Abstract
In the context of large-scale renewable energy integrated into an electrical power system, the effects of power forecast errors on the power balance equation of the power system unit commitment model is considered. In this paper, the problem of solving the power balance equation with uncertain variables was studied. The unit commitment model with random variables in the power balance equation was solved by establishing a power system day-ahead optimisation unit commitment model based on chance-constrained dependent chance goal programming. First, to achieve the solution of the power balance equation with random variables, the equality constraint is loosened into an inequality constraint, and the power balance equation constraint is transformed into a dependent chance programming model aimed at maximising the probability of occurrence of random events in an uncertain environment. Then, the dependent chance programming model is proposed to ensure the economy and security of the scheme, and the goal programming model is introduced to facilitate an efficient solution. By combining dependent chance programming and goal programming, a power system day-ahead unit commitment model based on chance-constrained dependent chance goal programming is established. Finally, an example is discussed to demonstrate the effectiveness of the proposed model.
Highlights
The unit commitment (UC) problem is defined as an optimization problem which determines the operating schedule of a set of generating units at each time interval with varying loads and generations, considering a set of operational, technical, economic, and environmental constraints [1,2].With increasing concerns about climate change and fossil energy depletion, the power system has seen a fast expansion of renewable energy (RE) in recent years
Combined with the dependent chance programming used for solving the power balance equation containing uncertain variables, we propose chance-constrained dependent chance goal programming and apply it to the day-ahead UC problem, which introduces a novel method to deal with the uncertainties in the UC
This is because the stochastic UC model based scenarios make the UC scheme based on the error distribution information obtained from the reserved scenarios, while the UC model proposed in this paper directly makes the UC scheme by using the original error distribution information, and there is no error in the expression of the forecast error distribution information caused by the scenario reduction process
Summary
The unit commitment (UC) problem is defined as an optimization problem which determines the operating schedule of a set of generating units at each time interval with varying loads and generations, considering a set of operational, technical, economic, and environmental constraints [1,2]. This paper no longer ignores the forecast error in the power balance equation of the UC model, and proposes a power system day-ahead UC model based on chance-constrained dependent chance goal programming. Combined with the dependent chance programming used for solving the power balance equation containing uncertain variables, we propose chance-constrained dependent chance goal programming and apply it to the day-ahead UC problem, which introduces a novel method to deal with the uncertainties in the UC.
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