# Cloud-AC-OPF: Model Reduction Technique for Multi-Scenario Optimal Power Flow via Chance-Constrained Optimization

## TL;DR

This paper introduces Cloud-AC-OPF, a model reduction technique for multi-scenario AC optimal power flow that uses statistical summaries to efficiently handle large numbers of operational conditions in long-term planning.

## Contribution

It proposes a novel model reduction approach that replaces multiple samples with mean and standard deviation, enabling efficient long-term planning in power systems.

## Key findings

- Effective reduction of computational complexity.
- Successful application to a 30-bus IEEE model.
- Comparable results to traditional methods.

## Abstract

Many practical planning and operational applications in power systems require simultaneous consideration of a large number of operating conditions or Multi-Scenario AC-Optimal Power Flow (MS-AC-OPF) solution. However, when the number of exogenously prescribed conditions is large, solving the problem as a collection of single AC-OPFs may be time-consuming or simply intractable computationally. In this paper, we suggest a model reduction approach, coined Cloud-AC-OPF, which replaces a collection of samples by their compact representation in terms of mean and standard deviation. Instead of determining an optimal generation dispatch for each sample individually, we parametrize the generation dispatch as an affine function. The Cloud-AC-OPF is mathematically similar to a generalized Chance-Constrained AC-OPF (CC-AC-OPF) of the type recently discussed in the literature, but conceptually different as it discusses applications to long-term planning. We further propose a tractable formulation and implementation, and illustrate our construction on the example of 30-bus IEEE model.