Empirical Analysis of Capacity Investment Solution in Distribution Grids

TL;DR

This paper analyzes the stability and quality of investment solutions for distributed generation in power grids using a stochastic programming model and scenario clustering, highlighting the importance of scenario selection.

Contribution

It introduces a scenario generation method based on meteorological data clustering for better modeling renewable energy stochasticity in investment planning.

Findings

Reduced scenario sets can lead to inadequate investment solutions.

Clustering meteorological data improves scenario representativeness.

Optimal capacities depend on scenario quality and quantity.

Abstract

This paper presents an analysis of the stability and quality of the distributed generation planning problem's investment solution. The entry of distributed generators power based on non-conventional energy sources has been extensively promoted in distribution grids. In this paper, a two-stage stochastic programming model is used to find the optimal distributed generators' installed capacities. We emphasize the design of scenarios to represent the stochasticity of power production on renewable sources. In the scenario generation, a method is proposed based on the clustering of real measurements of meteorological variables. We measure the quality and stability of the investment solution as a function of the number of scenarios. The results show that a reduced selection of scenarios can give an inadequate solution to distributed generators' investment strategy.