Stochastic unit commitment based on energy-intensive loads participating in wind and solar power consumption

The fluctuation and intermittency of wind and solar power outputs result in increased regulation pressure on thermal units in power systems. Adjustable energy-intensive loads (such as electrolytic aluminium and steel plants) have great potential for participating in demand response (DR) programs with the goal of reducing thermal unit regulation pressure. This paper proposes an optimal scheduling method of unit commitment (UC) which gives consideration to energy-intensive loads participating in wind and solar power consumption. The UC method adopts the nonparametric kernel density estimation method to model wind and solar power outputs and then uses the Frank-Copula function to describe the correlation between the scenarios of wind and solar power outputs. A stochastic unit commitment (SUC) model introduces a chance-constrained theory of a reserve coefficient to describe time-variant scenarios on the basis of the deviation between the typical and simulative scenarios. The simulation results based on the IEEE 118-bus system show that the energy-intensive load in the SUC model can flexibly adjust and respond to changes in wind and solar power output, reduce the impact of the uncertainties of wind and solar power output, and promote the consumption of wind and solar power. An optimal scheduling method of the unit commitment with consideration of the energy-intensive loads participating in wind and solar power consumption is proposed. It adopts the nonparametric kernel density estimation method to model wind and solar power outputs. And then it uses the Frank-Copula function to describe the correlation between the scenarios of wind and solar power outputs. A stochastic unit commitment model introduces chance constraints of the reserve coefficient to describe time-variant scenarios on the basis of the deviation between the typical and simulative scenarios.image