Presented at the Official Release of Smart Grids Mapping by Cluster Tweed. Belgium, 2016

1. Optimization for a Smarter Energy World !
Sortie officielle de la cartographie
SmartGrid
Namur, April 11 2016

2. The best of advanced analytics for
optimal decision-making
Mathematical sciences
Business engineering
Computer science
Our professionals provide you
with combined expertise in:
State-of-the-art mathematics and algorithms are at the heart of N-SIDE’s innovation
Providing tailored software solutions & services to optimize decision making
Maximize profitsBe agile Manage risks
Descrip ve
Detailed mathema cal model to describe complexity and
opportuni es
Predic ve
Advanced forecast to be ahead of risk/opportunity
Prescrip ve
Efficient algorithm to generate op mal
decisions
N-SIDEAPPROACH
2

3. 3
Descriptive
Detailed mathematical models to describe complexity and
opportunities
Predictive
Advanced forecast to be ahead of risk/opportunity
Prescriptive
Efficient algorithm to generate optimal
decisions
N-SIDEAPPROACH
The best of advanced analytics for
optimal decision-making

4. Market coupling
Optimization
Energy Flexibility
Optimization
How to design and optimize my
micro- grid in an optimal way ?
MicroGrid
Optimization
Optimization for a smarter Energy World
4

5. Market Coupling Optimization
EUPHEMIA by N-SIDE

6. Day-ahead electricity prices in Europe are
calculated everyday thanks to N-Side algorithms
> “EUPHEMIA”: market coupling algorithm for
European Power exchange, implemented and
developed in-house by N-SIDE, from theory
to operations
> Used daily by Power Exchanges to fix pan-
EU day-ahead electricity prices in 19 EU
countries
> Computing market prices & volumes by:
 coupling national markets
 maximizing total economical welfare
 optimizing network capacity utilization
 modeling complex constraints
Modeling and Optimization of
Electricity Markets
6

7. Energy Flexibility Optimization
ENERTOP by N-SIDE

8. Energy Flexibility Optimization with
the best of advanced analytics
Flexible Load Models
CHP Models
RES Models
Storage Models
EVs Models
Efficient Mathematical
Modellings
Planning
Optimization
Real-time Optim.
Investment
Optimization
Aggregation Optim.
Bidding Optimization
Advanced Optimization
Algorithms
+
DA Market Forecast
Balancing
Opporunities
Reserve Markets
Demand Forecast
Contracts Model
Accurate Forecasts
+ =
Customized
Flexibility
Optimization
Solutions
8

9. Mathematical models to describe plant complexity…
A mathematical model is key for considering all factors in an integrated way…
Limestone Crushers Storage Raw Mill Storage
Preheater Tower
Cement KilnClinker CoolerStorageGypsum
Cement Mill Storage Shipping
Grid and market
interaction
• Different electricity
contracts (OTC, spot
based)
• Capacity constraints
Storage facilities
• Min-max capacities
• Storage target
Industrial processes
• All input and output flows
• Maximal Stop/Day
• Minimal time OFF
• ON-Off procedure
• Operating rates
Economics
• RM, electricity costs
• Opportunity costs
• Fix and variable
operating costs
• Incentive from DR
programs
9
1
Example : Mathematical Model of Cement Plants
Product Demand
• Quantities and delivery dates
• Must / May serve

10. … and the differents energy flexibilities
10
Produce electricity at optimal moment
Electricity
Generation
Electricity
Consumption
Consume electricity at optimal moment
Load Shifting
Load Scheduling
Load Shedding
Electricity
Storage
B
A
C F
CHP ModulationE
Fuel SwitchingD

11. Advanced forecasts to be ahead of risk/opportunities
11
Statistics and Machine
learning techniques
Price forecast
Spot Price Forecast
2

12. 12
Probalistic Approach
Statistics and Machine
learning techniques
Price forecast
Spot Price Forecast
1° Reserve
composition: Quantity
reserved / Marginal cost
for each reserve
2° Imbalance
volume on previous
Quarter
3° External
unpredicted change
Imbalance orientation:
Level of Imbalance:
Balancing Opportunity Forecast
Advanced forecasts to be ahead of risk/opportunities2

13. 13
Probalistic Approach
Price forecast
Spot Price Forecast
1° Reserve composition:
Quantity reserved / Marginal
cost for each reserve
2° Imbalance volume on
previous Quarter
3° External unpredicted
changes
Imbalance orientation:
Level of Imbalance:
Stochastic tree to
generate what-if
scenarios
1° Demand : Order
book
2° Process:
Maintenance and
machine failure
3° External factors
Combined
What-if scenarios
Balancing Opportunity Forecast
Statistics and Machine
learning techniques
Advanced forecasts to be ahead of risk/opportunities2

14. 14
Price forecast
Spot Price Forecast
1° Reserve composition:
Quantity reserved / Marginal
cost for each reserve
2° Imbalance volume on
previous Quarter
3° External unpredicted
changes
Stochastic tree to generate
what-if scenarios
1° Demand : Order book
2° Process: Maintenance
and machine failure
3° External factors
Combined What-if Scenarios
Probalistic Approach
Imbalance orientation:
Level of Imbalance:
Balancing Opportunity Forecast
Statistics and Machine
learning techniques
Advanced forecasts to be ahead of risk/opportunities2

15. Efficient algorithms to generate optimal planning….
15
Advanced
Algorithm
Accurate results
Fast running
Robust Solution
Intuitive
Planning
Electricity price
forecast
Risk Factors
forecast
Mathematical
modeling
Optimized planning
3

16. … leveraging the different flexibility
levers in a integrated way…
16
Produce electricity at optimal moment
Electricity
Generation
Electricity
Consumption
Consume electricity at optimal moment
Load Shifting
Load Scheduling
Load Shedding
Electricity
Storage
B
A
C F
CHP ModulationE
Fuel SwitchingD
IntegratedOptimization

17. Strategic
Optimization
Reserve
Optimization
Scheduling
Optimization
Real-time
Optimization
… and maximize savings on the different
key timeframes
17
• Optimal
electricity
contract
• Optimal
investment in
flexibility assets
• Optimal choice of
flexibility products
and volumes
• Optimal power and
energy price
• Optimal
scheduling of
electricity load
• Optimal
planning of CHP
unit
• Optimal
imbalance
minimization
• Optimal
activation
management

18. InduStore
• Objective: Quantify and Optimize Demand
Response potential in industrial sector in
Wallonia
• 4 years project funded by walloon region
(started in Oct. 2014)
• Partners: N-SIDE, UCL, ULg and ICEDD
• Objective: Optimize interaction between
TSO and DSO to leverage flexibilities at
local level
• 3 years H2020 projects starting in 2016
• Partners: 22 including RSE, Siemens,
Vodafone, Energinet.dk, Terna, Sintef,
VTT, VITO.
Innovative Projects on Energy
Flexibility Optimization

19. MicroGrid Optimization
How to design and optimize my
micro-grid in an optimal way ?

20. E-Cloud: Project for an Open Microgrid Solution
20
• Optimized microgrids for industrial parks
(eco-zoning):
 Optimal investment in RES
 Optimal sharing of locally produced
electricity
 Optimal storage of electricity
 Optimal billing process managed by
DSO in charge of eco-zoning
 Optimal interaction with network
 Two pilots projects in Wallonia
Partnership

21. Interested to know more ?
Please contact us
Olivier Devolder
Energy Project Manager
Tel: +32 472 46 83 44
Email: ode@n-side.com
N-SIDE
Watson & Crick Hill Park – Bldg. H
Rue Granbonpré, 11
B- 1348 Louvain-la-Neuve