progress report (01.07.2012 to 31.12.2015) dr.e. chandira ... · site i (ganapathy) max 2,036 659...

PROGRESS REPORT

(01.07.2012 to 31.12.2015)

UNIVERSITY GRANTS COMMISSION (UGC) (Apex Body of the Government of India)

MAJOR RESEARCH PROJECT

RESEARCH INVESTIGATION, DEVELOPMENT AND PERFORMANCE

ANALYSIS OF MICRO GRID WITH EMBEDDED CONTROLLER FOR

ENERGY OPTIMIZATION IN HYBRID POWER SYSTEMS

(F. No. 41-656/2012 (SR) dated 23rd July 2012)

Dr.E. CHANDIRA SEKARAN Principal Investigator

Associate Professor

Department of Electrical and Electronics Engineering

COIMBATORE INSTITUTE OF TECHNOLOGY (Government Aided Autonomous Institution)

Avinashi Road, Civil Aerodrome Post, Coimbatore-641 014, Tamil Nadu, INDIA.

i

Table of Contents

Sl.No Description Page No.

Acknowledgement ii

1. Aim 01

2. Objectives 01

3.

Research Work Completed

3.1 Study of Micro Grid Possibilities in Coimbatore Region 01

3.2 Solar PV Resource Assessment System 03

3.3 Identification of Project Implementation Site 13

3.4 Optimization of Solar PV– Wind Hybrid Power Generation

System 14

3.5 Development of Automated Metering Infrastructure (AMI) for

Residential and Institutional Microgrid 28

3.6 Development of Solar PV-Wind Hybrid Power System Hardware

Model and Testing 45

4. Conclusion 63

ii

ACKNOWLEDGEMENT

We would like to express our sincere gratitude to University Grants Commission (UGC),

Ministry of Human Resource Development, Government of India, for providing the necessary

grants to complete this research work.

We would like to express our profound sense of gratitude to our respected correspondent

Dr.S.R.K.Prasad, our esteemed secretary Dr.R.Prabhakar and our adored principal

Dr.V.Selladurai for permitting and providing all the facilities to carry out of this research work.

We thank our beloved Professor and Head Dr.S.Vasantharathna for her support and

guidance to complete this research work.

We would express our thanks to Sri.N.M.Jayabal and Sri.P.Murugesan of Accounts

Section, Coimbatore Institute of Technology for extending their support and co-operation.

We would like to thank all the UG and PG students who participated in this research

work.

We thank all the teaching faculty and non-teaching staff of the Department of Electrical

and Electronics Engineering, Coimbatore Institute of Technology, for their support and help.

Finally we would like to thank our family members without their unrelenting support and

co-operation this research work would not have been completed.

CIT-EEE-UGC-MRP-FINAL PROJECT REPORT Page 1

1. AIM

To design, develop and implement an institutional micro grid based hybrid power system and

optimize the energy utilization using embedded controller.

2. OBJECTIVES

i. To develop an architecture for institutional micro grid control system with energy

optimization and control with predictive resource modeling and optimal dispatch capabilities

to seamlessly coordinate the generation, storage, transmission and control.

ii. To develop an algorithm for Solar PV resource assessment

iii. To develop an intelligent Micro Grid Base Controller (MGBC) with integrated power

management and control system to manage systems stability and reliability.

iv. To simulate and demonstrate the Micro Grid Base Controller (MGBC) and Micro Grid Sub

Controller (MGSC) operations and to study the economic benefits.

v. To examine the energy consumption pattern of each Distributed Energy Sources (DES) and

precise estimation of energy delivery by remote energy estimation method.

vi. To reduce the adverse effect of green house gases and to reduce the global energy shortage by

implementing the energy optimized integrated management system.

3. RESEARCH WORK COMPLETED

The following activities have been completed for successful implementation of the

Micro Grid System in the Institute under this research project.

3.1 STUDY OF MICRO GRID POSSIBILITIES IN COIMBATORE REGION

In this work it is proposed to implement the concept of Micro Grid (MG) in the

residential areas in and around Coimbatore city. Coimbatore is the third largest city in

Tamilnadu, with a population of more than 20 Lakhs. It is one of the fastest growing tier-II cities

in India and a major textile, industrial, commercial, educational, information technology,

healthcare and manufacturing hub of Tamil Nadu. Other important industries include software

services. The climate is comfortable round the year. Table I gives the geographical information’s

about the Coimbatore city.

Table I – Geographical Information’s of Coimbatore City

Latitude 11.0183 N

Longitude 76.9725 E

Elevation 1,350 ft (Approximately)

Soil Type Predominantly Black With Red Loamy Soil

Average Wind Velocity 4.8 m/s

Average Solar Irradiance 900 – 1180 W/m2 on clear sunny days

Average Sun Hour 4.8 – 5.6 kWh/m2

Average Rainfall 61.22 cm (Annually)

Average Temperature 34.7 o C Mean on Summer, 21.4 oC Mean on Winter


As a first step the electrical utilization pattern of the residential areas in and around the

Coimbatore Corporation has been studied.

The survey has been conducted in both corporation and non-corporation limits (Sub Urban) at

all levels of income group to estimate the load demand.

The number of sites surveyed was about ten in which five sites in corporation limit and

remaining five from non-corporation areas.

For each site, for about six nearby houses were surveyed so that this approach enables us to

identify the possibility of MG formation and energy transfer via MG.

The total loads available in a residential house are categorized as

i) General loads, ii) Lighting loads and iii) Kitchen loads.

Table II shows the energy demand for the corporation limit and Table III shows the

energy demand for non-corporation limit.

Table II Energy Demand for Corporation Limit

Location Levels General (W) Lightings (W) Kitchen (W)

Site I (Ganapathy)

Max 2,036 659 3,240 Min 1,658 350 840 Average 1,888 468 1,422

Site II (Saibaba colony)

Max 3,223 821 1,580 Min 3,006 514 658 Average 3109 643 1,073

Site III (Saravanampatty)

Max 5,224 676 1,008 Min 1,475 440 840 Average 2,924 540 904

Site IV (SIHS colony)

Max 3,866 2,410 1,550 Min 1,805 313 490 Average 2,488 937 1,012

Site V (Sundaraapuram)

Max 3,331 600 2,840 Min 1,395 303 790 Average 2,087 486 1,352

Table III Energy Demand for Non-Corporation Limit (Sub Urban)

Location Levels General (W) Lightings (W) Kitchen (W)

Site VI (Kinathukadavu)

Max 1,058 563 1,128 Min 945 432 674 Average 1,245 450 845

Site VII (Palladam)

Max 1,076 850 936 Min 625 347 532 Average 1,300 543 789

Site VIII (PN Palayam)

Max 1,218 765 1,087 Min 857 416 660 Average 1,374 632 959

Site IX (Somanur)

Max 976 754 1,061 Min 538 457 517 Average 992 520 871

Site X (Sulur)

Max 1,121 457 860 Min 719 320 519 Average 1,276 517 849


The other findings of the survey are mentioned in Table IV.

Table IV – Other Findings in the Survey

RC Buildings 99 % Single Storage Buildings 84 % Utilization of Roof Area 95 % Vacant Shading Possibility 40 % Average Family Members 4-5

Type of Supply Single Phase - 86 % Three Phase – 14 %

Availability of UPS 36 % Usage of RES < 3 % Willingness for RES 98 % Willingness for MG 100 %

This survey provides a lot more findings which are very much useful to implement Renewable

Energy Systems (RES) leading to Micro Grid projects. The summary of the survey are,

Coimbatore region has lot of potential for both solar and wind energy. The region is blessed

with abundant solar irradiance throughout (around 250 – 300 full sunny day) and reasonable

wind velocity at specified hub height.

Most of the residential homes are closely located with each other. This arrangement favors to

form an independent micro grid between selected communities.

The roof areas of single storage buildings are 95 % vacant. This area can be best suitable to

install solar PV systems as per the required of the load demand.

It is also possible to implement small wind energy systems either separately or added with

solar PV system since the wind velocity is about 14 – 22 m/s in windy seasons and 4 – 12 m/s

in non-windy seasons at a hub height of 20-40 m.

Around 36 % of the households are having Uninterrupted Power Supply (UPS) for power

backup and this may be converted into solar UPS.

The usage of Renewable Energy Systems (RES) in the residential application is limited

around 3 % only. But the people are very much eager to implement RES projects in their

homes. This would definitely improve their life style.

3.2 SOLAR PV RESOURCE ASSESSMENT SYSTEM

The design of optimal sizing of solar PV and wind are much important while designing a

micro grid based on the load requirement as they are weather dependent. Hence a proper

assessment of these resources based on both weather conditions and load requirements is vital.A

resource assessment model has been developed for assessing instantaneous solar irradiance,

insolation and solar inclination angle. It gives the Global Horizontal Irradiance (GHI) on a

horizontal surface, which includes the diffuse radiation and direct normal irradiance over the

surface of the site. Initially it is developed to access the solar PV parameters and their future

predictions by including the geographical and climatic variations. The model has been developed

as simplified clear sky model by considering UV, Aerosol concentration, perceptible water vapor,

optical depth and other atmospheric conditions. In the developed assessment system, the

estimation of global irradiance can be evaluated by considering direct, diffused and reflected


beam radiations including the estimation of Direct Normal Irradiance (DNI). The DNI estimation

has been evaluated based on the concept of “optical depth” i.e. each component of gases or

particulates in the atmosphere absorbs certain amount of solar energy which is expressed as

optical depth for a given wavelength. During the monsoon seasons the water vapour content,

aerosol and ozone in the atmosphere is quite high and is medium in other seasons. Based on

these facts, the mathematical modelling for estimating the optical depth over a year has been

categorized in to three different levels.

The optical depth of the period of first 165 in a year is given by,

= . ((

)(

()∗.

)) – (1)

The optical depth for period of 165th day to 350th day in a year is given by,

= . ((

)(

()∗.

)) – (2)

The optical depth for period of 350th day to 366th in a year is given by,

= . + . ((

)(

()∗.

)) – (3)

The estimation of direct normal irradiance is given by,

= + . ( + .

– (4)

Where A is an empirical constant and their optimized values is 1250 W/m2 and N is the number of

the day in a year (For example N = 1 for January 1).

The Diffused Radiation is given by,

= ∗ –(5)

Where,

- 0.105 for the period between 150th and 325th day, and 0.58 for rest of the days.

- AM is the considered Air Mass

Then the Global Horizontal Irradiance (GHI) is given by,

= ()+ –(6)

Where,

- Z is the Zenith Angle

In order to improve the reliability and minimizing the error of the proposed model, a

scaling factor for various weather conditions has been assigned in order to enable the consumer to

calculate the exact irradiance for different atmospheric conditions. In addition to that, for each


weather condition an empirical scaling factor is also introduced to accurately estimate the solar

irradiance. Table V gives various weather conditions and their optimal scaling factors.

Table V – Various Weather Condition and Scaling Factor

WEATHER TYPE SCALING FACTOR

Mostly Sunny 100.0 %

Sunny 92.5 %

Partly Sunny 78.75 %

Partly cloudy 63.75 %

Cloudy 48.75 %

Overcast 33.75 %

Heavy overcast 22.5 %

Rain/ Snow 12.5 %

The entire model was developed using MATLAB GUI. The results obtained from the proposed

model have been verified with real time data and it is found that least Root Mean Square Error

(RMSE) and Mean Error (ME) are less as compared to NASA weather data.

In addition to the solar irradiance estimation, this model can also be designed to estimate the

annual energy production of a Wind Energy Generation (WEG) system using wind resource

assessment methodology. Generation potential of a WEG can be estimated by considering the air

density, hub height, rotor diameter and average wind velocity of the modeled site.

The above estimated weather parameters are mainly used to optimize the source requirement of

any MG. Based on the resource assessment, one can easily optimize the power rating of each source

and be able to match their load condition at site location for any weather condition. Using this

software model, it is possible to obtain irradiation, maximum day length, sun shine hours, optimal

inclination angle (Tilt angle for placing the solar panels), wind velocity at a specified height, and

wind direction by entering the latitude, longitude and average temperature of the site to be considered

over an year. It is planned to compare more number of real time data in the coming days and to

validate for minimum RMSE and ME.

Test Results

The effectiveness of the developed resource assessment system has been tested in the various

weather conditions as mentioned in Table V and locations. The test results are as follows:

Location-I: CIT, Coimbatore (Lat: 11.0183 N & Lon: 76.9725 E) Weather Type: Over Cast

The real time solar irradiance meter reading (Amprobe 100) and respective sun photo for over

cast weather condition is shown in Fig. 1a, 1b. Fig. 1c shows the GUI estimated irradiance for the

same condition and it is found to be matching with the simulated condition.


Location-II: CIT, Coimbatore (Lat: 11.0183 N & Lon: 76.9725 E) Weather Type: Sunny

For the same location the simulated software was tested for sunny weather condition as

shown in Fig. 2a & 2b. The simulated GUI irradiance for the same condition is shown in Fig. 2c.

Fig.1a & 1b – Real time reading and sun photo for over cast

Fig. 1c – GUI Model of estimated irradiance for over cast


Location-III: CIT, Coimbatore (Lat: 11.0183 N & Lon: 76.9725 E), Weather Type: Partly

Sunny

In order to achieve better results in the developed model, it was tested for the same

location with partly sunny condition as shown in Fig. 3a and their simulated GUI result is

mentioned in Fig. 3b.

Fig.2a & 2b – Real time reading and sun photo for sunny

Fig. 2c – GUI Model of estimated irradiance for sunny


Location-IV: Nidadavole, Andhra Pradesh (Lat: 16.9167 N & Lon: 81.667 E),

Weather Type: Sunny

The developed algorithm was also tested in Nidadavole, Andhra Pradesh on 05-01-2013. The

weather condition at the time of testing is sunny and is shown in Fig. 4a & 4b. The respective

simulated GUI mode is shown in Fig. 4C.

Fig. 3a – Meter reading for partly sunny

Fig. 3b – GUI Model of estimated irradiance for partly sunny


The performance of the proposed models has been evaluated by comparing the estimated

irradiance with the measured daily solar radiation data. The accuracy of the estimated values is tested by

determining the Root Mean Square Error (RMSE) and Mean Bias Error (MBE). In general the RMSE is

given by,

= [

∗ ∑( − )]^. -- (7)

Fig.4a & 4b – Real time reading and sun photo for sunny

Fig. 4c – GUI Model of estimated irradiance for sunny at Nidadavole, AP


Similarly the MBE is expressed as,

= [

∗ ∑( − ) -- (8)

Where,

No - No. of Observations

Im - Measured Value of Irradiance (W/m2)

Ic - Calculated Value of Irradiance (W/m2)

The RMSE provides information on the short-term performance of the correlations by allowing a

term-by-term comparison of the deviation between the calculated and measured values. The values of the

MBE represent the systematic error or bias in which a positive value of MBE shows an over-estimate

while a negative value an under-estimate by the model.

The RMSE and MBE values for measured verses predicted values at Coimbatore on 03-11-2012

are shown in Fig. 4a & 4b. The positive value of the MBE indicates that the developed algorithm slightly

biasing in over estimation.

The second observation at the same location on 08-11-2012 is shown in Fig. 5a & 5b. In this

observation also it if found that the MB E value is slightly biasing towards positive.

Fig. 4a & 4b – Comparison graph, RMSE and MBE values on 03-11-2012



Location-V: Mohanur, Namakkal (Lat: 12.4257 N & Lon: 77.3216 E)

The proposed software has been testing in Mohanur, Namakkal on 11-11-2012 to verify

its correctness with the real time measured data. The comparison graph and RMSE and MBE

values are given in Fig. 6a & 6b.

In the sequel of Coimbatore and Namakkal, the proposed method has also been tested in Nidadavole,

Andhra Pradesh on 04-01-2013 and their comparison graph is shown in Fig. 7. In this place also the

predicated values are almost matching with the measured values with minimum error.


Fig. 7– Comparison graph measured on 04-01-2013


From the tested results, it is concluded that the proposed algorithm is producing more accurate

prediction with minimal input parameters. In addition to this, the developed model has the flowing

advantages over other existing methods,

Prediction is based on very simple clear sky model with minimum input data. It requires only

latitude and longitude of the place and date of prediction.

Inclusion of different weather condition gives best accurate prediction values.

It also provides other variables like sun rise time, sunset time, day length, solar noon time and

optimized inclination angle for that day.

3.3 IDENTIFICATION OF PROJECT IMPLEMENTATION SITE

It is proposed to implement the micro grid architecture to energize some of the laboratories located in

the Information Technology (IT) building in our institute. The details of the Information Technology

(IT) building are mentioned in Table VI.

Table VI – Details of Civil Structure about IT Buildings

Location IT Buildings, CIT Latitude and Longitude 11.0216 N, 76.9714 E Number of Floor -1,G,1,2,3 Height Apporx. 33 Feet

Roof Area 590.34 Sq.m (One Side), (36.6 m x 22 m) 1180.68 Sq.m (Both Sides)

Roof Type Weather Coarse Tiles

Shading Possibility Approx. 30 % towards North -West (Only in the evening) Wind Obstacle NIL Wind Direction Mostly from South – West

A detailed power and energy study of various laboratories have been carried for one week. After

analyzing various technical and feasibility studies, two laboratories have been identified as their demands

are most suitable to implement Micro Grid. They are,

i. Systems laboratory,

ii. MCA laboratory

In a given week of time, P1 and P2 are not in a position to avail the energy supplied by the micro

grid. In that case the energy generated from the source will be effectively utilized by identifying another

load with priority P3. For that IT 302 laboratory has been selected with priority 3 (P3). Table VII gives

the priority condition of the above mentioned laboratories with their running time and load details.

Table VII – Priority Loads in IT Buildings

Room No No. of

Computers

Type of the System Running Hours /Week Remarks Priority

HCL Zenith FN AN Total

System Lab 40 40 -- 6 6 12 TFT Monitor P1

MCA Lab 46 46 -- 13 12 25 TFT Monitor P2

IT 302 50 40 10 6 9 15 TFT Monitor P3


The power consumed by the P1 (system laboratory) is shown in Fig. 8a & 8b at different time period.

From Table VII the system laboratory has 40 computers with a power requirement of 120 watt per

computer. This gives a total power requirement of 4.8 kW is needed to connect all the computers. Fig.

8b shows the power reading of the system laboratory and it has maximum power demand of 3.15 kW in a

particular day of observation. Fig. 9a & 9b depicts the power readings for MCA laboratory and it has a

maximum demand of 3.67 kW. Fig.10 shows the power readings for IT 302 laboratory and it has a

maximum demand of 2.28 kW.

Fig.8a & 8b – Power & Energy Trend of Systems Laboratory

Fig.9a & 9b – Power & Energy Trend of MCA Laboratory


These readings of the load analysis are taken as a reference for optimizing the power rating of the

hybrid wind/solar PV systems.

The load analysis is very much useful to identify the various priority loads in the system, so that the

source may be diverted to other loads for effective utilization.

3.4 OPTIMIZATION OF SOLAR PV– WIND HYBRID POWER GENERATION SYSTEM

Optimization of hybrid system is important as the system must be sized to provide continuous

reliable power to the load and at the same time to be cost effective.

Optimization is needed for better efficiency and economic utilization of the renewable energy

resources at their fullest possible extend.

It also helps to endorse the lowest investment with adequate and full use of the solar PV system,

wind system and battery bank, so that the hybrid system can work at optimum conditions in terms

of investment and system reliability.

It is proposed to develop an optimization method to size the solar PV and wind power system

based on Geographical Based Data Observation.

This method will provide best possible combination of wind-solar PV system, the geographical

data of the proposed site like solar radiation, wind velocity, temperature etc, are collected from

the solar-wind resource assessment systems installed in the IT building of the institution and

taken as input for the system modeling.

In this optimization model, the wind speed and solar irradiance data are measured at an interval of

every 15min that can be downloaded to the PC through web server. For each combination, based on the

geographical data, the wind generator and turbine details are to be calculated. Then for all the wind

velocity the power output is determined. The power output from the PV system is also calculated based

on the irradiance input and then these two are added to get the total power generated for each combination

for a given day.

Fig. 10 – Power & Energy Trend of IT 302 Laboratory


Based on the load analysis, the power requirement of P1 is 4.8 kW. The load profile of P1 and

their running time is shown in Fig. 11.

Assuming an overall efficiency of 80 % for power conditioning system the power output of

source I will be 6 kW (ie., 4.8 kW/0.8). Then the input power 6 kW has to be configured in different

sizing as mentioned in Table VIII.

Table VIII – Load combination for initial selection optimization

PW (kW) PPV (kW)

1 kW 5 kW

2 kW 4 kW

3 kW 3 kW

4 kW 2 kW

5 kW 1 kW

The same procedure has to be repeated for all other day with their geographical data and best

value has to be selected based on the Loss of Power Supply Probability (LPSP). Lowest value of LPSP is

an index for achieving best the input value. The GUI model for LPSP selection is shown in Fig. 12.

Fig. 11 – Load profile of P1 in a week


Then any best LPSP value will be selected for next iteration. In the next level of selection, cost

per watt and cost per unit generation has been considered for selecting one among the best value. Finally

a 7 kW overall power capacity in which 4 kW of solar PV and 3 kW of wind generator is the best model

with 1400 Ah of battery capacity. This optimization is applicable for priority load P1 only. In the same

way the priority has an optimized value of 9 kW of total power capacity. Solar PV is about 6 kW and 3

kW is from wind power system.

3.4.1 METHODOLOGY

Based on the above resource assessment, load analysis and optimization the load demand of P1 is

4.8 kW and P2 is 5.52 kW and their input source values are 7 kW and 9 kW respectively. Integration of

hybrid sources in a micro grid and their demand side management is still a challenging tasks since the

power availability from the source are intermittent in nature. The architecture of the micro grid, operation

and control has to be decided based on the power demand, location of the site and variability of the input

sources.

Literature Review (Selected)

Bruno Belvedere, Michele Bianchi, Alberto Borghetti, Carlo Alberto Nucci, Mario Paolone,

IEEE, and Antonio Peretto, “A Microcontroller-Based Power Management System for

Standalone Microgrids with Hybrid Power Supply” IEEE Transactions on Sustainable

Energy, Vol. 3, No. 3, PP. 422 – 431, July 2012. The authors discussed the AC micro grid

implementation and their power management in various source. This paper only emphasis

the fixed load model and their demand side management.

Fig. 12 – Selection of optimal sources using LPSP


Z. Ye, R. Walling, N. Miller, P. Du, K. Nelson, “Facility Micro grids”, Subcontract Report

General Electric Global Research Centre, National Renewable Energy Laboratory, U.S.

Department of Energy, Office of Energy Efficiency & Renewable Energy, Niskayuna, New

York, NREL/SR-560-38019, May 2005. This paper describes the case studies of inverter

based distribution system. It also emphasis the micro grid topology for AC and DC supply

but mostly oriented towards AC micro grid operation.

Considering all the constrained studied in the literature survey, an improvised model of micro grid

architecture preferably for the above discussed load – source pattern is configured. The functional block

diagram of the proposed micro grid is shown in Fig. 13. The proposed MG architecture has following

advantages,

a) Operation and control of DC micro grid is better and easier since DC is frequency is free from

frequency, and phase angle variation

b) Individual converter (Decentralized inverter topology) for each load enables simple and reliable

operation. If any one of the inverter fails, that will not affect the other loads.

c) Modular expansion of the system is quite simple and any power rating of a load can able to added

to the grid provided the DC micro grid is initially designed for high current carrying capacity.

In this proposed systems, the DC micro grid voltage is designed to set at 400 V which reduces the

current in the bus for a specified power capacity. The DC 400 V input is best suited for inverters to

converter it to 230 V single phase AC supply.

Fig. 13 – Architecture of the proposed DC micro grid

400 V

DC Micro Bi-Directional

Charge

DC-DC 4 kW Solar PV

System

Active Front End

AC DC DC-DC 3 kW Wind System

Load

P1

DC-AC 1-Ph 230 V

AC Supply

Load

P2

DC-AC

1-Ph 230 V

AC Supply

Load

P3

DC-AC

1-Ph 230 V

AC Supply


The block diagram shown in Fig. 13 has solar PV and wind hybrid source of 7 kW (Considered as

source I) designed for P1 and the source II for the P2 of optimized size may be connected to MG. The

operating sequences for the proposed configuration are mentioned in Table IX.

Table IX – Operation sequence and load management in the MG

Condition

Function Source I Source II P1 P2 Battery

Case I A A A A Fully charged Energy transfer from

Source to load

Case II* A A A NA Partially charged

Check for P3, if available transfer the power to P3 If P3 is NA, charge the

battery

Case III A A A NA Fully charged If P3 is NA, Feed to grid or feed other lighting loads in

the same buildings Case IV$ A NA A A Fully charged Feed P2 through battery

Case V$ A NA A A Partially charged

Feed the P2 through grid

Case VI A A NA NA Fully charged Feed to grid or feed other lighting loads in the same

buildings

Case VII A A NA NA Partially charged

Charge the battery and feed the remaining power to grid or feed other lighting loads

in the same buildings

* Also applicable when P1 is not available

$ Also applicable when source I is not available

The technical specification for the proposed systems is given in Table X.

Table X Technical Systems Specifications

POWER REQUIREMENTS OF P1 AND P2 Load Demand of P1 4.8 kW Load Demand of P2 5.52 kW Optimized power capacity of P1 7 kW Optimized power capacity of P1 9 kW Power Source Solar PV + Small Wind Generator

SOLAR PV SYSTEMS FOR P1 Power Capacity of the Solar PV 4 kW PV Module Type Monocrstalline Number of Panels 20 Peak power capacity of each panel 200 Wp Peak Panel Voltage 26.1 V Peak Panel Voltage 7.66 A Solar PV String Voltage 132 V Min & Max String Voltage 96 V, 162.5 V PV Panel Orientation Single Axis Tracking


WIND ENERGY SYSTEM FOR P1 Power Capacity of the Wind 3 kW Generator Type 3 Ph – PMSM Coupling Gear Less direct through Power Converter Tower Type Lattice type Tower Mounting 3 direction stay connected SPECIFICATION OF MICRO GRID Capacity Addition of power sources Modularly added to the MG Type of the Micro Grid DC Micro Grid MG Operating Voltage 400 V +/- 3 % INVERTER SPECIFICATION FOR P1 Rating 5 kVA DC bus Voltage 400 V +/- 3 % Output Voltage Single phase 230 V +/- 1 % Output Frequency 50±0.5Hz Output Waveform Pure Sine Wave

Total Harmonic Distortion <3% on Linear Load , for both V & I (As per IEEE-519 std), For Non Linear Loads Voltage THD is <3%, Current THD depends on the load.

Over Load Capacity 100-120% for 60 Seconds 125% - 150% for 20 Seconds

BATTERY SPECIFICATION FOR P1 Battery Capacity 1,200 Ah (Approximated value for both P1 and P2) Battery Type Lead Acid Industrial Standard SMF Battery Battery Voltage 12 V (With necessary series and parallel combination) CONTROLLER SPECIFICATIONS Micro Grid Controller Embedded Altium 6.0 USB PCU Controller TMS 320 F 28335 (C 2000 Series) DSP Controller

3.4.2 Simulation Study

F1. Simulation Study of Load Modeling

The load study has been carried out using HIOKE 3196 Power Quality Analyzer for the laboratories

mentioned above. Fig. 14 shows the details of the power consumed by Systems Lab. By using this

data load Equivalent model is calculated. Fig. 17 shows that the equivalent model of the load, which

is modeled as R and L Component. Fig .18 shows the output waveform of the equivalent model,

Fig.19a and 19b shows the Power Factor and RMS values and Fig. 20a and 20b shows the Apparent

and Real Power of the Equivalent Model. Values specified in the table XI is used for the Calculation

of R and L Value.


Table XI Load Specifications

Power(kW) 1.99 Apparent Power(kVA) 2.807 Reactive Power(kVAR) 1.970 Load Voltage (Vrms) 227.52 V Load Current (Irms) 12.34 A Power Factor 0.7122

R and L calculation:

=

--(9)

Z = 18.14Ω

Cosφ = 0.7122

R = Zcosφ --(10)

R = 16.04 Ω

XL = Zsinφ --(11)

L = 77.55mH

Fig. 14 – Power & Energy of Systems Laboratory

Fig. 15 – Voltage & Current Waveform of Systems Laboratory


Fig. 18 – Voltage and Current Waveform of the Equivalent Load Circuit

Fig. 19a & 19b – Power Factor and RMS Value of the Equivalent Load Circuit

Fig. 17 – Equivalent Load Circuit


The results in the Figs.19a, 19b, 20a and 20b are compared with the Real Time data acquired

during the load study, which is shown in the Fig. 14, these results are approximately same. So the value

obtained in the calculation is used for the further simulation work.

F2. Simulation Modeling of Converters

Interleaved Boost (IB) Converter:

Interleaved Boost Converter is used in between Solar Panel and Micro Grid, to boost the Solar Output

Voltage to the Level of the Micro Grid working Voltage. Interleaved Boost Converter is considering

being Efficient than the Conventional Boost converter. The following reasons are made to choose the

Interleaved Boost converter,

1. Reduces the Conduction losses by sharing the Load Current in two branches.

2. Inductor Current rating is reduced so the conductor size reduces.

3. Output Voltage ripple is reducing.

4. Improves the Overall Efficiency of the converter.

Design of Interleaved Boost Converter

Input Voltage Vin = 96 to 132V

Output Voltage Vout = 400V

Duty Cycle kmin = 0.63

kmax = 0.76

Inductance, L1 = L2 = ∗

∗∗Δ -- (12)

= ∗.

∗∗∗.∗.

L1 = L2 = 0.901 mH

Capacitance, C1 = C2= ∗

∗∗Δ -- (13)

= .∗.

∗∗∗

C1 = C2= 60.5 µF

Fig. 20a & 20b – Apparent and Real Power of the Equivalent Load Circuit


Designed Values are simulated with PSIM, Fig. 21 shows the PSIM simulation of the Interleaved

Boost converter. Fig.22 shows the Output load Voltage and Current of the Interleaved Boost Converter.

F3. Inverter Study Based on the Converter

Inverter study is based on the front end with Interleaved Boost Converter. The voltage from the

solar panel will be 132V, under standard Conditions of the Solar Panel, which is boosted up to 400V

output. This is fed into the inverter to get the output AC voltage of 230V. This entire circuit is given

in the Fig. 23.Pulse width modulation is Sine Compared with the Triangular Wave. The load which is

calculated in above which is used in the load model section.

Fig. 21 – Interleaved Boost Converter Circuit

Fig. 22 – Output Voltage and Current Waveform of Interleaved Boost Converter


In the Fig.29 overall output waveform is given. In the Fig. 24, Solar PV Output Waveform, in Fig.25

Interleaved Boost Converter Output or MG, in Fig.26. Load Voltage, in Fig.27 Load Current is shown.

Fig. 23 – Inverter Circuit Coupled with Interleaved Boost

Fig. 24 – Solar PV Output Waveform

Fig. 25 – Interleaved Boost Output Waveform (or) MG Voltage


Fig. 26 – Load Voltage Output Waveform

Fig. 27 – Load Current Output Waveform

Fig. 28 – Load Voltage and Current Output Waveform


Fig. 30 a & 30 b – Real and Apparent Power of Equivalent Circuit connected with Inverter

Fig. 29 – Overall Output Waveform


The results obtained from the Inverter simulation which is shown in the Figs. 30a, 30b, 31a and 32a,

which are compared with Fig 20b, 20a, 19b and 19a are very much similar to the Data which collected

during the load study.

Fig. 31 a & 31 b – RMS and Average values of Equivalent Circuit connected with Inverter

Fig. 32 a & 32 b – Power Factor and THD values of Equivalent Circuit connected with Inverter


3.5 DEVELOPMENT OF AUTOMATED METERING INFRASTRUCTURE (AMI) FOR

RESIDENTIAL AND INSTITUTIONAL MICROGRID

This work provides the development of GUI based Automated Metering Infrastructure (AMI)

for the residential consumers connected to a microgrid. It describes the design and functioning of two

intelligent units called Salve Control Unit (SCU) located in the each homes, and Master Control Unit

(MCU) located in the MG premises. The residential loads are modeled it equivalent electrical circuit

model using Matlab-Simulink. The power transferring between home to home through MG during excess

and shortfall of generation is clearly explained. This work also describes how the recorded data can be

used to plot the graphical representation for further analysis. Further this work deals with the

development of Automated Metering Infrastructure for the laboratory loads connected in the institutional

microgrid system which monitoring of laboratory loads, priority load setting and will be implemented

during the complete installation of the MG system.

This work proposes the development of GUI based Automated Metering Infrastructure (AMI)

in distributed generation environment. The proposed AMI facilitates the consumer to predict their power

generation based on the data base in the meter and can able to connect the loads accordingly. Power

generation and status of the loads are being monitored by a control unit located in each home. This unit

records all the events at regular interval and at the same time it will communicate with the global unit

placed centrally. Two different GUI models were developed in the AMI, one is a group of residential

consumers connected in a microgrid system with different connected load sharing the power to each

other, and the second one is the development of AMI for the laboratory loads. It is essential to provide an

electric utility meter to measure and monitor the electrical variability and homes connected in the MG

being delivered with required power in the network. Many utility companies in the world are now

focusing to replace the legacy electricity metering with smart meters. A meter is said to be smart if it

includes significant data processing and storage. The schematic representation of the DG based MG is

shown in Fig.33.

Fig.33 General block diagram residential DC microgrid structure


The proposed smart meter has been developed for residential distributed generation system in

which all the homes are connected to a common MG. The metering system has to two major components

one will be located in each home called Slave Control Unit (SCU) and another one will be Master Control

Monitoring Unit (MCU).

Irrespective of the number of homes connected in the MG, only one MCU will be located to

monitor and control all the SCUs. A prototype model of four residential units is considered and each

equipped with individual SCU. The connected loads in each homes are powered by their own power

generating units by means of renewable energy sources like small wind energy generators, solar PV, bio

fuels etc.

The SCU monitors the instantaneous power generation and connected load in the respective

home at a regular interval. These informations will be communicated to MCU through data bus. MCU is a

central unit in this smart metering system, which receives the informations given by each SCU.

The MCU only knows the present position of power generation and consumption of all the

homes. Further it estimates which home has surplus/deficit power. This intelligent network enables both

to control the system remotely and manage consumption more efficiently with advanced data analysis.

The SCU is located in each of the individual homes through which all the electrical

equipments are connected. The functions of each SCU is to monitor the instantaneous power generation,

the connected loads, alert the consumer about the existing generated power and the consumption of power

if the connected load is close to the generation, communicates with the MCU for excess energy export

and/or energy import during shortage.

It also maintains a data base for power generation and status of each load. This will be

recorded instantaneously in the SCU. The functional flowchart performed by the SCU is shown in Fig.34.

Every SCU is designed to estimate the power generation for a particular day based on the

previous year geographical informations like (solar irradiance, wind velocity, temperature etc.). It always

gives an alert (message indication) to the consumer whenever the instantaneous power generation PG is

less than the estimated power generation PEST and hence the loading pattern has been changed

accordingly. All the electrical appliances in the home are connected through SCU. If any load is turned

ON will be sensed by SCU and hence increase the connected load PL. SCU also sense the instantaneous

power generation PG and compare with PL.

At any stage if PL exceeds PG, then the consumer will be advised to shut down the

apriority/additional loads. If all the loads are essential, then SCU will send a request to MCU for

additional power. All the functions right from the beginning to end will be updated in the internal

memory of each SCU. Similarly, the excess power available in the home will also be intimated to MCU at

regular interval. The consumer can able to process the stored informations for proper energy conservation.

They have an option to plot various graphs and prepare reports.

MCU is the master control unit located centrally in the MG having two way communications

with each SCU at regular interval. MCU processes the request send by each SCUs and control the energy

flow. Fig.35 shows the functional flowchart of MCU. Comprehensive power management with optimal

energy conservation is attained using MCU. MCU will always read the PG and PL of each home and

estimates the power in the MG PDG. It also monitors the request send by any home in the network, being

it for deficit power requirement or excess power availability.


Fig.34 Functional flowchart representation of SCU

YES

YES

Start

Estimate the power generation (PEST) on that day

If PG< PEST

Prioritize the load

Read the instantaneous power

generation (PG)

Read the weather data on a day (Irradiance, Wind velocity and

Temperature)

Read the instantaneous

connected load (PL)

Send request to MCU

Send request to MCU

Store the present value

Import the deficit power

Export the excess power

Update the data base

If PL = P

If PL> P

Stop

YES

NO

NO NO


Export

No Request

Import

NO

YES

Start

Read PG and PL of each home

Estimate the Power Request (PR)

Check PDG> PR

Estimate thePower in DG (PDG)

Transfer Power from MG

Export PDG to

Utility grid

Update the report

Stop

Check Import/Export

Request

Import Request Granted Import Request Denied

Transfer Power from Grid

Fig.35 Functional Flowchart of MCU

The power request PR will always be compared with PDG and power transfer happens when

PDG is higher than PR. On the other hand, if the PDG is lower that the PR, the request will be denied. In that

case, either the consumer is advised to shut off the loads based on their priority levels or the respective

home is connected to the utility grid for energy import. The request process has been streamlined using

queuing theory if two or more requests are raised simultaneously. The MCU will clear the first request on


the queue similar to First in First out (FIFO) and process the remaining requests. The process of queuing

theory is shown in Fig.36.

Fig.36 Priority request queuing process

In the first request, PR is greater than PDG and hence the request has been denied. The pointer

will move on to the next request immediately. Once the process is accepted, then the pointer will stay on

that position until it is being cleared. As of now, parallel processing is not being considered in the MCU

even if the MG has excess power other than power delivered to the request home.

The front panel of the SCU is modeled as menu based structure since the consumer and utility

companies can access easily. The functionalities of SCU are described in Table XI.

Table XI Menu Descriptions, Features & Functionalities of SCU

Menu Menu Descriptions Features and Functionality

SCU Meter Descriptions

SCU Meter No Set by the utility companies during system installation

SCU Authentication no Cannot be altered by the consumer

Consumer ID

Consumer Details

Name, Status of the service (Alive/Quiet) Set by the utility companies during system installation

Region/Circle/Section Cannot be altered by the consumer Consumer Address

Status of the service may change according to the consumer usage

Date of Installation Status of the service (Alive/Quiet)

Electrical Connection

No of phases (Single/Three Phase) Initially sanctioned by based on the consumer application Sanctioned Maximum Demand (kW)

Tariff Plans (Industry/Domestic/Residential),

Time based tariff rates may be altered by the utility companies at regular interval of time Tariff Rates (Time based/fixed)

Details of the DG

Nature of the source (Solar PV/Wind/Bio-fuels/Others)

Configuration for Solar PV Configured by the consumer during

the system installation Configuration for wind energy system

Inverter Description

Phase, Power rating, Rated voltage and current, THD

Consumer can modify the configuration using modify option

Battery Description Battery voltage, No. of battery in

Each modification should be updated in the SCU and in MCU

Pointer

Request I

PR> PDG

Request II

PR> PDG

Request III

PR> PDG

Pointer

Request I

PR> PDG

Request II

PR> PDG

Request III

PR> PDG


series and parallel Enables with username and password protection

General Display

Voltage, Current, Frequency, Power (Active/reactive/apparent), Energy (Active/reactive/apparent), THD, List of individual harmonics, Form factor, Crest factor

General display parameters on SCU

SCU is designed with five main menus namely i) SCU meter descriptions, ii) Consumer

details, iii) Electrical connection, iv) Details of DG and v) General display. Each menu has been modeled

with unique features, since the consumers have no control to change the descriptions and are set by the

utility companies during the installation of the service connection. The general layout of the SCU meter

descriptions is shown in Fig.37.

Fig.37 GUI Model of SCU meter description

It contains the SCU meter number, SCU authentication number and Consumer identity

number. These informations are set by the utility company during initial installation. Fig.38 shows the

consumer details and is initially configured by the service provider.


Fig.38 Consumer Details menu description

Fig.39 shows the informations about the electrical connection of a consumer. The proposed

meter has two tariff options either fixed rates or time based rates to fix the consumer tariff during energy

import/export from other consumers or from the utility grid.

Fig.39 Details about the electrical connection

Fig.40 shows the details of the distributed generation connected in each residential consumer

and it has variety of options like solar PV, Wind, Bio-fuel based energy generation and others. This menu

has an option namely “Modify and Update” and it will be used if the corresponding consumer adds

additional DG with the existing system or increases the capacity of the present DG.


Fig.40 Details of the DG resource capacity & configuration

All the SCUs connected in each residential homes are monitored by the central unit namely

Master Control Unit which has all the records of the entire DG system. Table XII shows the features and

functionalities of a MCU.

Table XII Menu descriptions, features & functionalities of MCU

Menu Menu Descriptions Features and Functionality

Meter

Descriptions

MCU meter No Set by the utility companies during

system installation

MCU authentication no SCU monitoring facility

Slave documents

(SCU-1/SCU-2/ SCU-3) Recording set of variables in SCU

Recording parameters Memory capacity (used and free)

Memory capacity

History

Details of Import/export Import/export details during specified

period from DD-MM-YY to

DD-MM-YY Total energy transferred

Price credit or debit System/meter fault history and their

details Cumulative energy import/export

Fault and history Details of meter outages

Meter outages

Plots/Tables Period (Day/Month/Year)

Individual/grouped graphs for

generated power, connected load and

all other recorded data

Fig.41 shows the menu structure of the MCU. It has three major menus namely i) MCU meter

descriptions, ii) History and iii) Plots and Graphs. The MCU meter description menu is same as the SCU

menu that contains the MCU meter number and MCU authentication number. The history menu shows


the complete energy transfer informations between consumers over a specified period of time. It also

shows the net energy transfer either in credit or debit for a consumer. The plot/graphs menu presents the

generated power and load duration curve for a specified. Here the + sign indicates the energy export and

– sign indicates the energy import.

Fig.41 GUI model of MCU description – History

The consumers loads are classified as kitchen, lighting and general loads. Inclusion of loads

at any time sums up the total connected load PL. This will immediately be communicated to their MCU

for further process. The load profile front end GUI for home II is shown in Fig.42.

Fig.42 Residential load profile for home II in the MG

Load profile GUI shows all the informations like instantaneous power generation, generation

positions in other homes connected in the MG etc. The assumed installed capacity of home II is 3.5 kW

whereas the instantaneous power generation is 2 kW. This is because of the seasonal availability of wind

and SPV. There are about 560 W loads are connected at present in the entire three category which leads to

1,440 W excess power availability in home II alone. This information will be communicated to MCU also

to other SCUs.

Communication network and selection of the protocol structure plays a major role in AMI.

The smart metering system proposed in this research work employs IEEE 802.15.8 protocol based Smart

Utility Network (SUN) has been adopted which involves majorly Peer Aware Communication (PAC) and

Key Management Protocol (KMP) (LI Bao-shu et al 2012). PAC is mainly used to create device identity

connected in the communication network. The SCU and MCU are assigned with unique identification


information element format like SCU_home number_0x03_0,98. The part in the syntax 0x03 indicates the

ID variability and this syntax belongs to IEEE 802.15.7. The last part of the syntax 0, 98 implies that only

one frame in the communication and 98 for KMP. Similar identities for second frame can also be named

for continuous data communication.

KMP is also a part of the SUN that defines a message exchange framework based on

information elements as a transport method. KMP ID ranges from 98-126 in older framework and in

revised it from 97-127. IEEE 802.15.8 support a fully distributed, decentralized, self organized system

composed of unique ID devices in the network. It supports one-to-one and one-to-many communication

for any topological structure (mostly suitable for mess topology). It also supports simultaneous

communication between devices for same or different applications. This feature is however useful in

handling multiple request sent by many SCU at a time. IEEE 802.15.8 extends high layer support for

device discovery using device ID, device group ID and application ID (TG 4k Coexistence Document on

‘IEEE P 802.15 Working Group for Wireless Personal Area Networks (WPAN)’, 2012). It operates in

synchronous or asynchronous mode, coordinating with devices in the network.

IEEE 802.15.8 is being provided with fully distributed scheduling mechanism, link

establishment between devices (Peering), prioritized service allocation, reduced power consumption (in

hardware model), and security. Scalability is an added advantage in the protocol sine the data rate

typically about 10 Mbps. IEEE 802.15.8 has minimum discovery latency (moment when first transmits or

receives the discovery signal to moment before establishes a communication link) but there is a trade-off

between the latency and power consumption. The Packet Error Rate (PER) without retransmission shall

be about 10% and link success probability of 95%. Higher link success rate will decrease the response

time of every request sent by SCU and least PER helps for accurate data base.

Initially the metering system is designed to implement four individual homes having different

source power generation and also different connected loads. The proposed systems initially consider only

two sources of energy generation namely wind and solar PV. A provision is given to the consumers to add

other kinds of loads in future (Chandira Sekaran et al 2013). The simulated GUI for home – I is shown in

Fig.43.

Fig.43 Simulated GUI model for Home I


The key ID for home I is SCU 1_0x03_0,98. It has a generation capacity of 2.5 kW and

during a particular time the generation is only 2 kW. At that time the connected load is 1, 985 W shown in

Section S1. Section S2 shows the PEXE in other homes. The excess power in home I is only 15 W.

If the consumer wants to connect a personal computer, then the PL is higher that PG. Personal

computer is simulated for 100 W therefore the home I needs an excess power of 85 W shown in section

S3. Once the consumer load PL is greater than PG, then a message appears asking the consumer to

connect in MG. The MCU will process the request send by SCU 1_0x03_0,98 and connect the home I to

MG if the MG has excess power. This is shown in Fig.44.

Fig.44 Simulated GUI model for power request of Home I

Section S4 shows the request status as “Granted”. It also shows that the connected load in the

general category has been increased to 1,735 W from 1,635 W. The total excess power PEXE is also

reduced to 6,485 W shown in section S5.

In this case, the excess power of 85 W required for the home I is delivered from home II and

hence the PEXE of home II is reduced from 2940 W to 2855 W and displayed in the respective column.

The key ID for the MCU is MCU 0x03_0, 98, 2. Fig.45 shows the simulated GUI model for MCU.

Section S5 indicates the power availability in all the homes and Home I takes additional power of 85 W

from home II.


Fig.45 Simulated GUI model of MCU with power status

The power export/import details are also updated in the corresponding SCU and remaining

homes are unaltered. The MCU has all the records in terms of power availability in each home, power

delivery status, how long the power delivered to a particular home, which home delivers the power,

current status of power generation and connected loads in all the homes.

Consumer loads are simulated by MATLAB /Simulink as a backend model. All the loads are

designed to simulate as per the standard power ratings. The power generation capacity of any home has

been finalized based on their connected load in that home. A provision is given to the consumer to add the

loads in future, but it should be within the limit of installed power capacity. Similarly the load may also

been withdrawn if it is not in regular usage. A simulated model of a load is shown in Fig.46. Each load

has a simulated time delay to avoid when two loads are acting at a time.

Fig.46 Simulink model of consumer loads


The developed AMI model is used to monitor and control the power generation/transfer

through SCU and MCU. This method has an important facility of storing all the events in the database so

that the consumer can revert back either in the form of graphs or MS Excel working table. Fig.47 shows a

graphical representation of generated power PG verses connected load PL on a specified date and period.

Similar types of graphs are useful for the MG operator, so that the system operation can effectively be

managed during power sharing between the homes. Further each consumer can able to know their

loading pattern of the electrical equipments and hence they can regulate the operation schedule of the

loads.

Fig.47 Graphs for PG verses PL

Similarly Fig.48 shows the PG and PL for weekly, fortnight and monthly graphs.

Fig.48 Graphs for weekly/fortnight/monthly

The prototype model of the MG system was installed to power the electrical laboratory loads.

For this system, the developed AMI model for residential MG system has modified to suit the

requirements of the laboratory loads. Here also the two same monitoring units namely Slave Control


Units (SCU) and Master Control Unit (MCU) are employed in which the SCU is located in the each

laboratory and the MCU is located centrally in the MG system. All the selected loads (Lightings and

Fans only) of each laboratory are connected through the SCU and it monitors the entire events taken place

in that laboratory. Table XIII shows the menus and their functions in SCU module.

Table XIII Menus and functions of SCU & MCU

Unit Menu

Slave Control Unit (SCU) SCU Description, Laboratory Details, Connected Electrical Load and Present Status

Master Control Unit (SCU) MCU Description, Details of Sources (Wind & Solar PV), Status of SCU and Priority Settings

The working schedules of each laboratory are different for both the eastern and western side

of the electrical block (Proposed site) and the connected loads are also not unique. Similar to the

residential SCU, the SCU in the laboratory application also record all the electrical parameters. Each

laboratory loads are connected through individual SCU and one set of SCUs (ie 5 SCUs) will be

connected to one MCU, controlling the eastern side parameters. Similarly, another set of 5 SCUs are

located in the western side loads and are connected to the second MCU which controls the western side

electrical parameters. Fig.49 shows the Laboratory details menu available in the main menu of the SCU.

Fig.49 Laboratory details of the main menu in SCU

In Fig.49, the name of the lab and location is initially filled. The working schedule of the

mentioned lab will be properly selected in the weekly schedule portion (either in Forenoon FN or

Afternoon AN or both). This information is very much essential for the MCU to set the load priority

during energy transfer. Since the working schedule of each labs may vary for each semester in an year,

the consumers is given with an option of “Modify”. After making the necessary correction in the

working schedule of the particular lab, one can finalize the all parameters using “Initialize”. These two

options are available only in Laboratory Details and Connected Load menus. Fig.50 shows the

connected electrical load in the mentioned laboratory.


Fig.50 Electrical load menu in SCU

Using the Modify option, the consumer may alter the number of electrical equipments, if it is

appended / withdrawn in future. Fig.51 shows the present status of the connected load for the specified

laboratory.

Fig.51 Instantaneous connected load menu in the SCU

Since the priority of the selected lab is set as 1, it must be supplied by the source at all the

time irrespective of other laboratory loads.

MCU unit is located centrally in the MG system to monitor and record all the events of SCU

and power generation from the source. Fig.52 shows the main menu GUI model of the MCU.


Fig.52 Main menu GUI model of the MCU

The MCU has four main menus in which the first menu contains the MCU description and

the second menu designates the sources employed in the MG. It is modeled with two sources namely

wind and solar PV. The specification of the resource system must be entered in the respective menu

during the installation of the MCU which put in to service in the MG system. Fig.53 shows configuration

menu for the input source.

Fig.53 GUI model of the input sources

The first two menus (namely MCU description and details of sources) have “Initialize” and

“modify” option and their applications are same as in the SCU. Figure 7.54 shows the status of SCU menu

which indicates the instantaneous running status of each SCU connected to that MCU.


Fig.54 GUI model of the monitoring status of the SCU

In this research work, the generated power from the solar PV and wind hybrid combination is

used efficiently to power the required laboratory loads by employing optimal load management technique.

This is enabled by properly selecting the loads as per the working schedule and set the load priority.

Fig.55 shows the priority setting menu which has tabled the entire laboratory loads.

Fig.55 GUI model of the priority setting

The consumer can select the loads and assign the priority for each day in a week as per the

working schedule of the laboratory. For example, lab 7 is selected as first priority on a specified date.

This indicates that the lab 7 (ie Power Electronics laboratory) is always connected imports from the MG

system if the sufficient power is available. Similarly if lab 3 is selected on the same date, then it is

assigned with second priority. If the power generation is sufficient to power these two loads at the

specified working time duration, then both the loads are connected to the MG. However, if the power is

insufficient (due to low wind velocity/low irradiance), then the low priority loads are disconnected from

the MG system. This menu has “Deselect” option using which the selected priority loads could be

withdrawn for the next date and new priority loads can be selected as per the working schedule.


This work provides the development of Automated Metering Infrastructure (AMI) model for

the residential and laboratory microgrid system. The objective of the AMI system is to control and

monitor the loads connected through the SCU and MCU located in the microgrids. The residential AMI

unit is developed to import and export the energy between the houses and MG system and all the

electrical parameters are recorded in the data base at regular interval. From the stored data, it is possible

to optimize the energy usage of any consumer which operates the proposed MG system in an effective

manner. In laboratory AMI system, all the laboratory loads are connected through the SCU and

communicates the recorded parameters to the MCU. This system has a unique function of setting the load

priority based on the working schedule. In future, this system can modified to set the load priority for the

entire semester of the academic year based on the time table. The proposed AMI model is initialized and

tested for residential consumers for their connected load and it can be extensively implemented to the

institutional laboratory loads.

3.6 DEVELOPMENT OF SOLAR PV-WIND HYBRID POWER SYSTEM HARDWARE MODEL

AND TESTING

This part of the work provides the development of Solar PV-Wind Hybrid Power System

Hardware Model to show the implementation strategies of DC microgrid system with battery backup.

Also this work emphasis the test setup hardware and their result analysis under various operating

condition. Finally this work ends with the testing of priority based energy management technique.

Based on the above power study, Fig.56 represents the proposed architecture of a microgrid

system with common DC bus, powered by group of solar Photovoltaics (SPV) array and wind energy

systems. Here the unregulated power inputs from both the sources are regulated using the power

electronic interfaces placed between sources to the common DC bus to maintain the level of voltage and

current.

Fig.56 Block diagram of the proposed MG system


The block diagram of the proposed system is integrated with small wind electric generator

along with solar photovoltaic energy source. The peak power capacity of each sources are, 300 Wp solar

PV modules, 500 Wp solar PV modules and 600 Wp horizontal axis small wind generator respectively.

The operation of the hybrid systems at any time is the combination of a) Solar PV and wind (when the

wind is available) or b) two solar PV (when the wind velocity is not available). This enables the consumer

to complementarily connect the sources based on resource availability i.e. the 500 Wp solar PV and 600

Wp Wind acts as primary sources and the 300 Wp solar serves compliment source. If the wind velocity is

inadequate, then the system will manually be connected to both the solar PV panels and hence the power

transferring will takes place. If the wind velocity is sufficient to generate the power, then the 300 Wp

solar PV module manually disconnected from the system and 600 Wp wind generator will be added along

with 500 Wp solar PV system. Table XIV shows different modes of operation based on the resource

availability. When the battery is charging from the sources, then the battery current IB is assumed as

negative, since the battery current is leaving from the DC bus.

Table XIV Possible modes of operation

Source I Source II Load Battery Strength

Description

Available Available Full Load Full Is1 + Is2 = IDC

Load is powered by Source

Available Available Full Load Partial Is1 + Is2 - IB = IDC

Load is powered by source & Batter is charging

Available Not

Available Full Load Full/Partial

Is1 + Is2 + IB = IDC Load is powered by source & Batter is

discharging

Available Available No Load Partial Is1 + Is2 = IB

Batter is charging

Available Available No Load Full Grid export

(Future expansion)

The selected site is blessed with wind resources to energize the consumer loads during the

windy session. Two input sources will complement each other in addition to the battery backup provided

as storage option for the proposed model. Table XV shows the specification of the wind turbine model

used in the system.

Table XV Specifications of the wind turbine model

Sl. No Description Specification

1. Rated Power Output 600 W

2. Peak Power Output 750 W

3. Rated Voltage 36 V AC

4. Cut-in-Velocity 2.5 m/s

5. Cut-out-Velocity 25 m/s

6. Rated Wind Velocity 12 m/s

7. Rated Rotor Speed 750 rpm

8. Number of Blades 3

9. Rotor Diameter 1.75 m

10. Rotor Swept Area 2.4 Sq.m

11. Blade Material Reinforced Nylon Glass-Fiber


12. Generator Type Brushless 3-phase PMSG with Neodymium Magnet

13. Generator Body Aluminum alloy body

14. Rotor Configuration Precision Stainless Steel Rotor

15. Noise Level < 20 dB

The selected site is also blessed with almost 270-300 clear sunny days to extract the

maximum possible power output from the solar PV panels. Here two set of solar PV panels of different

type and power rating is used to compliment the source with wind turbine system. Table XVI shows the

specifications of the single solar PV panel (used to 500 Wp capacity) used in the proposed system.

Table XVI Specifications of the 250 Wp single solar PV panel

Sl. No Description Specification 1. Maximum Power (Pmax) 250 W 2. Rated Voltage (Vmax) 31.56 V 3. Rated Current (Imax) 7.92 A 4. Open Circuit Voltage (Voc) 37.68 V 5. Short Circuit Current (Isc) 8.63 A 6. Cell Type Poly Crystalline 7. Cell Efficiency 14.71 % 8. Tolerance on Power ± 3 % 9. Weight 19.5 kg/Panel

Similarly, Table XVII shows the specifications of the single solar PV panel (used to 300 Wp

capacity) used in the proposed system.

Table XVII Specifications of the 75 Wp single solar PV panel


1. Maximum Power (Pmax) 75 W

2. Rated Voltage (Vmax) 17.38 V

3. Rated Current (Imax) 4.31 A

4. Open Circuit Voltage (Voc) 21.26 V

5. Short Circuit Current (Isc) 4.89 A

6. Cell Type Mono Crystalline

7. Cell Efficiency 14.02 %

8. Tolerance on Power ± 6.65 %

9. Weight 7.36 kg/Panel

The specification of the solar PV panel indicates that the proposed model is tested with both

mono and poly crystalline PV cells.

Table XVIII represents the complete hardware details of the common DC MG along with

battery system of the proposed model.

Table XVIII Specifications of DC microgrid and battery


1. DC Bus Voltage 24 V – 30 V

2. Maximum Current Limitations 20 A (Designed for Prototype Model)

3. Peak Power Capacity of the Wind

Turbine 600 W


4. Peak Power Capacity of Solar PV Panels

– I

300 W

(75 Wp of 4 panel with 2 in series and

2 in Parallel)

5. Peak Power Capacity of Solar PV Panels

– II

500 W

(250 Wp of 4 Modules with 2 in Parallel)

6. Battery Type Tall Tubular C10

Low Maintenance Battery

7. Battery Capacity/Voltage 75 Ah/12 V

8. Battery Configuration 2 number of 75 Ah connected in Series

9. Inverter Type Single Phase

10. Inverter Power Capacity 1 kW

11. Inverter Configuration 230 V/ 50 Hz

The outputs of the wind and solar PV sources are integrated into a common DC microgrid

system at an operating voltage of 24 – 30 V DC. A hybrid charge controller placed in between the source

to the microgrid regulates the DC voltage at a specified level. In addition to the existing sources, an

energy storage battery backup of 75 Ah, 12 V of two batteries are connected in parallel to form a 24 V

DC suitable to connect in the microgrid system. The different modes of charging and discharging of the

battery bank is designed based on the instantaneous availability of the source and load.

In the proposed hardware model two DC-DC converters are employed in the solar PV side,

One AC-DC and DC-DC converter is employed in the wind generator side. In addition to this a 1 kVA

inverter is connected in to the DC microgrid system converters the DC power to AC for AC loads. Table

XIX shows the hardware specifications of the power converters employed in the proposed system.

Table XIX Hardware specifications of the power converters

Sl.No Description Specification

DC-DC Converter on SPV I

1. Type Buck Converter

2. Input Voltage 28-36 V DC

3. Output Voltage 24 V DC

4. Duty Cycle 0.67- 0.85

5. Control Operation Closed loop PI Control

6. Controller dsPIC 30F2011 (Common Controller)

DC-DC Converter on SPV II


8. Input Voltage 26-32 V DC


10. Duty Cycle 0.75- 0.92


AC-DC Converter on Wind Side

12. Type Uncontrolled

13. Input Voltage 30-38 V AC

14. Output Voltage 27 – 34 V DC

DC-DC Converter on Wind Side



16. Input Voltage 27 – 34 V DC


18. Duty Cycle 0.7- 0.9


Inverter Specifications

20. Inverter Type Single Phase

21. Inverter Power Capacity 1 Kw

22. Inverter Configuration 230 V/ 50 Hz

Fig.57 shows the hardware installation of the solar PV and wind turbine sources installed in

the top floor of the library buildings, Coimbatore Institute of Technology, Coimbatore. It contains 600

Wp of wind turbine, 500 Wp solar PV panels and 300 Wp solar PV panels.

Fig.57 Test setup of input sources (Wind and Solar PV)

The DC MG system integrating solar PV and wind sources, power converter unit along with

the CFL (acts as a loading unit) is placed underneath the roof. Fig.58 shows the hardware setup of the

battery bank, DC microgrid and inverter employing with necessary measuring instruments.

600 Wp Wind Turbine

300 Wp Solar PV 500 Wp Solar PV


Fig.58 Test setup of the hardware model

Fig.59 shows the hardware model of the common DC MG system integrating solar PV, wind

system and battery units. Then this DC MG line is connected to the inverter in which the AC loads are

connected.

Fig.59 Hardware model of the DC MG system

The developed hardware model was tested under i) load is powered by source, ii) load is

powered by source and battery, iii) load is powered by the sources and the battery is charging, iv) load is

powered by battery only and v) only the battery charging.

3.6.1 During Charging Mode Only

In this mode, the load is not connected in the inverter and the energy from the sources are

supplied to charge the battery. Fig.60 shows the voltage variation of charging characteristics of 500 Wp

Wind Input

Solar Input Battery Input

DC MG Bus

Battery Bank

Inverter

Hybrid MG Bus

Loading Unit


solar PV module which is varying between 32.2 V to 34.0 V (Measuring Instrument – Krykard make

ALM 36 Power Quality Analyzer).

Fig.60 Voltage variation of Panel 500 Wp PV panel during charging

Fig.61 shows the current variation of the same 500 W PV panel during charging mode of

operation showing the variation between 0.1 A to 1.7 A. From this Figure 8.6, even though the panel

voltage reaches the maximum rated voltage, the current supplied by the panel is less due to the low

irradiance condition on the testing day.


Fig.61 Current variation of 500 Wp PV panel during charging

At the same time the voltage and current output from the 300 W panels is shown in Fig.62

which also indicates that the PV panel develops the rated voltage but delivers the minimum current due to

the insufficient solar irradiance. (Measuring Instrument – Hioki make 3196 - Power Quality Analyzer)

Fig.62 Voltage and current screen of 300 Wp solar PV panel

The two PV sources of 500 W and 300 W delivers the energy to the battery bank and the

battery voltage and current during this process is shown in Fig.63 (a)&(b). (Measuring Instrument –

Fluke make 345 - Power Quality Analyzer).


Fig.63 (a)&(b) Voltage and current values of battery during charging mode

From fig.63(b), it is noted that the DC current 2.09 is negative since it means that the battery

is charging through sources. During discharging mode, it will become positive. The voltage/current

waveforms and power window for this process is shown in Fig.64 (a)&(b).

Fig.64(a)&(b) Voltage/current waveforms and power during charging process

3.6.2 During Charging/Discharging with Connected Load

The developed prototype model is tested with two solar PV systems are connected in the MG

grid and the sources feeding power to the battery and also to the load is examined. Fig.65 shows the voltage

and current trend of the 500 Wp solar PV panel and current delivered by the system varies between 4.7 A

to 7.7 A.


Fig. 65 Voltage and current Trend of 500 Wp solar PV panel

During the charging and discharging mode of operation, the load is supplied from both the

sources and battery. The contribution of each system depends on the capacity of the connected load.

During the charging and discharging mode of operation the battery voltage and current values are shown

in Fig.66(a) & (b).

Fig.66 (a) &(b)Voltage and current values of battery during discharging

The voltage and current waveform trend and the power during this process is shown in

Fig.67(a)&(b).

Fig.67(a) & (b) Voltage/current waveforms and power during discharging process

From fig.67, the batter discharging current is 6.39 A along with the source current. At the

same time the inverter is loaded to utilize the power generated from the sources and battery. Fig.68

shows the voltage waveform and Fig.69 shows the current waveform of the inverter.


`

Fig.68 Voltage waveform of the inverter

Fig.69 Current waveform of the inverter

The inverter is loaded up approximately 2.3 A based on the power generation from the solar

PV panels and the storage strength of the battery. (Measuring Instrument – Fluke make 435 –Series II -

Power Quality Analyzer). Fig.70 shows the measured power window during inverter loaded condition.

The active power consumed by the load at the time of measurement is about 431 W, the apparent power is

about 523 VA maintains a power factor of 0.82.


Fig.70 Power and Energy Screen of the inverter under loaded condition

Table XX represents the results of load test conducted on the above system feeding power to

the load from 300 Wp and 500 Wp solar PV respectively along with the battery charging. The battery

current in the first three readings indicate the negative value for both charging and discharging modes.

The remaining readings represent that the battery is only discharging and delivers power to the load.

Table XX Test results when two solar PV systems connected with DC MG

Sl.

No

Source-1

(500 Wp)

Source-2

(300 Wp) Battery

Inverter

Input Inverter Output Irradiance

G

(W/m2) VPV1

(V)

IPV2

(A)

PPV1

(W)

VPV2

(V)

IPV2

(A)

PPV2

(W)

VB

(V)

IB

(A)

PB

(W)

IDCMEAS

(A)

ICAL

(A)

VIN

(V)

IIN

(A)

PIN

(W)

1. 25.7 5.45 140.07 27.67 2.67 73.08 24.63 -6.55 -

161.33 1.53 1.57 0 0 0 427

2. 25.6 5.40 138.24 27.13 2.69 72.98 24.39 -3.17 -77.32 4.89 4.92 225.75 0.5 73.11 423

3. 25.1 5.35 134.29 26.65 2.57 68.49 24.06 -0.01 -0.24 8.08 7.91 225.46 0.8 136.3 419

4. 24.7 5.34 131.9 26.35 2.65 69.83 23.62 4.63 109.36 11.89 12.62 225.06 1.2 204.7 418

5. 24.2 5.27 127.53 25.83 2.55 65.87 23.26 7.73 179.80 15.73 15.55 224.96 1.5 264.3 418

6. 23.0 5.25 125.38 25.56 2.59 66.20 22.84 11.9 271.80 19.53 19.74 224.25 1.8 328.7 426

7. 23.3 5.34 124.42 24.96 2.66 66.39 22.38 15.77 352.93 23.74 23.77 223.73 2.1 377.5 413

8. 23.2 6.04 140.73 24.87 3.0 74.61 22.12 20.42 451.69 29.46 29.46 214.22 2.5 460.2 431

3.6.3 OPTIMAL ENERGY MANAGEMENT

This research work has a novelty to design an optimal hybrid MG system in which the power

sources are optimally selected using newly developed real time GDO method based on that only the best

combination was selected. Similarly the energy generated from the prototype model should be utilized

optimally by introduction the load priority in the control algorithm. The method proposed here is based

on sensing the current and voltage profile the load priority has been done. The GDO control algorithm


proposed in section 3.4 has different control strategy and all are basically relies on the battery voltage

(i.e. more specifically the excess power generation ΔP). Hence this research work proposes a cost

effective, simple load priority algorithm based on the battery voltage status. Fig.71 shows the flowchart

representation of the optimal energy management algorithm.

Fig.71Flowchart representation for the optimal energy management

Start

Initialize ADC value & Make all Relay OFF

Collect 10 samples of VB and Average

If VB > 21 V

If VB > 23.6 V

If VB > 25.6 V

If VB < 24 V

If VB < 22 V

L1 ON

L2 ON

L3 ON

L3 OFF

L2 OFF


3.6.4 HARDWARE TEST SETUP FOR OPTIMAL ENERGY MANAGEMENT

In order to test the concept of the optimal energy management, three electrical loads

initialized as priority load I, II and III. These loads are connected to the output of the inverter through

separate relay control circuit which is controlled by separate control algorithm mentioned in the above

flow chart operation. The phase of all the priority loads are connected in parallel to the phase of the

inverter output whereas the neutral is commonly connected. A dedicated control circuit is employed with

dsPCI30F 2010 (28 pin) in which the Analog to Digital Converter (ADC) will read the battery voltage at

regular interval. Based on the instantaneous battery voltage, the control loop enables to turn on/off the

specified load. Fig.72 shows the control logic circuit having 230 V/5 A AC (5 pin) relay to control the

loads.

Fig.72 Relay logic based optimal energy management

3.6.5 TEST RESULTS FOR OPTIMAL ENERGY MANAGEMENT

A. During Battery Discharging Mode

The ADC of the controller will sense the battery voltage and based on the algorithm

represented in the flowchart, the loads must be operated. Fig.73 shows the voltage/current trend recorded

during the battery discharging mode.

AC Relay Units

Controller Units


Fig.73 Voltage/current trend during the battery discharging mode

From analyzing Fig.73, it is concluded that the three loads are ON initially. During the

discharging process, because of the reduction in the battery voltage, at the time of 05:13:37 PM on

24.06.2015, the L3 turns OFF. From that time onwards, L1 and L2 still continuing its operation. Fig.74

shows the hardware testing model during L3 OFF and L1 & L2 is ON.

Fig.74 L1 and L2 ON and L3 OFF

The current trend of the L3 is measured using a power quality analyzer and the result of L3

ON and consuming a current of 0.7 A at the time 05:13:18 PM shown in Fig.75, where it matches with

the time stamp of Fig.73.

L1 ON

L2 ON L3 OFF

L1, L2 & L3 ON

L3 OFF

L2 OFF

L1 & L2 ON


Fig.75 L3 is ON condition

Fig.76 shows the current trend during L3 is off at 05:13:22 PM (at the time the load current

comes to almost zero) which is almost matched with the time stamping in Fig.73.

Fig.76 L3 is OFF condition

Similarly, at the time of 08:04:27 PM, the L2 turns OFF because of reduction of battery

voltage. For this condition also the current trend is recorded when L2 is ON and consuming a current of

0.7 A represented in Fig.77.

Time Indication

Time Indication


Fig.77 L2 is ON condition

Fig.78 shows the current trend during L2 is OFF at 08:04:27 PM (at the time the load current

comes to almost zero) which is almost matched with the time stamping in Fig.73.

Fig.78 L2 is OFF condition

The recording process of the optimal energy management techniques was done when the

battery is in discharging mode and none of the sources are connected in the system. Hence during load

withdrawal from the inverter makes significant changes of electrical parameters in the battery. Fig.79

shows the battery voltage and current trend when the L2 is in ON.

Time Indication

Time Indication


Fig.79 Voltage and current trend when L2 is ON

Fig.80 shows the battery voltage and current trend when the L2 is in OFF condition.

Fig.80 Voltage and current trend when L2 is OFF

From Fig.79 and 80, one can note that the voltage and current during the L2 ON period is

22.7 V & 23.3 A respectively. However when the L2 turned OFF, the current drawn from the battery is

reduced to 12.7 A whereas the battery voltage is slightly shoot up to 23.1 V. In the voltage and current

trend recording of the battery parameters, there exists a small time variation persist due to the difference

in time setting mode from one meter to another meter.

B. During Battery Charging & Discharging Mode

When the sources of solar PV and wind energy is used to feed the power along with the

battery bank, then the voltage and current trends are recorded at the main output of the inverter and is

shown in Fig.81.

Time

V & I

Time

V & I L2 OFF


Fig.81 Voltage and current trend of inverter output

From Fig.81, the load L2 is on at 11:06:50 AM due to the rise in battery voltage. The battery

is in charging mode from 300 Wp solar PV panel and wind turbine. Then at time 11:27:34 AM, the L2

shut down due to insufficient battery voltage. The recording trends goes for entire day in which L2

experiences multiple turn ON and OFFs.

This work starts with the development of prototype hardware model of proposed DC bus

microgrid system integrating two solar PV systems with different power capacity and one wind energy

system. The system is designed to operate with two sources at a time based on their input availability.

Then the system is tested for feeding the power to charge the battery using two PV systems. Also the

system is tested with two solar PV system feeding power to the battery (charging & discharging modes)

and load. In all the cases, the electrical parameters like voltage, current, power and energy are recorded

using power quality analyzers and the test results are presented. Finally to operate the loads optimally, a

relay control optimal energy management system was developed. This system enables the consumer to

operate their loads by setting the load priority and hence the optimal energy usage was realized.

4 CONCLUSIONS

In this research project, the design, develop and implementation of residential and

institution microgrid system by integrating solar PV and wind energy connected to common DC bus is

completed. Initial investigations are made through the available literature survey and studied for the

problem description of this research work. The objectives of this research have been fulfilled by

developing a hardware model of micro grid comprising Solar PV-Wind Energy based Hybrid Power

system with optimized energy utilization through embedded controller. All the simulated and real time

test results are found to be very good for various conditions.

*****0*****

L1 ON

L2 ON

L2 OFF

L2 ON

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