Decentralized optimal operation model for cooperative microgrids considering renewable energy uncertainties
•The optimal operation of cooperative MGs with complementary features is proposed.•A distributed framework to coordinate energy trading among MGs is designed.•A two-stage adjustable robust method is devised to handle uncertainties.•The agreements among multiple MGs can be efficiently reached in seve...
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Veröffentlicht in: | Applied energy 2020-03, Vol.262, p.114579, Article 114579 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | •The optimal operation of cooperative MGs with complementary features is proposed.•A distributed framework to coordinate energy trading among MGs is designed.•A two-stage adjustable robust method is devised to handle uncertainties.•The agreements among multiple MGs can be efficiently reached in several iterations.
An increasing number of microgrids (MGs) with high penetration of renewables are being integrated into the distribution system (DS). Multiple MGs as stakeholders in the future retail markets may sign a cooperation contract when faced with high uncertainties of renewable energies and complementary features among MGs. The optimal coordinated operation mechanism for the MGs would be essential. In this paper, we propose a decentralized optimal operation framework to coordinate the power exchange among MGs abiding by a cooperative contract based on the analytical target cascading (ATC) algorithm. Furthermore, to address the renewable energy uncertainties in the operation strategy of each MG, a two-stage adjustable robust optimization model is devised to coordinate the power exchange between the DS and MGs, the demand response, the output of controlled distributed generations (DGs), and the charging/discharging behavior of energy storage system (ESS). Then, the column and constraint generation method (CCG) is applied to solve the proposed robust optimization model. Comparative studies based on a DS with three typical MGs are performed to validate the effectiveness of the proposed methods. |
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ISSN: | 0306-2619 1872-9118 |
DOI: | 10.1016/j.apenergy.2020.114579 |