Optimum Investment Planning and Operation of Local Hybrid Energy Systems from the End- User s Perspective

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Optimum Investment Planning and Operation

of Local Hybrid Energy Systems from the

End-User’s Perspective

Andreas Fleischhacker

Energy Economics Group (EEG), TU Wien

ENERDAY 2015, Session: Investment Planning

17.04.2015, Dresden (DE)

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Agenda

 Motivation  Methodology  Technical Model  Investment Model  Model‘s Assumptions  Scenario Definition − Disaggregated − Aggregated  Results  Disaggregated Results − Single-Family-House − Business  Aggregated Results  Conclusion

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Motivation and Central Question

 Expansion or ‘open field‘ planning of energy system in local areas requires a multiple energy carrier approach.

 Consider multiple final energy carriers to determine the optimum energy strategy.

 Multidimensional synergies: Co- and Trigeneration.  Reduce overall CO2 footprint.

 Increasing utilization of small distributed energy resources for generation of heat and electricity.



Which kind of technology or technology mix provides the most

economical energy supply of the different consumption types of

the considered project areas?

Developed within the project:

INFRA-PLAN „Planning energy-border infrastructure

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Methodology

to determine economic efficiency from the end user‘s perspective. Formulated by an opimization problem consisting of:

Investment Model

objective function = „net-present-value“ (NPV) restrictions:  economies of scale:  maintenance costs  investment costs  calculation of  fuel costs  own consumption  revenues Technical Model restrictions:  supply = demand  minimum/maximum power  conversion coefficients and

efficiency

 dependence on the weather  demand,

 solar thermal systems,  photovoltaic systems  decentralized feed-in

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Technical Model

input energy storages not grid connected demand output energy storages energy conversion grid connected Optimization Problem of one coupling point

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Investment Model (1/2)

The optimization problem‘s objective function (maximum „net-present-value“): ,y,t ,y,t 8760 ,Q , 0 , , ,y,t ,y,t 1 8760 , , , , ,y,t ,y,t 1 , , max 1 (1 ) (Q , 1 , , ( )(Q , wit ) h ( ) i i i bin L y Y y i i i y i i y t i i t i i i y i i i y t i y t i i i i t i y t NPV ( p) bin I bin CF L ( r)

bin I psf(r, p l Y)bin CIF COF L

CIF l ∈Ι ∈Ι = ∈Ι ∈Ι = = = + ∆ + = − + + = − + ∆ ∆ ∆ −

∑

∑∑

∑

,y,t ,y,t ,y, ,y,t ,y,t

, , ,y,t ,y,t ,y, ,y,t ,y,t ,y, ,y,t ,y,t

Q , (Q , (Q , (Q , (Q ) ) ) ) , ) i i i t i i i y t i i i t i i Maintenan i t i i L Revenues L COF L FuelCosts L ce L = = + Legend

bin ... binary variable

CF ... cash flow

CIF ... cash inflow

COF ... cash outflow

i ... investment possibility I ... investment costs ∆p ... price development factor r ... discount rate ∆l ... load development factor (e.g. renovation) psf ... price scenario factor

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Investment Model (2/2)

 including INV into the technical model:

available 2010-2020 available 2020-2030 available 2030-2040 available 2040-2050 invest in boiler 2010 1 1 0 0 invest in boiler 2020 0 1 1 0 invest in boiler 2030 0 0 1 1 invest in boiler 2040 0 0 0 1 ,

Investment Matrix: inv_{i y} = INV

, , ,y, y t i y i t i Demand inv L ∈Ι =

∑

Expand the investment possibilities by flexible investment time points and taking the lifetime into account.

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Model‘s Assumptions

 investment possibilities:

■ electrical grid

■ district heating system

■ heat pump (sole-water)

■ heat pump (air-water)

■ gas boiler ■ µCHP

■ photovoltaic ■ biomass boiler ■ solarthermal plant

 connection to not more than two grids:

 electricity and natural gas,  electricity and heating etc.

 preinstalled heat storage

 simulation of one year with a resolution of 15min

 modelled by MATLAB and YALMIP Toolbox

 solver: gurobi

 discount factor of 2%

 only maximum power can be installed of each investment − electricity generation

− heat generation

, max( ( ))

max electricity demand electricit

P = P y,t

, max( Deman ( ))

max heat d

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Scenario Definition – Disaggregated View (End User)

A1) baseline scenario:

 no increase in fuel prices

 reflect the consumers investment behavior A2) decrease in electricity prices:

 reduction in electricity prices by 5% annually

 increase in fossil fuel and district heating prices by 5% annually A3) increase in natural gas price:

 no increase in the electricity and district heating prices  increase in fossil fuel prices by 5% annually

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Scenario Definition – Aggregated View (Spatial Distribution of

Different End Users Types)

 considering only the heat supply with grid connected heat generators  project region „Reininghaus“ in Graz (AT)

 splitting the area in 44 zones

 modelling the investment path up to 2050 B1) baseline scenario:

 low renovation

 moderate price increase (electricity, natural gas, district heating) of 2% B2) high renovation scenario:

 high renovation

 moderate price increase (electricity, natural gas, district heating) of 2%

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Results – Overview

A) Disaggregated Results:

 single-family house (passive house standard) - three scenarios (A1-A3)

- „Levelized Costs of Electricity/Heat“ (scenario: decrease in electricity prices), calculation by:

 business customers: three scenarios (A1-A3) B) Aggregated Results:

 project region „Reininghaus“ in Graz (AT)  two scenarios (B1-B2) 8760 , 1 / 8760 / 1 ( , , , ) ( . ( , , ) ( ) ) norm i i t electricity heat electricity he t t i a I psf r p l Y COF t LC crf r l Y Demand t ∈ = = Ι   ₊ _{∆ ∆}         = ∆

∑

C Legend

C_norm,i ... normalized trans-formation matrix

bin ... binary variable

COF ... cash outflow

crf ... capital recovery factor

I ... investment costs

∆l ... load development factor (e.g renovation)

∆p ... price development factor

∆l ... load development factor (e.g renovation)

r ... discount rate

psf ... price scenario factor

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Rank

electrical grid district heating heat pump (sole) heat pump (air) gas boiler

A) Single-Family-House (Passiv)

A1) baseline scenario

A2) decrease in electricity prices

A3) increase in

natural gas price

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Rank

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

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0 50 100 150 200 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Level iz ed Co st s i n EUR/ M Wh Rank

A) Single-Family-House (Passiv) - Levelized Costs of Electricity/Heat

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Rank solarthermal plant biomass boiler photovoltaik µCHP gas boiler heat pump (air) heat pump (sole) district heating electrical grid Levelized Costs of Electricity Levelized Costs of Heat add A2) decrease in electricity prices 0 50 100 150 200 250 300 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Level iz ed Co st s i n EUR/ M Wh Rank

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Rank

electrical grid district heating heat pump (sole) heat pump (air) gas boiler

A) Business

A1) baseline scenario

A2) decrease in electricity prices

A3) increase in

natural gas price

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Rank

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

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B) Aggregated Results

B1) baseline scenario, with parameters: - rate of renovation: 3%

- renovation effect: 10% heat energy saving of construction class D, E, F and G

district heating natural gas not defined heat pump district heating natural gas not defined heat pump district heating natural gas not defined district heating natural gas not defined heat pump 2015 - 2020 2020 - 2030 2030 - 2040 2040 - 2050

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B) Aggregated Results

B2) high renovation scenario, with parameters: - rate of renovation: 3%

- renovation effect: 60% heat energy saving of construction class D, E, F and G

district heating natural gas not defined heat pump district heating natural gas not defined heat pump district heating natural gas not defined heat pump district heating natural gas not defined heat pump 2015 - 2020 2020 - 2030 2030 - 2040 2040 - 2050

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Conclusion (1/2)

Small-Scale Customers

 Most economic for heat generation: 1. natural gas boiler

2. district heating

3. electric heat pump (AT) / biomass (DE)

 Trade-Off between renovation costs and resulting high energy prices (especially district heating) due to high fix costs.

 low potential of PV surplus power in heat pumps for heating  cooling  small hybrid composite advantages

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Conclusion (2/2)

Medium/Large-Sized Customers

 Most economic for heat generation : 1. natural gas: boiler / µCHP (DE)

2. electric heat pump (AT) / biomass (DE) 3. district heating (AT/DE)

 high electricity prices support distributed self-generation (benefit of economies of scale)

− µCHP

− heat storages − PV

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Andreas Fleischhacker

Vienna University of Technology

Energy Economic Group, EEG

Gußhausstraße 25-29 / E370-3

1040 Vienna, Austria

[T] +43 1 58801 370 361

[F] +43 1 58801 370 397

[E] fleischhacker@eeg.tuwien.ac.at

[W] http://www.eeg.tuwien.ac.at

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… hybrid?

Source: IBA (2014)

http://www.iba-hamburg.de/projekte/energiebunke r/projekt/energiebunker.html

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Data

consumer energy prices (DE)

Households Business Source

Electricity 298 EUR/MWh 230 EUR/MWh eurostat (2014), BDEW (2014), own calculations

Natural Gas 68 EUR/MWh 57 EUR/MWh eurostat (2014), own calculations

District Heating 89 EUR/MWh 71 EUR/MWh Bundeskartellamt (2012), own calculations