Guillaume Guérard

Versailles - France

A generic modelling for

Smart Grid’s Design

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May 2015SmartGreens2015Lisboa

The current Energy Grid is based on Nikola Tesla work (1888).

Shortcoming:- Production: integration and management

of Renewable Energies, management of storage.

- Consumption: congestion, network latency, profitability of plants, DSM, demand-response, data security.

Industrial goals

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Global behavior:

- To smooth the curve

- To manage supply and demand

- To guarantee the QoS.

Source: ABB

Smart Grid : network integrating users behavior.

A smart system of systems

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• Self-Healing• Flexibility• Predictive• Interactive• Optimal• Secure.

A efficient smart grid should integrate:

Unlike its predecessor, it reacts in real time to the internal or external constraints.

Source: Siemens

smart infrastructure

smart management

smart protection

Smart Grid

Current models

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Most of simulations/models are done on a

specific case/technology with a limitedevolution perspective.

Drawbacks of most models:- Time of computation depends on size of

variables.

- Data storage, data mining are almost difficult to treat for real-time management.

- Models are not “plug-and-play” and not “friendly-user”.

Objective: to model a context-free Smart Grid.

Brandon Palacio

The modelling challenges

Challenges:• It is difficult to find an

objective function solving the overall problem.

• The number of variables involved range up to thousands of entities.

Response:• To find an appropriate

class of algorithms for optimizing local applications.

• Data involved in each application should be standardized.

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Goal: to find a method in order to analyse and define a model for managing a complex system.

Studying the smart grid through modelling and simulation provides us withvaluable results, which cannot be obtained in the real world due to time andcost-related constraints.

Optimization in a smart grid:

How to optimize the consumption, the production and thedistribution of the energy in a Smart Grid.

focus on the smart management system

Various optimization problems:

- Resiliency.- Reliability.- Minimal cost (flow, production, consumption).- Demand-Response.

Problematic

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Smart Grid

Complex system approach Algorithms Smart system

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Complex system approach

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Smart Grid: a complex system

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Complex system analysis

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Would it not be better and permissive to understand the fundamentals of Smart Grid rather than imposing new and often incompatible technologies?

Evolution

Self-organization

Composite

• Feedbacks

• Learning system

• Optimization

• Communication

• Agents/Structure

How to model an agent ?(MAS)

Smart Grid: network and local objectives

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Several goals in competition:

• To minimize the cost for producers, consumers and during distribution.

• To avoid congestion, under/overproduction.

• To maximize the use of local Renewable Energies.

• To manage energy storage.

Source: Siemens

Network structure:

3-layered Grid

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Loca

l lev

el

• Isolated, grouped in a tree structure.

• Local management:

• Domotic

• Renewable Energies

• V2G

• Energy distribution.

Mic

rogr

id

• Root station for local agents.

• DSM

• Supply and demandequilibrium

• Consumer’sbehaviour

• Local concensusbetween supply and demand

T&D

net

wo

rk

• 2-connected graph

• Demand-response:

• Production management

• Scheme of consumption

• Predict future production

• Energy ditribution

A generic model

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General process

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• Bottom-up– Scheme of consumption

following prognostics

– If prognostics are valid, then next step.

• Top-down– Equilibrium supply/demand

– Final allocation and prognostics update for future iterations.

Bottom - up

Top - down

Local level

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Update

• Data update (devices, sensors, batteries).

First allocation

• Comparing prognostics to data.

First allocation II

• Find the First Optimal Solution.

Iteration: each 5min.

Priority of consumption - local management

House1 House2 House3 House4 House5

1/0/811/1/803/0/835/2/7520/4/20

Forecast: 4FOS : 5

1/0/161/0/162/1/153/0/184/3/75/3/5

Forecast: 6FOS: 7

1/01/010/0

Forecast: 12FOS: 12

1/0/331/1/323/0/353/2/294/1/328/4/8

Forecast: 8FOS: 9

1/03/0

Forecast: 6FOS: 6

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Value function of device’s priority and its consumption: ui=(weightmax*prioritymax)-(weighti*priorityi) + weighti

Net consumption: a local agent will consume its production beforecomputing its needs.

DSM: only smart devices/domotics can have a priority value superior to 0.

Microgrid level

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Auction

• Demand-side management

• Bid system

• Feedback with T&D

Consensus

• Local knapsack problem

• Knapsack bottom-up resolution.

Strategies

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Strategies based on the priority value/consumption of each devices (one-sided)

DSM Strategies (two-sided)The behaviour of the consumer may differs to the producers’ one. Microgrid’s policies can’t impose a local strategy but influence all utilities.

Set of devices 1 l/r Response 1 … Response i

Set of devices 2 l/r DSM 1 l/r l/r l/r

Set of devices 3 l/r DSM 2 l/r l/r l/r

… l/r … l/r l/r l/r

Set of devices m l/r DSM j l/r l/r l/r

- l: utility of the strategy for the consumer- r: utility of the strategy for the producers (granted energy).

The strategy with the highest sum l+r is chosen (Pareto). The microgridbenefits depends on the benefits of both sides

How to build efficient strategies

• 0-1 Knapsack problem:

• Upper bound in real-time:

– our tout instant T

– max 𝑖=1𝑛 𝑥𝑖 𝑢𝑖

• xi=1 si la demande en énergie est satisfaite à l’instant T, 0 sinon.

• Ui= valeur calculée lors du sac-à-dos ou lors des enchères (modèle économique).

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– For each iteration

max

𝑖=1

𝑛

𝑥𝑖 𝑢𝑖

s.t. 𝑖=1𝑛 𝑥𝑖𝑤𝑖 ≤ 𝑊

𝑗

𝑖=1𝑛 𝑎𝑖

𝑘 𝑤𝑖 ≤ 𝑊𝑘

𝑖=1𝑚 𝑎𝑖

𝑘 = 𝑥𝑖

𝑗=1𝑎𝑙𝑙(𝑗)𝑊𝑗 = 𝑘=1

all(𝑘)𝑊𝑘

• j for each microgrid

• k for each flow• Wj consumption of each

microgrid.

How to increase the efficiency of algorithms ?

Parametrize the strategies in order to increase benefits

T&D level

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Graph update

• Network data update.

Max flow, min cost

• Find a valid distribution pattern.

Equilibrium

• Identify bottleneck and perform feedback.

T&D network

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• Network: pretopology

• Network updateHow to build a dynamic graph ?

Pros: real time management, detailed network, easy to parameterize, self-healingCons: the grid need to be covered by a lot of sensors, fault risk during data mining.

1. A graph for each criterion.

3. The final graph is a Boolean function of the pretopologicspaces.

3. Resolve the max flow at minimum cost problem.

A real-time management

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Demand-response management

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Feedback gives advices for current and further iterations.

Feedback n°j

• Current feedback: Microgrids can change their behaviour.

• Final decision:

2 𝑖=1𝑗𝑗∗(𝑥𝑟𝑒𝑠𝑢𝑙𝑡 𝑗 )

𝑗(𝑗−1)

• x : for each microgrid• x : for each producer• Building forecast at the

end of an iteration.

How to smooth the curve

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• Slope and regularity • K-Lipschitz function

Local algorithm cannot see the overall results.

Smart Grid: an iteration.

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1. Data update2. First Optimal Solution3. Auction – Network update4. Feedback – New auction5. Consensus: local distribution.

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Global benefits

• For the producers:– Production is predictive

– The use of fossil-fuel power plants is limited.

• For the consumers:– DSM reduces energy

cost.

– Reward for acceptance of Response strategies.

– Minimal use of general distribution network.

– Maximal use of local renewable energies and storage.

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Future works

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Smart management system:• Parametrize strategies• Parametrize utility values Real-time learning process• Adapt to local changes (IA)

Context-free and friendly tool to model an efficient Smart Grid:• Create a « plug-and-play » framework• Allow external device management • Allow competition between microgrids• Allow competition between producers• Allow consumers to choose DSM strategies

Multi-agent model

The presented model is a general framework for future Smart Grid design.

Quel message voulez-vous diffuser ?

A generic modelling for

Smart Grid’s DesignSmartGreens2015

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Guérard GuillaumeVersailles – PRiSMFrance

Obrigado pela sua atenção!

Publications

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International Journal / Revue

Guérard, G., Amor, S. B., & Bui, A. (2012). Survey on smart grid modelling. International Journal of Systems, Control and

Communications, 4(4), 262-279.

Ahat, M., Amor, S. B., Bui, M., Bui, A., Guérard, G., & Petermann, C. (2013). Smart grid and optimization. American Journal of

Operations Research, 3, 196.

Guérard, G., & Tseveendorj, I. (2014), Inscribed Ball and Enclosing Box for Convex Maximization Problems. Optimization

Letters (2nd revised edition).

International Conference

Guérard, G., Amor, S. B., & Bui, A. (2012). A Complex System Approach for Smart Grid Analysis and Modeling. In KES (pp.

788-797).

MAGO14, Guérard, G., & Tseveendorj, I. (2014) Largest Inscribed Ball and Minimal Enclosing Box for Convex Maximization

Problems.

IEEE/ACM'14, Guérard, G., Amor, S. B., & Bui, A. (2014). A Context-Free Smart Grid Model Using Complex System Approach.

ProjectEPIT 2.0 (Bouygues; Alstom; Renault; Supélec; Eurodecision) 2011-2014

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La simulation

Module réseau de transport

Module consommation et production

Module distribution de l’énergie

Module gestion des pannes

Module réseau de communication

Coralie Petermann - UVSQ 30