DYNAMIC P2X MODEL FOR FEASIBILITY CASE STUDIES AND OPERATION PLANNING. Researcher s seminar

Transcription

1 DYNAMIC P2X MODEL FOR FEASIBILITY CASE STUDIES AND OPERATION PLANNING Researcher s seminar Eero Inkeri, LUT

2 1. MOTIVATION 2. MODEL 3. OUTPUT

3 MOTIVATION

4 Dynamical nature of power-to-gas -system Mass flows and economics power power-to-gas = 5-7 % gas storage, = 1 % transport, = 3-5 % gas Figure: Fraunhofer ISE power, = 2-6 % heat, = 1 % storage other systems gas, heat and power Intermittent production + wrong forecast cheap electricity how is the future?

5 P2x system buffer storage requirements What size buffer storages are required for and CH4? Yearly basis Daily basis 1 1 January December : 24: methanation solar power power/heat demand methane storage storage

6 P2x system buffer storage requirements, Where, when and how is produced - continuous vs. intermittent source - large vs. small source - capture process that requires heat One week year example for consumption and storage: mas flow [kg/h] consumption 1 production storage [kg]

7 P2x economics at dynamical markets For economics, it matters at which hours electrolyser is operating Some very cheap and very expensive hours future? Electricity price, 213, Finland Grid balancing service price, 213, Finland spot price [ /MWh] spot price duration curve FCR-N price [ /MW,h] FCR-N price duration curve

8 MODEL

9 Boundary Outputs Physical models conditions Methanation Time Implement Historical Explicit series, time characteristics simplified average mass, seriesenergy correlations values future from and models accurate economical cash flow models numbers calculations with one hour time step liquefaction Markets power power-to-gas LNG storage storage transport gas gas power heat storage other system CHP plant, off-design gas, heat and power district heat demand Life cycle analysis - captured - Power used - capture Case studies - OPEX - average values -

10 Operation planning Electrolysis can be operated based on several rules Fixed limit electricity price based on CAPEX, OPEX etc. No limits or target for full load hours Easy planning Optimized operation for near time horizon, from one to several days Maximises the profit, satisfying restrictions like storages and up times Computationally time-consuming Variable limit price for electricity by duration curve of latest market history Specified full load hours are achieved Computationally fast

11 Operation planning By duration curve and full load hours target Choose full load hours target Create duration curve for market history period (week, month ) find limit electricity price and FCR-N price repeat every day spot price [ /MWh] Electricity price FCR-N price Net price of electricity spot price duration curve FCR-N price [ /MW,h] FCR-N price duration curve net cost [ /MWh]

12 OUTPUT

13 Output: realized electricity price Full load hours target & FCR-N market share realized electricity price Grid Full load balancing hours market target defines share has the a limit huge for impact spot price to net cost of electricity, because hourly markets are limited spot price [ /MWh] Different full load hour targets spot price 1 h 2 h 3 h 4 h spot power price [ /MWh] [MW,h] Total Different FCR-N volume market FCR-N of share hourly market = FCR-N 1 shares % market [%] FCR-N total market volume duration curve 1 % 4 % 8 % full load time hours [h] [h]

14 Output: time series Production, consumption and storage (9 MW electrolyser) Hydrogen Carbon dioxide Storages mass flow [kg/h] prod. cons mas flow [kg/h] prod. cons storage [kg] H Requires realistic control for process: characteristics, correlation, PID-control Try to maintain continuous operation or schedule shut-downs and start-ups? Cost and limits of H2 storage?

15 Output: economic analysis Operational costs of power-to-gas system, [ /MWh CH4 ] 7 Net cost of electricity for methane 6 OPEX [ /MWh] realized optimal full load hours [h] 4 % market share of grid balancing markets Grid balancing offers for 4 h