电力系统可靠性论文演讲.ppt

Yi Gao, Roy Billinton, Life Fellow, IEEE, And Rajesh Karki, Senior Member, IEEE Power Symposium, 2008. NAPS 08. 40th North American,Composite Generation and Transmission System Reliability Evaluation Incorporating Two Wind Energy Facilities Considering Wind Speed Correlation,报告人：路明 2013-11-29,I. Introduction:,Generating capacity from wind power behaves quite differently than that from conventional sources,wind is highly variable,wind is both site and terrain specific,Relatively little work has been done on the reliability evaluation of power systems incorporating multiple dependent wind energy facilities. This paper presents an approach to incorporate dependent wind energy facilities in composite generation and transmission adequacy evaluation using the state sampling Monte Carlo simulation technique and data from two wind facilities Regina and Swift Current.,II. Wind Energy Conversion Systems(WECS),A WECS has two basic parts: wind resource and the WTG units,If the WECS consists of identical WTG units with zero FOR, the WECS mult-state model is basically identical to that of a single WTG unit. If the FOR of the WTG units is not zero, the WECS capacity outage probability values are not the same as those of a single WTG unit.,可以假定FOR=0，因为与风况相比，FOR是个对风机实际出力影响可以忽略的随机 因素。,II(A). Wind Speed Model,The ARMA models for the two sites are given as follows:,The simulated wind speed SWt can be obtained as follows:,II(B). Correlated Wind Speed bewteen Two Wind Sites,In practice, wind farms are neither completely dependent nor independent if the distances between the sites are not very large. The wind speed correlation between two sites can be calculated using cross correlation as follows:,A correlation greater than 0.8 is generally described as strong, whereas a correlation less than 0.5 is generally described as weak.,To explore the relationship between the power system reliability and the wind correlation, we have to adjust the value of the Rxy between Regina and Swift Current.,II(B). Correlated Wind Speed bewteen Two Wind Sites,The ARMA model is composed of two subcomponents, the AR model involing lagged terms in the time series itself(wind speed from previous hours), and the MA model involving lagged terms in the noise or residuals(which are random). Therefore it is possible to adjust the wind speed correlation level by selecting the random number seeds for a random number generator process used in the MA model.,Assume the cross-correlation coefficient between Regina and Swift Curren is 0.48, and assume that Regina is used as the base case and that its random seed is X. Then assume the random seed for Swift Current case is a proportional value of X, i.e mX. The next step is to pick some values(m) and test it in the wind speed simulation process to determine the best ”m” that results in a correlation of 0.48.,在参考文献 Considering Load-Carrying Capability and Wind Speed Correlation of WECS in Generation Adequacy Assessment中提出了一种调整Rxy的方法。,II(C). Wind Farm Power Outputs Considering Wind Speed Correlation Levels for the Two Wind Sites,The WTG units used in this study have a rated capacity of 2 MW, and cut-in, rated, cut-out speeds of 14.4, 36 and 80 km/h. The cross-correlation coefficient of the wind power outputs for the two sites are shown in Table 2 for different wind speed cross-correlation coefficients.,II(D). WECS Models,A five-state WECS model can be used to provide a reasonable assessment in practical studies using state sampling Monte Carlo simulation.,III. State Sampling Simulation Method,The basic assumption of state sampling simulation method: 1. Behaviour of each component can be categorized by a uniform distribution under 0, 1; 2. Components outage are independent events.,IV. Adequacy Assessment of Composite Generation and Transmi- ssion Systems with Two Wind Farms Considering Wind Speed Correlation,Bus 3 has lowest priority and is the least reliable load point in MRBTS.,The Modified RBTS,随着风电相关性增加，系统的可靠性降低。引入风电的位置对系统可靠性有明显影响。随着风电相关性增加，Case1和2的可靠性差别更加明显。,Three cases are used in this study. A single transmission line with an unavailability of 0.00114 and an average repair time of 10 hrs is used to connect a WECS to a MRBTS bus. Case 1: 20 MW WECS are added at Buses 1 and 2. Case 2: 20 MW WECS are added at Buses 3 and 5. Case 3: 20 MW WECS are added at Buses 3 and 6.,风电穿透率提高，系统的可靠性提高。随着风电穿透率的提高，系统可靠性提高程度随着风电相关性增加而降低的幅度也随之变大。,利用Case2考虑在不同的风电穿透率下（14.3% 40MW和 29.4% 100MW），风电的相关性对系统可靠性的影响。,随着风电相关性增加，负荷点3的可靠性降低。同时，引入风电的位置对负荷点3的可靠性几乎没有影响。,负荷点可靠性指标。,The change in base case EENS at Bus 3 when the correlation changes from 0.2 to 0.8 is 5.9% while it becomes 11.1% when adding 100 MW WECS. 引入风电可以提高系统和负荷点的可靠性，但提高程度与风电相关性呈负相关。同时随着风电穿透率的增加，系统和负荷点的可靠性提高就越明显。,以Case 2 为例继续分析负荷点可靠性指标与风电穿透率的关系。,Total install capability: 3405 MW Peak load: 2850 MW Unavailability of the connection line: 0.00058 Average repair time of the connection line: 10 hrs,The IEEE-RTS Analysis,Case 1: WECS at Bus 1 and 3 Case 2: WECS at Bus 1 and 4 Case 3: WECS at Bus 1 and 6,Two identical installed capacity wind farms totalling 600 MW(penetration level 15.0%) and 1400 MW (penetration level 29.1%)are used.,系统可靠性受到风电相关性和风电穿透率的影响。但是受风电引入系统位置的影响不大。,The load point EENS values when adding two 300 MW WECS with a 0.5 correlation for these cases.,引入互相关联的风电之后，优先级低的负荷可靠性得到提升。提升程度与引入风电的位置几乎无关。,Take case 1 with a wind speed correlation of 0.5 to compare the load bus EENS under different wind penetrati