UNIVERSITY OF WISCONSIN - MADISON An ANN Approach to LMP Classification & Prediction ECE/CS/ME 539 2010 Fall Project Report ZHENG, HONGHAO 2010/12/21 Locational Marginal Price (LMP) gives efficient signals for the production and consumption of energy, and for the construction of new generation and transmission facilities. The report indicates the Multi Layer Perceptron (MLP) training & testing method to predict the future LMP value of the load zone. It also states the drawback of this methodology and provides the improvement ideas.ZHENG, HONGHAO Campus ID: 9063081385 ECE 539 2010 Fall Project Report 1 Context 1. Introduction ........................................................................................................................................ 2 1.1 Background .............................................................................................................................. 2 1.2 Motivation ................................................................................................................................ 2 2. Methodology ...................................................................................................................................... 3 2.1 Manually Filtering ................................................................................................................... 3 Fig. 2-1 Sample Data File of LMP......................................................................................... 3 Table 2-1 Possession of Different Nodes in the MISO Grid .................................................. 3 2.2 Feature Vector Selection .......................................................................................................... 4 Fig. 2-2 Geographic Distribution of Load Zones ............................................................... 4 Table 2-2 Weights of the Feature Vector ............................................................................ 5 2.3 MLP Training & Prediction ..................................................................................................... 5 3. Simulation Result & Analysis ............................................................................................................ 6 Table 3-1 Different Training & Testing Sequence ............................................................. 6 Table 3-2 Part of Simulation Result (Best Performance) ................................................... 6 4. Discussion .......................................................................................................................................... 7 4.1 Disturbance .............................................................................................................................. 7 4.2 Ways to Improve ...................................................................................................................... 7 5. Summary ............................................................................................................................................ 8 References .............................................................................................................................................. 9 Appendix: MATLAB FILE .................................................................................................................. 10ZHENG, HONGHAO Campus ID: 9063081385 ECE 539 2010 Fall Project Report 2 1. Introduction 1.1 Background With the economic recovery and population boom, the power grid expanded rapidly after the Second World War in the United States. The golden age of grid development had lasted for 30 years in twenty’s century until the arrival of the saturation of power demand in the beginning of 1980s. Until then, the power grid was still dominated by the government and electric power price was set to be constant. However, with the idea of power marketing came into existence, people gradually focused on the deregulation of the power market and put the idea into practice. Then the original government institutions that used to be in charge of the power grid were separated into three different parts: generation, transmission delivery and power dispatch. In the market, the different organizations and individuals are encouraged to bid for their own power price, which may exert paramount influence on the final price of the power in the local grid. Locational Marginal Price, as abbreviated as LMP, is the “shadow” price of the power system. Specifically, it is assumed that one additional kilowatt-hour is demanded at the node in question, and the hypothetical incremental cost to the system that would result from the optimized re-dispatch of available units establishes the hypothetical production cost of the hypothetical kilowatt-hour. In fact, LMP not only gives efficient signals for the production and consumption of energy, but for the construction of new generation and transmission facilities as well. The LMP representation allows people to model detailed power flows on specific lines and provides individual nodal pricing, also it could be used to define the zonal boundaries in the power grid [1]. 1.2 Motivation The LMP could provide detailed zonal information in a certain power system such as the reliability of the power grid [2], which it could also reveal part of the information for individual nodal pricing. Therefore, the prediction of LMP would be quite beneficial if we estimate the future LMP price at certain location, and then bid the price to the market so that we can get the maximum profit. Because the interested area is mainly residential zone, we would rather choose load zone LMP as our major testing case. With the knowledge of LMP values at the certain location and certain time, we may give out the relatively accurate prediction. Actually, we could roughly estimate the LMP into three categories: Low, Middle & High. With the existing LMP data, the potential bidding habit of the system may be revealed by Multi-Layer-Perceptron classification method. The existing data could be treated as the training data, while the future feature could be generated as the testing data. An important point that should be taken into consideration is that this project is comparatively ideal with certain assumptions, which make
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