Operations Research 2025
Abstract Submission

121. Aggregator-Based Local Energy Markets and Congestion Management

Invited abstract in session TD-9: Optimization in Energy Infrastructure Planning, stream Energy and Sustainability.

Thursday, 14:30-16:00
Room: H15

Authors (first author is the speaker)

1. Kai Hoth
Institute for Operations Research and Information Systems, Hamburg University of Technology (TUHH)
2. Kathrin Fischer
Institute for Operations Research and Information Systems, Hamburg University of Technology (TUHH)

Abstract

In this work, a day-ahead process for a local energy market is developed, allowing aggregators to trade energy on behalf of communities of prosumer households. This process is based on a MILP model that optimizes an aggregator’s decisions regarding the deployment of the households’ various flexible energy resources while considering multiple trading options, including a local market. The day-ahead local market operates through iterative bidding phases, during which matching bids are fixed and local prices are adjusted according to supply and demand. The objective of these iterations is to incentivize high local trading volumes, thereby exploiting the potential for local energy exchange.

The process is applied in a case study with ten aggregators that manage a total of 111 households. Characteristic summer and winter days are selected to represent a range of external conditions. Furthermore, two distinct configurations for the assignment of households to aggregators are compared. The results show that both external conditions and aggregator configurations influence local trading potential, which is highest when aggregators are assigned dissimilar household configurations and external conditions result in a nearly balanced local energy supply and demand. Extreme conditions like high heating loads due to low ambient temperatures cause an imbalance between demand and supply that inhibits local trading.

Additional analyses explore market interventions through targeted price adjustments as a means of congestion management. Four intervention schemes are tested in multiple variants. The most effective of these schemes reduces power line overloads by approximately 8 %, demonstrating that local market interventions can support congestion management.

Keywords

Status: accepted

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