Adaptive Decision-Objective Loss for Forecast-then-Optimize in Power Systems
CoRR(2023)
摘要
Forecast-then-optimize is a widely-used framework for decision-making
problems in power systems. Traditionally, statistical losses have been employed
to train forecasting models, but recent research demonstrated that improved
decision utility in downstream optimization tasks can be achieved by using
decision loss as an alternative. However, the implementation of decision loss
in power systems faces challenges in 1) accommodating multi-stage
decision-making problems where upstream optimality cannot guarantee final
optimality; 2) adapting to dynamic environments such as changing parameters and
nature of the problem like continuous or discrete optimization tasks. To this
end, this paper proposes a novel adaptive decision-objective loss (ADOL) to
address the above challenges. Specifically, ADOL first redefines the decision
loss as objective utilities rather than objective loss to eliminate the need to
manually set the optimal decision, thus ensuring the globally optimal decision.
ADOL enables one-off training in a dynamic environment by introducing
additional variables. The differentiability and convexity of ADOL provide
useful gradients for forecasting model training in conjunction with continuous
and discrete optimization tasks. Experiments are conducted for both linear
programming-based and mixed integer linear programming-based power system
two-stage dispatching cases with changing costs, and the results show that the
proposed ADOL is capable of achieving globally optimal decision-making and
adaptability to dynamic environments. The method can be extended to other
multi-stage tasks in complex systems.
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