Modelleringsresultater -Resultater fra modellering af VE-gas og biobrændsler i det fremtidige danske energisystem (2050)

Transcription

1 Modelleringsresultater -Resultater fra modellering af VE-gas og biobrændsler i det fremtidige danske energisystem (2050) Gastekniske dage Rasmus Bramstoft PhD candidate System Analysis

2 Outline Current Energy System Gas in Integrated Energy Systems Modelling of RE-gas and Fuels in Integrated Energy Systems Results Conclusions Future Work 2

3 Nordic energy system in 2014 Natural gas Biofuels and waste Crude oil Hydro Wind, Solar Nuclear Coal Source: IEA

4 Introduction Gas is accounting for approx. 17% of the total primary energy supply Current Danish gas demand is primarily supplied by the large natural gas reserves in the North Sea, making Denmark self-sufficient with natural gas The development of the gas system is important to investigate - given the long-term energy policy targets, - declining gas consumption, - limited natural gas resources in the North Sea - estimated to be depleted by 2040 Total Primary Energy Supply, 2014 Crude oil Natural gas Biofuels and waste Coal Wind, Solar Local biogas production is becoming (private-) economically attractive for both local use and grid injection Source: (Top) IEA 2017 (Bottom) Energinet.dk 4

5 Integrated energy systems 5

6 Integrated energy systems 6

7 Integrated energy systems 7

8 Methodology Balmorel Energy system optimization model covering the Nordic power and district heating sectors. OptiFlow Generalized spatiotemporal network optimization model which facilitates modelling of renewable gas and fuel production. Optimisation min. z (Money) Background system Electricity demand District heat demand Balmorel Electricity balance District heat balance OptiFlow Natural gas market Biofuel market Least-cost power and district heating system Hourly district heating prices Hourly electricity prices Optimisation of RE gas and fuel production Power and DH production from RE-gas Excess heat from thermal gasification Electricity consumption in electrolysis 8

9 Spatial resolution Spatial resolution in Balmorel and OptiFLow Energy system optimization model covering the Nordic power and district heating sectors. Optimizes (operation and investments in) generation, transmission and consumption of the power and district heating sectors. Power regions in Balmorel District heating areas in Balmorel and OptiFlow 9

10 Modelling of RE-gas and fuel in OptiFlow 10

11 Modelling of biogas in OptiFlow District heating grid Engine Biogas Upgrade Methanation Gas grid Modelling of biogas Parameters: Investment and O&M costs Efficiencies Emissions Energy demands (Space-time) Resource pot. (Space-time) Transportation costs Restrictions: Min/max ratio of input/output Ramping constraints Electrolysis Electricity grid Variables: Investments Operation Location 11

12 Main data assumptions In this study, the co-simulation of OptiFlow and Balmorel leads to the socio-economic optimal system, given specified demands, fuel potentials, and fuel prices. Fuel potentials for RE-gas production in Denmark [1,3,4] Manure 25.3 Mio tons Straw 55 PJ Refuse derived fuel (RDF) 5 PJ Wood 40 PJ Wood Pellets, import 40 PJ Electricity and district heating demands Techno-economic costs of REproduction plants Source [2] [3,5] Fuel prices [2] Natural Gas 6.98 /GJ Coal 2.35 /GJ Fueloil /GJ Gas oil /GJ Oil /GJ Straw 8.92 /GJ Wood chips 9.91 /GJ Wood pellets /GJ Nuclear - Uranium 1.94 /GJ CO 2 price /ton CO 2 [1]: DEA (2014), Energiscenarier frem mod 2020, 2035 og [4]: DTU(2018). [2]: IEA (2016), 2 DS in NETP2016. [5]: DEA, Energinet - Energikataloger [3]: EA Energy Analyses (2017), Integration af termisk forgasning i det danske energisystem. 12

13 Resource potentials Source: Based on data from: Kortlægning af hensigtsmæssig lokalisering af nye biogasanlæg i Danmark, 2015, AgroTech and SEGES 13

14 Resource potentials Source: Based on data from: Kortlægning af hensigtsmæssig lokalisering af nye biogasanlæg i Danmark, 2015, AgroTech and SEGES 14

15 Scenarios Scenarios Simulation year Scenarios Bio fuel demand (PJ) Bio-Jet demand (PJ) Gas price ( /GJ) CO2 price Base 50-6,31 YES High_gas_price YES Fuel ,31 YES Fuel ,31 YES Fuel100+25_ST_pot ,31 YES 15

16 Electricity and District heating systems

17 Electricity generation, demand and price

18 Energy flows Unit: PJ (except manure (in mio. tons)) 18

19 Renewable gas and fuel production Unit: PJ (except manure (in mio. tons)) 19

20 Electricity and District heating systems

21 Resource utilization for RE-gas production

22 RE-gas production

23 Biogas and biofuel production

24 Excess heat

25 Sensitivity analysis for biogas production

26 Conclusion Co-simulation of the spatiotemporal network optimization model, OptiFlow, and the energy system model, Balmorel - The modelling framework allowed modelling of the gas chain from up-stream renewable gas production, through storage facilities to end consumers, taking into account the spatial and temporal system integration between the gas, electricity, and district heating system - The results of the co-simulation of OptiFlow and Balmorel represents the socio-economic optimal system, where investments and operations optimization is undertaken for the integrated energy system. The results show that production of RE-gas is socio-economically attractive in the investigated scenarios. Furthermore, the results show that RE-gas directly injected into the natural gas pipeline network is preferred, under the given assumptions. In the high natural gas price scenarios, the catalytic methanation technology is used to produce additional methane. Development of gas technologies, such as thermal gasification, is needed for producing biofuels. The limited biomass resource needs to be utilized where it is most needed. And hydrogen is one efficient way to get more energy out of the limited biomass resource. The modelling approach applied in this study, allowed the investigation of RE-gas and RE-fuels production with a high temporal and spatial resolution. This was used to show that the deployment of the chosen technologies for producing RE-gas varies according to the resource allocation, and to show the effect of electricity price on hydrogen production. 26

27 Future work Optimisation min. z (Money) Background system Electricity demand District heat demand Balmorel Electricity balance District heat balance OptiFlow Natural gas market El Prices/ interconnectors Biofuel demand TIMES Optimisation min. z (Money) 27

28 Future work 28

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30 Methodology Optimisation min. z (Money) Background system Electricity demand District heat demand Balmorel Electricity balance District heat balance OptiFlow Natural gas market Electricity prices Biofuel demand TIMES Optimisation min. z (Money) 30

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