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.

Hydrogen from coal: Production and utilisation technologies

Error condition detected Although coal may be viewed as a dirty fuel due to its high greenhouse emissions when combusted, a strong case can be made for coal to be a major world source of clean H-2 energy. Apart from the fact that resources of coal will outlast oil and natural gas by centuries, there is a shift towards developing environmentally benign coal technologies, which can lead to high energy conversion efficiencies and low air pollution emissions as compared to conventional coal fired power generation plant. There are currently several world research and industrial development projects in the areas of Integrated Gasification Combined Cycles (IGCC) and Integrated Gasification Fuel Cell (IGFC) systems. In such systems, there is a need to integrate complex unit operations including gasifiers, gas separation and cleaning units, water gas shift reactors, turbines, heat exchangers, steam generators and fuel cells. IGFC systems tested in the USA, Europe and Japan employing gasifiers (Texaco, Lurgi and Eagle) and fuel cells have resulted in energy conversions at efficiency of 47.5% (HHV) which is much higher than the 30-35% efficiency of conventional coal fired power generation. Solid oxide fuel cells (SOFC) and molten carbonate fuel cells (MCFC) are the front runners in energy production from coal gases. These fuel cells can operate at high temperatures and are robust to gas poisoning impurities. IGCC and IGFC technologies are expensive and currently economically uncompetitive as compared to established and mature power generation technology. However, further efficiency and technology improvements coupled with world pressures on limitation of greenhouse gases and other gaseous pollutants could make IGCC/IGFC technically and economically viable for hydrogen production and utilisation in clean and environmentally benign energy systems. (c) 2005 Elsevier B.V. All rights reserved.

Impact of weather dependency on planning decisions in the australian national electricity market

A long-term planning method for the electricity market is to simulate market operation into the future. Outputs from market simulation include indicators for transmission augmentation and new generation investment. A key input to market simulations is demand forecasts. For market simulation purposes, regional demand forecasts for each half-hour interval of the forecasting horizon are required, and they must accurately represent realistic demand profiles and interregional demand relationships. In this paper, a demand model is developed to accurately model these relationships. The effects of uncertainty in weather patterns and inherent correlations between regional demands on market simulation results are presented. This work signifies the advantages of probabilistic modeling of demand levels when making market-based planning decisions.

A new methodology for generation investment in the national electricity market of Australia

Market administrators hold the vital role of maintaining sufficient generation capacity in their respective electricity market. However without the jurisdiction to dictate the generator types, locations and timing of new generation, the reliability of the system may be compromised by delayed entry of new generation. This paper illustrates a new generation investment methodology that can effectively present expected returns from the pool market; while concurrently searching for the type and placement of a new generator to fulfil system reliability requirements.

A genetic algorithm based method for deregulated electricity market dispatch

In a deregulated electricity market, optimizing dispatch capacity and transmission capacity are among the core concerns of market operators. Many market operators have capitalized on linear programming (LP) based methods to perform market dispatch operation in order to explore the computational efficiency of LP. In this paper, the search capability of genetic algorithms (GAs) is utilized to solve the market dispatch problem. The GA model is able to solve pool based capacity dispatch, while optimizing the interconnector transmission capacity. Case studies and corresponding analyses are performed to demonstrate the efficiency of the GA model.