Synthetic Natural Gas Production and Utilization in the North European Power System in 2050 Jussi Ikäheimo and Juha Kiviluoma Energy Systems VTT Technical Research Centre of Finland Espoo, Finland
[email protected]
Abstract— Power-to-gas technology (P2G), which produces synthetic natural gas (SNG), has been proposed as one way to overcome the variability of variable generation. Its advantage is the almost unlimited storage capacity but it is encumbered by low efficiency. In this study, the capacity investments in power and heat generation until 2050 and the hourly operation of the power system in Nordic countries and countries surrounding the Baltic Sea were studied. The focus was on nearly 100 % renewable power and district heat sectors. In addition, some of the energy demand of the transportation and industrial sectors was provided by SNG. P2G as well as other balancing technologies such as electric vehicles, heat storages and pumped hydro plants were also included in the simulation. Sensitivity analysis was performed for PV and P2G investment cost.
In this study we investigate the role of P2G within a future energy system via a complex dynamic optimisation models, taking into consideration other balancing technologies such as power-to-heat, heat storages, battery energy storage and electric vehicles. We perform a sensitivity analysis with respect to the investment cost of P2G and solar PV. The studied region included the Nordic countries, the Baltic countries, Germany and Poland, as shown in Figure 1.
It was found that consumption of SNG in the power and heat sector remained below 2–3 % of the electrical demand. More SNG was consumed in transportation and industrial sectors. Investment cost of solar PV and P2G greatly affect the utilization of P2G. Utilization of P2G had an increasing trend with respect to PV investment cost. Keywords- synthetic natural gas; power-to-gas; power system planning; energy storage; photovoltaics (PV)
I. INTRODUCTION One of the greatest challenges of wind and solar power generation technologies is that they lack a similar level of load-following capability compared to conventional thermal plants. Their maximum output is dictated by weather, which is notoriously variable and uncertain. This problem aggravates at high shares of wind and solar power generation. Power-to-gas (P2G) technology has been proposed as one way to balance the mismatch between generation and demand. By producing synthetic natural gas (SNG) this technology can store energy as fuel, as well as provide renewable fuel for the transportation, industrial and household sectors. P2G is especially suited for long-term storage. For example, in European latitudes wind power has typically generation patterns lasting several days [1] which creates an advantage for P2G over battery storage.
The authors acknowledge funding from the TEKES project Neocarbon Energy.
Figure 1. The studied region shown with the subdivision of countries into smaller model regions.
II. MODELING TOOLS The results were obtained in two stages. Optimal investments into new plants, storages and transmission lines were calculated with a generation planning model. Greenfield scenario was not assumed but instead the existing hydro power, a number of nuclear plants, and certain amount of existing combined cycle plants as well as wind power were assumed to be present. The operation of these plants and storages was then optimized in more detail with an unit commitment and economic dispatch model. In addition to these two models results from the TIMESVTT energy system model [2] were used to set the penetration of electric vehicles and SNG consumption in the transportation sector. TIMES-VTT is a partial equilibrium model containing all the important sectors of the energy system, from energy resource extraction via energy production to end-use sectors such as transport, residential, and industry. There are several interconnected regions in terms of energy commodity trade in the model. However, only results concerning Nordic Countries were available in this study and other countries were extrapolated from those. A. Generation planning model Balmorel considers hourly chronology when minimizing operational costs, fixed costs and annualized investment costs using an LP solver (CPLEX in this case). An energy storage has to have an equal energy content in the beginning and in the end of the period while bound by its capacity and charging/discharging limits. Thermal power plants do not have ramp constraints, part-load efficiencies or minimum load limits. Capacity adequacy constraints are taken into account. Investments into transmission capacity between regions are possible. The availability of some fuels such as biomass was limited. Since the analyzed cases include several areas and multiple investment options, it was not possible to solve the model for a full year hourly time series using a single work station. Instead algorithmically selected three weeks representing the full year were used. B. Operational scheduling model WILMAR power system simulation tool uses a GAMS based Joint Market Model (JMM) to optimize unit commitment and economic dispatch of power systems. JMM is solved several times in each simulated day to incorporate new forecasts for demand and generation as time draws closer to the operating period. It can utilize stochastic forecasts in order to achieve more robust and economic unit commitment decisions when minimizing power system operational costs with uncertainty from wind power and/or PV. In this case, a single forecast was used. Wilmar JMM can incorporate plant dynamic behavior such as minimum load and start-up time restrictions and start-up costs. In this work individual plants were mostly aggregated into larger groups where start-ups were approximated with continuous variables. Figure 2 shows the cascading structure of the models and Figure 3 shows the power system components and energy flow within the generation planning and operational scheduling models.
Figure 2. Parameter flows between the models in the simulation.
III.
DESCRIPTION OF TECHNOLOGIES AND INPUT DATA
A. Power-to-gas technology P2G technology produces gas for gas-fired power plants as well as for the transportation sector. P2G plant consists of water electrolysis and methanation processes and possibly CO2 capture process. For electrolysis we consider proton exchange membrane (PEM) electrolysis. Its capital costs in 2030 are estimated [3] to be 760 €/kW. For 2050 our simulation assumed the average efficiency of the electrolyser to be 79 % based on higher heating value and investment cost 600 €/kW. Methanation is an exothermic process which converts hydrogen and carbon dioxide into methane according to the Sabatier reaction. The efficiency of methanation was estimated as 80 % [4] and most of the losses can be utilized as high-temperature heat [5]. The investment cost of the methanation process today is 135–275 €/kWe per power input of the electrolyser [4]. In this study we assume capture of CO2 directly from air. Currently a wide variety of cost estimates of the future cost of direct air capture exist. We assume that the investment cost of this technology is reduced significantly to less than 500 €/kWe. We assume that direct air capture also reduces the efficiency of the P2G process by 6 percentage points. The resulting efficiency based on lower heating value would be 51 %. Assuming only a small cost for the balance of plant components, the total cost would be 1500 €/kW. For 2050 cost uncertainty is high and consequently we model a wide variation of potential P2G costs. The aim is to chart the impact of potential cost reductions, not to predict their likelihood. Therefore we also studied a far-fetched case of 500 €/kW investment cost for P2G. B. Battery energy storages Batteries were assumed to have 90 % round-trip efficiency as energy storages. Maximum charging and discharging was set to 1 C, i.e. they could be charged or discharged in one hour. Higher rates are achievable, but with a cost, and the application here is for energy arbitrage where high charging rate is not needed. According to [6] the capital cost of battery energy storage systems today ranges from 500 to 1500 €/kWh depending on the battery type and application. We assumed a significant cost reduction and in our sensitivity analysis used the values 150 €/kWh and 300 €/kWh. C. Plugin electric vehicles Penetration of plugin electric vehicles (EV) is given as a parameter to the Balmorel and Wilmar models. The source was the VTT TIMES simulation, which resulted in 23 TWh of EV electricity consumption in Nordic countries, equal to 5 million passenger vehicles, assuming 0.25 kWh/km
specific consumption and average 18000 km annual driving distance. The same proportion of plugin EV based on 2013 statistics were assumed to be present in Germany and Poland. Vehicle arrival and departure to/from charging station time series were modeled. Important parameters are the battery storage capacity, charging capacity and discharging (vehicle-to-grid) capacity. We assumed 60 kWh average battery storage capacity, 6 kW charging capacity. Vehicle-to-grid capability is not needed for the normal use of the cars and thus only a part of them were assumed to have this function. 1 kW average discharging capacity was thus assumed. EVs were assumed to have no regulatory payments or subsidies when charging or discharging electricity. D. Wind and solar power Wind power was divided into three tiers, with limited capacity and increasing investment cost in each tier. The same capacity factor and time series were used in each tier in order to keep the optimization model smaller. It was assumed that at least 130 GW of wind power was operational anyway in the simulated year as result of policy measures. No capacity limit was set for solar PV but the resulting capacities were checked ex-post. Hourly solar irradiation data was obtained from the MERRA database,
maintained by NASA, and was then converted to PV electricity output using typical parameters for crystalline silicon panels. All panels were assumed to be facing south, and their inclination (tilt) angle was set to the optimal value for that latitude. Solar thermal energy or CSP were not included as options. The investment cost of PV was varied between 300–600 €/kW. Table III shows the capacity factors of wind and solar PV power in the modeled countries during the simulated year. The maximum wind capacity is also shown. E. Power transmission Transmission capacities between regions were optimized separately for a case where the investment cost of PV was 300 €/kW and P2G cost 1500 €/kW, and the resulting transmission investments were used in all cases. Upper limit and investment cost were specified for each interconnection. As shown in Figure 1, in the Nordic region there were nine copperplate regions, whereas Germany, Poland and the Baltic countries were each modeled as single copperplate region. Consequently the investment options were concentrated in the Nordic region.
Figure 3. Power and heat sectors, and main conversion and storage technologies in the optimization models
TABLE I. FUEL, INVESTMENT COST, FIXED OPERATING AND MAINTENANCE COST AND MAXIMUM ELECTRICAL EFFICIENCY OF THE MOST IMPORTANT GENERATION TECHNOLOGIES. Conversion technology
fuel
Inv. cost e/kW
Nuclear
U-235
4800
Fixed O&M €/kW/a 96
OCGT
nat. gas
550
17
38 %
CCGT
nat. gas
1000
28
60 %
elec
(max) 33 %
CCGT-CHP
nat. gas
1300
40
60 %
ICE
nat. gas
670
17
45 %
ST condensing
coal
–
–
36 %
ST CHP
2000
60
39 %
heat pump
biomas s elec.
580a
15
280 %b
PV
–
300–600
10
– –
wind power
–
900–1100
27
gas boiler
nat. gas
100a
5
90 % a. In units of €/kW(thermal).
b. Thermal output/electricity input.
F. Other technologies A number of thermal conversion technologies were available as investment options. In the district heating sector CHP plants, heat pumps as well as electric, gas-fired and wood-fired boilers were available. The most important generation technologies have been listed in Table I and storage technologies in Table II. TABLE II.
ENERGY STORAGE TECHONOGIES INCLUDED IN THE SIMULATION.
Storage technology Battery storage
Investment cost €/kWh 150–300
Heat storage Pumped hydro
5
95 %
400 a
80 %
Plugin electric vehicles Power-to-gas
round-trip efficiency 90 %
–
80 %
500–1500a
55 %b a. In units of €/kW
b. Based on LHV. Does not include losses from DAC.
G. Other input data Consumption of SNG in the transportation and industrial sectors in Nordic countries were obtained from the TIMESVTT results and is shown in Figure 4. The results were extrapolated for the whole area assuming the same consumption per capita. TABLE III. Country
Figure 4. Transport and industry use of SNG as function of the P2G investment cost.
Electricity demand in the simulated region was assumed to be 1240 TWh/a, a 24 % increase with respect to 2014. Load patterns were not changed. Investment costs were annualized by using technology-specific lifetimes and real interest rate of 5 % p.a. CO2 emissions cost was set to 60 €/tCO2 in all cases. IV. RESULTS Capacity values are results of the generation planning model but annual generation values can be extracted from both generation planning and operational scheduling models. For some quantities we show both results, especially because the 450 €/kW PV cost was only available from the generation planning model. A. Optimal generation mix The power sector was dominated by wind, solar and hydro power and gas turbines for peak power generation. Figure 5 shows the annually generated power by the most important fuels as a function of PV and P2G investment cost. The category of electricity storages is dominated by plugin electric vehicles. The figure shows generation by both existing and new plants. New nuclear plants were too expensive to build. No dedicated battery storages were built in any of the cases.
WIND AND SOLAR RESOURCES IN THE STUDIED REGION Wind FLH h
Solar PV FLH h
Wind max capacity GW
Germany
2700
1000
244
Denmark
3500
888
9.2
Estonia
3200
822
8.5
Finland
3050
776
49.0
Latvia
3250
857
29.2
Lithuania
3150
830
30.6
Norway (north)
3750
648
48.8
Norway (south)
3670
771
36.0
Poland
2900
971
254
Sweden
3050
860
56.9
Figure 5. Annual power generation by fuels in different cases of PV and P2G investment cost.
The main primary energy sources in the simulations were wind and solar power, in addition to hydro power. However, total hydro power generation was almost constant across all cases. Figure 6 shows the dependence of wind power generation on PV and P2G investment cost. Figure 7
shows the corresponding results for solar PV. PV generation naturally decreases with increasing cost and the deficit is filled mostly by wind power.
TABLE IV.
ADDED TRANSMISSION CAPACITY (MW) BETWEEN MODEL REGIONS WITHIN AND BETWEEN BALTIC, CENTRAL EUROPEAN AND NORDIC COUNTRIES.
Baltic Central Europe Nordic
Figure 6. Annual wind power generation in different cases of PV and P2G investment cost. Results from both generation planning (Balmorel) and operational simulation (Wilmar JMM) are shown.
Baltic
Central Europe
Nordic
970
1400
1400
4200
11200 24660
B. Transmission The added transmission capacity between model regions is shown in Table IV. The new transmission capacity is concentrated in Nordic countries because of the number and geography of the Nordic model regions. For example, between Nordic countries and central European countries (e.g. Germany and Poland), the added transmission capacity was 11200 MW. C. SNG production Figure 9 shows the resulting power-to-gas electrical capacity as function of PV and P2G investment cost. Naturally the capacity increases with decreasing cost. The resulting annual operation times were in excess of 6000 hours. Figure 10 shows the production of SNG for the power and heat sector. Most of the produced SNG, especially in the 500 €/kW P2G case, goes into transport and industry use as shown by Figure 11. Both planning and operational model results are shown. They show a clear increasing trend with increasing PV investment cost. On the other hand, Figure 12 shows that the utilization of the vehicle-to-grid capability of electric vehicles has a decreasing trend with increasing PV investment cost.
Figure 7. Annual solar PV power generation in different cases of PV and P2G investment cost.
The district heating sector was dominated by heat pumps, electric boilers and heat storages. Figure 8 shows the annually generated district heat by the most important fuels. Majority of district heat was produced by heat pumps, followed by wood, gas and municipal waste fired plants as well as old coal-fired CHP plants, which each produced 1–2 % of total demand.
Figure 9. Power-to-gas capacity (electrolysis power) as function of PV investment cost.
D. Cost of SNG Figure 13 shows the long-term marginal cost of produced SNG. Naturally the cost increases with increasing PV cost and increasing P2G cost. We note the rather small marginal cost reduction when P2G cost is decreased.
Figure 8. Annual district heat generation by different fuels. Heat storages total output is also shown.
Figure 10. Annual production of synthetic natural gas for the energy sector as function of PV and P2G investment cost.
Figure 13. The long-term marginal cost of SNG as function of PV and P2G investment cost.
On the other hand, the utilization of electric vehicles in feeding power to the grid increased with decreasing PV investment cost, suggesting that they take partly the balancing function of P2G in a solar PV dominated system. The dependency of vehicle-to-grid operation on the investment cost of P2G was not clear. One should consider that the demand of SNG in the transportation and industrial sectors increases dramatically with decreasing P2G cost, which brings more wind and PV capacity and more need for balancing. Investments into battery energy storages were not necessary in this case. Figure 11. Annual share of SNG used in the power and heat sector as function of PV and P2G investment cost.
The resulting long-term marginal cost of SNG was very high compared to the current price of natural gas. Decreasing the investment cost did not decrease the marginal cost of SNG very much. This is most likely because of the increasing transportation and industrial demand, which requires building more generation capacity only for the purpose of producing SNG. The need for grid reinforcements within the model regions (e.g. southern Norway, Germany or Finland) was not included in the analysis. Because the generation peak power would be significantly increased from the current level, the corresponding cost would be high and would impact the results. The burden would especially be allocated to the high PV capacity (180–420 GW in the whole region). REFERENCES [1]
Figure 12. Annual power discharged to the grid by plugin electric vehicles.
V. CONCLUSIONS When simulating the hourly operation of the power system in Nordic countries, Baltic countries, Germany and Poland, as well as investments in new generation and storage capacity, the consumption of SNG in the power and heat sector remained below 2–3 % of the electrical demand. Only SNG was allowed in gas-fired generation, i.e. no conventional natural gas. The consumption of SNG increased with increasing PV investment cost. This could be explained by the increasing share of wind power. P2G and gas-fired generation are better suited for balancing long periods of low or high wind, than e.g. plugin electric vehicles. SNG consumption naturally increased with decreasing investment cost of P2G.
[2]
[3]
[4]
[5]
[6]
J. Kiviluoma, H. Holttinen, D. Weir, R. Scharff, L. Söder, N. Menemenlis, N. A. Cutululis, I. Danti Lopez, E. Lannoye, A. Estanqueiro, E. Gomez-Lazaro, Q. Zhang, J. Bai, Y.-H. Wan, and M. Milligan, “Variability in large-scale wind power generation,” Wind Energy, vol. 19, no. 9, pp. 1649–1665, Sep. 2016. E. Pursiheimo, “Path towards 100 % Renewable Energy Future and Feasibility of Power-to-Gas Technology in Nordic Countries,” 2016. (Unpublished) L. Bertuccioli, A. Chan, D. Hart, F. Lehner, B. Madden, and E. Standen, “Development of Water Electrolysis in the European Union,” Fuel Cells Hydrog. Jt. Undert., no. February, pp. 1–160, 2014. M. Lehner, R. Tichler, H. Steinmüller, and M. Koppe, Power-toGas: Technology and Business Models. Cham: Springer International Publishing, 2014. T. Schaaf, J. Grünig, M. R. Schuster, T. Rothenfluh, and A. Orth, “Methanation of CO2 - storage of renewable energy in a gas distribution system,” Energy. Sustain. Soc., vol. 4, no. 1, pp. 1–14, 2014. Lazard, “Lazard’s levelized cost of storage analysis — version 1.0,” 2015.
