Abstract
In many power systems, in particular in Great Britain (GB), significant wind generation is anticipated and gas-fired generation will continue to play an important role. Gas-fired generating units act as a link between the gas and electricity networks. The variability of wind power is, therefore, transferred to the gas network by influencing the gas demand for electricity generation. Operation of a GB integrated gas and electricity network considering the uncertainty in wind power forecast was investigated using three operational planning methods: deterministic, two-stage stochastic programming, and multistage stochastic programming. These methods were benchmarked against a perfect foresight model which has no uncertainty associated with the wind power forecast. In all the methods, thermal generators were controlled through an integrated unit commitment and economic dispatch algorithm that used mixed integer programming. The nonlinear characteristics of the gas network, including the gas flow along pipes and the operation of compressors, were taken into account and the resultant nonlinear problem was solved using successive linear programming. The operational strategies determined by the stochastic programming methods showed reductions of the operational costs compared to the solution of the deterministic method by almost 1%. Also, using the stochastic programming methods to schedule the thermal units was shown to make a better use of pumped storage plants to mitigate the variability and uncertainty of the net demand.
Highlights
A S THE fraction of wind power generation in a power system increases, it becomes important to take account of the wind variability and the uncertainty in the forecasts of wind power.Several studies have examined the effect of uncertainty in wind power forecasts on unit commitment
Operation of the Great Britain (GB) integrated gas and electricity network considering the uncertainty in wind power forecast was investigated using three operational planning methods: deterministic, two-stage stochastic programming, and multistage stochastic programming
These methods were benchmarked against a perfect foresight model which has no uncertainty associated with wind power forecast
Summary
A S THE fraction of wind power generation in a power system increases, it becomes important to take account of the wind variability and the uncertainty in the forecasts of wind power. Gonzalez et al [4] formulated a two-stage stochastic programming model to optimize the combined operation of a wind farm and a pumped storage facility in a market environment with wind generation and electricity price uncertainties. Tuohy et al [6] examined the effects of uncertain wind and load on the unit commitment and dispatch of power systems with high levels of wind power generation. Methaprayoon et al [7] developed an artificial neural network model to generate uncertain wind power forecasts. This model was integrated into unit commitment scheduling. An integrated model of gas and electricity networks was developed to take into account the uncertainty in wind power forecast and fuel availability to gas-fired generators. The uncertainty in the forecasts of electricity demand is significantly less than that of wind power; in this research, only the uncertainty of wind power forecasts are considered
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