- Email: [email protected]
A Multi-Agent Clustering-based Approach for the Distributed Planning of Wind Generators
10th IFAC Symposium on Control of Power and Energy Systems 10th IFAC Symposium on 4-6, Control of Power Power and and Energy Energy Systems Systems 10th IFAC Symposium on Control Tokyo, Japan, September 2018of 10th IFAC Symposium on Control of 10th IFAC Symposium on 4-6, Control of Power Power and and Energy Energy Systems Systems Tokyo, Japan, September 2018 Tokyo, Japan, September 4-6, 2018 Available online at www.sciencedirect.com Tokyo, Japan, September 4-6, 2018 Tokyo, Japan, September 2018of Power and Energy Systems 10th IFAC Symposium on 4-6, Control Tokyo, Japan, September 4-6, 2018
A A A A
ScienceDirect IFAC PapersOnLine 51-28 (2018) 138–142
Multi-Agent Clustering-based Approach Multi-Agent Clustering-based Approach Multi-Agent Clustering-based Approach for the Distributed Planning of Wind for the Distributed Planning of Wind Multi-Agent Clustering-based Approach for the Distributed Planning of Wind Generators for the Distributed Planning of Wind Generators Generators Khawaja KhalidGenerators Mehmood ∗∗∗ Saad Ullah Khan ∗∗∗
Khawaja Khalid Mehmood∗ ∗∗ Saad Saad Ullah Khan Khan ∗ ∗ Khawaja Khalid Mehmood Ullah Khawaja Khalid Saad Khan Zunaib Maqsood Haider ∗∗ Chul-Hwan ∗ ∗ Khawaja Khalid Mehmood Mehmood Saad Ullah Ullah Kim Khan ∗ Zunaib Maqsood Haider Chul-Hwan Kim ∗ ∗ Zunaib Maqsood Haider Chul-Hwan Kim ∗ ∗ Chul-Hwan Zunaib Maqsood Haider Kim Khawaja Khalid Mehmood Saad Ullah Khan Zunaib Maqsood Haider Chul-Hwan Kim ∗ ∗ ∗ ∗ College of Information and Communication Engineering, Zunaib Maqsood Haider Chul-Hwan Kim ∗ ∗ ∗ College of Information and Communication Engineering, ∗ College of Information and Communication Engineering, ∗ College of and Communication Engineering, Sungkyunkwan Suwon Korea (e-mail: khalidmzd@ College University, of Information Information and440-746, Communication Engineering, Sungkyunkwan University, Suwon 440-746, Korea (e-mail: khalidmzd@ Sungkyunkwan University, Suwon 440-746, Korea (e-mail: khalidmzd@ ∗ Sungkyunkwan University, Suwon 440-746, Korea (e-mail: khalidmzd@ skku.edu, [email protected], [email protected], [email protected]). College of Information and Communication Engineering, Sungkyunkwan University, Suwon 440-746, Korea (e-mail: khalidmzd@ skku.edu, [email protected], [email protected], [email protected]). skku.edu, [email protected], [email protected], [email protected]). skku.edu, [email protected], [email protected], [email protected]). Sungkyunkwan University, Suwon 440-746, Korea (e-mail: khalidmzd@ skku.edu, [email protected], [email protected], [email protected]). [email protected], [email protected], [email protected]). Abstract: Inskku.edu, this paper, a new approach for the distributed planning of the wind distributed Abstract: In this paper, aa new approach for the distributed planning of the wind distributed Abstract: In this paper, new approach for the distributed planning of theFirst, wind distributed Abstract: In this paper, a new approach for the distributed planning of wind distributed generators (DGs) in active distribution networks and microgrids is proposed. electrical Abstract: (DGs) In thisinpaper, adistribution new approach for theand distributed planning of the theFirst, windan distributed generators active networks microgrids is proposed. an electrical generators (DGs) in active distribution networks and microgrids is proposed. First, an electrical generators (DGs) inpaper, active distribution networks and microgrids isHaving proposed. First, an electrical distance matrix (EDM) isadistribution obtained fornetworks thefor distribution system. obtained the EDM, a Abstract: In this new approach the distributed planning of the wind distributed generators (DGs) in active and microgrids is proposed. First, an electrical distance matrix (EDM) is obtained for the distribution system. Having obtained the EDM, a distance matrix (EDM) is obtained for the distribution system. Having obtained the EDM, distance matrix (EDM) obtained for the distribution system. Having obtained the EDM, maximization problem is is formulated and solved for themicrogrids optimal clustering of the network. In theaaa generators (DGs) in active distribution networks and is proposed. First, an electrical distance matrix (EDM) is obtained for the distribution system. Having obtained the EDM, maximization problem is formulated and solved for the optimal clustering of the network. In the maximization problem is formulated solved for the optimal clustering of the network. In thea maximization problem is and solved for the clustering of In second agents are assigned to and each cluster, a system. multi-objective optimization distancestage, matrix (EDM) obtained for the distribution Having obtained the problem EDM, maximization problem is isformulated formulated and solved for and the optimal optimal clustering of the the network. network. In the the second stage, agents are assigned to each cluster, and aaa multi-objective optimization problem second stage, agents are assigned to each cluster, and multi-objective optimization problem second stage, agents are assigned to each cluster, and multi-objective optimization problem (MOOP) is formulated for optimal planning of wind DGs and assigned to a head agent; maximization problem is formulated and solved for the optimal clustering of the network. In the second stage, agents are assigned to each cluster, and a multi-objective optimization problem (MOOP) is for optimal planning of wind DGs and assigned to head the (MOOP) is formulated optimal planning DGs assigned to head agent; the (MOOP) is formulated formulated for optimal planning of wind DGs and assigned to aaaaobjective head agent; agent; the objective functions inare thefor MOOP are equalcluster, to of thewind number ofand agents, and the function second stage, agents assigned to each and a multi-objective optimization problem (MOOP) is formulated for optimal planning of wind DGs and assigned to head agent; the objective functions in the MOOP are equal to the number of agents, and the objective function objective functions in the MOOP are equal to the number of agents, and the objective function objective functions in the MOOP are equal to the number of agents, and the objective function of an agent isformulated composed ofMOOP annual energy losses. Anumber voltage improvement index for each cluster (MOOP) is for optimal planning of wind DGs and assigned to a head agent; the objective functions in the are equal to the of agents, and the objective function of an agent is composed of annual energy losses. A voltage improvement index for each cluster of an agent is composed of annual energy losses. voltage improvement index for each cluster of an agent is composed of annual energy losses. A voltage improvement index for each cluster is also calculated toinmeasure the improvement inA the voltage profile.and IEEE 37-node test feeder objective functions the MOOP are equal to the number of agents, the objective function of an agent is composed of annual energy losses. A voltage improvement index for each cluster is also calculated to measure the improvement in the voltage profile. IEEE 37-node test feeder is also calculated to measure the in the voltage profile. 37-node test feeder is calculated to measure the improvement in voltage profile. IEEE 37-node test feeder and testis cases taken intoimprovement account for the study. The results IEEE show thatforthe losses are of antwo agent composed of annual energy losses. voltage improvement index each cluster is also also calculated toare measure the improvement inAthe the voltage profile. IEEE 37-node test feeder and two test cases are taken into account for the study. The results show that the losses are and two test cases are taken into account for the study. The results show that the losses are and two test cases are taken intovoltage account for the the study. Theprofile. results show that thetest losses are reduced in each cluster, and the profile is also enhanced at theIEEE same37-node time.the is also calculated to measure the improvement in the voltage feeder and two test cases are taken into account for study. The results show that losses are reduced in each cluster, and the voltage profile is also enhanced at the same time. reduced in each cluster, and the voltage profile is also enhanced at the same time. reduced in each cluster, and the voltage profile is also enhanced at the same time. and two in test cases are taken intovoltage account for the study. The results show that the losses are reduced each cluster, and the profile is also enhanced at the same time. © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. reduced in Clustering, each cluster,distributed and the voltage profile is also enhanced the same time. Keywords: planning, distribution systems,atenergy losses, wind DG Keywords: Keywords: Clustering, Clustering, distributed distributed planning, planning, distribution distribution systems, systems, energy energy losses, losses, wind wind DG DG Keywords: Clustering, distributed planning, distribution systems, energy losses, wind planning. Keywords: Clustering, distributed planning, distribution systems, energy losses, wind DG DG planning. planning. planning. Keywords: Clustering, distributed planning, distribution systems, energy losses, wind DG planning. planning. 1. INTRODUCTION was employed for the planning of DERs in Othman et al. 1. INTRODUCTION was employed for the planning of DERs in Othman et al. 1. INTRODUCTION INTRODUCTION was employed employed for the the planning of DERs DERs in loss Othman et al. al. 1. was for of in Othman et (2015) considering theplanning energy and power minimiza1. INTRODUCTION was employed for the planning of DERs in loss Othman et al. (2015) considering the energy and power minimiza(2015) considering the energy and power loss minimiza(2015) considering the energy and power loss minimization. 1. INTRODUCTION was employed for the of DERs in loss Othman et al. (2015) considering theplanning energy and power minimizaTo reduce the environmental pollution, penetration of tion. tion. To reduce reduce the the environmental environmental pollution, pollution, penetration penetration of of tion. To (2015) considering the energy and power loss minimization. To reduce the environmental pollution, penetration of distributed energy resources (DERs) and electric vehicles To reduce energy the environmental pollution, penetration of In previous research, the planning of wind DERs is perdistributed resources (DERs) and electric vehicles distributed energy resources (DERs) and electric vehicles In previous research, the planning of wind DERs is pertion. In previous research, the planning of wind wind DERs DERs is is perdistributed energy resources (DERs) electric has increased the past few yearsand Khan et al. vehicles (2018). To reduce theover environmental pollution, penetration of In research, planning of distributed energy resources (DERs) and electric vehicles formed considering thethe whole distribution as perone In previous previous research, the planning of windnetwork DERs is perhas increased over the past few years Khan et al. (2018). has increased over the past few years Khan et al. (2018). formed considering the whole distribution network as one formed considering the whole distribution network as one has increased over the past few years Khan et al. (2018). In this context, Korean government has also made several formed distributed energy resources (DERs) and electric vehicles considering the whole distribution network as one has increased over the past few years Khan et al. (2018). search space. However, in real-world power networks, some In previous research, planning of windnetworks, DERs is performed considering thethe whole distribution network assome one In this this context, context, Korean Korean government government has has also also made made several several search In space. However, in real-world power search space. However, in real-world power networks, some In this context, Korean government has also made several policies to cut down the green house gas emission and have hasthis increased over the past few years Khan al.and (2018). search However, in real-world power networks, In context, Korean government has also et made several regions have strong mutual coupling between them;assome voltformed considering the whole distribution network one search space. space. However, in real-world power networks, some policies to cut down the green house gas emission have policies to cut down the green house gas emission and have regions have strong mutual coupling between them; voltregions have strong strong mutual coupling between them; voltpolicies to cut the gas emission and have plans its Jeju islandhouse carbon 2030. The regions In thisto context, Korean government hasfree alsoby made several have mutual coupling between them; voltpolicies tomake cut down down the green green house gas emission and have age variations in these closely coupled buses may occur search space. However, in real-world power networks, some regions have strong mutual coupling between them; voltplans to make its Jeju island carbon free by 2030. The plans to make its Jeju island carbon by 2030. The age variations in these closely coupled buses may occur age variations invariations these closely coupled buses may occur plans to its island carbon free by 2030. The total electric generated fromgas thefree wind policies cutenergy down the green emission and have variations in these closely buses may occur plans totomake make its Jeju Jeju islandhouse carbon by distributed 2030. The age because of loads incoupling onecoupled of these buses.them; Therefore, regions have strong mutual between voltage variations invariations these closely coupled buses may occur total electric energy generated from the thefree wind distributed total electric energy generated from wind distributed because of loads in one of these buses. Therefore, because of loads variations in one of these buses. Therefore, total electric energy generated from the wind distributed generators (DGs) would reach 2carbon GW the and 350 MW forThe off- because plans to make its Jeju island free by 2030. of loads variations in one of these buses. Therefore, total electric energy generated from wind distributed it is necessary for enhancing the voltage quality to identify age variations in these closely coupled buses may occur because of loads variations in one of these buses. Therefore, generators (DGs) would reach 2 GW and 350 MW for offgenerators (DGs) would reach 2 GW and 350 MW for offit is necessary for enhancing the voltage quality to identify it is necessary necessary forvariations enhancing the voltage quality to identify identify generators (DGs) would reach 22from GW and 350 MW for shore and on-shore wind power generation, total electric energy generated the distributed for the to generators (DGs) would reach GW andwind 350 respectively. MW for offoff- it these electrically coupled in buses before optimally placing because of loads onevoltage of thesequality buses. Therefore, it is is necessary for enhancing enhancing the voltage quality to identify shore and on-shore on-shore wind power generation, respectively. shore and wind power generation, respectively. these electrically coupled buses before optimally placing these electrically coupled buses before optimally placing shore and on-shore wind power generation, respectively. Due to substantial increase in the penetration of DERs generators (DGs) would reach 2 GW and 350 MW for offthese coupled buses before optimally placing shoretoand on-shore increase wind power generation, respectively. DGs inelectrically the distribution networks to mitigate thetoeffects of it is necessary for enhancing the voltage quality identify these electrically coupled buses before optimally placing Due substantial in the penetration of DERs Due to substantial increase in the penetration of DERs DGs in the distribution networks to mitigate the effects of DGs in the distribution networks to mitigate the effects of Due to substantial increase in the penetration of DERs and independent microgrids, new planning approaches are DGs shore and on-shore wind power generation, respectively. in the distribution networks to mitigate the effects of Due to substantial increase in the penetration of DERs load variations. In addition, since the problem of optimal these electrically coupled buses before optimally placing DGs in the distribution networks to mitigate the effects of and independent microgrids, new planning approaches are and independent microgrids, new planning approaches are load variations. In addition, since the problem of optimal load variations. In addition, since the problem of optimal and independent microgrids, new planning approaches are required for the distributed planning of the DERs by Due to substantial increase in the penetration of DERs load variations. In addition, since the problem of optimal and independent microgrids, new planning approaches are planning of DGs is a combinatorial optimization problem, DGs in the distribution networks to mitigate the effects of load variations. In addition, since the problem of optimal required for the distributed planning of the DERs by required for the distributed planning of the DERs by planning of DGs is aa combinatorial combinatorial optimization problem, planning ofspace DGsIn isthe combinatorial optimization problem, required for distributed planning of the DERs by identifying thethe areas which are closely connected to each and independent microgrids, new planning approaches are planning of DGs is a optimization problem, required for the distributed planning of the DERs by the search of problem can be reduced by the idenload variations. addition, since the problem of optimal planning of DGs is a combinatorial optimization problem, identifying the the areas areas which which are are closely closely connected connected to to each each the identifying search space of the problem can be reduced by the identhe searchof space ofisclosely the problem can areas. be reduced by problem, the idenidenidentifying areas which are closely connected to other. The distributed approach will eventually increase required forthe the distributed of the DERs by the search space of the problem can be reduced by identifying the areas which areplanning closely connected to each each tification these coupled Furthermore,the planning DGs a combinatorial optimization the searchof space of closely the problem can areas. be reduced by the the idenother. The distributed approach will eventually increase other. The distributed approach will eventually increase tification of these coupled Furthermore,the tification of these closely coupled areas. Furthermore,the other. The distributed approach will eventually increase the reliability of the which system and improve the planning identifying the areas are closely connected to each tification of these closely coupled areas. Furthermore,the other. The distributed approach will eventually increase identification of these regions in the distribution feeder the searchof space ofthese the problem can be reduced by thefeeder identification these closely coupled areas. Furthermore,the the reliability reliability of of the the system system and and improve improve the the planning planning identification the of regions in the distribution identification of these regions in the the distribution feeder the reliability of the improve the planning results for all the of theand distribution network. other. The distributed approach will eventually increase identification of regions in the distribution feeder the reliability of regions the system system and improve the planning may also help thethese utilities to vary importance given to tification of these closely coupled areas. Furthermore,the identification of these regions in the distribution feeder results for all the regions of the distribution network. results for all the regions of the distribution network. may also help the utilities to the importance given to may also regions help thefor utilities to vary vary the importance given to results for all the regions of the distribution network. the reliability of the system and improve the planning may also help the utilities to vary the importance given to results for all the regions of the distribution network. different installing the new DGs in the distriidentification of these regions in the distribution feeder may also help the utilities to vary the importance given to Several researchers have studied the optimal planning of different regions for installing the new DGs in in the the distridistridifferent regions for installing the new DGs in the distriSeveral researchers have studied the optimal planning of results for all the regions of the distribution network. different regions for installing the new DGs system. Allfor these issues require that the distributed Several researchers have studied the optimalofplanning planning of bution may also help the utilities to vary the importance given to different regions installing the new DGs in the distriSeveral researchers have studied the optimal of DERs. In Atwa et al. (2010), the planning DERs was bution system. All these issues require that the distributed Several In researchers have studied optimalofplanning of bution bution system. All these issues require that the distributed DERs. Atwa et et al. al. (2010), thethe planning DERs was was system. All these issues require that the distributed planning of the DGs should be performed. DERs. In Atwa (2010), the planning of DERs different regions for installing the new DGs in the distribution system. All these issues require that the distributed DERs. In Atwa et al. (2010), the planning of DERs was performed considering the annual energy loss minimizaSeveral In researchers have studied optimal of planning of the DGs should be performed. DERs. Atwa et al. (2010), thethe planning ofplanning DERs was planning of the the DGs DGs should berequire performed. performed considering the annual energy loss minimizaplanning of should be performed considering the annual energy loss minimizabution these issues thatdistributed the distributed planning of theAll DGs should be performed. performed. performed considering the annual energy minimization. In In Sheng et et al.al. (2015), new was proposed DERs. Atwa (2010), the algorithm planning of DERs was In performed considering the a annual energy loss loss minimizathis system. paper, we have proposed a new aption. In Sheng et al. (2015), a new algorithm was proposed tion. In Sheng et al. (2015), a new algorithm was proposed In this paper, we have proposed a new distributed applanning of the DGs should be performed. In this paper, we have proposed a new distributed aption. In Sheng et aaannual new algorithm was proposed for the planning wind generators con- In performed considering the energy loss this paper, we have proposed aa new distributed aption. In multi-objective Sheng et al. al. (2015), (2015), newof algorithm wasminimizaproposed proach for the optimal planning of wind DGs. The problem In this paper, we have proposed new distributed apfor the multi-objective planning of wind generators confor the multi-objective planning of wind generators conproach for the optimal planning of wind DGs. The problem proach for the optimal planning of wind DGs. The problem for the multi-objective planning of wind generators considering several objective functions such as losses, voltage tion.the In multi-objective Sheng etobjective al. (2015), a newof algorithm was proposed planning of DGs. The problem for planning windasgenerators con- proach is solved inthe twooptimal stages; inproposed the first stage, first, an electrical In this for paper, we have a new distributed approach for the optimal planning of wind wind DGs. The problem sidering several functions such losses, voltage sidering several objective functions such as losses, voltage is solved in two stages; in the first stage, first, an electrical is solvedforin in twooptimal stages; inisthe the first stage,DGs. first, an electrical sidering several objective functions such as losses, voltage deviation and voltage stability margins improvements. In is for the multi-objective planning of wind generators consolved two stages; in first stage, first, an electrical sidering several objective functions such as losses, voltage distance matrix (EDM) constructed for a distribution proach the planning of wind The problem is solved in two stages; in the first stage, first, an electrical deviation and and voltage voltage stability stability margins margins improvements. improvements. In In distance deviation matrix (EDM) is constructed for distribution distance matrix (EDM) isthe constructed for distribution deviation and voltage stability margins In Mehmood et al. (2017), optimal planning battery storsidering several objective functions such improvements. asof voltage matrix (EDM) for aaaaisan distribution deviation and voltage stability margins improvements. In distance system. an optimization problem formulated is solvedAfter in twothat, stages; inis first stage, first, electrical distance matrix (EDM) is constructed constructed for distribution Mehmood et al. al. (2017), optimal planning oflosses, battery storMehmood et (2017), optimal planning of battery storsystem. After that, an optimization problem is formulated system. After that, an optimization problem is formulated Mehmood et al. (2017), optimal planning of battery storage systems with windstability and solar DGs was carried outstorfor deviation and voltage margins improvements. In system. After that, an optimization problem is formulated Mehmood et al. (2017), optimal planning of battery for the optimal clustering of the distribution networks to distance matrix (EDM) is constructed for a distribution system. After that, an optimization problem is formulated age systems with wind and solar DGs was carried out for age systems with wind and solar DGs was carried out for for the optimal clustering of the distribution networks to for the optimal optimal clustering ofareas the distribution distribution networks to age systems with wind and solar DGs was carried out for the losses and investment cost minimization. In Mansor Mehmood et al. (2017), optimal planning of battery storfor the clustering of the networks to age systems with wind and solar DGs was carried out for identify the closely coupled in the network. Having system. After that, an optimization problem is formulated for the optimal clustering ofareas the distribution networks to the losses losses and and investment investment cost cost minimization. minimization. In In Mansor Mansor identify the the closely coupled in the network. Having identify the closely coupled areas in the network. Having the losses and investment cost minimization. In Mansor and Levi (2017), a two-stage procedure was proposed for age systems with wind and solar DGs was carried out identify the closely coupled areas inassigned the network. network. Having the losses and investment cost minimization. In Mansor generated clusters, agents to the clusfor the optimal clustering ofareas theare distribution networks to identify thethe closely coupled in the Having and Levi (2017), a two-stage procedure was proposed for and Levi (2017), a two-stage procedure was proposed for generated the clusters, agents are assigned to the clusgenerated the clusters, agents areinoptimization assigned to the the clusand Levi aa two-stage procedure was for the DER-planning which considered several operational and investment cost minimization. In Mansor clusters, agents are assigned to clusand losses Levi (2017), (2017), two-stage procedure was proposed proposed for generated ters, andthe athe second multi-objective problem identify closely coupled areas the network. Having generated the clusters, agents are assigned to the clusthe DER-planning which considered several operational the which considered several operational ters, and second multi-objective optimization problem ters, and aaathe second multi-objective optimization problem the DER-planning which several operational factors during thea planning. Big bang-big crunch method and DER-planning Levi (2017), two-stage procedure was proposed for ters, and multi-objective problem the DER-planning which considered considered several operational generated clusters, agents are optimization assigned to the clusters, and a second second multi-objective optimization problem factors during the planning. planning. Big bang-big crunch method factors during the Big bang-big crunch method factors during the planning. Big bang-big crunch method the DER-planning which considered several operational factors during the planning. Big bang-big crunch method ters, and a second multi-objective optimization problem factors planning. Big Federation bang-bigofcrunch method Copyright © 2018, 2018the IFAC 138Hosting by Elsevier Ltd. All rights reserved. 2405-8963during © IFAC (International Automatic Control) Copyright 2018 IFAC 138 Copyright ©under 2018 responsibility IFAC 138 Peer review© of International Federation of Automatic Control. Copyright 138 Copyright © © 2018 2018 IFAC IFAC 138 10.1016/j.ifacol.2018.11.691 Copyright © 2018 IFAC 138
IFAC CPES 2018 Tokyo, Japan, September 4-6, 2018 Khawaja Khalid Mehmood et al. / IFAC PapersOnLine 51-28 (2018) 138–142
is formulated for the annual energy loss minimization for a head-agent. Each agent attempts to minimize the losses for its own cluster and reports to the head-agent. IEEE 37-node test feeder and two test cases are studied for the simulations.
d¯ =
N di N i=1
(6)
subject to
The paper is organized as follows: Section 2 describes the clustering. An electrical distance matrix formation and problem formulation are also discussed in Section 2. The problem formulation for the sizing and placement of wind DGs is presented in Section 3. A test system and test cases are explained in Section 4, and the results are discussed in Section 5. Finally, the paper is concluded in Section 6.
ξ(i,i+1,c) = 1 ∀ (bs(i,c) ∧ bs(i+1,c) ) ∈ Nc (1)
(bs(i,c) ∧ bs(i+1,c) ) ∈ Nc ∀ Adj(i+1,c) ∈ Nc C
2. CLUSTERING OF A DISTRIBUTION SYSTEM
ic(c) = RCLmax
(7) (8)
(9)
c=1
In this section, we present the formation of electrical distance matrix and problem formulation for clustering. 2.1 Electrical distance matrix construction In literature, several researchers have used network clustering based method in distributed control schemes for the identification of closely related areas in the power systems Cotilla-Sanchez et al. (2013); Shahidehpour and Wang (2004). The most commonly used approach uses the construction of EDMs as they are capable of providing relationships between nodes and the sensitivity of active powers. The EDMs can be constructed using several approaches such as by using the admittance/susceptance or the Jacobian matrix of the system Poudel et al. (2016); Blumsack et al. (2009). The contruction of an EDM using admittance matrix method reduces the computational time of the simulations as the formation of the Jacobian is not required for the EDM construction. Because of the aforementioned reasons, we use the admittance matrix for obtaining the EDM using equation (1): ED = |Y(bus) −1 |
139
where zij is the electrical distance between buses i and j, L is the set containing the buses in a cluster, d and d∗ are the normal and optimal size of the cluster, N is the total number of buses in the system, ξ is the incidence matrix, bs is the identification constant for a bus, Adj is the adjacency list of the buses, Nc is the number of a buses in a c-th cluster, ic is the identification constant for the clusters and RCLmax is the maximum number of the desired clusters. In order to obtain feasible solutions, several constraints are also imposed on the optimization problem. In an optimal solution, all the buses must be connected in a cluster as given in equation (7). To verify the connectivity of the nodes, the incidence matrix for the distribution network is constructed. We have used admittance matrix to construct the incidence matrix. In case of two disconnected buses in the same cluster, the adjacency list of the nodes is used for the verification as shown in equation (8). The maximum number of clusters obtained are control by Equation (9). In the next section, the problem formulation for the wind DG planning is presented.
(1)
where Y(bus) is the admittance matrix of the system. 3. PLANNING OF WIND DGS
2.2 Problem formulation for clustering After obtaining the EDM, a maximization problem is formulated for the clustering of the distribution network. The objective function, given in equation (2), is composed of three parameters presented in equations (3)–(5) CotillaSanchez et al. (2013). First parameter, inter-cluster distance index (INI), controls and combine the buses with are closely located to each other. Second parameter, intracluster distance index (ITI), governs the distances between different clusters. Finally, third parameter, cluster size index (DCI), controls the size of the clusters. (2) max f1 = IN I × IT I × DCI n z ij j ∈L / i=1 IN I = 1 − n n i (3) z i=1 j=1 ij n 1 IT I = 1 −
i=1 n i=1
DCI = e
j ∈Li zij n 1 j=1 i =j zij
∗) ¯ −(lnd−lnd 2σ 2
(4) (5)
139
3.1 Problem formulation for Wind DG placement Having partitioned the network optimally, agents are assigned to each cluster. A multi-objective optimization problem, equation (10), is formulated and assigned to a head agent. The objective function of an agent, equation (11), is comprised of the annual energy losses. The importance given to a specific region can be varied by changing the weights for an agent in the objective function (10).
M in f2 =
C c=1
(α) f(c)
(α)
w(c) × f(c)
(10)
Nc S D H P(i,c,m,h,s) + Q(i,c,m,h,s) 2 = × R(ij) V(i,c,m,h,s) s= 1 h=1 m=1 i=1 (11)
IFAC CPES 2018 140 Tokyo, Japan, September 4-6, 2018 Khawaja Khalid Mehmood et al. / IFAC PapersOnLine 51-28 (2018) 138–142
subject to min max V(i,c) ≤ V(i,c,t) ≤ V(i,c)
(12)
max |I(ij,c,t) | ≤ I(ij,c)
Nc
(13)
W DG W DG ς(i,c) = R(c)
(14)
i=1
A
α=1 W DG P(i,c)
ia(α) = C
(15)
= 0 ∀i ∈ / Nc
(16)
W DG l(i,c) , P(i,c) ≥ + l(i,c) ∈ Z
0
(17)
Similar to the optimization problem of the preceding section, in order to obtain the feasible solutions, various constraints are designed for the planning optimization problem. Voltage magnitude of a bus in a cluster ‘c’ should be within the safe operating limits at time ‘t’ as given in equation (12), and Equation (13) shows that the current flowing through a line must be less than the maximum allowed current in the line. The total number of the DGs obtained from an optimal solution must be equal to the required number of DGs to be installed in the network as depicted in Equation (15). If a bus is not a member of a specific cluster, the DGs must not be placed on that bus as shown in Equation (16). The size of DGs must be positive numbers whereas the bus locations should be positive and integer numbers in accordance with Equations (17) and (18). In order to evaluate the quality of obtained solutions, voltage improvement index (VMI) values of the buses within each cluster are also calculated using Equation (19):
V
=
Nc i=1
avg V(c,i,t) =
ref avg |V(i,t) − V(i,t) |
N V p=1 (p,i,c,t)
P
ϕ(i,c)
Acquire the network admittance matrix Clustering Problem Obtain the EDM from the admittance matrix
Apply GA
(18)
where w(c) represents the weights given to the various (α) agents, f(c) is the objective function of the agent assigned to the c-th cluster. P(i,c,m,h,s) and Q(i,c,m,h,s) are the active and reactive loads connected to the i -th bus of the c-th cluster, V(i,c,t) is the voltage of the i -th bus of the c-th min min and V(i,c) are the minimum and maximum cluster, V(i,c) voltages, I(ij,c,t) is the current flowing between between max buses i and j, I(ij,c) is the maximum value of the line W DG current, ς(i,c) is the identification constant for installing W DG wind DG, R(c) is the maximum number of wind DGs W DG to be installed, P(i,c) is the power rating of a wind DG, l(i,c) is the bus number of the i -th bus of the c-th cluster.
(α) M I(c)
Start
2
(19) (20)
ref where V(i,t) is the reference voltage value for the i -th bus, avg V(i,t) is the average voltage of three phases, V(p,i,c,t) is the voltage of the i -th bus, N is the total number of buses in the system and ϕ(i,c) is the number of phases at the i -th bus.
140
Solve the clustering problem using genetic algorithm Obtain the optimal cluster and partition the network Planning Problem Assign agents to the clusters
Apply GA Solve the wind DG planning problem Obtain the optimal size and location
Calculate VMI values
Stop
Fig. 1. Flowchart representation of the proposed planning approach 3.2 Power model of a Wind DG For generating the electrical power from wind speed samples, the power model given in (21) is employed Hetzer et al. (2008); Mehmood et al. (2018). sw − sci for sci ≤ sw ≤ sco Pr × s − s r ci (21) Pw = P for sr ≤ sw ≤ sco r 0 otherwise
where Pw is power generated from a wind turbine, Pr is the rated power of a wind turbine. sw , sr , sci and sco are the current, rated, cut-in and cut-off wind speeds, respectively. Fig. 1 shows the steps performed in the proposed scheme. First, the network admittance matrix is acquired to obtain the EDM. Having obtained the EDM, an optimization
IFAC CPES 2018 Tokyo, Japan, September 4-6, 2018 Khawaja Khalid Mehmood et al. / IFAC PapersOnLine 51-28 (2018) 138–142
1
C 1 712 701
Load demand
742 705
0.8
0.6
06
12 Time (hour)
18
24
Fig. 2. Seasonal 5-min load curves of Korea problem is formulated for the optimal clustering of the distribution network.Thereafter, a genetic algorithm (GA) is used for solving the clustering problem, and the network is partitioned optimally. After the clustering problem, agents are assigned to each cluster, a second multi-objective optimization problem is formulated and solved using the GA. The solution of this problem results in optimal sizes and locations of wind DGs in each cluster. Finally, the VMI values are calculated for each cluster, which also finish the proposed scheme. In the next section, a test system and test cases are described. 4. TEST SYSTEM AND TEST CASES The proposed scheme has been verified using the IEEE 37-node test feeder. The desired number of cluster to be formed in the test system has been set to four. The 5min seasonal load curves of Korea, presented in Fig. 2 has been used for the loads and the wind speed data of six years have been used for generating wind speed samples for four seasons. Two test cases have been studied to verify the proposed scheme. In the first test case, no DGs have been installed in the distribution system whereas in the second test case, distributed installation of wind DGs has been carried out. Table 1. Optimal locations and sizes of wind DGs Cases Case 2 (37)
Clusters Cluster 1 Cluster 2 Cluster 3 Cluster 4
Size 1695.8 kW 500.8 kW 797 kW 846.8 kW
Buses 702 722 709 738
Table 2. Results obtained from the simulations Cases Case 1 (37)
Case 2 (37)
Clusters Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 1 Cluster 2 Cluster 3 Cluster 4
Losses (MWh) 229.19 24.35 84.31 21.44 75.64 13.62 32.77 11.54
VMI 0.0022 0.0058 0.0081 0.0081 0.0009 0.0025 0.0034 0.0033
722
713 704 702 714
744 727 703 729 728
0.7
0.5 00
C2
799
0.9
141
C3
730
732 708
709 731
724 707 720 706
718 725
C : Cluster
733 775 736 C4 710 734 740 741 737 738 711 735 Fig. 3. Clusters in IEEE 37-node test feeder 5. RESUTLS AND DISCUSSION In the this section, results of the test cases presented in the preceding section are presented and discussed. 5.1 Clustering Fig. 3 shows the results obtained from the optimization of the distribution system for the clustering problem. As the desired number of clusters were set to four, the network was partitioned into four connected clusters. The objective function value for the network was 0.20375. It can be seen that the adjacent buses within the clusters are connected. 5.2 Optimal planning of wind DGs Table 2 shows results obtained for both test cases. Since there were four clusters formed in the distribution feeder, four wind DGs were installed in the distribution system. In clusters one, two, three and four, wind DGs of power ratings 1740, 780.7, 1144.6 and 822.1 kWs were installed on buses 701, 720, 730 and 738, respectively. Table 2 shows the losses in each cluster for both test cases. It can be seen that in Case 1, losses were high in all clusters since there were no wind DGs installed in the distribution system. However, after the installation of wind DGs in each cluster, losses reduced. It can be noticed from Table 2 that the losses in clusters one, two, three and four reduced from 229, 210, 21 and 125 to 73, 61, 52 and 16 MWhs. At this stage, it is worth mentioning that we discovered that in conventional approach of DG planning, reduction in losses in a distribution network resulted in increase in losses in some region of the power system. However, the approach proposed in our study partitioned the network and considered the minimization of losses in each cluster as a multi-objective optimization problem, which resulted in loss minimization in all clusters simultaneously. In addition, the VMI results have also been tabulated in Table 2. In Case 1, the VMI values were higher. However, it can be seen that after the placement of DGs in Case 2, because of reduction in the losses, the voltage profiles
141
IFAC CPES 2018 142 Tokyo, Japan, September 4-6, 2018 Khawaja Khalid Mehmood et al. / IFAC PapersOnLine 51-28 (2018) 138–142
Cluster 4
Cluster 3
Cluster 2
Cluster 1 0
50
100 150 200 Losses (MWh)
250
300
Fig. 4. Comparison of losses in Cases 1 and 2 of the distribution system improved which caused the reduction in the VMI values from 0.0022, 0.0058, 0.0081 and 0.0081 to 0.0009, 0.0025, 0.0034 and 0.0033 in Case 2. Finally, Fig. 4 compares the results of Cases 1 and 2 and shows the reduction of losses in Case 2 for each cluster. All the results show that the proposed distributed planning scheme can be used for the distributed planning of wind DGs to reduce the losses and to improve the voltage in the distribution network. 6. CONCLUSION In this paper, a new multi-agent clustering based distributed approach was proposed for the distributed planning of wind DGs. First, an optimization problem was formulated for the network clustering; cluster size and inter and intra cluster distances were considered as the objective functions. After partitioning the network, agents were assigned to each cluster, and a second multi-objective optimization problem was formulated for a head agent; the objective function of an agent was composed of annual energy loss reduction. IEEE 37-node test feeder was taken into account for the simulations, and six year wind speed data and seasonal load curves of Korea were considered for the simulations. Moreover, two test cases were studied. Clustering problem optimally partitioned the network into four cluster, and in the second optimization problem, one wind DG was placed in each cluster. All the results showed that the losses were reduced in each cluster, and at the same time, voltage profile of all buses within each cluster was enhanced. The scheme proposed in this paper can be utilized for the distributed planning of distributed generators to increase the reliability of the power networks. In the future, we will extent and test the method for different test systems and different types of DGs. ACKNOWLEDGEMENTS This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (no. 2015R1A2A1A10052459). REFERENCES Atwa, Y.M., El-Saadany, E.F., Salama, M.M.A., and Seethapathy, R. (2010). Optimal renewable resources 142
mix for distribution system energy loss minimization. IEEE Transactions on Power Systems, 25(1), 360–370. doi:10.1109/TPWRS.2009.2030276. Blumsack, S., Hines, P., Patel, M., Barrows, C., and Sanchez, E.C. (2009). Defining power network zones from measures of electrical distance. In 2009 IEEE Power Energy Society General Meeting, 1–8. doi: 10.1109/PES.2009.5275353. Cotilla-Sanchez, E., Hines, P.D.H., Barrows, C., Blumsack, S., and Patel, M. (2013). Multi-attribute partitioning of power networks based on electrical distance. IEEE Transactions on Power Systems, 28(4), 4979–4987. doi: 10.1109/TPWRS.2013.2263886. Hetzer, J., Yu, D.C., and Bhattarai, K. (2008). An economic dispatch model incorporating wind power. IEEE Transactions on Energy Conversion, 23(2), 603– 611. doi:10.1109/TEC.2007.914171. Khan, S.U., Mehmood, K.K., Haider, Z.M., Bukhari, S.B.A., Lee, S.J., Rafique, M.K., and Kim, C.H. (2018). Energy management scheme for an ev smart charger V2G/G2V application with an EV power allocation technique and voltage regulation. Applied Sciences, 8(4). doi:10.3390/app8040648. Mansor, N.N. and Levi, V. (2017). Integrated planning of distribution networks considering utility planning concepts. IEEE Transactions on Power Systems, 32(6), 4656–4672. doi:10.1109/TPWRS.2017.2687099. Mehmood, K.K., Khan, S.U., Lee, S.J., Haider, Z.M., Rafique, M.K., and Kim, C.H. (2017). Optimal sizing and allocation of battery energy storage systems with wind and solar power DGs in a distribution network for voltage regulation considering the lifespan of batteries. IET Renewable Power Generation, 11(10), 1305–1315. doi:10.1049/iet-rpg.2016.0938. Mehmood, K.K., Khan, S.U., Lee, S.J., Haider, Z.M., Rafique, M.K., and Kim, C.H. (2018). A realtime optimal coordination scheme for the voltage regulation of a distribution network including an OLTC, capacitor banks, and multiple distributed energy resources. International Journal of Electrical Power & Energy Systems, 94, 1 – 14. doi: https://doi.org/10.1016/j.ijepes.2017.06.024. Othman, M.M., El-Khattam, W., Hegazy, Y.G., and Abdelaziz, A.Y. (2015). Optimal placement and sizing of distributed generators in unbalanced distribution systems using supervised big bang-big crunch method. IEEE Transactions on Power Systems, 30(2), 911–919. doi:10.1109/TPWRS.2014.2331364. Poudel, S., Ni, Z., and Sun, W. (2016). Electrical distance approach for searching vulnerable branches during contingencies. IEEE Transactions on Smart Grid, PP(99), 1–1. doi:10.1109/TSG.2016.2631622. Shahidehpour, M. and Wang, Y. (2004). Communication and Control in Electric Power Systems: Applications of Parallel and Distributed Processing. John Wiley & Sons, Inc., Hoboken, New Jersey, Hoboken, New Jersey, USA, 1st edition. Sheng, W., Liu, K.Y., Liu, Y., Meng, X., and Li, Y. (2015). Optimal placement and sizing of distributed generation via an improved nondominated sorting genetic algorithm-II. IEEE Transactions on Power Delivery, 30(2), 569–578. doi:10.1109/TPWRD.2014.2325938.
A A A A
ScienceDirect IFAC PapersOnLine 51-28 (2018) 138–142
Multi-Agent Clustering-based Approach Multi-Agent Clustering-based Approach Multi-Agent Clustering-based Approach for the Distributed Planning of Wind for the Distributed Planning of Wind Multi-Agent Clustering-based Approach for the Distributed Planning of Wind Generators for the Distributed Planning of Wind Generators Generators Khawaja KhalidGenerators Mehmood ∗∗∗ Saad Ullah Khan ∗∗∗
Khawaja Khalid Mehmood∗ ∗∗ Saad Saad Ullah Khan Khan ∗ ∗ Khawaja Khalid Mehmood Ullah Khawaja Khalid Saad Khan Zunaib Maqsood Haider ∗∗ Chul-Hwan ∗ ∗ Khawaja Khalid Mehmood Mehmood Saad Ullah Ullah Kim Khan ∗ Zunaib Maqsood Haider Chul-Hwan Kim ∗ ∗ Zunaib Maqsood Haider Chul-Hwan Kim ∗ ∗ Chul-Hwan Zunaib Maqsood Haider Kim Khawaja Khalid Mehmood Saad Ullah Khan Zunaib Maqsood Haider Chul-Hwan Kim ∗ ∗ ∗ ∗ College of Information and Communication Engineering, Zunaib Maqsood Haider Chul-Hwan Kim ∗ ∗ ∗ College of Information and Communication Engineering, ∗ College of Information and Communication Engineering, ∗ College of and Communication Engineering, Sungkyunkwan Suwon Korea (e-mail: khalidmzd@ College University, of Information Information and440-746, Communication Engineering, Sungkyunkwan University, Suwon 440-746, Korea (e-mail: khalidmzd@ Sungkyunkwan University, Suwon 440-746, Korea (e-mail: khalidmzd@ ∗ Sungkyunkwan University, Suwon 440-746, Korea (e-mail: khalidmzd@ skku.edu, [email protected], [email protected], [email protected]). College of Information and Communication Engineering, Sungkyunkwan University, Suwon 440-746, Korea (e-mail: khalidmzd@ skku.edu, [email protected], [email protected], [email protected]). skku.edu, [email protected], [email protected], [email protected]). skku.edu, [email protected], [email protected], [email protected]). Sungkyunkwan University, Suwon 440-746, Korea (e-mail: khalidmzd@ skku.edu, [email protected], [email protected], [email protected]). [email protected], [email protected], [email protected]). Abstract: Inskku.edu, this paper, a new approach for the distributed planning of the wind distributed Abstract: In this paper, aa new approach for the distributed planning of the wind distributed Abstract: In this paper, new approach for the distributed planning of theFirst, wind distributed Abstract: In this paper, a new approach for the distributed planning of wind distributed generators (DGs) in active distribution networks and microgrids is proposed. electrical Abstract: (DGs) In thisinpaper, adistribution new approach for theand distributed planning of the theFirst, windan distributed generators active networks microgrids is proposed. an electrical generators (DGs) in active distribution networks and microgrids is proposed. First, an electrical generators (DGs) inpaper, active distribution networks and microgrids isHaving proposed. First, an electrical distance matrix (EDM) isadistribution obtained fornetworks thefor distribution system. obtained the EDM, a Abstract: In this new approach the distributed planning of the wind distributed generators (DGs) in active and microgrids is proposed. First, an electrical distance matrix (EDM) is obtained for the distribution system. Having obtained the EDM, a distance matrix (EDM) is obtained for the distribution system. Having obtained the EDM, distance matrix (EDM) obtained for the distribution system. Having obtained the EDM, maximization problem is is formulated and solved for themicrogrids optimal clustering of the network. In theaaa generators (DGs) in active distribution networks and is proposed. First, an electrical distance matrix (EDM) is obtained for the distribution system. Having obtained the EDM, maximization problem is formulated and solved for the optimal clustering of the network. In the maximization problem is formulated solved for the optimal clustering of the network. In thea maximization problem is and solved for the clustering of In second agents are assigned to and each cluster, a system. multi-objective optimization distancestage, matrix (EDM) obtained for the distribution Having obtained the problem EDM, maximization problem is isformulated formulated and solved for and the optimal optimal clustering of the the network. network. In the the second stage, agents are assigned to each cluster, and aaa multi-objective optimization problem second stage, agents are assigned to each cluster, and multi-objective optimization problem second stage, agents are assigned to each cluster, and multi-objective optimization problem (MOOP) is formulated for optimal planning of wind DGs and assigned to a head agent; maximization problem is formulated and solved for the optimal clustering of the network. In the second stage, agents are assigned to each cluster, and a multi-objective optimization problem (MOOP) is for optimal planning of wind DGs and assigned to head the (MOOP) is formulated optimal planning DGs assigned to head agent; the (MOOP) is formulated formulated for optimal planning of wind DGs and assigned to aaaaobjective head agent; agent; the objective functions inare thefor MOOP are equalcluster, to of thewind number ofand agents, and the function second stage, agents assigned to each and a multi-objective optimization problem (MOOP) is formulated for optimal planning of wind DGs and assigned to head agent; the objective functions in the MOOP are equal to the number of agents, and the objective function objective functions in the MOOP are equal to the number of agents, and the objective function objective functions in the MOOP are equal to the number of agents, and the objective function of an agent isformulated composed ofMOOP annual energy losses. Anumber voltage improvement index for each cluster (MOOP) is for optimal planning of wind DGs and assigned to a head agent; the objective functions in the are equal to the of agents, and the objective function of an agent is composed of annual energy losses. A voltage improvement index for each cluster of an agent is composed of annual energy losses. voltage improvement index for each cluster of an agent is composed of annual energy losses. A voltage improvement index for each cluster is also calculated toinmeasure the improvement inA the voltage profile.and IEEE 37-node test feeder objective functions the MOOP are equal to the number of agents, the objective function of an agent is composed of annual energy losses. A voltage improvement index for each cluster is also calculated to measure the improvement in the voltage profile. IEEE 37-node test feeder is also calculated to measure the in the voltage profile. 37-node test feeder is calculated to measure the improvement in voltage profile. IEEE 37-node test feeder and testis cases taken intoimprovement account for the study. The results IEEE show thatforthe losses are of antwo agent composed of annual energy losses. voltage improvement index each cluster is also also calculated toare measure the improvement inAthe the voltage profile. IEEE 37-node test feeder and two test cases are taken into account for the study. The results show that the losses are and two test cases are taken into account for the study. The results show that the losses are and two test cases are taken intovoltage account for the the study. Theprofile. results show that thetest losses are reduced in each cluster, and the profile is also enhanced at theIEEE same37-node time.the is also calculated to measure the improvement in the voltage feeder and two test cases are taken into account for study. The results show that losses are reduced in each cluster, and the voltage profile is also enhanced at the same time. reduced in each cluster, and the voltage profile is also enhanced at the same time. reduced in each cluster, and the voltage profile is also enhanced at the same time. and two in test cases are taken intovoltage account for the study. The results show that the losses are reduced each cluster, and the profile is also enhanced at the same time. © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. reduced in Clustering, each cluster,distributed and the voltage profile is also enhanced the same time. Keywords: planning, distribution systems,atenergy losses, wind DG Keywords: Keywords: Clustering, Clustering, distributed distributed planning, planning, distribution distribution systems, systems, energy energy losses, losses, wind wind DG DG Keywords: Clustering, distributed planning, distribution systems, energy losses, wind planning. Keywords: Clustering, distributed planning, distribution systems, energy losses, wind DG DG planning. planning. planning. Keywords: Clustering, distributed planning, distribution systems, energy losses, wind DG planning. planning. 1. INTRODUCTION was employed for the planning of DERs in Othman et al. 1. INTRODUCTION was employed for the planning of DERs in Othman et al. 1. INTRODUCTION INTRODUCTION was employed employed for the the planning of DERs DERs in loss Othman et al. al. 1. was for of in Othman et (2015) considering theplanning energy and power minimiza1. INTRODUCTION was employed for the planning of DERs in loss Othman et al. (2015) considering the energy and power minimiza(2015) considering the energy and power loss minimiza(2015) considering the energy and power loss minimization. 1. INTRODUCTION was employed for the of DERs in loss Othman et al. (2015) considering theplanning energy and power minimizaTo reduce the environmental pollution, penetration of tion. tion. To reduce reduce the the environmental environmental pollution, pollution, penetration penetration of of tion. To (2015) considering the energy and power loss minimization. To reduce the environmental pollution, penetration of distributed energy resources (DERs) and electric vehicles To reduce energy the environmental pollution, penetration of In previous research, the planning of wind DERs is perdistributed resources (DERs) and electric vehicles distributed energy resources (DERs) and electric vehicles In previous research, the planning of wind DERs is pertion. In previous research, the planning of wind wind DERs DERs is is perdistributed energy resources (DERs) electric has increased the past few yearsand Khan et al. vehicles (2018). To reduce theover environmental pollution, penetration of In research, planning of distributed energy resources (DERs) and electric vehicles formed considering thethe whole distribution as perone In previous previous research, the planning of windnetwork DERs is perhas increased over the past few years Khan et al. (2018). has increased over the past few years Khan et al. (2018). formed considering the whole distribution network as one formed considering the whole distribution network as one has increased over the past few years Khan et al. (2018). In this context, Korean government has also made several formed distributed energy resources (DERs) and electric vehicles considering the whole distribution network as one has increased over the past few years Khan et al. (2018). search space. However, in real-world power networks, some In previous research, planning of windnetworks, DERs is performed considering thethe whole distribution network assome one In this this context, context, Korean Korean government government has has also also made made several several search In space. However, in real-world power search space. However, in real-world power networks, some In this context, Korean government has also made several policies to cut down the green house gas emission and have hasthis increased over the past few years Khan al.and (2018). search However, in real-world power networks, In context, Korean government has also et made several regions have strong mutual coupling between them;assome voltformed considering the whole distribution network one search space. space. However, in real-world power networks, some policies to cut down the green house gas emission have policies to cut down the green house gas emission and have regions have strong mutual coupling between them; voltregions have strong strong mutual coupling between them; voltpolicies to cut the gas emission and have plans its Jeju islandhouse carbon 2030. The regions In thisto context, Korean government hasfree alsoby made several have mutual coupling between them; voltpolicies tomake cut down down the green green house gas emission and have age variations in these closely coupled buses may occur search space. However, in real-world power networks, some regions have strong mutual coupling between them; voltplans to make its Jeju island carbon free by 2030. The plans to make its Jeju island carbon by 2030. The age variations in these closely coupled buses may occur age variations invariations these closely coupled buses may occur plans to its island carbon free by 2030. The total electric generated fromgas thefree wind policies cutenergy down the green emission and have variations in these closely buses may occur plans totomake make its Jeju Jeju islandhouse carbon by distributed 2030. The age because of loads incoupling onecoupled of these buses.them; Therefore, regions have strong mutual between voltage variations invariations these closely coupled buses may occur total electric energy generated from the thefree wind distributed total electric energy generated from wind distributed because of loads in one of these buses. Therefore, because of loads variations in one of these buses. Therefore, total electric energy generated from the wind distributed generators (DGs) would reach 2carbon GW the and 350 MW forThe off- because plans to make its Jeju island free by 2030. of loads variations in one of these buses. Therefore, total electric energy generated from wind distributed it is necessary for enhancing the voltage quality to identify age variations in these closely coupled buses may occur because of loads variations in one of these buses. Therefore, generators (DGs) would reach 2 GW and 350 MW for offgenerators (DGs) would reach 2 GW and 350 MW for offit is necessary for enhancing the voltage quality to identify it is necessary necessary forvariations enhancing the voltage quality to identify identify generators (DGs) would reach 22from GW and 350 MW for shore and on-shore wind power generation, total electric energy generated the distributed for the to generators (DGs) would reach GW andwind 350 respectively. MW for offoff- it these electrically coupled in buses before optimally placing because of loads onevoltage of thesequality buses. Therefore, it is is necessary for enhancing enhancing the voltage quality to identify shore and on-shore on-shore wind power generation, respectively. shore and wind power generation, respectively. these electrically coupled buses before optimally placing these electrically coupled buses before optimally placing shore and on-shore wind power generation, respectively. Due to substantial increase in the penetration of DERs generators (DGs) would reach 2 GW and 350 MW for offthese coupled buses before optimally placing shoretoand on-shore increase wind power generation, respectively. DGs inelectrically the distribution networks to mitigate thetoeffects of it is necessary for enhancing the voltage quality identify these electrically coupled buses before optimally placing Due substantial in the penetration of DERs Due to substantial increase in the penetration of DERs DGs in the distribution networks to mitigate the effects of DGs in the distribution networks to mitigate the effects of Due to substantial increase in the penetration of DERs and independent microgrids, new planning approaches are DGs shore and on-shore wind power generation, respectively. in the distribution networks to mitigate the effects of Due to substantial increase in the penetration of DERs load variations. In addition, since the problem of optimal these electrically coupled buses before optimally placing DGs in the distribution networks to mitigate the effects of and independent microgrids, new planning approaches are and independent microgrids, new planning approaches are load variations. In addition, since the problem of optimal load variations. In addition, since the problem of optimal and independent microgrids, new planning approaches are required for the distributed planning of the DERs by Due to substantial increase in the penetration of DERs load variations. In addition, since the problem of optimal and independent microgrids, new planning approaches are planning of DGs is a combinatorial optimization problem, DGs in the distribution networks to mitigate the effects of load variations. In addition, since the problem of optimal required for the distributed planning of the DERs by required for the distributed planning of the DERs by planning of DGs is aa combinatorial combinatorial optimization problem, planning ofspace DGsIn isthe combinatorial optimization problem, required for distributed planning of the DERs by identifying thethe areas which are closely connected to each and independent microgrids, new planning approaches are planning of DGs is a optimization problem, required for the distributed planning of the DERs by the search of problem can be reduced by the idenload variations. addition, since the problem of optimal planning of DGs is a combinatorial optimization problem, identifying the the areas areas which which are are closely closely connected connected to to each each the identifying search space of the problem can be reduced by the identhe searchof space ofisclosely the problem can areas. be reduced by problem, the idenidenidentifying areas which are closely connected to other. The distributed approach will eventually increase required forthe the distributed of the DERs by the search space of the problem can be reduced by identifying the areas which areplanning closely connected to each each tification these coupled Furthermore,the planning DGs a combinatorial optimization the searchof space of closely the problem can areas. be reduced by the the idenother. The distributed approach will eventually increase other. The distributed approach will eventually increase tification of these coupled Furthermore,the tification of these closely coupled areas. Furthermore,the other. The distributed approach will eventually increase the reliability of the which system and improve the planning identifying the areas are closely connected to each tification of these closely coupled areas. Furthermore,the other. The distributed approach will eventually increase identification of these regions in the distribution feeder the searchof space ofthese the problem can be reduced by thefeeder identification these closely coupled areas. Furthermore,the the reliability reliability of of the the system system and and improve improve the the planning planning identification the of regions in the distribution identification of these regions in the the distribution feeder the reliability of the improve the planning results for all the of theand distribution network. other. The distributed approach will eventually increase identification of regions in the distribution feeder the reliability of regions the system system and improve the planning may also help thethese utilities to vary importance given to tification of these closely coupled areas. Furthermore,the identification of these regions in the distribution feeder results for all the regions of the distribution network. results for all the regions of the distribution network. may also help the utilities to the importance given to may also regions help thefor utilities to vary vary the importance given to results for all the regions of the distribution network. the reliability of the system and improve the planning may also help the utilities to vary the importance given to results for all the regions of the distribution network. different installing the new DGs in the distriidentification of these regions in the distribution feeder may also help the utilities to vary the importance given to Several researchers have studied the optimal planning of different regions for installing the new DGs in in the the distridistridifferent regions for installing the new DGs in the distriSeveral researchers have studied the optimal planning of results for all the regions of the distribution network. different regions for installing the new DGs system. Allfor these issues require that the distributed Several researchers have studied the optimalofplanning planning of bution may also help the utilities to vary the importance given to different regions installing the new DGs in the distriSeveral researchers have studied the optimal of DERs. In Atwa et al. (2010), the planning DERs was bution system. All these issues require that the distributed Several In researchers have studied optimalofplanning of bution bution system. All these issues require that the distributed DERs. Atwa et et al. al. (2010), thethe planning DERs was was system. All these issues require that the distributed planning of the DGs should be performed. DERs. In Atwa (2010), the planning of DERs different regions for installing the new DGs in the distribution system. All these issues require that the distributed DERs. In Atwa et al. (2010), the planning of DERs was performed considering the annual energy loss minimizaSeveral In researchers have studied optimal of planning of the DGs should be performed. DERs. Atwa et al. (2010), thethe planning ofplanning DERs was planning of the the DGs DGs should berequire performed. performed considering the annual energy loss minimizaplanning of should be performed considering the annual energy loss minimizabution these issues thatdistributed the distributed planning of theAll DGs should be performed. performed. performed considering the annual energy minimization. In In Sheng et et al.al. (2015), new was proposed DERs. Atwa (2010), the algorithm planning of DERs was In performed considering the a annual energy loss loss minimizathis system. paper, we have proposed a new aption. In Sheng et al. (2015), a new algorithm was proposed tion. In Sheng et al. (2015), a new algorithm was proposed In this paper, we have proposed a new distributed applanning of the DGs should be performed. In this paper, we have proposed a new distributed aption. In Sheng et aaannual new algorithm was proposed for the planning wind generators con- In performed considering the energy loss this paper, we have proposed aa new distributed aption. In multi-objective Sheng et al. al. (2015), (2015), newof algorithm wasminimizaproposed proach for the optimal planning of wind DGs. The problem In this paper, we have proposed new distributed apfor the multi-objective planning of wind generators confor the multi-objective planning of wind generators conproach for the optimal planning of wind DGs. The problem proach for the optimal planning of wind DGs. The problem for the multi-objective planning of wind generators considering several objective functions such as losses, voltage tion.the In multi-objective Sheng etobjective al. (2015), a newof algorithm was proposed planning of DGs. The problem for planning windasgenerators con- proach is solved inthe twooptimal stages; inproposed the first stage, first, an electrical In this for paper, we have a new distributed approach for the optimal planning of wind wind DGs. The problem sidering several functions such losses, voltage sidering several objective functions such as losses, voltage is solved in two stages; in the first stage, first, an electrical is solvedforin in twooptimal stages; inisthe the first stage,DGs. first, an electrical sidering several objective functions such as losses, voltage deviation and voltage stability margins improvements. In is for the multi-objective planning of wind generators consolved two stages; in first stage, first, an electrical sidering several objective functions such as losses, voltage distance matrix (EDM) constructed for a distribution proach the planning of wind The problem is solved in two stages; in the first stage, first, an electrical deviation and and voltage voltage stability stability margins margins improvements. improvements. In In distance deviation matrix (EDM) is constructed for distribution distance matrix (EDM) isthe constructed for distribution deviation and voltage stability margins In Mehmood et al. (2017), optimal planning battery storsidering several objective functions such improvements. asof voltage matrix (EDM) for aaaaisan distribution deviation and voltage stability margins improvements. In distance system. an optimization problem formulated is solvedAfter in twothat, stages; inis first stage, first, electrical distance matrix (EDM) is constructed constructed for distribution Mehmood et al. al. (2017), optimal planning oflosses, battery storMehmood et (2017), optimal planning of battery storsystem. After that, an optimization problem is formulated system. After that, an optimization problem is formulated Mehmood et al. (2017), optimal planning of battery storage systems with windstability and solar DGs was carried outstorfor deviation and voltage margins improvements. In system. After that, an optimization problem is formulated Mehmood et al. (2017), optimal planning of battery for the optimal clustering of the distribution networks to distance matrix (EDM) is constructed for a distribution system. After that, an optimization problem is formulated age systems with wind and solar DGs was carried out for age systems with wind and solar DGs was carried out for for the optimal clustering of the distribution networks to for the optimal optimal clustering ofareas the distribution distribution networks to age systems with wind and solar DGs was carried out for the losses and investment cost minimization. In Mansor Mehmood et al. (2017), optimal planning of battery storfor the clustering of the networks to age systems with wind and solar DGs was carried out for identify the closely coupled in the network. Having system. After that, an optimization problem is formulated for the optimal clustering ofareas the distribution networks to the losses losses and and investment investment cost cost minimization. minimization. In In Mansor Mansor identify the the closely coupled in the network. Having identify the closely coupled areas in the network. Having the losses and investment cost minimization. In Mansor and Levi (2017), a two-stage procedure was proposed for age systems with wind and solar DGs was carried out identify the closely coupled areas inassigned the network. network. Having the losses and investment cost minimization. In Mansor generated clusters, agents to the clusfor the optimal clustering ofareas theare distribution networks to identify thethe closely coupled in the Having and Levi (2017), a two-stage procedure was proposed for and Levi (2017), a two-stage procedure was proposed for generated the clusters, agents are assigned to the clusgenerated the clusters, agents areinoptimization assigned to the the clusand Levi aa two-stage procedure was for the DER-planning which considered several operational and investment cost minimization. In Mansor clusters, agents are assigned to clusand losses Levi (2017), (2017), two-stage procedure was proposed proposed for generated ters, andthe athe second multi-objective problem identify closely coupled areas the network. Having generated the clusters, agents are assigned to the clusthe DER-planning which considered several operational the which considered several operational ters, and second multi-objective optimization problem ters, and aaathe second multi-objective optimization problem the DER-planning which several operational factors during thea planning. Big bang-big crunch method and DER-planning Levi (2017), two-stage procedure was proposed for ters, and multi-objective problem the DER-planning which considered considered several operational generated clusters, agents are optimization assigned to the clusters, and a second second multi-objective optimization problem factors during the planning. planning. Big bang-big crunch method factors during the Big bang-big crunch method factors during the planning. Big bang-big crunch method the DER-planning which considered several operational factors during the planning. Big bang-big crunch method ters, and a second multi-objective optimization problem factors planning. Big Federation bang-bigofcrunch method Copyright © 2018, 2018the IFAC 138Hosting by Elsevier Ltd. All rights reserved. 2405-8963during © IFAC (International Automatic Control) Copyright 2018 IFAC 138 Copyright ©under 2018 responsibility IFAC 138 Peer review© of International Federation of Automatic Control. Copyright 138 Copyright © © 2018 2018 IFAC IFAC 138 10.1016/j.ifacol.2018.11.691 Copyright © 2018 IFAC 138
IFAC CPES 2018 Tokyo, Japan, September 4-6, 2018 Khawaja Khalid Mehmood et al. / IFAC PapersOnLine 51-28 (2018) 138–142
is formulated for the annual energy loss minimization for a head-agent. Each agent attempts to minimize the losses for its own cluster and reports to the head-agent. IEEE 37-node test feeder and two test cases are studied for the simulations.
d¯ =
N di N i=1
(6)
subject to
The paper is organized as follows: Section 2 describes the clustering. An electrical distance matrix formation and problem formulation are also discussed in Section 2. The problem formulation for the sizing and placement of wind DGs is presented in Section 3. A test system and test cases are explained in Section 4, and the results are discussed in Section 5. Finally, the paper is concluded in Section 6.
ξ(i,i+1,c) = 1 ∀ (bs(i,c) ∧ bs(i+1,c) ) ∈ Nc (1)
(bs(i,c) ∧ bs(i+1,c) ) ∈ Nc ∀ Adj(i+1,c) ∈ Nc C
2. CLUSTERING OF A DISTRIBUTION SYSTEM
ic(c) = RCLmax
(7) (8)
(9)
c=1
In this section, we present the formation of electrical distance matrix and problem formulation for clustering. 2.1 Electrical distance matrix construction In literature, several researchers have used network clustering based method in distributed control schemes for the identification of closely related areas in the power systems Cotilla-Sanchez et al. (2013); Shahidehpour and Wang (2004). The most commonly used approach uses the construction of EDMs as they are capable of providing relationships between nodes and the sensitivity of active powers. The EDMs can be constructed using several approaches such as by using the admittance/susceptance or the Jacobian matrix of the system Poudel et al. (2016); Blumsack et al. (2009). The contruction of an EDM using admittance matrix method reduces the computational time of the simulations as the formation of the Jacobian is not required for the EDM construction. Because of the aforementioned reasons, we use the admittance matrix for obtaining the EDM using equation (1): ED = |Y(bus) −1 |
139
where zij is the electrical distance between buses i and j, L is the set containing the buses in a cluster, d and d∗ are the normal and optimal size of the cluster, N is the total number of buses in the system, ξ is the incidence matrix, bs is the identification constant for a bus, Adj is the adjacency list of the buses, Nc is the number of a buses in a c-th cluster, ic is the identification constant for the clusters and RCLmax is the maximum number of the desired clusters. In order to obtain feasible solutions, several constraints are also imposed on the optimization problem. In an optimal solution, all the buses must be connected in a cluster as given in equation (7). To verify the connectivity of the nodes, the incidence matrix for the distribution network is constructed. We have used admittance matrix to construct the incidence matrix. In case of two disconnected buses in the same cluster, the adjacency list of the nodes is used for the verification as shown in equation (8). The maximum number of clusters obtained are control by Equation (9). In the next section, the problem formulation for the wind DG planning is presented.
(1)
where Y(bus) is the admittance matrix of the system. 3. PLANNING OF WIND DGS
2.2 Problem formulation for clustering After obtaining the EDM, a maximization problem is formulated for the clustering of the distribution network. The objective function, given in equation (2), is composed of three parameters presented in equations (3)–(5) CotillaSanchez et al. (2013). First parameter, inter-cluster distance index (INI), controls and combine the buses with are closely located to each other. Second parameter, intracluster distance index (ITI), governs the distances between different clusters. Finally, third parameter, cluster size index (DCI), controls the size of the clusters. (2) max f1 = IN I × IT I × DCI n z ij j ∈L / i=1 IN I = 1 − n n i (3) z i=1 j=1 ij n 1 IT I = 1 −
i=1 n i=1
DCI = e
j ∈Li zij n 1 j=1 i =j zij
∗) ¯ −(lnd−lnd 2σ 2
(4) (5)
139
3.1 Problem formulation for Wind DG placement Having partitioned the network optimally, agents are assigned to each cluster. A multi-objective optimization problem, equation (10), is formulated and assigned to a head agent. The objective function of an agent, equation (11), is comprised of the annual energy losses. The importance given to a specific region can be varied by changing the weights for an agent in the objective function (10).
M in f2 =
C c=1
(α) f(c)
(α)
w(c) × f(c)
(10)
Nc S D H P(i,c,m,h,s) + Q(i,c,m,h,s) 2 = × R(ij) V(i,c,m,h,s) s= 1 h=1 m=1 i=1 (11)
IFAC CPES 2018 140 Tokyo, Japan, September 4-6, 2018 Khawaja Khalid Mehmood et al. / IFAC PapersOnLine 51-28 (2018) 138–142
subject to min max V(i,c) ≤ V(i,c,t) ≤ V(i,c)
(12)
max |I(ij,c,t) | ≤ I(ij,c)
Nc
(13)
W DG W DG ς(i,c) = R(c)
(14)
i=1
A
α=1 W DG P(i,c)
ia(α) = C
(15)
= 0 ∀i ∈ / Nc
(16)
W DG l(i,c) , P(i,c) ≥ + l(i,c) ∈ Z
0
(17)
Similar to the optimization problem of the preceding section, in order to obtain the feasible solutions, various constraints are designed for the planning optimization problem. Voltage magnitude of a bus in a cluster ‘c’ should be within the safe operating limits at time ‘t’ as given in equation (12), and Equation (13) shows that the current flowing through a line must be less than the maximum allowed current in the line. The total number of the DGs obtained from an optimal solution must be equal to the required number of DGs to be installed in the network as depicted in Equation (15). If a bus is not a member of a specific cluster, the DGs must not be placed on that bus as shown in Equation (16). The size of DGs must be positive numbers whereas the bus locations should be positive and integer numbers in accordance with Equations (17) and (18). In order to evaluate the quality of obtained solutions, voltage improvement index (VMI) values of the buses within each cluster are also calculated using Equation (19):
V
=
Nc i=1
avg V(c,i,t) =
ref avg |V(i,t) − V(i,t) |
N V p=1 (p,i,c,t)
P
ϕ(i,c)
Acquire the network admittance matrix Clustering Problem Obtain the EDM from the admittance matrix
Apply GA
(18)
where w(c) represents the weights given to the various (α) agents, f(c) is the objective function of the agent assigned to the c-th cluster. P(i,c,m,h,s) and Q(i,c,m,h,s) are the active and reactive loads connected to the i -th bus of the c-th cluster, V(i,c,t) is the voltage of the i -th bus of the c-th min min and V(i,c) are the minimum and maximum cluster, V(i,c) voltages, I(ij,c,t) is the current flowing between between max buses i and j, I(ij,c) is the maximum value of the line W DG current, ς(i,c) is the identification constant for installing W DG wind DG, R(c) is the maximum number of wind DGs W DG to be installed, P(i,c) is the power rating of a wind DG, l(i,c) is the bus number of the i -th bus of the c-th cluster.
(α) M I(c)
Start
2
(19) (20)
ref where V(i,t) is the reference voltage value for the i -th bus, avg V(i,t) is the average voltage of three phases, V(p,i,c,t) is the voltage of the i -th bus, N is the total number of buses in the system and ϕ(i,c) is the number of phases at the i -th bus.
140
Solve the clustering problem using genetic algorithm Obtain the optimal cluster and partition the network Planning Problem Assign agents to the clusters
Apply GA Solve the wind DG planning problem Obtain the optimal size and location
Calculate VMI values
Stop
Fig. 1. Flowchart representation of the proposed planning approach 3.2 Power model of a Wind DG For generating the electrical power from wind speed samples, the power model given in (21) is employed Hetzer et al. (2008); Mehmood et al. (2018). sw − sci for sci ≤ sw ≤ sco Pr × s − s r ci (21) Pw = P for sr ≤ sw ≤ sco r 0 otherwise
where Pw is power generated from a wind turbine, Pr is the rated power of a wind turbine. sw , sr , sci and sco are the current, rated, cut-in and cut-off wind speeds, respectively. Fig. 1 shows the steps performed in the proposed scheme. First, the network admittance matrix is acquired to obtain the EDM. Having obtained the EDM, an optimization
IFAC CPES 2018 Tokyo, Japan, September 4-6, 2018 Khawaja Khalid Mehmood et al. / IFAC PapersOnLine 51-28 (2018) 138–142
1
C 1 712 701
Load demand
742 705
0.8
0.6
06
12 Time (hour)
18
24
Fig. 2. Seasonal 5-min load curves of Korea problem is formulated for the optimal clustering of the distribution network.Thereafter, a genetic algorithm (GA) is used for solving the clustering problem, and the network is partitioned optimally. After the clustering problem, agents are assigned to each cluster, a second multi-objective optimization problem is formulated and solved using the GA. The solution of this problem results in optimal sizes and locations of wind DGs in each cluster. Finally, the VMI values are calculated for each cluster, which also finish the proposed scheme. In the next section, a test system and test cases are described. 4. TEST SYSTEM AND TEST CASES The proposed scheme has been verified using the IEEE 37-node test feeder. The desired number of cluster to be formed in the test system has been set to four. The 5min seasonal load curves of Korea, presented in Fig. 2 has been used for the loads and the wind speed data of six years have been used for generating wind speed samples for four seasons. Two test cases have been studied to verify the proposed scheme. In the first test case, no DGs have been installed in the distribution system whereas in the second test case, distributed installation of wind DGs has been carried out. Table 1. Optimal locations and sizes of wind DGs Cases Case 2 (37)
Clusters Cluster 1 Cluster 2 Cluster 3 Cluster 4
Size 1695.8 kW 500.8 kW 797 kW 846.8 kW
Buses 702 722 709 738
Table 2. Results obtained from the simulations Cases Case 1 (37)
Case 2 (37)
Clusters Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 1 Cluster 2 Cluster 3 Cluster 4
Losses (MWh) 229.19 24.35 84.31 21.44 75.64 13.62 32.77 11.54
VMI 0.0022 0.0058 0.0081 0.0081 0.0009 0.0025 0.0034 0.0033
722
713 704 702 714
744 727 703 729 728
0.7
0.5 00
C2
799
0.9
141
C3
730
732 708
709 731
724 707 720 706
718 725
C : Cluster
733 775 736 C4 710 734 740 741 737 738 711 735 Fig. 3. Clusters in IEEE 37-node test feeder 5. RESUTLS AND DISCUSSION In the this section, results of the test cases presented in the preceding section are presented and discussed. 5.1 Clustering Fig. 3 shows the results obtained from the optimization of the distribution system for the clustering problem. As the desired number of clusters were set to four, the network was partitioned into four connected clusters. The objective function value for the network was 0.20375. It can be seen that the adjacent buses within the clusters are connected. 5.2 Optimal planning of wind DGs Table 2 shows results obtained for both test cases. Since there were four clusters formed in the distribution feeder, four wind DGs were installed in the distribution system. In clusters one, two, three and four, wind DGs of power ratings 1740, 780.7, 1144.6 and 822.1 kWs were installed on buses 701, 720, 730 and 738, respectively. Table 2 shows the losses in each cluster for both test cases. It can be seen that in Case 1, losses were high in all clusters since there were no wind DGs installed in the distribution system. However, after the installation of wind DGs in each cluster, losses reduced. It can be noticed from Table 2 that the losses in clusters one, two, three and four reduced from 229, 210, 21 and 125 to 73, 61, 52 and 16 MWhs. At this stage, it is worth mentioning that we discovered that in conventional approach of DG planning, reduction in losses in a distribution network resulted in increase in losses in some region of the power system. However, the approach proposed in our study partitioned the network and considered the minimization of losses in each cluster as a multi-objective optimization problem, which resulted in loss minimization in all clusters simultaneously. In addition, the VMI results have also been tabulated in Table 2. In Case 1, the VMI values were higher. However, it can be seen that after the placement of DGs in Case 2, because of reduction in the losses, the voltage profiles
141
IFAC CPES 2018 142 Tokyo, Japan, September 4-6, 2018 Khawaja Khalid Mehmood et al. / IFAC PapersOnLine 51-28 (2018) 138–142
Cluster 4
Cluster 3
Cluster 2
Cluster 1 0
50
100 150 200 Losses (MWh)
250
300
Fig. 4. Comparison of losses in Cases 1 and 2 of the distribution system improved which caused the reduction in the VMI values from 0.0022, 0.0058, 0.0081 and 0.0081 to 0.0009, 0.0025, 0.0034 and 0.0033 in Case 2. Finally, Fig. 4 compares the results of Cases 1 and 2 and shows the reduction of losses in Case 2 for each cluster. All the results show that the proposed distributed planning scheme can be used for the distributed planning of wind DGs to reduce the losses and to improve the voltage in the distribution network. 6. CONCLUSION In this paper, a new multi-agent clustering based distributed approach was proposed for the distributed planning of wind DGs. First, an optimization problem was formulated for the network clustering; cluster size and inter and intra cluster distances were considered as the objective functions. After partitioning the network, agents were assigned to each cluster, and a second multi-objective optimization problem was formulated for a head agent; the objective function of an agent was composed of annual energy loss reduction. IEEE 37-node test feeder was taken into account for the simulations, and six year wind speed data and seasonal load curves of Korea were considered for the simulations. Moreover, two test cases were studied. Clustering problem optimally partitioned the network into four cluster, and in the second optimization problem, one wind DG was placed in each cluster. All the results showed that the losses were reduced in each cluster, and at the same time, voltage profile of all buses within each cluster was enhanced. The scheme proposed in this paper can be utilized for the distributed planning of distributed generators to increase the reliability of the power networks. In the future, we will extent and test the method for different test systems and different types of DGs. ACKNOWLEDGEMENTS This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (no. 2015R1A2A1A10052459). REFERENCES Atwa, Y.M., El-Saadany, E.F., Salama, M.M.A., and Seethapathy, R. (2010). Optimal renewable resources 142
mix for distribution system energy loss minimization. IEEE Transactions on Power Systems, 25(1), 360–370. doi:10.1109/TPWRS.2009.2030276. Blumsack, S., Hines, P., Patel, M., Barrows, C., and Sanchez, E.C. (2009). Defining power network zones from measures of electrical distance. In 2009 IEEE Power Energy Society General Meeting, 1–8. doi: 10.1109/PES.2009.5275353. Cotilla-Sanchez, E., Hines, P.D.H., Barrows, C., Blumsack, S., and Patel, M. (2013). Multi-attribute partitioning of power networks based on electrical distance. IEEE Transactions on Power Systems, 28(4), 4979–4987. doi: 10.1109/TPWRS.2013.2263886. Hetzer, J., Yu, D.C., and Bhattarai, K. (2008). An economic dispatch model incorporating wind power. IEEE Transactions on Energy Conversion, 23(2), 603– 611. doi:10.1109/TEC.2007.914171. Khan, S.U., Mehmood, K.K., Haider, Z.M., Bukhari, S.B.A., Lee, S.J., Rafique, M.K., and Kim, C.H. (2018). Energy management scheme for an ev smart charger V2G/G2V application with an EV power allocation technique and voltage regulation. Applied Sciences, 8(4). doi:10.3390/app8040648. Mansor, N.N. and Levi, V. (2017). Integrated planning of distribution networks considering utility planning concepts. IEEE Transactions on Power Systems, 32(6), 4656–4672. doi:10.1109/TPWRS.2017.2687099. Mehmood, K.K., Khan, S.U., Lee, S.J., Haider, Z.M., Rafique, M.K., and Kim, C.H. (2017). Optimal sizing and allocation of battery energy storage systems with wind and solar power DGs in a distribution network for voltage regulation considering the lifespan of batteries. IET Renewable Power Generation, 11(10), 1305–1315. doi:10.1049/iet-rpg.2016.0938. Mehmood, K.K., Khan, S.U., Lee, S.J., Haider, Z.M., Rafique, M.K., and Kim, C.H. (2018). A realtime optimal coordination scheme for the voltage regulation of a distribution network including an OLTC, capacitor banks, and multiple distributed energy resources. International Journal of Electrical Power & Energy Systems, 94, 1 – 14. doi: https://doi.org/10.1016/j.ijepes.2017.06.024. Othman, M.M., El-Khattam, W., Hegazy, Y.G., and Abdelaziz, A.Y. (2015). Optimal placement and sizing of distributed generators in unbalanced distribution systems using supervised big bang-big crunch method. IEEE Transactions on Power Systems, 30(2), 911–919. doi:10.1109/TPWRS.2014.2331364. Poudel, S., Ni, Z., and Sun, W. (2016). Electrical distance approach for searching vulnerable branches during contingencies. IEEE Transactions on Smart Grid, PP(99), 1–1. doi:10.1109/TSG.2016.2631622. Shahidehpour, M. and Wang, Y. (2004). Communication and Control in Electric Power Systems: Applications of Parallel and Distributed Processing. John Wiley & Sons, Inc., Hoboken, New Jersey, Hoboken, New Jersey, USA, 1st edition. Sheng, W., Liu, K.Y., Liu, Y., Meng, X., and Li, Y. (2015). Optimal placement and sizing of distributed generation via an improved nondominated sorting genetic algorithm-II. IEEE Transactions on Power Delivery, 30(2), 569–578. doi:10.1109/TPWRD.2014.2325938.