Composite auction method for suppressing unreasonable electricity price spikes in a competitive electricity market

The existence of undesirable electricity price spikes in a competitive electricity market requires an efficient auction mechanism. However, many of the existing auction mechanism have difficulties in suppressing such unreasonable price spikes effectively. A new auction mechanism is proposed to suppress effectively unreasonable price spikes in a competitive electricity market. It optimally combines system marginal price auction and pay as bid auction mechanisms. A threshold value is determined to activate the switching between the marginal price auction and the proposed composite auction. Basically when the system marginal price is higher than the threshold value, the composite auction for high price electricity market is activated. The winning electricity sellers will sell their electricity at the system marginal price or their own bid prices, depending on their rights of being paid at the system marginal price and their offers' impact on suppressing undesirable price spikes. Such economic stimuli discourage sellers from practising economic and physical withholdings. Multiple price caps are proposed to regulate strong market power. We also compare other auction mechanisms to highlight the characteristics of the proposed one. Numerical simulation using the proposed auction mechanism is given to illustrate the procedure of this new auction mechanism.

Electricity market price spike forecast with data mining techniques

Electricity market price forecast is a changeling yet very important task for electricity market managers and participants. Due to the complexity and uncertainties in the power grid, electricity prices are highly volatile and normally carry with spikes. which may be (ens or even hundreds of times higher than the normal price. Such electricity spikes are very difficult to be predicted. So far. most of the research on electricity price forecast is based on the normal range electricity prices. This paper proposes a data mining based electricity price forecast framework, which can predict the normal price as well as the price spikes. The normal price can be, predicted by a previously proposed wavelet and neural network based forecast model, while the spikes are forecasted based on a data mining approach. This paper focuses on the spike prediction and explores the reasons for price spikes based on the measurement of a proposed composite supply-demand balance index (SDI) and relative demand index (RDI). These indices are able to reflect the relationship among electricity demand, electricity supply and electricity reserve capacity. The proposed model is based on a mining database including market clearing price, trading hour. electricity), demand, electricity supply and reserve. Bayesian classification and similarity searching techniques are used to mine the database to find out the internal relationships between electricity price spikes and these proposed. The mining results are used to form the price spike forecast model. This proposed model is able to generate forecasted price spike, level of spike and associated forecast confidence level. The model is tested with the Queensland electricity market data with promising results. Crown Copyright (C) 2004 Published by Elsevier B.V. All rights reserved.