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Abstract-- This paper addresses the important issue of power

quality management for smart grids and proposes a load management strategy based on transformer derating for minimizing harmonic distortion in distribution feeders and transformers. Ongoing development of smart grid technologies such as smart metering and smart appliances are creating new opportunities for improving distribution system performance. One area undergoing study is effective control of demand response through (semi)automated load management practices (e.g., smart appliances). Despite these developments, the impact on power quality has not been taken into consideration from a demand side management point of view. Smart grids provide an excellent opportunity to better manage power quality and reduce harmonic distortions present in power networks. In this paper, it is proposed that the impact of harmonics generated by nonlinear loads should be factored into overall load control strategies of smart appliances. This work focuses on the impact on residential distribution transformers which are adversely impacted by harmonic current distortions. A growing concern is the potentially high penetration of plug-in electric vehicles in smart grids. Load management of electric vehicles is studied for an IEEE 30-bus 23 kV distribution system to demonstrate the benefits of the proposed power quality and load management strategy. This paper proposes computing transformer K-Factor derating to control scheduling of smart appliances/loads to reduce harmonic stresses.

Index Terms-- Smart grid, harmonic losses, K-factor, load management and nonlinear transformer.

I. INTRODUCTION MART GRID technologies are presently undergoing rapid development in an effort to modernize legacy power grids

to cope with increasing energy demands of the future [1]. High speed bi-directional communications networks will provide the framework for real time monitoring and control of transmission, distribution and end-user consumer assets for effective coordination and usage of available energy resources. Furthermore, integration of computer automation into all levels of power network operations, especially at the distribution and consumer level (e.g., smart meters), enables smart grids to rapidly self regulate and heal, improve system reliability and security, and more efficiently manage energy delivery and consumption [2]. These objectives are inline

M. A. S. Masoum, P. S. Moses and S. Deilami are with the Department of

Electrical and Computer Engineering, Curtin University of Technology, Perth, WA, 6845, Australia (e-mail: m.masoum@curtin.edu.au; paul.s.moses@gmail.com; s.deilami@postgrad.curtin.edu.au).

with the proposed load management techniques of this paper which concentrates on power quality management of smart grids.

So far there has been significant research in integrating customer demand side management into smart grids to improve the system load profile and reduce peak demand [3-7]. To achieve this, many countries are developing technologies such as smart metering and smart appliances. Italy and Sweden, for example, are approaching 100 percent deployment of smart meters for consumers. Furthermore, smart appliances for households such as air-conditioning, dishwashers, clothes dryers, washing machines, as well as plug-in electric vehicles (PEVs), could “talk” to the grid and decide how best to operate and automatically schedule their activity at strategic times based on available generation. This paper is proposing to go one step further and incorporate power quality into load scheduling, which has not been previously considered.

This paper proposes the novel application of using derating K-Factors for load and power quality management in a smart grid. A harmonic load flow algorithm is used to evaluate the harmonic stresses at the distribution transformer serving nonlinear loads. K-Factor derating is applied to determine the amount of load that must be curtailed or reconfigured to minimize harmonic losses at the transformer. The proposed load management strategy based on power quality is a crucial component for smart grids to achieve the goal of maximizing system reliability and improving overall distribution system efficiency.

II. HARMONIC POWER FLOW For the harmonic power-flow calculation, a decoupled

approach is employed. This is justified due to the acceptable accuracy of the proposed decoupled harmonic power flow (DHPF) and the fact that industrial distribution systems consist of a large number of linear and nonlinear loads that cause convergence and memory storage problems if the harmonic couplings are considered [8].

At harmonic frequencies, the system is modeled as a combination of passive elements and harmonic current sources. The related admittance matrix is modified according to the harmonic frequency [9], [10], [11]. The general model of linear load as resistance in parallel with a reactance is utilized [12]. Nonlinear loads are modeled as current sources that inject harmonic current into the system. The fundamental and the hth harmonic current of the nonlinear load installed at

Load Management in Smart Grids Considering Harmonic Distortion and Transformer Derating

M. A. S. Masoum, Senior Member, IEEE, P. S. Moses, Student Member, IEEE, and S. Deilami, Student Member, IEEE

S

978-1-4244-6266-7/10/$26.00 ©2010 IEEE

2

bus i with real power P and reactive power Q are modeled as

*]/)[( 1iii

1i VjQPI += (1)

1i

hi IhCI )(= (2)

where C(h) is the ratio of the hth harmonic current to its fundamental. The harmonic voltages are computed by solving the following load-flow equation:

.hhh IVY = (3)

The voltage at bus is defined as 21H

1h

2hii VV

/

⎟⎟⎠

⎞⎜⎜⎝

⎛= ∑

= (4)

and the related total harmonic distortions of voltage (THDv) and current (THDi) are

%/

%/

/

/

100IITHD

100VVTHD

1i

21H

1h

2hii

1i

21H

1h

2hiv

×⎥⎥

⎦

⎤

⎢⎢

⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛=

×⎥⎥⎦

⎤

⎢⎢⎣

⎡⎟⎟⎠

⎞⎜⎜⎝

⎛=

∑≠

∑≠

(5)

where H is the highest harmonic order considered (H = 49 for this paper). At the hth harmonic frequency, power loss in the line section between buses i and i+1 is

2h1ii

hi

h1ii1ii

h1iiloss yVVRP ⎟

⎠⎞⎜

⎝⎛ −= ++++ ,,,),( (6)

and the total power loss, including losses at harmonic frequencies, for an bus system is

∑=

∑−

=+ ⎟

⎠⎞

⎜⎝⎛=

H

1h

1m

0i

h1iiloss

hloss PP ),( (7)

III. IMPACT OF HARMONICS ON TRANSFORMERS This paper proposes using derating factors for load

management in smart grids. Derating is defined as the intentional reduction in load capacity of a transformer operating under nonsinusoidal conditions. Derating of transformers is necessary because of additional fundamental and harmonic losses generated by nonsinusoidal load currents which cause abnormal increases in transformer temperature beyond rated operation. Transformers can suffer age reduction and premature failure due to resulting thermal stresses in the windings and core structure. Therefore, it makes sense that any power quality improvement strategy should be aimed at protecting assets prone to harmonic disturbances. The goal of derating is to reduce the transformer kVA loading such that total transformer losses are limited to rated losses.

The main methods for estimating transformer derating are: K-Factor [8,27], Harmonic Loss Factor [8], online harmonic loss measurement [13] and computed harmonic losses [14-[16]. In this paper, K-Factor approach is selected because it can easily be estimated from transformer load current harmonic spectra. This approach quantifies the harmonic stress impact of given nonsinusoidal load currents on

transformer harmonic losses and temperature rise. The DHPF (Eqs. 1-7) is used to calculate the current harmonics. The full derivation for K-Factor is given in [8]. The definition of K-Factor is as follows,

∑=

=H

1h 21rms

22h

IhIK)(

)( (8)

where hI is the rms load current for harmonic order h , and 1rmsI is the rated rms load current of the transformer. Given

the load current harmonic spectra and rated eddy current loss coefficient ( RECP − ), the K-Factor can be used to calculate transformer derating [14]:

( ))(

)(

)(pu

PI

IK1

P1I

REC

HLF

H1h

2h

21rms

RECderated

−

=

∑ =

−

+

+=

4434421

(9)

The new (derated) apparent power capability of the transformer can be estimated as follows,

( )puIkVAkVA deratedratedderated ×= (10)

Finally, the percentage decrease in transformer kVA rating is

( ) 100puI1Derating derated ×−= )( (11)

In this paper, the derating factor (K-Factor) is proposed for load control of smart appliances to minimize harmonic stresses at the substation transformers and voltage/current distortions in distribution feeders.

IV. SMART LOAD MANAGEMENT (SLM) BASED ON THDI AND K-FACTOR DERATING

The main contribution of this work is the smart load management (SLM) strategy in response to excessive harmonic distortion on distribution feeders and transformers caused by nonlinear loads (e.g., battery chargers for electric vehicles) and smart appliances. Some assumptions are made to expedite the explanation of the proposed SLM concept:

• The smart grid infrastructure is in place to communicate necessary power quality information and harmonic load data to the utility (in this paper, harmonic load flow is used to extract this information).

• Bidirectional communication network is available to send and receive control and status signals between the utility and smart appliance loads to modify their operation.

• For the purpose of not distracting from ensuing explanations, conventional demand-side management based on shaping load profile and reducing peak demand is not directly addressed in this work.

It is proposed that when the K-Factor and computed derating of the distribution transformer exceeds a certain limit, the smart grid should act to curtail harmonic rich loads. An SLM algorithm is proposed to scan through the distribution system on a priority and THDi basis and request smart loads (possibly

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with user interaction) to modify their operation (e.g., defer operation, turn off non-essential lighting, change heater thermostat set-points and delay of charging of vehicles).

For example, if a neighborhood has 30 washing machines operating and the K-Factor dictates that the transformer is under stress and should be derated, the utility could schedule the smart loads to queue their operation such that only 5 washing machines operate at a time, which would reduce the overall load and total harmonic distortion. The customer may be given the option to override this recommendation signal, but at a penalty. The derated transformer load current can be recalculated to see if the smart load control actions have reduced transformer loading and harmonic stresses. If not, the process is repeated again for remaining smart appliances in the high THDi group, then polling smart appliances with lower THDi. If this is not sufficiently improving transformer performance, then the algorithm moves on to higher priority loads and repeats the same process, and then, if necessary, re-applies strategy to different locations along the feeder until derating is achieved. A case study applied to plug-in electric vehicle load management detailing the sequence of events is exemplified in Section VI. The scanning algorithm is proposed in Fig. 2.

This SLM algorithm may loop through all smart appliance loads on a distribution system. This may be computationally impractical due to the sheer number of appliances. Alternatively, SLM could be implemented multiple times operating in parallel at selected buses (e.g., at a ring main unit) to perform local load management closer to the customer.

V. STUDIED SYSTEM In this paper, the argument is made for taking advantage of

smart grids to more effectively manage loads to mitigate the impact of harmonic distortion in distribution systems. For example, highly distorted loads such as charging PEVs could be dispersed in their scheduling to avoid too many charger loads coming online at one time to pollute the electrical system. Such operation can cause unacceptable bus voltage distortions and increase harmonic losses. The focus is on coordinating loads in smart grids to reduce harmonic stresses in residential distribution transformers.

A. System Under Study To illustrate smart grid power quality load management

scenarios, the IEEE 30 bus 23 kV distribution system [17-19] is modified to include a residential feeder with high penetration of PEVs and another serving an industrial area with nonlinear loads. A 250 kVA 23 kV/415(240) V distribution transformer is placed at bus 4 feeding high penetration of PEV nonlinear loads and linear loads (buses 23-28) at the customer side.

B. Linear Loads A typical daily load curve will be used in this paper (Fig.

3). In the simulation results, peak load and off-peak load scenarios are demonstrated for the case study. The data for

active and reactive power is available in reference [18].

Fig 1. Proposed smart load management algorithm based on K-Factor derating of transformers

Residentia

l Area

Fig. 2. Modified IEEE 30-bus 23 kV distribution system with residential and industrial feeders [17].

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Fig. 3. Typical daily load curve [17]

C. Industrial Nonlinear Loads An industrial feeder lateral is implemented at buses 29 to

31 serving large industrial nonlinear loads consisting of a variable frequency drive (VFD) and PWM based adjustable speed drives (ASD). The current harmonic spectra for these loads are shown in Table I and are based on [17].

TABLE I HARMONIC CURRENT CONTENT FOR INDUSTRIAL LOADS

order

Rockwell-6pulse-VFD

Toshiba-PWM-ASD

h mag deg mag deg 1 100 0 100 0 5 23.52 111 23.52 111 7 6.08 109 6.08 109 11 4.57 -158 4.57 -158 13 4.2 -178 4.2 -178 17 1.8 -94 1.8 -94 19 1.37 -92 1.37 -92 23 0.75 -70 0.75 -70 25 0.56 -70 0.56 -70 29 0.49 -20 0.49 -20 31 0.54 7 0.54 7 35 0 0 0 0 37 0 0 0 0 41 0 0 0 0 43 0 0 0 0 47 0 0 0 0 49 0 0 0 0

THDi

25.2 % 7.1 %

D. Plug-in Electric Vehicles (PEVs) Plug-in electric vehicles are becoming popular as a low

emission mode of transport which will dramatically increase its presence in distribution systems in the near future. Smart grids provide the unique opportunity to manage not only the energy storage options, but also address power quality impacts presented by the highly nonlinear charging circuitry employed for PEVs. Many papers have already studied the harmonic distortions generated by AC-DC charging circuitry [20-25]. However, no studies have been put forth to exploit smart grids

to manage power quality in load management of PEV’s and other smart appliances.

In this study, high penetrations of PEVs are placed at various locations along a low voltage (415 V) residential distribution feeder. Based on [20], the assumed mean operating power level per PEV charger at a customers premise is 1.3 kW (5.3 Amps at 240 Vac single-phase). Reference [20] has performed a detailed statistical analysis based on Monte Carlo simulations justifying this power level for a group distribution of chargers. It stochastically takes into account uncertainties in load diversity due to different start-times, variable initial battery state-of-charge and different charging profiles.

Typical harmonic current content of PEV chargers obtained from [20] is shown in Table II. In the following studies, a maximum of 120 PEV chargers grouped at 6 nodes along the residential feeder is simulated (Fig. 2) with existing linear loads. In the next section, the impact on distribution transformer performance and system harmonics will be evaluated for different load scenarios.

TABLE II TYPICAL LINE CURRENT HARMONIC CONTENT OF

AN ELECTRIC VEHICLE CHARGER [20] order h mag * deg

1 100 -26 5 25 -94 7 17 -67

11 9 -67 13 5 -46

THDi 31.9%

Fig. 4. Waveform of input current for PEV charger (Table II)

VI. SIMULATION RESULTS To demonstrate the performance of the proposed smart load

management algorithm (Fig. 1) on the derating of distribution transformer of Fig. 2, three cases studies are simulated.

A. Case 1: Peak-Load with High Penetration of PEVs The impact of PEV chargers during peak load on the

distribution transformer (Fig. 2) is evaluated. The transformer is loaded to its rated capacity and the K-Factor and THDi are computed (Table IV) using the outputs of the harmonic load flow algorithm. Transformer load current is shown in Fig 5.

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Fig. 5. Waveform of distorted current supplied by distribution transformer (Fig. 2) during peak load with high penetration of PEV (THDi = 19.13 %).

B. Case 2: Smart Load Management (SLM) for Case 1 In order to address the deteriorated power quality

conditions caused by PEVs in case 1, the proposed SLM (Fig. 1) approach is applied. The algorithm relies on all smart appliances, in this case, PEVs, to have assigned load priorities. Individual PEV charger harmonics and THDi can vary depending on battery state of charge and thus classifies its THDi group at a certain time. For simplicity, “Low”, “Med”, or “High” priorities are randomly assigned to various PEV chargers connected to the smart grid. Furthermore, load priorities, based on user preferences are assigned to PEV charging schedules. The load priorities may be based on user needs and be appropriately priced to reflect this. Table III shows a sample of selected individual PEV assets for the residential feeder with randomly assigned load priorities and THDi groupings.

TABLE III SAMPLE OF PEV ASSETS INTERFACED TO THE SMART GRID WITH

ASSIGNED THDI AND PRIORITY GROUPS FOR LOAD MANAGEMENT

Bus PEV Asset ID Priority Group THDi Group 23 A-23 HIGH HIGH B-23 LOW LOW C-23 LOW HIGH 24 A-24 HIGH MED B-24 HIGH HIGH C-24 MED HIGH 25 A-25 HIGH LOW B-25 HIGH LOW C-25 LOW MED Due to the possibility of user interaction, there are many

possible outcomes. One possibility is shown in the following sequence based on the SLM algorithm (Fig. 1): 1. Algorithm starts at closest bus to the distribution

transformer (e.g., bus 23). 2. The low (deferrable) priority group is scanned first

starting with high THDi impact loads. Therefore asset C-23 would be identified.

3. The customer owning C-23 would be signaled to modify their behavior, and may be given a variety of options or automatically default to certain settings such as:

• Defer and queue charging load to a later time (user may be rewarded for complicity).

• PEV charger may reconfigure itself automatically to reduce charging power at the expense of prolonging recharge time.

• User may override signaled recommendation and remain at full charging power (possibly penalized or pay a premium for this action)

4. After querying this asset, the algorithm will recheck the K-Factor to see if sufficient transformer derating has been achieved. If the K-Factor derating is satisfactory, the algorithm exits the load management loop and enters an idle checking loop and no further action is taken until K-Factor derating is necessary again.

5. On the other hand, if further derating is required, the next PEV asset with equal or less THDi than previous asset in the low priority group is queried (e.g., B-23) in the same manner as step 3. The algorithm continues this action and progresses through lower THDi devices before advancing to the next highest priority group for that bus. (Cost incentives could increase with priority grouping to reward customers who choose to part with high priority items.)

6. After progressing from high THDi to low THDi items, and then low priority items to high, if K-Factor derating is still unsatisfactory, the next closest bus to the distribution transformer is scanned, repeating steps 1 onwards

After performing this procedure for several iterations, the number of simultaneous PEV charger loads was reduced to 102. The impact on THDi and K-Factor is shown in Table IV.

C. Case 3: Off-Peak with High Penetration of PEVs The impact of operating PEV chargers off-peak at the same

PEV charging power level as previous cases was tested. The resulting K-Factor and THDi at the transformer is 1.99 and 29.92 %, respectively.

VII. ANALYSIS In case 1, the operation of high penetration PEV loads with

no power quality load management strategy is demonstrated. Under this condition, the transformer is delivering rated fundamental load current with significant harmonic content. An increase of transformer losses is indicated by the large K-Factor of 2.67 and a high THDi level of 19.13%. Such operation should be avoided as it could lead to premature failure (or aging) of transformer component and reduced efficiency.

In case 2, the developed power quality load management strategy is applied to case 1 to derate the load current of the transformer. The SLM algorithm, through a combination of factoring load priorities and THDi impacts specified in Table III, requested complying PEVs to queue their operation. In this case, rescheduling of loads resulted in 102 PEV chargers being left on the line as oppose to the original 120 PEVs. At this load level (0.94 pu), the required 4.9% derating is achieved. The K-Factor and THDi are decreased to 2.05 and 17.9 %, respectively.

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In case 3, off-peak load with high penetration of PEV loads is considered. The THDi has actually increased because the transformer load current is dominated by the nonlinear charger loads; however, the fundamental load component of the transformer current is significantly less (0.67 pu) due to off-peak linear loads being substantially less. Therefore,

negligible derating is required. The K-Factor is not as significant as it was during peak loading however some corrective action could be taken similar to Case 2 to reduce the K-Factor.

TABLE IV

SUMMARY OF SIMULATION RESULTS FOR DIFFERENT LOAD SCENARIOS

Case I1

[pu]* K THDi [%]

Derating [%] Comment

1 1.02 2.67 19.1 4.9

• 120 PEVs • Peak load with high penetration of PEV and no power quality load

management. • Transformer delivering rated fundamental load current. • Significant current harmonic content. • Excessive transformer harmonic losses

2 0.94 2.05 17.9 -

• Developed power quality load management applied. • Rescheduled PEV loading. • 102 PEV loads remain. • Required derating of Case 1 is achieved. • Current distortions and harmonic loss reduced.

3 0.67 1.99 29.9 - • Off-peak with high penetration of PEV. • Transformer current dominated by PEV loads. • Current distortion still present.

*) I1 IS IN PER-UNIT OF RATED TRANSFORMER CURRENT.

VIII. CONCLUSION The goal of this paper was to put forth the concept of addressing power quality improvement in load-side management strategies of smart grids. A study of potential power quality benefits based on decoupled harmonic load flow and derating factors (K-Factor) has been performed. The main conclusions are: • A comparison of different load scenarios on a distribution

feeder with high penetration of nonlinear loads (PEVs) is performed and its impact on transformer performance and load management is demonstrated.

• The impact of smart grid load management of high penetration of PEVs operating during peak load time is shown to have significant benefits in reducing transformer loading and minimizing harmonic losses

• Charging PEVs off-peak showed a reduction in K-Factor, however, there were still significant current harmonics generated to warrant further load curtailment action.

• This paper uses decoupled harmonic power flow algorithms for assessment of distribution system harmonic stresses.

• A load management algorithm is proposed which polls smart appliances to modify their behavior to reduce K-Factor based on distance to substation, load priority and THDi level.

• The impact of PEV was the focus of this study however the proposed load management routine is applicable to all smart appliances in general.

• Future work needs to be performed to develop online monitoring and integration of smart meters to feed into harmonic load flow algorithm which updates the K-Factor and initiates the load management algorithm.

• K-Factor derating is proposed as an effective smart grid control parameter to control harmonic rich loads (curtailment/penalties/incentives) which ultimately reduces distribution system harmonics losses and prolong transformer lifetime.

• The proposed concept places importance on smart meters to provide power quality measurements in addition to energy consumption data for the utility.

• This paper focuses entirely on power quality management and ignores power demand of loads. In reality, the proposed method should be combined with existing customer-side load management strategies to jointly achieve reduced peak demands and low harmonic distortions.

• In networks implementing distributed generation, the proposed approach could be used to coordinate DG sources to reduce transformer loading, or, interface with active filters for corrective action against harmonics.

• Instead of curtailing loads to derate the transformer, in a distributed generation scheme, the affected transformer could be managed by having power injected into the network from another node with DG sources, thereby alleviating the stressed transformer.

• The proposed method is applied to reducing harmonic impacts at the substation transformer bus. An alternative

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approach is to apply the proposed strategy for low voltage distribution transformers instead.

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Mohammad A. S. Masoum (SM’05) received his B.S., M.S. and Ph.D. degrees in Electrical and Computer Engineering in 1983, 1985, and 1991, respectively, from the University of Colorado at Boulder, USA. Dr. Masoum’s research interests include optimization, power quality and stability of power systems/electric machines and distributed generation. Currently, he is an Associate Professor at the Electrical and Computer Engineering Department, Curtin University of Technology, Perth, Australia and a senior member of IEEE. Paul S. Moses (S’09) received his B.Eng. and B.Sc. degrees in Electrical Engineering and Physics in 2006 from Curtin University of Technology, Perth, Australia. He was awarded an Australian Postgraduate Award scholarship in 2009 and is presently working towards a PhD degree in Electrical Engineering. His interests include nonlinear electromagnetic phenomena, power quality and protection. He has also performed scientific research for the Defence Science and Technology Organisation, Department of Defence, Australia. Sara Deilami (S’09) received her B.S. degree in Electrical Engineering-Electronics in 2000 from Islamic Azad University, Tehran, Iran. She is presently working towards a Master degree in Electrical Engineering at Curtin University of Technology, Perth, Australia. Her interests include optimal dispatch of shunt capacitors and LTC, harmonics, power quality and protection and renewable energy systems. She has eight years of industry experience as an engineer working in consultant companies.