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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
2
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
4
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
5
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)
6
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
15
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
17
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Thanks for your attention!
Angelos I. NousdilisSchool of Electrical and Computer Engineering
Aristotle University of ThessalonikiThessaloniki, [email protected]