Computationally Efficient Market Simulation Tool for Future Grid Scenario Analysis

TL;DR

This paper introduces a computationally efficient electricity market simulation tool designed for future grid scenario analysis, incorporating emerging technologies and enabling stability assessments with reduced computational complexity.

Contribution

The paper presents a novel approach combining unit clustering, rolling horizon, and constraint reduction to make UC-based market simulation feasible for long-term future grid studies.

Findings

The tool produces results close to full UC models over long horizons.

It accurately estimates system inertia and stability indicators.

Validated using a simplified Australian market model.

Abstract

The paper proposes a computationally efficient electricity market simulation tool (MST) suitable for future grid scenario analysis. The market model is based on a unit commitment (UC) problem and takes into account the uptake of emerging technologies, like demand response, battery storage, concentrated solar thermal generation, and HVDC transmission lines. To allow for a subsequent stability assessment, the MST requires an explicit representation of the number of online generation units, which affects powers system inertia and reactive power support capability. These requirements render a fullfledged UC model computationally intractable, so we propose unit clustering, rolling horizon approach, and constraint reduction to increase the computational efficiency. To showcase the capability of the proposed tool, we use a simplified model of the Australian National Electricity Market with different penetrations of renewable generation. The results show that the number of online units resulting from the proposed tool is very close to the binary UC run over a week-long horizon, which is confirmed by the loadability and inertia analysis. That confirms the validity of the approach for long term future grid studies, where one is more interested in finding weak points in the system rather than in a detailed analysis of individual operating conditions.

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