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The potential of power-to-heat in Swedish district heating systems
Energy xxx (2017) 1e9
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The potential of power-to-heat in Swedish district heating systems Gerald Schweiger a, c, Jonatan Rantzer a, Karin Ericsson a, *, Patrick Lauenburg b a
Environmental and Energy Systems Studies, Department of Technology and Society, Lund University, PO Box 118, SE-221 00 Lund, Sweden Department of Energy Sciences, Lund University, PO Box 118, SE-221 00 Lund, Sweden c AEE INTEC Institut für Nachhaltige Technologien, Feldgasse 19, 8200 Gleisdorf, Austria b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 31 October 2016 Received in revised form 10 February 2017 Accepted 13 February 2017 Available online xxx
The main challenge for future electricity systems is to match the available electricity from variable renewable resources with the electricity demand in place, time and quantity. One option for increasing electricity system flexibility is to integrate the electricity system with the district heating systems via the use of power-to-heat technologies such as electric boilers. The overarching objective of this paper is to increase the understanding of what role power-to-heat could have in Sweden and to contribute to the development of methods and tools that can be applied when analysing the potential of power-to-heat. For that purpose we estimate the technical potential of power-to-heat for different power scenarios and assumptions and identify key parameters which have significant impact on the potential. The calculations are based on hourly simulations of electricity production, electricity consumption and district heat load. The power-to-heat potential was estimated to 0.2e8.6 TWh, where the potentials at the higher end pertain to scenarios with high amounts of wind and solar power production (corresponding to 54e64% of electricity consumption). Access to thermal storage increases the potential of power-to-heat while the use of industrial waste heat and heat from waste incineration in the district heat load reduces the potential. © 2017 Published by Elsevier Ltd.
Keywords: Power-to-heat Flexibility measure Sweden District heating systems
1. Introduction The installation of wind power and solar power is growing rapidly in many countries around the world [14]. With the expansion of electricity production from variable energy sources such as wind and solar energy, other parts of the energy systems must become more flexible. There are a number of options for increasing electricity system flexibility, including increasing supply and demand flexibility, developing energy storage technologies and systems services and increasing the transmission capacity of the national grid and interconnections to other countries [17]. Demand flexibility may be facilitated by the integration of the electricity system with the heating and gas systems. Such integration offers an opportunity to increase the electricity consumption during hours of very high electricity production from variable electricity sources by producing heat (power-to-heat) or gas (power-to-gas). This paper focuses on power-to-heat, which refers to heat production from electricity through heat pumps or electric boilers, and the application of this technology in the district heating sector. However,
* Corresponding author. E-mail address: [email protected] (K. Ericsson).
power-to-heat could also be applied in buildings and industries with a demand for heat. Power-to-heat is attracting growing interest in Europe (see for example Refs. [5,7,18,21]), especially in Denmark (see e.g. Refs. [16,20,23] and Germany (see e.g. Refs. [4,12]. The benefit of this technology is that it may reduce the need for curtailment and the hours of very low electricity prices while saving fuel in heat production [22]. Denmark and Germany, both of which feature a high percentage of variable, renewable energy sources in their electricity supply, have seen an increasing number of power-toheat projects being realised over the past few years. These projects mainly involve investments in the district heating systems, but also some in industry [1]. In other countries with very limited use of district heating, the opportunity for power-to-heat may primarily lie in industry. A study concerning the Netherlands shows that there is a considerable potential for utilising power-to-heat in Dutch industry, but that the potential is limited in the district heating systems [13]. This paper focuses on Sweden, which similar to Denmark, has extensive district heating systems. District heat accounted for 58% of the energy supplied to buildings in Sweden in 2014 [28]. During the past few years there has been a considerable growth in wind
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power production in Sweden. In 2015 wind power accounted for 10% of the Swedish electricity production. Solar electricity production is still negligible [31], but has potential for future growth. Several factors suggest that variable electricity production will expand considerably in Sweden over the coming decades. One important factor is the recent multi-party agreement which introduced a goal of 100% renewable energy sources in electricity production by 2040 [24]. Although the agreement states that the target should not be interpreted as an end date for nuclear power, it adds to the political uncertainty concerning the future of nuclear power which accounted for about 35% of Swedish electricity production in 2014 [31]. Nuclear power is furthermore fraught with high costs. Another factor is the falling costs of solar PVs and the customers' desire to produce their own electricity [15]. Sweden has by international comparison favourable conditions for integration of variable electricity production due to a robust transmission grid and large capacity of storage hydropower [35]. However, the ongoing development that suggests very high proportions of variable electricity production in Sweden and neighbouring countries within a few decades is raising concerns over future supply quality and security. This in turn motivates the analysis and development of flexibility measures [34]. Power-to-heat has so far received little attention in a Swedish €der [35]; p. 33e38) who has esticontext. One exception is So mated the technical potential of power-to-heat (as a flexibility measure) in the district heating systems to 1.2 TWh per year. That study was based on hourly simulations, the utilisation of electric boilers and a scenario with an annual solar and wind power production of 55 TWh. The heat from waste incineration and industrial waste heat was excluded from the district heat load which was simulated based on the load curve of Stockholm. € ldberg et al. [30] who have done a case Another example is Sko study on four district heating systems for which they analysed the impact of more variable electricity prices (as a result of high penetration of variable electricity production) on the operation of different heat production units and the choice of heat sources. Their results show that despite low electricity prices, the use of heat pumps and electric boilers increases only marginally due to the current tax on electricity consumption and the availability of low-cost waste heat from waste incineration and industries in the systems studied. The overarching objective of this paper is to increase the understanding of what role power-to-heat could have in Sweden and to contribute to the development of methods and tools that can be applied when analysing the potential of power-to-heat. For this purpose this paper addresses the following questions: i) What is the technical potential of power-to-heat in the Swedish district heating systems for different electricity scenarios, conditions and restrictions? ii) Which conditions and restrictions have considerable impact on the power-to-heat potential? iii) What is the economic potential of power-to-heat in the Swedish district heating systems for different electricity scenarios? These questions are addressed by first designing three electricity scenarios and identifying conditions and restrictions to be investigated. Two of the electricity scenarios involve a considerable growth in electricity production from variable renewable energy sources, and thus imply a time perspective some decades ahead (for simplicity we refer to this time to be around 2050). We estimate the technical potential based on hourly simulations of electricity
production, electricity consumption and district heat load. This € ttger et al. [4] who have estimated the approach was inspired by Bo technical potential of power-to-heat in Germany. However, our study also involves further development of their methods in order to account for various restrictions and conditions. For example, we have developed a storage model in order to analyse the effects of access to thermal storage on the potential for power-to-heat. We estimate the economic potential based on a straightforward cost model. This paper contributes to the research field by providing the first detailed study on the power-to-heat potential in Sweden for various conditions and restrictions. Moreover, unlike previous studies, this study is based on hourly simulations of all district heating systems using a GIS (geographical information systems) approach. The resulting GIS database, as well as the models (available as a Python library),1 are publicly available for further investigation. Another novelty of this paper is that it presents the development and implementation of a storage model and storage strategy to analyse the impact of different storage sizes on the potential of power-to-heat.
2. Methodology 2.1. Technical potential - overall approach The theoretical potential of power-to-heat is the annual district heat load for all district heating systems, since all this heat in theory could be produced from electricity. The current theoretical potential of power-to-heat in the Swedish district heating systems thus amounts to about 57 TWh which was the supply of district heat in 2014 [32]. In contrast, the technical potential specifies the amount of “surplus electricity” that can be converted to district heat load. A more appropriate term for “surplus electricity” is negative residual load, which is used onwards in this paper. Negative residual load and other important concepts that are used in this paper are defined in Table 1. The technical potential only includes the negative residual load that is matched in time with the available district heat load, unless there is access to thermal storage. In order to estimate the technical potential for power-to-heat we have to make a number of simplifications and rough assumptions. Three major simplifications are that we i) disregard the potential development of other flexibility measures than power-toheat, ii) disregard the export and import of electricity, and iii) neglect bottlenecks in future power transmission and distribution networks. The last simplification implies that the geographical location of electricity production units and district heating systems is not taken into account. The power-to-heat technology is assumed to be electric boilers which are cheaper, but less efficient than heat pumps. An argument for this choice is that the unit will only be operated at times of very high electricity production from variable renewable sources. The sections below describe how we calculate the technical potential based on different electricity scenarios, conditions and restrictions. The calculations are carried out by first simulating the hourly district heat load and residual power loads for different scenarios. The district heat load and the negative power residual loads are then matched.
1 The database (https://github.com/GersHub/P2HSweden) contains information on all Swedish district heating systems (production units, storage size/capacity, share of waste heat etc); data on outdoor temperatures in each county (to calculate the hourly heat load); data on the electricity system (hourly power consumption and power production for different technologies).
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Table 1 Definitions of important concepts that are used in this paper. Concept
Definition
District heat load
District heat load in this paper is defined as the heat power that represents the district heat supply that should satisfy customers' heat demand and the distribution losses in the network. The heat load varies over time and is normally expressed as average heat power with a certain time resolution such as hours [8]; p 67). The design head load is used for estimating the demand for the installed heat supply capacity of the system. Design heat loads can be calculated using either measurements of actual heat loads or relevant capacity factors. In this paper we calculate the design heat load by an extrapolation from the line representing the heat power signature to the design outdoor temperature [8]; p 90e91 and p 105). The residual load describes what is left of the hourly power demand after subtracting the hourly electricity production from variable renewable sources and certain must-run generators [26]. Negative residual load is the load that occurs when the electricity production from variable renewable sources and certain must-run generators is higher than electricity demand. The design outdoor temperature is the lowest outdoor temperature to be considered when designing for a particular desired heat capacity for space heating power. Design outdoor temperatures are calculated using design frequencies and building time constants [8]; p 48). In this paper we use extreme outdoor temperatures based on data from Frederiksen and Werner [8]; p 100).
Design heat load
Residual load Negative residual load Design outdoor temperature
2.2. Simulation of district heat load
2.3. Scenarios for electricity production and use
Sweden has more than 400 district heating networks [32]. In order to simulate the district heat load in Sweden on an hourly basis we use data on the annual supply of district heat in different networks in 2014 [32] and on hourly outdoor temperatures at regional level [36]. In order to limit the level of detail, all district heating networks are divided into regions, corresponding to Sweden's 21 counties. This is done by using a GIS approach. For each county, the hourly district heat load is simulated assuming a piecewise linear correlation between outdoor temperatures and district heat loads. At the same time, we calculate the design heat load for each network, which sets the limit for the installation of electric boilers and thus for the power-to-heat capacity in each network. Several assumptions are necessary for estimating the linear correlation between outdoor temperatures and heat loads. Firstly, we assume that there is no need for space heating at outdoor temperatures above 15 C when all heat is used for heating tap water and heat distribution losses. This implies that the district heat load is constant for temperatures above 15 C and a linear function of outdoor temperature with a negative slope for temperatures below 15 C. Secondly, the design outdoor temperature for each region is estimated based on extreme outdoor temperatures found in Frederiksen and Werner [8]; p.100). Thirdly, we assume that 30% of the annual district heat load is distributed evenly throughout the year based on measured data for a number of district heating networks in different parts of Sweden. The value of 30% is also in line with data for the district heating network in €, which is described in Frederiksen and Werner [8]; p. 94). Malmo The mean errors for the different systems lie between 13 and 19%, c.f. Appendix.
We design three scenarios (presented in Table 2) that illustrate what Swedish electricity production may look like around 2050. The scenarios are denoted based on the level of wind power and solar power, which are considered to be the most promising options for expansion of renewable electricity production in Sweden according to existing energy scenarios for 2050 (see e.g. Refs. [11,25,27]. Our scenarios have been designed based on rough assumptions that draw on the existing scenarios and with levels of variable electricity that are at the higher end of those in the existing scenarios. The Conservative scenario assumes that there has been a moderate development of wind and solar power, and that the existing nuclear reactors have been replaced by new ones, something that enables continued high nuclear power production. In this scenario, the annual wind power and solar power amount to 30 TWh and 5 TWh, respectively. The two other scenarios assume that there has been a considerable expansion of wind and solar power while nuclear power has been phased out. The annual wind power production is assumed to amount to 70 TWh in both the High Wind scenario and the High Wind & Solar scenario. The annual solar power is assumed to amount to 5 TWh in the High Wind scenario and to 20 TWh in the High Wind & Solar scenario. Electricity production in CHP plants and gas turbines is assumed to be 15 TWh in all three scenarios, i.e. to roughly remain at the current level. The production of hydropower is also assumed to remain at average annual production level of 65 TWh. The annual electricity consumption is assumed to be 140 TWh in all three scenarios and excludes the electricity used in power-toheat. All three scenarios assume higher production than consumption and thus imply net export of electricity.
Table 2 Electricity production and consumption in 2015 [31] and in our three scenarios in TWh/yr and the proportion of electricity from variable RES in relation to electricity consumption. Electricity production and consumption
Year 2015
Scenarios Conservative
High wind
High wind & solar
Nuclear power Hydropowera Other thermal power (CHP, gas turbines) Wind power Solar power Total production Variable electricity as percent of electricity consumption (%)
54.4 74.0 13.5c 16.6 0.1 158.6 12
55 65 15 30 5 170 25
0 65 15 70 5 155 54
0 65 15 70 20 170 64
Total consumption, including lossesb
135.9
140
140
140
a b c
The annual hydropower production typically varies between 55 and 75 TWh depending on the availability of water [31]. Distribution losses amounted to 10 TWh in 2015. Almost all of the electricity is produced in CHP plants. About half of the CHP production takes place in industry and the other half in the district heating systems.
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2.4. Simulation of residual load The residual load is usually defined as what is left of the power demand after subtracting the output of variable renewable electricity sources and certain must-run generators [26]. Based on this definition we calculate the residual load by subtracting the hourly generation of wind power, solar power, a minimum level of hydropower and must-run thermal generators from the hourly demand for electricity. As must-run thermal generation, we include 50% of the electricity load from CHP plants and 100% from nuclear power plants. This rough assumption considers the difficulty of ramping up/down nuclear power plants on short notice and the relatively low flexibility of industrial CHP plants, which account for about 50% of the co-generated electricity in Sweden [31]. The minimum level of hydropower is set to 1.682 GW, which is the lowest measured mean hourly power load over the last five years (2011e2015). This level corresponds to about 10% of the installed hydropower capacity [33]. The residual load is simulated based on historical hourly data from the Swedish TSO on the electricity consumption and production of each technology [33]. The historical hourly data are linearly scaled up to fit the scenarios. An exception has been made for nuclear power production, for which the load is simply distributed evenly throughout the year. The resulting hourly residual load is shown in Fig. 1. As can be seen in this figure, the hours of negative residual load are almost zero for the current situation. The total negative residual loads were calculated to 0.13 TWh for the Conservative scenario, 2.8 TWh for the High Wind scenario and 5.0 TWh for the High Wind & Solar scenario. 2.5. Calculation of the technical potential The power-to-heat potentials are calculated by matching the negative residual loads with the district heat load on an hourly basis. These calculations are carried out for a base case and two cases in which we assume access to thermal storage and priority for waste heat, respectively. In all three cases, the potential for powerto-heat is restricted by the installed capacity of electric boilers, which in each district heating system is assumed to be equal to 30% of the design heat load. This assumption is in line with that used in € ttger et al. [4]. Bo
Fig. 1. The simulated residual power loads for the current situation (2015) and the different scenarios. The power available for power-to-heat is proportional to the area between the curve and the x-axis for hours with negative residual load.
2.5.1. Base case Fig. 2 shows the hourly district heat load plotted against the corresponding residual load for the base case and High Wind scenario. The upper (blue) triangle frames the data for hours when all of the negative residual can be used for the district heat load. The lower (red) triangle frames the data for hours when the negative residual exceeds the district heat load, which means that the potential of power-to-heat is restricted by the district heat load. 2.5.2. Access to thermal storage In the base case we assumed that the negative residual load needs to be consumed during the same hour as it is produced. However, today many district heating networks have access to thermal storage which allows for a temporary mismatch in the generation of heat and the district heat load. The thermal storage usually consists of accumulators (hot water tanks). For this case we assume that every district heating network accommodates an accumulator with the capacity to store 25% of its mean daily demand and with a transfer capacity corresponding to 2.5% of its mean daily demand. This dimensioning is based on Gadd and Werner [9]; as well as data on accumulators present today in several district heating systems [10]. In addition, we study the impact of assuming access to thermal storage corresponding to 250% of the mean daily demand. We develop a storage model where the thermal storage is limited by storage size and transfer capacity. Heat losses are neglected. The storage strategy consists of storing as much as possible of the negative residual load as heat when this load exceeds heat demand. Correspondingly, as much as possible of the district heat load should be covered by stored heat when the negative residual load is smaller than the available district heat load. Furthermore, the model prioritises to fill up the accumulators that are relatively empty in relation to their storage capacity. 2.5.3. Priority for waste heat Industrial waste heat and heat from waste incineration plants accounted for 7 and 20%, respectively, of the heat supply in the Swedish district heating systems in 2014 [32]. Both of these heat sources are normally associated with low marginal costs and low
Fig. 2. The residual load plotted against the district heat load on an hourly basis. The upper (blue) triangle frames the data for hours when all of the negative residual load can be converted to district heat load. The lower (red) triangle frames the data for hours when the negative residual exceeds the district heat load. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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flexibility. For this case we include a restriction that the district heat loads that are currently met by industrial waste heat and heat from waste incineration are not available for power-to-heat and that the waste heat serves as base load (the heat is distributed evenly over the year). 2.6. Economic potential The economic potential describes the power-to-heat potential when the installation of electric boilers has been optimised from an economic point. The installation and operation of electric boilers for power-to-heat only makes sense if it reduces the use of fuel in other production units in the district heating system and if the electricity is cheaper than the fuel. In order to calculate the economic potential we develop a cost model where the levelised investment cost and operational cost of electric boilers are weighed against the reductions in fuel cost. The optimal installation of electric boilers is found when the cost model yields the greatest profit. Biomass is the dominant energy source in the supply of district heat in Sweden and accounted for around 60% of this supply in 2014 [32]5). The economic calculations are based on the assumption that the use of 1 MW of electricity for power-to-heat saves 1 MWh of wood chips. This assumption is motivated by the fairly similar energy efficiencies for production of heat from electricity and various fuels; in both cases the energy efficiency should be at least 90%. We calculate the economic potential for two cases based on different electricity prices. The price of electricity (including taxes and charges) is assumed to be 20 V/MWh in one case and zero in the other case. The low electricity prices are motivated by the assumption that the electric boilers are only operated at times of negative residual load. For both cases we assume that the price of wood chips is 30 V/MWh. The calculations are based on the assumption that the electric boilers are dimensioned for 10 kV and 20 MW and have a lifetime of 20 years. The real interest rate is set to 6%. The costs include an investment cost of 0.06 MV/MW, a fixed operation and maintenance cost of 1100 V/MW/yr and a variable operation and maintenance cost of 0.5 V/MWh [6]. We disregard costs associated with grid connection that may arise due to the installation of electric boilers since these investments should be fairly limited assuming these boilers are co-located with existing CHP plants. 3. Results 3.1. Technical potential for the base case The technical potentials for the base case are shown in Fig. 3. The potential for power-to-heat was estimated at 0.1 TWh for the current situation, 1.9 TWh for the Conservative scenario, 3.7 TWh for the High Wind scenario and 6.2 TWh for the High Wind & Solar scenario. As expected, the potential of power-to-heat increases with the amount of variable electricity production. Furthermore, the values of the technical potentials correspond to 89% 80% and 59% of the negative residual loads (presented in Section 2.4). The lower percentage for the High Wind & Solar scenario indicates the difficulty of converting the negative residual load resulting from solar power peaks to district heat load since this load appears during summer when the demand for district heat is small. The maximum hourly capacity of power-to-heat is calculated to 7.46 GW; this limit is reached for the highest production peaks of the High Wind scenario and the High Wind & Solar scenario.
Fig. 3. The hourly power-to-heat potentials for the current situation (2015) and three future scenarios. The numbers specify the total potential for the scenarios.
3.2. Technical potential assuming access to thermal storage Access to thermal storage naturally increases the potential of power-to-heat. If assuming access to large thermal storage, the technical potentials of power-to-heat increases by 7% for the Conservative scenario, by 26% for the High Wind scenario and by 75% for the High Wind & Solar scenario in comparison to the base case (Table 3). The district heat load is a greater limiting factor in the High Wind & Solar scenario than in the other scenarios. Fig. 4 shows the hourly result for the High Wind scenario and different thermal storage capacities. 3.3. Technical potential assuming priority for waste heat The technical potentials of power-to-heat are reduced by 20e40% compared to the base case when assuming priority for heat from waste incineration and industrial waste heat in the district heat load. The technical potentials were estimated to 1.2, 2.9 and 3.8 TWh for the Conservative, High Wind and High Wind & Solar scenarios, respectively (Table 3). Fig. 5 shows the result for the High Wind scenario on an hourly basis. The High Wind & Solar scenario is most strongly affected by this restriction since most of the solar power is produced during summer when waste heat accounts for a large part of the district heat load. However, the technical potentials of power-to-heat are only somewhat smaller than those in the base case if assuming the use of waste heat in combination with thermal storage. 3.4. Summary of technical potentials The technical potentials of power-to-heat for the base case and two other cases are summarised in Table 3. 3.5. Economic potential Table 4 shows the calculated economic potentials of power to heat and the optimal installation capacity of electric boilers for the two cases and three scenarios. The economic potential is higher in the second case which is based on a lower electricity price. Fig. 6 shows the profit and various costs associated with power-to-heat as functions of installed capacity; the results concern the High Wind scenario and case 2. The investment cost is a linear function
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G. Schweiger et al. / Energy xxx (2017) 1e9 Table 3 The technical potentials of power-to-heat (TWh) for three cases and different scenarios. The numbers in parenthesis show the increase/decrease of the potential compared to the base case. Scenarios
Base case
Priority for waste heat
Access to thermal storage
Conservative High wind High wind & solar
1.9 3.7 6.2
1.2 (37%) 2.9 (20%) 3.8 (39%)
2.0 (þ5%) - 2.1 (þ7%) 4.0 (þ9%) - 4.6 (þ26%) 8.5 (þ37%) - 10.8 (þ75%)
potential of power-to-heat is influenced by two parameters: the electricity consumption and the wind power production pattern. 4.1. Electricity consumption
Fig. 4. The hourly power-to-heat potentials for the High Wind Scenario when assuming access to thermal storage of different sizes. The numbers specify the total potential for the scenarios.
There is great uncertainty as to how Swedish electricity consumption will develop over the next decades. Continuous improvement in energy efficiency is a driver for reduced electricity consumption while economic growth, population growth and new applications for electricity in the industry and the transport sector (as a substitute for fossil fuels) are drivers for increased electricity n consumption. In the Swedish energy scenarios for 2050 by Ryde et al. [25]; the annual electricity consumption varies between 125 TWh and 170 TWh. The previous results in this study were based on an electricity consumption of 140 TWh. In this sensitivity analysis we simulate the power residual using the electricity consumption levels of 125 TWh and 170 TWh, respectively. Our results show that the size of electricity consumption greatly influences the power residual and thus the technical potential for power-to-heat (Fig. 7, Table 5). The technical potential of power-toheat is considerably smaller compared to the previous results (based on 140 TWh) if electricity consumption is high (170 TWh) and considerably larger if electricity consumption is low (125 TWh). 4.2. Wind power production pattern
Fig. 5. The power-to-heat potentials for the base case and when assuming priority for waste heat in the district heat load for the High Wind scenario. The numbers specify the total potential for the scenarios.
The wind production patterns differ between years since the weather and wind profiles change over time. The previous results in this study are based on the use of the wind power production pattern from 2014. In this sensitivity analysis we estimate the power-to-heat potential using data on hourly wind power production from 2012, 2013 and 2015 when simulating the residual load. The hourly electricity production from other technologies apart from hydropower is not adjusted to the wind power variations. The results of the sensitivity analysis show that variations in the wind power production pattern between years influence the power-to-heat potential. The use of the wind power production pattern from 2013 results in a higher potential for power-to-heat for all three scenarios, while the use of data from 2015 results in lower potential. In the Appendix, the same analysis is performed for variations in the solar power production patterns (Figure 10). In this case, the results vary marginally when using solar production patterns from different years. 5. Discussion
of installed power capacity, while the reduction in fuel cost is proportional to the power-to-heat production. The operation and maintenance cost has two components: one proportional to the installed power capacity and one proportional to the power-to-heat production. 4. Sensitivity analysis In the sensitivity analysis we investigate how the technical
The results of this study, including those from this sensitivity analysis, are summarised n Fig. 8. The figure shows that the technical potential of power-to-heat varies greatly depending on various assumptions, including for example electricity production and consumption, access to thermal storage and utilisation of waste heat in the district heat load. In addition, the economic calculations show that the price relation of electricity and biomass has large impact on the economic power-to-heat. Apart from these conditions and restrictions, the potential of power-to-heat is also
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Table 4 The economic potentials of power-to-heat, the optimal installation of electric boiler capacity and profits for the three scenarios' two cases. Scenario
Conservative High wind High wind & solar
Case 1 Electricity price ¼ 20 V/MWh Biomass price ¼ 30 V/MWh
Case 2 Electricity price ¼ 0 Biomass price ¼ 30 V/MWh
Power-to-heat capacity [GW]
Economical power-to-heat potential Profit [TWh] [MV]
Power-to-heat capacity [GW]
Economical power-to-heat potential Profit [TWh] [MV]
1.3 2.2 3.5
1.3 2.3 5.1
2.4 5.1 5.4
1.7 3.4 5.9
5.1 7.5 28
37 70 143
Table 5 The technical potentials of power-to-heat (TWh) for the three scenarios when assuming lower/higher electricity consumption and wind power production patterns from other years. The numbers in parenthesis show the increase/decrease of the potential compared to the base case. Scenarios
Electricity consumption ¼ 125 TWh
Electricity consumption ¼ 170 TWh
Wind power production pattern of 2013
Wind power production pattern of 2015
Conservative High wind High wind & solar
4.7 (þ66%) 5.7 (þ57%) 8.6 (þ39%)
0.2 (93%) 1.5 (60%) 3.1 (49%)
3.1 (þ9%) 4.6 (þ25%) 6.2 (þ10%)
2.6 (10%) 3.1 (16%) 5.4 (13%)
Fig. 6. The costs and profit in MV for different levels of installed power capacity of electric boilers for the High Wind scenario and economic case 2.
Fig. 7. The technical potential of power-to-heat on an hourly basis for the High Wind scenario and three different levels of electricity consumption.
influenced by a number of other factors that were not investigated in this study, but are discussed below. One obvious factor that influences the technical as well as the economic potential of power-to-heat is the choice of technology. If assuming the use of heat pumps instead of electric boilers, the potential of power-to-heat will be reduced by a factor of 2e4 since a heat pump produces 2e4 units of heat per unit of electricity. The choice of electric boilers makes economic sense if the unit is to be operated fairly few hours per year since this technology incurs a low investment cost compared to heat pumps. Another factor of major importance to the technical and economic potential of power-to-heat is the development of other flexibility measures, including the power transmission capacities to neighbouring countries. If assuming a considerable expansion of variable electricity production over the coming decades, a number of flexibility measures are likely to develop in parallel. For practical purposes, other flexibility measures, as well as the import and export of electricity, were disregarded in this study.
In reality, the power-to-heat potential could also be influenced by bottlenecks in the power transmission and distribution networks. This aspect was neglected in this study since a detailed simulation of the whole power system is beyond the scope of this paper. For a detailed discussion on possible future developments concerning transmission and distribution networks, we refer to Wangel [37]. The technical potential of power-to-heat is also influenced by the demand for district heat. Our estimates are based on the supply of district heat in 2014. Some studies suggest that the total demand for district heat in Sweden is expected to decrease somewhat after 2030 due to saturated markets (limited potential for future expansion) and decreasing heat demands per area unit (more energy efficient building stock) [19,29]. Power-to-heat involves mature technologies and an opportunity to avoid curtailment during hours of very high variable electricity production while reducing fuel consumption. In spite of these benefits, Blarke [2] and Blarke and Lund [3] argue that power-to-heat technologies (especially heat pumps) should be
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optimal level of installed electric boilers is in the range of 1.3e5.4 GW depending on electricity scenario etc and that the economic potentials are 5e20% lower than the technical potentials for the base case. In this paper we have estimated the potential for power-to-heat in the district heating systems. To get a comprehensive picture of the potential for power-to-heat, this technology should also be explored in industry and buildings with individual heating systems.
Acknowledgements The reported research has been conducted within the framework of the Research Studio Austria “EnergySimCity” funded by the Austrian Federal Ministry of Science, Research and Economics. The authors would like to thank Kraftringen AB for sharing data on the district heat load in their network in Lund.
Fig. 8. A spider plot summarising the estimated power-to-heat potentials (in TWh) for the different scenarios and cases.
Appendix Model validation
introduced with care in order not to jeopardise the economics and thus threaten the existence of distributed cogeneration. Distributed cogeneration could just as power-to-heat balance the electricity system, but in this case by ramping up electricity production during hours of low production from variable renewable sources. 6. Conclusions This study estimates the power-to-heat potentials in the Swedish district heating systems to 0.2e8.6 TWh, assuming that electric boilers are used as power-to-heat technology. The wide range of potentials indicates that the potential is highly dependent on various assumptions, most importantly the proportion of electricity production from variable energy sources. The results suggest that the potential of power-to-heat could be fairly high, assuming a development towards considerable amounts of wind and solar power production (75e90 TWh in total, corresponding to 54e64% of electricity consumption). Other factors that influence the potential include access to thermal storage and the use of waste heat from industries and waste incineration plants in the district heat load. Access to thermal storage could increase the potential for power-to-heat considerably depending on the size of the storage while the use of waste heat reduces the potential. The available district heat load, especially when assuming the use of waste heat, mainly pose a restriction to capturing negative residual loads during the summer, which is when solar power production is high. This study confirms the fact that Sweden has good conditions for integrating large amounts of variable electricity. Our simulations show moderate negative residual loads also in the scenarios with large volumes of variable electricity production. This can be explained by the high capacity of flexible storage hydropower (we assume 90% flexibility). Performing an equivalent study in another country could yield substantially larger power-to-heat potentials. Whether the technical potential of power-to-heat will be realised depends on the future economic viability of these technologies. A factor of key importance in this regard is the price of electricity (including taxes and charges) relative to the price of the fuel that is saved. Our rough economic calculations indicate that the
In order to validate the method of simulating the district heat load based on outdoor temperatures we compare the simulated data to measured data for a specific network. The data concerns the district heating network of Lund and was provided by Kraftringen AB. Figure 9 shows the comparison between simulated and measured data for 2014. Some outliers have been removed from the measured data. The mean error is 19% when comparing the simulated and measured data.
Fig. 9. The district heat load plotted against temperature for both simulated and measured data. The figure shows that the simulated data is a good approximation.
Sensitivity analysis on the influence of solar power production pattern Similar to wind power, the hourly solar power production may differ between years. In this sensitivity analysis we estimate the power-to-heat potentials based on residual loads that have been simulated using data on hourly solar power production from 2011 to 2015. Figure 10 shows that the power-to-heat potentials vary only marginally as a result of using solar power production patterns from different years.
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Fig. 10. The power-to-heat potential for the High Wind & Solar scenario based on the use of hourly solar power production from different years when simulating the residual load.
References [1] Agora Energiewende. Power-to-Heat zur Integration von ansonsten abgeregeltem Strom aus Erneuerbaren Energien, Berlin. 2014. [2] Blarke MB. Towards an intermittency-friendly energy system: comparing electric boilers and heat pumps in distributed cogeneration. Appl Energy 2012;91:349e65. http://dx.doi.org/10.1016/j.apenergy.2011.09.038. [3] Blarke MB, Lund H. Large-scale heat pumps in sustainable energy systems: system and project perspectives. Therm Sci 2007;11:143e52. €ttger D, Go €tz M, Lehr N, Kondziella H, Bruckner T. Potential of the power-to[4] Bo heat technology in district heating grids in Germany. Energy Proced 2014;46: 246e53. http://dx.doi.org/10.1016/j.egypro.2014.01.179. €ller B, Persson U, Boermans T, [5] Connolly D, Lund H, Mathiesen BV, Werner S, Mo Trier D, Østergaard PA, Nielsen S. Heat Roadmap Europe: Combining district heating with heat savings to decarbonise the EU energy system. Energy Policy 2014;65:475e89. http://dx.doi.org/10.1016/j.enpol.2013.10.035. [6] Energistyrelsen. Technology data for energy plants - generation of electricity and district heating, energy storage and energy carrier generation and conversion. 2012. https://www.energinet.dk/SiteCollectionDocuments/Danske% 20dokumenter/Forskning/Technology_data_for_energy_plants.pdf. [7] European Commission. An EU strategy on heating and cooling, COM (2016) 51 final, Brussels. 2016. [8] Frederiksen S, Werner S. District heating and cooling. Studentlitteratur; 2013. [9] Gadd H, Werner S. Daily heat load variations in Swedish district heating systems. Appl Energy 2013;106(2013):47e55. [10] Gadd H, Werner S. Thermal energy storage systems for district heating and cooling. In: Cabeza LF, editor. Advances in thermal energy storage systems methods and applications. Woodhead Publishing; 2015. €rnholm E, Stigson P, Zetterberg L. Energy scenario for Sweden [11] Gustafsson M, Sa 2050-Based on renewable energy technologies and sources. IVL Swedish Environment Institute and WWF Sweden; 2011. € rtz M, Bo €ttger D, Kondziella H, Bruckner T. Economic potential of the [12] Go "Power-to-Heat " technology in the 50 Hz control area. Germany: University of Leipzig; 2013. [13] Hers SJ, Afman M, Cherif S, Rooijers F. Potential for power-to-heat in The Netherlands. Delft: CE Delft; 2015. [14] IRENA. Renewable capacity statistics 2016. International Renewable Energy
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Agency; 2016. [15] Lindahl J. National survey report of PV power applications in Sweden 2015, IEA-PVPS. 2016. [16] Lund H, Mathiesen BV. Energy system analysis of 100% renewable energy systemsdthe case of Denmark in years 2030 and 2050. Energy 2009;34: 524e31. http://dx.doi.org/10.1016/j.energy.2008.04.003. [17] Lund PD, Lindgren J, Mikkola J, Salpakari J. Review of energy system flexibility measures to enable high levels of variable renewable electricity. Renew Sustain Energy Rev 2015;45:785e807. http://dx.doi.org/10.1016/ j.rser.2015.01.057. [18] Lund H, Werner S, Wiltshire R, Svendsen S, Thorsen JE, Hvelplund F, et al. 4th Generation District Heating (4GDH): integrating smart thermal grids into future sustainable energy systems. Energy 2014;68:1e11. http://dx.doi.org/ 10.1016/j.energy.2014.02.089. [19] Magnusson D. Swedish district heatingda system in stagnation: current and future trends in the district heating sector. Energy Policy 2012;48:449e59. http://dx.doi.org/10.1016/j.enpol.2012.05.047. [20] Mathiesen BV, Lund H. Comparative analyses of seven technologies to facilitate the integration of fluctuating renewable energy sources. Renew Power Gener IET 2009;3:190e204. http://dx.doi.org/10.1049/iet-rpg:20080049. €ller B, et al. [21] Mathiesen BV, Lund H, Connolly D, Wenzel H, Østergaard PA, Mo Smart Energy Systems for coherent 100% renewable energy and transport solutions. Appl Energy 2015;145:139e54. http://dx.doi.org/10.1016/ j.apenergy.2015.01.075. [22] Meibom P, Kiviluoma J, Barth R, Brand H, Weber C, Larsen HV. Value of electric heat boilers and heat pumps for wind power integration. Wind Energy 2007;10:321e37. http://dx.doi.org/10.1002/we.224. [23] Münster M, Morthorst PE, Larsen HV, Bregnbæk L, Werling J, Lindboe HH, et al. The role of district heating in the future Danish energy system. Energy 2012;48:47e55. http://dx.doi.org/10.1016/j.energy.2012.06.011. € verenskommelse mellan Socialdemokraterna, Moderaterna, [24] Regeringen. Ramo € partiet de gro € na, Centerpartiet och Kristdemokraterna, RegerMiljo ingskansliet, 2016-06-10. 2016. n B, Axelsson E, Unger T, Sko €ldberg H. In-depth scenario analysis and [25] Ryde €rdjupad scenarioanalys och quantification of the council's four scenarios [Fo kvantifiering av rådets fyra scenarier]. NEPP report, Profu, North European Power Perspectives. 2014. [26] Schill W-P. Residual load, renewable surplus generation and storage requirements in Germany. Energy Policy 2014;73:65e79. http://dx.doi.org/ 10.1016/j.enpol.2014.05.032. €rdplan 2050: El-och fja €rrva €rmeproduktion, ER 2012:30, Ener[27] SEA. Fa € r en fa €rdplan fo €r gimyndighetens underlag till Naturvårdsverkets uppdrag fo ett Sverige utan nettoutsl€ app av v€ axthusgaser år 2050, Eskilstuna. 2012. [28] SEA. Energy in Sweden - Facts and figures 2015, ET 2015:08. 2015. €ldberg H, Ryde n B. The heating market in Sweden - an overall view, [29] Sko €rmemarknad Sverige. 2014. Va €ldberg H, Unger T, Holmstro € m D. Electricity and district heat - interplay [30] Sko €rrva €rme - samverkan mellan marknaderna]. between the markets [El och fja Rapport 2015: 223, Energiforsk. 2015. [31] Svenska energi. The electricity year and business 2015 [Elåret och verksamheten 2015]. Stockholm. 2016. €rrva €rme. Fja €rrva €rmens bra €nslen och produktion 2014, Excel-file. [32] Svensk fja 2015. http://www.svenskfjarrvarme.se/StatistikePris/Fjarrvarme/ Energitillforsel/. [33] Svenska Kraftn€ at. Statistik. 2016. http://www.svk.se/aktorsportalen/ elmarknad/statistik/ (Visited 2016-06-30). [34] Svenska kraftn€ at. Adaption of the electricity system with a large amount of renewable electricity production [Anpassning av elsystemet med en stor € rnybar elproduktion]. Sundbyberg. 2015. m€ angd fo € der L. På v€ € rso € rjning baserad på enbart fo €rnybar el i Sverige [35] So ag mot en elfo en studie om kraftsystemets balansering, Version 3.0. Stockholm: KTH; 2013. [36] Temperatur.nu. Website visited 2016-05-15. 2016. [37] Wangel. Developing Sweden's transmission grid:what are the drivers and barriers? Stockholm Environment Institute, Project Report 2015-06. Stockholm Environment Institute; 2015. Project Report 2015-06 (Visited 2016-1010).
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Contents lists available at ScienceDirect
Energy journal homepage: www.elsevier.com/locate/energy
The potential of power-to-heat in Swedish district heating systems Gerald Schweiger a, c, Jonatan Rantzer a, Karin Ericsson a, *, Patrick Lauenburg b a
Environmental and Energy Systems Studies, Department of Technology and Society, Lund University, PO Box 118, SE-221 00 Lund, Sweden Department of Energy Sciences, Lund University, PO Box 118, SE-221 00 Lund, Sweden c AEE INTEC Institut für Nachhaltige Technologien, Feldgasse 19, 8200 Gleisdorf, Austria b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 31 October 2016 Received in revised form 10 February 2017 Accepted 13 February 2017 Available online xxx
The main challenge for future electricity systems is to match the available electricity from variable renewable resources with the electricity demand in place, time and quantity. One option for increasing electricity system flexibility is to integrate the electricity system with the district heating systems via the use of power-to-heat technologies such as electric boilers. The overarching objective of this paper is to increase the understanding of what role power-to-heat could have in Sweden and to contribute to the development of methods and tools that can be applied when analysing the potential of power-to-heat. For that purpose we estimate the technical potential of power-to-heat for different power scenarios and assumptions and identify key parameters which have significant impact on the potential. The calculations are based on hourly simulations of electricity production, electricity consumption and district heat load. The power-to-heat potential was estimated to 0.2e8.6 TWh, where the potentials at the higher end pertain to scenarios with high amounts of wind and solar power production (corresponding to 54e64% of electricity consumption). Access to thermal storage increases the potential of power-to-heat while the use of industrial waste heat and heat from waste incineration in the district heat load reduces the potential. © 2017 Published by Elsevier Ltd.
Keywords: Power-to-heat Flexibility measure Sweden District heating systems
1. Introduction The installation of wind power and solar power is growing rapidly in many countries around the world [14]. With the expansion of electricity production from variable energy sources such as wind and solar energy, other parts of the energy systems must become more flexible. There are a number of options for increasing electricity system flexibility, including increasing supply and demand flexibility, developing energy storage technologies and systems services and increasing the transmission capacity of the national grid and interconnections to other countries [17]. Demand flexibility may be facilitated by the integration of the electricity system with the heating and gas systems. Such integration offers an opportunity to increase the electricity consumption during hours of very high electricity production from variable electricity sources by producing heat (power-to-heat) or gas (power-to-gas). This paper focuses on power-to-heat, which refers to heat production from electricity through heat pumps or electric boilers, and the application of this technology in the district heating sector. However,
* Corresponding author. E-mail address: [email protected] (K. Ericsson).
power-to-heat could also be applied in buildings and industries with a demand for heat. Power-to-heat is attracting growing interest in Europe (see for example Refs. [5,7,18,21]), especially in Denmark (see e.g. Refs. [16,20,23] and Germany (see e.g. Refs. [4,12]. The benefit of this technology is that it may reduce the need for curtailment and the hours of very low electricity prices while saving fuel in heat production [22]. Denmark and Germany, both of which feature a high percentage of variable, renewable energy sources in their electricity supply, have seen an increasing number of power-toheat projects being realised over the past few years. These projects mainly involve investments in the district heating systems, but also some in industry [1]. In other countries with very limited use of district heating, the opportunity for power-to-heat may primarily lie in industry. A study concerning the Netherlands shows that there is a considerable potential for utilising power-to-heat in Dutch industry, but that the potential is limited in the district heating systems [13]. This paper focuses on Sweden, which similar to Denmark, has extensive district heating systems. District heat accounted for 58% of the energy supplied to buildings in Sweden in 2014 [28]. During the past few years there has been a considerable growth in wind
http://dx.doi.org/10.1016/j.energy.2017.02.075 0360-5442/© 2017 Published by Elsevier Ltd.
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power production in Sweden. In 2015 wind power accounted for 10% of the Swedish electricity production. Solar electricity production is still negligible [31], but has potential for future growth. Several factors suggest that variable electricity production will expand considerably in Sweden over the coming decades. One important factor is the recent multi-party agreement which introduced a goal of 100% renewable energy sources in electricity production by 2040 [24]. Although the agreement states that the target should not be interpreted as an end date for nuclear power, it adds to the political uncertainty concerning the future of nuclear power which accounted for about 35% of Swedish electricity production in 2014 [31]. Nuclear power is furthermore fraught with high costs. Another factor is the falling costs of solar PVs and the customers' desire to produce their own electricity [15]. Sweden has by international comparison favourable conditions for integration of variable electricity production due to a robust transmission grid and large capacity of storage hydropower [35]. However, the ongoing development that suggests very high proportions of variable electricity production in Sweden and neighbouring countries within a few decades is raising concerns over future supply quality and security. This in turn motivates the analysis and development of flexibility measures [34]. Power-to-heat has so far received little attention in a Swedish €der [35]; p. 33e38) who has esticontext. One exception is So mated the technical potential of power-to-heat (as a flexibility measure) in the district heating systems to 1.2 TWh per year. That study was based on hourly simulations, the utilisation of electric boilers and a scenario with an annual solar and wind power production of 55 TWh. The heat from waste incineration and industrial waste heat was excluded from the district heat load which was simulated based on the load curve of Stockholm. € ldberg et al. [30] who have done a case Another example is Sko study on four district heating systems for which they analysed the impact of more variable electricity prices (as a result of high penetration of variable electricity production) on the operation of different heat production units and the choice of heat sources. Their results show that despite low electricity prices, the use of heat pumps and electric boilers increases only marginally due to the current tax on electricity consumption and the availability of low-cost waste heat from waste incineration and industries in the systems studied. The overarching objective of this paper is to increase the understanding of what role power-to-heat could have in Sweden and to contribute to the development of methods and tools that can be applied when analysing the potential of power-to-heat. For this purpose this paper addresses the following questions: i) What is the technical potential of power-to-heat in the Swedish district heating systems for different electricity scenarios, conditions and restrictions? ii) Which conditions and restrictions have considerable impact on the power-to-heat potential? iii) What is the economic potential of power-to-heat in the Swedish district heating systems for different electricity scenarios? These questions are addressed by first designing three electricity scenarios and identifying conditions and restrictions to be investigated. Two of the electricity scenarios involve a considerable growth in electricity production from variable renewable energy sources, and thus imply a time perspective some decades ahead (for simplicity we refer to this time to be around 2050). We estimate the technical potential based on hourly simulations of electricity
production, electricity consumption and district heat load. This € ttger et al. [4] who have estimated the approach was inspired by Bo technical potential of power-to-heat in Germany. However, our study also involves further development of their methods in order to account for various restrictions and conditions. For example, we have developed a storage model in order to analyse the effects of access to thermal storage on the potential for power-to-heat. We estimate the economic potential based on a straightforward cost model. This paper contributes to the research field by providing the first detailed study on the power-to-heat potential in Sweden for various conditions and restrictions. Moreover, unlike previous studies, this study is based on hourly simulations of all district heating systems using a GIS (geographical information systems) approach. The resulting GIS database, as well as the models (available as a Python library),1 are publicly available for further investigation. Another novelty of this paper is that it presents the development and implementation of a storage model and storage strategy to analyse the impact of different storage sizes on the potential of power-to-heat.
2. Methodology 2.1. Technical potential - overall approach The theoretical potential of power-to-heat is the annual district heat load for all district heating systems, since all this heat in theory could be produced from electricity. The current theoretical potential of power-to-heat in the Swedish district heating systems thus amounts to about 57 TWh which was the supply of district heat in 2014 [32]. In contrast, the technical potential specifies the amount of “surplus electricity” that can be converted to district heat load. A more appropriate term for “surplus electricity” is negative residual load, which is used onwards in this paper. Negative residual load and other important concepts that are used in this paper are defined in Table 1. The technical potential only includes the negative residual load that is matched in time with the available district heat load, unless there is access to thermal storage. In order to estimate the technical potential for power-to-heat we have to make a number of simplifications and rough assumptions. Three major simplifications are that we i) disregard the potential development of other flexibility measures than power-toheat, ii) disregard the export and import of electricity, and iii) neglect bottlenecks in future power transmission and distribution networks. The last simplification implies that the geographical location of electricity production units and district heating systems is not taken into account. The power-to-heat technology is assumed to be electric boilers which are cheaper, but less efficient than heat pumps. An argument for this choice is that the unit will only be operated at times of very high electricity production from variable renewable sources. The sections below describe how we calculate the technical potential based on different electricity scenarios, conditions and restrictions. The calculations are carried out by first simulating the hourly district heat load and residual power loads for different scenarios. The district heat load and the negative power residual loads are then matched.
1 The database (https://github.com/GersHub/P2HSweden) contains information on all Swedish district heating systems (production units, storage size/capacity, share of waste heat etc); data on outdoor temperatures in each county (to calculate the hourly heat load); data on the electricity system (hourly power consumption and power production for different technologies).
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Table 1 Definitions of important concepts that are used in this paper. Concept
Definition
District heat load
District heat load in this paper is defined as the heat power that represents the district heat supply that should satisfy customers' heat demand and the distribution losses in the network. The heat load varies over time and is normally expressed as average heat power with a certain time resolution such as hours [8]; p 67). The design head load is used for estimating the demand for the installed heat supply capacity of the system. Design heat loads can be calculated using either measurements of actual heat loads or relevant capacity factors. In this paper we calculate the design heat load by an extrapolation from the line representing the heat power signature to the design outdoor temperature [8]; p 90e91 and p 105). The residual load describes what is left of the hourly power demand after subtracting the hourly electricity production from variable renewable sources and certain must-run generators [26]. Negative residual load is the load that occurs when the electricity production from variable renewable sources and certain must-run generators is higher than electricity demand. The design outdoor temperature is the lowest outdoor temperature to be considered when designing for a particular desired heat capacity for space heating power. Design outdoor temperatures are calculated using design frequencies and building time constants [8]; p 48). In this paper we use extreme outdoor temperatures based on data from Frederiksen and Werner [8]; p 100).
Design heat load
Residual load Negative residual load Design outdoor temperature
2.2. Simulation of district heat load
2.3. Scenarios for electricity production and use
Sweden has more than 400 district heating networks [32]. In order to simulate the district heat load in Sweden on an hourly basis we use data on the annual supply of district heat in different networks in 2014 [32] and on hourly outdoor temperatures at regional level [36]. In order to limit the level of detail, all district heating networks are divided into regions, corresponding to Sweden's 21 counties. This is done by using a GIS approach. For each county, the hourly district heat load is simulated assuming a piecewise linear correlation between outdoor temperatures and district heat loads. At the same time, we calculate the design heat load for each network, which sets the limit for the installation of electric boilers and thus for the power-to-heat capacity in each network. Several assumptions are necessary for estimating the linear correlation between outdoor temperatures and heat loads. Firstly, we assume that there is no need for space heating at outdoor temperatures above 15 C when all heat is used for heating tap water and heat distribution losses. This implies that the district heat load is constant for temperatures above 15 C and a linear function of outdoor temperature with a negative slope for temperatures below 15 C. Secondly, the design outdoor temperature for each region is estimated based on extreme outdoor temperatures found in Frederiksen and Werner [8]; p.100). Thirdly, we assume that 30% of the annual district heat load is distributed evenly throughout the year based on measured data for a number of district heating networks in different parts of Sweden. The value of 30% is also in line with data for the district heating network in €, which is described in Frederiksen and Werner [8]; p. 94). Malmo The mean errors for the different systems lie between 13 and 19%, c.f. Appendix.
We design three scenarios (presented in Table 2) that illustrate what Swedish electricity production may look like around 2050. The scenarios are denoted based on the level of wind power and solar power, which are considered to be the most promising options for expansion of renewable electricity production in Sweden according to existing energy scenarios for 2050 (see e.g. Refs. [11,25,27]. Our scenarios have been designed based on rough assumptions that draw on the existing scenarios and with levels of variable electricity that are at the higher end of those in the existing scenarios. The Conservative scenario assumes that there has been a moderate development of wind and solar power, and that the existing nuclear reactors have been replaced by new ones, something that enables continued high nuclear power production. In this scenario, the annual wind power and solar power amount to 30 TWh and 5 TWh, respectively. The two other scenarios assume that there has been a considerable expansion of wind and solar power while nuclear power has been phased out. The annual wind power production is assumed to amount to 70 TWh in both the High Wind scenario and the High Wind & Solar scenario. The annual solar power is assumed to amount to 5 TWh in the High Wind scenario and to 20 TWh in the High Wind & Solar scenario. Electricity production in CHP plants and gas turbines is assumed to be 15 TWh in all three scenarios, i.e. to roughly remain at the current level. The production of hydropower is also assumed to remain at average annual production level of 65 TWh. The annual electricity consumption is assumed to be 140 TWh in all three scenarios and excludes the electricity used in power-toheat. All three scenarios assume higher production than consumption and thus imply net export of electricity.
Table 2 Electricity production and consumption in 2015 [31] and in our three scenarios in TWh/yr and the proportion of electricity from variable RES in relation to electricity consumption. Electricity production and consumption
Year 2015
Scenarios Conservative
High wind
High wind & solar
Nuclear power Hydropowera Other thermal power (CHP, gas turbines) Wind power Solar power Total production Variable electricity as percent of electricity consumption (%)
54.4 74.0 13.5c 16.6 0.1 158.6 12
55 65 15 30 5 170 25
0 65 15 70 5 155 54
0 65 15 70 20 170 64
Total consumption, including lossesb
135.9
140
140
140
a b c
The annual hydropower production typically varies between 55 and 75 TWh depending on the availability of water [31]. Distribution losses amounted to 10 TWh in 2015. Almost all of the electricity is produced in CHP plants. About half of the CHP production takes place in industry and the other half in the district heating systems.
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2.4. Simulation of residual load The residual load is usually defined as what is left of the power demand after subtracting the output of variable renewable electricity sources and certain must-run generators [26]. Based on this definition we calculate the residual load by subtracting the hourly generation of wind power, solar power, a minimum level of hydropower and must-run thermal generators from the hourly demand for electricity. As must-run thermal generation, we include 50% of the electricity load from CHP plants and 100% from nuclear power plants. This rough assumption considers the difficulty of ramping up/down nuclear power plants on short notice and the relatively low flexibility of industrial CHP plants, which account for about 50% of the co-generated electricity in Sweden [31]. The minimum level of hydropower is set to 1.682 GW, which is the lowest measured mean hourly power load over the last five years (2011e2015). This level corresponds to about 10% of the installed hydropower capacity [33]. The residual load is simulated based on historical hourly data from the Swedish TSO on the electricity consumption and production of each technology [33]. The historical hourly data are linearly scaled up to fit the scenarios. An exception has been made for nuclear power production, for which the load is simply distributed evenly throughout the year. The resulting hourly residual load is shown in Fig. 1. As can be seen in this figure, the hours of negative residual load are almost zero for the current situation. The total negative residual loads were calculated to 0.13 TWh for the Conservative scenario, 2.8 TWh for the High Wind scenario and 5.0 TWh for the High Wind & Solar scenario. 2.5. Calculation of the technical potential The power-to-heat potentials are calculated by matching the negative residual loads with the district heat load on an hourly basis. These calculations are carried out for a base case and two cases in which we assume access to thermal storage and priority for waste heat, respectively. In all three cases, the potential for powerto-heat is restricted by the installed capacity of electric boilers, which in each district heating system is assumed to be equal to 30% of the design heat load. This assumption is in line with that used in € ttger et al. [4]. Bo
Fig. 1. The simulated residual power loads for the current situation (2015) and the different scenarios. The power available for power-to-heat is proportional to the area between the curve and the x-axis for hours with negative residual load.
2.5.1. Base case Fig. 2 shows the hourly district heat load plotted against the corresponding residual load for the base case and High Wind scenario. The upper (blue) triangle frames the data for hours when all of the negative residual can be used for the district heat load. The lower (red) triangle frames the data for hours when the negative residual exceeds the district heat load, which means that the potential of power-to-heat is restricted by the district heat load. 2.5.2. Access to thermal storage In the base case we assumed that the negative residual load needs to be consumed during the same hour as it is produced. However, today many district heating networks have access to thermal storage which allows for a temporary mismatch in the generation of heat and the district heat load. The thermal storage usually consists of accumulators (hot water tanks). For this case we assume that every district heating network accommodates an accumulator with the capacity to store 25% of its mean daily demand and with a transfer capacity corresponding to 2.5% of its mean daily demand. This dimensioning is based on Gadd and Werner [9]; as well as data on accumulators present today in several district heating systems [10]. In addition, we study the impact of assuming access to thermal storage corresponding to 250% of the mean daily demand. We develop a storage model where the thermal storage is limited by storage size and transfer capacity. Heat losses are neglected. The storage strategy consists of storing as much as possible of the negative residual load as heat when this load exceeds heat demand. Correspondingly, as much as possible of the district heat load should be covered by stored heat when the negative residual load is smaller than the available district heat load. Furthermore, the model prioritises to fill up the accumulators that are relatively empty in relation to their storage capacity. 2.5.3. Priority for waste heat Industrial waste heat and heat from waste incineration plants accounted for 7 and 20%, respectively, of the heat supply in the Swedish district heating systems in 2014 [32]. Both of these heat sources are normally associated with low marginal costs and low
Fig. 2. The residual load plotted against the district heat load on an hourly basis. The upper (blue) triangle frames the data for hours when all of the negative residual load can be converted to district heat load. The lower (red) triangle frames the data for hours when the negative residual exceeds the district heat load. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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flexibility. For this case we include a restriction that the district heat loads that are currently met by industrial waste heat and heat from waste incineration are not available for power-to-heat and that the waste heat serves as base load (the heat is distributed evenly over the year). 2.6. Economic potential The economic potential describes the power-to-heat potential when the installation of electric boilers has been optimised from an economic point. The installation and operation of electric boilers for power-to-heat only makes sense if it reduces the use of fuel in other production units in the district heating system and if the electricity is cheaper than the fuel. In order to calculate the economic potential we develop a cost model where the levelised investment cost and operational cost of electric boilers are weighed against the reductions in fuel cost. The optimal installation of electric boilers is found when the cost model yields the greatest profit. Biomass is the dominant energy source in the supply of district heat in Sweden and accounted for around 60% of this supply in 2014 [32]5). The economic calculations are based on the assumption that the use of 1 MW of electricity for power-to-heat saves 1 MWh of wood chips. This assumption is motivated by the fairly similar energy efficiencies for production of heat from electricity and various fuels; in both cases the energy efficiency should be at least 90%. We calculate the economic potential for two cases based on different electricity prices. The price of electricity (including taxes and charges) is assumed to be 20 V/MWh in one case and zero in the other case. The low electricity prices are motivated by the assumption that the electric boilers are only operated at times of negative residual load. For both cases we assume that the price of wood chips is 30 V/MWh. The calculations are based on the assumption that the electric boilers are dimensioned for 10 kV and 20 MW and have a lifetime of 20 years. The real interest rate is set to 6%. The costs include an investment cost of 0.06 MV/MW, a fixed operation and maintenance cost of 1100 V/MW/yr and a variable operation and maintenance cost of 0.5 V/MWh [6]. We disregard costs associated with grid connection that may arise due to the installation of electric boilers since these investments should be fairly limited assuming these boilers are co-located with existing CHP plants. 3. Results 3.1. Technical potential for the base case The technical potentials for the base case are shown in Fig. 3. The potential for power-to-heat was estimated at 0.1 TWh for the current situation, 1.9 TWh for the Conservative scenario, 3.7 TWh for the High Wind scenario and 6.2 TWh for the High Wind & Solar scenario. As expected, the potential of power-to-heat increases with the amount of variable electricity production. Furthermore, the values of the technical potentials correspond to 89% 80% and 59% of the negative residual loads (presented in Section 2.4). The lower percentage for the High Wind & Solar scenario indicates the difficulty of converting the negative residual load resulting from solar power peaks to district heat load since this load appears during summer when the demand for district heat is small. The maximum hourly capacity of power-to-heat is calculated to 7.46 GW; this limit is reached for the highest production peaks of the High Wind scenario and the High Wind & Solar scenario.
Fig. 3. The hourly power-to-heat potentials for the current situation (2015) and three future scenarios. The numbers specify the total potential for the scenarios.
3.2. Technical potential assuming access to thermal storage Access to thermal storage naturally increases the potential of power-to-heat. If assuming access to large thermal storage, the technical potentials of power-to-heat increases by 7% for the Conservative scenario, by 26% for the High Wind scenario and by 75% for the High Wind & Solar scenario in comparison to the base case (Table 3). The district heat load is a greater limiting factor in the High Wind & Solar scenario than in the other scenarios. Fig. 4 shows the hourly result for the High Wind scenario and different thermal storage capacities. 3.3. Technical potential assuming priority for waste heat The technical potentials of power-to-heat are reduced by 20e40% compared to the base case when assuming priority for heat from waste incineration and industrial waste heat in the district heat load. The technical potentials were estimated to 1.2, 2.9 and 3.8 TWh for the Conservative, High Wind and High Wind & Solar scenarios, respectively (Table 3). Fig. 5 shows the result for the High Wind scenario on an hourly basis. The High Wind & Solar scenario is most strongly affected by this restriction since most of the solar power is produced during summer when waste heat accounts for a large part of the district heat load. However, the technical potentials of power-to-heat are only somewhat smaller than those in the base case if assuming the use of waste heat in combination with thermal storage. 3.4. Summary of technical potentials The technical potentials of power-to-heat for the base case and two other cases are summarised in Table 3. 3.5. Economic potential Table 4 shows the calculated economic potentials of power to heat and the optimal installation capacity of electric boilers for the two cases and three scenarios. The economic potential is higher in the second case which is based on a lower electricity price. Fig. 6 shows the profit and various costs associated with power-to-heat as functions of installed capacity; the results concern the High Wind scenario and case 2. The investment cost is a linear function
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G. Schweiger et al. / Energy xxx (2017) 1e9 Table 3 The technical potentials of power-to-heat (TWh) for three cases and different scenarios. The numbers in parenthesis show the increase/decrease of the potential compared to the base case. Scenarios
Base case
Priority for waste heat
Access to thermal storage
Conservative High wind High wind & solar
1.9 3.7 6.2
1.2 (37%) 2.9 (20%) 3.8 (39%)
2.0 (þ5%) - 2.1 (þ7%) 4.0 (þ9%) - 4.6 (þ26%) 8.5 (þ37%) - 10.8 (þ75%)
potential of power-to-heat is influenced by two parameters: the electricity consumption and the wind power production pattern. 4.1. Electricity consumption
Fig. 4. The hourly power-to-heat potentials for the High Wind Scenario when assuming access to thermal storage of different sizes. The numbers specify the total potential for the scenarios.
There is great uncertainty as to how Swedish electricity consumption will develop over the next decades. Continuous improvement in energy efficiency is a driver for reduced electricity consumption while economic growth, population growth and new applications for electricity in the industry and the transport sector (as a substitute for fossil fuels) are drivers for increased electricity n consumption. In the Swedish energy scenarios for 2050 by Ryde et al. [25]; the annual electricity consumption varies between 125 TWh and 170 TWh. The previous results in this study were based on an electricity consumption of 140 TWh. In this sensitivity analysis we simulate the power residual using the electricity consumption levels of 125 TWh and 170 TWh, respectively. Our results show that the size of electricity consumption greatly influences the power residual and thus the technical potential for power-to-heat (Fig. 7, Table 5). The technical potential of power-toheat is considerably smaller compared to the previous results (based on 140 TWh) if electricity consumption is high (170 TWh) and considerably larger if electricity consumption is low (125 TWh). 4.2. Wind power production pattern
Fig. 5. The power-to-heat potentials for the base case and when assuming priority for waste heat in the district heat load for the High Wind scenario. The numbers specify the total potential for the scenarios.
The wind production patterns differ between years since the weather and wind profiles change over time. The previous results in this study are based on the use of the wind power production pattern from 2014. In this sensitivity analysis we estimate the power-to-heat potential using data on hourly wind power production from 2012, 2013 and 2015 when simulating the residual load. The hourly electricity production from other technologies apart from hydropower is not adjusted to the wind power variations. The results of the sensitivity analysis show that variations in the wind power production pattern between years influence the power-to-heat potential. The use of the wind power production pattern from 2013 results in a higher potential for power-to-heat for all three scenarios, while the use of data from 2015 results in lower potential. In the Appendix, the same analysis is performed for variations in the solar power production patterns (Figure 10). In this case, the results vary marginally when using solar production patterns from different years. 5. Discussion
of installed power capacity, while the reduction in fuel cost is proportional to the power-to-heat production. The operation and maintenance cost has two components: one proportional to the installed power capacity and one proportional to the power-to-heat production. 4. Sensitivity analysis In the sensitivity analysis we investigate how the technical
The results of this study, including those from this sensitivity analysis, are summarised n Fig. 8. The figure shows that the technical potential of power-to-heat varies greatly depending on various assumptions, including for example electricity production and consumption, access to thermal storage and utilisation of waste heat in the district heat load. In addition, the economic calculations show that the price relation of electricity and biomass has large impact on the economic power-to-heat. Apart from these conditions and restrictions, the potential of power-to-heat is also
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Table 4 The economic potentials of power-to-heat, the optimal installation of electric boiler capacity and profits for the three scenarios' two cases. Scenario
Conservative High wind High wind & solar
Case 1 Electricity price ¼ 20 V/MWh Biomass price ¼ 30 V/MWh
Case 2 Electricity price ¼ 0 Biomass price ¼ 30 V/MWh
Power-to-heat capacity [GW]
Economical power-to-heat potential Profit [TWh] [MV]
Power-to-heat capacity [GW]
Economical power-to-heat potential Profit [TWh] [MV]
1.3 2.2 3.5
1.3 2.3 5.1
2.4 5.1 5.4
1.7 3.4 5.9
5.1 7.5 28
37 70 143
Table 5 The technical potentials of power-to-heat (TWh) for the three scenarios when assuming lower/higher electricity consumption and wind power production patterns from other years. The numbers in parenthesis show the increase/decrease of the potential compared to the base case. Scenarios
Electricity consumption ¼ 125 TWh
Electricity consumption ¼ 170 TWh
Wind power production pattern of 2013
Wind power production pattern of 2015
Conservative High wind High wind & solar
4.7 (þ66%) 5.7 (þ57%) 8.6 (þ39%)
0.2 (93%) 1.5 (60%) 3.1 (49%)
3.1 (þ9%) 4.6 (þ25%) 6.2 (þ10%)
2.6 (10%) 3.1 (16%) 5.4 (13%)
Fig. 6. The costs and profit in MV for different levels of installed power capacity of electric boilers for the High Wind scenario and economic case 2.
Fig. 7. The technical potential of power-to-heat on an hourly basis for the High Wind scenario and three different levels of electricity consumption.
influenced by a number of other factors that were not investigated in this study, but are discussed below. One obvious factor that influences the technical as well as the economic potential of power-to-heat is the choice of technology. If assuming the use of heat pumps instead of electric boilers, the potential of power-to-heat will be reduced by a factor of 2e4 since a heat pump produces 2e4 units of heat per unit of electricity. The choice of electric boilers makes economic sense if the unit is to be operated fairly few hours per year since this technology incurs a low investment cost compared to heat pumps. Another factor of major importance to the technical and economic potential of power-to-heat is the development of other flexibility measures, including the power transmission capacities to neighbouring countries. If assuming a considerable expansion of variable electricity production over the coming decades, a number of flexibility measures are likely to develop in parallel. For practical purposes, other flexibility measures, as well as the import and export of electricity, were disregarded in this study.
In reality, the power-to-heat potential could also be influenced by bottlenecks in the power transmission and distribution networks. This aspect was neglected in this study since a detailed simulation of the whole power system is beyond the scope of this paper. For a detailed discussion on possible future developments concerning transmission and distribution networks, we refer to Wangel [37]. The technical potential of power-to-heat is also influenced by the demand for district heat. Our estimates are based on the supply of district heat in 2014. Some studies suggest that the total demand for district heat in Sweden is expected to decrease somewhat after 2030 due to saturated markets (limited potential for future expansion) and decreasing heat demands per area unit (more energy efficient building stock) [19,29]. Power-to-heat involves mature technologies and an opportunity to avoid curtailment during hours of very high variable electricity production while reducing fuel consumption. In spite of these benefits, Blarke [2] and Blarke and Lund [3] argue that power-to-heat technologies (especially heat pumps) should be
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G. Schweiger et al. / Energy xxx (2017) 1e9
optimal level of installed electric boilers is in the range of 1.3e5.4 GW depending on electricity scenario etc and that the economic potentials are 5e20% lower than the technical potentials for the base case. In this paper we have estimated the potential for power-to-heat in the district heating systems. To get a comprehensive picture of the potential for power-to-heat, this technology should also be explored in industry and buildings with individual heating systems.
Acknowledgements The reported research has been conducted within the framework of the Research Studio Austria “EnergySimCity” funded by the Austrian Federal Ministry of Science, Research and Economics. The authors would like to thank Kraftringen AB for sharing data on the district heat load in their network in Lund.
Fig. 8. A spider plot summarising the estimated power-to-heat potentials (in TWh) for the different scenarios and cases.
Appendix Model validation
introduced with care in order not to jeopardise the economics and thus threaten the existence of distributed cogeneration. Distributed cogeneration could just as power-to-heat balance the electricity system, but in this case by ramping up electricity production during hours of low production from variable renewable sources. 6. Conclusions This study estimates the power-to-heat potentials in the Swedish district heating systems to 0.2e8.6 TWh, assuming that electric boilers are used as power-to-heat technology. The wide range of potentials indicates that the potential is highly dependent on various assumptions, most importantly the proportion of electricity production from variable energy sources. The results suggest that the potential of power-to-heat could be fairly high, assuming a development towards considerable amounts of wind and solar power production (75e90 TWh in total, corresponding to 54e64% of electricity consumption). Other factors that influence the potential include access to thermal storage and the use of waste heat from industries and waste incineration plants in the district heat load. Access to thermal storage could increase the potential for power-to-heat considerably depending on the size of the storage while the use of waste heat reduces the potential. The available district heat load, especially when assuming the use of waste heat, mainly pose a restriction to capturing negative residual loads during the summer, which is when solar power production is high. This study confirms the fact that Sweden has good conditions for integrating large amounts of variable electricity. Our simulations show moderate negative residual loads also in the scenarios with large volumes of variable electricity production. This can be explained by the high capacity of flexible storage hydropower (we assume 90% flexibility). Performing an equivalent study in another country could yield substantially larger power-to-heat potentials. Whether the technical potential of power-to-heat will be realised depends on the future economic viability of these technologies. A factor of key importance in this regard is the price of electricity (including taxes and charges) relative to the price of the fuel that is saved. Our rough economic calculations indicate that the
In order to validate the method of simulating the district heat load based on outdoor temperatures we compare the simulated data to measured data for a specific network. The data concerns the district heating network of Lund and was provided by Kraftringen AB. Figure 9 shows the comparison between simulated and measured data for 2014. Some outliers have been removed from the measured data. The mean error is 19% when comparing the simulated and measured data.
Fig. 9. The district heat load plotted against temperature for both simulated and measured data. The figure shows that the simulated data is a good approximation.
Sensitivity analysis on the influence of solar power production pattern Similar to wind power, the hourly solar power production may differ between years. In this sensitivity analysis we estimate the power-to-heat potentials based on residual loads that have been simulated using data on hourly solar power production from 2011 to 2015. Figure 10 shows that the power-to-heat potentials vary only marginally as a result of using solar power production patterns from different years.
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Fig. 10. The power-to-heat potential for the High Wind & Solar scenario based on the use of hourly solar power production from different years when simulating the residual load.
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