Abstract
Bidding competition is one of the main transaction approaches in a deregulated electricity market. Locational marginal prices (LMPs) resulting from bidding competition and system operation conditions indicate electricity values at a node or in an area. The LMP reveals important information for market participants in developing their bidding strategies. Moreover, LMP is also a vital indicator for the Security Coordinator to perform market redispatch for congestion management. This paper presents a method using a principal component analysis (PCA) network cascaded with a multi-layer feedforward (MLF) network for forecasting LMPs in a day-ahead market. The PCA network extracts essential features from periodic information in the market. These features serve as inputs to the MLF network for forecasting LMPs. The historical LMPs in the PJM market are employed to test the proposed method. It is found that the proposed method is capable of forecasting day-ahead LMP values efficiently.
Highlights
There are two main transaction modes in a deregulated electric power industry, namely, competitive bidding and bilateral contract
A principal component analysis (PCA) neural network cascaded with the multi-layer-feedforward (MLF) neural network is proposed for day-ahead Locational marginal prices (LMPs) forecasting
(3) Because the purpose of the PCA neural network is to find a set of P orthonormal vectors (OVs) in a Q-dimensional space, P is expected to be smaller than the corresponding number of inputs
Summary
There are two main transaction modes in a deregulated electric power industry, namely, competitive bidding and bilateral contract. The recurrent neural network integrated with fuzzy-c-means was proposed for hour-ahead LMP forecasting in [7]. Linguistic descriptions in the PJM market were transformed into fuzzy membership functions associated with the recurrent neural network for forecasting volatile hour-ahead LMP variations when contingency occurs [8]. A principal component analysis (PCA) neural network cascaded with the multi-layer-feedforward (MLF) neural network is proposed for day-ahead LMP forecasting. The PCA neural network is used to extract essential features in the electricity market. It helps reduce high-dimensional data into low-dimensional ones, which serve as inputs for the MLF neural network.
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