optimized self-sustained renewable dg integration in … · [zbus] = [ybus]-1. since in this case,...

Optimized Self-Sustained Renewable DG Integration in the Lebanese Power Grid

1Martine M. Chlela, 2Kifah Daher, Students and 3Maged B. Najjar, IEEE Member Department of Electrical Engineering, University of Balamand

North Tripoli, Lebanon [email protected]

[email protected] [email protected]

Abstract— With the increasing demand of power generation and the exhaustibility of the current energy sources, smart introduction of renewable sources on the distribution level is crucial. Lebanon has been facing a disastrous situation regarding the electricity sector. Forced cutoffs and failures are daily matters with no approaching solution. This paper is intended to give insights about the sustainable sources that should be distributed in a manner which not only reduces the total power losses and improves the voltage profile, but also decreases the current dependency on the central generation unit. An algorithm which assures this sustainability and self-reliance of the grid was performed and will be shown in details. Results obtained clearly prove that in addition to its reliability and compatibility, the grid will be able of minimizing the power losses of the system and will function in absolute autonomy. Once the distributed generations of optimum size are installed at the optimum location and the algorithm ran, losses will be reduced to around 70% of their initial value, the voltage profile will be enhanced, and the main grid will not anymore supply power but instead will become a feeder to the neighboring branches of the system.

Keywords- Minimum power loss; Optimum size; Optimum location; Self-sufficiency; Distributed Generation; Renewable energy sources;

I. INTRODUCTION Defining distributed generations is a question to which no

precise and final answer has been found. However, applying some classification criteria might lead to a clearer understanding of the DG of concern. DGs can be either renewable or non-renewable energy sources. These generations could also be classified according to their types; they can be divided into wind, photovoltaic, small hydropower, micro turbines etc. Moreover, DGs could directly be associated with the power system or linked through the interconnected system [1]. DGs are capable of increasing or reducing losses, depending on the location at which they are installed, their capacity and the relative size of the load quantity. The losses of the network depend largely on the flow of power. Since the allocation of DGs in the grid affects the power flow, placing them in an optimal manner would ultimately reduce the losses [1].Optimal DG location and size can be determined by using loss sensitivity index (LSI) in order to maximize the net savings [2]. A study based on a load flow algorithm, where DGs were optimally installed, minimizes the electrical network losses and guarantees acceptable voltage profile [3]. Similarly,

the Cuckoo Search Algorithm can be followed in order to find the best size and location of the DGs. This method assures in addition to the reduction of power losses and improvement of the voltage profile, an enhancement of the voltage stability [4].DGs can either be used in an isolated manner to supply the consumers locally, or in an integrated way to supply power to the whole network. When installed on the distribution system, DGs are beneficial to both, the consumers and the utilities, especially when the central generating units are incapable of supplying the needed energy or when there are deficiencies on the transmission side [5]. This is exactly the situation of the Lebanese power system. Not even half of the amount of energy required to fulfill Lebanon’s demands is being produced by the available conventional generation plants. Therefore, finding the optimal size and location of the DGs in this study would guarantee, in addition to improved voltage profile and minimization of the power losses, a reduced reliance on the main central generating unit. The DGs installed in the system are classified to be renewable sources of energy. Accordingly, the main grid supplying power from exhaustible sources will no longer be needed; the system will become more sustainable and will function in autonomy.

II. PROPOSED MODEL The IEEE 69-bus radial distribution system consists of one

main branch and seven laterals containing different load buses. Buses 1 to 27 lie on the main branch. Bus 1 represents the main grid feeding the whole distribution system. The 69 bus-68 branches system has 48 load points totaling 3.80 MW and 2.69 MVAR and is represented in Fig. 1.

III. ALGORITHM FOLLOWED In order to increase the net savings and decrease the

system’s losses, the genetic algorithm was used to find the optimal location of capacitors [7]. Similarly, capacitor allocation could be performed by the mean of the widely used sensitivity factor method [6]. This method will now be introduced for the allocation of DGs.

The real power loss can be obtained from the so called “approximate loss” formula:

(1)

978-1-4799-1464-7/13/$31.00 ©2013 IEEE

2nd International Conference on Renewable Energy Research and Applications Madrid, Spain, 20-23 October 2013

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Figure 1. Single Line Diagram of the IEEE 69-bus distribution system

Where cos

sin

Zij represents the ijth element of the impedance matrix which is the inverse of the admittance matrix: [Zbus] = [Ybus]-1. Since in this case, there is no connection to ground, the inverse of the admittance matrix does not exist. Therefore, one should introduce a fictitious line which connects the swing bus to ground. This connection will not affect the power flow result [8]; instead, it will render the admittance matrix invertible. Once the admittance matrix inverted, the impedance matrix and the sensitivity factors will be directly obtained.

The sensitivity factor of real power loss with respect to real power injection is:

2 (2)

The total power loss against the injected power is a parabolic function and the rate of change of losses with respect to the injected power is zero at minimum losses. That is:

2 0 (3)

This yields the following:

0 (4)

1

Where Pi is the real power injection at node i which is also the difference between real power generated and that demanded at node i.

That is:

(5)

PDGi and PDi are the real power injection from DG and load demand at node i respectively.

It follows that: 1 (6)

Equation (6) is used to find the optimum size of the DG for each bus, in a way that minimizes losses. That is, for whatever DG size different than PDGi, higher losses are obtained. Even though this loss is a function of the loss coefficients, α and β, which change when DG is installed, experiments have shown that the error obtained due to lack of accuracy is very negligible, hence no need to run a new power flow. This yields that the optimum size of the DG can be obtained from the base case power flow.

Once the DG size obtained, the optimum DG location, which will guarantee the lowest possible total losses, will be calculated using (1). Therefore, the DG of concern has an optimum size and will be placed at the optimum location. For minimization of losses purposes, one would have stopped here. However, since this study consists on enhancing the self-sufficiency of the network with partial or total independency upon the main grid while decreasing the overall losses, the study was pursued and an algorithm was set and followed.


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Figure 2. Flow chart of the Algorithm followed

The algorithm of Fig. 2 will output the final values of both, the total power loss and the power generated by the main grid, once the power supplied by the swing bus is less than or equal to zero. This optimum solution highlights the importance of the distribution generations in the system.

IV. RESULTS AND DISCUSSION The size of the DGs represented by (6) can be obtained

using Matlab. Once this optimum size obtained, the real power loss can be computed from (1). For each bus, the power loss will be calculated by replacing its real power by the size of the DG previously obtained for that bus while maintaining the real power of all the other buses intact. The optimum location represents the bus at which power losses are minimal. In this case, the DG should be located at bus 61. Notice here that in order to attain this minimal value, one should place a DG of size 180.77 kW at bus 61. Initially, and with no DGs in the system, the amount of losses was 224.959 kW. The main grid supplies 402.68498 kW. Once the DG of optimal size is placed at bus 61, losses are reduced to 83.33961 kW, and the power generated by the main grid decreases to 207.745 kW. The ±5% voltage limit was violated when no DGs were installed in the system. However, once the DG is connected to bus 61, the voltage is enhanced and is contained within the constraint.

In order to achieve the goal of this study, which is reaching the self-sufficiency of the IEEE 69-bus distribution network; eighteen DGs had to be installed at different locations. One intermediate allocation will be analyzed before reaching the final result.

After installing the DG at bus 61, the power flow was run again in order to check for minimum losses. Bus 17 was chosen to be the optimum location. Again, the power flow was

obtained with buses 17 and 61 as part of the network. The total power loss and the optimum DG size were computed, and Fig. 3 and Fig. 4 were obtained.

Once these graphs are obtained, one can locate the bus at which power losses are minimal. In this case, bus 50 was the best location at which a DG of size 71.94 kW is to be installed. As soon as this bus is introduced in the system, the power losses decreased to 70.17616 kW. Similarly, the power generated by the swing bus was reduced to 83.356935 kW, and the voltage profile was better enhanced.

In order to cancel out the need of the main grid, and to supply the load buses by distributed renewable energy sources only, the algorithm was repeated for eighteen times. Each time, the installed DG was considered part of the system, and optimum allocation of another DG was studied. By the end of these consecutive allocations, a total power loss diminution reaching 69.58% was observed. Additionally, the voltage profile was further improved. More importantly, the overall power generated by the swing bus attained a value of -6.4 kW which infers that the network is running independently of the main grid. This central unit is therefore no longer necessary; it is instead fed by the network where DGs are installed. Here arises the ability of these DGs, once connected to the system in an optimum manner, to transform the main grid into a feeder capable of supplying power to neighboring branches or networks.

Table 1 summarizes the results of the study performed each time a DG is integrated, until finally reaching the target value.

Figure 3. Optimum size of DG at different buses (DGs integrated at buses 61

and 17)

Figure 4. Approximate power losses (DGs integrated at busses 61 and 17)


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Power supplied by the swing bus (MW)

Power loss without DG (kW) *

Bus at which losses are minimum

DG size at the corresponding bus (MW)

Number of DG(s) previously integrated

4.0226 224.9599289 61 1.8077 0

2.0774 83.3396108 17 0.5112 1

1.5545 71.69266971 50 0.7194 2

0.00161 68.43078467 5 0.007996 17

-0.0064 68.43058148

18

* power loss obtained after installing DG(s) of optimum size at optimum location specified in the previous iteration and considering them as a part of the system.

V. CONCLUSION

After highlighting the importance of renewable distributed generations, one would be interested in observing the amelioration of the network’s behavior to which they are connected. The IEEE 69-bus distribution benchmark network was taken as a test system; however the study performed would be easily applicable for the Lebanese distribution network whenever line and bus data are available.

Figure 5. Power generated by the swing bus each time a DG is installed in

the system (MW)

Figure 6. Total Power Loss each time a DG is integrated in the system

(kW)

Figure 7. Voltage Profile Improvement

Furthermore, in order to emphasize the importance of distributed renewable energy sources in distribution networks, and to explain their ability to compensate for the power generated by the main electricity unit, an algorithm was proposed. This algorithm does not only ensure minimum power losses in the distribution system; it also improves the voltage profile and guarantees that the network will become self-supported in case optimal locations and sizes of the DGs were employed.

Figures 5, 6 and 7 show the behavior of the power generated by the main grid, the power losses and the voltage profile each time a DG of optimal size is placed at its optimal location.

The IEEE 69-bus which is capable of satisfying itself with the least possible losses and improved voltage profile within voltage constraints is modeled in Figure 11.

As a summary, many features of the inexhaustible distributed generations have been studied and applied; compatibility, sustainability and self-sufficiency of the grid were obtained after allocating these DGs and following the proposed algorithm. The total power losses of the grid were also reduced and the voltage profile was greatly enhanced resulting in increased efficiency of the electricity sector of concern.


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Figure 8. Self-sufficient IEEE 69-bus with optimized DG integration

REFERENCES [1] F.H. Guan, D.Ｍ Zhao, X. Zhang, B.T Shan, Z. Liu, Research on

Distributed Generation Technologies and its impacts on Power System, International Conference on Sustainable Power Generation and Supply, 2009.

[2] B. Hanumantha Rao, S. Sivanagaraju, Optimum allocation and sizing of distributed generations based on clonal selection algorithm for loss reduction and technical benefits of energy savings, International Conference on Advances in Power Conversion and Energy Technologies (APCET), 2012.

[3] A.Kazemi, M.Sadeghi, Distributed Generation Allocation for Loss Reduction and Voltage Improvement, Power and Energy Engineering Conference, 2009.

[4] W. S. Tan, M. Y. Hassan, M. S. Majid, H. A. Rahman, Allocation and Sizing of DG Using Cuckoo Search Algorithm, International Conference on Power and Energy, 2012.

[5] C. L. T. Borges, Djalma M. Falcao, Impact of Distributed Generation Allocation and Sizing on Reliability, Losses and Voltage Profile, Power Tech Conference Proceedings, 2003.

[6] M. E. Baran, F. F. Wu, Optimal Capacitor Placement on Radial Distribution Systems, Transactions on Power Delivery, 1989.

[7] S. Neelima, P. S. Subramanyam, Optimal capacitor placement in distribution networks for loss reduction using differential evolution incorporating dimension reducing load flow for different load levels, Energytech, 2012.

[8] G. T. Heydt, Computer Analysis Methods for Power Systems, MacMillan 1st edition, 1986.


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optimized self-sustained renewable dg integration in … · [zbus] = [ybus]-1. since in this case,...

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