Local market framework considering flexible agents in energy communities: Theory and experimental validation

In this paper, we experimentally test a local electricity market framework, considering both passive and active market participants as well as uncertainty from production, consumption and network flows. By solving a chance-constrained optimal power flow and expressing the uncertainty using generalized polynomial chaos, we can express nodal active and reactive power prices as polynomial chaos coefficients eliminating the need to perform computationally expensive Monte Carlo simulations. In the day-ahead stage, this produces probabilistic nodal prices, giving prosumers the ability to make optimal strategies ex ante. In the realtime stage a simple measurement of the stochastic drivers is sufficient to calculate realtime nodal prices, avoiding the need to run near-realtime optimization routines. Results are shown on two operating days as well as 30 days of operation with simulated flexible agents.

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Publication Details

Journal
Electric Power Systems Research
Published
2026-07-16
DOI
https://doi.org/10.1016/j.epsr.2026.113795
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
0.00

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article

Local market framework considering flexible agents in energy communities: Theory and experimental validation

Enea Figini, Mario Paolone, Matthieu Jacobs, Pål Forr Austnes
Electric Power Systems Research
Smart Grid Energy Management
article

Local market framework considering flexible agents in energy communities: Theory and experimental validation

Enea Figini, Mario Paolone, Matthieu Jacobs, Pål Forr Austnes
article en

Abstract

In this paper, we experimentally test a local electricity market framework, considering both passive and active market participants as well as uncertainty from production, consumption and network flows. By solving a chance-constrained optimal power flow and expressing the uncertainty using generalized polynomial chaos, we can express nodal active and reactive power prices as polynomial chaos coefficients eliminating the need to perform computationally expensive Monte Carlo simulations. In the day-ahead stage, this produces probabilistic nodal prices, giving prosumers the ability to make optimal strategies ex ante. In the realtime stage a simple measurement of the stochastic drivers is sufficient to calculate realtime nodal prices, avoiding the need to run near-realtime optimization routines. Results are shown on two operating days as well as 30 days of operation with simulated flexible agents.

Electric Power Systems ResearchVol. 264
École Polytechnique Fédérale de Lausanne (CH)
Board of the Swiss Federal Institutes of Technology
Affordable and clean energy
Openalex Percentile: Top 15%
Smart Grid Energy Management
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