Optimal Planning of Microgrid Using Multi Criteria Decision Analysis

Transcription

1 Optmal Plannng of Mcrogrd Usng Mult Crtera Decson Analyss Z. Moravej 1 and H. Afshar 2 1,2 Electrcal and Computer Engneerng Faculty, Semnan Unversty, Semnan, Iran Abstract Today, ntellgent buldngs have receved more attenton, as well as, mcrogrds wll have the sgnfcant role n future smart power dstrbuton system. Smart buldng ntegrated wth mcrogrd can provde much comfort, qualty, safety and hgh energy effcency for the users. Mcrogrds can ntegrate the conventonal and renewable energy power generaton systems, energy storage systems and controllable loads. Mcrogrd plannng s a comple problem wth techncal, economc, and envronmental attrbutes that should be consdered. In ths paper, Mult Crtera Decson Makng (MCDM) approach s suggested for the selecton among varous mcrogrd plannng optons. Dfferent plans are generated by varous combnatons of conventonal and renewable energy resources. Fve attrbutes namely, profts from njectng power nto grd at peak load, captal costs, cost of energy (COE), total emssons, and energy not served are consdered. The Analytcal Herarchy Process (AHP) method s used for weghtng the mportance of the dfferent crtera, and MULTIMOORA method s proposed for prortzng of dfferent plans. The fnal rankng of the plans s obtaned consderng uncertanty n demand. Three loadng condtons are consdered as hgh, medum and low. The proposed procedure s appled to a sample system and numercal results are presented. Keywords Dstrbuted Generaton, MCDM and Mcrogrd T I. INTRODUCTION oday power system plannng s affected by techncal, Economc and envronmental ssues. Centralzed power systems are reformng to local scale dstrbuted generatons [1]. In future, mcrogrds wll undertake the domnant role n power dstrbuton system [2], [3]. Mcrogrd s a small power system consstng of one or more dstrbuted generaton unts that can operated ndependently from the bulk power system. Energy plannng usng mult crtera analyss has attracted the attenton of researchers for a long tme. Durng the 1970s, dealng wth energy problems by sngle crteron approaches whch amed at fndng the most effcent energy supply optons at a low cost was popular. However, n the 1980s, growng envronmental awareness modfed the above decson approach. The need to ncorporate envronmental consderatons n energy plannng resulted n the ncreasng usage of mult crtera methods [4]-[7]. In recent years many researches have been conducted n order to fnd optmal desgn and szng of hybrd energy systems for resdental applcaton [8]-[12]. Mcrogrd plannng process should nclude engneerng, fnancal and envronmental aspects to fnd an optmum soluton. Multple crtera decson-makng (MCDM) s an operatonal evaluaton and decson support approach sutable for addressng comple problems featurng hgh uncertanty, conflctng objectves, mult nterests and perspectves. MCDM methodologes are capable of provdng solutons to a wde range of energy management and plannng problems [13]-[15]. MCDM technques have been appled to dfferent energy plannng problems. References [6], [15] revewed the MCDM methods applcaton n sustanable energy plannng. The authors n [16] used two MCDM algorthms to choose the best prme mover for a mcro-cchp system, to be used n a resdental buldng. In [17] the authors appled the fuzzy MCDM model to select the best cool storage system n buldngs. Reference [18] presented a revew of mult-crtera decson-makng methods for boenergy systems. Reference [19] solved the problem of evaluaton and selecton of an optmum thermal power plant usng Mult Attrbute Decson Makng methodology. Reference [20] developed the mult-crtera decson support framework for fndng the most sustanable electrcty producton technologes. Applcaton of MCDM to Prortzng of demand response (DR) programs was addressed n [21]. The role of MCDM n load estmaton, load management, congeston management, epanson plannng, dstrbuted generaton plannng, unt commtment problem, mult-mcrogrd concept was presented n [22]-[30]. In ths paper, plannng of a grdconnected mcogrd s consdered. Varous possble combnatons of energy resources are evaluated for ths mcrogrd usng HOMER software [31], [32]. The dfferent attrbutes namely, profts from njectng power nto grd at peak load, captal costs, cost of energy (COE), total emssons, and energy not served are consdered. The plans are prortzed accordng to the attrbutes by usng MADM technques consderng uncertanty n demand. The remanng of ths paper s organzed as follows. In secton 2 the problem descrpton s llustrated. Secton 3 descrbes the MCDM methods and the algorthm for the proposed approach. The sample system and results are presented n secton 4. Fnally, Secton 5 concludes the paper. [ISSN: ] 1

2 II. PROBLEM DESCRIPTION Smultaneously wth the advent of the smart grd concept, as well as ncreasng concerns about the harmful effects of greenhouse gases, envronmental and techncal ssues n addton to economc ssues n the power system plannng need to be consdered more than ever before. The mcrogrd s an emergng techncal soluton to deal wth these problems. Decson maker (DM) has to evaluate all possble plans for the mcrogrd ncludng renewable and conventonal energy resources. Because of the conflctng nature of the aforementoned attrbutes, t s a comple problem to fnd the optmum selecton of the energy resources wth regard to these attrbutes. The plans can be prortzed usng advanced plannng technques presented n the net secton. The proposed approach can help the decson maker to fnd the perfect soluton for the plannng problem consderng the concerned ssues. III. THE PLANS PRIORITIZING PROCEDURE In ths secton, procedure of plannng wth respect to varous techncal, economc and envronmental attrbutes are consdered. Fve attrbutes are namely, profts from njectng power nto grd at peak load, captal costs, cost of energy, energy not served and total emssons. Frst, HOMER software s utlzed to generate the several plannng optons. All possble combnatons of DERs are evaluated by HOMER wth respect to the components nstallaton and replacement costs, fuel prce, and regonal envronmental data. And HOMER can calculate the values of the attrbutes for each plan too. A three-layer herarchy shown n Fgure 1 s used for the assessment of plannng optons. The am s n the frst layer, attrbutes are n the second layer and the plannng optons (alternatves) are located n the thrd layer. The am s to prortzng the plannng optons and selecton of the optmzed plans based on these attrbutes. In the proposed plannng process the attrbutes are weghted by Analytcal Herarchy Process (AHP) technque. Then, the decson maker sorts the plans (alternatves) by means of MULTIMOORA method. Then, fve best plans are selected, and senstvty analyss s appled to these selected plans. Fnally the best plan s suggested accordng to uncertanty n future load. A. AHP Method AHP method bulds on the par-wse comparson model for specfyng the relatve mportance of all the attrbutes. AHP was proposed prmarly by Saaty [33]. AHP s done accordng to the herarchy of the plannng process. The attrbutes consttutng the herarchy are allowed to rate each other, and fnally the weght of attrbutes s determned. In order to fnd the relatve mportance from dfferent attrbutes wth regards to the alternatve, a comparson matr among pars usng a relatve mportance scale s buld. The judgments are entered takng nto account the fundamental scale of the AHP. An attrbute compared wth tself wll always have the value 1, thus the man dagonal entres of the matr wll be all one. The numbers 3, 5, 7, and 9 correspond to the verbal judgments. moderate mportance, strong mportance, very strong mportance, and absolute mportance (wth 2, 4, 6, and 8 for compromse between the prevous values). Consderng n attrbutes, the comparson between pars of attrbutes wth j attrbutes generates Ann matr where aj denotes the comparatve mportance of attrbute regardng to j attrbute. In the matr, aj=1when =j and aj=1/aj. To obtan the attrbutes weghts (wj) from matr A, frst the normalzed matr A s bult and then wj s calculated as the average of the entres n row j of normalzed matr A [34]. B. The MULTIMOORA Method Brauers and Zavadskas ntroduced the MULTIMOORA method frstly as MOORA standng for Mult-Objectve Optmzaton by Rato Analyss. Then they etended the method makng t more robust as MULTIMOORA (MOORA plus the full multplcatve form) [35], [36]. The MULTIMOORA method begns wth a decson matr D where ts elements j denote the performance of the -th alternatve regardng j-th attrbute as followng: Fgure 1. The three-layer herarchy for the proposed plannng process [ISSN: ] 2

3 D 11 1n m1 mn The method ncludes three parts namely, the Rato System, the Reference Pont approach, and the Full Multplcatve Form. The Rato System of MOORA: Rato system utlzes the vector data normalzaton by comparng alternatve of an attrbute to all values of the attrbute: j rj w j m 0w 1,0 r 1 (2) 2 j j 1 j Where j denotes the performance of the -th alternatve regardng j-th attrbute and w j s weght of the j-th attrbute, w j 1 j. These Indees are subtracted (f desrable value of ndcator s mnmum) or added (f desrable value of ndcator s mamum). So, the summarzng ndcator of each alternatve s derved as followng: g c r r j j j 1 j g 1 n Where g = 1, 2,..., n denotes number of attrbutes to be mamzed, so the rest of attrbutes to be mnmzed. Rankng of the alternatves s based on the C value. The hgher C coeffcent s the better alternatve (plan). The Reference Pont of MOORA: The Mamal attrbute Reference Pont (vector) s found wth respect to ratos obtanng from Eq. (2). The j-th element of ths vector can be defned as y ma rj n case of mamzaton. Every member of the reference pont represents mamum or mnmum of certan attrbute. Then every element of the normalzed decson matr s recalculated and fnal rank s determned accordng to devaton from the reference pont and the normalzed values as below: mn [ma ( y r )] j j The Full Multplcatve Form and MULTIMOORA: Brauers and Zavadskas [26] mproved MOORA by Full Multplcatve, comprsng mamzaton and also mnmzaton of purely multplcatve utlty functon. The -th alternatve utlty s declared as: U A B g w A ( ) j j 1 j : The product of attrbutes of the -th alternatve to be mamzed. n w B ( ) j j g 1 j : The product of attrbutes of the -th alternatve to be mnmzed. Where j = 1,...,g are the numbers of attrbutes to be (1) (3) (4) (5) mamzed and j= g+1,,n are the numbers of attrbutes to be mnmzed [15], [20]. Fgure 2 shows the flowchart of the proposed plannng optons sortng algorthm. Fgure 2. Flowchart of the proposed plannng optons sortng algorthm IV. SAMPLE SYSTEM AND RESULTS The MADM technques developed n secton 2 are appled to the sample system usng the proposed procedure and results of the methodology are presented n ths secton. In ths research, a grd-connected mcrogrd s consdered. The annual load average of case study s 1000 kwh/d, wth 93 kw peak load and load factor of 0.45 whch has ablty to echange electrcal energy wth power grd. And also supples thermal load wth annual load average of 100 kwh/d, 9 kw peak load and load factor of All possble combnatons of electrcty producton technologes, namely, mcroturbne, wnd power generaton unt, PV solar unt, desel power generaton unt, bomass combuston power generaton unt and a set of storage batteres are consdered supplyng power to the mcrogrd. The system ncludes a converter, a boler, AC and DC buses and other essental components of the mcrogrd. The schematc dagram of the proposed system n ths research s shown n Fgure 3. The frst attrbute namely profts from njectng power nto grd at peak load should be mamzed and whle four [ISSN: ] 3

4 Plan TABLE I NORMALIZED VALUES OF ATTRIBUTES FOR VARIOUS CONFIGURATION PLANS Confguraton Plans Proft Receved COE Captal Energy not Total emssons no. Cost served 1 Grd+DSL+MT+Battery Grd+BIO+MT Grd+PV+DSL+MT+Battery Grd+DSL+BIO+MT Grd+PV+BIO+MT+Battery Grd+DSL+BIO+MT+Battery Grd+PV+DSL+BIO+MT+Battery Grd+PV+Wnd+DSL+MT+Battery Grd+Wnd+BIO+MT+Battery Grd+PV+Wnd+BIO+MT Grd+MT Grd+PV+MT Grd+DSL+MT+Battery Grd+PV+Wnd+MT+Battery Grd+DSL+BIO+Battery Grd+PV+DSL+BIO+Battery Grd+PV+DSL+BIO Grd+Wnd+DSL+BIO+Battery Grd+PV+Wnd+DSL+BIO+Battery Grd+BIO+Battery TABLE II ATTRIBUTES WEIGHTING BY AHP METHOD Proft Receved COE Captal cost Energy not served Total emssons Weght Proft Receved COE Captal cost Energy not served Total emssons attrbutes namely captal costs, cost of energy (COE), energy not served, and total emssons should be mnmzed. The value of each attrbute for dfferent possble confguraton plans s calculated and the normalzed values (r j ) of ths attrbutes are as shown n Table 1. The decson matr D s establshed usng Eq. (1) wth the results obtaned from Table I. The decson matr D epresses the values of each attrbute for each plan. Table II shows the weghts of attrbutes obtanng by AHP method. Now by usng the MULTIMOORA method the rankng of plannng optons can be calculated usng Eqs. (2)-(5), and the results are shown n Table III. As seen from Table III, fve best plans are 10,2,4,8 and 6 plans. Then the uncertanty n future load s consdered n these selected plans. 3 Uncertan futures namely, future 1 (F1), future 2 (F2) and future 3 (F3).F1 represents the base load, F2 hgh load and F3 low load wth annual load average of 1000, 1200 and 800, respectvely. TABLE III PRIORITY OF PLANNING OPTIONS Prorty Plan no. Prorty Plan no. 1 Plan Plan 13 2 Plan 2 12 Plan 14 3 Plan 4 13 Plan 3 4 Plan 8 14 Plan 5 5 Plan 6 15 Plan 7 6 Plan 9 16 Plan 15 7 Plan 1 17 Plan 16 8 Plan Plan 17 9 Plan Plan Plan Plan 19 Fgure 3. Schematc of mcrogrd system confguraton [ISSN: ] 4

5 TABLE IV NORMALIZED VALUES OF ATTRIBUTES FOR SELECTED PLANS CONSIDERING UNCERTAIN FUTURES Plan No. Future Proft Receved COE Captal cost Energy not served Total emssons 10 Future Future Future Future Future Future Future Future Future Future Future Future Future Future Future The value of each attrbute for selected plans consderng uncertan futures s calculated and the normalzed values of ths attrbutes are as shown n Table IV. Then the proposed procedure for prortzng of the plans s appled to the selected plans agan consderng varous futures. Table V demonstrates the preferental rankng of plans regardng uncertan futures by usng the AHP and MULTIMOORA methods. TABLE VI PROBABILITY OF FUTURE LOADING CONDITIONS Loadng condton Future Future Future Probablty TABLE V PRIORITY OF SELECTED PLANS CONSIDERING UNCERTAINTY IN FUTURE LOAD Prorty Plan no. Prorty Plan no. 1 Plan 2(3) 9 Plan 8(1) 2 Plan 4(3) 10 Plan 6(1) 3 Plan 10(3) 11 Plan 4(2) 4 Plan 8(3) 12 Plan 10(2) 5 Plan 6(3) 13 Plan 2(2) 6 Plan 10(1) 14 Plan 8(2) 7 Plan 2(1) 15 Plan 6(2) 8 Plan 4(1) Now probablty of each uncertan future s determned whch s depcted n Table VI. Fnally n order for fnd the effect of uncertan future loads to plans, fnal rankng of plans s determned accordng to probablty of each uncertan future load. Table VII shows the fnal prorty of plans. As t s seen from Table IV, Plan 4 has the hghest rank and plan 6 has lowest rank. Plan 4 ncludes mcroturbne, desel generator and bomass combuston power generator that can better meet all consdered attrbutes under dfferent future loads. Prorty TABLE VII FINAL PRIORITY OF PLANS Plans plan4 plan2 Accordng to the Table IV, plan 4 s the only plan wth zero value of energy not served for all future loads, ndcatng the hgh relablty of ths plan. It has also acceptable values for remanng attrbutes comparng wth other plans. The notable pont s that all fve plans nclude the mcroturbne, thus the mcroturbne technology can be an approprate choce n mcrogrd plannng process. V. CONCLUSION plan10 plan8 plan6 In ths paper, a MCDM approach was proposed to solve mcrogrd plannng problem for buldngs. Dstrbuted energy resources consdered for mcrogrd are mcroturbne, wnd turbne, photovoltac panels, bomass combuston power [ISSN: ] 5

6 generator and desel engne. The mcrogrd has the ablty to echange electrcal energy wth bulk power grd. Economc, techncal and envronmental attrbutes were dentfed, and weghtng process of all the attrbutes was performed usng AHP method. The proposed method was appled to a sample buldng. The prortzng of all possble plans was done usng MULTIMOORA method. Three loadng condtons were consdered as hgh, medum and low. The fnal rankng of the plans s obtaned consderng uncertanty n future load, In order to assess performance of selected plans. The best plan was ntroduced as plan wth better performance n dfferent loadng condtons. It should be noted that the proposed approach s a proper method n feld of ntellgent buldng plannng to fnd the optmum plan for mcrogrd. REFERENCES [1] S. Wencong, Y. Zhyong, C. Mo-Yuen, Mcrogrd plannng and operaton: Solar energy and wnd energy, Power and Energy Socety General Meetng, 2010 IEEE, Mnneapols, MN, USA, 2010, pp [2] N. Hatzargyrou, H. Asano, R. Iravan, C. Marnay, Mcrogrds, Power and Energy Magazne, IEEE, vol. 5, no. 4, pp , [3] J.S. Katz, Educatng the Smart Grd, Energy 2030 Conference. IEEE, Atlanta, GA, 2008, pp [4] J.E. Samoulds, C.S. Mtropoulos, Energy-economy models: A survey, European Journal of Operatonal Research, vol.11, no. 3, pp , [5] P. Meer, V. Mubay, Modellng energy-economc nteractons n developng countres: A lnear programmng approach, European Journal of Operatonal Research, vol.13, no. 1, pp , [6] S.D. Pohekar, M. Ramachandran, Applcaton of mult-crtera decson makng to sustanable energy plannng A revew, Renewable and Sustanable Energy Revews, vol. 8, no. 4, pp [7] T. Kaya, C. Kahraman, Multcrtera decson makng n energy plannng usng a modfed fuzzy TOPSIS methodology, Epert Systems wth Applcatons, vol. 38, no. 6, pp , [8] E. Entchev, L. Yang, F. Szadkowsk, M. Armstrong, M. Swnton, Applcaton of hybrd mcro-cogeneraton system Thermal and power energy solutons for Canadan resdences, Energy and Buldngs, vol. 60, pp , [9] A.T. Rezvan, N.S. Gharneh, G.B. Gharehpetan, Optmzaton of dstrbuted generaton capactes n buldngs under uncertanty n load demand, Energy and Buldngs, vol. 57, pp , [10] E. Cetn, A. Ylanc, H.K. Ozturk, M. Colak, I. Kaskc, S. Iplkc, A mcro-dc power dstrbuton system for a resdental applcaton energzed by photovoltac wnd/fuel cell hybrd energy systems, Energy and Buldngs, vol. 42, no. 8, pp , [11] Z. Zeng, R. Zhao, H. Yang, Mcro-sources desgn of an ntellgent buldng ntegrated wth mcro-grd, Energy and Buldngs, vol. 57, pp , [12] H.A. Kazem, T. Khatb, K. Sopan, Szng of a standalone photovoltac/battery system at mnmum cost for remote housng electrfcaton n Sohar, Oman, Energy and Buldngs, vol. 61, pp , [13] E. Løken, Use of multcrtera decson analyss methods for energy plannng problems, Renewable and Sustanable Energy Revews, vol. 11 no.7, pp , [14] T. Tsoutsos, M. Drandak, N. Frantzeskak, E. Iosfds, I. Kosses, Sustanable energy plannng by usng mult-crtera analyss applcaton n the sland of Crete, Energy Polcy, vol. 37 no.5,pp , [15] J.-J. Wang, Y.-Y. Jng, C.-F. Zhang, J.-H. Zhao, Revew on multcrtera decson analyss ad n sustanable energy decson-makng, Renewable and Sustanable Energy Revews, vol.13 no. 9, pp , [16] M. Ebrahm, A. Keshavarz, Prme mover selecton for a resdental mcro-cchp by usng two mult-crtera decson-makng methods, Energy and Buldngs, vol. 55, no , [17] J.-J. Wang, C.-F. Zhang, Y.-Y. Jng, G.-Z. Zheng, Usng the fuzzy mult-crtera model to select the optmal cool storage system for ar condtonng, Energy and Buldngs, vol. 40, no. 11, pp , [18] J.A. Scott, W. Ho, P.K. Dey, A revew of mult-crtera decsonmakng methods for boenergy systems, Energy, vol. 42, no. 1, pp , [19] R.K. Garg, V.P. Agrawal, V.K. Gupta, Codng, evaluaton and selecton of thermal power plants A MADM approach, Internatonal Journal of Electrcal Power & Energy Systems, vol. 29, no. 9, pp , [20] D. Stremkene, T. Balezents, I. Krscukatenė, A. Balezents, Prortzng sustanable electrcty producton technologes: MCDM approach, Renewable and Sustanable Energy Revews, vol. 16, no. 5, pp , [21] H.A. Aalam, M.P. Moghaddam, G.R. Yousef, Modelng and prortzng demand response programs n power markets, Electrc Power Systems Research, vol. 80, no. 4, pp , [22] M.P. Moghaddam, M.K. Shekh-El-Eslam, S. Jadd, A MADM framework for generaton epanson plannng n small electrcty frms, Power Engneerng Socety General Meetng, IEEE, 2005, pp Vol [23] Z. Tefeng, Y. Jnsha, Decson-ad for power dstrbuton system plannng problems usng ELECTRE III, Power Engneerng Conference, The 7th Internatonal, 2005, pp [24] A. Gabatt, Development of a mult crtera decson model to evaluate the resdental energy effcency programs elaborated by the electrc utltes, n: 18th Internatonal conference on multple crtera decson makng, [25] A.T. Sarć, R.M. Ćrć, Mult-objectve ntegraton of real-tme functons n dstrbuton management system, Internatonal Journal of Electrcal Power & Energy Systems, vol. 25, no.3, pp , [26] A. Zangeneh, S. Jadd, A. Rahm-Kan, A fuzzy envronmentaltechncal-economc model for dstrbuted generaton plannng, Energy, vol. 36, no. 5, pp , [27] A. Zangeneh, S. Jadd, A. Rahm-Kan, A herarchcal decson makng model for the prortzaton of dstrbuted generaton technologes: A case study for Iran, Energy Polcy, vol. 37, no. 12, pp , [28] J. Vasljevska, J.A. Peças Lopes, M.A. Matos, Evaluatng the mpacts of the mult-mcrogrd concept usng multcrtera decson ad, Electrc Power Systems Research, vol. 91, pp , [29] M.G. R.Effatnejad, H. Jokar and S. Shahd, AHP METHOD FOR UNIT COMMITMENT, Internatonal Journal on Techncal and Physcal Problems of Engneerng, vol. 4, no. 13, pp , [30] A.H. Mondal, M. Dench, Hybrd systems for decentralzed power generaton n Bangladesh, Energy for Sustanable Development, vol. 14, no. 1, pp , [31] [Onlne] Avalable: [32] N.-K.C. Nar, N. Garmella, Battery energy storage systems: Assessment for small-scale renewable energy ntegraton, Energy and Buldngs, vol. 42, no. 11, pp , [33] T.L. Saaty, Fundamentals of Decson Makng and Prorty Theory wth the Analytc Herarchy Process, RWS Publcatons, Ptsburg, [34] J.R. San Crstóbal, Mult-crtera decson-makng n the selecton of a renewable energy project n span: The Vkor method, Renewable Energy, vol. 36, no. 2, pp , [35] W.K.M. Brauers, E.K. Zavadskas, Multmoora Optmzaton Used to Decde on a Bank Loan to Buy Property, Technologcal and Economc Development of Economy, vol. 17, no. 1, pp , [36] W.K.M. Brauers, E.K. Zavadskas, Project management by multmoora as an nstrument for transton economes, Technologcal and Economc Development of Economy, vol. 16, no. 1, pp. 5-24, [ISSN: ] 6