ISGT-LA 2015 Tutorial part 02: Techno-economic Optimisation and Flexible Planning Under Uncertainty of Smart Distribution Networks, Microgrids and District Energy Systems

Abstract

ISGTLA 2015 MONTEVIDEO URUGUAY (http://isgtla.org/tutorial)

Full text

Techno-economic Optimisation and Flexible
Planning Under Uncertainty of Smart Distribution
Networks, Microgrids and District Energy Systems
Part 2 - Applications
Dr Eduardo Alejandro Martínez Ceseña
(Alex)
The University of Manchester
Eduardo.MartinezCesena@manchester.ac.uk
1
ISGTLA 2015 MONTEVIDEO URUGUAY

Outline
First part
•Overview
•Coordinated operation of DER
•Distribution network support
Break

Outline
Second part
•Support opportunities throughout the electricity chain
•Business cases
•Conclusion
Questions and Answers (15 minutes)

Overview

Overview: Methodologies (1)
•A wide range of techniques are available for optimal planning
under uncertainty of smart systems:
Decision theory
Robust optimisation
Real Options theory
Monte Carlo simulations
Hybrid methods

Overview: Methodologies (2)
•Decision theory (i.e., trees and risk management tools):
Provide strong means for optimisation under uncertainty
Large amount of scenarios can become an issue
Simplified models of the system may be required for stochastic
programming applications
•Robust optimisation based risk management:
Can be implemented in mathematical programming approaches
Improves the robustness of the solutions, but not the accuracy of
the assessments

Overview: Methodologies (3)
•Real Options theory:
Provides the thinking required to address flexibility
Traditional (finance based approaches) can be unrealistic for
engineering projects
•Monte Carlo simulations:
Provide highly accurate assessments
Can be challenging to implement in an optimisation context

Overview: Methodologies (4)
•Hybrid methods combine tools to minimise drawbacks
1) Screening for initial designs:
- Deterministic optimisation
- Sensitivity studies
(long-term uncertainty) - Robust optimisation
(short-term uncertainty)
- Monte Carlo simulations (short-term assessment)
2) Initial assessment of designs:
- Stochastic programming - Robust optimisation
- Real Options thinking
3) Investment decisions:
- Monte Carlo simulation (short/long-term assessment)
- Massive path-dependent trees (long-term assessment)
4) Detailed assessment:

Coordinated operation of Distributed Energy
Resources (DER)

DER: Emerging distribution system conditions
•New distribution level energy technologies, and novel Information
and Communication Technologies (ICT) to manage them
•Smart distribution networks, Microgrids and smart district energy
systems as a means to tackle carbon and uncertainty challenges
•Distribution system expansion to be framed in a whole-energy
system context, as heat (and cooling) are major contribution to
energy consumption and emissions

11
DER model: Operation (1)

•The operational model:
Provides an annual optimisation (half-hourly resolution) of DER
operation
Considers prediction errors
Can consider different objectives (and business models)
Is fully customisable
12
DER model: Operation (2)

13
Electricity (Retail)
Heat
Gas (Retail)
Gas network
Imports
Exports
Electricity network
Boiler
Heat
demand
Electricity
demand
Electricity (Retail)
Heat
Gas (Retail)
Gas network
Imports
Exports
Electricity network
Boiler
Heat
demand
Electricity
demand
∑
∑
CHP
TES
Imports
Exports
Electricity network
CHP
Gas network
Boiler
Heat
demand
Electricity
demand
∑
∑
Electricity
Heat
Gas
DER model: Operation (3)

14
Technical and economic
inputs Scenarios
A: Pre-processing
C: Investment optimisation Formulate scenario tree
Results Investment scheme optimisation
No
Yes
B: Screening
Operational
optimisation
System design
More designs?
DER model: Investment (1)

•The investment model:
Is deployed using commercial optimisation software
Can consider different paradigms
Can address risk preferences
Can address massive customised and path- dependent decision trees
spanning thousands of nodes (e.g., 3540)
Allows probabilistic assessments
15
DER model: Investment (2)

16
0
5
10
15
20
25
30
35
010 20
Probability (%)
NPC £x106
RO Multi-Stage
Best view Do-Nothing
DER model: Investment (3)

17
•26 Buildings
•Annual consumption of roughly 30
GWhe and 30 GWht
•Annual emissions of 20 000 tCO2
•Annual energy costs of 3.2 M£
Case study: The University of Manchester (1)

•Wide portfolio of multi-energy profiles and resources
18
Case study: The University of Manchester (2)
0
10
20
30
40
50
60
70
00:0002:00 04:00 06:0008:00 10:00 12:00 14:0016:00 18:00 20:00 22:00
Average Daily Demand (kW)
January February March April May June
July August September October November December
0
50
100
150
200
250
300
350
00:0002:00 04:00 06:00 08:0010:00 12:00 14:00 16:0018:00 20:00 22:00
Average Daily Demand (kW)
January February March April May June
July August September October November December
0
10
20
30
40
50
60
00:0002:00 04:00 06:00 08:0010:00 12:00 14:00 16:0018:00 20:00 22:00
Average Daily Demand (kW)
January February Ma rch April May June
July August September October November December
0
100
200
300
400
500
600
700
00:0002:00 04:00 06:00 08:0010:00 12:00 14:00 16:0018:00 20:00 22:00
Average Daily Demand (kW)
January February March April May June
July August September October November December

•Multi-energy networks
19
Case study: The University of Manchester (3)

20
Case study: Traditional approach
•The current energy consumption and characteristics of the
university are taken as a Baseline
•A target of 40% CO2 reduction has been set by the University
•A Reference multi-energy system is planned to reduce 30% CO2
using business-as-usual practices (i.e., energy exchanges between
building is not considered)

21
Case study: Smart strategies
•In addition, the following strategies are considered:
Strategy 1: Optimised multi-energy exchanges between buildings
Strategy 2: Optimised multi-energy exchange considering dynamic
price signals
Strategy 3: Optimised planning and operation

Strategy Expected
benefits compared
with the Reference (%)
Expected benefits compared
with the Baseline (%)
CO2
reduction
Costs
savings
CO2
reduction
Costs
savings
Strategy 1
(internal trading)
0
– 25
0
– 20
0
– 10
0
– 5
Strategy 2
(optimal operation)
0
– 30
0
– 50
0
– 12
0
– 5
Strategy 3
(Investment)
15
– 38
15
– 95
5
– 15
10
– 20
22
Case study: Results

Distribution network support

•Flexible customers, particularly in smart district energy systems and
Microgrids can provide Demand Side Response (DSR)
•DSR can be used to release untapped distribution network capacity;
effectively deferring (or avoiding) costly network reinforcements
•The use of active network reconfiguration and improved level of
automation can also about improved reliability levels and reduces
carbon emissions
Distribution network support: Generalities (1)

•Distribution network reinforcement planning is made in light of the
potential use of DSR to manage uncertain demand growth
•The networks are assessed based on current regulations
•DSR is modelled based on outputs from real applications
•Demand growth uncertainty is based on real forecasts
•A bespoke hybrid framework is developed
Distribution network support: Generalities (2)

•Distribution network reinforcements are assessed based on the
current CBA framework imposed by the UK’s regulator
•The analysis quantifies, economic costs, power losses, carbon
emissions, reliability and taxes, among other factors relevant to the
business of DNOs
Methodology: Cost Benefit Analysis (CBA)

Methodology: DSR model

•Demand growth scenarios (and trees) are formulated based on
information provided by the DNO
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
12345678910 11 12 13 14 15 16 17 18 19 20 21
Time period (years)
Scenario 1
Scenario 2
Scenario 3
Scenario 5
Scenario 4
Demand growth (%)
Network's firm capacity
Substation's firm capacity
Methodology: Uncertainty

Methodology: Imperfect forecasts

•Network reinforcements: Potential line and substation upgrades, as
well as DSR deployment (based on deterministic optimisations)
•Network capacity: Firm capacity of the network determined based
on hourly AC power flows (sensitivity studies)
•Power losses: Annual values for both radial and ring configurations
under different demand growth scenarios (sensitivity studies)
•Network reliability: Monte Carlo based studies
Methodology: Other inputs

Propose solution for
year y+1
Recursive function
yes
Best?
Feasible?
yes
no
Asses investment
scheme in year y
Implement proposed
solution for year y
CBA
y>45?
no yes
Methodology: Optimisation engine

•The Holme Road system (view Fig. 4) is an 11kV network that supplies
2888 urban customers via 2 radial feeders
MOOR AVE
MULBERRY AVENUE
POC
NOP
Priory Lane
Blundell Lane
Case study: Real UK distribution network

Intervention Description Trigger
(%)
Cost (k£)
IL1
-2
Reinforce lines to reach next immediate capacity
available
3
246
IL2
-3
After IL1
-
2, reinforce the lines again to reach the next
immediate capacity
available
15
193
IS
Reinforce the substation
5
338
IRC
Implement RC
2C method
Any of
19 +
IIC
Implement IC
2C method
the above
DSR cost
Case study: Interventions

Traditional investment strategies:
•Baseline: Traditional line and substation reinforcements
•RC2C: Only the use of DSR is considered
•IC2C: Only the use of DSR, active network reconfiguration and
increased automation levels are considered
Case study: Investment strategies (1)

Optimised investment strategies:
•OSI: Optimal combinations of solutions to minimise investment
costs for the DNO
•OSS: Optimal combination of solutions to minimise costs, losses
and emissions and increase reliability levels
Case study: Investment strategies (2)

Scenario Baseline RC2C IC2C OSI OSS
2
IL1
-2
IS
IL2
-3
4
5
15
IRC
IS
IL1
-2
5
17
17
IIC
IS
IL1
-2
5
17
17
IRC
IL1
-2
IS
5
17
17
IIC
IL1
-2
IS
1
17
17
6691 k£
12652 k£
6061 k£
12322 k£
6231 k£
10532 k£
6061 k£
12322 k£
6261 k£
10212 k£
4
IL1
-2
IS
9
10
IRC
10
IIC
10
IRC
10
IIC
1
4521 k£
10392 k£
2261 k£
8532 k£
2411 k£
7122 k£
2261 k£
8532 k£
2471 k£
6452 k£
5
IL1
-2
5
IRC
6
IIC
6
IRC
6
IIC
1
2271 k£
7802 k£
391 k£
6332 k£
551 k£
4682 k£
391 k£
6322 k£
591 k£
4282 k£
1NPCI excluding social costs
2NPCI+S including social costs
Case study: Results

0
50
100
150
200
250
300
350
020 40 60 80 100 120
NPCI
Line reinforcement costs (%)
Baseline RC2C IC2C OSI OSS
Case study: Sensitivity studies (1)

0
100
200
300
400
500
010 20 30 40 50
NPCI
Substation headroom (%)
Baseline RC2C IC2C OSI OSS
Case study: Sensitivity studies (2)

Support opportunities throughout the electricity chain

Central
Generation
HV HV / MV MV LVMV / LV
EHV / HV
EHV
TSO DSO
Technical Services
HV DER
HV HV / MV MV LVMV / LV
EHV / HV
EHV
TSO DSO
Technical Services
Microgrid
MV DER
LV DER
(Micro-
Sources)
Technical
Services Technical
Services Technical
Services
Central
Generation
’Macrogrid’
Nowadays
Grid and
Service Market
Future
Grid and
Service Market
Smart house, district, city, grid:
multi-level microgrid aggregation
DNO
Energy services: Overview (2)

•District energy systems and Microgrids may have the means to
provide different energy services with customers, the market
and/or the grid
•Some of these services can be:
Energy cost minimisation
Transmission charge reductions
Distribution investment deferral (or avoidance)
Reliability improvements
Emission reductions
Energy services: Overview (2)

•The multi-energy system couples different technologies,
including a gas boiler and CHP, EHP and TES units
ED
CHP
ŋe, ŋt
Boiler
ŋaux
EDS
CONSUMERS
HD, ED
Fchp
Faux Haux
Hchp
(1-α)Echp
Eexp Eimp
EHP
COP
αEchp
HEHP
EEHP
TES
HS
Electricity
Gas
Heat
Energy services: Cost minimisation (1)

•The Microgrid minimises energy costs by coupling different
technologies to exploit arbitrage between energy vectors at
different times
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0
1000
2000
3000
4000
5000
6000
Electricity prices (£/kWh)
Energy Demand (kW)
Time (h)
ED HD Sell EE price Buy EE price
Energy services: Cost minimisation (2)

1
2
3
4 5
6
7
8 9
10
11
12
13
14
•Transmission costs are levied on Retailers
via the TRIAD mechanism
•The microgrid can reduce its electricity
consumption during TRIAD warnings
•This service can be sold to the retailer
Energy services: Transmission support

•Distribution networks at the 11kV and 6.6kV levels are
oversized to meet security considerations
NOP
(closed)
(c) Emergency
Must be
oversized
Feeder 1 Feeder 2
NOP
(open)
(b) Contingency
Feeder 1 Feeder 2
NOP
(open)
(a) Normal operation
Feeder 1 Feeder 2
Energy services: Distribution support (1)

•The Microgrid can operate as an island while the fault is being
cleared, avoiding the need to oversize the feeders
NOP
(open)
(a) Normal operation
Feeder 1 Feeder 2
Microgrid
NOP
(open)
(b) Contingency
Feeder 1 Feeder 2
Microgrid
(Island)
NOP
(closed)
(c) Emergency
Feeder 1 Feeder 2
Microgrid
(Island)
Energy services: Distribution support (2)

•The service can be sold as DSR for a DNO
•The DNO would save investment costs in the form or network
reinforcement deferral or avoidance
0
5
10
15
20
25
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Time period (years)
Demand growth (%)
Investment deferral
Investment time
(without Microgrid) Investment time
(Microgrid)
Energy services: Distribution support (3)

•Customers in the Holme Road network perceive a level of
supply reliability based on their location
Feeder 1
Feeder 2
NOP
Point of connection
Microgrid
Energy services: Reliability improvements (1)

•Customers within the Microgrid perceive increased levels of
reliability, as they can be islanded from the network should a
contingency occur
•The average reliability of the distribution network increases as
less customers are exposed to interruptions
Energy services: Reliability improvements (2)

•The Microgrid can supply customers that have become islanded
during emergency conditions
NOP
(open)
(a) Normal operation
Feeder 1 Feeder 2
Microgrid
NOP
(open)
(b) Contingency
Feeder 1 Feeder 2
Microgrid
(Island)
NOP
(open)
(c) Emergency
Feeder 1 Feeder 2
Microgrid
Energy services: Reliability improvements (3)

•The low carbon energy produced by the Microgrid can replace
more carbon intense generation from the power grid (and the
boiler)
•However, the capacity services provided by the Microgrid lead
to higher asset utilization and thus increased power losses
Energy services: Emission reductions (1)

•The carbon reductions associated with energy consumption is
more significant than the emissions from increased power
losses
35
40
45
50
55
60
65
70
1700
2200
2700
3200
3700
4200
2014 2017 2020 2023 2026 2029 2032
tCO2/year (losses)
tCO2/year (community)
Year
Grid imports Microgrid Losses Losses Microgrid
Energy services: Emission reductions (2)

Business cases

•A mapping approach is used to sheds light on changes in the
cash flows perceived by different actors due to the deployment
of the Microgrid
•This provides insights on the incentives for other actors
(including customers) to support or oppose the deployment of
Microgrids under current (or proposed) regulatory frameworks
Business case: Mapping (1)

Business case: Mapping (2)

Business case: Mapping (3)
•The data collected through flow mapping is used to estimate
exchanges between actors/roles via interaction matrices
Cash
Flow 1
Cash
Flow 2
Cash
Flow 3
Cash
Flow 4
Cash
Flow 5
Cash
Flow 6
Cash
Flow 7
Cash
Flow 8
Cash
Flow 9
Cash
Flow 10
Role 1
1
0
-1
-1
0
1
-1
0
0
0
Role 2
0
0
1
0
0
0
0
0
0
0
Role 3
0
0
0
0
1
0
1
0
0
0
Role 4
-1
0
0
1
0
1
0
0
0
0
Role 5
0
-1
0
0
0
0
0
1
0
0
Role 6
0
0
0
0
1
0
0
0
1
-1
Role 7
0
0
0
0
0
0
0
0
0
1
Role 8
0
1
0
0
0
0
0
-1
-1
0
Role 1
Role 2
Role 3
Role 4
Role 5
Role 6
Role 7
Role 8
Actor 1
1
1
0
0
0
0
0
0
Actor 2
0
0
1
0
0
0
0
0
Actor 3
0
0
0
0
1
0
0
0
Actor 4
0
0
0
1
0
1
0
0
Actor 5
0
0
0
0
0
0
1
1

Business case: Mapping (3)

EPN
NEM
Buildings
CHP
TES
Q
DG (PV)
Zero flexibility
W
(EHP)
Zero flexibility
(Q)
Flexibility Q
(GHP)
Qenv
W
Storage
(immobile)
Boiler
n = 1
n = 2
n = ...
Flexibility W
n = 1
n = 2
n = ...
Zero flexibility
Q+W
Flexibility Q W
n = 1
n = 2
n = ...
Energy
Storage
(EV) Non-building
electricity
µCHP
DG (PV)
(EHP)
TES (GHP)
EES
Boiler
Building‘s DER
Central DER
Building‘s Demand
Microgrid
Business case: Mapping (4)

•Characteristics of the Holme Road District Energy
System/Microgrid:
Community owned
Installed in a residential area with 1000 customers
Connected to the Holme Road 11 kV network
•The system is optimised to maximise benefits for the
community from the provision or trade of services
Case study 1: Generalities

Actor Economic benefit (£x103)
Investment
Energy
Trans.
Emissions
Dist.
Reliability
Total
Microgrid
-
620
3014
0
-490
0
-337
0
-216
40
-56
2433
-
3493
Retailer
–
–
0
-490
–
–
–
0
-490
Society
–
–
–
0
-337
–
–
0
-337
DNO
–
–
–
–
0
-216
0
-17
0
-233
•The economic benefits from each service must be distributed
between the Microgrid and the corresponding actors
Case study 1: Results

•The results are consistent subject to reasonable variations of
the discount rate
0.0
1.0
2.0
3.0
4.0
1357911 13 15 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Value (M£) (Energy)
Discount rate (%)
Value (M£) (Other services)
Trans. Dist. Reliability Emissions Energy
Case study 1: Sensitivity studies (1)

•There is a strong business case for the Microgrid within the
scope of the sensitivity analysis
1357911 13 15
0
1
2
3
4
5
6
Discount rate (%)
NPV (M£)
NPV min NPV max
Case study 1: Sensitivity studies (2)

•The business case of the multi-energy system is affected by:
Ownership
Available signals
Flexibility and smartness of the system
Case study 2: Generalities (1)

•Current conditions are taken as a Baseline
Case study 2: Generalities (2)
No.
Description
Ownership
Signals
Control
1
Reference
3rd party
-
Fixed
2
Internal trading is allowed
EPN
Retail
Fixed
3
Operation
is optimised
based on retail
signals
EPN
Retail
Smart
4
Operation
is optimised
based on real
signals
EPN
Dynamic
Smart
5
Investments can be made
EPN
Dynamic
Smart

Case:
NPV (10%)
NPV (7%)
IRR
Payback
(years)
1:
Reference
£170,532
£266,114
22%
4
2: Internal
trading
£427,703
£595,102
38%
2
3: Smart w/o
signals
£410,225
£572,744
37%
2
4: Smart with
signals
£692,014
£933,224
54%
1
5:
Smart
investments
£790,318
£1,177,739
25%
3
65
Case study 2: Results

Conclusion

•Emerging concepts based on the optimal operation and
placement of DER (i.e., Smart networks and districts, and
Microgrids) have been discussed and demonstrated
•These new concepts provide flexibility to manage energy
consumption and provide different services in light of
uncertainty
•There is a strong business case for these concepts should the
proper regulation be put in place
Conclusion:

Acknowledgment
We would like to thank the European commission for its support via the
seventh framework programme for research, technological development and
demonstration
DIMMER: District Information Modeling and Management for Energy
Reduction
Grant Agreement number: 609084
COOPERaTE: Control and Optimization for Energy Positive Neighbourhoods
Grant agreement number: 600063
68

Questions
Thank you, any questions?
Eduardo.MartinezCesena@Manchester.ac.uk

Multi-energy systems and distributed multi-generation framework
•P.Mancarella, Multi-energy systems: an overview of models and evaluation concepts, Energy,
Vol. 65, 2014, 1-17, Invited paper
•P.Mancarella and G.Chicco, Distributed multi-generation systems. Energy models and
analyses, Nova Science Publishers, Hauppauge, NY, 2009
•G.Chicco and P.Mancarella, Distributed multi-generation: A comprehensive view, Renewable
and Sustainable Energy Reviews, Volume 13, No. 3, April 2009, Pages 535-551
•P.Mancarella, Urban energy supply technologies: multigeneration and district energy systems,
Book Chapter in the book “Urban energy systems: An integrated approach”, J.Keirstead and
N.Shah (eds.), Taylor and Francis, 2012
Selected references

Operational optimization and demand response in multi-energy systems
•T. Capuder and P. Mancarella, Techno-economic and environmental modelling and
optimization of flexible distributed multi-generation options, Energy, Vol. 71, 2014,
pages 516-533
•P. Mancarella and G.Chicco, Real-time demand response from energy shifting in
Distributed Multi-Generation, IEEE Transactions on Smart Grid, vol. 4, no. 4, Dec. 2013,
pp. 1928-1938
•N. Good, E. Karangelos, A. Navarro-Espinosa, and P. Mancarella, Optimization under
uncertainty of thermal storage based flexible demand response with quantification of
thermal users’ discomfort, IEEE Transactions on Smart Grid, 2015
•Y. Kitapbayev, J. Moriarty and P. Mancarella, Stochastic control and real options valuation
of thermal storage-enabled demand response from flexible district energy systems,
Applied Energy, 2014
•S. Altaher, P. Mancarella, and J. Mutale, Automated Demand Response from Home
Energy Management System under Dynamic Pricing and Power and Comfort Constraints,
IEEE Transactions on Smart Grid, January 2015.
•G.Chicco and P.Mancarella, Matrix modelling of small-scale trigeneration systems and
application to operational optimization, Energy, Volume 34, No. 3, March 2009, Pages
261-273.
Selected references

Planning under uncertainty
•E. A. Martinez Cesena, J. Mutale and F. Rivas-Davalos, “Real options theory applied to
electricity generation projects: A review,” Renewable and Sustainable Energy Reviews,
vol. 19, pp. 573 –581, 2013.
•B. Azzopardi, E.A. Martinez-Cesena and J. Mutale, “Decision support system for ranking
photovoltaic technologies”, IET Renewable Power Generation, vol. 7, no. 6, pp. 669 –679,
2013.
•E. A. Martinez-Cesena, B. Azzopardi, and J. Mutale, ”Assessment of domestic
photovoltaic systems based on real options theory,” Progress in Photovoltaics: Research
and Applications, vol. 21, no. 2, pp. 250 –262, 2013
•E. A. Martinez-Cesena and J. Mutale, “Wind power projects planning considering real
options for the wind resource assessment,” IEEE Transactions on Sustainable Energy, vol.
3, no. 1, pp. 158 –166, 2012.
•E. A. Martinez-Cesena and J. Mutale, “Application of an advanced real options approach
for renewable energy generation projects planning,” Renewable and Sustainable Energy
Reviews, vol. 15, no. 4, pp. 2087 –2094, 2011.
Selected references

Planning under uncertainty of multi-energy systems
•E.A. Martinez-Cesena, T. Capuder and P. Mancarella, Flexible Distributed Multi-Energy
Generation System Expansion Planning under Uncertainty, IEEE Transactions on Smart
Grid, 2015
•J. Schachter and P. Mancarella, Demand Response Contracts as Real Options: A
Probabilistic Evaluation Framework under Short-Term and Long-Term Uncertainties, IEEE
Transactions on Smart Grid, 2015.
•E.Carpaneto, G.Chicco, P.Mancarella, and A.Russo, Cogeneration planning under
uncertainty. Part I: Multiple time frame approach, Applied Energy, Vol. 88, Issue 4, April
2011, Pages 1059-1067
•E.Carpaneto, G.Chicco, P.Mancarella, and A.Russo, Cogeneration planning under
uncertainty. Part II: Decision theory-based assessment of planning alternatives, Applied
Energy, Vol. 88, Issue 4, April 2011, Pages 1075-1083
•G.Chicco and P.Mancarella, From cogeneration to trigeneration: profitable alternatives in
a competitive market, IEEE Transactions on Energy Conversion, Vol.21, No.1, March
2006, pp.265-272
Selected references

Business cases of multi-energy systems
•E.A. Martinez Cesena, N. Good, and P. Mancarella, Electrical Network Capacity Support
from Demand Side Response: Techno-Economic Assessment of Potential Business Cases
for Commercial and Residential End-Users, Energy Policy, 2015
•N. Good, E.A. Martinez-Cesena, and P. Mancarella, Techno-economic assessment and
business case modelling of low carbon technologies in distributed multi-energy systems,
submitted to Applied Energy, Special Issue on Integrated Energy Systems, May 2015,
accepted for publication
•N. Good, E. A. Martinez Cesena and P. Mancarella, “Mapping multi-form flows in smart
multi-energy districts to facilitate new business cases,” in Sustainable Places conference,
01 –03 October, France, 2014.
Selected references

Modelling of residential multi-energy technologies and multi-energy networks
•X. Liu and P. Mancarella, Integrated modelling and assessment of multi-vector district
energy systems, Invited Submission to Applied Energy, Special Issue on Integrated Energy
Systems, May 2015, accepted for publication, available online
•N. Good, L. Zhang, A. N. Espinosa, and P. Mancarella, High resolution modelling of multi-
energy demand profiles, Applied Energy, Volume 137, 1 January 2015, Pages 193–210
•A. Ahmed and P. Mancarella, Strategic techo-economic assessment of heat network
options in distributed energy systems in the UK, Energy 75 (2014) 182-193
•A. Navarro Espinosa and P. Mancarella, Probabilistic modeling and assessment of the
impact of electric heat pumps on low voltage distribution networks, Applied Energy, Vol.
127, 2014, pag. 249–26
Selected references