978–1–4673–2240–9/12/$31.00 ©2012 IEEE 297 35th Int. Spring Seminar on Electronics Technology

Microgrid Model for Fast Development of Energy Management Algorithms

Radu Etz, Toma Patarau, Stefan Daraban, Dorin Petreus

Applied Electronics Department, Technical University of Cluj–Napoca, Romania radu.etz@ael.utcluj.ro

Abstract: The demand for electricity production increased in the last ten years leading to large investments and losses in the electricity grids. This forms the basis of decentralized hybrid energy system where the energy is produced in close proximity to the end user. This paper presents a simulation model for intelligent microgrids with distributed generation, also known as Smart Microgrids, focusing on the reduction of the simulation time in order to allow fast development of energy management algorithms. Photovoltaic panels and wind generators will be used as renewable energy primary sources and batteries as storage.

1. INTRODUCTION

In our days, the contamination of air is progressing with the increase of energy consumption. Presently the most used sources of electrical energy are the conventional sources that use pollutant fuels like coal and natural gases. A lot of research has been carried out to promote renewable energies and make them accessible in large quantities and low prices with good efficiency [1]. Wind energy, solar energy and fuel cells hybrid systems have experienced rapid growth in the past 10 years because they are pollution-free resources of power.

In [3-5] different hybrid generation plants have been presented. Electrical power generators should ensure continuous power production to meet the energy demand of the end users. Wind and sun energy cannot fulfill this requirement. A good practice is to use hybrid energy systems combining photovoltaic panels with wind generators and storage to overcome this requirement. Different simulation models where developed in order to optimize the design of such microgrids and to improve the price/kWh produced in certain areas of the world, with different weather conditions [6].

In [2] several types of hybrid systems have been compared from the economical point of view: a Type A where all the energy produced is sold to the grid; a Type B where a part of the energy is sold to the grid and the other part converted to hydrogen; and a Type

C where a part of energy is sold to the grid, the other part is converted to hydrogen which later is converted to electricity and then sold to the grid. It was proved that each of these systems suit best for different weather scenarios.

In [7, 8] the studies are focused on the development of mathematical and simulation models for hybrid power systems which allow optimal sizing of the microgrid components. Negotiating the power flow inside a microgrid is also of great importance to reduce the price of the power produced and to fulfill the load demand. Energy management algorithms should be developed to ensure power flow equilibrium inside the microgrid.

This paper presents an improved simulation model for intelligent microgrids also known as Smart Microgrids which will enable fast development of new energy management algorithms. Each simulation model of the Smart Grid’s components will be presented in order to explain its functionality and point out methods of controlling it. The power flow in the system will be administered by the power converters together with the energy management system. Photovoltaic panels and wind generators will be used as renewable energy primary sources. The focus of the developed simulation models is on reducing the simulation time in order to allow fast development of energy management algorithms.

Section II describes each model proposed for the converters used. Section III discuses the entire

978–1–4673–2240–9/12/$31.00 ©2012 IEEE 298 35th Int. Spring Seminar on Electronics Technology

simulation model of the intelligent microgrid and the simulation results, and section IV draws the conclusions.

2. PROPOSED MODEL

The microgrid incorporates renewable energy as primary sources, energy storage and intelligent loads. The proposed microgrid simulation schematic is presented in Fig. 1. The microgrid has a DC bus of 400V which is built by stacking photovoltaic panels and by connecting a wind generator through a converter. This DC bus is maintained to a fixed voltage by controlling each block of the simulation by an energy management system. The input sources have to produce maximum energy, thus the solar panels and the wind generators have to work at their maximum power point (MPP).

Fig. 1. Proposed microgrid simulation schematic.

The excess energy produced can be delivered to the national grid. An inverter model is developed that allows active power injection into the grid. The inverter should be able to also correct the power factor of the grid in the common connection point area.

2.1. Solar Panel Model

This section defines a circuit model for the PV cell in order to estimate the electrical behavior of the panel with respect to environmental parameters like irradiance and temperature. Modeling the solar panel is very important in simulations but also in the laboratory where the model can help to reproduce the irradiance and temperature conditions all over again. The model of one cell is represented in Fig.2.

Fig. 2. Solar cell model.

The model presented above is derived from equation (1) which describes the behavior of the panel under different irradiation conditions.

sh

SssSssophshDphs R

RIV

a

RIVIIIIII

1exp (1)

were ID is the current through the diode, IPH is the short circuit current of the cell, RSH and RPH are the parasitic resistances of the cell, and a is a parameter used for curve fitting, Vs is the output voltage, Is is the load current and Rload is the load resistance.

Fig. 3 presents the current-voltage and power-voltage characteristics of the panel.

Fig. 3. Panel characteristics.

The model is designed to simulate a 45W, 24V solar panel.

2.2. Maximum Power Point Tracker (MPPT)

Usually the DC bus in a microgrid is 400V [10]. To keep the voltage up to this level, a bank of series connected capacitors are charged from the solar panels using MPPT circuits. The MPPT circuits are DC-DC converters which can modify their input impedance to match the panel output impedance for maximum power transfer [9]. A list of different power topologies used in MPPT circuits is presented in Fig. 4, [9].

978–1–4673–2240–9/12/$31.00 ©2012 IEEE 299 35th Int. Spring Seminar on Electronics Technology

Fig. 4. MPPT topologies.

It was demonstrated in [9] that the last topology presented in the figure above has the fastest luminosity step response and the best immunity to load changes and noise compared to the others. The circuit is formed of three loops: one internal current loop, one external voltage loop and a power loop to compute the maximum power point. A model for the MPPT was developed and is presented in Fig. 5.

Fig. 5. MPPT model.

If the real converter would be used to model the MPPT, the simulation time would increase considerably, thus the DC-DC converter together with the current loop have been replaced with a voltage controlled current source, Iconv. The output voltage loop is a PI controller composed of an operational amplifier, Rpi and Cpi. The output voltage controlled

current source Iconvout was introduced based on equation 2.

out

ininconvout V

IVI

(2)

In this way, a large signal model for the MPPT system was developed that will ensure fast simulation times compared to the use of the entire circuit for simulation.

Fig. 6 represents the panel voltage, panel current and the output voltage of the MPPT. To reduce the simulation time of the entire model even more, the steady state operating point of the MPPT is set to the maximum power point of the panel.

Fig. 6. Characteristic waveforms.

2.3. Bidirectional Converter

The model of the bidirectional converter is represented in Fig.7.

Fig. 7. Bidirectional converter model.

The role of the bidirectional converter in a microgrid is to charge and discharge the battery pack. In the direct transfer mode (charging state) the bidirectional converter is designed to implement a constant current– constant voltage charging algorithm. In the reverse transfer mode the converter supplies current to DC bus. The amount of current transfered is set by the energy management controller.

The output current, voltage and power are represented in Fig. 8.

978–1–4673–2240–9/12/$31.00 ©2012 IEEE 300 35th Int. Spring Seminar on Electronics Technology

Fig. 8. Bidirectional converter waveforms.

In the direct charging state the converter supplies the battery with constant current until the voltage reaches 48V then, the voltage is maintained constant and the current drops to zero. In the reverse transfer mode the converter supplies current to the DC bus. Because the battery pack is one of the most expansive components of the microgrid, the current is monitored in both charging and discharging modes.

2.4. Inverter

The role of the inverter is to supply the excess of power produced by the renewable energy sources to the grid. The inverter is designed to work as a constant current source (CSI). The current supplied to the grid is established by the power management controller. The inverter used is a single phase full bridge inverter, Fig. 9.

Fig. 9. Inverter power topology.

PWM driven voltage source inverters are widely used in drives applications especially where adjustable speed is necessary [11]. Consequently, their control has been studied for long in the past and three main possibilities were discovered convening to grid connected inverters: i) stationary abc reference frame, ii) stationary αβ reference frame and iii) synchronous rotating dq reference frame.

DQ rotating reference frame is used to control the converter in this case. In case of DQ frame, the control is implemented in a reference frame which rotates with the same frequency as the grid angular

frequency. Synchronization with the grid is necessary for this type of control. The model of the control algorithm is represented in Fig. 10.

Fig. 10. Inverter DQ control.

The control variables, Id, Iq, are obtained using a Clarke transformation [12]:

I

I

I

I

q

d

cossin

sincos (3)

where is the phase of the grid voltage, Iα is the grid current and Iβ is an orthogonal component obtained from the grid current.

The power transmission lines can be considered in most cases inductive. It is well known that, for an inductive transmission line, the frequency of the grid is influenced by the active power consumed and the voltage by the reactive power [13-14].

21VV

XP

; 121 V

XQVV (4)

where X is the grid impedance; V1 is the generator voltage; V2 is the voltage of the point of common connection; P the active power and Q the reactive power. In consequence the inverter should be developed to be able to control both the active and the reactive power injected into the grid. PQ control theory was developed by Akagi in 1983, to address this problem []. The inverter control is developed based on the following relationship detailed by the PQ theory.

22

2qd

qdd

vv

vqvpi

(5)

978–1–4673–2240–9/12/$31.00 ©2012 IEEE 301 35th Int. Spring Seminar on Electronics Technology

22

2qd

dqq

vv

vqvpi

(6)

The model developed based on equation (5) and (6) is presented in Fig. 11.

Fig. 11. Inverter PQ control.

The simulation results are represented in Fig.12. Two random active and reactive power references were chosen to show the proper functionality of the inverter. These levels will be set by the power management system based on local measurements.

Fig. 12. Inverter PQ control simulation results.

2.5. Capacitor Bank

It can be seen in Fig. 13 that when the input power of the inverter, Pin, is higher than the output power produced by the renewable energy sources, Pout, the capacitor bank, Cdc, connected on the DC bus is charged and if is lower, the capacitor bank is discharged. Consequently, it can be noticed on the DC bus a ripple voltage having its frequency two times higher than of the AC grid frequency. This ripple is inverse proportional with the capacitance value connected on the bus. The value of the capacitors that should be connected on the DC bus can be evaluated with:

dcdc

dcloadindc VV

PPC

2

(7)

where dcV is the DC bus ripple voltage and Pdcload, is the power consumed by the DC loads directly connected to the DC bus.

Fig. 13. DC Bus voltage.

The voltage of the DC bus can then be expressed as follows:

tVVtv dcDCDC 2sin (7)

3. RESULTS

The simulation results obtained using the proposed model are represented in Fig.14.

Fig. 14. Simulation results.

978–1–4673–2240–9/12/$31.00 ©2012 IEEE 302 35th Int. Spring Seminar on Electronics Technology

At a certain moment the current supplied to the grid is increased. By keeping constant the current produced by the solar panels and the battery charging current, it can be observed that the DC bus voltage decreases and reaches its new steady state operation point. The simulation time obtained with the converter’s circuits is 5 to 10 times longer than the proposed model.

4. CONCLUSIONS

The aim of this paper was to develop a smart microgrid simulation model for fast development of energy management algorithms. The proposed model can reduce the simulation time of the entire microgrid up to 10 times. Even a preliminary study, such as the one reported here, has highlighted the need for algorithms for power exchange balance between the power converters inside the microgrid. Clearly, further studies must be made to develop models for the converter for wind generator and to develop the energy management algorithms.

ACKNOWLEDGEMENT

This paper was supported by the project "Doctoral studies in engineering sciences for developing the knowledge based society-SIDOC” contract no. POSDRU/88/1.5/S/60078, project co-funded from European Social Fund through Sectorial Operational Program Human Resources 2007-2013.

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