Optimal sizing method for grid connected renewable ... - IEEE Xplore

2015 6th International Renewable Energy Congress (IREC)

Optimal sizing method for grid connected renewable energy system under Algerian climate T.Nacer O.Nadjemi

A.Hamidat

Department of Electronics, Faculty of Technology University of Blida 1 Blida, Algeria Abstract This paper presents a novel approach for optimal sizing of grid connected hybrid renewable energy systems including photovoltaic generator, wind turbine and without storage devices. A novel grid power absorption probability (GPAP) parameter is introduced as a decision variable in the optimization procedure. The sizing optimization consists on the identification of a cost effective feasible configuration with a permissible level of grid power purchase. The proposed approach has been tested on a case study of a residential load in Ghardaia city (northern-central Algeria). The optimized system offers a 65% renewable electricity fraction with a 0.47 grid power absorption probability and a total cost of 5788$. Keywords—hybrid system; optimal sizing; grid connected; GPAP; residential.

I.

INTRODUCTION

Renewable and clean energy is attracting more attention all over the world to overcome the increasing power demand and shortages of fossil fuels. Unlike traditional energy, the solar and wind energies are intermittent, uncertain and unpredictable. As a result, serious reliability concerns exist in both design and operation of such systems. Hybrid energy systems combining solar and wind energy are used to enhance the system reliability, especially when it is backed by efficient storage system [1]. Optimum sizing is an important issue in hybrid systems. Over sizing may conquer the reliability problem. However, it may be costly. The sizing optimization method consists on the identification of the optimal system (or near optimal) generating a reasonable amount of renewable energy that match the time distribution of the load demand with the lowest investment. Thus, Borowy and Salameh [2] have introduced loss of load probability (LLP) concept for finding the optimal size of a battery bank and the photovoltaic (PV) array in a hybrid windPV system. The minimum system cost is achieved by constructing the curve that represents the relationship between the number of PV modules and batteries. Kaabeche et al [3] have utilized the iterative optimization technique to follow the

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Deficiency of Power Supply Probability (DPSP) and the Levelised Cost of energy (LCE). Similar Loss of Power Supply Probability LPSP technique has been utilized by [4] to develop a techno-economic algorithm able to determine the system that would guarantee a reliable energy supply with a lowest investment. There are few studies of grid connected hybrid energy systems sizing. These systems are suitable in location with access to the grid. The hybrid system is used to produce green self-consumed energy. The unmet load demand is purchased from the grid, so the system is always reliable. Unlike standalone systems, the surplus energy is injected to the grid with prime price according to the local policy which makes the grid connected system more cost-effective. The grid can be considered as a high efficiency infinite storage system. Angel et al [5] have defined the sizing parameters for grid connected hybrid system as the inverter sizing factor, the solar and wind fractions, and the size of the batteries. This paper presents a novel sizing technique for grid connected hybrid PV-wind-battery system based on grid power absorption probability GPAP. The GPAP is defined as the ratio of grid purchased energy to satisfy the load demand. . II.

GRID CONNECTED SYSTEM DESCRIPTION

The considered hybrid energy system is a combination of a photovoltaic array and a wind energy system. The variations of solar and wind energy generally do not match the time distribution of the demand. Therefore, the grid connected system absorbs electricity from the grid to satisfy the load demand. A schematic diagram of the grid connected hybrid PV/Wind system is shown in Fig.1. An inverter is used to interface the DC bus voltage to the load AC requirements. The inverter AC power output is always synchronized to the grid characteristics (voltage and frequency) in order to respect protection standards when an interconnection to the utility grid is needed by the system. A control system allows to the grid utility to inject or absorb energy depending on the system production and demand. The surplus electricity is sold to the grid with a prime price according to the local energy policy.

Fig. 1. Grid connected hybrid PV/Wind system

III.

SYSTEM COMPONENTS MODELING

A. Photovoltaic generator The hourly output power of the PV generator with an area A_PV (m2) at a solar radiation on tilted plane module I(t) (W/m2) is given by the following (1):

PPV = η PV . APV .I (t )

(1)

Where ȘPV is the PV system efficiency. This value covers the reference module efficiency (ȘPR), the power conditioning efficiency (ȘPC) and power losses due to temperature change, shadows, dirt, losses in inverter, etc. B. Wind turbine power The power output of a wind turbine is determined by three main factors which are the wind speed distribution for the selected site, the hub height of the wind tower as well as the power output characteristic curve of the chosen wind turbine. The model used to simulate the power output of a wind turbine can be illustrated by (2). Three operating levels are described. When the wind speed V(t) is less than the cut-in value or exceeds the cut-off value, the wind turbine stops running. If the cut-in value is reached, the turbine generates an increasing power proportional to the wind speed until the exceeding of the rated speed of wind turbine where a constant output power is generated.

­0 V (t ) ≤ Vc or V (t ) ≥ V f ° ° V (t ) − Vc Vc ≤ V (t ) ≤ Vr ® Pr V − V r c ° ° Pr Vr ≤ V (t ) ≤ V f ¯

(2)

Where Pr is the rated electrical power of the wind turbine andVc, Vf,Vr are the cut-in, cut-off and the rated wind speeds, respectively. Since the wind speed varies with height, the measured wind speed at anemometer height must be adjusted to desired hub height of the wind turbine. The two mathematical models can be used to model the vertical profile of wind speed are the log law model and the power law model. In this study, the adjustment of the wind profile is calculated by using the power law model described by (3) α

V ( h) § h · =¨ ¸ V (ha ) © ha ¹

(3)

Where V(h) is the wind velocity at the height h, ha is the anemometer elevation and Į is the power-law exponent. Į varies with parameters such as roughness of terrain, wind speed, the height above ground, time of the day and various thermal and mechanical mixing parameters. In literature, the value of 1/7 is usually assumed. IV.

OPTIMAL SIZING PROCEDURE

The optimal sizing of the grid connected system consists on the identification of the most cost effective renewable energy configuration with a minimum conventional grid electricity use by the load. A novel grid power absorption probability GPAP is defined in this study as a performance indicator for grid connected systems with analogy to LPSP for stand-alone systems. The GPAP is the probability that the load need to purchase electricity from the utility grid when the renewable energy is unable to feed the load. The power management strategy utilized for the simulation can be described as: • Sufficient renewable energy is generated by the system then the load is satisfied. The surplus electricity is sold to the grid.

•

The renewable energy is less than the load demand then the energy deficit is purchased from the grid to satisfy the load demand.

The total renewable energy produced by the system at the time step [t, t+1] is calculated as follows (4)

ERE (t ) = EPV (t ) + EW (t )

(4)

costs for all components of the system are considered to be per unit cost according to the Algerian market for each item. The case study is a typical Algerian residential load profile (Fig.4)[7]. Hourly and daily random variations are taken as 10% and 20% respectively to model more realistic profiles. The time step is considered 1 h and during this time step the load is assumed to be constant. The simulations were executed using a 1 year hourly average values of solar radiation, wind speed (at a height of 25m) and load demand.

Where EPV(t) and EW(t) are the energy generated by PV and wind generators, respectively. The energy flow between the DC bus and the AC load E(Load ) (t) is limited by the inverter efficiency Șinv (in this study the inverter efficiency is considered constant).This energy flow is described by (5)

EEx (t ) = ERE (t ) − D(t ) E (t ) D(t ) = Load

ηinv

(5) (6)

Fig. 2. Global solar radiation of studied location

Where D(t) is the equivalent DC load demand corresponding to the inverter input. The inverter sizing ratio is taking as 0.7. The grid power absorption probability, GPAP, for a considered period T, can be defined as the ratio of purchased electricity over the total load required during that period. The GPAP technique is considered as technical performance criteria for sizing the grid connected hybrid PV/wind system without storage battery. The GPAP probability can be determined using (7)

¦ E (t ) GPAP = ¦ D(t )

Fig. 3. Wind speed profile of the studied location

T

GP

(7)

1

Where T is the operation time (in this study, T=8760hours for 1year analysis).

1.5

Load (kW)

1 T

1

0.5

0

V.

RESULT & DISCUSSION

The proposed algorithm has been applied to analyze a grid connected hybrid PV/wind energy system located in suburban location in Ghardaia city (northern-central Algeria). The solar radiation data (Fig.2) for the studied location (32°30'N latitude and 3°38'E longitude) was obtained from the NASA Surface Meteorology and Solar Energy (SSE) database [6] and a one year hourly average wind speed data is extracted from a long term recorded data (from 2006 to 2010) by the Algerian National Meteorological Office (ANMO) using an anemometer at a height of 10 m above ground level (Fig.3). The parameters related to the components have been given in Table. 1. The installation, maintenance and replacement

5

10

15 Time (hour)

20

Fig. 4. Typical residential load profile

In the optimization process, an iterative procedure is performed to simulate an hourly time electricity production and grid interactions for all feasible systems (respecting the roof area). Then, the feasible systems are ranked according to their total capacity as shown in Fig.5.It can be seen in this figure that the photovoltaic grid connected system can reach a minimum GPAP of 70%. However, the hybrid system PV-wind configuration can attain a GPAP of 30%. Without taking into account the renewable electricity selling incomes, the system

4 PV WT

3.5

System capacity (kW)

3 2.5 2 1.5 1 0.5 0 0.2

0.3

0.4

0.5

0.6 GPAP

0.7

0.8

0.9

1

Fig. 7. Net grid electricity cost and income

Fig. 5. System configuration according to GPAP

cost increase significantly as the GPAP decrease below 35%. Increasing the GPAP of the system from 55% to 100% decreases the system cost moderately (Fig.6).

6500

6000

5500 Net present cost($)

The net grid electricity cost is shown in Fig.7. The negative values show that the local produced renewable electricity income is more than the purchased electricity cost. It is clear from Fig.6 and Fig.7 that the optimal system has a GPAP between 35% and 55%. A carbon tax is considered as 20$ per ton of CO2 to take into account the environmental impact of the system in the sizing optimization. The total net present cost of the systems according to GPAP is presented in Fig.8. The optimal feasible hybrid configuration is found to be 1kWc PV panel and 1kW wind turbine. The GPAP of the system is 47% with a total cost of 5788$. The energy mix of the obtained system is 35% photovoltaic electricity, 30% wind turbine electricity and 35% from the grid.

5000

4500

4000

3500

3000 0.2

0.3

0.4

0.6 GPAP

0.7

0.8

0.9

1

Fig. 8. Systems net present cost

VI.

Fig. 6. System cost (excluding grid interaction)

0.5

CONCLUSION

The current study presents a techno-economic sizing optimization of grid connected hybrid PV-wind system for residential application in Ghardaia city (Algeria). A novel grid power absorption probability GPAP parameter is introduced as a decision variable in the optimization procedure. It has been shown that the hybrid grid/PV/wind 1kWc PV modules and 1kW wind turbine is the economically optimal solution. This system has a renewable fraction of 65% and a 47% GPAP. Furthermore, the optimal system injects 1.5MWh into the grid annually and helps to mitigate 135 tons of CO2 over the project lifetime. The added value of this study is to demonstrate the techno-economic and environmental impacts of using grid connected hybrid systems according to their levels of integration and make the decision easy for the investor to choose the optimal system.

TABLE I. Parameters Initial cost ($) Maintenance ($/y) Replacement cost ($) Lifetime (y) Grid purchase rate1($/kWh) Grid purchase Rate2($/kWh) Electricity sell to the grid ($/kWh) Interest rate (%)

COMPONENTS CHARACTERISTICS

REFERENCES

Characteristic PV

WT

3200

2000

20

50

22000

1600

25

20

[1]

[2]

[3]

0.01 0.044

[4]

0.12 4

[5]

[6] [7]

A. Maleki, A. Askarzadeh “Artificial bee swarm optimization for optimum sizing of a stand-alone PV/WT/FC hybrid system considering LPSP concept,” Solar Energy, vol. 107, pp. 227–235, 2014. B.S Borowy, Z.M Salameh, “Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system,” Energy Convers., IEEE Trans, vol. 11, pp. 367–375, 1996. A. Kaabeche, M. Belhamel, R. Ibtiouen, “Sizing optimization of gridindependent hybrid photovoltaic/wind power generation system,” Energy, vol. 36, pp. 1214-1222, 2011. H. Belmili, M. Haddadi, S. Bacha, M. Fayçal Almi, B. Bendib, “Sizing stand-alone photovoltaic–wind hybrid system: Techno-economic analysis and optimization,” Renewable and Sustainable Energy Reviews, vol. 30, pp. 821-832, 2014. Ángel A. Bayod-Rújula, Marta E. Haro-Larrodé, Amaya MartínezGracia, “Sizing criteria of hybrid photovoltaic–wind systems with battery storage and self-consumption considering interaction with the grid,” Solar Energy, vol. 98, Part C, pp. 582-591, December 2013. NASA Surface meteorology and Solar Energy. http://eosweb.larc.nasa.gov/sse/. Farouk Chellali, Adballah Khellafb, Adel Belouchranic, Abdelmadjid Reciouid, “A contribution in the actualization of wind map of Algeria,” Renewable and Sustainable Energy Reviews; vol. 15, pp. 993–1002, 2011.