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Smart Energy Markets - Future electricity, gas and heating markets
Renewable and Sustainable Energy Reviews 119 (2020) 109655
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Renewable and Sustainable Energy Reviews journal homepage: http://www.elsevier.com/locate/rser
Smart Energy Markets - Future electricity, gas and heating markets P. Sorknæs a, *, H. Lund a, I.R. Skov b, S. Djørup a, K. Skytte c, P.E. Morthorst c, F. Fausto c a
Department of Planning, Aalborg University, Rendsburggade 14, DK-9000, Aalborg, Denmark Department of Planning, Aalborg University, A.C. Meyers Vænge 15, DK-2450, Copenhagen, Denmark c DTU Management, Energy Economics and Regulation, Produktionstorvet 424, DK-2800, Kgs. Lyngby, Denmark b
A R T I C L E I N F O
A B S T R A C T
Keywords: Electricity markets Gas markets Heating markets Energy markets Smart energy system Renewable energy
This paper introduces the concept of Smart Energy Markets. Sustainable smart energy systems based on renewable energy cannot be implemented without addressing the issue of how to re-design existing electricity and gas markets. Moreover, markets for heating and transport fuels will also be challenged. In recent years, the re-design of the electricity market has attracted a lot of attention and is an area of focus in research. However, the re-design of electricity markets should not be seen isolated from the re-design of other energy markets. By applying a 100% renewable energy system scenario, this paper illustrates and quantifies how future renewable heating, green gas and liquid fuel markets will influence the electricity markets and vice versa. Based on this scenario, it is found that annual average market prices on the future green gas and liquid fuel markets may potentially be affected by the electricity and heating markets by 60–120 EUR/MWh, and the heating, green gas and liquid fuel markets could influence the annual average electricity prices by up to 28 EUR/MWh. The concept of Smart Energy Markets expresses the point that mutual influences become essential in the design of future energy markets facilitating the transition into smart energy systems based on variable renewable energy.
1. Introduction
Systems [9]. The concept of Smart Energy Systems highlights that not only should individual energy grids should be smart, but that all energy grids (electricity, thermal and gas) should be smart, and that these smart grids should be connected so to better exploit synergies across the different individual energy grids [9]. In such energy systems, it is possible to utilise the electricity produced by, e.g., wind turbines and PV in other sectors, such as heating and transport. This enables a more flexible use of different types of energy storage options, where e.g. thermal storages are about 100 times cheaper than electricity storages when comparing investments per unit of storage, which in turn can facilitate a cheaper and more energy efficient integration of VRE [10]. Within the transport sector, this is especially relevant with the use of electric vehicles and direct electrification of the sector, as the direct use of electricity is more efficient than storing electricity as fuel [11]. However, electrification will most likely not be relevant or possible in all modes of transport, such as long-haul transport (marine, heavy-duty road vehicles and aviation) [11]. As such, besides the direct electrifi cation of the transport sector, the smart energy system approach also emphasizes the production of renewable fuels for the modes of transport of which direct electrification is not possible [5,12]. Especially
Globally, energy systems are increasingly moving towards a pro duction primarily based on renewable energy sources (RES). Many countries have defined long-term goals of converting to energy systems based mainly or solely on RES [1]. In energy systems based on RES, variable RES (VRE), like wind power and PV, are expected to be important components [2,3]. The variability of the production from VRE has proven to require new methods for creating a balance between production and demand in the electricity system [4]. Research has found that, in order to implement large shares of VRE in energy systems, an increased interaction between the different energy sectors is required. Furthermore, the increased interaction will be beneficial in terms of making the transition fuel-efficient and cost-effective [5]. In many en ergy systems based on RES, another main fuel will be biomass; however, studies have found that the biomass potential for energy purposes is limited when considering other potential uses of biomass, such as food and materials, as well as the use of land for other purposes than agri culture [6–8]. Energy systems based on RES with a high degree of interaction between energy sectors are referred to as Smart Energy
Abbreviations: JP1, Jet fuel; O&M, Operation and maintenance; RES, Renewable energy sources; VRE, Variable Renewable Energy sources. * Corresponding author. E-mail address: [email protected] (P. Sorknæs). https://doi.org/10.1016/j.rser.2019.109655 Received 17 March 2019; Received in revised form 29 November 2019; Accepted 3 December 2019 Available online 7 December 2019 1364-0321/© 2019 Elsevier Ltd. All rights reserved.
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electrofuels have proven to be relevant by providing interaction be tween the energy sectors, while also keeping the consumption of biomass as low as possible [13,14]. Electrofuels are here understood as fuels merging a carbon source with electrolytic hydrogen to produce desired hydrocarbons [15] or merging a nitrogen source to produce ammonia. The selection of fuels produced is flexible and can be adapted to different needs. The carbon source can be obtained by carbon capture and utilisation from point sources, direct air carbon capture, biomass gasification, or biogas [16]. Parallel with the change towards more RES based energy systems, there has been an increasing movement towards market-based exchange and valuation of energy. This has especially been the case within the electricity sector where market-based wholesale exchanges have been developed and implemented in many countries, such as the USA and member states of the EU [17]. As such, the market-based wholesale exchange of electricity has also received a lot of attention within research. In particular, research has been conducted on the effect of increasing the shares of VRE in existing electricity market designs, where VRE has shown to reduce wholesale electricity market prices to a level at which both VRE and conventional thermal CHP and power plants become unprofitable [18,19]. As such, re-designs of electricity markets for energy systems based on RES have received a lot of atten tion. Djørup et al. [20] found that the current electricity market struc ture is not able to sustain the amount of wind power needed for the transition to 100% RES systems. Newbery et al. [21] discuss the Euro pean electricity market failures that need to be addressed in order to accommodate large shares of VRE and set up economic principles for an electricity wholesale market re-design to facilitate an increasing VRE production. Roques and Finon [22] take an institutional approach to the electricity market design, where the electricity market setup is divided into “modules”, and discuss how existing modules can be improved and which new modules could be introduced. Auer and Haas [23] analyse if the addition of capacity mechanisms to the current energy-only elec tricity market structure is relevant, but find that capacity mechanisms are not necessary. Instead, the challenges with VRE in energy-only markets will be solvable with increased flexibility measures and by ensuring that energy-only markets provide the correct price signals. Conejo and Sioshansi [24] review the restructured electricity market designs that are used today, and identify principles for an efficient electricity market design for future electricity markets. Coester et al. [25] develop a new electricity market design for Germany to test whether this new market design can improve the profitability of power plants at increasing levels of VRE in the electricity market. They find that the existing electricity market setup provides a higher profitability for power plants. Hu et al. [26] make a literature review of barriers to large-scale market integration of VRE in EU in order to suggest an overhaul of the EU electricity market to reduce integration costs of VRE. Besides the focus on the market-based wholesale electricity ex changes, new business models for electricity have also received atten tion within research. Such as, Ramos et al. [27] that investigates new business models for distributed generation of VRE in the medium- and low-voltage grid using the case of Brazil, and proposes four business models addressing issues of small electricity consumers. Hall and Roe lich [28] investigates different business models for local supply of electricity within the context of the UK. Mahdi Behrangrad [29] reviews different business models for energy efficiency and demand side man agement in different electricity market segments. Brown et al. [30] in vestigates new business models related to prosumers of electricity. Though the smart energy systems approach has found that the interaction between different energy sectors becomes increasingly important as more VRE is introduced, research on cross-sectoral energy markets and business models has been somewhat scarcer than research on electricity markets. Van Stiphout et al. [31] propose a multi-carrier day-ahead market in which electricity, gas, and heat are traded simul taneously. It is argued that the multi-carrier market reduces the need for forecasting prices of different energy markets, increases the flexibility
between energy sectors, and increases the optimality of the market outcome. Ordoudis et al. [32] propose a clearing algorithm for an in tegrated market for electricity and natural gas systems, and find that the integrated market results lower costs by reducing uncertainty. In economic theory, analysing how mutual markets interact is a wellestablished tradition. However, as shown, energy economics has tradi tionally been approached through single market analyses, e.g., ana lysing the electricity market [33] or analysing the oil and gas markets. In economics, this approach relies on the theoretical presumption that if single market prices are efficient, a general equilibrium is established. This cross-sector price based efficiency is, however, only valid if there are no institutional disturbances to the price formation. Institutional disturbances are plenty in the energy sector [34–36]. On the other hand, the physical integration has so far been somehow limited to the primary supply side – to the extent energy generation is based on oil, gas and coal. While analysing single energy commodities traditionally has been adequate, we hold that this is no longer the case. Because of the physical integration across sectors, single sector market analyses will to an increasingly extent fall short. Both in terms of analysing the financial basis for the individual types of technology as well as understanding the dynamics of the individual markets. For example, the electricity market cannot be understood through partial analysis of the electricity market, but must be analysed in its coherence with gas and liquid markets as well as the heat market. CHP units link the electricity market to heat and fuel markets on the supply side and heat pumps and electrolysers link the electricity to heat and fuel markets on the demand side. While fuels traditionally have affected the electricity market as input on the supply side, fuels also become an output on the demand side of the electricity sector in a smart energy system. It is therefore our hypothesis that the smart energy system design necessitates a broader approach to energy economics. We call the approach Smart Energy Markets and we define it as an approach in which each energy sector market is analysed as part of an energy system market where the latter consists of electricity, heat, gas and liquid energy markets. The contribution of the Smart Energy Market approach is the development of an energy economic discipline that is adequate for analysing the complex economic relations rising from a smart energy system. The aim is to define a theoretical concept which can analyse all energy markets as interdependent systems. To illustrate this, this paper quantifies how future renewable heating, gas and liquid fuel markets can influence the markets in other energy sectors and vice versa. The quantification is done based on a market-based wholesale exchange setup, which is widely used in Europe, however, the purpose of this is to exemplify the need to think across energy sectors when designing markets within all types of energy market structures and business models. Other research has been done within the production costs of renewable gas and liquid fuels. Brynolf et al. [13] review the production costs of electrofuels and found that production costs in 2030 are ex pected to be in the range of 160–210 EUR/MWh (2015 level) with the two most important factors being capital cost of electrolysers and elec tricity prices. McDonagh et al. [37] investigate the levelized costs of energy for a power-to-gas plant with 10 MWe PEM electrolyser and catalytic methanation in the Irish electricity market and at different levels of VRE in the electricity system, using different bidding strategies. With the optimal bidding strategy, the levelized costs of energy are found to be 100.9 EUR/MWh to 116.85 EUR/MWh depending on the level of VRE in the electricity system. Van Leeuwen and Mulder [38] investigate the profitability of alkaline electrolysers using historical electricity market prices from 2013 to 2017 for four different European countries, and find that alkaline electrolysers are not profitable under current electricity market conditions. In this paper, we use a holistic energy system analysis approach and the energy system analysis tool as 2
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short-term production costs of the energy system. Simulations presented in this paper are all based on the Market Economic simulation strategy. Fig. 1 shows a simplified overview of EnergyPLAN, and it is therefore relevant to detail how the modelling of the production and storage units able to produce and store renewable gas and liquid fuel is handled by EnergyPLAN. In Fig. 1, the units that can produce fuels are represented by the boxes Biomass conversion and Hydrogenation. Though, as can also be seen in Fig. 1, these units require inputs from other units, e.g. the electrolysers. Fig. 2 shows the details of how the creation and storage of renewable gas and liquid fuels are modelled in EnergyPLAN, alongside the direct interactions with other energy sectors and markets. The production of electrofuels integrates four markets: the elec tricity, the heat, the gas and the liquid fuel markets. The production of liquid and gaseous electrofuels can be modelled with three different pathways: biomass hydrogenation, biogas hydrogenation and CO2 hy drogenation. All three pathways are based on converting and storing electricity. Firstly, electricity is converted into hydrogen by an elec trolysis process and further into hydrocarbons with certain carbon sources. In the case of biomass hydrogenation, biomass is firstly gasified and the produced gas is boosted with hydrogen to the desired fuel. Biogas hydrogenation takes a portion of the CO2 in biogas and boosts it with hydrogen either via methanation to produce gas or via the synthesis of liquid fuel. CO2 hydrogenation uses captured carbon dioxide emis sions and electrolytic hydrogen to produce electrofuels. Besides the electrofuel pathways, it is possible to upgrade biogas by CO2 removal in order to distribute it to the gas grid, and deliver gas produced by the gasification plants to the gas grid. Generated gas or liquid fuels are then distributed to the gas and liquid fuel markets. All fuel pathways can generate excess heat that can be sold to local heat markets. The production setup shown in Fig. 2 is used to calculate the pro duction price for each of the types of fuels in the energy system pre sented and analysed in this paper. In this paper, the production costs are calculated as the long-term marginal costs, meaning that the investment costs, operation and maintenance (O&M) costs, the purchase of elec tricity, the purchase of biomass, and the purchase of biogas are all included in the calculated production costs. The investment costs are annualised applying a discount rate of 3% and the technical lifetime of
means of showing the importance of developing cross-sectoral market approaches. 2. Methods To quantify how future renewable gas and liquid fuel productions influence the markets in other energy sectors and vice versa, an existing 100% RES smart energy system scenario is analysed based on how the production of renewable gases and liquid fuels is affected by a variety of units and sectors. To analyse specific 100% RES smart energy system scenarios, it is important to use an energy system simulation tool that can identify the operation of the energy system at a suitable temporal level. The varia tions in VRE production make it important to identify the units that can integrate VRE into an energy system and other energy sectors. Likewise, the tool must be able to simulate cross-sectoral integration of energy to include the smart energy system approach. The tool should especially be able to simulate the production of electrofuels with all the possible connections to other energy sectors. The advanced energy systems analysis tool EnergyPLAN has been designed for such purpose. EnergyPLAN v14.1 has been made for simulating energy balances in smart energy systems, and enables the modelling of electricity, cooling, heating, transport, and process heat demands alongside production, storage, and transmission of energy in order to meet these energy demands [39]. Fig. 1 shows the energy sec tors modelled in EnergyPLAN and their interaction. EnergyPLAN simulates a leap year of operation using an hourly temporal resolution. EnergyPLAN is a well-established energy systems analysis tool that have been developed since 1999 [39], and has been used for a number of energy system studies at both the regional and national level, including analysing 100% renewable energy systems [2, 40–42]. Units and demands are aggregated depending on their type. EnergyPLAN enables the use of a Market Economic simulation strategy. With this simulation strategy, the electricity market setup is based on a short-term marginal price system, like existing electricity market ex changes such as the Nord Pool Spot market [20], and the units in the modelled energy system are operated with the goal of minimising the
Fig. 1. - Overview of energy sectors modelled in EnergyPLAN v14 and their interaction [39]. 3
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Fig. 2. – The gas and liquid fuel infrastructure and its connection to different markets. [43].
the different units. For units with a potential for selling excess heat to local heat markets, this income will be deducted from the production costs, where the value of this thermal energy is assumed to equal the change in the short-term marginal cost of the entire energy system, compared to if this excess thermal energy is not utilised. As such, the valuation of the excess heat does not account for potential new in vestments in replacement technologies. The production costs are iden tified for the output from the five end-producing units shown in Fig. 2: Gasification upgrade, CO2 hydrogenation, Biomass hydrogenation, Biogas upgrade, and Biogas hydrogenation. For units that deliver input to other units, the costs are allocated depending on how much of the energy output is used. E.g., all the production costs for the electrolysers and hydrogen storages are allocated to the units consuming the hydrogen based on the total consumption of hydrogen by these units. As such, the production costs for the five end-producing units do not only include the costs directly related to that unit. The production cost is not the potential market price for these fuels, as, e.g., revenue requirements are not included. Likewise, the costs are excluding potential subsidies, taxes, etc. Using this method, it is possible to identify the production costs for renewable gas and liquid fuels, which can be compared with the competing fossil fuels. The investment cost, technical lifetime and yearly O&M of each type of unit used in this paper are shown in Table 1. As the focus of this paper is on the production of renewable gas and liquid fuels in 100% RES energy systems, an existing 100% RES-based energy system scenario is used, where renewable gas and liquid fuels are utilised. It has been chosen to use the IDA2050 scenario from Ref. [2] with the updates made in Ref. [43]. IDA2050 represents a 100% RES energy system for Denmark in 2050, where the entire Danish energy system is based on 100% RES and all fuel demands are met by an internal Danish production of fuels. The scenario only includes the operation of a future Danish energy system with the possibility of importing and exporting electricity and gas with surrounding countries. The scenario is created so that all energy demands in principle can be met only with domestic production units, to ensure that the renewable energy system technically can be achieved without being dependent on being able to import renewable energy in any given hour. All import and export of
Table 1 Investment costs, technical lifetimes and yearly O&M costs for the units.
Electrolyser (SOEC) H2 storage (Hydrogen steel tanks) Gasification plant (Fixed bed) CO2 capture (industrial plants) Catalytical Methanation (with added hydrogen) Biogas plant (Basic configuration) Liquid fuel synthesis (Average) Liquid fuel synthesis (Additional for jet fuel (JP1))
Investment
Lifetime [Years]
Yearly O&M [% of investment]
Source
0.4 M EUR/ MWe 7.6 M EUR/ GWh 1.33 M EUR/ MWgas 33 M EUR/Mt CO2 0.2 EUR/ MWFuel
30
3
[44]
25
2.5
[45]
20
2.4
[44]
25
0
[46]
25
4
[13]
20
14
[44]
25
4
[13]
25
4
[47]
159.03 M EUR/ TWhbiogas 0.3 M EUR/ MWFuel 0.37 M EUR/ MWFuel
electricity in the scenario is therefore a result of differences in market prices. However, as the surrounding countries’ energy systems have not been modelled for 2050, import of electricity could in principle be from a non-renewable source. In the scenario, the hourly production of electricity from onshore wind power, offshore wind power and PV are based on measured hourly electricity production profiles for Denmark from 2013 which has been used alongside the total installed capacities in Denmark in 2013 and expectations for the future capacity factors [43]. The extra cost for upgrading liquid green fuel to jet fuel is not included in the money flow and production cost analyses, but is included in the CO2 quota analysis in “3 Results”. The scenario has been adapted to use the costs presented in Table 1. As the results are affected by the fuel prices and the international electricity market prices, it is relevant to investigate how different price assumptions affect the results. As also argued by Lund et al. [48], prices 4
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on future energy markets are difficult to predict, especially long-term predictions, and as such, it is often more relevant to investigate a range of options for fuel and electricity prices, rather than trying to identify exact prices in a certain future year. Therefore, the simulations of the scenario are done using two different levels of annual average electricity market price on international electricity markets that can be seen as extremes, being 16 EUR/MWh and 77 EUR/MWh. Using these two yearly averages, two hourly distributions are constructed by weighting the yearly average price according to actual hourly Nord Pool Spot system price from 2013. The low level of 16 EUR/MWh represents a situation assuming that the electricity market price in itself is not suf ficient to cover the long-term marginal costs of many electricity pro duction units. The high level of 77 EUR/MWh represents a situation in which most, if not all, electricity producing units are expected to be able to recover their long-term marginal costs. In EnergyPLAN, the con structed hourly international electricity market prices are affected by the operation of the energy system simulated in EnergyPLAN. In the modelled energy system each flexible electricity production and con sumption unit is activated in the electricity market according to their short-term marginal costs and the resulting market price in that hour taking into account technical restrictions, e.g. district heating demand or storage availability for the output. In cases where there is no bottleneck on the transmission to the external markets, an increased electricity production in the modelled energy system will, all things being equal, result in a reduced electricity price in that hour, and vice versa for electricity consumption. The method is detailed in Sorknæs et al. [18]. The resulting electricity market price in each hour is used to identify the electricity cost of each unit based on the operation of each unit. The electricity prices shown are excluding grid tariffs. Likewise, three different levels of fuel prices are used, which are shown in Table 2. The biomass input for the Gasification plant is assumed to equal the price of straw/wood chips. It is assumed that the material used to pro duce biogas is primarily manure, and that no costs are associated with purchasing manure from the farmers nor with the transportation of the manure. Besides the benefit of producing energy, biogas production from manure also provides several societal benefits, such as improving fertiliser quality and reducing odours from manure [49]; however, these benefits are not included here. The details of the IDA2050 scenario are explained in Ref. [2]; here, only the production and consumption of fuels are presented. Table 3 shows the production of electrofuels, and the consumption of these fuels can be seen in Table 4. Both are shown with an external average elec tricity market price of 77 EUR/MWh and medium fuel price level, as the consumption and the corresponding production for CHP and power plants depend on the import and export of electricity, which is again affected by the fuel and electricity prices used. For the case of an external average electricity market price of 77 EUR/MWh and medium fuel price level the yearly import of electricity is 2.99 TWh and the export is 18.95 TWh, as the CHP and power plants produce electricity for export at this high electricity price level. In the IDA2050 scenario, all biogas is used for either Biogas hydrogenation or is upgraded to natural gas quality by removal of CO2. In this analysis, these are treated as one combined category.
Table 3 Production of electrofuels in IDA2050 at an external electricity market price of 77 EUR/MWh and medium fuel price level. [TWh/year]
Liquid fuel
Gas
Gasification upgrade CO2 hydrogenation Biomass hydrogenation Biogas hydrogenation & biogas upgrade Total
0 15.5 15.6 0 31.1
26.5 0.5 0 15.1 42.1
Table 4 Consumption of electrofuels in IDA2050 at an external electricity market price of 77 EUR/MWh and medium fuel price level. [TWh/year]
Liquid fuel
Gas
Transport CHP in DH Power plants Industry and various Total
31.1 0 0 0 31.1
0 14.5 19.2 8.4 42.1
have on the others. First, some general energy and cost flows are illus trated. Then, the influence of the electricity market on the green gas and green liquid fuel markets is analysed. Next, the influence of the heating and the green gas and liquid fuel markets on the electricity markets is analysed and quantified. Finally, a first attempt to identify a balance between the markets is discussed. 3.1. Principal energy and cost flows To illustrate the principle of the method used, the energy and cost flows of the IDA2050 scenario are shown in detail at the high electricity price level of 77 EUR/MWh and the medium fuel price level (see Fig. 3 and Fig. 4). As seen in Fig. 3, in the IDA2050 scenario, the energy inputs for the fuel production are mainly electricity for the electrolysers and biomass for the gasification plants. Likewise, the excess heat is extracted from the gasification plants and electrolysers for the district heating systems. The largest loss of energy in the production of fuels is from the electrolysers, which have an electricity consumption of 40.28 TWh and produces 29.41 TWh H2 as well as 1.47 TWh of district heating. The CO2 hydro genation is the largest consumer of H2. 5.92 TWh of district heating is produced from the fuel production. For comparison, the total district heating demand including grid loss is about 35 TWh/year in the IDA2050 scenario. Using the energy flows shown in Fig. 3, the costs for producing electrofuels are allocated depending on the pathway in which the fuel is produced. The cost flow in the production of electrofuels in the scenario is shown in Fig. 4. 3.2. The electricity market’s influence on green gas and liquid fuel markets As can be seen in Fig. 4, the relatively high electricity price in troduces a high total electricity cost for the system, resulting in high production costs for the units that utilise hydrogen from the electro lysers. Using the energy flows shown in Fig. 3 and the cost flows shown in Fig. 4, production costs are found per unit of output for each of the
3. Results The results of the analysis are here used to illustrate and to some extent quantify the influence that each of the individual energy markets
Table 2 – The fuel prices at the three different fuel price levels. JP1 is jet fuel. Fuel price levels from Ref. [2]. [EUR/GJ]
Coal
Fuel oil
Diesel fuel/Gas Oil
Petrol/JP1
Natural gas
Straw/Wood chips
Wood pellets (general)
Low Medium High
2.2 2.8 3.5
6.1 11.6 17.0
11.0 16.0 20.9
11.9 16.4 20.8
6.3 8.3 10.4
4.6 6.0 7.3
10.0 10.9 11.9
5
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Figure 3. Sankey diagram of yearly energy flows in TWh for the production of electrofuels in the IDA2050 scenario with an international electricity market price of 77 EUR/MWh and medium fuel price assumption. The flows in and out are related to the energy type, and the flow between the conversion units is related to the type of conversion unit.
Figure 4. Sankey diagram of the cost flows in MEUR for the production of electrofuels in the IDA2050 scenario with an international electricity market price of 77 EUR/MWh and medium fuel price assumption. The value of heat corresponds to the reduction in the resulting fuel costs due to the sale of heat to local district heating systems.
fuel pathways. This can be seen in Fig. 5, where the production costs for each unit are divided into different cost categories for the medium fuel price level and the high electricity price level. Each cost category in cludes the allocation of costs from units delivering products for the unit. The results in Fig. 5 are based on an energy system in which the electrolysers purchase electricity on the market at an average electricity price of 56.1 EUR/MWh. The value of the 5.9 TWh of thermal energy produced for district heating is found to be 10.3 EUR/MWh, due to its
replacement ability. It may be replaced by 2 TWh of heat from CHP units, 1.9 TWh from electric driven heat pumps, and 0.2 TWh from electric boilers. 1.7 TWh of the produced thermal energy was not useable in the district heating systems, as it was produced in periods with low district heating demand. The replacement technologies are chosen using the hourly short-term marginal production cost of the different units; by selecting the units with the lowest short-term mar ginal cost at the beginning of each hour and including the potential of
Fig. 5. Output production cost for each of the fuel pathways in the IDA2050 scenario with an international average electricity market price of 77 EUR/MWh and medium fuel price assumption. Costs are divided into types of cost. 6
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the units to store the thermal energy. These costs are excluding potential extra costs for the energy grids. When deducting the income from the sale of district heating, the production cost for the Gasification plant is found to be 46 EUR/MWh; for Biomass hydrogenation it is 68 EUR/ MWh; for CO2 hydrogenation it is 121 EUR/MWh and for Biogas hy drogenation 60 EUR/MWh. The annualised investment costs presented in Fig. 5 are based on a discount rate of 3%, which is suitable for a socio-economic calculation in Denmark [2]. However, this discount rate might be low compared with requirements in business economic calculations. If instead a discount rate of 10% is used then the production costs of the Gasification plant would increase with 11 EUR/MWh; Biomass hydrogenation costs would increase with 13 EUR/MWh; CO2 hydrogenation with 19 EUR/MWh and Biogas hydrogenation with 12 EUR/MWh. As such, the production costs that are most sensitive to the discount rate are those where the annualised investment costs makes up the largest proportion of the total costs. The exception to this is the Biogas hydrogenation, where the discount rate also affects the costs of the needed biogas plants, which in turn increases the production costs of biogas, and thereby increases the fuel costs for Biogas hydrogenation. If instead the costs of the biogas were kept unchanged, then the increase in production costs for Biogas hydrogenation would only increase by 5 EUR/MWh. As shown in Table 2, with the medium fuel price level, the prices of international diesel, JP1, and petrol are set at around 58–59 EUR/MWh excluding taxes, and the international natural gas price is set at 29.88 EUR/MWh excluding potential CO2 quota costs. As shown in Table 4, the produced gas is used in CHP plants, power plants, and the industry. Most of these units are part of the current European CO2 trading scheme. As such, it is relevant to analyse the potential effect of CO2 quota costs for the use of natural gas, as it can be assumed that the produced renewable fuels will not be required to use CO2 quotas. Including a CO2 quota cost in the price of using natural gas and assuming a CO2 emission from natural gas of 56.7 kg/GJ, the Gasification plant would equal a natural gas price of around 78 EUR/t CO2; Biogas hydrogenation would equal a price at around 148 EUR/t CO2, and CO2 hydrogenation at around 445 EUR/t CO2. CO2 hydrogenation uses 0.252 t CO2/MWhout, and if capturing this CO2 would result in an income from the sale of CO2 quotas, the cost of production from CO2 hydrogenation would equal the natural gas price incl. CO2 quota costs at a price of around 200 EUR/t CO2. In late 2018, the average CO2 quota price was around 20 EUR/t CO2, where the quota price for the first time in a decade rose above 20 EUR/t CO2 [50]. The electrofuels used in the transport sector, except
aviation and international maritime shipping, are currently not included in the European CO2 trading scheme [51]. Producing JP1 for aviation increases the production cost to about 4.1 EUR/MWh, using to the costs shown in Table 1 and the aviation consumption of 10.12 TWh. With a CO2 emission from JP1 of 72 kg/GJ, a CO2 quota cost of 50 EUR/t CO2 would make JP1 production cost using Biomass hydrogenation equal to the fossil JP1 price, and at a CO2 emission cost of around 254 EUR/t CO2, the JP1 production cost using CO2 hydrogenation would equal the fossil JP1 price. Again, if CO2 capture would result in an income from the sale of CO2 quotas, this changes to around 129 EUR/t CO2. Fig. 6 shows the resulting production costs for each of the four output units in the IDA2050 scenario at the two different levels of electricity market prices and three different fuel price levels. As shown in Fig. 6, the electricity price especially affects the pro duction costs of the units utilising hydrogen from electrolysers. In particular, the production cost for CO2 hydrogenation is extremely dependent on the electricity price, which is due to the high usage of hydrogen from electrolysers. Likewise, the gasification plants generally have a relatively low production cost, though at low electricity prices and medium and high fuel prices, biogas hydrogenation has a lower production cost. The price of biogas is excluding the costs for manure and transportation of manure, but also excluding the societal benefits that can be derived from biogas production. Table 5 shows some of the different results that affect the production costs of the fuel producing units. As shown in Table 5, at the low external electricity market price level of a yearly average of 16 EUR/MWh, the Danish energy production re sults in a yearly average higher electricity market price in Denmark. This is due to the short-term electricity production costs of the CHP and power plants in the IDA2050 scenario, which are higher than the average electricity price on the external electricity markets and thus result in a net import of electricity to the Danish energy system. At the high electricity price level of 77 EUR/MWh on the external market, the CHP and power plants all have lower short-term electricity production costs than the average electricity market price on the external markets, resulting in lower electricity prices in Denmark and a net export of electricity. It can also be seen that the average cost of purchasing elec tricity for the electrolysers is close to the average electricity market price in Denmark in all cases. This is due to a total electricity consumption for fuel production at about 44% of the total electricity consumption in the IDA2050 scenario. The value of excess heat for district heating is in the range of 7.9–13 EUR/MWh depending on the scenario. The effect of the
Fig. 6. Output production cost for each of the fuel production pathways in the IDA2050 scenario at the two different electricity market price levels and three different fuel price levels. 7
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electricity price level and basic fuel price level (green), alongside the resulting electricity market price without the production of electrofuels (blue) and without the production of electrofuels and heat pumps and electric boilers in the district heating sector (orange), respectively. The marked blue area shows the effect of the electrofuel production on the electricity market price, and the marked orange area shows the effects of the heat pumps and electric boilers on the electricity market price. As can be seen, the electrofuel production increases the electricity market price significantly, which especially is due to the electricity consumption of the electrofuel production. The consumption corresponds to more than 40% of the total electricity consumption in the system, where in comparison, heat pumps and electric boilers in district heating only account for about 2–3% of the total electricity consumption. As presented in Table 4, a portion of produced gaseous electrofuels is used in CHP and power plants. It is therefore interesting to identify the outcome of increasing the gas price to a level corresponding to the marginal gas production price. As can be seen in Table 3, in the IDA2050 scenario, the gaseous electrofuels are primarily produced by Gasifica tion plants and Biogas hydrogenation. To identify the effect of equalising the gas price and the cost of producing the gas, the highest production cost of these two units is used as the new gas price in the IDA2050 scenario, and the model is simulated again. As a changed gas price af fects both the electricity market price and the district heating price, due to the gas consumption of e.g. CHP units, the fuel production cost is affected by this change. Again, a new highest production cost is found and used as the gas price in the model. This process is repeated until the production cost for gaseous electrofuels is unchanged. This analysis has only been done using the basic fuel price level for the other fuels than gas. Through this process, the gas price is found to be 45 EUR/MWh at the low electricity market price level, corresponding to a CO2 quota price of 71 EUR/ton for the natural gas price to equal this cost. At the low price level, the gasification plant is the unit with the highest pro duction costs. As the CHPs and power plants were only operating in cases of bottlenecks in the transmission lines at the basic gas price level, the increased gas price has no effect on the amount of energy produced by these units. In hours of bottlenecks, a marginal activated unit sets the price based on its short-term marginal costs, meaning that since these are only activated in hours of bottlenecks and the electricity market price is set according to their short-term marginal costs, there is no change in the total economy of these units at the low electricity market price level. At the high electricity market price level, the unit with the
Table 5 Data from the IDA2050 scenario at the two different electricity market price levels and three different fuel price levels. [EUR/MWh]
Average electricity price in Denmark Average electricity price for electrolysers Value of produced district heating
Low electricity price level
High electricity price level
Low fuel
Medium fuel
High fuel
Low fuel
Medium fuel
High fuel
19
20
21
53
57
61
19
20
21
53
56
60
7.9
11.5
13.0
10.6
10.3
10.5
different fuel price levels is larger at a low electricity price level, where the replacement technologies are heat pumps, fuel boilers, and electric boilers. At a high electricity price level, the replacement technologies are CHP, heat pumps, and electric boilers, and a high share of the CHP units are already producing in condensing mode due to high electricity market prices. As such, regardless of the electricity and fuel price level and when only accounting for the changes in short-term marginal costs of already installed district heating capacity, the value of the excess heat only corresponds to about 1–2% of the total costs of producing renew able gas and liquid fuels. 3.3. Heating, green gas, and liquid fuel markets’ influence on the electricity market As illustrated above, the electricity market prices have a high and direct influence on the cost of producing green gas and liquid fuels. If investments in the needed conversion units on the green gas and liquid fuel market should be feasible, the gas and liquid fuel prices should be relatively high, which in return would have an important influence on the electricity market. Likewise, the heating market will also influence the other markets. The electricity consumption for the production of electrofuels has a significant effect on the resulting electricity market price. Fig. 7 shows the duration curve for the hourly electricity market price at the high
Fig. 7. Duration curves for electricity market prices in three different technical setups at the high electricity market price level and basic fuel price level. The shaded areas represent the total effect of the different technologies on the hourly electricity market price. 8
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highest production cost is biogas hydrogenation with gas production cost of 65 EUR/MWh, corresponding to a CO2 quota price of 165 EUR/ ton for the natural gas price to equal this cost. With this specific fuel cost, the CHPs and power plants reduce their electricity production from 19.3 TWh to 8.1 TWh, due to their increased short-term marginal cost for producing electricity.
EUR/MWh (2015 level), and McDonagh et al. [37] who came to a range between 100.9 EUR/MWh to 116.85 EUR/MWh. Meanwhile, the work presented in this paper differs from these works by using a holistic en ergy system analysis approach. In order to calculate the above, the potential income of electrolysers from selling excess heat to the district heating market has been identi fied and included in the calculation, showing an average heating market price of around 10 EUR/MWh excluding taxes. As a next step, both the green gas and the heating market prices have been used to analyse and quantify the influence from these markets on the electricity market. The result shows that annual average electricity market prices may increase by as much as 28 EUR/MWh from 29 to 57 EUR/MWh, at the high electricity market price and basic fuel price level. Besides the effects investigated in this paper, other market effects could occur. E.g., the increased flexible electricity demand for electro fuel producers, which results in a higher demand elasticity, is relevant for preventing involuntary load shedding (blackouts) and aiding the integration of more VRE. Also, the increased flexible electricity demand means that extreme prices (scarcity prices) are less likely to occur, which impacts the business case for other flexibility sources, such as batteries and peak load plants.
4. Discussion The context and background of this paper is that the design of current electricity and green gas/liquid fuel markets will be challenged by the transition towards future low-carbon sustainable energy systems based on RES. Previous analyses show how the electricity prices will decrease to a level at which the needed investments in renewable energy and back-up and reserve capacity will not be feasible. The same challenge applies to the green gas and liquid fuel markets. Consequently, the transition cannot be implemented without a certain re-design of current market structures. From this point of departure, this paper introduces the concept of Smart Energy Markets, which expresses the idea that mutual influence becomes essential in the design of future energy markets, facilitating the transformation into low carbon sustainable energy solutions in the entire energy system and not only in one part of it. The hypothesis is that the re-designs of markets within the individual energy sectors should not be seen isolated from the re-design of markets within other energy sectors. It is not the ambition to propose specific market designs in this paper, but instead to illustrate this hypothesis. To illustrate this, this paper has quantified how future renewable heating, gas and liquid fuel markets can influence the markets in other energy sectors and vice versa. The analysis has been made for a 100% renewable energy system for 2050 based on the concept of a smart en ergy systems approach. This solution includes all sub-sectors of the entire energy systems and is based on a sustainable use of biomass as well as a sufficient hourly balancing of the electricity as well as the heating, the gas and the liquid fuel supplies and demands. In order to implement and economically sustain such an energy system, the market designs should make the needed investments feasible from an investor’s point of view. First, this paper has analysed the influence of electricity prices on the cost of producing green gas and green liquid fuels, resulting in prices between 60 and 120 EUR/MWh equal to 0.6–1.2 EUR/litre diesel (or similar fuel) or 0.7–1.4 EUR per m3 green gas (natural gas quality). Such prices correspond to average electricity prices between 20 and 60 EUR/ MWh. The reason for analysing a price interval is that international electricity prices are very likely to vary substantially between the years due to the influence of weather conditions on hydro power, wind, solar, and potentially also biomass yields. Prices of 0.6–1.2 EUR per litre diesel or similar are in the order of magnitude of the sales prices on many European gasoline stations today. However, such prices often include a high share of taxes, and therefore, in the future, gasoline prices should increase or liquid electrofuels should have lower or no taxes, if investments should be feasible. Prices of 0.7–1.4 EUR per m3 gas are higher than world market natural gas prices today; i.e., the average monthly natural gas price on the European wholesale market is typically in the interval of 0.1–0.3 EUR per m3 [52]. Consequently, in order to replace natural gas by green gas, important adjustments should be made. These could be either to abandon the use of natural gas or to introduce a carbon tax. In the latter case, the carbon tax needs to be in the order of magnitude of 31–480 EUR/ton, depending on technology and energy price forecast. If only including the most dominant gas producing technologies from the sce nario, the carbon tax interval is 31–183 EUR/ton. Nevertheless, in both cases, the green gas market would end in wholesale prices in the order of magnitude of 60–120 EUR/MWh. These costs are lower than the results of Brynolf et al. [13], who, as mentioned in the introduction, reached an expected range of 160–210
5. Conclusion This paper has introduced the concept of Smart Energy Markets, expressing the idea that mutual influence becomes essential in the design of future energy markets facilitating the transformation into lowcarbon sustainable energy solutions in the entire energy system and not only in one part of it. The hypothesis is that the re-designs of markets within the individual energy sectors should not be seen isolated from the re-design of markets within other energy sectors. The paper has analysed, identified and quantified a number of important mutual influences to verify the hypothesis. As part of this quantification, it has been identified that the market prices of the future green gas and liquid fuel markets may potentially be affected by the electricity and heating markets in the order of magnitude of 60–120 EUR/MWh. Heating markets seem to have prices around 10 EUR/MWh. Both markets could influence the annual average electricity prices by up to 28 EUR/MWh, at the high electricity market price and basic fuel price level. It has been beyond the scope of this paper to discuss the concrete design of future Smart Energy Markets. However, no matter if such markets should be based on “energy-only” and/or combinations of en ergy, capacity, etc., the point made here is that one should consider all the relevant energy markets including their mutual influence on each other in future energy systems. This point has in this paper been demonstrated and quantified using a specific smart energy system sce nario as an example. Author contribution statement Sorknæs, P: Conceptualization, Methodology, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Visualization, Lund, H: Conceptualization, Methodology, Writing - Original Draft, Skov, I. R: Methodology, Investigation, Data Curation, Writing - Original Draft, Djørup, S: Conceptualization, Writing - Original Draft, Skytte, K: Conceptualization, Writing - Review & Editing, Morthorst, P.E: Conceptualization, Writing - Review & Editing, Fausto, F: Conceptuali zation, Writing - Review & Editing. Acknowledgments The work presented in this paper is a result of the research activities of the project “Innovative re-making of markets and business models in a renewable energy system based on wind power (I-REMB)”. The work has received funding from the Danish research programme ForskEL. 9
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Smart Energy Markets - Future electricity, gas and heating markets P. Sorknæs a, *, H. Lund a, I.R. Skov b, S. Djørup a, K. Skytte c, P.E. Morthorst c, F. Fausto c a
Department of Planning, Aalborg University, Rendsburggade 14, DK-9000, Aalborg, Denmark Department of Planning, Aalborg University, A.C. Meyers Vænge 15, DK-2450, Copenhagen, Denmark c DTU Management, Energy Economics and Regulation, Produktionstorvet 424, DK-2800, Kgs. Lyngby, Denmark b
A R T I C L E I N F O
A B S T R A C T
Keywords: Electricity markets Gas markets Heating markets Energy markets Smart energy system Renewable energy
This paper introduces the concept of Smart Energy Markets. Sustainable smart energy systems based on renewable energy cannot be implemented without addressing the issue of how to re-design existing electricity and gas markets. Moreover, markets for heating and transport fuels will also be challenged. In recent years, the re-design of the electricity market has attracted a lot of attention and is an area of focus in research. However, the re-design of electricity markets should not be seen isolated from the re-design of other energy markets. By applying a 100% renewable energy system scenario, this paper illustrates and quantifies how future renewable heating, green gas and liquid fuel markets will influence the electricity markets and vice versa. Based on this scenario, it is found that annual average market prices on the future green gas and liquid fuel markets may potentially be affected by the electricity and heating markets by 60–120 EUR/MWh, and the heating, green gas and liquid fuel markets could influence the annual average electricity prices by up to 28 EUR/MWh. The concept of Smart Energy Markets expresses the point that mutual influences become essential in the design of future energy markets facilitating the transition into smart energy systems based on variable renewable energy.
1. Introduction
Systems [9]. The concept of Smart Energy Systems highlights that not only should individual energy grids should be smart, but that all energy grids (electricity, thermal and gas) should be smart, and that these smart grids should be connected so to better exploit synergies across the different individual energy grids [9]. In such energy systems, it is possible to utilise the electricity produced by, e.g., wind turbines and PV in other sectors, such as heating and transport. This enables a more flexible use of different types of energy storage options, where e.g. thermal storages are about 100 times cheaper than electricity storages when comparing investments per unit of storage, which in turn can facilitate a cheaper and more energy efficient integration of VRE [10]. Within the transport sector, this is especially relevant with the use of electric vehicles and direct electrification of the sector, as the direct use of electricity is more efficient than storing electricity as fuel [11]. However, electrification will most likely not be relevant or possible in all modes of transport, such as long-haul transport (marine, heavy-duty road vehicles and aviation) [11]. As such, besides the direct electrifi cation of the transport sector, the smart energy system approach also emphasizes the production of renewable fuels for the modes of transport of which direct electrification is not possible [5,12]. Especially
Globally, energy systems are increasingly moving towards a pro duction primarily based on renewable energy sources (RES). Many countries have defined long-term goals of converting to energy systems based mainly or solely on RES [1]. In energy systems based on RES, variable RES (VRE), like wind power and PV, are expected to be important components [2,3]. The variability of the production from VRE has proven to require new methods for creating a balance between production and demand in the electricity system [4]. Research has found that, in order to implement large shares of VRE in energy systems, an increased interaction between the different energy sectors is required. Furthermore, the increased interaction will be beneficial in terms of making the transition fuel-efficient and cost-effective [5]. In many en ergy systems based on RES, another main fuel will be biomass; however, studies have found that the biomass potential for energy purposes is limited when considering other potential uses of biomass, such as food and materials, as well as the use of land for other purposes than agri culture [6–8]. Energy systems based on RES with a high degree of interaction between energy sectors are referred to as Smart Energy
Abbreviations: JP1, Jet fuel; O&M, Operation and maintenance; RES, Renewable energy sources; VRE, Variable Renewable Energy sources. * Corresponding author. E-mail address: [email protected] (P. Sorknæs). https://doi.org/10.1016/j.rser.2019.109655 Received 17 March 2019; Received in revised form 29 November 2019; Accepted 3 December 2019 Available online 7 December 2019 1364-0321/© 2019 Elsevier Ltd. All rights reserved.
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electrofuels have proven to be relevant by providing interaction be tween the energy sectors, while also keeping the consumption of biomass as low as possible [13,14]. Electrofuels are here understood as fuels merging a carbon source with electrolytic hydrogen to produce desired hydrocarbons [15] or merging a nitrogen source to produce ammonia. The selection of fuels produced is flexible and can be adapted to different needs. The carbon source can be obtained by carbon capture and utilisation from point sources, direct air carbon capture, biomass gasification, or biogas [16]. Parallel with the change towards more RES based energy systems, there has been an increasing movement towards market-based exchange and valuation of energy. This has especially been the case within the electricity sector where market-based wholesale exchanges have been developed and implemented in many countries, such as the USA and member states of the EU [17]. As such, the market-based wholesale exchange of electricity has also received a lot of attention within research. In particular, research has been conducted on the effect of increasing the shares of VRE in existing electricity market designs, where VRE has shown to reduce wholesale electricity market prices to a level at which both VRE and conventional thermal CHP and power plants become unprofitable [18,19]. As such, re-designs of electricity markets for energy systems based on RES have received a lot of atten tion. Djørup et al. [20] found that the current electricity market struc ture is not able to sustain the amount of wind power needed for the transition to 100% RES systems. Newbery et al. [21] discuss the Euro pean electricity market failures that need to be addressed in order to accommodate large shares of VRE and set up economic principles for an electricity wholesale market re-design to facilitate an increasing VRE production. Roques and Finon [22] take an institutional approach to the electricity market design, where the electricity market setup is divided into “modules”, and discuss how existing modules can be improved and which new modules could be introduced. Auer and Haas [23] analyse if the addition of capacity mechanisms to the current energy-only elec tricity market structure is relevant, but find that capacity mechanisms are not necessary. Instead, the challenges with VRE in energy-only markets will be solvable with increased flexibility measures and by ensuring that energy-only markets provide the correct price signals. Conejo and Sioshansi [24] review the restructured electricity market designs that are used today, and identify principles for an efficient electricity market design for future electricity markets. Coester et al. [25] develop a new electricity market design for Germany to test whether this new market design can improve the profitability of power plants at increasing levels of VRE in the electricity market. They find that the existing electricity market setup provides a higher profitability for power plants. Hu et al. [26] make a literature review of barriers to large-scale market integration of VRE in EU in order to suggest an overhaul of the EU electricity market to reduce integration costs of VRE. Besides the focus on the market-based wholesale electricity ex changes, new business models for electricity have also received atten tion within research. Such as, Ramos et al. [27] that investigates new business models for distributed generation of VRE in the medium- and low-voltage grid using the case of Brazil, and proposes four business models addressing issues of small electricity consumers. Hall and Roe lich [28] investigates different business models for local supply of electricity within the context of the UK. Mahdi Behrangrad [29] reviews different business models for energy efficiency and demand side man agement in different electricity market segments. Brown et al. [30] in vestigates new business models related to prosumers of electricity. Though the smart energy systems approach has found that the interaction between different energy sectors becomes increasingly important as more VRE is introduced, research on cross-sectoral energy markets and business models has been somewhat scarcer than research on electricity markets. Van Stiphout et al. [31] propose a multi-carrier day-ahead market in which electricity, gas, and heat are traded simul taneously. It is argued that the multi-carrier market reduces the need for forecasting prices of different energy markets, increases the flexibility
between energy sectors, and increases the optimality of the market outcome. Ordoudis et al. [32] propose a clearing algorithm for an in tegrated market for electricity and natural gas systems, and find that the integrated market results lower costs by reducing uncertainty. In economic theory, analysing how mutual markets interact is a wellestablished tradition. However, as shown, energy economics has tradi tionally been approached through single market analyses, e.g., ana lysing the electricity market [33] or analysing the oil and gas markets. In economics, this approach relies on the theoretical presumption that if single market prices are efficient, a general equilibrium is established. This cross-sector price based efficiency is, however, only valid if there are no institutional disturbances to the price formation. Institutional disturbances are plenty in the energy sector [34–36]. On the other hand, the physical integration has so far been somehow limited to the primary supply side – to the extent energy generation is based on oil, gas and coal. While analysing single energy commodities traditionally has been adequate, we hold that this is no longer the case. Because of the physical integration across sectors, single sector market analyses will to an increasingly extent fall short. Both in terms of analysing the financial basis for the individual types of technology as well as understanding the dynamics of the individual markets. For example, the electricity market cannot be understood through partial analysis of the electricity market, but must be analysed in its coherence with gas and liquid markets as well as the heat market. CHP units link the electricity market to heat and fuel markets on the supply side and heat pumps and electrolysers link the electricity to heat and fuel markets on the demand side. While fuels traditionally have affected the electricity market as input on the supply side, fuels also become an output on the demand side of the electricity sector in a smart energy system. It is therefore our hypothesis that the smart energy system design necessitates a broader approach to energy economics. We call the approach Smart Energy Markets and we define it as an approach in which each energy sector market is analysed as part of an energy system market where the latter consists of electricity, heat, gas and liquid energy markets. The contribution of the Smart Energy Market approach is the development of an energy economic discipline that is adequate for analysing the complex economic relations rising from a smart energy system. The aim is to define a theoretical concept which can analyse all energy markets as interdependent systems. To illustrate this, this paper quantifies how future renewable heating, gas and liquid fuel markets can influence the markets in other energy sectors and vice versa. The quantification is done based on a market-based wholesale exchange setup, which is widely used in Europe, however, the purpose of this is to exemplify the need to think across energy sectors when designing markets within all types of energy market structures and business models. Other research has been done within the production costs of renewable gas and liquid fuels. Brynolf et al. [13] review the production costs of electrofuels and found that production costs in 2030 are ex pected to be in the range of 160–210 EUR/MWh (2015 level) with the two most important factors being capital cost of electrolysers and elec tricity prices. McDonagh et al. [37] investigate the levelized costs of energy for a power-to-gas plant with 10 MWe PEM electrolyser and catalytic methanation in the Irish electricity market and at different levels of VRE in the electricity system, using different bidding strategies. With the optimal bidding strategy, the levelized costs of energy are found to be 100.9 EUR/MWh to 116.85 EUR/MWh depending on the level of VRE in the electricity system. Van Leeuwen and Mulder [38] investigate the profitability of alkaline electrolysers using historical electricity market prices from 2013 to 2017 for four different European countries, and find that alkaline electrolysers are not profitable under current electricity market conditions. In this paper, we use a holistic energy system analysis approach and the energy system analysis tool as 2
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short-term production costs of the energy system. Simulations presented in this paper are all based on the Market Economic simulation strategy. Fig. 1 shows a simplified overview of EnergyPLAN, and it is therefore relevant to detail how the modelling of the production and storage units able to produce and store renewable gas and liquid fuel is handled by EnergyPLAN. In Fig. 1, the units that can produce fuels are represented by the boxes Biomass conversion and Hydrogenation. Though, as can also be seen in Fig. 1, these units require inputs from other units, e.g. the electrolysers. Fig. 2 shows the details of how the creation and storage of renewable gas and liquid fuels are modelled in EnergyPLAN, alongside the direct interactions with other energy sectors and markets. The production of electrofuels integrates four markets: the elec tricity, the heat, the gas and the liquid fuel markets. The production of liquid and gaseous electrofuels can be modelled with three different pathways: biomass hydrogenation, biogas hydrogenation and CO2 hy drogenation. All three pathways are based on converting and storing electricity. Firstly, electricity is converted into hydrogen by an elec trolysis process and further into hydrocarbons with certain carbon sources. In the case of biomass hydrogenation, biomass is firstly gasified and the produced gas is boosted with hydrogen to the desired fuel. Biogas hydrogenation takes a portion of the CO2 in biogas and boosts it with hydrogen either via methanation to produce gas or via the synthesis of liquid fuel. CO2 hydrogenation uses captured carbon dioxide emis sions and electrolytic hydrogen to produce electrofuels. Besides the electrofuel pathways, it is possible to upgrade biogas by CO2 removal in order to distribute it to the gas grid, and deliver gas produced by the gasification plants to the gas grid. Generated gas or liquid fuels are then distributed to the gas and liquid fuel markets. All fuel pathways can generate excess heat that can be sold to local heat markets. The production setup shown in Fig. 2 is used to calculate the pro duction price for each of the types of fuels in the energy system pre sented and analysed in this paper. In this paper, the production costs are calculated as the long-term marginal costs, meaning that the investment costs, operation and maintenance (O&M) costs, the purchase of elec tricity, the purchase of biomass, and the purchase of biogas are all included in the calculated production costs. The investment costs are annualised applying a discount rate of 3% and the technical lifetime of
means of showing the importance of developing cross-sectoral market approaches. 2. Methods To quantify how future renewable gas and liquid fuel productions influence the markets in other energy sectors and vice versa, an existing 100% RES smart energy system scenario is analysed based on how the production of renewable gases and liquid fuels is affected by a variety of units and sectors. To analyse specific 100% RES smart energy system scenarios, it is important to use an energy system simulation tool that can identify the operation of the energy system at a suitable temporal level. The varia tions in VRE production make it important to identify the units that can integrate VRE into an energy system and other energy sectors. Likewise, the tool must be able to simulate cross-sectoral integration of energy to include the smart energy system approach. The tool should especially be able to simulate the production of electrofuels with all the possible connections to other energy sectors. The advanced energy systems analysis tool EnergyPLAN has been designed for such purpose. EnergyPLAN v14.1 has been made for simulating energy balances in smart energy systems, and enables the modelling of electricity, cooling, heating, transport, and process heat demands alongside production, storage, and transmission of energy in order to meet these energy demands [39]. Fig. 1 shows the energy sec tors modelled in EnergyPLAN and their interaction. EnergyPLAN simulates a leap year of operation using an hourly temporal resolution. EnergyPLAN is a well-established energy systems analysis tool that have been developed since 1999 [39], and has been used for a number of energy system studies at both the regional and national level, including analysing 100% renewable energy systems [2, 40–42]. Units and demands are aggregated depending on their type. EnergyPLAN enables the use of a Market Economic simulation strategy. With this simulation strategy, the electricity market setup is based on a short-term marginal price system, like existing electricity market ex changes such as the Nord Pool Spot market [20], and the units in the modelled energy system are operated with the goal of minimising the
Fig. 1. - Overview of energy sectors modelled in EnergyPLAN v14 and their interaction [39]. 3
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Fig. 2. – The gas and liquid fuel infrastructure and its connection to different markets. [43].
the different units. For units with a potential for selling excess heat to local heat markets, this income will be deducted from the production costs, where the value of this thermal energy is assumed to equal the change in the short-term marginal cost of the entire energy system, compared to if this excess thermal energy is not utilised. As such, the valuation of the excess heat does not account for potential new in vestments in replacement technologies. The production costs are iden tified for the output from the five end-producing units shown in Fig. 2: Gasification upgrade, CO2 hydrogenation, Biomass hydrogenation, Biogas upgrade, and Biogas hydrogenation. For units that deliver input to other units, the costs are allocated depending on how much of the energy output is used. E.g., all the production costs for the electrolysers and hydrogen storages are allocated to the units consuming the hydrogen based on the total consumption of hydrogen by these units. As such, the production costs for the five end-producing units do not only include the costs directly related to that unit. The production cost is not the potential market price for these fuels, as, e.g., revenue requirements are not included. Likewise, the costs are excluding potential subsidies, taxes, etc. Using this method, it is possible to identify the production costs for renewable gas and liquid fuels, which can be compared with the competing fossil fuels. The investment cost, technical lifetime and yearly O&M of each type of unit used in this paper are shown in Table 1. As the focus of this paper is on the production of renewable gas and liquid fuels in 100% RES energy systems, an existing 100% RES-based energy system scenario is used, where renewable gas and liquid fuels are utilised. It has been chosen to use the IDA2050 scenario from Ref. [2] with the updates made in Ref. [43]. IDA2050 represents a 100% RES energy system for Denmark in 2050, where the entire Danish energy system is based on 100% RES and all fuel demands are met by an internal Danish production of fuels. The scenario only includes the operation of a future Danish energy system with the possibility of importing and exporting electricity and gas with surrounding countries. The scenario is created so that all energy demands in principle can be met only with domestic production units, to ensure that the renewable energy system technically can be achieved without being dependent on being able to import renewable energy in any given hour. All import and export of
Table 1 Investment costs, technical lifetimes and yearly O&M costs for the units.
Electrolyser (SOEC) H2 storage (Hydrogen steel tanks) Gasification plant (Fixed bed) CO2 capture (industrial plants) Catalytical Methanation (with added hydrogen) Biogas plant (Basic configuration) Liquid fuel synthesis (Average) Liquid fuel synthesis (Additional for jet fuel (JP1))
Investment
Lifetime [Years]
Yearly O&M [% of investment]
Source
0.4 M EUR/ MWe 7.6 M EUR/ GWh 1.33 M EUR/ MWgas 33 M EUR/Mt CO2 0.2 EUR/ MWFuel
30
3
[44]
25
2.5
[45]
20
2.4
[44]
25
0
[46]
25
4
[13]
20
14
[44]
25
4
[13]
25
4
[47]
159.03 M EUR/ TWhbiogas 0.3 M EUR/ MWFuel 0.37 M EUR/ MWFuel
electricity in the scenario is therefore a result of differences in market prices. However, as the surrounding countries’ energy systems have not been modelled for 2050, import of electricity could in principle be from a non-renewable source. In the scenario, the hourly production of electricity from onshore wind power, offshore wind power and PV are based on measured hourly electricity production profiles for Denmark from 2013 which has been used alongside the total installed capacities in Denmark in 2013 and expectations for the future capacity factors [43]. The extra cost for upgrading liquid green fuel to jet fuel is not included in the money flow and production cost analyses, but is included in the CO2 quota analysis in “3 Results”. The scenario has been adapted to use the costs presented in Table 1. As the results are affected by the fuel prices and the international electricity market prices, it is relevant to investigate how different price assumptions affect the results. As also argued by Lund et al. [48], prices 4
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on future energy markets are difficult to predict, especially long-term predictions, and as such, it is often more relevant to investigate a range of options for fuel and electricity prices, rather than trying to identify exact prices in a certain future year. Therefore, the simulations of the scenario are done using two different levels of annual average electricity market price on international electricity markets that can be seen as extremes, being 16 EUR/MWh and 77 EUR/MWh. Using these two yearly averages, two hourly distributions are constructed by weighting the yearly average price according to actual hourly Nord Pool Spot system price from 2013. The low level of 16 EUR/MWh represents a situation assuming that the electricity market price in itself is not suf ficient to cover the long-term marginal costs of many electricity pro duction units. The high level of 77 EUR/MWh represents a situation in which most, if not all, electricity producing units are expected to be able to recover their long-term marginal costs. In EnergyPLAN, the con structed hourly international electricity market prices are affected by the operation of the energy system simulated in EnergyPLAN. In the modelled energy system each flexible electricity production and con sumption unit is activated in the electricity market according to their short-term marginal costs and the resulting market price in that hour taking into account technical restrictions, e.g. district heating demand or storage availability for the output. In cases where there is no bottleneck on the transmission to the external markets, an increased electricity production in the modelled energy system will, all things being equal, result in a reduced electricity price in that hour, and vice versa for electricity consumption. The method is detailed in Sorknæs et al. [18]. The resulting electricity market price in each hour is used to identify the electricity cost of each unit based on the operation of each unit. The electricity prices shown are excluding grid tariffs. Likewise, three different levels of fuel prices are used, which are shown in Table 2. The biomass input for the Gasification plant is assumed to equal the price of straw/wood chips. It is assumed that the material used to pro duce biogas is primarily manure, and that no costs are associated with purchasing manure from the farmers nor with the transportation of the manure. Besides the benefit of producing energy, biogas production from manure also provides several societal benefits, such as improving fertiliser quality and reducing odours from manure [49]; however, these benefits are not included here. The details of the IDA2050 scenario are explained in Ref. [2]; here, only the production and consumption of fuels are presented. Table 3 shows the production of electrofuels, and the consumption of these fuels can be seen in Table 4. Both are shown with an external average elec tricity market price of 77 EUR/MWh and medium fuel price level, as the consumption and the corresponding production for CHP and power plants depend on the import and export of electricity, which is again affected by the fuel and electricity prices used. For the case of an external average electricity market price of 77 EUR/MWh and medium fuel price level the yearly import of electricity is 2.99 TWh and the export is 18.95 TWh, as the CHP and power plants produce electricity for export at this high electricity price level. In the IDA2050 scenario, all biogas is used for either Biogas hydrogenation or is upgraded to natural gas quality by removal of CO2. In this analysis, these are treated as one combined category.
Table 3 Production of electrofuels in IDA2050 at an external electricity market price of 77 EUR/MWh and medium fuel price level. [TWh/year]
Liquid fuel
Gas
Gasification upgrade CO2 hydrogenation Biomass hydrogenation Biogas hydrogenation & biogas upgrade Total
0 15.5 15.6 0 31.1
26.5 0.5 0 15.1 42.1
Table 4 Consumption of electrofuels in IDA2050 at an external electricity market price of 77 EUR/MWh and medium fuel price level. [TWh/year]
Liquid fuel
Gas
Transport CHP in DH Power plants Industry and various Total
31.1 0 0 0 31.1
0 14.5 19.2 8.4 42.1
have on the others. First, some general energy and cost flows are illus trated. Then, the influence of the electricity market on the green gas and green liquid fuel markets is analysed. Next, the influence of the heating and the green gas and liquid fuel markets on the electricity markets is analysed and quantified. Finally, a first attempt to identify a balance between the markets is discussed. 3.1. Principal energy and cost flows To illustrate the principle of the method used, the energy and cost flows of the IDA2050 scenario are shown in detail at the high electricity price level of 77 EUR/MWh and the medium fuel price level (see Fig. 3 and Fig. 4). As seen in Fig. 3, in the IDA2050 scenario, the energy inputs for the fuel production are mainly electricity for the electrolysers and biomass for the gasification plants. Likewise, the excess heat is extracted from the gasification plants and electrolysers for the district heating systems. The largest loss of energy in the production of fuels is from the electrolysers, which have an electricity consumption of 40.28 TWh and produces 29.41 TWh H2 as well as 1.47 TWh of district heating. The CO2 hydro genation is the largest consumer of H2. 5.92 TWh of district heating is produced from the fuel production. For comparison, the total district heating demand including grid loss is about 35 TWh/year in the IDA2050 scenario. Using the energy flows shown in Fig. 3, the costs for producing electrofuels are allocated depending on the pathway in which the fuel is produced. The cost flow in the production of electrofuels in the scenario is shown in Fig. 4. 3.2. The electricity market’s influence on green gas and liquid fuel markets As can be seen in Fig. 4, the relatively high electricity price in troduces a high total electricity cost for the system, resulting in high production costs for the units that utilise hydrogen from the electro lysers. Using the energy flows shown in Fig. 3 and the cost flows shown in Fig. 4, production costs are found per unit of output for each of the
3. Results The results of the analysis are here used to illustrate and to some extent quantify the influence that each of the individual energy markets
Table 2 – The fuel prices at the three different fuel price levels. JP1 is jet fuel. Fuel price levels from Ref. [2]. [EUR/GJ]
Coal
Fuel oil
Diesel fuel/Gas Oil
Petrol/JP1
Natural gas
Straw/Wood chips
Wood pellets (general)
Low Medium High
2.2 2.8 3.5
6.1 11.6 17.0
11.0 16.0 20.9
11.9 16.4 20.8
6.3 8.3 10.4
4.6 6.0 7.3
10.0 10.9 11.9
5
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Figure 3. Sankey diagram of yearly energy flows in TWh for the production of electrofuels in the IDA2050 scenario with an international electricity market price of 77 EUR/MWh and medium fuel price assumption. The flows in and out are related to the energy type, and the flow between the conversion units is related to the type of conversion unit.
Figure 4. Sankey diagram of the cost flows in MEUR for the production of electrofuels in the IDA2050 scenario with an international electricity market price of 77 EUR/MWh and medium fuel price assumption. The value of heat corresponds to the reduction in the resulting fuel costs due to the sale of heat to local district heating systems.
fuel pathways. This can be seen in Fig. 5, where the production costs for each unit are divided into different cost categories for the medium fuel price level and the high electricity price level. Each cost category in cludes the allocation of costs from units delivering products for the unit. The results in Fig. 5 are based on an energy system in which the electrolysers purchase electricity on the market at an average electricity price of 56.1 EUR/MWh. The value of the 5.9 TWh of thermal energy produced for district heating is found to be 10.3 EUR/MWh, due to its
replacement ability. It may be replaced by 2 TWh of heat from CHP units, 1.9 TWh from electric driven heat pumps, and 0.2 TWh from electric boilers. 1.7 TWh of the produced thermal energy was not useable in the district heating systems, as it was produced in periods with low district heating demand. The replacement technologies are chosen using the hourly short-term marginal production cost of the different units; by selecting the units with the lowest short-term mar ginal cost at the beginning of each hour and including the potential of
Fig. 5. Output production cost for each of the fuel pathways in the IDA2050 scenario with an international average electricity market price of 77 EUR/MWh and medium fuel price assumption. Costs are divided into types of cost. 6
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the units to store the thermal energy. These costs are excluding potential extra costs for the energy grids. When deducting the income from the sale of district heating, the production cost for the Gasification plant is found to be 46 EUR/MWh; for Biomass hydrogenation it is 68 EUR/ MWh; for CO2 hydrogenation it is 121 EUR/MWh and for Biogas hy drogenation 60 EUR/MWh. The annualised investment costs presented in Fig. 5 are based on a discount rate of 3%, which is suitable for a socio-economic calculation in Denmark [2]. However, this discount rate might be low compared with requirements in business economic calculations. If instead a discount rate of 10% is used then the production costs of the Gasification plant would increase with 11 EUR/MWh; Biomass hydrogenation costs would increase with 13 EUR/MWh; CO2 hydrogenation with 19 EUR/MWh and Biogas hydrogenation with 12 EUR/MWh. As such, the production costs that are most sensitive to the discount rate are those where the annualised investment costs makes up the largest proportion of the total costs. The exception to this is the Biogas hydrogenation, where the discount rate also affects the costs of the needed biogas plants, which in turn increases the production costs of biogas, and thereby increases the fuel costs for Biogas hydrogenation. If instead the costs of the biogas were kept unchanged, then the increase in production costs for Biogas hydrogenation would only increase by 5 EUR/MWh. As shown in Table 2, with the medium fuel price level, the prices of international diesel, JP1, and petrol are set at around 58–59 EUR/MWh excluding taxes, and the international natural gas price is set at 29.88 EUR/MWh excluding potential CO2 quota costs. As shown in Table 4, the produced gas is used in CHP plants, power plants, and the industry. Most of these units are part of the current European CO2 trading scheme. As such, it is relevant to analyse the potential effect of CO2 quota costs for the use of natural gas, as it can be assumed that the produced renewable fuels will not be required to use CO2 quotas. Including a CO2 quota cost in the price of using natural gas and assuming a CO2 emission from natural gas of 56.7 kg/GJ, the Gasification plant would equal a natural gas price of around 78 EUR/t CO2; Biogas hydrogenation would equal a price at around 148 EUR/t CO2, and CO2 hydrogenation at around 445 EUR/t CO2. CO2 hydrogenation uses 0.252 t CO2/MWhout, and if capturing this CO2 would result in an income from the sale of CO2 quotas, the cost of production from CO2 hydrogenation would equal the natural gas price incl. CO2 quota costs at a price of around 200 EUR/t CO2. In late 2018, the average CO2 quota price was around 20 EUR/t CO2, where the quota price for the first time in a decade rose above 20 EUR/t CO2 [50]. The electrofuels used in the transport sector, except
aviation and international maritime shipping, are currently not included in the European CO2 trading scheme [51]. Producing JP1 for aviation increases the production cost to about 4.1 EUR/MWh, using to the costs shown in Table 1 and the aviation consumption of 10.12 TWh. With a CO2 emission from JP1 of 72 kg/GJ, a CO2 quota cost of 50 EUR/t CO2 would make JP1 production cost using Biomass hydrogenation equal to the fossil JP1 price, and at a CO2 emission cost of around 254 EUR/t CO2, the JP1 production cost using CO2 hydrogenation would equal the fossil JP1 price. Again, if CO2 capture would result in an income from the sale of CO2 quotas, this changes to around 129 EUR/t CO2. Fig. 6 shows the resulting production costs for each of the four output units in the IDA2050 scenario at the two different levels of electricity market prices and three different fuel price levels. As shown in Fig. 6, the electricity price especially affects the pro duction costs of the units utilising hydrogen from electrolysers. In particular, the production cost for CO2 hydrogenation is extremely dependent on the electricity price, which is due to the high usage of hydrogen from electrolysers. Likewise, the gasification plants generally have a relatively low production cost, though at low electricity prices and medium and high fuel prices, biogas hydrogenation has a lower production cost. The price of biogas is excluding the costs for manure and transportation of manure, but also excluding the societal benefits that can be derived from biogas production. Table 5 shows some of the different results that affect the production costs of the fuel producing units. As shown in Table 5, at the low external electricity market price level of a yearly average of 16 EUR/MWh, the Danish energy production re sults in a yearly average higher electricity market price in Denmark. This is due to the short-term electricity production costs of the CHP and power plants in the IDA2050 scenario, which are higher than the average electricity price on the external electricity markets and thus result in a net import of electricity to the Danish energy system. At the high electricity price level of 77 EUR/MWh on the external market, the CHP and power plants all have lower short-term electricity production costs than the average electricity market price on the external markets, resulting in lower electricity prices in Denmark and a net export of electricity. It can also be seen that the average cost of purchasing elec tricity for the electrolysers is close to the average electricity market price in Denmark in all cases. This is due to a total electricity consumption for fuel production at about 44% of the total electricity consumption in the IDA2050 scenario. The value of excess heat for district heating is in the range of 7.9–13 EUR/MWh depending on the scenario. The effect of the
Fig. 6. Output production cost for each of the fuel production pathways in the IDA2050 scenario at the two different electricity market price levels and three different fuel price levels. 7
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electricity price level and basic fuel price level (green), alongside the resulting electricity market price without the production of electrofuels (blue) and without the production of electrofuels and heat pumps and electric boilers in the district heating sector (orange), respectively. The marked blue area shows the effect of the electrofuel production on the electricity market price, and the marked orange area shows the effects of the heat pumps and electric boilers on the electricity market price. As can be seen, the electrofuel production increases the electricity market price significantly, which especially is due to the electricity consumption of the electrofuel production. The consumption corresponds to more than 40% of the total electricity consumption in the system, where in comparison, heat pumps and electric boilers in district heating only account for about 2–3% of the total electricity consumption. As presented in Table 4, a portion of produced gaseous electrofuels is used in CHP and power plants. It is therefore interesting to identify the outcome of increasing the gas price to a level corresponding to the marginal gas production price. As can be seen in Table 3, in the IDA2050 scenario, the gaseous electrofuels are primarily produced by Gasifica tion plants and Biogas hydrogenation. To identify the effect of equalising the gas price and the cost of producing the gas, the highest production cost of these two units is used as the new gas price in the IDA2050 scenario, and the model is simulated again. As a changed gas price af fects both the electricity market price and the district heating price, due to the gas consumption of e.g. CHP units, the fuel production cost is affected by this change. Again, a new highest production cost is found and used as the gas price in the model. This process is repeated until the production cost for gaseous electrofuels is unchanged. This analysis has only been done using the basic fuel price level for the other fuels than gas. Through this process, the gas price is found to be 45 EUR/MWh at the low electricity market price level, corresponding to a CO2 quota price of 71 EUR/ton for the natural gas price to equal this cost. At the low price level, the gasification plant is the unit with the highest pro duction costs. As the CHPs and power plants were only operating in cases of bottlenecks in the transmission lines at the basic gas price level, the increased gas price has no effect on the amount of energy produced by these units. In hours of bottlenecks, a marginal activated unit sets the price based on its short-term marginal costs, meaning that since these are only activated in hours of bottlenecks and the electricity market price is set according to their short-term marginal costs, there is no change in the total economy of these units at the low electricity market price level. At the high electricity market price level, the unit with the
Table 5 Data from the IDA2050 scenario at the two different electricity market price levels and three different fuel price levels. [EUR/MWh]
Average electricity price in Denmark Average electricity price for electrolysers Value of produced district heating
Low electricity price level
High electricity price level
Low fuel
Medium fuel
High fuel
Low fuel
Medium fuel
High fuel
19
20
21
53
57
61
19
20
21
53
56
60
7.9
11.5
13.0
10.6
10.3
10.5
different fuel price levels is larger at a low electricity price level, where the replacement technologies are heat pumps, fuel boilers, and electric boilers. At a high electricity price level, the replacement technologies are CHP, heat pumps, and electric boilers, and a high share of the CHP units are already producing in condensing mode due to high electricity market prices. As such, regardless of the electricity and fuel price level and when only accounting for the changes in short-term marginal costs of already installed district heating capacity, the value of the excess heat only corresponds to about 1–2% of the total costs of producing renew able gas and liquid fuels. 3.3. Heating, green gas, and liquid fuel markets’ influence on the electricity market As illustrated above, the electricity market prices have a high and direct influence on the cost of producing green gas and liquid fuels. If investments in the needed conversion units on the green gas and liquid fuel market should be feasible, the gas and liquid fuel prices should be relatively high, which in return would have an important influence on the electricity market. Likewise, the heating market will also influence the other markets. The electricity consumption for the production of electrofuels has a significant effect on the resulting electricity market price. Fig. 7 shows the duration curve for the hourly electricity market price at the high
Fig. 7. Duration curves for electricity market prices in three different technical setups at the high electricity market price level and basic fuel price level. The shaded areas represent the total effect of the different technologies on the hourly electricity market price. 8
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highest production cost is biogas hydrogenation with gas production cost of 65 EUR/MWh, corresponding to a CO2 quota price of 165 EUR/ ton for the natural gas price to equal this cost. With this specific fuel cost, the CHPs and power plants reduce their electricity production from 19.3 TWh to 8.1 TWh, due to their increased short-term marginal cost for producing electricity.
EUR/MWh (2015 level), and McDonagh et al. [37] who came to a range between 100.9 EUR/MWh to 116.85 EUR/MWh. Meanwhile, the work presented in this paper differs from these works by using a holistic en ergy system analysis approach. In order to calculate the above, the potential income of electrolysers from selling excess heat to the district heating market has been identi fied and included in the calculation, showing an average heating market price of around 10 EUR/MWh excluding taxes. As a next step, both the green gas and the heating market prices have been used to analyse and quantify the influence from these markets on the electricity market. The result shows that annual average electricity market prices may increase by as much as 28 EUR/MWh from 29 to 57 EUR/MWh, at the high electricity market price and basic fuel price level. Besides the effects investigated in this paper, other market effects could occur. E.g., the increased flexible electricity demand for electro fuel producers, which results in a higher demand elasticity, is relevant for preventing involuntary load shedding (blackouts) and aiding the integration of more VRE. Also, the increased flexible electricity demand means that extreme prices (scarcity prices) are less likely to occur, which impacts the business case for other flexibility sources, such as batteries and peak load plants.
4. Discussion The context and background of this paper is that the design of current electricity and green gas/liquid fuel markets will be challenged by the transition towards future low-carbon sustainable energy systems based on RES. Previous analyses show how the electricity prices will decrease to a level at which the needed investments in renewable energy and back-up and reserve capacity will not be feasible. The same challenge applies to the green gas and liquid fuel markets. Consequently, the transition cannot be implemented without a certain re-design of current market structures. From this point of departure, this paper introduces the concept of Smart Energy Markets, which expresses the idea that mutual influence becomes essential in the design of future energy markets, facilitating the transformation into low carbon sustainable energy solutions in the entire energy system and not only in one part of it. The hypothesis is that the re-designs of markets within the individual energy sectors should not be seen isolated from the re-design of markets within other energy sectors. It is not the ambition to propose specific market designs in this paper, but instead to illustrate this hypothesis. To illustrate this, this paper has quantified how future renewable heating, gas and liquid fuel markets can influence the markets in other energy sectors and vice versa. The analysis has been made for a 100% renewable energy system for 2050 based on the concept of a smart en ergy systems approach. This solution includes all sub-sectors of the entire energy systems and is based on a sustainable use of biomass as well as a sufficient hourly balancing of the electricity as well as the heating, the gas and the liquid fuel supplies and demands. In order to implement and economically sustain such an energy system, the market designs should make the needed investments feasible from an investor’s point of view. First, this paper has analysed the influence of electricity prices on the cost of producing green gas and green liquid fuels, resulting in prices between 60 and 120 EUR/MWh equal to 0.6–1.2 EUR/litre diesel (or similar fuel) or 0.7–1.4 EUR per m3 green gas (natural gas quality). Such prices correspond to average electricity prices between 20 and 60 EUR/ MWh. The reason for analysing a price interval is that international electricity prices are very likely to vary substantially between the years due to the influence of weather conditions on hydro power, wind, solar, and potentially also biomass yields. Prices of 0.6–1.2 EUR per litre diesel or similar are in the order of magnitude of the sales prices on many European gasoline stations today. However, such prices often include a high share of taxes, and therefore, in the future, gasoline prices should increase or liquid electrofuels should have lower or no taxes, if investments should be feasible. Prices of 0.7–1.4 EUR per m3 gas are higher than world market natural gas prices today; i.e., the average monthly natural gas price on the European wholesale market is typically in the interval of 0.1–0.3 EUR per m3 [52]. Consequently, in order to replace natural gas by green gas, important adjustments should be made. These could be either to abandon the use of natural gas or to introduce a carbon tax. In the latter case, the carbon tax needs to be in the order of magnitude of 31–480 EUR/ton, depending on technology and energy price forecast. If only including the most dominant gas producing technologies from the sce nario, the carbon tax interval is 31–183 EUR/ton. Nevertheless, in both cases, the green gas market would end in wholesale prices in the order of magnitude of 60–120 EUR/MWh. These costs are lower than the results of Brynolf et al. [13], who, as mentioned in the introduction, reached an expected range of 160–210
5. Conclusion This paper has introduced the concept of Smart Energy Markets, expressing the idea that mutual influence becomes essential in the design of future energy markets facilitating the transformation into lowcarbon sustainable energy solutions in the entire energy system and not only in one part of it. The hypothesis is that the re-designs of markets within the individual energy sectors should not be seen isolated from the re-design of markets within other energy sectors. The paper has analysed, identified and quantified a number of important mutual influences to verify the hypothesis. As part of this quantification, it has been identified that the market prices of the future green gas and liquid fuel markets may potentially be affected by the electricity and heating markets in the order of magnitude of 60–120 EUR/MWh. Heating markets seem to have prices around 10 EUR/MWh. Both markets could influence the annual average electricity prices by up to 28 EUR/MWh, at the high electricity market price and basic fuel price level. It has been beyond the scope of this paper to discuss the concrete design of future Smart Energy Markets. However, no matter if such markets should be based on “energy-only” and/or combinations of en ergy, capacity, etc., the point made here is that one should consider all the relevant energy markets including their mutual influence on each other in future energy systems. This point has in this paper been demonstrated and quantified using a specific smart energy system sce nario as an example. Author contribution statement Sorknæs, P: Conceptualization, Methodology, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Visualization, Lund, H: Conceptualization, Methodology, Writing - Original Draft, Skov, I. R: Methodology, Investigation, Data Curation, Writing - Original Draft, Djørup, S: Conceptualization, Writing - Original Draft, Skytte, K: Conceptualization, Writing - Review & Editing, Morthorst, P.E: Conceptualization, Writing - Review & Editing, Fausto, F: Conceptuali zation, Writing - Review & Editing. Acknowledgments The work presented in this paper is a result of the research activities of the project “Innovative re-making of markets and business models in a renewable energy system based on wind power (I-REMB)”. The work has received funding from the Danish research programme ForskEL. 9
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