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 t...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 July7th 2010.展开更多
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 intr...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 8 weeks 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.展开更多
The continuous development of hydrogen-electrolyser and fuel-cell technologies not only reduces their investment and operating costs but also improves their technical performance to meet fast-acting requirements of el...The continuous development of hydrogen-electrolyser and fuel-cell technologies not only reduces their investment and operating costs but also improves their technical performance to meet fast-acting requirements of electrical grid balancing services such as frequency-response services.In order to project the feasibility of co-locating a hydrogen-storage system with a wind farm for the dynamic regulation frequency-response provision in Great Britain,this paper develops a modelling framework to coordinate the wind export and frequency responses to the main grid and manage the interaction of the electrolyser,compressor,storage tank and fuel cell within the hydrogen-storage system by respecting the market mechanisms and the balance and conversion of power and hydrogen flows.Then the revenue of frequency-response service provision and a variety of costs induced by the hydrogen-storage system are translated into the net profit of the co-location system,which is maximized by optimizing the capacities of hydrogen-storage-system components,hydrogen-storage levels that guide the hydrogen restoration via operational baselines and the power interchange between a wind-farm and hydrogen-storage system,as well as the capacities tendered for low-and high-frequency dynamic regulation services.The developed modelling framework is tested based on a particular 432-MW offshore wind farm in Great Britain combined with the techno-economics of electrolysers and fuel cells projected for 2030 and 2050 scenarios.The optimized system configuration and operation are compared between different operating scenarios and discussed alongside the prospect of applying hydrogen-storage systems for frequency-response provision.展开更多
Battery energy storage systems(BESS)are instrumental in the transition to a low carbon electrical network with enhanced flexibility,however,the set objective can be accomplished only through suitable scheduling of the...Battery energy storage systems(BESS)are instrumental in the transition to a low carbon electrical network with enhanced flexibility,however,the set objective can be accomplished only through suitable scheduling of their operation.This paper develops a dynamic optimal power flow(DOPF)-based scheduling framework to optimize the day(s)-ahead operation of a grid-scale BESS aiming to mitigate the predicted limits on the renewable energy generation as well as smooth out the network demand to be supplied by conventional generators.In DOPF,all the generating units,including the ones that model the exports and imports of the BESS,across the entire network and the complete time horizon are integrated on to a single network.Subsequently,an AC-OPF is applied to dispatch their power outputs to minimize the total generation cost,while satisfying the power balance equations,and handling the unit and network constraints at each time step coupled with intertemporal constraints associated with the state of charge(SOC).Furthermore,the DOPF developed here entails the frequently applied constant current-constant voltage charging profile,which is represented in the SOC domain.Considering the practical application of a 1 MW BESS on a particular 33 kV network,the scheduling framework is designed to meet the pragmatic requirements of the optimum utilization of the available energy capacity of BESS in each cycle,while completing up to one cycle per day.展开更多
文摘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 July7th 2010.
文摘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 8 weeks 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.
文摘The continuous development of hydrogen-electrolyser and fuel-cell technologies not only reduces their investment and operating costs but also improves their technical performance to meet fast-acting requirements of electrical grid balancing services such as frequency-response services.In order to project the feasibility of co-locating a hydrogen-storage system with a wind farm for the dynamic regulation frequency-response provision in Great Britain,this paper develops a modelling framework to coordinate the wind export and frequency responses to the main grid and manage the interaction of the electrolyser,compressor,storage tank and fuel cell within the hydrogen-storage system by respecting the market mechanisms and the balance and conversion of power and hydrogen flows.Then the revenue of frequency-response service provision and a variety of costs induced by the hydrogen-storage system are translated into the net profit of the co-location system,which is maximized by optimizing the capacities of hydrogen-storage-system components,hydrogen-storage levels that guide the hydrogen restoration via operational baselines and the power interchange between a wind-farm and hydrogen-storage system,as well as the capacities tendered for low-and high-frequency dynamic regulation services.The developed modelling framework is tested based on a particular 432-MW offshore wind farm in Great Britain combined with the techno-economics of electrolysers and fuel cells projected for 2030 and 2050 scenarios.The optimized system configuration and operation are compared between different operating scenarios and discussed alongside the prospect of applying hydrogen-storage systems for frequency-response provision.
文摘Battery energy storage systems(BESS)are instrumental in the transition to a low carbon electrical network with enhanced flexibility,however,the set objective can be accomplished only through suitable scheduling of their operation.This paper develops a dynamic optimal power flow(DOPF)-based scheduling framework to optimize the day(s)-ahead operation of a grid-scale BESS aiming to mitigate the predicted limits on the renewable energy generation as well as smooth out the network demand to be supplied by conventional generators.In DOPF,all the generating units,including the ones that model the exports and imports of the BESS,across the entire network and the complete time horizon are integrated on to a single network.Subsequently,an AC-OPF is applied to dispatch their power outputs to minimize the total generation cost,while satisfying the power balance equations,and handling the unit and network constraints at each time step coupled with intertemporal constraints associated with the state of charge(SOC).Furthermore,the DOPF developed here entails the frequently applied constant current-constant voltage charging profile,which is represented in the SOC domain.Considering the practical application of a 1 MW BESS on a particular 33 kV network,the scheduling framework is designed to meet the pragmatic requirements of the optimum utilization of the available energy capacity of BESS in each cycle,while completing up to one cycle per day.
