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A cluster-based optimization approach to support the participation of an aggregator of a larger number of prosumers in the day-ahead energy market
•Participation of an aggregator of prosumers in the day-ahead energy market.•A cluster-based approach to enable the optimization of millions of flexible resources.•The cluster-based approach includes two steps: clustering and optimization. Optimizing the participation of a large number of prosumers...
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Published in: | Electric power systems research 2019-03, Vol.168, p.324-335 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | •Participation of an aggregator of prosumers in the day-ahead energy market.•A cluster-based approach to enable the optimization of millions of flexible resources.•The cluster-based approach includes two steps: clustering and optimization.
Optimizing the participation of a large number of prosumers in the electricity markets is a challenging problem, especially for portfolios with thousands or millions of flexible resources. To address this problem, this paper proposes a cluster-based optimization approach to support an aggregator in the definition of demand and supply bids for the day-ahead energy market. This approach consists of two steps. In the first step, the aggregated flexibility of the entire portfolio is computed by a centroid-based clustering algorithm. In the second step, the supply and demand bids are defined by an optimization model that can assume the form of a deterministic or a two-stage stochastic problem. A case study of 10,000 prosumers from the Iberian market is used to evaluate and compare the performance of the bidding optimization models with and without pre-clustering. The numerical results show that the optimized bidding strategies outperform an inflexible strategy by more than 20% of cost savings. The centroid-based clustering algorithm reduces effectively the execution times of the bidding optimization problems, without affecting the quality of the energy bids. |
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ISSN: | 0378-7796 1873-2046 |
DOI: | 10.1016/j.epsr.2018.11.022 |