Economic Assessment of Lithium-Ion Battery Storage Systems

in the Nearly Zero Energy Building Environment

Angelos I. Nousdilis, E. O. Kontis, G. C. Kryonidis, G. C. Christoforidis, G. K. Papagiannis

1SIELA 2018 – Burgas, Bulgaria – 3 - 6 June 2018

Outline • NZEB environment

• Aim of work

• Building modelo Heating & cooling Load

o Heating and cooling system, DHW

o PV, Solar, Heat-pump generators

o Thermal and electrical storage system

• Economic evaluation of BSS

• Proposed techno-economic model

• Case study

• Simulation results

• Conclusions

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NZEB environment

• Nearly Zero Energy Building (NZEB) concept introduced by the recast of EPBD in 2010 in the EU

• All new buildings NZEB by 2019

• Transformation of building stock into NZEBs by 2030

• NZEB = reduced net-energy demand – high share of thermal & electrical needs covered locally by RES

• Considerable amount of PVs will be connected to the grid

• Technical challenges: overvoltages, protection, congestion issues

• SOLUTION! Locally store PV energy to battery ESSs

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Aim of work

• Deliver a complete techno-economic model to evaluate the economic viability of a Li-ion battery system in NZEB context

• The proposed model simulates the combined operation of

• Photovoltaics (PV)

• Solar thermal generators (ST)

• Heat pump generators (HP)

• Thermal storage system (TSS)

• Electrical storage (Li-ion battery storage system, BSS)

To represent typical characteristics of NZEBs.

• Propose an optimization algorithm to calculate the optimal size of ESS in terms of NPV

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Building model

• Inputs

• Thermal needs are covered following the priority sequence:

• 1) Thermal storage, 2) HP generator

• Expresses thermal needs and thermal energy systems outputsin terms of thermal power

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For the simulation of building energy needs and systems

• Technical datao Building energy systemso Building shell

• Economical datao Cost of Li-ion BSSo Electricity prices

• Typical electricity consumption profileso Lightingo Household appliances

• Weather data

Heating & cooling load

• Thermal power required each time-instant for the heating and cooling of the building

• Takes into consideration the surface & the thermal transmittance of each wall

• and the time-shift φ (ISO 13786:2007)

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Heating & cooling system | DHW system

• Radiant floor

• Simplified resistance model

• Takes into consideration

• Tfloor, Twater, Prequired, Surface, Thermal transmittance

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Heat terminal unit: Radiant floor

• DHW production

• dw: Supply of DHW (kg/min)

• cw: specific heat capacity of the water

PV, Solar, Heat-pump generator

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• PV system power

• Solar thermal system power

• Heat pump generator (electrical) power

(heating)

(cooling)

Thermal storage system

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• Thermal power balance equation

• VTS: Total volume of the TS

• ρw: density of the water

Gains from ST and HP

Supply to DHW and heating:

TS system losses (surface, wall thickness, etc):

STHP

DHWHeating

losses

Battery storage system

• Adopted BSS operation: maximization of SCR

PV power exceeds demand power

Battery charges

Demand power exceeds PV power

Battery discharges

SOC operational limits are preserved10

• Electrical power balance equation

where,

PPV Pgrid

Pbat Pload

Economic evaluation of BSS• Investigate the profit of installing BSS in a building with PV

• Net-metering scheme• Full NeM (all energy related costs are applied to net-energy)

• Net Energy – standalone PV: [ A + F + E ] - [ B + D ]

• Net Energy – PV+storage: [ A + F ] - B

11 Time

Pow

er (

kW)

F

Economic evaluation of BSS• Investigate the profit of installing BSS in a building with PV

• Net-metering scheme• Full NeM (all energy related costs are applied to net-energy)

• Netting period: 1 hour

• Compensation of excess energy• At system marginal price OR No compensation

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cfin = [avoided electricity cost] – [compensation lost due to BSS]

cfout = [operation and maintenance cost for BSS]

capexB = [capital investment cost of BSS]

Techno-economic model1. Required data input

o Technical parameters

o Economical parameters

o Typical consumption profiles

o Historical weather data

2. Evaluation of thermal & electrical energy needs

3. NPV is calculated & a vector is saved [npv, S]

4. Maximum desired BSS size to evaluate (breaks loop)

5. Maximum npv is determined, offering the optimal s [npvmax , Sopt]

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Model inputs

Building model

S Smax

Battery size (S)S=0.5 kWh

npv calculation

Optimal BSS size

Step 1

Step 2

Step 3

Step 4

Step 5

Case study

• Technical data

• Area: 100 m2, Height: 3 m

• Internal air: T=26oC (cooling) & T=20oC (heating) – constant

• DHW: Supply=10 kg/min , T=45oC – constant

• PV: 40 panels x 1.5 m2

• Battery life > 20 years (8000 cycles @ 80% DoD)

• Economical data

• Analysis period: 20 years

• Inflation rate: 2%

• Interest rate: 5%

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Essential parameters

Simulation results

• Current BSS prices (600 €/kWh)

• Investment on BSS NOT profitable

• Sensitivity analysis for different prices

• For BSS prices< 200 €/kWh

• Investment becomes profitable

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Case A) Full-NeM – Excess energy compensated at SMP

Simulation results

• Similarly, for current BSS prices

• A standalone PV is more profitable

• However, for lower BSS prices

• Investment becomes profitable

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Case B) Full-NeM – Excess energy NOT compensated

• Comparing with Case A, under a Full-NeM without excess energy compensation

• Profits are considerably increased

Conclusions• A techno-economic model was proposed for the economic

evaluation of BSS integration in NZEBs

• In this framework, a building model was developed to simulate thermal and electrical needs of the building

• Through an optimization procedure, optimal BSS size can be determined, in terms of maximizing prosumer profit

• A case study on a typical household in central Greece, revealed that with current Li-ion prices, a standalone PV can generate more profits, under a full-NeM scheme

• However, with decreasing BSS cost, investment on BSS becomes profitable

• In this context, the proposed method can be a valuable tool for optimal sizing of households BSS

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Thanks for your attention!

Angelos I. NousdilisSchool of Electrical and Computer Engineering

Aristotle University of ThessalonikiThessaloniki, Greeceangelos@ece.auth.gr