Optimal Load Distribution of Microgrid Including Vanadium Redox Flow Battery

LIN Wei Communication Automation Center Sichuan Electric Power Corporation

Chengdu, China

CHEN Guang-tang, QIU Xiao-yan School of Electrical Engineering and Information

Sichuan University Chengdu, China

E-mail: cgt_scu@yahoo.cn

Abstract— The applications of energy storage technologies will be more and more important for the security, economic and stability of microgrid. This paper chooses a vanadium redox flow battery (VRB) as a distributed energy storage unit. A multi-objective optimization model for a microgrid which contents VRB is proposed in this paper. It analysis economical benefits model of microgrid VRB bring to. And another model is given special attention to various factors such as operating modes, control strategies and the weight of optimization goals. The numerical solution based on genetic algorithm (GA) is worked out and an illustrative system is calculated with the algorithm proposed.

Keywords — VRB ； microgrid ； multi-objective ； load distribution optimization

I INTRODUCTION The need for more flexible electric systems, changing regulatory and economic scenarios, energy savings and environmental impact are providing impetus to the development of microgrid, which are predicted to play an increasing role in the electric power system of the near future [1-5]. But as so far, the key technology of microgrid is still in experimental stage. Due to containing a large proportion of intermittent renewable energy, it is difficult to withstand disturbance. It will play an important role on the operations of power system considering the flexibility and progress of the energy storage system. The important role of energy storage system used in microgrid is proposed in [5-10]. It can be used to improving efficiency of demand side facilities, maintain power quality, support of renewable energy, emergency power supply, and improve efficiency of supply side facilities. And it is also used to getting benefit using different energy price in different time, reducing transmission access cost, and deferring facility investment and so on. In addition to learning the principle economic dispatching and energy trading of traditional power system, optimal distribution of microgrid has its own unique characteristics [5,10-15]. For example, because of lower voltage level, power loss of transmission line is relatively large, so it can not be ignored, wind turbine (WT) and photovoltaic (PV) usually work in maximum power tracking mode so they could not be scheduled by human, and efficiency of some micro source change with

output power and so on. The economic dispatch model of microgrid containing energy battery devices considering operation mode and different objective were proposed in [5, 10-15]. But they do not considering benefits of energy battery devices such as reducing transmission access cost and deferring facility investment and so on. This paper chooses VRB as a distributed energy storage unit. A multi-objective optimization model for a microgrid which contents a vanadium redox flow battery is proposed in this paper. And it analysis economical benefits of microgrid vanadium redox flow battery bringing. The model is validated by studying a specified case with various factors such as operating mode, control ling strategies and the weight of optimization goals.

II VRB ENERGY STORAGE SYSTEM The use of stored energy is fundamental to the generation of electric power, whether in fuel stockpiles for fossil or nuclear power plants, or the seasonal runoff and dammed waterways for hydroelectric power plants. Recent developments in advanced energy storage technology are providing new opportunities for using energy storage in grid stabilization, grid operation support, distribution power quality, and load shifting applications [7, 9, 16]. An integrated VRB energy storage consists of three subsystems: The Energy Storage System (ESS), the Power Conversion System (PCS) and the Balance of Plant (BOP). As good energy storage system, VRB have many advantages that:

1 It can offer almost unlimited capacity simply by using larger and larger electrolyte storage tanks;

2 It can be left completely discharged for long periods with no ill effects;

3 It can be recharged simply by replacing the electrolyte, if no power source is available to charge it;

4 If the electrolytes are accidentally mixed, the battery suffers no permanent damage.

VRB technology does, however, have a couple of disadvantages:

1 Have a relatively poor energy-to-volume ratio. 2 System complexity in comparison with standard

storage batteries.

Project Supported by a Technology Project of Sichuan Electric Power Corporation (No.11H0892), Technology Project of Science & Technology Department of Sichuan Province (No.2011GZ0036).

978-1-4577-0547-2/12/$31.00 ©2012 IEEE

III MATHEMATICAL MODEL

A. Control Strategy As renewable energy, WT and PV are good for

environment and low operating cost. However, their installation cost is so high that economic efficiency can not compete with other forms of power generation. Meanwhile, they are influenced by weather so much that can not be scheduled. So output power of WT and PV is treated as a negative load in this paper.

Control strategies of energy interaction between main network and microgrid are different at different operating mode.

1 Connected mode: it can exchange power freedom within the limits of transmission capacity between microgrid and main network.

2 Island mode: Overall power load needed is supplied by micro-source.

B. Proposed Objective The proposed multi-objective economic dispatch model can be written as:

1

1 1

1m ax 25 0 ( )1

1 11 2 ( ) ( )1 1

m in (1 )

NT

rT

N NT T

r e a p MT T

g en em iss io n

iP Pd

i iT D C Cd d

F C Cω ω

=

= =

+⎧ = × × +⎪ +⎪+ +⎪ × × + − − ×⎨ + +⎪

⎪ = + −⎪⎩

∑

∑ ∑

(3)

Where 1

. .1( )

T

r batter t batter t tt

P P P pr+ −

== − ×∑ (4)

{ } { }

{ }

. . . .

. .

( ) ( )

( )l h

m

r b atter t ba tter t T l b atter t ba tter t T ht T t T

ba tter t b atter t T mt T

T P P pr P P pr

P P pr

+ − + −

∈ ∈

+ −

∈

= − × + − ×

+ − ×

∑ ∑

∑

(5)

in v1(1 ( ) )1

Ne

iD Cd

Δ+= × −+

(6)

l o g (1 )l o g (1 )

N ατ

+Δ =+

(7)

max maxap P WC C P C W= × + × (8)

m axM M f M v an nualC C P C W= × + × (9) 1

. . . . . . .1 1( ( ( ) ( )) )

T N

gen f i i t i i t buy t buy t sell t sell tt i

C C P M P C P C P= =

= + + −∑ ∑ (10)

13

. . . .1 1 1

( 1 0 ( ) )T M N

e m is s io n k i k i t g r id k b u y tt k i

C P Pα β β−

= = == +∑ ∑ ∑ (11)

Where the P is benefit VRB bring, the F is operating and emission cost, rP is the energy price of the ith hour,

.batter tP+ is the discharging power of VRB, .batter tP− is the charging power of VRB, T1 is cycle time of dispatching of grid, rT is the fee reduced in a month, Tl is time of low transmission cost, Tm is time of middle transmission cost, Th is time of high transmission cost,

Tlpr is the price of power in time of low transmission cost, Tmpr is the price of power in time of middle transmission cost, Thpr is the price of power in time of high transmission cost N is total number of micro-source,

invC is the investment of upgrading facility, eD is the benefit of deferring NΔ years, α is the percentage of peak load reduced after adding VRB energy storage,

apC is the capital cost of VRB energy storage system,

maxP and maxW are the peak power and maximum energy capacity of the energy storage system, PC and

WC are their related costs respectively, MC is the operating and maintenance cost, MfC and MvC are fixed and variable operating and maintenance specific costs, annualW is annual discharge energy of the energy storage system, genC is the operation cost, . .( )f i i tC P is

consumption cost of the ith micro-source, .( )i i tM P is maintenance cost of the ith micro-source, .buy tP is power

microgrid buying from main grid, .sell tP is power microgrid selling to main grid, .buy tC is price of power

microgrid buying from main grid, .sell tC is price of power microgrid selling to main grid, k is the number of pollutants (CO2, SO2, NOx , act), kα is the cost of managing pollutants, .i kβ is the emission factor of pollutants of the ith micro-source, .grid kβ is the emission factor of pollutants of main grid. Power balance constraints: To meet the active power balance, an equality constraint is imposed

. . . . . . . . .1

( )N

i t W t s t batter t batter t buy t sell t D t L ti

P P P P P P P P P+ −

=+ + + − + − = +∑ (12)

Where the .W tP is output power of WT, .s tP is output power of PV, .D tP is demanding power of load, .L tP is loss of power of transmission line. Micro-source operation constraints:

min maxGit Git GitP P P≤ ≤ (13)

VRB energy storage system constraints: The balance between charge and discharge of the energy storage system in one day

1

. .1

( ) 0T

b a t te r t b a t t e r tt

P P η+ −

=− × =∑ (14)

The maximum energy capacity constrain: 1

. m ax1

T

batter tt

P W+

=≤∑ (15)

Power limit of VRB energy storage in ith hour: . . . .max( ) (1 )D t batter t batter t DP P P Pα+ −− − ≤ − (16)

. m ax0 batter tP P+≤ ≤ (17)

. max0 batter tP P−≤ ≤ (18) Power interaction constraints between microgrid and main grid:

. m in . . m axb u y b u y t b u yP P P≤ ≤ (19)

.min . .maxsell sell t sellP P P≤ ≤ (20)

C. Optimization Algorithm Genetic algorithm (GA) is a evolutionary technique that can tackle complex optimization problems. Traditional binary GA has some drawbacks when applying to multidimensional and high-precision numerical problems. To overcome these drawbacks,

some measures had been proposed in [17-18]. To calculate this model, the improved idea proposed in [17] was used.

IV CASE STUDY BY SIMULATION

A. System Configuration The case based on the medium-voltage rural distribution network benchmark [5, 19] is proposed to study. The benchmark is shown in Fig.3. The benchmark network are supplied by 110/20 kV transformers, which are referred to as TR1 and TR2, respectively. The medium-voltage DC coupler (MVDC) is optional and the purpose of subnetwork 2 is to study such coupling. It is sufficient to consider subnetwork 1 only in this case.

Fig.1 CIGRE 6 middle-voltage benchmark system

Assume the benchmark system contain micro-turbine (MT), diesel generator (DG), WT and PV. And position and capacity of microgrid units are shown in TableⅠ.Externality costs and emission factors are shown in TableⅡ. Maximum interaction power between microgrid and network is 500 kW.

TableⅠ position and capacity of microgrid units

Node Capacity（kW）

PV MT PV DG VRB 3 1000 200 4 200 5 1200 6 1200 7 200 9 200

10 200

TableⅡ Externality costs and emission factors

Emission type

Externality Costs

RMB/kg

emission factors (g/kWh)

MT DG network VRB

CO2 0.210 724.6 649 889 0 SO2 14.842 0.004 0.206 1.8 0 NOx 62.964 0.2 9.890 1.6 0

Load and output power of PV and WT are shown in Fig.2. Price of power buying and selling of microgrid are shown in Table Ⅲ.

0 5 10 15 200

500

1000

1500

2000

2500

3000

3500

time(h)

Act

ive

pow

er (kW

)

PV

load

WT

Fig.2 Daily demand of load, output power of WT and PV

Table Ⅲ Price of power buying or selling of microgrid

Time Price (RMB/kWh)

buying selling

7:00-11:00,12:00-15:00 1.3 1.2

6:00-7:00, 11:00-12:00,15:00-22:00 0.9 0.8

22:00-6:00 0.45 0.4

B. Result and Discussion

The maximum benefit of VRB storage system is 163.43w RMB calculated by the optimization algorithm in its life expectancy. And the operation of the VRB energy storage system is shown in Fig.3.

0 5 10 15 20-200

0

200

Time (hour)po

wer (kW

)

Fig.3 The operation of the VRB energy storage system

Grid-connected Mode

The load distribution is different when ω is different. Optimal load distribution of microgrid in different ω at connected mode is shown in Fig.4. It can be seen that externality cost of network is higher than MT and lower than DG from TableⅠ. So it should consider MT priority, and then consider purchasing power from network, and finally to DG when ω =0. The results are same with Fig.4 (a). However, the result is different while ω =1, It is known from table Ⅲ and features of every micro-source that sum of operation costs and maintenance costs is higher than price purchasing power from network in valley load period. Meanwhile, efficiency of MT is affected by its output power. So it should consider purchasing power from network priority, and then consider MT and finally DG. But the sum of operation costs and maintenance costs is lower than price of selling power to network, so it should be sell power to network as much as possible in peak load period. This result is same with Fig.4 (c). The result is shown in Fig.4 (b) when ω =0.5.

0 5 10 15 20

0

500

1000

Time (hour)

Power (kW

)

MTDG

Pbuy

VRB

(a) ω =0

0 5 10 15 20

0

500

1000

Time (hour)

Pow

er (kW

)

Pbuy

VRB

MT

DG

(b) ω =0.5

0 5 10 15 20

0

500

1000

Time (hour)

Pow

er (kW

) Pbuy

MT DG

Psell

VRB

(c) ω =1

Fig.4 Optimal results under different ω at grid-connected mode

Island Mode

It will be disconnected from network when necessary. Obviously, the result is different from when it working in grid-connected mode. There is no interaction power between microgrid and network at grid-connected mode. The optimal result calculated by GA under different ω at this model is shown in Fig.5.

0 5 10 15 20

0

500

1000

Time (hour)

Pow

er (k

W) MT

VRB

DG

(a) ω =0

0 5 10 15 20

0

500

1000

Time (hour)

Power (

kW) MT

VRB

DG

(b) ω =0.5

0 5 10 15 20

0

500

1000

Time (hour)

Pow

er (kW

)

DG

VRB

MT

(c) ω =1

Fig.5 Optimal results under different ω at island mode

V CONCLUSION

This paper chooses VRB as a distributed energy storage unit. A multi-objective optimization model for a microgrid which contents a vanadium redox flow battery is proposed. And it analysis economical benefits of microgrid VRB bring to. The model is validated by studying a specified case with various factors such as operating mode, control strategies and the weight of optimization goals. References [1] LU Zhong-xiang，WANG Cai-xia， et al．Overview on

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