I. INTRODUCTION II. LOAD SURVEY STUDY

Transcription

1 Load Profile Synthesis and Wind Power Generation Prediction for An Isolated Power System M. S. Kang, Member, IEEE, C. S. Chen, Member, IEEE, Y. L. Ke, Member, IEEE, Department of Electrical Engineering Department of Electrical Engineering Department of Electrical Engineering Kao Yuan Institute of Technology National Sun Yat-sen University Kun Shan University of Technology Kaohsiung, Taiwan, R. O. C. Kaohsiung, Taiwan, R. O. C. Tainan, Taiwan, R. O. C. C. H. Lin, and C. W. Huang Department of Electrical Engineering Research Institute of Taipower Kao Yuan Institute of Technology Taipei, Taiwan, R. O. C. Kaohsiung, Taiwan, R. O. C. Abstract-- This paper investigates the load composition by load survey study and predicts the wind power generation with a probabilistic network for an isolated power system. The power consumption by each customer-class with the application of load patterns and the total power consumption of all customers within the same class can be obtained and calculated. Probabilistic neural network (PNN) solves the wind power generation based on the wind speed for an offshore island in Taiwan. With the hourly wind speed and load composition, the power generation of diesel generators has been obtained. Results of this study demonstrate that wind power generation can economically and effectively replace the generation of the diesel power plant and provide partial power supply capability for the net peak load requirement. Index Terms--Load survey, load profile synthesis, probabilistic neural network, wind power generation prediction. C I. INTRODUCTION USTOMER load study has been an crucial undertaking for utilities to offer the critical information of demand side to improve the correctness of load forecasting, to support better system planning and the design of tariff structure[-]. Moreover, more effective load management strategies can be named by investigating the customer load features so that the peak power decrease can be achieved. Performing a routine check on load study is necessary so that fluctuation of customer load behavior with time can be found. Deregulation of the power industry in Taiwan has caused Taiwan Power Company (Taipower) face very serious challenges from increasing independent power producers. More competitive marketing strategies by considering the customer power consumption characteristics have to be developed. Taipower recognizing the importance of load study started load survey study [-] in 99 by selecting test customers and installing intelligent meters to collect the power consumption. Load study information obtained will be utilized intensively to support diverse system operation functions in Taipower planning. A robust estimation technique must effectively handle the uncertainty in wind power generation. Probabilistic neural network (PNN) [-7] was therefore studied and proposed for wind power generation estimation. PNN can be curve fitted as a nonlinear system, to learn the relationship between inputs and outputs. PNN has many input nodes equal to the number of predictor variables, and has many hidden nodes equal to the number of training exemplars, with one hidden node specified to each training exemplar. Output nodes of PNN are equal to the number of dependent variables whose values are being predicted. Another advantage of PNN is the single-pass network training stage without any iteration for adjusting weights. PNN is trained by using the recorded data of previous day from the day before a forecast day, or past days before and after the forecast day in the previous year. PNN is used to determine the similarity trend and estimate the similarity between the forecast data and former data. The conventionally adopted back propagation algorithm [8] was used for the artificial neural network (ANN) training. The training data set selection significantly affects the model performance. ANN is retrained to obtain the relationship between wind speed and capture power if the record data changes. Conventional ANN would retrain the network with all past and new training data. The network has difficulties in adapting to a new environment. Due to speed training process, PNN is always growing and incremental learning with new training data. II. LOAD SURVEY STUDY To derive the customer load patterns, various customer data of power consumption is collected and analyzed instead of the power consumption of individual appliance. The load patterns derived can represent the load behavior of customers within the same class if there is adequate customer samples according to the variance of customer power consumption. Load survey study is to select the proper customer sampling size so that the load patterns of the selected customers can represent the load behavior of the entire population within the customer class. To identify the load demand by considering the load composition and customer load behavior for each substation in the Peng-Hu power system, the load survey study [9-] was performed in this work to find the load behavior of each customer class. By retrieving the power consumption of all customers from the customer information system (CIS), the

2 power profiles of each substation and whole power system can be obtained by using the typical load patterns derived. The stratified sampling method [] is applied to find the number of test customers within each customer class according to the standard deviation of customer power consumption as shown in () and (). By this method, the typical load patterns derived can represent the load behavior of each customer class with a specified confidential level. The system power profile can be obtained by synthesizing the power consumption by all customer classes. The actual system profile collected by the SCADA system is then used to verify the accuracy of the load survey study. Z ( N ) h S n = h () N X dr + Z N h S h n NhSh h = n () N h S h where Z : ( α) % confidence level, N h : population of power consumption of class h, S h : variance of power consumption of class h, dr : desired relative precision, X : sample mean. A. The power system in the Peng-Hu offshore island To serve Peng-Hu offshore island s load demand, Taipower has installed twelve diesel generation units with a total capacity of MW. To use the plentiful wind resources in the Peng-Hu area, eight wind generation units with a capacity of kw/each have been installed to work in parallel with the diesel generation units. The ac voltage of V generated by the wind power generation units is transformed to. kv by step up transformers and fed into the power system network to provide the customers power supply. B. The load composition of the Peng-Hu power system After collecting the actual power consumption of all test customers, the bad data detection is performed to identify any abnormal customer power consumption. The hourly power consumption of all test customers within the same class is then incorporated to solve the mean value and standard deviation of a typical load pattern for each customer class. Fig. displays the normalized load patterns of residential, commercial, and industrial customers solved by the load survey study. According to this figure, residential customers consume most of their power during the night when people are at home using many electric appliances such as air conditioner s (A/C s), lamps, and TV s. For the commercial customers, the peak loading occurred during the daytime business hours with a very high percentage of A/C loading. With high temperatures and humidity during the summer season in Taiwan, A/C units contribute to more than 7% of the power demand for commercial customers. Most industrial customers in the Peng- Hu area engage in the seafood processing, the power demand during the night exceeds that during the daytime because load management strategies are applied to increase plant productivity from P.M. to A.M. and save power consumption costs with lower electricity rates. With the typical load patterns derived and the total power consumption by all customers in each class within the service territory, the daily system power profile is obtained. Fig. describes the load composition and power profile of the Peng- Hu District in summer, indicating that residential and commercial customers contribute to most of the system demand. The day time system peak load demand is 9.8 MW at P.M. with load contribution by residential, commercial, and industrial customers as %, %, and %, respectively; meanwhile, the night time system peak load demand is.7 MW at 8 P.M. Fig. illustrates the load composition of the Peng-Hu District in winter. The system peak load demand is 8. MW at 7 P.M. with loading percentages of residential, commercial, and industrial customers as 7%, 8%, and %, respectively. Comparing Fig. and Fig. reveals significantly different daily power profiles for summer and winter seasons due to the variation of load composition % Load(MW) Residential Commercial Industrial Fig.. The typical load patterns of three customer classes. Commercial Residential Industrial Fig.. Synthesized daily power profiles of Peng-Hu District in summer.

3 Load(MW) I Industrial Commercial Residential Fig.. Synthesized daily power profiles of Peng-Hu District in winter. III. WIND POWER GENERATION PREDICTION OF PROBABILISTIC NEURAL NETWORK Among many multilayer feed-forward neural networks, conventional back-propagation network (BPN) has become the main architecture of choice. BPN is effective because its parallel distributed process and training capability, with one shortcoming such as the stochastic searching algorithm to find the minimum error in the error space, training BPN is time consuming and extremely slow without guarantee a global minimum. Another limitation is its difficulty in determining not only the number of hidden layers, but also the neural units in each layer. The probabilistic neural network (PNN) has demonstrated to be more time efficient than the conventional BPN [-7]. The PNN uses the Baysian decision rule to separate decision regions in a multidimensional input space, thus lowering the probable classification error and increasing its accuracy. The significant feature of the PNN is its speed and training process. The training process is one-pass and without any iteration for weight adjustment, and the network generalizes to the new incoming patterns without having to repeat the training process. Additionally, these features are used for real time applications, owing to its modular architecture and accelerated learning speed. A. Probabilistic neural network Figure depicts the architecture of the PNN []. PNN contains four units: input units, pattern units, summation units, and output unit. PNN predicts the value of one or more dependent variables, given the value of one or more independent variables. PNN can take an input vector X of length n (X=[x, x,, x i,, x n ])and generates an output vector Y of length K (Y =[y, y,, y k,, y K ]), where Y denotes the prediction of the actual Y. PNN does this by comparing a new input pattern X with a set of K stored patterns X k (K pattern units) for which the output y k is known. In each pattern unit, a normalize Gaussian function is applied to the distance measure (Euclidian norm) between the unknown input pattern X and the training pattern X k, which gives a measure of the distance or dissimilarity between two patterns. The property of h k is that its magnitudes for a stored pattern X k can be inversely relative to its distance from the input pattern X, if the distance is zero the h k is a maximum of unity. In the summation units, one unit must calculate N, the sums of the products of H k and associated known output y k. Another also must compute D, the sum of all H k. Lastly, the output unit divides N by D to produce the output Y. PNN is able of estimating any random function, either linear or non-linear relationships between input and output variables, drawing the function estimates directly []. In this study, the property is used to apply curve fitting, and replaces the conventional polynomial regression analysis. Expanding the architecture of PNN, allows us to obtain n input units x through x n, K pattern units (K training examples), m output units Y through Y m, and yield K input/output training pairs (X(k)/Y (k)). PNN algorithm has two stages: the learning stage and recalling stages as delineated below. Learning Stage Step) For each training example X(k), k=,,, K, create input weight W IP between input units and pattern units by () wki = xi (k) (i=,,,, n ) () with W IP =[w ki IP ] K n and X(k)=[x (k), x (k),, x i (k),, x n (k)] t. Step) Create output weight W PN between pattern units and summation unit N j by () ykj = y j (k) (j =,,,, m ). () with W PN =[y PN kj ] K m and Y (k)=[y (k), y (k),, y j (k),, y m (k)] t. Connection weights from pattern units to summation unit D j are set. Y Output Unit h y y N h... h K Summation Units x x i x n Fig.. Architecture of the PNN. D y K- y K h K Input Units Pattern Units

4 Recalling Stage Step) Derive network weights W IP and W PN. Step) Apply test vector X=[ x, x,, x i,, x n ] to the network. Step) Compute the Euclidian distance by (). dk = n ( IP xi wki ) () i= The outputs of pattern units are computed by (). d k hk = exp( ) () σ k where smoothing parameters are assumed to be equal such that σ =σ =σ k =σ K =σ. The suitable σ can be performed to obtain the minimum misclassification error based on the testing data []. Step) Compute the N j, the sums of the products of H k and associated known output y kj, is given by (7). rotor-blade. wind generator records were acquired during wintertime (from September to December ). This study addresses the captured power prediction of wind power generators. The proposed PNN model is used to learn the capture power curve and prediction capture capacity with various wind speeds. Figures and show the structure of the prediction system and PNN training results respectively in the wintertime. The wind power starts to be generated with the wind speed reaching the cut in speed of. m/s; the power output of the wind turbines is maintained at constant value of kw for the wind speed over m/s. The blade bearing is disconnected from wind turbines to avoid the mechanical damage of wind turbines when the wind speed exceeds the cut off speed of m/s. It is found that the wind power generation varies with cube of the wind speed for wind speed between. m/s to m/s by PNN model of wind generation, which is consistent to the output characteristics of wind turbine. Pitch angle δ Wind turbine 8 Local Network K PN N j = y kj H k k= (7) Wind Speed V IG Capture Power P the sum of all H k is given by (8) IG 8 D j K = H k k= (8) δ n P n Capture Power Prediction Step) Compute the output Y j by (9). V n j N j D j Y = (9) Y j is the localized average of the stored output patterns. Wind power is clean and renewable energy, and its energy conversion system is attached owing to a simple structure and easy maintenance. The wind farm was set up due to its special native environment and seasonal monsoons in Taiwan s Peng- Hu offshore island. Eight generators are in commercial operation, and more are planned to enlarge to the local system. The energy conversion system could capture the wind s energy through adjusting the wind turbine s rotation speed and the aerodynamic torque by controlling the pitch angle. The inputs of the system are wind speed V and pitch angle δ. The wind energy captured by the wind turbine depends on the wind speed, blade pitch angle, and rotation speed of the Capture power (kw) 7 Fig.. The Capture power prediction system. Actual Test Error Wind speed (m/sec) Fig.. Capture power versus wind speed in wintertime. % 8% % % % % Error(%)

5 Input and output training pairs are defined nonlinear mapping as (). X n (k)=[ δ n (k), V n (k)] y n (k)=p n (k) () where δ n : the pitch angle of n-th wind generator, V n : the wind speed of n-th wind generator, n: the number of wind generator, k: the number of training example. B. Wind power generation With its off-island geographic location, Peng-Hu District has a great wind energy resource. Figure 7 shows the hourly wind speed on Dec. 9,, indicating that the hourly wind speed is above 7. m/s and the mean value of daily wind speed is 8. m/s. With the probabilistic neural network trained and the actual hourly wind speed, the daily wind power generation has been solved as shown in Fig. 7. For the wind speed of. m/s at A.M. The output of wind power generation is therefore predicted as kw and the capacity factor of wind turbines reaches 9%. Figure 8 displays the system load demand, diesel power generation and wind power generation. A higher wind speed implies greater wind generation. For A.M., the system load demand is. MW and wind power generation is. MW, implying that wind energy can supply.% of the system load demand. Power generation (MW) Power generation Wind speed Wind speed (m/s) Fig. 7. Hourly wind speed and power generation on Dec. 9,. 8 Diesel generation & load (MW) Load demand Diesel generation Wind generation Wind generation (MW) Time (Hour) Fig. 8. System load demand, wind power generation, and diesel power generation on Dec. 9,. To understand the benefits of wind power generation over the period of a year, the hourly wind speeds within each month were collected for. Figure 9 describes the monthly average wind speed, indicating that the wind energy resource during the winter season is markedly larger than that during the summer season. With the monthly average wind speed less than. m/s from April to September, the benefit of monthly wind power generation is less than 8 MWh. The capacity factor of wind generators is below.8% for July and August with monthly average wind speed less than. m/s and the wind power generation is under MWh. From October to next January, the monthly average wind speed exceeds. m/s. The capacity factor of wind power generators reaches 7.% with monthly wind power generation surpassing MWh. For, the average capacity factor of wind power generators is.% and total wind power generation is 9 MWh. Wind power generation (MWh) Wind power generation Average wind speed 8 Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. Month Average monthly wind speed (m/s) 7 Fig. 9. Monthly avoided power generation by wind turbines in year.

6 IV. CONCLUSION A load survey study has been performed in this study to identify the typical load patterns of different customer classes. The power consumption of customers is collected and a statistical method is adopted to derive the typical load patterns of each customer class. Based on the load patterns derived and total power consumption within the service territory, the power system profiles of Peng-Hu service district are determined. The contribution of system power demand by each customer class can be identified in a somewhat accurate manner and to improve the unfair cross-subsidy tariff structure. A probabilistic neural network (PNN) is also proposed to accurately predict the wind power generation with variation of wind speed. Additionally, the wind power generation for an offshore island in Taipower is derived according to the actual hourly wind speed in. Moreover, the load survey study is executed to obtain the typical load patterns of various customer classes, which are applied to derive the load composition and power profiles of substations and the power system. For the eight units of wind power generators with a capacity of kw each, the wind power generation for the year is obtained as 9 MWh. The average capacity factor of wind power generators is.% for the yearly average wind speed of.m/s. The avoided generation cost of Peng-Hu power system by wind turbine is solved by the PNN network according to the hourly wind speed. Based on the analysis results of this study, Taipower is seriously considering how to implement more wind power generators for the offshore service district. ACKNOWLEDGMENT The authors would like to thank the National Science Council of the Republic of China for financially supporting this research under Contract No. NSC 9--E--. REFERENCES [] C. S. Chen, J. C. Hwang and C. W. Huang, Determination of Customer Load Characteristics by Load Survey System at Taipower, IEEE Trans. on Power Delivery, Vol., No., pp. -, July 99. [] C. S. Chen, J. C. Hwang and C. W. Huang, Application of Load Survey Systems to Proper Tariff Design, IEEE Trans. on Power Systems, Vol., No., pp. 7-7, November 997. [] C. F. Walker and J. J. Pokoski, Residential Load Shape Modeling Based on Customer Behavior, IEEE Trans. on Power Systems, Vol. PAS-, No.7, pp.7-7, July 98. [] C. S. Chen, M. S. Kang, J. C. Hwang, and C. W. Huang, Implementation of the Load Survey System in Taipower, in Proceeding of the 999 IEEE T&D Conference, Apr [] Load Research Manual, Association of Edison Illuminating Companies, Feb. 99. [] Mo and W. Kinsner, Probabilistic Neural Networks for Power Line Fault Classification, IEEE Canadian Conference on Electrical and Computer Engineering, Vol., pp. 8-88, -8 May 998. [7] W. M. Lin, C. H. Lin, and Z. C. 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Nam, Faults Diagnoses of Rotating Machines by Using Neural Nets: GRNN and BPN, in Proceeding of the 99 IEEE IECON, Vol., pp. -. BIOGRAPHIES Meei-Song Kang (M 99) received the M.S., Ph.D. degree in Electrical Engineering from the National Sun Yat-Sen University in 99 and respectively. Since August 99, he has been with Department of Electrical Engineering, Kao Yuan Institute of Technology, Kaohsiung, Taiwan. Currently he is an Associate Professor. His research interest is in the area of load survey and demand subscription service. Chao-Shun Chen received the B.S. degree from National Taiwan University in 97 and the M.S., Ph.D. degree in Electrical Engineering from the University of Texas at Arlington in 98 and 98 respectively. From 98 to 99 he was a professor of Electrical Engineering department at National Sun Yat-Sen University. Since 99, he works as the deputy director general of Department of Kaohsiung Mass Rapid Transit. From Feb.997 to July 998, he was with the National Taiwan University of Science and Technology as a professor. From August 998, he is with the National Sun Yat-Sen University as a full professor. His majors are computer control of power systems and distribution automation. Yu-Lung Ke (M 98) received his BS degree in Control Engineering from National Chiao Tung University, Hsin-Chu City, Taiwan, in 988. He received his MS degree in Electrical Engineering from National Taiwan University, Taipei City, Taiwan, in 99. He received his Ph.D. degree in Electrical Engineering from National Sun Yat-Sen University, Kaohsiung City, Taiwan, in. Since August 99, he has been with Department of Electrical Engineering, Kun Shan University of Technology, Yung-Kang City, Tainan Hsien, Taiwan. Currently he is an Associate Professor. His research interests include the power system, distribution automation, energy management, power quality and renewable energy. Chia-Hung Lin was born in 97. He received the B.S. degree in electrical engineering from the Tatung Institute of Technology, Taipei, Taiwan, in 998 and M.S. degree in electrical engineering from the National Sun Yat-Sen University, Kaohsiung, Taiwan, in. He got the Ph.D. degree in electrical engineering from the National Sun Yat-Sen University, Kaohsiung, Taiwan, in June. Currently he is an Assistant Professor. His research interests include fault diagnosis in power system, neural network computing, and harmonic analysis. Chia-Wen Huang received the B.S degree in Electronic Engineering from National Taiwan Ocean University in 97. He is a senior research engineer of Research Institute of Taipower and works as the project leader of the Taipower system load survey and the development of master plans for demand-side management and integrated resource planning.