the more widely discussed synthetic diesel and methanol.
Book
The Intergovernmental Panel on Climate Change (IPCC) is the leading international body for assessing the science related to climate change. It provides regular assessments of the scientific basis of climate change, its impacts and future risks, and options for adaptation and mitigation. This IPCC Special Report is a comprehensive assessment of our understanding of global warming of 1.5°C, future climate change, potential impacts and associated risks, emission pathways, and system transitions consistent with 1.5°C global warming, and strengthening the global response to climate change in the context of sustainable development and efforts to eradicate poverty. It serves policymakers, decision makers, stakeholders and all interested parties with unbiased, up-to-date, policy-relevant information. This title is also available as Open Access on Cambridge Core.