COST-OPTIMISATION MODELLING OF LOCAL LEVEL CROSS-VECTOR FLEXIBILITY IN COUPLED DISTRICT HEATING AND ELECTRICITY NETWORKS: A CASE STUDY OF KUNGSBACKA, SWEDEN
This study presents a local-level cost-optimization analysis of cross-vector flexibility between district heating (DH) and electricity systems in the municipality of Kungsbacka, Sweden. Using a TIMES model, we assess the techno-economic performance of integrated flexibility solutions, including elec...
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| Дата: | 2025 |
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| Автори: | , , |
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Institute of Renewable Energy National Academy of Sciences of Ukraine
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Vidnovluvana energetika| _version_ | 1871104011892948992 |
|---|---|
| author | Lysenko, O. Mata , É. Sobha , P. |
| author_facet | Lysenko, O. Mata , É. Sobha , P. |
| author_institution_txt_mv | [
{
"author": " O. Lysenko",
"institution": "IVL Svenska Miljöinstitutet AB, Stockholm, Sweden"
},
{
"author": "É. Mata ",
"institution": "IVL Svenska Miljöinstitutet AB, Gothenburg, Sweden"
},
{
"author": "P. Sobha ",
"institution": "IVL Svenska Miljöinstitutet AB, Stockholm, Sweden"
}
] |
| author_sort | Lysenko, O. |
| baseUrl_str | https://ve.org.ua/index.php/journal/oai |
| collection | OJS |
| datestamp_date | 2026-07-18T06:32:23Z |
| description | This study presents a local-level cost-optimization analysis of cross-vector flexibility between district heating (DH) and electricity systems in the municipality of Kungsbacka, Sweden. Using a TIMES model, we assess the techno-economic performance of integrated flexibility solutions, including electric boilers, heat pumps, rooftop photovoltaic (PV) panels, storage in electric battery and thermal storage, solar thermal collectors, and smart appliances, under varying electricity and biomass price scenarios. Results show that while the base and implemented configurations remain dominated by biomass-fired generation, the inclusion of electricity-based heating technologies, particularly the electric boiler, enables cost-efficient and low-emission operation during periods of high biomass prices. The analysis also reveals notable differences in system running costs across scenarios, with substantial long-term cost reductions observed only when investment flexibility allows broader adoption of power-to-heat options. The scenarios reveal a long-term transition toward electrified DH, where heat pumps and thermal storage become central to system optimization. Thermal energy storage contributes significantly to peak shaving and load shifting, enabling the system to respond to hourly electricity price variations. Consumer-side resources, including rooftop PV, batteries, and dual-input smart appliances, further enhance local flexibility by increasing self-consumption and shifting energy carriers between electricity and DH. The findings confirm that combining multiple cross-vector flexibility measures can reduce operating costs, enhance renewable integration, and strengthen local energy resilience, supporting a transition toward climate-neutral district energy systems. These results underscore the importance of price-responsive operation and coordinated deployment of flexibility technologies in local-scale decarbonisation pathways. |
| doi_str_mv | 10.36296/1819-8058.2025.4(83).8-17 |
| first_indexed | 2026-02-08T07:59:26Z |
| format | Article |
| fulltext |
8
Відновлювана енергетика. № 4/2025 | Комплексні проблеми енергетичних систем на основі НВДЕ
UDK 6210.91 https://doi.org/10.36296/1819-8058.2025.4(83).8-17
COST-OPTIMISATION MODELLING OF LOCAL LEVEL CROSS-VECTOR FLEXIBILITY IN COUPLED
DISTRICT HEATING AND ELECTRICITY NETWORKS: A CASE STUDY OF KUNGSBACKA, SWEDEN
Received Nov. 27, 2025; accepted Dec. 09, 2025
Available online Dec. 31, 2025
Lysenko O.1, Mata É.2, Sobha P.3
Author for correspondence: Lysenko Olga,
e-mail: olga.lysenko@ivl.se
Abstract. This study presents a local-level cost-optimization
analysis of cross-vector flexibility between district heating (DH)
and electricity systems in the municipality of Kungsbacka, Swe-
den. Using a TIMES model, we assess the techno-economic per-
formance of integrated flexibility solutions, including electric
boilers, heat pumps, rooftop photovoltaic (PV) panels, storage
in electric battery and thermal storage, solar thermal collec-
tors, and smart appliances, under varying electricity and biomass price scenarios. Results show that while the base
and implemented configurations remain dominated by biomass-fired generation, the inclusion of electricity-based
heating technologies, particularly the electric boiler, enables cost-efficient and low-emission operation during pe-
riods of high biomass prices. The analysis also reveals notable differences in system running costs across scenarios,
with substantial long-term cost reductions observed only when investment flexibility allows broader adoption of
power-to-heat options. The scenarios reveal a long-term transition toward electrified DH, where heat pumps and
thermal storage become central to system optimization. Thermal energy storage contributes significantly to peak
shaving and load shifting, enabling the system to respond to hourly electricity price variations. Consumer-side
resources, including rooftop PV, batteries, and dual-input smart appliances, further enhance local flexibility by
increasing self-consumption and shifting energy carriers between electricity and DH. The findings confirm that
combining multiple cross-vector flexibility measures can reduce operating costs, enhance renewable integration,
and strengthen local energy resilience, supporting a transition toward climate-neutral district energy systems.
These results underscore the importance of price-responsive operation and coordinated deployment of flexibility
technologies in local-scale decarbonisation pathways.
Keywords: Cross-vector flexibility, district heating, power-to-heat, TIMES model, energy system optimization,
renewable integration, local energy systems; decarbonization.
МОДЕЛЮВАННЯ ВАРТІСНОЇ ОПТИМІЗАЦІЇ ЛОКАЛЬНОГО РІВНЯ МІЖСИСТЕМНОЇ ГНУЧКОСТІ У
ПОЄДНАНИХ МЕРЕЖАХ ЦЕНТРАЛІЗОВАНОГО ТЕПЛОПОСТАЧАННЯ ТА ЕЛЕКТРОПОСТАЧАННЯ:
КЕЙС СТАДІ МІСТА КУНГСБАКА, ШВЕЦІЯ
Отримано 27 лист. 2025 р.; рекомендовано до публікації 09 груд. 2025 р.
Доступно онлайн 31 груд. 2025 р.
Лисенко О.¹, Мата Е.², Собха П.³
Автор для кореспонденції: Лисенко Ольга,
e-mail: e-mail: olga.lysenko@ivl.se
Анотація. У цьому дослідженні представлено аналіз ва-
ртісної оптимізації на локальному рівні щодо міжсисте-
мної гнучкості між системами централізованого тепло-
постачання (ЦТ) та електропостачання в муніципалі-
теті Кунгсбака (Швеція). За допомогою моделі TIMES оці-
нено техніко-економічну ефективність інтегрованих рі-
шень гнучкості, включно з електричними котлами, теп-
ловими насосами, даховими фотоелектричними системами, акумуляторами електроенергії, сонячними
тепловими колекторами та «розумними» побутовими приладами, в умовах різних сценаріїв цін на елек-
1 PhD
https://orcid.org/0000-0001-7085-7796
2 PhD
https://orcid.org/0000-0002-0735-3744
2 Energy system modeler
https://orcid.org/0000-0003-1022-6014
1, 3 IVL Svenska Miljöinstitutet AB, Stockholm,
Sweden
2 IVL Svenska Miljöinstitutet AB, Gothenburg,
Sweden
1 PhD
https://orcid.org/0000-0001-7085-7796
2 PhD
https://orcid.org/0000-0002-0735-3744
2 Фахівець з моделювання енергетичних систем
https://orcid.org/0000-0003-1022-6014
1,3 IVL Шведський інститут досліджень довкілля,
Стокгольм, Швеція
2 IVL Шведський інститут досліджень довкілля,
Гетеборг, Швеція
9
Відновлювана енергетика. № 4/2025 | Комплексні проблеми енергетичних систем на основі НВДЕ
троенергію та біомасу. Результати показують, що хоча базові та впроваджені конфігурації залиша-
ються переважно зосередженими на виробництві енергії на біомасі, залучення технологій опалення на
основі електроенергії, зокрема електричного котла забезпечує економічно ефективну та низьковугле-
цеву роботу системи в періоди високих цін на біомасу. Аналіз також виявляє помітні відмінності в експлу-
атаційних витратах системи в різних сценаріях, причому істотне довгострокове зниження витрат спо-
стерігається лише в тих випадках, коли гнучкість інвестицій дозволяє ширше застосовувати варіанти
перетворення електроенергії на тепло. Сценарії демонструють довгостроковий перехід до електрифі-
кованої системи ЦТ, де теплові насоси та теплові акумулятори стають ключовими елементами опти-
мізації. Зберігання теплової енергії значно сприяє згладжуванню пікових навантажень і перерозподілу на-
вантажень, дозволяючи системі реагувати на погодинні коливання цін на електроенергію. Ресурси на
стороні споживачів, включно з даховими фотоелектричними системами, акумуляторами та смартпри-
ладами з подвійним входом, ще більше підвищують місцеву гнучкість за рахунок збільшення власного спо-
живання та перерозподілу енергоносіїв між електроенергією та централізованим теплопостачанням.
Отримані результати підтверджують, що поєднання декількох заходів міжсистемної гнучкості може
знизити експлуатаційні витрати, підвищити рівень інтеграції відновлюваних джерел енергії та зміц-
нити локальну енергетичну стійкість, підтримуючи перехід до кліматично нейтральних систем центра-
лізованого теплопостачання. Ці результати підкреслюють важливість оперативного реагування на ці-
нові зміни та скоординованого впровадження гнучких технологій у процесах декарбонізації на місцевому
рівні.
Ключові слова: міжсистемна гнучкість; централізоване теплопостачання; power-to-heat; модель TIMES;
оптимізація енергосистем; інтеграція відновлюваних джерел; локальні енергосистеми; декарбонізація.
Introduction. In Sweden, Distribution System Operators
(DSOs) play a crucial role in managing local electricity distri-
bution networks. They manage the medium and low-volt-
age distribution networks that deliver electricity from the
transmission network to end-users and are responsible for
ensuring a reliable supply of electricity, maintaining and de-
veloping the grid infrastructure, and integrating renewable
energy sources. The regulation also encourages DSOs to
adopt innovative solutions and technologies that enhance
grid performance and flexibility.
The cooperation between DSOs and consumers is essential
to keep the supply and demand balanced and enhance grid
flexibility. Consumers may contribute through demand re-
sponse (DR) programs (by reducing electricity use during
peak periods and/or shifting high-load appliances to off-
peak hours, reducing the strain on the distribution network
and the risk of outages or the need for costly short-term
purchases [1]; through energy efficiency measures (which
decrease overall electricity demand and limit the need for
infrastructure upgrades and maintenance costs); through
distributed renewable generation and energy storage (with
smart meters and real-time data exchange enabling DSOs
to operate local grids more effectively); or through partici-
pation in energy communities (with collectively manage-
ment of energy production and consumption). Together,
these mechanisms consider flexibility across the broader
energy system rather than focusing solely on electricity
supply and demand.
At the local and urban scales, technologies like electric boil-
ers, heat pumps, and thermal storage integrated with roof-
top PV and battery systems provide enhanced operational
efficiency, better renewable resource usage, and reduced
carbon emissions [2], [3]. Heating systems, in particular, of-
fer multiple flexibility services to the electric power system
by either consuming excess electricity, e.g., during the ti-
mes of high renewable generation and consequently low
electricity prices, or by generating electricity (together with
heat – cogeneration plants) during the periods of low re-
newable output and high electricity prices. Both individual
technologies (e.g., electric boilers or heat pumps installed
in individual buildings) and centralized heating technolo-
gies as district heating (DH) have significant potentials of
providing such flexibility services and therefore contribute
to higher shares of renewable energy sources (RES) in the
energy system [4], [5], [6].
DH systems flexibility services are commonly categorised
into flexibility of heat sources, heat generation units, net-
works and thermal energy storage (TES), markets, and con-
sumers [7] [8] [9]. Flexibility at the heat-source level arises
from the ability to utilise a range of fuels and renewable
sources, such as biomass, waste heat, solar thermal energy,
or electricity-driven heating. Generation units—including
boilers, combined heat and power (CHP) plants, heat
pumps, and electric boilers—provide thermal or electrical
flexibility depending on their configuration. DH networks
and TES enable peak shaving and temporal shifting of heat
production, while buildings contribute through thermal in-
ertia, smart thermostats, decentralised production, and
controllable appliances [10]. Together, these mechanisms
illustrate the inherent potential of DH systems to interact
dynamically with electricity grids and support increased in-
tegration of renewable energy.
A substantial body of research has advanced the under-
standing of energy system flexibility, providing valuable in-
sights into long-term transitions and multi-sector interac-
tions. Studies at regional or national scales capture broad
trends but necessarily simplify the unique behaviours and
constraints that characterize local energy systems, such as
spatially diverse demand patterns, infrastructure limita-
tions, and the role of local actors, thereby underrepresent-
ing the complexity inherent in local-scale energy transitions
[11]. Progress has been make in modelling individual
10
Відновлювана енергетика. № 4/2025 | Комплексні проблеми енергетичних систем на основі НВДЕ
flexibility technologies, like batteries, PV systems, or heat
pumps, while integrated cost-optimisation frameworks
that combine multiple flexibility options remain scarce [11],
[12]. Modelling of dual-input smart appliances capable of
using either electricity or DH hot water, and hybrid system
models that combine electric boilers, heat pumps, and
thermal storage have shown cost effectiveness, yet remain
rare in mainstream frameworks [13], [14]. A few studies
have assessed how fluctuations in electricity or biomass
pricing influence investment decisions and operational
strategies for flexibility resources in local systems [15]. No-
tably, sector-coupled models are highly sensitive to price
changes, as shown by sensitivity analyses of production
mixes [16], [17] and stochastic operational planning ap-
proaches [18]. Furthermore, power-to-heat configurations
with thermal storage emerge as risk-mitigating solutions
under price volatility [15], [19]. In parallel, several articles
[20], [21] emphasize the critical need for aligning models
with real-world demonstration data to validate assump-
tions and ensure practical relevance for operational and de-
cision-making support. Yet there remains a need for local-
scale, integrated modelling approaches that combine mul-
tiple flexibility measures, account for price volatility, and
are validated against empirical implementation data.
Objective of the work. This study aims to investigate the po-
tential of cross-vector flexibility between DH and electricity
systems at the local level, with explicit assessment of how
different flexibility technologies interact under varying en-
ergy price conditions and with a focus on how combined flex-
ibility measures can enhance renewable energy utilisation,
reduce operational costs, and improve system resilience un-
der varying energy price conditions. Using the case of Kungs-
backa, Sweden, the study seeks to generate evidence on the
techno-economic value of integrating multiple flexibility op-
tions, and provide insights into their optimal deployment
strategies by comparing currently implemented solutions
with future cost-optimal configurations.
Materials and methods.
Energy flexibility. We consider five categories of flexibility
sources [7], [8], [9]:
Heat sources: DH systems can utilize a range of fuels and
renewable heat sources, such as biomass, waste heat, solar
thermal, and electricity-driven heating. Systems with mul-
tiple or switchable energy inputs are inherently flexible, as
operators can adjust production based on fuel availability
and market conditions. DH systems using electricity-based
heat technologies (e.g., heat pumps or electric boilers) also
provide direct power flexibility by shifting or modulating
their electricity consumption.
Heat generation units: The flexibility of DH systems largely de-
pends on their generation technologies. Combined heat and
power (CHP) plants can produce both heat and electricity, of-
fering direct power flexibility and supporting grid balancing.
Heat-only units, such as boilers, offer thermal flexibility within
the DH system, while power-to-heat (P2H) technologies like
heat pumps and electric boilers directly connect the DH and
electricity sectors, enabling cross-vector flexibility.
DH network and thermal energy storage (TES): TES systems
balance supply and demand over time, allowing peak
shaving and load shifting. They can be centralized or distrib-
uted and vary from simple hot-water tanks to large under-
ground storages. The DH network itself also provides short-
term storage capacity by modulating supply temperatures.
While TES does not directly interact with the electricity grid,
it enhances the operational flexibility of CHP and P2H units.
Heat consumers: Buildings connected to DH networks can
contribute to flexibility through demand response, thermal
storage, and decentralized generation. Smart thermostats,
control systems, and insulated water tanks allow heat de-
mand shifting in response to price or grid signals. Buildings
with on-site renewables or cogeneration units can also ex-
port surplus energy, further supporting system balancing.
Description of the Kungsbacka demo site. Demo case is lo-
cated in Kungsbacka Municipality (Halland County, Swe-
den) in the parish of Fjärås. Fjärås has a population of ap-
proximately 2700 and area of 269 hectares. It has a local DH
network, which comprises:
• Two biomass-fired (pellets) boilers of 3.5 MW and 1.5
MW of installed capacity
• Two fossil fuel oil-fired boilers of 4.0 MW and 1.5 MW
of installed capacity
• Solar collectors of 45 kW of installed capacity.
Annual heat generation in 2022 totalled 11,806 MWh from
biomass boilers, 25.5 MWh from the DH-connected solar col-
lectors, and 0.7 MWh from fossil oil. While the share of heat
generated from fossil oil in 2022 was insignificant, previous
years showed higher levels, i.e. 67 MWh in 2020 and 42
MWh in 2021. Heat generation from fossil oil increases the
production cost and conflicts with the environmental goals
of the DH company of fully eliminating fossil-based heat.
The total heat delivered to customers in 2022 was 8970
MWh, corresponding to heat losses in the DH network of
24%. The heat is distributes to substations, at a relatively
constant supply temperature of around 90 °C. In each sub-
station, the delivered heat is is transferred to the building
through heat exchangers that produce domestic hot water
and heating water for distribution to rooms and tapping
points. The connection between the substation and the
building heating system is regulated by a digital unit con-
troller (DUC) based on the outdoor temperature curve. The
indoor spaces are heated via radiators or underfloor heat-
ing. In the passive buildings which constitute the demo-site
(residential apartments, a pre-school and a group-home),
space heating follows a slightly different configuration: the
pre-school relies largely on a heat exchanger installed in the
ventilation system, while the group-home uses both the
heat exchanger, and radiators and/or floor heating. Each
indoor space, for example, an apartment, includes a sensor
that fine-tunes room temperature to approximately 21 °C;
temperatures can be lowered but not raised. Domestic hot
water is also controlled via a DUC to maintain a minimum
temperature of 55 °C. Overall heat control is managed
through indoor temperature and humidity sensors, sup-
ported by a Building Energy Management System (BEMS)
developed for the site.
In the substation at the demo site, there are individual heat
and electricity generation and storage units. 605 kW of solar
collectors are installed on buildings roofs of the demo-site
11
Відновлювана енергетика. № 4/2025 | Комплексні проблеми енергетичних систем на основі НВДЕ
area, providing 473 MWh of heat in 2022. These collectors
are not coupled to the DH system; instead, generated heat is
stored in on-site thermal storages (combined volume: 134
thousand litres, ~7.8 MWh heat capacity) and used to cover
the heating and hot water demand of the buildings on which
the collectors are installed. Buildings with these solar collec-
tors and storage units are also DH-connected, enabling flexi-
ble switching between local solar heat or DH based on the
cost of heat generation in the DH system, solar irradiation,
and the energy content of the storage units.
The demo-site area also contains distributed electricity re-
sources relevant for flexibility. Rooftop solar PV installa-
tions total 86 kW and there is an electric battery (17.4 kWh
of electric capacity). These assets allow buildings to choose
between the self-generated PV-electricity and electricity
purchased from the grid, providing a degree of power flex-
ibility that can support the local DSO.
Together, the existing DH infrastructure, decentralised so-
lar thermal systems, rooftop PV, and battery storage define
the technological baseline of the Kungsbacka demo site.
We incorporate these components into the TIMES model to
evaluate their current performance and potential contribu-
tions to cross-vector flexibility.
Model Description. The effects of activated flexibility
measures on the heat and electricity supply at the district
level, and consequently on the local electricity grid, are an-
alysed using TIMES (The Integrated MARKAL-EFOM System)
model generator [22], [23].
TIMES is a technology-rich, bottom-up model generator,
which uses linear programming to produce a least-cost en-
ergy system design, optimized according to several user
constraints over a predefined time horizon, usually a few
decades. The studied energy system is represented by dif-
ferent processes that are connected by flows of commodi-
ties. Each process (such as, e.g., an energy conversion tech-
nology – heat pump) is described by its input and output
commodities, efficiency, availability, lifetime, costs, and
possibly other parameters. Each commodity (such as e.g., a
fuel – biomass) is described by its availability, extraction or
import cost and environmental impacts. The model mini-
mizes the total system cost, i.e., the sum of the running and
investment costs, of the modelled energy system while as-
suring that the energy system meets the energy service de-
mands over a time horizon. The model assumes perfect
foresight in that all investment decisions are made in each
investment period with full knowledge of future events.
In this study we developed a TIMES model of the heating
and electricity system of the demo-site area (only buildings
connected to the DH system). The model minimizes the
cost of meeting the total heating and electricity demand of
the demo-site area (Fig. 1). The heating and electricity de-
mand of the demo-site area is given to the model exoge-
nously and can have different disaggregation levels (levels
of detail), which depend on the available statistical data.
The heat supply side includes both centralized (DH) and de-
centralized (individual) heating technologies. The electric-
ity supply side includes the possibility of importing (using)
electricity from the electric grid as well as generating elec-
tricity locally by distributed electricity generation technol-
ogies, such as solar panels and small-scale wind turbines
(technologies that do not need to sell the generated elec-
tricity to the wholesale market, as the national regulation
demands). The DH and local electrical grids are represented
in the model in a simplified way, in terms of energy flows
and losses. Key heating technologies are:
• Heat pumps (ambient and waste heat)
• Electric boiler
• Energy storage in buildings
• Grid-connected DH storage
• Solar thermal
Fig. 1. Schematics of the model
12
Відновлювана енергетика. № 4/2025 | Комплексні проблеми енергетичних систем на основі НВДЕ
Scenarios of cross-vectorial flexibility. Scenario analysis is
performed using the developed energy system model to
study the effects of flexibility options on the development
and operation of the district energy system of the Kungs-
backa demo-site. Three scenarios were defined to repre-
sent the three groups of flexibility options considered in
this project (Table).
A business as usual (BAU) scenario is designed to represent a
future up until 2050, in which the studied district energy sys-
tem is operated in a way closest to the operation as of 2022.
The heat and electricity will be supplied to the demo-site
area by the same technologies as of 2022, i.e., the DH system
will remain the main heat supplier and rely on the biomass-
fired boilers supported by fossil oil-fired units for peak gen-
eration. Electricity will be supplied form the local grid or by
the existing solar panels. The heat and electricity demand
and their respective profiles will also be unchanged. Invest-
ments in new heat and electricity generation and storage
technologies will not be allowed in the model with one ex-
ception - the existing boilers will be replaced in the future by
new ones of the same type and capacity (biomass boilers
phased out by 2040 and oil boilers – by 2030).
Table. Defined scenarios and cases modelled
Sce-
nario
name
Case
No.
Electricity price Biomass price
Refer-
ence
High Refer-
ence
High
BAU
sce-
nario
BAU YES NO YES NO
Imple-
mented
sce-
nario
I1 YES NO YES NO
I2 NO YES YES NO
I3 YES NO NO YES
I4 NO YES NO YES
Planned
sce-
nario
P1 YES NO YES NO
P2 NO YES YES NO
P3 YES NO NO YES
P4 NO YES NO YES
An implemented scenario is designed to study the effects of
the flexibility options implemented during the project’s
timeline on the district energy system of the Kungsbacka
demo-site area. It includes the same input data assump-
tions and identical constraints as in the reference scenario.
It also includes, i) electric boiler at the demo-site buildings,
connected to the thermal storage, ii) smart washing ma-
chines and dish washers, and iii) “passive customers” utili-
zation of the thermal inertia of buildings as thermal energy
storage. Other investments (except for reinvestments in
heat generation boilers) are still forbidden in the model.
A potential scenario is defined to explore other, cost-attrac-
tive flexibility options to be added (invested in) in the
Kungsbacka demo-site area. Here, all the input assump-
tions are the same as in the previous two scenarios, but
with one difference – the model is not forced to reinvest in
the same biomass- and oil-fired capacities in the future but
is allowed to choose freely the cost-optimal type and size
of new heat and electricity generation/storage technolo-
gies out of the possible investment options. This scenario’s
objective is to show the unconstrained (in terms of invest-
ments) energy system development pathway. A narrower
focus is on understanding if a stronger link (additional in-
vestments) between the heating and electricity systems
could be found by the model in addition to the solutions
implemented during the project’s lifetime.
The proposed scenarios represent alternative pathways for
the development of the local energy system, all evaluated
under the same set of input parameters, such as fuel prices,
fuel availability, and electricity prices. To explore a wider
range of possible system evolutions, these scenarios were
further tested by varying selected parameters in the model.
Results and discussion.
Running costs. Fig. 2 illustrates the annual running costs of
the energy system under different scenarios (BAU, Imple-
mented I1–I4, and Potential P1–P4) for the years 2023,
2030, 2040, and 2050. The BAU scenario maintains stable
but relatively high running costs, driven mainly by fuel ex-
penditures and taxes.
Fig. 2. Comparison of annual system running costs across scenarios (BAU, Business as usual; I, Implemented flexibility; P,
Potential flexibility), EURO
The implemented flexibility scenarios (I1–I4) introduce
modest cost variation. Although the cost reductions are
limited, these scenarios demonstrate how already-imple-
mented measures alter operational behaviour, especially
under high or volatile energy prices. Notably, I4, reflecting
high biomass and electricity prices, results in the highest
running costs, particularly in 2040 and 2050. In contrast,
the Potential scenarios (P1–P4) show a clear downward
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shift in total running costs, especially under P3 and P2,
driven by accelerated fuel switching, increased use of elec-
tricity-based technologies, and more efficient utilisation of
renewable and ambient heat. Fuel-related costs (blue and
orange) shrink significantly in the potential scenarios, due
to the adoption of low-cost and low-emission technologies
(e.g., solar, heat pumps), illustrating the system-wide eco-
nomic benefits of deeper cross-vector integration. Opera-
tion and maintenance (O&M) costs grow more prominently
in optimized systems but remain offset by lower fuel and
emissions costs, resulting in an overall cost reduction.
Together, these results show that cost savings depend
strongly on the availability of investment flexibility and the
ability of the system to respond to price signals through
power-to-heat technologies and thermal storage. While
BAU maintains a stable but costly path, more dynamic and
diversified system configurations (as seen in Potential sce-
narios) offer economic advantages and resilience against
future price volatility.
Fuel use in the DH system. Fig. 3 presents the annual fuel
consumption of the DH system across all modelled scenar-
ios—BAU, Implemented (I1), and Potential (P1) for the years
2023, 2030, 2040, and 2050. Wood pellets remain the domi-
nant fuel in BAU and implemented scenarios across all years,
reflecting the continuity of biomass-based generation.
In Potential scenarios (P1), the system transitions toward
diverse low-carbon energy sources, particularly:
• Electricity from the grid, used in electric boilers and
heat pumps,
• Outdoor air and waste heat, representing renewable
and recovered heat for heat pump operation.
This transition accelerates after 2030, demonstrating how in-
vestment flexibility enables a structural shift from combus-
tion-based to electrified and renewable heat supply. Starting
from 2030 and expanding through 2050, these cleaner en-
ergy sources significantly displace wood pellets, reflecting a
shift to electrified DH. This shows a clear trajectory of decar-
bonization and diversification in the DH system.
The BAU and Implemented scenarios remain reliant on bi-
omass and fossil-based fuels, with limited use of electrifica-
tion or renewable heat sources. This indicates that opera-
tional flexibility alone is insufficient to drive deep
decarbonisation without accompanying investment flexibil-
ity. In contrast, Potential scenarios unlock substantial fuel
switching, reducing dependency on combustion fuels and
integrating clean electricity and ambient heat. The less
prominent but relevant contributors, such as solar thermal
and bio-oils, enhance flexibility and support system resili-
ence.
These results confirm that system-wide transformation is
contingent on investment flexibility, enabling the DH sector
to adopt a broader mix of sustainable fuels. In both winter
and summer BAU scenarios, the DH system remains pre-
dominantly biomass-based, with minimal solar or storage
contributions. Solar thermal output is visible only during
daytime in summer, offsetting a small share of boiler oper-
ation but not significantly altering system behaviour. The
limited power-to-heat technologies or active storage imply
the limited influence of electricity price variations on DH
operations. Although their absolute contribution is modest,
they complement heat pumps and storage, strengthening
the system’s ability to respond to price and demand varia-
tions.
Fig. 3. Fuel use by DH facility by scenario and year, GJ
Implemented scenarios: electric boiler operation. The con-
tribution from the electric boiler remains relatively low, with
the wood pellet boiler continuing to dominate the heat sup-
ply to the DH network. However, the highest levels of heat
generation from the electric boiler are observed in the Im-
plemented Scenarios (I3 and I4) (Fig. 4). The cross-vectorial
approach, which facilitates the installation and integration of
the electric boiler, clearly demonstrates the technical and
operational potential of this technology within the district
heating system. This result highlights the electric boiler’s
ability to respond to electricity and biomass price signals and
confirms its role as a strategic complement to biomass-fired
boilers. This impact becomes particularly evident under con-
ditions of high biomass prices, where the electric boiler,
serves as a flexible and low-emission alternative to tradi-
tional biomass-based systems.
Although the current share remains limited, the results un-
derscore the value of cross-vectorial flexibility in enabling
future expansion, optimization, and decarbonization of
heat supply within the DH network. In particular, they show
how the introduction of even a single power-to-heat tech-
nology can support sector coupling and reduce system ex-
posure to fuel price volatility.
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Fig. 4. Electric boiler heat supply (left axis, GJ) and electricity price (right axis, €/MWh) comparison for Implemented Sce-
narios (I3 and I4), year 2050
Potential scenarios: heat pumps and thermal storage.
Fig. 5 shows the daily heat supply to the DH network and
the corresponding electricity market price during selected
weeks in 2050, under Scenario P1 (with full investment
freedom and low electricity price). They illustrate how var-
ious flexibility technologies interact with market conditions
to optimize system operation.
For example, in week 9, 2050 (Winter Week), the DH sys-
tem is predominantly supplied by heat pumps, which oper-
ate steadily across the week. A notable discharge event is
observed during day D2, when energy storage in buildings
is released, covering a significant portion of peak demand.
The electric boiler is not used this week, indicating that low
electricity prices are still not more favourable than heat
pump operation at that time. The integration of building-
level storage helps shave peak demand and reduce load on
the heat pumps during brief high-demand periods.
In week 44, 2050 (Autumn Week), during a lower-demand
period, heat pumps still operate as the main source, though
with more variability. As electricity prices drop, the system
takes advantage of grid- and building-level storage (days 2
and 3), filling and discharging based on cost optimization.
The bars show coordinated use of DH grid storage and
building thermal storage, with discharges aligned to peri-
ods of high electricity price, reflecting intelligent dispatch
and load shifting. Waste heat and solar thermal contribute
marginally during daytime hours.
As shown under Scenario P1, where full flexibility and in-
vestment freedom are permitted, heat pumps become the
dominant technology, thermal energy storage (both in
buildings and the DH grid) plays a crucial role in flattening
peaks, shifting load, and reducing costs. The system re-
sponds smoothly to electricity price signals, demonstrating
that cross-sectorial flexibility enhances economic and oper-
ational efficiency in a low-carbon DH system.
Fig. 5. Heat supply to DH (left axis, GJ) and electricity price (right axis, Euro/MW) for scenario P1, year 2050
Local electricity management. Fig. 6 illustrates the operation
of the different local electricity systems across the scenarios
I1 (2050), and P1 (2050), based on hourly data from Week 9.
BAU scenario results are very similar to those of Scenario I1.
They show how electricity supply composition, rooftop PV
generation, and battery storage operation respond to elec-
tricity price signals and available flexibility options.
In the I1 scenario, grid electricity remains dominant, but bat-
tery use becomes more active and targeted. This indicates
that implemented flexibility measures already enhance local
responsiveness to price variations, even without new invest-
ments. Discharge is visible in mid-morning (W09D7H10).
However, there is still no discharge during the highest price
periods, suggesting improved but still limited flexibility.
Solar PV input is also minimal in I1, as no new rooftop PV is
added. P1 represents a fully optimized system with ex-
panded rooftop PV (pink) and coordinated storage integra-
tion. Under these conditions, the demo-case exhibits clear
self-consumption optimisation and reduced dependence
on the grid.
During midday hours (W09D7H12–H16), rooftop PV
meets most of the demand. In the evening, grid storage
(orange) supply electricity during price peaks, effectively
reducing reliance on the grid when prices are highest. This
behaviour demonstrates a high degree of price-respon-
sive flexibility and highlights how investments in distrib-
uted PV and storage enhance resilience and reduce oper-
ational costs.
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Відновлювана енергетика. № 4/2025 | Комплексні проблеми енергетичних систем на основі НВДЕ
Fig. 6. Operation of local electricity systems: PV, battery storage, and grid interaction (left axis, GJ) and electricity price
(right axis, Euro/MW) for winter week, 2050, scenarios I1 and P1
Smart appliance flexibility. Fig. 7 compares hourly smart ap-
pliance energy use across four scenarios: BAU, Implemented
I1, Implemented I4, and Potential P1, for a representative
weekday in 2050. These appliances, such as dishwashers and
washing machines, are capable of using either electricity or
DH hot water. The total appliance load is shown in gigajoules
(GJ), with energy carrier breakdown: electricity and DH.
In the BAU scenario, all appliance energy demand is met
with electricity, following a conventional use pattern that
gradually increases during the day and peaks in the even-
ing. In Scenario I1, most appliance operation shifts to DH
hot water, reducing electricity demand.
A small share of electricity use remains visible during mid-
day hours. Scenario I4, which assumes higher energy prices,
reflects further substitution of electricity with DH energy.
Nearly all appliance demand is met with DH, with only a
minimal share of electricity used. The daily use pattern re-
mains similar, indicating that while energy carrier choice
has shifted, the appliance schedule has not.
In the P1 scenario, a more balanced and dynamic configu-
ration appears. Both DH and electricity are used across the
day, with electricity dominating from late morning through
early evening. This reflects a system where smart appli-
ances are configured to select between DH and electricity
based on predefined rules or cost-effectiveness, though
again without altering the time of use.
These results show that smart appliances primarily contrib-
ute through energy-carrier switching rather than temporal
load shifting. Even so, this switching provides meaningful
demand-side flexibility, helping to reduce peak electricity
use and supporting integration of low-cost or renewable
heat sources when available.
Fig. 7. Smart appliances operation 2040 (winter day), GJ
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Conclusion. This study investigated the techno-economic
potential of cross-vector flexibility in a local energy system
coupling district heating (DH) and electricity networks, us-
ing the Kungsbacka demo site in Sweden as a case study.
The TIMES cost-optimisation model enabled an integrated
assessment of electricity, heating, and storage technologies
under varying energy price scenarios. The results show that
while biomass remains the dominant heat source in the ref-
erence (BAU) and implemented scenarios, introducing flex-
ibility technologies such as electric boilers, heat pumps, and
smart appliances leads to gradual diversification of the en-
ergy mix and improved operational adaptability. In particu-
lar, the installation of the electric boiler further enables
sector coupling. Although its contribution to total heat sup-
ply remains moderate, the technology shows clear opera-
tional and environmental advantages under high biomass
price conditions, offering a low-emission and cost-compet-
itive complement to traditional bio-based generation.
A key finding is the substantial reduction in long-term op-
erating costs observed in the potential scenarios, driven by
fuel switching away from biomass, increased use of elec-
tricity-based heating, and reduced emissions-related costs.
The scenarios also reveal a pronounced transition from bi-
omass to electrified heat supply, with heat pumps becom-
ing the dominant technology by 2050 and ambient and
waste heat replacing combustion fuels. Thermal energy
storage—both at the building level and within the DH net-
work—plays a central role in enabling load shifting, peak
shaving, and cost-efficient utilisation of low-price electric-
ity, illustrating its importance in fully flexible systems.
The analysis further highlights the strong price-responsive-
ness of flexible technologies: electric boilers operate pri-
marily under high biomass prices, heat pumps adapt to
electricity price variations, and storage systems charge and
discharge according to hourly market conditions. Con-
sumer-side resources also contribute meaningfully to sys-
tem performance; rooftop PV and batteries increase local
self-consumption and reduce grid imports during high-price
hours, while smart appliances provide additional sector-
coupling flexibility by shifting energy carriers between elec-
tricity and DH. Notably, smart appliances shift the energy
carrier rather than the timing of energy use, indicating that
their flexibility is primarily related to fuel choice rather than
load shifting. Overall, the findings confirm that local-scale
cross-vector flexibility—particularly by combining power-
to-heat technologies with intelligent control—can signifi-
cantly lower operational costs, improve renewable integra-
tion, and contribute to deep decarbonisation of urban
heating systems. We see a high potential for future work to
focus on the dynamic operation of such hybrid systems,
real-time market participation strategies, and the replica-
tion of the approach demonstrated here in other munici-
palities to accelerate the transition toward climate-neutral
and resilient local energy systems. Further refinement of
consumer-side flexibility modelling and continued valida-
tion against demonstration data would strengthen the ap-
plicability of these results.
Funding
All authors are funded by the ENFLATE project, with sup-
port from the EU Horizon program, grant agreement
101075783.
Availability of data and materials
Data available on request from the authors.
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| id | veorgua-article-571 |
| institution | Vidnovluvana energetika |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2026-07-19T01:17:19Z |
| publishDate | 2025 |
| publisher | Institute of Renewable Energy National Academy of Sciences of Ukraine |
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| resource_txt_mv | veorgua/e1/fd64d2f5d20502715bd3d15061d306e1.pdf |
| spelling | veorgua-article-5712026-07-18T06:32:23Z COST-OPTIMISATION MODELLING OF LOCAL LEVEL CROSS-VECTOR FLEXIBILITY IN COUPLED DISTRICT HEATING AND ELECTRICITY NETWORKS: A CASE STUDY OF KUNGSBACKA, SWEDEN МОДЕЛЮВАННЯ ВАРТІСНОЇ ОПТИМІЗАЦІЇ ЛОКАЛЬНОГО РІВНЯ МІЖСИСТЕМНОЇ ГНУЧКОСТІ У ПОЄДНАНИХ МЕРЕЖАХ ЦЕНТРАЛІЗОВАНОГО ТЕПЛОПОСТАЧАННЯ ТА ЕЛЕКТРОПОСТАЧАННЯ: КЕЙС СТАДІ МІСТА КУНГСБАКА, ШВЕЦІЯ Lysenko, O. Mata , É. Sobha , P. Cross-vector flexibility, district heating, power-to-heat, TIMES model, energy system optimization, renewable integration, local energy systems; decarbonization. міжсистемна гнучкість; централізоване теплопостачання; power-to-heat; модель TIMES; оптимізація енергосистем; інтеграція відновлюваних джерел; локальні енергосистеми; декарбонізація. This study presents a local-level cost-optimization analysis of cross-vector flexibility between district heating (DH) and electricity systems in the municipality of Kungsbacka, Sweden. Using a TIMES model, we assess the techno-economic performance of integrated flexibility solutions, including electric boilers, heat pumps, rooftop photovoltaic (PV) panels, storage in electric battery and thermal storage, solar thermal collectors, and smart appliances, under varying electricity and biomass price scenarios. Results show that while the base and implemented configurations remain dominated by biomass-fired generation, the inclusion of electricity-based heating technologies, particularly the electric boiler, enables cost-efficient and low-emission operation during periods of high biomass prices. The analysis also reveals notable differences in system running costs across scenarios, with substantial long-term cost reductions observed only when investment flexibility allows broader adoption of power-to-heat options. The scenarios reveal a long-term transition toward electrified DH, where heat pumps and thermal storage become central to system optimization. Thermal energy storage contributes significantly to peak shaving and load shifting, enabling the system to respond to hourly electricity price variations. Consumer-side resources, including rooftop PV, batteries, and dual-input smart appliances, further enhance local flexibility by increasing self-consumption and shifting energy carriers between electricity and DH. The findings confirm that combining multiple cross-vector flexibility measures can reduce operating costs, enhance renewable integration, and strengthen local energy resilience, supporting a transition toward climate-neutral district energy systems. These results underscore the importance of price-responsive operation and coordinated deployment of flexibility technologies in local-scale decarbonisation pathways. У цьому дослідженні представлено аналіз вартісної оптимізації на локальному рівні щодо міжсистемної гнучкості між системами централізованого теплопостачання (ЦТ) та електропостачання в муніципалітеті Кунгсбака (Швеція). За допомогою моделі TIMES оцінено техніко-економічну ефективність інтегрованих рішень гнучкості, включно з електричними котлами, тепловими насосами, даховими фотоелектричними системами, акумуляторами електроенергії, сонячними тепловими колекторами та «розумними» побутовими приладами, в умовах різних сценаріїв цін на електроенергію та біомасу. Результати показують, що хоча базові та впроваджені конфігурації залишаються переважно зосередженими на виробництві енергії на біомасі, залучення технологій опалення на основі електроенергії, зокрема електричного котла забезпечує економічно ефективну та низьковуглецеву роботу системи в періоди високих цін на біомасу. Аналіз також виявляє помітні відмінності в експлуатаційних витратах системи в різних сценаріях, причому істотне довгострокове зниження витрат спостерігається лише в тих випадках, коли гнучкість інвестицій дозволяє ширше застосовувати варіанти перетворення електроенергії на тепло. Сценарії демонструють довгостроковий перехід до електрифікованої системи ЦТ, де теплові насоси та теплові акумулятори стають ключовими елементами оптимізації. Зберігання теплової енергії значно сприяє згладжуванню пікових навантажень і перерозподілу навантажень, дозволяючи системі реагувати на погодинні коливання цін на електроенергію. Ресурси на стороні споживачів, включно з даховими фотоелектричними системами, акумуляторами та смартприладами з подвійним входом, ще більше підвищують місцеву гнучкість за рахунок збільшення власного споживання та перерозподілу енергоносіїв між електроенергією та централізованим теплопостачанням. Отримані результати підтверджують, що поєднання декількох заходів міжсистемної гнучкості може знизити експлуатаційні витрати, підвищити рівень інтеграції відновлюваних джерел енергії та зміцнити локальну енергетичну стійкість, підтримуючи перехід до кліматично нейтральних систем централізованого теплопостачання. Ці результати підкреслюють важливість оперативного реагування на цінові зміни та скоординованого впровадження гнучких технологій у процесах декарбонізації на місцевому рівні. Institute of Renewable Energy National Academy of Sciences of Ukraine 2025-12-27 Article Article application/pdf https://ve.org.ua/index.php/journal/article/view/571 10.36296/1819-8058.2025.4(83).8-17 Vidnovluvana energetika ; No. 4(83) (2025): Scientific and applied Journal renewable energy ; 8-17 Возобновляемая энергетика; ##issue.no## 4(83) (2025): Scientific and applied Journal renewable energy ; 8-17 Відновлювана енергетика; № 4(83) (2025): Науково-прикладний журнал Відновлювана енергетика; 8-17 2664-8172 1819-8058 10.36296/1819-8058.2025.4(83) en https://ve.org.ua/index.php/journal/article/view/571/482 Copyright (c) 2025 O. Lysenko, É. Mata , P. Sobha https://creativecommons.org/licenses/by-nc-nd/4.0 |
| spellingShingle | Cross-vector flexibility district heating power-to-heat TIMES model energy system optimization renewable integration local energy systems; decarbonization. Lysenko, O. Mata , É. Sobha , P. COST-OPTIMISATION MODELLING OF LOCAL LEVEL CROSS-VECTOR FLEXIBILITY IN COUPLED DISTRICT HEATING AND ELECTRICITY NETWORKS: A CASE STUDY OF KUNGSBACKA, SWEDEN |
| title | COST-OPTIMISATION MODELLING OF LOCAL LEVEL CROSS-VECTOR FLEXIBILITY IN COUPLED DISTRICT HEATING AND ELECTRICITY NETWORKS: A CASE STUDY OF KUNGSBACKA, SWEDEN |
| title_alt | МОДЕЛЮВАННЯ ВАРТІСНОЇ ОПТИМІЗАЦІЇ ЛОКАЛЬНОГО РІВНЯ МІЖСИСТЕМНОЇ ГНУЧКОСТІ У ПОЄДНАНИХ МЕРЕЖАХ ЦЕНТРАЛІЗОВАНОГО ТЕПЛОПОСТАЧАННЯ ТА ЕЛЕКТРОПОСТАЧАННЯ: КЕЙС СТАДІ МІСТА КУНГСБАКА, ШВЕЦІЯ |
| title_full | COST-OPTIMISATION MODELLING OF LOCAL LEVEL CROSS-VECTOR FLEXIBILITY IN COUPLED DISTRICT HEATING AND ELECTRICITY NETWORKS: A CASE STUDY OF KUNGSBACKA, SWEDEN |
| title_fullStr | COST-OPTIMISATION MODELLING OF LOCAL LEVEL CROSS-VECTOR FLEXIBILITY IN COUPLED DISTRICT HEATING AND ELECTRICITY NETWORKS: A CASE STUDY OF KUNGSBACKA, SWEDEN |
| title_full_unstemmed | COST-OPTIMISATION MODELLING OF LOCAL LEVEL CROSS-VECTOR FLEXIBILITY IN COUPLED DISTRICT HEATING AND ELECTRICITY NETWORKS: A CASE STUDY OF KUNGSBACKA, SWEDEN |
| title_short | COST-OPTIMISATION MODELLING OF LOCAL LEVEL CROSS-VECTOR FLEXIBILITY IN COUPLED DISTRICT HEATING AND ELECTRICITY NETWORKS: A CASE STUDY OF KUNGSBACKA, SWEDEN |
| title_sort | cost-optimisation modelling of local level cross-vector flexibility in coupled district heating and electricity networks: a case study of kungsbacka, sweden |
| topic | Cross-vector flexibility district heating power-to-heat TIMES model energy system optimization renewable integration local energy systems; decarbonization. |
| topic_facet | Cross-vector flexibility district heating power-to-heat TIMES model energy system optimization renewable integration local energy systems; decarbonization. міжсистемна гнучкість централізоване теплопостачання power-to-heat модель TIMES оптимізація енергосистем інтеграція відновлюваних джерел локальні енергосистеми декарбонізація. |
| url | https://ve.org.ua/index.php/journal/article/view/571 |
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