International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineer6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March0500100015002000...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) V...
Upcoming SlideShare
Loading in …5
×

Vagueness concern in bulk power system reliability assessment methodology 2-3-4

297 views

Published on

Published in: Technology, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
297
On SlideShare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
8
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Vagueness concern in bulk power system reliability assessment methodology 2-3-4

  1. 1. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME372VAGUENESS CONCERN IN BULK POWER SYSTEMRELIABILITY ASSESSMENT METHODOLOGYMr. N.M.G KUMAR1, Dr.P.SANGAMEWARA RAJU21(Research scholar, Department of EEE, S.V.U. College of Engineering.&Associate Professor, Department of EEE., Sree Vidyanikethan Engineering College)2(Professor, Department of E.E.E., S.V.U. College of Engineering,Tirupati, Andhra Pradesh, India)ABSTRACTThis paper has illustrated the development of a technique for examining the reliabilityassociated with a generation configuration using an energy based index. The approach is basedupon the Expected Loss of Energy Approach and extends the technique to include theconsideration of energy limitations associated with generation facilities.Safe, secure anduninterrupted electric power supply plays very important in the operation of the complex electricpower system that provides the efficient electrical infrastructure to supporting all economic,community progress, social security and to live quality of modern living life. The large utility ofelectricity has led to a high vulnerability to power failures. In this way, reliability of powersupply has gained focus and it is important for electric power system planning and operation.Thispaper illustrates a method for evaluating the significance of reliability indices for bulk powersystems. The technique utilizes a continuous representation of a generating capacity model forLOLP (Loss of Load Probability), LOLE (Loss of Load Expectation) and EENS (ExpectedEnergy Not Supplied) for single area. The objective paper is to describe a load and generationmodel for analysis of generation reliability index. This paper illustrates a well known techniquefor generating capacity evaluation which includes limited energy sources. The most populartechnique at the present time for assessing the adequacy of an existing or proposed generatingcapacity configuration is the Loss of Load Probability or Expectation Method. As an energyindex in bulk power system reliability assessment is EENS (Expected Energy not supplied) isof great significance for economic analysis and power system planning. This paper mainlyfocuses on the following two categories among which one is the establishment of new reliabilityindex frame work that meets the developing power market and integrates reliability assessmentcalled Expected Energy Not Supplied (EENS). Here we are considering IEEE-Reliability TestSystem (RTS).INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING& TECHNOLOGY (IJEET)ISSN 0976 – 6545(Print)ISSN 0976 – 6553(Online)Volume 4, Issue 2, March – April (2013), pp. 372-392© IAEME: www.iaeme.com/ijeet.aspJournal Impact Factor (2013): 5.5028 (Calculated by GISI)www.jifactor.comIJEET© I A E M E
  2. 2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME373Total system GenerationTransmission SystemDistribution SystemHierarchical Level 1Hierarchical Level 2Hierarchical Level 3Keywords: Reliability assessment, power failure, FOR (Forced Outage Rate), EENS(Expected Energy not Supplied), LOLP (Loss of Load Probability), and LOLE (Loss of LoadExpectation), analytical method, sensitivity analysis, bulk power system reliability, reliabilityindices.I. INTRODUCTIONReliability is an abstract term meaning endurance, dependability, and goodperformance. For engineering systems, however, it is more than an abstract term; it issomething that can be computed, measured, evaluated, planned, and designed into a piece ofequipment or a system. Reliability means the ability of a system to perform the function it isdesigned for under the operating conditions encountered during its projected lifetime.Reliability analysis has a wide range of applications in the engineering field. Many of theseuses can be implemented with either qualitative or quantitative techniques. Qualitativetechniques imply that reliability assessment must depend solely upon engineering experienceand judgment. Quantitative methodologies use statistical approaches to reinforce engineeringjudgments. Quantitative techniques describe the historical performance of existing systemsand utilize the historical performance to predict the effects of changing conditions on systemperformance [1].Continuity of electric power supply plays very important in the modern days ofcomplex electric power system that describes the efficient electrical operation and economic,community progress, social security and growth of country. The modern days of electricpower system is complex and is always subjected to disturbance around the clock, and isgenerally composed of three parts (1) generation, (2) transmission, and (3) distributionsystems, all of which contribute to the production and transportation of electric energy tocustomers. The reliability of an electric power system is defined as the probability that thepower system will perform the function of delivering electric energy to customers on acontinuous basis and with acceptable service quality. Power system reliability assessment, thethree power system parts are combined into different system hierarchical levels, as shown inFigure 1.Hierarchical level 1 (HL1) involves the reliability analysis of only the generationsystem, hierarchical level 2 (HL2) includes the reliability evaluation of the composite of bothgeneration and transmission systems, referred to as the bulk power system or the compositepower system, and hierarchical level 3 (HL3) consists of a reliability study of the entirepower system.Fig. 1. Hierarchical levels for power system reliability assessment.
  3. 3. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME374At the present stage of development, the reliability evaluation of the entire powersystem (HL3) is usually not conducted because of the immensity and complexity of theproblem in a practical system. Power system reliability is assessed separately for thegeneration system (HL1), the bulk power system (HL2), and the distribution system.Reliability analysis methods for generation and distribution systems are well developed. Bulkpower system reliability assessment refers to the process of estimating the ability of thesystem to simultaneously (a) generate and (b) move energy to load supply points.Traditionally, it has formed an important element of both power system planning andoperating procedures. The main objective of power system planning is to achieve the leastcostly design with acceptable system reliability. For this purpose, long-term reliabilityevaluation is usually executed to assist long-range system planning in the following aspects:[1, 11, 12](1)The determination of whether the system has sufficient capacity to meet system loaddemands,(2)The development of a suitable transmission network to transfer generated energy tocustomer load points,(3) A comparative evaluation of expansion plans, and(4) A review of maintenance schedules for preventive and correctivePower system operating conditions are subject to changes such as loadabilityuncertainty, i.e., the load may be different from that assumed in design studies, andunplanned component outages. To deliver electricity with acceptable quality and continuity tocustomers at minimum cost and to prevent cascading sequences after possible disturbances,short-term reliability prediction that assists operators in day-to-day operating decisions isneeded. These decisions include determining short-term operating reserves and maintenanceschedules, adding additional control aids and short lead-time equipment, and utilizing specialprotection systems.II. POWER SYSTEM ADEQUACY VERSUS SYSTEM SECURITYToday’s new operating environment for electrical power system is to supply itscustomers with electrical energy as economically as possible and with an acceptable level ofreliability. The prerequisite of reliable electric power supply enhance the significance ofdependence of modern society on electrical energy. Electric power utilities therefore mustprovide a reasonable assurance of quality and continuity of service to their customers. Ingeneral, more reliable systems involve more financial investment. It is, however unrealistic totry to design a power system with a hundred percent reliability and therefore, power systemplanners and engineers have always attempted to achieve a reasonable level of reliability atan affordable cost. It is clear that reliability and related cost/worth evaluation are importantaspects in power system planning and operation reliability of a power system is defined as theability of power system to supply consumers demand continuously with acceptable quality.The concept of power-system reliability is extremely broad and covers all aspects of theability of the power system to satisfy the customer requirements. The perception of powersystem reliability may be reasonable involves the security and adequacy and can berecognized an healthy, Marginal (alert) and emergency (at risk) concerned and designated as“system reliability”, which is shown in Fig. 2
  4. 4. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME375Fig. 2 Subdivision of System ReliabilityThe Figure 2 represents two basic aspects of power system reliability depends onsystem adequacy and System Security. Adequacy relates to the existence of sufficientfacilities within the system to satisfy the consumer load demand or operational constraints.These include the facilities necessary to generate sufficient energy and the associatedtransmission and distribution facilities required to transport the energy to the actual consumerload points. Adequacy is therefore associated with static conditions which don’t includesystem disturbance. Security relates to the ability of the system to respond to disturbancesarising within that system. Security is therefore associated with the response of the system toperturbations it is subjected to. These include the conditions associated with local andwidespread disturbance of major generation, transmission, services etc. Another aspect ofreliability is system integrity, the ability to maintain interconnected operations. Integration isviolated causes an uncontrolled separation occurs in presence of severe disturbance (Blockout of grid or regional grid).Most of the probabilistic techniques presently available for powersystem reliability evaluations are in the domain of adequacy assessment.[1-3]There are two basically and conceptually different mythologies are present in thepower system reliability studies i.e. the analytical approaches and Monte Carlo simulationapproaches, used in power system reliability evaluation. This is shown is shown in belowFigure 3. An analytical approach represents the system by a mathematical model andevaluates the reliability indices from this model using analytical solutions. The Monte Carlosimulation approaches, however, estimates the reliability indices by simulating the actualprocess and random behavior of the system and treats the problem as a series of realexperiments.III. PROBLEM OF STATEMENT FOR BULK POWER SYSTEM RELIABILITYThe bulk power system therefore can be simply represented by a single bus as shownin Figure 4, at which the total generation and total load demand are connected is normally asSMLB(single machine connected to load bus) system in power system stability and securitystudies. The main objective in HL-I assessment is the evaluation of the system reserverequired to satisfy the system demand and to accommodate the failure and maintenance of thegenerating facilities in addition to satisfying any load growth in excess of the forecast. Thisarea of study can be categorized into two different aspects designated as static and operatingcapacity assessment. Static assessment deals with the planning of the capacity required toAnalytical approaches MCS ApproachesBasic Approaches
  5. 5. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME376satisfy the total system load demand and maintain the required level of reliability. Operatingcapacity assessment, on the other hand, is mainly focused on the determination of therequired capacity to satisfy the load demand in the short term (usually a few hours) whilemaintaining a specified level of reliability. This thesis is focused on static capacity adequacyevaluation and corresponding cost/worth assessment of generating systemsFig. 4. System representation at HL-IThere is a wide range of power system reliability assessment techniques are used inthe generating capacity planning and operation [1]. Basically, generating capacity adequacyevaluation involves the development of a generation model, the development of a load modeland the combination of the two models to produce a risk model as shown in Figure 5. Thesystem risk is usually expressed by one or more quantitative risk indices. In the directanalytical method for generating capacity adequacy evaluation, the generation model isusually in the form of a generating capacity outage probability table, which can be calculatedas a indices in HL-I evaluation simply indicate the overall ability of the generating facilitiesto satisfy the total system demand. Generating unit unavailability is an important parameter ina probabilistic analysis.Fig..5 Conceptual tasks for HL- I evaluationIV. RELIABILITY EVALUATION METHODSReliability techniques can be divided into the two general categories of probabilisticand deterministic methods. Both methods are used by electric power utilities at the presenttime. Most large power utilities, however, use a probabilistic approach.Risk modelGeneration model Load modelTotalSystemLoadTotalgeneration
  6. 6. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME377IV.I. Deterministic MethodsOver the years, a range of deterministic methods have been developed by the powerindustry for generating capacity planning and operating. These methods evaluate the systemadequacy on the basis of simple and subjective criteria generally termed as “rule of thumbmethods” [1]. Different criteria have been utilized to determine the system reserve capacity.The following is a brief description of the most commonly used deterministic criteria withoutconsidering energy storage capability.1. Capacity Reserve Margin (CRM)In this approach, the reserve capacity (RC), which is normally the differencebetween the system total installed capacity (IC =Σ Gi, G , is the capacity of Unit i in thesystem) and the system peak load (PL), is expressed as a fixed percentage of the totalinstalled capacity as shown in Equation (1). This method is easy to apply and to understand,but it does not incorporate any individual generating unit reliability data or load shapeinformation%100xICPLICRC−= (1)2. Loss of the Largest Unit (LLU)In this approach, the required reserve capacity in a system is at least equal to thecapacity of the largest unit (CLU) as expressed in Equation (2). This method is also easy toapply. Although it incorporates the size of the largest unit in the system, it does not recognizethe system risk due to an outage of one or more generating units. The system reserveincreases with the addition of larger units to the system.RC ≥ CLU (2)3. Percentage reserve margin methodIn this method the reserve capacity is equal to or greater than the capacity of thelargest unit plus a fixed percentage of either the capacity installed or the peak load as shownin Equations (3) and (.4). It also incorporates not only the size of the largest unit in theevaluation but also some measure of load forecast uncertainty. It does not reflect the systemrisk as the multiplication factor x (normally in the range of 0-15%) is usually subjectivelydetermined by the system planner.RC= CLU + x*IC (.3)RC= CLU + x*PL (.4)PL is peak load in MW; IC is Installed capacity in MWThe main disadvantage of deterministic techniques is that they do not consider theinherent random nature of system component operating failures, of the customer load demandand of the system behavior. The system risk cannot be determined using deterministiccriteria. Conventional deterministic methods and procedures are severely limited in theirapplication to modern integrated complex power systems.
  7. 7. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME378V. PROBABILISTIC METHODSThe benefits of utilizing probabilistic methods have been recognized since at leastthe 1930s and have been applied by utilities in power system reliability analyses since thattime. The unavailability (U) of a generating unit is the basic parameter in building aprobabilistic generation model. This statistic is known as the generating unit forced outagerate (FOR). It is defined as the probability of finding the unit on forced outage at some distanttime in the future. The unit FOR is obtained using Equation (5).∑ ∑∑+=][][][timeuptimedowntimedownFOR (5)The load model should provide an appropriate representation of the system load overa specified period of time, which is usually one calendar year in a planning study. Thegeneration model is normally in the form of an array of capacity levels and their associatedprobabilities. This representation is known as a capacity outage probability table (COPT) [1].Each generating unit in the system is represented by either a two-state or a multi-state model.Case 1: No Derated State [1]In this case, the generating unit is considered to be either fully available (UP) ortotally out of service (Down) as shown in Figure 6. The availability (A) and the unavailability(U) of the generating unit are given by Equations (6) and (7) respectivelyFig. 6. Two state model for generating unitWhere λ= unit failure rate and µ = unit repair rate.µλµ+=A (6)µλλ+=U (7)A Recursive Algorithm for Capacity Model BuildingThe capacity model can be created using a simple algorithm with a multi-state unit,i.e. a unit which can exist in one or more derated or partial output states as well as in the fullyup and fully down states. The technique is illustrated for a two-state unit addition followed bythe more general case of a multi-state unit. The probability of a capacity outage state of XMW can be calculated using Equation (8).C))-(XP*(U+(X)P*U)-(I=P(X) (8)µλUp0 Down1
  8. 8. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME379Where the cumulative probabilities of a capacity outage level of X MW before andafter the unit of capacity C is added respectively. Equation (8) is initialized by setting P’(X) =1.0 for X≤ 0 and P’(X)=0 otherwise.Case 2: Inclusion of De-rated statesIn addition to being in the full capacity and completely failed states, a generatingunit can exist in other states where it operates i.e reduced operating capacity state as shown inFigure 7. Such states are called derated states. The simplest model that incorporates de-ratingstate. This three-state model includes a single derated state in addition to the full capacity andfailed states. The Equation (9) can be used to add multi-state units to a capacity outageprobability table.Fig. 7. Three state model for generating unit∑=−=nti CXPpXP1)()( (9)Where n- the number of unit states, Ci - Capacity outage state i for the unit being added pi -Probability of existences of the unit state iCase3: Recursive algorithm for unit removalGenerating units are periodically scheduled for unit overhaul and preventivemaintenance. During these scheduled outages, the unit is available neither for service nor forfailure. This situation requires a capacity model which does not include the unit on scheduledoutage. The new model could be created by simply building it from the beginning usingEquation (10).)1()(*)()(UCXPUXPXP−−−= (10)In above equation P(X - C) = 1.0 for X < CCase4: Procedure for Rounding Off Value[1-3]Bulk power generation system having large number of generating units of differentcapacities, the table will contain several tens or hundreds possible discrete levels of capacityoutage levels. This outage levels can be reduced by grouping and rounding the capacityoutage into the possible discrete levels. The capacity outage table introduces unnecessaryapproximations which can be avoided by the table rounding approach and reduces thecomplexity. The capacity rounding increment used depends upon the accuracy desired. Thefinal rounded table contains capacity outage magnitudes that are multiples of the roundingincrement are calculated by equations (11), and (12). The number of capacity levels decreasesas the rounding increment increases, with a corresponding decrease in accuracy.Derated2 Down1Up0
  9. 9. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME380For rounding off the values we use the formulaCk=capacity of higher (kth) state,Cj=capacity of lower (jth) state,Ci=capacity of variable (ith) state)(*)( ijkikj CPCCCCCP−−= (11))(*)( ijkjik CPCCCCCP−−= (12)For all the states i falling between the required rounding states j and kThe Equation (11) is used for rounding off values for exact state and in betweenstates and Equation (12) is used for rounding off values for previous state The use of arounded table in combination with the load model to calculate the risk level introduces certaininaccuracies. The error depends upon the rounding increment used and on the slope of theload characteristic. The error decreases with increasing slope of the load characteristic and fora given load characteristic the error increases with increased rounding increment. Therounding increment used should be related to the system size and composition. Also the firstnon-zero capacity-on-outage state should not be less than the capacity of the smallest unit.VI. LOSS OF LOAD .ENERGY INDICES (LOLP, LOLE, EENS) LOSS OF LOADPROBABILITY [1]The generation system model can be convolved with an appropriate load model toproduce a system risk index. The simplest load model that can be used quite extensively, inwhich each day is represented by its daily peak load or weekly peak load duration. Prior tocombining the outage probability table it should be realized that there is a difference betweenthe terms capacity outage and loss of load. The term capacity outage indicates a loss ofgeneration which may or may not result in a loss of load. This condition depends upon thegenerating capacity reserve margin and the system load level. A loss of load will occur onlywhen the capability of the generating capacity remaining in service is exceeded by the systemload level.In this approach, the generation system represented by the COPT and the loadcharacteristic represented by either the DPLVC or the LDC are convolved to calculate theLOLE index. Figure 8 shows a typical load-capacity relationship where the load model isrepresented by the DPLVC or LDC, capacity outage exceeds the reserve, causes a load loss.Each such outage state contributes to the system LOLE by an amount equal to the product ofthe probability and the corresponding time unit. The summation of all such products gives thesystem LOLE in a specified period, as expressed mathematically in Equation (13). Capacityoutage less than the reserve do not contribute to the system LOLE The main objective ofpower system planning is to achieve the least costly design with acceptable system reliability.For this purpose, long-term reliability evaluation is usually executed to assist long-rangesystem planning in the following aspects: (1) the determination of whether the system has
  10. 10. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME381Fig. 8 Relationship between load, capacity and reservesufficient capacity to meet system load demands, (2) the development of a suitabletransmission network to transfer generated energy to customer load points, (3) a comparativeevaluation of expansion plans, and (4) a review of maintenance schedules [5, 6]. Powersystem operating conditions are subject to changes such as load uncertainty, i.e., the load maybe different from that assumed in design studies, and unplanned component outages. Todeliver electricity with acceptable quality to customers at minimum cost and to preventcascading sequences after possible disturbances, short-term reliability prediction that assistsoperators in day-to-day operating decisions is needed. These decisions include determiningshort-term operating reserves and maintenance schedules, adding additional control aids andshort lead-time equipment, and utilizing special protection systems [7, 8].∑==nKkk txpLOLE1(13)Where n is the number of capacity outage state in excess of the reserve, pk-probability of the capacity outage Ok ,tk- the time for which load loss will occur, the values inEquation (13) are the individual probabilities associated with the COPT. The equation can bemodified to use the cumulative probabilities as expressed in Equation (14).)( 11−=−×= ∑ kknkk ttPLOLE (14)Where Pk is the cumulative outage probability for capacity outage Ok, tk- the time forwhich load loss will occur. The LOLE is expressed as the number of days or number weeksduring the study period if the DPLVC or WPLVC is used. The unit of LOLE is in hours perperiod if the LDC is used. If the time tk is the per unit value of the total period considered, theindex calculated by Equation (13) or (14) is called the loss of load probability (LOLP).VII. LOSS OF ENERGY METHOD (LOEE) [1-3]The standard LOLE technique uses the daily peak load variation curve or theindividual daily peak loads to calculate the expected number of days in the period that thedaily peak load exceeds the available installed capacity. The area under the load durationcurve represents the energy utilized during the specified period and can be used to calculatean expected energy not supplied due to insufficient installed capacity. The results of this
  11. 11. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME382approach can also be expressed in terms of the probable ratio between the load energycurtailed due to deficiencies in the generating capacity available and the total load energyrequired to serve the requirements of the system. The ratio is generally an extremely small infigure less than one and can be defined as the ‘Energy Index of Unreliability’. It is moreusual, however, to subtract this quantity from unity and thus obtain the probable ratiobetween the load energy that will be supplied and the total load energy required by thesystem. This is known as ‘Energy Index of Reliability’ (EIR). The probabilities of havingvarying amounts of capacity unavailable are combined with the system load as shown inFigure 9. Any outage of generating capacity exceeding the reserve will result in a curtailmentof system load energy. In this method the generation system and the load are represented bythe COPT and the LDC respectively. These two models are convolved to produce a range ofenergy-based risk indices such as the LOEE, units per million (UPM), system minutes (SM)and energy index of reliability (EIR) [1]. The area under the LDC, in Figure 9, represents thetotal energy demand (E) of the system during the specific period considered. When an outagewith probability occurs, it causes an energy curtailment of, shown as the shaded area inFigure 9.Fig. 9. Evaluation of LOEE using LDCOk= magnitude of the capacity outage, pk = probability of a capacity outage equal to Ok,,Ek = energy curtailed by a capacity outage equal to Ok. The total expected energy curtailed orthe LOEE is expressed mathematically in Equation (15). The other indices are expressed inEquations (16) to (19) respectively. [11]Knkk EpLOEE ×= ∑=1(15)610×=ELOEEUPM (16)60×=PLLOEESM (17)60×=PLLOEESM (18)(19)∑=×−=nkkkEEpEIR11
  12. 12. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME383Algorithm for the Program1. Read system data table.2. Find Availability =A , =FOR=U.3. Find binomial co-efficient = nCr .To find binomial co- efficient create a function factorial4. Calculate Individual Probability(IP)pk=nCrx(p^(n-r)x(q^ r)Initialize Cumulative probability Pk =1.0, CP=1-IP5. Calculation of LOLEi) Read the peak load data weekly or dailyii) Read peak load= 2850 MWiii) Calculate tk = (Ci – 2850) / (slope of the load line)iv) Calculate LOLE = ∑ pk x tk Calculation of EENS6. i)Calculation of Energy Available=Capacity available*8760ii) Calculation of ELC = Total – Energy availableiii) Calculate EENS = ELC * pkVIII. SYSTEM DATAThe RTS-96 generating system contains 32 units, ranging from 12 MW to 400 MW.The system contains buses connected by 38 lines or autotransformers at two voltages, 138and 230 kV shown in figure 10. The total installed generation capacity is 3405MW, Thereserve capacity is 555MW, The peak load of system is 2850MW, The Minimum load ofsystem is 1981MW, The average load of system is 2336MW, It gives data on weekly peakloads in per cent of the annual peak load. The annual peak occurs in week 52Figure 10 IEEE one area RTS-96[7]
  13. 13. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME384Table 1 System Generation data for IEEE-RTS SystemVIII.I Energy Required From Load Duration CurveThe figure 11 to figure 14 shows the LDC and modified LDC.From the weekly loaddata for one year the load duration curve is drawn with a peak load of 2850MW. The totalenergy required for the corresponding data is calculated by finding out the area of loadduration curve Total energy required = Area of Load Duration Curve = 21154305unitsFig. 11. Original Weekly load duration CharacteristicsFig. 12. Modified weekly load patternsSize (MW) 12 20 50 76 100 155 197 350 400No. of Units 5 4 6 4 3 4 3 1 2ForcedOutage Rate0.02 0.10 0.01 0.02 0.04 0.04 0.05 0.08 0.12MTTF (hours) 2940 450 1980 1960 1200 960 950 1150 1100MTTR (hours) 60 50 20 40 50 40 50 100 150
  14. 14. International Journal of Electrical Engineer6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March050010001500200025003000113253749617385LoadDemandinMWModified Dailyload duration curveFig. 13.Fig. 14.VIII. II. FORMATION OF COPTThe below Table 2- 9 shows the capacity outage probability tables for the 24 bussystem. By using the generation dcalculate(a) THE CAPACITY OUTAGE PROBABILITY TABLESUnit-1.(1).No. of units =5 (2).Unit size (MW) =12, (3).Total capacity of system=60MWInternational Journal of Electrical Engineering and Technology (IJEET), ISSN 09766553(Online) Volume 4, Issue 2, March – April (2013), © IAEME385No of Days8597109121133145157169181193205217229241253265277289301313Modified Dailyload duration curveFig. 13. Original Daily load patternsFig. 14. Modified daily load patternsOPT9 shows the capacity outage probability tables for the 24 bussystem. By using the generation data and the failure rate and the repair rates of each unit andROBABILITY TABLES1.(1).No. of units =5 (2).Unit size (MW) =12, (3).Total capacity of system=60MWing and Technology (IJEET), ISSN 0976 –April (2013), © IAEME3133253373499 shows the capacity outage probability tables for the 24 busata and the failure rate and the repair rates of each unit and1.(1).No. of units =5 (2).Unit size (MW) =12, (3).Total capacity of system=60MW
  15. 15. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME386Table 2 Capacity outage probability for unit- 1No.ofUnitsCapacity (MW) IndividualProbabilityCumulativeProbabilityAvailable Unavailable5 60 0 0.903920 1.0000004 48 12 0.092236 0.096083 36 24 0.003764 0.0038442 24 36 0.000076 0.000081 12 48 0.0000007 0.0000030 0 60 0 0.000002Unit-2.(1) .No. of units =4 (2).Unit size (MW)=20, (3).Total capacity of system=80MWTable 3 Capacity Outage Probability for unit -2No. ofUnitsCapacity (MW) IndividualProbabilityCumulativeProbabilityAvailable Unavailable4 80 0 0.65651 1.000003 60 20 0.29160 0.34392 40 40 0.048600 0.05231 20 60 0.003600 0.00370 0 80 0.000100 0.0001Unit-3 (1).No. of units =3(2).Unit size (MW) =197MW (3).Total capacity of system=591MWTable 4 Capacity Outage Probability for unit-4No. ofUnitsCapacity (MW) IndividualprobabilityCumulativeprobabilityAvailable unavailable3 591 0 0.857375 1.0000002 394 197 0.135375 0.1426251 197 394 0.007125 0.007250 0 591 0.000125 0.000125Unit-4(1).No. of units = 4(2).Unit size (MW) =76 (3).Total capacity of system=304MWTable.5 Capacity Outage Probability for unit-4No. ofUnitsCapacity (MW) IndividualprobabilityCumulativeprobabilityAvailable unavailable4 304 0 0.922368 1.0000003 228 76 0.075295 0.0776322 152 152 0.002304 0.0023361 76 228 0.000031 0.0000320 0 304 0 0Unit-5 (1).No. of units =1 (2).Unit size (MW) =350 (3). Total capacity of system=350MWTable 6 Capacity Outage Probability for unit-8No. ofUnitsCapacity (MW) IndividualprobabilityCumulativeprobabilityAvailable unavailable1 350 0 0.92 1.000 0 350 0.08 0.08Unit-6 (1).No. of units =3 (2).Unit size (MW) =100 3).Total capacity of system=300 MW
  16. 16. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME387Table 7 Capacity Outage Probability for unit-5No. ofUnitsCapacity (MW) IndividualprobabilityCumulativeprobabilityAvailable Unavailable3 300 0 0.884736 1.0000002 200 100 0.110592 0.1152641 100 200 0.004608 0.0046720 0 300 0.000064 0.000064Unit-7 (1).No. of units =4MW (2).Unit size =155 MW(3).Total capacity of system=620MWTable 8 Capacity Outage Probability for unit-6No.ofUnitsCapacity (MW)IndividualprobabilityCumulativeprobabilityAvailable unavailable4 620 0 0.849345 1.0000003 465 155 0.1415577 0.1506532 310 310 0.0088473 0.0090951 155 465 0.0002476 0.0002480 0 620 0.000025 0.000003Unit-8 (1). No. of units = 6 (2).Unit size (MW) = 50, (3).Total capacity of system=300MWTable 9 Capacity Outage Probability for unit-7No. ofUnitsCapacity (MW) IndividualprobabilityCumulativeprobabilityAvailable Unavailable6 300 0 0.9414801 1.0000005 250 50 0.0570594 0.05851994 200 100 0.0014409 0.00146053 150 150 0.0000194 0.00001962 100 200 1.4*E-7 2.1*E-71 50 250 5*E-9 7*E-90 0 300 1*E-12 1*E-12Unit-9 (1). No. of units =2 (2).Unit size (MW) =400(3). Total capacity of system=800MWTable 10 Capacity Outage Probability for unit-9No. ofUnitsCapacity (MW) IndividualprobabilityCumulativeprobabilityAvailable Unavailable2 800 0 0.7744 1.0000001 400 400 0.2112 0.22560 0 800 0.0144 0.0144
  17. 17. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME388(b)FORMATION OF MERGED TABLE OF TWO UNITS i.e. Table 1 & 2Table 11 Combination of first two Table Merged datacapacityunavailabilityIndividualprobability0+0=0 0.5936062440+12=12 0.060516550+24=24 0.002470070+36=36 0.000050410+48=48 0.000000510+60=60 0.0000504120+0=20 0.2635833120+12=32 0.0268962620+24=44 0.0010978120+36=56 0.0000224020+48=68 0.0000002320+60=80 0.0000903940+0=40 0.0439305540+12=52 0.0044827140+36=76 0.0000037340+48=88 0.0000000440+60=100 060+0=60 060+12=72 0.0003820560+24=84 0.0000135560+36=96 0.0000002860+48=108 060+60=120 080+0=80 080+12=92 0.0000092280+24=104 0.0000003880+36=116 0.0000000180+48=128 080+60=140 0C) ROUNDING OFF TABLE FOR ENTIRE SYSTEM – 8Table 12 Rounding OFFCapacityunavailabilityIndividualprobability0 0.4561740200 0.22512662400 0.19669503600 0.0957801800 0.028259761000 0.009369141200 0.002091051400 0.000399571600 0.000053081800 0.000004252000 0.000000202200 0.00000001
  18. 18. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME389Table 13 Calculation of LOLE for the system dataCapacityin serviceIndividualprobabilitytk LOLE3405 0.4561740 0 -3205 0.22512662 0 -3005 0.19669503 0 -2805 0.02825976 330.88(3.78%) 0.1068218922605 0.00936914 1801.47(20.56%) 0.1926295182405 0.00209105 3271.1(37.035%) 0.0781007172205 0.00039957 4742.6(54.013%) 0.0212628722005 0.00005308 6213.23(70.92%) 0.0037644331805 0.00000425 7683.82(87.71%) 0.000017541605 0.00000020 - -1405 0.00000001 - -Total LOLE 0.40220896The tables11 shows a merged table one for the first two units and then merge the nine unitswe can get the more number of combinations of capacity unavailability and become complexand get nearly 5000 states. So to reduce the complexity and uncertainty in table and loadduration curve can develop the rounding off the merged tables. After rounding off table witha nearest discrete level (i.e 100MW, 200 MW…..etc) their probabilities in decrement order asshown Rounding off tables. The evaluation of individually probability pk by using equations(11) and (12)Table 1.14 Summaries of EENS for the given systemPriorityUnit capacity(MW)EENS(MU)Expected energyoutput (MU)1 60 20579.216 577.0882 80 20147.421 1006.683 300 19078.294 2076.014 304 17889..2 3265.1045 300 17045.876 4108.426 620 12881.556 8272.77 591 11387.849 9766.458 350 9880.602 11273.79 800 6.6220 21147.68
  19. 19. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME390(d) EXPECTED ENERGY NOT SUPPLIEDTable 1.15 Calculation of EENS for merged tableCapacity inserviceIndividualprobabilityELC Expectation(ELC*I.P)0 0.45617401 0 00 0.22512662 0 00 0.19669503 0 024544800 0.07957801 0 021067800 0.0282597 0 021067800 0.00936914 86505 810.47719315800 0.00220910 1838505 4061.4517563800 0.00039957 3590505 1434.6515811800 0.00005308 5342505 283.5814059800 0.00000425 7094505 30.1512307800 0.00000020 8846505 1.76910555800 0.00000001 10598505 0Total 6622.076IX. CONCLUSIONSAs an energy index in bulk power system reliability assessment, EENS (ExpectedEnergy Not Supplied) is of great significance to reliability and economic analysis, optimalreliability, power system planning, and so on. Based on the analytical formula, sensitivityindices can help to identify the system “bottlenecks” effectively and provide essentialinformation for power system planning and operation. The technique can effectively alleviatethe question of “calculation catastrophe” and provide more detailed valuable information toplanners and designers, as well as important guidance to component maintenance strategies.Probabilistic methods for the reliability assessment of the composite bulk power generationand transmission in electric power systems are still under development. It concludes that thecapacity outage Probability tables, process of merging tables, LOLE and EENS for givenIEEE-RTS 24 bus system energy indices and load indices areLOLP = 0.004022089.EENS (PU) = 0.000313UPM = 313.04.SM = 139.41EIR = 99.96%The system peak load is 2850MW with a reserve of 555MW only, but the system risklevel will vary as variations in the units Forced outage rates and peak load variation.Additional investments in terms of Design, construction, reliability, Maintainability and spareparts provisioning can results in improved unit’s unavailability levels. The system risk levelcan also reduces with good load forecasting techniques such as artificial intelligenttechniques causes reduced reserve level. The loss of load probability approach gives thereliability of the system adequacy and security accurately. The test system will be a great helpfor illustrating power system measures and gaining new insights into their meaning. One areato explore involves the loss of load probability quantity. The COPT is not easily calculatedwithout the use of a digital computer and the table will identify the maintenance schedulingor new unit addition may be started.
  20. 20. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME391REFERENCESJournal Papers[1] M. Tanriovena, Q.H. Wub,*, D.R. Turnerb, C. Kocatepea, J. Wangbl, “A new approach toreal-time reliability analysis of transmission system using fuzzy Markov Model”, ElectricalPower and Energy Systems 26, 2004 pp. 821–832[2] Reliability Test System Task Force of the Application of Probability. Methods Subcommittee,"IEEE Reliability Test System," on Power Apparatus and Systems, Vol. PAS-98, No.6, pp. 2047-2054, Nov.lDec. 1979.[3] Grigg, C ,Billinton, R.; Chen, Q.; et al “The IEEE Reliability Test System-1996. A reportprepared by the Reliability Test System Task Force of the Application of Probability MethodsSubcommittee” on IEEE Transactions on Power Systems,Volume: 14 , Issue: 3 Page(s): 1010-1020 Aug 1999[4] Hamoud, G., Billinton, R., “An approximate and practical approach to include uncertaintyconcept in generating capacity reliability evaluation” IEEE transaction on power apparatus andsystem, vol. PAS-100, no.3, March 1981.[5] Roy Billinton., P.G.Harrington., “Reliability evaluation in energy limited generating capacitystudies”, IEEE transaction on power apparatus and system, vol.PAS-97,no.6, nov/dec 1978[6] R. Allan and R. Billinton, “Power System Reliability and its Assessment. Part 1 Backgroundand Generating Capacity,” Power Engineering Journal, Vol. 6, No. 4, pp. 191-196, July 1992.[7] R. Allan and R. Billinton,“Power System Reliability and its Assessment. Part 2 Compositegeneration and transmission systems,” Power Engineering Journal,Vol. 6, No. 6, pp. 291- 297,November 1992.[8] D Devendra Mittal, Om Prakash Mahela and Rohit Jain, “Detection and Analysis of PowerQuality Disturbances under Faulty Conditions in Electrical Power System”, International Journalof Electrical Engineering & Technology (IJEET), Volume 4, Issue 2, 2013, pp. 25 - 36, ISSNPrint : 0976-6545, ISSN Online: 0976-6553.[9] Dr C.K.Panigrahi, P.K.Mohanty, A.Nimje, N.Soren, A.Sahu, R.K.Pati, “Enhancing PowerQuality and Reliability in Deregulated Environment”, International Journal of ElectricalEngineering & Technology (IJEET), Volume 2, Issue 2, 2011, pp. 1-11, ISSN Print :0976-6545, ISSN Online: 0976-6553.[10] D Devendra Mittal, Om Prakash Mahela and Rohit Jain, “Detection and Analysis of PowerQuality Disturbances under Faulty Conditions in Electrical Power System”, International Journalof Electrical Engineering & Technology (IJEET), Volume 4, Issue 2, 2013, pp. 25 - 36, ISSNPrint : 0976-6545, ISSN Online: 0976-6553.[11] Preethi Thekkath and Dr. G. Gurusamy, “Effect of Power Quality on Stand by PowerSystems”, International Journal of Electrical Engineering & Technology (IJEET), Volume 1,Issue 1, 2010, pp. 118 - 126, ISSN Print : 0976-6545, ISSN Online: 0976-6553.Books[8]Billinton, R, and Allan, R.N, “Reliability evaluation of power systems”, New York,Plenum Press, Second Edition, 1996.[9]Billinton, R, and Allan, R.N, “Reliability evaluation of engineering systems", New YorkSecond Edition, 1983.[10]Endrenyi.J., “Reliability Modeling in Electrical Power Systems", A Wiley –Inter sciencePublication, 1978.
  21. 21. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME392Theses[11]Bagen Ph.d. thesis on “Reliability and Cost/Worth Evaluation of Generating SystemsUtilizing Wind and Solar Energy” University of Saskatchewan Saskatoon, Saskatchewan,Canada pp.15-25. In the year 2005[12] Fang Yang Ph.d. thesis “ A Comprehensive Approach for bulk Power System ReliabilityAssessment” School of Electrical and Computer Engineering Georgia Institute of Tech.pp.50-90.in the year 2007.Proceedings Papers[13] M.Fotuhi, A.Ghafouri, “Uncertainty Consideration in Power System Reliability IndicesAssessment Using Fuzzy Logic Method”, Sharif University of technology, IEEE Conferenceon Power Engineering, 2007, Large Engineering Systems, Sharif University of Technology,Tehran 10-12 Oct. 2007, Page(s): 305 - 309[14]A Reliability Test System Task Force of the Application of Probability. MethodsSubcommittee, "IEEE Reliability Test System,"on Power Apparatus and Systems, Vol. PAS-98, No.6, pp. 2047-2054, Nov./Dec. 1979.[15] Fang Yang, A.P. Sakis Meliopoulos, “A Bulk Power System Reliability AssessmentMethodology”, 8th International Conference on Probabilistic Methods Applied to PowerSystems, IOWA University, Annes, September 16 2004.[16]X. Zhu, “A New Methodology of Analytical Formula Deduction and Sensitivity Analysisof EENS in Bulk Power System Reliability Assessment” IEEE Power Systems Conferenceand Exposition, IEEEPES2006,(PSCE 06). Page(s): 825 - 831

×