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International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME & TECHNOLOGY (IJEET) ISSN 0976 – 6545(Print) ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), pp. 372-392 IJEET © IAEME: www.iaeme.com/ijeet.asp Journal Impact Factor (2013): 5.5028 (Calculated by GISI) ©IAEME www.jifactor.com VAGUENESS CONCERN IN BULK POWER SYSTEM RELIABILITY ASSESSMENT METHODOLOGY Mr. N.M.G KUMAR1, Dr.P.SANGAMEWARA RAJU2 1 (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) ABSTRACT This paper has illustrated the development of a technique for examining the reliability associated with a generation configuration using an energy based index. The approach is based upon the Expected Loss of Energy Approach and extends the technique to include the consideration of energy limitations associated with generation facilities.Safe, secure and uninterrupted electric power supply plays very important in the operation of the complex electric power 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 of electricity has led to a high vulnerability to power failures. In this way, reliability of power supply has gained focus and it is important for electric power system planning and operation.This paper illustrates a method for evaluating the significance of reliability indices for bulk power systems. The technique utilizes a continuous representation of a generating capacity model for LOLP (Loss of Load Probability), LOLE (Loss of Load Expectation) and EENS (Expected Energy Not Supplied) for single area. The objective paper is to describe a load and generation model for analysis of generation reliability index. This paper illustrates a well known technique for generating capacity evaluation which includes limited energy sources. The most popular technique at the present time for assessing the adequacy of an existing or proposed generating capacity configuration is the Loss of Load Probability or Expectation Method. As an energy index in bulk power system reliability assessment is EENS (Expected Energy not supplied) is of great significance for economic analysis and power system planning. This paper mainly focuses on the following two categories among which one is the establishment of new reliability index frame work that meets the developing power market and integrates reliability assessment called Expected Energy Not Supplied (EENS). Here we are considering IEEE-Reliability Test System (RTS). 372 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME Keywords: Reliability assessment, power failure, FOR (Forced Outage Rate), EENS (Expected Energy not Supplied), LOLP (Loss of Load Probability), and LOLE (Loss of Load Expectation), analytical method, sensitivity analysis, bulk power system reliability, reliability indices. I. INTRODUCTION Reliability is an abstract term meaning endurance, dependability, and good performance. For engineering systems, however, it is more than an abstract term; it is something that can be computed, measured, evaluated, planned, and designed into a piece of equipment or a system. Reliability means the ability of a system to perform the function it is designed for under the operating conditions encountered during its projected lifetime. Reliability analysis has a wide range of applications in the engineering field. Many of these uses can be implemented with either qualitative or quantitative techniques. Qualitative techniques imply that reliability assessment must depend solely upon engineering experience and judgment. Quantitative methodologies use statistical approaches to reinforce engineering judgments. Quantitative techniques describe the historical performance of existing systems and utilize the historical performance to predict the effects of changing conditions on system performance [1]. Continuity of electric power supply plays very important in the modern days of complex electric power system that describes the efficient electrical operation and economic, community progress, social security and growth of country. The modern days of electric power system is complex and is always subjected to disturbance around the clock, and is generally composed of three parts (1) generation, (2) transmission, and (3) distribution systems, all of which contribute to the production and transportation of electric energy to customers. The reliability of an electric power system is defined as the probability that the power system will perform the function of delivering electric energy to customers on a continuous basis and with acceptable service quality. Power system reliability assessment, the three power system parts are combined into different system hierarchical levels, as shown in Figure 1.Hierarchical level 1 (HL1) involves the reliability analysis of only the generation system, hierarchical level 2 (HL2) includes the reliability evaluation of the composite of both generation and transmission systems, referred to as the bulk power system or the composite power system, and hierarchical level 3 (HL3) consists of a reliability study of the entire power system. Hierarchical Level 1 Total system Generation Transmission System Hierarchical Level 2 Distribution System Hierarchical Level 3 Fig. 1. Hierarchical levels for power system reliability assessment. 373 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME At the present stage of development, the reliability evaluation of the entire power system (HL3) is usually not conducted because of the immensity and complexity of the problem in a practical system. Power system reliability is assessed separately for the generation system (HL1), the bulk power system (HL2), and the distribution system. Reliability analysis methods for generation and distribution systems are well developed. Bulk power system reliability assessment refers to the process of estimating the ability of the system to simultaneously (a) generate and (b) move energy to load supply points. Traditionally, it has formed an important element of both power system planning and operating procedures. The main objective of power 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-range system planning in the following aspects: [1, 11, 12] (1)The determination of whether the system has sufficient capacity to meet system load demands, (2)The development of a suitable transmission network to transfer generated energy to customer load points, (3) A comparative evaluation of expansion plans, and (4) A review of maintenance schedules for preventive and corrective Power system operating conditions are subject to changes such as loadability uncertainty, i.e., the load may be different from that assumed in design studies, and unplanned component outages. To deliver electricity with acceptable quality and continuity to customers at minimum cost and to prevent cascading sequences after possible disturbances, short-term reliability prediction that assists operators in day-to-day operating decisions is needed. These decisions include determining short-term operating reserves and maintenance schedules, adding additional control aids and short lead-time equipment, and utilizing special protection systems. II. POWER SYSTEM ADEQUACY VERSUS SYSTEM SECURITY Today’s new operating environment for electrical power system is to supply its customers with electrical energy as economically as possible and with an acceptable level of reliability. The prerequisite of reliable electric power supply enhance the significance of dependence of modern society on electrical energy. Electric power utilities therefore must provide a reasonable assurance of quality and continuity of service to their customers. In general, more reliable systems involve more financial investment. It is, however unrealistic to try to design a power system with a hundred percent reliability and therefore, power system planners and engineers have always attempted to achieve a reasonable level of reliability at an affordable cost. It is clear that reliability and related cost/worth evaluation are important aspects in power system planning and operation reliability of a power system is defined as the ability 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 the ability of the power system to satisfy the customer requirements. The perception of power system reliability may be reasonable involves the security and adequacy and can be recognized an healthy, Marginal (alert) and emergency (at risk) concerned and designated as “system reliability”, which is shown in Fig. 2 374 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME Basic Approaches Analytical approaches MCS Approaches Fig. 2 Subdivision of System Reliability The Figure 2 represents two basic aspects of power system reliability depends on system adequacy and System Security. Adequacy relates to the existence of sufficient facilities within the system to satisfy the consumer load demand or operational constraints. These include the facilities necessary to generate sufficient energy and the associated transmission and distribution facilities required to transport the energy to the actual consumer load points. Adequacy is therefore associated with static conditions which don’t include system disturbance. Security relates to the ability of the system to respond to disturbances arising within that system. Security is therefore associated with the response of the system to perturbations it is subjected to. These include the conditions associated with local and widespread disturbance of major generation, transmission, services etc. Another aspect of reliability is system integrity, the ability to maintain interconnected operations. Integration is violated causes an uncontrolled separation occurs in presence of severe disturbance (Block out of grid or regional grid).Most of the probabilistic techniques presently available for power system reliability evaluations are in the domain of adequacy assessment.[1-3] There are two basically and conceptually different mythologies are present in the power system reliability studies i.e. the analytical approaches and Monte Carlo simulation approaches, used in power system reliability evaluation. This is shown is shown in below Figure 3. An analytical approach represents the system by a mathematical model and evaluates the reliability indices from this model using analytical solutions. The Monte Carlo simulation approaches, however, estimates the reliability indices by simulating the actual process and random behavior of the system and treats the problem as a series of real experiments. III. PROBLEM OF STATEMENT FOR BULK POWER SYSTEM RELIABILITY The bulk power system therefore can be simply represented by a single bus as shown in Figure 4, at which the total generation and total load demand are connected is normally as SMLB(single machine connected to load bus) system in power system stability and security studies. The main objective in HL-I assessment is the evaluation of the system reserve required to satisfy the system demand and to accommodate the failure and maintenance of the generating facilities in addition to satisfying any load growth in excess of the forecast. This area of study can be categorized into two different aspects designated as static and operating capacity assessment. Static assessment deals with the planning of the capacity required to 375 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME satisfy the total system load demand and maintain the required level of reliability. Operating capacity assessment, on the other hand, is mainly focused on the determination of the required capacity to satisfy the load demand in the short term (usually a few hours) while maintaining a specified level of reliability. This thesis is focused on static capacity adequacy evaluation and corresponding cost/worth assessment of generating systems Total Total generation System Load Fig. 4. System representation at HL-I There is a wide range of power system reliability assessment techniques are used in the generating capacity planning and operation [1]. Basically, generating capacity adequacy evaluation involves the development of a generation model, the development of a load model and the combination of the two models to produce a risk model as shown in Figure 5. The system risk is usually expressed by one or more quantitative risk indices. In the direct analytical method for generating capacity adequacy evaluation, the generation model is usually in the form of a generating capacity outage probability table, which can be calculated as a indices in HL-I evaluation simply indicate the overall ability of the generating facilities to satisfy the total system demand. Generating unit unavailability is an important parameter in a probabilistic analysis. Generation model Load model Risk model Fig..5 Conceptual tasks for HL- I evaluation IV. RELIABILITY EVALUATION METHODS Reliability techniques can be divided into the two general categories of probabilistic and deterministic methods. Both methods are used by electric power utilities at the present time. Most large power utilities, however, use a probabilistic approach. 376 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME IV.I. Deterministic Methods Over the years, a range of deterministic methods have been developed by the power industry for generating capacity planning and operating. These methods evaluate the system adequacy on the basis of simple and subjective criteria generally termed as “rule of thumb methods” [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 without considering energy storage capability. 1. Capacity Reserve Margin (CRM) In this approach, the reserve capacity (RC), which is normally the difference between the system total installed capacity (IC =Σ Gi, G , is the capacity of Unit i in the system) and the system peak load (PL), is expressed as a fixed percentage of the total installed 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 shape information IC − PL RC = x100% (1) IC 2. Loss of the Largest Unit (LLU) In this approach, the required reserve capacity in a system is at least equal to the capacity of the largest unit (CLU) as expressed in Equation (2). This method is also easy to apply. Although it incorporates the size of the largest unit in the system, it does not recognize the system risk due to an outage of one or more generating units. The system reserve increases with the addition of larger units to the system. RC ≥ CLU (2) 3. Percentage reserve margin method In this method the reserve capacity is equal to or greater than the capacity of the largest unit plus a fixed percentage of either the capacity installed or the peak load as shown in Equations (3) and (.4). It also incorporates not only the size of the largest unit in the evaluation but also some measure of load forecast uncertainty. It does not reflect the system risk as the multiplication factor x (normally in the range of 0-15%) is usually subjectively determined 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 MW The main disadvantage of deterministic techniques is that they do not consider the inherent random nature of system component operating failures, of the customer load demand and of the system behavior. The system risk cannot be determined using deterministic criteria. Conventional deterministic methods and procedures are severely limited in their application to modern integrated complex power systems. 377 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME V. PROBABILISTIC METHODS The benefits of utilizing probabilistic methods have been recognized since at least the 1930s and have been applied by utilities in power system reliability analyses since that time. The unavailability (U) of a generating unit is the basic parameter in building a probabilistic generation model. This statistic is known as the generating unit forced outage rate (FOR). It is defined as the probability of finding the unit on forced outage at some distant time in the future. The unit FOR is obtained using Equation (5). FOR= ∑[downtime] (5) ∑[downtime] + ∑[up time] The load model should provide an appropriate representation of the system load over a specified period of time, which is usually one calendar year in a planning study. The generation model is normally in the form of an array of capacity levels and their associated probabilities. 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) or totally 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) respectively λ 0 Up 1 Down µ Fig. 6. Two state model for generating unit Where λ= unit failure rate and µ = unit repair rate. µ A = (6) λ + µ λ U = (7) λ + µ A Recursive Algorithm for Capacity Model Building The 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 fully up and fully down states. The technique is illustrated for a two-state unit addition followed by the more general case of a multi-state unit. The probability of a capacity outage state of X MW can be calculated using Equation (8). P(X) = (I - U) * P' (X) + (U * P' (X - C)) (8) 378 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME Where the cumulative probabilities of a capacity outage level of X MW before and after 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 states In addition to being in the full capacity and completely failed states, a generating unit can exist in other states where it operates i.e reduced operating capacity state as shown in Figure 7. Such states are called derated states. The simplest model that incorporates de-rating state. This three-state model includes a single derated state in addition to the full capacity and failed states. The Equation (9) can be used to add multi-state units to a capacity outage probability table. 0 Up 2 Derated 1 Down Fig. 7. Three state model for generating unit n P(X ) = ∑t =1 piP '(X − C ) (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 i Case3: Recursive algorithm for unit removal Generating units are periodically scheduled for unit overhaul and preventive maintenance. During these scheduled outages, the unit is available neither for service nor for failure. This situation requires a capacity model which does not include the unit on scheduled outage. The new model could be created by simply building it from the beginning using Equation (10). P( X ) − U * P'( X − C ) (10) P'( X ) = (1 − U ) In above equation P(X - C) = 1.0 for X < C Case4: Procedure for Rounding Off Value[1-3] Bulk power generation system having large number of generating units of different capacities, the table will contain several tens or hundreds possible discrete levels of capacity outage levels. This outage levels can be reduced by grouping and rounding the capacity outage into the possible discrete levels. The capacity outage table introduces unnecessary approximations which can be avoided by the table rounding approach and reduces the complexity. The capacity rounding increment used depends upon the accuracy desired. The final rounded table contains capacity outage magnitudes that are multiples of the rounding increment are calculated by equations (11), and (12). The number of capacity levels decreases as the rounding increment increases, with a corresponding decrease in accuracy. 379 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME For rounding off the values we use the formula Ck=capacity of higher (kth) state, Cj=capacity of lower (jth) state, Ci=capacity of variable (ith) state Ck − Ci P (C j ) = * P (C i ) (11) Ck − C j Ci − C j P (C k ) = * P (C i ) (12) Ck − C j For all the states i falling between the required rounding states j and k The Equation (11) is used for rounding off values for exact state and in between states and Equation (12) is used for rounding off values for previous state The use of a rounded table in combination with the load model to calculate the risk level introduces certain inaccuracies. The error depends upon the rounding increment used and on the slope of the load characteristic. The error decreases with increasing slope of the load characteristic and for a given load characteristic the error increases with increased rounding increment. The rounding increment used should be related to the system size and composition. Also the first non-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 LOAD PROBABILITY [1] The generation system model can be convolved with an appropriate load model to produce a system risk index. The simplest load model that can be used quite extensively, in which each day is represented by its daily peak load or weekly peak load duration. Prior to combining the outage probability table it should be realized that there is a difference between the terms 'capacity outage' and 'loss of load'. The term 'capacity outage' indicates a loss of generation which may or may not result in a loss of load. This condition depends upon the generating capacity reserve margin and the system load level. A 'loss of load' will occur only when the capability of the generating capacity remaining in service is exceeded by the system load level. In this approach, the generation system represented by the COPT and the load characteristic represented by either the DPLVC or the LDC are convolved to calculate the LOLE index. Figure 8 shows a typical load-capacity relationship where the load model is represented 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 of the probability and the corresponding time unit. The summation of all such products gives the system LOLE in a specified period, as expressed mathematically in Equation (13). Capacity outage less than the reserve do not contribute to the system LOLE The main objective of power 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-range system planning in the following aspects: (1) the determination of whether the system has 380 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME Fig. 8 Relationship between load, capacity and reserve sufficient capacity to meet system load demands, (2) the development of a suitable transmission network to transfer generated energy to customer load points, (3) a comparative evaluation of expansion plans, and (4) a review of maintenance schedules [5, 6]. Power system operating conditions are subject to changes such as load uncertainty, i.e., the load may be different from that assumed in design studies, and unplanned component outages. To deliver electricity with acceptable quality to customers at minimum cost and to prevent cascading sequences after possible disturbances, short-term reliability prediction that assists operators in day-to-day operating decisions is needed. These decisions include determining short-term operating reserves and maintenance schedules, adding additional control aids and short lead-time equipment, and utilizing special protection systems [7, 8]. n LOLE = ∑ p k x t k (13) K =1 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 in Equation (13) are the individual probabilities associated with the COPT. The equation can be modified to use the cumulative probabilities as expressed in Equation (14). n LOLE = ∑P k =1 k × (t k − t k −1 ) (14) Where Pk is the cumulative outage probability for capacity outage Ok, tk- the time for which load loss will occur. The LOLE is expressed as the number of days or number weeks during the study period if the DPLVC or WPLVC is used. The unit of LOLE is in hours per period if the LDC is used. If the time tk is the per unit value of the total period considered, the index 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 the individual daily peak loads to calculate the expected number of days in the period that the daily peak load exceeds the available installed capacity. The area under the load duration curve represents the energy utilized during the specified period and can be used to calculate an expected energy not supplied due to insufficient installed capacity. The results of this 381 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME approach can also be expressed in terms of the probable ratio between the load energy curtailed due to deficiencies in the generating capacity available and the total load energy required to serve the requirements of the system. The ratio is generally an extremely small in figure less than one and can be defined as the ‘Energy Index of Unreliability’. It is more usual, however, to subtract this quantity from unity and thus obtain the probable ratio between the load energy that will be supplied and the total load energy required by the system. This is known as ‘Energy Index of Reliability’ (EIR). The probabilities of having varying amounts of capacity unavailable are combined with the system load as shown in Figure 9. Any outage of generating capacity exceeding the reserve will result in a curtailment of system load energy. In this method the generation system and the load are represented by the COPT and the LDC respectively. These two models are convolved to produce a range of energy-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 the total energy demand (E) of the system during the specific period considered. When an outage with probability occurs, it causes an energy curtailment of, shown as the shaded area in Figure 9. Fig. 9. Evaluation of LOEE using LDC Ok= 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 or the LOEE is expressed mathematically in Equation (15). The other indices are expressed in Equations (16) to (19) respectively. [11] n LOEE = ∑ pk × EK (15) k =1 LOEE (16) UPM = × 10 6 E LOEE (17) SM = × 60 PL LOEE SM = × 60 (18) PL n pk × Ek (19) EIR = 1 − ∑ k =1 E 382 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME Algorithm for the Program 1. Read system data table. 2. Find Availability =A , =FOR=U. 3. Find binomial co-efficient = nCr .To find binomial co- efficient create a function factorial 4. Calculate Individual Probability(IP)pk=nCrx(p^(n-r)x(q^ r) Initialize Cumulative probability Pk =1.0, CP=1-IP 5. Calculation of LOLE i) Read the peak load data weekly or daily ii) Read peak load= 2850 MW iii) Calculate tk = (Ci – 2850) / (slope of the load line) iv) Calculate LOLE = ∑ pk x tk Calculation of EENS 6. i)Calculation of Energy Available=Capacity available*8760 ii) Calculation of ELC = Total – Energy available iii) Calculate EENS = ELC * pk VIII. SYSTEM DATA The 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, 138 and 230 kV shown in figure 10. The total installed generation capacity is 3405MW, The reserve capacity is 555MW, The peak load of system is 2850MW, The Minimum load of system is 1981MW, The average load of system is 2336MW, It gives data on weekly peak loads in per cent of the annual peak load. The annual peak occurs in week 52 Figure 10 IEEE one area RTS-96[7] 383 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME Table 1 System Generation data for IEEE-RTS System Size (MW) 12 20 50 76 100 155 197 350 400 No. of Units 5 4 6 4 3 4 3 1 2 Forced 0.02 0.10 0.01 0.02 0.04 0.04 0.05 0.08 0.12 Outage Rate MTTF (hours) 2940 450 1980 1960 1200 960 950 1150 1100 MTTR (hours) 60 50 20 40 50 40 50 100 150 VIII.I Energy Required From Load Duration Curve The figure 11 to figure 14 shows the LDC and modified LDC.From the weekly load data for one year the load duration curve is drawn with a peak load of 2850MW. The total energy required for the corresponding data is calculated by finding out the area of load duration curve Total energy required = Area of Load Duration Curve = 21154305units Fig. 11. Original Weekly load duration Characteristics Fig. 12. Modified weekly load patterns 384 Engineering International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME Fig. 13. Original Daily load patterns Modified Dailyload duration curve 3000 Load Demand in MW 2500 2000 1500 1000 500 0 1 13 25 37 49 61 73 85 97 109 121 133 145 157 169 181 193 205 217 229 241 253 265 277 289 301 313 325 337 349 No of Days Fig. 14. Modified daily load patterns VIII. II. FORMATION OF COPT The below Table 2- 9 shows the capacity outage probability tables for the 24 bus data system. By using the generation data and the failure rate and the repair rates of each unit and calculate (a) THE CAPACITY OUTAGE PROBABILITY TABLES ROBABILITY 1.(1).No. Unit-1.(1).No. of units =5 (2).Unit size (MW) =12, (3).Total capacity of system=60MW 385 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME Table 2 Capacity outage probability for unit- 1 No.of Capacity (MW) Individual Cumulative Units Available Unavailable Probability Probability 5 60 0 0.903920 1.000000 4 48 12 0.092236 0.09608 3 36 24 0.003764 0.003844 2 24 36 0.000076 0.00008 1 12 48 0.0000007 0.000003 0 0 60 0 0.000002 Unit-2.(1) .No. of units =4 (2).Unit size (MW)=20, (3).Total capacity of system=80MW Table 3 Capacity Outage Probability for unit -2 No. of Capacity (MW) Individual Cumulative Units Available Unavailable Probability Probability 4 80 0 0.65651 1.00000 3 60 20 0.29160 0.3439 2 40 40 0.048600 0.0523 1 20 60 0.003600 0.0037 0 0 80 0.000100 0.0001 Unit-3 (1).No. of units =3(2).Unit size (MW) =197MW (3).Total capacity of system=591MW Table 4 Capacity Outage Probability for unit-4 No. of Capacity (MW) Individual Cumulative Units Available unavailable probability probability 3 591 0 0.857375 1.000000 2 394 197 0.135375 0.142625 1 197 394 0.007125 0.00725 0 0 591 0.000125 0.000125 Unit-4(1).No. of units = 4(2).Unit size (MW) =76 (3).Total capacity of system=304MW Table.5 Capacity Outage Probability for unit-4 No. of Capacity (MW) Individual Cumulative Units Available unavailable probability probability 4 304 0 0.922368 1.000000 3 228 76 0.075295 0.077632 2 152 152 0.002304 0.002336 1 76 228 0.000031 0.000032 0 0 304 0 0 Unit-5 (1).No. of units =1 (2).Unit size (MW) =350 (3). Total capacity of system=350MW Table 6 Capacity Outage Probability for unit-8 No. of Capacity (MW) Individual Cumulative Units Available unavailable probability probability 1 350 0 0.92 1.00 0 0 350 0.08 0.08 Unit-6 (1).No. of units =3 (2).Unit size (MW) =100 3).Total capacity of system=300 MW 386 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME Table 7 Capacity Outage Probability for unit-5 No. of Capacity (MW) Individual Cumulative Units Available Unavailable probability probability 3 300 0 0.884736 1.000000 2 200 100 0.110592 0.115264 1 100 200 0.004608 0.004672 0 0 300 0.000064 0.000064 Unit-7 (1).No. of units =4MW (2).Unit size =155 MW(3).Total capacity of system=620MW Table 8 Capacity Outage Probability for unit-6 No. Capacity (MW) Individual Cumulative of Available unavailable probability probability Units 4 620 0 0.849345 1.000000 3 465 155 0.1415577 0.150653 2 310 310 0.0088473 0.009095 1 155 465 0.0002476 0.000248 0 0 620 0.000025 0.000003 Unit-8 (1). No. of units = 6 (2).Unit size (MW) = 50, (3).Total capacity of system=300MW Table 9 Capacity Outage Probability for unit-7 No. of Capacity (MW) Individual Cumulative Units Available Unavailable probability probability 6 300 0 0.9414801 1.000000 5 250 50 0.0570594 0.0585199 4 200 100 0.0014409 0.0014605 3 150 150 0.0000194 0.0000196 2 100 200 1.4*E-7 2.1*E-7 1 50 250 5*E-9 7*E-9 0 0 300 1*E-12 1*E-12 Unit-9 (1). No. of units =2 (2).Unit size (MW) =400(3). Total capacity of system=800MW Table 10 Capacity Outage Probability for unit-9 No. of Capacity (MW) Individual Cumulative Units Available Unavailable probability probability 2 800 0 0.7744 1.000000 1 400 400 0.2112 0.2256 0 0 800 0.0144 0.0144 387 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME (b)FORMATION OF MERGED TABLE OF TWO UNITS i.e. Table 1 & 2 Table 11 Combination of first two Table Merged data capacity Individual unavailability probability 0+0=0 0.593606244 0+12=12 0.06051655 0+24=24 0.00247007 0+36=36 0.00005041 0+48=48 0.00000051 0+60=60 0.00005041 20+0=20 0.26358331 20+12=32 0.02689626 20+24=44 0.00109781 20+36=56 0.00002240 20+48=68 0.00000023 20+60=80 0.00009039 40+0=40 0.04393055 40+12=52 0.00448271 40+36=76 0.00000373 40+48=88 0.00000004 40+60=100 0 60+0=60 0 60+12=72 0.00038205 60+24=84 0.00001355 60+36=96 0.00000028 60+48=108 0 60+60=120 0 80+0=80 0 80+12=92 0.00000922 80+24=104 0.00000038 80+36=116 0.00000001 80+48=128 0 80+60=140 0 C) ROUNDING OFF TABLE FOR ENTIRE SYSTEM – 8 Table 12 Rounding OFF Capacity Individual unavailability probability 0 0.4561740 200 0.22512662 400 0.19669503 600 0.0957801 800 0.02825976 1000 0.00936914 1200 0.00209105 1400 0.00039957 1600 0.00005308 1800 0.00000425 2000 0.00000020 2200 0.00000001 388 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME Table 13 Calculation of LOLE for the system data Capacity Individual tk LOLE in service probability 3405 0.4561740 0 - 3205 0.22512662 0 - 3005 0.19669503 0 - 2805 0.02825976 330.88(3.78%) 0.106821892 2605 0.00936914 1801.47(20.56%) 0.192629518 2405 0.00209105 3271.1(37.035%) 0.078100717 2205 0.00039957 4742.6(54.013%) 0.021262872 2005 0.00005308 6213.23(70.92%) 0.003764433 1805 0.00000425 7683.82(87.71%) 0.00001754 1605 0.00000020 - - 1405 0.00000001 - - Total LOLE 0.40220896 The tables11 shows a merged table one for the first two units and then merge the nine units we can get the more number of combinations of capacity unavailability and become complex and get nearly 5000 states. So to reduce the complexity and uncertainty in table and load duration curve can develop the rounding off the merged tables. After rounding off table with a nearest discrete level (i.e 100MW, 200 MW…..etc) their probabilities in decrement order as shown Rounding off tables. The evaluation of individually probability pk by using equations (11) and (12) Table 1.14 Summaries of EENS for the given system Unit capacity EENS Expected energy Priority (MW) (MU) output (MU) 1 60 20579.216 577.088 2 80 20147.421 1006.68 3 300 19078.294 2076.01 4 304 17889..2 3265.104 5 300 17045.876 4108.42 6 620 12881.556 8272.7 7 591 11387.849 9766.45 8 350 9880.602 11273.7 9 800 6.6220 21147.68 389 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME (d) EXPECTED ENERGY NOT SUPPLIED Table 1.15 Calculation of EENS for merged table Capacity in Individual ELC Expectation service probability (ELC*I.P) 0 0.45617401 0 0 0 0.22512662 0 0 0 0.19669503 0 0 24544800 0.07957801 0 0 21067800 0.0282597 0 0 21067800 0.00936914 86505 810.477 19315800 0.00220910 1838505 4061.45 17563800 0.00039957 3590505 1434.65 15811800 0.00005308 5342505 283.58 14059800 0.00000425 7094505 30.15 12307800 0.00000020 8846505 1.769 10555800 0.00000001 10598505 0 Total 6622.076 IX. CONCLUSIONS As an energy index in bulk power system reliability assessment, EENS (Expected Energy Not Supplied) is of great significance to reliability and economic analysis, optimal reliability, power system planning, and so on. Based on the analytical formula, sensitivity indices can help to identify the system “bottlenecks” effectively and provide essential information for power system planning and operation. The technique can effectively alleviate the question of “calculation catastrophe” and provide more detailed valuable information to planners and designers, as well as important guidance to component maintenance strategies. Probabilistic methods for the reliability assessment of the composite bulk power generation and transmission in electric power systems are still under development. It concludes that the capacity outage Probability tables, process of merging tables, LOLE and EENS for given IEEE-RTS 24 bus system energy indices and load indices are LOLP = 0.004022089. EENS (PU) = 0.000313 UPM = 313.04. SM = 139.41 EIR = 99.96% The system peak load is 2850MW with a reserve of 555MW only, but the system risk level will vary as variations in the units Forced outage rates and peak load variation. Additional investments in terms of Design, construction, reliability, Maintainability and spare parts provisioning can results in improved unit’s unavailability levels. The system risk level can also reduces with good load forecasting techniques such as artificial intelligent techniques causes reduced reserve level. The loss of load probability approach gives the reliability of the system adequacy and security accurately. The test system will be a great help for illustrating power system measures and gaining new insights into their meaning. One area to explore involves the loss of load probability quantity. The COPT is not easily calculated without the use of a digital computer and the table will identify the maintenance scheduling or new unit addition may be started. 390 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME REFERENCES Journal Papers [1] M. Tanriovena, Q.H. Wub,*, D.R. Turnerb, C. Kocatepea, J. Wangbl, “A new approach to real-time reliability analysis of transmission system using fuzzy Markov Model”, Electrical Power 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 report prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee” 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 uncertainty concept in generating capacity reliability evaluation” IEEE transaction on power apparatus and system, vol. PAS-100, no.3, March 1981. [5] Roy Billinton., P.G.Harrington., “Reliability evaluation in energy limited generating capacity studies”, 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 Background and 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 Composite generation 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 Power Quality Disturbances under Faulty Conditions in Electrical Power System”, International Journal of Electrical Engineering & Technology (IJEET), Volume 4, Issue 2, 2013, pp. 25 - 36, ISSN Print : 0976-6545, ISSN Online: 0976-6553. [9] Dr C.K.Panigrahi, P.K.Mohanty, A.Nimje, N.Soren, A.Sahu, R.K.Pati, “Enhancing Power Quality and Reliability in Deregulated Environment”, International Journal of Electrical Engineering & 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 Power Quality Disturbances under Faulty Conditions in Electrical Power System”, International Journal of Electrical Engineering & Technology (IJEET), Volume 4, Issue 2, 2013, pp. 25 - 36, ISSN Print : 0976-6545, ISSN Online: 0976-6553. [11] Preethi Thekkath and Dr. G. Gurusamy, “Effect of Power Quality on Stand by Power Systems”, 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 York Second Edition, 1983. [10]Endrenyi.J., “Reliability Modeling in Electrical Power Systems", A Wiley –Inter science Publication, 1978. 391 International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 – 6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 2, March – April (2013), © IAEME Theses [11]Bagen Ph.d. thesis on “Reliability and Cost/Worth Evaluation of Generating Systems Utilizing 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 Reliability Assessment” 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 Indices Assessment Using Fuzzy Logic Method”, Sharif University of technology, IEEE Conference on 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. Methods Subcommittee, "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 Assessment Methodology”, 8th International Conference on Probabilistic Methods Applied to Power Systems, IOWA University, Annes, September 16 2004. [16]X. Zhu, “A New Methodology of Analytical Formula Deduction and Sensitivity Analysis of EENS in Bulk Power System Reliability Assessment” IEEE Power Systems Conference and Exposition, IEEEPES2006,(PSCE '06). Page(s): 825 - 831 392

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