The 19th Electrical Power Distribution Conference (EPDC2014), 6-7 May, 2014, Niroo Research Institute

Techno-Economic Considerations on Distributed Generations (DGs) Planning Studies in Power

Distribution Systems

Abolfazl Asadi Department of Electrical Engineering

[slamic Azad University of Science and Research Tehran, Iran

abasadiz@gmail.com

Abstract- Power system restructuring has led to a significant

change on how to design, operate, and decision making in power

systems. This major change has brought with itself lots of

concerns and challenges in power system studies concerning the

generation adequacy issue. Lower amount of costs and

investment risk associated with the distributed generation (DG) units has made them a promising opportunity for the power

system operators to expand the system capacity. This has made

them attractive enough justifying their growing penetration

levels in the electrical industry. According to this, effect analysis

of the DG units on improving the technical and economical

performance of the power distribution systems has to be well

surveyed. In this regard, this paper proposes a comprehensive

decision making approach in order to reach some optimal and

also practical DG investment schemes. In each level of the

decision making process and for the sake of planning on the DG capacity expansion mechanisms, critical cost-based indices are

defined from the perspective of different involved investors, i.e.,

distribution utility, customers, and private investors to identify

the optimal size and location of DG units. The customer

interruption costs and reliability worth criteria are among the

most critical ones which have been taken care of in this analysis.

An optimization algorithm, namely particle swarm optimization

(PSO) technique, is employed to effectively deal with the

extracted mathematical optimization problem. 2-point estimation

method (2-PEM) is also giving the authors a hand in dealing with

the probabilistic factors involved.

Keywords-component; Distributed generation (DG); renewable; reliability; particle swarm; placement.

I. [NTRODUCTION

Distributed generators (DGs) are the generating units with relatively lower capacities compared to the conventional units and are commonly located in vicinity of the consumers and load centers. DG units are attractive in the planner's mind since their initial investment costs and investment risks compared to the other expansion planning scenarios are fairly lower. DG units, if properly planned and operated, can postpone the new reinforcements and installation of new units and components especially in power distribution sector. They also could lead to some technical consequences such as the

978-1-4799-5636-4/[4/$31.00 ©20 [4 IEEE 82

Mahmud Fotuhi-Friuzabad, Moein Moeini-Aghtaie Department of Electrical Engineering

Sharif University of Technology Tehran, Iran

fotuhi@sharif.edu; m.moeini@ieee.org

improvement of the system voltage profile and network losses [1], [2]. The system reliability enhancement and considerable reductions in the customer interruption costs are also among the other incentives followed on the growing adoption of the DG units in power distributions systems [3].

On the other hand, lots of challenges and technical/economical concerns would be brought into play in presence of DG units in power distribution systems once designing, planning, and operating the system. In response, Considerable number of past works has been hence devoted to the topic to assess the impact of DG units on the system parameters and critical technical/economical performance indices. The focuses investigated through the previous researches include the following main road lines:

• Environmental and geographical constraints • Technical concerns such as system stability,

reliability, security, and so on. • Load ever-increasing growth in distribution level and

the need for the expansion planning • Renewable and clean energy integration in the

decision making scenarios • Independency encouragement to the conventional

fossil fuels • The network losses and their efficiency • Placement and installation problem

This paper takes a long pace in the assessment of the main criteria interrelated in the DG unit sizing and siting problem in both traditional and restructured environments. Traditionally, the installation and operation of DG units were done in power distribution systems for the main sake of improving the system technical performance, e.g., voltage profile [[], minimizing network losses [2], and system reliability [3]. Having the privatization process in electric industry into account and with the arrival of restructuring trend, the economical factors also gain twice importance and the DG units have been then installed and operated not only with having the technical factors into account, but also for the sake of lowering down the total amounts of cost and maximizing the benefits gained.

The increasing importance of the economical factors may lead to, in some cases, the degradation of the system technical performance. Moreover, in most of the studies done on the subject, the reliability worth and the customer interruption cost are neglected once planning for the sizing and siting of DG units while the sole expected energy not served and the outage durations are being considered [4].

it should be notified that in the DG placement planning studies in the restructured environment, both the technical and economical aspects interrelated with the problem have to be taken into account concurrently so that a techno-economical solution can be gained. Three different types of investors can participate once dealing with the investments on the DG units and the associated expansion planning decision makings (sizing and siting) in deregulated environments each of which tries to find the optimum solution according to its requirements. The three investors are the distribution utility as the system main operator, the customers as the energy consumers, and the private investors. In order to find the optimal solution from the viewpoint of each of the above aspects, the existent uncertainties (in electricity and gas) have to be also regarded as the main factor contributing the most in the costs and investment risks. It is also of utmost importance to incorporate the reliability issues and interruptions occurred with each outage of electricity to the customers especially when the problem is being approached via the utility or from the customer viewpoint.

Having defined the main criteria in the decision making problem from different aspects, an appropriate methodology has to be called upon. Different approaches can be selected in different systems with various characteristics and also considering different scenarios. These special cases may include the continuous/discrete decision making spaces, single or multiple objectives, qualitative or quantitative criteria and so on, which all matter once deciding on the best possible approach and policy in dealing with the problem. In this paper, a strong and well-proven optimization technique, namely particle swarm optimization (PSO), has been selected to be employed by which the proposed multi-objective formulation has been applied and verified on a test distribution system.

II. DGs PRESENCE IN PO WER S YSTE M

DG unit integration in power systems can be regarded as a new move and trend in its operation though no unique definition has been yet devised or available to identify its roles and the associated technologies. DG units can be generally defined as all the energy provision units in a small scale or in vicinity of the load centers whose produced power and energy can either directly be fed into the distribution system or directly supply the customers/loads [5]. This definition is actually a complex and somehow ambiguous in comparing the DG applications with those of the other means of power generation. Consequently, some more precise and accurate

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definitions for DGs have been introduced in the literature regarding the objective, application, capacity, deployment, or benefiting from the renewable sources of energy. In the following, the proposed structure embracing all the above concerns is being introduced.

III. PRO POSED PRO B A BILISTIC FR A ME WORK FOR DG PLACE MENT PRO BLE M

According to the 20-year smart grid vision in Iran, several main objectives have been described for the power industry in Iran and specifically for power distribution system design, planning and operation. Deployment of private DG units especially the renewable-based ones, such as wind turbines and photovoltaic cells, is popular in distribution systems worldwide. Anyway, one of the most important factors for installation and selection of the DG units and their associated technologies is the total amount of imposed costs to the distribution utility. Utilities usually seek the solutions with the minimum possible amount of costs of any type in such a way to be able to maximize their benefits. The costs associated with the DG unit placements can be divided as the fixed and variable costs. The former includes the investment costs majorly depending on the selected capacity level and the DG technology. The latter includes the operation and maintenance costs of DG units which is majorly depending on the DG usage pattern, type and technology. In general, the total imposed costs of DG investment and operation can be expressed as follows:

IC = It{_I_, .CC,.p,Cap +( 1+ i )lp,capCF,.oc.8760} (1)

'�l ,�l (1 + d) 1 + d

In which IC , CCj, �Cap , CFi , OC , i, d, T, and N are respectively, the total imposed costs associated with the ith DG unit, the capacity factor of the ith DG unit, the variable costs of operation, interest rate, inflation rate, studies time period for planning, and the number of DG units in the expansion planning of distribution system.

Due to the restructuring trend in electrical industry and the ever-increasing dependency of the human being to the electricity, power quality has gained twice importance than before which is reflected as the customer interruption costs in this study. Any outages in the network not only can lead to lost revenue of the utility due to the unsold amount of power, but also may improve the customer interruption costs or outage damage costs to the utility. This financial term is of great importance in planning and reinforcement of the distribution system.

IV. PER FOR M ANCE-BASED REGULATION (PBR) MECH ANIS M

One of the main triggers for the customer interruptions is the distribution system itself and the associated reliability level. Of the main reasons which traditionally has led to a poor

understanding and not well-focuses considerations on the distribution level of power systems compared to the generation and transmission levels is the limited and dispersed impact of the faults in distribution level once occurred. The financial regulations in power distribution systems are considered as of the most considerable achievements of the restructuring trend during the past two decades. Competitive nature of the power market in various levels of power and energy industry has led the Distribution System Operators (DSOs) to respond to the customer interruptions according to certain rules and regulations. The regulations can be set based on the cost or the performance indices [6]. In the former way, the objective is to restrict the DSO's revenue in such a way that they operate within a limited generation cost or within a maximum amount of revenue as the benefit. In the latter, which is a main advantage of power system restructuring, the direct connection and dependence of the costs and revenue, which is the case in the cost-based regulation mechanism, is looser or even does not exist sometimes.

Most of the strategies and incentives concerning the cost reductions have been improved and what really matters more is the performance improvement and condition/quality enhancement of the delivered power. This policy would lead to a considerable increase in the system risk due to the delay in expansion, operation and maintenance costs. In order to compensate for this risk, the PBR mechanism is further equipped and organized with a type of regulation based on the quality, which is called quality regulation (QR). How the connection between the PBR and QR is identified is the main factor in defining the incentives for the DSOs to increase the system reliability [6]. This performance is valued according to the economic efficiency and quality regulations.

One of the main reasons to consider the supply voltage as a PBR indicator is its undeniable role in convenience of the customers. In other words, rather than the fact that the power supply is connected or not, power quality has to be also taken into account. Otherwise, this can lead to a huge financial damage and irretrievable consequence in the case of some industrial or commercial consumers [6], [7].

The network reliability indices indicate the system inability in terms of expected energy not served, average interruption frequency, and the average interruption duration index [8]. The reliability indices can be categorized into two main groups: weighted indices reflecting the customer types and weighted indices reflecting the load capacities. The noteworthy here is that the indices concerning the voltage (power) quality is not considered the same for different types of customers. As an example, the vallies or drops in the voltage profile would be of huge consequence for industrial customers while it will lead to a far lower financial loss for the residential customers. the employed PBR mechanism should reflect the main requirements of different customers. Moreover, the rules have to be well identified, to be certain for different policies, and to be applicable in practice for a predefined horizon. The regulator, hence, has to define and set

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some parameters and economic values for the quality indices for each DSO top make the proper incentives for them to operate upon.

One of the main concerns which needs to be considered once facing with the cost reduction policies is the postponed costs of expansion, reinforcement, investment, operation and maintenance which, for sure as a consequence, would lead to a steeped decrease in the system reliability performance. In order to mitigate the concerns, efforts can be made in the regulations to identity some quality indices. The more decrease in the cost incentives, the more important is the role of quality indicators in the regulation mechanisms.

Several approaches are being employed in order to increase the system reliability including the improvement of the network protective devices, overhead line investments in order to change the radial distribution network to the meshed one with higher reliability performance (though is costly), investments on the underground cables, and so on. The important consideration is that the investment and expansion costs must justify the benefits gained. In order to quantify the obtained benefits according to a certain level of power quality, the regulator conducts some studies on the basis of customer interruption costs. That is the negative benefit due to the undesirable quality or the positive benefit due to the desirable power quality is assessed and some levels of rewards and penalties are then defined for the DSOs accordingly, In order to improve the accuracy, it would be desirable to consider both the costs associated with the interruption frequency and duration (in the literature, the few number of long interruptions or the considerable number of short outages are commonly considered). In so doing, the customer interruption cost is taken as the promising index for the DG placement problem from the PBR perspective. As it is demonstrated in Fig. 1, the vertical axis indicates the utility benefits in terms of revenue and the horizontal axis is the indicator of the customer interruption cost [6]-[10].

According to the figure, as the interrupted energy increases (and according to the increase in the customer interruption cost), the utility revenue (benefit) would decrease. The amount of decrease falls within a certain level and the decrement trend

I' Allowable revenue

NP A.u.x -+-------1,. �

::::� -- -----------------�-----�_'i_: ------

[C="

I lCvah.-.:d 1Cn= IC

Figure I. The perfonnance based regulation ( P BR) mechanism.

is assumed to be linear. As a result, one can conclude that with the PBR policy and mechanism applied, the utilities would have to find some ways to decrease the frequency and duration of the outages (customer interruptions which not only would lead to an increase of their revenue in one hand, but also would last into the system reliability improvements.

This paper proposed an optimization-based approach for the DG placement near the load centers in distribution systems to increase the system reliability. The increase in the system reliability, as explained earlier, is translated as the lower customer interruption costs resulting from the proper DG installations. In order to evaluate the annual costs associated with the adoption of the PBR policy, the customer interruption cost is defined as follows. cac = I (EENS).(JEAR) + PEN -REV (2)

n PEN = I(�'m)(penJ (3)

i=!

n REV = -I (PRJCv, )(revi ) (4)

i=i

where, cac, EENS, lEAR, PEN, REV , �'mj , and PRJCv, are, respectively, the customer interruption costs, the expected energy not served, the interrupted energy assessment rate, the penalty rate, the reward rate, and the probability of facing with the reward and penalty conditions. According to the defined cost function, the objective function in this paper is defined as follows for the sake of optimal placement of DG units in distribution systems. f = min( cac + IC) (5)

The installation of DG units in power distribution systems depending on the location and operational conditions can decrease the network losses by 53% and can lead to a significant improvement in the system techno-economical performance [II]. On the other hand, if not located properly in the network, the increased loss would be the consequential outcome. So, the studies have to be focused on the network topology, location, capacity, and the load distribution. Once dealing with objective of loss minimization on the network, we have the following relations in mind [3],

n � = v: I �j Vj cos( � -6j -r;) (6)

i�l

n Q = v:I �jVj sin(� -6j - r;) (7)

i�l

In which � and Q are, respectively, the real and reactive power at bus-bar i; v: is the /h bus-bar voltage; � and Yare respectively representatives of the ith bus-bar voltage angle and the elements of the network admittance matrix. The real power loss can be then quantified as follows.

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n n P. -"P. -"P, loss - � (Ii � Dj

1=1 1=1 (8)

Studies reveal that the voltage profile at various bus-bars can be improved in presence of DG units. In order to take this technical index into account once deciding on the DG placement problem, the following two constraints have to be considered,

P Loss = � Index pNoDC;

Loss

VDmax = maxlV: -11

V:ndex = V D max

V:ndex ::;; VI && LOSSIndex ::;; L1

(9)

(10)

(II)

(12)

In which LOSSlndex' PJ���)(] , Vrndex' VDmax' VI , and LI are representatives of the loss index, the network total losses with no DG unit penetrated, voltage profile index, the maximum amount of the voltage profile index and the acceptable amounts of the voltage and losses.

V. SI MULATIONS AND CASE STU Y

The test system applied in this paper is a 27-bus test system including 24 load points [12]. The data on the lines and load points are demonstrated in Table I. Other data on the average costs of energy interruptions associated with various types of customers and maintenance data associated with the system components are borrowed from [3] and [12]. At first, 7 points are assumed in the system as the candidate to install the DG units. Sensitivity analysis has been conducted on the basis of the loss and voltage profile variations in the presence of DG units in various points to select the candidates. The MATPOWER software in Matlab environment has been used as the open source software in doing so. The identified candidate points for DG placement are demonstrated in Fig. 2. Load growth rate is assumed to be 8% in the studies within the 10-year time period. The DG technology includes the thermal units as well as the PV panels as the renewable based DGs.

17 18 19 20 21

11 12 13 14

Figure 2. The identified candidate points for DG placement .

180

160 f'"

f"'I

140

I 120 � 100 �

- .. ,. f'" ,. 1"'1 jill jill jill 0 ...l 80 5 60 0 I-

40

20

0 '" f- r- - '- - '- - - f- f- 7' 4.8 5.2 11.1 14.8 19.1 23.6 29.7 33.5 38 42.6 47.3

DGs Penetration ('X,) Figure 3. Power loss variations in various penetration levels of DG units in

the system.

In order to model the uncertainty associated with the PV output power, the sun irradiance analysis has been done for the past 5 years, five clusters have been recognized according to the levels of the produced power, and the associated probability has been also estimated. The proposed approach on the basis of PSO algorithm with the initial population of 200 particles is simulated which converged to the optimal results demonstrated in Table II. The results show that the optimization process identifies which DG technology to be selected at various candidate points as well.

Table III demonstrates the final optimal values of the proposed objective function, the customer interruption costs, and the network losses for different amounts of VI and LI. As can be shown, once the constraints associated with the network losses and voltage profile are defined conservatively (that is the operator in utility seeks the policy with the minimum amount of network losses and voltage drops), the total imposed costs to the utility increases while the customer interruption costs decreases due to the deployment of local DG units in the optimal manner.

However, in case where it is allowed to have an increase in the network losses and bus-bar voltage drops (for example in the case of L1=1 and VI=0.12 in Table III), although the total imposed costs of operation, DG installation and customer interruptions decrease, the network losses and customer interruption costs would drastically increase. As a result, it can be interpreted in this way that selection of the LI and VI values would provide the decision maker with the possibility of having a wider and comprehensive perspective on the DG placement decision making in distribution network. In other words, it can be concluded that considering the LI and VI parameters can well meet the network requirements and is able to mitigate the operator concerns in the optimization studies. Fig. 3 shows the total network losses for different penetration percentages of DG units in the system. As can be concluded, with the increase in the DG penetration level in the network, the network losses consequently goes down since the optimal location and output power has been decided and employed in the system. The point to be emphasized in the above figure is that the network losses decrease with different penetration

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levels of DG units. As the penetration level grows from 5.2% to 11.1 %, it is observed that the total network losses would drastically diminish. However, the detrimental rate is not that high as the penetration level increases from those values. So, for the penetration levels higher than 40%, the network loss improvement is trivial.

TABLE!. DATA OF THE SYSTEM UNDERSTUDY R(Q/Km) X(QlKm)

0.1233 0.4127 Line No From To Line Length Load (KVA) Power Factor

1 0 1 0 2 1 2 2.5 0 3 2 3 4.7354 1350 0.8 4 3 4 2.5712 0 5 4 5 7.3123 1300 0.95 6 5 6 3.3381 0 7 6 7 4.5219 1250 0.9 8 7 8 10.5507 0 9 8 9 11.7567 1000 0.9

10 9 10 4.7354 1313 0.8 11 2 11 2.5712 1100 0.95 12 11 12 7.3123 400 0.75 13 12 13 3.3381 1350 0.85 14 13 14 4.5219 1225 0.82 15 14 15 10.5507 1125 0.93 16 15 16 11.7567 1300 0.75 17 3 17 4.7354 300 0.9 18 17 18 2.5712 1150 0.8 19 18 19 7.3123 350 0.85 20 19 20 3.3381 400 0.8 21 20 21 4.5219 1100 0.8

22 4 22 10.5507 125 0.93 23 5 23 11.7567 965 0.89 24 6 24 4.7354 982 0.88 25 8 25 2.5712 1300 0.9 26 8 26 7.3123 875 0.75 27 26 27 3.3381 200 0.9

TABLE II . THE FINAL OPTIMAL VALUES FOR THE DG PLACEMENT

DG Busbar 2in

Type line 3

PV x MT x FC ,f

TABLE 1\1.

LI VI

1 0.08 0.9 0.08 0.8 0.08 1 0.12

0.9 0.12 0.8 0.12

PROBLEM Location of the various DG types

Busbar Busbar Busbar Busbar Busbar Busbar 2in 13 in 3 in 4in 6in 8 in

line 11 line 14 line 17 line 5 line 7 line 26 x ,f ,f x ,f x x ,f ,f ,f ,f x ,f x x x ,f ,f

THE FINAL OPTIMAL SELECTION OF THE DG UNITS FOR DIFFERENT LI AND VI VALUES

Total Cost (M$) COC(M$) Total Loss (MW) 3.96 0.318 0.127 4.08 0.316 0.112 4.24 0.301 0.094 3.83 0.343 0.169 4.01 0.306 0.129 4.19 0.296 0.107

VI. CONCLUSION

In this paper, first the techno-economical impacts of DG penetration in power distribution systems have been discussed. A new approach was proposed in response to the common decision makings on the DG placement problem considering the uncertain energy markets. Various forms of imposed costs to the distribution system utility, DG installation and technology-related fees, and customer interruption costs have been all taken into account as a part of the reward/penalty regulation mechanism designed. Two main constraints, namely the total network losses and the maximum allowable voltage profile variations, have been incorporated into the optimization mathematical model. PSO algorithm, due to its strength in fining the global optima of the optimization problems, is employed and the results on the application of the proposed model on a 27-bus test system were investigated. The observations made it clear that the proposed methodology can well provide a practical understanding to the system operator from a techno-economical viewpoint once dealing with the DG unit placement decision making problem in power distribution systems.

RE FERENCES

[ 1] M. Saedighizadeh, A. Rezazadaeh, Using genetic algorithm for distributed generation allocation to reduce losses and im prove voltage profile, World Academy of Science, Engineering and Technology

2008; 37: 251- 256. [ 2] A. K. Singh, S. K. Parida, Selection of load buses for DG placement

based on loss reduction and voltage im provement sensitivity, International Conference on Power Engineering, Energy and Electrical Drives, 11- 13 May, 2011.

[ 3] T. Jen-Hao, L. Tain-Syh, L. Yi-Hwa, Strategic distributed generator placements for service reliability im provements, in: Proceedings of the

IEEE Power Engineering Society Summer Meeting, 2002, p p .719- 724. [ 4] P. Dehghanian, S. H. Hosseini, M. Moeini-Aghtaie, and S. Arabali,

"O ptimal Siting of DG Units in Power Systems form a Probabilistic

87

Multi-Objective O ptimization Pers pective", International Journal of

Electrical Power and Energy Systems, vo1 .51, p p .l 4- 26, October 2013. [ 5] N. Jenkins, R. Allan, P. Crossley, D. Kirschen, G. Strbac, Embeddded

Generation, 1st ed ., Cambridge University, UK, 2008. [ 6] M. Moeini-Aghtaie, P. Dehghanian, E. Vaezizade, and M. Fotuhi­

Firuzabad, "Embedded Generation Planning in Presence of Renewable Energies Using a Probabilistic Scenario- Based Multi-Objective O ptimization A p proach," The 1st International Conference on Integration of Renewable into Power Distributions Systems, CIRED­Worksho p, May 2012, Lisbon, Portugal .

[ 7] G. Erbic, C. A. Canizares, 2006, "Integration of clustering analysis and reward/ penalty mechanisms for regulating service reliability in distribution systems," lET Generation, Transmission & Distribution, vo1 .5, no . 11, p p .1192- 1200.

[ 8] H. Mohammadnezhad-Shourkaei, M. Fotuhi- Firuzabad, R. Billinton, "Integration of Clustering Analysis and Reward/ Penalty Mechanisms for Regulating Service Reliability in Distribution Systems," IETGeneration, Transmission & Distribution, Vo1 . 5, Issue 11, p p . 1192- 1200, Nov . 2011.

[ 9] P. Dehghanian, M. Fotuhi- Firuzabad, F. Aminifar, and R . Billinton, "A Comprehensive Scheme for Reliability Centered Maintenance Implementation in Power Distribution Systems- Part 1: Methodology", IEEE Transactions on Power Delivery, vo1 .28, no .2, p p .761- 770, A pril 2013.

[ 10] P. Dehghanian, M. Fotuhi- Firuzabad, F. Aminifar, and R . Billinton, "A Comprehensive Scheme for Reliability Centered Maintenance

Implementation in Power Distribution Systems- Part IT: Numerical Analysis", IEEE Transactions on Power Delivery, vo1 .28, no .2, p p .771- 778, A pril 2013.

[ 11] 1. Wood, Local Energy: distributed generation of heat and power, lET, London, 2008.

[ 12] M. Moeini-Aghtaie, P. Dehghanian, and S. H. Hosseini, "O ptimal Distributed Generation Placement in a Restructured Environment via a multi-Objective O ptimization A p proach", 16th Conference on Electric

Power Distribution Networks, E PDC, May 2011, Bandar-Abbas, Iran . [ 13] M. Moeini-Aghtaie, P. Dehghanian, M. Fotuhi-Firuzabad, and A.

Abbaspour, "Multi-Agent Genetic Algorithm: An Online

Probabilistic View on Economic Dispatch of Energy Hubs

Constrained by Wind Availability," IEEE Transactions on Sustainable Energy, Special Issue on Real Time Applications of Intelligent Methods in Sustainable Power and Energy Systems, Accepted for Publication, in Press, 2013.