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1 Provded by the author(s) and Unversty College Dubln Lbrary n accordance wth publsher polces. Please cte the publshed verson when avalable. Ttle Restoraton n a Self-healng Dstrbuton Network wth DER and Flexble Loads Author(s) Ansar, Bananeh; Smoes, Marcelo G.; Soroud, Alreza; Keane, Andrew Publcaton date Conference detals IEEE 16th Internatonal Conference on Envronment and Electrcal Engneerng (EEEIC 2016), 7-10 June 2016, Florence, Italy Publsher IEEE Item record/more nformaton Publsher's statement Publsher's verson (DOI) IEEE. Personal use of ths materal s permtted. Permsson from IEEE must be obtaned for all other uses, n any current or future meda, ncludng reprntng/republshng ths materal for advertsng or promotonal purposes, creatng new collectve works, for resale or redstrbuton to servers or lsts, or reuse of any copyrghted component of ths work n other works. Downloaded T06:32:57Z The UCD communty has made ths artcle openly avalable. Please share how ths access benefts you. Your story matters! (@ucd_oa) Some rghts reserved. For more nformaton, please see the tem record lnk above.

2 Restoraton n a Self-healng Dstrbuton Network wth DER and Flexble Loads Bananeh Ansar Marcelo G. Smoes Electrcal Engneerng and Computer Scence Department Colorado School of Mnes Golden, Colorado Alreza Soroud Andrew Keane School of Electrcal Engneerng Unversty College Dubln Dubln, Ireland alreza.soroud,andrew.keane@ucd.e Abstract Ths paper develops an algorthm for energy management of a dstrbuton network consderng a restoratve plan for most possble devastatng contngences n the network. The dstrbuton network s selfhealng and conssts of smart meters and remotelycontrolled automated swtches. Dstrbuted energy resources and flexble loads provde the redundant capacty for restoraton. Under normal operatng condtons, the objectve of the energy management system s to mnmze the generaton cost, whle under emergency condtons, the objectve s to mnmze the amount of shed load, gvng prorty to crtcal loads. The energy management problem forms a non-lnear programmng problem. Smulaton results verfy the effectveness of the proposed algorthm n energy management and restoraton of a dstrbuton network. I. INTRODUCTION Negatve socal and economc mpacts of servce nterruptons force the dstrbuton network operators to reduce the frequency and duraton of network outages. Grd upgrades and renforcement can sgnfcantly reduce the frequency of outages, but cannot totally elmnate the rsk of equpment falure, especally n dstrbuton networks located n areas prone to natural dsasters such as earthquakes and hurrcanes. These events are subject to uncertanty [?], [?], [?] and they need to be properly modeled. Therefore, t s necessary for dstrbuton network operators to be able to reduce the duraton and severty of servce nterruptons once they occur. Under faulty condtons, protecton devces dentfy the fault and solate t from the rest of the dstrbuton network. Durng swtchng operatons for fault solaton, n addton to the faulty sectons, some healthy sectons of the network wll also experence servce nterrupton. The network operator needs to restore the servce to these areas, n the shortest tme possble. Servce restoraton capablty of a dstrbuton network depends on two factors: the avalable capacty for restoraton, and the speed of mplementng the restoraton plan. Tradtonally, neghborng feeders have been responsble for provdng the requred capacty for restoraton [?]. However, the ntegraton of dstrbuted energy resources (DER) ncludng renewable energy resources nto the network, and customers wllng to partcpate n demand response programs have created new sources of flexblty capacty n the dstrbuton network. Ths redundant capacty ncreases the flexblty and feasblty of restoraton operaton. In a smart self-healng dstrbuton network wth numerous smart meters capable of recordng and reportng network data, and automated swtches wth remote control capablty, servce restoraton can be faster and more effectve. Smart meters can report data on the sze and locaton of an outage, avalable flexblty capacty n the vcnty of outage area, and several other nformaton useful for servce restoraton. On the other hand, remotely-controlled automated swtches can mplement the restoraton plan wthout human nterventon and ncrease the rapdty of servce restoraton. Ths paper develops an algorthm for servce restoraton n selfhealng dstrbuton networks wth DER and flexble loads. The algorthm assumes two operatng condtons for the dstrbuton network: normal and emergency. The reslency of a dstrbuton network s defned wth respect to ts ablty to wthstand rare and extreme emergency events [?]. Dstrbuton network operator cannot precsely predct when and where an outage may occur [?]. Hence, even under normal operatng condtons, there must be a level of preparedness for emergency events such as component falures or natural dsasters that may lead to servce nterruptons. Consderng the topology of the network, hstorcal data about prevous outages, and the locaton of crtcal loads, the network operator can come up wth worst case scenaros, and prepare a plan for servce restoraton n case the network enters an emergency state of operaton. Under normal operaton, the objectve of dstrbuton network operator s to supply the demand at mnmum cost. However, when the network enters an emergency state, the objectve wll

3 be maxmzng the amount of restored loads, gvng prorty to crtcal loads. Under emergency operaton, dstrbuton network operator seeks alternatve routes and energy resources to restore the servce to outage areas. Alternatve routes for power flow can be created through proper swtchng operatons. In a self-healng network, the swtchng operatons do not requre human nterventon, and hence network restoraton s faster. Neghborng feeders, DER, and flexble loads can provde the requred flexblty capacty for restoraton. The proposed algorthm consders most probable and most devastatng contngency scenaros and ncludes them n the normal energy management of dstrbuton network. The uncertanty of dfferent scenaros s modeled usng probablstc approach [?]. Ths way, t ensures that the dstrbuton network successfully restores as much crtcal load as possble n case of a contngency. Smart meters record and report nformaton on the avalable capacty of dstrbuted generaton and the wllngness of flexble loads to reduce ther demand. The algorthm receves all the data from smart meters, and determnes the normal schedulng of dstrbuton network along wth a restoratve plan. The restoratve plan follows the objectve of mnmzng the amount of shed load (nterchangeably, maxmzng restored load) consderng techncal constrants such as network topology, branch flow lmts,avalablty of DER and comfort level of flexble loads. The energy management problem consderng restoratve actons forms a non-lnear programmng problem that commercal packages can solve. The man contrbutons of ths work are frst combnng normal and emergency operatons of a dstrbuton network n one optmzaton problem, and second, consderng the role of demand flexblty and DER n servce restoraton. We analyze the performance of proposed algorthm for dfferent case studes on IEEE 33-bus system as the test dstrbuton network. Smulaton results show the algorthm can successfully restore the servce to outage areas n each case study. II. PROBLEM FORMULATION Under normal operatng condtons, the objectve s to mnmze generaton cost, (OF n ) mn OF n = J (P g ), (1) =1 subject to the followng techncal constrants: Actve/Reactve power balance: P g P d = (2) V V j (G j cos (δ j ) + B j sn (δ j )) Q g Qd = (3) V V j (G j sn (δ j ) B j cos (δ j )) δ j = δ δ j (4) Branches flow lmts: Voltage lmts: Generaton lmts: V mn S j S max j (5) V V max (6) P g,mn P g P g,max (7) Q g,mn Q g Qg,max (8) where: P/Q g : Actve/reactve power generaton at node. P/Q d : Total actve/reactve load at node. S j : Branch j flow. V : Voltage magntude at node. δ : Voltage phase angle at node. G j : Conductance matrx whch s the real part of network s Y bus matrx. B j : Susceptance matrx whch s the magnary part of network s Y bus matrx. J (.): Generaton cost functon at node whch s a quadratc functon: J (P g ) = a P g2 + b P g + c. When a contngency occurs n the dstrbuton network, the objectve s to mnmze the amount of load sheddng and generaton cost, wth prorty gven to load sheddng mnmzaton. Therefore: Ω S mn OF c = λ π s τ s s=1 N =1 w P ls s, (9) Under emergency condtons, the techncal constrants change accordngly: Actve/Reactve power balance: P g s, P s, d + Ps, ls = (10) V s, V s,j (G j cos (δ s,j ) + B j sn (δ s,j )) Q g s, Qd s, + Q ls s, = (11) V s, V s,j (G j sn (δ s,j ) B j cos (δ s,j )) δ s,j = δ s, δ s,j (12)

4 Branches flow lmts: Voltage lmts: Generaton lmts: P g,mn Q g,mn S s,j (1 u s, u s,j γ s,j ) S max j (13) V mn Flexble load lmts: V s, V max (14) P g s, (1 u s,κ s, ) P g,max (15) Q g s, (1 u s,κ s, ) Q g,max (16) 0 Ps, ls P s, d (17) 0 Q ls s, Q d s, (18) where: P/Q g s, : Actve/reactve power generaton at node n scenaro s. P/Q d s, : Total actve/reactve load at node n scenaro s. P/Q ls s, : Total actve/reactve load shed at node n scenaro s. V s, : Voltage magntude at node n scenaro s. δ s, : Voltage phase angle at node n scenaro s. S s,j : Branch j flow n scenaro s. w : Load prorty weghtng factor at node. λ: Load sheddng penalty factor. u s, : A bnary parameter to show f the contngency has affected node ; 1: affected 0: not affected γ s,j : Percent reducton n dstrbuton capacty of branch j n scenaro s. Note that a branch s affected only f both ts sendng and recevng ends are nvolved. κ s, : Percent reducton n generaton capacty of node (f any DER s connected to the node) n scenaro s. Note that f node 1 s nvolved, substaton generaton capacty wll decrease by κ s,1 %. The energy management system mnmze the overall objectve, OF, whch s comprsed of total costs n normal and contngency condtons: OF = OF n + OF c (19) III. SIMULATION RESULTS In ths secton, we conduct two case studes to compare the performance of proposed automated selfhealng plan wth an operator-dependent sem-manual restoratve procedure. Our test system s IEEE 33- bus test feeder shown n Fgure??. We modfy the system to add DER and flexble demand to t. Also, we assume that the dstrbuton feeder s located n an area prone to natural dsasters. Smulaton assumptons are as followed: DER penetraton s 34.6% of feeder s total apparent power. Table?? shows the capacty of each DER. Fg. 1. IEEE 33-bus test feeder Generaton cost functon coeffcents (a, b and c ) at each DER bus as well as the substaton are accordng to Table??. Loads are dvded nto three categores: Noncrtcal non-flexble, non-crtcal flexble, and crtcal. Table?? shows the weghtng factor for each crtcal load. Natural dsasters tend to affect the upstream feeder more than downstream feeder [?]. We dentfy four most probable contngency scenaros, S 1, S 2, S 3, and S 4. Table?? shows the characterstcs of each scenaro. If the dsaster affects a bus, DER connected to t cannot generate any power (κ s, = 100%). Also the capactes of all dstrbuton branches n an affected area decrease by the same factor (same γ s,j for all affected branches). In scenaro II, node 1 and the substaton are nvolved, and the substaton capacty decreases by 70%. TABLE I DER CONNECTION POINTS AND THEIR CAPACITIES DER Crtcal Loads A. Sem-manual Restoraton Locaton 5, 7, 9, 11, 13, 17, 20, 22, 24 26, 29, 31 Capacty 67, 224, 63, 54, 69, 63, 98, 98, 47 [kw] 65, 139, 166 Locaton 7, 10, 14,24, 25, 32 w -factor 0.06, 0.07, 0.1, 0.20, 0.15, 0.20 In the frst case study, we assume that followng each contngency, a human operator takes correctve actons to restore the servce n affected areas. The operator runs a smple optmzaton program to determne what

5 TABLE II COST COEFFICIENTS OF GENERATING UNITS Bus 5,7,9 11,13,17 20,22,24 26,29 31 Sub a [$/MWh 2 ] b [$/MWh] c [$] TABLE III CONTINGENCY SCENARIOS Scenaro Bus κ s,sub κ s, γ s,j π s τ s [%] [%] [%] [h] S 1 3-5, S 2 1,2, S , S , correctve actons to take. The objectve of optmzaton program s to mnmze load sheddng cost, subject to system techncal constrants. Compared to the automated self-healng restoraton plan, the sem-manual plan: forces DER to operate at unty power factor, and does not dstngush between non-crtcal nonflexble and non-crtcal flexble loads, and hence assumes the same weghtng factors for all noncrtcal loads (that s ). Snce contngences are not antcpated, the operator has to take the most approprate actons on very short notce. Hence the restoratve algorthm must be as smple as possble. Ths justfes the assumptons we have made regardng the sem-manual restoraton procedure. Table?? shows the correctve actons the operator has taken n each scenaro. The last column of the Table shows the restoraton cost for each scenaro. The second scenaro, S 2 whch affects buses 1,2 and 19, as well as the substaton, s the most dsastrous contngency and requres a sgnfcant amount of load sheddng. Consderng the probablty of each scenaro, total restoraton cost n the sem-manual case wll be $31. B. Automated Self-healng Restoraton In the second case study, we assume that the dstrbuton network s self-healng, and n case of emergency, uses the automated restoraton plan augmented n ts energy management system. Table?? shows the system dspatch under normal operatng condtons. The Generaton cost s $8.2. The energy management system consders contngency scenaros, S 1, S 2, S 3, and S 4 even when the system s operatng under normal condtons. Ths adds a level of preparedness to the system. The automated self-healng restoraton plan: allows DER to change ther power factors between 0.9 to 1 (laggng or leadng), and TABLE IV SIMULATION RESULTS FOR THE SEMI-MANUAL RESTORATION PLAN Gen. S 1 S 2 S 3 S 4 P Q P Q P Q P Q DER kw kvar kw kvar kw kvar kw kvar Sub LS ΣLS Σ Cost [$] TABLE V SYSTEM DISPATCH UNDER NORMAL CONDITIONS Bus 5,7, , ,29 31 Sub P [kw] Q [kvar] assumes that non-crtcal non-flexble have weghtng factors of 0.01, whle non-crtcal flexble loads have weghtng factors of Table?? shows how the self-healng network restores power n affected areas. If we compare ths Table to Table??, we can see that the total amount of shed load and restoraton costs are much lower n the selfhealng network whch uses the proposed automated restoraton plan. Consderng the probabltes of scenaros, total restoraton cost n ths case wll be $17, whch s much lower than that of sem-manual case. IV. CONCLUSION Ths paper proposes an automated restoraton algorthm to be ntegrated n the energy management system of a self-healng dstrbuton network whch contans DER and flexble loads. Under normal operatng condtons, the energy management system mnmzes the generaton cost, whle under emergency condtons t mnmzes the amount of load shed. The algorthm consders the most probable and most devastatng contngences that may occur n the network. Moreover, the algorthm s flexble n terms of allowng generaton unts to control ther reactve power, and

6 TABLE VI SIMULATION RESULTS FOR THE AUTOMATED SELF-HEALING PLAN Gen. S 1 S 2 S 3 S 4 P Q P Q P Q P Q DER kw kvar kw kvar kw kvar kw kvar Sub LS Σ LS Σ Cost [$] [6] A. Soroud and T. Amraee, Decson makng under uncertanty n energy systems: State of the art, Renewable and Sustanable Energy Revews, vol. 28, pp , [7] A. Soroud, Possblstc-scenaro model for dg mpact assessment on dstrbuton networks n an uncertan envronment, IEEE Transactons on Power Systems,, vol. PP, no. 99, p. 1, [8] B. Ansar and S. Mohaghegh, Optmal energy dspatch of the power dstrbuton network durng the course of a progressng wldfre, Internatonal Transactons on Electrcal Energy Systems, vol. 25, no. 12, pp , usng flexble load to provde restoraton capacty. Smulaton results showed that compared to a less flexble sem-manual plan, the automated self-healng plan has a consderably less restoraton cost. ACKNOWLEDGMENT Ths work was conducted n the Electrcty Research Centre, Unversty College Dubln, Ireland, whch s supported by the Electrcty Research Centres Industry Afflates Programme ( Ths materal s based upon works supported by the Scence Foundaton Ireland, by fundng Alreza Soroud, under Grant No. SFI/09/SRC/E1780. The opnons, fndngs and conclusons or recommendatons expressed n ths materal are those of the author(s) and do not necessarly reflect the vews of the Scence Foundaton Ireland. REFERENCES [1] A. Soroud, P. Sano, and A. Keane, Optmal DR and ESS schedulng for dstrbuton losses payments mnmzaton under electrcty prce uncertanty, IEEE Transactons on Smart Grd, vol. 7, no. 1, pp , Jan [2] C. Murphy, A. Soroud, and A. Keane, Informaton gap decson theory-based congeston and voltage management n the presence of uncertan wnd power, IEEE Transactons on Sustanable Energy, vol. 7, no. 2, pp , Aprl [3] P. Maghoul, A. Soroud, and A. Keane, Robust computatonal framework for md-term techno-economcal assessment of energy storage, IET Generaton, Transmsson Dstrbuton, vol. 10, no. 3, pp , [4] B. Ansar and S. Mohaghegh, Electrc servce restoraton usng mcrogrds, n 2014 IEEE PES General Meetng Conference Exposton, July 2014, pp [5] M. Olken, The reslency of the grd: Puttng the plans n place [from the edtor], IEEE Power and Energy Magazne, vol. 13, no. 3, pp. 4 4, May 2015.