Modeling Decentralized Generation and Storage Technologies...
Transcript of Modeling Decentralized Generation and Storage Technologies...
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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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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)
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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
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Cap
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(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)
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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
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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
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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
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CO
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Storage Scenario (BAU) Storage Scenario (NEP)
CO2 Emissions Across Scenarios
Feed-in Tariffs
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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
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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