Cooperative Operation of Chemical-Free Energy Storage System with Solar Photovoltaic for Resilient

Power Distribution in Buildings – A Case Study

Jin Guang Yu, Tseng King Jet and Nguyen Hoan Thong School of Electrical and Electronic Engineering, Nanyang Technological University

Singapore, 639798

Abstract—This paper introduces a method to cooperatively operate the chemical-free energy storage system (ESS) with the solar photovoltaic (PV) system for more resilient power distribution in buildings. Specifically, the power distribution network in a commercial building located in Singapore is investigated as a case study. The re-design of building power distribution network and the ESS operation strategy are proposed to resolve the fluctuations from PV and building loads locally, instead of passing those fluctuations into the utility grid. Moreover, the ESS operation strategy shall help to achieve the peak curtailment of the building daily electricity demand.

Keywords—power distribution; chemical-free; ESS; PV; flywheel

I. INTRODUCTION Building integrated photovoltaic (BIPV) systems will in

future be the prevailing renewable energy supplies in highly urbanized cities with high density in buildings, for example in Singapore. However, most of the BIPV systems are tied with utility grid through a conventional building power distribution network. Due to the weather, the electricity generated by the BIPV systems is naturally intermittent and cannot be precisely predicted. If a large amount of BIPV systems’ unprocessed, wildly fluctuating “dirty power” is injected into the utility grid, this can cause serious stability problems. Another irony of many BIPV systems is that they may not be able to supply any power to the buildings in event of utility grid failure, even though they are in bright sunlight. This is because the solar inverters are designed to work only when the external grid voltage is present. Therefore, aiming for more resilient and a smarter power distribution in building, the cooperative use of energy storage systems with a re-design of the conventional power distribution network may be a possible solution, as investigated in [1]. Through the use of hybrid energy storage systems, the fluctuating BIPV power can be absorbed within the local building’s power distribution network instead of passing the fluctuation to the grid power [2]-[3], and to supply power to the critical building loads in case there is utility grid failure.

Generally, chemical batteries, e.g., lead acid batteries, are used as energy storage in many applications including uninterruptible power supplies (UPS). However, most of the

chemical batteries cannot withstand high cycling rates, and the chemical substances within the batteries can present environment problems, as described in [4]. Hence we have decided to consider the use of chemical-free storage technologies e.g. flywheel energy storage system (FESS). FESS offers a means of storing energy in a rotating disk with efficiencies higher than 90%. Reference [5] describes why the FESSs are preferred over the conventional chemical batteries in many micro-grid applications, which is presented in Table 1.

TABLE I. ENERGY STORAGE CHARACTERISTICS AND THE RESULTING BENEFITS OF FESS

Energy Storage Characteristic Resulting Benefits 5 to 10+ times greater specific energy

Lower mass

Long life (20 yr.) Unaffected by number of charge/discharge cycles

Reduced logistics, maintenance, life cycle costs and enhanced vehicle integration

85-95% round-trip efficiency More usable power, lower thermal loads, compared with <70-80% for battery system

High charge/discharge rates & no taper charge required

Peak load capability, 5-10% smaller solar array

Deterministic state-of-charge Improved operability Inherent bus regulation and power shunt capability

Fewer regulators needed

In this paper, we propose a solution based on a re-designed building power distribution network integrated with advanced FESS. Specifically, the cooperative operation strategy of the FESS is developed to realize the resilient power distribution performance targets, in terms of demand profile smoothing and peak demand curtailment. The proposed solution “should be made as simple as possible, but not simpler”, and should be developed to fit the local conditions.

The rest of the paper is organized as follows. The existing power distribution network, load characteristics and BIPV systems’ performance of the case study building are described and analyzed in section 2. Based on the analytic conclusion, the solution is proposed in section 3. In section 4, to examine the effectiveness of the proposed solution, simulations are conducted based on the measured data, and the results are analyzed. Finally, the conclusions and possible future work are discussed in section 5.

II. SYSTEM DESCRIPTION AND ANALYSIS In this paper, the building for the case study is a 16-floor

office building with 34,000 gross square meters located in central Singapore. The building is leased to different tenants, each floor are occupied by one or two tenants. This building’s roof-top are installed with BIPV system to sell its output electricity to the power grid.

A. Building Electrical Power Network Fig. 1 shows the existing power distribution network of

this office building.

Fig. 1. Power network of the building under study Within the building, there are two parallel power

distribution networks, one for emergency loads and the other for normal loads. The emergency loads are those associated with red switches/power points, which will be supplied from standby generator (the standby generator is not drawn in the figure) in case of power interruption to the incoming to the building. Generally, for the office buildings in Singapore, the emergency loads are usually those at the building common area and corridors. For this building, it means the electricity consumed by the selected building lifts, lights, power points at toilet and pantry, exit lights and any other building facilities not inside the tenants’ area but at the common area. In case of power failure at the building, the power points, lights and lifts connected to this emergency network will have a power from the standby generator that will come online within 15~20 second. On the other hand, as its name implies, the normal loads means the other various loads at the tenant area and other building facilities which are not connected to the emergency network.

B. Load Characteristics This building is equipped with a sophisticated building

energy management system (BEMS). A cluster of electrical power meters are installed to measure the power demand of the loads attached to the emergency network and normal network, respectively. The data from the electrical power meter are transferred to the database of the BEMS system via the supervisory control and data acquisition (SCADA) system. From the BEMS’s database we can export the logged power meter data for analysis. Fig. 2 shows the one month’s (October, 2012) power demand profiles of the building emergency loads, normal loads and total loads, respectively.

Here we set the sampling time interval to 15 minutes, therefore, there are 2976 sets of data in 31 days.

Fig. 2. Power demand of the building loads during October, 2012

From the data we can conclude two important points: 1) The power demand of the emergency loads is less

fluctuating, ranging from 20.44 kW to 28.92 kW, and is much lower than that of the normal loads which ranging from 45.04 kW to 163.72 kW

2) The power demand profiles seem to be predictable, as they vary periodically within the certain ranges.

III. BIPV SYSTEM The building roof-mounted BIPV system consists of 252

pieces of REC 230 PE polycrystalline silicon PV panels [6], with the total system capacity of 57.96 kWp. Four Sunny Tripower 15000Tl (STP-15000TL) PV inverters [7] integrated with maximum power point tracker (MPPT), are installed to invert the DC power to the AC power which then be coupled to the utility grid. It is a multi-string system; each inverter is associated with a string of 63 pieces of PV modules. Electrical data are obtained from the inverters. Solar irradiance, ambient temperature and relative humidity readings were taken simultaneously with the power and energy output of solar panels every 15 minutes. Fig. 3 shows the solar irradiance and corresponding BIPV system power and energy output in a typical day.

Fig. 3. The 24-hour solar irradiance and electricity yield of the above mentioned BIPV system in a typical day (Nov’06, 2012)

From Fig. 3, we have the evidence that the power output of the BIPV system can fluctuate widely within a short time period. In this typical day of November 6th, 2012, this BIPV system produces the electricity mainly between 7:00 to 19:00 PM, the peak power out is 49.56 kW, and the total energy yield is 237.78 kWh.

0.000

50.000

100.000

150.000

200.000

250.000

0.0 500.0 1000.0 1500.0 2000.0 2500.0 3000.0

Pow

er D

eman

d (k

W)

Time step (15 min)

Normal loadEmergency loadTotal load

0

50

100

150

200

250

0

200

400

600

800

1000

1200

0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00

Pow

er (k

W)/E

nerg

y (k

Wh)

Irra

dian

ce (W

/m^2

)

Time

Irradiance (W/m^2)System Power (kW)System Eenergy (kWh)

IV. SOLUTION Obviously, the existing power distribution network shown

in Fig. 1 may cause the effects of wildly fluctuating, unpredictable power output of the BIPV to be injected to the utility grid. As aforementioned, one possible solution would be to absorb the fluctuating PV power at the local building’s power distribution network instead of passing the fluctuation to the utility grid. This makes sense since the PV generation is also at the local building level. The solution would rely on energy storage technologies, simple re-design of the building power distribution network and a cooperative operation strategy of the storage system.

Fig. 4. Proposed system designs for building electrical power network

Specifically, the proposed design may help to gain the following benefits:

1) Self-sustaining power in times of emergency such as the utility grid failure, the resilient network, originally emergency network, shall work completely in isolated mode with the storage electricity within the FESS as well as the BIPV power output during the day time,

2) Negligible fluctuation power drawn from the utility gird and peak demand curtailment on the distribution feeder of the entire building through the cooperative operation of FEES,

3) Flexibility of operating in both grid parallel and islanded modes; allow the BIPV power to be consumed locally, thereby deferring the investment of upgrading the transmission and distribution (T&D) system when the power demand of the building increase.

4) The potential to participate in the ancillary service through the existing infrastructure with the help of the intelligent switches, and to enhance the reliability and stability of the utility grid.

V. COOPERATIVE OPERATION STRATEGY In this paper, the cooperative operation strategy of the

FESS is analyzed by considering its coordination with the BIPV system to regulate the total power demand of the building at the feeder.

If assuming the power output of the BIPV system is in real-time consumed by the building loads without the power flow regulation from the FESS, and the resistive transmission losses are neglected, the hypothetical net building power demand, denoted by ( )tPnet , can be expressed by:

)()()( tPtPtP BIPVtotalnet −= (1) where, )(tPBIPV is the BIPV system power output, and

)(tPtotal is the total building electricity demand of the building

loads tied to the normal and emergency power networks. Meanwhile, the )(tPtotal can be expressed by

)()()( tPtPtP emergencynormaltotal += (2)

where, )(tPnormal and )(tPemergency denote the electricity power demand at the building normal power network and emergency power network, respectively.

FESS must be operated to regulate the power flow among the ever-changing building loads, the BIPV system and the utility grid. Fig. 5 gives a graphic explanation of the operation principle of the FESS [8]. The FESS has to react to the net building power demand to eliminate the fluctuation from BIPV system and building loads as a whole. In Fig.5, )(tPreg is the regulated power demand of the building loads.

Fig. 5. Conceptual explanation of the FESS operation strategy, modified from [8]

VI. REDESIGN OF POWER DISTRIBUTION NETWORK Based on the analysis of the exiting power distribution

network, load characteristics and BIPV system performances, the power distribution network integrated with the FESS is re-designed, which is shown in Fig. 4. The proposed power distribution network is expected to realize the resilient power distribution within the building, and enable the coupled BIPV system and FESS assembly to facilitate advanced local electricity demand and production management rather than to use the existing permanent grid injection of BIPV power.

And the cooperative operation strategy is based on the following rules:

1) When )()( tPtP regnet ≤ , charging is favored, the power generated by BIPV system is used to charge the FESS as the first priority, and the surplus power is delivered to the emergency network and the normal network as the second priority.

2) When )()( tPtP regnet > , discharging is favored, the power discharged from FESS combining all the power generated by BIPV system is feed to the emergency network and the normal network in turn.

To maintain the regulated power demand of the building loads at )(tPreg , the integrated proportional-integral (PI) controller (not drawn in Fig.4) is used and gives the value of FESS charging power, i.e., )(tPfess . A positive )(tPfess means the electricity is charged to the FESS, and in the contrary, a

netPregP

0

t

t

Charging

Discharging

fessP

negative )(tPfess means the electricity are discharged from the FESS to supply power to the building loads. The FESS charging power can be determined by:

( ) ( ) ( )tPtPtP regnetfess −= (3) If we define the demand regulation period is from time

point 1t to time point 2t , the energy balance expression can be obtained by integrating both sides of the (3), as below,

ttPttPttPt

treg

t

tnet

t

tfess d )(d )(d )(

2

1

2

1

2

1∫∫∫ −= (4)

Aiming to the demand regulation targets of zero fluctuation and peak curtailment, the FESS should be operated to maintain

)(tPreg at a constant value during this certain time period, and the idea case is:

0d )(2

1

=∫ ttPt

tfess (5)

which means that FESS just performs as an energy buffer to balance the electricity flows between BIPV system and building total loads, the net energy charged to the FESS should be zero.

And if we assume all the daily electricity generated by the BIPV system shall be consumed during 21 ~ tt and by substituting )(tPnet by (1) in (4), for the idea case, we can rewrite (4) to:

12

24

2

1

d)(d)(

tt

ttPttP

P

t

thrt

BIPVtotal

reg −

−

=∫ ∫

= (6)

However, for the real case, the value of regP is unknown for the coming demand regulation time period as the future building power demand and BIPV system power output is unknown. Thus, a prediction value of regP , denoted by regpreP , , should be predetermined before we apply the FESS cooperative operation strategy. Based on the relationship expressed in (6), the regpreP , can be calculated by:

12

24,,

,

2

1

d)(d)(

tt

ttPttP

P

t

thrt

BIPVpretotalpre

regpre −

−

=∫ ∫

= (7)

where, )(, tP totalpre is the predicted power demand of the building total loads tied to emergency network and normal network, and )(, tP BIPVpre is the predicted power output of the BIPV system, respectively. Consequently, term

∫2

1

d)(,t

ttotalpre ttP is the accumulated energy demand of

building loads during 1t to 2t , and term ∫= hrt

BIPVpre ttP24

, d)( is the

accumulated daily electrical energy yield of the BIPV system. Therefore, the problem can be transferred to how to

determine the regpreP , . In the next section, two simple methods

are used to predict ∫2

1

d)(,t

ttotalpre ttP and ∫

= hrtBIPVpre ttP

24, d)( ,

respectively. And then the value of regpreP , is calculated.

VII. SIMULATION To evaluate the effectiveness of the proposed FESS

cooperative operation strategy, we select November 6’s data for the case study, the sampling time interval is 15 minutes. Because the building office time is from 9:00 to 18:00, which also regarded as the peak demand period, we set 1t = 9:00 and

2t =18:00. As aforementioned, to determine the regpreP , for 9:00 ~18:00, we first need to predict the energy demand of building loads and energy yield of the BIPV system.

A. Load prediction Usually, to predict the building electricity demand, the

following information is considered as explanatory factors: 1) Weather conditions: Possible variables to consider in

this category are wind speed, cloudiness, rainfall, temperature, etc., which are very related to the performance of the building heating, ventilation and air conditioning (HVAC) system. However, in this case study, the factor of the weather is limitedly related to the building electricity demand since the building are cooled by the district cooling system (DCS), in which the primary chilled water is provided by a centralized chiller plant outside the building. And the indoor ventilation fans’ power consumption is mainly related to the time of the day.

2) Time of the day: Obviously, electricity demand of the building loads depends on the time of day for which the forecast is made. Here, as we set the sampling time interval at 15 minute. Accordingly, 96 predictions are to be calculated to obtain the forecasting daily electricity demand of a building.

3) Type of day: Clearly, the electricity consumption on a working Tuesday is not the same as that of a holiday Tuesday, so it is necessary to consider the type of day, must be considered in the demand prediction.

4) Unpredictable factors: There are factors that may affect the consumption, such as a failure of the building facilities, or another major production failure, strikes, etc., difficult to factor into the demand prediction.

Based on the above points, the factors of time of the day and type of day are considered to forecast the load. And as the building under study is a recent construction, not enough data is available to train the sophisticated load forecasting model. Here, we use a simple method to predict the building electricity demand; we use the average electricity demand of the same weekly day within the previous month to predict the demand of the target day in each corresponding time interval. In the case study, we use the average power demand data of working Tuesdays of October, year 2012 (i.e., October 2nd, 9th, 16th, 23rd and 30th) to predict the power demand of a working Tuesday of November (i.e., November 6th, year 2012).

0

500

1000

1500

2000

2500

3000

0

40

80

120

160

200

0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00

Tot

al e

nerg

y de

man

d (k

Wh)

Tot

al p

ower

dem

and

(kW

h)

Time of the day

PredictedMeasured

Oct-30 Oct-31 Nov-1 Nov-2 Nov-3 Nov -4 Nov -5 Average Nov -6

Energy yield (kWh) 301.45 231.75 280.87 194.41 178.83 286.21 261.31 247.83 237.78

0.00

50.00

100.00

150.00

200.00

250.00

300.00

350.00

BIPV

sys

tem

ener

gy yi

eld

(kW

h)

The predicted and real measured electricity demand profiles are represented by curve “Predicted” and curve “Measured” respectively in Fig. 6. The left axis is for power demand profiles, while the right axis is for accumulated energy demand profiles.

Fig. 6. Comparison of predicted and measure total power demand and accumulated energy demand

We can observe from Fig. 6 that the predictions are very close to the measured results, which indicates that this simple method can predict the demand with an acceptable accuracy. To quantify the prediction error, the root mean square error (RMSE) of power demand prediction is calculated by (8), and the value is 5.66 kW.

( ) ( )( )96

96

2∑ −=

tPtPRMSE

measuredpredicted

(8)

where, predictedP denotes the prediction values, i.e., the average power demand of the each same time interval of the five Tuesdays; and measuredP denotes the measured power demands of the corresponding time intervals in November 6th. In this case study, November 6th is a typical day, not specifically selected. Therefore, this simple method used here makes sense for the demand prediction with certain generality.

B. BIPV Performance Prediction There are various methodologies to estimate the electricity

yield of an installed BIPV system, ranging from data driven performance data based method to rigorous physical models. However, most of them required the weather data and/or system characteristic data. As the prediction accuracy is not the first priority concern in this paper, here we utilize limited historical performance data to estimate the electricity yield of the BIPV system. In this case study, the average energy yield of the BIPV system in the previous successive seven days (from October 30th to November 5th) is used to approximate the energy yield in November 6th. The results are shown in Fig. 7.

Fig. 7. BIPV system energy yield from October 30th to November 6th

Fig. 7 shows that the predicted value, i.e., average BIPV system energy yield of previous seven days’, is 247.83 kWh, which is very close to the measured result, 237.78 kWh which is already shown in Fig. 3.

C. Simulation & Analysis With the predictions of predictedP , the accumulated energy

demand between 9:00 to 18:00 can be easily calculated, and the value is 1485.972 kWh. And by combining the prediction result of BIPV system energy yield, 247.83 kWh, we can obtain the value of regpreP , by (1), the value is 134.94 kW.

Fig. 8 shows the measured power output of BIPV system, measured power demand, calculated net power demand and simulated regulated power demand of the building loads, respectively.

Figure 8: Measured and simulated results

As the peak of the power demand without the FESS regulation is 169.92kW, despite leading to zero fluctuation power demand, the proposed method can achieve a 20.6% of peak power curtailment, calculated by (9):

%100,

,, ×−

=peaktotal

regprepeaktotal

PPP

R (9)

where, peaktotalP , is the peak value of the building daily demand profile, R is the percentage of the peak curtailment.

Figure 9: Power and accumulated energy charged to the FESS

Fig. 9 shows the profile of FESS charging power, scaled by the left axis; and the corresponding accumulated energy levels in the FESS, scaled by the right axis. The lowest point of the energy curve is -46.355kWh, indicates that the FESS shall have to store at least 46.355 kWh of energy, which is adequate for discharging during 9:00 to 18:00. The end point of the

-50-40-30-20-1001020304050

-50-40-30-20-10

01020304050

9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00

Ene

rgy

to th

e FE

SS (k

Wh)

Pow

er to

the

FESS

(kW

)

Time of the day

Charging power

Charging energy

0

20

40

60

80

100

120

140

160

180

200

9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00

Pow

er D

eman

d (k

W) /

BIP

V P

ouw

er

Out

put (

kW)

Time of the day

BIPV Output

Total Demand

Net Demand

Regulated Demand

energy charging curve is -19.655 kWh; means that after 9 hours’ operation, the net energy flows from FESS to the building power distribution networks is 19.655 kWh. Thus, after 18:00, the FESS shall buy back certain amount of electricity from the grid to maintain a certain energy level for next day’s use.

VIII. CONCLUSION & FUTURE WORK

A. Conclusion The project uses an office building located in Singapore as

a case study. At this early stage, this phase of the project is more of a feasibility study or pre-proposal of the energy retrofit for a conventional building power distribution network. Aiming to achieve a more resilient energy distribution within the building, a solution of re-design of existing power distribution network integrated with the FESS is proposed based on the analysis of the building load characteristics. Then the cooperative operation strategy for the FESS is investigated based on predicting the building power demand and BIPV system performance. Specifically, simple, yet accurate forecasting methods are introduced and used to predict the building power demand and BIPV system performance. Simulations are conducted, and the results show that the proposed methods can achieve the regulation targets: negligible fluctuation power demand profile and a 20.6% of power demand curtailment in the typical day.

B. Future work As the method is proposed based on a single case study, it

general applicability still remains to be investigated. A more general and sophisticated method shall be investigated in future. In addition, the prediction methods used in this paper shall be examined by more case studies. And the experiments and/or emulations shall be conducted to evaluate the effectiveness of the method.

ACKNOWLEDGMENT This research is funded by the Republic of Singapore’s

National Research Foundation through a grant to the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) Program. BEARS has been established by the University of California, Berkeley as a center for intellectual excellence in research and education in Singapore.

REFERENCES [1] Liu, B., Duan, S., and Cai, T., “Photovoltaic DC building module based

BIPV system – concept and design considerations”, IEEE Transactions on Power Electronics, vol. 26, pp. 1418–1429, 2011.

[2] Manuela Sechilariu, Baochao Wang, and Fabrice Locment, “Building-integrated microgrid: Advanced local energy management for forthcoming smart power grid communication”, Energy and Buildings, vol. 59, pp. 236–243, 2013.

[3] Manuela Sechilariu, Baochao Wang, and Fabrice Locment, “Building Integrated Photovoltaic System With Energy Storage and Smart Grid Communication”, IEEE Transactions on Industrial Electronics, vol. 60, no.4, Apr. 2013.

[4] Nirmal-Kumar C. Nair and Niraj Garimella, “Battery energy storage systems: Assessment for small-scale renewable energy integration”, Energy and Buildings, vol.42, no. 11, pp. 2124–2130, Nov.2010.

[5] Ibrahim, H., A. Ilinca, and J. Perron. “Energy Storage Systems-- Characteristics and Comparisons”, Renewable and Sustainable Energy Reviews, vol. 12, no. 5, pp. 1221-250, 2008.

[6] Specification of Renewable Energy Corporation (REC) 230 PE Polycrystalline Silicon Solar Panel.Available: http://www.recgroup.com.

[7] Specification of Sunny Tripower 15000Tl (STP-15000TL) PV inverter. Available: http://www.sma.de.

[8] Cimuca, Gabriel O., Saudemont, Christophe, Robyns, Benoît, and Radulescu, Mircea M., “Control and Performance Evaluation of a Flywheel Energy-Storage System Associated to a Variable-Speed Wind Generator”, ”, IEEE Transactions on Industrial Electronics, vol. 53, no.4, Aug. 2006.