Management of a Group of Distributed Generators Aimed at Cooperation with theBulk Power System

KOICHI TANAKA, HIROYUKI KITA, EIICHI TANAKA, and JUN HASEGAWAHokkaido University, Japan

SUMMARY

Recently, the basic framework of electric power sys-tems has been changed significantly by deregulation ofelectric power industry. Also, distributed generators (DGs)such as renewable energy generations, co-generations, andenergy storage systems have been introduced into the de-mand sides not only for saving energy and the globalenvironment, but also for compensating voltage drops andsupply interruption. In the future power systems, a lot ofsmall-sized DGs will supply the electricity with existinglarge-scale generators under a competitive environment. Inthis context, it is desirable that DGs contribute to stable,reliable and economic operations by coordinating withexisting large-scale generators. For example, power fromlarge-scale generators could be leveled if DGs are operatedduring the peak period. Also, ancillary services such asfrequency control, voltage control, and supply of reservecapacity could be executed by controlling DGs directly orindirectly. Under these circumstances, the authors haveproposed a new power distribution system: Flexible, Reli-able and Intelligent ENergy Delivery System (FRIENDS).This paper investigates some kinds of power supply sys-tems which coordinate DGs with existing large-scale gen-erators including the concept of FRIENDS and consideringhow DGs are managed. The effects of DGs which contrib-ute to supply the energy and/or the reserve in every powersystem are evaluated. © 2008 Wiley Periodicals, Inc. ElectrEng Jpn, 163(1): 48–56, 2008; Published online in WileyInterScience (www.interscience.wiley.com). DOI10.1002/eej.20358

Key words: distributed generator; FRIENDS;multi-quality power supply; integrated control.

1. Introduction

Distributed generators (DG) of various kinds havebeen introduced recently into power systems. In the futurewe may expect hybrid power systems that combine multipleDGs and conventional large-scale centralized power net-works. This requires the development of new system con-figurations offering stable power supply while not onlypreventing DGs from degrading system performance, butalso maintaining cooperation among different powersources and offering ancillary services. All aspects of suchsystem configurations have been investigated [1, 2]. Theauthors too have developed a concept of next-generationpower distribution system called FRIENDS (Flexible, Re-liable and Intelligent ENergy Delivery System) [3]. InFRIENDS, multiple quality control centers (QCC) are in-stalled between distribution substations and customers, andappropriate control of such facilities provides power con-ditioning [4], multi-quality power supply [5], and otherfunctions. In addition, if these multiple QCCs are connectednot only in a power system but also in a heat system, fuelsystem, and information network, the DG groups can con-tribute to energy supply to points of demand by interchangeof energy and information.

DGs installed inside QCCs and those owned by cus-tomers are operated so as to maximize the benefits of therespective owners, and direct or indirect integrated control(management) must be applied in order to contribute to theoperation of the whole power system. The objective of thisstudy is to develop a framework for managing a DG groupin terms of multi-quality power supply, power interchange,and regional distribution of system operation responsibility.We assume several management models, and examine howdifferent DG operation patterns affect the capacity andoutput of the power system as well as its economic perform-ance. In this study, we evaluate the power supply (includingreserve power) of DGs installed in QCCs, while disregard-ing other functions such as energy storage.

© 2008 Wiley Periodicals, Inc.

Electrical Engineering in Japan, Vol. 163, No. 1, 2008Translated from Denki Gakkai Ronbunshi, Vol. 125-B, No. 7, July 2005, pp. 647–654

Cotnract grant sponsor: Grant-in-Aid for Scientific Research (B-15360144, C-15560232).

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2. System Model with Backup DG Group

We assume that all customers can be divided intohigh-quality loads with very high requirements for powersupply reliability, and ordinary-quality loads that allowsome degradation of reliability. For high-quality loads, abackup power source must be provided in case the commer-cial power supply is interrupted. In particular, customersmay either provide backup generators themselves (suchcustomers are referred to below as self-help, and mayinclude hospitals, data centers, semiconductor factories,etc.), or rely on the power supplier (such customers arereferred to below as dependent; normally, their supplyreliability is lower than that of self-help customers). In thisstudy, we consider the following models with regard tocustomers’ needs.

Model 1 A general power utility supplier or inde-pendent system operator (ISO) is responsible for stableoperation of the whole system, and power is supplied whileproviding a necessary reserve. Here self-help demand cus-tomers cannot be satisfied with supply reliability and mustinstall backup DGs themselves. This model is shown inFig. 1.

Model 2 Here the FRIENDS concept is introducedin part of the power system. Specifically, the QCCs in thepower distribution network receive power of uniform reli-ability from the system and then supply power to the loads,while providing higher reliability as an added value byoperating backup DGs. Thus, in case of a system failure,the self-help customers take care of reserve power them-selves, and the dependent customers are supported by theQCCs. Investment to assure reliability of power supply onthe system side can be reduced by avoiding redundantreserve. This model is shown in Fig. 2.

The QCCs can manage the DGs of self-help custom-ers to some extent via information and communicationsnetworks. If such contracts are concluded, the QCCs canreduce the capacity of their own DGs, and the self-help

consumers can benefit from maximally efficient utilizationof their DGs.

Model 3a This model assumes the existence of areserve power market (on a pool basis or a bilateral basis)in which the power deficits or excesses of every QCC canbe adjusted via the market. In Model 2, power is managedon the individual QCC level. On the other hand, Model 3aallows for power interchange among QCCs. Here we mayconsider various reliability levels of electric power on themarket. For example, when electricity is traded under acontract requiring the seller to guarantee reserve power (afirm or semifirm contract), a high reliability level is nor-mally expected; on the other hand, the reliability level israther low if the contract allows for power interruption incase of emergency (interruptible contract).

Model 3b This model assumes a micro-grid systemin which the responsibility for system operation is dele-gated to small communities with appropriate demand (herereferred to as areas). Power exchange among QCCs isallowed as in Model 3a; however, area operation of thepower system is controlled by area ISOs, who manage allQCCs in order to maintain orderly power interchange.Thus, the area ISOs collect data on supply and demand fromall QCCs via information and communications networks,compare these data with the supply available from the

Fig. 1. Model 1.

Fig. 2. Model 2.

Fig. 3. Model 3a.

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upstream power system, and determine the optimal opera-tion patterns for the area system in terms of reliability, price,etc. This model is shown in Fig. 4.

Model 4 Here the area ISOs are fully responsible forpower system operation in their respective areas, as inModel 3b, but a market for energy and ancillary services isassumed as in Model 3a; therefore, all area ISOs cancooperate in the market as shown in Fig. 5.

3. Operation Strategies and Evaluation of DGGroups in Different Models

As explained in the previous section, DGs owned byself-help customers or installed in the QCC are used pri-marily as backup generators to support high-quality loads.However, power demand for high-quality loads may be lowin certain time slots, and all available DGs need not neces-sarily stand by for their respective loads. That is, providedthat backup power for the respective loads is assured, theremaining power may be used to back up other loads. Inaddition, in case of an interruptible contract, backup gener-ators can be operated constantly. Normally, such generatorssupply power to the respective interruptible loads; however,when reserve power becomes necessary, the generators stop

supply to these loads and switch to high-quality loads, thusperforming their original backup function.

Thus, we may consider two operation strategies for aDG group introduced for the purpose of power backup. Inthe first case, the DGs are put on standby for an emergency.In the second case, the DGs are constantly operated tosupply interruptible loads. One of the two strategies isselected by the decision maker on the basis of his ownprofit. However, the above models differ in the contributionof the DGs to the whole system. This difference is examinedbelow, and the features of each operation strategy are iden-tified.

3.1 Effect of DG introduction for every model

Model 1 Here we assume that the self-help custom-ers introduce DGs with a capacity corresponding to themaximum demand of the high-quality loads for the purposeof backup in case of power interruption. These DGs areoperated constantly, thus reducing the burden on the powersystem.

Model 2 Here the reliability of supply to high-qual-ity loads is improved for the self-help customers by meansof proprietary DGs, and for the dependent customers by theDGs installed in the QCCs; therefore, the reserve rate of thepower system can be set lower than in Model 1 (the sameapplies to the other models considered below). In addition,if all QCCs use their DGs to supply power to customers,the generated output on the system side becomes lower thanin Model 1. When a DG fails, backup from the powersystem is required. However, since the DG capacity is verysmall compared to the power grid, such backup can beeasily covered by the system reserve power.

Model 3a If DGs are put on standby as an emer-gency power source, excess power appears in a QCC when-ever the high-quality loads of the dependent customers donot reach their peak. Therefore, this excess power may beoffered to other QCCs via the reserve power market. Thisassures efficient use of the reserve power provided by DGgroups, and we may expect a reduction of the required DGcapacity compared to Model 2. In addition, if interruptiblepower can be traded on the market, the DGs may be usednot only as reserve power for other QCCs but also for powersupply, which implies a smaller output of the power grid(large-scale generators) than in Model 2. However, if thepower market deals with firm or semifirm contracts, QCCscan participate in the market only if reserve power isassured.

Model 3b Here the DG capacity can be reduced,although not to such an extent as in Model 3a, by inter-change of reserve power among QCCs within the area. Inaddition, once reserve power for high-quality loads in thearea is assured, interruptible power can be interchanged

Fig. 4. Model 3b.

Fig. 5. Model 4.

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among QCCs, thus allowing a reduction of power gridoutput. In addition, even in case of power system interrup-tion, continuous power supply to high-quality loads can beassured by building autonomous area power networks andusing available DGs.

Model 4 As in Model 3b, the area ISOs providebackup power sources as necessary to supply high-qualityloads in the area, and hence there may be excess powersupply but a deficiency of power supply (here we do notconsider power interchange among areas). In this case, thesurplus of reserve power generated in the whole area isassumed to contribute to reduction of reserve power on thepower grid side. In addition, the same degree of reductionof power grid output can be achieved by interarea powersupply.

3.2 Model evaluation with regard to DGoperation strategy

We evaluated the equipment capacity and energysupply of the power grid and DG group for every model.We assumed that the reserve power in Models 2 to 4 couldbe reduced compared to Model 1 due to the introduction ofQCCs. Thus, in the calculations, we set the reserve rate ofthe power grid to 10% for Model 1, and to 3% for Models2 to 4. In addition, the DG capacity was assumed to be 110%of the maximum demand of the high-quality loads, andModels 2 to 4 were evaluated by the same standard (whichdoes not necessarily mean the same reliability). As regardsthe power demand of the whole system, we assumed threeareas with DG groups (residential, commercial, and indus-trial), and other loads (without DGs) supplied directly fromthe power grid (Fig. 6). We also assumed that every sectorincluded four blocks, and the one QCC supplied power toevery block. High-quality loads for every block were simu-lated by using the load patterns shown in Fig. 7, withrandom fluctuations added to reflect the fact that the loadsof different QCCs had different patterns of variation. In

addition, the quantity of ordinary-quality loads was as-sumed to be one-third that of the high-quality loads. Sincethe DGs are introduced as emergency power source forhigh-quality loads, the DG capacity decreases as the shareof ordinary quality loads increases. The total demand curvefor the three areas is shown in Fig. 8. The ratio of self-helpcustomers and dependent customers is 1:3 for the residen-tial area, 1:1 for the commercial area, and 3:1 for theindustrial area.

The calculation results are presented in Fig. 9. In thediagrams, the vertical axis represents the overall capacityof the power grid and DGs as well as their respective energysupplies, and the horizontal axis represents the maximumDG output when the DGs are operated for power supply.That is, a value of 0 on the horizontal axis represents thecase in which all DGs are put on standby as a power reserve.The following can be inferred from Fig. 9.

Model 1 Here the reserve rate is set higher than inthe other models in order to satisfy the demand of depend-ent customers for high-quality power by means of the powergrid. Therefore, the capacity of the upstream system ishigher. On the other hand, the capacity and energy supplyof the DGs are about half as great as in the other modelsbecause QCCs are not introduced.

Fig. 6. Duration curve for general loads (supplied frompower grid).

Fig. 7. Load curves for every area.

Fig. 8. Total load curves.

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Model 2 With the introduction of QCCs, the DGcapacity grows just to the extent of the high-quality loadsof the dependent customers. On the other hand, the reserverate of the power grid is set correspondingly lower, and thecapacity of the upstream system becomes smaller. Thistrend becomes more pronounced as DGs are increasinglyused for power supply.

Model 3a Due to interchange of reserve power onthe market, the DG capacity is lower than in the othermodels (the part of the diagram with a DG output below 12MW). When DGs are used for power supply too, efficientutilization is achieved via market interchange, and the DGenergy supply increases compared to Models 2 and 3b,where the market is not available.

Model 3b Here DGs are introduced to the extentnecessary to satisfy the high-quality loads of dependentcustomers within the area. Due to area management, the DGcapacity can be somewhat decreased compared to Model3a. As regards the DG energy supply and the capacity of the

upstream system, the results are the same as in Model 2because market interchange is not provided.

Model 4 Here power interchange on the market isavailable as in Model 3a, and the excess power reserve ofthe area DGs is assumed to contribute to a decrease in thereserve rate of the power grid. Therefore, when the DGs areused as power reserve, the capacity of the power system canbe decreased. When the DG output is below 12 MW, thistrend becomes pronounced, in contrast to Model 3a, wherethe DG capacity decreases.

3.3 Economic evaluation of every model withregard to DG operation strategy

A power cost evaluation of the whole system wasperformed for every model described above. We consideredfixed and variable costs for the power grid and DGs, whiledisregarding equipment other than DGs in the QCCs anddistribution lines.

Fig. 9. Capacity and energy supply of power grid and DGs.

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The costs of various generators are given in Table 1.Here we assumed a micro gas turbine as the DG, and itsfixed cost was calculated by multiplying the expected con-struction cost of 10,000 yen/kW [7] by the annual expenserate of 0.096 (interest rate 5%, service life 15 years). Basedon the data in Table 1, we found the best mix of generatorsby using a screening method. We assumed the same reserverate of the power grid as in the previous section, withreserve power being provided by LNG-fired thermal power.In addition, we assumed an ideal market where all transac-tions are always completed, while omitting evaluation ofmarket prices.

The results thus obtained are presented in Table 2. Inthe table, Total cost represents the minimum cost for everymodel, and DG-EO represents the DG capacity used forpower supply to obtain the minimum cost. The DGs offer alower fixed cost than the large-scale generators, but thevariable cost is rather high. Therefore, the DGs are likelyto be used as reserve power rather than for power supply.When the DGs are forced to supply power, we may wellexpect higher costs. This is particularly true for Models 3aand 4, which allow for area market transactions, and theminimum cost is obtained when all DGs are used as reservepower. In addition, although these models offer the widestrange of market transactions, the cost is higher than inModel 3b, which allows only for area management.

We also calculated the cost while varying the reserverate of the power grid from 2% to 7% in Models 2 to 4. Wefound that the total cost of every model increases with thereserve rate. In addition, when the rate exceeds 4% inModels 2 and 3b, the DG capacity used for power supplyincreases to 35 MW at the minimum cost, while remainingunchanged in Models 3a and 4. However, cost with theshare of DG capacity used for power supply is only a fewpercent in all cases.

3.4 Evaluation for various loads of the powergrid

We may assume that the features of every modelrevealed by the calculations are reflected in nonsynchron-ism of the area loads and in the amount of the loads supplied

from the power grid. We examined how the power costvaries with the amount of power demand satisfied by thepower grid (large-scale generators).

The obtained results for Models 1 and 4 are comparedin Fig. 10. Here the vertical axis represents the total cost peryear, and the horizontal axis represents the load ratio shownin Fig. 6. As the amount of the load covered by the upstreamsystem grows, the cost increase of Model 1 is relativelyhigh. This is because in Model 1, reserve power is providedconventionally only by the power grid, except for self-helpcustomers, and therefore the cost of reserve power increaseswith the share of demand supplied by the power grid. Onthe contrary, in Model 4, which offers a reduction of reservepower in the grid by market transactions, the increase incost is restricted compared to Model 1.

3.5 Calculation for different DG fixed costs

We also examined the influence of the DGs intro-duced in an area. Since we assumed the introduction ofmany DGs, the fixed cost of the DGs was of great influence.Above, the fixed cost of the DGs was assumed to be 10,000yen/kW. We also performed calculations at costs of 8000yen/kW and 6000 yen/kW as shown in Fig. 11.

Table 2. Minimum cost for each model

Fig. 10. Total cost versus load ratio of power grid.

Table 1. Costs of generators

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The diagram indicates that Model 1 is better than theothers when the DG fixed cost is 10,000 yen/kW, andsimilar results are obtained at 8000 yen/kW. However,Models 2 to 4 perform better at 6000 yen/kW, which agreeswith the data in Table 2 showing a great influence of the DGfixed cost. On the other hand, the introduced DG capacityin Model 1 is small compared to Models 2 to 4, and hencethe effect of reduction of the DG fixed cost is small as well.

3.6 Calculation for different DG variable costs

We performed calculations at DG fuel prices of 7yen/kWh and 5 yen/kWh. The minimum cost and the DGcapacity used for energy supply at these price levels aregiven in Table 3 for every model.

Compared with Table 2, we see that the percentageof DG capacity used for energy supply increases withdecreasing fuel prices. At a price of 7 yen/kWh as shown inTable 3(a), the lowest cost is offered by Model 3b, whichprovides a rather large share of DG capacity used for energysupply. When the fuel price is reduced to 5 yen/kWh,Models 3a and 4 offer the lowest cost, as shown in Table3(b). This may be attributed to the best utilization of theDGs by market transactions.

4. Conclusions

In this study we have considered several models of acombined configuration that adds DGs installed on thecustomer side to the conventional power grid, and haveexamined how appropriate management of DG groups cancontribute to overall power supply and maintenance ofreserve power. We also evaluated how DG operation strate-gies affect equipment capacity, amount of power supply,and economic performance. In addition, we examined DGmanagement at various system loads and for differentfixed/variable costs of DGs.

Calculations for various DG operation strategiesshowed that with models allowing for power interchangeamong QCCs and areas, equipment capacity can be reducedalong with the burden on the power grid. As regards theoverall load amount supplied from the power grid, we foundthat in case of considerable nonsynchronism and the possi-bility of power interchange, appropriate reliability manage-ment on the customer side proves more economical thanmaintaining uniform reliability in the upstream system. Inaddition, we showed that partial replacement of conven-tional large-scale generators with DG proves advantageousif a suitable environment is created by lower DG costs.

Acknowledgments

Part of the calculations in this study were carried outin cooperation with first-year postgraduate student Y.Sasaki. This study was assisted by a Grant-in-Aid forScientific Research (B-15360144, C-15560232).

REFERENCES

1. Report on new electric power networks. The Instituteof Applied Energy; 2003.

2. Ichikawa T, Rehatanz C. Recent trends in distributedgeneration—Technology, grid integration, systemoperations. Proc 14-th Power Systems ComputationConference, Seville, Spain, 2002.

3. Nara K, Hasegawa J. A new flexible, reliable andintelligent electrical energy delivery system. TransIEE Japan 1997;117-B:47–53.

4. Sasaki T, Kita H, Hara R, Tanaka E, Hasegawa J.Interior structure of quality control center at thelow voltage side considering the power condition-ing by SMES. Trans IEE Japan 2001;121-B:1353–1360. (in Japanese)

5. Mitsukuri Y, Kita H, Hara R, Tanaka E, Hasegawa J.Reliability evaluation considering customized powerquality supply in FRIENDS. IEEJ Trans PE2004;124:43–52. (in Japanese)

Fig. 11. Total cost at varied DG fixed cost.

Table 3. Minimum cost for each model

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6. Ueno F, Kita H, Tanaka E, Hasegawa J. Study ondistributed autonomous supply system in the deregu-lation power system. Trans IEE Japan 2001;121-B:1757–1764. (in Japanese)

7. Ishii K (editor). Micro gas turbine systems. OhmPress; 2002.

APPENDIX

Introduced capacity PDG of DGs and capacity PU ofupstream power system for every model were formulatedas follows.

Model 1

Here PDG_kS is the DG capacity of self-help customer k;

LH_kS (t) is the high-quality load of self-help customer k at

time slot t; L(t) is the power grid load at time slot t;PDG_k

S (t) is the DG output of self-help customer k at timeslot t; and R is the power grid reserve.

Model 2

Here P DG_ jQCC is the DG capacity of QCC j; LH_ jm

D (t) is thehigh-quality load of dependent customer m of QCC j at timeslot t; and P DG_ j

QCC (t) is the total DG output of QCC j at timeslot t.

Model 3a

Here P DGQCC is the total DG capacity of QCC; and P DG

QCC(t)is the total DG output of QCC at time slot t.

Model 3b

Here P DG_iQCC is the total DG capacity of QCC in area i; and

P DG_iQCC (t) is the total DG output of QCC in area i at time slot

t.Model 4

The DG capacity PDG4 was set the same as in Model3b. On the other hand, the capacity of the power grid PU4 iscloser to Model 3a than to Model 3b because of powerinterchange. If transactions on power market are madeideally, PU in Model 4 would become equal to that in Model3a. However, the DG capacity PDG3a in Model 3a becomessmaller than PDG4(= PDG3b) due to power interchange whenthe share of DG output used for power supply is small[PDG(t) ≤ 12 MW in Fig. 9]. Therefore, PU in Model 4becomes smaller than that in Model 3a.

(A.1)

(A.3)

(A.2)

(A.4)

(A.6)

(A.5)

(A.7)

(A.9)

(A.8)

(A.10)

(A.12)

(A.11)

(A.10)

(A.14)

(A.13)

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AUTHORS (from left to right)

Koichi Tanaka (student member) completed the M.E. program at Hokkaido University (systems and informationengineering) in 2004 and joined Kansai Electric Power Co. His student research focused on management of distributed generatorgroups.

Hiroyuki Kita (member) completed the M.E. program at Hokkaido University (electrical engineering) in 1988 and becamea research associate there in 1989. He has been an associate professor since 1995. He received an IEEJ Paper Award in 1997.His research interests are planning, operation, and control of power systems. He holds a D.Eng. degree, and is a member ofIEEE, ORSJ, IEIEJ, and IEIJ.

Eiichi Tanaka (member) completed the M.E. program at Hokkaido University (electrical engineering) in 1977 and joinedthe faculty as a research associate. His research interests are analysis and control of power systems. He is a member of ORSJ,IEIEJ, and SICE.

Jun Hasegawa (member) completed the doctoral program at Hokkaido University (electrical engineering) in 1971 andjoined the faculty as a lecturer, subsequently becoming an associate professor, and a professor in 1985. Since 2004 he has beenpresident of Hakodate National College of Technology. He received an IEEJ Paper Award in 1997. He was chairman of IEEJDept. B in 1994, IEEJ vice chair in 1997, and IEEJ acting chair in 2004. His research interests are planning, operation, analysis,and control of power systems, and electrical power engineering. He holds a D.Eng. degree, and is a member of IEEE, IEIJ,JSER, CAJ, ORSJ, and IEIEJ.

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