lucian toma romania paper af-0192

7/26/2019 Lucian Toma Romania Paper AF-0192

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Optimal generation scheduling strategy in a microgrid

Lucian Toma, Ion Tristiu, Constantin Bulac, Andreea-Georgiana Neagoe-Stefana

Department of Electrical Power Systems,

University POLITEHNICA of BucharestRomania

Email: [email protected]

AbstractThis paper presents a strategy for optimal

generation scheduling in a microgrid. Several small generation

units of different type are considered, that is a wind system, a

photovoltaic system and a gas engine unit. In order to maximize

the use of renewable energy sources (RES) a storage battery is

also integrated. Two generation scheduling algorithms are

elaborated, either aiming at minimizing the use of fuel based unit

or at maximizing the lifetime of the battery. The simulations have

been performed in Matlab.

Keywordsmicrogrid, battery management, renewable energy

sources

I. INTRODUCTION

With the advent of new technologies running on renewableenergy sources (e.g. wind and solar) and small size but highlyefficient fossil fueled power plants (i.e. natural gas and

petroleum), the number of distributed generators in the lowvoltage and medium voltage networks has significantlyincreased in the last decade and will continue to increase in thenear future. Generation scheduling of small size units, similarto large generators, requires market strategy in terms of

generation availability and generation costs. Under the actualpower market conditions, a market strategy is strongly relatedto an accurate generation forecast and generation/load bidding.

It is well known that the renewable energy sources show astochastic behavior, thus there is always a risk for balancingthe commercial agreements in which such units are part of.Flexible generation solutions, e.g. gas fired units or flexiblehydro units, are thus required to appropriately balance themismatches in generation caused by renewables.

The generation units are differently considered forbalancing responsibility in terms of the installed power. InRomania, for instance, the owner of a power plant of installed

power totaling over 10 MW can sell the energy either on the

day ahead market (DAM) or by bilateral contracts and mustenter into balancing agreements, while units under 10 MW areallowed to produce the available power without penalizationsfor unbalancing. Besides, units under 10 MW can sell energyto the balancing responsible parties (BRP) only, through a

bilateral agreement. Thereby, the greater the unbalance adistributed generator (DG) produces the smaller the energy

price it can get. Vice versa, the smaller the unbalance thegreater the energy price can be negotiated. Therefore, ifcapable, the owner of a DG or group of DGs is interested to

balance the energy bids, minimizing the unbalances, in order toget higher price.

There is an important support for development ofrenewable energy sources (RES) at international level. TheseRES units are replacing, from year to year, the classical unitsthereby generating a bigger risk in ensuring proper powerreserves either for frequency control or for congestion

management. Solutions should be found to optimally operatethe small size, but flexible, units in order to limit the problemscreated by the RES.

II. THE MICROGRID CONCEPT

The concept of microgrid was first introduced in thetechnical literature in [1] and [2] as a solution for the reliable

integration of distributed energy resources (DERs), including

Energy Storage Systems (ESSs) and controllable loads [3].The microgrids are electrical networks of various size

delimited from the main grid by the power transformer or the

electrical interface. A microgrid can operate either

synchronized to the main grid or insulated from it. When

operating in islanding mode, a local control system to providegeneration-load balancing is required.

Figure 1 suggests an example of a microgrid. The loads

can be monitored using Advanced Metering Infrasctrure(AMI), and the generation units can be remotely controlled via

SCADA and remote terminal units (RTU).

Fig. 1. Generic representation of a microgrid.


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Various mathematical models are provided in the

literature. A battery energy storage system (BESS) wasconsidered in [4] as a main way of balancing the powers

within and isolated microgrid. A multiagent system was

designed to govern each entity to participate in the balancingprocess based on predefined rules. The modeling and control

of a battery management system used in a microgrid for both

grid-connected and autonomous modes is investigated in [5].

The overall management system is demonstrated to beeffective by six case studies at different microgrid operationmodes. The authors of [6] propose a solution for aggregation

of distributed generators in order to reduce the imbalance risk

in the market, by means of an existing methodology based onstochastic programming. The authors of [7] and [8] propose a

bidding strategy on the electricity market, as a non-

equilibrium model based on the deterministic price-based unitcommitment which takes the supply-demand balancing

constraint and security constraints of VPP itself into account.

The storage battery systems gains more and more space in

the power system applications. However, the technology

cannot be considered mature as they are limited to a certain

number of years of operation of a certain charging/dischargingcycles [9].

III. MATHEMATICAL MODEL

Three types of generation units are considered in the

microgrid, that is a photovoltaic power plant, a wind powerplant, and a gas engine unit. Additionally, a battery is used to

store the renewable energy when there is a surplus from the

two units.The mathematical model is formulated to run in discrete

steps of 1 minute, and the simulation is performed for a 24hour period, which means 1440 minutes.

The main objective of mathematical model is to minimize

the power generation cost with the gas engine

MIN

60x24

1

( )GEt

P t

=

subject to load-generation balance

( ) ( ) ( ) ( ) ( ) ( )load pv w GE bat sysP t P t P t P t P t P t= + + + +

and other operational constraints that are presented as follows:

A. Characteristics of the wind and solar power plants.

The main purpose of the renewable energy sources (RES)

is to replace the classical power plants to reduce the emissions

and protect the environment. The RES are characterized bylarge initial investments. Then, in operation only maintenance

costs are incurred. Therefore, since no fuel costs are involved,

the highest priority is assigned for operation to such

generation units. For this reason, in our application, the RES

are simply represented by generation curves Ppv(t) and Pw(t),as real time values.

There are many techniques for forecasting the generation

from wind and solar power plants. For national power

systems, the share of generation from RES can be significant

and the forecast errors may take acceptable values. However,in a microgrid, since the RES are located geographically near,

the error can be very high.

B. Characteristics of the gas engine

Small gas engine driven generators, of installed power

from hundreds of kW to few MW, are currently in operation in

distribution networks for combined heat and power. Similar toan internal combustion vehicle, the gas engine is very flexible

and is capable of changing the mechanical load very quickly

so that the synchronous generator can change the set-pointwithin few seconds.

A SCADA system is updating the data acquired from the

network on a 2-3 seconds rate basis, while a primary

frequency control system is set to intervene for frequencydeviations after 30 seconds. For this reason, in our application,

the classical generator is assumed to change the generated

power instantly.

The classical generator is defined by its installed powerPGE,inst, in MW, and the instantaneous power generation

PGE(t), in MW.

In our application, we consider that the gas enginegenerator produces energy when RES and battery cannot

supply the load, that is ( ) ( ) ( ) ( )load pv w bat P t P t P t P t> + + .

C. Characteristics of the battery

A battery is characterized by the total installed energyEbat,inst, in MWh, and the maximum instantaneous powerPbat,max, in MW.

The battery charges when there is a surplus of energy from

the renewable energy units only, that is when

( ) ( ) ( )pv w loadP t P t P t+ > .

The battery charging/discharging management is ensured

using two generation scheduling algorithms. The storagebattery can therefore be charged and discharged in cycles to

store energy from the RES and use it efficiently based on the

scheduling algorithm.In order to increase the lifetime of the battery, a minimum

and a maximum state of charge, SOCmin and SOCmax, are

considered.

Negative values of Pbatshow that the battery is charging,and positive values show that the battery is producing energy.

D. Grid connection

An interfacing with the main grid is considered. When

there is a surplus of energy from the renewable energy sources

and the battery is fully charged, the surplus of energy isinjected into the main power grid. When

The power exchanged with the main grid is denoted by

Psurplus. Positive values represent import from the grid,

whereas negative values represent export to the grid.

E.Management Algorithm

Two generation scheduling algorithms are developed andimplemented for simulation. The first algorithm aims to reducethe number of charging/discharging cycles. This can beachieved if the battery system will not change the operation


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mode until it is fully charged or fully discharged. The secondalgorithm aims to maximize the use of the renewable energysources and thus to minimize the generation from the gasengine unit. This algorithm will allow the battery to chargingany time there is a surplus of generation from RES ordischarging to cover the load.

The two algorithms are shown as follows.

ALGORITM 1Step 1. Set the initial values for generation and load powers

Step 2. Calculate generated power at time instant t

IF Cbat=1 [the battery is in discharging mode]

IF PL>(PW+PPV) and PL(PW+PPV)(PW+PPV) and PL(PW+PPV)>Pbat,max

Pbat=Pbat,max

PGE=PL(PW+PPV+Pbat)

IF PGE>PGE,inst

PGE=PGE,inst

ENDIF

ELSE

Pbat=0

PGE=0

ENDIF

ELSEIF Cbat=0 [the battery is in charging mode]

IF PW+PPV>PLand PW+PPV-PLPLand (PW+PPV)-PL>=Pbat,max

Pbat=-Pbat,max

PGE=0ELSE

Pbat=-Pbat,max

PGE=0

ENDIF

Pbat=0

PGE= PL-(PW+PPV)

IF PGE>Pgas,inst

PGE=Pgas,inst

ENDIF

ENDIF

Step 3. Calculate the power surplus

Step 4. Update the instantaneous battery energy

Ebat(t)=Ebat(t-1)-Pbat/60

and calculate the state of charge

SoC(t)=Ebat(t)/Ebat,inst

ALGORITHM 2

Step 1. Set the initial values for generation and load powers

Step 2. Calculate generated power at time instant t

IF PL>(PW+PPV)

IF SoCmin< SoC < SoCmax

IF PW+PPV+Pbat,max < PL

Pbat=Pbat,max

PGE=PL-(PW+PPV+Pbat,max)

IF PGE>Pgas,inst

PGE=Pgas,inst

ENDIF

ELSE

Pbat=PL-(PW+PPV)

PGE=0

ENDIF

ELSE

Pbat=0

PGE=PL-(PW+PPV)

IF PGE>Pgas,inst

PGE=Pgas,inst

ENDIF

ENDIF

ELSEIF PL


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- the battery is represented in magenta; positive values

shows power generation (the battery is discharging),and negative values shows power consumption (the

battery is charging)

All simulated cases assumes that the initial state of charge

of the battery is SoC = 0.56 (56%), and the battery operates indischarging mode with Cbat= 1.

A. Case 1

The first case assumes employing the first algorithm, in

which the battery is not allowed to discharged when operates

in charging mode, and is not allowed to charge when operatesin discharging mode. This means that the battery is used to

produce energy after a full charging cycle.

Figure Fig. 2 shows the load and generation curves for

Case 1. The algorithm applied has resulted in unused power(Surplus) from the renewable sources. This is due to that fact

that the battery is not allowed to charge when in discharging

mode although there is a surplus of power from renewables.

Since the battery is not fully charged and thus is notavailable, for generation, the gas engine is used. This results in

a total energy generation from the gas engine unit of 5.65

MWh, while the unused energy (Surplus) is 0.523 MWh.

0 500 1000 1500-0.5

0

0.5

1

1.5

2

2.5

Time [minutes]

G

eneration-Loadprofile[MW]

Ppv

Pw

Pgas

Pbat

Pload

Surplus

Fig. 2. Generation scheduling in the microgrid for Case 1.

Figure Fig. 3 shows the battery state of charge. Since the

battery is set to operate initially in the discharging mode, it

continues to produce energy until it reaches the minimum

accepted SoC, then starts charging when the energy generation

from renewables is greater than the load. The full charging of

the batter takes too long for two reasons: there generationfrom renewables is mainly smaller than the load, and the

maximum capacity of the battery might be too large.The change of the operating mode is illustrated in Figure

Fig. 4. When operating in the charging mode, a Cbat index is

set to 1, while when operating in discharging mode the index

is set to 0. When using Algorithm 1, the number ofcharging/discharging cycles is reduced very much. This may

increase the lifetime of the battery but may lead to

unoptimized use of the gas engine unit.

0 500 1000 15000.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Time [minutes]

Battery-s

tateofcharge[-]

Fig. 3. State of charge of battery for Case 1.

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]

Chargingmode

Fig. 4.

Charging mode of the battery for Case 1.

B. Case 2

In order to overcome the inconvenience met in the Case 1,

we assume a smaller size battery, with the installed power of

0.3 MW. All the other parameters and characteristics were

maintained the same. Reduction of the battery size wasdecided in order to allow achieving more charging/discharging

cycles.

The simulation with the new battery size has resulted in anew generation scheduling, for the battery and the gas engine

unit (Fig. 6). The total energy produced by the gas engine in

the Case 2 was 5.37 MWh, and the energy not used from therenewable sources was 0.437 kWh. Comparing these valuewith the ones obtained in Case 1 we may say that when

Algorithm 1 is employed, smaller size batteries should be

used. However, the results depend on both the load profile and

the wind and PV generation profiles. In order to maximize theuse of renewable energy, more than one battery should be

used.


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0 500 1000 1500-0.5

0

0.5

1

1.5

2

2.5

Time [minutes]

Generation-L

oadprofile[MW]

Ppv

Pw

Pgas

Pbat

Pload

Surplus


Figure Fig. 6 shows the battery state of charge for Case 2.It is obvious that a smaller size battery exhibits more

charging/discharging cycles, mainly in the first half of the day

when the load is smaller than the power generation from

renewable sources. If several small batteries are used, one

battery can be scheduled to produce energy during the peakload and charger during night periods or during high

availability of renewable sources. However, more batteries

requires larger investment costs. Beyond any short-term

optimization, a long term analysis to choose the number andsize of the batteries may be required.

0 500 1000 15000.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Time [minutes]

Battery

-state

ofcharge

[-]


Figure Fig. 7 shows the charging mode changes for Case 2.

In the second half of the day the charging mode do not change

because the power generation from renewable sources issmaller than the load.

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

ingmode

Fig. 7. Charging mode of the battery for Case 2.

C. Case 3

The third case considers the same data used for Case 1.

The second scheduling algorithm is employed for this case,which assumes that the battery is charged any time there is a

surplus of generation from renewable sources, and is

discharged any time the renewable sources cannot cover theload. This algorithm allows thus the battery to change its

charging mode.

Figure Fig. 8 illustrates the generation scheduling for the24 hours window. The figure shows that there is no waste

energy since the surplus line is always zero. On the other

hand, the battery is used mainly in the half part of the day

because it was fully charged during the night while the loadwas also at low values. The gas engine is used during the peak

load because the PV system is not available, and the wind

system is not capable of covering the entire load.

0 500 1000 1500-0.5

0

0.5

1

1.5

2

2.5

Time [minutes]

Generation-Loadprofile[MW]

Ppv

Pw

Pgas

Pbat

Pload

Surplus


Figure Fig. 9 shows the state, SoC, of charge of the

battery. Since renewable generation is available in the half


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part of the day, the battery changes from charging mode to

discharging mode to maximize the use of renewable energy.On the second half of the day the battery remains discharged

because the load is higher than the renewable generation.

0 500 1000 15000.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Time [minutes]

Battery-stateofcharge[-]


The changes between charging and discharging modes for

the battery is also observed in Figure Fig. 10.

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]

Chargingmode

Fig. 10.Charging mode of the battery for Case 3.

This case required the use of the gas engine mostly during

the peak load period, and the total generation provided was

4.98 kWh. Compared with the first two cases, this case

ensures the smallest cost for additional fuel. However, thebattery exhibit a large number of charging/discharging cycles.

V. CONCLUSIONS

The microgrid concept is a way of integrating the

renewable energy resources into the electrical networks. The

control implemented at the microgrid level allows a local

generation-load balancing thus reducing the negative effectsof the intermittency shown by RES. In order to fully benefit

from the availability of the RES generation, storage batteries

can be used.

Two algorithms have been used for simulations. The firstalgorithm assumes that if the battery operates in charging

mode it is not allowed to produce energy, whereas when

operating in discharging mode it is not allowed to absorbenergy from the network. The second algorithm has no

restriction on the operating mode of the battery. The batterycan be switched to charging or discharging mode any time is

necessary in order to fully use the energy from the renewablesources.

The simulations have shown that the second algorithm

achieves minimum generation from the gas engine unit and

thus less fuel. On the other hand, the first algorithm involves a

smaller number of charging/discharging cycles. It is importantto note that, when employing the first algorithm a smaller size

battery should be used.

The simulations have been performed using a code

implemented in Matlab, using also the same input data. It isexpected that if different load profile or RES generation

profile are used, more conclusions may be drawn regarding

the size and the strategy for battery use may be required.

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