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

Jungyeon Park, Estêvão Alvarenga, Jooyoung Jeon, Ran Li, Fotios Petropoulos, Hokyun Kim, Kwangwon Ahn

Research output: Contribution to journalArticlepeer-review

1 Citation (SciVal)

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.
Original languageEnglish
Article number122109
JournalApplied Energy
Volume353
Issue numberPart B
Early online date23 Oct 2023
DOIs
Publication statusPublished - 1 Jan 2024

Bibliographical note

The research was supported by the EPSRC grant (EP/N03466X/1), a National Research Foundation of Korea grant and the KISTI Super Computing Center. We are grateful for the insight comments of participants at the International Symposium on Forecasting, 2018.

Data Availability Statement

The data that has been used is confidential.

Keywords

  • Aggregated electricity demand
  • Low-aggregation load
  • Portfolio optimization
  • Probabilistic forecasts
  • Short-term load forecasting

ASJC Scopus subject areas

  • Mechanical Engineering
  • Energy(all)
  • Management, Monitoring, Policy and Law
  • Building and Construction
  • Renewable Energy, Sustainability and the Environment

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