Scholarly article on topic 'A Computer Program Development for Sizing Stand-alone Photovoltaic-Wind Hybrid Systems'

A Computer Program Development for Sizing Stand-alone Photovoltaic-Wind Hybrid Systems Academic research paper on "Electrical engineering, electronic engineering, information engineering"

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Software / Sizing / Photovoltaic / Wind / LPSP Algorithms / Hybrid System ;

Abstract of research paper on Electrical engineering, electronic engineering, information engineering, author of scientific article — H. Belmili, M.F. Almi, B.Bendib, S. Bolouma

Abstract The exhaustion and all the drawbacks of fossil fuels are the main elements that led to the development and use of new alternativesfor power generation based on renewable energy,amongthem: photovoltaic energy systems, windenergy systems and their combination in a hybrid photovoltaic-wind system. In this paper we proposed a sizing approach of stand-alone Photovoltaic-Wind systems which is evaluated by the development of a computer applicationbased essentially on Loss of Power Supply Probability (LPSP) algorithmto provide an optimal technical-economic configuration. An example of a PV-Wind plant sizing is presented and discussed.

Academic research paper on topic "A Computer Program Development for Sizing Stand-alone Photovoltaic-Wind Hybrid Systems"

Available online at www.sciencedirect.com

ScienceDirect

Energy Procedía 36 (2013) 546 - 557

TerraGreen 13 International Conference 2013 - Advancements in Renewable Energy

and Clean Environment

A computer program development for sizing stand-alone Photovoltaic-Wind hybrid systems

H. Belmilia, M. F. Almia, B.Bendiba, S. Boloumaa,*

aGroup of research: Photovoltaic system, Unit of Development of Solar Equipments (UDES)/EPST-CDER Route Nationale N°: 11 Bou-Ismail LP 365, Tipaza 42415, Algeria,

Abstract

The exhaustion and all the drawbacks of fossil fuels are the main elements that led to the development and use of new alternativesfor power generation based on renewable energy,amongthem: photovoltaic energy systems, windenergy systems and their combination in a hybrid photovoltaic-wind system.

In this paper we proposed a sizing approach of stand-alone Photovoltaic-Wind systems which is evaluated by the development of a computer applicationbased essentially on Loss of Power Supply Probability (LPSP) algorithmto provide an optimal technical-economic configuration. An example of a PV-Wind plant sizing is presented and discussed.

© 2013 The Authors. Published by Elsevier Ltd.

Selection and/or peer-review under responsibility of the TerraGreen Academy Keywords: Software, Sizing, Photovoltaic, Wind, LPSP Algorithms, Hybrid System;

1. Introduction

In a general context, Hybrid Energy Systems (HES) combine two or more complementary renewable sources like wind turbines and photovoltaic generators and/or one or more conventional sources like diesel generators [1]. Naturally, renewable sources are not constant, so their combination with conventional ones allowsan uninterrupted power generation. Most hybrid systems have an energy storage system [2].There are many systems for storage; electrochemical batteries, inertial storage and hydrogen.

* Corresponding author. Tel.: +213-24-41-02-00; fax: +213-24-41-01-33. E-mail address: belmilih@yah oo.fr.

1876-6102 © 2013 The Authors. Published by Elsevier Ltd.

Selection and/or peer-review under responsibility of the TerraGreen Academy

doi: 10. 1016/j .egypro .2013.07.063

The latter is limited in storage capacity and has a high cost. In general there are three main aspects to consider for a hybrid system [3]:

• The hybrid system configuration with respect to the available resources and constraints utilization.

• The optimization of the available renewable resources exploitation.

• The optimization of the output power quality.

There are many configurations of hybrid systems. The most popular are: DC-bus configuration and DC/AC mixed bus configuration. In what follows we present a brief description of these architectures.

1.1 DC bus architecture

In this case the power provided by each source is centralized on a DC-bus, figure. 1. The matching between the DC bus and the AC loads is possible by using DC-AC inverter, the advantage of this architecture is that the control system is relatively simple [4].

Fig.1. DC Bus architecture

1.2 Mixed-bus AC / DC Architecture

This architecture is more efficient compared to the DC bus architecture. Indeed in this case the wind generator output power can be directly feed the AC load which increases the system performance. Where there is a surplus of energy, the batteries will start charging, figure.2. For the converters, it can be a single bi-directional between the two buses DC and AC replaces the other two converters unidirectional [2, 4].

liUS J

Fig.2. DC - AC Bus Architecture

1.3 operating strategy

It is an algorithm that manages the flow of energy in the different system components. Depending on the load profile and the characteristics of system, as well as the requirements on power quality [2, 3]. The operation of a hybrid system depends on the following parameters:

• The load profile, diurnal variations, seasonal variations, peak dips.. .etc.

• Renewable resources: the mean, standard deviation, frequencies of events, extreme values, diurnal variations, seasonal variations

• The system configuration: number and types of components

• Standards of power quality.

1.4 Storage Management

• The storage strategy in the short-term "Shaving pecker Strategy", allows you to filter out fluctuations in renewable energy and/or load.

• The long-term strategy "Cycle Charge Strategy" is used to supply the load for a long period of time; it also improves energy balance [4].

1.5 Load management

It can be seen as short-term or long-term expenses that are connected or disconnected according to their priority:

HES Generated Energy 1st priority Main Load

Accumulator Deferred load

Energy Excess 2nd priority ■-* 3rd priority 4th priority

Optional load Ballast load

Fig.3. Load management

2. The studied hybrid system

Figure 4 presents the hybrid system. This system is based on a photovoltaic and an asynchronous machine wind turbine. The storage part is connected to DC-bus through a shopper. DC loads are supplied directly from DC-bus by using DC/DC chopper and AC loads are fed using DC/AC inverter.

The photovoltaic generator is connected to the DC-bus through a DC/DC chopper controlled by an MPPT controller; however the wind generator is connected to both the DC and AC buses.

We denote the energy produced for a period of a typical day Ep, where Ep = EPV + EW, and on the other side the energy consumption: Ec. the flow of energy is shown in the Fig.5.

Fig.4.Configuration of the standalone studied PV-Wind hybrid system.

Fig.5. Energy flow diagram

Assuming the bus voltage is still constant, it can resonate in current such as:

p 24hUc

(1) (2)

We can establish the nodes equation: ip = ic + ib , where ib is the equivalent battery current. If there is an imbalance between production and consumption, this difference will vary the DC-bus voltage for a period of time AT, the timewhen the battery is working: either to charge or to discharge, we can write:

( AU} i i ATJl„ - Ic|

ic = c| —| = \i„ - iJ ^ c = —L-P—cA (3)

c ^ AT) |p c AU

In this way we can estimate a priori the value of the battery capacity. The following diagram gives a synopsis of the developed software

Fig.6. synopsis of the developed software

3. Explanation of the developed interface:

In this section we have developed a software code for sizing PV-Wind hybrid system which is based on the loss of power supply probability (LPSP) method [5,9]. The design of a facility window for LPSP sizing appears with five buttons in order to carry the different tasks in the sizing procedure, figure 7.

9 LPSP Methode

1 5ities Settings

Load Characteristics

Technical Parameters Economic Parameters System Sizing

Fig.7. LPSP based window

When a button is clicked, a new window appears, allowing the user to define the various quantities and constants that characterize his system size. These windows are discussed below.

3.1 Sites Sittings: Selecting this button will lead us to the next window:

In this window the user can choose the location where he will implement his installation: a database provided by NASA gives us the different values necessary for our design: temperature, sunlight, pressure, wind speed...etc. Once the location is well chosen it is validated by clicking the OK button: the icon that was red turns green to confirm this validation. We reduce the window and we move to the second button.

000H0E0 S

Citie EES3 1

Latitude 36,8

Longitude

E levât ion 60

A i r Te m pe ratu °C horizontal re Irradiation kW/raA2/d Atmospheric Wjnd spped pressure kPa m/s Ground emperature °C

January 12,2 3,2 97,2 3 11,6

February 12,6 3 97,1 3 12,4

March 13,8 4,1 96,9 3 14,6

April 16 4,9 96,7 3 17,1

May 18,5 6 96,7 3 21

June 22,1 6,2 96,8 3 25,8

July 24,3 7 96,8 3 28,9

August 25,2 6,4 96,8 3 29

September 23,2 5,1 96,8 2 25,8

October 20 3,3 96,9 3 21,5

November 16,7 2,7 96,9 3 16,6

December 13,9 2 97,1 3 13

Annual 18,2 4,42 2.9 19,8 I Valid

Measured at ID m O Print

Fig.8. Sitecharacterizations.

3.2 Load characteristics This step is crucial, the user must specify the load profile [6, 12]; this can be done in two ways:

• Daily average load: where the user should provide the load values during 24 hours within a typical day in each month.

• Monthly average load: where the user is prompted to enter the daily average load value of his load.

For the daily average load whenever the user entered a value, he must increment time by clicking the increment button.

At the end user must provide the rated values: number of days of autonomy, to be able to operate the system using the storage and the probability of dissatisfaction LPSP in the load [7, 13]. The validation is done by a click on the submit button, the icon red becomes green for the confirmation of validation.

Fig.9. Load profile window

Load Consumption W Hours

January 5400| < | Increment |

February 7800 7 {increment |

Fig.10. monthly Average consumption

3.3 Technical parameters

When clicking the technical parameters button, a new window appears, which bring together all the parameters characterizing the system, it is unscrewed in four fields:

• Adjustment of the range of system production: the user must specify the maximum power, the power and the minimum increment step for each PV system and wind, figure11.

Variation ranges of production systems

Minimum Power Maximum Power Increment Step

Wind Generator PV-Generator 9000 10100 100

2000 3000 100

Fig.11. Variation interval of production sources

Parameters of the photovoltaic generators: to calculate the power of PV generator, the user is prompted for this field: the performance of the panel, NOTC temperature, reference temperature (usually it is equal to 25°C), the temperature coefficient p (generally between 0.004 and 0.006) [8], figure.12.

Photovoltaic Generator Parameters

Nominal Operating Cell Temperature °c 45

Reference Cell Temperature °c 25

Temperature Coef Temperature 1 / =c -0,0033

PV module effeciency 12

Fig.12. PV Generator parameters

• Parameters of wind generators: in this field the user must specify the ranges of variation of the used wind turbines. There are three tracks to complete, and every power range of wind turbines must enter their speed characteristics: Release, Nominal, and Maximal (that according to Power (speed) turbines gives by manufacturers) [9].

Wind Generator Parameters

Power Curves Parameters w w Starting Speed mf s Nominal Speed m/s Maximum Speed my s

Between 1600 and t000 4 £ 13 16

Between 4000 and ™00 4, 13 16

Between 7000 and 9600 4 5 13 16

Fig.13. Wind generator parameters

3.4 Technical parameters

When clicking the technical parameters button, a new window appears, which bring together all the parameters characterizing the system, it is unscrewed in four fields:

Adjustment of the range of system production: the user must specify the maximum power, the power and

the minimum increment step for each PV system and wind, figure11.

3.5 Storage Settings

To define the storage capacity of the overall system, the user must enter the storage capacity in [Ah]of a single battery, the performance of charging and discharging, the depth of discharge and voltage rating [10] (it depends on the continuous bus used in the system), figure 14.

At the end make sure to fill the box that defines the performance of the inverter use, typically it is around 90-98% [11], and once the user finishes entering All parameters specified must be validated by a click on the red icon and the green confirms validation.

3.4 Economic Parameters

The economic parameters are in a direct relationship with the overall cost of the installation, selecting this button will lead us to a new window where the user is prompted for each component: the photovoltaic panel, the wind, batteries, inverter and its initial price, and maintenance price in $/W and its life time [12, 13], figure 15.

Storage System Parametes

Storage Capacity [Ah] 200

Nominal Voltage [ vj 12

Discharging Depth ["V® ] 60

Batteries Charging Effeciency (%] SO

Inverter Parameters

Inverter Effeciency [% j 85

Fig.14. Storage system and inverter parameters

lY Economic Parameters

Economic Parameters №1

Inlttll Cott t/w

Photovoltaic System 2.5

Wind Gtntrjtor 2.6

Invtrttr 0-49

Storage System 0.042

Annual Maintenance Cost Lifetime [Years] Of Initial Cost V >'_

Fig.15. Economic parameters window

3.4 System sizing

On reaching this stage, the user had finished entering all parameters and constants characterizing the hybrid system, it should be emphasized that, before the design, the user is strongly advised to check the validation of the values recorded after each step, verifying that the red icon before the OK button is set to green each time. Clicking this button will lead us to a new summary window across the sizing; it appears as shown in the figure below:

The user will find this window in the optimal configuration of the hybrid system design:

4 The optimal power of the wind turbine to be used;

5 optimal power photovoltaic panels;

6 The number of batteries;

7 The number of autonomy days;

8 The overall cost of the facility;

9 LPSP.

Fig.16. Screen of simulation results

Other configurations that satisfy the condition specified in the value of the IPPL are classified in a table, to fill the calculated results from the user presses the button "Add Results". Graphs are provided by clicking different buttons:

10 "Draw the graph of PV power"

11 "Draw the graph of wind power"

12 "Draw the graph of the number of battery"

13 "Draw the graph of the overall cost"

3. Conclusion :

In this work we have implemented the LPSP method for the sizing of a standalone PV-Wind system. This method is based in principle on a technical-economic strategy that's depending on the cost study taking into account the different equipment's, the load profile and the meteorological characteristics of

each installation site. This method allows to define several configurations results that satisfy the profile load. The economical study is then performed to determine the optimal configuration. The following step of this work is the design and the realization of the software which can support this analysis study. A presentation of this software is established carefully to explain the LPSP sizing technique of PV-Wind hybrid system. Our software has become practical, interactive and easy to use. This elaborated simulation program allows as to determine the optimum size of battery bank and PV array for an autonomous PV-wind hybrid energy system for a given load and a desired loss of power supply probability based on the minimum cost of the system. The total cost also depends on investment cost, operation and maintenance costs, depreciation period and energy produced in one year, in addition to external trends such as the cost of batteries (subject to legislation affecting the cost of new materials and the cost of disposal), the potential downward trend of equipment costs with rising volumes etc. The competitiveness of a hybrid system also depends on the relative cost of fossil fuels and the demand for renewable energy from the market. The competitiveness of a hybrid system also depends on the relative cost of fossil fuels and the demand for renewable energy from the market.

Reference

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