In collaboration with Decube Consult, IDEA and UMONS

Industrial microgrids planning including different time horizons and all stakeholders by the use of game theory

Charline Stevanoni, Electrical Power Engineering UnitSupervisor: Prof. Olivier Deblecker, Co-supervisor: Prof. François Vallée

Organisation

Stakeholders

Consumers: industrial or

offices companies

Prosumers: consumers withtheir own RES

DSO: Distribution System Operator

IEO: IndustrialEstate Owner (e.g.

IDEA)

Aggregator = manager of the microgrid• Management of data forecasting (consumption and production)• Management of the microgrid operation (internal exchanges and

exchanges between the microgrid and the distribution network)

Multi-time horizon planning

Long-terminvestments

Short-termmanagement

Week-ahead

Day-ahead

MicrogridOperation

LoadManagement

Combination of the different time horizons and their connection

Data modelling

Data pre-processing

Pricing definition

Long-termExtensive

Game

Scenario definition

Week-aheaddecisions and contracts W

Day-ahead decisions and

contracts D: Short-term extensive

game

LoadMana-

gement

Microgrid operation Deviation

NPVn for long-termextensive game:

Equilibrium

Year =1

Day =1

Day =7

Year =Ytot

Stakeholder Objectives

DSO

Sign of profit:> or = 0

• Decreasing transmission costs, even if there is less energy transiting in the distribution network

• Maintaining or improving the quality of electricity in the distribution system and in the microgrid and ensuring their stability

Prosumers/Consumers

Sign of profit: > 0

• Reducing of the difference between the current purchasing price of electricity to supply their needs and selling price of the excess of electricity produced

• Improving their self-consumption (for the prosumers)• Decreasing the amount of electricity exchanged with the distribution system

IEO

Sign of profit: <, = or > 0

• Optimizing the proper operation of the microgrid by promoting the RESs• Providing a social and global welfare• Developing the industrial estate by luring new companies thanks to attractive

prices of electricity

Multi-agent and multi-objective problem

Prosumer 1

Prosumer k

Prosumer 2

.

.

.

IEO

InternalMarket

Aggregator = DSOExternalMarket

Distribution Network

Industrial microgrid

TSO + taxes

Flexibiliy

Management & pricing

RES+ storage

RES+ storage

Interaction model

Flowchart of the long-term planning tool

Outputs of the tool

NPV global over the 20 years and their evolution over the years:• NPV if the situation doesn’t change (𝑁𝑃𝑉𝑛,0 )• NPV with investments but without microgrid (𝑁𝑃𝑉𝑛,𝑖𝑛𝑣)• NPV with microgrid which correponds to the equilibrium

of the long-term game (𝑁𝑃𝑉𝑛,𝑚𝑔 ) Results of a load flow for a technical analysis Number of days with load managament for each consumer/prosumer

and profiles with and without load management All exchanges without microgrid All exchanges inside the microgrid and between the microgrid and the

distribution system Metrics in order to analyse the performances of the microgrid

𝑁𝑃𝑉𝑛 = 𝜌𝑛𝐿𝑇 +

𝑌=1

𝑌𝑡𝑜𝑡

𝑑=1

365

h=1

24𝜌𝑛𝑆𝑇

1 + 𝑟 𝑌

Objective function

With:𝑁𝑃𝑉𝑛,𝑚𝑔: Net Present Value, payoff of the

long-term game for a stakeholder 𝑛

𝜌𝑛𝐿𝑇 : cost of the long-term investments

(negative value)

𝜌𝑛𝑆𝑇 : difference between the short-term

incomes and expenses (exchanges of energy), cumulated payoff of the short-term games

• Prosumer 2 : Investment in a PV installation at year 1

• Without microgrid: Time of return on investment ≈ 8 years

• With microgrid: Time of return on investment ≈ 6 years

• Prosumer 1 (who already has a PV installation)

• No investment at year 1 : 𝑁𝑃𝑉𝑛,0 = 𝑁𝑃𝑉𝑛,𝑖𝑛𝑣

• Benefits thanks to the microgrid operation !

Game Theory

“Game theory is a concept which allows for describing and analyzing different dealings among agents who need to take decisions to fulfill their own objectives. It involves using the interaction between them

to optimize their respective objectives.“

. . . . . .

. . . . . .

Player 2

Player Ntot

a11 a1m1

a21

aNtot1

a2m2

aNtotmtot

(ρ1(τ1), … , ρ𝑁𝑡𝑜𝑡(τ1)) (ρ1(τtot), … , ρ𝑁𝑡𝑜𝑡(τtot))

Player 1

. . .

Player 2a21 a2m2

. . .

Player Ntot

aNtot1aNtotmtot

τ = 1 τ= τ totτ = 2

Nash equilibrium=

global solution suchthat, if any player

would change its decision, the risk of weakening the global

solution would increase

Extensive Game

Payoff attached by each player to the corresponding combination of decisions τtot

Illustrations (aggregator =DSO)

Context and planning problem definition

Planning tool

• Aggregator

• Lower NPV if only investments of prosumers (without microgrid)

• Benefits thanks to the microgrid operation(new pricing and exchanges between prosumersinside the microgrid)