Autor:Milan Prodanovic

1. Smart Energy Management Algorithms
Dr. Milan Prodanović
EOI, Madrid, November 2010

2. Introduction
Mission of IMDEA Energy is to promote renewable and clean energy technologies
Formed of six research units:
Thermo-Chemical (production of sustainable fuels, CO2 confinement and valorisation)
Bio-Chemical (production of sustainable fuels, CO2 confinement and valorisation)
Electrochemical (energy storage, development of systems with enhanced efficiency)
High Temperature Processes (solar energy, energy storage)
Energy Systems Analysis (CO2 confinement and valorisation, life-cycle analysis)
Electrical Processes (Smart management of networks, renewable energy, energy storage)
Collaboration with other IMDEA institutes
Research objectives of Electrical Processes Unit
Development of Smart management techniques for future power networks
Active demand side management and energy efficiency improvement
Management of energy storage devices across the network
Electric vehicles
Key technologies
ICT
Power electronics
Embedded RT control systems

3. SmartGrids
EU Deployment Priorities for SmartGrids
IMDEA Energy
“A SmartGrid is an electricity network that can intelligently integrate the actions
of all users connected to it - generators, consumers and those that do both – in
order to efficiently deliver sustainable, economic and secure electricity supplies.”

4. SmartGrids
According to Strategic Deployment Document of European
Technology Platform, Key Challenges for SmartGrids are:
Strengthening the grid – ensuring transmission capacity
Moving offshore
Developing decentralized architectures
Communications – allowing RT operating and trading
Active demand side – all consumers play an active role
Integrating intermittent generation
Enhanced intelligence of generation, demand and the grid
Capturing the benefits of DG and storage
Preparing for electric vehicles

5. SmartGrids
According to Strategic Deployment Document of European
Technology Platform, Key Challenges for SmartGrids are:
Strengthening the grid – ensuring transmission capacity
Moving offshore
Developing decentralized architectures
Communications – allowing RT operating and trading
Active demand side – all consumers play an active role
Integrating intermittent generation
Enhanced intelligence of generation, demand and the grid
Capturing the benefits of DG and storage
Preparing for electric vehicles

6. G
Feeder 1
WAN
CONTROL
Bus 1 Bus 2
Bus 7
Bus 9
Bus 10
Gen 10
Load 10
Load 9
Load 2
Load 7
PF1
,QF1
PG5,QG5
PL10,QL10
PL9
,QL9
PL7,QL7
PL2
,QL2
Bus 4
Load 4
PL4
,QL4
Bus 8
Bus 6
Load 6
PL6
,QL6
Bus 3
Bus 5
Load 5
PL5
,QL5
Load 3
PL3
,QL3
Tr 8
Tr 4Tr 1
SW1-2
SW6-7
SW7-5
SW3-9
Feeder 2
PF2
,QF2
Feeder 3
PF3
,QF3
SW1 SW2
SW3
SW4-10
Distribution Networks
Conventional distribution networks:
Unidirectional power flows
Limited number of generators
Passive loads
No active control, only reactive
(protection) functions
Voltage levels and power flows easily
maintained by open-loop control
Limited measurement and control
required

7. Energy storage
G
Feeder 1
WAN
CONTROL
Bus 1 Bus 2
Bus 7
Bus 9
Bus 10
Gen 10
Gen 8
Load 10
Load 9
Load 2
Load 7
PF1
,QF1
PG8
,QG8
PG5
,QG5
PL10
,QL10
PL9
,QL9
PL7
,QL7
PE
,QE
PL2
,QL2
Bus 4
Load 4
PL4
,QL4
Bus 8
Bus 6
Load 6
PL6
,QL6
Bus 3
Bus 5
Load 5
PL5
,QL5
Load 3
PL3
,QL3
Gen 3
PG3
,QG3
Tr 8
Tr 4Tr 1
SW1-2
SW6-7
SW7-5
SW3-9
Feeder 2
PF2
,QF2
Feeder 3
PF3
,QF3
SW1 SW2
SW3
SW4-10
G
G
AC
DC
Distribution Networks
Networks with DGs and active loads and:
Bidirectional power flows
Line congestion problems
Voltage excursions
Protection issues
Only limited measurement and control
provided
Limited use of energy storage

8. Energy storage
G
Feeder 1
MU
WAN
CONTROL
MU
MU
Bus 1 Bus 2
Bus 7
Bus 9
Bus 10
Gen 10
Gen 8
Load 10
Load 9
Load 2
Load 7
PF1,QF1
PG8,QG8
PG5,QG5
PL10,QL10
PL9,QL9
PL7,QL7
PE,QE
PL2,QL2
Bus 4
Load 4
PL4,QL4
Bus 8
Bus 6
Load 6
PL6,QL6
Bus 3
Bus 5
Load 5
PL5,QL5
Load 3
PL3,QL3
Gen 3
PG3,QG3
Tr 8
Tr 4Tr 1
SW1-2
SW6-7
SW7-5
SW3-9
Feeder 2
PF2,QF2
Feeder 3
PF3,QF3
Fragment 1
SW1 SW2
SW3
SW4-10
Fragment 3
Fragment 2
G
G
AC
DC
Distribution Networks
Future distribution networks:
Fragmented networks
Various generators connected
Active demand management and
Smart loads
Large scale and aggregated
energy storage devices deployed
Future distribution networks:
RT measurements and control
available
RT Active and reactive control
and protection functions
RT arbitration for the resources
RT energy trading between the
new entities in the network

9. Distribution Networks
Research Objectives
Devising algorithms for flexible real-time management of networks
Integration of distributed generation
Medium level generation 1MW-100MW
Aggregated small scale generation
Integration of large scale energy storage elements
Reversible hydro, electrochemical, mechanical
Aggregated storage such as electric vehicles
Decentralised management functions
Active demand side management
More efficient use of installed network capacity
Real-time energy trading
Real-time active and reactive network control
Network modelling assuming RT active management
Developing scenarios for fragmented use of distribution networks

10. Smart Energy Consumption
Small Networks, Microgrids, Smart Buildings and Residential Loads
Real-time demand side management and control
Advanced measurement and load prediction
Ability to control and limit consumption (Smart Appliances)
Energy efficiency improvement
Integration of local and on-site generation
Renewable energy (solar, wind, geo-thermal)
Gas micro-turbines, diesel generators, CHP
Integration and management of energy storage elements
Electrochemical (batteries, capacitor banks, fuel-cells)
Exploiting the effects of thermal capacitance
Security of supply
Real-time energy trading

11. Smart Energy Consumption
A conventional microgrid:
Only few generators and loads
Islanded or grid-connected
With or without energy storage
elements

12. Smart Energy Consumption
Smart microgrids:
Smart load controls and times
energy consumption
A consumer can also store energy
and act as a generator too!
Smart Generators benefit from
embedded energy storage
Network energy storage elements

13. Smart Energy Consumption
MU
NETWORK
MANAGER
Control Room
MU
MU
Network management:
RT measurement and control
Improved energy efficiency
Improved security of supply
RT energy trading between the
entities in and out of the microgrid

14. Electric Vehicles
NETWORK
MANAGER
P,QAC
DC
DC
DC
DC
DC
DC
DC
Power Network
Recharging Station - Provider Green Recharging Station - provider SuperGreen
MU
MU MU
Recharging Station - Provider Green
P1 P2
P3
Usage patterns and scenarios:
Vehicles require recharging
More than 90% of all vehicles stationary
at any time
New entities in the network
An example of service based approach
Car owner options
Choosing the recharging station
Recharging only
Fast charging
Timed charging
Car owner services
Energy storage
Recharging station functions
Energy management
Optimisation of energy cost
Recharging station services
Fast charging
Network energy storage
Reactive power control
Emergency power supply
Energy trading
Network manager services
Energy trading
Energy storage
Energy transfer

15. Electric Vehicles
Investigating the impact of electric vehicle connection
Network reinforcement
Benefits analysis
Development of recharging points and station
Devising scenarios and usage patterns for vehicle recharging
Using car batteries as an aggregated energy storage
Providing service based solutions for:
Battery charging
Reactive power control
Emergency power
Demand side management
RT energy trading
Battery and supercapacitor technologies
Investigating static and dynamic properties
Life-cycle analysis

16. Electrical Processes Lab
Various IT equipment (PCs, routers)
Network sensors (voltage, current, etc.)
Ambient sensors (temperature, insolation, wind-speed)
Distribution level automation (tele-controlled switchgear)
Various energy source models (gas, solar, wind, fuel-cells)
Energy storage elements (batteries, capacitors, fly-wheels)
Various power converters (DC/DC, AC/DC, DC/AC)
Distribution network impedance
Flexible controller development and programming platforms

17. IMDEA Lab

18. Concluding Remarks
SmartGrids will provide flexible, real-time management of the
energy balance in the networks
A number of new entities (smart loads, generators and storage)
will be able to connect and offer their services in the energy
market
Network optimisation targets can be easily changed according to
the market and economic conditions
New, real-time, Smart energy management algorithms are
needed and should be deployed in all levels of power networks