6 research outputs found

    Comparison of ARIMA and ANN models used in electricity price forecasting for power market

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    In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps ahead in the UK electricity market. The half hourly data of historical prices are obtained from UK Reference Price Data from March 22nd to July 14th 2010 and the predictions are derived from a sliding training window with a length of 8 weeks. The ARIMA with various AR and MA orders and the ANN with different numbers of delays and neurons have been established and compared in terms of the root mean square errors (RMSEs) of price forecasts. The experimental results illustrate that the ARIMA (4,1,2) model gives greater improvement over persistence than the ANN (20 neurons, 4 delays) model

    Forecasting the impact of CCGT-CCS on the UK's electricity market by LCOE

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    To achieve the target for building a low-carbon economy, the UK will have to build more low-carbon power plants to reduce carbon dioxide emissions from electricity generation. However, renewable energy is difficult to meet the in-creasing energy demand and keep lights on. This limitation of renewable could be solved by coal and gas-fired power station fitted with carbon capture storage (CCS) technology. CCS technology could capture up to 90% of carbon dioxide from emissions and allow fossil fuel power station to provide conti-nuous low-carbon electricity power. This paper presents the levelised cost of electricity of CCGT with CCS and compared with renewable technology to forecast the impact of CCGT with CCS on the UK’s electricity market

    Calculating size of pump-hydro combined energy storage system in wind-diesel systems based on PHCES dynamic Model

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    This paper will propose an approach to calculate and evaluate the reserve capacity and energy size of Pumping-Hydro Combined Energy Storage (PHCES) when wind power is integrated to power grid while considering the scheme of generation capacity allocation and operation of PHCES. This approach will use Monte Carlo Method to simulate large amount of samples to obtain the minimum value of capacity and energy size that could satisfy the requirement of system reliability. Finally this approach will apply in a RBTS system to assess the project feasibilit

    A short-term electricity price forecasting scheme for power market

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    Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model’s parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1st to July 7th 2010

    An approach to calculate the capacity of pump-hydro combined energy storage with wind power integration

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    In this paper, an approach is presented to calculate the reserve capacity of Pump-Hydro Combined Energy Storage (PHCES) integrated with wind generation. The proposed approach utilizes Monte Carlo methods to obtain all the reasonable capacity of PHCES based on the power system reliability requirement. The pumping and hydro period of PHCES will be taken into consideration to estimate the reliability of wind power generation with EES. Finally this approach is applied in a RBTS system to calculate the minimum capacity of PHCES

    Risk assessment due to electricity price forecast uncertainty in UK electricity market

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    This paper illustrates the risk assessment on electricity price forecast uncertainty. The high-risk periods under different time have been indicated. Autoregressive integrated moving average (ARIMA) models and artificial neural network (ANN) techniques are introduced to forecast electricity prices in UK electricity market. Also, this paper investigates the risk index of electricity prices due to forecast uncertainties in the competitive power market through two aspects – daily and seasonal. This risk index is calculated using the errors of short-term electricity price forecast. The input data of forecasting models is divided into weekday and weekend profiles and this is done to observe the different electricity price dynamic risks between weekdays and weekends
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