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November 6, 2015

Modeling Decentralized Generation and Storage Technologies on a Community Level Using the TIMES FrameworkMashael YazdanieLaboratory for Energy Systems Analysis, Paul Scherrer InstituteSimLab Educational Workshop 2016

Our Changing Energy Networks

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Source: http://ilsr.org/challenge-reconciling-centralized-v-decentralized-electricity-system/

Pros and Cons

Decentralized

• Small to medium scale

+ modular+ wide range of technologies+ potential to reduce CO2 emissions+ grid upgrade cost deferrals+ peak shaving system cost savings+ reduced transmission losses

- potential diseconomies of scale- increased complexity of grid operation and

integration- non-dispatchable generation (renewables)

Centralized

• Large scale

+ economies of scale+ infrastructure established

- modular operation difficult- relies on costly grid infrastructure and

upgrades- grid congestion and losses

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Objective

To quantify the benefits and potential of decentralized generation and storage technologies

(DGSTs) in future energy systems planning for urban and rural Swiss communities.

Several scenarios are developed and analyzed using cost-optimization models (built in the

TIMES framework) to measure the impacts of DGSTs compared to a baseline scenario.

Carbon mitigation policy measures are also investigated.

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Main Scenarios

• Baseline scenario (“baseline”)

• Decentralized systems scenario without storage (“decentralized”)

• Decentralized adaptive systems scenario with storage (“storage”)

Sub-scenarios

• Carbon mitigation policies

Business-as-usual (BAU) and new energy policy (NEP) CO2 price scenarios from

Swiss Energy Strategy 2050

Feed-in-tariffs (Swiss Energy Strategy 2050)

• Energy carrier and technology cost sensitivity analysis

Scenarios

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Modeling Approach

TIMES framework (Loulou et al., 2005)

• Cost optimization modeling tool, providing details on capacity planning and dispatch

• Linear programming, least-cost optimization

• Detailed bottom-up, techno-economic, energy systems modeling

• Range of energy conversion technologies, storage technologies, energy carriers, and demand sectors can be considered

• Enables development and analysis of scenarios considering various uncertainties within energy systems (e.g., technology mix, costs, etc.)

• Modeling time horizon: 2010-2050

• Time resolution: hourly profiles for typical days (weekday/weekend) across four seasons

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Technology Considerations

• Electricity production (renewable and non-renewable DG and centralized generation)

• Heat production (CHP, heat pumps, district heating, renew., non-renew.)

• Storage (heat, electricity, seasonal, short-term)

• Transmission and distribution grid

Technology Parameters

• Cost (investment, fixed O&M, variable O&M, decommissioning)

• Efficiency, availability factor

• Installed capacity (past investment, current stock)

• Lifetime, construction time

Technologies

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Basel Case Study

• Population: 175’000

• Industrial, residential and commercial/service sectors

• Gas and district heating network

• 6 gas, wood and waste-fueled plants for district heating (CHP, boilers and

wastewater treatment)

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D DemandDG Output from distributed

generation technologyDN Output from distribution

networkDNi Input to distribution network

elc Electricity

Legend

Basel Reference Energy System (RES) – Base Year

* Modeled by building type (residential single and multi family, commercial, industrial, and mixed use buildings for different age/energy efficiency categories)** Modeled by sector (residential, commercial and industrial

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Basel Energy Supply

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Total: 5.2 TWh

End-use heat demand by source

*Gas, waste and wood-fueled heat and CHP plants

Energy Carrier Demand in Base Year

Energy carrier demand for heat and electricity

Source data: Industrielle Werke Basel (2013), Bundesamt für Statistik (2015), Basel Statistics Office.(2012), Berger et al. (2011), Aksoezen et al. (2014)

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Technology Options

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Generation Category

ScenarioBaseline Decentralized Storage

Electricity • Transmission grid (centralized)

Baseline techs +• Rooftop photovoltaics

(PV)

Decentralized techs +• Batteries

Heat / CHP Decentralized:• Boilers (gas, oil, waste,

and wood-fueled)• Heat pumps• Solar thermal heating

District heating:• CHP (gas, oil, waste,

and wood-fueled)• Boiler heat plants (gas,

oil, waste, and wood)

Baseline techs +• Building-level CHP

(gas, wood, hydrogen fuel cell)

Decentralized techs +• Seasonal heat storage

(district heating)• Day/night heat storage• Hydrogen storage

• Constant demand and load curves based on statistical data

• Energy carrier costs:

Swiss oil and gas pricing based on IEA 4D scenario (STEM)

Wood price based on statistics (Waldwirtschaft Schweiz), price varies with oil

Electricity price based on statistics, varies according to BAU scenario of

Swiss Energy Perspectives 2050

• Technology parameters based on STEM family of models

• Constraints on resource potentials according to Berger, et al. (2011) and Basel

statistics

• Initial stock estimated according to load profile peak

Model Assumptions

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0.0

0.5

1.0

1.5

2.0

2010 2020 2030 2040 2050

TWh

0.0

0.5

1.0

1.5

2.0

2010 2020 2030 2040 2050

TWh

Electricity Supply Across Scenarios – BAU CO2 Price

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Baseline

Decentralized Storage

• Grid replaced with micro CHP and PV generation

• Grid imports in 2050 reduced by ~60% in decentralized and storage scenario

• Battery storage enables increase in PV

Grid PV Gas CHP (DH) Gas CHP (bldg.) Waste/wood CHP (DH)

0.0

0.5

1.0

1.5

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2010 2020 2030 2040 2050

TWh

0.0

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2010 2020 2030 2040 2050

TWh

0.0

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1.0

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2010 2020 2030 2040 2050

TWh

0.0

0.5

1.0

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2010 2020 2030 2040 2050

TWh

Heat Supply Across Scenarios – BAU CO2 Price

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Baseline

Decentralized Storage

• Oil, gas and waste energy carriers for heating• Heat pump potential exhausted• Building-level CHP replaces district heating CHP• Heat storage enables increase in solar thermal

and a reduction in local emissions

Gas boiler (DH) Gas CHP (DH) Gas boiler Small gas CHP Oil boilerWaste boiler (DH) Waste/wood CHP (DH) Waste boiler Heat pump Solar thermal

0

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2010 2015 2020 2025 2030 2035 2040 2045 2050

Cap

acity

(MW

h)

Storage - battery Storage - heat (day/night)

Storage

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• Significant investment in building-level heat storage• Investment in battery storage prompts increase in PV investment• No seasonal heat storage

Storage Scenario (BAU CO2 Price)

0

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0 2 4 6 8 10 12 14 16 18 20 22

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erat

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(MW

)

Hour

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0 2 4 6 8 10 12 14 16 18 20 22

Gen

erat

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(MW

)

Hour

Hourly Heat Dispatch - Winter Weekday 2050

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Decentralized Scenario Storage Scenario

• Heat storage enables: peak shaving increase in solar thermal dispatch

• Storage of heat from solar thermal during day, heat release during peak demand (morning) and evening hours

Gas boiler Small gas CHP Oil boiler Waste boiler (DH) Waste boiler Heat pump Solar thermalHeat strg. - out Heat strg. - in

-150

-100

-50

0

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0 2 4 6 8 10 12 14 16 18 20 22

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rage

Flo

w (M

W)

Hour

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(MW

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Hour

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0 2 4 6 8 10 12 14 16 18 20 22

Gen

erat

ion

(MW

)

Hour

Hourly Electricity Dispatch - Winter Weekday 2050

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Decentralized Scenario Storage Scenario

• Battery storage enables: peak shaving slight increase in PV dispatch

• Storage of PV electricity during day, dispatch during peak evening hours

Small gas CHP PV Batt. strg. - out Batt. strg. - inGrid

-40

-30

-20

-10

0

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30

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0 2 4 6 8 10 12 14 16 18 20 22

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rage

Flo

w (M

W)

Hour

CO2 Emissions and Pricing Scenarios

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• Dramatic reduction in fossil fuel combustion emissions in NEP carbon price scenario compared to BAU

• Emissions more sensitive to carbon price than technology options across scenarios

• Oil boilers replaced with wood boilers0

100

200

300

400

500

2010 2020 2030 2040 2050

CO

2(k

t)

Base BAU

Base NEP

Dec. BAU

Dec. NEP

Strg. BAU

Strg. NEP

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2010 2020 2030 2040 2050

CO

2(k

t) OilGas

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2010 2020 2030 2040 2050

CO

2(k

t)

Storage Scenario (BAU) Storage Scenario (NEP)

CO2 Emissions Across Scenarios

Feed-in Tariffs

13.04.2016ETH, Page 20

0.0

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2010 2020 2030 2040 2050

Ele

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ener

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Wh)

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Wh)

Grid PV Gas CHP (DH) Gas CHP (bldg.) Waste/wood CHP (DH)

Storage Scenario (BAU CO2, w/o feed-in-tariff) Storage Scenario (BAU CO2, feed-in-tariff)

• Feed-in tariffs (FIT) based on Swiss Energy Strategy 2050 for solar and biomass-based electricity generation (i.e., from PV, wood CHP, and waste CHP), phased out by 2035

• FITs prompt earlier investment in PV, but does not prompt investment in biomass-based generation in this case study

• Local CO2 emissions and long-term energy technology mix largely unaffected

Grid Upgrade Deferrals

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• Decentralized generation and storage technologies enable significant transmission grid upgrade deferrals

• Upgrades delayed to 2030 in decentralized and storage scenarios• Storage scenario transmission upgrades remarkably reduced• Results do not consider factors outside of system boundaries however• Distribution grid upgrades required across all scenarios

05

101520253035404550

2015 2025 2030 2035 2040 2045

New

Tra

nsm

issi

on C

apac

ity (M

W)

BaseDec.Strg.

• Decentralized generation and storage technologies play a significant role in the cost optimal, future energy system of Basel

• Total discounted system costs reduced by approx. 4% in decentralized and storage scenarios compared to baseline case

• Building-level gas CHP and PV enable the community to reduce reliance on grid and move towards self-sufficiency

• Decentralized technologies enable reduced and deferred transmission grid upgrades

• Storage technologies enable increased use of solar thermal and PV technologies, resulting in further transmission grid upgrade deferrals/capacity reductions

• Carbon pricing policies are effective in reducing local CO2 emissions from fossil fuels

• Feed-in tariffs prompt earlier investment in PV, but do not significantly impact CO2 emissions and capacity planning

Conclusions

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• Technology and energy carrier price sensitivity• Building renovation potential• Electricity price signals and demand-side management• Grid model improvement

Outlook and Further Considerations

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Thank You

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Aksoezen, M., Daniel, M., Hassler, U., & Kohler, N. (2015). Building age as an indicator for energy consumption. Energy & Buildings, 87, 74–86. doi:10.1016/j.enbuild.2014.10.074

Amt für Umwelt und Energie. (2012). City of Basel Statistics. Bern.

Basel Statistics Office. (2012). City of Basel Energy Demand Statistics. Basel.

Berger, T., Genske, D., Hüsler, L., Jödecke, T., Menn, A., & Ruff, A. (2011). Energetische Optimierung des Kantons Basel-Stadt. Basel.

Bundesamt für Statistik. (2015). Gebäude- und Wohnungsregister. Bern

Geoportal Kanton Basel-Stadt. (2015). GeoViewer Solar Cadastre. Basel.

Industrielle Werke Basel. (2013). Basel Energy Statistics. Basel.

Loulou, R., Remne, U., Kanudia, A., Lehtila, A., Goldstein, G., 2005. Documentation for the TIMES Model

Ramachandran, K., Turton, H., 2013. A Long-Term Electricity Dispatch Model with the TIMES Framework. Environ. Model. Assess. 18, 325–343. doi:10.1007/s10666-012-9346-y

References