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These projects have received funding from the European Union’s Horizon 2020
research and innovation program under grant agreements No 774199 and 864400
Webinar: How numerical software tools
support the creation of replication plans in
smart cities energy projects
December 17th, 2019
Agenda
IRIS Webinar - How numerical software tools support the creation of replication plans in smart cities energy projects
Software tools for evaluating replication strategies for
near zero energy districts – Case study on the city of
Alexandroupolis
Nikos Nikolopoulos, CERTH
Battery sizing – Case study on IMREDD building
Christian Keim, EDF
Q&A - Discussion
IRIS Webinar
17/12/2019
Panagiotis Tsarchopoulos, Thanasis Tryferidis, Komninos
Angelakoglou, Paraskevi Giourka, Emmanouil Kakaras,
Nikos Nikolopoulos, Konstantinos Lymperopoulos, Dimitrios
Kourtidis
Software tools for evaluating replication
strategies for near zero energy districts – Case
study on the city of Alexandroupolis
Table of contents
❑ Aim and objectives;
❑ Alexandroupolis replication activities for TT1;
❑ Software selection;
❑ Case Study Analysis;
❑ Replication measures evaluation;
❑ Summary and lessons learnt;
❑ The case of a Battery Sizing (from EDF by Christian Keim)
4
Aim and objectives
Aim: Demonstrate capacities of potential software for evaluating replication strategies for TT1
Integrated Solutions 1.1 and 1.2 in the city of Alexandroupolis
The objectives of this webinar are:
❑ Present a) available software, and b) CERTH’s and Alexandroupolis selection for feasibility
studies on building level and district level analysis;
❑ Present the Linking and interoperability of selected software;
❑ Discuss information required and potential sources of information;
❑ Demonstrate Case Study Analysis;
❑ Evaluate the replication measures on a techno-economical and environmental basis;
❑ Discuss strengths and weaknesses of selected approach;
❑ Present the case of a Battery Sizing, by commercial oriented partners, with the use of Software
5
The city of Alexandroupolis
• IRIS replication activities fit well with the city’s ambitions and targets
IRIS Solutions 1.1 (Positive Energy Buildings)
• Individual Buildings retrofitted
• New-built neighbourhood
IRIS Solutions 1.2 (Near Zero Energy District)
• Retrofit at district level to reach near zero energy performance
6
• Administrative Centre of Regional Unit of Evros;
• Member of Covenant of Mayors;
• Founding member of Greek Green Cities Network;
• Vision to become a sustainable city and an innovation hub
• Sustainable Energy Action Plan target is to reduce emissions by
20% by 2020 through energy upgrade of buildings and increase of
RES
Replication buildings for TT1 – IS1.1 existing
buildings
Alexandroupolis buildings
• 1st Nursery school;
• 2nd Nursery school;
• 7th Nursery school;
• 1st senior citizen community centre (KAPI);
• 2nd senior citizen community centre (KAPI);
• Office building (Polidinamo);
• Urban setting;
7
Replication buildings for TT1 – IS1.1 New-built
neighbourhood
8
Area: 42 acres
Construction requirements
Coverage ratio: 40%;
Maximum height: 8m;
Neighbourhood
• 100 detached 2-storey dwellings;
• 6 x 10m footprint;
• E-W axis for maximizing solar gains;
• Large south facing glazing;
Replication buildings for TT1 – IS1.2 Near
Zero Energy District
9
• 95 2-storey dwellings;
• Built in 1970s
• No insulation was considered at design
stage
• Semi-detached and mid-terrace houses
• Different orientations
IS 1.1 IS 1.2
Retrofitted
buildings
New-built
neighbourhood
Retrofitted district
Replicated technology
Increasing insulation levels ✓ ✓ ✓
Photovoltaic ✓ ✓ ✓
Electrical Storage ✓ ✓ ✓
District Heating and Cooling  ✓ ✓
Geothermal Heat Pump ✓ ✓ ✓
Other technologies
ASHP ✓  
Solar thermal ✓  
Solar powered ORC unit ✓  
Biomass ✓  
10
Transition Track 1 – Technologies Replicated
Tool Selection
General Requirements
• Feasibility Analysis
• Building level;
• District level;
• Performance Evaluation
• Technical;
• Financial;
• Environmental;
11
Selection Criteria
• Technical capacity to model as many technologies as possible
(building retrofit, PV, heat pump, etc);
• Cost effectiveness – if possible, no license fee;
• Ease of use;
• Ability to conduct preliminary analysis fast, allowing the fast
comparison of alternative scenarios;
• Ability to provide results with simplified input;
• Ability to provide results in the right format;
• Technical and financial capabilities;
• Databases included that will assist the users in finding technical
information;
Building System
District
/grid
Heat Electricity Transport Storage Simulation level Access
TRNSYS X X X X X X X Detailed generic simulations of transient systems Priced
HOMER Pro - X X X X X Advanced simulation for assessing power plant and grid
performance
Priced
PV SYST - X - - X - X Advanced simulation for assessing PV performance Priced
T*sol - X - X - - X Advanced simulation for solar thermal systems performance Priced
PV*sol - X - - X - X Advanced simulation for assessing PV performance Priced
GeoT*sol - X X X X - X Advanced simulation for assessing heat pump and/or solar
thermal system performance
Priced
IDA ICE X X - X X - X Detailed simulations Priced
ESP-r X X - X X - X Detailed simulation of building thermal and electricity loads and
HVAC systems. Capacity for façade integrated PV
Free
EDSL Tas X X - X X - Detailed simulation of building thermal and electricity loads and
HVAC systems
Priced
Design
Builder
X X X X - Detailed simulation of building thermal and electricity loads and
HVAC systems
Priced
Energy Plus X X - X X - X Detailed simulation of building thermal and electricity loads and
HVAC systems
Free
RETScreen X X X X X X X Preliminary analysis of various renewable energy and energy
efficiency measures
Priced
EnergyPLAN - - X X X X X Advanced simulation of complex energy systems at regional
level
Free
Energy Pro - X X X X - X Advanced simulation of complex energy systems at
system/regional level
Priced
12
13
Steady (state) simulations – representation of all three energy
vectors (electricity, heating, cooling) on a common platform, to
investigate the behavioral characteristics of integrated grids
Tools selection
RETScreen for the building level analysis
• Simplified technical modelling of a range of technologies (power,
heating, cooling);
• Simulation as well as performance analysis tool for existing
projects;
• Extensive databases for various technologies;
• Fast results;
• Extensive financial and risk analysis capabilities;
• User friendly;
• Tutorials available for training;
• Cost effective - Free distribution for academic and research
institutes at the moment;
• Annual fee for 10 PCs ~ €620
14
Tools selection
EnergyPLAN for the district level analysis
• Developed to analyse the energy, environmental, and economic impact of various energy strategies
on national and regional energy systems;
• simulates the operation of energy systems on an hourly basis, including the electricity, heating,
cooling, industry, and transport sectors;
• User friendly;
• Deterministic in nature;
• Allows fast comparison of different scenarios;
• Freeware distribution;
• Training available;
15
Simulation Process
16
Climate
Internal conditions
Building fabric
Building systems
Renewable Energy
Heating loads
Cooling loads
Electricity loads
Energy production
Emissions reduction
InputOutput
Aggregated
Energy demands
Aggregated
Energy balances
Output
Step 1
Building
Step 2
District
Analysis – Pre-processing
• Prior to the analysis, a great deal of information was required to be gathered;
• Central to this, information is collected onsite regarding the local conditions and context mapping;
• Core information of the preliminary analysis is collected onsite (site, building, planning terms and
restrictions, etc)
• This is then supplemented with information obtained through
• Relative literature and online sources and databases;
• Software databases;
• Feedback from partners adjusted to site localities;
• Expert feedback from previous projects
17
Onsite / Local
context
mapping
Literature/
online
Feedback
Software
databases
Previous
experience
Onsite/Local context mapping
• A two-stage approach has been followed including a broader and a specific local context mapping
process
Stage Α:
• Broader approach includes
• assessment of city needs & challenges,
• policy context and stakeholder network analysis,
*All in respect to the IS to be replicated
18
“Rs” selection
SEAP,
technical program
οperational plan
…
Decision makers,
Experts,
Market,
…
Peer to peer
Case studies selection
Onsite/Local context mapping
Stage B - Specific local context (case studies) information collection
• Collaboration with the technical department of Municipality
• Collaboration with local technical experts (engineers) (Energy HIVE Cluster, technical chamber of
Thrace)
• Collaboration with local academic organization (Democritus University Thrace)
19
Input data for Retscreen & EnergyPlan
Information
• Feedback from previous experience and partners
• Cost of measures (insulation, PV, battery, GSHP, District Heating and Cooling network,
conventional equipment);
• Efficiency of GSHP with long term borehole storage in summer and winter;
• DHC Network losses;
• Literature/online/databases
• Technical directives (indoor conditions, water consumption, electrical appliances consumption,
equipment efficiencies etc);
• DHC network losses;
• Online data and information (hourly weather files, DHC network losses etc);
• Software databases
• RETScreen databases (weather data, equipment efficiencies, SHGC coefficients etc);
20
RETScreen Analysis Steps
Building level analysis using RETScreen
21
Step 1: Defining base
information (climate,
occupancy and thermostat
settings)
Step 2: Defining
equipment characteristics
(HVAC)
Step 3: Defining end-use
and energy production
Step 4: Results
Analysis – Building level
22
Climate Data
• RETScreen includes
extensive worldwide climate
database;
• Alternatively, the user may
enter their own climate data
manually;
Schedule and thermostat settings
• 24/7 use considered;
• Thermostat settings for the heating and cooling season;
• External temperature when system switches from heating to
cooling;
Step 1: Defining base
information
Analysis – Building level
23
Heating and cooling system:
• Input required is the fuel
type and seasonal
efficiency;
• District heating;
• District cooling;
• In this case, this information
is used in the analysis
conducted in EnergyPlan
Step 2: Equipment
characteristics
Analysis – Building level
24
Input data
• Building element areas and
U-values (walls, windows,
roof, floor etc);
• Solar Heat Gain Coefficient –
Shading;
• Infiltration;
• Heating/cooling system for
base case and retrofit;
• Intervention costs;
Output
• Fabric Heating/cooling Load;
• Energy saved;
Step 3: Defining end-
use and energy
production
Information from national technical guidelines may be used in filling in information on
this section (SHGC, thermophysical properties, infiltration etc). Alternatively,
RETScreen databases can be very useful for quick analysis.
Analysis – Building level
25
Input data
• Lighting characteristics for
base case and proposed;
• Impact on space cooling
and heating (unwanted and
wanted heat gains);
• Electrical appliances (hours
of usage, power rating)
Output
Energy consumption for
• lighting
Step 3: Defining end-
use and energy
production
Analysis – Building level
26
Input data
• Electrical appliances (hours
of usage, power rating);
• Impact on space cooling
and heating (unwanted and
wanted heat gains);
Output
Energy consumption for
• appliances
Step 3: Defining end-
use and energy
production
Analysis – Building level
27
Input data
• Typical usage;
• Water temperature (supply,
usable);
• Equipment for hot water
supply;
Output
• Energy consumption for hot
water usage;
Step 3: Defining end-
use and energy
production
Analysis – Building level
28
Renewable Energy production
is considered for:
• Electricity (PV, wind or
generic green energy
source);
• hot water (solar water
heater);
• space heating requirements
(solar air heater);
• In this case, a PV system
was considered
Step 3: Defining end-
use and energy
production
Analysis – Building level
29
Results presented for:
• Space Heating
requirements;
• Space cooling;
• Lighting;
• Appliances;
• Hot water;
• A simple payback period for
each measure is provided;
• Provides a fast estimation of
the contribution of each
measure and the change in
the payback period of the
investment;
Step 4: Results
Analysis – District level
From building to district level…
30
District level analysis using EnergyPLAN
• EnergyPLAN was developed model various
energy strategies on national and regional energy
systems;
• However, with minor data manipulation/
restructuring it was found useful for conducting the
technical evaluation at the district scale;
• Technical Analysis is conducted in three steps
Step 1: Defining
energy demands
Step 2: Defining
energy supply
systems
Step 3: Defining
system balancing
and storage
Analysis – District level
31
Building level
(RETScreen
output)
kWh
District level
(EnergyPlan
input)
Heating
demand
11,150 1,115,000
Cooling
demand
8,017 801,700
Electricity
demand
2,715 271,500
• Inputs are provided in the form of an annual value
and an hourly distribution profile;
• Annual values were obtained from RETScreen
considering the district level;
• Hourly profiles were required to be developed for:
• Electricity demand;
• Heating demand;
• Cooling demand;
• PV production;
Analysis – District level
32
• Electricity profile was developed assuming
typical schedule (winter/summer) for the
appliances and lighting considered in the
analysis in RETScreen.
Analysis – District level
• Heating and cooling demand profiles were determined with the use of Heating and Cooling Degree
Days (HDDs and CDDs). Weather data in hourly format and choosing an appropriate base
temperature were required;
• Base temperature of 18oC for heating and 24oC for cooling were considered as suitable for the Greek
climate (Papakostas et al., 2010).
33
• Weather data in an hourly format were obtained from climate.onebuilding.org.
Typical Meteorological Year files (TMYs) for building simulations are provided for
various locations worldwide
• Another source for TMY weather data is the JRC TMY generator (available at:
https://re.jrc.ec.europa.eu/tmy.html). TMY files are provided for a given longitude
and latitude
Analysis – District level
34
• Due to the lack of available data from local
DH networks, information was sought from
literature;
• Heating demand was increased by 7% to
account for district heating network losses;
• 7% losses was considered conservative
value based on findings from literature1,2 and
the size of the network.
1 Elmegaard, B., Schmidt O.T., Markussen, M. and Iversen, J. (2016) Integration of space heating and hot water supply in low temperature district heating, Energy and Buildings, vol. 124, pp. 255 – 264
2 Marguerite C., Geyer, R., Hangartner, D., Lindhal M. and Pedersen S.V. IEA Heat Pumping Technlogies Annex 47 - Heat Pumps in District Heating and Cooling Systems – Task 3: Review of
concepts and solutions of heat pump integration IEA-HPT
Analysis – District level
35
• Due to the lack of available data from local
DC networks, information was sought from
literature;
• 4% network losses was considered based on
findings from literature3,4 and the size of the
network (conservative value);
• For this reason, cooling demand was
increased by 4%;
3 Dominković, D., & Krajačić, G. (2019). District Cooling Versus Individual Cooling in Urban Energy Systems: The Impact of District Energy Share in Cities on the Optimal Storage Sizing. Energies, 12(3), 407
4 Calance M.A. (2014) Energy Losses Study on District Cooling Pipes – Steady State Modeling and Simulation (Master Thesis), University of Gävle, Gävle, Sweden
Analysis – District level
36
• Temporal electricity production from the PV
system is determined through the PV capacity
and the hourly distribution
• An hourly PV production profile that takes into
account the effect of irradiation levels and pane
temperature was developed based on the
formulas provided by the relevant technical
directive on climatic conditions5
5 TOTEE 20701-3/2010
6 Available at: https://heatroadmap.eu/energy-models/
• A simple approximation method would be to assess the PV production in RETScreen and
then creating the distribution profile using the hourly solar irradiance data from the TMY file;
• Alternatively, PV production profiles, as well as other Renewable Energy and typical
demand profiles, for 14 EU countries may be found from the Heat Roadmap Europe 4
project6. These are available at country-level, not site specific;
Analysis – District level
37
• Battery capacity was determined at 1500
kWh corresponded to one-day’s autonomy;
• Parametric analysis was also conducted for:
• 750 kWh (half-day’s autonomy);
• 2,250 kWh (1.5 days autonomy);
• 3,000 kWh (2 days) autonomy;
• Geothermal Heat Pump providing thermal energy
to the District Heating Network;
• Capacity of the system to cover the needs of
district and the COP required;
• COP: 7 based on assumption on the
performance of the borehole storage system
Post Processing - Results
• Results are obtained manually on an hourly
basis in excel
• Heating and cooling demand (kWh);
• Electricity demand (kWh);
• PV production (kWh);
• PV electricity direct use (kWh);
• PV electricity stored and used later (kWh);
• Electricity imported from grid;
• Electricity exported to the grid;
38
Assessment of replication measures was done using
project KPI’s and common financial criteria
Some degree of manipulation required to obtain:
• Degree of Energetic Self supply;
• Emissions reduction;
• Payback Period;
• IRR;
• NPV;
Replication measures are assessed against a Business-as-Usual Scenario
Replication Strategy Assessment
39
BAU
• Building regulations insulation;
• No energy production;
• Heating oil boiler;
• A/C units;
• Heating requirements: 1,225,895 kWh heating
oil;
• Cooling requirements: 810,000 kWh;
• Electricity requirements: 433,500 kWh;
• Electricity production: 0 kWh;
• Electricity imports: 433,500 kWh;
• CO2 emissions (primary): 1,438, 406 kg CO2;
• Capital Cost: € 300,000;
• Operating cost: € 174,270.80;
Zero energy scenario
• Improved insulation;
• PV - 500 kW;
• Battery 1500 kWh/day;
• GSHP + DHC network;
• Heating requirements: 1,115,000 kWh;
• Cooling requirements: 801,700 kWh;
• Electricity requirements: 611,484;
• Electricity production: 789,090 kWh;
• Electricity exports: 269,440 kWh;
• Electricity imports: 91,874 kWh;
• CO2 emissions (primary): -2,588 kg CO2
(savings);
• Capital Cost: € 3,054,000;
• Operating cost: € 5,614.66;
Replication Strategy Assessment
• Degree of Energetic self-supply
DET = 100% (all thermal energy is geothermal)
DEE=
PV production
PV direct + PV stored + (electricity import x 2,9)
=
789,090
189,833 + 329,769 + (91,874 x 2,9)
= 100.4%
(Electrical Energy consumption includes appliances, lighting and electrical power for GSHP)
• Emissions reduction
ER = Emissions of positive energy scenario - CO2 emissions of BAU scenario = -1,440,994 kg CO2
40
Replication Strategy Assessment
Financial Evaluation – Assumptions
• Cost of Geothermal Heat Pumps: €1,500/kW;
• Cost of District Heating and Cooling Network:
€1,000,000;
• Cost of the PV system (PV panels, inverters etc):
€1,000/kW;
• Cost of the battery storage: €400/kW;
• Cost of heating oil boilers: €1,500/house;
• Cost of A/C units: €1,500/house;
• Selling Price of electricity: €0.065/kWh;
• Cost of electricity purchase: €0.10 /kWh;
• Heating oil costs: €1.10/litre;
• Additional insulation costs: € 3/m2 per 0.05W/m2K
reduction in the U-value;
41
• Project lifetime: 25 years;
• Energy price increase: 2% annually;
• PV performance reduction: 0.5% annually;
Financial criteria
Simple payback period = 14,7 years
Discounted payback period = 11,6 years
Net Present Value = € 276,860
Internal Rate of Return = 6 %
This strategy is feasible both technically and
economically
Replication Strategy Assessment
42
Case A – Building Regulation insulation levels
Case B – increased insulation
Case C – further increased insulation and triple glazing
The case of dynamic simulations – representation of all three
energy vectors (electricity, heating, cooling) on a common
platform, to investigate dynamic behavioral characteristics of
integrated grids - INTEMA
43
Near Zero-Energy Districts Energy analysis
using INTEMA framework
• Examined Case: Electrification of existing
DH network
• District Heating Network to be heated by Heat
Pump (HP) power by RES electricity;
• Use of Thermal Energy Storage to increase
RES penetration and HP operation reliability
44
district
CO2
coal biomass
• Alternative software tools for advanced
energy management analysis;
• Operation optimization using appropriate
EMS algorithms;
• INTEMA framework is capable of performing
multi-domain transient simulation runs
Heat pump
District
RES
Thermal
energy storage
The INTEMA Framework
In the context of INTEMA framework a number of tools have been developed in
order to asses:
• Electrical Grid Performance
• Multidomain EMS (e.g heating/cooling/electricity)
• Grid Ancillary Services
• Economic Dispatch Strategies
• Energy Storage Technologies
The INTEMA framework contains:
• The INTEMA Library: Energy (production, consumption and storage) and control
components’ models, in Modelica as core Simulation Module
• Energy Demand and Production Forecasting Module (Python)
• Demand Response Module(Python)
• Optimal Power Flow Module (Python)
45
INTEMA Applications
INTEMA (in Modelica) can perform:
• Short-Term Simulations, to evaluate and address specific technical
challenges
• Examine solutions that can offer ancillary services in electrical
grid
• Estimate the response of electrical grid, electrical / thermal
systems and specific components
• Assess efficiency and performance of specific components
under various conditions
• Long-Term Simulations, to evaluate and optimize seasonal, annual
or life cycle systems’ performance
• Perform power flow studies
• Identify rules and scenarios in energy management systems
• Propose economic dispatch strategies
46
Modelica Environment
INTEMA Example – Electrification of Heating
• District with Electric & Heating Demand
• Local Energy Production from PV & Wind
Turbines
• District Heating Network with Heat Pump, Solar
Heater and Thermal Storage
• Excess Electric energy will provide heat to
network through heat pump
• Heat pump electrical load will be added to total
load
• Storage and Heat Pump Controller (rules and set
points) will define the operation (ON-OFF) of the
Heat Pump
• With INTEMA advance Energy Management
Rules can be implemented
47
District DH System
PV System
Wind System
Outer Grid
Connection
INTEMA Example – Electrification of Heating
48
District (Detail)
Loads / Houses
Solar Heater
Heat
Pump
DH System (Detail)
HP
Controller
Storage
RE Systems
Lines
Input Variables Design
Parameters
Output Variables
Heat Demand TS HP Electric Power Electrical Demand TS
Elect. Energy send to
mainland signal
Heat Tank Size Thermal Peak Power
TS
Solar Irradiation TS COP vs Temp
Ambient Temp TS Solar System Size
and efficiency
Heat Pump (Detail)
Indicative Results
Simulation Data (for approx. 100 houses)
• Solar heater area: 500m2
• Heat pump electric power: 70kW
• Storage Thermal: 8.3MWh
• PV install power: 500kWp
• Wind Turbine install power: 500kW
• Mean Annual Electrical Demand: 30kW
• Mean Annual Heat Demand: 127kW
49
Annual: Before
Electrification
(MWh)
After
Electrification
(MWh)
Elect2Grid (Net) 1827 1485
Elect2Heat 272
Wind Production 1546
PV Production 554
Elec Demand 273 273
• Before Electrification no need for energy
imports
• Elect2Grid + Elec2Heat After
electrification is lower than the Elec2Grid
Before (70MWh)
• There are small periods that the district
has to import energy from the grid to
cover the combined needs from the
Grid (heat + electricity)
INTEMA Example – Electrification of
Heating
System Operation – Weekly profile
50
DH Thermal
Storage SOC
SOC
1.0
0.5
0.05
Heat Pump
Operation
Charges from
Solar Heater
Power to
Grid
Power from
Grid
PV
Production
Wind
Production
Elec Demand
Heat Demand
Excess
Energy and
HP Starts
0
100
200
500
400
300
800
700
600
Power(kW)
Summary and Lessons Learnt
51
Approach:
1. The analysis followed a bottom ->> top approach (Level of Detail)
From low (building) to higher level (district), input for was obtained from simulations at building level;
2. The tools involved were able to provide results with simplified top-level input;
3. Fast analysis allowed the conduction of parametric analysis examining several different variations of
replication measures in short time;
4. Gathering of Input Data (Technical, Economic, at least estimations) through LHs, onsite collection,
literature, online and software databases and assumptions
5. Calculation of basic expected KPIs (Technical, Economic) to answer key questions (is it worth
investing?)
6. The output data can act as the basis for tenders;
7. Identification of Investors;
Summary and Lessons Learnt
• RETScreen includes various databases that may significantly reduce simulation time and includes the
majority of renewable energy technologies. However, in certain cases external software was required
to model specific technologies (outside the scope of the replication measures)
• RETScreen models a base case and a retrofit scenario simultaneously. Renewable energy
technologies are considered for the retrofit case only. Care should be taken when examining a case
study where renewable energy in included in the pre-retrofit stage. Energy requirements and
production may be obtained but manual calculations are recommended for the financial evaluation.
• EnergyPLAN is developed for simulating much larger systems (national, regional level) and has the
capacity to conduct financial and emissions analysis. For the purpose of this study, it was found
suitable for performing technical analysis only at the district level with minor adjustments. Financial
and emissions analysis was conducted manually.
52
For ease of use, RETScreen may even be
used for conducting generic financial and risk
analysis
These projects have received funding from the European Union’s Horizon 2020
research and innovation program under grant agreements No 774199 and 864400
Battery sizing
Case study on IMREDD building
These project have received funding from the European Union’s Horizon 2020
research and innovation program under grant agreement No 774199
Battery sizing – Case study on IMREDD building
NEXITY
Common self consumption
Tertiary building
90 KWp PV + 79 kW/88 kWh battery storage
IMREDD
Individual self consumption
University building
175 KWp PV + 100kW/150kWh battery storage
36 kW 2nd life batteries compared to V2G with 41 kW
Background 1/3
Battery sizing – Case study on IMREDD building
PV production vs building’s needs for a week of June
0
50
100
150
200
250
Building consumption
PV production
weekend
Background 2/3
Battery sizing – Case study on IMREDD building
1 32
Selection of representative
pixelsSatellite image analysis PV production calculation
KT
Psat
Isat
reflectance r for
each pixel
Clear sky index = f(r)
x Imax (t) projected on the panels
Irradiance
PV production
Transfer function (production P, T°, …)
1.1. Estimation of PV production (2015)
At a 15 min step
Background 3/3
Battery sizing – Case study on IMREDD building
1.2. Estimation of the building consumption
0
50
100
150
200
250
Building consumption
Battery sizing – Case study on IMREDD building
26/06 27/06
PV surplus injected to
the grid
PV production totally consumed by the
building
0% extraction
from the grid
hours
100%
12
76%
PV SURPLUS
LOSSES
SELF CONSUMPTION
RATE *
PROFITABILITY
(years)
21%
SELF PRODUCTION
RATE **
PV surplus represents a substantial
amount especially during weekends
and holidays.
* Self consumption rate = PV production consumed on site / total PV production
** Self production rate = PV production consumed on site / total site consumption
Holiday
PV production
Building needs
Extraction from the grid
Injection to the grid
3.1. Hypothesis 1 : PV only
Scenario 1 : PV only
Battery sizing – Case study on IMREDD building
59
59%
0% 21% 40%
STORAGE
CAPACITY(kWh)
PROFITABILITY
(years)
1505001400 250
45 24 18 15
100
14
72%
PVSURPLUS
LOSSES
SELFCONSUMPTION
RATE
85%88%91%95%100%
PV production totally consumed
by the building
Scenario 2 : Maximisation of self consumption
rate
3.2. Hypothesis 2 : Maximisation of
the self consumption rate
26/06 27/06
Battery
not used
150 kWh / 100 kW battery
Battery
charging
Battery
discharging
Battery sizing – Case study on IMREDD building
12/01 13/01
Battery discharging to lower
the peak
Power < 170 kW
Battery charging
with PV production
Charging in off-peak
hours
Battery discharging in
peak hours
SELF CONSUMPTION
RATE *
SELF PRODUCTION
RATE **
With both tariff optimisation and peak
shaving services, less PV is injected to
the grid. However, the losses are more
important as the battery is more used
and the tariff optimisation is less
efficient.
PV production
Building needs
Battery power
Battery state of charge
Extraction from the grid
Injection to the grid
150 kWh / 50 kW battery
84%
13 MWh
Losses + auxiliaries
consumption
Scenario 4 : Tariff optimisation + peak shaving
3.4. Hypothesis 4 : Tariff optimisation
+ peak shaving
23%
Sensibility analysis
Battery sizing – Case study on IMREDD building
Technical and economic optimum of battery power and capacity
versus contract power
Power (kW)
Capacity (kWh)
Exponential
The decrease of contract power thanks to the battery is limited by the
exponential growth of the capacity installed. Therefore a contract power
below 160 kW doesn’t seem to appropriate.
Contract power - kW
kW/kWh
Capacity (kWh)
PMax : 180 kW
PMax : 170 kW
PMax : 160 kW
NPV15years(k€)
The peak shaving and tariff optimisation services lower the amount of
PV sold. However the profit linked to the reduction of contract power is
lower than the resale to the grid, hence the negative NPV.
1.1. Influence of the choice of contract power
1. Economic optimisation
Battery sizing – Case study on IMREDD building
➢ The decrease of PV resale
price improves the
profitability of the battery.
NPV15years(k€)
PV resale price becomes more important as the
battery capacity increases
Sensibility analysis
Capacity (kWh)
70 €/MWh
42 €/MWh
0 €/MWh
26 kW /
124 kWh
50 kW /
150 kWh
75 kW /
175 kWh
100 kW /
200kWh
125 kW /
225 kWh
The economic optimal sizing of the battery is 150 kWh – 50 kW
1.2. Influence of PV resale price – contract power 170 kW
1. Economic optimisation
Battery sizing – Case study on IMREDD building
Battery sizing
2. Self consumption rate maximisation
0
100
200
300
400
500
600
700
800
1 6 11 15 20 25 30 35 39 44 49 54 59 63 68 73 78 83 88 92 97
Daily
PV surplus in weekends (kWh)
39% of PV surplus in weekends can be
stored with a 150 kWh battery
150
All the simulations
presented lead to the choice
of a 150 kWh / 100 kW
battery.
50 kWh battery : 10% of PV surplus can be stored
100 kWh battery : 25% of PV surplus can be stored
200 kWh battery : 41% of PV surplus can be stored
--> 150 kWh seems to be a good compromise
50
Battery sizing – Case study on IMREDD building
Integration of grid services
To provide the 2 hour block erasing service, the minimum dimensioning to be considered in capacity is 2 times the
installed capacity.
No assumption of inflation of the erasure service remuneration is taken.
Tertiary reserves would, under current conditions, be cumulative with peak-shaving and rate optimization services. Thus, for a 50
kW - 150 kWh battery, an updated flow of gains of around 24 k € over 15 years could be obtained in addition without
oversizing the battery.
Evolution of the NPV (15years) depending on the capacity made available for the III service [50 - 500 kW]
Battery power (kW)
Scenario 1: 80% available. If calls < 60
days/an
Scenario 2: 80% available. + no additional
investment for a 50 kW / 150 kWh battery
7,2k€
NPV15ans(€)
Battery sizing – Case study on IMREDD building
Simulations on a power range engaged on the Primary Reserve (RP - FCR) market from 50 kW to 500 kW over 15 years
based on the previously exposed assumptions:
> By pooling the battery investments for the 3 Peak-Shaving services, tariff optimization and Primary Reserve, the availability in
RP is reduced to 28% but the model still seems profitable with a NPV15years of 7.1 k €,
> By participating in the RP 80% of the time, an addition of battery sizing is required because this service cannot be
combined with the previous services. Profitability is found for a minimum commitment of 250 kW with a battery of 300 kW -
185 kWh (optimal steering margins for the RP taken into account).
Evolution of the NPV15years as a function of the power engaged for the FCR service
7,1 k€
NPV15ans(€)
Battery power (kW)
Integration of grid services
Scenario 1: 80% available.
Scenario 2: 28% available. + no additional
investment for a 50 kW / 150 kWh battery
Thank you for your time
Thank you for your attention!
Any Questions?
@IRISsmartcities
https://irissmartcities.eu
@ pocityf
https://pocityf.eu

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IRIS Webinar: How can software support smart cities and energy projects?

  • 1. These projects have received funding from the European Union’s Horizon 2020 research and innovation program under grant agreements No 774199 and 864400 Webinar: How numerical software tools support the creation of replication plans in smart cities energy projects December 17th, 2019
  • 2. Agenda IRIS Webinar - How numerical software tools support the creation of replication plans in smart cities energy projects Software tools for evaluating replication strategies for near zero energy districts – Case study on the city of Alexandroupolis Nikos Nikolopoulos, CERTH Battery sizing – Case study on IMREDD building Christian Keim, EDF Q&A - Discussion
  • 3. IRIS Webinar 17/12/2019 Panagiotis Tsarchopoulos, Thanasis Tryferidis, Komninos Angelakoglou, Paraskevi Giourka, Emmanouil Kakaras, Nikos Nikolopoulos, Konstantinos Lymperopoulos, Dimitrios Kourtidis Software tools for evaluating replication strategies for near zero energy districts – Case study on the city of Alexandroupolis
  • 4. Table of contents ❑ Aim and objectives; ❑ Alexandroupolis replication activities for TT1; ❑ Software selection; ❑ Case Study Analysis; ❑ Replication measures evaluation; ❑ Summary and lessons learnt; ❑ The case of a Battery Sizing (from EDF by Christian Keim) 4
  • 5. Aim and objectives Aim: Demonstrate capacities of potential software for evaluating replication strategies for TT1 Integrated Solutions 1.1 and 1.2 in the city of Alexandroupolis The objectives of this webinar are: ❑ Present a) available software, and b) CERTH’s and Alexandroupolis selection for feasibility studies on building level and district level analysis; ❑ Present the Linking and interoperability of selected software; ❑ Discuss information required and potential sources of information; ❑ Demonstrate Case Study Analysis; ❑ Evaluate the replication measures on a techno-economical and environmental basis; ❑ Discuss strengths and weaknesses of selected approach; ❑ Present the case of a Battery Sizing, by commercial oriented partners, with the use of Software 5
  • 6. The city of Alexandroupolis • IRIS replication activities fit well with the city’s ambitions and targets IRIS Solutions 1.1 (Positive Energy Buildings) • Individual Buildings retrofitted • New-built neighbourhood IRIS Solutions 1.2 (Near Zero Energy District) • Retrofit at district level to reach near zero energy performance 6 • Administrative Centre of Regional Unit of Evros; • Member of Covenant of Mayors; • Founding member of Greek Green Cities Network; • Vision to become a sustainable city and an innovation hub • Sustainable Energy Action Plan target is to reduce emissions by 20% by 2020 through energy upgrade of buildings and increase of RES
  • 7. Replication buildings for TT1 – IS1.1 existing buildings Alexandroupolis buildings • 1st Nursery school; • 2nd Nursery school; • 7th Nursery school; • 1st senior citizen community centre (KAPI); • 2nd senior citizen community centre (KAPI); • Office building (Polidinamo); • Urban setting; 7
  • 8. Replication buildings for TT1 – IS1.1 New-built neighbourhood 8 Area: 42 acres Construction requirements Coverage ratio: 40%; Maximum height: 8m; Neighbourhood • 100 detached 2-storey dwellings; • 6 x 10m footprint; • E-W axis for maximizing solar gains; • Large south facing glazing;
  • 9. Replication buildings for TT1 – IS1.2 Near Zero Energy District 9 • 95 2-storey dwellings; • Built in 1970s • No insulation was considered at design stage • Semi-detached and mid-terrace houses • Different orientations
  • 10. IS 1.1 IS 1.2 Retrofitted buildings New-built neighbourhood Retrofitted district Replicated technology Increasing insulation levels ✓ ✓ ✓ Photovoltaic ✓ ✓ ✓ Electrical Storage ✓ ✓ ✓ District Heating and Cooling  ✓ ✓ Geothermal Heat Pump ✓ ✓ ✓ Other technologies ASHP ✓   Solar thermal ✓   Solar powered ORC unit ✓   Biomass ✓   10 Transition Track 1 – Technologies Replicated
  • 11. Tool Selection General Requirements • Feasibility Analysis • Building level; • District level; • Performance Evaluation • Technical; • Financial; • Environmental; 11 Selection Criteria • Technical capacity to model as many technologies as possible (building retrofit, PV, heat pump, etc); • Cost effectiveness – if possible, no license fee; • Ease of use; • Ability to conduct preliminary analysis fast, allowing the fast comparison of alternative scenarios; • Ability to provide results with simplified input; • Ability to provide results in the right format; • Technical and financial capabilities; • Databases included that will assist the users in finding technical information;
  • 12. Building System District /grid Heat Electricity Transport Storage Simulation level Access TRNSYS X X X X X X X Detailed generic simulations of transient systems Priced HOMER Pro - X X X X X Advanced simulation for assessing power plant and grid performance Priced PV SYST - X - - X - X Advanced simulation for assessing PV performance Priced T*sol - X - X - - X Advanced simulation for solar thermal systems performance Priced PV*sol - X - - X - X Advanced simulation for assessing PV performance Priced GeoT*sol - X X X X - X Advanced simulation for assessing heat pump and/or solar thermal system performance Priced IDA ICE X X - X X - X Detailed simulations Priced ESP-r X X - X X - X Detailed simulation of building thermal and electricity loads and HVAC systems. Capacity for façade integrated PV Free EDSL Tas X X - X X - Detailed simulation of building thermal and electricity loads and HVAC systems Priced Design Builder X X X X - Detailed simulation of building thermal and electricity loads and HVAC systems Priced Energy Plus X X - X X - X Detailed simulation of building thermal and electricity loads and HVAC systems Free RETScreen X X X X X X X Preliminary analysis of various renewable energy and energy efficiency measures Priced EnergyPLAN - - X X X X X Advanced simulation of complex energy systems at regional level Free Energy Pro - X X X X - X Advanced simulation of complex energy systems at system/regional level Priced 12
  • 13. 13 Steady (state) simulations – representation of all three energy vectors (electricity, heating, cooling) on a common platform, to investigate the behavioral characteristics of integrated grids
  • 14. Tools selection RETScreen for the building level analysis • Simplified technical modelling of a range of technologies (power, heating, cooling); • Simulation as well as performance analysis tool for existing projects; • Extensive databases for various technologies; • Fast results; • Extensive financial and risk analysis capabilities; • User friendly; • Tutorials available for training; • Cost effective - Free distribution for academic and research institutes at the moment; • Annual fee for 10 PCs ~ €620 14
  • 15. Tools selection EnergyPLAN for the district level analysis • Developed to analyse the energy, environmental, and economic impact of various energy strategies on national and regional energy systems; • simulates the operation of energy systems on an hourly basis, including the electricity, heating, cooling, industry, and transport sectors; • User friendly; • Deterministic in nature; • Allows fast comparison of different scenarios; • Freeware distribution; • Training available; 15
  • 16. Simulation Process 16 Climate Internal conditions Building fabric Building systems Renewable Energy Heating loads Cooling loads Electricity loads Energy production Emissions reduction InputOutput Aggregated Energy demands Aggregated Energy balances Output Step 1 Building Step 2 District
  • 17. Analysis – Pre-processing • Prior to the analysis, a great deal of information was required to be gathered; • Central to this, information is collected onsite regarding the local conditions and context mapping; • Core information of the preliminary analysis is collected onsite (site, building, planning terms and restrictions, etc) • This is then supplemented with information obtained through • Relative literature and online sources and databases; • Software databases; • Feedback from partners adjusted to site localities; • Expert feedback from previous projects 17 Onsite / Local context mapping Literature/ online Feedback Software databases Previous experience
  • 18. Onsite/Local context mapping • A two-stage approach has been followed including a broader and a specific local context mapping process Stage Α: • Broader approach includes • assessment of city needs & challenges, • policy context and stakeholder network analysis, *All in respect to the IS to be replicated 18 “Rs” selection SEAP, technical program οperational plan … Decision makers, Experts, Market, … Peer to peer Case studies selection
  • 19. Onsite/Local context mapping Stage B - Specific local context (case studies) information collection • Collaboration with the technical department of Municipality • Collaboration with local technical experts (engineers) (Energy HIVE Cluster, technical chamber of Thrace) • Collaboration with local academic organization (Democritus University Thrace) 19 Input data for Retscreen & EnergyPlan
  • 20. Information • Feedback from previous experience and partners • Cost of measures (insulation, PV, battery, GSHP, District Heating and Cooling network, conventional equipment); • Efficiency of GSHP with long term borehole storage in summer and winter; • DHC Network losses; • Literature/online/databases • Technical directives (indoor conditions, water consumption, electrical appliances consumption, equipment efficiencies etc); • DHC network losses; • Online data and information (hourly weather files, DHC network losses etc); • Software databases • RETScreen databases (weather data, equipment efficiencies, SHGC coefficients etc); 20
  • 21. RETScreen Analysis Steps Building level analysis using RETScreen 21 Step 1: Defining base information (climate, occupancy and thermostat settings) Step 2: Defining equipment characteristics (HVAC) Step 3: Defining end-use and energy production Step 4: Results
  • 22. Analysis – Building level 22 Climate Data • RETScreen includes extensive worldwide climate database; • Alternatively, the user may enter their own climate data manually; Schedule and thermostat settings • 24/7 use considered; • Thermostat settings for the heating and cooling season; • External temperature when system switches from heating to cooling; Step 1: Defining base information
  • 23. Analysis – Building level 23 Heating and cooling system: • Input required is the fuel type and seasonal efficiency; • District heating; • District cooling; • In this case, this information is used in the analysis conducted in EnergyPlan Step 2: Equipment characteristics
  • 24. Analysis – Building level 24 Input data • Building element areas and U-values (walls, windows, roof, floor etc); • Solar Heat Gain Coefficient – Shading; • Infiltration; • Heating/cooling system for base case and retrofit; • Intervention costs; Output • Fabric Heating/cooling Load; • Energy saved; Step 3: Defining end- use and energy production Information from national technical guidelines may be used in filling in information on this section (SHGC, thermophysical properties, infiltration etc). Alternatively, RETScreen databases can be very useful for quick analysis.
  • 25. Analysis – Building level 25 Input data • Lighting characteristics for base case and proposed; • Impact on space cooling and heating (unwanted and wanted heat gains); • Electrical appliances (hours of usage, power rating) Output Energy consumption for • lighting Step 3: Defining end- use and energy production
  • 26. Analysis – Building level 26 Input data • Electrical appliances (hours of usage, power rating); • Impact on space cooling and heating (unwanted and wanted heat gains); Output Energy consumption for • appliances Step 3: Defining end- use and energy production
  • 27. Analysis – Building level 27 Input data • Typical usage; • Water temperature (supply, usable); • Equipment for hot water supply; Output • Energy consumption for hot water usage; Step 3: Defining end- use and energy production
  • 28. Analysis – Building level 28 Renewable Energy production is considered for: • Electricity (PV, wind or generic green energy source); • hot water (solar water heater); • space heating requirements (solar air heater); • In this case, a PV system was considered Step 3: Defining end- use and energy production
  • 29. Analysis – Building level 29 Results presented for: • Space Heating requirements; • Space cooling; • Lighting; • Appliances; • Hot water; • A simple payback period for each measure is provided; • Provides a fast estimation of the contribution of each measure and the change in the payback period of the investment; Step 4: Results
  • 30. Analysis – District level From building to district level… 30 District level analysis using EnergyPLAN • EnergyPLAN was developed model various energy strategies on national and regional energy systems; • However, with minor data manipulation/ restructuring it was found useful for conducting the technical evaluation at the district scale; • Technical Analysis is conducted in three steps Step 1: Defining energy demands Step 2: Defining energy supply systems Step 3: Defining system balancing and storage
  • 31. Analysis – District level 31 Building level (RETScreen output) kWh District level (EnergyPlan input) Heating demand 11,150 1,115,000 Cooling demand 8,017 801,700 Electricity demand 2,715 271,500 • Inputs are provided in the form of an annual value and an hourly distribution profile; • Annual values were obtained from RETScreen considering the district level; • Hourly profiles were required to be developed for: • Electricity demand; • Heating demand; • Cooling demand; • PV production;
  • 32. Analysis – District level 32 • Electricity profile was developed assuming typical schedule (winter/summer) for the appliances and lighting considered in the analysis in RETScreen.
  • 33. Analysis – District level • Heating and cooling demand profiles were determined with the use of Heating and Cooling Degree Days (HDDs and CDDs). Weather data in hourly format and choosing an appropriate base temperature were required; • Base temperature of 18oC for heating and 24oC for cooling were considered as suitable for the Greek climate (Papakostas et al., 2010). 33 • Weather data in an hourly format were obtained from climate.onebuilding.org. Typical Meteorological Year files (TMYs) for building simulations are provided for various locations worldwide • Another source for TMY weather data is the JRC TMY generator (available at: https://re.jrc.ec.europa.eu/tmy.html). TMY files are provided for a given longitude and latitude
  • 34. Analysis – District level 34 • Due to the lack of available data from local DH networks, information was sought from literature; • Heating demand was increased by 7% to account for district heating network losses; • 7% losses was considered conservative value based on findings from literature1,2 and the size of the network. 1 Elmegaard, B., Schmidt O.T., Markussen, M. and Iversen, J. (2016) Integration of space heating and hot water supply in low temperature district heating, Energy and Buildings, vol. 124, pp. 255 – 264 2 Marguerite C., Geyer, R., Hangartner, D., Lindhal M. and Pedersen S.V. IEA Heat Pumping Technlogies Annex 47 - Heat Pumps in District Heating and Cooling Systems – Task 3: Review of concepts and solutions of heat pump integration IEA-HPT
  • 35. Analysis – District level 35 • Due to the lack of available data from local DC networks, information was sought from literature; • 4% network losses was considered based on findings from literature3,4 and the size of the network (conservative value); • For this reason, cooling demand was increased by 4%; 3 Dominković, D., & Krajačić, G. (2019). District Cooling Versus Individual Cooling in Urban Energy Systems: The Impact of District Energy Share in Cities on the Optimal Storage Sizing. Energies, 12(3), 407 4 Calance M.A. (2014) Energy Losses Study on District Cooling Pipes – Steady State Modeling and Simulation (Master Thesis), University of Gävle, Gävle, Sweden
  • 36. Analysis – District level 36 • Temporal electricity production from the PV system is determined through the PV capacity and the hourly distribution • An hourly PV production profile that takes into account the effect of irradiation levels and pane temperature was developed based on the formulas provided by the relevant technical directive on climatic conditions5 5 TOTEE 20701-3/2010 6 Available at: https://heatroadmap.eu/energy-models/ • A simple approximation method would be to assess the PV production in RETScreen and then creating the distribution profile using the hourly solar irradiance data from the TMY file; • Alternatively, PV production profiles, as well as other Renewable Energy and typical demand profiles, for 14 EU countries may be found from the Heat Roadmap Europe 4 project6. These are available at country-level, not site specific;
  • 37. Analysis – District level 37 • Battery capacity was determined at 1500 kWh corresponded to one-day’s autonomy; • Parametric analysis was also conducted for: • 750 kWh (half-day’s autonomy); • 2,250 kWh (1.5 days autonomy); • 3,000 kWh (2 days) autonomy; • Geothermal Heat Pump providing thermal energy to the District Heating Network; • Capacity of the system to cover the needs of district and the COP required; • COP: 7 based on assumption on the performance of the borehole storage system
  • 38. Post Processing - Results • Results are obtained manually on an hourly basis in excel • Heating and cooling demand (kWh); • Electricity demand (kWh); • PV production (kWh); • PV electricity direct use (kWh); • PV electricity stored and used later (kWh); • Electricity imported from grid; • Electricity exported to the grid; 38 Assessment of replication measures was done using project KPI’s and common financial criteria Some degree of manipulation required to obtain: • Degree of Energetic Self supply; • Emissions reduction; • Payback Period; • IRR; • NPV; Replication measures are assessed against a Business-as-Usual Scenario
  • 39. Replication Strategy Assessment 39 BAU • Building regulations insulation; • No energy production; • Heating oil boiler; • A/C units; • Heating requirements: 1,225,895 kWh heating oil; • Cooling requirements: 810,000 kWh; • Electricity requirements: 433,500 kWh; • Electricity production: 0 kWh; • Electricity imports: 433,500 kWh; • CO2 emissions (primary): 1,438, 406 kg CO2; • Capital Cost: € 300,000; • Operating cost: € 174,270.80; Zero energy scenario • Improved insulation; • PV - 500 kW; • Battery 1500 kWh/day; • GSHP + DHC network; • Heating requirements: 1,115,000 kWh; • Cooling requirements: 801,700 kWh; • Electricity requirements: 611,484; • Electricity production: 789,090 kWh; • Electricity exports: 269,440 kWh; • Electricity imports: 91,874 kWh; • CO2 emissions (primary): -2,588 kg CO2 (savings); • Capital Cost: € 3,054,000; • Operating cost: € 5,614.66;
  • 40. Replication Strategy Assessment • Degree of Energetic self-supply DET = 100% (all thermal energy is geothermal) DEE= PV production PV direct + PV stored + (electricity import x 2,9) = 789,090 189,833 + 329,769 + (91,874 x 2,9) = 100.4% (Electrical Energy consumption includes appliances, lighting and electrical power for GSHP) • Emissions reduction ER = Emissions of positive energy scenario - CO2 emissions of BAU scenario = -1,440,994 kg CO2 40
  • 41. Replication Strategy Assessment Financial Evaluation – Assumptions • Cost of Geothermal Heat Pumps: €1,500/kW; • Cost of District Heating and Cooling Network: €1,000,000; • Cost of the PV system (PV panels, inverters etc): €1,000/kW; • Cost of the battery storage: €400/kW; • Cost of heating oil boilers: €1,500/house; • Cost of A/C units: €1,500/house; • Selling Price of electricity: €0.065/kWh; • Cost of electricity purchase: €0.10 /kWh; • Heating oil costs: €1.10/litre; • Additional insulation costs: € 3/m2 per 0.05W/m2K reduction in the U-value; 41 • Project lifetime: 25 years; • Energy price increase: 2% annually; • PV performance reduction: 0.5% annually; Financial criteria Simple payback period = 14,7 years Discounted payback period = 11,6 years Net Present Value = € 276,860 Internal Rate of Return = 6 % This strategy is feasible both technically and economically
  • 42. Replication Strategy Assessment 42 Case A – Building Regulation insulation levels Case B – increased insulation Case C – further increased insulation and triple glazing
  • 43. The case of dynamic simulations – representation of all three energy vectors (electricity, heating, cooling) on a common platform, to investigate dynamic behavioral characteristics of integrated grids - INTEMA 43
  • 44. Near Zero-Energy Districts Energy analysis using INTEMA framework • Examined Case: Electrification of existing DH network • District Heating Network to be heated by Heat Pump (HP) power by RES electricity; • Use of Thermal Energy Storage to increase RES penetration and HP operation reliability 44 district CO2 coal biomass • Alternative software tools for advanced energy management analysis; • Operation optimization using appropriate EMS algorithms; • INTEMA framework is capable of performing multi-domain transient simulation runs Heat pump District RES Thermal energy storage
  • 45. The INTEMA Framework In the context of INTEMA framework a number of tools have been developed in order to asses: • Electrical Grid Performance • Multidomain EMS (e.g heating/cooling/electricity) • Grid Ancillary Services • Economic Dispatch Strategies • Energy Storage Technologies The INTEMA framework contains: • The INTEMA Library: Energy (production, consumption and storage) and control components’ models, in Modelica as core Simulation Module • Energy Demand and Production Forecasting Module (Python) • Demand Response Module(Python) • Optimal Power Flow Module (Python) 45
  • 46. INTEMA Applications INTEMA (in Modelica) can perform: • Short-Term Simulations, to evaluate and address specific technical challenges • Examine solutions that can offer ancillary services in electrical grid • Estimate the response of electrical grid, electrical / thermal systems and specific components • Assess efficiency and performance of specific components under various conditions • Long-Term Simulations, to evaluate and optimize seasonal, annual or life cycle systems’ performance • Perform power flow studies • Identify rules and scenarios in energy management systems • Propose economic dispatch strategies 46 Modelica Environment
  • 47. INTEMA Example – Electrification of Heating • District with Electric & Heating Demand • Local Energy Production from PV & Wind Turbines • District Heating Network with Heat Pump, Solar Heater and Thermal Storage • Excess Electric energy will provide heat to network through heat pump • Heat pump electrical load will be added to total load • Storage and Heat Pump Controller (rules and set points) will define the operation (ON-OFF) of the Heat Pump • With INTEMA advance Energy Management Rules can be implemented 47 District DH System PV System Wind System Outer Grid Connection
  • 48. INTEMA Example – Electrification of Heating 48 District (Detail) Loads / Houses Solar Heater Heat Pump DH System (Detail) HP Controller Storage RE Systems Lines Input Variables Design Parameters Output Variables Heat Demand TS HP Electric Power Electrical Demand TS Elect. Energy send to mainland signal Heat Tank Size Thermal Peak Power TS Solar Irradiation TS COP vs Temp Ambient Temp TS Solar System Size and efficiency Heat Pump (Detail)
  • 49. Indicative Results Simulation Data (for approx. 100 houses) • Solar heater area: 500m2 • Heat pump electric power: 70kW • Storage Thermal: 8.3MWh • PV install power: 500kWp • Wind Turbine install power: 500kW • Mean Annual Electrical Demand: 30kW • Mean Annual Heat Demand: 127kW 49 Annual: Before Electrification (MWh) After Electrification (MWh) Elect2Grid (Net) 1827 1485 Elect2Heat 272 Wind Production 1546 PV Production 554 Elec Demand 273 273 • Before Electrification no need for energy imports • Elect2Grid + Elec2Heat After electrification is lower than the Elec2Grid Before (70MWh) • There are small periods that the district has to import energy from the grid to cover the combined needs from the Grid (heat + electricity)
  • 50. INTEMA Example – Electrification of Heating System Operation – Weekly profile 50 DH Thermal Storage SOC SOC 1.0 0.5 0.05 Heat Pump Operation Charges from Solar Heater Power to Grid Power from Grid PV Production Wind Production Elec Demand Heat Demand Excess Energy and HP Starts 0 100 200 500 400 300 800 700 600 Power(kW)
  • 51. Summary and Lessons Learnt 51 Approach: 1. The analysis followed a bottom ->> top approach (Level of Detail) From low (building) to higher level (district), input for was obtained from simulations at building level; 2. The tools involved were able to provide results with simplified top-level input; 3. Fast analysis allowed the conduction of parametric analysis examining several different variations of replication measures in short time; 4. Gathering of Input Data (Technical, Economic, at least estimations) through LHs, onsite collection, literature, online and software databases and assumptions 5. Calculation of basic expected KPIs (Technical, Economic) to answer key questions (is it worth investing?) 6. The output data can act as the basis for tenders; 7. Identification of Investors;
  • 52. Summary and Lessons Learnt • RETScreen includes various databases that may significantly reduce simulation time and includes the majority of renewable energy technologies. However, in certain cases external software was required to model specific technologies (outside the scope of the replication measures) • RETScreen models a base case and a retrofit scenario simultaneously. Renewable energy technologies are considered for the retrofit case only. Care should be taken when examining a case study where renewable energy in included in the pre-retrofit stage. Energy requirements and production may be obtained but manual calculations are recommended for the financial evaluation. • EnergyPLAN is developed for simulating much larger systems (national, regional level) and has the capacity to conduct financial and emissions analysis. For the purpose of this study, it was found suitable for performing technical analysis only at the district level with minor adjustments. Financial and emissions analysis was conducted manually. 52 For ease of use, RETScreen may even be used for conducting generic financial and risk analysis
  • 53. These projects have received funding from the European Union’s Horizon 2020 research and innovation program under grant agreements No 774199 and 864400 Battery sizing Case study on IMREDD building These project have received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 774199
  • 54. Battery sizing – Case study on IMREDD building NEXITY Common self consumption Tertiary building 90 KWp PV + 79 kW/88 kWh battery storage IMREDD Individual self consumption University building 175 KWp PV + 100kW/150kWh battery storage 36 kW 2nd life batteries compared to V2G with 41 kW
  • 55. Background 1/3 Battery sizing – Case study on IMREDD building PV production vs building’s needs for a week of June 0 50 100 150 200 250 Building consumption PV production weekend
  • 56. Background 2/3 Battery sizing – Case study on IMREDD building 1 32 Selection of representative pixelsSatellite image analysis PV production calculation KT Psat Isat reflectance r for each pixel Clear sky index = f(r) x Imax (t) projected on the panels Irradiance PV production Transfer function (production P, T°, …) 1.1. Estimation of PV production (2015) At a 15 min step
  • 57. Background 3/3 Battery sizing – Case study on IMREDD building 1.2. Estimation of the building consumption 0 50 100 150 200 250 Building consumption
  • 58. Battery sizing – Case study on IMREDD building 26/06 27/06 PV surplus injected to the grid PV production totally consumed by the building 0% extraction from the grid hours 100% 12 76% PV SURPLUS LOSSES SELF CONSUMPTION RATE * PROFITABILITY (years) 21% SELF PRODUCTION RATE ** PV surplus represents a substantial amount especially during weekends and holidays. * Self consumption rate = PV production consumed on site / total PV production ** Self production rate = PV production consumed on site / total site consumption Holiday PV production Building needs Extraction from the grid Injection to the grid 3.1. Hypothesis 1 : PV only Scenario 1 : PV only
  • 59. Battery sizing – Case study on IMREDD building 59 59% 0% 21% 40% STORAGE CAPACITY(kWh) PROFITABILITY (years) 1505001400 250 45 24 18 15 100 14 72% PVSURPLUS LOSSES SELFCONSUMPTION RATE 85%88%91%95%100% PV production totally consumed by the building Scenario 2 : Maximisation of self consumption rate 3.2. Hypothesis 2 : Maximisation of the self consumption rate 26/06 27/06 Battery not used 150 kWh / 100 kW battery Battery charging Battery discharging
  • 60. Battery sizing – Case study on IMREDD building 12/01 13/01 Battery discharging to lower the peak Power < 170 kW Battery charging with PV production Charging in off-peak hours Battery discharging in peak hours SELF CONSUMPTION RATE * SELF PRODUCTION RATE ** With both tariff optimisation and peak shaving services, less PV is injected to the grid. However, the losses are more important as the battery is more used and the tariff optimisation is less efficient. PV production Building needs Battery power Battery state of charge Extraction from the grid Injection to the grid 150 kWh / 50 kW battery 84% 13 MWh Losses + auxiliaries consumption Scenario 4 : Tariff optimisation + peak shaving 3.4. Hypothesis 4 : Tariff optimisation + peak shaving 23%
  • 61. Sensibility analysis Battery sizing – Case study on IMREDD building Technical and economic optimum of battery power and capacity versus contract power Power (kW) Capacity (kWh) Exponential The decrease of contract power thanks to the battery is limited by the exponential growth of the capacity installed. Therefore a contract power below 160 kW doesn’t seem to appropriate. Contract power - kW kW/kWh Capacity (kWh) PMax : 180 kW PMax : 170 kW PMax : 160 kW NPV15years(k€) The peak shaving and tariff optimisation services lower the amount of PV sold. However the profit linked to the reduction of contract power is lower than the resale to the grid, hence the negative NPV. 1.1. Influence of the choice of contract power 1. Economic optimisation
  • 62. Battery sizing – Case study on IMREDD building ➢ The decrease of PV resale price improves the profitability of the battery. NPV15years(k€) PV resale price becomes more important as the battery capacity increases Sensibility analysis Capacity (kWh) 70 €/MWh 42 €/MWh 0 €/MWh 26 kW / 124 kWh 50 kW / 150 kWh 75 kW / 175 kWh 100 kW / 200kWh 125 kW / 225 kWh The economic optimal sizing of the battery is 150 kWh – 50 kW 1.2. Influence of PV resale price – contract power 170 kW 1. Economic optimisation
  • 63. Battery sizing – Case study on IMREDD building Battery sizing 2. Self consumption rate maximisation 0 100 200 300 400 500 600 700 800 1 6 11 15 20 25 30 35 39 44 49 54 59 63 68 73 78 83 88 92 97 Daily PV surplus in weekends (kWh) 39% of PV surplus in weekends can be stored with a 150 kWh battery 150 All the simulations presented lead to the choice of a 150 kWh / 100 kW battery. 50 kWh battery : 10% of PV surplus can be stored 100 kWh battery : 25% of PV surplus can be stored 200 kWh battery : 41% of PV surplus can be stored --> 150 kWh seems to be a good compromise 50
  • 64. Battery sizing – Case study on IMREDD building Integration of grid services To provide the 2 hour block erasing service, the minimum dimensioning to be considered in capacity is 2 times the installed capacity. No assumption of inflation of the erasure service remuneration is taken. Tertiary reserves would, under current conditions, be cumulative with peak-shaving and rate optimization services. Thus, for a 50 kW - 150 kWh battery, an updated flow of gains of around 24 k € over 15 years could be obtained in addition without oversizing the battery. Evolution of the NPV (15years) depending on the capacity made available for the III service [50 - 500 kW] Battery power (kW) Scenario 1: 80% available. If calls < 60 days/an Scenario 2: 80% available. + no additional investment for a 50 kW / 150 kWh battery 7,2k€ NPV15ans(€)
  • 65. Battery sizing – Case study on IMREDD building Simulations on a power range engaged on the Primary Reserve (RP - FCR) market from 50 kW to 500 kW over 15 years based on the previously exposed assumptions: > By pooling the battery investments for the 3 Peak-Shaving services, tariff optimization and Primary Reserve, the availability in RP is reduced to 28% but the model still seems profitable with a NPV15years of 7.1 k €, > By participating in the RP 80% of the time, an addition of battery sizing is required because this service cannot be combined with the previous services. Profitability is found for a minimum commitment of 250 kW with a battery of 300 kW - 185 kWh (optimal steering margins for the RP taken into account). Evolution of the NPV15years as a function of the power engaged for the FCR service 7,1 k€ NPV15ans(€) Battery power (kW) Integration of grid services Scenario 1: 80% available. Scenario 2: 28% available. + no additional investment for a 50 kW / 150 kWh battery
  • 66. Thank you for your time
  • 67. Thank you for your attention! Any Questions? @IRISsmartcities https://irissmartcities.eu @ pocityf https://pocityf.eu