Probabilistic Forecast-based Portfolio Optimization of Electricity Demand at Low Aggregation Levels

TL;DR

This paper introduces a portfolio-based method for aggregating low-level electricity demand forecasts to improve accuracy, utilizing probabilistic models like ARMA-GARCH and KDE, with applications in decentralized energy markets.

Contribution

It proposes three novel portfolio approaches for demand aggregation that enhance probabilistic forecast accuracy at low aggregation levels, addressing a gap in demand-side research.

Findings

Seasonal Residual approach outperforms others in accuracy and efficiency

All three methods improve forecast accuracy over random portfolios

Methods are effective for Korea and Ireland datasets

Abstract

In the effort to achieve carbon neutrality through a decentralized electricity market, accurate short-term load forecasting at low aggregation levels has become increasingly crucial for various market participants' strategies. Accurate probabilistic forecasts at low aggregation levels can improve peer-to-peer energy sharing, demand response, and the operation of reliable distribution networks. However, these applications require not only probabilistic demand forecasts, which involve quantification of the forecast uncertainty, but also determining which consumers to include in the aggregation to meet electricity supply at the forecast lead time. While research papers have been proposed on the supply side, no similar research has been conducted on the demand side. This paper presents a method for creating a portfolio that optimally aggregates demand for a given energy demand, minimizing forecast inaccuracy of overall low-level aggregation. Using probabilistic load forecasts produced by either ARMA-GARCH models or kernel density estimation (KDE), we propose three approaches to creating a portfolio of residential households' demand: Forecast Validated, Seasonal Residual, and Seasonal Similarity. An evaluation of probabilistic load forecasts demonstrates that all three approaches enhance the accuracy of forecasts produced by random portfolios, with the Seasonal Residual approach for Korea and Ireland outperforming the others in terms of both accuracy and computational efficiency.