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Essay: Selection of Battery size by using Power Flow decision program for Microgrids

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Abstract. This paper concentrates on determining the size of energy storage system (ESS) for microgrid (MG). Renewable energies are intermittent and their productions are stochastic can cause insufficiency of supply in electrical systems. Applying an energy storage system (ESS) can alleviate the impact of renewable energies. ESS plays an important role in the MG, which is advantageous to shave the peak demand and store the surplus energy. Sizing of ESS is to be viewed as first while considering ESS in the MG. Most MGs work in the grid connected mode. ESS is utilized to store the additional power created by renewable energy sources during the low demand time and re dispatching it during the peak load period. In order to determine the size of an ESS the objective function is to minimize the total Annual operating cost, this can be accomplished by using power flow decision.

Keywords: Energy storage system (ESS), ESS sizing, Power flow decision

1 Introduction

To meet the raise of the power demand, and 2 reduce the use of non-renewable energy sources and the need of reducing CO2 emission; grid associated renewable power frameworks have increased to answer this. Photovoltaic (PV) generation show up as the most encouraging issues. PV is exceedingly increased in the building energy sector for which it is especially relevant. The grid associated PV framework is an electrical power producing system that uses a PV as the essential of power generation and is proposed to work synchronously and in parallel with the utility grid. Such systems may incorporate energy storage systems and other generating sources for providing supply to nearby loads for the period of network blackouts and peak load hours [1]. We consider three methods of operation for PV/energy storage system.

1. ESS charging state: When the demand is low, PV as well as grid will charge the energy storage system.

2. ESS Idle state: PV supplies the nearby load directly at specific hours when demand is low, both PV power and load are high but the PV is not sufficient to meet the load that time ESS discharge up to it maximum after it is idle state.

3. ESS discharging state: Both PV and ESS supply the peak load at specific hours (like peak hours); energy storage system supplies the evening peak load hours when the PV power is insufficient and the marginal cost of grid power is high.

The above modes are considered under ordinary climate conditions when the PV power is available. However, on cloudy days, the PV/energy storage system will only function as ESS because of the non available of PV output. In such cases, In such cases, if the daily load profile creates sufficient price differences between peak and off-peak load periods, battery will be charged during off-peak periods and discharged during peak periods for economic operation or else ESS will be idle state [2].

One of the real difficulties for PV systems stays in the coordinating of the irregular power generation with the dynamic power demand. An answer is to add a ESS component to these nonconventional and irregular power sources. For this situation, the hybrid system, made out of a PV generator, nearby loads, ESS, and the grid, can perform numerous applications. In a grid connected PV system with ESS, if the energy is not adequate to take care of the load demand, the rest of the power will be provided by the grid. Then again, when the energy is produced more than the demand, the surplus power is sold to the grid [3].

Various potential advantages can be gotten from the utilization of ESS in systems. They are Shifting the energy buy from peak demand periods to low demand periods; Reduction of power charges; Deferral of investment on transmission and distribution; Avoidance of blackouts when demand exceeds a basic level; Improved power quality; Reduction of losses[4].

1.1 Basic model design

In this, the ESS is connected with the AC bus through the inverter/charger unit and the PV output also sends through the inverter towards change the DC into AC. The PV modules, utility grid, ESS and the load are connected with AC bus bar. A PV inverter has been used to change the DC of the PV modules into AC output at the AC bus bar. The ESS works on DC conditions, thusly an inverter/charger unit has been utilized among battery at AC bust bar to change over AC to DC and DC to AC, while charge and discharge of the battery [3].

Figure (1) Basic system configuration

1.2 Solar Photovoltaic Power

Solar Photovoltaic Power is a specific term utilized for electrical power produced sunlight. A PV system converts the sunlight in to electricity. In the PV system, we assume that a maximum power point tracker will be used. The DC output power produced from PV an area Apvg (m2) at a solar radiation on tilted module plane Gt (W/m2) can be given by [5,6,7,8].

(1)

Represents radiation of solar panel at tilted module plane at hour t, , represents derating factors, Represents the PV generator efficiency. The Ac output from the PV can be written as:

Epv_ac (t) = Epv_dc (t)

(2)

1.3 Energy storage system modelling

By means of continuously growing energy requirement and in future more expansion of renewable power, circulated power and smart grid, the request for rising power storage systems will be keep on increase. The usage of energy storage system reduces cost of electricity purchase and improves reliability of the power system. The state charge of the battery is updated each hour, charge and discharge equations for battery banks (BBs) are written in the following equations [7,8, 9].

The charge and discharge power transfer to and from the battery in a specific sampling hour can be defined as

(3)

The battery DC power is converted into AC power when it discharges through the inverter/charger unit to the AC side. AC power at the AC bus is converted into DC when it passes through the inverter/charger unit. The conversion efficiency of the inverter bat is assumed to be constant while charging and discharging the battery.

(4)

Charging

(5)

Discharging

(6)

During charge and discharge process of battery storage system, it can loss the some amount of energy i.e., cumulative battery capacity loss, it can be calculated as

(7)

ESS capacity loss during any sampling time can be found by using the equation

BCL (d,t) =Ecumi, bat, loss (t) – Ecumi, bat, loss (t-1) (8)

Usable battery capacity for the next hour in KWh

(9)

2.0 Optimal selection and sizing of battery storage system

The main purpose of optimal selection of battery storage system is to minimize the annual operation cost (AOC) as minimum as possible, it is defined as

Qobj = min (AOC) (10)

Where AOC includes the net electricity cost , ESS capacity loss cost and Annualized inverter cost. The cost of electricity can be given as

Electricity_cost (d,t) =E_price (d,t) E_grid (d,t) sampling time interval (11)

Where E_price (t) is the instantaneous electricity tariff in ($/KWH), E_grid (t) is the power transfer to and from the grid. Electricity is purchased from grid when E_grid (t)>0 and sold back to the grid when E_grid (t) <0.

ESS capacity loss cost during any particular hour can be calculated as

(12)

Annualized inverter cost = inverter capital cost CRF (13)

Where CRF can be given as

(14)

(15)

2.1 Flow chart for the optimal sizing of Energy storage system

Figure 2 Process for sizing of energy storage system

Table 1. Input parameter values for Matlab optimization

Sl.no Symbol Variable Value

1 PV system size 100kWdc

2 Z Ageing coefficient 3e-4

3 Socmin Minimum state
of charge 30%

4 Socmax Maximum state of charge 90%

5 a Self discharging factor 2.5%(per month)

6 tmin Minimum charging/discharging time 10hrs

7

PV inverter efficiency 97%

8

Battery inverter efficiency 94%

9

Efficiency of the battery charge 90%

10

Efficiency of the battery discharge 90%

11 V Nominal Voltage of the battery 12v

12 t Sampling Time Interval 1 hour

13 CRF Capital Recovery Factor 0.1233

14 i Real interest rate 4%

15 N Inverter Lifetime 10years

16 Battery investment cost rate 200$/kwh

17 Battery inverter cost rate 606$/kwh

3 Results

In order to obtain the program results, hourly PV panel output, load data and electricity price data given as input and use the energy flow decision based on cost of energy for a particular hour subjected to the objective function of total annual operating cost (AOC) as minimum.

Figure 3 Variation of active power in PV, load, grid, and ESS

From figure 3 during off peak time (from 10 pm to 7 am & 1pm to 4 pm) the amount energy required to feed the load will be taken from grid, ESS will also charging during this period, during peak times (from 7am to 1pm and 4pm to 10pm) the amount energy required for load will be taken from PV , if PV is not sufficient to meet the load the priority will be given to the ESS , if excessive PV and energy available at peak times will be supplied to the grid in order to getting the economic benefits.

Figure 4 charging state of battery

It is observed from figure 4 battery is well bounded between SOCmin and SOCmax we ensure that battery will be working as good condition. This is the main parameter that reveals that current energy storage in the battery. During this peak hour period customer sell all his battery power to the utility grid, until the battery state of charge is becoming to the minimum of the SOC. During off peak time the required power to feed the load will be taken from grid this condition battery will be charging up to it maximum value of the SOC.

Figure 5 Cumulative battery capacity loss (kWh)

An atypical day starting, cumulative battery capacity loss (CBCL) will be 9.289 it will remain until the battery will start discharge at 7 am it will continue up to 1 pm during the battery discharges its energy from 7 a.m. to 1 p.m., battery capacity is getting reduced due to the ageing effect.

Figure 6 Optimum value of the energy storage system

Figure 6 shows the variation of annual operating cost with different battery capacities for 100KWdc system. For different battery capacity sizes starting with 500 Ah to 40000 Ah are entered in the Matlab programme and annual operating cost for each of the battery size is obtained. The critical size of the battery, which gave the minimum annual operating cost of the system, is selected as 18500 Ah. The annual operating cost of the system by using the battery is 11421 $.

Figure 7 without battery storage system for 100KWdc system

Table 2 Optimal battery capacity results

Name of the parameter value

Optimal battery size (Ah) 18500

Total annual operating cost with battery storage system BSS($) 11421

Total annual operating cost without battery storage system BSS($) 11818

Conclusion

In this the optimal value of battery storage system for grid connected PV systems can be found using the power flow programm choice on particular hour subjected to the objective of total annual operating cost as a minimum. Without a energy storage system the total annual operating cost will be more compared to the with energy storage system of same capacity rating. Here we have to considered some varying parameters will be constant like power transmission loss and maintenance cost of the battery will be negligible.

References

1. S Riffonneau.Y, Bacha.S, Barruel.F and Ploix.S. Optimal Power Flow Management for Grid Connected PV Systems with Batteries. In Sustainable Energy, IEEE Transactions, vol. 2, no. 3, pp. 309 – 320, 2011.

2. Bo Lu, Member, and Mohammad Shahidehpour.: Short-Term Scheduling of Battery in a Grid-Connected PV/Battery System. In IEEE transactions on power systems, vol. 20, no. 2, may 2005

3. Chinmay Kumar Nayak, Manas Ranjan Nayak.: optimal battery energy storage sizing for grid connected PV system using IHSA. In IEEE Conference Publications, 2016.

4. Tan Zhang, Stephen Cialdea, Alexander E. Emanuel, John A.: Electric Energy Cost Reduction by Shifting Energy Purchases from On-Peak Times. In 2013 IEEE Electrical Power & Energy Conference.

5. S.Diaf, M. Belhamelb, M. Haddaic, A.Louchea: Technical and assessment of hybrid photovoltaic/wind system with battery storage in Corsica Island. In Energy Policy, vol. 36, no 2, pp.743-754, 2008.

6. H.X. Yang, J.Burnett, L. Lu.: Wether data and probability analysis of hybrid photovoltaic/wind power generation systems in Hong Kong. In Renewable Energy, vol.28, pp.1813-1824, 2003.

7. H.X. Yang, L. Lu, W. Zhou.: A novel optimization sizing model for hybrid solar-wind power generation system. In Solar Energy, vol.81, no 1, pp. 76-84, 2007.

8. A. Kaabeche, M.Belhamel, R. Ibtiouen.: Sizing optimization of grid independent hybrid photovoltaic/wind power generation system. In Energy, vol. 36, pp. 1214-1222,2011

9. Kusakana Kanzumba.: optimal scheduled power flow for distributed photovoltaic/wind/diesel generators with battery storage system. In IET Renewable Power Generation, pp. 1-9, 2015.

10. Y. Ru, J. Kleissl and S. Martinez.: Storage Size Determination for Grid-Connected Photovoltaic Systems. In Sustainable Energy, IEEE Transactions, vol. 4, no. 1, pp. 68 – 81, 2012.

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