ECE 539 Project 2010 Fall 2 Motivation Locational Marginal Price (LMP), which is usually referred to as “shadow price” of the power grid, gives efficient measurement of power production and the consumption of energy at the different bus nodes. The prediction of LMP at different zonal price could benefit the individual biding for the electricity at different nodes in the power system. If we could locate the feature vector, then we could use ANN method to predict the PMU value at certain time in certain place.
ECE 539 Project 2010 Fall 3 Previous WORK  Zonal Prices Analysis Supported by a Data Mining Based Methodology, J. Ferreira, S. Ramos, Z. Vale and J. P. Soares. IEEE Conference Proceedings,  Zone Clustering LMP with Location information using an Improved Fuzzy C-Mean, Se-Hwan Jang, Jin-Ho Kim, Sang-Hyuk Lee and June-Ho Park, IEEE Conference Proceedings.  High value wind: A method to explore the relationship between wind speed and electricity locational marginal price, Geoffrey McD. Lewis
ECE 539 Project 2010 Fall 4 The first step of the project would be manually filtering the large amount of LMP hourly data into different groups. The LMP data is downloaded from the website of Midwest Independent System Operator (Midwest ISO). Filters: Time = April, Value = LMP, Type = LoadZone Methodology Step No.1
ECE 539 Project 2010 Fall 5 Methodology Step No.2 Step No.2 mainly concerns with the feature vector selection. Major Issue that may influence the value of LMP: 1. Grid structure; 2. Weekday or Weekend (7 days in one week); 3. Different period in a day (Morning/Noon/Evening) 1.Generate physical position of different load zones; 2.Grant different weights to the seven days; 3.Choose 4 hours to be one period, all have high LMP. Feature Vector Dimension: 4
ECE 539 Project 2010 Fall 6 ALTEALTWAMRNCILCCINCONSCWLDCWLP DECODPCEKPCFEGREHEIPIPL IGEEMDUMGEMPNIPSNSPOTPSIGE SIPCTVAUPPCWECWPSWR TOTAL Methodology Step No.2(Cont’d) Generate Geographic Location:
ECE 539 Project 2010 Fall 7 Methodology Step No.3 The Step No.3 Using MLP Mapping to Test the data 1.Classification Criterion: 50 HIGH LMP 2.Separate the 28 days in Apr into 4 weeks, labeled W1, W2, W3, W4. 3.Formulate 3 tests: Training Set (W1, W1&W2, W1&W2&W3), Testing Set (W2&W3&W4, W3&W4, W4) Here the testing set functions as the prediction, because in the future if we know the feature vector, we could use MLP to predict the LMP value directly.
ECE 539 Project 2010 Fall 8 Simulation Result Ways of TrainingLayer = 3, Neurons/Layer = 5Layer = 4, Neurons/Layer = 6 Training RatePrediction RateTraining RatePrediction Rate T %55.12%83.87%50.26% T %54.89%67.467%53.12% T %53.20%63.15%61.50% TrainingTraining SetTesting Set T.1W1W2, W3, W4 T.2W1, W2W3, W4 T.3W1, W2, W3W4 Comment: 1.Training Rate does not have necessary relationship with the Prediction Rate 2.Prediction Rate (Testing Rate) is not that high as expected. 3.The randomly-generated location may result in the inconsistency.
ECE 539 Project 2010 Fall 9 Discussion Disturbance & Ways to Improve Disturbance: 1.Inconsistency in the location 2.The classification of the LMP may be too rough to determine the exact position of LMP. 3.Possible feature difference not quite clear. Ways to Improve: 1.Acquire actual geographic location (longitude, latitude). 2.Classify the LMP value range smaller. 3.To make the range difference between the features to be obvious.
ECE 539 Project 2010 Fall 10 Conclusion 1.ANN: quite a useful tool in the power system, yet the application of prediction for LMP value is rare. 2.The result that has the best performance (63%) is roughly acceptable, yet not the expected value. 3.Outlook: make the model more realistic; trying to get the location data from the government; change MLP algorithm to better suitable for LMP Prediction