Optimal generation scheduling

in a microgrid

Lucian Toma, Ion Triştiu,

Constantin Bulac, Andreea ŞtefanaDepartment of Electrical Power Systems

University POLITEHNICA of Bucharest

2016 ITEC ASIA, Busan, June 1-4

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Transmission network

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The future of power systems

AMI

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Substation

Dispatching and Strategy

PV

OPTIMIZATION

& CONTROL

Gas Engine

RTU RTU

RTU

Communication

Main

electrical

network

Microgrid

Wind

The future of power systems

Battery

Microgrid

Substation

Militari

Distrib. St.

Cotroceni

Distrib. St.

Gas Engine P.P

PV power plant

Microgrid – University “Politehnica” of Bucharest

Cable 1

Cable 2

Pinst = 30 kW

CF = 20%

2 x 800 kWel

ηel = 38%

PolyGrid

Substation

Militari

Distrib. St.

Cotroceni

Distrib. St.

Gas Engine P.P

PV power plant

Microgrid – University “Politehnica” of Bucharest

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The optimization objective:

The mathematical model

subject to load-generation balance:

60x24

1

( )GE

t

P t

MIN

( ) ( ) ( ) ( ) ( ) ( )load pv w GE bat surplusP t P t P t P t P t P t

Ppv – power generation from PV power plantPw – power generation from wind power plantPGE – power generation from gas enginePbat – power from battery (positive = generation; negative = load)Psurplus – power unbalance

and capability limits

maxgenP P

Characteristics of the wind and solar power plants

The mathematical model

- have the highest priority and are given by generation profiles

Characteristics of the gas engine

- installed power from hundreds of kW to few MW- very fast; can change the generation within few seconds- it has the lowest priority, thus they produce power when

( ) ( ) ( ) ( )load pv w batP t P t P t P t

The mathematical model

Characteristics of the battery

- is characterized by the total installed energy Ebat,inst, in MWh, and the maximum instantaneous power Pbat,max, in MW.

- battery charges when there is a surplus of energy from the renewable energyunits only

( ) ( ) ( )pv w loadP t P t P t

- in order to increase the lifetime of the battery, a minimum and a maximum state of charge, SOCmin and SOCmax, are considered

The mathematical model

Algorithm 1 assumes that the battery’s operating mode ischanged when it completes a full charging / discharging;no charging is allowed when in discharging mode, and nodischarging is allowed when in charging mode;

Algorithm 2 assumes that the battery is charging any time thereis a surplus from renewables, and discharging when theload is greater than the available generation fromrenewables.

Two algorithms are used:

Main data gas engine installed power, PGE,inst = 1.4 MW; battery’s size is decided battery minimum state o charge, SOCmin = 25%; battery maximum state of charge, SOCmax = 75%; load, wind generation and solar generation profiles are given

Microgrid – case studies

WindPhotovoltaicGas Engine

Uncontrolled: Load, Wind, Solar

Controlled: Gas engine, Battery

Microgrid – case studies

• installed power Pbat,max = 0.6 MW;

• installed energy, Ebat,inst = 1 MWh;

• rule: full charging/discharging is required until the battery changes its operating mode: Algorithm 1 is applied

Case 1

Microgrid – case studies

0 500 1000 1500-0.5

0

0.5

1

1.5

2

2.5

Time [minutes]

Genera

tion-L

oad p

rofile

[M

W]

Ppv

Pw

Pgas

Pbat

Pload

Surplus

0 500 1000 15000.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Time [minutes]

Batt

ery

- s

tate

of

charg

e [

-]

0 500 1000 15000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time [minutes]

Charg

ing m

ode

Case 1 Battery state of charge

Battery operating mode

PGE = 5.65 MWh

Psurplus = 0.523 MWh

Microgrid – case studies

• installed power Pbat,max = 0.6 MW;

• installed energy, Ebat,inst = 1 MWh;

• rule: full charging/discharging is required until the battery changes itsoperating mode: Algorithm 1 is applied

Case 2

Microgrid – case studies

Case 2

0 500 1000 1500-0.5

0

0.5

1

1.5

2

2.5

Time [minutes]

Genera

tion-L

oad p

rofile

[M

W]

Ppv

Pw

Pgas

Pbat

Pload

Surplus0 500 1000 1500

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Time [minutes]

Batt

ery

- s

tate

of

charg

e [

-]

0 500 1000 15000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time [minutes]

Charg

ing m

ode

Battery state of charge

Battery operating mode

PGE = 5.37 MWh

Psurplus = 0.437 MWh

Microgrid – case studies

• installed power Pbat,max = 0.6 MW;

• installed energy, Ebat,inst = 1 MWh;

• rule: the battery charges any time there is a surplus of generation fromrenewables: Algorithm 2 is applied

Case 3

Case 3

Microgrid – case studies

0 500 1000 1500-0.5

0

0.5

1

1.5

2

2.5

Time [minutes]

Genera

tion-L

oad p

rofile

[M

W]

Ppv

Pw

Pgas

Pbat

Pload

Surplus

0 500 1000 15000.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Time [minutes]

Batt

ery

- s

tate

of

charg

e [

-]

0 500 1000 15000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time [minutes]

Charg

ing m

ode

Battery state of charge

Battery operating mode

PGE = 4.98 MWh

Psurplus = 0 MWh

the first algorithm involves a smaller number ofcharging/discharging cycles

Conclusions

the microgrid allows a local generation-load balancingthus reducing the negative effects of the intermittencyshown by RES

the second algorithm achieves minimum generation fromthe gas engine unit and thus less fuel

Thank you