Tractable Probabilistic Models for Investment Planning

2025-11-17

AI Summary

Long-term investment planning in power systems confronts high-dimensional, long-horizon uncertainties, where conventional finite-scenario approaches—such as Dirac mixture modeling—fail to adequately capture stochastic volatility, thereby hindering risk-aware decision-making. To address this, this paper introduces sum-product networks (SPNs) into power system planning for the first time, establishing a unified probabilistic modeling framework capable of scenario generation, exact marginal and conditional probability inference, and chance-constrained optimization. Unlike traditional stochastic programming methods constrained by fixed scenario sets, the proposed SPN-based framework lifts the cardinality limitation on scenarios, enabling efficient, scalable uncertainty quantification and robust optimization. Validated on benchmark power system test cases, the method achieves significant improvements in computational efficiency and solution reliability, demonstrating superior performance in supporting risk-informed, long-term infrastructure investment decisions.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Planning, Routing, and Scheduling: Planning under Uncertainty
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Novel mobile and WoT systems, and system-of-systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Investment planning in power utilities, such as generation and transmission expansion, requires decade-long forecasts under profound uncertainty. Forecasting of energy mix and energy use decades ahead is nontrivial. Classical approaches focus on generating a finite number of scenarios (modeled as a mixture of Diracs in statistical theory terms), which limits insight into scenario-specific volatility and hinders robust decision-making. We propose an alternative using tractable probabilistic models (TPMs), particularly sum-product networks (SPNs). These models enable exact, scalable inference of key quantities such as scenario likelihoods, marginals, and conditional probabilities, supporting robust scenario expansion and risk assessment. This framework enables direct embedding of chance-constrained optimization into investment planning, enforcing safety or reliability with prescribed confidence levels. TPMs allow both scenario analysis and volatility quantification by compactly representing high-dimensional uncertainties. We demonstrate the approach's effectiveness through a representative power system planning case study, illustrating computational and reliability advantages over traditional scenario-based models.

Problem

• Investment planning requires long-term forecasts under deep uncertainty
• Classical scenario methods limit insight into volatility and robustness
• Tractable probabilistic models enable exact inference for risk assessment

Innovation

• Using tractable probabilistic models for investment planning
• Applying sum-product networks for exact scalable inference
• Embedding chance-constrained optimization with confidence levels