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Power-to-heat as a flexibility measure for integration of renewable energy
Accepted Manuscript Power-to-heat as a flexibility measure for integration of renewable energy Jon Gustav Kirkerud, Torjus Folsland Bolkesjø, Erik Trømborg PII:
S0360-5442(17)30547-9
DOI:
10.1016/j.energy.2017.03.153
Reference:
EGY 10620
To appear in:
Energy
Received Date: 7 November 2016 Revised Date:
27 March 2017
Accepted Date: 29 March 2017
Please cite this article as: Kirkerud JG, Bolkesjø TF, Trømborg E, Power-to-heat as a flexibility measure for integration of renewable energy, Energy (2017), doi: 10.1016/j.energy.2017.03.153. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Power-to-heat as a flexibility measure for integration of renewable energy
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Jon Gustav Kirkerud
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Corresponding author
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Norwegian University of Life Sciences
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Faculty of Environmental Sciences and Natural Resource Management
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Norwegian University of Life Sciences
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Faculty of Environmental Sciences and Natural Resource Management
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Erik Trømborg
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Norwegian University of Life Sciences
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Faculty of Environmental Sciences and Natural Resource Management
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Corresponding author contact details:
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E-Mail: [email protected]
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P.O. Box 5003, NO-1432 Ås
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Phone: + 47 64965800
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Fax: + 47 64965801
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Abstract Higher shares of variable renewable energy (VRE) in energy systems reduce electricity spot prices at
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times when the supply of these sources are most abundant, leading to a lower market value per unit for
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these sources compared to energy sources with a constant or adjustable supply. This disadvantage for
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VRE can be offset by evolving the energy system towards wider market integration and flexible demand
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or supply. This study analyses how increased integration of heat and power markets by use of power-to-
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heat in district heating system could increase the value VRE sources in the Northern European power
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system. For the quantification, we apply a partial equilibrium model with a detailed representation of
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power and district heat generation for a likely 2030 energy system. The results show a markedly
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increase in VRE market value when installed capacity of power-to-heat is increased, especially in
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scenarios with a large Nordic power surplus. The study concludes that power-to-heat solutions in district
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heating systems can increase system flexibility in a short time perspective that considers hours to
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weeks, but also in a longer perspective that accounts for the significant inter-annual variability in
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hydropower supply.
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Keywords: Variable renewable energy, flexibility, district heating, energy system modelling
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1. Introduction
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To reach the ambitious energy and climate goals a growing share of variable renewable energy sources
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(VRE) is deployed in Europe, especially wind power and solar photovoltaics (PV). By nature, the temporal
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supply of VRE is highly variable because it is determined by weather conditions, it is uncertain due to
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forecasting errors, and it is location specific, as the primary energy carrier cannot be transported such as
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coal or biomass. As pointed out by [1] these characteristics have implications for welfare, cost-benefit
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and competitiveness analysis. From the energy system viewpoint, the variability imply major integration
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challenges of VRE with the energy system [2-7]. VRE will affect balancing costs as a consequence of
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forecasting errors [7-10], the costs of distribution and transmission networks [11-14], provision of firm
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reserve capacity [15, 16] cause lower utilization rates and more cycling and ramping of traditional plants
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[6, 17]. At high VRE penetration rates, the integration costs could be substantial [1, 18, 19].
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The system challenges of high VRE shares are substantial and they receive much attention in the
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literature. Large VRE shares, however, also have implications to the VRE competitiveness, i.e. from the
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private economic perspective, since prices received by VRE producers decrease more than the average
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price due to the merit order effect [7, 20]. The market price impacts of VRE are already observed in the
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day ahead wholesale electricity price. In markets with high VRE shares, electricity prices are reduced
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[21] and the price pattern changes; there are lower hourly electricity prices when VRE supply is high [7,
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22]. VRE has practically zero marginal costs and replaces the most expensive electricity production in the
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merit order, thus lowering the electricity price. Hirth [7] finds that higher shares of wind or solar power
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leads to a drop in the market value for these sources, defined as average revenues per unit produced on
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the spot market, in both absolute value and relative value compared to the average power price. This
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effect is often referred to as the merit order effect and it is already significant in some areas: Fraunhofer
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ISE [23] report that the German wind power market value was 26.8 €/MWh in 2015, well below an
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average price of 31.2 €/MWh giving a relative market value, called value factor [7], of 86 %. Huge
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depressions in market values will reduce the potential of VRE as means for reaching the energy and
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climate goals.
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As stated by IEA [19], the key to integrate VRE is flexibility, which describes the extent to which a power
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system can adapt the patterns of electricity generation and consumption in a cost effective manner
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while maintaining balance and security. Introducing more flexible elements in the energy system have
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several benefits from a system perspective, and it could increase the market value of variable renewable
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generation [24]. Using electric power to produce heat (P2H) in district heating (DH) plants is seen as a
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promising solution for increased flexibility because the technology needed is mature, can provide
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flexibility both over short and long time spans, and the potential is vast [24]. The flexibility occurs when
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heat pumps or electric boilers replace other boilers in periods when the VRE supply is high with
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corresponding low electricity prices. Increasing the demand in these periods can raise the price and
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hence the VRE market value [25]. At the same time, in periods of low VRE supply, electricity demand is
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being unchanged since other fuels have lower costs in DH plants under such market conditions.
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Consequently, the electricity price increases more in low price hours than in high price hours, resulting
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in a more stable price both over shorter and longer time horizons. This implies a lower price risk [22]
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that together with higher market values for VRE will improve the competitiveness of VRE.
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Heating and cooling accounts for 50 % of EU’s final energy demand and the renewable share is only 18 %
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[26]. From the heating sector point of view, using variable renewable energy through P2H represent an
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opportunity to reduce both fuel costs and emissions from fossil fuels. The advantages of using electricity
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in DH has been analyzed at a plant level in several studies such as [27, 28] and at a regional or national
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level in for example [29-34]. Generally, allowing for stronger integration between the heat and power
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market could diversify the fuel supply and open for more renewable heat production. District heat as an
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energy carrier also has the advantages of low cost storage [35, 36] and peak load capacity. With better
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P2H integration, these advantages can benefit the whole energy system. There are, however, few
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studies addressing increased power and heat market integration in a broader power market perspective.
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The present paper attempt to take the power market perspective when analyzing power and heat
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market integration by quantifying how increased use of flexible electricity in the district heating sector
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affects the power and heat market and VRE competitiveness through changes in power prices. The study
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uses a comprehensive linear partial equilibrium model, Balmorel, which is calibrated with data for the
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expected Nordic power and district heat market in 2030. The Nordic market is characterized by a large
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district heat sector in combination with high and growing VRE shares domestically and in neighboring
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countries and is hence a particularly interesting region in this context. Moreover, the Nordic countries
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were an early mover in electricity market deregulation, and may be so for a stronger coupling of the
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power and heat markets to mitigate the challenges of high VRE shares.
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2. Materials and methods 2.1. The Balmorel model
The market impacts of P2H is analyzed with a detailed partial equilibrium model for the Nordic power
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and district heating sector, Balmorel [30, 37, 38]. The Balmorel model finds the least cost power and
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heat operation strategy in meeting the demand, given predefined power and heat generation capacities
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and transmission bottlenecks. The model solution provides market-clearing production, transmission
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levels and prices for each geographical unit and time step, under the assumption of competitive
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markets.
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The current model version is updated with 2012 base year data. Full model and data documentation are
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found in [38]. The exogenous model parameters like demand, capacities of the different generation
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technologies, transmission capacity, and availability of variable renewable energy sources are specified
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individually for each power region and heat area. The model calculates the electricity and heat
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production per technology, time unit, and region, minimizing total system costs for a given electricity
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and heat demand. Market-clearing conditions in Balmorel are analyzed by applying two different modes
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of the model: i) a long-term (one year) optimization horizon where the total reservoir hydropower
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generation is allocated on week level, and ii) a short-term (weekly) optimization horizon with an hourly
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time resolution where the weekly hydropower supply is allocated on an hourly basis.
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The model version used for this study uses an exogenously decided generation capacity mix for a 2030
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scenario. The production capacities are determined exogenously based on external sources such as the
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European commission roadmap to 2050 [39].
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Power sector
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has a detailed representation of the Nordic countries and especially the Norwegian and Swedish
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hydropower system, which may provide large amounts of flexibility (figure 1).
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Other interconnected Northern European energy markets are handled as third countries with
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exogenously given power exchange. The main assumptions regarding installed capacities and
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consumption [38] in the different modelled countries are shown in table 1. For the electricity
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consumption, we apply the observed hourly consumption profile of 2012 to distribute the annual
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consumption over the modelled 8760 hours. The same year is applied as base year for hourly profile of
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wind and solar power.
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Fuel price assumptions are based on [40, 41] and they are shown in table 2. Prices for biomass are 25 %
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higher than a medium price development, representing a high price scenario.
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District heating sector
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the Nordic members of the EU (Denmark, Finland and Sweden) account for 29% of all district heating
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demand for households and services in the EU [42]. The energy demand served by the Nordic district
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heating sector accounted for 119 TWh in 2013, having the size around one third of the electricity market
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in the region. A slight decrease in Nordic district heating demand by 3.4 % from 2013 to 2030 is
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assumed, following [39]. The assumed increase in Norway’s district heating demand offsets some of the
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decline in the other countries. In Denmark and Finland, district heating systems are built around larger
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combined heat and power stations with a high share of fossil fuels and peat. In Sweden and Finland, a
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large share also comes from byproducts from the forest industries. In Norway, the main share comes
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from waste incineration plants and more electricity is used in the heating plants.
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The power market module covers the Nordic countries, Germany, the Netherlands and the UK [38]. It
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The Nordic energy system is characterized by well-developed district heating systems. As a comparison,
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The district heating system is modeled in the Nordic countries with a finer spatial resolution: there are
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fourteen district heating areas in Norway, thirteen in Sweden, eight in Denmark, and seven in Finland.
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Assumptions regarding installed DH boiler capacity in 2030 is based on [43-46], and the assumptions
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aggregated to country level are shown in figure 3.
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For the DH demand we have used [39] to estimate the annual consumption growth in each Nordic
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country except Norway by 2030. The annual figures was transformed to an hourly consumption profile
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over the year by assuming the same load characteristics as consumers in Trondheim. Statkraft Varme
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has provided hourly data for year 2012 on DH consumption for 776 consumers in the Trondheim DH
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system in Norway. The data has been indexed by consumer category and compared to the measured
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hourly outdoor temperature for Trondheim [47]. To correct the load pattern for different temperature
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and consumer mix in the DH system, the relationships are found using linear regression models with
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auto regressive terms. A regression model (Eq.1) is created for each season of the year (s), consumer
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category (c) and hour of the day (h), assuming a linear relationship between outdoor temperatures ( )
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up to 17 degrees Celsius, and consumption ( ). The consumption in hour h is also dependent on the
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consumption in hour h-1 ( ). The values for 1, 2 and 3 are then connected to observed hourly
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outdoor temperatures [47-49] and consumer mixes [42] in key locations that represent the different
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heat areas in Balmorel.
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(Eq.1)
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The estimation model is validated against observed heat consumption in the Oslo DH system. The mean
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absolute error of the model is measured to 10.0 % compared to 21.1 % for uncorrected data.
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2.2. Model simulation assumptions
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Initially, the impacts on VRE integration of P2H is analyzed by comparing the simulation results for four
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different scenarios regarding electric boiler capacity in the district heating sector (Table 3). We assume a
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low grid rent of 5 €/MWh, a CO2 price of 35 €/MWh and a hydro power supply corresponding to a
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normal hydrological year. A ramping cost is introduced for thermal power plants producing only power
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[14], but are in the current model version not included for technologies generating heat.
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3. Results and discussion 3.1. The modelled 2030 power generation mix
Table 4 summarizes the annual power generation level for different technologies and boiler types in the
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“Base” scenario – i.e. the scenario assuming no electric boiler capacity. For power generation we notice
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that hydropower remains the most important power source in the Nordic countries. Wind power
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production is significant and accounts for 70.5 TWh or 16.6 % of Nordic electricity production. The wind
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power shares in surrounding regions are higher: 26 %, 27 % and 41 % for Germany, the Netherlands and
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the UK respectively.
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3.2. Simulated use of P2H and corresponding change in average electricity prices and price variation Table 5 displays the modelled use of electric boilers in district heating (P2H) and the corresponding
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electricity prices and price variations for the different electric boiler capacity scenarios. All scenarios are
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simulated for a normal and a wet hydrological year where the total hydropower generation is 215 and
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249 TWh/year respectively. The electricity prices are calculated as a Nordic index price, which is a
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consumption weighted average price in the Nordic countries.
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According to the model results, the use of P2H will increase more than 400% in a normal year and more
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than 700% in a wet year if electric boilers corresponding to 20% of the peak consumption is installed
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(Medium scenario). The electricity price increase resulting from increased P2H is modest in the normal
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hydrological year. In the wet year, however, the price difference between “Base” and “Medium” is 49 %.
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Table 5 also shows that the variations in hourly electricity prices decreases when P2H increases, and the
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increased P2H internally in the Nordic countries causes lower net electricity export to Continental
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Europe.
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Figures 4 and 5 show how the prices are affected during the seasons. For the normal hydrological year,
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there is generally little price impact, but the most pronounced effect is observed during the summer
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months when the electricity price is low due to high supply and low demand. During the peak electricity
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demand in winter there is no price difference between the different P2H scenarios, since a high
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electricity price makes other fuels more competitive in the DH plants in this period. Also in the wet year,
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the peak winter prices are unaltered, but there is a substantial price increase in the spring, summer, and
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autumn. We also observe that the price increase from “Low” to “High” scenario is modest – i.e. electric
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boilers corresponding to 10 % of the peak DH consumption covers most of the P2H potential in our
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simulations.
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3.3. Main factors impacting P2H volumes
As discussed in chapter 2, the use of P2H in DH with multiple boilers will depend on the market
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conditions, such as the heat demand, the electricity price level, and the short-term electricity price
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variation, which in the future will be heavily affected by wind power generation. These relationships are
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shown for the modelled 2030 North European case in Figure 6. The weekly patterns of electric boilers to
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a large extent follows the wind power generation variations, especially during spring and summer, but a
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correlation with heating demand is also visible.
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An analysis of the correlations between the electric boiler use at an hourly level on the one hand and
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the electricity price, wind power generation, and heat demand on the other hand gives further insight
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on how flexible and system friendly the P2H option is (Table 6). In a normal year the highest correlation
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is found between electric boiler use and heat demand, but there is also a correlation of 0.35-0.36 to
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wind power generation. In the wet year (i.e. with assumptions of a larger export surplus from Norway
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and Sweden) there is a stronger correlation between electric boiler use and wind power generation, i.e.
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a stronger tendency of high electric boiler use in periods with high VRE supply – which is favorable from
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an energy system viewpoint. The correlations between electric boiler generation and electricity price is
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slightly negative. On the one hand, high prices occur in periods with high total demand for heat. On the
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other hand, the electric boiler is more competitive to other boiler options in the DH plant in periods with
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low electricity prices. The slightly negative correlation indicates that the latter effect is the stronger one.
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From the viewpoint of a robust energy system and integration of VRE, an even stronger correlation to
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wind power and more negative correlation to electricity prices would be favorable. As shown by [50],
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the flexible use of electric boilers in DH may be further increased if the electricity grid tariffs are
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structured to follow the electricity price.
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3.4. Impact on VRE market values and value factor
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The increase in power price shown in figure 5 and 6 indicates that increased P2H improves profitability
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of VRE. However, a general price increase also increase the profitability of other power generation
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technologies. To assess whether the competitiveness of VRE is improved, the price received by VRE
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producers, or the producer’s price of VRE relative to the average power prices, should be assessed.
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Figures 7 and 8 show the modelled P2H in DH and the corresponding increase in market value (i.e.
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producer’s price multiplied by the amount generated) for wind power and run-of-river hydropower in a
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normal and wet year. When assuming a normal hydrological year, our results indicate a potential of 3-9
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% increase in the VRE market value for extensive utilization of P2H. In wet years, with a significant
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generation surplus in the Nordic region, prices will generally be depressed. Increased use of P2H may in
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such conditions increase the VRE market share as much as 70 % according to our model results.
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The value factor shows the increase in the producer’s price of one technology relative to the average
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spot price. An increase in the value factor for wind power will hence reflect an increase in the
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competitiveness of wind power relative to other technologies. According to the model results, there is a
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slight increase in the wind power value factor when increasing the electric boiler capacity in a normal
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year (figure 9). The increase is most substantial for eastern Denmark, having a large wind power share
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and a large amount of DH. The change in wind power value factors are, however, more significant when
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assuming a wet year (i.e. a larger export surplus in the Nordic market).
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The value factor assessment shows that the increase in VRE competitiveness from a more active use of
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P2H in DH is heavily dependent on the market’s supply-demand balance. Assuming a Nordic hydropower
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generation of 215 TWh (normal year) the value factor increase is rather modest, but if the assumed
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hydropower generation is increased to 249 TWh (wet year) the wind power value factor increase 10-15
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% with realistic increases in P2H in DH (fig 10). Furthermore, it should be noted that we are analyzing
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the value of P2H flexibility for VRE integration in a market with very large shares of easily regulated
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hydropower. We observe that the largest change in value factor occurs in the areas having small
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amounts of hydropower. The increase in VRE competitiveness as a result of flexible P2H in DH is likely
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larger in other regions having less flexible hydro power on the supply side. From the VRE producers’
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viewpoint, electric boilers in DH represent a hedge against very low revenues; under market conditions
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that cause a low power price, more electricity is used in DH, and this limits the price decline. For the
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integration of VRE this effect is particularly important if the merit order effect of VRE is the main reason
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for low prices, since P2H in DH will have the opposite effect of the merit order effect in periods of high
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VRE supply.
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3.5. Impact on P2H of CO2 price assumptions The future CO2 price is highly uncertain and at the same time a major electricity price driver towards
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2030. The importance of the CO2 price on the magnitude of P2H is illustrated by running four scenarios
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for the 2030 CO2 price: 5, 20, 35, and 50 €/t. Figure 11 shows the modelled electricity price (x-axis) and
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the corresponding use of electricity in DH for the different CO2 price scenarios (High electric boiler
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capacity scenario). As expected, we observe that the electricity price increases with increased CO2
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prices. As a result, the usage of P2H decreases when the CO2 price increases, since a high electricity price
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level makes electricity less competitive in DH plants. Again, the option of using electricity in flexible DH
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plants represent a hedge against low electricity prices for VRE generators. However, the decrease in
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power price due to reduced CO2 prices show that there is still fossil fuel based power generation in the
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production mix in 2030.
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Ideally, electric boilers should only be used in times with no fossil fuel based electricity generation, as
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generation units in DH plants are more fuel-efficient. This underlines the importance of a firm CO2 price
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and suggests a holistic view of energy taxation for the power and heat sector. To evaluate this more
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appropriately it is recommended to apply a long-run modelling approach, where improved economic
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sustainability for VRE is analyzed.
3.6. Discussion
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The present study uses a set of assumptions regarding the future energy system, and these assumptions
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do to some extent affect the results. For example, the future demand for DH will be affected by means
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for improved energy efficiency through stricter building standards aiming to reduce heat losses, and
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might lead to lower space heating demand. On the other hand, increased population with higher
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standards in terms of indoor comfort and housing space pulls heating demand in the opposite direction.
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In this study, a slight decrease in Nordic district heating demand by 3.4 % is assumed. While demand for
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space heat seemingly will decrease, demand for domestic hot water and district cooling probably will
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increase. Consequently, heat demand might be more evenly distributed over the year. Effects of
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changes in heat demand profiles is an interesting topic for further research.
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The results are sensitive to other changes to the district heating system such as inclusion of waste heat
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and thermal storage [51]. [51] finds that the potential use of P2H in Sweden decreases if there is high
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access to heat sources with low marginal heat costs, such as solar heat, industrial waste heat, or heat
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from waste incineration. Heat from such sources might play a larger role in the future, e.g.? if district
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heating systems are operated on a lower temperature [52]. The present study, however, assumes only a
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slight increase in low marginal cost heat sources, which might lead to an overestimation of the P2H
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potential. [51] also concludes that access to thermal storage increases the potential of P2H. Thermal
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storage is not addressed in this study.
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4. Conclusions
A partial equilibrium model with a simultaneous modelling of the expected 2030 power and district
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heating sector was developed to analyze the energy system impacts of increased utilization of electricity
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for heating in the Nordic district heating sector. We find that the use of electric boilers in district heating
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will correlate positively with high wind speed levels and negatively with electricity prices, and that
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electricity to a very limited extent is used in district heating plants in peak electricity demand periods.
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Furthermore, we find that P2H improve VRE integration since it mitigates some of the negative
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economic effects of the merit order effect. The simulation results with high use of P2H show significantly
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higher average electricity prices, with an even higher increase in VRE market values, indicating that the
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system gain flexibility. The effect is more prominent in wet years with an increase in average electricity
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price of 49 % and an increase in the value factor for onshore wind power by up to 13 percentage points.
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The highest increase in wind value factor is found in eastern Denmark (DK2), a region that is highly
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exposed to wind power, has a high district heating demand, and a weak connection to flexible
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hydropower compared to other Nordic regions. The model simulations demonstrate that P2H is mainly
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used under market conditions causing low electricity prices, for example in periods of high hydropower
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supply, high wind power supply, or low CO2 prices. Hence, the P2H option in district heating plants
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represent a hedge against low electricity prices and revenues. In addition, P2H is higher in periods of
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high VRE supply and it therefore increases the competitiveness of VRE by mitigating the merit order
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effect reducing the VRE producer’s prices. This implies that less subsidies for increasing the VRE share is
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needed if electric boilers are installed in the district heating plants. This study focuses on the Nordic
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power market, which has a large share of flexible hydropower on the supply side. The benefits of P2H is
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probably even larger in regions with less supply-side flexibility.
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ACKNOWLEGDMENT
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This study is funded through the Flexelterm (www.flexelterm.no), and the Flex4RES project
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(www.Flex4RES.org). Flexelterm is owned and funded by Energy Norway with co-funding from the
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Norwegian Research Council under project no. 226260. Flex4RES is funded by Nordic Energy Research
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under grant 76084.
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Ravn, H., et al., Balmorel: A model for analyses of the electricity and CHP markets in the Baltic Sea region, in Balmorel Project. 2001: Denmark. Tveten, Å.G., Renewable energy in Northern European power markets: effects, challenges and integration options (Ph.D. Thesis), in Department of Ecology and Natural Resource Management. 2015, Norwegian University of Life Sciences. Capros, P., et al., EU energy, transport and GHG emissions, trends to 2050. 2014, European Commission: European Union. IEA, World Energy Outlook 2014. 2014. EA-Analyse, Analysis of biomass prices. 2013. Eurostat, Supply, transformation and consumption of heat - annual data (nrg_105a). 2012. SSB, District heating 2012, S. Norway, Editor. 2013: restricted access. DEA, Energiproducenttælling 2012, D.E. Agency, Editor. 2013: restricted access. energia, District heating in Finland 2012, tables, F. Energy, Editor. 2013: energi.fi. Svensk_Fjärrvärme, Fjärrvärmens bränslen och produktion 2012, S. Fjärrvärme, Editor. 2013. eKlima, eKlima - Free access to weather- and climate data from Norwegian Meteorological Institute from historical data to real time observations. 2013. SMHI, SMHI - Öppna data: Vindhastighet. 2015, Swedish Meteorological and Hydrological Institute. FMI, Wind speed time series. 2015, Finnish Meteorological Institute. Kirkerud, J.G., E. Trømborg, and T.F. Bolkesjø, Impacts of electricity grid tariffs on flexible use of electricity to heat generation. Energy. Schweiger, G., et al., The potential of power-to-heat in Swedish district heating systems. Energy. Lund, H., et al., 4th Generation District Heating (4GDH): Integrating smart thermal grids into future sustainable energy systems. Energy, 2014. 68: p. 1-11.
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1 Area 31 GWh
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1 Area .3 TWh
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1 Area 10 GWh 1 Area .5 TWh
1 Area 2 Areas .2 TWh 4.9 TWh 1 Area .1 TWh
1 Area .5 TWh
7 Areas 36.4 TWh
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4 Areas 8.6 TWh
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Figure 2. Number of district heating areas and the assumed total district heat generation 2030 in Balmorel.
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Installed capacity heat (GW)
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4 2 0 Denmark
Heat pump
Boilers bio
Finland
Boilers fossil
CHP bio
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CHP fossil
Figure 1: Assumed installed district heating boiler generation capacity for 2030 (MW)
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Figure 4: Weekly average price (Nordic index) for the different electric boiler scenarios in a normal year (€/MWh).
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8
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Week Base
Low
Medium
High
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Figure 5: Weekly average price (Nordic index) for the different electric boiler scenarios in a wet year (€/MWh).
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Heat demand
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Figure 6: Weekly heat demand (left axis), wind and electric boiler generation (right axis) for the “High” scenario in a wet year (GWh/week).
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60%
High Medium
50% Low
40% 30% 20% 10%
Low
Base
0%
Medium High
0% 0
5000
10000
15000
20000
Wind onshore normal year
Wind onshore wet year
461
Figure 7. Modelled P2H in normal and wet year under the different electric boiler scenarios (x-axis) and corresponding increase in market value for wind power (% relative to baseline) (y-axis).
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P2H (GWh)
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Run-of-river hydro wet year
Figure 8. Modelled P2H in normal and wet year under the different electric boiler scenarios (x-axis) and corresponding increase in market value for run-of-river hydropower (% relative to baseline) (y-axis).
8% Medium
7% 6% 5% Low
4% 3% 2% Base
1% 0% -
2 000
4 000
6 000
8 000
10 000
Denmark west
Denmark east
Norway east
Sweden mid
Finland
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Figure 9: Percentage point (pp) change in value factor for onshore wind when introducing more P2H capacity in a normal hydro year.
471
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6% 4% Base
0% -2% -
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Finland
Figure 10: Percentage point (pp) change in onshore wind value factor when introducing more P2H capacity in a wet hydro year.
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40,000 5 €/t 35,000 30,000
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5 €/t
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50 €/t 15,000
Wet year
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30
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Average Nordic electricity price (€/MWh)
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Figure 11: Change in average Nordic electricity price and P2H use with changing in CO2 price. Normal hydrological conditions and high level of electric boiler installations are assumed.
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Table 1. Installed electrical capacities (MW) and assumed annual net electricity consumption (TWh) in 2030. Nuclear
Thermal flexible
Thermal must- run
CHP DH
Wind power
Solar power
Hydropower
Net electricity consumption (TWh)
Norway
0
0
677
72
4960
0
36139
118.5
Denmark
0
559
116
4113
8021
1440
0
30.9
4510
1171
3230
4031
4692
0
3426
83.0
0
53516
31177
0
67118
68000
610
13979
14082
0
12684
1478
Finland Germany Netherlands
RI PT
479
10669
548.8
37
120.2
482
Table 2. Assumed fuel and emission prices in 2030 (€/MWh). Fuel Hard coal Lignite Light oil
Wood chips
32.0
Wood pellets Bio oil
40.0 60.3
CO2 (€/t)
35.0
Table 3. Specification of the four scenarios for electric boiler capacity in the DH sector. Scenario Base Low Medium High
Electric boiler capacity, as % of peak district heating capacity 0% 10% 20% 50%
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4.7 66.1
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31.5
12.2
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Price (€/MWh)
M AN U
Natural gas
SC
480 481
6673 1185 1420 3762 11300 0 16921 137.8 Sweden United 8433 42837 11439 0 51344 23300 4483 338.7 Kingdom Notes: CHP DH in non-Nordic countries are modeled as must-run. Net electricity consumption excludes consumption for pumped hydro storage, own consumption in electricity plants and losses in transmission and distribution.
Electric boiler capacity (MW) 0 MW 4809 MW 9617 MW 24043 MW
Table 4: Modelled 2030 power generation (TWh) in the “Base” scenario. Nuclear
Norway
-
Thermal Flexible -
Thermal must-run 3.5
CHP DH 0.6
Wind power 13.8
Solar power 0.0
Hydropower 133.1
Denmark
-
0.0
0.5
13.1
24.8
1.3
-
Finland Germany
35.9 -
0.0 176.4
10.3 160.3
12.9 -
8.0 163.1
0.0 67.5
14.4 27.5
Netherlands
4.5
26.0
53.4
-
33.8
1.5
0.2
Sweden United Kingdom
43.8
0.0
6.6
9.4
23.8
0.0
67.9
58.3
80.8
45.2
-
151.8
22.3
9.4
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Table 5: Modelled electric boiler generation in district heating and the corresponding average power price and price variation under different electric boiler scenarios in a normal hydrological year. Wet year
Normal year Low
Medium
High
Base
Low
Medium
High
Electric boiler use (TWh)
2.5
8.6
10.4
11.3
2.5
14.9
17.8
19.1
Average power price (€/MWh)
45.6
47.6
48.1
48.2
26.9
38.6
40.2
40.8
Standard deviation (€/MWh)
11.9
10.9
10.7
10.8
16.8
13.2
13.0
13.0
489 490
RI PT
Base
Table 6: Cross correlation between electric boiler generation and possible drivers for P2H generation. Normal Hydrology
Wet Hydrology
Medium
High
Low
Medium
High
Wind power generation Heat demand
0.36 0.62
0.36 0.63
0.35 0.61
0.46 0.40
0.45 0.49
0.45 0.51
Electricity prices
-0.10
-0.03
-0.01
-0.19
-0.06
-0.01
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Highlights
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• Power-to-heat (P2H) has a potential of up to 19.1 TWh electricity in the Nordic energy system • Modeled P2H use is very limited in peak hours • Variable renewable energy are more competitive with high rates of P2H • Investments in P2H can represent a hedge against wet years and low CO2 prices
S0360-5442(17)30547-9
DOI:
10.1016/j.energy.2017.03.153
Reference:
EGY 10620
To appear in:
Energy
Received Date: 7 November 2016 Revised Date:
27 March 2017
Accepted Date: 29 March 2017
Please cite this article as: Kirkerud JG, Bolkesjø TF, Trømborg E, Power-to-heat as a flexibility measure for integration of renewable energy, Energy (2017), doi: 10.1016/j.energy.2017.03.153. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Power-to-heat as a flexibility measure for integration of renewable energy
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Jon Gustav Kirkerud
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Corresponding author
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Norwegian University of Life Sciences
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Faculty of Environmental Sciences and Natural Resource Management
8 Torjus Folsland Bolkesjø
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Norwegian University of Life Sciences
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Faculty of Environmental Sciences and Natural Resource Management
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Erik Trømborg
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Norwegian University of Life Sciences
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Faculty of Environmental Sciences and Natural Resource Management
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Corresponding author contact details:
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E-Mail: [email protected]
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P.O. Box 5003, NO-1432 Ås
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Phone: + 47 64965800
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Fax: + 47 64965801
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Abstract Higher shares of variable renewable energy (VRE) in energy systems reduce electricity spot prices at
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times when the supply of these sources are most abundant, leading to a lower market value per unit for
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these sources compared to energy sources with a constant or adjustable supply. This disadvantage for
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VRE can be offset by evolving the energy system towards wider market integration and flexible demand
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or supply. This study analyses how increased integration of heat and power markets by use of power-to-
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heat in district heating system could increase the value VRE sources in the Northern European power
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system. For the quantification, we apply a partial equilibrium model with a detailed representation of
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power and district heat generation for a likely 2030 energy system. The results show a markedly
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increase in VRE market value when installed capacity of power-to-heat is increased, especially in
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scenarios with a large Nordic power surplus. The study concludes that power-to-heat solutions in district
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heating systems can increase system flexibility in a short time perspective that considers hours to
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weeks, but also in a longer perspective that accounts for the significant inter-annual variability in
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hydropower supply.
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Keywords: Variable renewable energy, flexibility, district heating, energy system modelling
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1. Introduction
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To reach the ambitious energy and climate goals a growing share of variable renewable energy sources
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(VRE) is deployed in Europe, especially wind power and solar photovoltaics (PV). By nature, the temporal
49
supply of VRE is highly variable because it is determined by weather conditions, it is uncertain due to
50
forecasting errors, and it is location specific, as the primary energy carrier cannot be transported such as
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coal or biomass. As pointed out by [1] these characteristics have implications for welfare, cost-benefit
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and competitiveness analysis. From the energy system viewpoint, the variability imply major integration
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challenges of VRE with the energy system [2-7]. VRE will affect balancing costs as a consequence of
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forecasting errors [7-10], the costs of distribution and transmission networks [11-14], provision of firm
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reserve capacity [15, 16] cause lower utilization rates and more cycling and ramping of traditional plants
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[6, 17]. At high VRE penetration rates, the integration costs could be substantial [1, 18, 19].
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The system challenges of high VRE shares are substantial and they receive much attention in the
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literature. Large VRE shares, however, also have implications to the VRE competitiveness, i.e. from the
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private economic perspective, since prices received by VRE producers decrease more than the average
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price due to the merit order effect [7, 20]. The market price impacts of VRE are already observed in the
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day ahead wholesale electricity price. In markets with high VRE shares, electricity prices are reduced
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[21] and the price pattern changes; there are lower hourly electricity prices when VRE supply is high [7,
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22]. VRE has practically zero marginal costs and replaces the most expensive electricity production in the
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merit order, thus lowering the electricity price. Hirth [7] finds that higher shares of wind or solar power
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leads to a drop in the market value for these sources, defined as average revenues per unit produced on
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the spot market, in both absolute value and relative value compared to the average power price. This
67
effect is often referred to as the merit order effect and it is already significant in some areas: Fraunhofer
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ISE [23] report that the German wind power market value was 26.8 €/MWh in 2015, well below an
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average price of 31.2 €/MWh giving a relative market value, called value factor [7], of 86 %. Huge
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depressions in market values will reduce the potential of VRE as means for reaching the energy and
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climate goals.
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As stated by IEA [19], the key to integrate VRE is flexibility, which describes the extent to which a power
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system can adapt the patterns of electricity generation and consumption in a cost effective manner
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while maintaining balance and security. Introducing more flexible elements in the energy system have
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several benefits from a system perspective, and it could increase the market value of variable renewable
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generation [24]. Using electric power to produce heat (P2H) in district heating (DH) plants is seen as a
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promising solution for increased flexibility because the technology needed is mature, can provide
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flexibility both over short and long time spans, and the potential is vast [24]. The flexibility occurs when
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heat pumps or electric boilers replace other boilers in periods when the VRE supply is high with
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corresponding low electricity prices. Increasing the demand in these periods can raise the price and
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hence the VRE market value [25]. At the same time, in periods of low VRE supply, electricity demand is
82
being unchanged since other fuels have lower costs in DH plants under such market conditions.
83
Consequently, the electricity price increases more in low price hours than in high price hours, resulting
84
in a more stable price both over shorter and longer time horizons. This implies a lower price risk [22]
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that together with higher market values for VRE will improve the competitiveness of VRE.
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Heating and cooling accounts for 50 % of EU’s final energy demand and the renewable share is only 18 %
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[26]. From the heating sector point of view, using variable renewable energy through P2H represent an
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opportunity to reduce both fuel costs and emissions from fossil fuels. The advantages of using electricity
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in DH has been analyzed at a plant level in several studies such as [27, 28] and at a regional or national
90
level in for example [29-34]. Generally, allowing for stronger integration between the heat and power
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market could diversify the fuel supply and open for more renewable heat production. District heat as an
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energy carrier also has the advantages of low cost storage [35, 36] and peak load capacity. With better
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P2H integration, these advantages can benefit the whole energy system. There are, however, few
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studies addressing increased power and heat market integration in a broader power market perspective.
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The present paper attempt to take the power market perspective when analyzing power and heat
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market integration by quantifying how increased use of flexible electricity in the district heating sector
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affects the power and heat market and VRE competitiveness through changes in power prices. The study
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uses a comprehensive linear partial equilibrium model, Balmorel, which is calibrated with data for the
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expected Nordic power and district heat market in 2030. The Nordic market is characterized by a large
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district heat sector in combination with high and growing VRE shares domestically and in neighboring
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countries and is hence a particularly interesting region in this context. Moreover, the Nordic countries
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were an early mover in electricity market deregulation, and may be so for a stronger coupling of the
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power and heat markets to mitigate the challenges of high VRE shares.
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2. Materials and methods 2.1. The Balmorel model
The market impacts of P2H is analyzed with a detailed partial equilibrium model for the Nordic power
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and district heating sector, Balmorel [30, 37, 38]. The Balmorel model finds the least cost power and
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heat operation strategy in meeting the demand, given predefined power and heat generation capacities
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and transmission bottlenecks. The model solution provides market-clearing production, transmission
112
levels and prices for each geographical unit and time step, under the assumption of competitive
113
markets.
114
The current model version is updated with 2012 base year data. Full model and data documentation are
115
found in [38]. The exogenous model parameters like demand, capacities of the different generation
116
technologies, transmission capacity, and availability of variable renewable energy sources are specified
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individually for each power region and heat area. The model calculates the electricity and heat
118
production per technology, time unit, and region, minimizing total system costs for a given electricity
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and heat demand. Market-clearing conditions in Balmorel are analyzed by applying two different modes
120
of the model: i) a long-term (one year) optimization horizon where the total reservoir hydropower
121
generation is allocated on week level, and ii) a short-term (weekly) optimization horizon with an hourly
122
time resolution where the weekly hydropower supply is allocated on an hourly basis.
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The model version used for this study uses an exogenously decided generation capacity mix for a 2030
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scenario. The production capacities are determined exogenously based on external sources such as the
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European commission roadmap to 2050 [39].
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Power sector
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has a detailed representation of the Nordic countries and especially the Norwegian and Swedish
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hydropower system, which may provide large amounts of flexibility (figure 1).
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Other interconnected Northern European energy markets are handled as third countries with
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exogenously given power exchange. The main assumptions regarding installed capacities and
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consumption [38] in the different modelled countries are shown in table 1. For the electricity
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consumption, we apply the observed hourly consumption profile of 2012 to distribute the annual
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consumption over the modelled 8760 hours. The same year is applied as base year for hourly profile of
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wind and solar power.
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Fuel price assumptions are based on [40, 41] and they are shown in table 2. Prices for biomass are 25 %
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higher than a medium price development, representing a high price scenario.
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District heating sector
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the Nordic members of the EU (Denmark, Finland and Sweden) account for 29% of all district heating
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demand for households and services in the EU [42]. The energy demand served by the Nordic district
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heating sector accounted for 119 TWh in 2013, having the size around one third of the electricity market
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in the region. A slight decrease in Nordic district heating demand by 3.4 % from 2013 to 2030 is
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assumed, following [39]. The assumed increase in Norway’s district heating demand offsets some of the
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decline in the other countries. In Denmark and Finland, district heating systems are built around larger
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combined heat and power stations with a high share of fossil fuels and peat. In Sweden and Finland, a
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large share also comes from byproducts from the forest industries. In Norway, the main share comes
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from waste incineration plants and more electricity is used in the heating plants.
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The power market module covers the Nordic countries, Germany, the Netherlands and the UK [38]. It
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The Nordic energy system is characterized by well-developed district heating systems. As a comparison,
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The district heating system is modeled in the Nordic countries with a finer spatial resolution: there are
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fourteen district heating areas in Norway, thirteen in Sweden, eight in Denmark, and seven in Finland.
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Assumptions regarding installed DH boiler capacity in 2030 is based on [43-46], and the assumptions
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aggregated to country level are shown in figure 3.
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For the DH demand we have used [39] to estimate the annual consumption growth in each Nordic
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country except Norway by 2030. The annual figures was transformed to an hourly consumption profile
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over the year by assuming the same load characteristics as consumers in Trondheim. Statkraft Varme
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has provided hourly data for year 2012 on DH consumption for 776 consumers in the Trondheim DH
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system in Norway. The data has been indexed by consumer category and compared to the measured
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hourly outdoor temperature for Trondheim [47]. To correct the load pattern for different temperature
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and consumer mix in the DH system, the relationships are found using linear regression models with
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auto regressive terms. A regression model (Eq.1) is created for each season of the year (s), consumer
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category (c) and hour of the day (h), assuming a linear relationship between outdoor temperatures ( )
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up to 17 degrees Celsius, and consumption ( ). The consumption in hour h is also dependent on the
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consumption in hour h-1 ( ). The values for 1, 2 and 3 are then connected to observed hourly
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outdoor temperatures [47-49] and consumer mixes [42] in key locations that represent the different
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heat areas in Balmorel.
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(Eq.1)
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The estimation model is validated against observed heat consumption in the Oslo DH system. The mean
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absolute error of the model is measured to 10.0 % compared to 21.1 % for uncorrected data.
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2.2. Model simulation assumptions
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Initially, the impacts on VRE integration of P2H is analyzed by comparing the simulation results for four
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different scenarios regarding electric boiler capacity in the district heating sector (Table 3). We assume a
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low grid rent of 5 €/MWh, a CO2 price of 35 €/MWh and a hydro power supply corresponding to a
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normal hydrological year. A ramping cost is introduced for thermal power plants producing only power
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[14], but are in the current model version not included for technologies generating heat.
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3. Results and discussion 3.1. The modelled 2030 power generation mix
Table 4 summarizes the annual power generation level for different technologies and boiler types in the
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“Base” scenario – i.e. the scenario assuming no electric boiler capacity. For power generation we notice
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that hydropower remains the most important power source in the Nordic countries. Wind power
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production is significant and accounts for 70.5 TWh or 16.6 % of Nordic electricity production. The wind
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power shares in surrounding regions are higher: 26 %, 27 % and 41 % for Germany, the Netherlands and
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the UK respectively.
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3.2. Simulated use of P2H and corresponding change in average electricity prices and price variation Table 5 displays the modelled use of electric boilers in district heating (P2H) and the corresponding
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electricity prices and price variations for the different electric boiler capacity scenarios. All scenarios are
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simulated for a normal and a wet hydrological year where the total hydropower generation is 215 and
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249 TWh/year respectively. The electricity prices are calculated as a Nordic index price, which is a
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consumption weighted average price in the Nordic countries.
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According to the model results, the use of P2H will increase more than 400% in a normal year and more
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than 700% in a wet year if electric boilers corresponding to 20% of the peak consumption is installed
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(Medium scenario). The electricity price increase resulting from increased P2H is modest in the normal
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hydrological year. In the wet year, however, the price difference between “Base” and “Medium” is 49 %.
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Table 5 also shows that the variations in hourly electricity prices decreases when P2H increases, and the
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increased P2H internally in the Nordic countries causes lower net electricity export to Continental
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Europe.
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Figures 4 and 5 show how the prices are affected during the seasons. For the normal hydrological year,
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there is generally little price impact, but the most pronounced effect is observed during the summer
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months when the electricity price is low due to high supply and low demand. During the peak electricity
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demand in winter there is no price difference between the different P2H scenarios, since a high
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electricity price makes other fuels more competitive in the DH plants in this period. Also in the wet year,
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the peak winter prices are unaltered, but there is a substantial price increase in the spring, summer, and
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autumn. We also observe that the price increase from “Low” to “High” scenario is modest – i.e. electric
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boilers corresponding to 10 % of the peak DH consumption covers most of the P2H potential in our
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simulations.
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3.3. Main factors impacting P2H volumes
As discussed in chapter 2, the use of P2H in DH with multiple boilers will depend on the market
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conditions, such as the heat demand, the electricity price level, and the short-term electricity price
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variation, which in the future will be heavily affected by wind power generation. These relationships are
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shown for the modelled 2030 North European case in Figure 6. The weekly patterns of electric boilers to
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a large extent follows the wind power generation variations, especially during spring and summer, but a
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correlation with heating demand is also visible.
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An analysis of the correlations between the electric boiler use at an hourly level on the one hand and
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the electricity price, wind power generation, and heat demand on the other hand gives further insight
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on how flexible and system friendly the P2H option is (Table 6). In a normal year the highest correlation
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is found between electric boiler use and heat demand, but there is also a correlation of 0.35-0.36 to
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wind power generation. In the wet year (i.e. with assumptions of a larger export surplus from Norway
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and Sweden) there is a stronger correlation between electric boiler use and wind power generation, i.e.
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a stronger tendency of high electric boiler use in periods with high VRE supply – which is favorable from
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an energy system viewpoint. The correlations between electric boiler generation and electricity price is
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slightly negative. On the one hand, high prices occur in periods with high total demand for heat. On the
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other hand, the electric boiler is more competitive to other boiler options in the DH plant in periods with
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low electricity prices. The slightly negative correlation indicates that the latter effect is the stronger one.
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From the viewpoint of a robust energy system and integration of VRE, an even stronger correlation to
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wind power and more negative correlation to electricity prices would be favorable. As shown by [50],
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the flexible use of electric boilers in DH may be further increased if the electricity grid tariffs are
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structured to follow the electricity price.
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3.4. Impact on VRE market values and value factor
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The increase in power price shown in figure 5 and 6 indicates that increased P2H improves profitability
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of VRE. However, a general price increase also increase the profitability of other power generation
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technologies. To assess whether the competitiveness of VRE is improved, the price received by VRE
239
producers, or the producer’s price of VRE relative to the average power prices, should be assessed.
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Figures 7 and 8 show the modelled P2H in DH and the corresponding increase in market value (i.e.
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producer’s price multiplied by the amount generated) for wind power and run-of-river hydropower in a
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normal and wet year. When assuming a normal hydrological year, our results indicate a potential of 3-9
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% increase in the VRE market value for extensive utilization of P2H. In wet years, with a significant
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generation surplus in the Nordic region, prices will generally be depressed. Increased use of P2H may in
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such conditions increase the VRE market share as much as 70 % according to our model results.
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The value factor shows the increase in the producer’s price of one technology relative to the average
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spot price. An increase in the value factor for wind power will hence reflect an increase in the
248
competitiveness of wind power relative to other technologies. According to the model results, there is a
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slight increase in the wind power value factor when increasing the electric boiler capacity in a normal
250
year (figure 9). The increase is most substantial for eastern Denmark, having a large wind power share
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and a large amount of DH. The change in wind power value factors are, however, more significant when
252
assuming a wet year (i.e. a larger export surplus in the Nordic market).
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The value factor assessment shows that the increase in VRE competitiveness from a more active use of
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P2H in DH is heavily dependent on the market’s supply-demand balance. Assuming a Nordic hydropower
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generation of 215 TWh (normal year) the value factor increase is rather modest, but if the assumed
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hydropower generation is increased to 249 TWh (wet year) the wind power value factor increase 10-15
257
% with realistic increases in P2H in DH (fig 10). Furthermore, it should be noted that we are analyzing
258
the value of P2H flexibility for VRE integration in a market with very large shares of easily regulated
259
hydropower. We observe that the largest change in value factor occurs in the areas having small
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amounts of hydropower. The increase in VRE competitiveness as a result of flexible P2H in DH is likely
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larger in other regions having less flexible hydro power on the supply side. From the VRE producers’
262
viewpoint, electric boilers in DH represent a hedge against very low revenues; under market conditions
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that cause a low power price, more electricity is used in DH, and this limits the price decline. For the
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integration of VRE this effect is particularly important if the merit order effect of VRE is the main reason
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for low prices, since P2H in DH will have the opposite effect of the merit order effect in periods of high
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VRE supply.
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3.5. Impact on P2H of CO2 price assumptions The future CO2 price is highly uncertain and at the same time a major electricity price driver towards
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2030. The importance of the CO2 price on the magnitude of P2H is illustrated by running four scenarios
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for the 2030 CO2 price: 5, 20, 35, and 50 €/t. Figure 11 shows the modelled electricity price (x-axis) and
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the corresponding use of electricity in DH for the different CO2 price scenarios (High electric boiler
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capacity scenario). As expected, we observe that the electricity price increases with increased CO2
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prices. As a result, the usage of P2H decreases when the CO2 price increases, since a high electricity price
275
level makes electricity less competitive in DH plants. Again, the option of using electricity in flexible DH
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plants represent a hedge against low electricity prices for VRE generators. However, the decrease in
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power price due to reduced CO2 prices show that there is still fossil fuel based power generation in the
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production mix in 2030.
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Ideally, electric boilers should only be used in times with no fossil fuel based electricity generation, as
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generation units in DH plants are more fuel-efficient. This underlines the importance of a firm CO2 price
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and suggests a holistic view of energy taxation for the power and heat sector. To evaluate this more
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appropriately it is recommended to apply a long-run modelling approach, where improved economic
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sustainability for VRE is analyzed.
3.6. Discussion
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The present study uses a set of assumptions regarding the future energy system, and these assumptions
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do to some extent affect the results. For example, the future demand for DH will be affected by means
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for improved energy efficiency through stricter building standards aiming to reduce heat losses, and
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might lead to lower space heating demand. On the other hand, increased population with higher
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standards in terms of indoor comfort and housing space pulls heating demand in the opposite direction.
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In this study, a slight decrease in Nordic district heating demand by 3.4 % is assumed. While demand for
292
space heat seemingly will decrease, demand for domestic hot water and district cooling probably will
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increase. Consequently, heat demand might be more evenly distributed over the year. Effects of
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changes in heat demand profiles is an interesting topic for further research.
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The results are sensitive to other changes to the district heating system such as inclusion of waste heat
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and thermal storage [51]. [51] finds that the potential use of P2H in Sweden decreases if there is high
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access to heat sources with low marginal heat costs, such as solar heat, industrial waste heat, or heat
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from waste incineration. Heat from such sources might play a larger role in the future, e.g.? if district
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heating systems are operated on a lower temperature [52]. The present study, however, assumes only a
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slight increase in low marginal cost heat sources, which might lead to an overestimation of the P2H
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potential. [51] also concludes that access to thermal storage increases the potential of P2H. Thermal
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storage is not addressed in this study.
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4. Conclusions
A partial equilibrium model with a simultaneous modelling of the expected 2030 power and district
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heating sector was developed to analyze the energy system impacts of increased utilization of electricity
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for heating in the Nordic district heating sector. We find that the use of electric boilers in district heating
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will correlate positively with high wind speed levels and negatively with electricity prices, and that
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electricity to a very limited extent is used in district heating plants in peak electricity demand periods.
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Furthermore, we find that P2H improve VRE integration since it mitigates some of the negative
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economic effects of the merit order effect. The simulation results with high use of P2H show significantly
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higher average electricity prices, with an even higher increase in VRE market values, indicating that the
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system gain flexibility. The effect is more prominent in wet years with an increase in average electricity
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price of 49 % and an increase in the value factor for onshore wind power by up to 13 percentage points.
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The highest increase in wind value factor is found in eastern Denmark (DK2), a region that is highly
317
exposed to wind power, has a high district heating demand, and a weak connection to flexible
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hydropower compared to other Nordic regions. The model simulations demonstrate that P2H is mainly
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used under market conditions causing low electricity prices, for example in periods of high hydropower
320
supply, high wind power supply, or low CO2 prices. Hence, the P2H option in district heating plants
321
represent a hedge against low electricity prices and revenues. In addition, P2H is higher in periods of
322
high VRE supply and it therefore increases the competitiveness of VRE by mitigating the merit order
323
effect reducing the VRE producer’s prices. This implies that less subsidies for increasing the VRE share is
324
needed if electric boilers are installed in the district heating plants. This study focuses on the Nordic
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power market, which has a large share of flexible hydropower on the supply side. The benefits of P2H is
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probably even larger in regions with less supply-side flexibility.
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ACKNOWLEGDMENT
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This study is funded through the Flexelterm (www.flexelterm.no), and the Flex4RES project
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(www.Flex4RES.org). Flexelterm is owned and funded by Energy Norway with co-funding from the
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Norwegian Research Council under project no. 226260. Flex4RES is funded by Nordic Energy Research
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under grant 76084.
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1 Area 31 GWh
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60%
High Medium
50% Low
40% 30% 20% 10%
Low
Base
0%
Medium High
0% 0
5000
10000
15000
20000
Wind onshore normal year
Wind onshore wet year
461
Figure 7. Modelled P2H in normal and wet year under the different electric boiler scenarios (x-axis) and corresponding increase in market value for wind power (% relative to baseline) (y-axis).
M AN U
462 463
25000
SC
P2H (GWh)
RI PT
Change in market value
70%
464
High
70%
Medium
60%
Low
40% 30% 20% Base
10% 0%
Low
0% 0
TE D
50%
5000
Medium
High
10000
EP
Change in market value
80%
15000
20000
25000
P2H (GWh)
466 467
AC C
Run-of-river hydro normal year
465
Run-of-river hydro wet year
Figure 8. Modelled P2H in normal and wet year under the different electric boiler scenarios (x-axis) and corresponding increase in market value for run-of-river hydropower (% relative to baseline) (y-axis).
8% Medium
7% 6% 5% Low
4% 3% 2% Base
1% 0% -
2 000
4 000
6 000
8 000
10 000
Denmark west
Denmark east
Norway east
Sweden mid
Finland
468
M AN U
Figure 9: Percentage point (pp) change in value factor for onshore wind when introducing more P2H capacity in a normal hydro year.
471
14% 12%
6% 4% Base
0% -2% -
5 000
TE D
8%
2%
High
Low
10%
10 000
15 000
20 000
Nordic P2H (GWh)
Denmark west
Denmark east
Norway east
Sweden mid
AC C
-4%
473 474
Medium
EP
Change in value factor (pp)
16%
472
12 000
SC
Nordic P2H (GWh)
469 470
High
RI PT
Change in value factor (pp)
ACCEPTED MANUSCRIPT
Finland
Figure 10: Percentage point (pp) change in onshore wind value factor when introducing more P2H capacity in a wet hydro year.
ACCEPTED MANUSCRIPT
40,000 5 €/t 35,000 30,000
35 €/t
5 €/t
20,000
RI PT
P2H (GWh)
20€/t 25,000 Nomal year
50 €/t 15,000
Wet year
20 €/t
10,000
50€/t 0
10
20
30
40
50
475 476 477
60
M AN U
Average Nordic electricity price (€/MWh)
SC
35 €/t
5,000
Figure 11: Change in average Nordic electricity price and P2H use with changing in CO2 price. Normal hydrological conditions and high level of electric boiler installations are assumed.
AC C
EP
TE D
478
ACCEPTED MANUSCRIPT
Table 1. Installed electrical capacities (MW) and assumed annual net electricity consumption (TWh) in 2030. Nuclear
Thermal flexible
Thermal must- run
CHP DH
Wind power
Solar power
Hydropower
Net electricity consumption (TWh)
Norway
0
0
677
72
4960
0
36139
118.5
Denmark
0
559
116
4113
8021
1440
0
30.9
4510
1171
3230
4031
4692
0
3426
83.0
0
53516
31177
0
67118
68000
610
13979
14082
0
12684
1478
Finland Germany Netherlands
RI PT
479
10669
548.8
37
120.2
482
Table 2. Assumed fuel and emission prices in 2030 (€/MWh). Fuel Hard coal Lignite Light oil
Wood chips
32.0
Wood pellets Bio oil
40.0 60.3
CO2 (€/t)
35.0
Table 3. Specification of the four scenarios for electric boiler capacity in the DH sector. Scenario Base Low Medium High
Electric boiler capacity, as % of peak district heating capacity 0% 10% 20% 50%
AC C
485 486
4.7 66.1
EP
484
31.5
12.2
TE D
483
Price (€/MWh)
M AN U
Natural gas
SC
480 481
6673 1185 1420 3762 11300 0 16921 137.8 Sweden United 8433 42837 11439 0 51344 23300 4483 338.7 Kingdom Notes: CHP DH in non-Nordic countries are modeled as must-run. Net electricity consumption excludes consumption for pumped hydro storage, own consumption in electricity plants and losses in transmission and distribution.
Electric boiler capacity (MW) 0 MW 4809 MW 9617 MW 24043 MW
Table 4: Modelled 2030 power generation (TWh) in the “Base” scenario. Nuclear
Norway
-
Thermal Flexible -
Thermal must-run 3.5
CHP DH 0.6
Wind power 13.8
Solar power 0.0
Hydropower 133.1
Denmark
-
0.0
0.5
13.1
24.8
1.3
-
Finland Germany
35.9 -
0.0 176.4
10.3 160.3
12.9 -
8.0 163.1
0.0 67.5
14.4 27.5
Netherlands
4.5
26.0
53.4
-
33.8
1.5
0.2
Sweden United Kingdom
43.8
0.0
6.6
9.4
23.8
0.0
67.9
58.3
80.8
45.2
-
151.8
22.3
9.4
ACCEPTED MANUSCRIPT
487 488
Table 5: Modelled electric boiler generation in district heating and the corresponding average power price and price variation under different electric boiler scenarios in a normal hydrological year. Wet year
Normal year Low
Medium
High
Base
Low
Medium
High
Electric boiler use (TWh)
2.5
8.6
10.4
11.3
2.5
14.9
17.8
19.1
Average power price (€/MWh)
45.6
47.6
48.1
48.2
26.9
38.6
40.2
40.8
Standard deviation (€/MWh)
11.9
10.9
10.7
10.8
16.8
13.2
13.0
13.0
489 490
RI PT
Base
Table 6: Cross correlation between electric boiler generation and possible drivers for P2H generation. Normal Hydrology
Wet Hydrology
Medium
High
Low
Medium
High
Wind power generation Heat demand
0.36 0.62
0.36 0.63
0.35 0.61
0.46 0.40
0.45 0.49
0.45 0.51
Electricity prices
-0.10
-0.03
-0.01
-0.19
-0.06
-0.01
AC C
EP
M AN U
TE D
491
SC
Low
ACCEPTED MANUSCRIPT
Highlights
AC C
EP
TE D
M AN U
SC
RI PT
• Power-to-heat (P2H) has a potential of up to 19.1 TWh electricity in the Nordic energy system • Modeled P2H use is very limited in peak hours • Variable renewable energy are more competitive with high rates of P2H • Investments in P2H can represent a hedge against wet years and low CO2 prices