Multiple Joint Chance Constraints Approximation for Uncertainty Modeling in Dispatch Problems
arxiv(2024)
摘要
Uncertainty modeling has become increasingly important in power system
decision-making. The widely-used tractable uncertainty modeling method-chance
constraints with Conditional Value at Risk (CVaR) approximation, can be
overconservative and even turn an originally feasible problem into an
infeasible one. This paper proposes a new approximation method for multiple
joint chance constraints (JCCs) to model the uncertainty in dispatch problems,
which solves the conservativeness and potential infeasibility concerns of CVaR.
The proposed method is also convenient for controlling the risk levels of
different JCCs, which is necessary for power system applications since
different resources may be affected by varying degrees of uncertainty or have
different importance to the system. We then formulate a data-driven
distributionally robust chance-constrained programming model for the power
system multiperiod dispatch problem and leverage the proposed approximation
method to solve it. In the numerical simulations, two small general examples
clearly demonstrate the superiority of the proposed method, and the results of
the multiperiod dispatch problem on IEEE test cases verify its practicality.
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