Adaptive Decision-Objective Loss for Forecast-then-Optimize in Power Systems

TL;DR

This paper introduces an adaptive decision-objective loss (ADOL) that improves power system decision-making by ensuring global optimality and adaptability in dynamic, multi-stage environments, outperforming traditional forecasting methods.

Contribution

The paper proposes ADOL, a novel loss function that redefines decision loss as utility, enabling globally optimal and adaptable decisions in power systems with dynamic conditions.

Findings

ADOL achieves globally optimal decisions in power dispatching.

ADOL adapts effectively to changing costs and environments.

Experimental results show improved decision utility over traditional methods.

Abstract

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.